diff --git a/backend/routes/allowlist.py b/backend/routes/allowlist.py index d1a576aeb33..2f65f99c292 100644 --- a/backend/routes/allowlist.py +++ b/backend/routes/allowlist.py @@ -120,6 +120,9 @@ BACKEND_PATH_PREFIXES: tuple[str, ...] = ( "/robots.txt", # Health (k8s probes) "/health", + # Plugin system + "/api/plugins", + "/plugin-proxy/", ) BACKEND_EXACT_PATHS: frozenset[str] = frozenset( diff --git a/docs/plugin_architecture.md b/docs/plugin_architecture.md new file mode 100644 index 00000000000..8801761531d --- /dev/null +++ b/docs/plugin_architecture.md @@ -0,0 +1,141 @@ +# LiteLLM Plugin Architecture + +Plugins let external services appear as selectable modes in the litellm UI sidebar alongside the AI Gateway. + +--- + +## Quick start + +### 1. Configure the plugin + +Add a `plugins` block to your litellm `config.yaml`: + +```yaml +general_settings: + master_key: sk-... + plugins: + - name: my-plugin # unique identifier (no spaces) + display_name: My Plugin # shown in the UI dropdown + url: "https://my-plugin.example.com" + plugin_key: "sk-..." # plugin's own auth credential +``` + +`plugin_key` is injected as `Authorization: Bearer ` on every +request proxied through `/plugin-proxy/my-plugin/*`. The caller's litellm +credential is stripped before forwarding so the plugin never receives a live +litellm API key. + +### 2. Implement two endpoints on your service + +| Endpoint | Method | Purpose | +|---|---|---| +| `GET /api/plugin-manifest` | public | Returns plugin metadata for the UI | +| `POST /api/plugin-auth` | public | Decrypts the identity claim for seamless sign-in | + +#### `GET /api/plugin-manifest` + +```json +{ + "name": "my-plugin", + "display_name": "My Plugin", + "version": "1.0.0", + "nav_items": [ + { "key": "home", "label": "Home", "icon": "HomeOutlined", "path": "/" }, + { "key": "reports", "label": "Reports", "icon": "BarChartOutlined", "path": "/reports" } + ], + "capabilities": ["reports", "data"] +} +``` + +#### `POST /api/plugin-auth` + +Receives `{ "session_claim": "" }`. + +The proxy never shares `LITELLM_SALT_KEY` with your plugin. Each plugin is +provisioned with its own dedicated key, derived as +`HMAC-SHA256(LITELLM_SALT_KEY, plugin_name)`. Compute it once on the proxy +host and hand the result to your plugin as a secret (e.g. `PLUGIN_AUTH_KEY`): + +```bash +python -c 'import base64,hmac,hashlib,os; \ +print(base64.urlsafe_b64encode(hmac.new(os.environ["LITELLM_SALT_KEY"].encode(), b"my-plugin", hashlib.sha256).digest()).decode())' +``` + +A compromised plugin holding only this scoped key cannot recover +`LITELLM_SALT_KEY` or decrypt any other litellm secret. + +Decrypt and validate the claim with that key: + +```python +import json, os, time +from cryptography.fernet import Fernet + +_CLAIM_TTL_SECONDS = 30 + +def plugin_auth(session_claim: str) -> dict: + cipher = Fernet(os.environ["PLUGIN_AUTH_KEY"].encode()) + claim = json.loads(cipher.decrypt(session_claim.encode(), ttl=_CLAIM_TTL_SECONDS)) + if claim.get("plugin") != "my-plugin": + raise ValueError("claim audience mismatch") + if int(claim.get("exp", 0)) < int(time.time()): + raise ValueError("claim expired") + return claim +``` + +The claim is `{ "plugin", "user_id", "user_role", "exp" }`; it carries no +litellm bearer token. Establish the plugin's own session from `user_id` / +`user_role` and authenticate API calls back to litellm through the +`/plugin-proxy/my-plugin/*` reverse proxy, which injects `plugin_key` for you. + +--- + +## How iframe auth works + +``` +litellm UI + ├─ GET /api/plugins/auth-token -> { session_claim } + └─ postMessage({ type:"litellm-auth", session_claim }, pluginOrigin) + │ + ▼ +Plugin iframe browser + └─ POST /api/plugin-auth { session_claim } + │ + ▼ +Plugin server + ├─ decrypt(session_claim, PLUGIN_AUTH_KEY) -> { user_id, user_role, exp } + └─ establish plugin session -> stored in sessionStorage +``` + +No litellm bearer token ever leaves the proxy; the claim only conveys the +caller's identity and expires after 30 seconds. A postMessage intercept +yields ciphertext that is useless without the plugin's scoped key. + +--- + +## Proxy routes + +- `GET /api/plugins` — list registered plugins (`name`, `display_name`, `url`). `plugin_key` is **never** returned; it stays server-side. Requires an authenticated caller. +- `GET /api/plugins/auth-token?plugin_name=` — short-lived encrypted identity claim for the named plugin. Requires `LITELLM_SALT_KEY` to be set (503 otherwise) and the plugin to be registered (404 otherwise). +- `ANY /plugin-proxy/{name}/{path}` — authenticated reverse proxy to the plugin backend. Restricted to `proxy_admin`. + +--- + +## Reverse proxy behaviour + +When an admin (or server-to-server caller) hits `/plugin-proxy//`, the proxy authenticates the caller locally, then rewrites the request before forwarding it to the plugin's `url`: + +- **Every litellm credential header is stripped** — `Authorization`, `x-api-key`, `API-Key`, `x-goog-api-key`, `Ocp-Apim-Subscription-Key`, `x-litellm-api-key`, any configured `litellm_key_header_name`, plus `Cookie`. The plugin can never be handed the caller's live litellm key. +- **`plugin_key` is injected** as `Authorization: Bearer ` — the only credential the plugin receives. +- **Caller identity is forwarded** as `x-litellm-user-id` and `x-litellm-user-role` so the plugin can run its own authorization. These are informational, not credentials. +- **Responses are sandboxed** — `Content-Security-Policy: sandbox` and `X-Content-Type-Options: nosniff` are set so plugin-controlled bytes served from the litellm origin cannot execute against the dashboard. + +--- + +## Security checklist + +- [ ] `LITELLM_SALT_KEY` is set on the proxy and never shared with the plugin +- [ ] The plugin holds only its derived `HMAC(LITELLM_SALT_KEY, plugin_name)` key, provisioned as a dedicated secret +- [ ] `plugin_key` is a dedicated credential scoped to the plugin (not your litellm master key) +- [ ] Plugin's `POST /api/plugin-auth` enforces the claim's `plugin` audience and `exp` (30s TTL) +- [ ] Plugin treats `x-litellm-user-id` / `x-litellm-user-role` as identity hints, not as proof of authentication +- [ ] Plugin service URL uses HTTPS in production diff --git a/litellm/integrations/code_interpreter_interception/__init__.py b/litellm/integrations/code_interpreter_interception/__init__.py new file mode 100644 index 00000000000..2256356b6f5 --- /dev/null +++ b/litellm/integrations/code_interpreter_interception/__init__.py @@ -0,0 +1,15 @@ +""" +Code Interpreter Interception Module + +Converts the native OpenAI Responses ``code_interpreter`` tool into a function +tool, runs the model-emitted code in a sandbox, and feeds the result back into +the agentic loop. +""" + +from litellm.integrations.code_interpreter_interception.handler import ( + CodeInterpreterInterceptionLogger, +) + +__all__ = [ + "CodeInterpreterInterceptionLogger", +] diff --git a/litellm/integrations/code_interpreter_interception/handler.py b/litellm/integrations/code_interpreter_interception/handler.py new file mode 100644 index 00000000000..da8149eab9b --- /dev/null +++ b/litellm/integrations/code_interpreter_interception/handler.py @@ -0,0 +1,473 @@ +""" +Code Interpreter Interception Handler + +CustomLogger that swaps the native OpenAI Responses ``code_interpreter`` tool for +a function tool, executes the code the model emits inside a sandbox, and feeds the +captured stdout back through the typed agentic loop plan. +""" + +import json +import time +import uuid +from typing import Any, cast + +import litellm +from litellm._logging import verbose_logger +from litellm.integrations.custom_logger import CustomLogger +from litellm.types.integrations.code_interpreter_interception