diff --git a/.circleci/config.yml b/.circleci/config.yml index a518628afb9..0adfd5be529 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -3496,8 +3496,13 @@ jobs: command: | npx playwright test e2e_ui_tests/ --reporter=html --output=test-results no_output_timeout: 120m - - store_test_results: + - store_artifacts: path: test-results + destination: playwright-results + + - store_artifacts: + path: playwright-report + destination: playwright-report test_nonroot_image: machine: diff --git a/Dockerfile b/Dockerfile index f75706805e0..d8397ec4811 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,8 +1,8 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base # Builder stage FROM $LITELLM_BUILD_IMAGE AS builder @@ -12,11 +12,9 @@ WORKDIR /app USER root # Install build dependencies -RUN apk add --no-cache gcc python3-dev openssl openssl-dev +RUN apk add --no-cache bash gcc py3-pip python3 python3-dev openssl openssl-dev - -RUN pip install --upgrade pip>=24.3.1 && \ - pip install build +RUN python -m pip install build # Copy the current directory contents into the container at /app COPY . . @@ -48,10 +46,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # Install runtime dependencies -RUN apk add --no-cache openssl tzdata nodejs npm - -# Upgrade pip to fix CVE-2025-8869 -RUN pip install --upgrade pip>=24.3.1 +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip WORKDIR /app # Copy the current directory contents into the container at /app diff --git a/README.md b/README.md index 20483ce70c0..4f1a86a648f 100644 --- a/README.md +++ b/README.md @@ -348,7 +348,7 @@ curl 'http://0.0.0.0:4000/key/generate' \ | [Fireworks AI (`fireworks_ai`)](https://docs.litellm.ai/docs/providers/fireworks_ai) | ✅ | ✅ | ✅ | | | | | | | | | [FriendliAI (`friendliai`)](https://docs.litellm.ai/docs/providers/friendliai) | ✅ | ✅ | ✅ | | | | | | | | | [Galadriel (`galadriel`)](https://docs.litellm.ai/docs/providers/galadriel) | ✅ | ✅ | ✅ | | | | | | | | -| [GitHub Copilot (`github_copilot`)](https://docs.litellm.ai/docs/providers/github_copilot) | ✅ | ✅ | ✅ | | | | | | | | +| [GitHub Copilot (`github_copilot`)](https://docs.litellm.ai/docs/providers/github_copilot) | ✅ | ✅ | ✅ | ✅ | | | | | | | | [GitHub Models (`github`)](https://docs.litellm.ai/docs/providers/github) | ✅ | ✅ | ✅ | | | | | | | | | [Google - PaLM](https://docs.litellm.ai/docs/providers/palm) | ✅ | ✅ | ✅ | | | | | | | | | [Google - Vertex AI (`vertex_ai`)](https://docs.litellm.ai/docs/providers/vertex) | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | diff --git a/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py b/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py new file mode 100644 index 00000000000..fd53ece6604 --- /dev/null +++ b/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py @@ -0,0 +1,573 @@ +#!/usr/bin/env python3 +""" +Mock Bedrock Guardrail API Server + +This is a FastAPI server that mimics the AWS Bedrock Guardrail API for testing purposes. +It follows the same API spec as the real Bedrock guardrail endpoint. + +Usage: + python mock_bedrock_guardrail_server.py + +The server will start on http://localhost:8080 +""" + +import os +import re +from typing import Any, Dict, List, Literal, Optional + +from fastapi import Depends, FastAPI, Header, HTTPException, status +from fastapi.responses import JSONResponse +from pydantic import BaseModel, Field + +# ============================================================================ +# Request/Response Models (matching Bedrock API spec) +# ============================================================================ + + +class BedrockTextContent(BaseModel): + text: str + + +class BedrockContentItem(BaseModel): + text: BedrockTextContent + + +class BedrockRequest(BaseModel): + source: Literal["INPUT", "OUTPUT"] + content: List[BedrockContentItem] = Field(default_factory=list) + + +class BedrockGuardrailOutput(BaseModel): + text: Optional[str] = None + + +class TopicPolicyItem(BaseModel): + name: str + type: str + action: Literal["BLOCKED", "NONE"] + + +class TopicPolicy(BaseModel): + topics: List[TopicPolicyItem] = Field(default_factory=list) + + +class ContentFilterItem(BaseModel): + type: str + confidence: str + action: Literal["BLOCKED", "NONE"] + + +class ContentPolicy(BaseModel): + filters: List[ContentFilterItem] = Field(default_factory=list) + + +class CustomWord(BaseModel): + match: str + action: Literal["BLOCKED", "NONE"] + + +class WordPolicy(BaseModel): + customWords: List[CustomWord] = Field(default_factory=list) + managedWordLists: List[Dict[str, Any]] = Field(default_factory=list) + + +class PiiEntity(BaseModel): + type: str + match: str + action: Literal["BLOCKED", "ANONYMIZED", "NONE"] + + +class RegexMatch(BaseModel): + name: str + match: str + regex: str + action: Literal["BLOCKED", "ANONYMIZED", "NONE"] + + +class SensitiveInformationPolicy(BaseModel): + piiEntities: List[PiiEntity] = Field(default_factory=list) + regexes: List[RegexMatch] = Field(default_factory=list) + + +class ContextualGroundingFilter(BaseModel): + type: str + threshold: float + score: float + action: Literal["BLOCKED", "NONE"] + + +class ContextualGroundingPolicy(BaseModel): + filters: List[ContextualGroundingFilter] = Field(default_factory=list) + + +class Assessment(BaseModel): + topicPolicy: Optional[TopicPolicy] = None + contentPolicy: Optional[ContentPolicy] = None + wordPolicy: Optional[WordPolicy] = None + sensitiveInformationPolicy: Optional[SensitiveInformationPolicy] = None + contextualGroundingPolicy: Optional[ContextualGroundingPolicy] = None + + +class BedrockGuardrailResponse(BaseModel): + usage: Dict[str, int] = Field( + default_factory=lambda: {"topicPolicyUnits": 1, "contentPolicyUnits": 1} + ) + action: Literal["NONE", "GUARDRAIL_INTERVENED"] = "NONE" + outputs: List[BedrockGuardrailOutput] = Field(default_factory=list) + assessments: List[Assessment] = Field(default_factory=list) + + +# ============================================================================ +# Mock Guardrail Configuration +# ============================================================================ + + +class GuardrailConfig(BaseModel): + """Configuration for mock guardrail behavior""" + + blocked_words: List[str] = Field( + default_factory=lambda: ["offensive", "inappropriate", "badword"] + ) + blocked_topics: List[str] = Field(default_factory=lambda: ["violence", "illegal"]) + pii_patterns: Dict[str, str] = Field( + default_factory=lambda: { + "EMAIL": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", + "PHONE": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b", + "SSN": r"\b\d{3}-\d{2}-\d{4}\b", + "CREDIT_CARD": r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b", + } + ) + anonymize_pii: bool = True # If True, ANONYMIZE PII; if False, BLOCK it + bearer_token: str = "mock-bedrock-token-12345" + + +# Global config +GUARDRAIL_CONFIG = GuardrailConfig() + +# ============================================================================ +# FastAPI App Setup +# ============================================================================ + +app = FastAPI( + title="Mock Bedrock Guardrail API", + description="Mock server mimicking AWS Bedrock Guardrail API", + version="1.0.0", +) + + +# ============================================================================ +# Authentication +# ============================================================================ + + +async def verify_bearer_token(authorization: Optional[str] = Header(None)) -> str: + """ + Verify the Bearer token from the Authorization header. + + Args: + authorization: The Authorization header value + + Returns: + The token if valid + + Raises: + HTTPException: If token is missing or invalid + """ + if authorization is None: + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Missing Authorization header", + headers={"WWW-Authenticate": "Bearer"}, + ) + + # Check if it's a Bearer token + parts = authorization.split() + print(f"parts: {parts}") + if len(parts) != 2 or parts[0].lower() != "bearer": + raise HTTPException( + status_code=status.HTTP_401_UNAUTHORIZED, + detail="Invalid Authorization header format. Expected: Bearer ", + headers={"WWW-Authenticate": "Bearer"}, + ) + + token = parts[1] + + # Verify token + if token != GUARDRAIL_CONFIG.bearer_token: + raise HTTPException( + status_code=status.HTTP_403_FORBIDDEN, + detail="Invalid bearer token", + ) + + return token + + +# ============================================================================ +# Guardrail Logic +# ============================================================================ + + +def check_blocked_words(text: str) -> Optional[WordPolicy]: + """Check if text contains blocked words""" + found_words = [] + text_lower = text.lower() + + for word in GUARDRAIL_CONFIG.blocked_words: + if word.lower() in text_lower: + found_words.append(CustomWord(match=word, action="BLOCKED")) + + if found_words: + return WordPolicy(customWords=found_words) + return None + + +def check_blocked_topics(text: str) -> Optional[TopicPolicy]: + """Check if text contains blocked topics""" + found_topics = [] + text_lower = text.lower() + + for topic in GUARDRAIL_CONFIG.blocked_topics: + if topic.lower() in text_lower: + found_topics.append( + TopicPolicyItem(name=topic, type=topic.upper(), action="BLOCKED") + ) + + if found_topics: + return TopicPolicy(topics=found_topics) + return None + + +def check_pii(text: str) -> tuple[Optional[SensitiveInformationPolicy], str]: + """ + Check for PII in text and return policy + anonymized text + + Returns: + Tuple of (SensitiveInformationPolicy or None, anonymized_text) + """ + pii_entities = [] + anonymized_text = text + action = "ANONYMIZED" if GUARDRAIL_CONFIG.anonymize_pii else "BLOCKED" + + for pii_type, pattern in GUARDRAIL_CONFIG.pii_patterns.items(): + try: + # Compile the regex pattern with a timeout to prevent ReDoS attacks + compiled_pattern = re.compile(pattern) + matches = compiled_pattern.finditer(text) + for match in matches: + matched_text = match.group() + pii_entities.append( + PiiEntity(type=pii_type, match=matched_text, action=action) + ) + + # Anonymize the text if configured + if GUARDRAIL_CONFIG.anonymize_pii: + anonymized_text = anonymized_text.replace( + matched_text, f"[{pii_type}_REDACTED]" + ) + except re.error: + # Invalid regex pattern - skip it and log a warning + print(f"Warning: Invalid regex pattern for PII type {pii_type}: {pattern}") + continue + + if pii_entities: + return SensitiveInformationPolicy(piiEntities=pii_entities), anonymized_text + + return None, text + + +def process_guardrail_request( + request: BedrockRequest, +) -> tuple[BedrockGuardrailResponse, List[str]]: + """ + Process a guardrail request and return the response. + + Returns: + Tuple of (response, list of output texts) + """ + all_text_content = [] + output_texts = [] + + # Extract all text from content items + for content_item in request.content: + if content_item.text and content_item.text.text: + all_text_content.append(content_item.text.text) + + # Combine all text for analysis + combined_text = " ".join(all_text_content) + + # Initialize response + response = BedrockGuardrailResponse() + assessment = Assessment() + has_intervention = False + + # Check for blocked words + word_policy = check_blocked_words(combined_text) + if word_policy: + assessment.wordPolicy = word_policy + has_intervention = True + + # Check for blocked topics + topic_policy = check_blocked_topics(combined_text) + if topic_policy: + assessment.topicPolicy = topic_policy + has_intervention = True + + # Check for PII + for text in all_text_content: + pii_policy, anonymized_text = check_pii(text) + if pii_policy: + assessment.sensitiveInformationPolicy = pii_policy + if GUARDRAIL_CONFIG.anonymize_pii: + # If anonymizing, we don't block, we modify the text + output_texts.append(anonymized_text) + has_intervention = True + else: + # If not anonymizing PII, we block it + output_texts.append(text) + has_intervention = True + else: + output_texts.append(text) + + # Build response + if has_intervention: + response.action = "GUARDRAIL_INTERVENED" + # Only add assessment if there were interventions + response.assessments = [assessment] + + # Add outputs (modified or original text) + response.outputs = [BedrockGuardrailOutput(text=txt) for txt in output_texts] + + return response, output_texts + + +# ============================================================================ +# API Endpoints +# ============================================================================ + + +@app.get("/") +async def root(): + """Health check endpoint""" + return { + "service": "Mock Bedrock Guardrail API", + "status": "running", + "endpoint_format": "/guardrail/{guardrailIdentifier}/version/{guardrailVersion}/apply", + } + + +@app.get("/health") +async def health(): + """Health check endpoint""" + return {"status": "healthy"} + + +@app.post( + "/guardrail/{guardrailIdentifier}/version/{guardrailVersion}/apply", + response_model=BedrockGuardrailResponse, +) +async def apply_guardrail( + guardrailIdentifier: str, + guardrailVersion: str, + request: BedrockRequest, + token: str = Depends(verify_bearer_token), +) -> BedrockGuardrailResponse: + """ + Apply guardrail to input or output content. + + This endpoint mimics the AWS Bedrock ApplyGuardrail API. + + Args: + guardrailIdentifier: The guardrail ID + guardrailVersion: The guardrail version + request: The guardrail request containing content to analyze + token: Bearer token (verified by dependency) + + Returns: + BedrockGuardrailResponse with analysis results + """ + # Process the request + response, output_texts = process_guardrail_request(request) + + # Log the request (optional, for debugging) + print(f"Guardrail applied: {guardrailIdentifier} v{guardrailVersion}") + print(f"Source: {request.source}") + print(f"Action: {response.action}") + + return response + + +""" +LiteLLM exposes a basic guardrail API with the text extracted from the request and sent to the guardrail API, as well as the received request body for any further processing. + +This works across all LiteLLM endpoints (completion, anthropic /v1/messages, responses api, image generation, embedding, etc.) + +This makes it easy to support your own guardrail API without having to make a PR to LiteLLM. + +LiteLLM supports passing any provider specific params from LiteLLM config.yaml to the guardrail API. + +Example: + +```yaml +guardrails: + - guardrail_name: "bedrock-content-guard" + litellm_params: + guardrail: generic_guardrail_api + mode: "pre_call" + api_key: os.environ/GUARDRAIL_API_KEY + api_base: os.environ/GUARDRAIL_API_BASE + additional_provider_specific_params: + api_version: os.environ/GUARDRAIL_API_VERSION # additional provider specific params +``` + +This is a beta API. Please help us improve it. +""" + + +class LitellmBasicGuardrailRequest(BaseModel): + texts: List[str] + images: Optional[List[str]] = None + tools: Optional[List[dict]] = None + request_data: Dict[str, Any] = Field(default_factory=dict) + additional_provider_specific_params: Dict[str, Any] = Field(default_factory=dict) + input_type: Literal["request", "response"] + litellm_call_id: Optional[str] = None + litellm_trace_id: Optional[str] = None + + +class LitellmBasicGuardrailResponse(BaseModel): + action: Literal[ + "BLOCKED", "NONE", "GUARDRAIL_INTERVENED" + ] # BLOCKED = litellm will raise an error, NONE = litellm will continue, GUARDRAIL_INTERVENED = litellm will continue, but the text was modified by the guardrail + blocked_reason: Optional[str] = None # only if action is BLOCKED, otherwise None + texts: Optional[List[str]] = None + images: Optional[List[str]] = None + + +@app.post( + "/beta/litellm_basic_guardrail_api", + response_model=LitellmBasicGuardrailResponse, +) +async def beta_litellm_basic_guardrail_api( + request: LitellmBasicGuardrailRequest, +) -> LitellmBasicGuardrailResponse: + """ + Apply guardrail to input or output content. + + This endpoint mimics the AWS Bedrock ApplyGuardrail API. + + Args: + request: The guardrail request containing content to analyze + token: Bearer token (verified by dependency) + + Returns: + LitellmBasicGuardrailResponse with analysis results + """ + print(f"request: {request}") + if any("ishaan" in text.lower() for text in request.texts): + return LitellmBasicGuardrailResponse( + action="BLOCKED", blocked_reason="Ishaan is not allowed" + ) + elif any("pii_value" in text for text in request.texts): + return LitellmBasicGuardrailResponse( + action="GUARDRAIL_INTERVENED", + texts=[ + text.replace("pii_value", "pii_value_redacted") + for text in request.texts + ], + ) + return LitellmBasicGuardrailResponse(action="NONE") + + +@app.post("/config/update") +async def update_config( + config: GuardrailConfig, token: str = Depends(verify_bearer_token) +): + """ + Update the guardrail configuration. + + This is a testing endpoint to modify the mock guardrail behavior. + + Args: + config: New guardrail configuration + token: Bearer token (verified by dependency) + + Returns: + Updated configuration + """ + global GUARDRAIL_CONFIG + GUARDRAIL_CONFIG = config + return {"status": "updated", "config": GUARDRAIL_CONFIG} + + +@app.get("/config") +async def get_config(token: str = Depends(verify_bearer_token)): + """ + Get the current guardrail configuration. + + Args: + token: Bearer token (verified by dependency) + + Returns: + Current configuration + """ + return GUARDRAIL_CONFIG + + +# ============================================================================ +# Error Handlers +# ============================================================================ + + +@app.exception_handler(HTTPException) +async def http_exception_handler(request, exc: HTTPException): + """Custom error handler for HTTP exceptions""" + return JSONResponse( + status_code=exc.status_code, + content={"error": exc.detail}, + headers=exc.headers, + ) + + +# ============================================================================ +# Main +# ============================================================================ + +if __name__ == "__main__": + import uvicorn + + # Get configuration from environment + host = os.getenv("MOCK_BEDROCK_HOST", "0.0.0.0") + port = int(os.getenv("MOCK_BEDROCK_PORT", "8080")) + bearer_token = os.getenv("MOCK_BEDROCK_TOKEN", "mock-bedrock-token-12345") + + # Update config with environment token + GUARDRAIL_CONFIG.bearer_token = bearer_token + + print("=" * 80) + print("Mock Bedrock Guardrail API Server") + print("=" * 80) + print(f"Server starting on: http://{host}:{port}") + print(f"Bearer Token: {bearer_token}") + print(f"Endpoint: POST /guardrail/{{id}}/version/{{version}}/apply") + print("=" * 80) + print("\nExample curl command:") + print( + f""" +curl -X POST "http://{host}:{port}/guardrail/test-guardrail/version/1/apply" \\ + -H "Authorization: Bearer {bearer_token}" \\ + -H "Content-Type: application/json" \\ + -d '{{ + "source": "INPUT", + "content": [ + {{ + "text": {{ + "text": "Hello, my email is test@example.com" + }} + }} + ] + }}' + """ + ) + print("=" * 80) + + uvicorn.run(app, host=host, port=port) diff --git a/deploy/charts/litellm-helm/Chart.yaml b/deploy/charts/litellm-helm/Chart.yaml index eedadebaa8e..7f14af7db5d 100644 --- a/deploy/charts/litellm-helm/Chart.yaml +++ b/deploy/charts/litellm-helm/Chart.yaml @@ -18,7 +18,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 0.4.8 +version: 0.4.9 # This is the version number of the application being deployed. This version number should be # incremented each time you make changes to the application. Versions are not expected to @@ -33,5 +33,5 @@ dependencies: condition: db.deployStandalone - name: redis version: ">=18.0.0" - repository: oci://registry-1.docker.io/bitnamicharts + repository: oci://registry-1.docker.io/bitnamicharts condition: redis.enabled diff --git a/deploy/charts/litellm-helm/README.md b/deploy/charts/litellm-helm/README.md index 352c3e9ddff..6fdc423a177 100644 --- a/deploy/charts/litellm-helm/README.md +++ b/deploy/charts/litellm-helm/README.md @@ -10,46 +10,48 @@ - Helm 3.8.0+ If `db.deployStandalone` is used: + - PV provisioner support in the underlying infrastructure If `db.useStackgresOperator` is used (not yet implemented): -- The Stackgres Operator must already be installed in the Kubernetes Cluster. This chart will **not** install the operator if it is missing. + +- The Stackgres Operator must already be installed in the Kubernetes Cluster. This chart will **not** install the operator if it is missing. ## Parameters ### LiteLLM Proxy Deployment Settings -| Name | Description | Value | -| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | -| `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | -| `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | -| `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | -| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | -| `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | -| `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | -| `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | -| `image.pullPolicy` | LiteLLM Proxy image pull policy | `IfNotPresent` | -| `image.tag` | Overrides the image tag whose default the latest version of LiteLLM at the time this chart was published. | `""` | -| `imagePullSecrets` | Registry credentials for the LiteLLM and initContainer images. | `[]` | -| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` | -| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` | -| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` | -| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` | -| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A | -| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` | -| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` | -| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` | -| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` | -| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. -| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` | -| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | -| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | -| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` | -| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` | +| Name | Description | Value | +| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------- | +| `replicaCount` | The number of LiteLLM Proxy pods to be deployed | `1` | +| `masterkeySecretName` | The name of the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use the generated secret name. | N/A | +| `masterkeySecretKey` | The key within the Kubernetes Secret that contains the Master API Key for LiteLLM. If not specified, use `masterkey` as the key. | N/A | +| `masterkey` | The Master API Key for LiteLLM. If not specified, a random key in the `sk-...` format is generated. | N/A | +| `environmentSecrets` | An optional array of Secret object names. The keys and values in these secrets will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | +| `environmentConfigMaps` | An optional array of ConfigMap object names. The keys and values in these configmaps will be presented to the LiteLLM proxy pod as environment variables. See below for an example Secret object. | `[]` | +| `image.repository` | LiteLLM Proxy image repository | `ghcr.io/berriai/litellm` | +| `image.pullPolicy` | LiteLLM Proxy image pull policy | `IfNotPresent` | +| `image.tag` | Overrides the image tag whose default the latest version of LiteLLM at the time this chart was published. | `""` | +| `imagePullSecrets` | Registry credentials for the LiteLLM and initContainer images. | `[]` | +| `serviceAccount.create` | Whether or not to create a Kubernetes Service Account for this deployment. The default is `false` because LiteLLM has no need to access the Kubernetes API. | `false` | +| `service.type` | Kubernetes Service type (e.g. `LoadBalancer`, `ClusterIP`, etc.) | `ClusterIP` | +| `service.port` | TCP port that the Kubernetes Service will listen on. Also the TCP port within the Pod that the proxy will listen on. | `4000` | +| `service.loadBalancerClass` | Optional LoadBalancer implementation class (only used when `service.type` is `LoadBalancer`) | `""` | +| `ingress.labels` | Additional labels for the Ingress resource | `{}` | +| `ingress.*` | See [values.yaml](./values.yaml) for example settings | N/A | +| `proxyConfigMap.create` | When `true`, render a ConfigMap from `.Values.proxy_config` and mount it. | `true` | +| `proxyConfigMap.name` | When `create=false`, name of the existing ConfigMap to mount. | `""` | +| `proxyConfigMap.key` | Key in the ConfigMap that contains the proxy config file. | `"config.yaml"` | +| `proxy_config.*` | See [values.yaml](./values.yaml) for default settings. Rendered into the ConfigMap’s `config.yaml` only when `proxyConfigMap.create=true`. See [example_config_yaml](../../../litellm/proxy/example_config_yaml/) for configuration examples. | `N/A` | +| `extraContainers[]` | An array of additional containers to be deployed as sidecars alongside the LiteLLM Proxy. | +| `pdb.enabled` | Enable a PodDisruptionBudget for the LiteLLM proxy Deployment | `false` | +| `pdb.minAvailable` | Minimum number/percentage of pods that must be available during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | +| `pdb.maxUnavailable` | Maximum number/percentage of pods that can be unavailable during **voluntary** disruptions (choose **one** of minAvailable/maxUnavailable) | `null` | +| `pdb.annotations` | Extra metadata annotations to add to the PDB | `{}` | +| `pdb.labels` | Extra metadata labels to add to the PDB | `{}` | #### Example `proxy_config` ConfigMap from values (default): - ``` proxyConfigMap: create: true @@ -67,7 +69,6 @@ proxy_config: #### Example using existing `proxyConfigMap` instead of creating it: - ``` proxyConfigMap: create: false @@ -77,8 +78,7 @@ proxyConfigMap: # proxy_config is ignored in this mode ``` -#### Example `environmentSecrets` Secret - +#### Example `environmentSecrets` Secret ``` apiVersion: v1 @@ -91,21 +91,23 @@ type: Opaque ``` ### Database Settings -| Name | Description | Value | -| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | -| `db.useExisting` | Use an existing Postgres database. A Kubernetes Secret object must exist that contains credentials for connecting to the database. An example secret object definition is provided below. | `false` | -| `db.endpoint` | If `db.useExisting` is `true`, this is the IP, Hostname or Service Name of the Postgres server to connect to. | `localhost` | -| `db.database` | If `db.useExisting` is `true`, the name of the existing database to connect to. | `litellm` | -| `db.url` | If `db.useExisting` is `true`, the connection url of the existing database to connect to can be overwritten with this value. | `postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_HOST)/$(DATABASE_NAME)` | -| `db.secret.name` | If `db.useExisting` is `true`, the name of the Kubernetes Secret that contains credentials. | `postgres` | -| `db.secret.usernameKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the username for authenticating with the Postgres instance. | `username` | -| `db.secret.passwordKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the password associates with the above user. | `password` | -| `db.useStackgresOperator` | Not yet implemented. | `false` | -| `db.deployStandalone` | Deploy a standalone, single instance deployment of Postgres, using the Bitnami postgresql chart. This is useful for getting started but doesn't provide HA or (by default) data backups. | `true` | -| `postgresql.*` | If `db.deployStandalone` is `true`, configuration passed to the Bitnami postgresql chart. See the [Bitnami Documentation](https://github.com/bitnami/charts/tree/main/bitnami/postgresql) for full configuration details. See [values.yaml](./values.yaml) for the default configuration. | See [values.yaml](./values.yaml) | -| `postgresql.auth.*` | If `db.deployStandalone` is `true`, care should be taken to ensure the default `password` and `postgres-password` values are **NOT** used. | `NoTaGrEaTpAsSwOrD` | + +| Name | Description | Value | +| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------ | +| `db.useExisting` | Use an existing Postgres database. A Kubernetes Secret object must exist that contains credentials for connecting to the database. An example secret object definition is provided below. | `false` | +| `db.endpoint` | If `db.useExisting` is `true`, this is the IP, Hostname or Service Name of the Postgres server to connect to. | `localhost` | +| `db.database` | If `db.useExisting` is `true`, the name of the existing database to connect to. | `litellm` | +| `db.url` | If `db.useExisting` is `true`, the connection url of the existing database to connect to can be overwritten with this value. | `postgresql://$(DATABASE_USERNAME):$(DATABASE_PASSWORD)@$(DATABASE_HOST)/$(DATABASE_NAME)` | +| `db.secret.name` | If `db.useExisting` is `true`, the name of the Kubernetes Secret that contains credentials. | `postgres` | +| `db.secret.usernameKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the username for authenticating with the Postgres instance. | `username` | +| `db.secret.passwordKey` | If `db.useExisting` is `true`, the name of the key within the Kubernetes Secret that holds the password associates with the above user. | `password` | +| `db.useStackgresOperator` | Not yet implemented. | `false` | +| `db.deployStandalone` | Deploy a standalone, single instance deployment of Postgres, using the Bitnami postgresql chart. This is useful for getting started but doesn't provide HA or (by default) data backups. | `true` | +| `postgresql.*` | If `db.deployStandalone` is `true`, configuration passed to the Bitnami postgresql chart. See the [Bitnami Documentation](https://github.com/bitnami/charts/tree/main/bitnami/postgresql) for full configuration details. See [values.yaml](./values.yaml) for the default configuration. | See [values.yaml](./values.yaml) | +| `postgresql.auth.*` | If `db.deployStandalone` is `true`, care should be taken to ensure the default `password` and `postgres-password` values are **NOT** used. | `NoTaGrEaTpAsSwOrD` | #### Example Postgres `db.useExisting` Secret + ```yaml apiVersion: v1 kind: Secret @@ -143,7 +145,7 @@ metadata: name: litellm-env-secret type: Opaque data: - SOME_PASSWORD: cDZbUGVXeU5e0ZW # base64 encoded + SOME_PASSWORD: cDZbUGVXeU5e0ZW # base64 encoded ANOTHER_PASSWORD: AAZbUGVXeU5e0ZB # base64 encoded ``` @@ -153,23 +155,23 @@ Source: [GitHub Gist from troyharvey](https://gist.github.com/troyharvey/4506472 The migration job supports both ArgoCD and Helm hooks to ensure database migrations run at the appropriate time during deployments. -| Name | Description | Value | -| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----- | -| `migrationJob.enabled` | Enable or disable the schema migration Job | `true` | -| `migrationJob.backoffLimit` | Backoff limit for Job restarts | `4` | -| `migrationJob.ttlSecondsAfterFinished` | TTL for completed migration jobs | `120` | -| `migrationJob.annotations` | Additional annotations for the migration job pod | `{}` | -| `migrationJob.extraContainers` | Additional containers to run alongside the migration job | `[]` | -| `migrationJob.hooks.argocd.enabled` | Enable ArgoCD hooks for the migration job (uses PreSync hook with BeforeHookCreation delete policy) | `true` | -| `migrationJob.hooks.helm.enabled` | Enable Helm hooks for the migration job (uses pre-install,pre-upgrade hooks with before-hook-creation delete policy) | `false` | -| `migrationJob.hooks.helm.weight` | Helm hook execution order (lower weights executed first). Optional - defaults to "1" if not specified. | N/A | - +| Name | Description | Value | +| -------------------------------------- | -------------------------------------------------------------------------------------------------------------------- | ------- | +| `migrationJob.enabled` | Enable or disable the schema migration Job | `true` | +| `migrationJob.backoffLimit` | Backoff limit for Job restarts | `4` | +| `migrationJob.ttlSecondsAfterFinished` | TTL for completed migration jobs | `120` | +| `migrationJob.annotations` | Additional annotations for the migration job pod | `{}` | +| `migrationJob.extraContainers` | Additional containers to run alongside the migration job | `[]` | +| `migrationJob.hooks.argocd.enabled` | Enable ArgoCD hooks for the migration job (uses PreSync hook with BeforeHookCreation delete policy) | `true` | +| `migrationJob.hooks.helm.enabled` | Enable Helm hooks for the migration job (uses pre-install,pre-upgrade hooks with before-hook-creation delete policy) | `false` | +| `migrationJob.hooks.helm.weight` | Helm hook execution order (lower weights executed first). Optional - defaults to "1" if not specified. | N/A | ## Accessing the Admin UI + When browsing to the URL published per the settings in `ingress.*`, you will -be prompted for **Admin Configuration**. The **Proxy Endpoint** is the internal +be prompted for **Admin Configuration**. The **Proxy Endpoint** is the internal (from the `litellm` pod's perspective) URL published by the `-litellm` -Kubernetes Service. If the deployment uses the default settings for this +Kubernetes Service. If the deployment uses the default settings for this service, the **Proxy Endpoint** should be set to `http://-litellm:4000`. The **Proxy Key** is the value specified for `masterkey` or, if a `masterkey` @@ -181,7 +183,8 @@ kubectl -n litellm get secret -litellm-masterkey -o jsonpath="{.data.ma ``` ## Admin UI Limitations -At the time of writing, the Admin UI is unable to add models. This is because + +At the time of writing, the Admin UI is unable to add models. This is because it would need to update the `config.yaml` file which is a exposed ConfigMap, and -therefore, read-only. This is a limitation of this helm chart, not the Admin UI +therefore, read-only. This is a limitation of this helm chart, not the Admin UI itself. diff --git a/deploy/charts/litellm-helm/templates/ingress.yaml b/deploy/charts/litellm-helm/templates/ingress.yaml index 09e8d715ab8..ea9ffcbb54c 100644 --- a/deploy/charts/litellm-helm/templates/ingress.yaml +++ b/deploy/charts/litellm-helm/templates/ingress.yaml @@ -18,6 +18,9 @@ metadata: name: {{ $fullName }} labels: {{- include "litellm.labels" . | nindent 4 }} + {{- with .Values.ingress.labels }} + {{- toYaml . | nindent 4 }} + {{- end }} {{- with .Values.ingress.annotations }} annotations: {{- toYaml . | nindent 4 }} diff --git a/deploy/charts/litellm-helm/tests/ingress_tests.yaml b/deploy/charts/litellm-helm/tests/ingress_tests.yaml new file mode 100644 index 00000000000..aad6ecfcee8 --- /dev/null +++ b/deploy/charts/litellm-helm/tests/ingress_tests.yaml @@ -0,0 +1,45 @@ +suite: Ingress Configuration Tests +templates: + - ingress.yaml +tests: + - it: should not create Ingress by default + asserts: + - hasDocuments: + count: 0 + + - it: should create Ingress when enabled + set: + ingress.enabled: true + asserts: + - hasDocuments: + count: 1 + - isKind: + of: Ingress + + - it: should add custom labels + set: + ingress.enabled: true + ingress.labels: + custom-label: "true" + another-label: "value" + asserts: + - isKind: + of: Ingress + - equal: + path: metadata.labels.custom-label + value: "true" + - equal: + path: metadata.labels.another-label + value: "value" + + - it: should add annotations + set: + ingress.enabled: true + ingress.annotations: + kubernetes.io/ingress.class: "nginx" + asserts: + - isKind: + of: Ingress + - equal: + path: metadata.annotations["kubernetes.io/ingress.class"] + value: "nginx" diff --git a/deploy/charts/litellm-helm/values.yaml b/deploy/charts/litellm-helm/values.yaml index acb8c9ca32f..35021157826 100644 --- a/deploy/charts/litellm-helm/values.yaml +++ b/deploy/charts/litellm-helm/values.yaml @@ -35,7 +35,8 @@ podAnnotations: {} podLabels: {} terminationGracePeriodSeconds: 90 -topologySpreadConstraints: [] +topologySpreadConstraints: + [] # - maxSkew: 1 # topologyKey: kubernetes.io/hostname # whenUnsatisfiable: DoNotSchedule @@ -46,7 +47,8 @@ topologySpreadConstraints: [] # At the time of writing, the litellm docker image requires write access to the # filesystem on startup so that prisma can install some dependencies. podSecurityContext: {} -securityContext: {} +securityContext: + {} # capabilities: # drop: # - ALL @@ -57,13 +59,15 @@ securityContext: {} # A list of Kubernetes Secret objects that will be exported to the LiteLLM proxy # pod as environment variables. These secrets can then be referenced in the # configuration file (or "litellm" ConfigMap) with `os.environ/` -environmentSecrets: [] +environmentSecrets: + [] # - litellm-env-secret # A list of Kubernetes ConfigMap objects that will be exported to the LiteLLM proxy # pod as environment variables. The ConfigMap kv-pairs can then be referenced in the # configuration file (or "litellm" ConfigMap) with `os.environ/` -environmentConfigMaps: [] +environmentConfigMaps: + [] # - litellm-env-configmap service: @@ -82,7 +86,9 @@ separateHealthPort: 8081 ingress: enabled: false className: "nginx" - annotations: {} + labels: {} + annotations: + {} # kubernetes.io/ingress.class: nginx # kubernetes.io/tls-acme: "true" hosts: @@ -129,7 +135,8 @@ proxy_config: general_settings: master_key: os.environ/PROXY_MASTER_KEY -resources: {} +resources: + {} # We usually recommend not to specify default resources and to leave this as a conscious # choice for the user. This also increases chances charts run on environments with little # resources, such as Minikube. If you do want to specify resources, uncomment the following @@ -231,7 +238,7 @@ migrationJob: # cpu: 100m # memory: 100Mi extraContainers: [] - + # Hook configuration hooks: argocd: @@ -240,30 +247,30 @@ migrationJob: enabled: false # Additional environment variables to be added to the deployment as a map of key-value pairs -envVars: { - # USE_DDTRACE: "true" -} +envVars: {} +# USE_DDTRACE: "true" # Additional environment variables to be added to the deployment as a list of k8s env vars -extraEnvVars: { - # - name: EXTRA_ENV_VAR - # value: EXTRA_ENV_VAR_VALUE -} +extraEnvVars: {} +# - name: EXTRA_ENV_VAR +# value: EXTRA_ENV_VAR_VALUE # Pod Disruption Budget pdb: enabled: false # Set exactly one of the following. If both are set, minAvailable takes precedence. - minAvailable: null # e.g. "50%" or 1 - maxUnavailable: null # e.g. 1 or "20%" + minAvailable: null # e.g. "50%" or 1 + maxUnavailable: null # e.g. 1 or "20%" annotations: {} labels: {} serviceMonitor: enabled: false - labels: {} + labels: + {} # test: test - annotations: {} + annotations: + {} # kubernetes.io/test: test interval: 15s scrapeTimeout: 10s @@ -273,4 +280,4 @@ serviceMonitor: # action: replace namespaceSelector: matchNames: [] - # - test-namespace \ No newline at end of file + # - test-namespace diff --git a/docker/Dockerfile.database b/docker/Dockerfile.database index 09b5265191b..0e804cbfd12 100644 --- a/docker/Dockerfile.database +++ b/docker/Dockerfile.database @@ -1,8 +1,8 @@ # Base image for building -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base # Runtime image -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base # Builder stage FROM $LITELLM_BUILD_IMAGE AS builder @@ -13,13 +13,15 @@ USER root # Install build dependencies RUN apk add --no-cache \ - build-base \ + bash \ + gcc \ + py3-pip \ + python3 \ python3-dev \ + openssl \ openssl-dev - -RUN pip install --upgrade pip && \ - pip install build +RUN python -m pip install build # Copy the current directory contents into the container at /app COPY . . @@ -46,7 +48,7 @@ FROM $LITELLM_RUNTIME_IMAGE AS runtime USER root # Install runtime dependencies -RUN apk add --no-cache openssl +RUN apk add --no-cache bash openssl tzdata nodejs npm python3 py3-pip WORKDIR /app # Copy the current directory contents into the container at /app diff --git a/docker/Dockerfile.non_root b/docker/Dockerfile.non_root index 2dcb7cb4787..cd1633e319c 100644 --- a/docker/Dockerfile.non_root +++ b/docker/Dockerfile.non_root @@ -1,6 +1,6 @@ # Base images -ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/python:latest-dev -ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/python:latest-dev +ARG LITELLM_BUILD_IMAGE=cgr.dev/chainguard/wolfi-base +ARG LITELLM_RUNTIME_IMAGE=cgr.dev/chainguard/wolfi-base # ----------------- # Builder Stage @@ -10,7 +10,18 @@ WORKDIR /app # Install build dependencies including Node.js for UI build USER root -RUN apk add --no-cache build-base bash nodejs npm \ +RUN apk add --no-cache \ + python3 \ + py3-pip \ + clang \ + llvm \ + lld \ + gcc \ + linux-headers \ + build-base \ + bash \ + nodejs \ + npm \ && pip install --no-cache-dir --upgrade pip build # Copy project files @@ -62,7 +73,7 @@ WORKDIR /app # Install runtime dependencies USER root RUN apk upgrade --no-cache && \ - apk add --no-cache bash libstdc++ ca-certificates openssl supervisor + apk add --no-cache python3 py3-pip bash openssl tzdata nodejs npm supervisor # Copy only necessary artifacts from builder stage for runtime COPY . . diff --git a/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md b/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md new file mode 100644 index 00000000000..1e5f968b2ca --- /dev/null +++ b/docs/my-website/blog/anthropic_opus_4_5_and_advanced_features/index.md @@ -0,0 +1,1069 @@ +--- +slug: anthropic_advanced_features +title: "Day 0 Support: Claude 4.5 Opus (+Advanced Features)" +date: 2025-11-25T10:00:00 +authors: + - name: Sameer Kankute + title: SWE @ LiteLLM (LLM Translation) + url: https://www.linkedin.com/in/sameer-kankute/ + image_url: https://media.licdn.com/dms/image/v2/D4D03AQHB_loQYd5gjg/profile-displayphoto-shrink_800_800/profile-displayphoto-shrink_800_800/0/1719137160975?e=1765411200&v=beta&t=c8396f--_lH6Fb_pVvx_jGholPfcl0bvwmNynbNdnII + - name: Krrish Dholakia + title: "CEO, LiteLLM" + url: https://www.linkedin.com/in/krish-d/ + image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg + - name: Ishaan Jaff + title: "CTO, LiteLLM" + url: https://www.linkedin.com/in/reffajnaahsi/ + image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg +tags: [anthropic, claude, tool search, programmatic tool calling, effort, advanced features] +hide_table_of_contents: false +--- + +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +This guide covers Anthropic's latest model (Claude Opus 4.5) and its advanced features now available in LiteLLM: Tool Search, Programmatic Tool Calling, Tool Input Examples, and the Effort Parameter. + +--- + +| Feature | Supported Models | +|---------|-----------------| +| Tool Search | Claude Opus 4.5, Sonnet 4.5 | +| Programmatic Tool Calling | Claude Opus 4.5, Sonnet 4.5 | +| Input Examples | Claude Opus 4.5, Sonnet 4.5 | +| Effort Parameter | Claude Opus 4.5 only | + +Supported Providers: [Anthropic](../../docs/providers/anthropic), [Bedrock](../../docs/providers/bedrock), [Vertex AI](../../docs/providers/vertex_partner#vertex-ai---anthropic-claude), [Azure AI](../../docs/providers/azure_ai). + +## Usage + + + + + +```python +import os +from litellm import completion + +# set env - [OPTIONAL] replace with your anthropic key +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +messages = [{"role": "user", "content": "Hey! how's it going?"}] + +## OPENAI /chat/completions API format +response = completion(model="claude-opus-4-5-20251101", messages=messages) +print(response) + +``` + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-4 ### RECEIVED MODEL NAME ### + litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input + model: claude-opus-4-5-20251101 ### MODEL NAME sent to `litellm.completion()` ### + api_key: "os.environ/ANTHROPIC_API_KEY" # does os.getenv("ANTHROPIC_API_KEY") +``` + +**2. Start the proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Test it!** + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "max_tokens": 1024, + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + + + +## Usage - Bedrock + +:::info + +LiteLLM uses the boto3 library to authenticate with Bedrock. + +For more ways to authenticate with Bedrock, see the [Bedrock documentation](../../docs/providers/bedrock#authentication). + +::: + + + + + +```python +import os +from litellm import completion + +os.environ["AWS_ACCESS_KEY_ID"] = "" +os.environ["AWS_SECRET_ACCESS_KEY"] = "" +os.environ["AWS_REGION_NAME"] = "" + +## OPENAI /chat/completions API format +response = completion( + model="bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0", + messages=[{ "content": "Hello, how are you?","role": "user"}] +) +``` + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-4 ### RECEIVED MODEL NAME ### + litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input + model: bedrock/us.anthropic.claude-opus-4-5-20251101-v1:0 ### MODEL NAME sent to `litellm.completion()` ### + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/AWS_REGION_NAME +``` + +**2. Start the proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Test it!** + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "max_tokens": 1024, + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + +```bash +curl --location 'http://0.0.0.0:4000/bedrock/model/claude-4/invoke' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "max_tokens": 1024, + "messages": [{"role": "user", "content": "Hello, how are you?"}] + }' +``` + + +```bash +curl --location 'http://0.0.0.0:4000/bedrock/model/claude-4/converse' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "messages": [{"role": "user", "content": "Hello, how are you?"}] + }' +``` + + + + + + +## Usage - Vertex AI + + + + + +```python +from litellm import completion +import json + +## GET CREDENTIALS +## RUN ## +# !gcloud auth application-default login - run this to add vertex credentials to your env +## OR ## +file_path = 'path/to/vertex_ai_service_account.json' + +# Load the JSON file +with open(file_path, 'r') as file: + vertex_credentials = json.load(file) + +# Convert to JSON string +vertex_credentials_json = json.dumps(vertex_credentials) + +## COMPLETION CALL +response = completion( + model="vertex_ai/claude-opus-4-5@20251101", + messages=[{ "content": "Hello, how are you?","role": "user"}], + vertex_credentials=vertex_credentials_json, + vertex_project="your-project-id", + vertex_location="us-east5" +) +``` + + + + +**1. Setup config.yaml** + +```yaml +model_list: + - model_name: claude-4 ### RECEIVED MODEL NAME ### + litellm_params: + model: vertex_ai/claude-opus-4-5@20251101 + vertex_credentials: "/path/to/service_account.json" + vertex_project: "your-project-id" + vertex_location: "us-east5" +``` + +**2. Start the proxy** + +```bash +litellm --config /path/to/config.yaml +``` + +**3. Test it!** + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + +```bash +curl --location 'http://0.0.0.0:4000/v1/messages' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "max_tokens": 1024, + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ] + } +' +``` + + + + + +## Usage - Azure Anthropic (Azure Foundry Claude) + +LiteLLM funnels Azure Claude deployments through the `azure_ai/` provider so Claude Opus models on Azure Foundry keep working with Tool Search, Effort, streaming, and the rest of the advanced feature set. Point `AZURE_AI_API_BASE` to `https://.services.ai.azure.com/anthropic` (LiteLLM appends `/v1/messages` automatically) and authenticate with `AZURE_AI_API_KEY` or an Azure AD token. + + + + +```python +import os +from litellm import completion + +# Configure Azure credentials +os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key" +os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic" + +response = completion( + model="azure_ai/claude-opus-4-1", + messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}], + max_tokens=1200, + temperature=0.7, + stream=True, +) + +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="", flush=True) +``` + + + + +**1. Set environment variables** + +```bash +export AZURE_AI_API_KEY="your-azure-ai-api-key" +export AZURE_AI_API_BASE="https://my-resource.services.ai.azure.com/anthropic" +``` + +**2. Configure the proxy** + +```yaml +model_list: + - model_name: claude-4-azure + litellm_params: + model: azure_ai/claude-opus-4-1 + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE +``` + +**3. Start LiteLLM** + +```bash +litellm --config /path/to/config.yaml +``` + +**4. Test the Azure Claude route** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer $LITELLM_KEY' \ + --data '{ + "model": "claude-4-azure", + "messages": [ + { + "role": "user", + "content": "How do I use Claude Opus 4 via Azure Anthropic in LiteLLM?" + } + ], + "max_tokens": 1024 + }' +``` + + + + + +## Tool Search {#tool-search} + +This lets Claude work with thousands of tools, by dynamically loading tools on-demand, instead of loading all tools into the context window upfront. + +### Usage Example + + + + +```python +import litellm +import os + +# Configure your API key +os.environ["ANTHROPIC_API_KEY"] = "your-api-key" + +# Define your tools with defer_loading +tools = [ + # Tool search tool (regex variant) + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + # Deferred tools - loaded on-demand + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location. Returns temperature and conditions.", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "Temperature unit" + } + }, + "required": ["location"] + } + }, + "defer_loading": True # Load on-demand + }, + { + "type": "function", + "function": { + "name": "search_files", + "description": "Search through files in the workspace using keywords", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"}, + "file_types": { + "type": "array", + "items": {"type": "string"} + } + }, + "required": ["query"] + } + }, + "defer_loading": True + }, + { + "type": "function", + "function": { + "name": "query_database", + "description": "Execute SQL queries against the database", + "parameters": { + "type": "object", + "properties": { + "sql": {"type": "string"} + }, + "required": ["sql"] + } + }, + "defer_loading": True + } +] + +# Make a request - Claude will search for and use relevant tools +response = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{ + "role": "user", + "content": "What's the weather like in San Francisco?" + }], + tools=tools +) + +print("Claude's response:", response.choices[0].message.content) +print("Tool calls:", response.choices[0].message.tool_calls) + +# Check tool search usage +if hasattr(response.usage, 'server_tool_use'): + print(f"Tool searches performed: {response.usage.server_tool_use.tool_search_requests}") +``` + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: claude-4 + litellm_params: + model: anthropic/claude-opus-4-5-20251101 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [{ + "role": "user", + "content": "What's the weather like in San Francisco?" + }], + "tools": [ + # Tool search tool (regex variant) + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + # Deferred tools - loaded on-demand + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location. Returns temperature and conditions.", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "Temperature unit" + } + }, + "required": ["location"] + } + }, + "defer_loading": True # Load on-demand + }, + { + "type": "function", + "function": { + "name": "search_files", + "description": "Search through files in the workspace using keywords", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"}, + "file_types": { + "type": "array", + "items": {"type": "string"} + } + }, + "required": ["query"] + } + }, + "defer_loading": True + }, + { + "type": "function", + "function": { + "name": "query_database", + "description": "Execute SQL queries against the database", + "parameters": { + "type": "object", + "properties": { + "sql": {"type": "string"} + }, + "required": ["sql"] + } + }, + "defer_loading": True + } + ] +} +' +``` + + + +### BM25 Variant (Natural Language Search) + +For natural language queries instead of regex patterns: + +```python +tools = [ + { + "type": "tool_search_tool_bm25_20251119", # Natural language variant + "name": "tool_search_tool_bm25" + }, + # ... your deferred tools +] +``` + +--- + +## Programmatic Tool Calling {#programmatic-tool-calling} + +Programmatic tool calling allows Claude to write code that calls your tools programmatically. [Learn more](https://platform.claude.com/docs/en/agents-and-tools/tool-use/programmatic-tool-calling) + + + + +```python +import litellm +import json + +# Define tools that can be called programmatically +tools = [ + # Code execution tool (required for programmatic calling) + { + "type": "code_execution_20250825", + "name": "code_execution" + }, + # Tool that can be called from code + { + "type": "function", + "function": { + "name": "query_database", + "description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.", + "parameters": { + "type": "object", + "properties": { + "sql": { + "type": "string", + "description": "SQL query to execute" + } + }, + "required": ["sql"] + } + }, + "allowed_callers": ["code_execution_20250825"] # Enable programmatic calling + } +] + +# First request +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[{ + "role": "user", + "content": "Query sales data for West, East, and Central regions, then tell me which had the highest revenue" + }], + tools=tools +) + +print("Claude's response:", response.choices[0].message) + +# Handle tool calls +messages = [ + {"role": "user", "content": "Query sales data for West, East, and Central regions, then tell me which had the highest revenue"}, + {"role": "assistant", "content": response.choices[0].message.content, "tool_calls": response.choices[0].message.tool_calls} +] + +# Process each tool call +for tool_call in response.choices[0].message.tool_calls: + # Check if it's a programmatic call + if hasattr(tool_call, 'caller') and tool_call.caller: + print(f"Programmatic call to {tool_call.function.name}") + print(f"Called from: {tool_call.caller}") + + # Simulate tool execution + if tool_call.function.name == "query_database": + args = json.loads(tool_call.function.arguments) + # Simulate database query + result = json.dumps([ + {"region": "West", "revenue": 150000}, + {"region": "East", "revenue": 180000}, + {"region": "Central", "revenue": 120000} + ]) + + messages.append({ + "role": "user", + "content": [{ + "type": "tool_result", + "tool_use_id": tool_call.id, + "content": result + }] + }) + +# Get final response +final_response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=messages, + tools=tools +) + +print("\nFinal answer:", final_response.choices[0].message.content) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: claude-4 + litellm_params: + model: anthropic/claude-opus-4-5-20251101 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [{ + "role": "user", + "content": "Query sales data for West, East, and Central regions, then tell me which had the highest revenue" + }], + "tools": [ + # Code execution tool (required for programmatic calling) + { + "type": "code_execution_20250825", + "name": "code_execution" + }, + # Tool that can be called from code + { + "type": "function", + "function": { + "name": "query_database", + "description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.", + "parameters": { + "type": "object", + "properties": { + "sql": { + "type": "string", + "description": "SQL query to execute" + } + }, + "required": ["sql"] + } + }, + "allowed_callers": ["code_execution_20250825"] # Enable programmatic calling + } + ] +} +' +``` + + + +--- + +## Tool Input Examples {#tool-input-examples} + +You can now provide Claude with examples of how to use your tools. [Learn more](https://platform.claude.com/docs/en/agents-and-tools/tool-use/tool-input-examples) + + + + + +```python +import litellm + +tools = [ + { + "type": "function", + "function": { + "name": "create_calendar_event", + "description": "Create a new calendar event with attendees and reminders", + "parameters": { + "type": "object", + "properties": { + "title": {"type": "string"}, + "start_time": { + "type": "string", + "description": "ISO 8601 format: YYYY-MM-DDTHH:MM:SS" + }, + "duration_minutes": {"type": "integer"}, + "attendees": { + "type": "array", + "items": { + "type": "object", + "properties": { + "email": {"type": "string"}, + "optional": {"type": "boolean"} + } + } + }, + "reminders": { + "type": "array", + "items": { + "type": "object", + "properties": { + "minutes_before": {"type": "integer"}, + "method": {"type": "string", "enum": ["email", "popup"]} + } + } + } + }, + "required": ["title", "start_time", "duration_minutes"] + } + }, + # Provide concrete examples + "input_examples": [ + { + "title": "Team Standup", + "start_time": "2025-01-15T09:00:00", + "duration_minutes": 30, + "attendees": [ + {"email": "alice@company.com", "optional": False}, + {"email": "bob@company.com", "optional": False} + ], + "reminders": [ + {"minutes_before": 15, "method": "popup"} + ] + }, + { + "title": "Lunch Break", + "start_time": "2025-01-15T12:00:00", + "duration_minutes": 60 + # Demonstrates optional fields can be omitted + } + ] + } +] + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[{ + "role": "user", + "content": "Schedule a team meeting for tomorrow at 2pm for 45 minutes with john@company.com and sarah@company.com" + }], + tools=tools +) + +print("Tool call:", response.choices[0].message.tool_calls[0].function.arguments) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: claude-4 + litellm_params: + model: anthropic/claude-opus-4-5-20251101 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [{ + "role": "user", + "content": "Schedule a team meeting for tomorrow at 2pm for 45 minutes with john@company.com and sarah@company.com" + }], + "tools": [ + { + "type": "function", + "function": { + "name": "create_calendar_event", + "description": "Create a new calendar event with attendees and reminders", + "parameters": { + "type": "object", + "properties": { + "title": {"type": "string"}, + "start_time": { + "type": "string", + "description": "ISO 8601 format: YYYY-MM-DDTHH:MM:SS" + }, + "duration_minutes": {"type": "integer"}, + "attendees": { + "type": "array", + "items": { + "type": "object", + "properties": { + "email": {"type": "string"}, + "optional": {"type": "boolean"} + } + } + }, + "reminders": { + "type": "array", + "items": { + "type": "object", + "properties": { + "minutes_before": {"type": "integer"}, + "method": {"type": "string", "enum": ["email", "popup"]} + } + } + } + }, + "required": ["title", "start_time", "duration_minutes"] + } + }, + # Provide concrete examples + "input_examples": [ + { + "title": "Team Standup", + "start_time": "2025-01-15T09:00:00", + "duration_minutes": 30, + "attendees": [ + {"email": "alice@company.com", "optional": False}, + {"email": "bob@company.com", "optional": False} + ], + "reminders": [ + {"minutes_before": 15, "method": "popup"} + ] + }, + { + "title": "Lunch Break", + "start_time": "2025-01-15T12:00:00", + "duration_minutes": 60 + # Demonstrates optional fields can be omitted + } + ] + } +] +} +' +``` + + + +--- + +## Effort Parameter: Control Token Usage {#effort-parameter} + +Control how much effort Claude puts into its response using the `reasoning_effort` parameter. This allows you to trade off between response thoroughness and token efficiency. + +:::info +LiteLLM automatically maps `reasoning_effort` to Anthropic's `output_config` format and adds the required `effort-2025-11-24` beta header for Claude Opus 4.5. +::: + +Potential values for `reasoning_effort` parameter: `"high"`, `"medium"`, `"low"`. + +### Usage Example + + + + +```python +import litellm + +message = "Analyze the trade-offs between microservices and monolithic architectures" + +# High effort (default) - Maximum capability +response_high = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{"role": "user", "content": message}], + reasoning_effort="high" +) + +print("High effort response:") +print(response_high.choices[0].message.content) +print(f"Tokens used: {response_high.usage.completion_tokens}\n") + +# Medium effort - Balanced approach +response_medium = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{"role": "user", "content": message}], + reasoning_effort="medium" +) + +print("Medium effort response:") +print(response_medium.choices[0].message.content) +print(f"Tokens used: {response_medium.usage.completion_tokens}\n") + +# Low effort - Maximum efficiency +response_low = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{"role": "user", "content": message}], + reasoning_effort="low" +) + +print("Low effort response:") +print(response_low.choices[0].message.content) +print(f"Tokens used: {response_low.usage.completion_tokens}\n") + +# Compare token usage +print("Token Comparison:") +print(f"High: {response_high.usage.completion_tokens} tokens") +print(f"Medium: {response_medium.usage.completion_tokens} tokens") +print(f"Low: {response_low.usage.completion_tokens} tokens") +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: claude-4 + litellm_params: + model: anthropic/claude-opus-4-5-20251101 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +2. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +3. Test it! + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--header 'Authorization: Bearer $LITELLM_KEY' \ +--data ' { + "model": "claude-4", + "messages": [{ + "role": "user", + "content": "Analyze the trade-offs between microservices and monolithic architectures" + }], + "reasoning_effort": "high" + } +' +``` + + diff --git a/docs/my-website/docs/a2a.md b/docs/my-website/docs/a2a.md new file mode 100644 index 00000000000..ef19e22dab3 --- /dev/null +++ b/docs/my-website/docs/a2a.md @@ -0,0 +1,204 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# /a2a - Agent Gateway (A2A Protocol) + +| Feature | Supported | +|---------|-----------| +| Logging | ✅ | +| Load Balancing | ✅ | +| Streaming | ✅ | + +:::tip + +LiteLLM follows the [A2A (Agent-to-Agent) Protocol](https://github.com/google/A2A) for invoking agents. + +::: + +## Adding your Agent + +You can add A2A-compatible agents through the LiteLLM Admin UI. + +1. Navigate to the **Agents** tab +2. Click **Add Agent** +3. Enter the agent name (e.g., `ij-local`) and the URL of your A2A agent + + + +The URL should be the invocation URL for your A2A agent (e.g., `http://localhost:10001`). + +## Invoking your Agents + +Use the [A2A Python SDK](https://pypi.org/project/a2a/) to invoke agents through LiteLLM: + +- `base_url`: Your LiteLLM proxy URL + `/a2a/{agent_name}` +- `headers`: Include your LiteLLM Virtual Key for authentication + +```python showLineNumbers title="invoke_a2a_agent.py" +from uuid import uuid4 +import httpx +import asyncio +from a2a.client import A2ACardResolver, A2AClient +from a2a.types import MessageSendParams, SendMessageRequest + +# === CONFIGURE THESE === +LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL +LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key +LITELLM_AGENT_NAME = "ij-local" # Agent name registered in LiteLLM +# ======================= + +async def main(): + base_url = f"{LITELLM_BASE_URL}/a2a/{LITELLM_AGENT_NAME}" + headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"} + + async with httpx.AsyncClient(headers=headers) as httpx_client: + # Resolve agent card and create client + resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url) + agent_card = await resolver.get_agent_card() + client = A2AClient(httpx_client=httpx_client, agent_card=agent_card) + + # Send a message + request = SendMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Hello, what can you do?"}], + "messageId": uuid4().hex, + } + ), + ) + response = await client.send_message(request) + print(response.model_dump(mode="json", exclude_none=True)) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +### Streaming Responses + +For streaming responses, use `send_message_streaming`: + +```python showLineNumbers title="invoke_a2a_agent_streaming.py" +from uuid import uuid4 +import httpx +import asyncio +from a2a.client import A2ACardResolver, A2AClient +from a2a.types import MessageSendParams, SendStreamingMessageRequest + +# === CONFIGURE THESE === +LITELLM_BASE_URL = "http://localhost:4000" # Your LiteLLM proxy URL +LITELLM_VIRTUAL_KEY = "sk-1234" # Your LiteLLM Virtual Key +LITELLM_AGENT_NAME = "ij-local" # Agent name registered in LiteLLM +# ======================= + +async def main(): + base_url = f"{LITELLM_BASE_URL}/a2a/{LITELLM_AGENT_NAME}" + headers = {"Authorization": f"Bearer {LITELLM_VIRTUAL_KEY}"} + + async with httpx.AsyncClient(headers=headers) as httpx_client: + # Resolve agent card and create client + resolver = A2ACardResolver(httpx_client=httpx_client, base_url=base_url) + agent_card = await resolver.get_agent_card() + client = A2AClient(httpx_client=httpx_client, agent_card=agent_card) + + # Send a streaming message + request = SendStreamingMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Hello, what can you do?"}], + "messageId": uuid4().hex, + } + ), + ) + + # Stream the response + async for chunk in client.send_message_streaming(request): + print(chunk.model_dump(mode="json", exclude_none=True)) + +if __name__ == "__main__": + asyncio.run(main()) +``` + +## Tracking Agent Logs + +After invoking an agent, you can view the request logs in the LiteLLM **Logs** tab. + +The logs show: +- **Request/Response content** sent to and received from the agent +- **User, Key, Team** information for tracking who made the request +- **Latency and cost** metrics + + + +## API Reference + +### Endpoint + +``` +POST /a2a/{agent_name}/message/send +``` + +### Authentication + +Include your LiteLLM Virtual Key in the `Authorization` header: + +``` +Authorization: Bearer sk-your-litellm-key +``` + +### Request Format + +LiteLLM follows the [A2A JSON-RPC 2.0 specification](https://github.com/google/A2A): + +```json title="Request Body" +{ + "jsonrpc": "2.0", + "id": "unique-request-id", + "method": "message/send", + "params": { + "message": { + "role": "user", + "parts": [{"kind": "text", "text": "Your message here"}], + "messageId": "unique-message-id" + } + } +} +``` + +### Response Format + +```json title="Response" +{ + "jsonrpc": "2.0", + "id": "unique-request-id", + "result": { + "kind": "task", + "id": "task-id", + "contextId": "context-id", + "status": {"state": "completed", "timestamp": "2025-01-01T00:00:00Z"}, + "artifacts": [ + { + "artifactId": "artifact-id", + "name": "response", + "parts": [{"kind": "text", "text": "Agent response here"}] + } + ] + } +} +``` + +## Agent Registry + +Want to create a central registry so your team can discover what agents are available within your company? + +Use the [AI Hub](./proxy/ai_hub) to make agents public and discoverable across your organization. This allows developers to browse available agents without needing to rebuild them. diff --git a/docs/my-website/docs/adding_provider/generic_guardrail_api.md b/docs/my-website/docs/adding_provider/generic_guardrail_api.md new file mode 100644 index 00000000000..f8c07b25f9d --- /dev/null +++ b/docs/my-website/docs/adding_provider/generic_guardrail_api.md @@ -0,0 +1,269 @@ +# [BETA] Generic Guardrail API - Integrate Without a PR + +## The Problem + +As a guardrail provider, integrating with LiteLLM traditionally requires: +- Making a PR to the LiteLLM repository +- Waiting for review and merge +- Maintaining provider-specific code in LiteLLM's codebase +- Updating the integration for changes to your API + +## The Solution + +The **Generic Guardrail API** lets you integrate with LiteLLM **instantly** by implementing a simple API endpoint. No PR required. + +### Key Benefits + +1. **No PR Needed** - Deploy and integrate immediately +2. **Universal Support** - Works across ALL LiteLLM endpoints (chat, embeddings, image generation, etc.) +3. **Simple Contract** - One endpoint, three response types +4. **Multi-Modal Support** - Handle both text and images in requests/responses +5. **Custom Parameters** - Pass provider-specific params via config +6. **Full Control** - You own and maintain your guardrail API + +## Supported Endpoints + +The Generic Guardrail API works with the following LiteLLM endpoints: + +- `/v1/chat/completions` - OpenAI Chat Completions +- `/v1/completions` - OpenAI Text Completions +- `/v1/responses` - OpenAI Responses API +- `/v1/images/generations` - OpenAI Image Generation +- `/v1/audio/transcriptions` - OpenAI Audio Transcriptions +- `/v1/audio/speech` - OpenAI Text-to-Speech +- `/v1/messages` - Anthropic Messages +- `/v1/rerank` - Cohere Rerank +- Pass-through endpoints + +## How It Works + +1. LiteLLM extracts text and images from any request (chat messages, embeddings, image prompts, etc.) +2. Sends extracted content + metadata to your API endpoint +3. Your API responds with: `BLOCKED`, `NONE`, or `GUARDRAIL_INTERVENED` +4. LiteLLM enforces the decision and applies any modifications + +## API Contract + +### Endpoint + +Implement `POST /beta/litellm_basic_guardrail_api` + +### Request Format + +```json +{ + "texts": ["extracted text from the request"], // array of text strings + "images": ["base64_encoded_image_data"], // optional array of images + "tools": [ // optional array of tools (OpenAI ChatCompletionToolParam format) + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + } + } + } + } + ], + "request_data": { + "user_api_key_hash": "hash of the litellm virtual key used", + "user_api_key_alias": "alias of the litellm virtual key used", + "user_api_key_user_id": "user id associated with the litellm virtual key used", + "user_api_key_user_email": "user email associated with the litellm virtual key used", + "user_api_key_team_id": "team id associated with the litellm virtual key used", + "user_api_key_team_alias": "team alias associated with the litellm virtual key used", + "user_api_key_end_user_id": "end user id associated with the litellm virtual key used", + "user_api_key_org_id": "org id associated with the litellm virtual key used" + }, + "input_type": "request", // "request" or "response" + "litellm_call_id": "unique_call_id", // the call id of the individual LLM call + "litellm_trace_id": "trace_id", // the trace id of the LLM call - useful if there are multiple LLM calls for the same conversation + "additional_provider_specific_params": { + // your custom params from config + } +} +``` + +### Response Format + +```json +{ + "action": "BLOCKED" | "NONE" | "GUARDRAIL_INTERVENED", + "blocked_reason": "why content was blocked", // required if action=BLOCKED + "texts": ["modified text"], // optional array of modified text strings + "images": ["modified_base64_image"] // optional array of modified images +} +``` + +**Actions:** +- `BLOCKED` - LiteLLM raises error and blocks request +- `NONE` - Request proceeds unchanged +- `GUARDRAIL_INTERVENED` - Request proceeds with modified texts/images (provide `texts` and/or `images` fields) + +## Parameters + +### `tools` Parameter + +The `tools` parameter provides information about available function/tool definitions in the request. + +**Format:** OpenAI `ChatCompletionToolParam` format (see [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools)) + +**Example:** +```json +{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "City and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + } +} +``` + +**Limitations:** +- **Input only:** Tools are only passed for `input_type="request"` (pre-call guardrails). Output/response guardrails do not currently receive tool information. +- **Supported endpoints:** The `tools` parameter is supported on: `/v1/chat/completions`, `/v1/responses`, and `/v1/messages`. Other endpoints do not have tool support. + +**Use cases:** +- Enforce tool permission policies (e.g., only allow certain users/teams to access specific tools) +- Validate tool schemas before sending to LLM +- Log tool usage for audit purposes +- Block sensitive tools based on user context + +## LiteLLM Configuration + +Add to `config.yaml`: + +```yaml +litellm_settings: + guardrails: + - guardrail_name: "my-guardrail" + litellm_params: + guardrail: generic_guardrail_api + mode: pre_call # or post_call, during_call + api_base: https://your-guardrail-api.com + api_key: os.environ/YOUR_GUARDRAIL_API_KEY # optional + additional_provider_specific_params: + # your custom parameters + threshold: 0.8 + language: "en" +``` + +## Usage + +Users apply your guardrail by name: + +```python +response = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "hello"}], + guardrails=["my-guardrail"] +) +``` + +Or with dynamic parameters: + +```python +response = client.chat.completions.create( + model="gpt-4", + messages=[{"role": "user", "content": "hello"}], + guardrails=[{ + "my-guardrail": { + "extra_body": { + "custom_threshold": 0.9 + } + } + }] +) +``` + +## Implementation Example + +See [mock_bedrock_guardrail_server.py](https://github.com/BerriAI/litellm/blob/main/cookbook/mock_guardrail_server/mock_bedrock_guardrail_server.py) for a complete reference implementation. + +**Minimal FastAPI example:** + +```python +from fastapi import FastAPI +from pydantic import BaseModel +from typing import List, Optional, Dict, Any + +app = FastAPI() + +class GuardrailRequest(BaseModel): + texts: List[str] + images: Optional[List[str]] = None + tools: Optional[List[Dict[str, Any]]] = None # OpenAI ChatCompletionToolParam format + request_data: Dict[str, Any] + input_type: str # "request" or "response" + litellm_call_id: Optional[str] = None + litellm_trace_id: Optional[str] = None + additional_provider_specific_params: Dict[str, Any] + +class GuardrailResponse(BaseModel): + action: str # BLOCKED, NONE, or GUARDRAIL_INTERVENED + blocked_reason: Optional[str] = None + texts: Optional[List[str]] = None + images: Optional[List[str]] = None + +@app.post("/beta/litellm_basic_guardrail_api") +async def apply_guardrail(request: GuardrailRequest): + # Your guardrail logic here + + # Example: Check text content + for text in request.texts: + if "badword" in text.lower(): + return GuardrailResponse( + action="BLOCKED", + blocked_reason="Content contains prohibited terms" + ) + + # Example: Check tools (if present in request) + if request.tools: + for tool in request.tools: + if tool.get("type") == "function": + function_name = tool.get("function", {}).get("name", "") + # Block sensitive tools + if function_name in ["delete_data", "access_admin_panel"]: + return GuardrailResponse( + action="BLOCKED", + blocked_reason=f"Tool '{function_name}' is not allowed" + ) + + return GuardrailResponse(action="NONE") +``` + +## When to Use This + +✅ **Use Generic Guardrail API when:** +- You want instant integration without waiting for PRs +- You maintain your own guardrail service +- You need full control over updates and features +- You want to support all LiteLLM endpoints automatically + +❌ **Make a PR when:** +- You want deeper integration with LiteLLM internals +- Your guardrail requires complex LiteLLM-specific logic +- You want to be featured as a built-in provider + +## Questions? + +This is a **beta API**. We're actively improving it based on feedback. Open an issue or PR if you need additional capabilities. + diff --git a/docs/my-website/docs/audio_transcription.md b/docs/my-website/docs/audio_transcription.md index fd55cc66e92..5853b5c1872 100644 --- a/docs/my-website/docs/audio_transcription.md +++ b/docs/my-website/docs/audio_transcription.md @@ -13,7 +13,7 @@ import TabItem from '@theme/TabItem'; | Fallbacks | ✅ | Works between supported models | | Loadbalancing | ✅ | Works between supported models | | Guardrails | ✅ | Applies to output transcribed text (non-streaming only) | -| Supported Providers | `openai`, `azure`, `vertex_ai`, `gemini`, `deepgram`, `groq`, `fireworks_ai` | | +| Supported Providers | `openai`, `azure`, `vertex_ai`, `gemini`, `deepgram`, `groq`, `fireworks_ai`, `ovhcloud` | | ## Quick Start @@ -126,6 +126,7 @@ transcript = client.audio.transcriptions.create( - [Fireworks AI](./providers/fireworks_ai.md#audio-transcription) - [Groq](./providers/groq.md#speech-to-text---whisper) - [Deepgram](./providers/deepgram.md) +- [OVHcloud AI Endpoints](./providers/ovhcloud.md) --- diff --git a/docs/my-website/docs/completion/image_generation_chat.md b/docs/my-website/docs/completion/image_generation_chat.md index 5538b7f8ff3..83488ac7ce8 100644 --- a/docs/my-website/docs/completion/image_generation_chat.md +++ b/docs/my-website/docs/completion/image_generation_chat.md @@ -224,8 +224,8 @@ asyncio.run(generate_image()) | Provider | Model | |----------|--------| -| Google AI Studio | `gemini/gemini-2.0-flash-preview-image-generation`, `gemini/gemini-2.5-flash-image-preview` | -| Vertex AI | `vertex_ai/gemini-2.0-flash-preview-image-generation`, `vertex_ai/gemini-2.5-flash-image-preview` | +| Google AI Studio | `gemini/gemini-2.0-flash-preview-image-generation`, `gemini/gemini-2.5-flash-image-preview`, `gemini/gemini-3-pro-image-preview` | +| Vertex AI | `vertex_ai/gemini-2.0-flash-preview-image-generation`, `vertex_ai/gemini-2.5-flash-image-preview`, `vertex_ai/gemini-3-pro-image-preview` | ## Spec diff --git a/docs/my-website/docs/completion/knowledgebase.md b/docs/my-website/docs/completion/knowledgebase.md index 3040f7f1cc0..fd6ef7a9982 100644 --- a/docs/my-website/docs/completion/knowledgebase.md +++ b/docs/my-website/docs/completion/knowledgebase.md @@ -20,6 +20,8 @@ LiteLLM integrates with vector stores, allowing your models to access your organ - [OpenAI Vector Stores](https://platform.openai.com/docs/api-reference/vector-stores/search) - [Azure Vector Stores](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/file-search?tabs=python#vector-stores) (Cannot be directly queried. Only available for calling in Assistants messages. We will be adding Azure AI Search Vector Store API support soon.) - [Vertex AI RAG API](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-overview) +- [Gemini File Search](https://ai.google.dev/gemini-api/docs/file-search) +- [RAGFlow Datasets](/docs/providers/ragflow_vector_store.md) (Dataset management only, search not supported) ## Quick Start diff --git a/docs/my-website/docs/contribute_integration/custom_webhook_api.md b/docs/my-website/docs/contribute_integration/custom_webhook_api.md new file mode 100644 index 00000000000..499c7fd51de --- /dev/null +++ b/docs/my-website/docs/contribute_integration/custom_webhook_api.md @@ -0,0 +1,106 @@ +# Contribute Custom Webhook API + +If your API just needs a Webhook event from LiteLLM, here's how to add a 'native' integration for it on LiteLLM: + +1. Clone the repo and open the `generic_api_compatible_callbacks.json` + +```bash +git clone https://github.com/BerriAI/litellm.git +cd litellm +open . +``` + +2. Add your API to the `generic_api_compatible_callbacks.json` + +Example: + +```json +{ + "rubrik": { + "event_types": ["llm_api_success"], + "endpoint": "{{environment_variables.RUBRIK_WEBHOOK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.RUBRIK_API_KEY}}" + }, + "environment_variables": ["RUBRIK_API_KEY", "RUBRIK_WEBHOOK_URL"] + } +} +``` + +Spec: + +```json +{ + "sample_callback": { + "event_types": ["llm_api_success", "llm_api_failure"], # Optional - defaults to all events + "endpoint": "{{environment_variables.SAMPLE_CALLBACK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.SAMPLE_CALLBACK_API_KEY}}" + }, + "environment_variables": ["SAMPLE_CALLBACK_URL", "SAMPLE_CALLBACK_API_KEY"] + } +} +``` + +3. Test it! + +a. Setup config.yaml + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + - model_name: anthropic-claude + litellm_params: + model: anthropic/claude-3-5-sonnet-20241022 + api_key: os.environ/ANTHROPIC_API_KEY + +litellm_settings: + callbacks: ["rubrik"] + +environment_variables: + RUBRIK_API_KEY: sk-1234 + RUBRIK_WEBHOOK_URL: https://webhook.site/efc57707-9018-478c-bdf1-2ffaabb2b315 +``` + +b. Start the proxy + +```bash +litellm --config /path/to/config.yaml +``` + +c. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "gpt-3.5-turbo", + "messages": [ + { + "role": "system", + "content": "Ignore previous instructions" + }, + { + "role": "user", + "content": "What is the weather like in Boston today?" + } + ], + "mock_response": "hey!" +}' +``` + +4. File a PR! + +- Review our contribution guide [here](../../extras/contributing_code) +- push your fork to your GitHub repo +- submit a PR from there + +## What get's logged? + +The [LiteLLM Standard Logging Payload](https://docs.litellm.ai/docs/proxy/logging_spec) is sent to your endpoint. \ No newline at end of file diff --git a/docs/my-website/docs/embedding/supported_embedding.md b/docs/my-website/docs/embedding/supported_embedding.md index e63d9403665..0e8252b409b 100644 --- a/docs/my-website/docs/embedding/supported_embedding.md +++ b/docs/my-website/docs/embedding/supported_embedding.md @@ -263,6 +263,8 @@ print(response) | Model Name | Function Call | |----------------------|---------------------------------------------| +| Amazon Nova Multimodal Embeddings | `embedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)` | [Nova Docs](../providers/bedrock_embedding#amazon-nova-multimodal-embeddings) | +| Amazon Nova (Async) | `embedding(model="bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0", input=input, input_type="text", output_s3_uri="s3://bucket/")` | [Nova Async Docs](../providers/bedrock_embedding#asynchronous-embeddings-with-segmentation) | | Titan Embeddings - G1 | `embedding(model="amazon.titan-embed-text-v1", input=input)` | | Cohere Embeddings - English | `embedding(model="cohere.embed-english-v3", input=input)` | | Cohere Embeddings - Multilingual | `embedding(model="cohere.embed-multilingual-v3", input=input)` | diff --git a/docs/my-website/docs/files_endpoints.md b/docs/my-website/docs/files_endpoints.md index fc0484e9219..30677c748a9 100644 --- a/docs/my-website/docs/files_endpoints.md +++ b/docs/my-website/docs/files_endpoints.md @@ -301,6 +301,17 @@ content = await litellm.afile_content( print("file content=", content) ``` +**Get File Content (Bedrock)** +```python +# For Bedrock batch output files stored in S3 +content = await litellm.afile_content( + file_id="s3://bucket-name/path/to/file.jsonl", # S3 URI or unified file ID + custom_llm_provider="bedrock", + aws_region_name="us-west-2" +) +print("file content=", content.text) +``` + @@ -313,4 +324,6 @@ print("file content=", content) ### [Vertex AI](./providers/vertex#batch-apis) +### [Bedrock](./providers/bedrock_batches#4-retrieve-batch-results) + ## [Swagger API Reference](https://litellm-api.up.railway.app/#/files) diff --git a/docs/my-website/docs/getting_started.md b/docs/my-website/docs/getting_started.md deleted file mode 100644 index 6b2c1fd531e..00000000000 --- a/docs/my-website/docs/getting_started.md +++ /dev/null @@ -1,108 +0,0 @@ -# Getting Started - -import QuickStart from '../src/components/QuickStart.js' - -LiteLLM simplifies LLM API calls by mapping them all to the [OpenAI ChatCompletion format](https://platform.openai.com/docs/api-reference/chat). - -## basic usage - -By default we provide a free $10 community-key to try all providers supported on LiteLLM. - -```python -from litellm import completion - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "your-api-key" -os.environ["COHERE_API_KEY"] = "your-api-key" - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -# openai call -response = completion(model="gpt-3.5-turbo", messages=messages) - -# cohere call -response = completion("command-nightly", messages) -``` - -**Need a dedicated key?** -Email us @ krrish@berri.ai - -Next Steps 👉 [Call all supported models - e.g. Claude-2, Llama2-70b, etc.](./proxy_api.md#supported-models) - -More details 👉 - -- [Completion() function details](./completion/) -- [Overview of supported models / providers on LiteLLM](./providers/) -- [Search all models / providers](https://models.litellm.ai/) -- [Build your own OpenAI proxy](https://github.com/BerriAI/liteLLM-proxy/tree/main) - -## streaming - -Same example from before. Just pass in `stream=True` in the completion args. - -```python -from litellm import completion - -## set ENV variables -os.environ["OPENAI_API_KEY"] = "openai key" -os.environ["COHERE_API_KEY"] = "cohere key" - -messages = [{ "content": "Hello, how are you?","role": "user"}] - -# openai call -response = completion(model="gpt-3.5-turbo", messages=messages, stream=True) - -# cohere call -response = completion("command-nightly", messages, stream=True) - -print(response) -``` - -More details 👉 - -- [streaming + async](./completion/stream.md) -- [tutorial for streaming Llama2 on TogetherAI](./tutorials/TogetherAI_liteLLM.md) - -## exception handling - -LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM. - -```python -from openai.error import OpenAIError -from litellm import completion - -os.environ["ANTHROPIC_API_KEY"] = "bad-key" -try: - # some code - completion(model="claude-instant-1", messages=[{"role": "user", "content": "Hey, how's it going?"}]) -except OpenAIError as e: - print(e) -``` - -## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks)) - -LiteLLM exposes pre defined callbacks to send data to MLflow, Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack - -```python -from litellm import completion - -## set env variables for logging tools (API key set up is not required when using MLflow) -os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key" # get your public key at https://app.lunary.ai/settings -os.environ["HELICONE_API_KEY"] = "your-helicone-key" -os.environ["LANGFUSE_PUBLIC_KEY"] = "" -os.environ["LANGFUSE_SECRET_KEY"] = "" - -os.environ["OPENAI_API_KEY"] - -# set callbacks -litellm.success_callback = ["lunary", "mlflow", "langfuse", "helicone"] # log input/output to MLflow, langfuse, lunary, helicone - -#openai call -response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}]) -``` - -More details 👉 - -- [exception mapping](./exception_mapping.md) -- [retries + model fallbacks for completion()](./completion/reliable_completions.md) -- [tutorial for model fallbacks with completion()](./tutorials/fallbacks.md) diff --git a/docs/my-website/docs/observability/datadog.md b/docs/my-website/docs/observability/datadog.md index 5cb5ab3af2d..b2901650ea6 100644 --- a/docs/my-website/docs/observability/datadog.md +++ b/docs/my-website/docs/observability/datadog.md @@ -71,17 +71,19 @@ DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source. use to different Send logs through a local DataDog agent (useful for containerized environments): ```shell -DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent -DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518) -DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth) -DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source +LITELLM_DD_AGENT_HOST="localhost" # hostname or IP of DataDog agent +LITELLM_DD_AGENT_PORT="10518" # [OPTIONAL] port of DataDog agent (default: 10518) +DD_API_KEY="5f2d0f310***********" # [OPTIONAL] your datadog API Key (agent handles auth) +DD_SOURCE="litellm_dev" # [OPTIONAL] your datadog source ``` -When `DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for: +When `LITELLM_DD_AGENT_HOST` is set, logs are sent to the agent instead of directly to DataDog API. This is useful for: - Centralized log shipping in containerized environments - Reducing direct API calls from multiple services - Leveraging agent-side processing and filtering +**Note:** We use `LITELLM_DD_AGENT_HOST` instead of `DD_AGENT_HOST` to avoid conflicts with `ddtrace` which automatically sets `DD_AGENT_HOST` for APM tracing. + **Step 3**: Start the proxy, make a test request Start proxy @@ -191,8 +193,8 @@ LiteLLM supports customizing the following Datadog environment variables |---------------------|-------------|---------------|----------| | `DD_API_KEY` | Your Datadog API key for authentication (required for direct API, optional for agent) | None | Conditional* | | `DD_SITE` | Your Datadog site (e.g., "us5.datadoghq.com") (required for direct API) | None | Conditional* | -| `DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No | -| `DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No | +| `LITELLM_DD_AGENT_HOST` | Hostname or IP of DataDog agent (e.g., "localhost"). When set, logs are sent to agent instead of direct API | None | ❌ No | +| `LITELLM_DD_AGENT_PORT` | Port of DataDog agent for log intake | "10518" | ❌ No | | `DD_ENV` | Environment tag for your logs (e.g., "production", "staging") | "unknown" | ❌ No | | `DD_SERVICE` | Service name for your logs | "litellm-server" | ❌ No | | `DD_SOURCE` | Source name for your logs | "litellm" | ❌ No | @@ -201,5 +203,5 @@ LiteLLM supports customizing the following Datadog environment variables | `POD_NAME` | Pod name tag (useful for Kubernetes deployments) | "unknown" | ❌ No | \* **Required when using Direct API** (default): `DD_API_KEY` and `DD_SITE` are required -\* **Optional when using DataDog Agent**: Set `DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required +\* **Optional when using DataDog Agent**: Set `LITELLM_DD_AGENT_HOST` to use agent mode; `DD_API_KEY` and `DD_SITE` are not required diff --git a/docs/my-website/docs/observability/generic_api.md b/docs/my-website/docs/observability/generic_api.md new file mode 100644 index 00000000000..2d1a24c317b --- /dev/null +++ b/docs/my-website/docs/observability/generic_api.md @@ -0,0 +1,110 @@ +# Generic API Callback (Webhook) + +Send LiteLLM logs to any HTTP endpoint. + +## Quick Start + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: openai/gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + +litellm_settings: + callbacks: ["custom_api_name"] + +callback_settings: + custom_api_name: + callback_type: generic_api + endpoint: https://your-endpoint.com/logs + headers: + Authorization: Bearer sk-1234 +``` + +## Configuration + +### Basic Setup + +```yaml +callback_settings: + : + callback_type: generic_api + endpoint: https://your-endpoint.com # required + headers: # optional + Authorization: Bearer + Custom-Header: value + event_types: # optional, defaults to all events + - llm_api_success + - llm_api_failure +``` + +### Parameters + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `callback_type` | string | Yes | Must be `generic_api` | +| `endpoint` | string | Yes | HTTP endpoint to send logs to | +| `headers` | dict | No | Custom headers for the request | +| `event_types` | list | No | Filter events: `llm_api_success`, `llm_api_failure`. Defaults to all events. | + +## Pre-configured Callbacks + +Use built-in configurations from `generic_api_compatible_callbacks.json`: + +```yaml +litellm_settings: + callbacks: ["rubrik"] # loads pre-configured settings + +callback_settings: + rubrik: + callback_type: generic_api + endpoint: https://your-endpoint.com # override defaults + headers: + Authorization: Bearer ${RUBRIK_API_KEY} +``` + +## Payload Format + +Logs are sent as `StandardLoggingPayload` [objects](https://docs.litellm.ai/docs/proxy/logging_spec) in JSON format: + +```json +[ + { + "id": "chatcmpl-123", + "call_type": "litellm.completion", + "model": "gpt-3.5-turbo", + "messages": [...], + "response": {...}, + "usage": {...}, + "cost": 0.0001, + "startTime": "2024-01-01T00:00:00", + "endTime": "2024-01-01T00:00:01", + "metadata": {...} + } +] +``` + +## Environment Variables + +Set via environment variables instead of config: + +```bash +export GENERIC_LOGGER_ENDPOINT=https://your-endpoint.com +export GENERIC_LOGGER_HEADERS="Authorization=Bearer token,Custom-Header=value" +``` + +## Batch Settings + +Control batching behavior (inherits from `CustomBatchLogger`): + +```yaml +callback_settings: + my_api: + callback_type: generic_api + endpoint: https://your-endpoint.com + batch_size: 100 # default: 100 + flush_interval: 60 # seconds, default: 60 +``` + + diff --git a/docs/my-website/docs/observability/phoenix_integration.md b/docs/my-website/docs/observability/phoenix_integration.md index ad337439934..898d780668d 100644 --- a/docs/my-website/docs/observability/phoenix_integration.md +++ b/docs/my-website/docs/observability/phoenix_integration.md @@ -6,7 +6,7 @@ Open source tracing and evaluation platform :::tip -This is community maintained, Please make an issue if you run into a bug +This is community maintained. Please make an issue if you run into a bug: https://github.com/BerriAI/litellm ::: @@ -31,19 +31,16 @@ litellm.callbacks = ["arize_phoenix"] import litellm import os -os.environ["PHOENIX_API_KEY"] = "" # Necessary only using Phoenix Cloud -os.environ["PHOENIX_COLLECTOR_HTTP_ENDPOINT"] = "" # The URL of your Phoenix OSS instance e.g. http://localhost:6006/v1/traces -os.environ["PHOENIX_PROJECT_NAME"]="litellm" # OPTIONAL: you can configure project names, otherwise traces would go to "default" project +# Set env variables +os.environ["PHOENIX_API_KEY"] = "d0*****" # Set the Phoenix API key here. It is necessary only when using Phoenix Cloud. +os.environ["PHOENIX_COLLECTOR_HTTP_ENDPOINT"] = "https://app.phoenix.arize.com/s//v1/traces" # Set the URL of your Phoenix OSS instance, otherwise tracer would use https://app.phoenix.arize.com/v1/traces for Phoenix Cloud. +os.environ["PHOENIX_PROJECT_NAME"] = "litellm" # Configure the project name, otherwise traces would go to "default" project. +os.environ['OPENAI_API_KEY'] = "fake-key" # Set the OpenAI API key here. -# This defaults to https://app.phoenix.arize.com/v1/traces for Phoenix Cloud - -# LLM API Keys -os.environ['OPENAI_API_KEY']="" - -# set arize as a callback, litellm will send the data to arize +# Set arize_phoenix as a callback & LiteLLM will send the data to Phoenix. litellm.callbacks = ["arize_phoenix"] - -# openai call + +# OpenAI call response = litellm.completion( model="gpt-3.5-turbo", messages=[ @@ -52,8 +49,9 @@ response = litellm.completion( ) ``` -### Using with LiteLLM Proxy +## Using with LiteLLM Proxy +1. Setup config.yaml ```yaml model_list: @@ -66,12 +64,63 @@ model_list: litellm_settings: callbacks: ["arize_phoenix"] +general_settings: + master_key: "sk-1234" + environment_variables: PHOENIX_API_KEY: "d0*****" - PHOENIX_COLLECTOR_ENDPOINT: "https://app.phoenix.arize.com/v1/traces" # OPTIONAL, for setting the GRPC endpoint - PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/v1/traces" # OPTIONAL, for setting the HTTP endpoint + PHOENIX_COLLECTOR_ENDPOINT: "https://app.phoenix.arize.com/s//v1/traces" # OPTIONAL - For setting the gRPC endpoint + PHOENIX_COLLECTOR_HTTP_ENDPOINT: "https://app.phoenix.arize.com/s//v1/traces" # OPTIONAL - For setting the HTTP endpoint ``` +2. Start the proxy + +```bash +litellm --config config.yaml +``` + +3. Test it! + +```bash +curl -X POST 'http://0.0.0.0:4000/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ "model": "gpt-4o", "messages": [{"role": "user", "content": "Hi 👋 - i'm openai"}]}' +``` + +## Supported Phoenix Endpoints +Phoenix now supports multiple deployment types. The correct endpoint depends on which version of Phoenix Cloud you are using. + +**Phoenix Cloud (With Spaces - New Version)** +Use this if your Phoenix URL contains `/s/` path. + +```bash +https://app.phoenix.arize.com/s//v1/traces +``` + +**Phoenix Cloud (Legacy - Deprecated)** +Use this only if your deployment still shows the `/legacy` pattern. + +```bash +https://app.phoenix.arize.com/legacy/v1/traces +``` + +**Phoenix Cloud (Without Spaces - Old Version)** +Use this if your Phoenix Cloud URL does not contain `/s/` or `/legacy` path. + +```bash +https://app.phoenix.arize.com/v1/traces +``` + +**Self-Hosted Phoenix (Local Instance)** +Use this when running Phoenix on your machine or a private server. + +```bash +http://localhost:6006/v1/traces +``` + +Depending on which Phoenix Cloud version or deployment you are using, you should set the corresponding endpoint in `PHOENIX_COLLECTOR_HTTP_ENDPOINT` or `PHOENIX_COLLECTOR_ENDPOINT`. + ## Support & Talk to Founders - [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) diff --git a/docs/my-website/docs/projects/Agent Lightning.md b/docs/my-website/docs/projects/Agent Lightning.md new file mode 100644 index 00000000000..28e5546e398 --- /dev/null +++ b/docs/my-website/docs/projects/Agent Lightning.md @@ -0,0 +1,10 @@ + +# Agent Lightning + +[Agent Lightning](https://github.com/microsoft/agent-lightning) is Microsoft's open-source framework for training and optimizing AI agents with Reinforcement Learning, Automatic Prompt Optimization, and Supervised Fine-tuning — with almost zero code changes. + +It works with any agent framework including LangChain, OpenAI Agents SDK, AutoGen, and CrewAI. Agent Lightning uses LiteLLM Proxy under the hood to route LLM requests and collect traces that power its training algorithms. + +- [GitHub](https://github.com/microsoft/agent-lightning) +- [Docs](https://microsoft.github.io/agent-lightning/) +- [arXiv Paper](https://arxiv.org/abs/2508.03680) diff --git a/docs/my-website/docs/projects/Google ADK.md b/docs/my-website/docs/projects/Google ADK.md new file mode 100644 index 00000000000..25e910dcbad --- /dev/null +++ b/docs/my-website/docs/projects/Google ADK.md @@ -0,0 +1,21 @@ + +# Google ADK (Agent Development Kit) + +[Google ADK](https://github.com/google/adk-python) is an open-source, code-first Python framework for building, evaluating, and deploying sophisticated AI agents. While optimized for Gemini, ADK is model-agnostic and supports LiteLLM for using 100+ providers. + +```python +from google.adk.agents.llm_agent import Agent +from google.adk.models.lite_llm import LiteLlm + +root_agent = Agent( + model=LiteLlm(model="openai/gpt-4o"), # Or any LiteLLM-supported model + name="my_agent", + description="An agent using LiteLLM", + instruction="You are a helpful assistant.", + tools=[your_tools], +) +``` + +- [GitHub](https://github.com/google/adk-python) +- [Documentation](https://google.github.io/adk-docs) +- [LiteLLM Samples](https://github.com/google/adk-python/tree/main/contributing/samples/hello_world_litellm) diff --git a/docs/my-website/docs/projects/Harbor.md b/docs/my-website/docs/projects/Harbor.md new file mode 100644 index 00000000000..684dfa93720 --- /dev/null +++ b/docs/my-website/docs/projects/Harbor.md @@ -0,0 +1,24 @@ + +# Harbor + +[Harbor](https://github.com/laude-institute/harbor) is a framework from the creators of Terminal-Bench for evaluating and optimizing agents and language models. It uses LiteLLM to call 100+ LLM providers. + +```bash +# Install +pip install harbor + +# Run a benchmark with any LiteLLM-supported model +harbor run --dataset terminal-bench@2.0 \ + --agent claude-code \ + --model anthropic/claude-opus-4-1 \ + --n-concurrent 4 +``` + +Key features: +- Evaluate agents like Claude Code, OpenHands, Codex CLI +- Build and share benchmarks and environments +- Run experiments in parallel across cloud providers (Daytona, Modal) +- Generate rollouts for RL optimization + +- [GitHub](https://github.com/laude-institute/harbor) +- [Documentation](https://harborframework.com/docs) diff --git a/docs/my-website/docs/projects/openai-agents.md b/docs/my-website/docs/projects/openai-agents.md new file mode 100644 index 00000000000..95a2191b883 --- /dev/null +++ b/docs/my-website/docs/projects/openai-agents.md @@ -0,0 +1,22 @@ + +# OpenAI Agents SDK + +The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) is a lightweight framework for building multi-agent workflows. +It includes an official LiteLLM extension that lets you use any of the 100+ supported providers (Anthropic, Gemini, Mistral, Bedrock, etc.) + +```python +from agents import Agent, Runner +from agents.extensions.models.litellm_model import LitellmModel + +agent = Agent( + name="Assistant", + instructions="You are a helpful assistant.", + model=LitellmModel(model="provider/model-name") +) + +result = Runner.run_sync(agent, "your_prompt_here") +print("Result:", result.final_output) +``` + +- [GitHub](https://github.com/openai/openai-agents-python) +- [LiteLLM Extension Docs](https://openai.github.io/openai-agents-python/ref/extensions/litellm/) diff --git a/docs/my-website/docs/providers/anthropic.md b/docs/my-website/docs/providers/anthropic.md index 45edd184d5d..f78af51bd90 100644 --- a/docs/my-website/docs/providers/anthropic.md +++ b/docs/my-website/docs/providers/anthropic.md @@ -18,11 +18,11 @@ LiteLLM supports all anthropic models. | Property | Details | |-------|-------| -| Description | Claude is a highly performant, trustworthy, and intelligent AI platform built by Anthropic. Claude excels at tasks involving language, reasoning, analysis, coding, and more. | -| Provider Route on LiteLLM | `anthropic/` (add this prefix to the model name, to route any requests to Anthropic - e.g. `anthropic/claude-3-5-sonnet-20240620`) | -| Provider Doc | [Anthropic ↗](https://docs.anthropic.com/en/docs/build-with-claude/overview) | -| API Endpoint for Provider | https://api.anthropic.com | -| Supported Endpoints | `/chat/completions` | +| Description | Claude is a highly performant, trustworthy, and intelligent AI platform built by Anthropic. Claude excels at tasks involving language, reasoning, analysis, coding, and more. Also available via Azure Foundry. | +| Provider Route on LiteLLM | `anthropic/` (add this prefix to the model name, to route any requests to Anthropic - e.g. `anthropic/claude-3-5-sonnet-20240620`). For Azure Foundry deployments, use `azure/claude-*` (see [Azure Anthropic documentation](../providers/azure/azure_anthropic)) | +| Provider Doc | [Anthropic ↗](https://docs.anthropic.com/en/docs/build-with-claude/overview), [Azure Foundry Claude ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude) | +| API Endpoint for Provider | https://api.anthropic.com (or Azure Foundry endpoint: `https://.services.ai.azure.com/anthropic`) | +| Supported Endpoints | `/chat/completions`, `/v1/messages` (passthrough) | ## Supported OpenAI Parameters @@ -41,7 +41,8 @@ Check this in code, [here](../completion/input.md#translated-openai-params) "extra_headers", "parallel_tool_calls", "response_format", -"user" +"user", +"reasoning_effort", ``` :::info @@ -49,6 +50,7 @@ Check this in code, [here](../completion/input.md#translated-openai-params) **Notes:** - Anthropic API fails requests when `max_tokens` are not passed. Due to this litellm passes `max_tokens=4096` when no `max_tokens` are passed. - `response_format` is fully supported for Claude Sonnet 4.5 and Opus 4.1 models (see [Structured Outputs](#structured-outputs) section) +- `reasoning_effort` is automatically mapped to `output_config={"effort": ...}` for Claude Opus 4.5 models (see [Effort Parameter](./anthropic_effort.md)) ::: @@ -163,6 +165,22 @@ os.environ["ANTHROPIC_API_KEY"] = "your-api-key" # os.environ["LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX"] = "true" # [OPTIONAL] Disable automatic URL suffix appending ``` +:::tip Azure Foundry Support + +Claude models are also available via Microsoft Azure Foundry. Use the `azure/` prefix instead of `anthropic/` and configure Azure authentication. See the [Azure Anthropic documentation](../providers/azure/azure_anthropic) for details. + +Example: +```python +response = completion( + model="azure/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + api_key="your-azure-api-key", + messages=[{"role": "user", "content": "Hello!"}] +) +``` + +::: + ### Custom API Base When using a custom API base for Anthropic (e.g., a proxy or custom endpoint), LiteLLM automatically appends the appropriate suffix (`/v1/messages` or `/v1/complete`) to your base URL. @@ -183,6 +201,30 @@ Without `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX`: With `LITELLM_ANTHROPIC_DISABLE_URL_SUFFIX=true`: - Base URL `https://my-proxy.com/custom/path` → `https://my-proxy.com/custom/path` (unchanged) +### Azure AI Foundry (Alternative Method) + +:::tip Recommended Method +For full Azure support including Azure AD authentication, use the dedicated [Azure Anthropic provider](./azure/azure_anthropic) with `azure_ai/` prefix. +::: + +As an alternative, you can use the `anthropic/` provider directly with your Azure endpoint since Azure exposes Claude using Anthropic's native API. + +```python +from litellm import completion + +response = completion( + model="anthropic/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + api_key="", + messages=[{"role": "user", "content": "Hello!"}], +) +print(response) +``` + +:::info +**Finding your Azure endpoint:** Go to Azure AI Foundry → Your deployment → Overview. Your base URL will be `https://.services.ai.azure.com/anthropic` +::: + ## Usage ```python diff --git a/docs/my-website/docs/providers/anthropic_effort.md b/docs/my-website/docs/providers/anthropic_effort.md new file mode 100644 index 00000000000..e4bfd50e6c2 --- /dev/null +++ b/docs/my-website/docs/providers/anthropic_effort.md @@ -0,0 +1,286 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Anthropic Effort Parameter + +Control how many tokens Claude uses when responding with the `effort` parameter, trading off between response thoroughness and token efficiency. + +## Overview + +The `effort` parameter allows you to control how eager Claude is about spending tokens when responding to requests. This gives you the ability to trade off between response thoroughness and token efficiency, all with a single model. + +**Note**: The effort parameter is currently in beta and only supported by Claude Opus 4.5. LiteLLM automatically adds the `effort-2025-11-24` beta header when: +- `reasoning_effort` parameter is provided (for Claude Opus 4.5 only) + +For Claude Opus 4.5, `reasoning_effort="medium"`—both are automatically mapped to the correct format. + +## How Effort Works + +By default, Claude uses maximum effort—spending as many tokens as needed for the best possible outcome. By lowering the effort level, you can instruct Claude to be more conservative with token usage, optimizing for speed and cost while accepting some reduction in capability. + +**Tip**: Setting `effort` to `"high"` produces exactly the same behavior as omitting the `effort` parameter entirely. + +The effort parameter affects **all tokens** in the response, including: +- Text responses and explanations +- Tool calls and function arguments +- Extended thinking (when enabled) + +This approach has two major advantages: +1. It doesn't require thinking to be enabled in order to use it. +2. It can affect all token spend including tool calls. For example, lower effort would mean Claude makes fewer tool calls. + +This gives a much greater degree of control over efficiency. + +## Effort Levels + +| Level | Description | Typical use case | +|-------|-------------|------------------| +| `high` | Maximum capability—Claude uses as many tokens as needed for the best possible outcome. Equivalent to not setting the parameter. | Complex reasoning, difficult coding problems, agentic tasks | +| `medium` | Balanced approach with moderate token savings. | Agentic tasks that require a balance of speed, cost, and performance | +| `low` | Most efficient—significant token savings with some capability reduction. | Simpler tasks that need the best speed and lowest costs, such as subagents | + +## Quick Start + +### Using LiteLLM SDK + + + + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{ + "role": "user", + "content": "Analyze the trade-offs between microservices and monolithic architectures" + }], + reasoning_effort="medium" # Automatically mapped to output_config for Opus 4.5 +) + +print(response.choices[0].message.content) +``` + + + + +```typescript +import Anthropic from "@anthropic-ai/sdk"; + +const client = new Anthropic({ + apiKey: process.env.ANTHROPIC_API_KEY, +}); + +const response = await client.messages.create({ + model: "claude-opus-4-5-20251101", + max_tokens: 4096, + messages: [{ + role: "user", + content: "Analyze the trade-offs between microservices and monolithic architectures" + }], + output_config: { + effort: "medium" + } +}); + +console.log(response.content[0].text); +``` + + + + +### Using LiteLLM Proxy + +```bash +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_API_KEY" \ + -d '{ + "model": "anthropic/claude-opus-4-5-20251101", + "messages": [{ + "role": "user", + "content": "Analyze the trade-offs between microservices and monolithic architectures" + }], + "output_config": { + "effort": "medium" + } + }' +``` + +### Direct Anthropic API Call + +```bash +curl https://api.anthropic.com/v1/messages \ + --header "x-api-key: $ANTHROPIC_API_KEY" \ + --header "anthropic-version: 2023-06-01" \ + --header "anthropic-beta: effort-2025-11-24" \ + --header "content-type: application/json" \ + --data '{ + "model": "claude-opus-4-5-20251101", + "max_tokens": 4096, + "messages": [{ + "role": "user", + "content": "Analyze the trade-offs between microservices and monolithic architectures" + }], + "output_config": { + "effort": "medium" + } + }' +``` + +## Model Compatibility + +The effort parameter is currently only supported by: +- **Claude Opus 4.5** (`claude-opus-4-5-20251101`) + +## When Should I Adjust the Effort Parameter? + +- Use **high effort** (the default) when you need Claude's best work—complex reasoning, nuanced analysis, difficult coding problems, or any task where quality is the top priority. + +- Use **medium effort** as a balanced option when you want solid performance without the full token expenditure of high effort. + +- Use **low effort** when you're optimizing for speed (because Claude answers with fewer tokens) or cost—for example, simple classification tasks, quick lookups, or high-volume use cases where marginal quality improvements don't justify additional latency or spend. + +## Effort with Tool Use + +When using tools, the effort parameter affects both the explanations around tool calls and the tool calls themselves. Lower effort levels tend to: +- Combine multiple operations into fewer tool calls +- Make fewer tool calls +- Proceed directly to action + +Example with tools: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{ + "role": "user", + "content": "Check the weather in multiple cities" + }], + tools=[{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather for a location", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + }, + "required": ["location"] + } + } + }], + output_config={ + "effort": "low" # Will make fewer tool calls + } +) +``` + +## Effort with Extended Thinking + +The effort parameter works seamlessly with extended thinking. When both are enabled, effort controls the token budget across all response types: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{ + "role": "user", + "content": "Solve this complex problem" + }], + thinking={ + "type": "enabled", + "budget_tokens": 5000 + }, + output_config={ + "effort": "medium" # Affects both thinking and response tokens + } +) +``` + +## Best Practices + +1. **Start with the default (high)** for new tasks, then experiment with lower effort levels if you're looking to optimize costs. + +2. **Use medium effort for production agentic workflows** where you need a balance of quality and efficiency. + +3. **Reserve low effort for high-volume, simple tasks** like classification, routing, or data extraction where speed matters more than nuanced responses. + +4. **Monitor token usage** to understand the actual savings from different effort levels for your specific use cases. + +5. **Test with your specific prompts** as the impact of effort levels can vary based on task complexity. + +## Provider Support + +The effort parameter is supported across all Anthropic-compatible providers: + +- **Standard Anthropic API**: ✅ Supported (Claude Opus 4.5) +- **Azure Anthropic / Microsoft Foundry**: ✅ Supported (Claude Opus 4.5) +- **Amazon Bedrock**: ✅ Supported (Claude Opus 4.5) +- **Google Cloud Vertex AI**: ✅ Supported (Claude Opus 4.5) + +LiteLLM automatically handles: +- Beta header injection (`effort-2025-11-24`) for all providers +- Parameter mapping: `reasoning_effort` → `output_config={"effort": ...}` for Claude Opus 4.5 + +## Usage and Pricing + +Token usage with different effort levels is tracked in the standard usage object. Lower effort levels result in fewer output tokens, which directly reduces costs: + +```python +response = litellm.completion( + model="anthropic/claude-opus-4-5-20251101", + messages=[{"role": "user", "content": "Analyze this"}], + output_config={"effort": "low"} +) + +print(f"Output tokens: {response.usage.completion_tokens}") +print(f"Total tokens: {response.usage.total_tokens}") +``` + +## Troubleshooting + +### Beta header not being added + +LiteLLM automatically adds the `effort-2025-11-24` beta header when: +- `reasoning_effort` parameter is provided (for Claude Opus 4.5 only) + +If you're not seeing the header: + +1. Ensure you're using `reasoning_effort` parameter +2. Verify the model is Claude Opus 4.5 +3. Check that LiteLLM version supports this feature + +### Invalid effort value error + +Only three values are accepted: `"high"`, `"medium"`, `"low"`. Any other value will raise a validation error: + +```python +# ❌ This will raise an error +output_config={"effort": "very_low"} + +# ✅ Use one of the valid values +output_config={"effort": "low"} +``` + +### Model not supported + +Currently, only Claude Opus 4.5 supports the effort parameter. Using it with other models may result in the parameter being ignored or an error. + +## Related Features + +- [Extended Thinking](/docs/providers/anthropic_extended_thinking) - Control Claude's reasoning process +- [Tool Use](/docs/providers/anthropic_tools) - Enable Claude to use tools and functions +- [Programmatic Tool Calling](/docs/providers/anthropic_programmatic_tool_calling) - Let Claude write code that calls tools +- [Prompt Caching](/docs/providers/anthropic_prompt_caching) - Cache prompts to reduce costs + +## Additional Resources + +- [Anthropic Effort Documentation](https://docs.anthropic.com/en/docs/build-with-claude/effort) +- [LiteLLM Anthropic Provider Guide](/docs/providers/anthropic) +- [Cost Optimization Best Practices](/docs/guides/cost_optimization) + diff --git a/docs/my-website/docs/providers/anthropic_programmatic_tool_calling.md b/docs/my-website/docs/providers/anthropic_programmatic_tool_calling.md new file mode 100644 index 00000000000..574dd7b0935 --- /dev/null +++ b/docs/my-website/docs/providers/anthropic_programmatic_tool_calling.md @@ -0,0 +1,435 @@ +# Anthropic Programmatic Tool Calling + +Programmatic tool calling allows Claude to write code that calls your tools programmatically within a code execution container, rather than requiring round trips through the model for each tool invocation. This reduces latency for multi-tool workflows and decreases token consumption by allowing Claude to filter or process data before it reaches the model's context window. + +:::info +Programmatic tool calling is currently in public beta. LiteLLM automatically detects tools with the `allowed_callers` field and adds the appropriate beta header based on your provider: + +- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20` +- **Amazon Bedrock**: `advanced-tool-use-2025-11-20` +- **Google Cloud Vertex AI**: Not supported + +This feature requires the code execution tool to be enabled. +::: + +## Model Compatibility + +Programmatic tool calling is available on the following models: + +| Model | Tool Version | +|-------|--------------| +| Claude Opus 4.5 (`claude-opus-4-5-20251101`) | `code_execution_20250825` | +| Claude Sonnet 4.5 (`claude-sonnet-4-5-20250929`) | `code_execution_20250825` | + +## Quick Start + +Here's a simple example where Claude programmatically queries a database multiple times and aggregates results: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[ + { + "role": "user", + "content": "Query sales data for the West, East, and Central regions, then tell me which region had the highest revenue" + } + ], + tools=[ + { + "type": "code_execution_20250825", + "name": "code_execution" + }, + { + "type": "function", + "function": { + "name": "query_database", + "description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.", + "parameters": { + "type": "object", + "properties": { + "sql": { + "type": "string", + "description": "SQL query to execute" + } + }, + "required": ["sql"] + } + }, + "allowed_callers": ["code_execution_20250825"] + } + ] +) + +print(response) +``` + +## How It Works + +When you configure a tool to be callable from code execution and Claude decides to use that tool: + +1. Claude writes Python code that invokes the tool as a function, potentially including multiple tool calls and pre/post-processing logic +2. Claude runs this code in a sandboxed container via code execution +3. When a tool function is called, code execution pauses and the API returns a `tool_use` block with a `caller` field +4. You provide the tool result, and code execution continues (intermediate results are not loaded into Claude's context window) +5. Once all code execution completes, Claude receives the final output and continues working on the task + +This approach is particularly useful for: + +- **Large data processing**: Filter or aggregate tool results before they reach Claude's context +- **Multi-step workflows**: Save tokens and latency by calling tools serially or in a loop without sampling Claude in-between tool calls +- **Conditional logic**: Make decisions based on intermediate tool results + +## The `allowed_callers` Field + +The `allowed_callers` field specifies which contexts can invoke a tool: + +```python +{ + "type": "function", + "function": { + "name": "query_database", + "description": "Execute a SQL query against the database", + "parameters": {...} + }, + "allowed_callers": ["code_execution_20250825"] +} +``` + +**Possible values:** + +- `["direct"]` - Only Claude can call this tool directly (default if omitted) +- `["code_execution_20250825"]` - Only callable from within code execution +- `["direct", "code_execution_20250825"]` - Callable both directly and from code execution + +:::tip +We recommend choosing either `["direct"]` or `["code_execution_20250825"]` for each tool rather than enabling both, as this provides clearer guidance to Claude for how best to use the tool. +::: + +## The `caller` Field in Responses + +Every tool use block includes a `caller` field indicating how it was invoked: + +**Direct invocation (traditional tool use):** + +```python +{ + "type": "tool_use", + "id": "toolu_abc123", + "name": "query_database", + "input": {"sql": ""}, + "caller": {"type": "direct"} +} +``` + +**Programmatic invocation:** + +```python +{ + "type": "tool_use", + "id": "toolu_xyz789", + "name": "query_database", + "input": {"sql": ""}, + "caller": { + "type": "code_execution_20250825", + "tool_id": "srvtoolu_abc123" + } +} +``` + +The `tool_id` references the code execution tool that made the programmatic call. + +## Container Lifecycle + +Programmatic tool calling uses code execution containers: + +- **Container creation**: A new container is created for each session unless you reuse an existing one +- **Expiration**: Containers expire after approximately 4.5 minutes of inactivity (subject to change) +- **Container ID**: Pass the `container` parameter to reuse an existing container +- **Reuse**: Pass the container ID to maintain state across requests + +```python +# First request - creates a new container +response1 = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[{"role": "user", "content": "Query the database"}], + tools=[...] +) + +# Get container ID from response (if available in response metadata) +container_id = response1.get("container", {}).get("id") + +# Second request - reuse the same container +response2 = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[...], + tools=[...], + container=container_id # Reuse container +) +``` + +:::warning +When a tool is called programmatically and the container is waiting for your tool result, you must respond before the container expires. Monitor the `expires_at` field. If the container expires, Claude may treat the tool call as timed out and retry it. +::: + +## Example Workflow + +### Step 1: Initial Request + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[{ + "role": "user", + "content": "Query customer purchase history from the last quarter and identify our top 5 customers by revenue" + }], + tools=[ + { + "type": "code_execution_20250825", + "name": "code_execution" + }, + { + "type": "function", + "function": { + "name": "query_database", + "description": "Execute a SQL query against the sales database. Returns a list of rows as JSON objects.", + "parameters": { + "type": "object", + "properties": { + "sql": {"type": "string", "description": "SQL query to execute"} + }, + "required": ["sql"] + } + }, + "allowed_callers": ["code_execution_20250825"] + } + ] +) +``` + +### Step 2: API Response with Tool Call + +Claude writes code that calls your tool. The response includes: + +```python +{ + "role": "assistant", + "content": [ + { + "type": "text", + "text": "I'll query the purchase history and analyze the results." + }, + { + "type": "server_tool_use", + "id": "srvtoolu_abc123", + "name": "code_execution", + "input": { + "code": "results = await query_database('')\ntop_customers = sorted(results, key=lambda x: x['revenue'], reverse=True)[:5]" + } + }, + { + "type": "tool_use", + "id": "toolu_def456", + "name": "query_database", + "input": {"sql": ""}, + "caller": { + "type": "code_execution_20250825", + "tool_id": "srvtoolu_abc123" + } + } + ], + "stop_reason": "tool_use" +} +``` + +### Step 3: Provide Tool Result + +```python +# Add assistant's response and tool result to conversation +messages = [ + {"role": "user", "content": "Query customer purchase history..."}, + { + "role": "assistant", + "content": response.choices[0].message.content, + "tool_calls": response.choices[0].message.tool_calls + }, + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": "toolu_def456", + "content": '[{"customer_id": "C1", "revenue": 45000}, ...]' + } + ] + } +] + +# Continue the conversation +response2 = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=messages, + tools=[...] +) +``` + +### Step 4: Final Response + +Once code execution completes, Claude provides the final response: + +```python +{ + "content": [ + { + "type": "code_execution_tool_result", + "tool_use_id": "srvtoolu_abc123", + "content": { + "type": "code_execution_result", + "stdout": "Top 5 customers by revenue:\n1. Customer C1: $45,000\n...", + "stderr": "", + "return_code": 0 + } + }, + { + "type": "text", + "text": "I've analyzed the purchase history from last quarter. Your top 5 customers generated $167,500 in total revenue..." + } + ], + "stop_reason": "end_turn" +} +``` + +## Advanced Patterns + +### Batch Processing with Loops + +Claude can write code that processes multiple items efficiently: + +```python +# Claude writes code like this: +regions = ["West", "East", "Central", "North", "South"] +results = {} +for region in regions: + data = await query_database(f"SELECT SUM(revenue) FROM sales WHERE region='{region}'") + results[region] = data[0]["total"] + +top_region = max(results.items(), key=lambda x: x[1]) +print(f"Top region: {top_region[0]} with ${top_region[1]:,}") +``` + +This pattern: +- Reduces model round-trips from N (one per region) to 1 +- Processes large result sets programmatically before returning to Claude +- Saves tokens by only returning aggregated conclusions + +### Early Termination + +Claude can stop processing as soon as success criteria are met: + +```python +endpoints = ["us-east", "eu-west", "apac"] +for endpoint in endpoints: + status = await check_health(endpoint) + if status == "healthy": + print(f"Found healthy endpoint: {endpoint}") + break # Stop early +``` + +### Data Filtering + +```python +logs = await fetch_logs(server_id) +errors = [log for log in logs if "ERROR" in log] +print(f"Found {len(errors)} errors") +for error in errors[-10:]: # Only return last 10 errors + print(error) +``` + +## Best Practices + +### Tool Design + +- **Provide detailed output descriptions**: Since Claude deserializes tool results in code, clearly document the format (JSON structure, field types, etc.) +- **Return structured data**: JSON or other easily parseable formats work best for programmatic processing +- **Keep responses concise**: Return only necessary data to minimize processing overhead + +### When to Use Programmatic Calling + +**Good use cases:** + +- Processing large datasets where you only need aggregates or summaries +- Multi-step workflows with 3+ dependent tool calls +- Operations requiring filtering, sorting, or transformation of tool results +- Tasks where intermediate data shouldn't influence Claude's reasoning +- Parallel operations across many items (e.g., checking 50 endpoints) + +**Less ideal use cases:** + +- Single tool calls with simple responses +- Tools that need immediate user feedback +- Very fast operations where code execution overhead would outweigh the benefit + +## Token Efficiency + +Programmatic tool calling can significantly reduce token consumption: + +- **Tool results from programmatic calls are not added to Claude's context** - only the final code output is +- **Intermediate processing happens in code** - filtering, aggregation, etc. don't consume model tokens +- **Multiple tool calls in one code execution** - reduces overhead compared to separate model turns + +For example, calling 10 tools directly uses ~10x the tokens of calling them programmatically and returning a summary. + +## Provider Support + +LiteLLM supports programmatic tool calling across the following Anthropic-compatible providers: + +- **Standard Anthropic API** (`anthropic/claude-sonnet-4-5-20250929`) ✅ +- **Azure Anthropic / Microsoft Foundry** (`azure/claude-sonnet-4-5-20250929`) ✅ +- **Amazon Bedrock** (`bedrock/invoke/anthropic.claude-sonnet-4-5-20250929-v1:0`) ✅ +- **Google Cloud Vertex AI** (`vertex_ai/claude-sonnet-4-5-20250929`) ❌ Not supported + +The beta header (`advanced-tool-use-2025-11-20`) is automatically added when LiteLLM detects tools with the `allowed_callers` field. + +## Limitations + +### Feature Incompatibilities + +- **Structured outputs**: Tools with `strict: true` are not supported with programmatic calling +- **Tool choice**: You cannot force programmatic calling of a specific tool via `tool_choice` +- **Parallel tool use**: `disable_parallel_tool_use: true` is not supported with programmatic calling + +### Tool Restrictions + +The following tools cannot currently be called programmatically: + +- Web search +- Web fetch +- Tools provided by an MCP connector + +## Troubleshooting + +### Common Issues + +**"Tool not allowed" error** + +- Verify your tool definition includes `"allowed_callers": ["code_execution_20250825"]` +- Check that you're using a compatible model (Claude Sonnet 4.5 or Opus 4.5) + +**Container expiration** + +- Ensure you respond to tool calls within the container's lifetime (~4.5 minutes) +- Consider implementing faster tool execution + +**Beta header not added** + +- LiteLLM automatically adds the beta header when it detects `allowed_callers` +- If you're manually setting headers, ensure you include `advanced-tool-use-2025-11-20` + +## Related Features + +- [Anthropic Tool Search](./anthropic_tool_search.md) - Dynamically discover and load tools on-demand +- [Anthropic Provider](./anthropic.md) - General Anthropic provider documentation + diff --git a/docs/my-website/docs/providers/anthropic_tool_input_examples.md b/docs/my-website/docs/providers/anthropic_tool_input_examples.md new file mode 100644 index 00000000000..39f4d8555f4 --- /dev/null +++ b/docs/my-website/docs/providers/anthropic_tool_input_examples.md @@ -0,0 +1,445 @@ +# Anthropic Tool Input Examples + +Provide concrete examples of valid tool inputs to help Claude understand how to use your tools more effectively. This is particularly useful for complex tools with nested objects, optional parameters, or format-sensitive inputs. + +:::info +Tool input examples is a beta feature. LiteLLM automatically detects tools with the `input_examples` field and adds the appropriate beta header based on your provider: + +- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20` +- **Amazon Bedrock**: `advanced-tool-use-2025-11-20` (Claude Opus 4.5 only) +- **Google Cloud Vertex AI**: Not supported + +You don't need to manually specify beta headers—LiteLLM handles this automatically. +::: + +## When to Use Input Examples + +Input examples are most helpful for: + +- **Complex nested objects**: Tools with deeply nested parameter structures +- **Optional parameters**: Showing when optional parameters should be included +- **Format-sensitive inputs**: Demonstrating expected formats (dates, addresses, etc.) +- **Enum values**: Illustrating valid enum choices in context +- **Edge cases**: Showing how to handle special cases + +:::tip +**Prioritize descriptions first!** Clear, detailed tool descriptions are more important than examples. Use `input_examples` as a supplement for complex tools where descriptions alone may not be sufficient. +::: + +## Quick Start + +Add an `input_examples` field to your tool definition with an array of example input objects: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[ + {"role": "user", "content": "What's the weather like in San Francisco?"} + ], + tools=[ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + }, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The unit of temperature" + } + }, + "required": ["location"] + } + }, + "input_examples": [ + { + "location": "San Francisco, CA", + "unit": "fahrenheit" + }, + { + "location": "Tokyo, Japan", + "unit": "celsius" + }, + { + "location": "New York, NY" # 'unit' is optional + } + ] + } + ] +) + +print(response) +``` + +## How It Works + +When you provide `input_examples`: + +1. **LiteLLM detects** the `input_examples` field in your tool definition +2. **Beta header added automatically**: The `advanced-tool-use-2025-11-20` header is injected +3. **Examples included in prompt**: Anthropic includes the examples alongside your tool schema +4. **Claude learns patterns**: The model uses examples to understand proper tool usage +5. **Better tool calls**: Claude makes more accurate tool calls with correct parameter formats + +## Example Formats + +### Simple Tool with Examples + +```python +{ + "type": "function", + "function": { + "name": "send_email", + "description": "Send an email to a recipient", + "parameters": { + "type": "object", + "properties": { + "to": {"type": "string", "description": "Email address"}, + "subject": {"type": "string"}, + "body": {"type": "string"} + }, + "required": ["to", "subject", "body"] + } + }, + "input_examples": [ + { + "to": "user@example.com", + "subject": "Meeting Reminder", + "body": "Don't forget our meeting tomorrow at 2 PM." + }, + { + "to": "team@company.com", + "subject": "Weekly Update", + "body": "Here's this week's progress report..." + } + ] +} +``` + +### Complex Nested Objects + +```python +{ + "type": "function", + "function": { + "name": "create_calendar_event", + "description": "Create a new calendar event", + "parameters": { + "type": "object", + "properties": { + "title": {"type": "string"}, + "start": { + "type": "object", + "properties": { + "date": {"type": "string"}, + "time": {"type": "string"} + } + }, + "attendees": { + "type": "array", + "items": { + "type": "object", + "properties": { + "email": {"type": "string"}, + "optional": {"type": "boolean"} + } + } + } + }, + "required": ["title", "start"] + } + }, + "input_examples": [ + { + "title": "Team Standup", + "start": { + "date": "2025-01-15", + "time": "09:00" + }, + "attendees": [ + {"email": "alice@example.com", "optional": False}, + {"email": "bob@example.com", "optional": True} + ] + }, + { + "title": "Lunch Break", + "start": { + "date": "2025-01-15", + "time": "12:00" + } + # No attendees - showing optional field + } + ] +} +``` + +### Format-Sensitive Parameters + +```python +{ + "type": "function", + "function": { + "name": "search_flights", + "description": "Search for available flights", + "parameters": { + "type": "object", + "properties": { + "origin": {"type": "string", "description": "Airport code"}, + "destination": {"type": "string", "description": "Airport code"}, + "date": {"type": "string", "description": "Date in YYYY-MM-DD format"}, + "passengers": {"type": "integer"} + }, + "required": ["origin", "destination", "date"] + } + }, + "input_examples": [ + { + "origin": "SFO", + "destination": "JFK", + "date": "2025-03-15", + "passengers": 2 + }, + { + "origin": "LAX", + "destination": "ORD", + "date": "2025-04-20", + "passengers": 1 + } + ] +} +``` + +## Requirements and Limitations + +### Schema Validation + +- Each example **must be valid** according to the tool's `input_schema` +- Invalid examples will return a **400 error** from Anthropic +- Validation happens server-side (LiteLLM passes examples through) + +### Server-Side Tools Not Supported + +Input examples are **only supported for user-defined tools**. The following server-side tools do NOT support `input_examples`: + +- `web_search` (web search tool) +- `code_execution` (code execution tool) +- `computer_use` (computer use tool) +- `bash_tool` (bash execution tool) +- `text_editor` (text editor tool) + +### Token Costs + +Examples add to your prompt tokens: + +- **Simple examples**: ~20-50 tokens per example +- **Complex nested objects**: ~100-200 tokens per example +- **Trade-off**: Higher token cost for better tool call accuracy + +### Model Compatibility + +Input examples work with all Claude models that support the `advanced-tool-use-2025-11-20` beta header: + +- Claude Opus 4.5 (`claude-opus-4-5-20251101`) +- Claude Sonnet 4.5 (`claude-sonnet-4-5-20250929`) +- Claude Opus 4.1 (`claude-opus-4-1-20250805`) + +:::note +On Google Cloud's Vertex AI and Amazon Bedrock, only Claude Opus 4.5 supports tool input examples. +::: + +## Best Practices + +### 1. Show Diverse Examples + +Include examples that demonstrate different use cases: + +```python +"input_examples": [ + {"location": "San Francisco, CA", "unit": "fahrenheit"}, # US city + {"location": "Tokyo, Japan", "unit": "celsius"}, # International + {"location": "New York, NY"} # Optional param omitted +] +``` + +### 2. Demonstrate Optional Parameters + +Show when optional parameters should and shouldn't be included: + +```python +"input_examples": [ + { + "query": "machine learning", + "filters": {"year": 2024, "category": "research"} # With optional filters + }, + { + "query": "artificial intelligence" # Without optional filters + } +] +``` + +### 3. Illustrate Format Requirements + +Make format expectations clear through examples: + +```python +"input_examples": [ + { + "phone": "+1-555-123-4567", # Shows expected phone format + "date": "2025-01-15", # Shows date format (YYYY-MM-DD) + "time": "14:30" # Shows time format (HH:MM) + } +] +``` + +### 4. Keep Examples Realistic + +Use realistic, production-like examples rather than placeholder data: + +```python +# ✅ Good - realistic examples +"input_examples": [ + {"email": "alice@company.com", "role": "admin"}, + {"email": "bob@company.com", "role": "user"} +] + +# ❌ Bad - placeholder examples +"input_examples": [ + {"email": "test@test.com", "role": "role1"}, + {"email": "example@example.com", "role": "role2"} +] +``` + +### 5. Limit Example Count + +Provide 2-5 examples per tool: + +- **Too few** (1): May not show enough variation +- **Just right** (2-5): Demonstrates patterns without bloating tokens +- **Too many** (10+): Wastes tokens, diminishing returns + +## Integration with Other Features + +Input examples work seamlessly with other Anthropic tool features: + +### With Tool Search + +```python +{ + "type": "function", + "function": { + "name": "query_database", + "description": "Execute a SQL query", + "parameters": {...} + }, + "defer_loading": True, # Tool search + "input_examples": [ # Input examples + {"sql": "SELECT * FROM users WHERE id = 1"} + ] +} +``` + +### With Programmatic Tool Calling + +```python +{ + "type": "function", + "function": { + "name": "fetch_data", + "description": "Fetch data from API", + "parameters": {...} + }, + "allowed_callers": ["code_execution_20250825"], # Programmatic calling + "input_examples": [ # Input examples + {"endpoint": "/api/users", "method": "GET"} + ] +} +``` + +### All Features Combined + +```python +{ + "type": "function", + "function": { + "name": "advanced_tool", + "description": "A complex tool", + "parameters": {...} + }, + "defer_loading": True, # Tool search + "allowed_callers": ["code_execution_20250825"], # Programmatic calling + "input_examples": [ # Input examples + {"param1": "value1", "param2": "value2"} + ] +} +``` + +## Provider Support + +LiteLLM supports input examples across the following Anthropic-compatible providers: + +- **Standard Anthropic API** (`anthropic/claude-sonnet-4-5-20250929`) ✅ +- **Azure Anthropic / Microsoft Foundry** (`azure/claude-sonnet-4-5-20250929`) ✅ +- **Amazon Bedrock** (`bedrock/invoke/anthropic.claude-opus-4-5-20251101-v1:0`) ✅ (Opus 4.5 only) +- **Google Cloud Vertex AI** (`vertex_ai/claude-sonnet-4-5-20250929`) ❌ Not supported + +The beta header (`advanced-tool-use-2025-11-20`) is automatically added when LiteLLM detects tools with the `input_examples` field. + +## Troubleshooting + +### "Invalid request" error with examples + +**Problem**: Receiving 400 error when using input examples + +**Solution**: Ensure each example is valid according to your `input_schema`: + +```python +# Check that: +# 1. All required fields are present in examples +# 2. Field types match the schema +# 3. Enum values are valid +# 4. Nested objects follow the schema structure +``` + +### Examples not improving tool calls + +**Problem**: Adding examples doesn't seem to help + +**Solution**: +1. **Check descriptions first**: Ensure tool descriptions are detailed and clear +2. **Review example quality**: Make sure examples are realistic and diverse +3. **Verify schema**: Confirm examples actually match your schema +4. **Add more variation**: Include examples showing different use cases + +### Token usage too high + +**Problem**: Input examples consuming too many tokens + +**Solution**: +1. **Reduce example count**: Use 2-3 examples instead of 5+ +2. **Simplify examples**: Remove unnecessary fields from examples +3. **Consider descriptions**: If descriptions are clear, examples may not be needed + +## When NOT to Use Input Examples + +Skip input examples if: + +- **Tool is simple**: Single parameter tools with clear descriptions +- **Schema is self-explanatory**: Well-structured schema with good descriptions +- **Token budget is tight**: Examples add 20-200 tokens each +- **Server-side tools**: web_search, code_execution, etc. don't support examples + +## Related Features + +- [Anthropic Tool Search](./anthropic_tool_search.md) - Dynamically discover and load tools on-demand +- [Anthropic Programmatic Tool Calling](./anthropic_programmatic_tool_calling.md) - Call tools from code execution +- [Anthropic Provider](./anthropic.md) - General Anthropic provider documentation + diff --git a/docs/my-website/docs/providers/anthropic_tool_search.md b/docs/my-website/docs/providers/anthropic_tool_search.md new file mode 100644 index 00000000000..28ce5688eeb --- /dev/null +++ b/docs/my-website/docs/providers/anthropic_tool_search.md @@ -0,0 +1,412 @@ +# Anthropic Tool Search + +Tool search enables Claude to dynamically discover and load tools on-demand from large tool catalogs (10,000+ tools). Instead of loading all tool definitions into the context window upfront, Claude searches your tool catalog and loads only the tools it needs. + +## Benefits + +- **Context efficiency**: Avoid consuming massive portions of your context window with tool definitions +- **Better tool selection**: Claude's tool selection accuracy degrades with more than 30-50 tools. Tool search maintains accuracy even with thousands of tools +- **On-demand loading**: Tools are only loaded when Claude needs them + +## Supported Models + +Tool search is available on: +- Claude Opus 4.5 +- Claude Sonnet 4.5 + +## Supported Platforms + +- Anthropic API (direct) +- Azure Anthropic (Microsoft Foundry) +- Google Cloud Vertex AI +- Amazon Bedrock (invoke API only, not converse API) + +## Tool Search Variants + +LiteLLM supports both tool search variants: + +### 1. Regex Tool Search (`tool_search_tool_regex_20251119`) + +Claude constructs regex patterns to search for tools. + +### 2. BM25 Tool Search (`tool_search_tool_bm25_20251119`) + +Claude uses natural language queries to search for tools using the BM25 algorithm. + +## Quick Start + +### Basic Example with Regex Tool Search + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[ + {"role": "user", "content": "What is the weather in San Francisco?"} + ], + tools=[ + # Tool search tool (regex variant) + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + # Deferred tool - will be loaded on-demand + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the weather at a specific location", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"}, + "unit": { + "type": "string", + "enum": ["celsius", "fahrenheit"] + } + }, + "required": ["location"] + } + }, + "defer_loading": True # Mark for deferred loading + }, + # Another deferred tool + { + "type": "function", + "function": { + "name": "search_files", + "description": "Search through files in the workspace", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"}, + "file_types": { + "type": "array", + "items": {"type": "string"} + } + }, + "required": ["query"] + } + }, + "defer_loading": True + } + ] +) + +print(response.choices[0].message.content) +``` + +### BM25 Tool Search Example + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[ + {"role": "user", "content": "Search for Python files containing 'authentication'"} + ], + tools=[ + # Tool search tool (BM25 variant) + { + "type": "tool_search_tool_bm25_20251119", + "name": "tool_search_tool_bm25" + }, + # Deferred tools... + { + "type": "function", + "function": { + "name": "search_codebase", + "description": "Search through codebase files by content and filename", + "parameters": { + "type": "object", + "properties": { + "query": {"type": "string"}, + "file_pattern": {"type": "string"} + }, + "required": ["query"] + } + }, + "defer_loading": True + } + ] +) +``` + +## Using with Azure Anthropic + +```python +import litellm + +response = litellm.completion( + model="azure_anthropic/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + api_key="your-azure-api-key", + messages=[ + {"role": "user", "content": "What's the weather like?"} + ], + tools=[ + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get current weather", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + }, + "required": ["location"] + } + }, + "defer_loading": True + } + ] +) +``` + +## Using with Vertex AI + +```python +import litellm + +response = litellm.completion( + model="vertex_ai/claude-sonnet-4-5", + vertex_project="your-project-id", + vertex_location="us-central1", + messages=[ + {"role": "user", "content": "Search my documents"} + ], + tools=[ + { + "type": "tool_search_tool_bm25_20251119", + "name": "tool_search_tool_bm25" + }, + # Your deferred tools... + ] +) +``` + +## Streaming Support + +Tool search works with streaming: + +```python +import litellm + +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[ + {"role": "user", "content": "Get the weather"} + ], + tools=[ + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather information", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + }, + "required": ["location"] + } + }, + "defer_loading": True + } + ], + stream=True +) + +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="") +``` + +## LiteLLM Proxy + +Tool search works automatically through the LiteLLM proxy: + +### Proxy Config + +```yaml +model_list: + - model_name: claude-sonnet + litellm_params: + model: anthropic/claude-sonnet-4-5-20250929 + api_key: os.environ/ANTHROPIC_API_KEY +``` + +### Client Request + +```python +import openai + +client = openai.OpenAI( + api_key="your-litellm-proxy-key", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="claude-sonnet", + messages=[ + {"role": "user", "content": "What's the weather?"} + ], + tools=[ + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get weather information", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"} + }, + "required": ["location"] + } + }, + "defer_loading": True + } + ] +) +``` + +## Important Notes + +### Beta Header + +LiteLLM automatically detects tool search tools and adds the appropriate beta header based on your provider: + +- **Anthropic API & Microsoft Foundry**: `advanced-tool-use-2025-11-20` +- **Google Cloud Vertex AI**: `tool-search-tool-2025-10-19` +- **Amazon Bedrock** (Invoke API, Opus 4.5 only): `tool-search-tool-2025-10-19` + +You don't need to manually specify beta headers—LiteLLM handles this automatically. + +### Deferred Loading + +- Tools with `defer_loading: true` are only loaded when Claude discovers them via search +- At least one tool must be non-deferred (the tool search tool itself) +- Keep your 3-5 most frequently used tools as non-deferred for optimal performance + +### Tool Descriptions + +Write clear, descriptive tool names and descriptions that match how users describe tasks. The search algorithm uses: +- Tool names +- Tool descriptions +- Argument names +- Argument descriptions + +### Usage Tracking + +Tool search requests are tracked in the usage object: + +```python +response = litellm.completion( + model="anthropic/claude-sonnet-4-5-20250929", + messages=[{"role": "user", "content": "Search for tools"}], + tools=[...] +) + +# Check tool search usage +if response.usage.server_tool_use: + print(f"Tool search requests: {response.usage.server_tool_use.tool_search_requests}") +``` + +## Error Handling + +### All Tools Deferred + +```python +# ❌ This will fail - at least one tool must be non-deferred +tools = [ + { + "type": "function", + "function": {...}, + "defer_loading": True + } +] + +# ✅ Correct - tool search tool is non-deferred +tools = [ + { + "type": "tool_search_tool_regex_20251119", + "name": "tool_search_tool_regex" + }, + { + "type": "function", + "function": {...}, + "defer_loading": True + } +] +``` + +### Missing Tool Definition + +If Claude references a tool that isn't in your deferred tools list, you'll get an error. Make sure all tools that might be discovered are included in the tools parameter with `defer_loading: true`. + +## Best Practices + +1. **Keep frequently used tools non-deferred**: Your 3-5 most common tools should not have `defer_loading: true` + +2. **Use semantic descriptions**: Tool descriptions should use natural language that matches user queries + +3. **Choose the right variant**: + - Use **regex** for exact pattern matching (faster) + - Use **BM25** for natural language semantic search + +4. **Monitor usage**: Track `tool_search_requests` in the usage object to understand search patterns + +5. **Optimize tool catalog**: Remove unused tools and consolidate similar functionality + +## When to Use Tool Search + +**Good use cases:** +- 10+ tools available in your system +- Tool definitions consuming >10K tokens +- Experiencing tool selection accuracy issues +- Building systems with multiple tool categories +- Tool library growing over time + +**When traditional tool calling is better:** +- Less than 10 tools total +- All tools are frequently used +- Very small tool definitions (\<100 tokens total) + +## Limitations + +- Not compatible with tool use examples +- Requires Claude Opus 4.5 or Sonnet 4.5 +- On Bedrock, only available via invoke API (not converse API) +- On Bedrock, only supported for Claude Opus 4.5 (not Sonnet 4.5) +- BM25 variant (`tool_search_tool_bm25_20251119`) is not supported on Bedrock +- Maximum 10,000 tools in catalog +- Returns 3-5 most relevant tools per search + +### Bedrock-Specific Notes + +When using Bedrock's Invoke API: +- The regex variant (`tool_search_tool_regex_20251119`) is automatically normalized to `tool_search_tool_regex` +- The BM25 variant (`tool_search_tool_bm25_20251119`) is automatically filtered out as it's not supported +- Tool search is only available for Claude Opus 4.5 models + +## Additional Resources + +- [Anthropic Tool Search Documentation](https://docs.anthropic.com/en/docs/build-with-claude/tool-use/tool-search) +- [LiteLLM Tool Calling Guide](https://docs.litellm.ai/docs/completion/function_call) + diff --git a/docs/my-website/docs/providers/azure/azure.md b/docs/my-website/docs/providers/azure/azure.md index 0ff5b2a5a77..0b9fd29e680 100644 --- a/docs/my-website/docs/providers/azure/azure.md +++ b/docs/my-website/docs/providers/azure/azure.md @@ -9,10 +9,10 @@ import TabItem from '@theme/TabItem'; | Property | Details | |-------|-------| -| Description | Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o1, o1-mini, GPT-5, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series | -| Provider Route on LiteLLM | `azure/`, [`azure/o_series/`](#o-series-models), [`azure/gpt5_series/`](#gpt-5-models) | -| Supported Operations | [`/chat/completions`](#azure-openai-chat-completion-models), [`/responses`](./azure_responses), [`/completions`](#azure-instruct-models), [`/embeddings`](./azure_embedding), [`/audio/speech`](azure_speech), [`/audio/transcriptions`](../audio_transcription), `/fine_tuning`, [`/batches`](#azure-batches-api), `/files`, [`/images`](../image_generation#azure-openai-image-generation-models) | -| Link to Provider Doc | [Azure OpenAI ↗](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) +| Description | Azure OpenAI Service provides REST API access to OpenAI's powerful language models including o1, o1-mini, GPT-5, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. Also supports Claude models via Azure Foundry. | +| Provider Route on LiteLLM | `azure/`, [`azure/o_series/`](#o-series-models), [`azure/gpt5_series/`](#gpt-5-models), [`azure/claude-*`](./azure_anthropic) (Claude models via Azure Foundry) | +| Supported Operations | [`/chat/completions`](#azure-openai-chat-completion-models), [`/responses`](./azure_responses), [`/completions`](#azure-instruct-models), [`/embeddings`](./azure_embedding), [`/audio/speech`](azure_speech), [`/audio/transcriptions`](../audio_transcription), `/fine_tuning`, [`/batches`](#azure-batches-api), `/files`, [`/images`](../image_generation#azure-openai-image-generation-models), [`/anthropic/v1/messages`](./azure_anthropic) | +| Link to Provider Doc | [Azure OpenAI ↗](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview), [Azure Foundry Claude ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude) ## API Keys, Params api_key, api_base, api_version etc can be passed directly to `litellm.completion` - see here or set as `litellm.api_key` params see here @@ -27,6 +27,12 @@ os.environ["AZURE_AD_TOKEN"] = "" os.environ["AZURE_API_TYPE"] = "" ``` +:::info Azure Foundry Claude Models + +Azure also supports Claude models via Azure Foundry. Use `azure/claude-*` model names (e.g., `azure/claude-sonnet-4-5`) with Azure authentication. See the [Azure Anthropic documentation](./azure_anthropic) for details. + +::: + ## **Usage - LiteLLM Python SDK** Open In Colab diff --git a/docs/my-website/docs/providers/azure/azure_anthropic.md b/docs/my-website/docs/providers/azure/azure_anthropic.md new file mode 100644 index 00000000000..4c722b30397 --- /dev/null +++ b/docs/my-website/docs/providers/azure/azure_anthropic.md @@ -0,0 +1,378 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Azure Anthropic (Claude via Azure Foundry) + +LiteLLM supports Claude models deployed via Microsoft Azure Foundry, including Claude Sonnet 4.5, Claude Haiku 4.5, and Claude Opus 4.1. + +## Available Models + +Azure Foundry supports the following Claude models: + +- `claude-sonnet-4-5` - Anthropic's most capable model for building real-world agents and handling complex, long-horizon tasks +- `claude-haiku-4-5` - Near-frontier performance with the right speed and cost for high-volume use cases +- `claude-opus-4-1` - Industry leader for coding, delivering sustained performance on long-running tasks + +| Property | Details | +|-------|-------| +| Description | Claude models deployed via Microsoft Azure Foundry. Uses the same API as Anthropic's Messages API but with Azure authentication. | +| Provider Route on LiteLLM | `azure_ai/` (add this prefix to Claude model names - e.g. `azure_ai/claude-sonnet-4-5`) | +| Provider Doc | [Azure Foundry Claude Models ↗](https://learn.microsoft.com/en-us/azure/ai-services/foundry-models/claude) | +| API Endpoint | `https://.services.ai.azure.com/anthropic/v1/messages` | +| Supported Endpoints | `/chat/completions`, `/anthropic/v1/messages`| + +## Key Features + +- **Extended thinking**: Enhanced reasoning capabilities for complex tasks +- **Image and text input**: Strong vision capabilities for analyzing charts, graphs, technical diagrams, and reports +- **Code generation**: Advanced thinking with code generation, analysis, and debugging (Claude Sonnet 4.5 and Claude Opus 4.1) +- **Same API as Anthropic**: All request/response transformations are identical to the main Anthropic provider + +## Authentication + +Azure Anthropic supports two authentication methods: + +1. **API Key**: Use the `api-key` header +2. **Azure AD Token**: Use `Authorization: Bearer ` header (Microsoft Entra ID) + +## API Keys and Configuration + +```python +import os + +# Option 1: API Key authentication +os.environ["AZURE_API_KEY"] = "your-azure-api-key" +os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" + +# Option 2: Azure AD Token authentication +os.environ["AZURE_AD_TOKEN"] = "your-azure-ad-token" +os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" + +# Optional: Azure AD Token Provider (for automatic token refresh) +os.environ["AZURE_TENANT_ID"] = "your-tenant-id" +os.environ["AZURE_CLIENT_ID"] = "your-client-id" +os.environ["AZURE_CLIENT_SECRET"] = "your-client-secret" +os.environ["AZURE_SCOPE"] = "https://cognitiveservices.azure.com/.default" +``` + +## Usage - LiteLLM Python SDK + +### Basic Completion + +```python +from litellm import completion + +# Set environment variables +os.environ["AZURE_API_KEY"] = "your-azure-api-key" +os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" + +# Make a completion request +response = completion( + model="azure_ai/claude-sonnet-4-5", + messages=[ + {"role": "user", "content": "What are 3 things to visit in Seattle?"