import ( + CodeInterpreterInterceptionConfig, +) +from litellm.types.integrations.custom_logger import ( + AgenticLoopPlan, + AgenticLoopRequestPatch, +) +from litellm.types.utils import CallTypes + +LITELLM_CODE_EXECUTION_TOOL_NAME = "litellm_code_execution" +_INTERCEPTION_ACTIVE_KEY = "_code_interpreter_interception_active" +_SANDBOX_KEY = "_code_interpreter_interception_sandbox_key" +_CACHE_TTL_SECONDS = 15 * 60 + + +def _resolve_sandbox_tool(sandbox_tool_name: str | None) -> dict[str, Any] | None: + try: + from litellm.sandbox.sandbox_tools import resolve_sandbox_tool + except ImportError: + return None + return resolve_sandbox_tool(sandbox_tool_name) + + +class CodeInterpreterInterceptionLogger(CustomLogger): + """ + CustomLogger that implements transparent code-interpreter execution loops. + + Flow: + 1. Replace the native ``code_interpreter`` tool with a function tool in the + pre-call hook so the model emits code as function-call arguments. + 2. Detect ``litellm_code_execution`` function calls in the model response. + 3. Run the emitted code in a sandbox (reused per request via a server-minted + sandbox key) and build a typed rerun plan that appends the + function_call_output. + """ + + def __init__( + self, + enabled: bool = True, + enabled_providers: list[str] | None = None, + sandbox_tool_name: str | None = None, + sandbox_config: Any | None = None, + ): + super().__init__() + self.enabled = enabled + self.enabled_providers = enabled_providers + self.sandbox_tool_name = sandbox_tool_name + self.sandbox_config = sandbox_config + self._container_cache: dict[str, tuple[Any, dict[str, Any] | None, float]] = {} + + @classmethod + def from_config_yaml( + cls, config: CodeInterpreterInterceptionConfig + ) -> "CodeInterpreterInterceptionLogger": + return cls( + enabled=bool(config.get("enabled", True)), + enabled_providers=config.get("enabled_providers"), + sandbox_tool_name=config.get("sandbox_tool_name"), + ) + + @staticmethod + def initialize_from_proxy_config( + litellm_settings: dict[str, Any], + callback_specific_params: dict[str, Any], + ) -> "CodeInterpreterInterceptionLogger": + params: CodeInterpreterInterceptionConfig = {} + if "code_interpreter_interception_params" in litellm_settings: + params = litellm_settings["code_interpreter_interception_params"] + elif "code_interpreter_interception" in callback_specific_params and isinstance( + callback_specific_params["code_interpreter_interception"], dict + ): + params = cast( + CodeInterpreterInterceptionConfig, + callback_specific_params["code_interpreter_interception"], + ) + return CodeInterpreterInterceptionLogger.from_config_yaml(params) + + async def async_pre_call_deployment_hook( + self, kwargs: dict[str, Any], call_type: CallTypes | None + ) -> dict | None: + if not kwargs.get("_agentic_loop_depth"): + kwargs.pop(_INTERCEPTION_ACTIVE_KEY, None) + kwargs.pop(_SANDBOX_KEY, None) + if not self.enabled: + return None + if call_type not in (CallTypes.responses, CallTypes.aresponses): + return None + if ( + self.enabled_providers is not None + and self._resolve_provider(kwargs) not in self.enabled_providers + ): + return None + + tools = kwargs.get("tools") + if not isinstance(tools, list): + return None + if not any( + isinstance(tool, dict) and tool.get("type") == "code_interpreter" + for tool in tools + ): + return None + + kwargs[_INTERCEPTION_ACTIVE_KEY] = True + kwargs[_SANDBOX_KEY] = uuid.uuid4().hex + if kwargs.get("stream"): + kwargs["stream"] = False + kwargs["_code_interpreter_interception_converted_stream"] = True + + function_tool = { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "description": "Execute python code in a sandbox and return stdout.", + "parameters": { + "type": "object", + "properties": {"code": {"type": "string"}}, + "required": ["code"], + }, + } + kwargs["tools"] = [ + ( + function_tool + if isinstance(tool, dict) and tool.get("type") == "code_interpreter" + else tool + ) + for tool in tools + ] + if self._tool_choice_targets_code_interpreter(kwargs.get("tool_choice")): + kwargs["tool_choice"] = { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + } + return kwargs + + @staticmethod + def _tool_choice_targets_code_interpreter(tool_choice: Any) -> bool: + if not isinstance(tool_choice, dict): + return False + return ( + tool_choice.get("type") == "code_interpreter" + or tool_choice.get("name") == "code_interpreter" + ) + + def _resolve_provider(self, kwargs: dict[str, Any]) -> str | None: + provider = kwargs.get("custom_llm_provider") + if provider: + return provider + model = kwargs.get("model") + if not isinstance(model, str): + return None + try: + return litellm.get_llm_provider(model=model)[1] + except Exception: + return None + + async def async_should_run_agentic_loop( + self, + response: Any, + model: str, + messages: list[dict], + tools: list[dict] | None, + stream: bool, + custom_llm_provider: str, + kwargs: dict, + ) -> tuple[bool, dict]: + if not self.enabled: + return False, {} + if not kwargs.get(_INTERCEPTION_ACTIVE_KEY): + return False, {} + if ( + self.enabled_providers is not None + and custom_llm_provider not in self.enabled_providers + ): + return False, {} + + tool_calls = self._extract_code_execution_tool_calls(response=response) + if not tool_calls: + return False, {} + + return True, {"tool_calls": tool_calls} + + async def async_build_agentic_loop_plan( + self, + tools: dict, + model: str, + messages: list[dict], + response: Any, + anthropic_messages_provider_config: Any, + anthropic_messages_optional_request_params: dict, + logging_obj: Any, + stream: bool, + kwargs: dict, + ) -> AgenticLoopPlan: + await self._prune_expired_cache() + tool_calls = cast(list[dict[str, Any]], tools.get("tool_calls", [])) + sandbox_key = kwargs.get(_SANDBOX_KEY) + container, params = await self._get_or_create_container(cache_key=sandbox_key) + + try: + container_id = getattr(container, "id", None) + input_list = self._normalize_messages(messages) + code_interpreter_calls = [] + for tool_call in tool_calls: + arguments = tool_call.get("arguments", "") + code = self._parse_code(arguments) + stdout = await self._run_tool_call( + container=container, params=params, arguments=arguments + ) + input_list.append( + { + "type": "function_call", + "call_id": tool_call.get("call_id"), + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": arguments, + } + ) + input_list.append( + { + "type": "function_call_output", + "call_id": tool_call.get("call_id"), + "output": stdout, + } + ) + code_interpreter_calls.append( + { + "id": f"ci_{uuid.uuid4().hex}", + "type": "code_interpreter_call", + "status": "completed", + "code": code, + "container_id": container_id, + "outputs": ( + [{"type": "logs", "logs": stdout}] if stdout else [] + ), + } + ) + except Exception: + await self._delete_container_for_cache_key(sandbox_key) + raise + + optional_params = anthropic_messages_optional_request_params + request_patch = AgenticLoopRequestPatch( + model=model, + messages=input_list, + tools=optional_params.get("tools"), + optional_params={k: v for k, v in optional_params.items() if k != "tools"}, + kwargs={k: v for k, v in kwargs.items() if k != "litellm_logging_obj"}, + ) + + return AgenticLoopPlan( + run_agentic_loop=True, + request_patch=request_patch, + metadata={ + "tool_type": "code_interpreter", + "sandbox_key": sandbox_key or "", + "code_interpreter_calls": code_interpreter_calls, + }, + ) + + async def async_agentic_loop_cleanup_hook( + self, plan: AgenticLoopPlan, kwargs: dict + ) -> None: + metadata = plan.metadata or {} if plan else {} + await self._delete_container_for_cache_key(metadata.get("sandbox_key")) + + async def async_post_agentic_loop_response_hook( + self, response: Any, plan: AgenticLoopPlan, kwargs: dict + ) -> Any: + metadata = plan.metadata or {} if plan else {} + await self._delete_container_for_cache_key(metadata.get("sandbox_key")) + + calls = metadata.get("code_interpreter_calls") + if not calls: + return response + + is_dict = isinstance(response, dict) + output = ( + response.get("output") if is_dict else getattr(response, "output", None) + ) + if not isinstance(output, list): + return response + + def _item_type(item: Any) -> Any: + return ( + item.get("type") + if isinstance(item, dict) + else getattr(item, "type", None) + ) + + insert_at = next( + (i for i, item in enumerate(output) if _item_type(item) == "message"), + len(output), + ) + new_output = output[:insert_at] + list(calls) + output[insert_at:] + if is_dict: + response["output"] = new_output + else: + response.output = new_output + return response + + @staticmethod + def _parse_code(arguments: str) -> str: + try: + return json.loads(arguments).get("code", "") if arguments else "" + except (json.JSONDecodeError, TypeError, AttributeError): + return "" + + async def _run_tool_call( + self, container: Any, params: dict[str, Any] | None, arguments: str + ) -> str: + try: + code = json.loads(arguments).get("code", "") if arguments else "" + except (json.JSONDecodeError, TypeError): + return "[invalid tool arguments: could not parse code]" + + result = await self._run_code(container=container, params=params, code=code) + if getattr(result, "error", None): + error = result.error + message = ( + error.get("value") or error.get("name") + if isinstance(error, dict) + else str(error) + ) + return f"[execution error] {message}" + return getattr(result, "stdout", "") or "" + + async def _get_or_create_container( + self, cache_key: str | None + ) -> tuple[Any, dict[str, Any] | None]: + if cache_key: + cached = self._container_cache.get(cache_key) + if cached is not None: + return cached[0], cached[1] + + container, params = await self._create_container() + if cache_key: + self._container_cache[cache_key] = (container, params, time.time()) + return container, params + + async def _create_container(self) -> tuple[Any, dict[str, Any] | None]: + if self.sandbox_config is not None: + return await self.sandbox_config.acreate_sandbox(), None + + params = _resolve_sandbox_tool(self.sandbox_tool_name) + if params is None: + raise ValueError( + "CodeInterpreterInterception: no sandbox available. Provide a " + "sandbox_config or configure a sandbox tool resolvable via " + "sandbox_tool_name." + ) + container = await litellm.acreate_sandbox( + provider=params["sandbox_provider"], + api_key=params.get("api_key"), + api_base=params.get("api_base"), + ) + return container, params + + async def _run_code( + self, container: Any, params: dict[str, Any] | None, code: str + ) -> Any: + if self.sandbox_config is not None: + return await self.sandbox_config.arun_code(container=container, code=code) + if params is None: + raise ValueError( + "CodeInterpreterInterception: no sandbox available to run code." + ) + return await litellm.arun_code( + provider=params["sandbox_provider"], + container=container, + code=code, + api_key=params.get("api_key"), + ) + + async def _delete_container( + self, container: Any, params: dict[str, Any] | None + ) -> None: + try: + if self.sandbox_config is not None: + await self.sandbox_config.adelete_sandbox(container=container) + return + if params is None: + return + await litellm.adelete_sandbox( + provider=params["sandbox_provider"], + container=container, + api_key=params.get("api_key"), + api_base=params.get("api_base"), + ) + except Exception: + verbose_logger.exception( + "CodeInterpreterInterception: failed to delete sandbox container" + ) + + async def _delete_container_for_cache_key(self, cache_key: str | None) -> None: + if not cache_key: + return + cached = self._container_cache.pop(cache_key, None) + if cached is None: + return + await self._delete_container(container=cached[0], params=cached[1]) + + def _normalize_messages(self, messages: Any) -> list[dict[str, Any]]: + if isinstance(messages, str): + return [{"role": "user", "content": messages}] + if isinstance(messages, list): + return list(messages) + return [] + + def _extract_code_execution_tool_calls(self, response: Any) -> list[dict[str, Any]]: + if isinstance(response, dict): + output = response.get("output", []) + else: + output = getattr(response, "output", []) or [] + if not isinstance(output, list): + return [] + + return [ + { + "call_id": ( + item.get("call_id") + if isinstance(item, dict) + else getattr(item, "call_id", None) + ), + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": ( + item.get("arguments") + if isinstance(item, dict) + else getattr(item, "arguments", "") + ), + } + for item in output + if self._is_code_execution_call(item) + ] + + def _is_code_execution_call(self, item: Any) -> bool: + if isinstance(item, dict): + return ( + item.get("type") == "function_call" + and item.get("name") == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + return ( + getattr(item, "type", None) == "function_call" + and getattr(item, "name", None) == LITELLM_CODE_EXECUTION_TOOL_NAME + ) + + async def _prune_expired_cache(self) -> None: + now = time.time() + expired = [ + (cache_key, container, params) + for cache_key, ( + container, + params, + created_at, + ) in self._container_cache.items() + if now - created_at > _CACHE_TTL_SECONDS + ] + for cache_key, container, params in expired: + self._container_cache.pop(cache_key, None) + await self._delete_container(container=container, params=params) diff --git a/litellm/integrations/custom_logger.py b/litellm/integrations/custom_logger.py index 481cf7fce8e..94fb97dff53 100644 --- a/litellm/integrations/custom_logger.py +++ b/litellm/integrations/custom_logger.py @@ -718,6 +718,24 @@ class CustomLogger: # https://docs.litellm.ai/docs/observability/custom_callbac """ return response + async def async_agentic_loop_cleanup_hook( + self, + plan: AgenticLoopPlan, + kwargs: dict, + ) -> None: + """ + Release resources held for an agentic-loop iteration. + + Runs in a ``finally`` around the follow-up provider call, so it fires + whether the rerun returns normally, hits a loop safety abort, or raises + an upstream error. Implementations must be idempotent because the + post-response hook may already have released the same resource on the + success path. Use ``plan.metadata`` to locate what to clean up. + + Default does nothing. + """ + return None + async def async_should_run_chat_completion_agentic_loop( self, response: Any, diff --git a/litellm/integrations/otel/model/config.py b/litellm/integrations/otel/model/config.py index a109ba898ff..991b156ae64 100644 --- a/litellm/integrations/otel/model/config.py +++ b/litellm/integrations/otel/model/config.py @@ -1,6 +1,7 @@ """Typed configuration for the OpenTelemetry instrumentation.""" from enum import Enum +from functools import lru_cache from typing import Any, List from pydantic import AliasChoices, BaseModel, Field, field_validator, model_validator @@ -47,7 +48,12 @@ class _OTelV2Flag(BaseSettings): enabled: bool = Field(default=False, validation_alias=AliasChoices(OTEL_V2_ENV)) +@lru_cache(maxsize=1) def is_otel_v2_enabled() -> bool: + # Resolved once at startup and cached: constructing the pydantic-settings + # model re-scans the environment and cost ~28us, which on the proxy hot path + # (auth, logging-callback setup) compounded into a measurable throughput + # regression. Tests that toggle the env must call ``is_otel_v2_enabled.cache_clear()``. return _OTelV2Flag().enabled diff --git a/litellm/litellm_core_utils/exception_mapping_utils.py b/litellm/litellm_core_utils/exception_mapping_utils.py