} + ], + max_tokens=1000, + temperature=0.7, +) + +print(response) +``` + +### Completion with API Key Parameter + +```python +import litellm + +response = litellm.completion( + model="azure_ai/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + api_key="your-azure-api-key", + messages=[ + {"role": "user", "content": "Hello!"} + ], + max_tokens=1000, +) +``` + +### Completion with Azure AD Token + +```python +import litellm + +response = litellm.completion( + model="azure_ai/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + azure_ad_token="your-azure-ad-token", + messages=[ + {"role": "user", "content": "Hello!"} + ], + max_tokens=1000, +) +``` + +### Streaming + +```python +from litellm import completion + +response = completion( + model="azure_ai/claude-sonnet-4-5", + messages=[ + {"role": "user", "content": "Write a short story"} + ], + stream=True, + max_tokens=1000, +) + +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="", flush=True) +``` + +### Tool Calling + +```python +from litellm import completion + +response = completion( + model="azure_ai/claude-sonnet-4-5", + messages=[ + {"role": "user", "content": "What's the weather in Seattle?"} + ], + tools=[ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA" + } + }, + "required": ["location"] + } + } + } + ], + tool_choice="auto", + max_tokens=1000, +) + +print(response) +``` + +## Usage - LiteLLM Proxy Server + +### 1. Save key in your environment + +```bash +export AZURE_API_KEY="your-azure-api-key" +export AZURE_API_BASE="https://.services.ai.azure.com/anthropic" +``` + +### 2. Configure the proxy + +```yaml +model_list: + - model_name: claude-sonnet-4-5 + litellm_params: + model: azure_ai/claude-sonnet-4-5 + api_base: https://.services.ai.azure.com/anthropic + api_key: os.environ/AZURE_API_KEY +``` + +### 3. Test it + + + + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data '{ + "model": "claude-sonnet-4-5", + "messages": [ + { + "role": "user", + "content": "Hello!" + } + ], + "max_tokens": 1000 +}' +``` + + + + +```python +from openai import OpenAI + +client = OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="claude-sonnet-4-5", + messages=[ + {"role": "user", "content": "Hello!"} + ], + max_tokens=1000 +) + +print(response) +``` + + + + +## Messages API + +Azure Anthropic also supports the native Anthropic Messages API. The endpoint structure is the same as Anthropic's `/v1/messages` API. + +### Using Anthropic SDK + +```python +from anthropic import Anthropic + +client = Anthropic( + api_key="your-azure-api-key", + base_url="https://.services.ai.azure.com/anthropic" +) + +response = client.messages.create( + model="claude-sonnet-4-5", + max_tokens=1000, + messages=[ + {"role": "user", "content": "Hello, world"} + ] +) + +print(response) +``` + +### Using LiteLLM Proxy + +```bash +curl --request POST \ + --url http://0.0.0.0:4000/anthropic/v1/messages \ + --header 'accept: application/json' \ + --header 'content-type: application/json' \ + --header "Authorization: bearer sk-anything" \ + --data '{ + "model": "claude-sonnet-4-5", + "max_tokens": 1024, + "messages": [ + {"role": "user", "content": "Hello, world"} + ] +}' +``` + +## Supported OpenAI Parameters + +Azure Anthropic supports the same parameters as the main Anthropic provider: + +``` +"stream", +"stop", +"temperature", +"top_p", +"max_tokens", +"max_completion_tokens", +"tools", +"tool_choice", +"extra_headers", +"parallel_tool_calls", +"response_format", +"user", +"thinking", +"reasoning_effort" +``` + +:::info + +Azure Anthropic API requires `max_tokens` to be passed. LiteLLM automatically passes `max_tokens=4096` when no `max_tokens` are provided. + +::: + +## Differences from Standard Anthropic Provider + +The only difference between Azure Anthropic and the standard Anthropic provider is authentication: + +- **Standard Anthropic**: Uses `x-api-key` header +- **Azure Anthropic**: Uses `api-key` header or `Authorization: Bearer ` for Azure AD authentication + +All other request/response transformations, tool calling, streaming, and feature support are identical. + +## API Base URL Format + +The API base URL should follow this format: + +``` +https://.services.ai.azure.com/anthropic +``` + +LiteLLM will automatically append `/v1/messages` if not already present in the URL. + +## Example: Full Configuration + +```python +import os +from litellm import completion + +# Configure Azure Anthropic +os.environ["AZURE_API_KEY"] = "your-azure-api-key" +os.environ["AZURE_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic" + +# Make a request +response = completion( + model="azure_ai/claude-sonnet-4-5", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Explain quantum computing in simple terms."} + ], + max_tokens=1000, + temperature=0.7, + stream=False, +) + +print(response.choices[0].message.content) +``` + +## Troubleshooting + +### Missing API Base Error + +If you see an error about missing API base, ensure you've set: + +```python +os.environ["AZURE_API_BASE"] = "https://.services.ai.azure.com/anthropic" +``` + +Or pass it directly: + +```python +response = completion( + model="azure_ai/claude-sonnet-4-5", + api_base="https://.services.ai.azure.com/anthropic", + # ... +) +``` + +### Authentication Errors + +- **API Key**: Ensure `AZURE_API_KEY` is set or passed as `api_key` parameter +- **Azure AD Token**: Ensure `AZURE_AD_TOKEN` is set or passed as `azure_ad_token` parameter +- **Token Provider**: For automatic token refresh, configure `AZURE_TENANT_ID`, `AZURE_CLIENT_ID`, and `AZURE_CLIENT_SECRET` + +## Related Documentation + +- [Anthropic Provider Documentation](./anthropic.md) - For standard Anthropic API usage +- [Azure OpenAI Documentation](./azure.md) - For Azure OpenAI models +- [Azure Authentication Guide](../secret_managers/azure_key_vault.md) - For Azure AD token setup + diff --git a/docs/my-website/docs/providers/azure_ai.md b/docs/my-website/docs/providers/azure_ai.md index b1b5de5bb34..68e2df676e6 100644 --- a/docs/my-website/docs/providers/azure_ai.md +++ b/docs/my-website/docs/providers/azure_ai.md @@ -312,6 +312,82 @@ LiteLLM supports **ALL** azure ai models. Here's a few examples: | mistral-large-latest | `completion(model="azure_ai/mistral-large-latest", messages)` | | AI21-Jamba-Instruct | `completion(model="azure_ai/ai21-jamba-instruct", messages)` | +## Usage - Azure Anthropic (Azure Foundry Claude) + +LiteLLM funnels Azure Claude deployments through the `azure_ai/` provider so Claude Opus models on Azure Foundry keep working with Tool Search, Effort, streaming, and the rest of the advanced feature set. Point `AZURE_AI_API_BASE` to `https://.services.ai.azure.com/anthropic` (LiteLLM appends `/v1/messages` automatically) and authenticate with `AZURE_AI_API_KEY` or an Azure AD token. + + + + +```python +import os +from litellm import completion + +# Configure Azure credentials +os.environ["AZURE_AI_API_KEY"] = "your-azure-ai-api-key" +os.environ["AZURE_AI_API_BASE"] = "https://my-resource.services.ai.azure.com/anthropic" + +response = completion( + model="azure_ai/claude-opus-4-1", + messages=[{"role": "user", "content": "Explain how Azure Anthropic hosts Claude Opus differently from the public Anthropic API."}], + max_tokens=1200, + temperature=0.7, + stream=True, +) + +for chunk in response: + if chunk.choices[0].delta.content: + print(chunk.choices[0].delta.content, end="", flush=True) +``` + + + + +**1. Set environment variables** + +```bash +export AZURE_AI_API_KEY="your-azure-ai-api-key" +export AZURE_AI_API_BASE="https://my-resource.services.ai.azure.com/anthropic" +``` + +**2. Configure the proxy** + +```yaml +model_list: + - model_name: claude-4-azure + litellm_params: + model: azure_ai/claude-opus-4-1 + api_key: os.environ/AZURE_AI_API_KEY + api_base: os.environ/AZURE_AI_API_BASE +``` + +**3. Start LiteLLM** + +```bash +litellm --config /path/to/config.yaml +``` + +**4. Test the Azure Claude route** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Content-Type: application/json' \ + --header 'Authorization: Bearer $LITELLM_KEY' \ + --data '{ + "model": "claude-4-azure", + "messages": [ + { + "role": "user", + "content": "How do I use Claude Opus 4 via Azure Anthropic in LiteLLM?" + } + ], + "max_tokens": 1024 + }' +``` + + + + ## Rerank Endpoint @@ -397,4 +473,5 @@ curl http://0.0.0.0:4000/rerank \ ``` - \ No newline at end of file + + diff --git a/docs/my-website/docs/providers/bedrock.md b/docs/my-website/docs/providers/bedrock.md index f0b89615a0d..17c0d38111d 100644 --- a/docs/my-website/docs/providers/bedrock.md +++ b/docs/my-website/docs/providers/bedrock.md @@ -7,7 +7,7 @@ ALL Bedrock models (Anthropic, Meta, Deepseek, Mistral, Amazon, etc.) are Suppor | Property | Details | |-------|-------| | Description | Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs). | -| Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models) | +| Provider Route on LiteLLM | `bedrock/`, [`bedrock/converse/`](#set-converse--invoke-route), [`bedrock/invoke/`](#set-invoke-route), [`bedrock/converse_like/`](#calling-via-internal-proxy), [`bedrock/llama/`](#deepseek-not-r1), [`bedrock/deepseek_r1/`](#deepseek-r1), [`bedrock/qwen3/`](#qwen3-imported-models), [`bedrock/qwen2/`](./bedrock_imported.md#qwen2-imported-models), [`bedrock/openai/`](./bedrock_imported.md#openai-compatible-imported-models-qwen-25-vl-etc) | | Provider Doc | [Amazon Bedrock ↗](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) | | Supported OpenAI Endpoints | `/chat/completions`, `/completions`, `/embeddings`, `/images/generations` | | Rerank Endpoint | `/rerank` | @@ -43,6 +43,8 @@ export AWS_BEARER_TOKEN_BEDROCK="your-api-key" Option 2: use the api_key parameter to pass in API key for completion, embedding, image_generation API calls. + + ```python response = completion( model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0", @@ -50,7 +52,17 @@ response = completion( api_key="your-api-key" ) ``` - + + +```yaml +model_list: + - model_name: bedrock-claude-3-sonnet + litellm_params: + model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0 + api_key: os.environ/AWS_BEARER_TOKEN_BEDROCK +``` + + ## Usage @@ -1598,206 +1610,6 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \ -## Bedrock Imported Models (Deepseek, Deepseek R1) - -### Deepseek R1 - -This is a separate route, as the chat template is different. - -| Property | Details | -|----------|---------| -| Provider Route | `bedrock/deepseek_r1/{model_arn}` | -| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html), [Deepseek Bedrock Imported Model](https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import/) | - - - - -```python -from litellm import completion -import os - -response = completion( - model="bedrock/deepseek_r1/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n", # bedrock/deepseek_r1/{your-model-arn} - messages=[{"role": "user", "content": "Tell me a joke"}], -) -``` - - - - - - -**1. Add to config** - -```yaml -model_list: - - model_name: DeepSeek-R1-Distill-Llama-70B - litellm_params: - model: bedrock/deepseek_r1/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n - -``` - -**2. Start proxy** - -```bash -litellm --config /path/to/config.yaml - -# RUNNING at http://0.0.0.0:4000 -``` - -**3. Test it!** - -```bash -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Authorization: Bearer sk-1234' \ - --header 'Content-Type: application/json' \ - --data '{ - "model": "DeepSeek-R1-Distill-Llama-70B", # 👈 the 'model_name' in config - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - }' -``` - - - - - -### Deepseek (not R1) - -| Property | Details | -|----------|---------| -| Provider Route | `bedrock/llama/{model_arn}` | -| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html), [Deepseek Bedrock Imported Model](https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import/) | - - - -Use this route to call Bedrock Imported Models that follow the `llama` Invoke Request / Response spec - - - - - -```python -from litellm import completion -import os - -response = completion( - model="bedrock/llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n", # bedrock/llama/{your-model-arn} - messages=[{"role": "user", "content": "Tell me a joke"}], -) -``` - - - - - - -**1. Add to config** - -```yaml -model_list: - - model_name: DeepSeek-R1-Distill-Llama-70B - litellm_params: - model: bedrock/llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n - -``` - -**2. Start proxy** - -```bash -litellm --config /path/to/config.yaml - -# RUNNING at http://0.0.0.0:4000 -``` - -**3. Test it!** - -```bash -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Authorization: Bearer sk-1234' \ - --header 'Content-Type: application/json' \ - --data '{ - "model": "DeepSeek-R1-Distill-Llama-70B", # 👈 the 'model_name' in config - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - }' -``` - - - - -### Qwen3 Imported Models - -| Property | Details | -|----------|---------| -| Provider Route | `bedrock/qwen3/{model_arn}` | -| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html), [Qwen3 Models](https://aws.amazon.com/about-aws/whats-new/2025/09/qwen3-models-fully-managed-amazon-bedrock/) | - - - - -```python -from litellm import completion -import os - -response = completion( - model="bedrock/qwen3/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen3-model", # bedrock/qwen3/{your-model-arn} - messages=[{"role": "user", "content": "Tell me a joke"}], - max_tokens=100, - temperature=0.7 -) -``` - - - - - -**1. Add to config** - -```yaml -model_list: - - model_name: Qwen3-32B - litellm_params: - model: bedrock/qwen3/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen3-model - -``` - -**2. Start proxy** - -```bash -litellm --config /path/to/config.yaml - -# RUNNING at http://0.0.0.0:4000 -``` - -**3. Test it!** - -```bash -curl --location 'http://0.0.0.0:4000/chat/completions' \ - --header 'Authorization: Bearer sk-1234' \ - --header 'Content-Type: application/json' \ - --data '{ - "model": "Qwen3-32B", # 👈 the 'model_name' in config - "messages": [ - { - "role": "user", - "content": "what llm are you" - } - ], - }' -``` - - - - ### OpenAI GPT OSS | Property | Details | @@ -1883,6 +1695,131 @@ curl --location 'http://0.0.0.0:4000/chat/completions' \ +## TwelveLabs Pegasus - Video Understanding + +TwelveLabs Pegasus 1.2 is a video understanding model that can analyze and describe video content. LiteLLM supports this model through Bedrock's `/invoke` endpoint. + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/us.twelvelabs.pegasus-1-2-v1:0`, `bedrock/eu.twelvelabs.pegasus-1-2-v1:0` | +| Provider Documentation | [TwelveLabs Pegasus Docs ↗](https://docs.twelvelabs.io/docs/models/pegasus) | +| Supported Parameters | `max_tokens`, `temperature`, `response_format` | +| Media Input | S3 URI or base64-encoded video | + +### Supported Features + +- **Video Analysis**: Analyze video content from S3 or base64 input +- **Structured Output**: Support for JSON schema response format +- **S3 Integration**: Support for S3 video URLs with bucket owner specification + +### Usage with S3 Video + + + + +```python title="TwelveLabs Pegasus SDK Usage" showLineNumbers +from litellm import completion +import os + +# Set AWS credentials +os.environ["AWS_ACCESS_KEY_ID"] = "your-aws-access-key" +os.environ["AWS_SECRET_ACCESS_KEY"] = "your-aws-secret-key" +os.environ["AWS_REGION_NAME"] = "us-east-1" + +response = completion( + model="bedrock/us.twelvelabs.pegasus-1-2-v1:0", + messages=[{"role": "user", "content": "Describe what happens in this video."}], + mediaSource={ + "s3Location": { + "uri": "s3://your-bucket/video.mp4", + "bucketOwner": "123456789012", # 12-digit AWS account ID + } + }, + temperature=0.2 +) + +print(response.choices[0].message.content) +``` + + + + + +**1. Add to config** + +```yaml title="config.yaml" showLineNumbers +model_list: + - model_name: pegasus-video + litellm_params: + model: bedrock/us.twelvelabs.pegasus-1-2-v1:0 + aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID + aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY + aws_region_name: os.environ/AWS_REGION_NAME +``` + +**2. Start proxy** + +```bash title="Start LiteLLM Proxy" showLineNumbers +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash title="Test Pegasus via Proxy" showLineNumbers +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "pegasus-video", + "messages": [ + { + "role": "user", + "content": "Describe what happens in this video." + } + ], + "mediaSource": { + "s3Location": { + "uri": "s3://your-bucket/video.mp4", + "bucketOwner": "123456789012" + } + }, + "temperature": 0.2 + }' +``` + + + + +### Usage with Base64 Video + +You can also pass video content directly as base64: + +```python title="Base64 Video Input" showLineNumbers +from litellm import completion +import base64 + +# Read video file and encode to base64 +with open("video.mp4", "rb") as video_file: + video_base64 = base64.b64encode(video_file.read()).decode("utf-8") + +response = completion( + model="bedrock/us.twelvelabs.pegasus-1-2-v1:0", + messages=[{"role": "user", "content": "What is happening in this video?"}], + mediaSource={ + "base64String": video_base64 + }, + temperature=0.2, +) + +print(response.choices[0].message.content) +``` + +### Important Notes + +- **Response Format**: The model supports structured output via `response_format` with JSON schema + ## Provisioned throughput models To use provisioned throughput Bedrock models pass - `model=bedrock/`, example `model=bedrock/anthropic.claude-v2`. Set `model` to any of the [Supported AWS models](#supported-aws-bedrock-models) @@ -1943,6 +1880,8 @@ Here's an example of using a bedrock model with LiteLLM. For a complete list, re | Meta Llama 2 Chat 70b | `completion(model='bedrock/meta.llama2-70b-chat-v1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mistral 7B Instruct | `completion(model='bedrock/mistral.mistral-7b-instruct-v0:2', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | | Mixtral 8x7B Instruct | `completion(model='bedrock/mistral.mixtral-8x7b-instruct-v0:1', messages=messages)` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| TwelveLabs Pegasus 1.2 (US) | `completion(model='bedrock/us.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | +| TwelveLabs Pegasus 1.2 (EU) | `completion(model='bedrock/eu.twelvelabs.pegasus-1-2-v1:0', messages=messages, mediaSource={...})` | `os.environ['AWS_ACCESS_KEY_ID']`, `os.environ['AWS_SECRET_ACCESS_KEY']`, `os.environ['AWS_REGION_NAME']` | ## Bedrock Embedding diff --git a/docs/my-website/docs/providers/bedrock_batches.md b/docs/my-website/docs/providers/bedrock_batches.md index a1116f41076..19446fda837 100644 --- a/docs/my-website/docs/providers/bedrock_batches.md +++ b/docs/my-website/docs/providers/bedrock_batches.md @@ -172,6 +172,97 @@ curl http://localhost:4000/v1/batches \ +### 4. Retrieve batch results + +Once the batch job is completed, download the results from S3: + + + + +```python showLineNumbers title="bedrock_batch.py" +... +# Wait for batch completion (check status periodically) +batch_status = client.batches.retrieve(batch_id=batch.id) + +if batch_status.status == "completed": + # Download the output file + result = client.files.content( + file_id=batch_status.output_file_id, + extra_headers={"custom-llm-provider": "bedrock"} + ) + + # Save or process the results + with open("batch_output.jsonl", "wb") as f: + f.write(result.content) + + # Parse JSONL results + for line in result.text.strip().split('\n'): + record = json.loads(line) + print(f"Record ID: {record['recordId']}") + print(f"Output: {record.get('modelOutput', {})}") +``` + + + + +```bash showLineNumbers title="Download Batch Results" +# First retrieve batch to get output_file_id +curl http://localhost:4000/v1/batches/batch_abc123 \ + -H "Authorization: Bearer sk-1234" + +# Then download the output file +curl http://localhost:4000/v1/files/{output_file_id}/content \ + -H "Authorization: Bearer sk-1234" \ + -H "custom-llm-provider: bedrock" \ + -o batch_output.jsonl +``` + + + + +```python showLineNumbers title="bedrock_batch.py" +import litellm +from litellm import file_content + +# Download using litellm directly (bypasses proxy managed files) +result = file_content( + file_id=batch_status.output_file_id, # Can be S3 URI or unified file ID + custom_llm_provider="bedrock", + aws_region_name="us-west-2", +) + +# Process results +print(result.text) +``` + + + + +**Output Format:** + +The batch output file is in JSONL format with each line containing: + +```json +{ + "recordId": "request-1", + "modelInput": { + "messages": [...], + "max_tokens": 1000 + }, + "modelOutput": { + "content": [...], + "id": "msg_abc123", + "model": "claude-3-5-sonnet-20240620-v1:0", + "role": "assistant", + "stop_reason": "end_turn", + "usage": { + "input_tokens": 15, + "output_tokens": 10 + } + } +} +``` + ## FAQ ### Where are my files written? diff --git a/docs/my-website/docs/providers/bedrock_embedding.md b/docs/my-website/docs/providers/bedrock_embedding.md index 76c9606533e..e2e7c0dcedd 100644 --- a/docs/my-website/docs/providers/bedrock_embedding.md +++ b/docs/my-website/docs/providers/bedrock_embedding.md @@ -4,7 +4,8 @@ | Provider | LiteLLM Route | AWS Documentation | Cost Tracking | |----------|---------------|-------------------|---------------| -| Amazon Titan | `bedrock/amazon.*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | ✅ | +| Amazon Titan | `bedrock/amazon.titan-*` | [Amazon Titan Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html) | ✅ | +| Amazon Nova | `bedrock/amazon.nova-*` | [Amazon Nova Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/nova-embed.html) | ✅ | | Cohere | `bedrock/cohere.*` | [Cohere Embeddings](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-cohere-embed.html) | ✅ | | TwelveLabs | `bedrock/us.twelvelabs.*` | [TwelveLabs](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-twelvelabs.html) | ✅ | @@ -16,6 +17,7 @@ LiteLLM supports AWS Bedrock's async-invoke feature for embedding models that re | Provider | Async Invoke Route | Use Case | |----------|-------------------|----------| +| Amazon Nova | `bedrock/async_invoke/amazon.nova-2-multimodal-embeddings-v1:0` | Multimodal embeddings with segmentation for long text, video, and audio | | TwelveLabs Marengo | `bedrock/async_invoke/us.twelvelabs.marengo-embed-2-7-v1:0` | Video, audio, image, and text embeddings | ### Required Parameters @@ -116,7 +118,7 @@ def check_async_job_status(invocation_arn, aws_region_name="us-east-1"): """Check the status of an async invoke job using LiteLLM batch API""" try: response = retrieve_batch( - batch_id=invocation_arn, + batch_id=invocation_arn, # Pass the invocation ARN here custom_llm_provider="bedrock", aws_region_name=aws_region_name ) @@ -128,11 +130,47 @@ def check_async_job_status(invocation_arn, aws_region_name="us-east-1"): # Check status status = check_async_job_status(invocation_arn, "us-east-1") if status: - print(f"Job Status: {status.status}") - print(f"Output Location: {status.output_file_id}") + print(f"Job Status: {status.status}") # "in_progress", "completed", or "failed" + print(f"Output Location: {status.metadata['output_file_id']}") # S3 URI where results are stored ``` -**Note:** The actual embedding results are stored in S3. The `output_file_id` from the batch status can be used to locate the results file in your S3 bucket. +#### Polling Until Complete + +Here's a complete example of polling for job completion: + +```python +def wait_for_async_job(invocation_arn, aws_region_name="us-east-1", max_wait=3600): + """Poll job status until completion""" + start_time = time.time() + + while True: + status = retrieve_batch( + batch_id=invocation_arn, + custom_llm_provider="bedrock", + aws_region_name=aws_region_name, + ) + + if status.status == "completed": + print("✅ Job completed!") + return status + elif status.status == "failed": + error_msg = status.metadata.get('failure_message', 'Unknown error') + raise Exception(f"❌ Job failed: {error_msg}") + else: + elapsed = time.time() - start_time + if elapsed > max_wait: + raise TimeoutError(f"Job timed out after {max_wait} seconds") + + print(f"⏳ Job still processing... (elapsed: {elapsed:.0f}s)") + time.sleep(10) # Wait 10 seconds before checking again + +# Wait for completion +completed_status = wait_for_async_job(invocation_arn) +output_s3_uri = completed_status.metadata['output_file_id'] +print(f"Results available at: {output_s3_uri}") +``` + +**Note:** The actual embedding results are stored in S3. When the job is completed, download the results from the S3 location specified in `status.metadata['output_file_id']`. The results will be in JSON/JSONL format containing the embedding vectors. ### Error Handling @@ -179,7 +217,7 @@ except Exception as e: ### Limitations -- Async-invoke is currently only supported for TwelveLabs Marengo models +- Async-invoke is supported for TwelveLabs Marengo and Amazon Nova models - Results are stored in S3 and must be retrieved separately using the output file ID - Job status checking requires using LiteLLM's `retrieve_batch()` function - No built-in polling mechanism in LiteLLM (must implement your own status checking loop) @@ -259,6 +297,7 @@ print(response) | Model Name | Usage | Supported Additional OpenAI params | |----------------------|---------------------------------------------|-----| +| **Amazon Nova Multimodal Embeddings** | `embedding(model="bedrock/amazon.nova-2-multimodal-embeddings-v1:0", input=input)` | Supports multimodal input (text, image, video, audio), multiple purposes, dimensions (256, 384, 1024, 3072) | | Titan Embeddings V2 | `embedding(model="bedrock/amazon.titan-embed-text-v2:0", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_v2_transformation.py#L59) | | Titan Embeddings - V1 | `embedding(model="bedrock/amazon.titan-embed-text-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_g1_transformation.py#L53) | Titan Multimodal Embeddings | `embedding(model="bedrock/amazon.titan-embed-image-v1", input=input)` | [here](https://github.com/BerriAI/litellm/blob/f5905e100068e7a4d61441d7453d7cf5609c2121/litellm/llms/bedrock/embed/amazon_titan_multimodal_transformation.py#L28) | diff --git a/docs/my-website/docs/providers/bedrock_imported.md b/docs/my-website/docs/providers/bedrock_imported.md new file mode 100644 index 00000000000..0784f716925 --- /dev/null +++ b/docs/my-website/docs/providers/bedrock_imported.md @@ -0,0 +1,434 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Bedrock Imported Models + +Bedrock Imported Models (Deepseek, Deepseek R1, Qwen, OpenAI-compatible models) + +### Deepseek R1 + +This is a separate route, as the chat template is different. + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/deepseek_r1/{model_arn}` | +| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html), [Deepseek Bedrock Imported Model](https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import/) | + + + + +```python +from litellm import completion +import os + +response = completion( + model="bedrock/deepseek_r1/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n", # bedrock/deepseek_r1/{your-model-arn} + messages=[{"role": "user", "content": "Tell me a joke"}], +) +``` + + + + + + +**1. Add to config** + +```yaml +model_list: + - model_name: DeepSeek-R1-Distill-Llama-70B + litellm_params: + model: bedrock/deepseek_r1/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n + +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "DeepSeek-R1-Distill-Llama-70B", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + + +### Deepseek (not R1) + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/llama/{model_arn}` | +| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html), [Deepseek Bedrock Imported Model](https://aws.amazon.com/blogs/machine-learning/deploy-deepseek-r1-distilled-llama-models-with-amazon-bedrock-custom-model-import/) | + + + +Use this route to call Bedrock Imported Models that follow the `llama` Invoke Request / Response spec + + + + + +```python +from litellm import completion +import os + +response = completion( + model="bedrock/llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n", # bedrock/llama/{your-model-arn} + messages=[{"role": "user", "content": "Tell me a joke"}], +) +``` + + + + + + +**1. Add to config** + +```yaml +model_list: + - model_name: DeepSeek-R1-Distill-Llama-70B + litellm_params: + model: bedrock/llama/arn:aws:bedrock:us-east-1:086734376398:imported-model/r4c4kewx2s0n + +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "DeepSeek-R1-Distill-Llama-70B", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + +### Qwen3 Imported Models + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/qwen3/{model_arn}` | +| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html), [Qwen3 Models](https://aws.amazon.com/about-aws/whats-new/2025/09/qwen3-models-fully-managed-amazon-bedrock/) | + + + + +```python +from litellm import completion +import os + +response = completion( + model="bedrock/qwen3/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen3-model", # bedrock/qwen3/{your-model-arn} + messages=[{"role": "user", "content": "Tell me a joke"}], + max_tokens=100, + temperature=0.7 +) +``` + + + + + +**1. Add to config** + +```yaml +model_list: + - model_name: Qwen3-32B + litellm_params: + model: bedrock/qwen3/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen3-model + +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "Qwen3-32B", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + +### Qwen2 Imported Models + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/qwen2/{model_arn}` | +| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html) | +| Note | Qwen2 and Qwen3 architectures are mostly similar. The main difference is in the response format: Qwen2 uses "text" field while Qwen3 uses "generation" field. | + + + + +```python +from litellm import completion +import os + +response = completion( + model="bedrock/qwen2/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen2-model", # bedrock/qwen2/{your-model-arn} + messages=[{"role": "user", "content": "Tell me a joke"}], + max_tokens=100, + temperature=0.7 +) +``` + + + + + +**1. Add to config** + +```yaml +model_list: + - model_name: Qwen2-72B + litellm_params: + model: bedrock/qwen2/arn:aws:bedrock:us-east-1:086734376398:imported-model/your-qwen2-model + +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "Qwen2-72B", # 👈 the 'model_name' in config + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + }' +``` + + + + +### OpenAI-Compatible Imported Models (Qwen 2.5 VL, etc.) + +Use this route for Bedrock imported models that follow the **OpenAI Chat Completions API spec**. This includes models like Qwen 2.5 VL that accept OpenAI-formatted messages with support for vision (images), tool calling, and other OpenAI features. + +| Property | Details | +|----------|---------| +| Provider Route | `bedrock/openai/{model_arn}` | +| Provider Documentation | [Bedrock Imported Models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html) | +| Supported Features | Vision (images), tool calling, streaming, system messages | + +#### LiteLLMSDK Usage + +**Basic Usage** + +```python +from litellm import completion + +response = completion( + model="bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z", # bedrock/openai/{your-model-arn} + messages=[{"role": "user", "content": "Tell me a joke"}], + max_tokens=300, + temperature=0.5 +) +``` + +**With Vision (Images)** + +```python +import base64 +from litellm import completion + +# Load and encode image +with open("image.jpg", "rb") as f: + image_base64 = base64.b64encode(f.read()).decode("utf-8") + +response = completion( + model="bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z", + messages=[ + { + "role": "system", + "content": "You are a helpful assistant that can analyze images." + }, + { + "role": "user", + "content": [ + {"type": "text", "text": "What's in this image?"}, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"} + } + ] + } + ], + max_tokens=300, + temperature=0.5 +) +``` + +**Comparing Multiple Images** + +```python +import base64 +from litellm import completion + +# Load images +with open("image1.jpg", "rb") as f: + image1_base64 = base64.b64encode(f.read()).decode("utf-8") +with open("image2.jpg", "rb") as f: + image2_base64 = base64.b64encode(f.read()).decode("utf-8") + +response = completion( + model="bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z", + messages=[ + { + "role": "system", + "content": "You are a helpful assistant that can analyze images." + }, + { + "role": "user", + "content": [ + {"type": "text", "text": "Spot the difference between these two images?"}, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{image1_base64}"} + }, + { + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{image2_base64}"} + } + ] + } + ], + max_tokens=300, + temperature=0.5 +) +``` + +#### LiteLLM Proxy Usage (AI Gateway) + +**1. Add to config** + +```yaml +model_list: + - model_name: qwen-25vl-72b + litellm_params: + model: bedrock/openai/arn:aws:bedrock:us-east-1:046319184608:imported-model/0m2lasirsp6z +``` + +**2. Start proxy** + +```bash +litellm --config /path/to/config.yaml + +# RUNNING at http://0.0.0.0:4000 +``` + +**3. Test it!** + +Basic text request: + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "qwen-25vl-72b", + "messages": [ + { + "role": "user", + "content": "what llm are you" + } + ], + "max_tokens": 300 + }' +``` + +With vision (image): + +```bash +curl --location 'http://0.0.0.0:4000/chat/completions' \ + --header 'Authorization: Bearer sk-1234' \ + --header 'Content-Type: application/json' \ + --data '{ + "model": "qwen-25vl-72b", + "messages": [ + { + "role": "system", + "content": "You are a helpful assistant that can analyze images." + }, + { + "role": "user", + "content": [ + {"type": "text", "text": "What is in this image?"}, + { + "type": "image_url", + "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZ..."} + } + ] + } + ], + "max_tokens": 300, + "temperature": 0.5 + }' +``` \ No newline at end of file diff --git a/docs/my-website/docs/providers/gemini_file_search.md b/docs/my-website/docs/providers/gemini_file_search.md new file mode 100644 index 00000000000..947715218a3 --- /dev/null +++ b/docs/my-website/docs/providers/gemini_file_search.md @@ -0,0 +1,414 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Gemini File Search + +Use Google Gemini's File Search for Retrieval Augmented Generation (RAG) with LiteLLM. + +Gemini File Search imports, chunks, and indexes your data to enable fast retrieval of relevant information based on user prompts. This information is then provided as context to the model for more accurate and relevant answers. + +[Official Gemini File Search Documentation](https://ai.google.dev/gemini-api/docs/file-search) + +## Features + +| Feature | Supported | Notes | +|---------|-----------|-------| +| Cost Tracking | ❌ | Cost calculation not yet implemented | +| Logging | ✅ | Full request/response logging | +| RAG Ingest API | ✅ | Upload → Chunk → Embed → Store | +| Vector Store Search | ✅ | Search with metadata filters | +| Custom Chunking | ✅ | Configure chunk size and overlap | +| Metadata Filtering | ✅ | Filter by custom metadata | +| Citations | ✅ | Extract from grounding metadata | + +## Quick Start + +### Setup + +Set your Gemini API key: + +```bash +export GEMINI_API_KEY="your-api-key" +# or +export GOOGLE_API_KEY="your-api-key" +``` + +### Basic RAG Ingest + + + + +```python +import litellm + +# Ingest a document +response = await litellm.aingest( + ingest_options={ + "name": "my-document-store", + "vector_store": { + "custom_llm_provider": "gemini" + } + }, + file_data=("document.txt", b"Your document content", "text/plain") +) + +print(f"Vector Store ID: {response['vector_store_id']}") +print(f"File ID: {response['file_id']}") +``` + + + + + +```bash +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "file": { + "filename": "document.txt", + "content": "'$(base64 -i document.txt)'", + "content_type": "text/plain" + }, + "ingest_options": { + "name": "my-document-store", + "vector_store": { + "custom_llm_provider": "gemini" + } + } + }' +``` + + + + +### Search Vector Store + + + + +```python +import litellm + +# Search the vector store +response = await litellm.vector_stores.asearch( + vector_store_id="fileSearchStores/your-store-id", + query="What is the main topic?", + custom_llm_provider="gemini", + max_num_results=5 +) + +for result in response["data"]: + print(f"Score: {result.get('score')}") + print(f"Content: {result['content'][0]['text']}") +``` + + + + + +```bash +curl -X POST "http://localhost:4000/v1/vector_stores/fileSearchStores/your-store-id/search" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "query": "What is the main topic?", + "custom_llm_provider": "gemini", + "max_num_results": 5 + }' +``` + + + + +## Advanced Features + +### Custom Chunking Configuration + +Control how documents are split into chunks: + +```python +import litellm + +response = await litellm.aingest( + ingest_options={ + "name": "custom-chunking-store", + "vector_store": { + "custom_llm_provider": "gemini" + }, + "chunking_strategy": { + "white_space_config": { + "max_tokens_per_chunk": 200, + "max_overlap_tokens": 20 + } + } + }, + file_data=("document.txt", document_content, "text/plain") +) +``` + +**Chunking Parameters:** +- `max_tokens_per_chunk`: Maximum tokens per chunk (default: 800, min: 100, max: 4096) +- `max_overlap_tokens`: Overlap between chunks (default: 400) + +### Metadata Filtering + +Attach custom metadata to files and filter searches: + +#### Attach Metadata During Ingest + +```python +import litellm + +response = await litellm.aingest( + ingest_options={ + "name": "metadata-store", + "vector_store": { + "custom_llm_provider": "gemini", + "custom_metadata": [ + {"key": "author", "string_value": "John Doe"}, + {"key": "year", "numeric_value": 2024}, + {"key": "category", "string_value": "documentation"} + ] + } + }, + file_data=("document.txt", document_content, "text/plain") +) +``` + +#### Search with Metadata Filter + +```python +import litellm + +response = await litellm.vector_stores.asearch( + vector_store_id="fileSearchStores/your-store-id", + query="What is LiteLLM?", + custom_llm_provider="gemini", + filters={"author": "John Doe", "category": "documentation"} +) +``` + +**Filter Syntax:** +- Simple equality: `{"key": "value"}` +- Gemini converts to: `key="value"` +- Multiple filters combined with AND + +### Using Existing Vector Store + +Ingest into an existing File Search store: + +```python +import litellm + +# First, create a store +create_response = await litellm.vector_stores.acreate( + name="My Persistent Store", + custom_llm_provider="gemini" +) +store_id = create_response["id"] + +# Then ingest multiple documents into it +for doc in documents: + await litellm.aingest( + ingest_options={ + "vector_store": { + "custom_llm_provider": "gemini", + "vector_store_id": store_id # Reuse existing store + } + }, + file_data=(doc["name"], doc["content"], doc["type"]) + ) +``` + +### Citation Extraction + +Gemini provides grounding metadata with citations: + +```python +import litellm + +response = await litellm.vector_stores.asearch( + vector_store_id="fileSearchStores/your-store-id", + query="Explain the concept", + custom_llm_provider="gemini" +) + +for result in response["data"]: + # Access citation information + if "attributes" in result: + print(f"URI: {result['attributes'].get('uri')}") + print(f"Title: {result['attributes'].get('title')}") + + # Content with relevance score + print(f"Score: {result.get('score')}") + print(f"Text: {result['content'][0]['text']}") +``` + +## Complete Example + +End-to-end workflow: + +```python +import litellm + +# 1. Create a File Search store +store_response = await litellm.vector_stores.acreate( + name="Knowledge Base", + custom_llm_provider="gemini" +) +store_id = store_response["id"] +print(f"Created store: {store_id}") + +# 2. Ingest documents with custom chunking and metadata +documents = [ + { + "name": "intro.txt", + "content": b"Introduction to LiteLLM...", + "metadata": [ + {"key": "section", "string_value": "intro"}, + {"key": "priority", "numeric_value": 1} + ] + }, + { + "name": "advanced.txt", + "content": b"Advanced features...", + "metadata": [ + {"key": "section", "string_value": "advanced"}, + {"key": "priority", "numeric_value": 2} + ] + } +] + +for doc in documents: + ingest_response = await litellm.aingest( + ingest_options={ + "name": f"ingest-{doc['name']}", + "vector_store": { + "custom_llm_provider": "gemini", + "vector_store_id": store_id, + "custom_metadata": doc["metadata"] + }, + "chunking_strategy": { + "white_space_config": { + "max_tokens_per_chunk": 300, + "max_overlap_tokens": 50 + } + } + }, + file_data=(doc["name"], doc["content"], "text/plain") + ) + print(f"Ingested: {doc['name']}") + +# 3. Search with filters +search_response = await litellm.vector_stores.asearch( + vector_store_id=store_id, + query="How do I get started?", + custom_llm_provider="gemini", + filters={"section": "intro"}, + max_num_results=3 +) + +# 4. Process results +for i, result in enumerate(search_response["data"]): + print(f"\nResult {i+1}:") + print(f" Score: {result.get('score')}") + print(f" File: {result.get('filename')}") + print(f" Content: {result['content'][0]['text'][:100]}...") +``` + +## Supported File Types + +Gemini File Search supports a wide range of file formats: + +### Documents +- PDF (`application/pdf`) +- Microsoft Word (`.docx`, `.doc`) +- Microsoft Excel (`.xlsx`, `.xls`) +- Microsoft PowerPoint (`.pptx`) +- OpenDocument formats (`.odt`, `.ods`, `.odp`) + +### Text Files +- Plain text (`text/plain`) +- Markdown (`text/markdown`) +- HTML (`text/html`) +- CSV (`text/csv`) +- JSON (`application/json`) +- XML (`application/xml`) + +### Code Files +- Python, JavaScript, TypeScript, Java, C/C++, Go, Rust, etc. +- Most common programming languages supported + +See [Gemini's full list of supported file types](https://ai.google.dev/gemini-api/docs/file-search#supported-file-types). + +## Pricing + +- **Indexing**: $0.15 per 1M tokens (embedding pricing) +- **Storage**: Free +- **Query embeddings**: Free +- **Retrieved tokens**: Charged as regular context tokens + +## Supported Models + +File Search works with: +- `gemini-3-pro-preview` +- `gemini-2.5-pro` +- `gemini-2.5-flash` (and preview versions) +- `gemini-2.5-flash-lite` (and preview versions) + +## Troubleshooting + +### Authentication Errors + +```python +# Ensure API key is set +import os +os.environ["GEMINI_API_KEY"] = "your-api-key" + +# Or pass explicitly +response = await litellm.aingest( + ingest_options={ + "vector_store": { + "custom_llm_provider": "gemini", + "api_key": "your-api-key" + } + }, + file_data=(...) +) +``` + +### Store Not Found + +Ensure you're using the full store name format: +- ✅ `fileSearchStores/abc123` +- ❌ `abc123` + +### Large Files + +For files >100MB, split them into smaller chunks before ingestion. + +### Slow Indexing + +After ingestion, Gemini may need time to index documents. Wait a few seconds before searching: + +```python +import time + +# After ingest +await litellm.aingest(...) + +# Wait for indexing +time.sleep(5) + +# Then search +await litellm.vector_stores.asearch(...) +``` + +## Related Resources + +- [Gemini File Search Official Docs](https://ai.google.dev/gemini-api/docs/file-search) +- [LiteLLM RAG Ingest API](/docs/rag_ingest) +- [LiteLLM Vector Store Search](/docs/vector_stores/search) +- [Using Vector Stores with Chat](/docs/completion/knowledgebase) + diff --git a/docs/my-website/docs/providers/github_copilot.md b/docs/my-website/docs/providers/github_copilot.md index 2ebe6eacb1c..306c9f949ec 100644 --- a/docs/my-website/docs/providers/github_copilot.md +++ b/docs/my-website/docs/providers/github_copilot.md @@ -15,7 +15,7 @@ https://docs.github.com/en/copilot |-------|-------| | Description | GitHub Copilot Chat API provides access to GitHub's AI-powered coding assistant. | | Provider Route on LiteLLM | `github_copilot/` | -| Supported Endpoints | `/chat/completions` | +| Supported Endpoints | `/chat/completions`, `/embeddings` | | API Reference | [GitHub Copilot docs](https://docs.github.com/en/copilot) | ## Authentication @@ -62,6 +62,34 @@ for chunk in stream: print(chunk.choices[0].delta.content, end="") ``` +### Responses + +For GPT Codex models, only responses API is supported. + +```python showLineNumbers title="GitHub Copilot Responses" +import litellm + +response = await litellm.aresponses( + model="github_copilot/gpt-5.1-codex", + input="Write a Python hello world", + max_output_tokens=500 +) + +print(response) +``` + +### Embedding + +```python showLineNumbers title="GitHub Copilot Embedding" +import litellm + +response = litellm.embedding( + model="github_copilot/text-embedding-3-small", + input=["good morning from litellm"] +) +print(response) +``` + ## Usage - LiteLLM Proxy Add the following to your LiteLLM Proxy configuration file: @@ -71,6 +99,16 @@ model_list: - model_name: github_copilot/gpt-4 litellm_params: model: github_copilot/gpt-4 + - model_name: github_copilot/gpt-5.1-codex + model_info: + mode: responses + litellm_params: + model: github_copilot/gpt-5.1-codex + - model_name: github_copilot/text-embedding-ada-002 + model_info: + mode: embedding + litellm_params: + model: github_copilot/text-embedding-ada-002 ``` Start your LiteLLM Proxy server: @@ -180,7 +218,7 @@ extra_headers = { "editor-version": "vscode/1.85.1", # Editor version "editor-plugin-version": "copilot/1.155.0", # Plugin version "Copilot-Integration-Id": "vscode-chat", # Integration ID - "user-agent": "GithubCopilot/1.155.0" # User agent + "user-agent": "GithubCopilot/1.155.0" # User agent } ``` diff --git a/docs/my-website/docs/providers/ovhcloud.md b/docs/my-website/docs/providers/ovhcloud.md index 6c42208f2cc..94625b0f2ed 100644 --- a/docs/my-website/docs/providers/ovhcloud.md +++ b/docs/my-website/docs/providers/ovhcloud.md @@ -311,6 +311,21 @@ response = embedding( print(response.data) ``` +### Audio Transcription + +```python +from litellm import transcription + +audio_file = open("path/to/your/audio.wav", "rb") + +response = transcription( + model="ovhcloud/whisper-large-v3-turbo", + file=audio_file +) + +print(response.text) +``` + ## Usage with LiteLLM Proxy Server Here's how to call a OVHCloud AI Endpoints model with the LiteLLM Proxy Server diff --git a/docs/my-website/docs/providers/publicai.md b/docs/my-website/docs/providers/publicai.md new file mode 100644 index 00000000000..1ab8bd5a06c --- /dev/null +++ b/docs/my-website/docs/providers/publicai.md @@ -0,0 +1,209 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# PublicAI + +## Overview + +| Property | Details | +|-------|-------| +| Description | PublicAI provides large language models including essential models like the swiss-ai apertus model. | +| Provider Route on LiteLLM | `publicai/` | +| Link to Provider Doc | [PublicAI ↗](https://platform.publicai.co/) | +| Base URL | `https://platform.publicai.co/` | +| Supported Operations | [`/chat/completions`](#sample-usage) | + +
+
+ +https://platform.publicai.co/ + +**We support ALL PublicAI models, just set `publicai/` as a prefix when sending completion requests** + +## Required Variables + +```python showLineNumbers title="Environment Variables" +os.environ["PUBLICAI_API_KEY"] = "" # your PublicAI API key +``` + +You can overwrite the base url with: + +``` +os.environ["PUBLICAI_API_BASE"] = "https://platform.publicai.co/v1" +``` + +## Usage - LiteLLM Python SDK + +### Non-streaming + +```python showLineNumbers title="PublicAI Non-streaming Completion" +import os +import litellm +from litellm import completion + +os.environ["PUBLICAI_API_KEY"] = "" # your PublicAI API key + +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# PublicAI call +response = completion( + model="publicai/swiss-ai/apertus-8b-instruct", + messages=messages +) + +print(response) +``` + +### Streaming + +```python showLineNumbers title="PublicAI Streaming Completion" +import os +import litellm +from litellm import completion + +os.environ["PUBLICAI_API_KEY"] = "" # your PublicAI API key + +messages = [{"content": "Hello, how are you?", "role": "user"}] + +# PublicAI call with streaming +response = completion( + model="publicai/swiss-ai/apertus-8b-instruct", + messages=messages, + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Usage - LiteLLM Proxy + +Add the following to your LiteLLM Proxy configuration file: + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: swiss-ai-apertus-8b + litellm_params: + model: publicai/swiss-ai/apertus-8b-instruct + api_key: os.environ/PUBLICAI_API_KEY + + - model_name: swiss-ai-apertus-70b + litellm_params: + model: publicai/swiss-ai/apertus-70b-instruct + api_key: os.environ/PUBLICAI_API_KEY +``` + +Start your LiteLLM Proxy server: + +```bash showLineNumbers title="Start LiteLLM Proxy" +litellm --config config.yaml + +# RUNNING on http://0.0.0.0:4000 +``` + + + + +```python showLineNumbers title="PublicAI via Proxy - Non-streaming" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-proxy-api-key" # Your proxy API key +) + +# Non-streaming response +response = client.chat.completions.create( + model="swiss-ai-apertus-8b", + messages=[{"role": "user", "content": "hello from litellm"}] +) + +print(response.choices[0].message.content) +``` + +```python showLineNumbers title="PublicAI via Proxy - Streaming" +from openai import OpenAI + +# Initialize client with your proxy URL +client = OpenAI( + base_url="http://localhost:4000", # Your proxy URL + api_key="your-proxy-api-key" # Your proxy API key +) + +# Streaming response +response = client.chat.completions.create( + model="swiss-ai-apertus-8b", + messages=[{"role": "user", "content": "hello from litellm"}], + stream=True +) + +for chunk in response: + if chunk.choices[0].delta.content is not None: + print(chunk.choices[0].delta.content, end="") +``` + + + + + +```python showLineNumbers title="PublicAI via Proxy - LiteLLM SDK" +import litellm + +# Configure LiteLLM to use your proxy +response = litellm.completion( + model="litellm_proxy/swiss-ai-apertus-8b", + messages=[{"role": "user", "content": "hello from litellm"}], + api_base="http://localhost:4000", + api_key="your-proxy-api-key" +) + +print(response.choices[0].message.content) +``` + +```python showLineNumbers title="PublicAI via Proxy - LiteLLM SDK Streaming" +import litellm + +# Configure LiteLLM to use your proxy with streaming +response = litellm.completion( + model="litellm_proxy/swiss-ai-apertus-8b", + messages=[{"role": "user", "content": "hello from litellm"}], + api_base="http://localhost:4000", + api_key="your-proxy-api-key", + stream=True +) + +for chunk in response: + if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content is not None: + print(chunk.choices[0].delta.content, end="") +``` + + + + + +```bash showLineNumbers title="PublicAI via Proxy - cURL" +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-proxy-api-key" \ + -d '{ + "model": "swiss-ai-apertus-8b", + "messages": [{"role": "user", "content": "hello from litellm"}] + }' +``` + +```bash showLineNumbers title="PublicAI via Proxy - cURL Streaming" +curl http://localhost:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer your-proxy-api-key" \ + -d '{ + "model": "swiss-ai-apertus-8b", + "messages": [{"role": "user", "content": "hello from litellm"}], + "stream": true + }' +``` + + + + +For more detailed information on using the LiteLLM Proxy, see the [LiteLLM Proxy documentation](../providers/litellm_proxy). diff --git a/docs/my-website/docs/providers/ragflow.md b/docs/my-website/docs/providers/ragflow.md new file mode 100644 index 00000000000..73223bd07b5 --- /dev/null +++ b/docs/my-website/docs/providers/ragflow.md @@ -0,0 +1,244 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# RAGFlow + +Litellm supports Ragflow's chat completions APIs + +## Supported Features + +- ✅ Chat completions +- ✅ Streaming responses +- ✅ Both chat and agent endpoints +- ✅ Multiple credential sources (params, env vars, litellm_params) +- ✅ OpenAI-compatible API format + + +## API Key + +```python +# env variable +os.environ['RAGFLOW_API_KEY'] +``` + +## API Base + +```python +# env variable +os.environ['RAGFLOW_API_BASE'] +``` + +## Overview + +RAGFlow provides OpenAI-compatible APIs with unique path structures that include chat and agent IDs: + +- **Chat endpoint**: `/api/v1/chats_openai/{chat_id}/chat/completions` +- **Agent endpoint**: `/api/v1/agents_openai/{agent_id}/chat/completions` + +The model name format embeds the endpoint type and ID: +- Chat: `ragflow/chat/{chat_id}/{model_name}` +- Agent: `ragflow/agent/{agent_id}/{model_name}` + + +## Sample Usage - Chat Endpoint + +```python +from litellm import completion +import os + +os.environ['RAGFLOW_API_KEY'] = "your-ragflow-api-key" +os.environ['RAGFLOW_API_BASE'] = "http://localhost:9380" # or your hosted URL + +response = completion( + model="ragflow/chat/my-chat-id/gpt-4o-mini", + messages=[{"role": "user", "content": "How does the deep doc understanding work?"}] +) +print(response) +``` + +## Sample Usage - Agent Endpoint + +```python +from litellm import completion +import os + +os.environ['RAGFLOW_API_KEY'] = "your-ragflow-api-key" +os.environ['RAGFLOW_API_BASE'] = "http://localhost:9380" # or your hosted URL + +response = completion( + model="ragflow/agent/my-agent-id/gpt-4o-mini", + messages=[{"role": "user", "content": "What are the key features?"}] +) +print(response) +``` + +## Sample Usage - With Parameters + +You can also pass `api_key` and `api_base` directly as parameters: + +```python +from litellm import completion + +response = completion( + model="ragflow/chat/my-chat-id/gpt-4o-mini", + messages=[{"role": "user", "content": "Hello!"}], + api_key="your-ragflow-api-key", + api_base="http://localhost:9380" +) +print(response) +``` + +## Sample Usage - Streaming + +```python +from litellm import completion +import os + +os.environ['RAGFLOW_API_KEY'] = "your-ragflow-api-key" +os.environ['RAGFLOW_API_BASE'] = "http://localhost:9380" + +response = completion( + model="ragflow/agent/my-agent-id/gpt-4o-mini", + messages=[{"role": "user", "content": "Explain RAGFlow"}], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Model Name Format + +The model name must follow one of these formats: + +### Chat Endpoint +``` +ragflow/chat/{chat_id}/{model_name} +``` + +Example: `ragflow/chat/my-chat-id/gpt-4o-mini` + +### Agent Endpoint +``` +ragflow/agent/{agent_id}/{model_name} +``` + +Example: `ragflow/agent/my-agent-id/gpt-4o-mini` + +Where: +- `{chat_id}` or `{agent_id}` is the ID of your chat or agent in RAGFlow +- `{model_name}` is the actual model name (e.g., `gpt-4o-mini`, `gpt-4o`, etc.) + +## Configuration Sources + +LiteLLM supports multiple ways to provide credentials, checked in this order: + +1. **Function parameters**: `api_key="..."`, `api_base="..."` +2. **litellm_params**: `litellm_params={"api_key": "...", "api_base": "..."}` +3. **Environment variables**: `RAGFLOW_API_KEY`, `RAGFLOW_API_BASE` +4. **Global litellm settings**: `litellm.api_key`, `litellm.api_base` + +## Usage - LiteLLM Proxy Server + +### 1. Save key in your environment + +```bash +export RAGFLOW_API_KEY="your-ragflow-api-key" +export RAGFLOW_API_BASE="http://localhost:9380" +``` + +### 2. Start the proxy + + + + +```yaml +model_list: + - model_name: ragflow-chat-gpt4 + litellm_params: + model: ragflow/chat/my-chat-id/gpt-4o-mini + api_key: os.environ/RAGFLOW_API_KEY + api_base: os.environ/RAGFLOW_API_BASE + - model_name: ragflow-agent-gpt4 + litellm_params: + model: ragflow/agent/my-agent-id/gpt-4o-mini + api_key: os.environ/RAGFLOW_API_KEY + api_base: os.environ/RAGFLOW_API_BASE +``` + + + + +```bash +$ litellm --config /path/to/config.yaml + +# Server running on http://0.0.0.0:4000 +``` + + + + +### 3. Test it + + + + +```bash +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "ragflow-chat-gpt4", + "messages": [ + {"role": "user", "content": "How does RAGFlow work?"} + ] + }' +``` + + + + +```python +from openai import OpenAI + +client = OpenAI( + api_key="sk-1234", # Your LiteLLM proxy key + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="ragflow-chat-gpt4", + messages=[ + {"role": "user", "content": "How does RAGFlow work?"} + ] +) +print(response) +``` + + + + +## API Base URL Handling + +The `api_base` parameter can be provided with or without `/v1` suffix. LiteLLM will automatically handle it: + +- `http://localhost:9380` → `http://localhost:9380/api/v1/chats_openai/{chat_id}/chat/completions` +- `http://localhost:9380/v1` → `http://localhost:9380/api/v1/chats_openai/{chat_id}/chat/completions` +- `http://localhost:9380/api/v1` → `http://localhost:9380/api/v1/chats_openai/{chat_id}/chat/completions` + +All three formats will work correctly. + +## Error Handling + +If you encounter errors: + +1. **Invalid model format**: Ensure your model name follows `ragflow/{chat|agent}/{id}/{model_name}` format +2. **Missing api_base**: Provide `api_base` via parameter, environment variable, or litellm_params +3. **Connection errors**: Verify your RAGFlow server is running and accessible at the provided `api_base` + +:::info + +For more information about passing provider-specific parameters, [go here](../completion/provider_specific_params.md) + +::: + diff --git a/docs/my-website/docs/providers/ragflow_vector_store.md b/docs/my-website/docs/providers/ragflow_vector_store.md new file mode 100644 index 00000000000..bc014cacbe6 --- /dev/null +++ b/docs/my-website/docs/providers/ragflow_vector_store.md @@ -0,0 +1,349 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; +import Image from '@theme/IdealImage'; + +# RAGFlow Vector Stores + +Litellm support creation and management of datasets for document processing and knowledge base management in Ragflow. + +| Property | Details | +|----------|---------| +| Description | RAGFlow datasets enable document processing, chunking, and knowledge base management for RAG applications. | +| Provider Route on LiteLLM | `ragflow` in the litellm vector_store_registry | +| Provider Doc | [RAGFlow API Documentation ↗](https://ragflow.io/docs) | +| Supported Operations | Dataset Management (Create, List, Update, Delete) | +| Search/Retrieval | ❌ Not supported (management only) | + +## Quick Start + +### LiteLLM Python SDK + +```python showLineNumbers title="Example using LiteLLM Python SDK" +import os +import litellm + +# Set RAGFlow credentials +os.environ["RAGFLOW_API_KEY"] = "your-ragflow-api-key" +os.environ["RAGFLOW_API_BASE"] = "http://localhost:9380" # Optional, defaults to localhost:9380 + +# Create a RAGFlow dataset +response = litellm.vector_stores.create( + name="my-dataset", + custom_llm_provider="ragflow", + metadata={ + "description": "My knowledge base dataset", + "embedding_model": "BAAI/bge-large-zh-v1.5@BAAI", + "chunk_method": "naive" + } +) + +print(f"Created dataset ID: {response.id}") +print(f"Dataset name: {response.name}") +``` + +### LiteLLM Proxy + +#### 1. Configure your vector_store_registry + + + + +```yaml +model_list: + - model_name: gpt-4o-mini + litellm_params: + model: gpt-4o-mini + api_key: os.environ/OPENAI_API_KEY + +vector_store_registry: + - vector_store_name: "ragflow-knowledge-base" + litellm_params: + vector_store_id: "your-dataset-id" + custom_llm_provider: "ragflow" + api_key: os.environ/RAGFLOW_API_KEY + api_base: os.environ/RAGFLOW_API_BASE # Optional + vector_store_description: "RAGFlow dataset for knowledge base" + vector_store_metadata: + source: "Company documentation" +``` + + + + + +On the LiteLLM UI, Navigate to Experimental > Vector Stores > Create Vector Store. On this page you can create a vector store with a name, vector store id and credentials. + + + + + + +#### 2. Create a dataset via Proxy + + + + +```bash +curl http://localhost:4000/v1/vector_stores \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer $LITELLM_API_KEY" \ + -d '{ + "name": "my-ragflow-dataset", + "custom_llm_provider": "ragflow", + "metadata": { + "description": "Test dataset", + "chunk_method": "naive" + } + }' +``` + + + + + +```python +from openai import OpenAI + +# Initialize client with your LiteLLM proxy URL +client = OpenAI( + base_url="http://localhost:4000", + api_key="your-litellm-api-key" +) + +# Create a RAGFlow dataset +response = client.vector_stores.create( + name="my-ragflow-dataset", + custom_llm_provider="ragflow", + metadata={ + "description": "Test dataset", + "chunk_method": "naive" + } +) + +print(f"Created dataset: {response.id}") +``` + + + + +## Configuration + +### Environment Variables + +RAGFlow vector stores support configuration via environment variables: + +- `RAGFLOW_API_KEY` - Your RAGFlow API key (required) +- `RAGFLOW_API_BASE` - RAGFlow API base URL (optional, defaults to `http://localhost:9380`) + +### Parameters + +You can also pass these via `litellm_params`: + +- `api_key` - RAGFlow API key (overrides `RAGFLOW_API_KEY` env var) +- `api_base` - RAGFlow API base URL (overrides `RAGFLOW_API_BASE` env var) + +## Dataset Creation Options + +### Basic Dataset Creation + +```python +response = litellm.vector_stores.create( + name="basic-dataset", + custom_llm_provider="ragflow" +) +``` + +### Dataset with Chunk Method + +RAGFlow supports various chunk methods for different document types: + + + + +```python +response = litellm.vector_stores.create( + name="general-dataset", + custom_llm_provider="ragflow", + metadata={ + "chunk_method": "naive", + "parser_config": { + "chunk_token_num": 512, + "delimiter": "\n", + "html4excel": False, + "layout_recognize": "DeepDOC" + } + } +) +``` + + + + + +```python +response = litellm.vector_stores.create( + name="book-dataset", + custom_llm_provider="ragflow", + metadata={ + "chunk_method": "book", + "parser_config": { + "raptor": { + "use_raptor": False + } + } + } +) +``` + + + + + +```python +response = litellm.vector_stores.create( + name="qa-dataset", + custom_llm_provider="ragflow", + metadata={ + "chunk_method": "qa", + "parser_config": { + "raptor": { + "use_raptor": False + } + } + } +) +``` + + + + + +```python +response = litellm.vector_stores.create( + name="paper-dataset", + custom_llm_provider="ragflow", + metadata={ + "chunk_method": "paper", + "parser_config": { + "raptor": { + "use_raptor": False + } + } + } +) +``` + + + + +### Dataset with Ingestion Pipeline + +Instead of using a chunk method, you can use an ingestion pipeline: + +```python +response = litellm.vector_stores.create( + name="pipeline-dataset", + custom_llm_provider="ragflow", + metadata={ + "parse_type": 2, # Number of parsers in your pipeline + "pipeline_id": "d0bebe30ae2211f0970942010a8e0005" # 32-character hex ID + } +) +``` + +**Note**: `chunk_method` and `pipeline_id` are mutually exclusive. Use one or the other. + +### Advanced Parser Configuration + +```python +response = litellm.vector_stores.create( + name="advanced-dataset", + custom_llm_provider="ragflow", + metadata={ + "chunk_method": "naive", + "description": "Advanced dataset with custom parser config", + "embedding_model": "BAAI/bge-large-zh-v1.5@BAAI", + "permission": "me", # or "team" + "parser_config": { + "chunk_token_num": 1024, + "delimiter": "\n!?;。；！？", + "html4excel": True, + "layout_recognize": "DeepDOC", + "auto_keywords": 5, + "auto_questions": 3, + "task_page_size": 12, + "raptor": { + "use_raptor": True + }, + "graphrag": { + "use_graphrag": False + } + } + } +) +``` + +## Supported Chunk Methods + +RAGFlow supports the following chunk methods: + +- `naive` - General purpose (default) +- `book` - For book documents +- `email` - For email documents +- `laws` - For legal documents +- `manual` - Manual chunking +- `one` - Single chunk +- `paper` - For academic papers +- `picture` - For image documents +- `presentation` - For presentation documents +- `qa` - Q&A format +- `table` - For table documents +- `tag` - Tag-based chunking + +## RAGFlow-Specific Parameters + +All RAGFlow-specific parameters should be passed via the `metadata` field: + +| Parameter | Type | Description | +|-----------|------|-------------| +| `avatar` | string | Base64 encoding of the avatar (max 65535 chars) | +| `description` | string | Brief description of the dataset (max 65535 chars) | +| `embedding_model` | string | Embedding model name (e.g., "BAAI/bge-large-zh-v1.5@BAAI") | +| `permission` | string | Access permission: "me" (default) or "team" | +| `chunk_method` | string | Chunking method (see supported methods above) | +| `parser_config` | object | Parser configuration (varies by chunk_method) | +| `parse_type` | int | Number of parsers in pipeline (required with pipeline_id) | +| `pipeline_id` | string | 32-character hex pipeline ID (required with parse_type) | + +## Error Handling + +RAGFlow returns error responses in the following format: + +```json +{ + "code": 101, + "message": "Dataset name 'my-dataset' already exists" +} +``` + +LiteLLM automatically maps these to appropriate exceptions: + +- `code != 0` → Raises exception with the error message +- Missing required fields → Raises `ValueError` +- Mutually exclusive parameters → Raises `ValueError` + +## Limitations + +- **Search/Retrieval**: RAGFlow vector stores support dataset management only. Search operations are not supported and will raise `NotImplementedError`. +- **List/Update/Delete**: These operations are not yet implemented through the standard vector store API. Use RAGFlow's native API endpoints directly. + +## Further Reading + +Vector Stores: +- [Vector Store Creation](../vector_stores/create.md) +- [Using Vector Stores with Completions](../completion/knowledgebase.md) +- [Vector Store Registry](../completion/knowledgebase.md#vectorstoreregistry) + diff --git a/docs/my-website/docs/providers/vertex.md b/docs/my-website/docs/providers/vertex.md index 70babea3814..da2997f6202 100644 --- a/docs/my-website/docs/providers/vertex.md +++ b/docs/my-website/docs/providers/vertex.md @@ -2550,355 +2550,6 @@ print(response) - -## **Gemini TTS (Text-to-Speech) Audio Output** - -:::info - -LiteLLM supports Gemini TTS models on Vertex AI that can generate audio responses using the OpenAI-compatible `audio` parameter format. - -::: - -### Supported Models - -LiteLLM supports Gemini TTS models with audio capabilities on Vertex AI (e.g. `vertex_ai/gemini-2.5-flash-preview-tts` and `vertex_ai/gemini-2.5-pro-preview-tts`). For the complete list of available TTS models and voices, see the [official Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation). - -### Limitations - -:::warning - -**Important Limitations**: -- Gemini TTS models only support the `pcm16` audio format -- **Streaming support has not been added** to TTS models yet -- The `modalities` parameter must be set to `['audio']` for TTS requests - -::: - -### Quick Start - - - - -```python -from litellm import completion -import json - -## GET CREDENTIALS -file_path = 'path/to/vertex_ai_service_account.json' - -# Load the JSON file -with open(file_path, 'r') as file: - vertex_credentials = json.load(file) - -# Convert to JSON string -vertex_credentials_json = json.dumps(vertex_credentials) - -response = completion( - model="vertex_ai/gemini-2.5-flash-preview-tts", - messages=[{"role": "user", "content": "Say hello in a friendly voice"}], - modalities=["audio"], # Required for TTS models - audio={ - "voice": "Kore", - "format": "pcm16" # Required: must be "pcm16" - }, - vertex_credentials=vertex_credentials_json -) - -print(response) -``` - - - - -1. Setup config.yaml - -```yaml -model_list: - - model_name: gemini-tts-flash - litellm_params: - model: vertex_ai/gemini-2.5-flash-preview-tts - vertex_project: "your-project-id" - vertex_location: "us-central1" - vertex_credentials: "/path/to/service_account.json" - - model_name: gemini-tts-pro - litellm_params: - model: vertex_ai/gemini-2.5-pro-preview-tts - vertex_project: "your-project-id" - vertex_location: "us-central1" - vertex_credentials: "/path/to/service_account.json" -``` - -2. Start proxy - -```bash -litellm --config /path/to/config.yaml -``` - -3. Make TTS request - -```bash -curl http://0.0.0.0:4000/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer " \ - -d '{ - "model": "gemini-tts-flash", - "messages": [{"role": "user", "content": "Say hello in a friendly voice"}], - "modalities": ["audio"], - "audio": { - "voice": "Kore", - "format": "pcm16" - } - }' -``` - - - - -### Advanced Usage - -You can combine TTS with other Gemini features: - -```python -response = completion( - model="vertex_ai/gemini-2.5-pro-preview-tts", - messages=[ - {"role": "system", "content": "You are a helpful assistant that speaks clearly."}, - {"role": "user", "content": "Explain quantum computing in simple terms"} - ], - modalities=["audio"], - audio={ - "voice": "Charon", - "format": "pcm16" - }, - temperature=0.7, - max_tokens=150, - vertex_credentials=vertex_credentials_json -) -``` - -For more information about Gemini's TTS capabilities and available voices, see the [official Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation). - -## **Text to Speech APIs** - -:::info - -LiteLLM supports calling [Vertex AI Text to Speech API](https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech) in the OpenAI text to speech API format - -::: - - - -### Usage - Basic - - - - -Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - -**Sync Usage** - -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" -response = litellm.speech( - model="vertex_ai/", - input="hello what llm guardrail do you have", -) -response.stream_to_file(speech_file_path) -``` - -**Async Usage** -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" -response = litellm.aspeech( - model="vertex_ai/", - input="hello what llm guardrail do you have", -) -response.stream_to_file(speech_file_path) -``` - - - - -1. Add model to config.yaml -```yaml -model_list: - - model_name: vertex-tts - litellm_params: - model: vertex_ai/ # Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - vertex_project: "adroit-crow-413218" - vertex_location: "us-central1" - vertex_credentials: adroit-crow-413218-a956eef1a2a8.json - -litellm_settings: - drop_params: True -``` - -2. Start Proxy - -``` -$ litellm --config /path/to/config.yaml -``` - -3. Make Request use OpenAI Python SDK - - -```python -import openai - -client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -# see supported values for "voice" on vertex here: -# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech -response = client.audio.speech.create( - model = "vertex-tts", - input="the quick brown fox jumped over the lazy dogs", - voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'} -) -print("response from proxy", response) -``` - - - - - -### Usage - `ssml` as input - -Pass your `ssml` as input to the `input` param, if it contains ``, it will be automatically detected and passed as `ssml` to the Vertex AI API - -If you need to force your `input` to be passed as `ssml`, set `use_ssml=True` - - - - -Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - - -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" - - -ssml = """ - -

Hello, world!

-

This is a test of the text-to-speech API.

- -""" - -response = litellm.speech( - input=ssml, - model="vertex_ai/test", - voice={ - "languageCode": "en-UK", - "name": "en-UK-Studio-O", - }, - audioConfig={ - "audioEncoding": "LINEAR22", - "speakingRate": "10", - }, -) -response.stream_to_file(speech_file_path) -``` - - - - - -```python -import openai - -client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -ssml = """ - -

Hello, world!

-

This is a test of the text-to-speech API.

- -""" - -# see supported values for "voice" on vertex here: -# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech -response = client.audio.speech.create( - model = "vertex-tts", - input=ssml, - voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'}, -) -print("response from proxy", response) -``` - - - - - -### Forcing SSML Usage - -You can force the use of SSML by setting the `use_ssml` parameter to `True`. This is useful when you want to ensure that your input is treated as SSML, even if it doesn't contain the `` tags. - -Here are examples of how to force SSML usage: - - - - - -Vertex AI does not support passing a `model` param - so passing `model=vertex_ai/` is the only required param - - -```python -speech_file_path = Path(__file__).parent / "speech_vertex.mp3" - - -ssml = """ - -

Hello, world!

-

This is a test of the text-to-speech API.

- -""" - -response = litellm.speech( - input=ssml, - use_ssml=True, - model="vertex_ai/test", - voice={ - "languageCode": "en-UK", - "name": "en-UK-Studio-O", - }, - audioConfig={ - "audioEncoding": "LINEAR22", - "speakingRate": "10", - }, -) -response.stream_to_file(speech_file_path) -``` - - - - - -```python -import openai - -client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") - -ssml = """ - -

Hello, world!

-

This is a test of the text-to-speech API.

- -""" - -# see supported values for "voice" on vertex here: -# https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech -response = client.audio.speech.create( - model = "vertex-tts", - input=ssml, # pass as None since OpenAI SDK requires this param - voice={'languageCode': 'en-US', 'name': 'en-US-Studio-O'}, - extra_body={"use_ssml": True}, -) -print("response from proxy", response) -``` - - - - ## **Fine Tuning APIs** diff --git a/docs/my-website/docs/providers/vertex_image.md b/docs/my-website/docs/providers/vertex_image.md index 27e584cb222..c4d5d554088 100644 --- a/docs/my-website/docs/providers/vertex_image.md +++ b/docs/my-website/docs/providers/vertex_image.md @@ -1,18 +1,65 @@ # Vertex AI Image Generation -Vertex AI Image Generation uses Google's Imagen models to generate high-quality images from text descriptions. +Vertex AI supports two types of image generation: + +1. **Gemini Image Generation Models** (Nano Banana 🍌) - Conversational image generation using `generateContent` API +2. **Imagen Models** - Traditional image generation using `predict` API | Property | Details | |----------|---------| -| Description | Vertex AI Image Generation uses Google's Imagen models to generate high-quality images from text descriptions. | +| Description | Vertex AI Image Generation supports both Gemini image generation models | | Provider Route on LiteLLM | `vertex_ai/` | | Provider Doc | [Google Cloud Vertex AI Image Generation ↗](https://cloud.google.com/vertex-ai/docs/generative-ai/image/generate-images) | +| Gemini Image Generation Docs | [Gemini Image Generation ↗](https://ai.google.dev/gemini-api/docs/image-generation) | ## Quick Start -### LiteLLM Python SDK +### Gemini Image Generation Models -```python showLineNumbers title="Basic Image Generation" +Gemini image generation models support conversational image creation with features like: +- Text-to-Image generation +- Image editing (text + image → image) +- Multi-turn image refinement +- High-fidelity text rendering +- Up to 4K resolution (Gemini 3 Pro) + +```python showLineNumbers title="Gemini 2.5 Flash Image" +import litellm + +# Generate a single image +response = await litellm.aimage_generation( + prompt="A nano banana dish in a fancy restaurant with a Gemini theme", + model="vertex_ai/gemini-2.5-flash-image", + vertex_ai_project="your-project-id", + vertex_ai_location="us-central1", + n=1, + size="1024x1024", +) + +print(response.data[0].b64_json) # Gemini returns base64 images +``` + +```python showLineNumbers title="Gemini 3 Pro Image Preview (4K output)" +import litellm + +# Generate high-resolution image +response = await litellm.aimage_generation( + prompt="Da Vinci style anatomical sketch of a dissected Monarch butterfly", + model="vertex_ai/gemini-3-pro-image-preview", + vertex_ai_project="your-project-id", + vertex_ai_location="us-central1", + n=1, + size="1024x1024", + # Optional: specify image size for Gemini 3 Pro + # imageSize="4K", # Options: "1K", "2K", "4K" +) + +print(response.data[0].b64_json) +``` + +### Imagen Models + +```python showLineNumbers title="Imagen Image Generation" import litellm # Generate a single image @@ -21,9 +68,11 @@ response = await litellm.aimage_generation( model="vertex_ai/imagen-4.0-generate-001", vertex_ai_project="your-project-id", vertex_ai_location="us-central1", + n=1, + size="1024x1024", ) -print(response.data[0].url) +print(response.data[0].b64_json) # Imagen also returns base64 images ``` ### LiteLLM Proxy @@ -70,6 +119,18 @@ print(response.data[0].url) ## Supported Models +### Gemini Image Generation Models + +- `vertex_ai/gemini-2.5-flash-image` - Fast, efficient image generation (1024px resolution) +- `vertex_ai/gemini-3-pro-image-preview` - Advanced model with 4K output, Google Search grounding, and thinking mode +- `vertex_ai/gemini-2.0-flash-preview-image` - Preview model +- `vertex_ai/gemini-2.5-flash-image-preview` - Preview model + +### Imagen Models + +- `vertex_ai/imagegeneration@006` - Legacy Imagen model +- `vertex_ai/imagen-4.0-generate-001` - Latest Imagen model +- `vertex_ai/imagen-3.0-generate-001` - Imagen 3.0 model :::tip @@ -77,7 +138,5 @@ print(response.data[0].url) ::: -LiteLLM supports all Vertex AI Imagen models available through Google Cloud. - For the complete and up-to-date list of supported models, visit: [https://models.litellm.ai/](https://models.litellm.ai/) diff --git a/docs/my-website/docs/providers/vertex_speech.md b/docs/my-website/docs/providers/vertex_speech.md new file mode 100644 index 00000000000..d0acacb5aec --- /dev/null +++ b/docs/my-website/docs/providers/vertex_speech.md @@ -0,0 +1,423 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Vertex AI Text to Speech + +| Property | Details | +|-------|-------| +| Description | Google Cloud Text-to-Speech with Chirp3 HD voices and Gemini TTS | +| Provider Route on LiteLLM | `vertex_ai/chirp` (Chirp), `vertex_ai/gemini-*-tts` (Gemini) | + +## Chirp3 HD Voices + +Google Cloud Text-to-Speech API with high-quality Chirp3 HD voices. + +### Quick Start + +#### LiteLLM Python SDK + +```python showLineNumbers title="Chirp3 Quick Start" +from litellm import speech +from pathlib import Path + +speech_file_path = Path(__file__).parent / "speech.mp3" +response = speech( + model="vertex_ai/chirp", + voice="alloy", # OpenAI voice name - automatically mapped + input="Hello, this is Vertex AI Text to Speech", + vertex_project="your-project-id", + vertex_location="us-central1", +) +response.stream_to_file(speech_file_path) +``` + +#### LiteLLM AI Gateway + +**1. Setup config.yaml** + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: vertex-tts + litellm_params: + model: vertex_ai/chirp + vertex_project: "your-project-id" + vertex_location: "us-central1" + vertex_credentials: "/path/to/service_account.json" +``` + +**2. Start the proxy** + +```bash title="Start LiteLLM Proxy" +litellm --config /path/to/config.yaml +``` + +**3. Make requests** + + + + +```bash showLineNumbers title="Chirp3 Quick Start" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": "alloy", + "input": "Hello, this is Vertex AI Text to Speech" + }' \ + --output speech.mp3 +``` + + + + +```python showLineNumbers title="Chirp3 Quick Start" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.audio.speech.create( + model="vertex-tts", + voice="alloy", + input="Hello, this is Vertex AI Text to Speech", +) +response.stream_to_file("speech.mp3") +``` + + + + +### Voice Mapping + +LiteLLM maps OpenAI voice names to Google Cloud voices. You can use either OpenAI voices or Google Cloud voices directly. + +| OpenAI Voice | Google Cloud Voice | +|-------------|-------------------| +| `alloy` | en-US-Studio-O | +| `echo` | en-US-Studio-M | +| `fable` | en-GB-Studio-B | +| `onyx` | en-US-Wavenet-D | +| `nova` | en-US-Studio-O | +| `shimmer` | en-US-Wavenet-F | + +### Using Google Cloud Voices Directly + +#### LiteLLM Python SDK + +```python showLineNumbers title="Chirp3 HD Voice" +from litellm import speech + +# Pass Chirp3 HD voice name directly +response = speech( + model="vertex_ai/chirp", + voice="en-US-Chirp3-HD-Charon", + input="Hello with a Chirp3 HD voice", + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +```python showLineNumbers title="Voice as Dict (Multilingual)" +from litellm import speech + +# Pass as dict for full control over language and voice +response = speech( + model="vertex_ai/chirp", + voice={ + "languageCode": "de-DE", + "name": "de-DE-Chirp3-HD-Charon", + }, + input="Hallo, dies ist ein Test", + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +#### LiteLLM AI Gateway + + + + +```bash showLineNumbers title="Chirp3 HD Voice" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": "en-US-Chirp3-HD-Charon", + "input": "Hello with a Chirp3 HD voice" + }' \ + --output speech.mp3 +``` + +```bash showLineNumbers title="Voice as Dict (Multilingual)" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": {"languageCode": "de-DE", "name": "de-DE-Chirp3-HD-Charon"}, + "input": "Hallo, dies ist ein Test" + }' \ + --output speech.mp3 +``` + + + + +```python showLineNumbers title="Chirp3 HD Voice" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.audio.speech.create( + model="vertex-tts", + voice="en-US-Chirp3-HD-Charon", + input="Hello with a Chirp3 HD voice", +) +response.stream_to_file("speech.mp3") +``` + +```python showLineNumbers title="Voice as Dict (Multilingual)" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.audio.speech.create( + model="vertex-tts", + voice={"languageCode": "de-DE", "name": "de-DE-Chirp3-HD-Charon"}, + input="Hallo, dies ist ein Test", +) +response.stream_to_file("speech.mp3") +``` + + + + +Browse available voices: [Google Cloud Text-to-Speech Console](https://console.cloud.google.com/vertex-ai/generative/speech/text-to-speech) + +### Passing Raw SSML + +LiteLLM auto-detects SSML when your input contains `` tags and passes it through unchanged. + +#### LiteLLM Python SDK + +```python showLineNumbers title="SSML Input" +from litellm import speech + +ssml = """ + +

Hello, world!

+

This is a test of the text-to-speech API.

+ +""" + +response = speech( + model="vertex_ai/chirp", + voice="en-US-Studio-O", + input=ssml, # Auto-detected as SSML + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +```python showLineNumbers title="Force SSML Mode" +from litellm import speech + +# Force SSML mode with use_ssml=True +response = speech( + model="vertex_ai/chirp", + voice="en-US-Studio-O", + input="Speaking slowly", + use_ssml=True, + vertex_project="your-project-id", +) +response.stream_to_file("speech.mp3") +``` + +#### LiteLLM AI Gateway + + + + +```bash showLineNumbers title="SSML Input" +curl http://0.0.0.0:4000/v1/audio/speech \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "vertex-tts", + "voice": "en-US-Studio-O", + "input": "

Hello!

How are you?

" + }' \ + --output speech.mp3 +``` + + + + +```python showLineNumbers title="SSML Input" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +ssml = """

Hello!

How are you?

""" + +response = client.audio.speech.create( + model="vertex-tts", + voice="en-US-Studio-O", + input=ssml, +) +response.stream_to_file("speech.mp3") +``` + + + + +### Supported Parameters + +| Parameter | Description | Values | +|-----------|-------------|--------| +| `voice` | Voice selection | OpenAI voice, Google Cloud voice name, or dict | +| `input` | Text to convert | Plain text or SSML | +| `speed` | Speaking rate | 0.25 to 4.0 (default: 1.0) | +| `response_format` | Audio format | `mp3`, `opus`, `wav`, `pcm`, `flac` | +| `use_ssml` | Force SSML mode | `True` / `False` | + +### Async Usage + +```python showLineNumbers title="Async Speech Generation" +import asyncio +from litellm import aspeech + +async def main(): + response = await aspeech( + model="vertex_ai/chirp", + voice="alloy", + input="Hello from async", + vertex_project="your-project-id", + ) + response.stream_to_file("speech.mp3") + +asyncio.run(main()) +``` + +--- + +## Gemini TTS + +Gemini models with audio output capabilities using the chat completions API. + +:::warning +**Limitations:** +- Only supports `pcm16` audio format +- Streaming not yet supported +- Must set `modalities: ["audio"]` +::: + +### Quick Start + +#### LiteLLM Python SDK + +```python showLineNumbers title="Gemini TTS Quick Start" +from litellm import completion +import json + +# Load credentials +with open('path/to/service_account.json', 'r') as file: + vertex_credentials = json.dumps(json.load(file)) + +response = completion( + model="vertex_ai/gemini-2.5-flash-preview-tts", + messages=[{"role": "user", "content": "Say hello in a friendly voice"}], + modalities=["audio"], + audio={ + "voice": "Kore", + "format": "pcm16" + }, + vertex_credentials=vertex_credentials +) +print(response) +``` + +#### LiteLLM AI Gateway + +**1. Setup config.yaml** + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: gemini-tts + litellm_params: + model: vertex_ai/gemini-2.5-flash-preview-tts + vertex_project: "your-project-id" + vertex_location: "us-central1" + vertex_credentials: "/path/to/service_account.json" +``` + +**2. Start the proxy** + +```bash title="Start LiteLLM Proxy" +litellm --config /path/to/config.yaml +``` + +**3. Make requests** + + + + +```bash showLineNumbers title="Gemini TTS Request" +curl http://0.0.0.0:4000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "gemini-tts", + "messages": [{"role": "user", "content": "Say hello in a friendly voice"}], + "modalities": ["audio"], + "audio": {"voice": "Kore", "format": "pcm16"} + }' +``` + + + + +```python showLineNumbers title="Gemini TTS Request" +import openai + +client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") + +response = client.chat.completions.create( + model="gemini-tts", + messages=[{"role": "user", "content": "Say hello in a friendly voice"}], + modalities=["audio"], + audio={"voice": "Kore", "format": "pcm16"}, +) +print(response) +``` + + + + +### Supported Models + +- `vertex_ai/gemini-2.5-flash-preview-tts` +- `vertex_ai/gemini-2.5-pro-preview-tts` + +See [Gemini TTS documentation](https://ai.google.dev/gemini-api/docs/speech-generation) for available voices. + +### Advanced Usage + +```python showLineNumbers title="Gemini TTS with System Prompt" +from litellm import completion + +response = completion( + model="vertex_ai/gemini-2.5-pro-preview-tts", + messages=[ + {"role": "system", "content": "You are a helpful assistant that speaks clearly."}, + {"role": "user", "content": "Explain quantum computing in simple terms"} + ], + modalities=["audio"], + audio={"voice": "Charon", "format": "pcm16"}, + temperature=0.7, + max_tokens=150, + vertex_credentials=vertex_credentials +) +``` diff --git a/docs/my-website/docs/providers/watsonx.md b/docs/my-website/docs/providers/watsonx.md deleted file mode 100644 index 23d8d259ac0..00000000000 --- a/docs/my-website/docs/providers/watsonx.md +++ /dev/null @@ -1,287 +0,0 @@ -import Tabs from '@theme/Tabs'; -import TabItem from '@theme/TabItem'; - -# IBM watsonx.ai - -LiteLLM supports all IBM [watsonx.ai](https://watsonx.ai/) foundational models and embeddings. - -## Environment Variables -```python -os.environ["WATSONX_URL"] = "" # (required) Base URL of your WatsonX instance -# (required) either one of the following: -os.environ["WATSONX_APIKEY"] = "" # IBM cloud API key -os.environ["WATSONX_TOKEN"] = "" # IAM auth token -# optional - can also be passed as params to completion() or embedding() -os.environ["WATSONX_PROJECT_ID"] = "" # Project ID of your WatsonX instance -os.environ["WATSONX_DEPLOYMENT_SPACE_ID"] = "" # ID of your deployment space to use deployed models -os.environ["WATSONX_ZENAPIKEY"] = "" # Zen API key (use for long-term api token) -``` - -See [here](https://cloud.ibm.com/apidocs/watsonx-ai#api-authentication) for more information on how to get an access token to authenticate to watsonx.ai. - -## Usage - - - Open In Colab - - -```python -import os -from litellm import completion - -os.environ["WATSONX_URL"] = "" -os.environ["WATSONX_APIKEY"] = "" - -## Call WATSONX `/text/chat` endpoint - supports function calling -response = completion( - model="watsonx/meta-llama/llama-3-1-8b-instruct", - messages=[{ "content": "what is your favorite colour?","role": "user"}], - project_id="" # or pass with os.environ["WATSONX_PROJECT_ID"] -) - -## Call WATSONX `/text/generation` endpoint - not all models support /chat route. -response = completion( - model="watsonx/ibm/granite-13b-chat-v2", - messages=[{ "content": "what is your favorite colour?","role": "user"}], - project_id="" -) -``` - -## Usage - Streaming -```python -import os -from litellm import completion - -os.environ["WATSONX_URL"] = "" -os.environ["WATSONX_APIKEY"] = "" -os.environ["WATSONX_PROJECT_ID"] = "" - -response = completion( - model="watsonx/meta-llama/llama-3-1-8b-instruct", - messages=[{ "content": "what is your favorite colour?","role": "user"}], - stream=True -) -for chunk in response: - print(chunk) -``` - -#### Example Streaming Output Chunk -```json -{ - "choices": [ - { - "finish_reason": null, - "index": 0, - "delta": { - "content": "I don't have a favorite color, but I do like the color blue. What's your favorite color?" - } - } - ], - "created": null, - "model": "watsonx/ibm/granite-13b-chat-v2", - "usage": { - "prompt_tokens": null, - "completion_tokens": null, - "total_tokens": null - } -} -``` - -## Usage - Models in deployment spaces - -Models that have been deployed to a deployment space (e.g.: tuned models) can be called using the `deployment/` format (where `` is the ID of the deployed model in your deployment space). - -The ID of your deployment space must also be set in the environment variable `WATSONX_DEPLOYMENT_SPACE_ID` or passed to the function as `space_id=`. - -```python -import litellm -response = litellm.completion( - model="watsonx/deployment/", - messages=[{"content": "Hello, how are you?", "role": "user"}], - space_id="" -) -``` - -## Usage - Embeddings - -LiteLLM also supports making requests to IBM watsonx.ai embedding models. The credential needed for this is the same as for completion. - -```python -from litellm import embedding - -response = embedding( - model="watsonx/ibm/slate-30m-english-rtrvr", - input=["What is the capital of France?"], - project_id="" -) -print(response) -# EmbeddingResponse(model='ibm/slate-30m-english-rtrvr', data=[{'object': 'embedding', 'index': 0, 'embedding': [-0.037463713, -0.02141933, -0.02851813, 0.015519324, ..., -0.0021367231, -0.01704561, -0.001425816, 0.0035238306]}], object='list', usage=Usage(prompt_tokens=8, total_tokens=8)) -``` - -## OpenAI Proxy Usage - -Here's how to call IBM watsonx.ai with the LiteLLM Proxy Server - -### 1. Save keys in your environment - -```bash -export WATSONX_URL="" -export WATSONX_APIKEY="" -export WATSONX_PROJECT_ID="" -``` - -### 2. Start the proxy - - - - -```bash -$ litellm --model watsonx/meta-llama/llama-3-8b-instruct - -# Server running on http://0.0.0.0:4000 -``` - - - - -```yaml -model_list: - - model_name: llama-3-8b - litellm_params: - # all params accepted by litellm.completion() - model: watsonx/meta-llama/llama-3-8b-instruct - api_key: "os.environ/WATSONX_API_KEY" # does os.getenv("WATSONX_API_KEY") -``` - - - -### 3. Test it - - - - - -```shell -curl --location 'http://0.0.0.0:4000/chat/completions' \ ---header 'Content-Type: application/json' \ ---data ' { - "model": "llama-3-8b", - "messages": [ - { - "role": "user", - "content": "what is your favorite colour?" - } - ] - } -' -``` - - - -```python -import openai -client = openai.OpenAI( - api_key="anything", - base_url="http://0.0.0.0:4000" -) - -# request sent to model set on litellm proxy, `litellm --model` -response = client.chat.completions.create(model="llama-3-8b", messages=[ - { - "role": "user", - "content": "what is your favorite colour?" - } -]) - -print(response) - -``` - - - -```python -from langchain.chat_models import ChatOpenAI -from langchain.prompts.chat import ( - ChatPromptTemplate, - HumanMessagePromptTemplate, - SystemMessagePromptTemplate, -) -from langchain.schema import HumanMessage, SystemMessage - -chat = ChatOpenAI( - openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy - model = "llama-3-8b", - temperature=0.1 -) - -messages = [ - SystemMessage( - content="You are a helpful assistant that im using to make a test request to." - ), - HumanMessage( - content="test from litellm. tell me why it's amazing in 1 sentence" - ), -] -response = chat(messages) - -print(response) -``` - - - - -## Authentication - -### Passing credentials as parameters - -You can also pass the credentials as parameters to the completion and embedding functions. - -```python -import os -from litellm import completion - -response = completion( - model="watsonx/ibm/granite-13b-chat-v2", - messages=[{ "content": "What is your favorite color?","role": "user"}], - url="", - api_key="", - project_id="" -) -``` - - -## Supported IBM watsonx.ai Models - -Here are some examples of models available in IBM watsonx.ai that you can use with LiteLLM: - -| Mode Name | Command | -|------------------------------------|------------------------------------------------------------------------------------------| -| Flan T5 XXL | `completion(model=watsonx/google/flan-t5-xxl, messages=messages)` | -| Flan Ul2 | `completion(model=watsonx/google/flan-ul2, messages=messages)` | -| Mt0 XXL | `completion(model=watsonx/bigscience/mt0-xxl, messages=messages)` | -| Gpt Neox | `completion(model=watsonx/eleutherai/gpt-neox-20b, messages=messages)` | -| Mpt 7B Instruct2 | `completion(model=watsonx/ibm/mpt-7b-instruct2, messages=messages)` | -| Starcoder | `completion(model=watsonx/bigcode/starcoder, messages=messages)` | -| Llama 2 70B Chat | `completion(model=watsonx/meta-llama/llama-2-70b-chat, messages=messages)` | -| Llama 2 13B Chat | `completion(model=watsonx/meta-llama/llama-2-13b-chat, messages=messages)` | -| Granite 13B Instruct | `completion(model=watsonx/ibm/granite-13b-instruct-v1, messages=messages)` | -| Granite 13B Chat | `completion(model=watsonx/ibm/granite-13b-chat-v1, messages=messages)` | -| Flan T5 XL | `completion(model=watsonx/google/flan-t5-xl, messages=messages)` | -| Granite 13B Chat V2 | `completion(model=watsonx/ibm/granite-13b-chat-v2, messages=messages)` | -| Granite 13B Instruct V2 | `completion(model=watsonx/ibm/granite-13b-instruct-v2, messages=messages)` | -| Elyza Japanese Llama 2 7B Instruct | `completion(model=watsonx/elyza/elyza-japanese-llama-2-7b-instruct, messages=messages)` | -| Mixtral 8X7B Instruct V01 Q | `completion(model=watsonx/ibm-mistralai/mixtral-8x7b-instruct-v01-q, messages=messages)` | - - -For a list of all available models in watsonx.ai, see [here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx&locale=en&audience=wdp). - - -## Supported IBM watsonx.ai Embedding Models - -| Model Name | Function Call | -|------------|------------------------------------------------------------------------| -| Slate 30m | `embedding(model="watsonx/ibm/slate-30m-english-rtrvr", input=input)` | -| Slate 125m | `embedding(model="watsonx/ibm/slate-125m-english-rtrvr", input=input)` | - - -For a list of all available embedding models in watsonx.ai, see [here](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). \ No newline at end of file diff --git a/docs/my-website/docs/providers/watsonx/audio_transcription.md b/docs/my-website/docs/providers/watsonx/audio_transcription.md new file mode 100644 index 00000000000..37b4bb438a2 --- /dev/null +++ b/docs/my-website/docs/providers/watsonx/audio_transcription.md @@ -0,0 +1,57 @@ +# WatsonX Audio Transcription + +## Overview + +| Property | Details | +|----------|---------| +| Description | WatsonX audio transcription using Whisper models for speech-to-text | +| Provider Route on LiteLLM | `watsonx/` | +| Supported Operations | `/v1/audio/transcriptions` | +| Link to Provider Doc | [IBM WatsonX.ai ↗](https://www.ibm.com/watsonx) | + +## Quick Start + +### **LiteLLM SDK** + +```python showLineNumbers title="transcription.py" +import litellm + +response = litellm.transcription( + model="watsonx/whisper-large-v3-turbo", + file=open("audio.mp3", "rb"), + api_base="https://us-south.ml.cloud.ibm.com", + api_key="your-api-key", + project_id="your-project-id" +) +print(response.text) +``` + +### **LiteLLM Proxy** + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: whisper-large-v3-turbo + litellm_params: + model: watsonx/whisper-large-v3-turbo + api_key: os.environ/WATSONX_APIKEY + api_base: os.environ/WATSONX_URL + project_id: os.environ/WATSONX_PROJECT_ID +``` + +```bash title="Request" +curl http://localhost:4000/v1/audio/transcriptions \ + -H "Authorization: Bearer sk-1234" \ + -F file="@audio.mp3" \ + -F model="whisper-large-v3-turbo" +``` + +## Supported Parameters + +| Parameter | Type | Description | +|-----------|------|-------------| +| `model` | string | Model ID (e.g., `watsonx/whisper-large-v3-turbo`) | +| `file` | file | Audio file to transcribe | +| `language` | string | Language code (e.g., `en`) | +| `prompt` | string | Optional prompt to guide transcription | +| `temperature` | float | Sampling temperature (0-1) | +| `response_format` | string | `json`, `text`, `srt`, `verbose_json`, `vtt` | diff --git a/docs/my-website/docs/providers/watsonx/index.md b/docs/my-website/docs/providers/watsonx/index.md new file mode 100644 index 00000000000..14e0c07c081 --- /dev/null +++ b/docs/my-website/docs/providers/watsonx/index.md @@ -0,0 +1,230 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# IBM watsonx.ai + +LiteLLM supports all IBM [watsonx.ai](https://watsonx.ai/) foundational models and embeddings. + +## Environment Variables +```python +os.environ["WATSONX_URL"] = "" # (required) Base URL of your WatsonX instance +# (required) either one of the following: +os.environ["WATSONX_APIKEY"] = "" # IBM cloud API key +os.environ["WATSONX_TOKEN"] = "" # IAM auth token +# optional - can also be passed as params to completion() or embedding() +os.environ["WATSONX_PROJECT_ID"] = "" # Project ID of your WatsonX instance +os.environ["WATSONX_DEPLOYMENT_SPACE_ID"] = "" # ID of your deployment space to use deployed models +os.environ["WATSONX_ZENAPIKEY"] = "" # Zen API key (use for long-term api token) +``` + +See [here](https://cloud.ibm.com/apidocs/watsonx-ai#api-authentication) for more information on how to get an access token to authenticate to watsonx.ai. + +## Usage + + + Open In Colab + + +```python showLineNumbers title="Chat Completion" +import os +from litellm import completion + +os.environ["WATSONX_URL"] = "" +os.environ["WATSONX_APIKEY"] = "" + +response = completion( + model="watsonx/meta-llama/llama-3-1-8b-instruct", + messages=[{ "content": "what is your favorite colour?","role": "user"}], + project_id="" +) +``` + +## Usage - Streaming +```python showLineNumbers title="Streaming" +import os +from litellm import completion + +os.environ["WATSONX_URL"] = "" +os.environ["WATSONX_APIKEY"] = "" +os.environ["WATSONX_PROJECT_ID"] = "" + +response = completion( + model="watsonx/meta-llama/llama-3-1-8b-instruct", + messages=[{ "content": "what is your favorite colour?","role": "user"}], + stream=True +) +for chunk in response: + print(chunk) +``` + +## Usage - Models in deployment spaces + +Models deployed to a deployment space (e.g.: tuned models) can be called using the `deployment/` format. + +```python showLineNumbers title="Deployment Space" +import litellm + +response = litellm.completion( + model="watsonx/deployment/", + messages=[{"content": "Hello, how are you?", "role": "user"}], + space_id="" +) +``` + +## Usage - Embeddings + +```python showLineNumbers title="Embeddings" +from litellm import embedding + +response = embedding( + model="watsonx/ibm/slate-30m-english-rtrvr", + input=["What is the capital of France?"], + project_id="" +) +``` + +## LiteLLM Proxy Usage + +### 1. Save keys in your environment + +```bash +export WATSONX_URL="" +export WATSONX_APIKEY="" +export WATSONX_PROJECT_ID="" +``` + +### 2. Start the proxy + + + + +```bash +$ litellm --model watsonx/meta-llama/llama-3-8b-instruct +``` + + + + +```yaml showLineNumbers title="config.yaml" +model_list: + - model_name: llama-3-8b + litellm_params: + model: watsonx/meta-llama/llama-3-8b-instruct + api_key: "os.environ/WATSONX_API_KEY" +``` + + + +### 3. Test it + + + + + +```shell +curl --location 'http://0.0.0.0:4000/chat/completions' \ +--header 'Content-Type: application/json' \ +--data '{ + "model": "llama-3-8b", + "messages": [ + { + "role": "user", + "content": "what is your favorite colour?" + } + ] + }' +``` + + + +```python showLineNumbers +import openai + +client = openai.OpenAI( + api_key="anything", + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="llama-3-8b", + messages=[{"role": "user", "content": "what is your favorite colour?"}] +) +print(response) +``` + + + + +## Supported Models + +| Model Name | Command | +|------------------------------------|------------------------------------------------------------------------------------------| +| Llama 3.1 8B Instruct | `completion(model="watsonx/meta-llama/llama-3-1-8b-instruct", messages=messages)` | +| Llama 2 70B Chat | `completion(model="watsonx/meta-llama/llama-2-70b-chat", messages=messages)` | +| Granite 13B Chat V2 | `completion(model="watsonx/ibm/granite-13b-chat-v2", messages=messages)` | +| Mixtral 8X7B Instruct | `completion(model="watsonx/ibm-mistralai/mixtral-8x7b-instruct-v01-q", messages=messages)` | + +For all available models, see [watsonx.ai documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx). + +## Supported Embedding Models + +| Model Name | Function Call | +|------------|------------------------------------------------------------------------| +| Slate 30m | `embedding(model="watsonx/ibm/slate-30m-english-rtrvr", input=input)` | +| Slate 125m | `embedding(model="watsonx/ibm/slate-125m-english-rtrvr", input=input)` | + +For all available embedding models, see [watsonx.ai embedding documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). + + +## Advanced + +### Using Zen API Key + +You can use a Zen API key for long-term authentication instead of generating IAM tokens. Pass it either as an environment variable or as a parameter: + +```python +import os +from litellm import completion + +# Option 1: Set as environment variable +os.environ["WATSONX_ZENAPIKEY"] = "your-zen-api-key" + +response = completion( + model="watsonx/ibm/granite-13b-chat-v2", + messages=[{"content": "What is your favorite color?", "role": "user"}], + project_id="your-project-id" +) + +# Option 2: Pass as parameter +response = completion( + model="watsonx/ibm/granite-13b-chat-v2", + messages=[{"content": "What is your favorite color?", "role": "user"}], + zen_api_key="your-zen-api-key", + project_id="your-project-id" +) +``` + +**Using with LiteLLM Proxy via OpenAI client:** + +```python +import openai + +client = openai.OpenAI( + api_key="sk-1234", # LiteLLM proxy key + base_url="http://0.0.0.0:4000" +) + +response = client.chat.completions.create( + model="watsonx/ibm/granite-3-3-8b-instruct", + messages=[{"role": "user", "content": "What is your favorite color?"}], + max_tokens=2048, + extra_body={ + "project_id": "your-project-id", + "zen_api_key": "your-zen-api-key" + } +) +``` + +See [IBM documentation](https://www.ibm.com/docs/en/watsonx/w-and-w/2.2.0?topic=keys-generating-zenapikey-authorization-tokens) for more information on generating Zen API keys. + + diff --git a/docs/my-website/docs/providers/zai.md b/docs/my-website/docs/providers/zai.md new file mode 100644 index 00000000000..5055d0c1cdd --- /dev/null +++ b/docs/my-website/docs/providers/zai.md @@ -0,0 +1,135 @@ +import Tabs from '@theme/Tabs'; +import TabItem from '@theme/TabItem'; + +# Z.AI (Zhipu AI) +https://z.ai/ + +**We support Z.AI GLM text/chat models, just set `zai/` as a prefix when sending completion requests** + +## API Key +```python +# env variable +os.environ['ZAI_API_KEY'] +``` + +## Sample Usage +```python +from litellm import completion +import os + +os.environ['ZAI_API_KEY'] = "" +response = completion( + model="zai/glm-4.6", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], +) +print(response) +``` + +## Sample Usage - Streaming +```python +from litellm import completion +import os + +os.environ['ZAI_API_KEY'] = "" +response = completion( + model="zai/glm-4.6", + messages=[ + {"role": "user", "content": "hello from litellm"} + ], + stream=True +) + +for chunk in response: + print(chunk) +``` + +## Supported Models + +We support ALL Z.AI GLM models, just set `zai/` as a prefix when sending completion requests. + +| Model Name | Function Call | Notes | +|------------|---------------|-------| +| glm-4.6 | `completion(model="zai/glm-4.6", messages)` | Latest flagship model, 200K context | +| glm-4.5 | `completion(model="zai/glm-4.5", messages)` | 128K context | +| glm-4.5v | `completion(model="zai/glm-4.5v", messages)` | Vision model | +| glm-4.5-x | `completion(model="zai/glm-4.5-x", messages)` | Premium tier | +| glm-4.5-air | `completion(model="zai/glm-4.5-air", messages)` | Lightweight | +| glm-4.5-airx | `completion(model="zai/glm-4.5-airx", messages)` | Fast lightweight | +| glm-4-32b-0414-128k | `completion(model="zai/glm-4-32b-0414-128k", messages)` | 32B parameter model | +| glm-4.5-flash | `completion(model="zai/glm-4.5-flash", messages)` | **FREE tier** | + +## Model Pricing + +| Model | Input ($/1M tokens) | Output ($/1M tokens) | Context Window | +|-------|---------------------|----------------------|----------------| +| glm-4.6 | $0.60 | $2.20 | 200K | +| glm-4.5 | $0.60 | $2.20 | 128K | +| glm-4.5v | $0.60 | $1.80 | 128K | +| glm-4.5-x | $2.20 | $8.90 | 128K | +| glm-4.5-air | $0.20 | $1.10 | 128K | +| glm-4.5-airx | $1.10 | $4.50 | 128K | +| glm-4-32b-0414-128k | $0.10 | $0.10 | 128K | +| glm-4.5-flash | **FREE** | **FREE** | 128K | + +## Using with LiteLLM Proxy + + + + +```python +from litellm import completion +import os + +os.environ['ZAI_API_KEY'] = "" +response = completion( + model="zai/glm-4.6", + messages=[{"role": "user", "content": "Hello, how are you?"}], +) + +print(response.choices[0].message.content) +``` + + + + +1. Setup config.yaml + +```yaml +model_list: + - model_name: glm-4.6 + litellm_params: + model: zai/glm-4.6 + api_key: os.environ/ZAI_API_KEY + - model_name: glm-4.5-flash # Free tier + litellm_params: + model: zai/glm-4.5-flash + api_key: os.environ/ZAI_API_KEY +``` + +2. Run proxy + +```bash +litellm --config config.yaml +``` + +3. Test it! + +```bash +curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \ +-H 'Content-Type: application/json' \ +-H 'Authorization: Bearer sk-1234' \ +-d '{ + "model": "glm-4.6", + "messages": [ + { + "role": "user", + "content": "Hello, how are you?" + } + ] +}' +``` + + + diff --git a/docs/my-website/docs/proxy/config_settings.md b/docs/my-website/docs/proxy/config_settings.md index 4d1bc549e05..b71e100e157 100644 --- a/docs/my-website/docs/proxy/config_settings.md +++ b/docs/my-website/docs/proxy/config_settings.md @@ -104,6 +104,7 @@ general_settings: disable_responses_id_security: boolean # turn off response ID security checks that prevent users from accessing other users' responses enable_jwt_auth: boolean # allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims enforce_user_param: boolean # requires all openai endpoint requests to have a 'user' param + reject_clientside_metadata_tags: boolean # if true, rejects requests with client-side 'metadata.tags' to prevent users from influencing budgets allowed_routes: ["route1", "route2"] # list of allowed proxy API routes - a user can access. (currently JWT-Auth only) key_management_system: google_kms # either google_kms or azure_kms master_key: string @@ -112,7 +113,7 @@ general_settings: # Database Settings database_url: string - database_connection_pool_limit: 0 # default 100 + database_connection_pool_limit: 0 # default 10 database_connection_timeout: 0 # default 60s allow_requests_on_db_unavailable: boolean # if true, will allow requests that can not connect to the DB to verify Virtual Key to still work @@ -201,6 +202,7 @@ router_settings: | disable_responses_id_security | boolean | If true, disables response ID security checks that prevent users from accessing response IDs from other users. When false (default), response IDs are encrypted with user information to ensure users can only access their own responses. Applies to /v1/responses endpoints | | enable_jwt_auth | boolean | allow proxy admin to auth in via jwt tokens with 'litellm_proxy_admin' in claims. [Doc on JWT Tokens](token_auth) | | enforce_user_param | boolean | If true, requires all OpenAI endpoint requests to have a 'user' param. [Doc on call hooks](call_hooks)| +| reject_clientside_metadata_tags | boolean | If true, rejects requests that contain client-side 'metadata.tags' to prevent users from influencing budgets by sending different tags. Tags can only be inherited from the API key metadata. | | allowed_routes | array of strings | List of allowed proxy API routes a user can access [Doc on controlling allowed routes](enterprise#control-available-public-private-routes)| | key_management_system | string | Specifies the key management system. [Doc Secret Managers](../secret) | | master_key | string | The master key for the proxy [Set up Virtual Keys](virtual_keys) | @@ -232,7 +234,7 @@ router_settings: | max_response_size_mb | int | The maximum size for responses in MB. LLM Responses above this size will not be sent. | | proxy_budget_rescheduler_min_time | int | The minimum time (in seconds) to wait before checking db for budget resets. **Default is 597 seconds** | | proxy_budget_rescheduler_max_time | int | The maximum time (in seconds) to wait before checking db for budget resets. **Default is 605 seconds** | -| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 30 seconds** | +| proxy_batch_write_at | int | Time (in seconds) to wait before batch writing spend logs to the db. **Default is 10 seconds** | | proxy_batch_polling_interval | int | Time (in seconds) to wait before polling a batch, to check if it's completed. **Default is 6000 seconds (1 hour)** | | alerting_args | dict | Args for Slack Alerting [Doc on Slack Alerting](./alerting.md) | | custom_key_generate | str | Custom function for key generation [Doc on custom key generation](./virtual_keys.md#custom--key-generate) | @@ -375,6 +377,7 @@ router_settings: | ATHINA_API_KEY | API key for Athina service | ATHINA_BASE_URL | Base URL for Athina service (defaults to `https://log.athina.ai`) | AUTH_STRATEGY | Strategy used for authentication (e.g., OAuth, API key) +| AUTO_REDIRECT_UI_LOGIN_TO_SSO | Flag to enable automatic redirect of UI login page to SSO when SSO is configured. Default is **true** | ANTHROPIC_API_KEY | API key for Anthropic service | ANTHROPIC_API_BASE | Base URL for Anthropic API. Default is https://api.anthropic.com | AWS_ACCESS_KEY_ID | Access Key ID for AWS services @@ -473,6 +476,8 @@ router_settings: | DEFAULT_ALLOWED_FAILS | Maximum failures allowed before cooling down a model. Default is 3 | DEFAULT_ANTHROPIC_CHAT_MAX_TOKENS | Default maximum tokens for Anthropic chat completions. Default is 4096 | DEFAULT_BATCH_SIZE | Default batch size for operations. Default is 512 +| DEFAULT_CHUNK_OVERLAP | Default chunk overlap for RAG text splitters. Default is 200 +| DEFAULT_CHUNK_SIZE | Default chunk size for RAG text splitters. Default is 1000 | DEFAULT_CLIENT_DISCONNECT_CHECK_TIMEOUT_SECONDS | Timeout in seconds for checking client disconnection. Default is 1 | DEFAULT_COOLDOWN_TIME_SECONDS | Duration in seconds to cooldown a model after failures. Default is 5 | DEFAULT_CRON_JOB_LOCK_TTL_SECONDS | Time-to-live for cron job locks in seconds. Default is 60 (1 minute) @@ -572,6 +577,8 @@ router_settings: | GENERIC_USER_PROVIDER_ATTRIBUTE | Attribute specifying the user's provider | GENERIC_USER_ROLE_ATTRIBUTE | Attribute specifying the user's role | GENERIC_USERINFO_ENDPOINT | Endpoint to fetch user information in generic OAuth +| GENERIC_LOGGER_ENDPOINT | Endpoint URL for the Generic Logger callback to send logs to +| GENERIC_LOGGER_HEADERS | JSON string of headers to include in Generic Logger callback requests | GEMINI_API_BASE | Base URL for Gemini API. Default is https://generativelanguage.googleapis.com | GALILEO_BASE_URL | Base URL for Galileo platform | GALILEO_PASSWORD | Password for Galileo authentication @@ -757,7 +764,7 @@ router_settings: | PROMPTLAYER_API_KEY | API key for PromptLayer integration | PROXY_ADMIN_ID | Admin identifier for proxy server | PROXY_BASE_URL | Base URL for proxy service -| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 30 +| PROXY_BATCH_WRITE_AT | Time in seconds to wait before batch writing spend logs to the database. Default is 10 | PROXY_BATCH_POLLING_INTERVAL | Time in seconds to wait before polling a batch, to check if it's completed. Default is 6000s (1 hour) | PROXY_BUDGET_RESCHEDULER_MAX_TIME | Maximum time in seconds to wait before checking database for budget resets. Default is 605 | PROXY_BUDGET_RESCHEDULER_MIN_TIME | Minimum time in seconds to wait before checking database for budget resets. Default is 597 diff --git a/docs/my-website/docs/proxy/configs.md b/docs/my-website/docs/proxy/configs.md index 18177b7c4d2..77ab3158f74 100644 --- a/docs/my-website/docs/proxy/configs.md +++ b/docs/my-website/docs/proxy/configs.md @@ -576,7 +576,7 @@ custom_tokenizer: ```yaml general_settings: - database_connection_pool_limit: 100 # sets connection pool for prisma client to postgres db at 100 + database_connection_pool_limit: 10 # sets connection pool for prisma client to postgres db (default: 10, recommended: 10-20) database_connection_timeout: 60 # sets a 60s timeout for any connection call to the db ``` diff --git a/docs/my-website/docs/proxy/error_diagnosis.md b/docs/my-website/docs/proxy/error_diagnosis.md new file mode 100644 index 00000000000..9629fc52b0c --- /dev/null +++ b/docs/my-website/docs/proxy/error_diagnosis.md @@ -0,0 +1,90 @@ +# Diagnosing Errors - Provider vs Gateway + +Having trouble diagnosing if an error is from the **LLM Provider** (OpenAI, Anthropic, etc.) or from the **LiteLLM AI Gateway** itself? Here's how to tell. + +## Quick Rule + +**If the error contains `Exception`, it's from the provider.** + +| Error Contains | Error Source | +|----------------|--------------| +| `AnthropicException` | Anthropic | +| `OpenAIException` | OpenAI | +| `AzureException` | Azure | +| `BedrockException` | AWS Bedrock | +| `VertexAIException` | Google Vertex AI | +| No provider name | LiteLLM AI Gateway | + +## Examples + +### Provider Error (from AWS Bedrock) + +``` +{ + "error": { + "message": "litellm.BadRequestError: BedrockException - {\"message\":\"The model returned the following errors: messages.1.content.0.type: Expected `thinking` or `redacted_thinking`, but found `text`.\"}", + "type": "invalid_request_error", + "param": null, + "code": "400" + } +} +``` + +This error is from **AWS Bedrock** (notice `BedrockException`). The Bedrock API is rejecting the request due to invalid message format - this is not a LiteLLM issue. + +### Provider Error (from OpenAI) + +``` +{ + "error": { + "message": "litellm.AuthenticationError: OpenAIException - Incorrect API key provided: . You can find your API key at https://platform.openai.com/account/api-keys.", + "type": "invalid_request_error", + "param": null, + "code": "invalid_api_key" + } +} +``` + +This error is from **OpenAI** (notice `OpenAIException`). The OpenAI API key configured in LiteLLM is invalid. + +### Provider Error (from Anthropic) + +``` +{ + "error": { + "message": "litellm.InternalServerError: AnthropicException - Overloaded. Handle with `litellm.InternalServerError`.", + "type": "internal_server_error", + "param": null, + "code": "500" + } +} +``` + +This error is from **Anthropic** (notice `AnthropicException`). The Anthropic API is overloaded - this is not a LiteLLM issue. + +### Gateway Error (from LiteLLM) + +``` +{ + "error": { + "message": "Invalid API Key. Please check your LiteLLM API key.", + "type": "auth_error", + "param": null, + "code": "401" + } +} +``` + +This error is from the **LiteLLM AI Gateway** (no provider name). Your LiteLLM virtual key is invalid. + +## What to do? + +| Error Source | Action | +|--------------|--------| +| Provider Error | Check the provider's status page, adjust rate limits, or retry later | +| Gateway Error | Check your LiteLLM configuration, API keys, or [open an issue](https://github.com/BerriAI/litellm/issues) | + +## See Also + +- [Debugging](/docs/proxy/debugging) - Enable debug logs to see detailed request/response info +- [Exception Mapping](/docs/exception_mapping) - Full list of LiteLLM exception types diff --git a/docs/my-website/docs/proxy/guardrails/bedrock.md b/docs/my-website/docs/proxy/guardrails/bedrock.md index 4a1a0a246f8..8c71508fd23 100644 --- a/docs/my-website/docs/proxy/guardrails/bedrock.md +++ b/docs/my-website/docs/proxy/guardrails/bedrock.md @@ -188,6 +188,28 @@ My email is [EMAIL] and my phone number is [PHONE_NUMBER] This helps protect sensitive information while still allowing the model to understand the context of the request. +## Experimental: Only Send Latest User Message + +When you're chaining long conversations through Bedrock guardrails, you can opt into a lighter, experimental behavior by setting `experimental_use_latest_role_message_only: true` in the guardrail's `litellm_params`. When enabled, LiteLLM only sends the most recent `user` message (or assistant output during post-call checks) to Bedrock, which: + +- prevents unintended blocks on older system/dev messages +- keeps Bedrock payloads smaller, reducing latency and cost +- applies to proxy hooks (`pre_call`, `during_call`) and the `/guardrails/apply_guardrail` testing endpoint + +```yaml showLineNumbers title="litellm proxy config.yaml" +guardrails: + - guardrail_name: "bedrock-pre-guard" + litellm_params: + guardrail: bedrock + mode: "pre_call" + guardrailIdentifier: wf0hkdb5x07f + guardrailVersion: "DRAFT" + aws_region_name: os.environ/AWS_REGION + experimental_use_latest_role_message_only: true # NEW +``` + +> ⚠️ This flag is currently experimental and defaults to `false` to preserve the legacy behavior (entire message history). We'll be listening to user feedback to decide if this becomes the default or rolls out more broadly. + ## Disabling Exceptions on Bedrock BLOCK By default, when Bedrock guardrails block content, LiteLLM raises an HTTP 400 exception. However, you can disable this behavior by setting `disable_exception_on_block: true`. This is particularly useful when integrating with **OpenWebUI**, where exceptions can interrupt the chat flow and break the user experience. diff --git a/docs/my-website/docs/proxy/guardrails/lasso_security.md b/docs/my-website/docs/proxy/guardrails/lasso_security.md index 21528790afe..113e3f8974a 100644 --- a/docs/my-website/docs/proxy/guardrails/lasso_security.md +++ b/docs/my-website/docs/proxy/guardrails/lasso_security.md @@ -35,7 +35,7 @@ guardrails: guardrail: lasso mode: "pre_call" api_key: os.environ/LASSO_API_KEY - api_base: "https://server.lasso.security" + api_base: "https://server.lasso.security/gateway/v3" - guardrail_name: "lasso-post-guard" litellm_params: guardrail: lasso @@ -228,7 +228,7 @@ Expected response: ## PII Masking with Lasso -Lasso supports automatic PII detection and masking using the `/gateway/v1/classifix` endpoint. When enabled, sensitive information like emails, phone numbers, and other PII will be automatically masked with appropriate placeholders. +Lasso supports automatic PII detection and masking using the `/classifix` endpoint. When enabled, sensitive information like emails, phone numbers, and other PII will be automatically masked with appropriate placeholders. ### Enabling PII Masking diff --git a/docs/my-website/docs/proxy/guardrails/pillar_security.md b/docs/my-website/docs/proxy/guardrails/pillar_security.md index 5ab9f9bf8cb..9632376768b 100644 --- a/docs/my-website/docs/proxy/guardrails/pillar_security.md +++ b/docs/my-website/docs/proxy/guardrails/pillar_security.md @@ -60,6 +60,8 @@ litellm_settings: set_verbose: true # Enable detailed logging ``` +**Note:** Virtual key context is **automatically passed** as headers - no additional configuration needed! + ### 3. Start the Proxy ```bash @@ -210,7 +212,7 @@ export PILLAR_API_KEY="your_api_key_here" export PILLAR_API_BASE="https://api.pillar.security" export PILLAR_ON_FLAGGED_ACTION="monitor" export PILLAR_FALLBACK_ON_ERROR="allow" -export PILLAR_TIMEOUT="30.0" +export PILLAR_TIMEOUT="5.0" ``` ### Session Tracking diff --git a/docs/my-website/docs/proxy/guardrails/tool_permission.md b/docs/my-website/docs/proxy/guardrails/tool_permission.md index 19b674c9e55..1827333654f 100644 --- a/docs/my-website/docs/proxy/guardrails/tool_permission.md +++ b/docs/my-website/docs/proxy/guardrails/tool_permission.md @@ -1,4 +1,3 @@ -import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; @@ -7,9 +6,34 @@ import TabItem from '@theme/TabItem'; LiteLLM provides the LiteLLM Tool Permission Guardrail that lets you control which **tool calls** a model is allowed to invoke, using configurable allow/deny rules. This offers fine-grained, provider-agnostic control over tool execution (e.g., OpenAI Chat Completions `tool_calls`, Anthropic Messages `tool_use`, MCP tools). ## Quick Start -### 1. Define Guardrails on your LiteLLM config.yaml -Define your guardrails under the `guardrails` section +### LiteLLM UI + +#### Step 1: Select Tool Permission Guardrail + +Open the LiteLLM Dashboard, click **Add New Guardrail**, and choose **LiteLLM Tool Permission Guardrail**. This loads the rule builder UI. + +#### Step 2: Define Regex Rules + +1. Click **Add Rule**. +2. Enter a unique Rule ID. +3. Provide a regex for the tool name (e.g., `^mcp__github_.*$`). +4. Optionally add a regex for tool type (e.g., `^function$`). +5. Pick **Allow** or **Deny**. + +#### Step 3: Restrict Tool Arguments (Optional) + +Select **+ Restrict tool arguments** to attach regex validations to nested paths (dot + `[]` notation). This enforces that sensitive parameters (such as `arguments.to[]`) conform to pre-approved formats. + +#### Step 4: Choose Defaults & Actions + +- Set the fallback decision (`default_action`) for tools that do not hit any rule. +- Decide how disallowed tools behave: **Block** halts the request, **Rewrite** strips forbidden tools and returns an error message inside the response. +- Customize `violation_message_template` if you want branded error copy. +- Save the guardrail. + +### LiteLLM Config.yaml Setup + ```yaml guardrails: - guardrail_name: "tool-permission-guardrail" @@ -21,16 +45,17 @@ guardrails: tool_name: "Bash" decision: "allow" - id: "allow_github_mcp" - tool_name: "mcp__github_*" + tool_name: "^mcp__github_.*$" decision: "allow" - id: "allow_aws_documentation" - tool_name: "mcp__aws-documentation_*_documentation" + tool_name: "^mcp__aws-documentation_.*_documentation$" decision: "allow" - id: "deny_read_commands" tool_name: "Read" - decision: "Deny" + decision: "deny" - id: "mail-domain" - tool_name: "send_email" + tool_name: "^send_email$" + tool_type: "^function$" decision: "allow" allowed_param_patterns: "to[]": "^.+@berri\\.ai$" @@ -44,7 +69,8 @@ guardrails: ```yaml - id: "unique_rule_id" # Unique identifier for the rule - tool_name: "pattern" # Tool name or pattern to match + tool_name: "^regex$" # Regex for tool name (optional, at least one of name/type required) + tool_type: "^function$" # Regex for tool type (optional) decision: "allow" # "allow" or "deny" allowed_param_patterns: # Optional - regex map for argument paths (dot + [] notation) "path.to[].field": "^regex$" diff --git a/docs/my-website/docs/proxy/pass_through.md b/docs/my-website/docs/proxy/pass_through.md index 7309cdeda26..03454004b8c 100644 --- a/docs/my-website/docs/proxy/pass_through.md +++ b/docs/my-website/docs/proxy/pass_through.md @@ -275,6 +275,20 @@ In this video, we'll add the Azure OpenAI Assistants API as a pass through endpo - Check LiteLLM proxy logs for error details - Verify the target API's expected request format +### Allowing Team JWTs to use pass-through routes + +If you are using pass-through provider routes (e.g., `/anthropic/*`) and want your JWT team tokens to access these routes, add `mapped_pass_through_routes` to the `team_allowed_routes` in `litellm_jwtauth` or explicitly add the relevant route(s). + +Example (`proxy_server_config.yaml`): + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + team_ids_jwt_field: "team_ids" + team_allowed_routes: ["openai_routes","info_routes","mapped_pass_through_routes"] +``` + ### Getting Help [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version) diff --git a/docs/my-website/docs/proxy/pass_through_guardrails.md b/docs/my-website/docs/proxy/pass_through_guardrails.md new file mode 100644 index 00000000000..cc3d36c866e --- /dev/null +++ b/docs/my-website/docs/proxy/pass_through_guardrails.md @@ -0,0 +1,250 @@ +# Guardrails on Pass-Through Endpoints + +import Image from '@theme/IdealImage'; + +## Overview + +| Property | Details | +|----------|---------| +| Description | Enable guardrail execution on LiteLLM pass-through endpoints with opt-in activation and automatic inheritance from org/team/key levels | +| Supported Guardrails | All LiteLLM guardrails (Bedrock, Aporia, Lakera, etc.) | +| Default Behavior | Guardrails are **disabled** on pass-through endpoints unless explicitly enabled | + +## Quick Start + +You can configure guardrails on pass-through endpoints either via the **UI** (recommended) or **config file**. + +### Using the UI + +#### 1. Navigate to Pass-Through Endpoints + +Go to **Models + Endpoints** → Click **+ Add Pass-Through Endpoint** + +Add guardrails to pass-through endpoint + +Scroll to the **Guardrails** section and select which guardrails to enforce. + +:::tip Default Behavior +By default, you don't need to specify fields - LiteLLM will JSON dump the entire request/response payload and send it to the guardrail. +::: + +#### 2. Target Specific Fields (Optional) + +Configure field-level targeting + +To check only specific fields instead of the entire payload: + +1. Select your guardrails +2. In **Field Targeting (Optional)**, specify fields for each guardrail +3. Use the quick-add buttons (`+ query`, `+ documents[*]`) or type custom JSONPath expressions +4. **Request Fields (pre_call)**: Fields to check before sending to target API +5. **Response Fields (post_call)**: Fields to check in the response from target API + +**Example**: In the screenshot above, we set `query` as a request field, so only the `query` field is sent to the guardrail instead of the entire request. + +--- + +### Using Config File + +#### 1. Define guardrails and pass-through endpoint + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-guard" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "your-guardrail-id" + guardrailVersion: "1" + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + headers: + Authorization: "bearer os.environ/COHERE_API_KEY" + guardrails: + pii-guard: +``` + +#### 2. Start proxy + +```bash +litellm --config config.yaml +``` + +#### 3. Test request + +```bash +curl -X POST "http://localhost:4000/v1/rerank" \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer sk-1234" \ + -d '{ + "model": "rerank-english-v3.0", + "query": "What is the capital of France?", + "documents": ["Paris is the capital of France."] + }' +``` + +--- + +## Opt-In Behavior + +| Configuration | Behavior | +|--------------|----------| +| `guardrails` not set | No guardrails execute (default) | +| `guardrails` set | All org/team/key + pass-through guardrails execute | + +When guardrails are enabled, the system collects and executes: +- Org-level guardrails +- Team-level guardrails +- Key-level guardrails +- Pass-through specific guardrails + +--- + + +## How It Works + +The diagram below shows what happens when a client makes a request to `/special/rerank` - a pass-through endpoint configured with guardrails in your `config.yaml`. + +When guardrails are configured on a pass-through endpoint: +1. **Pre-call guardrails** run on the request before forwarding to the target API +2. If `request_fields` is specified (e.g., `["query"]`), only those fields are sent to the guardrail. Otherwise, the entire request payload is evaluated. +3. The request is forwarded to the target API only if guardrails pass +4. **Post-call guardrails** run on the response from the target API +5. If `response_fields` is specified (e.g., `["results[*].text"]`), only those fields are evaluated. Otherwise, the entire response is checked. + +:::info +If the `guardrails` block is omitted or empty in your pass-through endpoint config, the request skips the guardrail flow entirely and goes directly to the target API. +::: + +```mermaid +sequenceDiagram + participant Client + box rgb(200, 220, 255) LiteLLM Proxy + participant PassThrough as Pass-through Endpoint + participant Guardrails + end + participant Target as Target API (Cohere, etc.) + + Client->>PassThrough: POST /special/rerank + Note over PassThrough,Guardrails: Collect passthrough + org/team/key guardrails + PassThrough->>Guardrails: Run pre_call (request_fields or full payload) + Guardrails-->>PassThrough: ✓ Pass / ✗ Block + PassThrough->>Target: Forward request + Target-->>PassThrough: Response + PassThrough->>Guardrails: Run post_call (response_fields or full payload) + Guardrails-->>PassThrough: ✓ Pass / ✗ Block + PassThrough-->>Client: Return response (or error) +``` + +--- + +## Field-Level Targeting + +Target specific JSON fields instead of the entire request/response payload. + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-detection" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "pii-guard-id" + guardrailVersion: "1" + + - guardrail_name: "content-moderation" + litellm_params: + guardrail: bedrock + mode: post_call + guardrailIdentifier: "content-guard-id" + guardrailVersion: "1" + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + headers: + Authorization: "bearer os.environ/COHERE_API_KEY" + guardrails: + pii-detection: + request_fields: ["query", "documents[*].text"] + content-moderation: + response_fields: ["results[*].text"] +``` + +### Field Options + +| Field | Description | +|-------|-------------| +| `request_fields` | JSONPath expressions for input (pre_call) | +| `response_fields` | JSONPath expressions for output (post_call) | +| Neither specified | Guardrail runs on entire payload | + +### JSONPath Examples + +| Expression | Matches | +|------------|---------| +| `query` | Single field named `query` | +| `documents[*].text` | All `text` fields in `documents` array | +| `messages[*].content` | All `content` fields in `messages` array | + +--- + +## Configuration Examples + +### Single guardrail on entire payload + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-detection" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "your-id" + guardrailVersion: "1" + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + guardrails: + pii-detection: +``` + +### Multiple guardrails with mixed settings + +```yaml showLineNumbers title="config.yaml" +guardrails: + - guardrail_name: "pii-detection" + litellm_params: + guardrail: bedrock + mode: pre_call + guardrailIdentifier: "pii-id" + guardrailVersion: "1" + + - guardrail_name: "content-moderation" + litellm_params: + guardrail: bedrock + mode: post_call + guardrailIdentifier: "content-id" + guardrailVersion: "1" + + - guardrail_name: "prompt-injection" + litellm_params: + guardrail: lakera + mode: pre_call + api_key: os.environ/LAKERA_API_KEY + +general_settings: + pass_through_endpoints: + - path: "/v1/rerank" + target: "https://api.cohere.com/v1/rerank" + guardrails: + pii-detection: + request_fields: ["input", "query"] + content-moderation: + prompt-injection: + request_fields: ["messages[*].content"] +``` diff --git a/docs/my-website/docs/proxy/reject_clientside_metadata_tags.md b/docs/my-website/docs/proxy/reject_clientside_metadata_tags.md new file mode 100644 index 00000000000..534c65939eb --- /dev/null +++ b/docs/my-website/docs/proxy/reject_clientside_metadata_tags.md @@ -0,0 +1,120 @@ +# Reject Client-Side Metadata Tags + +## Overview + +The `reject_clientside_metadata_tags` setting allows you to prevent users from passing client-side `metadata.tags` in their API requests. This ensures that tags are only inherited from the API key metadata and cannot be overridden by users to potentially influence budget tracking or routing decisions. + +## Use Case + +This feature is particularly useful in multi-tenant scenarios where: +- You want to enforce strict budget tracking based on API key tags +- You want to prevent users from manipulating routing decisions by sending custom client-side tags +- You need to ensure consistent tag-based filtering and reporting + +## Configuration + +Add the following to your `config.yaml`: + +```yaml +general_settings: + reject_clientside_metadata_tags: true # Default is false/null +``` + +## Behavior + +### When `reject_clientside_metadata_tags: true` + +**Rejected Request Example:** +```bash +curl -X POST http://localhost:4000/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello"}], + "metadata": { + "tags": ["custom-tag"] # This will be rejected + } + }' +``` + +**Error Response:** +```json +{ + "error": { + "message": "Client-side 'metadata.tags' not allowed in request. 'reject_clientside_metadata_tags'=True. Tags can only be set via API key metadata.", + "type": "bad_request_error", + "param": "metadata.tags", + "code": 400 + } +} +``` + +**Allowed Request Example:** +```bash +curl -X POST http://localhost:4000/chat/completions \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "model": "gpt-3.5-turbo", + "messages": [{"role": "user", "content": "Hello"}], + "metadata": { + "custom_field": "value" # Other metadata fields are allowed + } + }' +``` + +### When `reject_clientside_metadata_tags: false` or not set + +All requests are allowed, including those with client-side `metadata.tags`. + +## Setting Tags via API Key + +When `reject_clientside_metadata_tags` is enabled, tags should be set on the API key metadata: + +```bash +curl -X POST http://localhost:4000/key/generate \ + -H "Authorization: Bearer sk-master-key" \ + -H "Content-Type: application/json" \ + -d '{ + "metadata": { + "tags": ["team-a", "production"] + } + }' +``` + +These tags will be automatically inherited by all requests made with that API key. + +## Complete Example Configuration + +```yaml +model_list: + - model_name: gpt-3.5-turbo + litellm_params: + model: gpt-3.5-turbo + api_key: os.environ/OPENAI_API_KEY + +general_settings: + master_key: sk-1234 + database_url: "postgresql://user:password@localhost:5432/litellm" + + # Reject client-side tags + reject_clientside_metadata_tags: true + + # Optional: Also enforce user parameter + enforce_user_param: true +``` + +## Similar Features + +- `enforce_user_param` - Requires all requests to include a 'user' parameter +- Tag-based routing - Use tags for intelligent request routing +- Budget tracking - Track spending per tag + +## Notes + +- This check only applies to LLM API routes (e.g., `/chat/completions`, `/embeddings`) +- Management endpoints (e.g., `/key/generate`) are not affected +- The check validates that client-side `metadata.tags` is not present in the request body +- Other metadata fields can still be passed in requests +- Tags set on API keys will still be applied to all requests diff --git a/docs/my-website/docs/proxy/token_auth.md b/docs/my-website/docs/proxy/token_auth.md index c2a88010d79..1db1b2a8965 100644 --- a/docs/my-website/docs/proxy/token_auth.md +++ b/docs/my-website/docs/proxy/token_auth.md @@ -338,6 +338,58 @@ general_settings: team_allowed_routes: ["/v1/chat/completions"] # 👈 Set accepted routes ``` +### Allowing other provider routes for Teams + +To enable team JWT tokens to access Anthropic-style endpoints such as `/v1/messages`, update `team_allowed_routes` in your `litellm_jwtauth` configuration. `team_allowed_routes` supports the following values: + +- Named route groups from `LiteLLMRoutes` (e.g., `openai_routes`, `anthropic_routes`, `info_routes`, `mapped_pass_through_routes`). + +Below is a quick reference for the route groups you can use and example representative routes from each group. If you need the exhaustive list, see the `LiteLLMRoutes` enum in `litellm/proxy/_types.py` for the authoritative list. + +| Route Group | What it contains | Representative routes | +|-------------|------------------|-----------------------| +| `openai_routes` | OpenAI-compatible REST endpoints (chat, completion, embeddings, images, responses, models, etc.) | `/v1/chat/completions`, `/v1/completions`, `/v1/embeddings`, `/v1/images/generations`, `/v1/models` | +| `anthropic_routes` | Anthropic-style endpoints (`/v1/messages` and related) | `/v1/messages`, `/v1/messages/count_tokens`, `/v1/skills` | +| `mapped_pass_through_routes` | Provider-specific pass-through route prefixes (e.g., Anthropic when proxied via `/anthropic`). Use with `mapped_pass_through_routes` for provider wildcard mapping | `/anthropic/*`, `/vertex-ai/*`, `/bedrock/*` | +| `passthrough_routes_wildcard` | Wildcard mapping for providers (e.g., `/anthropic/*`) - precomputed wildcard list used by the proxy | `/anthropic/*`, `/vllm/*` | +| `google_routes` | Google-specific (e.g., Vertex / Batching endpoints) | `/v1beta/models/{model_name}:generateContent` | +| `mcp_routes` | Internal MCP management endpoints | `/mcp/tools`, `/mcp/tools/call` | +| `info_routes` | Read-only & info endpoints used by the UI | `/key/info`, `/team/info`, `/v1/models` | +| `management_routes` | Admin-only management endpoints (create/update/delete user/team/model) | `/team/new`, `/key/generate`, `/model/new` | +| `spend_tracking_routes` | Budget/spend related endpoints | `/spend/logs`, `/spend/keys` | +| `public_routes` | Public and unauthenticated endpoints | `/`, `/routes`, `/.well-known/litellm-ui-config` | + +Note: `llm_api_routes` is the union of OpenAI, Anthropic, Google, pass-through and other LLM routes (`openai_routes + anthropic_routes + google_routes + mapped_pass_through_routes + passthrough_routes_wildcard + apply_guardrail_routes + mcp_routes + litellm_native_routes`). + +Defaults (what the proxy uses if you don't override them in `litellm_jwtauth`): + +- `admin_jwt_scope`: `litellm_proxy_admin` +- `admin_allowed_routes` (default): `management_routes`, `spend_tracking_routes`, `global_spend_tracking_routes`, `info_routes` +- `team_allowed_routes` (default): `openai_routes`, `info_routes` +- `public_allowed_routes` (default): `public_routes` + + +Example: Allow team JWTs to call Anthropic `/v1/messages` (either by route group or by explicit route string): + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + team_ids_jwt_field: "team_ids" + team_allowed_routes: ["openai_routes", "info_routes", "anthropic_routes"] +``` + +Or selectively allow the exact Anthropic message endpoint only: + +```yaml +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + team_ids_jwt_field: "team_ids" + team_allowed_routes: ["/v1/messages", "info_routes"] +``` + + ### Caching Public Keys Control how long public keys are cached for (in seconds). @@ -407,6 +459,72 @@ general_settings: user_id_upsert: true # 👈 upserts the user to db, if valid email but not in db ``` +## OIDC UserInfo Endpoint + +Use this when your JWT/access token doesn't contain user-identifying information. LiteLLM will call your identity provider's UserInfo endpoint to fetch user details. + +### When to Use + +- Your JWT is opaque (not self-contained) or lacks user claims +- You need to fetch fresh user information from your identity provider +- Your access tokens don't include email, roles, or other identifying data + +### Configuration + +```yaml title="config.yaml" showLineNumbers +general_settings: + enable_jwt_auth: True + litellm_jwtauth: + # Enable OIDC UserInfo endpoint + oidc_userinfo_enabled: true + oidc_userinfo_endpoint: "https://your-idp.com/oauth2/userinfo" + oidc_userinfo_cache_ttl: 300 # Cache for 5 minutes (default: 300) + + # Map fields from UserInfo response + user_id_jwt_field: "sub" + user_email_jwt_field: "email" + user_roles_jwt_field: "roles" +``` + +### Flow Diagram + +```mermaid +sequenceDiagram + participant Client + participant LiteLLM + participant IdP as Identity Provider + + Client->>LiteLLM: Request with Bearer token + Note over LiteLLM: Check cache for UserInfo + + LiteLLM->>IdP: GET /userinfo (if not cached)
Authorization: Bearer {token} + IdP-->>LiteLLM: User data (sub, email, roles) + + Note over LiteLLM: Cache response (TTL: 5min)
Extract user_id, email, roles
Perform RBAC checks + + LiteLLM-->>Client: Authorized/Denied +``` + +### Example: Azure AD + +```yaml title="config.yaml" showLineNumbers +litellm_jwtauth: + oidc_userinfo_enabled: true + oidc_userinfo_endpoint: "https://graph.microsoft.com/oidc/userinfo" + user_id_jwt_field: "sub" + user_email_jwt_field: "email" +``` + +### Example: Keycloak + +```yaml title="config.yaml" showLineNumbers +litellm_jwtauth: + oidc_userinfo_enabled: true + oidc_userinfo_endpoint: "https://keycloak.example.com/realms/your-realm/protocol/openid-connect/userinfo" + user_id_jwt_field: "sub" + user_roles_jwt_field: "resource_access.your-client.roles" +``` + ## [BETA] Control Access with OIDC Roles Allow JWT tokens with supported roles to access the proxy. diff --git a/docs/my-website/docs/rag_ingest.md b/docs/my-website/docs/rag_ingest.md new file mode 100644 index 00000000000..536151febdc --- /dev/null +++ b/docs/my-website/docs/rag_ingest.md @@ -0,0 +1,305 @@ +# /rag/ingest + +All-in-one document ingestion pipeline: **Upload → Chunk → Embed → Vector Store** + +| Feature | Supported | +|---------|-----------| +| Logging | ✅ | +| Supported Providers | `openai`, `bedrock`, `vertex_ai`, `gemini` | + +## Quick Start + +### OpenAI + +```bash showLineNumbers title="Ingest to OpenAI vector store" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"document.txt\", + \"content\": \"$(base64 -i document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"vector_store\": { + \"custom_llm_provider\": \"openai\" + } + } + }" +``` + +### Bedrock + +```bash showLineNumbers title="Ingest to Bedrock Knowledge Base" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"document.txt\", + \"content\": \"$(base64 -i document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"vector_store\": { + \"custom_llm_provider\": \"bedrock\" + } + } + }" +``` + +### Vertex AI RAG Engine + +```bash showLineNumbers title="Ingest to Vertex AI RAG Corpus" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"document.txt\", + \"content\": \"$(base64 -i document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"vector_store\": { + \"custom_llm_provider\": \"vertex_ai\", + \"vector_store_id\": \"your-corpus-id\", + \"gcs_bucket\": \"your-gcs-bucket\" + } + } + }" +``` + +## Response + +```json +{ + "id": "ingest_abc123", + "status": "completed", + "vector_store_id": "vs_xyz789", + "file_id": "file_123" +} +``` + +## Query the Vector Store + +After ingestion, query with `/vector_stores/{vector_store_id}/search`: + +```bash showLineNumbers title="Search the vector store" +curl -X POST "http://localhost:4000/v1/vector_stores/vs_xyz789/search" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "query": "What is the main topic?", + "max_num_results": 5 + }' +``` + +## End-to-End Example + +### OpenAI + +#### 1. Ingest Document + +```bash showLineNumbers title="Step 1: Ingest" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"test_document.txt\", + \"content\": \"$(base64 -i test_document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"name\": \"test-basic-ingest\", + \"vector_store\": { + \"custom_llm_provider\": \"openai\" + } + } + }" +``` + +Response: +```json +{ + "id": "ingest_d834f544-fc5e-4751-902d-fb0bcc183b85", + "status": "completed", + "vector_store_id": "vs_692658d337c4819183f2ad8488d12fc9", + "file_id": "file-M2pJJiWH56cfUP4Fe7rJay" +} +``` + +#### 2. Query + +```bash showLineNumbers title="Step 2: Query" +curl -X POST "http://localhost:4000/v1/vector_stores/vs_692658d337c4819183f2ad8488d12fc9/search" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "query": "What is LiteLLM?", + "custom_llm_provider": "openai" + }' +``` + +Response: +```json +{ + "object": "vector_store.search_results.page", + "search_query": ["What is LiteLLM?"], + "data": [ + { + "file_id": "file-M2pJJiWH56cfUP4Fe7rJay", + "filename": "test_document.txt", + "score": 0.4004629778869299, + "attributes": {}, + "content": [ + { + "type": "text", + "text": "Test document abc123 for RAG ingestion.\nThis is a sample document to test the RAG ingest API.\nLiteLLM provides a unified interface for vector stores." + } + ] + } + ], + "has_more": false, + "next_page": null +} +``` + +## Request Parameters + +### Top-Level + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `file` | object | One of file/file_url/file_id required | Base64-encoded file | +| `file.filename` | string | Yes | Filename with extension | +| `file.content` | string | Yes | Base64-encoded content | +| `file.content_type` | string | Yes | MIME type (e.g., `text/plain`) | +| `file_url` | string | One of file/file_url/file_id required | URL to fetch file from | +| `file_id` | string | One of file/file_url/file_id required | Existing file ID | +| `ingest_options` | object | Yes | Pipeline configuration | + +### ingest_options + +| Parameter | Type | Required | Description | +|-----------|------|----------|-------------| +| `vector_store` | object | Yes | Vector store configuration | +| `name` | string | No | Pipeline name for logging | + +### vector_store (OpenAI) + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `custom_llm_provider` | string | - | `"openai"` | +| `vector_store_id` | string | auto-create | Existing vector store ID | + +### vector_store (Bedrock) + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `custom_llm_provider` | string | - | `"bedrock"` | +| `vector_store_id` | string | auto-create | Existing Knowledge Base ID | +| `wait_for_ingestion` | boolean | `false` | Wait for indexing to complete | +| `ingestion_timeout` | integer | `300` | Timeout in seconds (if waiting) | +| `s3_bucket` | string | auto-create | S3 bucket for documents | +| `s3_prefix` | string | `"data/"` | S3 key prefix | +| `embedding_model` | string | `amazon.titan-embed-text-v2:0` | Bedrock embedding model | +| `aws_region_name` | string | `us-west-2` | AWS region | + +:::info Bedrock Auto-Creation +When `vector_store_id` is omitted, LiteLLM automatically creates: +- S3 bucket for document storage +- OpenSearch Serverless collection +- IAM role with required permissions +- Bedrock Knowledge Base +- Data Source +::: + +### vector_store (Vertex AI) + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `custom_llm_provider` | string | - | `"vertex_ai"` | +| `vector_store_id` | string | **required** | RAG corpus ID | +| `gcs_bucket` | string | **required** | GCS bucket for file uploads | +| `vertex_project` | string | env `VERTEXAI_PROJECT` | GCP project ID | +| `vertex_location` | string | `us-central1` | GCP region | +| `vertex_credentials` | string | ADC | Path to credentials JSON | +| `wait_for_import` | boolean | `true` | Wait for import to complete | +| `import_timeout` | integer | `600` | Timeout in seconds (if waiting) | + +:::info Vertex AI Prerequisites +1. Create a RAG corpus in Vertex AI console or via API +2. Create a GCS bucket for file uploads +3. Authenticate via `gcloud auth application-default login` +4. Install: `pip install 'google-cloud-aiplatform>=1.60.0'` +::: + +## Input Examples + +### File (Base64) + +```json title="Request body" +{ + "file": { + "filename": "document.txt", + "content": "", + "content_type": "text/plain" + }, + "ingest_options": { + "vector_store": {"custom_llm_provider": "openai"} + } +} +``` + +### File URL + +```bash showLineNumbers title="Ingest from URL" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d '{ + "file_url": "https://example.com/document.pdf", + "ingest_options": {"vector_store": {"custom_llm_provider": "openai"}} + }' +``` + +## Chunking Strategy + +Control how documents are split into chunks before embedding. Specify `chunking_strategy` in `ingest_options`. + +| Parameter | Type | Default | Description | +|-----------|------|---------|-------------| +| `chunk_size` | integer | `1000` | Maximum size of each chunk | +| `chunk_overlap` | integer | `200` | Overlap between consecutive chunks | + +### Vertex AI RAG Engine + +Vertex AI RAG Engine supports custom chunking via the `chunking_strategy` parameter. Chunks are processed server-side during import. + +```bash showLineNumbers title="Vertex AI with custom chunking" +curl -X POST "http://localhost:4000/v1/rag/ingest" \ + -H "Authorization: Bearer sk-1234" \ + -H "Content-Type: application/json" \ + -d "{ + \"file\": { + \"filename\": \"document.txt\", + \"content\": \"$(base64 -i document.txt)\", + \"content_type\": \"text/plain\" + }, + \"ingest_options\": { + \"chunking_strategy\": { + \"chunk_size\": 500, + \"chunk_overlap\": 100 + }, + \"vector_store\": { + \"custom_llm_provider\": \"vertex_ai\", + \"vector_store_id\": \"your-corpus-id\", + \"gcs_bucket\": \"your-gcs-bucket\" + } + } + }" +``` + diff --git a/docs/my-website/docs/secret_managers/cyberark.md b/docs/my-website/docs/secret_managers/cyberark.md index 37aa1086691..c33aa286703 100644 --- a/docs/my-website/docs/secret_managers/cyberark.md +++ b/docs/my-website/docs/secret_managers/cyberark.md @@ -41,6 +41,7 @@ CYBERARK_CLIENT_KEY="path/to/client.key" # OPTIONAL CYBERARK_REFRESH_INTERVAL="300" # defaults to 300 seconds (5 minutes), frequency of token refresh +CYBERARK_SSL_VERIFY="true" # defaults to true, set to "false" to disable SSL verification (for self-signed certificates) ``` **Step 2.** Add to proxy config.yaml @@ -172,6 +173,24 @@ If these commands work successfully against your CyberArk instance, then CyberAr - The `CYBERARK_API_BASE` URL is accessible from your LiteLLM instance - Your API key or certificates have the necessary permissions in CyberArk +### SSL Certificate Errors + +If you encounter SSL certificate verification errors like: + +``` +RuntimeError: Could not authenticate to CyberArk Conjur: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain +``` + +This typically occurs when your CyberArk Conjur instance uses a self-signed certificate. You can disable SSL verification by setting: + +```bash +CYBERARK_SSL_VERIFY="false" +``` + +:::warning +Disabling SSL verification is insecure and should only be used for testing or development environments with self-signed certificates. For production, configure your certificate chain properly or use certificate-based authentication with `CYBERARK_CLIENT_CERT` and `CYBERARK_CLIENT_KEY`. +::: + ## Video Walkthrough This video walks through using CyberArk Conjur as a secret manager with LiteLLM. We create a virtual key in the LiteLLM Admin UI and verify it exists in CyberArk. Then we rotate the secret key and verify it exists in CyberArk. diff --git a/docs/my-website/docs/vector_stores/create.md b/docs/my-website/docs/vector_stores/create.md index 19b4f39cd9e..7025c490a32 100644 --- a/docs/my-website/docs/vector_stores/create.md +++ b/docs/my-website/docs/vector_stores/create.md @@ -14,6 +14,7 @@ Create a vector store which can be used to store and search document chunks for | End-user Tracking | ✅ | | | Support LLM Providers (OpenAI `/vector_stores` API) | **OpenAI** | Full vector stores API support across providers | | Support LLM Providers (Passthrough API) | [**Azure AI**](/docs/providers/azure_ai/azure_ai_vector_stores_passthrough) | Full vector stores API support across providers | +| Support LLM Providers (Dataset Management) | [**RAGFlow**](/docs/providers/ragflow_vector_store.md) | Dataset creation and management (search not supported) | ## Usage diff --git a/docs/my-website/docs/vector_stores/search.md b/docs/my-website/docs/vector_stores/search.md index 2ffc8ef12e5..3286b3b01e5 100644 --- a/docs/my-website/docs/vector_stores/search.md +++ b/docs/my-website/docs/vector_stores/search.md @@ -12,7 +12,7 @@ Search a vector store for relevant chunks based on a query and file attributes f | Cost Tracking | ✅ | Tracked per search operation | | Logging | ✅ | Works across all integrations | | End-user Tracking | ✅ | | -| Support LLM Providers | **OpenAI, Azure OpenAI, Bedrock, Vertex RAG Engine, Azure AI, Milvus** | Full vector stores API support across providers | +| Support LLM Providers | **OpenAI, Azure OpenAI, Bedrock, Vertex RAG Engine, Azure AI, Milvus, Gemini** | Full vector stores API support across providers | ## Usage @@ -164,6 +164,41 @@ print(response) [See full Milvus vector store documentation](../providers/milvus_vector_stores.md) + + + + +#### Using Gemini File Search +```python showLineNumbers title="Search Vector Store - Gemini Provider" +import litellm +import os + +# Set credentials +os.environ["GEMINI_API_KEY"] = "your-gemini-api-key" + +response = await litellm.vector_stores.asearch( + vector_store_id="fileSearchStores/your-store-id", + query="What is the capital of France?", + custom_llm_provider="gemini", + max_num_results=5 +) +print(response) +``` + +**With Metadata Filter:** +```python showLineNumbers title="Search with Metadata Filter" +response = await litellm.vector_stores.asearch( + vector_store_id="fileSearchStores/your-store-id", + query="What is LiteLLM?", + custom_llm_provider="gemini", + filters={"author": "John Doe", "category": "documentation"}, + max_num_results=5 +) +print(response) +``` + +[See full Gemini File Search documentation](../providers/gemini_file_search.md) + diff --git a/docs/my-website/img/add_agent1.png b/docs/my-website/img/add_agent1.png new file mode 100644 index 00000000000..e69de29bb2d diff --git a/docs/my-website/img/add_agent_1.png b/docs/my-website/img/add_agent_1.png new file mode 100644 index 00000000000..e60435996a9 Binary files /dev/null and b/docs/my-website/img/add_agent_1.png differ diff --git a/docs/my-website/img/agent2.png b/docs/my-website/img/agent2.png new file mode 100644 index 00000000000..412047a6aa3 Binary files /dev/null and b/docs/my-website/img/agent2.png differ diff --git a/docs/my-website/img/create_guard_tool_permission.png b/docs/my-website/img/create_guard_tool_permission.png new file mode 100644 index 00000000000..f6e0e77b1aa Binary files /dev/null and b/docs/my-website/img/create_guard_tool_permission.png differ diff --git a/docs/my-website/img/create_rule_tool_permission.png b/docs/my-website/img/create_rule_tool_permission.png new file mode 100644 index 00000000000..2944136e3ed Binary files /dev/null and b/docs/my-website/img/create_rule_tool_permission.png differ diff --git a/docs/my-website/img/pt_guard1.png b/docs/my-website/img/pt_guard1.png new file mode 100644 index 00000000000..85b094a14b9 Binary files /dev/null and b/docs/my-website/img/pt_guard1.png differ diff --git a/docs/my-website/img/pt_guard2.png b/docs/my-website/img/pt_guard2.png new file mode 100644 index 00000000000..32481109bcd Binary files /dev/null and b/docs/my-website/img/pt_guard2.png differ diff --git a/docs/my-website/package-lock.json b/docs/my-website/package-lock.json index aef7bc1fe96..9c7ee7741cd 100644 --- a/docs/my-website/package-lock.json +++ b/docs/my-website/package-lock.json @@ -16891,9 +16891,9 @@ } }, "node_modules/node-forge": { - "version": "1.3.1", - "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.1.tgz", - "integrity": "sha512-dPEtOeMvF9VMcYV/1Wb8CPoVAXtp6MKMlcbAt4ddqmGqUJ6fQZFXkNZNkNlfevtNkGtaSoXf/vNNNSvgrdXwtA==", + "version": "1.3.2", + "resolved": "https://registry.npmjs.org/node-forge/-/node-forge-1.3.2.tgz", + "integrity": "sha512-6xKiQ+cph9KImrRh0VsjH2d8/GXA4FIMlgU4B757iI1ApvcyA9VlouP0yZJha01V+huImO+kKMU7ih+2+E14fw==", "license": "(BSD-3-Clause OR GPL-2.0)", "engines": { "node": ">= 6.13.0" diff --git a/docs/my-website/package.json b/docs/my-website/package.json index 4895b6f518b..f217cef7fcc 100644 --- a/docs/my-website/package.json +++ b/docs/my-website/package.json @@ -52,13 +52,15 @@ "webpack-dev-server": ">=5.2.1", "form-data": ">=4.0.4", "mermaid": ">=11.10.0", - "gray-matter": "4.0.3" + "gray-matter": "4.0.3", + "node-forge": ">=1.3.2" }, "overrides": { "webpack-dev-server": ">=5.2.1", "form-data": ">=4.0.4", "mermaid": ">=11.10.0", "gray-matter": "4.0.3", - "glob": ">=11.1.0" + "glob": ">=11.1.0", + "node-forge": ">=1.3.2" } } diff --git a/docs/my-website/release_notes/v1.80.5-stable/index.md b/docs/my-website/release_notes/v1.80.5-stable/index.md index 4324fdef776..7c3283ce349 100644 --- a/docs/my-website/release_notes/v1.80.5-stable/index.md +++ b/docs/my-website/release_notes/v1.80.5-stable/index.md @@ -1,5 +1,5 @@ --- -title: "[PREVIEW] v1.80.5.rc.2 - Gemini 3.0 Support" +title: "v1.80.5-stable - Gemini 3.0 Support" slug: "v1-80-5" date: 2025-11-22T10:00:00 authors: @@ -27,7 +27,7 @@ import TabItem from '@theme/TabItem'; docker run \ -e STORE_MODEL_IN_DB=True \ -p 4000:4000 \ -ghcr.io/berriai/litellm:v1.80.5.rc.2 +ghcr.io/berriai/litellm:v1.80.5-stable ``` diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js index 6fa5fdeced0..cb8f3be0344 100644 --- a/docs/my-website/sidebars.js +++ b/docs/my-website/sidebars.js @@ -20,6 +20,16 @@ const sidebars = { type: "category", label: "Observability", items: [ + { + type: "category", + label: "Contributing to Integrations", + items: [ + { + type: "autogenerated", + dirName: "contribute_integration" + } + ] + }, { type: "autogenerated", dirName: "observability" @@ -35,6 +45,7 @@ const sidebars = { type: "category", "label": "Contributing to Guardrails", items: [ + "adding_provider/generic_guardrail_api", "adding_provider/simple_guardrail_tutorial", "adding_provider/adding_guardrail_support", ] @@ -130,6 +141,7 @@ const sidebars = { "proxy/quick_start", "proxy/cli", "proxy/debugging", + "proxy/error_diagnosis", "proxy/deploy", "proxy/health", "proxy/master_key_rotations", @@ -304,6 +316,7 @@ const sidebars = { slug: "/supported_endpoints", }, items: [ + "a2a", "assistants", { type: "category", @@ -409,9 +422,11 @@ const sidebars = { ] }, "pass_through/vllm", - "proxy/pass_through" + "proxy/pass_through", + "proxy/pass_through_guardrails" ] }, + "rag_ingest", "realtime", "rerank", "response_api", @@ -507,6 +522,7 @@ const sidebars = { "providers/vertex_partner", "providers/vertex_self_deployed", "providers/vertex_image", + "providers/vertex_speech", "providers/vertex_batch", "providers/vertex_ocr", ] @@ -530,6 +546,7 @@ const sidebars = { items: [ "providers/bedrock", "providers/bedrock_embedding", + "providers/bedrock_imported", "providers/bedrock_image_gen", "providers/bedrock_rerank", "providers/bedrock_agentcore", @@ -609,7 +626,9 @@ const sidebars = { "providers/ovhcloud", "providers/perplexity", "providers/petals", + "providers/publicai", "providers/predibase", + "providers/ragflow", "providers/recraft", "providers/replicate", { @@ -631,9 +650,17 @@ const sidebars = { "providers/volcano", "providers/voyage", "providers/wandb_inference", - "providers/watsonx", + { + type: "category", + label: "WatsonX", + items: [ + "providers/watsonx/index", + "providers/watsonx/audio_transcription", + ] + }, "providers/xai", "providers/xinference", + "providers/zai", ], }, { @@ -798,6 +825,10 @@ const sidebars = { items: [ "projects/smolagents", "projects/mini-swe-agent", + "projects/openai-agents", + "projects/Google ADK", + "projects/Agent Lightning", + "projects/Harbor", "projects/Docq.AI", "projects/PDL", "projects/OpenInterpreter", diff --git a/docs/my-website/src/pages/intro.md b/docs/my-website/src/pages/intro.md deleted file mode 100644 index 8a2e69d95f9..00000000000 --- a/docs/my-website/src/pages/intro.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Tutorial Intro - -Let's discover **Docusaurus in less than 5 minutes**. - -## Getting Started - -Get started by **creating a new site**. - -Or **try Docusaurus immediately** with **[docusaurus.new](https://docusaurus.new)**. - -### What you'll need - -- [Node.js](https://nodejs.org/en/download/) version 16.14 or above: - - When installing Node.js, you are recommended to check all checkboxes related to dependencies. - -## Generate a new site - -Generate a new Docusaurus site using the **classic template**. - -The classic template will automatically be added to your project after you run the command: - -```bash -npm init docusaurus@latest my-website classic -``` - -You can type this command into Command Prompt, Powershell, Terminal, or any other integrated terminal of your code editor. - -The command also installs all necessary dependencies you need to run Docusaurus. - -## Start your site - -Run the development server: - -```bash -cd my-website -npm run start -``` - -The `cd` command changes the directory you're working with. In order to work with your newly created Docusaurus site, you'll need to navigate the terminal there. - -The `npm run start` command builds your website locally and serves it through a development server, ready for you to view at http://localhost:3000/. - -Open `docs/intro.md` (this page) and edit some lines: the site **reloads automatically** and displays your changes. diff --git a/docs/my-website/src/pages/tutorial-basics/_category_.json b/docs/my-website/src/pages/tutorial-basics/_category_.json deleted file mode 100644 index 2e6db55b1eb..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/_category_.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "label": "Tutorial - Basics", - "position": 2, - "link": { - "type": "generated-index", - "description": "5 minutes to learn the most important Docusaurus concepts." - } -} diff --git a/docs/my-website/src/pages/tutorial-basics/congratulations.md b/docs/my-website/src/pages/tutorial-basics/congratulations.md deleted file mode 100644 index 04771a00b72..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/congratulations.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -sidebar_position: 6 ---- - -# Congratulations! - -You have just learned the **basics of Docusaurus** and made some changes to the **initial template**. - -Docusaurus has **much more to offer**! - -Have **5 more minutes**? Take a look at **[versioning](../tutorial-extras/manage-docs-versions.md)** and **[i18n](../tutorial-extras/translate-your-site.md)**. - -Anything **unclear** or **buggy** in this tutorial? [Please report it!](https://github.com/facebook/docusaurus/discussions/4610) - -## What's next? - -- Read the [official documentation](https://docusaurus.io/) -- Modify your site configuration with [`docusaurus.config.js`](https://docusaurus.io/docs/api/docusaurus-config) -- Add navbar and footer items with [`themeConfig`](https://docusaurus.io/docs/api/themes/configuration) -- Add a custom [Design and Layout](https://docusaurus.io/docs/styling-layout) -- Add a [search bar](https://docusaurus.io/docs/search) -- Find inspirations in the [Docusaurus showcase](https://docusaurus.io/showcase) -- Get involved in the [Docusaurus Community](https://docusaurus.io/community/support) diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md b/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md deleted file mode 100644 index ea472bbaf87..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/create-a-blog-post.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -sidebar_position: 3 ---- - -# Create a Blog Post - -Docusaurus creates a **page for each blog post**, but also a **blog index page**, a **tag system**, an **RSS** feed... - -## Create your first Post - -Create a file at `blog/2021-02-28-greetings.md`: - -```md title="blog/2021-02-28-greetings.md" ---- -slug: greetings -title: Greetings! -authors: - - name: Joel Marcey - title: Co-creator of Docusaurus 1 - url: https://github.com/JoelMarcey - image_url: https://github.com/JoelMarcey.png - - name: Sébastien Lorber - title: Docusaurus maintainer - url: https://sebastienlorber.com - image_url: https://github.com/slorber.png -tags: [greetings] ---- - -Congratulations, you have made your first post! - -Feel free to play around and edit this post as much you like. -``` - -A new blog post is now available at [http://localhost:3000/blog/greetings](http://localhost:3000/blog/greetings). diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-document.md b/docs/my-website/src/pages/tutorial-basics/create-a-document.md deleted file mode 100644 index ffddfa8eb8a..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/create-a-document.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Create a Document - -Documents are **groups of pages** connected through: - -- a **sidebar** -- **previous/next navigation** -- **versioning** - -## Create your first Doc - -Create a Markdown file at `docs/hello.md`: - -```md title="docs/hello.md" -# Hello - -This is my **first Docusaurus document**! -``` - -A new document is now available at [http://localhost:3000/docs/hello](http://localhost:3000/docs/hello). - -## Configure the Sidebar - -Docusaurus automatically **creates a sidebar** from the `docs` folder. - -Add metadata to customize the sidebar label and position: - -```md title="docs/hello.md" {1-4} ---- -sidebar_label: 'Hi!' -sidebar_position: 3 ---- - -# Hello - -This is my **first Docusaurus document**! -``` - -It is also possible to create your sidebar explicitly in `sidebars.js`: - -```js title="sidebars.js" -module.exports = { - tutorialSidebar: [ - 'intro', - // highlight-next-line - 'hello', - { - type: 'category', - label: 'Tutorial', - items: ['tutorial-basics/create-a-document'], - }, - ], -}; -``` diff --git a/docs/my-website/src/pages/tutorial-basics/create-a-page.md b/docs/my-website/src/pages/tutorial-basics/create-a-page.md deleted file mode 100644 index 20e2ac30055..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/create-a-page.md +++ /dev/null @@ -1,43 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Create a Page - -Add **Markdown or React** files to `src/pages` to create a **standalone page**: - -- `src/pages/index.js` → `localhost:3000/` -- `src/pages/foo.md` → `localhost:3000/foo` -- `src/pages/foo/bar.js` → `localhost:3000/foo/bar` - -## Create your first React Page - -Create a file at `src/pages/my-react-page.js`: - -```jsx title="src/pages/my-react-page.js" -import React from 'react'; -import Layout from '@theme/Layout'; - -export default function MyReactPage() { - return ( - -