index edb97b310d7..9b2a9af4126 100644 --- a/litellm/litellm_core_utils/exception_mapping_utils.py +++ b/litellm/litellm_core_utils/exception_mapping_utils.py @@ -1,7 +1,7 @@ import json import re import traceback -from typing import Any, Optional +from typing import Any, Optional, Protocol, cast import httpx @@ -244,6 +244,2011 @@ def extract_and_raise_litellm_exception( ) +class _ProviderHTTPException(Protocol): + status_code: int + message: str + response: httpx.Response + request: httpx.Request + body: object + code: str + llm_provider: str + + +def _map_openai_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + # custom_llm_provider is openai, make it OpenAI + message = get_error_message(error_obj=original_exception) + if message is None: + if hasattr(original_exception, "message"): + message = original_exception.message + else: + message = str(original_exception) + + if message is not None and isinstance( + message, str + ): # done to prevent user-confusion. Relevant issue - https://github.com/BerriAI/litellm/issues/1414 + message = message.replace("OPENAI", custom_llm_provider.upper()) + message = message.replace( + "openai.OpenAIError", + "{}.{}Error".format(custom_llm_provider, custom_llm_provider), + ) + if custom_llm_provider == "openai": + exception_provider = "OpenAI" + "Exception" + else: + exception_provider = ( + custom_llm_provider[0].upper() + custom_llm_provider[1:] + "Exception" + ) + + if ExceptionCheckers.is_error_str_rate_limit(error_str): + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"ContextWindowExceededError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "invalid_request_error" in error_str and "model_not_found" in error_str: + raise NotFoundError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "A timeout occurred" in error_str: + raise Timeout( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + ( + "invalid_request_error" in error_str + and "content_policy_violation" in error_str + ) + or ("Invalid prompt" in error_str and "violating our usage policy" in error_str) + or ( + "request was rejected as a result of the safety system" in error_str.lower() + ) + ): + raise ContentPolicyViolationError( + message=f"ContentPolicyViolationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ( + "invalid_encrypted_content" in error_str or "could not be verified" in error_str + ): + helpful_message = ( + f"{exception_provider} - {message}\n\n" + " This error occurs when load balancing Responses API across deployments with different API keys.\n" + " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" + " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" + " router_settings:\n" + " enable_pre_call_checks: true\n" + " optional_pre_call_checks:\n" + " - encrypted_content_affinity\n\n" + " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" + ) + raise BadRequestError( + message=helpful_message, + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif ( + "invalid_request_error" in error_str + and "Incorrect API key provided" not in error_str + ): + raise BadRequestError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif ( + "Web server is returning an unknown error" in error_str + or "The server had an error processing your request." in error_str + ): + raise litellm.InternalServerError( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + ) + elif "Request too large" in error_str: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ( + "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" + in error_str + ): + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "Mistral API raised a streaming error" in error_str: + _request = httpx.Request(method="POST", url="https://api.openai.com/v1") + raise APIError( + status_code=500, + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + request=_request, + litellm_debug_info=extra_information, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"Timeout Error: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"{exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + body=getattr(original_exception, "body", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 500: + raise InternalServerError( + message=f"InternalServerError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"BadGatewayError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"Timeout Error: {exception_provider} - {message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"APIError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + request=getattr(original_exception, "request", None), + litellm_debug_info=extra_information, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + # exception_mapping_worked = True + raise APIConnectionError( + message=f"APIConnectionError: {exception_provider} - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) + + +def _map_anthropic_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "prompt is too long" in error_str + or "prompt: length" in error_str + or ExceptionCheckers.is_error_str_context_window_exceeded(error_str) + ): + raise ContextWindowExceededError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + elif "overloaded_error" in error_str or "Overloaded" in error_str: + raise InternalServerError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "Invalid API Key" in error_str: + raise AuthenticationError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "content filtering policy" in error_str: + raise ContentPolicyViolationError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if "Client error '400 Bad Request'" in error_str: + raise BadRequestError( + message="AnthropicError - {}".format(error_str), + model=model, + llm_provider="anthropic", + ) + if hasattr(original_exception, "status_code"): + verbose_logger.debug(f"status_code: {original_exception.status_code}") + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + raise BadRequestError( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AnthropicException - {error_str}", + model=model, + llm_provider="anthropic", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AnthropicException - {error_str}", + llm_provider="anthropic", + model=model, + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 529 + ): + raise litellm.InternalServerError( + message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"AnthropicException BadGatewayError - {error_str}", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise litellm.ServiceUnavailableError( + message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.", + llm_provider="anthropic", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"AnthropicException Timeout - {error_str}", + model=model, + llm_provider="anthropic", + exception_status_code=original_exception.status_code, + ) + + +def _map_replicate_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Incorrect authentication token" in error_str: + raise AuthenticationError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "input is too long" in error_str: + raise ContextWindowExceededError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif exception_type == "ModelError": + raise BadRequestError( + message=f"ReplicateException - {error_str}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif "Request was throttled" in error_str: + raise RateLimitError( + message=f"ReplicateException - {error_str}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 400 + or original_exception.status_code == 413 + ): + raise BadRequestError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 422: + raise UnprocessableEntityError( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"ReplicateException - {original_exception.message}", + model=model, + llm_provider="replicate", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"ReplicateException - {original_exception.message}", + llm_provider="replicate", + model=model, + response=getattr(original_exception, "response", None), + ) + raise APIError( + status_code=500, + message=f"ReplicateException - {str(original_exception)}", + llm_provider="replicate", + model=model, + request=httpx.Request( + method="POST", + url="https://api.replicate.com/v1/deployments", + ), + ) + + +def _map_openai_like_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "authorization denied for" in error_str: + + # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception + if ( + error_str is not None + and isinstance(error_str, str) + and "bearer" in error_str.lower() + ): + # only keep the first 10 chars after the occurnence of "bearer" + _bearer_token_start_index = error_str.lower().find("bearer") + error_str = error_str[: _bearer_token_start_index + 14] + error_str += "XXXXXXX" + '"' + + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif "token_quota_reached" in error_str: + raise RateLimitError( + message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + "The server received an invalid response from an upstream server." in error_str + ): + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif "model_no_support_for_function" in error_str: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + original_exception.status_code == 422 + or original_exception.status_code == 424 + ): + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_bedrock_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "too many tokens" in error_str + or "expected maxLength:" in error_str + or "Input is too long" in error_str + or "prompt is too long" in error_str + or "prompt: length: 1.." in error_str + or "Too many input tokens" in error_str + ): + raise ContextWindowExceededError( + message=f"BedrockException: Context Window Error - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif ( + "Conversation blocks and tool result blocks cannot be provided in the same turn." + in error_str + ): + raise BadRequestError( + message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "Malformed input request" in error_str: + raise BadRequestError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "A conversation must start with a user message." in error_str: + raise BadRequestError( + message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif ( + "Unable to locate credentials" in error_str + or "The security token included in the request is invalid" in error_str + ): + raise AuthenticationError( + message=f"BedrockException Invalid Authentication - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "AccessDeniedException" in error_str: + raise PermissionDeniedError( + message=f"BedrockException PermissionDeniedError - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "throttlingException" in error_str or "ThrottlingException" in error_str: + raise RateLimitError( + message=f"BedrockException: Rate Limit Error - {error_str}", + model=model, + llm_provider="bedrock", + response=getattr(original_exception, "response", None), + ) + elif "Connect timeout on endpoint URL" in error_str or "timed out" in error_str: + raise Timeout( + message=f"BedrockException: Timeout Error - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif "Could not process image" in error_str: + raise litellm.InternalServerError( + message=f"BedrockException - {error_str}", + model=model, + llm_provider="bedrock", + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=httpx.Response( + status_code=500, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), + ), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"BedrockException - {original_exception.message}", + llm_provider="bedrock", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"BedrockException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_sagemaker_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Unable to locate credentials" in error_str: + raise BadRequestError( + message=f"litellm.BadRequestError: SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif "Input validation error: `best_of` must be > 0 and <= 2" in error_str: + raise BadRequestError( + message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif ( + "`inputs` tokens + `max_new_tokens` must be <=" in error_str + or "instance type with more CPU capacity or memory" in error_str + ): + raise ContextWindowExceededError( + message=f"SagemakerException - {error_str}", + model=model, + llm_provider="sagemaker", + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=500, + request=httpx.Request( + method="POST", url="https://api.openai.com/v1/" + ), + ), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"SagemakerException - {original_exception.message}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif ( + original_exception.status_code == 422 + or original_exception.status_code == 424 + ): + raise BadRequestError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"SagemakerException - {original_exception.message}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + + +def _map_vertex_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if ( + "Vertex AI API has not been used in project" in error_str + or "Unable to find your project" in error_str + ): + raise BadRequestError( + message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + litellm_debug_info=extra_information, + ) + if "400 Request payload size exceeds" in error_str: + raise ContextWindowExceededError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + ) + elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): + raise ContextWindowExceededError( + message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif "None Unknown Error." in error_str or "Content has no parts." in error_str: + raise litellm.InternalServerError( + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=500, + content=str(original_exception), + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + litellm_debug_info=extra_information, + ) + elif "API key not valid." in error_str: + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif "403" in error_str: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + litellm_debug_info=extra_information, + ) + elif ( + "The response was blocked." in error_str + or "Output blocked by content filtering policy" + in error_str # anthropic on vertex ai + ): + raise ContentPolicyViolationError( + message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + "429 Quota exceeded" in error_str + or "Quota exceeded for" in error_str + or "Resource exhausted" in error_str + or "IndexError: list index out of range" in error_str + or "429 Unable to submit request because the service is temporarily out of capacity." + in error_str + ): + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + isinstance(getattr(original_exception, "status_code", None), int) + and 500 <= original_exception.status_code < 600 + and _get_body_error_code(error_str) == 429 + ): + # upstream gateway wraps a 429 inside a 5xx envelope + # e.g. HTTP 500/503 with {"error":{"code":429,...}}. + # Scoped to 5xx so HTTP 400/401 with body code:429 + # still maps to BadRequestError / AuthenticationError. + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + elif ( + "500 Internal Server Error" in error_str + or "The model is overloaded." in error_str + ): + raise litellm.InternalServerError( + message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=400, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 403: + raise PermissionDeniedError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=httpx.Response( + status_code=403, + request=httpx.Request( + method="POST", + url="https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 404: + raise NotFoundError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 408: + raise Timeout( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + + if original_exception.status_code == 429: + raise RateLimitError( + message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=429, + request=httpx.Request( + method="POST", + url=" https://cloud.google.com/vertex-ai/", + ), + ), + ) + if original_exception.status_code == 500: + raise litellm.InternalServerError( + message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + response=httpx.Response( + status_code=500, + content=str(original_exception), + request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore + ), + ) + if original_exception.status_code == 502: + raise APIConnectionError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + if original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", + llm_provider=custom_llm_provider, + model=model, + ) + + +def _map_cloudflare_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "Authentication error" in error_str: + raise AuthenticationError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=getattr(original_exception, "response", None), + ) + if "must have required property" in error_str: + raise BadRequestError( + message=f"Cloudflare Exception - {original_exception.message}", + llm_provider="cloudflare", + model=model, + response=getattr(original_exception, "response", None), + ) + + +def _map_cohere_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "invalid api token" in error_str or "No API key provided." in error_str: + raise AuthenticationError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "invalid type: parameter" in error_str: + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "too many tokens" in error_str: + raise ContextWindowExceededError( + message=f"CohereException - {original_exception.message}", + model=model, + llm_provider="cohere", + response=getattr(original_exception, "response", None), + ) + elif "internal server error" in error_str.lower(): + raise InternalServerError( + message=f"CohereException - {error_str}", + model=model, + llm_provider="cohere", + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + if ( + original_exception.status_code == 400 + or original_exception.status_code == 498 + ): + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + ) + elif original_exception.status_code == 500: + raise InternalServerError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + "CohereConnectionError" in exception_type + ): # cohere seems to fire these errors when we load test it (1k+ messages / min) + raise RateLimitError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "invalid type:" in error_str: + raise BadRequestError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Unexpected server error" in error_str: + raise InternalServerError( + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + if hasattr(original_exception, "status_code"): + raise APIError( + status_code=original_exception.status_code, + message=f"CohereException - {original_exception.message}", + llm_provider="cohere", + model=model, + request=getattr(original_exception, "request", None), + ) + raise cast(Exception, original_exception) + + +def _map_huggingface_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "length limit exceeded" in error_str: + raise ContextWindowExceededError( + message=error_str, + model=model, + llm_provider="huggingface", + response=getattr(original_exception, "response", None), + ) + elif "A valid user token is required" in error_str: + raise BadRequestError( + message=error_str, + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Rate limit reached" in error_str: + raise RateLimitError( + message=error_str, + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"HuggingfaceException - {original_exception.message}", + model=model, + llm_provider="huggingface", + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"HuggingfaceException - {original_exception.message}", + llm_provider="huggingface", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_ai21_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "message"): + if "Prompt has too many tokens" in original_exception.message: + raise ContextWindowExceededError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + if "Bad or missing API token." in original_exception.message: + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + ) + if original_exception.status_code == 422: + raise BadRequestError( + message=f"AI21Exception - {original_exception.message}", + model=model, + llm_provider="ai21", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"AI21Exception - {original_exception.message}", + llm_provider="ai21", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_nlp_cloud_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "detail" in error_str: + if "Input text length should not exceed" in error_str: + raise ContextWindowExceededError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + elif "value is not a valid" in error_str: + raise BadRequestError( + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=500, + message=f"NLPCloudException - {error_str}", + model=model, + llm_provider="nlp_cloud", + request=getattr(original_exception, "request", None), + ) + if hasattr( + original_exception, "status_code" + ): # https://docs.nlpcloud.com/?shell#errors + if ( + original_exception.status_code == 400 + or original_exception.status_code == 406 + or original_exception.status_code == 413 + or original_exception.status_code == 422 + ): + raise BadRequestError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 401 + or original_exception.status_code == 403 + ): + raise AuthenticationError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 522 + or original_exception.status_code == 524 + ): + raise Timeout( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + ) + elif ( + original_exception.status_code == 429 + or original_exception.status_code == 402 + ): + raise RateLimitError( + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + response=getattr(original_exception, "response", None), + ) + elif ( + original_exception.status_code == 500 + or original_exception.status_code == 503 + ): + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=getattr(original_exception, "request", None), + ) + elif ( + original_exception.status_code == 504 + or original_exception.status_code == 520 + ): + raise ServiceUnavailableError( + message=f"NLPCloudException - {original_exception.message}", + model=model, + llm_provider="nlp_cloud", + response=getattr(original_exception, "response", None), + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"NLPCloudException - {original_exception.message}", + llm_provider="nlp_cloud", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_together_ai_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + try: + error_response = json.loads(error_str) + except Exception: + error_response = {"error": error_str} + if ( + "error" in error_response + and "`inputs` tokens + `max_new_tokens` must be <=" in error_response["error"] + ): + raise ContextWindowExceededError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif "error" in error_response and "invalid private key" in error_response["error"]: + raise AuthenticationError( + message=f"TogetherAIException - {error_response['error']}", + llm_provider="together_ai", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "error" in error_response and "INVALID_ARGUMENT" in error_response["error"]: + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif "A timeout occurred" in error_str: + raise Timeout( + message=f"TogetherAIException - {error_str}", + model=model, + llm_provider="together_ai", + ) + elif ( + "error" in error_response + and "API key doesn't match expected format." in error_response["error"] + ): + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif ( + "error_type" in error_response and error_response["error_type"] == "validation" + ): + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 408: + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + model=model, + llm_provider="together_ai", + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"TogetherAIException - {error_response['error']}", + model=model, + llm_provider="together_ai", + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 524: + raise Timeout( + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"TogetherAIException - {original_exception.message}", + llm_provider="together_ai", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_aleph_alpha_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if "This is longer than the model's maximum context length" in error_str: + raise ContextWindowExceededError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "InvalidToken" in error_str or "No token provided" in error_str: + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif hasattr(original_exception, "status_code"): + verbose_logger.debug(f"status code: {original_exception.status_code}") + if original_exception.status_code == 401: + raise AuthenticationError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + ) + elif original_exception.status_code == 400: + raise BadRequestError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 500: + raise ServiceUnavailableError( + message=f"AlephAlphaException - {original_exception.message}", + llm_provider="aleph_alpha", + model=model, + response=getattr(original_exception, "response", None), + ) + raise cast(Exception, original_exception) + raise cast(Exception, original_exception) + + +def _map_ollama_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if isinstance(original_exception, dict): + error_str = original_exception.get("error", "") + else: + error_str = str(original_exception) + if "no such file or directory" in error_str: + raise BadRequestError( + message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", + model=model, + llm_provider="ollama", + response=getattr(original_exception, "response", None), + ) + elif "Failed to establish a new connection" in error_str: + raise ServiceUnavailableError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Invalid response object from API" in error_str: + raise BadRequestError( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + response=getattr(original_exception, "response", None), + ) + elif "Read timed out" in error_str: + raise Timeout( + message=f"OllamaException: {original_exception}", + llm_provider="ollama", + model=model, + ) + + +def _map_vllm_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 0: + raise APIConnectionError( + message=f"VLLMException - {original_exception.message}", + llm_provider="vllm", + model=model, + request=getattr(original_exception, "request", None), + ) + + +def _map_azure_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + message = get_error_message(error_obj=original_exception) + if message is None: + if hasattr(original_exception, "message"): + message = original_exception.message + else: + message = str(original_exception) + + # Azure OpenAI (especially Images) often nests error details under + # body["error"]. Detect content policy violations using the structured + # payload in addition to string matching. + azure_error_code: Optional[str] = None + try: + body_dict = getattr(original_exception, "body", None) or {} + if isinstance(body_dict, dict): + if isinstance(body_dict.get("error"), dict): + azure_error_code = body_dict["error"].get("code") # type: ignore[index] + # Also check inner_error for + # ResponsibleAIPolicyViolation which indicates a + # content policy violation even when the top-level + # code is generic (e.g. "invalid_request_error"). + if azure_error_code != "content_policy_violation": + _inner = body_dict["error"].get( + "inner_error" + ) or body_dict[ # type: ignore[index] + "error" + ].get( + "innererror" + ) # type: ignore[index] + if ( + isinstance(_inner, dict) + and _inner.get("code") == "ResponsibleAIPolicyViolation" + ): + azure_error_code = "content_policy_violation" + else: + azure_error_code = body_dict.get("code") + except Exception: + azure_error_code = None + + if "Internal server error" in error_str: + raise litellm.InternalServerError( + message=f"AzureException Internal server error - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "This model's maximum context length is" in error_str: + raise ContextWindowExceededError( + message=f"AzureException ContextWindowExceededError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "DeploymentNotFound" in error_str: + raise NotFoundError( + message=f"AzureException NotFoundError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif ( + azure_error_code == "content_policy_violation" + or ExceptionCheckers.is_azure_content_policy_violation_error(error_str) + ): + from litellm.llms.azure.exception_mapping import ( + AzureOpenAIExceptionMapping, + ) + + raise AzureOpenAIExceptionMapping.create_content_policy_violation_error( + message=message, + model=model, + extra_information=extra_information, + original_exception=original_exception, + ) + elif ( + azure_error_code == "invalid_encrypted_content" + or "could not be verified" in error_str + ): + helpful_message = ( + f"AzureException - {message}\n\n" + "This error occurs when load balancing Responses API across deployments with different API keys.\n" + " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" + " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" + " router_settings:\n" + " enable_pre_call_checks: true\n" + " optional_pre_call_checks:\n" + " - encrypted_content_affinity\n\n" + " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" + ) + raise BadRequestError( + message=helpful_message, + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif "invalid_request_error" in error_str: + raise BadRequestError( + message=f"AzureException BadRequestError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif ( + "The api_key client option must be set either by passing api_key to the client or by setting" + in error_str + ): + raise AuthenticationError( + message=f"{exception_provider} AuthenticationError - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif "Connection error" in error_str: + raise APIConnectionError( + message=f"{exception_provider} APIConnectionError - {message}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + ) + elif hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"AzureException - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + body=getattr(original_exception, "body", None), + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AzureException AuthenticationError - {message}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"AzureException Timeout - {message}", + model=model, + litellm_debug_info=extra_information, + llm_provider="azure", + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"AzureException BadRequestError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"AzureException RateLimitError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 502: + raise BadGatewayError( + message=f"AzureException BadGatewayError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"AzureException ServiceUnavailableError - {message}", + model=model, + llm_provider="azure", + litellm_debug_info=extra_information, + response=getattr(original_exception, "response", None), + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"AzureException