My React page

-

This is a React page

- - ); -} -``` - -A new page is now available at [http://localhost:3000/my-react-page](http://localhost:3000/my-react-page). - -## Create your first Markdown Page - -Create a file at `src/pages/my-markdown-page.md`: - -```mdx title="src/pages/my-markdown-page.md" -# My Markdown page - -This is a Markdown page -``` - -A new page is now available at [http://localhost:3000/my-markdown-page](http://localhost:3000/my-markdown-page). diff --git a/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md b/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md deleted file mode 100644 index 1c50ee063ef..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/deploy-your-site.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -sidebar_position: 5 ---- - -# Deploy your site - -Docusaurus is a **static-site-generator** (also called **[Jamstack](https://jamstack.org/)**). - -It builds your site as simple **static HTML, JavaScript and CSS files**. - -## Build your site - -Build your site **for production**: - -```bash -npm run build -``` - -The static files are generated in the `build` folder. - -## Deploy your site - -Test your production build locally: - -```bash -npm run serve -``` - -The `build` folder is now served at [http://localhost:3000/](http://localhost:3000/). - -You can now deploy the `build` folder **almost anywhere** easily, **for free** or very small cost (read the **[Deployment Guide](https://docusaurus.io/docs/deployment)**). diff --git a/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx b/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx deleted file mode 100644 index 0337f34d6a5..00000000000 --- a/docs/my-website/src/pages/tutorial-basics/markdown-features.mdx +++ /dev/null @@ -1,150 +0,0 @@ ---- -sidebar_position: 4 ---- - -# Markdown Features - -Docusaurus supports **[Markdown](https://daringfireball.net/projects/markdown/syntax)** and a few **additional features**. - -## Front Matter - -Markdown documents have metadata at the top called [Front Matter](https://jekyllrb.com/docs/front-matter/): - -```text title="my-doc.md" -// highlight-start ---- -id: my-doc-id -title: My document title -description: My document description -slug: /my-custom-url ---- -// highlight-end - -## Markdown heading - -Markdown text with [links](./hello.md) -``` - -## Links - -Regular Markdown links are supported, using url paths or relative file paths. - -```md -Let's see how to [Create a page](/create-a-page). -``` - -```md -Let's see how to [Create a page](./create-a-page.md). -``` - -**Result:** Let's see how to [Create a page](./create-a-page.md). - -## Images - -Regular Markdown images are supported. - -You can use absolute paths to reference images in the static directory (`static/img/docusaurus.png`): - -```md -![Docusaurus logo](/img/docusaurus.png) -``` - -![Docusaurus logo](/img/docusaurus.png) - -You can reference images relative to the current file as well. This is particularly useful to colocate images close to the Markdown files using them: - -```md -![Docusaurus logo](./img/docusaurus.png) -``` - -## Code Blocks - -Markdown code blocks are supported with Syntax highlighting. - - ```jsx title="src/components/HelloDocusaurus.js" - function HelloDocusaurus() { - return ( -

Hello, Docusaurus!

- ) - } - ``` - -```jsx title="src/components/HelloDocusaurus.js" -function HelloDocusaurus() { - return

Hello, Docusaurus!

; -} -``` - -## Admonitions - -Docusaurus has a special syntax to create admonitions and callouts: - - :::tip My tip - - Use this awesome feature option - - ::: - - :::danger Take care - - This action is dangerous - - ::: - -:::tip My tip - -Use this awesome feature option - -::: - -:::danger Take care - -This action is dangerous - -::: - -## MDX and React Components - -[MDX](https://mdxjs.com/) can make your documentation more **interactive** and allows using any **React components inside Markdown**: - -```jsx -export const Highlight = ({children, color}) => ( - { - alert(`You clicked the color ${color} with label ${children}`) - }}> - {children} - -); - -This is Docusaurus green ! - -This is Facebook blue ! -``` - -export const Highlight = ({children, color}) => ( - { - alert(`You clicked the color ${color} with label ${children}`); - }}> - {children} - -); - -This is Docusaurus green ! - -This is Facebook blue ! diff --git a/docs/my-website/src/pages/tutorial-extras/_category_.json b/docs/my-website/src/pages/tutorial-extras/_category_.json deleted file mode 100644 index a8ffcc19300..00000000000 --- a/docs/my-website/src/pages/tutorial-extras/_category_.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "label": "Tutorial - Extras", - "position": 3, - "link": { - "type": "generated-index" - } -} diff --git a/docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png b/docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png deleted file mode 100644 index 97e4164618b..00000000000 Binary files a/docs/my-website/src/pages/tutorial-extras/img/docsVersionDropdown.png and /dev/null differ diff --git a/docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png b/docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png deleted file mode 100644 index e257edc1f93..00000000000 Binary files a/docs/my-website/src/pages/tutorial-extras/img/localeDropdown.png and /dev/null differ diff --git a/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md b/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md deleted file mode 100644 index e12c3f3444f..00000000000 --- a/docs/my-website/src/pages/tutorial-extras/manage-docs-versions.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -sidebar_position: 1 ---- - -# Manage Docs Versions - -Docusaurus can manage multiple versions of your docs. - -## Create a docs version - -Release a version 1.0 of your project: - -```bash -npm run docusaurus docs:version 1.0 -``` - -The `docs` folder is copied into `versioned_docs/version-1.0` and `versions.json` is created. - -Your docs now have 2 versions: - -- `1.0` at `http://localhost:3000/docs/` for the version 1.0 docs -- `current` at `http://localhost:3000/docs/next/` for the **upcoming, unreleased docs** - -## Add a Version Dropdown - -To navigate seamlessly across versions, add a version dropdown. - -Modify the `docusaurus.config.js` file: - -```js title="docusaurus.config.js" -module.exports = { - themeConfig: { - navbar: { - items: [ - // highlight-start - { - type: 'docsVersionDropdown', - }, - // highlight-end - ], - }, - }, -}; -``` - -The docs version dropdown appears in your navbar: - -![Docs Version Dropdown](./img/docsVersionDropdown.png) - -## Update an existing version - -It is possible to edit versioned docs in their respective folder: - -- `versioned_docs/version-1.0/hello.md` updates `http://localhost:3000/docs/hello` -- `docs/hello.md` updates `http://localhost:3000/docs/next/hello` diff --git a/docs/my-website/src/pages/tutorial-extras/translate-your-site.md b/docs/my-website/src/pages/tutorial-extras/translate-your-site.md deleted file mode 100644 index caeaffb0554..00000000000 --- a/docs/my-website/src/pages/tutorial-extras/translate-your-site.md +++ /dev/null @@ -1,88 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Translate your site - -Let's translate `docs/intro.md` to French. - -## Configure i18n - -Modify `docusaurus.config.js` to add support for the `fr` locale: - -```js title="docusaurus.config.js" -module.exports = { - i18n: { - defaultLocale: 'en', - locales: ['en', 'fr'], - }, -}; -``` - -## Translate a doc - -Copy the `docs/intro.md` file to the `i18n/fr` folder: - -```bash -mkdir -p i18n/fr/docusaurus-plugin-content-docs/current/ - -cp docs/intro.md i18n/fr/docusaurus-plugin-content-docs/current/intro.md -``` - -Translate `i18n/fr/docusaurus-plugin-content-docs/current/intro.md` in French. - -## Start your localized site - -Start your site on the French locale: - -```bash -npm run start -- --locale fr -``` - -Your localized site is accessible at [http://localhost:3000/fr/](http://localhost:3000/fr/) and the `Getting Started` page is translated. - -:::caution - -In development, you can only use one locale at a same time. - -::: - -## Add a Locale Dropdown - -To navigate seamlessly across languages, add a locale dropdown. - -Modify the `docusaurus.config.js` file: - -```js title="docusaurus.config.js" -module.exports = { - themeConfig: { - navbar: { - items: [ - // highlight-start - { - type: 'localeDropdown', - }, - // highlight-end - ], - }, - }, -}; -``` - -The locale dropdown now appears in your navbar: - -![Locale Dropdown](./img/localeDropdown.png) - -## Build your localized site - -Build your site for a specific locale: - -```bash -npm run build -- --locale fr -``` - -Or build your site to include all the locales at once: - -```bash -npm run build -``` diff --git a/document.txt b/document.txt new file mode 100644 index 00000000000..4a91207970a --- /dev/null +++ b/document.txt @@ -0,0 +1,19 @@ +LiteLLM provides a unified interface for calling 100+ different LLM providers. + +Key capabilities: +- Translate requests to provider-specific formats +- Consistent OpenAI-compatible responses +- Retry and fallback logic across deployments +- Proxy server with authentication and rate limiting +- Support for streaming, function calling, and embeddings + +Popular providers supported: +- OpenAI (GPT-4, GPT-3.5) +- Anthropic (Claude) +- AWS Bedrock +- Azure OpenAI +- Google Vertex AI +- Cohere +- And 95+ more + +This allows developers to easily switch between providers without code changes. diff --git a/enterprise/litellm_enterprise/proxy/enterprise_routes.py b/enterprise/litellm_enterprise/proxy/enterprise_routes.py index f3227892bbd..e28d8b8a4c6 100644 --- a/enterprise/litellm_enterprise/proxy/enterprise_routes.py +++ b/enterprise/litellm_enterprise/proxy/enterprise_routes.py @@ -5,14 +5,10 @@ from litellm_enterprise.enterprise_callbacks.send_emails.endpoints import ( ) from .audit_logging_endpoints import router as audit_logging_router -from .guardrails.endpoints import router as guardrails_router from .management_endpoints import management_endpoints_router from .utils import _should_block_robots -from .vector_stores.endpoints import router as vector_stores_router router = APIRouter() -router.include_router(vector_stores_router) -router.include_router(guardrails_router) router.include_router(email_events_router) router.include_router(audit_logging_router) router.include_router(management_endpoints_router) diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.7-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.7-py3-none-any.whl new file mode 100644 index 00000000000..376c1e0d070 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.7-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.7.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.7.tar.gz new file mode 100644 index 00000000000..0bb0fd9c74a Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.7.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.8-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.8-py3-none-any.whl new file mode 100644 index 00000000000..39c5f97d2f4 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.8-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.8.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.8.tar.gz new file mode 100644 index 00000000000..9c463cd8b2a Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.8.tar.gz differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.9-py3-none-any.whl b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.9-py3-none-any.whl new file mode 100644 index 00000000000..513acf49257 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.9-py3-none-any.whl differ diff --git a/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.9.tar.gz b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.9.tar.gz new file mode 100644 index 00000000000..75c06dffe95 Binary files /dev/null and b/litellm-proxy-extras/dist/litellm_proxy_extras-0.4.9.tar.gz differ diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql new file mode 100644 index 00000000000..74e0eea3134 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251114180624_Add_org_usage_table/migration.sql @@ -0,0 +1,42 @@ +-- CreateTable +CREATE TABLE "LiteLLM_DailyOrganizationSpend" ( + "id" TEXT NOT NULL, + "organization_id" TEXT, + "date" TEXT NOT NULL, + "api_key" TEXT NOT NULL, + "model" TEXT, + "model_group" TEXT, + "custom_llm_provider" TEXT, + "mcp_namespaced_tool_name" TEXT, + "prompt_tokens" BIGINT NOT NULL DEFAULT 0, + "completion_tokens" BIGINT NOT NULL DEFAULT 0, + "cache_read_input_tokens" BIGINT NOT NULL DEFAULT 0, + "cache_creation_input_tokens" BIGINT NOT NULL DEFAULT 0, + "spend" DOUBLE PRECISION NOT NULL DEFAULT 0.0, + "api_requests" BIGINT NOT NULL DEFAULT 0, + "successful_requests" BIGINT NOT NULL DEFAULT 0, + "failed_requests" BIGINT NOT NULL DEFAULT 0, + "created_at" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP, + "updated_at" TIMESTAMP(3) NOT NULL, + + CONSTRAINT "LiteLLM_DailyOrganizationSpend_pkey" PRIMARY KEY ("id") +); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_date_idx" ON "LiteLLM_DailyOrganizationSpend"("date"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_organization_id_idx" ON "LiteLLM_DailyOrganizationSpend"("organization_id"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_api_key_idx" ON "LiteLLM_DailyOrganizationSpend"("api_key"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_model_idx" ON "LiteLLM_DailyOrganizationSpend"("model"); + +-- CreateIndex +CREATE INDEX "LiteLLM_DailyOrganizationSpend_mcp_namespaced_tool_name_idx" ON "LiteLLM_DailyOrganizationSpend"("mcp_namespaced_tool_name"); + +-- CreateIndex +CREATE UNIQUE INDEX "LiteLLM_DailyOrganizationSpend_organization_id_date_api_key_key" ON "LiteLLM_DailyOrganizationSpend"("organization_id", "date", "api_key", "model", "custom_llm_provider", "mcp_namespaced_tool_name"); + diff --git a/litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql new file mode 100644 index 00000000000..4ea082f2750 --- /dev/null +++ b/litellm-proxy-extras/litellm_proxy_extras/migrations/20251122125322_Add organization_id to spend logs/migration.sql @@ -0,0 +1,3 @@ +-- AlterTable +ALTER TABLE "LiteLLM_SpendLogs" ADD COLUMN "organization_id" TEXT; + diff --git a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma index 6cfbb90c362..2883dfc4b82 100644 --- a/litellm-proxy-extras/litellm_proxy_extras/schema.prisma +++ b/litellm-proxy-extras/litellm_proxy_extras/schema.prisma @@ -304,6 +304,7 @@ model LiteLLM_SpendLogs { cache_key String? @default("") request_tags Json? @default("[]") team_id String? + organization_id String? end_user String? requester_ip_address String? messages Json? @default("{}") @@ -432,6 +433,35 @@ model LiteLLM_DailyUserSpend { @@index([mcp_namespaced_tool_name]) } +// Track daily organization spend metrics per model and key +model LiteLLM_DailyOrganizationSpend { + id String @id @default(uuid()) + organization_id String? + date String + api_key String + model String? + model_group String? + custom_llm_provider String? + mcp_namespaced_tool_name String? + prompt_tokens BigInt @default(0) + completion_tokens BigInt @default(0) + cache_read_input_tokens BigInt @default(0) + cache_creation_input_tokens BigInt @default(0) + spend Float @default(0.0) + api_requests BigInt @default(0) + successful_requests BigInt @default(0) + failed_requests BigInt @default(0) + created_at DateTime @default(now()) + updated_at DateTime @updatedAt + + @@unique([organization_id, date, api_key, model, custom_llm_provider, mcp_namespaced_tool_name]) + @@index([date]) + @@index([organization_id]) + @@index([api_key]) + @@index([model]) + @@index([mcp_namespaced_tool_name]) +} + // Track daily team spend metrics per model and key model LiteLLM_DailyTeamSpend { id String @id @default(uuid()) diff --git a/litellm-proxy-extras/poetry.lock b/litellm-proxy-extras/poetry.lock index f526fec8da0..301d0d2b073 100644 --- a/litellm-proxy-extras/poetry.lock +++ b/litellm-proxy-extras/poetry.lock @@ -1,7 +1,7 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.2.0 and should not be changed by hand. package = [] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = ">=3.8.1,<4.0, !=3.9.7" content-hash = "2cf39473e67ff0615f0a61c9d2ac9f02b38cc08cbb1bdb893d89bee002646623" diff --git a/litellm-proxy-extras/pyproject.toml b/litellm-proxy-extras/pyproject.toml index 78e34ccd01a..6bc576e4f3d 100644 --- a/litellm-proxy-extras/pyproject.toml +++ b/litellm-proxy-extras/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "litellm-proxy-extras" -version = "0.4.6" +version = "0.4.9" description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package." authors = ["BerriAI"] readme = "README.md" @@ -22,7 +22,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.commitizen] -version = "0.4.6" +version = "0.4.9" version_files = [ "pyproject.toml:version", "../requirements.txt:litellm-proxy-extras==", diff --git a/litellm/__init__.py b/litellm/__init__.py index 9b28c65072a..ee9a0568d1e 100644 --- a/litellm/__init__.py +++ b/litellm/__init__.py @@ -20,6 +20,9 @@ from typing import ( Literal, get_args, TYPE_CHECKING, + Tuple, + overload, + Type, ) from litellm.types.integrations.datadog_llm_obs import DatadogLLMObsInitParams from litellm.types.integrations.datadog import DatadogInitParams @@ -148,6 +151,7 @@ _custom_logger_compatible_callbacks_literal = Literal[ "mlflow", "langfuse", "langfuse_otel", + "weave_otel", "pagerduty", "humanloop", "gcs_pubsub", @@ -175,6 +179,7 @@ _known_custom_logger_compatible_callbacks: List = list( callbacks: List[ Union[Callable, _custom_logger_compatible_callbacks_literal, CustomLogger] ] = [] +callback_settings: Dict[str, Dict[str, Any]] = {} initialized_langfuse_clients: int = 0 langfuse_default_tags: Optional[List[str]] = None langsmith_batch_size: Optional[int] = None @@ -531,6 +536,7 @@ featherless_ai_models: Set = set() palm_models: Set = set() groq_models: Set = set() azure_models: Set = set() +azure_anthropic_models: Set = set() azure_text_models: Set = set() anyscale_models: Set = set() cerebras_models: Set = set() @@ -551,6 +557,7 @@ deepgram_models: Set = set() elevenlabs_models: Set = set() dashscope_models: Set = set() moonshot_models: Set = set() +publicai_models: Set = set() v0_models: Set = set() morph_models: Set = set() lambda_ai_models: Set = set() @@ -735,6 +742,8 @@ def add_known_models(): groq_models.add(key) elif value.get("litellm_provider") == "azure": azure_models.add(key) + elif value.get("litellm_provider") == "azure_anthropic": + azure_anthropic_models.add(key) elif value.get("litellm_provider") == "anyscale": anyscale_models.add(key) elif value.get("litellm_provider") == "cerebras": @@ -775,6 +784,8 @@ def add_known_models(): dashscope_models.add(key) elif value.get("litellm_provider") == "moonshot": moonshot_models.add(key) + elif value.get("litellm_provider") == "publicai": + publicai_models.add(key) elif value.get("litellm_provider") == "v0": v0_models.add(key) elif value.get("litellm_provider") == "morph": @@ -874,6 +885,7 @@ model_list = list( | palm_models | groq_models | azure_models + | azure_anthropic_models | anyscale_models | cerebras_models | galadriel_models @@ -892,6 +904,7 @@ model_list = list( | elevenlabs_models | dashscope_models | moonshot_models + | publicai_models | v0_models | morph_models | lambda_ai_models @@ -963,6 +976,7 @@ models_by_provider: dict = { "palm": palm_models, "groq": groq_models, "azure": azure_models | azure_text_models, + "azure_anthropic": azure_anthropic_models, "azure_text": azure_text_models, "anyscale": anyscale_models, "cerebras": cerebras_models, @@ -984,6 +998,7 @@ models_by_provider: dict = { "heroku": heroku_models, "dashscope": dashscope_models, "moonshot": moonshot_models, + "publicai": publicai_models, "v0": v0_models, "morph": morph_models, "lambda_ai": lambda_ai_models, @@ -1040,62 +1055,13 @@ openai_image_generation_models = ["dall-e-2", "dall-e-3"] openai_video_generation_models = ["sora-2"] from .timeout import timeout -from .cost_calculator import completion_cost -from litellm.litellm_core_utils.litellm_logging import Logging, modify_integration from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.litellm_core_utils.core_helpers import remove_index_from_tool_calls from litellm.litellm_core_utils.token_counter import get_modified_max_tokens -from .utils import ( - client, - exception_type, - get_optional_params, - get_response_string, - token_counter, - create_pretrained_tokenizer, - create_tokenizer, - supports_function_calling, - supports_web_search, - supports_url_context, - supports_response_schema, - supports_parallel_function_calling, - supports_vision, - supports_audio_input, - supports_audio_output, - supports_system_messages, - supports_reasoning, - get_litellm_params, - acreate, - get_max_tokens, - get_model_info, - register_prompt_template, - validate_environment, - check_valid_key, - register_model, - encode, - decode, - _calculate_retry_after, - _should_retry, - get_supported_openai_params, - get_api_base, - get_first_chars_messages, - ModelResponse, - ModelResponseStream, - EmbeddingResponse, - ImageResponse, - TranscriptionResponse, - TextCompletionResponse, - get_provider_fields, - ModelResponseListIterator, - get_valid_models, -) - -ALL_LITELLM_RESPONSE_TYPES = [ - ModelResponse, - EmbeddingResponse, - ImageResponse, - TranscriptionResponse, - TextCompletionResponse, -] +# client must be imported immediately as it's used as a decorator at function definition time +from .utils import client +# Note: Most other utils imports are lazy-loaded via __getattr__ to avoid loading utils.py +# (which imports tiktoken) at import time from .llms.bytez.chat.transformation import BytezChatConfig from .llms.custom_llm import CustomLLM @@ -1114,6 +1080,7 @@ from .llms.openrouter.chat.transformation import OpenrouterConfig from .llms.datarobot.chat.transformation import DataRobotConfig from .llms.anthropic.chat.transformation import AnthropicConfig from .llms.anthropic.common_utils import AnthropicModelInfo +from .llms.azure_ai.anthropic.transformation import AzureAnthropicConfig from .llms.groq.stt.transformation import GroqSTTConfig from .llms.anthropic.completion.transformation import AnthropicTextConfig from .llms.triton.completion.transformation import TritonConfig @@ -1198,6 +1165,9 @@ from .llms.bedrock.chat.invoke_transformations.amazon_ai21_transformation import from .llms.bedrock.chat.invoke_transformations.amazon_nova_transformation import ( AmazonInvokeNovaConfig, ) +from .llms.bedrock.chat.invoke_transformations.amazon_qwen2_transformation import ( + AmazonQwen2Config, +) from .llms.bedrock.chat.invoke_transformations.amazon_qwen3_transformation import ( AmazonQwen3Config, ) @@ -1222,9 +1192,15 @@ from .llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation imp from .llms.bedrock.chat.invoke_transformations.amazon_titan_transformation import ( AmazonTitanConfig, ) +from .llms.bedrock.chat.invoke_transformations.amazon_twelvelabs_pegasus_transformation import ( + AmazonTwelveLabsPegasusConfig, +) from .llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( AmazonInvokeConfig, ) +from .llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import ( + AmazonBedrockOpenAIConfig, +) from .llms.bedrock.image.amazon_stability1_transformation import AmazonStabilityConfig from .llms.bedrock.image.amazon_stability3_transformation import AmazonStability3Config @@ -1242,6 +1218,9 @@ from .llms.bedrock.embed.cohere_transformation import BedrockCohereEmbeddingConf from .llms.bedrock.embed.twelvelabs_marengo_transformation import ( TwelveLabsMarengoEmbeddingConfig, ) +from .llms.bedrock.embed.amazon_nova_transformation import ( + AmazonNovaEmbeddingConfig, +) from .llms.openai.openai import OpenAIConfig, MistralEmbeddingConfig from .llms.openai.image_variations.transformation import OpenAIImageVariationConfig from .llms.deepinfra.chat.transformation import DeepInfraConfig @@ -1349,18 +1328,24 @@ from .llms.azure.chat.o_series_transformation import AzureOpenAIO1Config from .llms.watsonx.completion.transformation import IBMWatsonXAIConfig from .llms.watsonx.chat.transformation import IBMWatsonXChatConfig from .llms.watsonx.embed.transformation import IBMWatsonXEmbeddingConfig +from .llms.watsonx.audio_transcription.transformation import ( + IBMWatsonXAudioTranscriptionConfig, +) from .llms.github_copilot.chat.transformation import GithubCopilotConfig from .llms.github_copilot.responses.transformation import ( GithubCopilotResponsesAPIConfig, ) +from .llms.github_copilot.embedding.transformation import GithubCopilotEmbeddingConfig from .llms.nebius.chat.transformation import NebiusConfig from .llms.wandb.chat.transformation import WandbConfig from .llms.dashscope.chat.transformation import DashScopeChatConfig from .llms.moonshot.chat.transformation import MoonshotChatConfig +from .llms.publicai.chat.transformation import PublicAIChatConfig from .llms.docker_model_runner.chat.transformation import DockerModelRunnerChatConfig from .llms.v0.chat.transformation import V0ChatConfig from .llms.oci.chat.transformation import OCIChatConfig from .llms.morph.chat.transformation import MorphChatConfig +from .llms.ragflow.chat.transformation import RAGFlowConfig from .llms.lambda_ai.chat.transformation import LambdaAIChatConfig from .llms.hyperbolic.chat.transformation import HyperbolicChatConfig from .llms.vercel_ai_gateway.chat.transformation import VercelAIGatewayConfig @@ -1431,6 +1416,7 @@ from .skills.main import ( ) from .containers.main import * from .ocr.main import * +from .rag.main import * from .search.main import * from .realtime_api.main import _arealtime from .fine_tuning.main import * @@ -1450,7 +1436,6 @@ from .vector_store_files.main import ( update as vector_store_file_update, ) from .scheduler import * -from .cost_calculator import response_cost_calculator, cost_per_token ### ADAPTERS ### from .types.adapter import AdapterItem @@ -1467,6 +1452,9 @@ from .vector_stores.vector_store_registry import ( vector_store_registry: Optional[VectorStoreRegistry] = None vector_store_index_registry: Optional[VectorStoreIndexRegistry] = None +### RAG ### +from . import rag + ### CUSTOM LLMs ### from .types.llms.custom_llm import CustomLLMItem from .types.utils import GenericStreamingChunk @@ -1505,3 +1493,58 @@ def set_global_gitlab_config(config: Dict[str, Any]) -> None: """Set global BitBucket configuration for prompt management.""" global global_gitlab_config global_gitlab_config = config + + +# Lazy loading system for heavy modules to reduce initial import time and memory usage + +if TYPE_CHECKING: + cost_per_token: Callable[..., Tuple[float, float]] + completion_cost: Callable[..., float] + response_cost_calculator: Any + modify_integration: Any + + +def __getattr__(name: str) -> Any: + """Lazy import handler for cost_calculator and litellm_logging functions.""" + # Lazy load cost_calculator functions + _cost_calculator_names = ( + "completion_cost", + "cost_per_token", + "response_cost_calculator", + ) + if name in _cost_calculator_names: + from ._lazy_imports import _lazy_import_cost_calculator + return _lazy_import_cost_calculator(name) + + # Lazy load litellm_logging functions + _litellm_logging_names = ( + "Logging", + "modify_integration", + ) + if name in _litellm_logging_names: + from ._lazy_imports import _lazy_import_litellm_logging + return _lazy_import_litellm_logging(name) + + # Lazy load utils functions + _utils_names = ( + "exception_type", "get_optional_params", "get_response_string", "token_counter", + "create_pretrained_tokenizer", "create_tokenizer", "supports_function_calling", + "supports_web_search", "supports_url_context", "supports_response_schema", + "supports_parallel_function_calling", "supports_vision", "supports_audio_input", + "supports_audio_output", "supports_system_messages", "supports_reasoning", + "get_litellm_params", "acreate", "get_max_tokens", "get_model_info", + "register_prompt_template", "validate_environment", "check_valid_key", + "register_model", "encode", "decode", "_calculate_retry_after", "_should_retry", + "get_supported_openai_params", "get_api_base", "get_first_chars_messages", + "ModelResponse", "ModelResponseStream", "EmbeddingResponse", "ImageResponse", + "TranscriptionResponse", "TextCompletionResponse", "get_provider_fields", + "ModelResponseListIterator", "get_valid_models", + ) + if name in _utils_names: + from ._lazy_imports import _lazy_import_utils + return _lazy_import_utils(name) + + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + + +# ALL_LITELLM_RESPONSE_TYPES is lazy-loaded via __getattr__ to avoid loading utils at import time diff --git a/litellm/_lazy_imports.py b/litellm/_lazy_imports.py new file mode 100644 index 00000000000..91b16864de1 --- /dev/null +++ b/litellm/_lazy_imports.py @@ -0,0 +1,259 @@ +from typing import Any +import sys + +def _get_litellm_globals() -> dict: + """Helper to get the globals dictionary of the litellm module.""" + return sys.modules["litellm"].__dict__ + +# Lazy import for utils module - imports only the requested item by name. +# Note: PLR0915 (too many statements) is suppressed because the many if statements +# are intentional - each attribute is imported individually only when requested, +# ensuring true lazy imports rather than importing the entire utils module. +def _lazy_import_utils(name: str) -> Any: # noqa: PLR0915 + """Lazy import for utils module - imports only the requested item by name.""" + _globals = _get_litellm_globals() + if name == "exception_type": + from .utils import exception_type as _exception_type + _globals["exception_type"] = _exception_type + return _exception_type + + if name == "get_optional_params": + from .utils import get_optional_params as _get_optional_params + _globals["get_optional_params"] = _get_optional_params + return _get_optional_params + + if name == "get_response_string": + from .utils import get_response_string as _get_response_string + _globals["get_response_string"] = _get_response_string + return _get_response_string + + if name == "token_counter": + from .utils import token_counter as _token_counter + _globals["token_counter"] = _token_counter + return _token_counter + + if name == "create_pretrained_tokenizer": + from .utils import create_pretrained_tokenizer as _create_pretrained_tokenizer + _globals["create_pretrained_tokenizer"] = _create_pretrained_tokenizer + return _create_pretrained_tokenizer + + if name == "create_tokenizer": + from .utils import create_tokenizer as _create_tokenizer + _globals["create_tokenizer"] = _create_tokenizer + return _create_tokenizer + + if name == "supports_function_calling": + from .utils import supports_function_calling as _supports_function_calling + _globals["supports_function_calling"] = _supports_function_calling + return _supports_function_calling + + if name == "supports_web_search": + from .utils import supports_web_search as _supports_web_search + _globals["supports_web_search"] = _supports_web_search + return _supports_web_search + + if name == "supports_url_context": + from .utils import supports_url_context as _supports_url_context + _globals["supports_url_context"] = _supports_url_context + return _supports_url_context + + if name == "supports_response_schema": + from .utils import supports_response_schema as _supports_response_schema + _globals["supports_response_schema"] = _supports_response_schema + return _supports_response_schema + + if name == "supports_parallel_function_calling": + from .utils import supports_parallel_function_calling as _supports_parallel_function_calling + _globals["supports_parallel_function_calling"] = _supports_parallel_function_calling + return _supports_parallel_function_calling + + if name == "supports_vision": + from .utils import supports_vision as _supports_vision + _globals["supports_vision"] = _supports_vision + return _supports_vision + + if name == "supports_audio_input": + from .utils import supports_audio_input as _supports_audio_input + _globals["supports_audio_input"] = _supports_audio_input + return _supports_audio_input + + if name == "supports_audio_output": + from .utils import supports_audio_output as _supports_audio_output + _globals["supports_audio_output"] = _supports_audio_output + return _supports_audio_output + + if name == "supports_system_messages": + from .utils import supports_system_messages as _supports_system_messages + _globals["supports_system_messages"] = _supports_system_messages + return _supports_system_messages + + if name == "supports_reasoning": + from .utils import supports_reasoning as _supports_reasoning + _globals["supports_reasoning"] = _supports_reasoning + return _supports_reasoning + + if name == "get_litellm_params": + from .utils import get_litellm_params as _get_litellm_params + _globals["get_litellm_params"] = _get_litellm_params + return _get_litellm_params + + if name == "acreate": + from .utils import acreate as _acreate + _globals["acreate"] = _acreate + return _acreate + + if name == "get_max_tokens": + from .utils import get_max_tokens as _get_max_tokens + _globals["get_max_tokens"] = _get_max_tokens + return _get_max_tokens + + if name == "get_model_info": + from .utils import get_model_info as _get_model_info + _globals["get_model_info"] = _get_model_info + return _get_model_info + + if name == "register_prompt_template": + from .utils import register_prompt_template as _register_prompt_template + _globals["register_prompt_template"] = _register_prompt_template + return _register_prompt_template + + if name == "validate_environment": + from .utils import validate_environment as _validate_environment + _globals["validate_environment"] = _validate_environment + return _validate_environment + + if name == "check_valid_key": + from .utils import check_valid_key as _check_valid_key + _globals["check_valid_key"] = _check_valid_key + return _check_valid_key + + if name == "register_model": + from .utils import register_model as _register_model + _globals["register_model"] = _register_model + return _register_model + + if name == "encode": + from .utils import encode as _encode + _globals["encode"] = _encode + return _encode + + if name == "decode": + from .utils import decode as _decode + _globals["decode"] = _decode + return _decode + + if name == "_calculate_retry_after": + from .utils import _calculate_retry_after as __calculate_retry_after + _globals["_calculate_retry_after"] = __calculate_retry_after + return __calculate_retry_after + + if name == "_should_retry": + from .utils import _should_retry as __should_retry + _globals["_should_retry"] = __should_retry + return __should_retry + + if name == "get_supported_openai_params": + from .utils import get_supported_openai_params as _get_supported_openai_params + _globals["get_supported_openai_params"] = _get_supported_openai_params + return _get_supported_openai_params + + if name == "get_api_base": + from .utils import get_api_base as _get_api_base + _globals["get_api_base"] = _get_api_base + return _get_api_base + + if name == "get_first_chars_messages": + from .utils import get_first_chars_messages as _get_first_chars_messages + _globals["get_first_chars_messages"] = _get_first_chars_messages + return _get_first_chars_messages + + if name == "ModelResponse": + from .utils import ModelResponse as _ModelResponse + _globals["ModelResponse"] = _ModelResponse + return _ModelResponse + + if name == "ModelResponseStream": + from .utils import ModelResponseStream as _ModelResponseStream + _globals["ModelResponseStream"] = _ModelResponseStream + return _ModelResponseStream + + if name == "EmbeddingResponse": + from .utils import EmbeddingResponse as _EmbeddingResponse + _globals["EmbeddingResponse"] = _EmbeddingResponse + return _EmbeddingResponse + + if name == "ImageResponse": + from .utils import ImageResponse as _ImageResponse + _globals["ImageResponse"] = _ImageResponse + return _ImageResponse + + if name == "TranscriptionResponse": + from .utils import TranscriptionResponse as _TranscriptionResponse + _globals["TranscriptionResponse"] = _TranscriptionResponse + return _TranscriptionResponse + + if name == "TextCompletionResponse": + from .utils import TextCompletionResponse as _TextCompletionResponse + _globals["TextCompletionResponse"] = _TextCompletionResponse + return _TextCompletionResponse + + if name == "get_provider_fields": + from .utils import get_provider_fields as _get_provider_fields + _globals["get_provider_fields"] = _get_provider_fields + return _get_provider_fields + + if name == "ModelResponseListIterator": + from .utils import ModelResponseListIterator as _ModelResponseListIterator + _globals["ModelResponseListIterator"] = _ModelResponseListIterator + return _ModelResponseListIterator + + if name == "get_valid_models": + from .utils import get_valid_models as _get_valid_models + _globals["get_valid_models"] = _get_valid_models + return _get_valid_models + + raise AttributeError(f"Utils lazy import: unknown attribute {name!r}") + + +def _lazy_import_cost_calculator(name: str) -> Any: + """Lazy import for cost_calculator functions.""" + _globals = _get_litellm_globals() + from .cost_calculator import ( + completion_cost as _completion_cost, + cost_per_token as _cost_per_token, + response_cost_calculator as _response_cost_calculator, + ) + + _cost_functions = { + "completion_cost": _completion_cost, + "cost_per_token": _cost_per_token, + "response_cost_calculator": _response_cost_calculator, + } + + func = _cost_functions[name] + _globals[name] = func + return func + + +def _lazy_import_litellm_logging(name: str) -> Any: + """Lazy import for litellm_logging module.""" + _globals = _get_litellm_globals() + try: + from litellm.litellm_core_utils.litellm_logging import ( + Logging as _Logging, + modify_integration as _modify_integration, + ) + + _logging_objects = { + "Logging": _Logging, + "modify_integration": _modify_integration, + } + + obj = _logging_objects[name] + _globals[name] = obj + return obj + except Exception as e: + raise AttributeError( + f"module 'litellm' has no attribute {name!r}. " + f"Lazy import failed: {e}" + ) from e \ No newline at end of file diff --git a/litellm/a2a_protocol/__init__.py b/litellm/a2a_protocol/__init__.py new file mode 100644 index 00000000000..d8d349bb98a --- /dev/null +++ b/litellm/a2a_protocol/__init__.py @@ -0,0 +1,59 @@ +""" +LiteLLM A2A - Wrapper for invoking A2A protocol agents. + +This module provides a thin wrapper around the official `a2a` SDK that: +- Handles httpx client creation and agent card resolution +- Adds LiteLLM logging via @client decorator +- Matches the A2A SDK interface (SendMessageRequest, SendMessageResponse, etc.) + +Example usage (standalone functions with @client decorator): + ```python + from litellm.a2a_protocol import asend_message + from a2a.types import SendMessageRequest, MessageSendParams + from uuid import uuid4 + + request = SendMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Hello!"}], + "messageId": uuid4().hex, + } + ) + ) + response = await asend_message( + base_url="http://localhost:10001", + request=request, + ) + print(response.model_dump(mode='json', exclude_none=True)) + ``` + +Example usage (class-based): + ```python + from litellm.a2a_protocol import A2AClient + + client = A2AClient(base_url="http://localhost:10001") + response = await client.send_message(request) + ``` +""" + +from litellm.a2a_protocol.client import A2AClient +from litellm.a2a_protocol.main import ( + aget_agent_card, + asend_message, + asend_message_streaming, + create_a2a_client, + send_message, +) +from litellm.types.agents import LiteLLMSendMessageResponse + +__all__ = [ + "A2AClient", + "asend_message", + "send_message", + "asend_message_streaming", + "aget_agent_card", + "create_a2a_client", + "LiteLLMSendMessageResponse", +] diff --git a/litellm/a2a_protocol/client.py b/litellm/a2a_protocol/client.py new file mode 100644 index 00000000000..31f7c3b6a90 --- /dev/null +++ b/litellm/a2a_protocol/client.py @@ -0,0 +1,107 @@ +""" +LiteLLM A2A Client class. + +Provides a class-based interface for A2A agent invocation. +""" + +from typing import TYPE_CHECKING, AsyncIterator, Dict, Optional + +from litellm.types.agents import LiteLLMSendMessageResponse + +if TYPE_CHECKING: + from a2a.client import A2AClient as A2AClientType + from a2a.types import ( + AgentCard, + SendMessageRequest, + SendStreamingMessageRequest, + SendStreamingMessageResponse, + ) + + +class A2AClient: + """ + LiteLLM wrapper for A2A agent invocation. + + Creates the underlying A2A client once on first use and reuses it. + + Example: + ```python + from litellm.a2a_protocol import A2AClient + from a2a.types import SendMessageRequest, MessageSendParams + from uuid import uuid4 + + client = A2AClient(base_url="http://localhost:10001") + + request = SendMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Hello!"}], + "messageId": uuid4().hex, + } + ) + ) + response = await client.send_message(request) + ``` + """ + + def __init__( + self, + base_url: str, + timeout: float = 60.0, + extra_headers: Optional[Dict[str, str]] = None, + ): + """ + Initialize the A2A client wrapper. + + Args: + base_url: The base URL of the A2A agent (e.g., "http://localhost:10001") + timeout: Request timeout in seconds (default: 60.0) + extra_headers: Optional additional headers to include in requests + """ + self.base_url = base_url + self.timeout = timeout + self.extra_headers = extra_headers + self._a2a_client: Optional["A2AClientType"] = None + + async def _get_client(self) -> "A2AClientType": + """Get or create the underlying A2A client.""" + if self._a2a_client is None: + from litellm.a2a_protocol.main import create_a2a_client + + self._a2a_client = await create_a2a_client( + base_url=self.base_url, + timeout=self.timeout, + extra_headers=self.extra_headers, + ) + return self._a2a_client + + async def get_agent_card(self) -> "AgentCard": + """Fetch the agent card from the server.""" + from litellm.a2a_protocol.main import aget_agent_card + + return await aget_agent_card( + base_url=self.base_url, + timeout=self.timeout, + extra_headers=self.extra_headers, + ) + + async def send_message( + self, request: "SendMessageRequest" + ) -> LiteLLMSendMessageResponse: + """Send a message to the A2A agent.""" + from litellm.a2a_protocol.main import asend_message + + a2a_client = await self._get_client() + return await asend_message(a2a_client=a2a_client, request=request) + + async def send_message_streaming( + self, request: "SendStreamingMessageRequest" + ) -> AsyncIterator["SendStreamingMessageResponse"]: + """Send a streaming message to the A2A agent.""" + from litellm.a2a_protocol.main import asend_message_streaming + + a2a_client = await self._get_client() + async for chunk in asend_message_streaming(a2a_client=a2a_client, request=request): + yield chunk diff --git a/litellm/a2a_protocol/cost_calculator.py b/litellm/a2a_protocol/cost_calculator.py new file mode 100644 index 00000000000..2d821bb9c3b --- /dev/null +++ b/litellm/a2a_protocol/cost_calculator.py @@ -0,0 +1,36 @@ +""" +Cost calculator for A2A (Agent-to-Agent) calls. +""" + +from typing import TYPE_CHECKING, Any, Optional + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import ( + Logging as LitellmLoggingObject, + ) +else: + LitellmLoggingObject = Any + + +class A2ACostCalculator: + @staticmethod + def calculate_a2a_cost( + litellm_logging_obj: Optional[LitellmLoggingObject], + ) -> float: + """ + Calculate the cost of an A2A send_message call. + + Default is 0.0. In the future, users can configure cost per agent call. + """ + if litellm_logging_obj is None: + return 0.0 + + # Check if user set a custom response cost + response_cost = litellm_logging_obj.model_call_details.get( + "response_cost", None + ) + if response_cost is not None: + return response_cost + + # Default to 0.0 for A2A calls + return 0.0 diff --git a/litellm/a2a_protocol/main.py b/litellm/a2a_protocol/main.py new file mode 100644 index 00000000000..5cb238904f0 --- /dev/null +++ b/litellm/a2a_protocol/main.py @@ -0,0 +1,298 @@ +""" +LiteLLM A2A SDK functions. + +Provides standalone functions with @client decorator for LiteLLM logging integration. +""" + +import asyncio +from typing import TYPE_CHECKING, Any, AsyncIterator, Coroutine, Dict, Optional, Union + +from litellm._logging import verbose_logger +from litellm.llms.custom_httpx.http_handler import ( + get_async_httpx_client, + httpxSpecialProvider, +) +from litellm.types.agents import LiteLLMSendMessageResponse +from litellm.utils import client + +if TYPE_CHECKING: + from a2a.client import A2AClient as A2AClientType + from a2a.types import ( + AgentCard, + SendMessageRequest, + SendStreamingMessageRequest, + SendStreamingMessageResponse, + ) + +# Runtime imports with availability check +A2A_SDK_AVAILABLE = False +A2ACardResolver: Any = None +_A2AClient: Any = None + +try: + from a2a.client import A2ACardResolver # type: ignore[no-redef] + from a2a.client import A2AClient as _A2AClient # type: ignore[no-redef] + + A2A_SDK_AVAILABLE = True +except ImportError: + pass + + +def _get_a2a_model_info(a2a_client: Any, kwargs: Dict[str, Any]) -> str: + """ + Extract agent info and set model/custom_llm_provider for cost tracking. + + Sets model info on the litellm_logging_obj if available. + Returns the agent name for logging. + """ + agent_name = "unknown" + + # Try to get agent card from our stored attribute first, then fallback to SDK attribute + agent_card = getattr(a2a_client, "_litellm_agent_card", None) + if agent_card is None: + agent_card = getattr(a2a_client, "agent_card", None) + + if agent_card is not None: + agent_name = getattr(agent_card, "name", "unknown") or "unknown" + + # Build model string + model = f"a2a_agent/{agent_name}" + custom_llm_provider = "a2a_agent" + + # Set on litellm_logging_obj if available (for standard logging payload) + litellm_logging_obj = kwargs.get("litellm_logging_obj") + if litellm_logging_obj is not None: + litellm_logging_obj.model = model + litellm_logging_obj.custom_llm_provider = custom_llm_provider + litellm_logging_obj.model_call_details["model"] = model + litellm_logging_obj.model_call_details["custom_llm_provider"] = custom_llm_provider + + return agent_name + + +@client +async def asend_message( + a2a_client: "A2AClientType", + request: "SendMessageRequest", + **kwargs: Any, +) -> LiteLLMSendMessageResponse: + """ + Async: Send a message to an A2A agent. + + Uses the @client decorator for LiteLLM logging and tracking. + + Args: + a2a_client: An initialized a2a.client.A2AClient instance + request: SendMessageRequest from a2a.types + **kwargs: Additional arguments passed to the client decorator + + Returns: + LiteLLMSendMessageResponse (wraps a2a SendMessageResponse with _hidden_params) + + Example: + ```python + from litellm.a2a_protocol import asend_message, create_a2a_client + from a2a.types import SendMessageRequest, MessageSendParams + from uuid import uuid4 + + # Create client once + a2a_client = await create_a2a_client(base_url="http://localhost:10001") + + # Use it for multiple requests + request = SendMessageRequest( + id=str(uuid4()), + params=MessageSendParams( + message={ + "role": "user", + "parts": [{"kind": "text", "text": "Hello!"}], + "messageId": uuid4().hex, + } + ) + ) + response = await asend_message(a2a_client=a2a_client, request=request) + ``` + """ + agent_name = _get_a2a_model_info(a2a_client, kwargs) + + verbose_logger.info(f"A2A send_message request_id={request.id}, agent={agent_name}") + + a2a_response = await a2a_client.send_message(request) + + verbose_logger.info(f"A2A send_message completed, request_id={request.id}") + + # Wrap in LiteLLM response type for _hidden_params support + response = LiteLLMSendMessageResponse.from_a2a_response(a2a_response) + + return response + + +@client +def send_message( + a2a_client: "A2AClientType", + request: "SendMessageRequest", + **kwargs: Any, +) -> Union[LiteLLMSendMessageResponse, Coroutine[Any, Any, LiteLLMSendMessageResponse]]: + """ + Sync: Send a message to an A2A agent. + + Uses the @client decorator for LiteLLM logging and tracking. + + Args: + a2a_client: An initialized a2a.client.A2AClient instance + request: SendMessageRequest from a2a.types + **kwargs: Additional arguments passed to the client decorator + + Returns: + LiteLLMSendMessageResponse (wraps a2a SendMessageResponse with _hidden_params) + """ + try: + loop = asyncio.get_running_loop() + except RuntimeError: + loop = None + + if loop is not None: + return asend_message(a2a_client=a2a_client, request=request, **kwargs) + else: + return asyncio.run(asend_message(a2a_client=a2a_client, request=request, **kwargs)) + + +async def asend_message_streaming( + a2a_client: "A2AClientType", + request: "SendStreamingMessageRequest", +) -> AsyncIterator["SendStreamingMessageResponse"]: + """ + Async: Send a streaming message to an A2A agent. + + Args: + a2a_client: An initialized a2a.client.A2AClient instance + request: SendStreamingMessageRequest from a2a.types + + Yields: + SendStreamingMessageResponse chunks from the agent + """ + verbose_logger.info(f"A2A send_message_streaming request_id={request.id}") + + stream = a2a_client.send_message_streaming(request) + + chunk_count = 0 + async for chunk in stream: + chunk_count += 1 + yield chunk + + verbose_logger.info( + f"A2A send_message_streaming completed, request_id={request.id}, chunks={chunk_count}" + ) + + +async def create_a2a_client( + base_url: str, + timeout: float = 60.0, + extra_headers: Optional[Dict[str, str]] = None, +) -> "A2AClientType": + """ + Create an A2A client for the given agent URL. + + This resolves the agent card and returns a ready-to-use A2A client. + The client can be reused for multiple requests. + + Args: + base_url: The base URL of the A2A agent (e.g., "http://localhost:10001") + timeout: Request timeout in seconds (default: 60.0) + extra_headers: Optional additional headers to include in requests + + Returns: + An initialized a2a.client.A2AClient instance + + Example: + ```python + from litellm.a2a_protocol import create_a2a_client, asend_message + + # Create client once + client = await create_a2a_client(base_url="http://localhost:10001") + + # Reuse for multiple requests + response1 = await asend_message(a2a_client=client, request=request1) + response2 = await asend_message(a2a_client=client, request=request2) + ``` + """ + if not A2A_SDK_AVAILABLE: + raise ImportError( + "The 'a2a' package is required for A2A agent invocation. " + "Install it with: pip install a2a" + ) + + verbose_logger.info(f"Creating A2A client for {base_url}") + + # Use LiteLLM's cached httpx client + http_handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.A2A, + params={"timeout": timeout}, + ) + httpx_client = http_handler.client + + # Resolve agent card + resolver = A2ACardResolver( + httpx_client=httpx_client, + base_url=base_url, + ) + agent_card = await resolver.get_agent_card() + + verbose_logger.debug( + f"Resolved agent card: {agent_card.name if hasattr(agent_card, 'name') else 'unknown'}" + ) + + # Create A2A client + a2a_client = _A2AClient( + httpx_client=httpx_client, + agent_card=agent_card, + ) + + # Store agent_card on client for later retrieval (SDK doesn't expose it) + a2a_client._litellm_agent_card = agent_card # type: ignore[attr-defined] + + verbose_logger.info(f"A2A client created for {base_url}") + + return a2a_client + + +async def aget_agent_card( + base_url: str, + timeout: float = 60.0, + extra_headers: Optional[Dict[str, str]] = None, +) -> "AgentCard": + """ + Fetch the agent card from an A2A agent. + + Args: + base_url: The base URL of the A2A agent (e.g., "http://localhost:10001") + timeout: Request timeout in seconds (default: 60.0) + extra_headers: Optional additional headers to include in requests + + Returns: + AgentCard from the A2A agent + """ + if not A2A_SDK_AVAILABLE: + raise ImportError( + "The 'a2a' package is required for A2A agent invocation. " + "Install it with: pip install a2a" + ) + + verbose_logger.info(f"Fetching agent card from {base_url}") + + # Use LiteLLM's cached httpx client + http_handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.A2A, + params={"timeout": timeout}, + ) + httpx_client = http_handler.client + + resolver = A2ACardResolver( + httpx_client=httpx_client, + base_url=base_url, + ) + agent_card = await resolver.get_agent_card() + + verbose_logger.info( + f"Fetched agent card: {agent_card.name if hasattr(agent_card, 'name') else 'unknown'}" + ) + return agent_card diff --git a/litellm/batches/batch_utils.py b/litellm/batches/batch_utils.py index 8289801ee30..50b48321db5 100644 --- a/litellm/batches/batch_utils.py +++ b/litellm/batches/batch_utils.py @@ -14,8 +14,7 @@ from litellm.utils import token_counter async def calculate_batch_cost_and_usage( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"], - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"], ) -> Tuple[float, Usage, List[str]]: """ Calculate the cost and usage of a batch @@ -37,8 +36,7 @@ async def calculate_batch_cost_and_usage( async def _handle_completed_batch( batch: Batch, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"], - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"], ) -> Tuple[float, Usage, List[str]]: """Helper function to process a completed batch and handle logging""" # Get batch results @@ -84,8 +82,7 @@ def _get_batch_models_from_file_content( def _batch_cost_calculator( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> float: """ Calculate the cost of a batch based on the output file id @@ -186,7 +183,7 @@ def calculate_vertex_ai_batch_cost_and_usage( async def _get_batch_output_file_content_as_dictionary( batch: Batch, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> List[dict]: """ Get the batch output file content as a list of dictionaries @@ -225,7 +222,7 @@ def _get_file_content_as_dictionary(file_content: bytes) -> List[dict]: def _get_batch_job_cost_from_file_content( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> float: """ Get the cost of a batch job from the file content @@ -253,8 +250,7 @@ def _get_batch_job_cost_from_file_content( def _get_batch_job_total_usage_from_file_content( file_content_dictionary: List[dict], - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", - model_name: Optional[str] = None, + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "hosted_vllm"] = "openai", ) -> Usage: """ Get the tokens of a batch job from the file content diff --git a/litellm/batches/main.py b/litellm/batches/main.py index 838ee95b2b5..b99f4a628dc 100644 --- a/litellm/batches/main.py +++ b/litellm/batches/main.py @@ -23,6 +23,7 @@ import litellm from litellm._logging import verbose_logger from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.batches.handler import AzureBatchesAPI +from litellm.llms.bedrock.batches.handler import BedrockBatchesHandler from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.llms.openai.openai import OpenAIBatchesAPI @@ -35,7 +36,11 @@ from litellm.types.llms.openai import ( RetrieveBatchRequest, ) from litellm.types.router import GenericLiteLLMParams -from litellm.types.utils import LiteLLMBatch, LlmProviders +from litellm.types.utils import ( + OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS, + LiteLLMBatch, + LlmProviders, +) from litellm.utils import ( ProviderConfigManager, client, @@ -100,7 +105,7 @@ async def acreate_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -148,7 +153,7 @@ def create_batch( completion_window: Literal["24h"], endpoint: Literal["/v1/chat/completions", "/v1/embeddings", "/v1/completions"], input_file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -235,7 +240,7 @@ def create_batch( ) return response api_base: Optional[str] = None - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -350,7 +355,7 @@ def create_batch( @client async def aretrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -396,10 +401,10 @@ def _handle_retrieve_batch_providers_without_provider_config( litellm_params: dict, _retrieve_batch_request: RetrieveBatchRequest, _is_async: bool, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", ): api_base: Optional[str] = None - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -512,7 +517,7 @@ def _handle_retrieve_batch_providers_without_provider_config( @client def retrieve_batch( batch_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -576,7 +581,7 @@ def retrieve_batch( async_kwargs = kwargs.copy() async_kwargs.pop("aws_region_name", None) - return _handle_async_invoke_status( + return BedrockBatchesHandler._handle_async_invoke_status( batch_id=batch_id, aws_region_name=kwargs.get("aws_region_name", "us-east-1"), logging_obj=litellm_logging_obj, @@ -644,7 +649,7 @@ def retrieve_batch( async def alist_batches( after: Optional[str] = None, limit: Optional[int] = None, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "vertex_ai"] = "openai", metadata: Optional[Dict[str, str]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -687,7 +692,7 @@ async def alist_batches( def list_batches( after: Optional[str] = None, limit: Optional[int] = None, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm", "vertex_ai"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -727,7 +732,7 @@ def list_batches( timeout = 600.0 _is_async = kwargs.pop("alist_batches", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -784,9 +789,36 @@ def list_batches( max_retries=optional_params.max_retries, litellm_params=litellm_params, ) + elif custom_llm_provider == "vertex_ai": + api_base = optional_params.api_base or "" + vertex_ai_project = ( + optional_params.vertex_project + or litellm.vertex_project + or get_secret_str("VERTEXAI_PROJECT") + ) + vertex_ai_location = ( + optional_params.vertex_location + or litellm.vertex_location + or get_secret_str("VERTEXAI_LOCATION") + ) + vertex_credentials = optional_params.vertex_credentials or get_secret_str( + "VERTEXAI_CREDENTIALS" + ) + + response = vertex_ai_batches_instance.list_batches( + _is_async=_is_async, + after=after, + limit=limit, + api_base=api_base, + vertex_project=vertex_ai_project, + vertex_location=vertex_ai_location, + vertex_credentials=vertex_credentials, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'list_batch'. Only 'openai' is supported.".format( + message="LiteLLM doesn't support {} for 'list_batch'. Supported providers: openai, azure, vertex_ai.".format( custom_llm_provider ), model="n/a", @@ -901,7 +933,7 @@ def cancel_batch( _is_async = kwargs.pop("acancel_batch", False) is True api_base: Optional[str] = None - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: api_base = ( optional_params.api_base or litellm.api_base @@ -1016,30 +1048,49 @@ def _handle_async_invoke_status( ) # Transform response to a LiteLLMBatch object + from litellm.types.llms.openai import BatchJobStatus from litellm.types.utils import LiteLLMBatch + # Normalize status to lowercase (AWS returns 'Completed', 'Failed', etc.) + aws_status_raw = status_response.get("status", "") + aws_status_lower = aws_status_raw.lower() + # Map AWS status values to LiteLLM expected values + status_mapping: dict[str, BatchJobStatus] = { + "completed": "completed", + "failed": "failed", + "inprogress": "in_progress", + "in_progress": "in_progress", + } + normalized_status: BatchJobStatus = status_mapping.get(aws_status_lower, "failed") # Default to "failed" if unknown status + + # Get output S3 URI safely + output_s3_uri = "" + try: + output_s3_uri = status_response["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"] + except (KeyError, TypeError): + pass + + # Use BedrockBatchesConfig's timestamp parsing method (expects raw AWS status string) + import time + + from litellm.llms.bedrock.batches.transformation import BedrockBatchesConfig + created_at, in_progress_at, completed_at, failed_at, _, _ = BedrockBatchesConfig()._parse_timestamps_and_status(status_response, aws_status_raw) result = LiteLLMBatch( id=status_response["invocationArn"], object="batch", - status=status_response["status"], - created_at=status_response["submitTime"], - in_progress_at=status_response["lastModifiedTime"], - completed_at=status_response.get("endTime"), - failed_at=( - status_response.get("endTime") - if status_response["status"] == "failed" - else None - ), + status=normalized_status, + created_at=created_at or int(time.time()), # Provide default timestamp if None + in_progress_at=in_progress_at, + completed_at=completed_at, + failed_at=failed_at, request_counts=BatchRequestCounts( total=1, - completed=1 if status_response["status"] == "completed" else 0, - failed=1 if status_response["status"] == "failed" else 0, + completed=1 if normalized_status == "completed" else 0, + failed=1 if normalized_status == "failed" else 0, ), metadata=dict( **{ - "output_file_id": status_response["outputDataConfig"][ - "s3OutputDataConfig" - ]["s3Uri"], + "output_file_id": output_s3_uri, "failure_message": status_response.get("failureMessage") or "", "model_arn": status_response["modelArn"], } diff --git a/litellm/completion_extras/litellm_responses_transformation/transformation.py b/litellm/completion_extras/litellm_responses_transformation/transformation.py index e37be860b52..2045836387f 100644 --- a/litellm/completion_extras/litellm_responses_transformation/transformation.py +++ b/litellm/completion_extras/litellm_responses_transformation/transformation.py @@ -148,7 +148,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): if role == "system": # Extract system message as instructions if isinstance(content, str): - instructions = content + if instructions: + # Concatenate multiple system prompts with a space + instructions = f"{instructions} {content}" + else: + instructions = content else: input_items.append( { @@ -234,6 +238,11 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): cast(List[Dict[str, Any]], value) ) ) + elif key == "response_format": + # Convert response_format to text.format + text_format = self._transform_response_format_to_text_format(value) + if text_format: + responses_api_request["text"] = text_format # type: ignore elif key in ResponsesAPIOptionalRequestParams.__annotations__.keys(): responses_api_request[key] = value # type: ignore elif key == "metadata": @@ -666,6 +675,63 @@ class LiteLLMResponsesTransformationHandler(CompletionTransformationBridge): return Reasoning(effort="minimal") return None + def _transform_response_format_to_text_format( + self, response_format: Union[Dict[str, Any], Any] + ) -> Optional[Dict[str, Any]]: + """ + Transform Chat Completion response_format parameter to Responses API text.format parameter. + + Chat Completion response_format structure: + { + "type": "json_schema", + "json_schema": { + "name": "schema_name", + "schema": {...}, + "strict": True + } + } + + Responses API text parameter structure: + { + "format": { + "type": "json_schema", + "name": "schema_name", + "schema": {...}, + "strict": True + } + } + """ + if not response_format: + return None + + if isinstance(response_format, dict): + format_type = response_format.get("type") + + if format_type == "json_schema": + json_schema = response_format.get("json_schema", {}) + return { + "format": { + "type": "json_schema", + "name": json_schema.get("name", "response_schema"), + "schema": json_schema.get("schema", {}), + "strict": json_schema.get("strict", False), + } + } + elif format_type == "json_object": + return { + "format": { + "type": "json_object" + } + } + elif format_type == "text": + return { + "format": { + "type": "text" + } + } + + return None + def _map_responses_status_to_finish_reason(self, status: Optional[str]) -> str: """Map responses API status to chat completion finish_reason""" if not status: diff --git a/litellm/constants.py b/litellm/constants.py index 41441e21f75..fa9f1d527af 100644 --- a/litellm/constants.py +++ b/litellm/constants.py @@ -1,4 +1,5 @@ import os +import sys from typing import List, Literal DEFAULT_HEALTH_CHECK_PROMPT = str( @@ -99,10 +100,18 @@ RUNWAYML_POLLING_TIMEOUT = int( ########## Networking constants ############################################################## _DEFAULT_TTL_FOR_HTTPX_CLIENTS = 3600 # 1 hour, re-use the same httpx client for 1 hour -# Aiohttp connection pooling constants -AIOHTTP_CONNECTOR_LIMIT = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT", 0)) +# Aiohttp connection pooling - prevents memory leaks from unbounded connection growth +# Set to 0 for unlimited (not recommended for production) +AIOHTTP_CONNECTOR_LIMIT = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT", 300)) +AIOHTTP_CONNECTOR_LIMIT_PER_HOST = int(os.getenv("AIOHTTP_CONNECTOR_LIMIT_PER_HOST", 50)) AIOHTTP_KEEPALIVE_TIMEOUT = int(os.getenv("AIOHTTP_KEEPALIVE_TIMEOUT", 120)) AIOHTTP_TTL_DNS_CACHE = int(os.getenv("AIOHTTP_TTL_DNS_CACHE", 300)) +# enable_cleanup_closed is only needed for Python versions with the SSL leak bug +# Fixed in Python 3.12.7+ and 3.13.1+ (see https://github.com/python/cpython/pull/118960) +# Reference: https://github.com/aio-libs/aiohttp/blob/master/aiohttp/connector.py#L74-L78 +AIOHTTP_NEEDS_CLEANUP_CLOSED = ( + (3, 13, 0) <= sys.version_info < (3, 13, 1) or sys.version_info < (3, 12, 7) +) # WebSocket constants # Default to None (unlimited) to match OpenAI's official agents SDK behavior @@ -139,6 +148,7 @@ DEFAULT_SSL_CIPHERS = os.getenv( REDIS_UPDATE_BUFFER_KEY = "litellm_spend_update_buffer" REDIS_DAILY_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_spend_update_buffer" REDIS_DAILY_TEAM_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_team_spend_update_buffer" +REDIS_DAILY_ORG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_org_spend_update_buffer" REDIS_DAILY_TAG_SPEND_UPDATE_BUFFER_KEY = "litellm_daily_tag_spend_update_buffer" MAX_REDIS_BUFFER_DEQUEUE_COUNT = int(os.getenv("MAX_REDIS_BUFFER_DEQUEUE_COUNT", 100)) MAX_SIZE_IN_MEMORY_QUEUE = int(os.getenv("MAX_SIZE_IN_MEMORY_QUEUE", 10000)) @@ -254,6 +264,9 @@ TOGETHER_AI_EMBEDDING_350_M = int(os.getenv("TOGETHER_AI_EMBEDDING_350_M", 350)) QDRANT_SCALAR_QUANTILE = float(os.getenv("QDRANT_SCALAR_QUANTILE", 0.99)) QDRANT_VECTOR_SIZE = int(os.getenv("QDRANT_VECTOR_SIZE", 1536)) CACHED_STREAMING_CHUNK_DELAY = float(os.getenv("CACHED_STREAMING_CHUNK_DELAY", 0.02)) +AUDIO_SPEECH_CHUNK_SIZE = int( + os.getenv("AUDIO_SPEECH_CHUNK_SIZE", 8192) +) # chunk_size for audio speech streaming. Balance between latency and memory usage MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB = int( os.getenv("MAX_SIZE_PER_ITEM_IN_MEMORY_CACHE_IN_KB", 512) ) @@ -276,10 +289,16 @@ REDACTED_BY_LITELM_STRING = "REDACTED_BY_LITELM" MAX_LANGFUSE_INITIALIZED_CLIENTS = int( os.getenv("MAX_LANGFUSE_INITIALIZED_CLIENTS", 50) ) -LOGGING_WORKER_CONCURRENCY = int(os.getenv("LOGGING_WORKER_CONCURRENCY", 100)) # Must be above 0 +LOGGING_WORKER_CONCURRENCY = int( + os.getenv("LOGGING_WORKER_CONCURRENCY", 100) +) # Must be above 0 LOGGING_WORKER_MAX_QUEUE_SIZE = int(os.getenv("LOGGING_WORKER_MAX_QUEUE_SIZE", 50_000)) -LOGGING_WORKER_MAX_TIME_PER_COROUTINE = float(os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0)) -LOGGING_WORKER_CLEAR_PERCENTAGE = int(os.getenv("LOGGING_WORKER_CLEAR_PERCENTAGE", 50)) # Percentage of queue to clear (default: 50%) +LOGGING_WORKER_MAX_TIME_PER_COROUTINE = float( + os.getenv("LOGGING_WORKER_MAX_TIME_PER_COROUTINE", 20.0) +) +LOGGING_WORKER_CLEAR_PERCENTAGE = int( + os.getenv("LOGGING_WORKER_CLEAR_PERCENTAGE", 50) +) # Percentage of queue to clear (default: 50%) MAX_ITERATIONS_TO_CLEAR_QUEUE = int(os.getenv("MAX_ITERATIONS_TO_CLEAR_QUEUE", 200)) MAX_TIME_TO_CLEAR_QUEUE = float(os.getenv("MAX_TIME_TO_CLEAR_QUEUE", 5.0)) LOGGING_WORKER_AGGRESSIVE_CLEAR_COOLDOWN_SECONDS = float( @@ -383,6 +402,7 @@ LITELLM_CHAT_PROVIDERS = [ "nebius", "dashscope", "moonshot", + "publicai", "v0", "heroku", "oci", @@ -525,6 +545,7 @@ openai_compatible_endpoints: List = [ "api.studio.nebius.ai/v1", "https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "https://api.moonshot.ai/v1", + "https://platform.publicai.co/v1", "https://api.v0.dev/v1", "https://api.morphllm.com/v1", "https://api.lambda.ai/v1", @@ -551,6 +572,7 @@ openai_compatible_providers: List = [ "perplexity", "xinference", "xai", + "zai", "together_ai", "fireworks_ai", "empower", @@ -570,6 +592,7 @@ openai_compatible_providers: List = [ "nebius", "dashscope", "moonshot", + "publicai", "v0", "morph", "lambda_ai", @@ -580,6 +603,7 @@ openai_compatible_providers: List = [ "cometapi", "clarifai", "docker_model_runner", + "ragflow", ] openai_text_completion_compatible_providers: List = ( [ # providers that support `/v1/completions` @@ -592,6 +616,7 @@ openai_text_completion_compatible_providers: List = ( "nebius", "dashscope", "moonshot", + "publicai", "v0", "lambda_ai", "hyperbolic", @@ -851,12 +876,16 @@ BEDROCK_INVOKE_PROVIDERS_LITERAL = Literal[ "nova", "deepseek_r1", "qwen3", + "qwen2", + "twelvelabs", + "openai", ] BEDROCK_EMBEDDING_PROVIDERS_LITERAL = Literal[ "cohere", "amazon", "twelvelabs", + "nova", ] BEDROCK_CONVERSE_MODELS = [ @@ -899,6 +928,9 @@ BEDROCK_CONVERSE_MODELS = [ "meta.llama3-2-3b-instruct-v1:0", "meta.llama3-2-11b-instruct-v1:0", "meta.llama3-2-90b-instruct-v1:0", + "amazon.nova-lite-v1:0", + "amazon.nova-2-lite-v1:0", + "amazon.nova-pro-v1:0", ] @@ -917,6 +949,7 @@ cohere_embedding_models: set = set( bedrock_embedding_models: set = set( [ "amazon.titan-embed-text-v1", + "amazon.nova-2-multimodal-embeddings-v1:0", "cohere.embed-english-v3", "cohere.embed-multilingual-v3", "cohere.embed-v4:0", @@ -1211,3 +1244,7 @@ SENTRY_PII_DENYLIST = [ COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY = int( os.getenv("COROUTINE_CHECKER_MAX_SIZE_IN_MEMORY", 1000) ) + +########################### RAG Text Splitter Constants ########################### +DEFAULT_CHUNK_SIZE = int(os.getenv("DEFAULT_CHUNK_SIZE", 1000)) +DEFAULT_CHUNK_OVERLAP = int(os.getenv("DEFAULT_CHUNK_OVERLAP", 200)) diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py index 0f5195e31af..57eab6d29a3 100644 --- a/litellm/cost_calculator.py +++ b/litellm/cost_calculator.py @@ -95,6 +95,7 @@ from litellm.utils import ( EmbeddingResponse, ImageResponse, ModelResponse, + ModelResponseStream, ProviderConfigManager, TextCompletionResponse, TranscriptionResponse, @@ -654,7 +655,9 @@ def _infer_call_type( if completion_response is None: return None - if isinstance(completion_response, ModelResponse): + if isinstance(completion_response, ModelResponse) or isinstance( + completion_response, ModelResponseStream + ): return "completion" elif isinstance(completion_response, EmbeddingResponse): return "embedding" @@ -934,6 +937,17 @@ def completion_cost( # noqa: PLR0915 prompt_tokens = token_counter(model=model, text=prompt) completion_tokens = token_counter(model=model, text=completion) + # Handle A2A calls before model check - A2A doesn't require a model + if call_type in ( + CallTypes.asend_message.value, + CallTypes.send_message.value, + ): + from litellm.a2a_protocol.cost_calculator import A2ACostCalculator + + return A2ACostCalculator.calculate_a2a_cost( + litellm_logging_obj=litellm_logging_obj + ) + if model is None: raise ValueError( f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}" @@ -1031,6 +1045,61 @@ def completion_cost( # noqa: PLR0915 billed_units.get("search_units") or 1 ) # cohere charges per request by default. completion_tokens = search_units + elif ( + call_type == CallTypes.search.value + or call_type == CallTypes.asearch.value + ): + from litellm.search import search_provider_cost_per_query + + # Extract number_of_queries from optional_params or default to 1 + number_of_queries = 1 + if optional_params is not None: + # Check if query is a list (multiple queries) + query = optional_params.get("query") + if isinstance(query, list): + number_of_queries = len(query) + elif query is not None: + number_of_queries = 1 + + search_model = model or "" + if custom_llm_provider and "/" not in search_model: + # If model is like "tavily-search", construct "tavily/search" for cost lookup + search_model = f"{custom_llm_provider}/search" + + prompt_cost, completion_cost_result = ( + search_provider_cost_per_query( + model=search_model, + custom_llm_provider=custom_llm_provider, + number_of_queries=number_of_queries, + optional_params=optional_params, + ) + ) + + # Return the total cost (prompt_cost + completion_cost, but for search it's just prompt_cost) + _final_cost = prompt_cost + completion_cost_result + + # Apply discount + original_cost = _final_cost + _final_cost, discount_percent, discount_amount = ( + _apply_cost_discount( + base_cost=_final_cost, + custom_llm_provider=custom_llm_provider, + ) + ) + + # Store cost breakdown in logging object if available + _store_cost_breakdown_in_logging_obj( + litellm_logging_obj=litellm_logging_obj, + prompt_tokens_cost_usd_dollar=prompt_cost, + completion_tokens_cost_usd_dollar=completion_cost_result, + cost_for_built_in_tools_cost_usd_dollar=0.0, + total_cost_usd_dollar=_final_cost, + original_cost=original_cost, + discount_percent=discount_percent, + discount_amount=discount_amount, + ) + + return _final_cost elif call_type == CallTypes.arealtime.value and isinstance( completion_response, LiteLLMRealtimeStreamLoggingObject ): diff --git a/litellm/files/main.py b/litellm/files/main.py index 535772fa42c..9378715a472 100644 --- a/litellm/files/main.py +++ b/litellm/files/main.py @@ -18,6 +18,7 @@ from litellm import get_secret_str from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.llms.azure.files.handler import AzureOpenAIFilesAPI +from litellm.llms.bedrock.files.handler import BedrockFilesHandler from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler from litellm.llms.openai.openai import FileDeleted, FileObject, OpenAIFilesAPI @@ -30,7 +31,10 @@ from litellm.types.llms.openai import ( OpenAIFileObject, ) from litellm.types.router import * -from litellm.types.utils import LlmProviders +from litellm.types.utils import ( + OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS, + LlmProviders, +) from litellm.utils import ( ProviderConfigManager, client, @@ -44,6 +48,7 @@ base_llm_http_handler = BaseLLMHTTPHandler() openai_files_instance = OpenAIFilesAPI() azure_files_instance = AzureOpenAIFilesAPI() vertex_ai_files_instance = VertexAIFilesHandler() +bedrock_files_instance = BedrockFilesHandler() ################################################# @@ -51,7 +56,7 @@ vertex_ai_files_instance = VertexAIFilesHandler() async def acreate_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -95,9 +100,7 @@ async def acreate_file( def create_file( file: FileTypes, purpose: Literal["assistants", "batch", "fine-tune"], - custom_llm_provider: Optional[ - Literal["openai", "azure", "vertex_ai", "bedrock"] - ] = None, + custom_llm_provider: Optional[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"]] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -165,7 +168,7 @@ def create_file( ), timeout=timeout, ) - elif custom_llm_provider == "openai": + elif custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -276,7 +279,7 @@ def create_file( @client async def afile_retrieve( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -317,7 +320,7 @@ async def afile_retrieve( @client def file_retrieve( file_id: str, - custom_llm_provider: Literal["openai", "azure"] = "openai", + custom_llm_provider: Literal["openai", "azure", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -347,7 +350,7 @@ def file_retrieve( _is_async = kwargs.pop("is_async", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -514,7 +517,7 @@ def file_delete( elif timeout is None: timeout = 600.0 _is_async = kwargs.pop("is_async", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -670,7 +673,7 @@ def file_list( timeout = 600.0 _is_async = kwargs.pop("is_async", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -754,7 +757,7 @@ def file_list( @client async def afile_content( file_id: str, - custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai", + custom_llm_provider: Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"] = "openai", extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, **kwargs, @@ -799,7 +802,7 @@ def file_content( file_id: str, model: Optional[str] = None, custom_llm_provider: Optional[ - Union[Literal["openai", "azure", "vertex_ai"], str] + Union[Literal["openai", "azure", "vertex_ai", "bedrock", "hosted_vllm"], str] ] = None, extra_headers: Optional[Dict[str, str]] = None, extra_body: Optional[Dict[str, str]] = None, @@ -846,7 +849,7 @@ def file_content( _is_async = kwargs.pop("afile_content", False) is True - if custom_llm_provider == "openai": + if custom_llm_provider in OPENAI_COMPATIBLE_BATCH_AND_FILES_PROVIDERS: # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there api_base = ( optional_params.api_base @@ -937,9 +940,18 @@ def file_content( timeout=timeout, max_retries=optional_params.max_retries, ) + elif custom_llm_provider == "bedrock": + response = bedrock_files_instance.file_content( + _is_async=_is_async, + file_content_request=_file_content_request, + api_base=optional_params.api_base, + optional_params=litellm_params_dict, + timeout=timeout, + max_retries=optional_params.max_retries, + ) else: raise litellm.exceptions.BadRequestError( - message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format( + message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai', 'bedrock'.".format( custom_llm_provider ), model="n/a", diff --git a/litellm/images/main.py b/litellm/images/main.py index 333a751b045..770b16c1ed2 100644 --- a/litellm/images/main.py +++ b/litellm/images/main.py @@ -6,10 +6,13 @@ from typing import Any, Coroutine, Dict, List, Literal, Optional, Union, cast, o import httpx import litellm -from litellm import Logging, client, exception_type, get_litellm_params +from litellm.utils import exception_type, get_litellm_params +# client is imported from litellm as it's a decorator +from litellm import client from litellm.constants import DEFAULT_IMAGE_ENDPOINT_MODEL from litellm.constants import request_timeout as DEFAULT_REQUEST_TIMEOUT from litellm.exceptions import LiteLLMUnknownProvider +from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj from litellm.litellm_core_utils.mock_functions import mock_image_generation from litellm.llms.base_llm import BaseImageEditConfig, BaseImageGenerationConfig @@ -19,6 +22,8 @@ from litellm.llms.custom_llm import CustomLLM #################### Initialize provider clients #################### llm_http_handler: BaseLLMHTTPHandler = BaseLLMHTTPHandler() +from openai.types.audio.transcription_create_params import FileTypes # type: ignore + from litellm.main import ( azure_chat_completions, base_llm_aiohttp_handler, @@ -26,7 +31,6 @@ from litellm.main import ( bedrock_image_generation, openai_chat_completions, openai_image_variations, - vertex_image_generation, ) ########################################### @@ -36,7 +40,6 @@ from litellm.types.llms.openai import ImageGenerationRequestQuality from litellm.types.router import GenericLiteLLMParams from litellm.types.utils import ( LITELLM_IMAGE_VARIATION_PROVIDERS, - FileTypes, LlmProviders, all_litellm_params, ) @@ -344,12 +347,17 @@ def image_generation( # noqa: PLR0915 litellm.LlmProviders.GEMINI, litellm.LlmProviders.FAL_AI, litellm.LlmProviders.RUNWAYML, + litellm.LlmProviders.VERTEX_AI, ): if image_generation_config is None: raise ValueError( f"image generation config is not supported for {custom_llm_provider}" ) + # Resolve api_base from litellm.api_base if not explicitly provided + _api_base = api_base or litellm.api_base + litellm_params_dict["api_base"] = _api_base + return llm_http_handler.image_generation_handler( api_key=api_key, model=model, @@ -430,46 +438,6 @@ def image_generation( # noqa: PLR0915 api_base=api_base, api_key=api_key, ) - elif custom_llm_provider == "vertex_ai": - vertex_ai_project = ( - optional_params.pop("vertex_project", None) - or optional_params.pop("vertex_ai_project", None) - or litellm.vertex_project - or get_secret_str("VERTEXAI_PROJECT") - ) - vertex_ai_location = ( - optional_params.pop("vertex_location", None) - or optional_params.pop("vertex_ai_location", None) - or litellm.vertex_location - or get_secret_str("VERTEXAI_LOCATION") - ) - vertex_credentials = ( - optional_params.pop("vertex_credentials", None) - or optional_params.pop("vertex_ai_credentials", None) - or get_secret_str("VERTEXAI_CREDENTIALS") - ) - - api_base = ( - api_base - or litellm.api_base - or get_secret_str("VERTEXAI_API_BASE") - or get_secret_str("VERTEX_API_BASE") - ) - - model_response = vertex_image_generation.image_generation( - model=model, - prompt=prompt, - timeout=timeout, - logging_obj=litellm_logging_obj, - optional_params=optional_params, - model_response=model_response, - vertex_project=vertex_ai_project, - vertex_location=vertex_ai_location, - vertex_credentials=vertex_credentials, - aimg_generation=aimg_generation, - api_base=api_base, - client=client, - ) elif ( custom_llm_provider in litellm._custom_providers ): # Assume custom LLM provider diff --git a/litellm/integrations/SlackAlerting/budget_alert_types.py b/litellm/integrations/SlackAlerting/budget_alert_types.py index 1e9ad286e37..dadfef3fc40 100644 --- a/litellm/integrations/SlackAlerting/budget_alert_types.py +++ b/litellm/integrations/SlackAlerting/budget_alert_types.py @@ -50,6 +50,14 @@ class TeamBudgetAlert(BaseBudgetAlertType): return user_info.team_id or "default_id" +class OrganizationBudgetAlert(BaseBudgetAlertType): + def get_event_message(self) -> str: + return "Organization Budget: " + + def get_id(self, user_info: CallInfo) -> str: + return user_info.organization_id or "default_id" + + class TokenBudgetAlert(BaseBudgetAlertType): def get_event_message(self) -> str: return "Key Budget: " @@ -72,6 +80,7 @@ def get_budget_alert_type( "soft_budget", "user_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], @@ -83,6 +92,7 @@ def get_budget_alert_type( "soft_budget": SoftBudgetAlert(), "user_budget": UserBudgetAlert(), "team_budget": TeamBudgetAlert(), + "organization_budget": OrganizationBudgetAlert(), "token_budget": TokenBudgetAlert(), "projected_limit_exceeded": ProjectedLimitExceededAlert(), } diff --git a/litellm/integrations/SlackAlerting/slack_alerting.py b/litellm/integrations/SlackAlerting/slack_alerting.py index 3efe5873786..0e691e2c43f 100644 --- a/litellm/integrations/SlackAlerting/slack_alerting.py +++ b/litellm/integrations/SlackAlerting/slack_alerting.py @@ -134,19 +134,25 @@ class SlackAlerting(CustomBatchLogger): if llm_router is not None: self.llm_router = llm_router - def _prepare_outage_value_for_cache(self, outage_value: Union[dict, ProviderRegionOutageModel, OutageModel]) -> dict: + def _prepare_outage_value_for_cache( + self, outage_value: Union[dict, ProviderRegionOutageModel, OutageModel] + ) -> dict: """ Helper method to prepare outage value for Redis caching. Converts set objects to lists for JSON serialization. """ # Convert to dict for processing cache_value = dict(outage_value) - - if "deployment_ids" in cache_value and isinstance(cache_value["deployment_ids"], set): + + if "deployment_ids" in cache_value and isinstance( + cache_value["deployment_ids"], set + ): cache_value["deployment_ids"] = list(cache_value["deployment_ids"]) return cache_value - def _restore_outage_value_from_cache(self, outage_value: Optional[dict]) -> Optional[dict]: + def _restore_outage_value_from_cache( + self, outage_value: Optional[dict] + ) -> Optional[dict]: """ Helper method to restore outage value after retrieving from cache. Converts list objects back to sets for proper handling. @@ -528,6 +534,7 @@ class SlackAlerting(CustomBatchLogger): "soft_budget", "user_budget", "team_budget", + "organization_budget", "proxy_budget", "projected_limit_exceeded", ], @@ -1338,7 +1345,7 @@ Model Info: subject=email_event["subject"], html=email_event["html"], ) - if webhook_event.event_group == "team": + if webhook_event.event_group == Litellm_EntityType.TEAM: from litellm.integrations.email_alerting import send_team_budget_alert await send_team_budget_alert(webhook_event=webhook_event) @@ -1399,7 +1406,7 @@ Model Info: current_time = datetime.now().strftime("%H:%M:%S") _proxy_base_url = os.getenv("PROXY_BASE_URL", None) # Use .name if it's an enum, otherwise use as is - alert_type_name = getattr(alert_type, 'name', alert_type) + alert_type_name = getattr(alert_type, "name", alert_type) alert_type_formatted = f"Alert type: `{alert_type_name}`" if alert_type == "daily_reports" or alert_type == "new_model_added": formatted_message = alert_type_formatted + message diff --git a/litellm/integrations/arize/arize_phoenix.py b/litellm/integrations/arize/arize_phoenix.py index ab70dd9d0e2..4a6e0cec8ca 100644 --- a/litellm/integrations/arize/arize_phoenix.py +++ b/litellm/integrations/arize/arize_phoenix.py @@ -1,18 +1,20 @@ import os -from typing import TYPE_CHECKING, Any, Union +from typing import TYPE_CHECKING, Any, Optional, Union +from datetime import datetime from litellm._logging import verbose_logger from litellm.integrations.arize import _utils from litellm.integrations.arize._utils import ArizeOTELAttributes from litellm.types.integrations.arize_phoenix import ArizePhoenixConfig +from litellm.types.services import ServiceLoggerPayload +from litellm.integrations.opentelemetry import OpenTelemetry if TYPE_CHECKING: from opentelemetry.trace import Span as _Span + from litellm.integrations.opentelemetry import OpenTelemetryConfig as _OpenTelemetryConfig from litellm.types.integrations.arize import Protocol as _Protocol - from .opentelemetry import OpenTelemetryConfig as _OpenTelemetryConfig - Protocol = _Protocol OpenTelemetryConfig = _OpenTelemetryConfig Span = Union[_Span, Any] @@ -25,7 +27,11 @@ else: ARIZE_HOSTED_PHOENIX_ENDPOINT = "https://otlp.arize.com/v1/traces" -class ArizePhoenixLogger: +class ArizePhoenixLogger(OpenTelemetry): + def set_attributes(self, span: Span, kwargs, response_obj: Optional[Any]): + ArizePhoenixLogger.set_arize_phoenix_attributes(span, kwargs, response_obj) + return + @staticmethod def set_arize_phoenix_attributes(span: Span, kwargs, response_obj): _utils.set_attributes(span, kwargs, response_obj, ArizeOTELAttributes) @@ -97,3 +103,46 @@ class ArizePhoenixLogger: endpoint=endpoint, project_name=project_name, ) + + async def async_service_success_hook( + self, + payload: ServiceLoggerPayload, + parent_otel_span: Optional[Span] = None, + start_time: Optional[Union[datetime, float]] = None, + end_time: Optional[Union[datetime, float]] = None, + event_metadata: Optional[dict] = None, + ): + pass # suppress additional spans + + async def async_service_failure_hook( + self, + payload: ServiceLoggerPayload, + error: Optional[str] = "", + parent_otel_span: Optional[Span] = None, + start_time: Optional[Union[datetime, float]] = None, + end_time: Optional[Union[float, datetime]] = None, + event_metadata: Optional[dict] = None, + ): + pass # suppress additional spans + + def create_litellm_proxy_request_started_span( + self, + start_time: datetime, + headers: dict, + ): + pass # suppress additional spans + + async def async_health_check(self): + + config = self.get_arize_phoenix_config() + + if not config.otlp_auth_headers: + return { + "status": "unhealthy", + "error_message": "PHOENIX_API_KEY environment variable not set", + } + + return { + "status": "healthy", + "message": "Arize-Phoenix credentials are configured properly", + } \ No newline at end of file diff --git a/litellm/integrations/custom_guardrail.py b/litellm/integrations/custom_guardrail.py index 42555841401..782f9460044 100644 --- a/litellm/integrations/custom_guardrail.py +++ b/litellm/integrations/custom_guardrail.py @@ -1,15 +1,26 @@ from datetime import datetime -from typing import Any, Dict, List, Optional, Type, Union, get_args +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + Literal, + Optional, + Tuple, + Type, + Union, + get_args, +) from litellm._logging import verbose_logger from litellm.caching import DualCache from litellm.integrations.custom_logger import CustomLogger from litellm.types.guardrails import ( DynamicGuardrailParams, + GenericGuardrailAPIInputs, GuardrailEventHooks, LitellmParams, Mode, - PiiEntityType, ) from litellm.types.llms.openai import AllMessageValues from litellm.types.proxy.guardrails.guardrail_hooks.base import GuardrailConfigModel @@ -20,6 +31,8 @@ from litellm.types.utils import ( StandardLoggingGuardrailInformation, ) +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj dc = DualCache() @@ -437,30 +450,33 @@ class CustomGuardrail(CustomLogger): async def apply_guardrail( self, - text: str, - language: Optional[str] = None, - entities: Optional[List[PiiEntityType]] = None, - request_data: Optional[dict] = None, - ) -> str: + inputs: GenericGuardrailAPIInputs, + request_data: dict, + input_type: Literal["request", "response"], + logging_obj: Optional["LiteLLMLoggingObj"] = None, + ) -> GenericGuardrailAPIInputs: """ - Apply your guardrail logic to the given text + Apply your guardrail logic to the given inputs Args: - text: The text to apply the guardrail to - language: The language of the text - entities: The entities to mask, optional - request_data: The request data dictionary to store guardrail metadata + inputs: Dictionary containing: + - texts: List of texts to apply the guardrail to + - images: Optional list of images to apply the guardrail to + - tool_calls: Optional list of tool calls to apply the guardrail to + request_data: The request data dictionary - containing user api key metadata (e.g. user_id, team_id, etc.) + input_type: The type of input to apply the guardrail to - "request" or "response" + logging_obj: Optional logging object for tracking the guardrail execution Any of the custom guardrails can override this method to provide custom guardrail logic - Returns the text with the guardrail applied + Returns the texts with the guardrail applied and the images with the guardrail applied (if any) Raises: Exception: - If the guardrail raises an exception """ - return text + return inputs def _process_response( self, diff --git a/litellm/integrations/datadog/datadog.py b/litellm/integrations/datadog/datadog.py index 46e1a2c201f..21e1d562224 100644 --- a/litellm/integrations/datadog/datadog.py +++ b/litellm/integrations/datadog/datadog.py @@ -65,11 +65,11 @@ class DataDogLogger( `DD_SITE` - your datadog site, example = `"us5.datadoghq.com"` Optional environment variables (DataDog Agent): - `DD_AGENT_HOST` - hostname or IP of DataDog agent, example = `"localhost"` - `DD_AGENT_PORT` - port of DataDog agent (default: 10518 for logs) + `LITELLM_DD_AGENT_HOST` - hostname or IP of DataDog agent, example = `"localhost"` + `LITELLM_DD_AGENT_PORT` - port of DataDog agent (default: 10518 for logs) - Note: If DD_AGENT_HOST is set, logs will be sent to the agent instead of directly to DataDog API. - In this case, DD_API_KEY and DD_SITE are not required (agent handles authentication). + Note: We use LITELLM_DD_AGENT_HOST instead of DD_AGENT_HOST to avoid conflicts + with ddtrace which automatically sets DD_AGENT_HOST for APM tracing. """ try: verbose_logger.debug("Datadog: in init datadog logger") @@ -85,7 +85,8 @@ class DataDogLogger( ) # Configure DataDog endpoint (Agent or Direct API) - dd_agent_host = os.getenv("DD_AGENT_HOST") + # Use LITELLM_DD_AGENT_HOST to avoid conflicts with ddtrace's DD_AGENT_HOST + dd_agent_host = os.getenv("LITELLM_DD_AGENT_HOST") if dd_agent_host: self._configure_dd_agent(dd_agent_host=dd_agent_host) else: @@ -127,7 +128,7 @@ class DataDogLogger( Args: dd_agent_host: Hostname or IP of DataDog agent """ - dd_agent_port = os.getenv("DD_AGENT_PORT", "10518") # default port for logs + dd_agent_port = os.getenv("LITELLM_DD_AGENT_PORT", "10518") # default port for logs self.intake_url = f"http://{dd_agent_host}:{dd_agent_port}/api/v2/logs" self.DD_API_KEY = os.getenv("DD_API_KEY") # Optional when using agent verbose_logger.debug(f"Datadog: Using DD Agent at {self.intake_url}") diff --git a/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py b/litellm/integrations/generic_api/generic_api_callback.py similarity index 67% rename from enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py rename to litellm/integrations/generic_api/generic_api_callback.py index 7e259d4e19d..1c8a5b883da 100644 --- a/enterprise/litellm_enterprise/enterprise_callbacks/generic_api_callback.py +++ b/litellm/integrations/generic_api/generic_api_callback.py @@ -7,13 +7,15 @@ Callback to log events to a Generic API Endpoint """ import asyncio +import json import os +import re import traceback -from litellm._uuid import uuid -from typing import Dict, List, Optional, Union +from typing import Dict, List, Literal, Optional, Union import litellm from litellm._logging import verbose_logger +from litellm._uuid import uuid from litellm.integrations.custom_batch_logger import CustomBatchLogger from litellm.litellm_core_utils.safe_json_dumps import safe_dumps from litellm.llms.custom_httpx.http_handler import ( @@ -22,12 +24,83 @@ from litellm.llms.custom_httpx.http_handler import ( ) from litellm.types.utils import StandardLoggingPayload +API_EVENT_TYPES = Literal["llm_api_success", "llm_api_failure"] + + +def load_compatible_callbacks() -> Dict: + """ + Load the generic_api_compatible_callbacks.json file + + Returns: + Dict: Dictionary of compatible callbacks configuration + """ + try: + json_path = os.path.join( + os.path.dirname(__file__), "generic_api_compatible_callbacks.json" + ) + with open(json_path, "r") as f: + return json.load(f) + except Exception as e: + verbose_logger.warning( + f"Error loading generic_api_compatible_callbacks.json: {str(e)}" + ) + return {} + + +def is_callback_compatible(callback_name: str) -> bool: + """ + Check if a callback_name exists in the compatible callbacks list + + Args: + callback_name: Name of the callback to check + + Returns: + bool: True if callback_name exists in the compatible callbacks, False otherwise + """ + compatible_callbacks = load_compatible_callbacks() + return callback_name in compatible_callbacks + + +def get_callback_config(callback_name: str) -> Optional[Dict]: + """ + Get the configuration for a specific callback + + Args: + callback_name: Name of the callback to get config for + + Returns: + Optional[Dict]: Configuration dict for the callback, or None if not found + """ + compatible_callbacks = load_compatible_callbacks() + return compatible_callbacks.get(callback_name) + + +def substitute_env_variables(value: str) -> str: + """ + Replace {{environment_variables.VAR_NAME}} patterns with actual environment variable values + + Args: + value: String that may contain {{environment_variables.VAR_NAME}} patterns + + Returns: + str: String with environment variables substituted + """ + pattern = r"\{\{environment_variables\.([A-Z_]+)\}\}" + + def replace_env_var(match): + env_var_name = match.group(1) + return os.getenv(env_var_name, "") + + return re.sub(pattern, replace_env_var, value) + class GenericAPILogger(CustomBatchLogger): def __init__( self, endpoint: Optional[str] = None, headers: Optional[dict] = None, + event_types: Optional[List[API_EVENT_TYPES]] = None, + callback_name: Optional[str] = None, **kwargs, ): """ @@ -36,7 +109,37 @@ class GenericAPILogger(CustomBatchLogger): Args: endpoint: Optional[str] = None, headers: Optional[dict] = None, + event_types: Optional[List[API_EVENT_TYPES]] = None, + callback_name: Optional[str] = None - If provided, loads config from generic_api_compatible_callbacks.json """ + ######################################################### + # Check if callback_name is provided and load config + ######################################################### + if callback_name: + if is_callback_compatible(callback_name): + verbose_logger.debug( + f"Loading configuration for callback: {callback_name}" + ) + callback_config = get_callback_config(callback_name) + + # Use config from JSON if not explicitly provided + if callback_config: + if endpoint is None and "endpoint" in callback_config: + endpoint = substitute_env_variables(callback_config["endpoint"]) + + if "headers" in callback_config: + headers = headers or {} + for key, value in callback_config["headers"].items(): + if key not in headers: + headers[key] = substitute_env_variables(value) + + if event_types is None and "event_types" in callback_config: + event_types = callback_config["event_types"] + else: + verbose_logger.warning( + f"callback_name '{callback_name}' not found in generic_api_compatible_callbacks.json" + ) + ######################################################### # Init httpx client ######################################################### @@ -51,8 +154,10 @@ class GenericAPILogger(CustomBatchLogger): self.headers: Dict = self._get_headers(headers) self.endpoint: str = endpoint + self.event_types: Optional[List[API_EVENT_TYPES]] = event_types + self.callback_name: Optional[str] = callback_name verbose_logger.debug( - f"in init GenericAPILogger, endpoint {self.endpoint}, headers {self.headers}" + f"in init GenericAPILogger, callback_name: {self.callback_name}, endpoint {self.endpoint}, headers {self.headers}, event_types: {self.event_types}" ) ######################################################### @@ -114,9 +219,9 @@ class GenericAPILogger(CustomBatchLogger): Raises: Raises a NON Blocking verbose_logger.exception if an error occurs """ - from litellm.proxy.utils import _premium_user_check - _premium_user_check() + if self.event_types is not None and "llm_api_success" not in self.event_types: + return try: verbose_logger.debug( @@ -153,9 +258,8 @@ class GenericAPILogger(CustomBatchLogger): - Creates a StandardLoggingPayload - Adds to batch queue """ - from litellm.proxy.utils import _premium_user_check - - _premium_user_check() + if self.event_types is not None and "llm_api_failure" not in self.event_types: + return try: verbose_logger.debug( diff --git a/litellm/integrations/generic_api/generic_api_compatible_callbacks.json b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json new file mode 100644 index 00000000000..1e88a39e0a7 --- /dev/null +++ b/litellm/integrations/generic_api/generic_api_compatible_callbacks.json @@ -0,0 +1,20 @@ +{ + "sample_callback": { + "event_types": ["llm_api_success", "llm_api_failure"], + "endpoint": "{{environment_variables.SAMPLE_CALLBACK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.SAMPLE_CALLBACK_API_KEY}}" + }, + "environment_variables": ["SAMPLE_CALLBACK_URL", "SAMPLE_CALLBACK_API_KEY"] + }, + "rubrik": { + "event_types": ["llm_api_success"], + "endpoint": "{{environment_variables.RUBRIK_WEBHOOK_URL}}", + "headers": { + "Content-Type": "application/json", + "Authorization": "Bearer {{environment_variables.RUBRIK_API_KEY}}" + }, + "environment_variables": ["RUBRIK_API_KEY", "RUBRIK_WEBHOOK_URL"] + } +} \ No newline at end of file diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 90bf19b21fe..9f9d45d0e7d 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -1065,14 +1065,7 @@ class OpenTelemetry(CustomLogger): self, span: Span, kwargs, response_obj: Optional[Any] ): try: - if self.callback_name == "arize_phoenix": - from litellm.integrations.arize.arize_phoenix import ArizePhoenixLogger - - ArizePhoenixLogger.set_arize_phoenix_attributes( - span, kwargs, response_obj - ) - return - elif self.callback_name == "langtrace": + if self.callback_name == "langtrace": from litellm.integrations.langtrace import LangtraceAttributes LangtraceAttributes().set_langtrace_attributes( @@ -1088,6 +1081,11 @@ class OpenTelemetry(CustomLogger): span, kwargs, response_obj ) return + elif self.callback_name == "weave_otel": + from litellm.integrations.weave.weave_otel import set_weave_otel_attributes + + set_weave_otel_attributes(span, kwargs, response_obj) + return from litellm.proxy._types import SpanAttributes optional_params = kwargs.get("optional_params", {}) diff --git a/litellm/integrations/prometheus.py b/litellm/integrations/prometheus.py index 8186006f8c8..4ce818f0cef 100644 --- a/litellm/integrations/prometheus.py +++ b/litellm/integrations/prometheus.py @@ -24,13 +24,29 @@ from litellm.proxy._types import LiteLLM_TeamTable, UserAPIKeyAuth from litellm.types.integrations.prometheus import * from litellm.types.integrations.prometheus import _sanitize_prometheus_label_name from litellm.types.utils import StandardLoggingPayload -from litellm.utils import get_end_user_id_for_cost_tracking if TYPE_CHECKING: from apscheduler.schedulers.asyncio import AsyncIOScheduler else: AsyncIOScheduler = Any +# Cached lazy import for get_end_user_id_for_cost_tracking +# Module-level cache to avoid repeated imports while preserving memory benefits +_get_end_user_id_for_cost_tracking = None + + +def _get_cached_end_user_id_for_cost_tracking(): + """ + Get cached get_end_user_id_for_cost_tracking function. + Lazy imports on first call to avoid loading utils.py at import time (60MB saved). + Subsequent calls use cached function for better performance. + """ + global _get_end_user_id_for_cost_tracking + if _get_end_user_id_for_cost_tracking is None: + from litellm.utils import get_end_user_id_for_cost_tracking + _get_end_user_id_for_cost_tracking = get_end_user_id_for_cost_tracking + return _get_end_user_id_for_cost_tracking + class PrometheusLogger(CustomLogger): # Class variables or attributes @@ -778,6 +794,8 @@ class PrometheusLogger(CustomLogger): model = kwargs.get("model", "") litellm_params = kwargs.get("litellm_params", {}) or {} _metadata = litellm_params.get("metadata", {}) + get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() + end_user_id = get_end_user_id_for_cost_tracking( litellm_params, service_type="prometheus" ) @@ -1164,6 +1182,8 @@ class PrometheusLogger(CustomLogger): "standard_logging_object", {} ) litellm_params = kwargs.get("litellm_params", {}) or {} + get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() + end_user_id = get_end_user_id_for_cost_tracking( litellm_params, service_type="prometheus" ) @@ -2249,6 +2269,8 @@ def prometheus_label_factory( } if UserAPIKeyLabelNames.END_USER.value in filtered_labels: + get_end_user_id_for_cost_tracking = _get_cached_end_user_id_for_cost_tracking() + filtered_labels["end_user"] = get_end_user_id_for_cost_tracking( litellm_params={"user_api_key_end_user_id": enum_values.end_user}, service_type="prometheus", diff --git a/litellm/integrations/weave/__init__.py b/litellm/integrations/weave/__init__.py new file mode 100644 index 00000000000..49af77b55e8 --- /dev/null +++ b/litellm/integrations/weave/__init__.py @@ -0,0 +1,7 @@ +""" +Weave (W&B) integration for LiteLLM via OpenTelemetry. +""" + +from litellm.integrations.weave.weave_otel import WeaveOtelLogger + +__all__ = ["WeaveOtelLogger"] diff --git a/litellm/integrations/weave/weave_otel.py b/litellm/integrations/weave/weave_otel.py new file mode 100644 index 00000000000..167deaf2cdc --- /dev/null +++ b/litellm/integrations/weave/weave_otel.py @@ -0,0 +1,329 @@ +from __future__ import annotations + +import base64 +import json +import os +from typing import TYPE_CHECKING, Any, Optional + +from opentelemetry.trace import Status, StatusCode +from typing_extensions import override + +from litellm._logging import verbose_logger +from litellm.integrations._types.open_inference import SpanAttributes as OpenInferenceSpanAttributes +from litellm.integrations.arize import _utils +from litellm.integrations.opentelemetry import OpenTelemetry, OpenTelemetryConfig +from litellm.integrations.opentelemetry_utils.base_otel_llm_obs_attributes import ( + BaseLLMObsOTELAttributes, + safe_set_attribute, +) +from litellm.litellm_core_utils.safe_json_dumps import safe_dumps +from litellm.types.integrations.weave_otel import WeaveOtelConfig, WeaveSpanAttributes +from litellm.types.utils import StandardCallbackDynamicParams + +if TYPE_CHECKING: + from opentelemetry.trace import Span + + +# Weave OTEL endpoint +# Multi-tenant cloud: https://trace.wandb.ai/otel/v1/traces +# Dedicated cloud: https://.wandb.io/traces/otel/v1/traces +WEAVE_BASE_URL = "https://trace.wandb.ai" +WEAVE_OTEL_ENDPOINT = "/otel/v1/traces" + + +class WeaveLLMObsOTELAttributes(BaseLLMObsOTELAttributes): + """ + Weave-specific LLM observability OTEL attributes. + + Weave automatically maps attributes from multiple frameworks including + GenAI, OpenInference, Langfuse, and others. + """ + + @staticmethod + @override + def set_messages(span: "Span", kwargs: dict[str, Any]): + """Set input messages as span attributes using OpenInference conventions.""" + + messages = kwargs.get("messages") or [] + optional_params = kwargs.get("optional_params") or {} + + prompt = {"messages": messages} + functions = optional_params.get("functions") + tools = optional_params.get("tools") + if functions is not None: + prompt["functions"] = functions + if tools is not None: + prompt["tools"] = tools + safe_set_attribute(span, OpenInferenceSpanAttributes.INPUT_VALUE, json.dumps(prompt)) + + +def _set_weave_specific_attributes(span: Span, kwargs: dict[str, Any], response_obj: Any): + """ + Sets Weave-specific metadata attributes onto the OTEL span. + + Based on Weave's OTEL attribute mappings from: + https://github.com/wandb/weave/blob/master/weave/trace_server/opentelemetry/constants.py + """ + + # Extract all needed data upfront + litellm_params = kwargs.get("litellm_params") or {} + # optional_params = kwargs.get("optional_params") or {} + metadata = kwargs.get("metadata") or {} + model = kwargs.get("model") or "" + custom_llm_provider = litellm_params.get("custom_llm_provider") or "" + + # Weave supports a custom display name and will default to the model name if not provided. + display_name = metadata.get("display_name") + if not display_name and model: + if custom_llm_provider: + display_name = f"{custom_llm_provider}/{model}" + else: + display_name = model + if display_name: + display_name = display_name.replace("/", "__") + safe_set_attribute(span, WeaveSpanAttributes.DISPLAY_NAME.value, display_name) + + # Weave threads are OpenInference sessions. + if (session_id := metadata.get("session_id")) is not None: + if isinstance(session_id, (list, dict)): + session_id = safe_dumps(session_id) + safe_set_attribute(span, WeaveSpanAttributes.THREAD_ID.value, session_id) + safe_set_attribute(span, WeaveSpanAttributes.IS_TURN.value, True) + + # Response attributes are already set by _utils.set_attributes, + # but we override them here to better match Weave's expectations + if response_obj: + output_dict = None + if hasattr(response_obj, "model_dump"): + output_dict = response_obj.model_dump() + elif hasattr(response_obj, "get"): + output_dict = response_obj + + if output_dict: + safe_set_attribute(span, OpenInferenceSpanAttributes.OUTPUT_VALUE, safe_dumps(output_dict)) + + +def _get_weave_authorization_header(api_key: str) -> str: + """ + Get the authorization header for Weave OpenTelemetry. + + Weave uses Basic auth with format: api: + """ + auth_string = f"api:{api_key}" + auth_header = base64.b64encode(auth_string.encode()).decode() + return f"Basic {auth_header}" + + +def get_weave_otel_config() -> WeaveOtelConfig: + """ + Retrieves the Weave OpenTelemetry configuration based on environment variables. + + Environment Variables: + WANDB_API_KEY: Required. W&B API key for authentication. + WANDB_PROJECT_ID: Required. Project ID in format /. + WANDB_HOST: Optional. Custom Weave host URL. Defaults to cloud endpoint. + + Returns: + WeaveOtelConfig: A Pydantic model containing Weave OTEL configuration. + + Raises: + ValueError: If required environment variables are missing. + """ + api_key = os.getenv("WANDB_API_KEY") + project_id = os.getenv("WANDB_PROJECT_ID") + host = os.getenv("WANDB_HOST") + + if not api_key: + raise ValueError("WANDB_API_KEY must be set for Weave OpenTelemetry integration.") + + if not project_id: + raise ValueError( + "WANDB_PROJECT_ID must be set for Weave OpenTelemetry integration. Format: /" + ) + + if host: + if not host.startswith("http"): + host = "https://" + host + # Self-managed instances use a different path + endpoint = host.rstrip("/") + WEAVE_OTEL_ENDPOINT + verbose_logger.debug(f"Using Weave OTEL endpoint from host: {endpoint}") + else: + endpoint = WEAVE_BASE_URL + WEAVE_OTEL_ENDPOINT + verbose_logger.debug(f"Using Weave cloud endpoint: {endpoint}") + + # Weave uses Basic auth with format: api: + auth_header = _get_weave_authorization_header(api_key=api_key) + otlp_auth_headers = f"Authorization={auth_header},project_id={project_id}" + + # Set standard OTEL environment variables + os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = endpoint + os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = otlp_auth_headers + + return WeaveOtelConfig( + otlp_auth_headers=otlp_auth_headers, + endpoint=endpoint, + project_id=project_id, + protocol="otlp_http", + ) + + +def set_weave_otel_attributes(span: Span, kwargs: dict[str, Any], response_obj: Any): + """ + Sets OpenTelemetry span attributes for Weave observability. + Uses the same attribute setting logic as other OTEL integrations for consistency. + """ + _utils.set_attributes(span, kwargs, response_obj, WeaveLLMObsOTELAttributes) + _set_weave_specific_attributes(span=span, kwargs=kwargs, response_obj=response_obj) + + +class WeaveOtelLogger(OpenTelemetry): + """ + Weave (W&B) OpenTelemetry Logger for LiteLLM. + + Sends LLM traces to Weave via the OpenTelemetry Protocol (OTLP). + + Environment Variables: + WANDB_API_KEY: Required. Weights & Biases API key for authentication. + WANDB_PROJECT_ID: Required. Project ID in format /. + WANDB_HOST: Optional. Custom Weave host URL. Defaults to cloud endpoint. + + Usage: + litellm.callbacks = ["weave_otel"] + + Or manually: + from litellm.integrations.weave.weave_otel import WeaveOtelLogger + weave_logger = WeaveOtelLogger(callback_name="weave_otel") + litellm.callbacks = [weave_logger] + + Reference: + https://docs.wandb.ai/weave/guides/tracking/otel + """ + + def __init__( + self, + config: Optional[OpenTelemetryConfig] = None, + callback_name: Optional[str] = "weave_otel", + **kwargs, + ): + """ + Initialize WeaveOtelLogger. + + If config is not provided, automatically configures from environment variables + (WANDB_API_KEY, WANDB_PROJECT_ID, WANDB_HOST) via get_weave_otel_config(). + """ + if config is None: + # Auto-configure from Weave environment variables + weave_config = get_weave_otel_config() + + config = OpenTelemetryConfig( + exporter=weave_config.protocol, + endpoint=weave_config.endpoint, + headers=weave_config.otlp_auth_headers, + ) + + super().__init__(config=config, callback_name=callback_name, **kwargs) + + def _maybe_log_raw_request(self, kwargs, response_obj, start_time, end_time, parent_span): + """ + Override to skip creating the raw_gen_ai_request child span. + + For Weave, we only want a single span per LLM call. The parent span + already contains all the necessary attributes, so the child span + is redundant. + """ + pass + + def _start_primary_span( + self, + kwargs, + response_obj, + start_time, + end_time, + context, + parent_span=None, + ): + """ + Override to always create a child span instead of reusing the parent span. + + This ensures that wrapper spans (like "B", "C", "D", "E") remain separate + from the LiteLLM LLM call spans, creating proper nesting in Weave. + """ + + otel_tracer = self.get_tracer_to_use_for_request(kwargs) + # Always create a new child span, even if parent_span is provided + # This ensures wrapper spans remain separate from LLM call spans + span = otel_tracer.start_span( + name=self._get_span_name(kwargs), + start_time=self._to_ns(start_time), + context=context, + ) + span.set_status(Status(StatusCode.OK)) + self.set_attributes(span, kwargs, response_obj) + span.end(end_time=self._to_ns(end_time)) + return span + + def _handle_success(self, kwargs, response_obj, start_time, end_time): + """ + Override to prevent ending externally created parent spans. + + When wrapper spans (like "B", "C", "D", "E") are provided as parent spans, + they should be managed by the user code, not ended by LiteLLM. + """ + + verbose_logger.debug( + "Weave OpenTelemetry Logger: Logging kwargs: %s, OTEL config settings=%s", + kwargs, + self.config, + ) + ctx, parent_span = self._get_span_context(kwargs) + + # Always create a child span (handled by _start_primary_span override) + primary_span_parent = None + + # 1. Primary span + span = self._start_primary_span(kwargs, response_obj, start_time, end_time, ctx, primary_span_parent) + + # 2. Raw-request sub-span (skipped for Weave via _maybe_log_raw_request override) + self._maybe_log_raw_request(kwargs, response_obj, start_time, end_time, span) + + # 3. Guardrail span + self._create_guardrail_span(kwargs=kwargs, context=ctx) + + # 4. Metrics & cost recording + self._record_metrics(kwargs, response_obj, start_time, end_time) + + # 5. Semantic logs. + if self.config.enable_events: + self._emit_semantic_logs(kwargs, response_obj, span) + + # 6. Don't end parent span - it's managed by user code + # Since we always create a child span (never reuse parent), the parent span + # lifecycle is owned by the user. This prevents double-ending of wrapper spans + # like "B", "C", "D", "E" that users create and manage themselves. + + def construct_dynamic_otel_headers( + self, standard_callback_dynamic_params: StandardCallbackDynamicParams + ) -> dict | None: + """ + Construct dynamic Weave headers from standard callback dynamic params. + + This is used for team/key based logging. + + Returns: + dict: A dictionary of dynamic Weave headers + """ + dynamic_headers = {} + + dynamic_wandb_api_key = standard_callback_dynamic_params.get("wandb_api_key") + dynamic_weave_project_id = standard_callback_dynamic_params.get("weave_project_id") + + if dynamic_wandb_api_key: + auth_header = _get_weave_authorization_header( + api_key=dynamic_wandb_api_key, + ) + dynamic_headers["Authorization"] = auth_header + + if dynamic_weave_project_id: + dynamic_headers["project_id"] = dynamic_weave_project_id + + return dynamic_headers if dynamic_headers else None diff --git a/litellm/litellm_core_utils/README.md b/litellm/litellm_core_utils/README.md index 6494041291b..b61c8982762 100644 --- a/litellm/litellm_core_utils/README.md +++ b/litellm/litellm_core_utils/README.md @@ -9,4 +9,5 @@ Core files: - `default_encoding.py`: code for loading the default encoding (tiktoken) - `get_llm_provider_logic.py`: code for inferring the LLM provider from a given model name. - `duration_parser.py`: code for parsing durations - e.g. "1d", "1mo", "10s" +- `api_route_to_call_types.py`: mapping of API routes to their corresponding CallTypes (e.g., `/chat/completions` -> [acompletion, completion]) diff --git a/litellm/litellm_core_utils/api_route_to_call_types.py b/litellm/litellm_core_utils/api_route_to_call_types.py new file mode 100644 index 00000000000..35f83de1dd7 --- /dev/null +++ b/litellm/litellm_core_utils/api_route_to_call_types.py @@ -0,0 +1,38 @@ +""" +Dictionary mapping API routes to their corresponding CallTypes in LiteLLM. + +This dictionary maps each API endpoint to the CallTypes that can be used for that route. +Each route can have both async (prefixed with 'a') and sync call types. +""" + +from litellm.types.utils import API_ROUTE_TO_CALL_TYPES, CallTypes + + +def get_call_types_for_route(route: str) -> list: + """ + Get the list of CallTypes for a given API route. + + Args: + route: API route path (e.g., "/chat/completions") + + Returns: + List of CallTypes for that route, or empty list if route not found + """ + return API_ROUTE_TO_CALL_TYPES.get(route, []) + + +def get_routes_for_call_type(call_type: CallTypes) -> list: + """ + Get all routes that use a specific CallType. + + Args: + call_type: The CallType to search for + + Returns: + List of routes that use this CallType + """ + routes = [] + for route, types in API_ROUTE_TO_CALL_TYPES.items(): + if call_type in types: + routes.append(route) + return routes diff --git a/litellm/litellm_core_utils/custom_logger_registry.py b/litellm/litellm_core_utils/custom_logger_registry.py index 2e996f42fde..a6b916d3800 100644 --- a/litellm/litellm_core_utils/custom_logger_registry.py +++ b/litellm/litellm_core_utils/custom_logger_registry.py @@ -76,6 +76,7 @@ class CustomLoggerRegistry: "langfuse_otel": OpenTelemetry, "arize_phoenix": OpenTelemetry, "langtrace": OpenTelemetry, + "weave_otel": OpenTelemetry, "mlflow": MlflowLogger, "langfuse": LangfusePromptManagement, "otel": OpenTelemetry, @@ -97,9 +98,6 @@ class CustomLoggerRegistry: } try: - from litellm_enterprise.enterprise_callbacks.generic_api_callback import ( - GenericAPILogger, - ) from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( PagerDutyAlerting, ) @@ -110,6 +108,10 @@ class CustomLoggerRegistry: SMTPEmailLogger, ) + from litellm.integrations.generic_api.generic_api_callback import ( + GenericAPILogger, + ) + enterprise_loggers = { "pagerduty": PagerDutyAlerting, "generic_api": GenericAPILogger, diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index f8c786daef3..056522e2a64 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -77,6 +77,7 @@ class ExceptionCheckers: "model's maximum context limit", "is longer than the model's context length", "input tokens exceed the configured limit", + "`inputs` tokens + `max_new_tokens` must be", ] for substring in known_exception_substrings: if substring in _error_str_lowercase: diff --git a/litellm/litellm_core_utils/get_litellm_params.py b/litellm/litellm_core_utils/get_litellm_params.py index 5279cb26b69..4dcc08e284e 100644 --- a/litellm/litellm_core_utils/get_litellm_params.py +++ b/litellm/litellm_core_utils/get_litellm_params.py @@ -118,6 +118,10 @@ def get_litellm_params( "bucket_name": kwargs.get("bucket_name"), "vertex_credentials": kwargs.get("vertex_credentials"), "vertex_project": kwargs.get("vertex_project"), + "vertex_location": kwargs.get("vertex_location"), + "vertex_ai_project": kwargs.get("vertex_ai_project"), + "vertex_ai_location": kwargs.get("vertex_ai_location"), + "vertex_ai_credentials": kwargs.get("vertex_ai_credentials"), "use_litellm_proxy": use_litellm_proxy, "litellm_request_debug": litellm_request_debug, "aws_region_name": kwargs.get("aws_region_name"), diff --git a/litellm/litellm_core_utils/get_llm_provider_logic.py b/litellm/litellm_core_utils/get_llm_provider_logic.py index eefe680217d..a90d16dba49 100644 --- a/litellm/litellm_core_utils/get_llm_provider_logic.py +++ b/litellm/litellm_core_utils/get_llm_provider_logic.py @@ -22,6 +22,18 @@ def _is_non_openai_azure_model(model: str) -> bool: return False +def _is_azure_claude_model(model: str) -> bool: + """ + Check if a model name contains 'claude' (case-insensitive). + Used to detect Claude models that need Anthropic-specific handling. + """ + try: + model_lower = model.lower() + return "claude" in model_lower or model_lower.startswith("claude") + except Exception: + return False + + def handle_cohere_chat_model_custom_llm_provider( model: str, custom_llm_provider: Optional[str] = None ) -> Tuple[str, Optional[str]]: @@ -240,6 +252,9 @@ def get_llm_provider( # noqa: PLR0915 elif endpoint == "api.moonshot.ai/v1": custom_llm_provider = "moonshot" dynamic_api_key = get_secret_str("MOONSHOT_API_KEY") + elif endpoint == "platform.publicai.co/v1": + custom_llm_provider = "publicai" + dynamic_api_key = get_secret_str("PUBLICAI_API_KEY") elif endpoint == "https://api.v0.dev/v1": custom_llm_provider = "v0" dynamic_api_key = get_secret_str("V0_API_KEY") @@ -647,6 +662,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.XAIChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "zai": + api_base = ( + api_base + or get_secret_str("ZAI_API_BASE") + or "https://api.z.ai/api/paas/v4" + ) + dynamic_api_key = api_key or get_secret_str("ZAI_API_KEY") elif custom_llm_provider == "together_ai": api_base = ( api_base @@ -741,6 +763,13 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.MoonshotChatConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "publicai": + ( + api_base, + dynamic_api_key, + ) = litellm.PublicAIChatConfig()._get_openai_compatible_provider_info( + api_base, api_key + ) elif custom_llm_provider == "docker_model_runner": ( api_base, @@ -811,6 +840,16 @@ def _get_openai_compatible_provider_info( # noqa: PLR0915 ) = litellm.ClarifaiConfig()._get_openai_compatible_provider_info( api_base, api_key ) + elif custom_llm_provider == "ragflow": + full_model = f"ragflow/{model}" + ( + api_base, + dynamic_api_key, + _, + ) = litellm.RAGFlowConfig()._get_openai_compatible_provider_info( + full_model, api_base, api_key, "ragflow" + ) + model = full_model if api_base is not None and not isinstance(api_base, str): raise Exception("api base needs to be a string. api_base={}".format(api_base)) diff --git a/litellm/litellm_core_utils/get_supported_openai_params.py b/litellm/litellm_core_utils/get_supported_openai_params.py index 06e650f938d..19b52d2dace 100644 --- a/litellm/litellm_core_utils/get_supported_openai_params.py +++ b/litellm/litellm_core_utils/get_supported_openai_params.py @@ -266,6 +266,15 @@ def get_supported_openai_params( # noqa: PLR0915 model=model ) ) + elif custom_llm_provider == "ovhcloud": + if request_type == "transcription": + from litellm.llms.ovhcloud.audio_transcription.transformation import ( + OVHCloudAudioTranscriptionConfig, + ) + + return OVHCloudAudioTranscriptionConfig().get_supported_openai_params( + model=model + ) elif custom_llm_provider == "elevenlabs": if request_type == "transcription": from litellm.llms.elevenlabs.audio_transcription.transformation import ( diff --git a/litellm/litellm_core_utils/litellm_logging.py b/litellm/litellm_core_utils/litellm_logging.py index 4218cd6c903..cdb5b05a2ab 100644 --- a/litellm/litellm_core_utils/litellm_logging.py +++ b/litellm/litellm_core_utils/litellm_logging.py @@ -58,7 +58,6 @@ from litellm.integrations.custom_guardrail import CustomGuardrail from litellm.integrations.custom_logger import CustomLogger from litellm.integrations.deepeval.deepeval import DeepEvalLogger from litellm.integrations.mlflow import MlflowLogger -from litellm.integrations.prometheus import PrometheusLogger from litellm.integrations.sqs import SQSLogger from litellm.litellm_core_utils.get_litellm_params import get_litellm_params from litellm.litellm_core_utils.llm_cost_calc.tool_call_cost_tracking import ( @@ -70,7 +69,9 @@ from litellm.litellm_core_utils.redact_messages import ( redact_message_input_output_from_logging, ) from litellm.llms.base_llm.ocr.transformation import OCRResponse +from litellm.llms.base_llm.search.transformation import SearchResponse from litellm.responses.utils import ResponseAPILoggingUtils +from litellm.types.agents import LiteLLMSendMessageResponse from litellm.types.containers.main import ContainerObject from litellm.types.llms.openai import ( AllMessageValues, @@ -165,9 +166,6 @@ try: from litellm_enterprise.enterprise_callbacks.callback_controls import ( EnterpriseCallbackControls, ) - from litellm_enterprise.enterprise_callbacks.generic_api_callback import ( - GenericAPILogger, - ) from litellm_enterprise.enterprise_callbacks.pagerduty.pagerduty import ( PagerDutyAlerting, ) @@ -181,6 +179,8 @@ try: StandardLoggingPayloadSetup as EnterpriseStandardLoggingPayloadSetup, ) + from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger + EnterpriseStandardLoggingPayloadSetupVAR: Optional[ Type[EnterpriseStandardLoggingPayloadSetup] ] = EnterpriseStandardLoggingPayloadSetup @@ -247,6 +247,23 @@ class ServiceTraceIDCache: in_memory_trace_id_cache = ServiceTraceIDCache() in_memory_dynamic_logger_cache = DynamicLoggingCache() +# Cached lazy import for PrometheusLogger +# Module-level cache to avoid repeated imports while preserving memory benefits +_PrometheusLogger = None + + +def _get_cached_prometheus_logger(): + """ + Get cached PrometheusLogger class. + Lazy imports on first call to avoid loading prometheus.py and utils.py at import time (60MB saved). + Subsequent calls use cached class for better performance. + """ + global _PrometheusLogger + if _PrometheusLogger is None: + from litellm.integrations.prometheus import PrometheusLogger + _PrometheusLogger = PrometheusLogger + return _PrometheusLogger + class Logging(LiteLLMLoggingBaseClass): global supabaseClient, promptLayerLogger, weightsBiasesLogger, logfireLogger, capture_exception, add_breadcrumb, lunaryLogger, logfireLogger, prometheusLogger, slack_app @@ -298,6 +315,7 @@ class Logging(LiteLLMLoggingBaseClass): for m in messages: new_messages.append({"role": "user", "content": m}) messages = new_messages + self.model = model self.messages = copy.deepcopy(messages) self.stream = stream @@ -357,6 +375,9 @@ class Logging(LiteLLMLoggingBaseClass): # Init Caching related details self.caching_details: Optional[CachingDetails] = None + # Passthrough endpoint guardrails config for field targeting + self.passthrough_guardrails_config: Optional[Dict[str, Any]] = None + self.model_call_details: Dict[str, Any] = { "litellm_trace_id": litellm_trace_id, "litellm_call_id": litellm_call_id, @@ -1298,6 +1319,7 @@ class Logging(LiteLLMLoggingBaseClass): OpenAIFileObject, LiteLLMRealtimeStreamLoggingObject, OpenAIModerationResponse, + "SearchResponse", ], cache_hit: Optional[bool] = None, litellm_model_name: Optional[str] = None, @@ -1710,10 +1732,14 @@ class Logging(LiteLLMLoggingBaseClass): or isinstance(logging_result, LiteLLMRealtimeStreamLoggingObject) or isinstance(logging_result, OpenAIModerationResponse) or isinstance(logging_result, OCRResponse) # OCR + or isinstance(logging_result, SearchResponse) # Search API or isinstance(logging_result, dict) and logging_result.get("object") == "vector_store.search_results.page" + or isinstance(logging_result, dict) + and logging_result.get("object") == "search" # Search API (dict format) or isinstance(logging_result, VideoObject) or isinstance(logging_result, ContainerObject) + or isinstance(logging_result, LiteLLMSendMessageResponse) # A2A or (self.call_type == CallTypes.call_mcp_tool.value) ): return True @@ -3457,6 +3483,8 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 _in_memory_loggers.append(_literalai_logger) return _literalai_logger # type: ignore elif logging_integration == "prometheus": + PrometheusLogger = _get_cached_prometheus_logger() + for callback in _in_memory_loggers: if isinstance(callback, PrometheusLogger): return callback # type: ignore @@ -3591,15 +3619,15 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if ( - isinstance(callback, OpenTelemetry) + isinstance(callback, ArizePhoenixLogger) and callback.callback_name == "arize_phoenix" ): return callback # type: ignore - _otel_logger = OpenTelemetry( + _arize_phoenix_otel_logger = ArizePhoenixLogger( config=otel_config, callback_name="arize_phoenix" ) - _in_memory_loggers.append(_otel_logger) - return _otel_logger # type: ignore + _in_memory_loggers.append(_arize_phoenix_otel_logger) + return _arize_phoenix_otel_logger # type: ignore elif logging_integration == "otel": from litellm.integrations.opentelemetry import OpenTelemetry @@ -3774,6 +3802,31 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915 ) _in_memory_loggers.append(_otel_logger) return _otel_logger # type: ignore + elif logging_integration == "weave_otel": + from litellm.integrations.opentelemetry import ( + OpenTelemetryConfig, + ) + from litellm.integrations.weave.weave_otel import WeaveOtelLogger, get_weave_otel_config + + weave_otel_config = get_weave_otel_config() + + otel_config = OpenTelemetryConfig( + exporter=weave_otel_config.protocol, + endpoint=weave_otel_config.endpoint, + headers=weave_otel_config.otlp_auth_headers, + ) + + for callback in _in_memory_loggers: + if ( + isinstance(callback, WeaveOtelLogger) + and callback.callback_name == "weave_otel" + ): + return callback # type: ignore + _otel_logger = WeaveOtelLogger( + config=otel_config, callback_name="weave_otel" + ) + _in_memory_loggers.append(_otel_logger) + return _otel_logger # type: ignore elif logging_integration == "pagerduty": for callback in _in_memory_loggers: if isinstance(callback, PagerDutyAlerting): @@ -3943,7 +3996,8 @@ def get_custom_logger_compatible_class( # noqa: PLR0915 for callback in _in_memory_loggers: if isinstance(callback, LiteralAILogger): return callback - elif logging_integration == "prometheus" and PrometheusLogger is not None: + elif logging_integration == "prometheus": + PrometheusLogger = _get_cached_prometheus_logger() for callback in _in_memory_loggers: if isinstance(callback, PrometheusLogger): return callback @@ -4097,10 +4151,8 @@ def _get_custom_logger_settings_from_proxy_server(callback_name: str) -> Dict: otel: message_logging: False """ - from litellm.proxy.proxy_server import callback_settings - - if callback_settings: - return dict(callback_settings.get(callback_name, {})) + if litellm.callback_settings: + return dict(litellm.callback_settings.get(callback_name, {})) return {} @@ -4177,6 +4229,39 @@ class StandardLoggingPayloadSetup: return start_time_float, end_time_float, completion_start_time_float + @staticmethod + def append_system_prompt_messages( + kwargs: Optional[Dict] = None, messages: Optional[Any] = None + ): + """ + Append system prompt messages to the messages + """ + if kwargs is not None: + if kwargs.get("system") is not None and isinstance( + kwargs.get("system"), str + ): + if messages is None: + return [{"role": "system", "content": kwargs.get("system")}] + elif isinstance(messages, list): + if len(messages) == 0: + return [{"role": "system", "content": kwargs.get("system")}] + # check for duplicates + if messages[0].get("role") == "system" and messages[0].get( + "content" + ) == kwargs.get("system"): + return messages + messages = [ + {"role": "system", "content": kwargs.get("system")} + ] + messages + elif isinstance(messages, str): + messages = [ + {"role": "system", "content": kwargs.get("system")}, + {"role": "user", "content": messages}, + ] + return messages + + return messages + @staticmethod def get_standard_logging_metadata( metadata: Optional[Dict[str, Any]], @@ -4899,7 +4984,9 @@ def get_standard_logging_object_payload( model_group=_model_group, model_id=_model_id, requester_ip_address=clean_metadata.get("requester_ip_address", None), - messages=kwargs.get("messages"), + messages=StandardLoggingPayloadSetup.append_system_prompt_messages( + kwargs=kwargs, messages=kwargs.get("messages") + ), response=final_response_obj, model_parameters=ModelParamHelper.get_standard_logging_model_parameters( kwargs.get("optional_params", None) or {} diff --git a/litellm/litellm_core_utils/logging_callback_manager.py b/litellm/litellm_core_utils/logging_callback_manager.py index 9ec346c20a1..349cb6f3ceb 100644 --- a/litellm/litellm_core_utils/logging_callback_manager.py +++ b/litellm/litellm_core_utils/logging_callback_manager.py @@ -1,9 +1,10 @@ -from typing import TYPE_CHECKING, Callable, List, Optional, Set, Type, Union +from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Set, Type, Union import litellm from litellm._logging import verbose_logger from litellm.integrations.additional_logging_utils import AdditionalLoggingUtils from litellm.integrations.custom_logger import CustomLogger +from litellm.integrations.generic_api.generic_api_callback import GenericAPILogger from litellm.types.utils import CallbacksByType if TYPE_CHECKING: @@ -11,6 +12,8 @@ if TYPE_CHECKING: else: _custom_logger_compatible_callbacks_literal = str +_generic_api_logger_cache: Dict[str, GenericAPILogger] = {} + class LoggingCallbackManager: """ @@ -138,6 +141,57 @@ class LoggingCallbackManager: return False return True + @staticmethod + def _add_custom_callback_generic_api_str( + callback: str, + ) -> Union[GenericAPILogger, str]: + """ + litellm_settings: + success_callback: ["custom_callback_name"] + + callback_settings: + custom_callback_name: + callback_type: generic_api + endpoint: https://webhook-test.com/30343bc33591bc5e6dc44217ceae3e0a + headers: + Authorization: Bearer sk-1234 + """ + callback_config = litellm.callback_settings.get(callback) + + if not isinstance(callback_config, dict): + return callback + + if callback_config.get("callback_type") != "generic_api": + return callback + + endpoint = callback_config.get("endpoint") + headers = callback_config.get("headers") + event_types = callback_config.get("event_types") + + if endpoint is None or headers is None: + verbose_logger.warning( + "generic_api callback '%s' is missing endpoint or headers, skipping.", + callback, + ) + return callback + + cached_logger = _generic_api_logger_cache.get(callback) + if ( + isinstance(cached_logger, GenericAPILogger) + and cached_logger.endpoint == endpoint + and cached_logger.headers == headers + and cached_logger.event_types == event_types + ): + return cached_logger + + new_logger = GenericAPILogger( + endpoint=endpoint, + headers=headers, + event_types=event_types, + ) + _generic_api_logger_cache[callback] = new_logger + return new_logger + def _safe_add_callback_to_list( self, callback: Union[CustomLogger, Callable, str], @@ -152,15 +206,24 @@ class LoggingCallbackManager: if not self._check_callback_list_size(parent_list): return + # Check if the callback is a custom callback + + if isinstance(callback, str): + callback = LoggingCallbackManager._add_custom_callback_generic_api_str( + callback + ) + if isinstance(callback, str): self._add_string_callback_to_list( callback=callback, parent_list=parent_list ) elif isinstance(callback, CustomLogger): + self._add_custom_logger_to_list( custom_logger=callback, parent_list=parent_list, ) + elif callable(callback): self._add_callback_function_to_list( callback=callback, parent_list=parent_list @@ -348,7 +411,6 @@ class LoggingCallbackManager: elif callable(callback): return getattr(callback, "__name__", str(callback)) return str(callback) - def get_active_custom_logger_for_callback_name( self, @@ -362,12 +424,16 @@ class LoggingCallbackManager: ) # get the custom logger class type - custom_logger_class_type = CustomLoggerRegistry.get_class_type_for_custom_logger_name(callback_name) + custom_logger_class_type = ( + CustomLoggerRegistry.get_class_type_for_custom_logger_name(callback_name) + ) # get the active custom logger custom_logger = self.get_custom_loggers_for_type(custom_logger_class_type) if len(custom_logger) == 0: - raise ValueError(f"No active custom logger found for callback name: {callback_name}") + raise ValueError( + f"No active custom logger found for callback name: {callback_name}" + ) return custom_logger[0] diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py index 262692d6d1a..04c8c235557 100644 --- a/litellm/litellm_core_utils/prompt_templates/factory.py +++ b/litellm/litellm_core_utils/prompt_templates/factory.py @@ -3446,8 +3446,25 @@ class BedrockConverseMessagesProcessor: @staticmethod def _initial_message_setup( messages: List, + model: str, + llm_provider: str, user_continue_message: Optional[ChatCompletionUserMessage] = None, ) -> List: + # gracefully handle base case of no messages at all + if len(messages) == 0: + if user_continue_message is not None: + messages.append(user_continue_message) + elif litellm.modify_params: + messages.append(DEFAULT_USER_CONTINUE_MESSAGE) + else: + raise litellm.BadRequestError( + message=BAD_MESSAGE_ERROR_STR + + "bedrock requires at least one non-system message", + model=model, + llm_provider=llm_provider, + ) + + # if initial message is assistant message if messages[0].get("role") is not None and messages[0]["role"] == "assistant": if user_continue_message is not None: messages.insert(0, user_continue_message) @@ -3475,18 +3492,8 @@ class BedrockConverseMessagesProcessor: contents: List[BedrockMessageBlock] = [] msg_i = 0 - ## BASE CASE ## - if len(messages) == 0: - raise litellm.BadRequestError( - message=BAD_MESSAGE_ERROR_STR - + "bedrock requires at least one non-system message", - model=model, - llm_provider=llm_provider, - ) - - # if initial message is assistant message messages = BedrockConverseMessagesProcessor._initial_message_setup( - messages, user_continue_message + messages, model, llm_provider, user_continue_message ) while msg_i < len(messages): @@ -3847,28 +3854,9 @@ def _bedrock_converse_messages_pt( # noqa: PLR0915 contents: List[BedrockMessageBlock] = [] msg_i = 0 - ## BASE CASE ## - if len(messages) == 0: - raise litellm.BadRequestError( - message=BAD_MESSAGE_ERROR_STR - + "bedrock requires at least one non-system message", - model=model, - llm_provider=llm_provider, - ) - - # if initial message is assistant message - if messages[0].get("role") is not None and messages[0]["role"] == "assistant": - if user_continue_message is not None: - messages.insert(0, user_continue_message) - elif litellm.modify_params: - messages.insert(0, DEFAULT_USER_CONTINUE_MESSAGE) - - # if final message is assistant message - if messages[-1].get("role") is not None and messages[-1]["role"] == "assistant": - if user_continue_message is not None: - messages.append(user_continue_message) - elif litellm.modify_params: - messages.append(DEFAULT_USER_CONTINUE_MESSAGE) + messages = BedrockConverseMessagesProcessor._initial_message_setup( + messages, model, llm_provider, user_continue_message + ) while msg_i < len(messages): user_content: List[BedrockContentBlock] = [] diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py index ddcf81b5ba5..c332e5f88f7 100644 --- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py +++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py @@ -18,6 +18,7 @@ from litellm.types.utils import ( ModelResponseStream, PromptTokensDetailsWrapper, Usage, + ServerToolUse ) from litellm.utils import print_verbose, token_counter @@ -418,7 +419,8 @@ class ChunkProcessor: ## anthropic prompt caching information ## cache_creation_input_tokens: Optional[int] = None cache_read_input_tokens: Optional[int] = None - + + server_tool_use: Optional[ServerToolUse] = None web_search_requests: Optional[int] = None completion_tokens_details: Optional[CompletionTokensDetails] = None prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None @@ -462,6 +464,8 @@ class ChunkProcessor: completion_tokens_details = usage_chunk_dict[ "completion_tokens_details" ] + if hasattr(usage_chunk, 'server_tool_use') and usage_chunk.server_tool_use is not None: + server_tool_use = usage_chunk.server_tool_use if ( usage_chunk_dict["prompt_tokens_details"] is not None and getattr( @@ -483,6 +487,7 @@ class ChunkProcessor: completion_tokens=completion_tokens, cache_creation_input_tokens=cache_creation_input_tokens, cache_read_input_tokens=cache_read_input_tokens, + server_tool_use=server_tool_use, web_search_requests=web_search_requests, completion_tokens_details=completion_tokens_details, prompt_tokens_details=prompt_tokens_details, @@ -513,6 +518,9 @@ class ChunkProcessor: "cache_read_input_tokens" ] + server_tool_use: Optional[ServerToolUse] = calculated_usage_per_chunk[ + "server_tool_use" + ] web_search_requests: Optional[int] = calculated_usage_per_chunk[ "web_search_requests" ] @@ -576,6 +584,8 @@ class ChunkProcessor: if prompt_tokens_details is not None: returned_usage.prompt_tokens_details = prompt_tokens_details + if server_tool_use is not None: + returned_usage.server_tool_use = server_tool_use if web_search_requests is not None: if returned_usage.prompt_tokens_details is None: returned_usage.prompt_tokens_details = PromptTokensDetailsWrapper( diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index 4d8e109d882..a7f460fab59 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -96,9 +96,9 @@ class CustomStreamWrapper: self.system_fingerprint: Optional[str] = None self.received_finish_reason: Optional[str] = None - self.intermittent_finish_reason: Optional[str] = ( - None # finish reasons that show up mid-stream - ) + self.intermittent_finish_reason: Optional[ + str + ] = None # finish reasons that show up mid-stream self.special_tokens = [ "<|assistant|>", "<|system|>", @@ -735,7 +735,7 @@ class CustomStreamWrapper: and completion_obj["function_call"] is not None ) or ( - "tool_calls" in model_response.choices[0].delta + "tool_calls" in model_response.choices[0].delta and model_response.choices[0].delta["tool_calls"] is not None ) or ( @@ -889,7 +889,6 @@ class CustomStreamWrapper: ## check if openai/azure chunk original_chunk = response_obj.get("original_chunk", None) if original_chunk: - if len(original_chunk.choices) > 0: choices = [] for choice in original_chunk.choices: @@ -906,7 +905,6 @@ class CustomStreamWrapper: print_verbose(f"choices in streaming: {choices}") setattr(model_response, "choices", choices) else: - return model_response.system_fingerprint = ( original_chunk.system_fingerprint @@ -1435,9 +1433,9 @@ class CustomStreamWrapper: _json_delta = delta.model_dump() print_verbose(f"_json_delta: {_json_delta}") if "role" not in _json_delta or _json_delta["role"] is None: - _json_delta["role"] = ( - "assistant" # mistral's api returns role as None - ) + _json_delta[ + "role" + ] = "assistant" # mistral's api returns role as None if "tool_calls" in _json_delta and isinstance( _json_delta["tool_calls"], list ): @@ -1533,7 +1531,7 @@ class CustomStreamWrapper: async def _call_post_streaming_deployment_hook(self, chunk): """ Call the post-call streaming deployment hook for callbacks. - + This allows callbacks to modify streaming chunks before they're returned. """ try: @@ -1544,15 +1542,17 @@ class CustomStreamWrapper: # Get request kwargs from logging object request_data = self.logging_obj.model_call_details call_type_str = self.logging_obj.call_type - + try: typed_call_type = CallTypes(call_type_str) except ValueError: typed_call_type = None - + # Call hooks for all callbacks for callback in litellm.callbacks: - if isinstance(callback, CustomLogger) and hasattr(callback, "async_post_call_streaming_deployment_hook"): + if isinstance(callback, CustomLogger) and hasattr( + callback, "async_post_call_streaming_deployment_hook" + ): result = await callback.async_post_call_streaming_deployment_hook( request_data=request_data, response_chunk=chunk, @@ -1560,11 +1560,14 @@ class CustomStreamWrapper: ) if result is not None: chunk = result - + return chunk except Exception as e: from litellm._logging import verbose_logger - verbose_logger.exception(f"Error in post-call streaming deployment hook: {str(e)}") + + verbose_logger.exception( + f"Error in post-call streaming deployment hook: {str(e)}" + ) return chunk def cache_streaming_response(self, processed_chunk, cache_hit: bool): @@ -1687,7 +1690,7 @@ class CustomStreamWrapper: response, "usage" ): # remove usage from chunk, only send on final chunk # Convert the object to a dictionary - obj_dict = response.dict() + obj_dict = response.model_dump() # Remove an attribute (e.g., 'attr2') if "usage" in obj_dict: @@ -1852,7 +1855,7 @@ class CustomStreamWrapper: processed_chunk, "usage" ): # remove usage from chunk, only send on final chunk # Convert the object to a dictionary - obj_dict = processed_chunk.dict() + obj_dict = processed_chunk.model_dump() # Remove an attribute (e.g., 'attr2') if "usage" in obj_dict: @@ -1872,11 +1875,15 @@ class CustomStreamWrapper: if self.sent_last_chunk is True and self.stream_options is None: usage = calculate_total_usage(chunks=self.chunks) processed_chunk._hidden_params["usage"] = usage - + # Call post-call streaming deployment hook for final chunk if self.sent_last_chunk is True: - processed_chunk = await self._call_post_streaming_deployment_hook(processed_chunk) - + processed_chunk = ( + await self._call_post_streaming_deployment_hook( + processed_chunk + ) + ) + return processed_chunk raise StopAsyncIteration else: # temporary patch for non-aiohttp async calls @@ -1890,9 +1897,9 @@ class CustomStreamWrapper: chunk = next(self.completion_stream) if chunk is not None and chunk != b"": print_verbose(f"PROCESSED CHUNK PRE CHUNK CREATOR: {chunk}") - processed_chunk: Optional[ModelResponseStream] = ( - self.chunk_creator(chunk=chunk) - ) + processed_chunk: Optional[ + ModelResponseStream + ] = self.chunk_creator(chunk=chunk) print_verbose( f"PROCESSED CHUNK POST CHUNK CREATOR: {processed_chunk}" ) diff --git a/litellm/llms/aiml/image_generation/transformation.py b/litellm/llms/aiml/image_generation/transformation.py index 006a2c16d7e..d8f3e23fe7e 100644 --- a/litellm/llms/aiml/image_generation/transformation.py +++ b/litellm/llms/aiml/image_generation/transformation.py @@ -97,6 +97,9 @@ class AimlImageGenerationConfig(BaseImageGenerationConfig): ) complete_url = complete_url.rstrip("/") + # Strip /v1 suffix if present since IMAGE_GENERATION_ENDPOINT already includes v1 + if complete_url.endswith("/v1"): + complete_url = complete_url[:-3] complete_url = f"{complete_url}/{self.IMAGE_GENERATION_ENDPOINT}" return complete_url diff --git a/litellm/llms/anthropic/chat/guardrail_translation/handler.py b/litellm/llms/anthropic/chat/guardrail_translation/handler.py index 06a1b92e1b0..9b6511f151c 100644 --- a/litellm/llms/anthropic/chat/guardrail_translation/handler.py +++ b/litellm/llms/anthropic/chat/guardrail_translation/handler.py @@ -12,11 +12,17 @@ Pattern Overview: 4. Apply guardrail responses back to the original structure """ -import asyncio -from typing import TYPE_CHECKING, Any, Coroutine, Dict, List, Optional, Tuple, cast +import json +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, cast from litellm._logging import verbose_proxy_logger +from litellm.llms.anthropic.experimental_pass_through.adapters.transformation import ( + LiteLLMAnthropicMessagesAdapter, +) from litellm.llms.base_llm.guardrail_translation.base_translation import BaseTranslation +from litellm.types.guardrails import GenericGuardrailAPIInputs +from litellm.types.llms.anthropic import AllAnthropicToolsValues +from litellm.types.llms.openai import ChatCompletionToolParam if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail @@ -37,42 +43,69 @@ class AnthropicMessagesHandler(BaseTranslation): Methods can be overridden to customize behavior for different message formats. """ + def __init__(self): + super().__init__() + self.adapter = LiteLLMAnthropicMessagesAdapter() + async def process_input_messages( self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, ) -> Any: """ Process input messages by applying guardrails to text content. """ messages = data.get("messages") + tools = data.get("tools", None) if messages is None: return data - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] + tools_to_check: List[ChatCompletionToolParam] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (message_index, content_index) for each task + # Track (message_index, content_index) for each text # content_index is None for string content, int for list content - # Step 1: Extract all text content and create guardrail tasks + # Step 1: Extract all text content and images for msg_idx, message in enumerate(messages): - await self._extract_input_text_and_create_tasks( + self._extract_input_text_and_images( message=message, msg_idx=msg_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - responses = await asyncio.gather(*tasks) + if tools is not None: + self._extract_input_tools( + tools=tools, + tools_to_check=tools_to_check, + ) - # Step 3: Map guardrail responses back to original message structure - await self._apply_guardrail_responses_to_input( - messages=messages, - responses=responses, - task_mappings=task_mappings, - ) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + if images_to_check: + inputs["images"] = images_to_check + if tools_to_check: + inputs["tools"] = tools_to_check + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=data, + input_type="request", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + + # Step 3: Map guardrail responses back to original message structure + await self._apply_guardrail_responses_to_input( + messages=messages, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) verbose_proxy_logger.debug( "Anthropic Messages: Processed input messages: %s", messages @@ -80,36 +113,63 @@ class AnthropicMessagesHandler(BaseTranslation): return data - async def _extract_input_text_and_create_tasks( + def _extract_input_text_and_images( self, message: Dict[str, Any], msg_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", ) -> None: """ - Extract text content from a message and create guardrail tasks. + Extract text content and images from a message. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ content = message.get("content", None) - if content is None: + tools = message.get("tools", None) + if content is None and tools is None: return - if isinstance(content, str): + ## CHECK FOR TEXT + IMAGES + if content is not None and isinstance(content, str): # Simple string content - tasks.append(guardrail_to_apply.apply_guardrail(text=content)) + texts_to_check.append(content) task_mappings.append((msg_idx, None)) - elif isinstance(content, list): + elif content is not None and isinstance(content, list): # List content (e.g., multimodal with text and images) for content_idx, content_item in enumerate(content): + # Extract text text_str = content_item.get("text", None) - if text_str is None: - continue - tasks.append(guardrail_to_apply.apply_guardrail(text=text_str)) - task_mappings.append((msg_idx, int(content_idx))) + if text_str is not None: + texts_to_check.append(text_str) + task_mappings.append((msg_idx, int(content_idx))) + + # Extract images + if content_item.get("type") == "image": + source = content_item.get("source", {}) + if isinstance(source, dict): + # Could be base64 or url + data = source.get("data") + if data: + images_to_check.append(data) + + def _extract_input_tools( + self, + tools: List[Dict[str, Any]], + tools_to_check: List[ChatCompletionToolParam], + ) -> None: + """ + Extract tools from a message. + """ + ## CHECK FOR TOOLS + if tools is not None and isinstance(tools, list): + # TRANSFORM ANTHROPIC TOOLS TO OPENAI TOOLS + openai_tools = self.adapter.translate_anthropic_tools_to_openai( + tools=cast(List[AllAnthropicToolsValues], tools) + ) + tools_to_check.extend(openai_tools) async def _apply_guardrail_responses_to_input( self, @@ -145,6 +205,8 @@ class AnthropicMessagesHandler(BaseTranslation): self, response: "AnthropicMessagesResponse", guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, ) -> Any: """ Process output response by applying guardrails to text content. @@ -152,6 +214,8 @@ class AnthropicMessagesHandler(BaseTranslation): Args: response: Anthropic MessagesResponse object guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata to pass to guardrails Returns: Modified response with guardrail applied to content @@ -166,35 +230,58 @@ class AnthropicMessagesHandler(BaseTranslation): ) return response - tasks: List[Coroutine[Any, Any, str]] = [] + texts_to_check: List[str] = [] + images_to_check: List[str] = [] task_mappings: List[Tuple[int, Optional[int]]] = [] - # Track (choice_index, content_index) for each task + # Track (content_index, None) for each text response_content = response.get("content", []) if not response_content: return response - # Step 1: Extract all text content from response choices + + # Step 1: Extract all text content from response for content_idx, content_block in enumerate(response_content): # Check if this is a text block by checking the 'type' field if isinstance(content_block, dict) and content_block.get("type") == "text": # Cast to dict to handle the union type properly - await self._extract_output_text_and_create_tasks( + self._extract_output_text_and_images( content_block=cast(Dict[str, Any], content_block), content_idx=content_idx, - tasks=tasks, + texts_to_check=texts_to_check, + images_to_check=images_to_check, task_mappings=task_mappings, - guardrail_to_apply=guardrail_to_apply, ) - # Step 2: Run all guardrail tasks in parallel - responses = await asyncio.gather(*tasks) + # Step 2: Apply guardrail to all texts in batch + if texts_to_check: + # Create a request_data dict with response info and user API key metadata + request_data: dict = {"response": response} - # Step 3: Map guardrail responses back to original response structure - await self._apply_guardrail_responses_to_output( - response=response, - responses=responses, - task_mappings=task_mappings, - ) + # Add user API key metadata with prefixed keys + user_metadata = self.transform_user_api_key_dict_to_metadata( + user_api_key_dict + ) + if user_metadata: + request_data["litellm_metadata"] = user_metadata + + inputs = GenericGuardrailAPIInputs(texts=texts_to_check) + if images_to_check: + inputs["images"] = images_to_check + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( + inputs=inputs, + request_data=request_data, + input_type="response", + logging_obj=litellm_logging_obj, + ) + + guardrailed_texts = guardrailed_inputs.get("texts", []) + + # Step 3: Map guardrail responses back to original response structure + await self._apply_guardrail_responses_to_output( + response=response, + responses=guardrailed_texts, + task_mappings=task_mappings, + ) verbose_proxy_logger.debug( "Anthropic Messages: Processed output response: %s", response @@ -202,6 +289,112 @@ class AnthropicMessagesHandler(BaseTranslation): return response + async def process_output_streaming_response( + self, + responses_so_far: List[Any], + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional[Any] = None, + user_api_key_dict: Optional[Any] = None, + ) -> List[Any]: + """ + Process output streaming response by applying guardrails to text content. + + Get the string so far, check the apply guardrail to the string so far, and return the list of responses so far. + """ + string_so_far = self.get_streaming_string_so_far(responses_so_far) + guardrailed_inputs = await guardrail_to_apply.apply_guardrail( # allow rejecting the response, if invalid + inputs={"texts": [string_so_far]}, + request_data={}, + input_type="response", + logging_obj=litellm_logging_obj, + ) + return responses_so_far + + def get_streaming_string_so_far(self, responses_so_far: List[Any]) -> str: + """ + Parse streaming responses and extract accumulated text content. + + Handles two formats: + 1. Raw bytes in SSE (Server-Sent Events) format from Anthropic API + 2. Parsed dict objects (for backwards compatibility) + + SSE format example: + b'event: content_block_delta\\ndata: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":" curious"}}\\n\\n' + + Dict format example: + { + "type": "content_block_delta", + "index": 0, + "delta": { + "type": "text_delta", + "text": " curious" + } + } + """ + text_so_far = "" + for response in responses_so_far: + # Handle raw bytes in SSE format + if isinstance(response, bytes): + text_so_far += self._extract_text_from_sse(response) + # Handle already-parsed dict format + elif isinstance(response, dict): + delta = response.get("delta") if response.get("delta") else None + if delta and delta.get("type") == "text_delta": + text = delta.get("text", "") + if text: + text_so_far += text + return text_so_far + + def _extract_text_from_sse(self, sse_bytes: bytes) -> str: + """ + Extract text content from Server-Sent Events (SSE) format. + + Args: + sse_bytes: Raw bytes in SSE format + + Returns: + Accumulated text from all content_block_delta events + """ + text = "" + try: + # Decode bytes to string + sse_string = sse_bytes.decode("utf-8") + + # Split by double newline to get individual events + events = sse_string.split("\n\n") + + for event in events: + if not event.strip(): + continue + + # Parse event lines + lines = event.strip().split("\n") + event_type = None + data_line = None + + for line in lines: + if line.startswith("event:"): + event_type = line[6:].strip() + elif line.startswith("data:"): + data_line = line[5:].strip() + + # Only process content_block_delta events + if event_type == "content_block_delta" and data_line: + try: + data = json.loads(data_line) + delta = data.get("delta", {}) + if delta.get("type") == "text_delta": + text += delta.get("text", "") + except json.JSONDecodeError: + verbose_proxy_logger.warning( + f"Failed to parse JSON from SSE data: {data_line}" + ) + + except Exception as e: + verbose_proxy_logger.error(f"Error extracting text from SSE: {e}") + + return text + def _has_text_content(self, response: "AnthropicMessagesResponse") -> bool: """ Check if response has any text content to process. @@ -219,23 +412,23 @@ class AnthropicMessagesHandler(BaseTranslation): return True return False - async def _extract_output_text_and_create_tasks( + def _extract_output_text_and_images( self, content_block: Dict[str, Any], content_idx: int, - tasks: List, + texts_to_check: List[str], + images_to_check: List[str], task_mappings: List[Tuple[int, Optional[int]]], - guardrail_to_apply: "CustomGuardrail", ) -> None: """ - Extract text content from a response choice and create guardrail tasks. + Extract text content and images from a response content block. - Override this method to customize text extraction logic. + Override this method to customize text/image extraction logic. """ content_text = content_block.get("text") if content_text and isinstance(content_text, str): # Simple string content - tasks.append(guardrail_to_apply.apply_guardrail(text=content_text)) + texts_to_check.append(content_text) task_mappings.append((content_idx, None)) async def _apply_guardrail_responses_to_output( diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py index b7b39f10395..36156d56a59 100644 --- a/litellm/llms/anthropic/chat/handler.py +++ b/litellm/llms/anthropic/chat/handler.py @@ -42,6 +42,7 @@ from litellm.types.llms.openai import ( ChatCompletionRedactedThinkingBlock, ChatCompletionThinkingBlock, ChatCompletionToolCallChunk, + ChatCompletionToolCallFunctionChunk, ) from litellm.types.utils import ( Delta, @@ -435,9 +436,7 @@ class AnthropicChatCompletion(BaseLLM): else: if client is None or not isinstance(client, HTTPHandler): - client = _get_httpx_client( - params={"timeout": timeout} - ) + client = _get_httpx_client(params={"timeout": timeout}) else: client = client @@ -527,9 +526,7 @@ class ModelResponseIterator: usage_object=cast(dict, anthropic_usage_chunk), reasoning_content=None ) - def _content_block_delta_helper( - self, chunk: dict - ) -> Tuple[ + def _content_block_delta_helper(self, chunk: dict) -> Tuple[ str, Optional[ChatCompletionToolCallChunk], List[Union[ChatCompletionThinkingBlock, ChatCompletionRedactedThinkingBlock]], @@ -550,15 +547,18 @@ class ModelResponseIterator: if "text" in content_block["delta"]: text = content_block["delta"]["text"] elif "partial_json" in content_block["delta"]: - tool_use = { - "id": None, - "type": "function", - "function": { - "name": None, - "arguments": content_block["delta"]["partial_json"], + tool_use = cast( + ChatCompletionToolCallChunk, + { + "id": None, + "type": "function", + "function": { + "name": None, + "arguments": content_block["delta"]["partial_json"], + }, + "index": self.tool_index, }, - "index": self.tool_index, - } + ) elif "citation" in content_block["delta"]: provider_specific_fields["citation"] = content_block["delta"]["citation"] elif ( @@ -569,7 +569,7 @@ class ModelResponseIterator: ChatCompletionThinkingBlock( type="thinking", thinking=content_block["delta"].get("thinking") or "", - signature=content_block["delta"].get("signature"), + signature=str(content_block["delta"].get("signature") or ""), ) ] provider_specific_fields["thinking_blocks"] = thinking_blocks @@ -625,7 +625,7 @@ class ModelResponseIterator: return content_block_start - def chunk_parser(self, chunk: dict) -> ModelResponseStream: + def chunk_parser(self, chunk: dict) -> ModelResponseStream: # noqa: PLR0915 try: type_chunk = chunk.get("type", "") or "" @@ -672,15 +672,32 @@ class ModelResponseIterator: text = content_block_start["content_block"]["text"] elif content_block_start["content_block"]["type"] == "tool_use": self.tool_index += 1 - tool_use = { - "id": content_block_start["content_block"]["id"], - "type": "function", - "function": { - "name": content_block_start["content_block"]["name"], - "arguments": "", - }, - "index": self.tool_index, - } + tool_use = ChatCompletionToolCallChunk( + id=content_block_start["content_block"]["id"], + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=content_block_start["content_block"]["name"], + arguments="", + ), + index=self.tool_index, + ) + # Include caller information if present (for programmatic tool calling) + if "caller" in content_block_start["content_block"]: + caller_data = content_block_start["content_block"]["caller"] + if caller_data: + tool_use["caller"] = cast(Dict[str, Any], caller_data) # type: ignore[typeddict-item] + elif content_block_start["content_block"]["type"] == "server_tool_use": + # Handle server tool use (for tool search) + self.tool_index += 1 + tool_use = ChatCompletionToolCallChunk( + id=content_block_start["content_block"]["id"], + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=content_block_start["content_block"]["name"], + arguments="", + ), + index=self.tool_index, + ) elif ( content_block_start["content_block"]["type"] == "redacted_thinking" ): @@ -696,17 +713,21 @@ class ModelResponseIterator: # check if tool call content block is_empty = self.check_empty_tool_call_args() if is_empty: - tool_use = { - "id": None, - "type": "function", - "function": { - "name": None, - "arguments": "{}", - }, - "index": self.tool_index, - } + tool_use = ChatCompletionToolCallChunk( + id=None, # type: ignore[typeddict-item] + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=None, # type: ignore[typeddict-item] + arguments="{}", + ), + index=self.tool_index, + ) # Reset response_format tool tracking when block stops self.is_response_format_tool = False + elif type_chunk == "tool_result": + # Handle tool_result blocks (for tool search results with tool_reference) + # These are automatically handled by Anthropic API, we just pass them through + pass elif type_chunk == "message_delta": finish_reason, usage = self._handle_message_delta(chunk) elif type_chunk == "message_start": diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py index 623a98c132f..bdc986ae27f 100644 --- a/litellm/llms/anthropic/chat/transformation.py +++ b/litellm/llms/anthropic/chat/transformation.py @@ -54,7 +54,10 @@ from litellm.types.utils import ( CompletionTokensDetailsWrapper, ) from litellm.types.utils import Message as LitellmMessage -from litellm.types.utils import PromptTokensDetailsWrapper, ServerToolUse +from litellm.types.utils import ( + PromptTokensDetailsWrapper, + ServerToolUse, +) from litellm.utils import ( ModelResponse, Usage, @@ -116,6 +119,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): def get_config(cls): return super().get_config() + def _is_claude_opus_4_5(self, model: str) -> bool: + """Check if the model is Claude Opus 4.5.""" + return "opus-4-5" in model.lower() or "opus_4_5" in model.lower() + def get_supported_openai_params(self, model: str): params = [ "stream", @@ -187,7 +194,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): ) return _tool_choice - def _map_tool_helper( + def _map_tool_helper( # noqa: PLR0915 self, tool: ChatCompletionToolParam ) -> Tuple[Optional[AllAnthropicToolsValues], Optional[AnthropicMcpServerTool]]: returned_tool: Optional[AllAnthropicToolsValues] = None @@ -250,9 +257,10 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): returned_tool = _computer_tool elif any(tool["type"].startswith(t) for t in ANTHROPIC_HOSTED_TOOLS): - function_name = tool.get("name", tool.get("function", {}).get("name")) - if function_name is None or not isinstance(function_name, str): + function_name_obj = tool.get("name", tool.get("function", {}).get("name")) + if function_name_obj is None or not isinstance(function_name_obj, str): raise ValueError("Missing required parameter: name") + function_name = function_name_obj additional_tool_params = {} for k, v in tool.items(): @@ -268,6 +276,30 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): mcp_server = self._map_openai_mcp_server_tool( cast(OpenAIMcpServerTool, tool) ) + elif tool["type"] == "tool_search_tool_regex_20251119": + # Tool search tool using regex + from litellm.types.llms.anthropic import AnthropicToolSearchToolRegex + + tool_name_obj = tool.get("name", "tool_search_tool_regex") + if not isinstance(tool_name_obj, str): + raise ValueError("Tool search tool must have a valid name") + tool_name = tool_name_obj + returned_tool = AnthropicToolSearchToolRegex( + type="tool_search_tool_regex_20251119", + name=tool_name, + ) + elif tool["type"] == "tool_search_tool_bm25_20251119": + # Tool search tool using BM25 + from litellm.types.llms.anthropic import AnthropicToolSearchToolBM25 + + tool_name_obj = tool.get("name", "tool_search_tool_bm25") + if not isinstance(tool_name_obj, str): + raise ValueError("Tool search tool must have a valid name") + tool_name = tool_name_obj + returned_tool = AnthropicToolSearchToolBM25( + type="tool_search_tool_bm25_20251119", + name=tool_name, + ) if returned_tool is None and mcp_server is None: raise ValueError(f"Unsupported tool type: {tool['type']}") @@ -275,14 +307,67 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): _cache_control = tool.get("cache_control", None) _cache_control_function = tool.get("function", {}).get("cache_control", None) if returned_tool is not None: - if _cache_control is not None: - returned_tool["cache_control"] = _cache_control - elif _cache_control_function is not None and isinstance( - _cache_control_function, dict - ): - returned_tool["cache_control"] = ChatCompletionCachedContent( - **_cache_control_function # type: ignore - ) + # Only set cache_control on tools that support it (not tool search tools) + tool_type = returned_tool.get("type", "") + if tool_type not in ("tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119"): + if _cache_control is not None: + returned_tool["cache_control"] = _cache_control # type: ignore[typeddict-item] + elif _cache_control_function is not None and isinstance( + _cache_control_function, dict + ): + returned_tool["cache_control"] = ChatCompletionCachedContent( # type: ignore[typeddict-item] + **_cache_control_function # type: ignore + ) + + ## check if defer_loading is set in the tool + _defer_loading = tool.get("defer_loading", None) + _defer_loading_function = tool.get("function", {}).get("defer_loading", None) + if returned_tool is not None: + # Only set defer_loading on tools that support it (not tool search tools or computer tools) + tool_type = returned_tool.get("type", "") + if tool_type not in ("tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119", "computer_20241022", "computer_20250124"): + if _defer_loading is not None: + if not isinstance(_defer_loading, bool): + raise ValueError("defer_loading must be a boolean") + returned_tool["defer_loading"] = _defer_loading # type: ignore[typeddict-item] + elif _defer_loading_function is not None: + if not isinstance(_defer_loading_function, bool): + raise ValueError("defer_loading must be a boolean") + returned_tool["defer_loading"] = _defer_loading_function # type: ignore[typeddict-item] + + ## check if allowed_callers is set in the tool + _allowed_callers = tool.get("allowed_callers", None) + _allowed_callers_function = tool.get("function", {}).get("allowed_callers", None) + if returned_tool is not None: + # Only set allowed_callers on tools that support it (not tool search tools or computer tools) + tool_type = returned_tool.get("type", "") + if tool_type not in ("tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119", "computer_20241022", "computer_20250124"): + if _allowed_callers is not None: + if not isinstance(_allowed_callers, list) or not all( + isinstance(item, str) for item in _allowed_callers + ): + raise ValueError("allowed_callers must be a list of strings") + returned_tool["allowed_callers"] = _allowed_callers # type: ignore[typeddict-item] + elif _allowed_callers_function is not None: + if not isinstance(_allowed_callers_function, list) or not all( + isinstance(item, str) for item in _allowed_callers_function + ): + raise ValueError("allowed_callers must be a list of strings") + returned_tool["allowed_callers"] = _allowed_callers_function # type: ignore[typeddict-item] + + ## check if input_examples is set in the tool + _input_examples = tool.get("input_examples", None) + _input_examples_function = tool.get("function", {}).get("input_examples", None) + if returned_tool is not None: + # Only set input_examples on user-defined tools (type "custom" or no type) + tool_type = returned_tool.get("type", "") + if tool_type == "custom" or (tool_type == "" and "name" in returned_tool): + if _input_examples is not None and isinstance(_input_examples, list): + returned_tool["input_examples"] = _input_examples # type: ignore[typeddict-item] + elif _input_examples_function is not None and isinstance( + _input_examples_function, list + ): + returned_tool["input_examples"] = _input_examples_function # type: ignore[typeddict-item] return returned_tool, mcp_server @@ -334,6 +419,82 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): mcp_servers.append(mcp_server_tool) return anthropic_tools, mcp_servers + def _detect_tool_search_tools(self, tools: Optional[List]) -> bool: + """Check if tool search tools are present in the tools list.""" + if not tools: + return False + + for tool in tools: + tool_type = tool.get("type", "") + if tool_type in ["tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119"]: + return True + return False + + def _separate_deferred_tools( + self, tools: List + ) -> Tuple[List, List]: + """ + Separate tools into deferred and non-deferred lists. + + Returns: + Tuple of (non_deferred_tools, deferred_tools) + """ + non_deferred = [] + deferred = [] + + for tool in tools: + if tool.get("defer_loading", False): + deferred.append(tool) + else: + non_deferred.append(tool) + + return non_deferred, deferred + + def _expand_tool_references( + self, + content: List, + deferred_tools: List, + ) -> List: + """ + Expand tool_reference blocks to full tool definitions. + + When Anthropic's tool search returns results, it includes tool_reference blocks + that reference tools by name. This method expands those references to full + tool definitions from the deferred_tools catalog. + + Args: + content: Response content that may contain tool_reference blocks + deferred_tools: List of deferred tools that can be referenced + + Returns: + Content with tool_reference blocks expanded to full tool definitions + """ + if not deferred_tools: + return content + + # Create a mapping of tool names to tool definitions + tool_map = {} + for tool in deferred_tools: + tool_name = tool.get("name") or tool.get("function", {}).get("name") + if tool_name: + tool_map[tool_name] = tool + + # Expand tool references in content + expanded_content = [] + for item in content: + if isinstance(item, dict) and item.get("type") == "tool_reference": + tool_name = item.get("tool_name") + if tool_name and tool_name in tool_map: + # Replace reference with full tool definition + expanded_content.append(tool_map[tool_name]) + else: + # Keep the reference if we can't find the tool + expanded_content.append(item) + else: + expanded_content.append(item) + + return expanded_content + def _map_stop_sequences( self, stop: Optional[Union[str, List[str]]] ) -> Optional[List[str]]: @@ -469,7 +630,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return hosted_web_search_tool - def map_openai_params( + def map_openai_params( # noqa: PLR0915 self, non_default_params: dict, optional_params: dict, @@ -555,9 +716,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): if param == "thinking": optional_params["thinking"] = value elif param == "reasoning_effort" and isinstance(value, str): - optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( - value - ) + # For Claude Opus 4.5, map reasoning_effort to output_config + if self._is_claude_opus_4_5(model): + optional_params["output_config"] = {"effort": value} + else: + # For other models, map to thinking parameter + optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( + value + ) elif param == "web_search_options" and isinstance(value, dict): hosted_web_search_tool = self.map_web_search_tool( cast(OpenAIWebSearchOptions, value) @@ -620,6 +786,9 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): valid_content: bool = False system_message_block = ChatCompletionSystemMessage(**message) if isinstance(system_message_block["content"], str): + # Skip empty text blocks - Anthropic API raises errors for empty text + if not system_message_block["content"]: + continue anthropic_system_message_content = AnthropicSystemMessageContent( type="text", text=system_message_block["content"], @@ -634,10 +803,14 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): valid_content = True elif isinstance(message["content"], list): for _content in message["content"]: + # Skip empty text blocks - Anthropic API raises errors for empty text + text_value = _content.get("text") + if _content.get("type") == "text" and not text_value: + continue anthropic_system_message_content = ( AnthropicSystemMessageContent( type=_content.get("type"), - text=_content.get("text"), + text=text_value, ) ) if "cache_control" in _content: @@ -822,6 +995,17 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): "messages": anthropic_messages, **optional_params, } + + ## Handle output_config (Anthropic-specific parameter) + if "output_config" in optional_params: + output_config = optional_params.get("output_config") + if output_config and isinstance(output_config, dict): + effort = output_config.get("effort") + if effort and effort not in ["high", "medium", "low"]: + raise ValueError( + f"Invalid effort value: {effort}. Must be one of: 'high', 'medium', 'low'" + ) + data["output_config"] = output_config return data @@ -870,18 +1054,40 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): text_content += content["text"] ## TOOL CALLING elif content["type"] == "tool_use": - tool_calls.append( - ChatCompletionToolCallChunk( - id=content["id"], - type="function", - function=ChatCompletionToolCallFunctionChunk( - name=content["name"], - arguments=json.dumps(content["input"]), - ), - index=idx, - ) + tool_call = ChatCompletionToolCallChunk( + id=content["id"], + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=content["name"], + arguments=json.dumps(content["input"]), + ), + index=idx, ) - + # Include caller information if present (for programmatic tool calling) + if "caller" in content: + tool_call["caller"] = cast(Dict[str, Any], content["caller"]) # type: ignore[typeddict-item] + tool_calls.append(tool_call) + ## SERVER TOOL USE (for tool search) + elif content["type"] == "server_tool_use": + # Server tool use blocks are for tool search - treat as tool calls + tool_call = ChatCompletionToolCallChunk( + id=content["id"], + type="function", + function=ChatCompletionToolCallFunctionChunk( + name=content["name"], + arguments=json.dumps(content.get("input", {})), + ), + index=idx, + ) + # Include caller information if present (for programmatic tool calling) + if "caller" in content: + tool_call["caller"] = cast(Dict[str, Any], content["caller"]) # type: ignore[typeddict-item] + tool_calls.append(tool_call) + ## TOOL SEARCH TOOL RESULT (skip - this is metadata about tool discovery) + elif content["type"] == "tool_search_tool_result": + # This block contains tool_references that were discovered + # We don't need to include this in the response as it's internal metadata + pass elif content.get("thinking", None) is not None: if thinking_blocks is None: thinking_blocks = [] @@ -916,7 +1122,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): return text_content, citations, thinking_blocks, reasoning_content, tool_calls def calculate_usage( - self, usage_object: dict, reasoning_content: Optional[str] + self, usage_object: dict, reasoning_content: Optional[str], completion_response: Optional[dict] = None ) -> Usage: # NOTE: Sometimes the usage object has None set explicitly for token counts, meaning .get() & key access returns None, and we need to account for this prompt_tokens = usage_object.get("input_tokens", 0) or 0 @@ -926,6 +1132,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): cache_read_input_tokens: int = 0 cache_creation_token_details: Optional[CacheCreationTokenDetails] = None web_search_requests: Optional[int] = None + tool_search_requests: Optional[int] = None if ( "cache_creation_input_tokens" in _usage and _usage["cache_creation_input_tokens"] is not None @@ -946,6 +1153,25 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): web_search_requests = cast( int, _usage["server_tool_use"]["web_search_requests"] ) + if ( + "tool_search_requests" in _usage["server_tool_use"] + and _usage["server_tool_use"]["tool_search_requests"] is not None + ): + tool_search_requests = cast( + int, _usage["server_tool_use"]["tool_search_requests"] + ) + + # Count tool_search_requests from content blocks if not in usage + # Anthropic doesn't always include tool_search_requests in the usage object + if tool_search_requests is None and completion_response is not None: + tool_search_count = 0 + for content in completion_response.get("content", []): + if content.get("type") == "server_tool_use": + tool_name = content.get("name", "") + if "tool_search" in tool_name: + tool_search_count += 1 + if tool_search_count > 0: + tool_search_requests = tool_search_count if "cache_creation" in _usage and _usage["cache_creation"] is not None: cache_creation_token_details = CacheCreationTokenDetails( @@ -982,8 +1208,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): cache_read_input_tokens=cache_read_input_tokens, completion_tokens_details=completion_token_details, server_tool_use=( - ServerToolUse(web_search_requests=web_search_requests) - if web_search_requests is not None + ServerToolUse( + web_search_requests=web_search_requests, + tool_search_requests=tool_search_requests, + ) + if (web_search_requests is not None or tool_search_requests is not None) else None ), ) @@ -1077,6 +1306,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig): usage = self.calculate_usage( usage_object=completion_response["usage"], reasoning_content=reasoning_content, + completion_response=completion_response, ) setattr(model_response, "usage", usage) # type: ignore diff --git a/litellm/llms/anthropic/common_utils.py b/litellm/llms/anthropic/common_utils.py index 0d00a3b4632..246618ad9e2 100644 --- a/litellm/llms/anthropic/common_utils.py +++ b/litellm/llms/anthropic/common_utils.py @@ -88,6 +88,93 @@ class AnthropicModelInfo(BaseLLMModelInfo): return True return False + def is_tool_search_used(self, tools: Optional[List]) -> bool: + """ + Check if tool search tools are present in the tools list. + """ + if not tools: + return False + + for tool in tools: + tool_type = tool.get("type", "") + if tool_type in ["tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119"]: + return True + return False + + def is_programmatic_tool_calling_used(self, tools: Optional[List]) -> bool: + """ + Check if programmatic tool calling is being used (tools with allowed_callers field). + + Returns True if any tool has allowed_callers containing 'code_execution_20250825'. + """ + if not tools: + return False + + for tool in tools: + # Check top-level allowed_callers + allowed_callers = tool.get("allowed_callers", None) + if allowed_callers and isinstance(allowed_callers, list): + if "code_execution_20250825" in allowed_callers: + return True + + # Check function.allowed_callers for OpenAI format tools + function = tool.get("function", {}) + if isinstance(function, dict): + function_allowed_callers = function.get("allowed_callers", None) + if function_allowed_callers and isinstance(function_allowed_callers, list): + if "code_execution_20250825" in function_allowed_callers: + return True + + return False + + def is_input_examples_used(self, tools: Optional[List]) -> bool: + """ + Check if input_examples is being used in any tools. + + Returns True if any tool has input_examples field. + """ + if not tools: + return False + + for tool in tools: + # Check top-level input_examples + input_examples = tool.get("input_examples", None) + if input_examples and isinstance(input_examples, list) and len(input_examples) > 0: + return True + + # Check function.input_examples for OpenAI format tools + function = tool.get("function", {}) + if isinstance(function, dict): + function_input_examples = function.get("input_examples", None) + if function_input_examples and isinstance(function_input_examples, list) and len(function_input_examples) > 0: + return True + + return False + + def is_effort_used(self, optional_params: Optional[dict], model: Optional[str] = None) -> bool: + """ + Check if effort parameter is being used. + + Returns True if effort-related parameters are present. + """ + if not optional_params: + return False + + # Check if reasoning_effort is provided for Claude Opus 4.5 + if model and ("opus-4-5" in model.lower() or "opus_4_5" in model.lower()): + reasoning_effort = optional_params.get("reasoning_effort") + if reasoning_effort and isinstance(reasoning_effort, str): + return True + + # Check if output_config is directly provided + output_config = optional_params.get("output_config") + if output_config and isinstance(output_config, dict): + effort = output_config.get("effort") + if effort and isinstance(effort, str): + return True + + return False + def _get_user_anthropic_beta_headers( self, anthropic_beta_header: Optional[str] ) -> Optional[List[str]]: @@ -113,6 +200,49 @@ class AnthropicModelInfo(BaseLLMModelInfo): computer_tool_version, "computer-use-2024-10-22" # Default fallback ) + def get_anthropic_beta_list( + self, + model: str, + optional_params: Optional[dict] = None, + computer_tool_used: Optional[str] = None, + prompt_caching_set: bool = False, + file_id_used: bool = False, + mcp_server_used: bool = False, + ) -> List[str]: + """ + Get list of common beta headers based on the features that are active. + + Returns: + List of beta header strings + """ + from litellm.types.llms.anthropic import ( + ANTHROPIC_EFFORT_BETA_HEADER, + ) + + betas = [] + + # Detect features + effort_used = self.is_effort_used(optional_params, model) + + if effort_used: + betas.append(ANTHROPIC_EFFORT_BETA_HEADER) # effort-2025-11-24 + + if computer_tool_used: + beta_header = self.get_computer_tool_beta_header(computer_tool_used) + betas.append(beta_header) + + if prompt_caching_set: + betas.append("prompt-caching-2024-07-31") + + if file_id_used: + betas.append("files-api-2025-04-14") + betas.append("code-execution-2025-05-22") + + if mcp_server_used: + betas.append("mcp-client-2025-04-04") + + return list(set(betas)) + def get_anthropic_headers( self, api_key: str, @@ -122,6 +252,10 @@ class AnthropicModelInfo(BaseLLMModelInfo): pdf_used: bool = False, file_id_used: bool = False, mcp_server_used: bool = False, + tool_search_used: bool = False, + programmatic_tool_calling_used: bool = False, + input_examples_used: bool = False, + effort_used: bool = False, is_vertex_request: bool = False, user_anthropic_beta_headers: Optional[List[str]] = None, ) -> dict: @@ -138,6 +272,15 @@ class AnthropicModelInfo(BaseLLMModelInfo): betas.add("code-execution-2025-05-22") if mcp_server_used: betas.add("mcp-client-2025-04-04") + # Tool search, programmatic tool calling, and input_examples all use the same beta header + if tool_search_used or programmatic_tool_calling_used or input_examples_used: + from litellm.types.llms.anthropic import ANTHROPIC_TOOL_SEARCH_BETA_HEADER + betas.add(ANTHROPIC_TOOL_SEARCH_BETA_HEADER) + + # Effort parameter uses a separate beta header + if effort_used: + from litellm.types.llms.anthropic import ANTHROPIC_EFFORT_BETA_HEADER + betas.add(ANTHROPIC_EFFORT_BETA_HEADER) headers = { "anthropic-version": anthropic_version or "2023-06-01", @@ -182,6 +325,10 @@ class AnthropicModelInfo(BaseLLMModelInfo): ) pdf_used = self.is_pdf_used(messages=messages) file_id_used = self.is_file_id_used(messages=messages) + tool_search_used = self.is_tool_search_used(tools=tools) + programmatic_tool_calling_used = self.is_programmatic_tool_calling_used(tools=tools) + input_examples_used = self.is_input_examples_used(tools=tools) + effort_used = self.is_effort_used(optional_params=optional_params, model=model) user_anthropic_beta_headers = self._get_user_anthropic_beta_headers( anthropic_beta_header=headers.get("anthropic-beta") ) @@ -194,6 +341,10 @@ class AnthropicModelInfo(BaseLLMModelInfo): is_vertex_request=optional_params.get("is_vertex_request", False), user_anthropic_beta_headers=user_anthropic_beta_headers, mcp_server_used=mcp_server_used, + tool_search_used=tool_search_used, + programmatic_tool_calling_used=programmatic_tool_calling_used, + input_examples_used=input_examples_used, + effort_used=effort_used, ) headers = {**headers, **anthropic_headers} diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py index 0e905014fe2..98e57f279cf 100644 --- a/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py +++ b/litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py @@ -645,7 +645,7 @@ class LiteLLMAnthropicMessagesAdapter: type="tool_use", id=choice.delta.tool_calls[0].id or str(uuid.uuid4()), name=choice.delta.tool_calls[0].function.name or "", - input={}, + input={}, # type: ignore[typeddict-item] ) elif isinstance(choice, StreamingChoices) and hasattr( choice.delta, "thinking_blocks" diff --git a/litellm/llms/anthropic/skills/readme.md b/litellm/llms/anthropic/skills/readme.md index 898639cd44b..0602272256c 100644 --- a/litellm/llms/anthropic/skills/readme.md +++ b/litellm/llms/anthropic/skills/readme.md @@ -1,17 +1,279 @@ -# Anthropic Skills API +# Anthropic Skills API Integration -This folder maintains the integration for the Anthropic Skills API. +This module provides comprehensive support for the Anthropic Skills API through LiteLLM. -You can do the following with the Anthropic Skills API: +## Features -1. Create a new skill -2. List all skills -3. Get a skill -4. Delete a skill +The Skills API allows you to: +- **Create skills**: Define reusable AI capabilities +- **List skills**: Browse all available skills +- **Get skills**: Retrieve detailed information about a specific skill +- **Delete skills**: Remove skills that are no longer needed +## Quick Start -Versions: - - Create Skill Version - - List Skill Versions - - Get Skill Version - - Delete Skill Version \ No newline at end of file +### Prerequisites + +Set your Anthropic API key: +```python +import os +os.environ["ANTHROPIC_API_KEY"] = "your-api-key-here" +``` + +### Basic Usage + +#### Create a Skill + +```python +import litellm + +# Create a skill with files +# Note: All files must be in the same top-level directory +# and must include a SKILL.md file at the root +skill = litellm.create_skill( + files=[ + # List of file objects to upload + # Must include SKILL.md + ], + display_title="Python Code Generator", + custom_llm_provider="anthropic" +) +print(f"Created skill: {skill.id}") + +# Asynchronous version +skill = await litellm.acreate_skill( + files=[...], # Your files here + display_title="Python Code Generator", + custom_llm_provider="anthropic" +) +``` + +#### List Skills + +```python +# List all skills +skills = litellm.list_skills( + custom_llm_provider="anthropic" +) + +for skill in skills.data: + print(f"{skill.display_title}: {skill.id}") + +# With pagination and filtering +skills = litellm.list_skills( + limit=20, + source="custom", # Filter by 'custom' or 'anthropic' + custom_llm_provider="anthropic" +) + +# Get next page if available +if skills.has_more: + next_page = litellm.list_skills( + page=skills.next_page, + custom_llm_provider="anthropic" + ) +``` + +#### Get a Skill + +```python +skill = litellm.get_skill( + skill_id="skill_abc123", + custom_llm_provider="anthropic" +) + +print(f"Skill: {skill.display_title}") +print(f"Created: {skill.created_at}") +print(f"Latest version: {skill.latest_version}") +print(f"Source: {skill.source}") +``` + +#### Delete a Skill + +```python +result = litellm.delete_skill( + skill_id="skill_abc123", + custom_llm_provider="anthropic" +) + +print(f"Deleted skill {result.id}, type: {result.type}") +``` + +## API Reference + +### `create_skill()` + +Create a new skill. + +**Parameters:** +- `files` (List[Any], optional): Files to upload for the skill. All files must be in the same top-level directory and must include a SKILL.md file at the root. +- `display_title` (str, optional): Display title for the skill +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `Skill`: The created skill object + +**Async version:** `acreate_skill()` + +### `list_skills()` + +List all skills. + +**Parameters:** +- `limit` (int, optional): Number of results to return per page (max 100, default 20) +- `page` (str, optional): Pagination token for fetching a specific page of results +- `source` (str, optional): Filter skills by source ('custom' or 'anthropic') +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `ListSkillsResponse`: Object containing a list of skills and pagination info + +**Async version:** `alist_skills()` + +### `get_skill()` + +Get a specific skill by ID. + +**Parameters:** +- `skill_id` (str, required): The skill ID +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `Skill`: The requested skill object + +**Async version:** `aget_skill()` + +### `delete_skill()` + +Delete a skill. + +**Parameters:** +- `skill_id` (str, required): The skill ID to delete +- `custom_llm_provider` (str, optional): Provider name (default: "anthropic") +- `extra_headers` (dict, optional): Additional HTTP headers +- `timeout` (float, optional): Request timeout + +**Returns:** +- `DeleteSkillResponse`: Object with `id` and `type` fields + +**Async version:** `adelete_skill()` + +## Response Types + +### `Skill` + +Represents a skill from the Anthropic Skills API. + +**Fields:** +- `id` (str): Unique identifier +- `created_at` (str): ISO 8601 timestamp +- `display_title` (str, optional): Display title +- `latest_version` (str, optional): Latest version identifier +- `source` (str): Source ("custom" or "anthropic") +- `type` (str): Object type (always "skill") +- `updated_at` (str): ISO 8601 timestamp + +### `ListSkillsResponse` + +Response from listing skills. + +**Fields:** +- `data` (List[Skill]): List of skills +- `next_page` (str, optional): Pagination token for the next page +- `has_more` (bool): Whether more skills are available + +### `DeleteSkillResponse` + +Response from deleting a skill. + +**Fields:** +- `id` (str): The deleted skill ID +- `type` (str): Deleted object type (always "skill_deleted") + +## Architecture + +The Skills API implementation follows LiteLLM's standard patterns: + +1. **Type Definitions** (`litellm/types/llms/anthropic_skills.py`) + - Pydantic models for request/response types + - TypedDict definitions for request parameters + +2. **Base Configuration** (`litellm/llms/base_llm/skills/transformation.py`) + - Abstract base class `BaseSkillsAPIConfig` + - Defines transformation interface for provider-specific implementations + +3. **Provider Implementation** (`litellm/llms/anthropic/skills/transformation.py`) + - `AnthropicSkillsConfig` - Anthropic-specific transformations + - Handles API authentication, URL construction, and response mapping + +4. **Main Handler** (`litellm/skills/main.py`) + - Public API functions (sync and async) + - Request validation and routing + - Error handling + +5. **HTTP Handlers** (`litellm/llms/custom_httpx/llm_http_handler.py`) + - Low-level HTTP request/response handling + - Connection pooling and retry logic + +## Beta API Support + +The Skills API is in beta. The beta header (`skills-2025-10-02`) is automatically added by the Anthropic provider configuration. You can customize it if needed: + +```python +skill = litellm.create_skill( + display_title="My Skill", + extra_headers={ + "anthropic-beta": "skills-2025-10-02" # Or any other beta version + }, + custom_llm_provider="anthropic" +) +``` + +The default beta version is configured in `litellm.constants.ANTHROPIC_SKILLS_API_BETA_VERSION`. + +## Error Handling + +All Skills API functions follow LiteLLM's standard error handling: + +```python +import litellm + +try: + skill = litellm.create_skill( + display_title="My Skill", + custom_llm_provider="anthropic" + ) +except litellm.exceptions.AuthenticationError as e: + print(f"Authentication failed: {e}") +except litellm.exceptions.RateLimitError as e: + print(f"Rate limit exceeded: {e}") +except litellm.exceptions.APIError as e: + print(f"API error: {e}") +``` + +## Contributing + +To add support for Skills API to a new provider: + +1. Create provider-specific configuration class inheriting from `BaseSkillsAPIConfig` +2. Implement all abstract methods for request/response transformations +3. Register the config in `ProviderConfigManager.get_provider_skills_api_config()` +4. Add appropriate tests + +## Related Documentation + +- [Anthropic Skills API Documentation](https://platform.claude.com/docs/en/api/beta/skills/create) +- [LiteLLM Responses API](../../../responses/) +- [Provider Configuration System](../../base_llm/) + +## Support + +For issues or questions: +- GitHub Issues: https://github.com/BerriAI/litellm/issues +- Discord: https://discord.gg/wuPM9dRgDw diff --git a/litellm/llms/azure/azure.py b/litellm/llms/azure/azure.py index e7aa93ac882..994afa26e9c 100644 --- a/litellm/llms/azure/azure.py +++ b/litellm/llms/azure/azure.py @@ -1020,7 +1020,7 @@ class AzureChatCompletion(BaseAzureLLM, BaseLLM): headers: dict, client=None, timeout=None, - ) -> litellm.ImageResponse: + ) -> ImageResponse: response: Optional[dict] = None try: diff --git a/litellm/llms/azure/chat/gpt_5_transformation.py b/litellm/llms/azure/chat/gpt_5_transformation.py index d563a2889ca..87f81d117f0 100644 --- a/litellm/llms/azure/chat/gpt_5_transformation.py +++ b/litellm/llms/azure/chat/gpt_5_transformation.py @@ -2,6 +2,8 @@ from typing import List +import litellm +from litellm.exceptions import UnsupportedParamsError from litellm.llms.openai.chat.gpt_5_transformation import OpenAIGPT5Config from litellm.types.llms.openai import AllMessageValues @@ -33,7 +35,38 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): drop_params: bool, api_version: str = "", ) -> dict: - return OpenAIGPT5Config.map_openai_params( + reasoning_effort_value = ( + non_default_params.get("reasoning_effort") + or optional_params.get("reasoning_effort") + ) + + # gpt-5.1 supports reasoning_effort='none', but other gpt-5 models don't + # See: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning + is_gpt_5_1 = self.is_model_gpt_5_1_model(model) + + if reasoning_effort_value == "none" and not is_gpt_5_1: + if litellm.drop_params is True or ( + drop_params is not None and drop_params is True + ): + non_default_params = non_default_params.copy() + optional_params = optional_params.copy() + if non_default_params.get("reasoning_effort") == "none": + non_default_params.pop("reasoning_effort") + if optional_params.get("reasoning_effort") == "none": + optional_params.pop("reasoning_effort") + else: + raise UnsupportedParamsError( + status_code=400, + message=( + "Azure OpenAI does not support reasoning_effort='none' for this model. " + "Supported values are: 'low', 'medium', and 'high'. " + "To drop this parameter, set `litellm.drop_params=True` or for proxy:\n\n" + "`litellm_settings:\n drop_params: true`\n" + "Issue: https://github.com/BerriAI/litellm/issues/16704" + ), + ) + + result = OpenAIGPT5Config.map_openai_params( self, non_default_params=non_default_params, optional_params=optional_params, @@ -41,6 +74,12 @@ class AzureOpenAIGPT5Config(AzureOpenAIConfig, OpenAIGPT5Config): drop_params=drop_params, ) + # Only drop reasoning_effort='none' for non-gpt-5.1 models + if result.get("reasoning_effort") == "none" and not is_gpt_5_1: + result.pop("reasoning_effort") + + return result + def transform_request( self, model: str, diff --git a/litellm/llms/azure/realtime/handler.py b/litellm/llms/azure/realtime/handler.py index 8e5581206de..217a05c83a4 100644 --- a/litellm/llms/azure/realtime/handler.py +++ b/litellm/llms/azure/realtime/handler.py @@ -12,6 +12,7 @@ from ....litellm_core_utils.litellm_logging import Logging as LiteLLMLogging from ....litellm_core_utils.realtime_streaming import RealTimeStreaming from ....llms.custom_httpx.http_handler import get_shared_realtime_ssl_context from ..azure import AzureChatCompletion +from litellm._logging import verbose_proxy_logger # BACKEND_WS_URL = "ws://localhost:8080/v1/realtime?model=gpt-4o-realtime-preview-2024-10-01" @@ -28,16 +29,41 @@ async def forward_messages(client_ws: Any, backend_ws: Any): class AzureOpenAIRealtime(AzureChatCompletion): - def _construct_url(self, api_base: str, model: str, api_version: str) -> str: + def _construct_url( + self, + api_base: str, + model: str, + api_version: str, + realtime_protocol: Optional[str] = None, + ) -> str: """ - Example output: - "wss://my-endpoint-sweden-berri992.openai.azure.com/openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview"; + Construct Azure realtime WebSocket URL. + Args: + api_base: Azure API base URL (will be converted from https:// to wss://) + model: Model deployment name + api_version: Azure API version + realtime_protocol: Protocol version to use: + - "GA" or "v1": Uses /openai/v1/realtime (GA path) + - "beta" or None: Uses /openai/realtime (beta path, default) + + Returns: + WebSocket URL string + + Examples: + beta/default: "wss://.../openai/realtime?api-version=2024-10-01-preview&deployment=gpt-4o-realtime-preview" + GA/v1: "wss://.../openai/v1/realtime?model=gpt-realtime-deployment" """ api_base = api_base.replace("https://", "wss://") - return ( - f"{api_base}/openai/realtime?api-version={api_version}&deployment={model}" - ) + + # Determine path based on realtime_protocol + if realtime_protocol in ("GA", "v1"): + path = "/openai/v1/realtime" + return f"{api_base}{path}?model={model}" + else: + # Default to beta path for backwards compatibility + path = "/openai/realtime" + return f"{api_base}{path}?api-version={api_version}&deployment={model}" async def async_realtime( self, @@ -50,6 +76,7 @@ class AzureOpenAIRealtime(AzureChatCompletion): azure_ad_token: Optional[str] = None, client: Optional[Any] = None, timeout: Optional[float] = None, + realtime_protocol: Optional[str] = None, ): import websockets from websockets.asyncio.client import ClientConnection @@ -59,7 +86,9 @@ class AzureOpenAIRealtime(AzureChatCompletion): if api_version is None: raise ValueError("api_version is required for Azure OpenAI calls") - url = self._construct_url(api_base, model, api_version) + url = self._construct_url( + api_base, model, api_version, realtime_protocol=realtime_protocol + ) try: ssl_context = get_shared_realtime_ssl_context() @@ -79,4 +108,5 @@ class AzureOpenAIRealtime(AzureChatCompletion): except websockets.exceptions.InvalidStatusCode as e: # type: ignore await websocket.close(code=e.status_code, reason=str(e)) except Exception: + verbose_proxy_logger.exception("Error in AzureOpenAIRealtime.async_realtime") pass diff --git a/litellm/llms/azure_ai/anthropic/__init__.py b/litellm/llms/azure_ai/anthropic/__init__.py new file mode 100644 index 00000000000..233f22999f0 --- /dev/null +++ b/litellm/llms/azure_ai/anthropic/__init__.py @@ -0,0 +1,12 @@ +""" +Azure Anthropic provider - supports Claude models via Azure Foundry +""" +from .handler import AzureAnthropicChatCompletion +from .transformation import AzureAnthropicConfig + +try: + from .messages_transformation import AzureAnthropicMessagesConfig + __all__ = ["AzureAnthropicChatCompletion", "AzureAnthropicConfig", "AzureAnthropicMessagesConfig"] +except ImportError: + __all__ = ["AzureAnthropicChatCompletion", "AzureAnthropicConfig"] + diff --git a/litellm/llms/azure_ai/anthropic/handler.py b/litellm/llms/azure_ai/anthropic/handler.py new file mode 100644 index 00000000000..fe4524fd5be --- /dev/null +++ b/litellm/llms/azure_ai/anthropic/handler.py @@ -0,0 +1,227 @@ +""" +Azure Anthropic handler - reuses AnthropicChatCompletion logic with Azure authentication +""" +import copy +import json +from typing import TYPE_CHECKING, Callable, Union + +import httpx + +from litellm.llms.anthropic.chat.handler import AnthropicChatCompletion +from litellm.llms.custom_httpx.http_handler import ( + AsyncHTTPHandler, + HTTPHandler, +) +from litellm.types.utils import ModelResponse +from litellm.utils import CustomStreamWrapper + +from .transformation import AzureAnthropicConfig + +if TYPE_CHECKING: + pass + + +class AzureAnthropicChatCompletion(AnthropicChatCompletion): + """ + Azure Anthropic chat completion handler. + Reuses all Anthropic logic but with Azure authentication. + """ + + def __init__(self) -> None: + super().__init__() + + def completion( + self, + model: str, + messages: list, + api_base: str, + custom_llm_provider: str, + custom_prompt_dict: dict, + model_response: ModelResponse, + print_verbose: Callable, + encoding, + api_key, + logging_obj, + optional_params: dict, + timeout: Union[float, httpx.Timeout], + litellm_params: dict, + acompletion=None, + logger_fn=None, + headers={}, + client=None, + ): + """ + Completion method that uses Azure authentication instead of Anthropic's x-api-key. + All other logic is the same as AnthropicChatCompletion. + """ + + optional_params = copy.deepcopy(optional_params) + stream = optional_params.pop("stream", None) + json_mode: bool = optional_params.pop("json_mode", False) + is_vertex_request: bool = optional_params.pop("is_vertex_request", False) + _is_function_call = False + messages = copy.deepcopy(messages) + + # Use AzureAnthropicConfig for both azure_anthropic and azure_ai Claude models + config = AzureAnthropicConfig() + + headers = config.validate_environment( + api_key=api_key, + headers=headers, + model=model, + messages=messages, + optional_params={**optional_params, "is_vertex_request": is_vertex_request}, + litellm_params=litellm_params, + ) + + data = config.transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + + ## LOGGING + logging_obj.pre_call( + input=messages, + api_key=api_key, + additional_args={ + "complete_input_dict": data, + "api_base": api_base, + "headers": headers, + }, + ) + print_verbose(f"_is_function_call: {_is_function_call}") + if acompletion is True: + if ( + stream is True + ): # if function call - fake the streaming (need complete blocks for output parsing in openai format) + print_verbose("makes async azure anthropic streaming POST request") + data["stream"] = stream + return self.acompletion_stream_function( + model=model, + messages=messages, + data=data, + api_base=api_base, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + encoding=encoding, + api_key=api_key, + logging_obj=logging_obj, + optional_params=optional_params, + stream=stream, + _is_function_call=_is_function_call, + json_mode=json_mode, + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=headers, + timeout=timeout, + client=( + client + if client is not None and isinstance(client, AsyncHTTPHandler) + else None + ), + ) + else: + return self.acompletion_function( + model=model, + messages=messages, + data=data, + api_base=api_base, + custom_prompt_dict=custom_prompt_dict, + model_response=model_response, + print_verbose=print_verbose, + encoding=encoding, + api_key=api_key, + provider_config=config, + logging_obj=logging_obj, + optional_params=optional_params, + stream=stream, + _is_function_call=_is_function_call, + litellm_params=litellm_params, + logger_fn=logger_fn, + headers=headers, + client=client, + json_mode=json_mode, + timeout=timeout, + ) + else: + ## COMPLETION CALL + if ( + stream is True + ): # if function call - fake the streaming (need complete blocks for output parsing in openai format) + data["stream"] = stream + # Import the make_sync_call from parent + from litellm.llms.anthropic.chat.handler import make_sync_call + + completion_stream, response_headers = make_sync_call( + client=client, + api_base=api_base, + headers=headers, # type: ignore + data=json.dumps(data), + model=model, + messages=messages, + logging_obj=logging_obj, + timeout=timeout, + json_mode=json_mode, + ) + from litellm.llms.anthropic.common_utils import ( + process_anthropic_headers, + ) + + return CustomStreamWrapper( + completion_stream=completion_stream, + model=model, + custom_llm_provider="azure_ai", + logging_obj=logging_obj, + _response_headers=process_anthropic_headers(response_headers), + ) + + else: + if client is None or not isinstance(client, HTTPHandler): + from litellm.llms.custom_httpx.http_handler import _get_httpx_client + + client = _get_httpx_client(params={"timeout": timeout}) + else: + client = client + + try: + response = client.post( + api_base, + headers=headers, + data=json.dumps(data), + timeout=timeout, + ) + except Exception as e: + from litellm.llms.anthropic.common_utils import AnthropicError + + status_code = getattr(e, "status_code", 500) + error_headers = getattr(e, "headers", None) + error_text = getattr(e, "text", str(e)) + error_response = getattr(e, "response", None) + if error_headers is None and error_response: + error_headers = getattr(error_response, "headers", None) + if error_response and hasattr(error_response, "text"): + error_text = getattr(error_response, "text", error_text) + raise AnthropicError( + message=error_text, + status_code=status_code, + headers=error_headers, + ) + + return config.transform_response( + model=model, + raw_response=response, + model_response=model_response, + logging_obj=logging_obj, + api_key=api_key, + request_data=data, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + encoding=encoding, + json_mode=json_mode, + ) + diff --git a/litellm/llms/azure_ai/anthropic/messages_transformation.py b/litellm/llms/azure_ai/anthropic/messages_transformation.py new file mode 100644 index 00000000000..55818cc07d6 --- /dev/null +++ b/litellm/llms/azure_ai/anthropic/messages_transformation.py @@ -0,0 +1,117 @@ +""" +Azure Anthropic messages transformation config - extends AnthropicMessagesConfig with Azure authentication +""" +from typing import TYPE_CHECKING, Any, List, Optional, Tuple + +from litellm.llms.anthropic.experimental_pass_through.messages.transformation import ( + AnthropicMessagesConfig, +) +from litellm.llms.azure.common_utils import BaseAzureLLM +from litellm.types.router import GenericLiteLLMParams + +if TYPE_CHECKING: + pass + + +class AzureAnthropicMessagesConfig(AnthropicMessagesConfig): + """ + Azure Anthropic messages configuration that extends AnthropicMessagesConfig. + The only difference is authentication - Azure uses x-api-key header (not api-key) + and Azure endpoint format. + """ + + def validate_anthropic_messages_environment( + self, + headers: dict, + model: str, + messages: List[Any], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Tuple[dict, Optional[str]]: + """ + Validate environment and set up Azure authentication headers for /v1/messages endpoint. + Azure Anthropic uses x-api-key header (not api-key). + """ + # Convert dict to GenericLiteLLMParams if needed + if isinstance(litellm_params, dict): + if api_key and "api_key" not in litellm_params: + litellm_params = {**litellm_params, "api_key": api_key} + litellm_params_obj = GenericLiteLLMParams(**litellm_params) + else: + litellm_params_obj = litellm_params or GenericLiteLLMParams() + if api_key and not litellm_params_obj.api_key: + litellm_params_obj.api_key = api_key + + # Use Azure authentication logic + headers = BaseAzureLLM._base_validate_azure_environment( + headers=headers, litellm_params=litellm_params_obj + ) + + # Azure Anthropic uses x-api-key header (not api-key) + # Convert api-key to x-api-key if present + if "api-key" in headers and "x-api-key" not in headers: + headers["x-api-key"] = headers.pop("api-key") + + # Set anthropic-version header + if "anthropic-version" not in headers: + headers["anthropic-version"] = "2023-06-01" + + # Set content-type header + if "content-type" not in headers: + headers["content-type"] = "application/json" + + # Update headers with optional anthropic beta features + headers = self._update_headers_with_optional_anthropic_beta( + headers=headers, + context_management=optional_params.get("context_management"), + ) + + return headers, api_base + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for Azure Anthropic /v1/messages endpoint. + Azure Foundry endpoint format: https://.services.ai.azure.com/anthropic/v1/messages + """ + from litellm.secret_managers.main import get_secret_str + + api_base = api_base or get_secret_str("AZURE_API_BASE") + if api_base is None: + raise ValueError( + "Missing Azure API Base - Please set `api_base` or `AZURE_API_BASE` environment variable. " + "Expected format: https://.services.ai.azure.com/anthropic" + ) + + # Ensure the URL ends with /v1/messages + api_base = api_base.rstrip("/") + if api_base.endswith("/v1/messages"): + # Already correct + pass + elif api_base.endswith("/anthropic/v1/messages"): + # Already correct + pass + else: + # Check if /anthropic is already in the path + if "/anthropic" in api_base: + # /anthropic exists, ensure we end with /anthropic/v1/messages + # Extract the base URL up to and including /anthropic + parts = api_base.split("/anthropic", 1) + api_base = parts[0] + "/anthropic" + else: + # /anthropic not in path, add it + api_base = api_base + "/anthropic" + # Add /v1/messages + api_base = api_base + "/v1/messages" + + return api_base + diff --git a/litellm/llms/azure_ai/anthropic/transformation.py b/litellm/llms/azure_ai/anthropic/transformation.py new file mode 100644 index 00000000000..150ad0a48b2 --- /dev/null +++ b/litellm/llms/azure_ai/anthropic/transformation.py @@ -0,0 +1,122 @@ +""" +Azure Anthropic transformation config - extends AnthropicConfig with Azure authentication +""" +from typing import TYPE_CHECKING, Dict, List, Optional, Union + +from litellm.llms.anthropic.chat.transformation import AnthropicConfig +from litellm.llms.azure.common_utils import BaseAzureLLM +from litellm.types.llms.openai import AllMessageValues +from litellm.types.router import GenericLiteLLMParams + +if TYPE_CHECKING: + pass + + +class AzureAnthropicConfig(AnthropicConfig): + """ + Azure Anthropic configuration that extends AnthropicConfig. + The only difference is authentication - Azure uses api-key header or Azure AD token + instead of x-api-key header. + """ + + @property + def custom_llm_provider(self) -> Optional[str]: + return "azure_ai" + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: Union[dict, GenericLiteLLMParams], + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> Dict: + """ + Validate environment and set up Azure authentication headers. + Azure supports: + 1. API key via 'api-key' header + 2. Azure AD token via 'Authorization: Bearer ' header + """ + # Convert dict to GenericLiteLLMParams if needed + if isinstance(litellm_params, dict): + # Ensure api_key is included if provided + if api_key and "api_key" not in litellm_params: + litellm_params = {**litellm_params, "api_key": api_key} + litellm_params_obj = GenericLiteLLMParams(**litellm_params) + else: + litellm_params_obj = litellm_params or GenericLiteLLMParams() + # Set api_key if provided and not already set + if api_key and not litellm_params_obj.api_key: + litellm_params_obj.api_key = api_key + + # Use Azure authentication logic + headers = BaseAzureLLM._base_validate_azure_environment( + headers=headers, litellm_params=litellm_params_obj + ) + + # Azure Anthropic uses x-api-key header (not api-key) + # Convert api-key to x-api-key if present + if "api-key" in headers and "x-api-key" not in headers: + headers["x-api-key"] = headers.pop("api-key") + + # Get tools and other anthropic-specific setup + tools = optional_params.get("tools") + prompt_caching_set = self.is_cache_control_set(messages=messages) + computer_tool_used = self.is_computer_tool_used(tools=tools) + mcp_server_used = self.is_mcp_server_used( + mcp_servers=optional_params.get("mcp_servers") + ) + pdf_used = self.is_pdf_used(messages=messages) + file_id_used = self.is_file_id_used(messages=messages) + user_anthropic_beta_headers = self._get_user_anthropic_beta_headers( + anthropic_beta_header=headers.get("anthropic-beta") + ) + + # Get anthropic headers (but we'll replace x-api-key with Azure auth) + anthropic_headers = self.get_anthropic_headers( + computer_tool_used=computer_tool_used, + prompt_caching_set=prompt_caching_set, + pdf_used=pdf_used, + api_key=api_key or "", # Azure auth is already in headers + file_id_used=file_id_used, + is_vertex_request=optional_params.get("is_vertex_request", False), + user_anthropic_beta_headers=user_anthropic_beta_headers, + mcp_server_used=mcp_server_used, + ) + # Merge headers - Azure auth (api-key or Authorization) takes precedence + headers = {**anthropic_headers, **headers} + + # Ensure anthropic-version header is set + if "anthropic-version" not in headers: + headers["anthropic-version"] = "2023-06-01" + + return headers + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform request using parent AnthropicConfig, then remove extra_body if present. + Azure Anthropic doesn't support extra_body parameter. + """ + # Call parent transform_request + data = super().transform_request( + model=model, + messages=messages, + optional_params=optional_params, + litellm_params=litellm_params, + headers=headers, + ) + + # Remove extra_body if present (Azure Anthropic doesn't support it) + data.pop("extra_body", None) + + return data + diff --git a/litellm/llms/azure_ai/embed/handler.py b/litellm/llms/azure_ai/embed/handler.py index 13b8cc4cf29..67733d1ccb5 100644 --- a/litellm/llms/azure_ai/embed/handler.py +++ b/litellm/llms/azure_ai/embed/handler.py @@ -58,7 +58,7 @@ class AzureAIEmbedding(OpenAIChatCompletion): data: ImageEmbeddingRequest, timeout: float, logging_obj, - model_response: litellm.EmbeddingResponse, + model_response: EmbeddingResponse, optional_params: dict, api_key: Optional[str], api_base: Optional[str], @@ -138,7 +138,7 @@ class AzureAIEmbedding(OpenAIChatCompletion): input: List, timeout: float, logging_obj, - model_response: litellm.EmbeddingResponse, + model_response: EmbeddingResponse, optional_params: dict, api_key: Optional[str] = None, api_base: Optional[str] = None, diff --git a/litellm/llms/base_llm/guardrail_translation/base_translation.py b/litellm/llms/base_llm/guardrail_translation/base_translation.py index 4599af1b745..7106c207bd6 100644 --- a/litellm/llms/base_llm/guardrail_translation/base_translation.py +++ b/litellm/llms/base_llm/guardrail_translation/base_translation.py @@ -1,17 +1,69 @@ from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, Dict, List, Optional if TYPE_CHECKING: from litellm.integrations.custom_guardrail import CustomGuardrail + from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj + from litellm.proxy._types import UserAPIKeyAuth class BaseTranslation(ABC): + @staticmethod + def transform_user_api_key_dict_to_metadata( + user_api_key_dict: Optional[Any], + ) -> Dict[str, Any]: + """ + Transform user_api_key_dict to a metadata dict with prefixed keys. + + Converts keys like 'user_id' to 'user_api_key_user_id' to clearly indicate + the source of the metadata. + + Args: + user_api_key_dict: UserAPIKeyAuth object or dict with user information + + Returns: + Dict with keys prefixed with 'user_api_key_' + """ + if user_api_key_dict is None: + return {} + + # Convert to dict if it's a Pydantic object + user_dict = ( + user_api_key_dict.model_dump() + if hasattr(user_api_key_dict, "model_dump") + else user_api_key_dict + ) + + if not isinstance(user_dict, dict): + return {} + + # Transform keys to be prefixed with 'user_api_key_' + transformed = {} + for key, value in user_dict.items(): + # Skip None values and internal fields + if value is None or key.startswith("_"): + continue + + # If key already has the prefix, use as-is, otherwise add prefix + if key.startswith("user_api_key_"): + transformed[key] = value + else: + transformed[f"user_api_key_{key}"] = value + + return transformed + @abstractmethod async def process_input_messages( self, data: dict, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, ) -> Any: + """ + Process input messages with guardrails. + + Note: user_api_key_dict metadata should be available in the data dict. + """ pass @abstractmethod @@ -19,5 +71,30 @@ class BaseTranslation(ABC): self, response: Any, guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, ) -> Any: + """ + Process output response with guardrails. + + Args: + response: The response object from the LLM + guardrail_to_apply: The guardrail instance to apply + litellm_logging_obj: Optional logging object + user_api_key_dict: User API key metadata (passed separately since response doesn't contain it) + """ pass + + async def process_output_streaming_response( + self, + responses_so_far: List[Any], + guardrail_to_apply: "CustomGuardrail", + litellm_logging_obj: Optional["LiteLLMLoggingObj"] = None, + user_api_key_dict: Optional["UserAPIKeyAuth"] = None, + ) -> Any: + """ + Process output streaming response with guardrails. + + Optional to override in subclasses. + """ + return responses_so_far diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 72e270428ac..816b93edd20 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -353,6 +353,10 @@ class BaseAWSLLM: model_id = BaseAWSLLM._get_model_id_from_model_with_spec( model_id, spec="deepseek_r1" ) + elif provider == "openai" and "openai/" in model_id: + model_id = BaseAWSLLM._get_model_id_from_model_with_spec( + model_id, spec="openai" + ) return model_id @staticmethod @@ -387,9 +391,16 @@ class BaseAWSLLM: Handles scenarios like: 1. model=cohere.embed-english-v3:0 -> Returns `cohere` 2. model=amazon.titan-embed-text-v1 -> Returns `amazon` - 3. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` - 4. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + 3. model=amazon.nova-2-multimodal-embeddings-v1:0 -> Returns `nova` + 4. model=us.twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` + 5. model=twelvelabs.marengo-embed-2-7-v1:0 -> Returns `twelvelabs` """ + # Special case: Check for "nova" in model name first (before "amazon") + # This handles amazon.nova-* models + if "nova" in model.lower(): + if "nova" in get_args(BEDROCK_EMBEDDING_PROVIDERS_LITERAL): + return cast(BEDROCK_EMBEDDING_PROVIDERS_LITERAL, "nova") + # Handle regional models like us.twelvelabs.marengo-embed-2-7-v1:0 if "." in model: parts = model.split(".") diff --git a/litellm/llms/bedrock/batches/handler.py b/litellm/llms/bedrock/batches/handler.py new file mode 100644 index 00000000000..4a26bd43348 --- /dev/null +++ b/litellm/llms/bedrock/batches/handler.py @@ -0,0 +1,96 @@ +from openai.types.batch import BatchRequestCounts +from openai.types.batch import Metadata as OpenAIBatchMetadata + +from litellm.types.utils import LiteLLMBatch + + +class BedrockBatchesHandler: + """ + Handler for Bedrock Batches. + + Specific providers/models needed some special handling. + + E.g. Twelve Labs Embedding Async Invoke + """ + @staticmethod + def _handle_async_invoke_status( + batch_id: str, aws_region_name: str, logging_obj=None, **kwargs + ) -> "LiteLLMBatch": + """ + Handle async invoke status check for AWS Bedrock. + + This is for Twelve Labs Embedding Async Invoke. + + Args: + batch_id: The async invoke ARN + aws_region_name: AWS region name + **kwargs: Additional parameters + + Returns: + dict: Status information including status, output_file_id (S3 URL), etc. + """ + import asyncio + + from litellm.llms.bedrock.embed.embedding import BedrockEmbedding + + async def _async_get_status(): + # Create embedding handler instance + embedding_handler = BedrockEmbedding() + + # Get the status of the async invoke job + status_response = await embedding_handler._get_async_invoke_status( + invocation_arn=batch_id, + aws_region_name=aws_region_name, + logging_obj=logging_obj, + **kwargs, + ) + + # Transform response to a LiteLLMBatch object + from litellm.types.utils import LiteLLMBatch + + openai_batch_metadata: OpenAIBatchMetadata = { + "output_file_id": status_response["outputDataConfig"][ + "s3OutputDataConfig" + ]["s3Uri"], + "failure_message": status_response.get("failureMessage") or "", + "model_arn": status_response["modelArn"], + } + + result = LiteLLMBatch( + id=status_response["invocationArn"], + object="batch", + status=status_response["status"], + created_at=status_response["submitTime"], + in_progress_at=status_response["lastModifiedTime"], + completed_at=status_response.get("endTime"), + failed_at=status_response.get("endTime") + if status_response["status"] == "failed" + else None, + request_counts=BatchRequestCounts( + total=1, + completed=1 if status_response["status"] == "completed" else 0, + failed=1 if status_response["status"] == "failed" else 0, + ), + metadata=openai_batch_metadata, + completion_window="24h", + endpoint="/v1/embeddings", + input_file_id="", + ) + + return result + + # Since this function is called from within an async context via run_in_executor, + # we need to create a new event loop in a thread to avoid conflicts + import concurrent.futures + + def run_in_thread(): + new_loop = asyncio.new_event_loop() + asyncio.set_event_loop(new_loop) + try: + return new_loop.run_until_complete(_async_get_status()) + finally: + new_loop.close() + + with concurrent.futures.ThreadPoolExecutor() as executor: + future = executor.submit(run_in_thread) + return future.result() diff --git a/litellm/llms/bedrock/chat/converse_handler.py b/litellm/llms/bedrock/chat/converse_handler.py index fd1f6f0c893..d5bd054118d 100644 --- a/litellm/llms/bedrock/chat/converse_handler.py +++ b/litellm/llms/bedrock/chat/converse_handler.py @@ -29,6 +29,7 @@ def make_sync_call( logging_obj: LiteLLMLoggingObject, json_mode: Optional[bool] = False, fake_stream: bool = False, + stream_chunk_size: int = 1024, ): if client is None: client = _get_httpx_client() # Create a new client if none provided @@ -66,7 +67,7 @@ def make_sync_call( ) else: decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) # LOGGING logging_obj.post_call( @@ -102,6 +103,7 @@ class BedrockConverseLLM(BaseAWSLLM): fake_stream: bool = False, json_mode: Optional[bool] = False, api_key: Optional[str] = None, + stream_chunk_size: int = 1024, ) -> CustomStreamWrapper: request_data = await litellm.AmazonConverseConfig()._async_transform_request( model=model, @@ -143,6 +145,7 @@ class BedrockConverseLLM(BaseAWSLLM): logging_obj=logging_obj, fake_stream=fake_stream, json_mode=json_mode, + stream_chunk_size=stream_chunk_size, ) streaming_response = CustomStreamWrapper( completion_stream=completion_stream, @@ -260,6 +263,7 @@ class BedrockConverseLLM(BaseAWSLLM): ): ## SETUP ## stream = optional_params.pop("stream", None) + stream_chunk_size = optional_params.pop("stream_chunk_size", 1024) unencoded_model_id = optional_params.pop("model_id", None) fake_stream = optional_params.pop("fake_stream", False) json_mode = optional_params.get("json_mode", False) @@ -356,7 +360,8 @@ class BedrockConverseLLM(BaseAWSLLM): json_mode=json_mode, fake_stream=fake_stream, credentials=credentials, - api_key=api_key + api_key=api_key, + stream_chunk_size=stream_chunk_size, ) # type: ignore ### ASYNC COMPLETION return self.async_completion( @@ -433,6 +438,7 @@ class BedrockConverseLLM(BaseAWSLLM): logging_obj=logging_obj, json_mode=json_mode, fake_stream=fake_stream, + stream_chunk_size=stream_chunk_size, ) streaming_response = CustomStreamWrapper( completion_stream=completion_stream, diff --git a/litellm/llms/bedrock/chat/converse_transformation.py b/litellm/llms/bedrock/chat/converse_transformation.py index d76a3c31b51..705f3c9e630 100644 --- a/litellm/llms/bedrock/chat/converse_transformation.py +++ b/litellm/llms/bedrock/chat/converse_transformation.py @@ -246,6 +246,93 @@ class AmazonConverseConfig(BaseConfig): llm_provider="bedrock", ) + def _is_nova_lite_2_model(self, model: str) -> bool: + """ + Check if the model is a Nova Lite 2 model that supports reasoningConfig. + + Nova Lite 2 models use a different reasoning configuration structure compared to + Anthropic's thinking parameter and GPT-OSS's reasoning_effort parameter. + + Supported models: + - amazon.nova-2-lite-v1:0 + - us.amazon.nova-2-lite-v1:0 + - eu.amazon.nova-2-lite-v1:0 + - apac.amazon.nova-2-lite-v1:0 + + Args: + model: The model identifier + + Returns: + True if the model is a Nova Lite 2 model, False otherwise + + Examples: + >>> config = AmazonConverseConfig() + >>> config._is_nova_lite_2_model("amazon.nova-2-lite-v1:0") + True + >>> config._is_nova_lite_2_model("us.amazon.nova-2-lite-v1:0") + True + >>> config._is_nova_lite_2_model("amazon.nova-pro-1-5-v1:0") + False + >>> config._is_nova_lite_2_model("amazon.nova-pro-v1:0") + False + """ + # Remove regional prefix if present (us., eu., apac.) + model_without_region = model + for prefix in ["us.", "eu.", "apac."]: + if model.startswith(prefix): + model_without_region = model[len(prefix) :] + break + + # Check if the model is specifically Nova Lite 2 + return "nova-2-lite" in model_without_region + + def _transform_reasoning_effort_to_reasoning_config( + self, reasoning_effort: str + ) -> dict: + """ + Transform reasoning_effort parameter to Nova 2 reasoningConfig structure. + + Nova 2 models use a reasoningConfig structure in additionalModelRequestFields + that differs from both Anthropic's thinking parameter and GPT-OSS's reasoning_effort. + + Args: + reasoning_effort: The reasoning effort level, must be "low" or "high" + + Returns: + dict: A dictionary containing the reasoningConfig structure: + { + "reasoningConfig": { + "type": "enabled", + "maxReasoningEffort": "low" | "medium" |"high" + } + } + + Raises: + BadRequestError: If reasoning_effort is not "low", "medium" or "high" + + Examples: + >>> config = AmazonConverseConfig() + >>> config._transform_reasoning_effort_to_reasoning_config("high") + {'reasoningConfig': {'type': 'enabled', 'maxReasoningEffort': 'high'}} + >>> config._transform_reasoning_effort_to_reasoning_config("low") + {'reasoningConfig': {'type': 'enabled', 'maxReasoningEffort': 'low'}} + """ + valid_values = ["low", "medium", "high"] + if reasoning_effort not in valid_values: + raise litellm.exceptions.BadRequestError( + message=f"Invalid reasoning_effort value '{reasoning_effort}' for Nova 2 models. " + f"Supported values: {valid_values}", + model="amazon.nova-2-lite-v1:0", + llm_provider="bedrock_converse", + ) + + return { + "reasoningConfig": { + "type": "enabled", + "maxReasoningEffort": reasoning_effort, + } + } + def get_supported_openai_params(self, model: str) -> List[str]: from litellm.utils import supports_function_calling @@ -299,6 +386,10 @@ class AmazonConverseConfig(BaseConfig): if "gpt-oss" in model: supported_params.append("reasoning_effort") + elif self._is_nova_lite_2_model(model): + # Nova Lite 2 models support reasoning_effort (transformed to reasoningConfig) + # These models use a different reasoning structure than Anthropic's thinking parameter + supported_params.append("reasoning_effort") elif ( "claude-3-7" in model or "claude-sonnet-4" in model @@ -564,6 +655,12 @@ class AmazonConverseConfig(BaseConfig): # GPT-OSS models: keep reasoning_effort as-is # It will be passed through to additionalModelRequestFields optional_params["reasoning_effort"] = value + elif self._is_nova_lite_2_model(model): + # Nova Lite 2 models: transform to reasoningConfig + reasoning_config = ( + self._transform_reasoning_effort_to_reasoning_config(value) + ) + optional_params.update(reasoning_config) else: # Anthropic and other models: convert to thinking parameter optional_params["thinking"] = AnthropicConfig._map_reasoning_effort( @@ -574,12 +671,27 @@ class AmazonConverseConfig(BaseConfig): self._validate_request_metadata(value) # type: ignore optional_params["requestMetadata"] = value - # Only update thinking tokens for non-GPT-OSS models - if "gpt-oss" not in model: + # Only update thinking tokens for non-GPT-OSS models and non-Nova-Lite-2 models + # Nova Lite 2 handles token budgeting differently through reasoningConfig + if "gpt-oss" not in model and not self._is_nova_lite_2_model(model): self.update_optional_params_with_thinking_tokens( non_default_params=non_default_params, optional_params=optional_params ) + final_is_thinking_enabled = self.is_thinking_enabled(optional_params) + if ( + final_is_thinking_enabled + and "tool_choice" in optional_params + ): + tool_choice_block = optional_params["tool_choice"] + if isinstance(tool_choice_block, dict): + if "any" in tool_choice_block or "tool" in tool_choice_block: + verbose_logger.info( + f"{model} does not support forced tool use (tool_choice='required' or specific tool) " + f"when reasoning is enabled. Changing tool_choice to 'auto'." + ) + optional_params["tool_choice"] = ToolChoiceValuesBlock(auto={}) + return optional_params def _translate_response_format_param( @@ -815,11 +927,21 @@ class AmazonConverseConfig(BaseConfig): user_betas = get_anthropic_beta_from_headers(headers) anthropic_beta_list.extend(user_betas) + # Filter out tool search tools - Bedrock Converse API doesn't support them + filtered_tools = [] + if original_tools: + for tool in original_tools: + tool_type = tool.get("type", "") + if tool_type in ("tool_search_tool_regex_20251119", "tool_search_tool_bm25_20251119"): + # Tool search not supported in Converse API - skip it + continue + filtered_tools.append(tool) + # Only separate tools if computer use tools are actually present - if original_tools and self.is_computer_use_tool_used(original_tools, model): + if filtered_tools and self.is_computer_use_tool_used(filtered_tools, model): # Separate computer use tools from regular function tools computer_use_tools, regular_tools = self._separate_computer_use_tools( - original_tools, model + filtered_tools, model ) # Process regular function tools using existing logic @@ -835,7 +957,7 @@ class AmazonConverseConfig(BaseConfig): additional_request_params["tools"] = transformed_computer_tools else: # No computer use tools, process all tools as regular tools - bedrock_tools = _bedrock_tools_pt(original_tools) + bedrock_tools = _bedrock_tools_pt(filtered_tools) # Set anthropic_beta in additional_request_params if we have any beta features if anthropic_beta_list: diff --git a/litellm/llms/bedrock/chat/invoke_handler.py b/litellm/llms/bedrock/chat/invoke_handler.py index b35e86cabd2..5e33a266449 100644 --- a/litellm/llms/bedrock/chat/invoke_handler.py +++ b/litellm/llms/bedrock/chat/invoke_handler.py @@ -73,6 +73,9 @@ bedrock_tool_name_mappings: InMemoryCache = InMemoryCache( max_size_in_memory=50, default_ttl=600 ) from litellm.llms.bedrock.chat.converse_transformation import AmazonConverseConfig +from litellm.llms.bedrock.chat.invoke_transformations.amazon_openai_transformation import ( + AmazonBedrockOpenAIConfig, +) converse_config = AmazonConverseConfig() @@ -189,6 +192,7 @@ async def make_call( fake_stream: bool = False, json_mode: Optional[bool] = False, bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None, + stream_chunk_size: int = 1024, ): try: if client is None: @@ -232,7 +236,7 @@ async def make_call( json_mode=json_mode, ) completion_stream = decoder.aiter_bytes( - response.aiter_bytes(chunk_size=1024) + response.aiter_bytes(chunk_size=stream_chunk_size) ) elif bedrock_invoke_provider == "deepseek_r1": decoder = AmazonDeepSeekR1StreamDecoder( @@ -240,12 +244,12 @@ async def make_call( sync_stream=False, ) completion_stream = decoder.aiter_bytes( - response.aiter_bytes(chunk_size=1024) + response.aiter_bytes(chunk_size=stream_chunk_size) ) else: decoder = AWSEventStreamDecoder(model=model) completion_stream = decoder.aiter_bytes( - response.aiter_bytes(chunk_size=1024) + response.aiter_bytes(chunk_size=stream_chunk_size) ) # LOGGING @@ -278,6 +282,7 @@ def make_sync_call( fake_stream: bool = False, json_mode: Optional[bool] = False, bedrock_invoke_provider: Optional[litellm.BEDROCK_INVOKE_PROVIDERS_LITERAL] = None, + stream_chunk_size: int = 1024, ): try: if client is None: @@ -318,16 +323,16 @@ def make_sync_call( sync_stream=True, json_mode=json_mode, ) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) elif bedrock_invoke_provider == "deepseek_r1": decoder = AmazonDeepSeekR1StreamDecoder( model=model, sync_stream=True, ) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) else: decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) # LOGGING logging_obj.post_call( @@ -401,6 +406,10 @@ class BedrockLLM(BaseAWSLLM): prompt = prompt_factory( model=model, messages=messages, custom_llm_provider="bedrock" ) + elif provider == "openai": + # OpenAI uses messages directly, no prompt conversion needed + # Return empty prompt as it won't be used + prompt = "" elif provider == "cohere": prompt, chat_history = cohere_message_pt(messages=messages) else: @@ -578,6 +587,30 @@ class BedrockLLM(BaseAWSLLM): ) elif provider == "meta" or provider == "llama": outputText = completion_response["generation"] + elif provider == "openai": + # OpenAI imported models use OpenAI Chat Completions format + if "choices" in completion_response and len(completion_response["choices"]) > 0: + choice = completion_response["choices"][0] + if "message" in choice: + outputText = choice["message"].get("content") + elif "text" in choice: # fallback for completion format + outputText = choice["text"] + + # Set finish reason + if "finish_reason" in choice: + model_response.choices[0].finish_reason = map_finish_reason( + choice["finish_reason"] + ) + + # Set usage if available + if "usage" in completion_response: + usage = completion_response["usage"] + _usage = litellm.Usage( + prompt_tokens=usage.get("prompt_tokens", 0), + completion_tokens=usage.get("completion_tokens", 0), + total_tokens=usage.get("total_tokens", 0), + ) + setattr(model_response, "usage", _usage) elif provider == "mistral": outputText = completion_response["outputs"][0]["text"] model_response.choices[0].finish_reason = completion_response[ @@ -698,6 +731,7 @@ class BedrockLLM(BaseAWSLLM): ## SETUP ## stream = optional_params.pop("stream", None) + stream_chunk_size = optional_params.pop("stream_chunk_size", 1024) provider = self.get_bedrock_invoke_provider(model) modelId = self.get_bedrock_model_id( @@ -895,6 +929,20 @@ class BedrockLLM(BaseAWSLLM): ): # completion(top_k=3) > anthropic_config(top_k=3) <- allows for dynamic variables to be passed in inference_params[k] = v data = json.dumps({"prompt": prompt, **inference_params}) + elif provider == "openai": + ## OpenAI imported models use OpenAI Chat Completions format (messages-based) + # Use AmazonBedrockOpenAIConfig for proper OpenAI transformation + openai_config = AmazonBedrockOpenAIConfig() + supported_params = openai_config.get_supported_openai_params(model=model) + + # Filter to only supported OpenAI params + filtered_params = { + k: v for k, v in inference_params.items() + if k in supported_params + } + + # OpenAI uses messages format, not prompt + data = json.dumps({"messages": messages, **filtered_params}) else: ## LOGGING logging_obj.pre_call( @@ -958,6 +1006,7 @@ class BedrockLLM(BaseAWSLLM): headers=prepped.headers, timeout=timeout, client=client, + stream_chunk_size=stream_chunk_size, ) # type: ignore ### ASYNC COMPLETION return self.async_completion( @@ -1003,7 +1052,7 @@ class BedrockLLM(BaseAWSLLM): decoder = AWSEventStreamDecoder(model=model) - completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=1024)) + completion_stream = decoder.iter_bytes(response.iter_bytes(chunk_size=stream_chunk_size)) streaming_response = CustomStreamWrapper( completion_stream=completion_stream, model=model, @@ -1123,6 +1172,7 @@ class BedrockLLM(BaseAWSLLM): logger_fn=None, headers={}, client: Optional[AsyncHTTPHandler] = None, + stream_chunk_size: int = 1024, ) -> CustomStreamWrapper: # The call is not made here; instead, we prepare the necessary objects for the stream. @@ -1138,6 +1188,7 @@ class BedrockLLM(BaseAWSLLM): messages=messages, logging_obj=logging_obj, fake_stream=True if "ai21" in api_base else False, + stream_chunk_size=stream_chunk_size, ), model=model, custom_llm_provider="bedrock", diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py index a81d55f0ad2..3506c8f1cc0 100644 --- a/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_nova_transformation.py @@ -10,7 +10,6 @@ from typing import Any, List, Optional import httpx -import litellm from litellm.litellm_core_utils.litellm_logging import Logging from litellm.types.llms.bedrock import BedrockInvokeNovaRequest from litellm.types.llms.openai import AllMessageValues @@ -80,7 +79,7 @@ class AmazonInvokeNovaConfig(AmazonInvokeConfig, AmazonConverseConfig): encoding: Any, api_key: Optional[str] = None, json_mode: Optional[bool] = None, - ) -> litellm.ModelResponse: + ) -> ModelResponse: return AmazonConverseConfig.transform_response( self, model, diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_openai_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_openai_transformation.py new file mode 100644 index 00000000000..ee07b71ef15 --- /dev/null +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_openai_transformation.py @@ -0,0 +1,186 @@ +""" +Transformation for Bedrock imported models that use OpenAI Chat Completions format. + +Use this for models imported into Bedrock that accept the OpenAI API format. +Model format: bedrock/openai/ + +Example: bedrock/openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/abc123 +""" + +from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union + +import httpx + +from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM +from litellm.llms.bedrock.common_utils import BedrockError +from litellm.llms.openai.chat.gpt_transformation import OpenAIGPTConfig +from litellm.types.llms.openai import AllMessageValues + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class AmazonBedrockOpenAIConfig(OpenAIGPTConfig, BaseAWSLLM): + """ + Configuration for Bedrock imported models that use OpenAI Chat Completions format. + + This class handles the transformation of requests and responses for Bedrock + imported models that accept the OpenAI API format directly. + + Inherits from OpenAIGPTConfig to leverage standard OpenAI parameter handling + and response transformation, while adding Bedrock-specific URL generation + and AWS request signing. + + Usage: + model = "bedrock/openai/arn:aws:bedrock:us-east-1:123456789012:imported-model/abc123" + """ + + def __init__(self, **kwargs): + OpenAIGPTConfig.__init__(self, **kwargs) + BaseAWSLLM.__init__(self, **kwargs) + + @property + def custom_llm_provider(self) -> Optional[str]: + return "bedrock" + + def _get_openai_model_id(self, model: str) -> str: + """ + Extract the actual model ID from the LiteLLM model name. + + Input format: bedrock/openai/ + Returns: + """ + # Remove bedrock/ prefix if present + if model.startswith("bedrock/"): + model = model[8:] + + # Remove openai/ prefix + if model.startswith("openai/"): + model = model[7:] + + return model + + def get_complete_url( + self, + api_base: Optional[str], + api_key: Optional[str], + model: str, + optional_params: dict, + litellm_params: dict, + stream: Optional[bool] = None, + ) -> str: + """ + Get the complete URL for the Bedrock invoke endpoint. + + Uses the standard Bedrock invoke endpoint format. + """ + model_id = self._get_openai_model_id(model) + + # Get AWS region + aws_region_name = self._get_aws_region_name( + optional_params=optional_params, model=model + ) + + # Get runtime endpoint + aws_bedrock_runtime_endpoint = optional_params.get( + "aws_bedrock_runtime_endpoint", None + ) + endpoint_url, proxy_endpoint_url = self.get_runtime_endpoint( + api_base=api_base, + aws_bedrock_runtime_endpoint=aws_bedrock_runtime_endpoint, + aws_region_name=aws_region_name, + ) + + # Build the invoke URL + if stream: + endpoint_url = f"{endpoint_url}/model/{model_id}/invoke-with-response-stream" + else: + endpoint_url = f"{endpoint_url}/model/{model_id}/invoke" + + return endpoint_url + + def sign_request( + self, + headers: dict, + optional_params: dict, + request_data: dict, + api_base: str, + api_key: Optional[str] = None, + model: Optional[str] = None, + stream: Optional[bool] = None, + fake_stream: Optional[bool] = None, + ) -> Tuple[dict, Optional[bytes]]: + """ + Sign the request using AWS Signature Version 4. + """ + return self._sign_request( + service_name="bedrock", + headers=headers, + optional_params=optional_params, + request_data=request_data, + api_base=api_base, + api_key=api_key, + model=model, + stream=stream, + fake_stream=fake_stream, + ) + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + """ + Transform the request to OpenAI Chat Completions format for Bedrock imported models. + + Removes AWS-specific params and stream param (handled separately in URL), + then delegates to parent class for standard OpenAI request transformation. + """ + # Remove stream from optional_params as it's handled separately in URL + optional_params.pop("stream", None) + + # Remove AWS-specific params that shouldn't be in the request body + inference_params = { + k: v + for k, v in optional_params.items() + if k not in self.aws_authentication_params + } + + # Use parent class transform_request for OpenAI format + return super().transform_request( + model=self._get_openai_model_id(model), + messages=messages, + optional_params=inference_params, + litellm_params=litellm_params, + headers=headers, + ) + + def validate_environment( + self, + headers: dict, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + api_key: Optional[str] = None, + api_base: Optional[str] = None, + ) -> dict: + """ + Validate the environment and return headers. + + For Bedrock, we don't need Bearer token auth since we use AWS SigV4. + """ + return headers + + def get_error_class( + self, error_message: str, status_code: int, headers: Union[dict, httpx.Headers] + ) -> BedrockError: + """Return the appropriate error class for Bedrock.""" + return BedrockError(status_code=status_code, message=error_message) diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py new file mode 100644 index 00000000000..c532d8ea27c --- /dev/null +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_qwen2_transformation.py @@ -0,0 +1,98 @@ +""" +Handles transforming requests for `bedrock/invoke/{qwen2} models` + +Inherits from `AmazonQwen3Config` since Qwen2 and Qwen3 architectures are mostly similar. +The main difference is in the response format: Qwen2 uses "text" field while Qwen3 uses "generation" field. + +Qwen2 + Invoke API Tutorial: https://docs.aws.amazon.com/bedrock/latest/userguide/invoke-imported-model.html +""" + +from typing import Any, List, Optional + +import httpx + +from litellm.llms.bedrock.chat.invoke_transformations.amazon_qwen3_transformation import ( + AmazonQwen3Config, +) +from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( + LiteLLMLoggingObj, +) +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import ModelResponse + + +class AmazonQwen2Config(AmazonQwen3Config): + """ + Config for sending `qwen2` requests to `/bedrock/invoke/` + + Inherits from AmazonQwen3Config since Qwen2 and Qwen3 architectures are mostly similar. + The main difference is in the response format: Qwen2 uses "text" field while Qwen3 uses "generation" field. + + Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/invoke-imported-model.html + """ + + def transform_response( + self, + model: str, + raw_response: httpx.Response, + model_response: ModelResponse, + logging_obj: LiteLLMLoggingObj, + request_data: dict, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + encoding: Any, + api_key: Optional[str] = None, + json_mode: Optional[bool] = None, + ) -> ModelResponse: + """ + Transform Qwen2 Bedrock response to OpenAI format + + Qwen2 uses "text" field, but we also support "generation" field for compatibility. + """ + try: + if hasattr(raw_response, 'json'): + response_data = raw_response.json() + else: + response_data = raw_response + + # Extract the generated text - Qwen2 uses "text" field, but also support "generation" for compatibility + generated_text = response_data.get("generation", "") or response_data.get("text", "") + + # Clean up the response (remove assistant start token if present) + if generated_text.startswith("<|im_start|>assistant\n"): + generated_text = generated_text[len("<|im_start|>assistant\n"):] + if generated_text.endswith("<|im_end|>"): + generated_text = generated_text[:-len("<|im_end|>")] + + # Set the content in the existing model_response structure + if hasattr(model_response, 'choices') and len(model_response.choices) > 0: + choice = model_response.choices[0] + if hasattr(choice, 'message'): + choice.message.content = generated_text + choice.finish_reason = "stop" + else: + # Handle streaming choices + choice.delta.content = generated_text + choice.finish_reason = "stop" + + # Set usage information if available in response + if "usage" in response_data: + usage_data = response_data["usage"] + if hasattr(model_response, 'usage'): + model_response.usage.prompt_tokens = usage_data.get("prompt_tokens", 0) + model_response.usage.completion_tokens = usage_data.get("completion_tokens", 0) + model_response.usage.total_tokens = usage_data.get("total_tokens", 0) + + return model_response + + except Exception as e: + if logging_obj: + logging_obj.post_call( + input=messages, + api_key=api_key, + original_response=raw_response, + additional_args={"error": str(e)}, + ) + raise e + diff --git a/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py new file mode 100644 index 00000000000..62e98f7472f --- /dev/null +++ b/litellm/llms/bedrock/chat/invoke_transformations/amazon_twelvelabs_pegasus_transformation.py @@ -0,0 +1,280 @@ +""" +Transforms OpenAI-style requests into TwelveLabs Pegasus 1.2 requests for Bedrock. + +Reference: +https://docs.twelvelabs.io/docs/models/pegasus +""" + +import json +import time +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +import httpx + +import litellm +from litellm._logging import verbose_logger +from litellm.litellm_core_utils.core_helpers import map_finish_reason +from litellm.llms.base_llm.base_utils import type_to_response_format_param +from litellm.llms.base_llm.chat.transformation import BaseConfig +from litellm.llms.bedrock.chat.invoke_transformations.base_invoke_transformation import ( + AmazonInvokeConfig, +) +from litellm.llms.bedrock.common_utils import BedrockError +from litellm.types.llms.openai import AllMessageValues +from litellm.types.utils import ModelResponse, Usage +from litellm.utils import get_base64_str + +if TYPE_CHECKING: + from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj + + LiteLLMLoggingObj = _LiteLLMLoggingObj +else: + LiteLLMLoggingObj = Any + + +class AmazonTwelveLabsPegasusConfig(AmazonInvokeConfig, BaseConfig): + """ + Handles transforming OpenAI-style requests into Bedrock InvokeModel requests for + `twelvelabs.pegasus-1-2-v1:0`. + + Pegasus 1.2 requires an `inputPrompt` and a `mediaSource` that either references + an S3 object or a base64-encoded clip. Optional OpenAI params (temperature, + response_format, max_tokens) are translated to the TwelveLabs schema. + """ + + def get_supported_openai_params(self, model: str) -> List[str]: + return [ + "max_tokens", + "max_completion_tokens", + "temperature", + "response_format", + ] + + def map_openai_params( + self, + non_default_params: dict, + optional_params: dict, + model: str, + drop_params: bool, + ) -> dict: + for param, value in non_default_params.items(): + if param in {"max_tokens", "max_completion_tokens"}: + optional_params["maxOutputTokens"] = value + if param == "temperature": + optional_params["temperature"] = value + if param == "response_format": + optional_params["responseFormat"] = self._normalize_response_format( + value + ) + return optional_params + + def _normalize_response_format(self, value: Any) -> Any: + """Normalize response_format to TwelveLabs format. + + TwelveLabs expects: + { + "jsonSchema": {...} + } + + But OpenAI format is: + { + "type": "json_schema", + "json_schema": { + "name": "...", + "schema": {...} + } + } + """ + if isinstance(value, dict): + # If it has json_schema field, extract and transform it + if "json_schema" in value: + json_schema = value["json_schema"] + # Extract the schema if nested + if isinstance(json_schema, dict) and "schema" in json_schema: + return {"jsonSchema": json_schema["schema"]} + # Otherwise use json_schema directly + return {"jsonSchema": json_schema} + # If it already has jsonSchema, return as is + if "jsonSchema" in value: + return value + # Otherwise return the dict as is + return value + return type_to_response_format_param(response_format=value) or value + + def transform_request( + self, + model: str, + messages: List[AllMessageValues], + optional_params: dict, + litellm_params: dict, + headers: dict, + ) -> dict: + input_prompt = self._convert_messages_to_prompt(messages=messages) + request_data: Dict[str, Any] = {"inputPrompt": input_prompt} + + media_source = self._build_media_source(optional_params) + if media_source is not None: + request_data["mediaSource"] = media_source + + # Handle temperature and maxOutputTokens + for key in ("temperature", "maxOutputTokens"): + if key in optional_params: + request_data[key] = optional_params.get(key) + + # Handle responseFormat - transform to TwelveLabs format + if "responseFormat" in optional_params: + response_format = optional_params["responseFormat"] + transformed_format = self._normalize_response_format(response_format) + if transformed_format: + request_data["responseFormat"] = transformed_format + + return request_data + + def _build_media_source(self, optional_params: dict) -> Optional[dict]: + direct_source = optional_params.get("mediaSource") or optional_params.get( + "media_source" + ) + if isinstance(direct_source, dict): + return direct_source + + base64_input = optional_params.get("video_base64") or optional_params.get( + "base64_string" + ) + if base64_input: + return {"base64String": get_base64_str(base64_input)} + + s3_uri = ( + optional_params.get("video_s3_uri") + or optional_params.get("s3_uri") + or optional_params.get("media_source_s3_uri") + ) + if s3_uri: + s3_location = {"uri": s3_uri} + bucket_owner = ( + optional_params.get("video_s3_bucket_owner") + or optional_params.get("s3_bucket_owner") + or optional_params.get("media_source_bucket_owner") + ) + if bucket_owner: + s3_location["bucketOwner"] = bucket_owner + return {"s3Location": s3_location} + return None + + def _convert_messages_to_prompt(self, messages: List[AllMessageValues]) -> str: + prompt_parts: List[str] = [] + for message in messages: + role = message.get("role", "user") + content = message.get("content", "") + if isinstance(content, list): + text_fragments = [] + for item in content: + if isinstance(item, dict): + item_type = item.get("type") + if item_type == "text": + text_fragments.append(item.get("text", "")) + elif item_type == "image_url": + text_fragments.append("") + elif item_type == "video_url": + text_fragments.append("