Timeout - {message}", + model=model, + litellm_debug_info=extra_information, + llm_provider="azure", + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"AzureException APIError - {message}", + llm_provider="azure", + litellm_debug_info=extra_information, + model=model, + request=httpx.Request(method="POST", url="https://openai.com/"), + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + message=f"{exception_provider} APIConnectionError - {message}\n{_redact_string(traceback.format_exc())}", + llm_provider="azure", + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://openai.com/"), + ) + + +def _map_openrouter_exception( + *, + model: str, + original_exception: _ProviderHTTPException, + custom_llm_provider: str, + error_str: str, + exception_type: str, + exception_provider: str, + extra_information: str, +) -> None: + if hasattr(original_exception, "status_code"): + if original_exception.status_code == 400: + raise BadRequestError( + message=f"{exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 401: + raise AuthenticationError( + message=f"AuthenticationError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 404: + raise NotFoundError( + message=f"NotFoundError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 408: + raise Timeout( + message=f"Timeout Error: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 422: + raise BadRequestError( + message=f"BadRequestError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 429: + raise RateLimitError( + message=f"RateLimitError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 503: + raise ServiceUnavailableError( + message=f"ServiceUnavailableError: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + response=getattr(original_exception, "response", None), + litellm_debug_info=extra_information, + ) + elif original_exception.status_code == 504: # gateway timeout error + raise Timeout( + message=f"Timeout Error: {exception_provider} - {error_str}", + model=model, + llm_provider=custom_llm_provider, + litellm_debug_info=extra_information, + exception_status_code=original_exception.status_code, + ) + else: + raise APIError( + status_code=original_exception.status_code, + message=f"APIError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + request=getattr(original_exception, "request", None), + litellm_debug_info=extra_information, + ) + else: + # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors + raise APIConnectionError( + message=f"APIConnectionError: {exception_provider} - {error_str}", + llm_provider=custom_llm_provider, + model=model, + litellm_debug_info=extra_information, + request=httpx.Request(method="POST", url="https://api.openai.com/v1/"), + ) + + def exception_type( # type: ignore model, original_exception, @@ -259,6 +2264,9 @@ def exception_type( # type: ignore return original_exception exception_mapping_worked = False exception_provider = custom_llm_provider + mappable_exception: _ProviderHTTPException = cast( + "_ProviderHTTPException", original_exception + ) if litellm.suppress_debug_info is False: print() # noqa: T201 print( # noqa: T201 @@ -382,2078 +2390,199 @@ def exception_type( # type: ignore or custom_llm_provider in litellm.openai_compatible_providers or custom_llm_provider == "mistral" ): - # custom_llm_provider is openai, make it OpenAI - message = get_error_message(error_obj=original_exception) - if message is None: - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - - if message is not None and isinstance( - message, str - ): # done to prevent user-confusion. Relevant issue - https://github.com/BerriAI/litellm/issues/1414 - message = message.replace("OPENAI", custom_llm_provider.upper()) - message = message.replace( - "openai.OpenAIError", - "{}.{}Error".format(custom_llm_provider, custom_llm_provider), - ) - if custom_llm_provider == "openai": - exception_provider = "OpenAI" + "Exception" - else: - exception_provider = ( - custom_llm_provider[0].upper() - + custom_llm_provider[1:] - + "Exception" - ) - - if ExceptionCheckers.is_error_str_rate_limit(error_str): - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ContextWindowExceededError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "invalid_request_error" in error_str - and "model_not_found" in error_str - ): - exception_mapping_worked = True - raise NotFoundError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "A timeout occurred" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - ( - "invalid_request_error" in error_str - and "content_policy_violation" in error_str - ) - or ( - "Invalid prompt" in error_str - and "violating our usage policy" in error_str - ) - or ( - "request was rejected as a result of the safety system" - in error_str.lower() - ) - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"ContentPolicyViolationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "invalid_encrypted_content" in error_str - or "could not be verified" in error_str - ): - exception_mapping_worked = True - helpful_message = ( - f"{exception_provider} - {message}\n\n" - " This error occurs when load balancing Responses API across deployments with different API keys.\n" - " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" - " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" - " router_settings:\n" - " enable_pre_call_checks: true\n" - " optional_pre_call_checks:\n" - " - encrypted_content_affinity\n\n" - " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" - ) - raise BadRequestError( - message=helpful_message, - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif ( - "invalid_request_error" in error_str - and "Incorrect API key provided" not in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif ( - "Web server is returning an unknown error" in error_str - or "The server had an error processing your request." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - ) - elif "Request too large" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting the OPENAI_API_KEY environment variable" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "Mistral API raised a streaming error" in error_str: - exception_mapping_worked = True - _request = httpx.Request( - method="POST", url="https://api.openai.com/v1" - ) - raise APIError( - status_code=500, - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=_request, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - body=getattr(original_exception, "body", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise InternalServerError( - message=f"InternalServerError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"BadGatewayError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"APIError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - request=getattr(original_exception, "request", None), - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - # exception_mapping_worked = True - raise APIConnectionError( - message=f"APIConnectionError: {exception_provider} - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) + _map_openai_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "anthropic" or custom_llm_provider == "anthropic_text" ): # one of the anthropics - if ( - "prompt is too long" in error_str - or "prompt: length" in error_str - or ExceptionCheckers.is_error_str_context_window_exceeded(error_str) - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - elif "overloaded_error" in error_str or "Overloaded" in error_str: - exception_mapping_worked = True - raise InternalServerError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "Invalid API Key" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "content filtering policy" in error_str: - exception_mapping_worked = True - raise ContentPolicyViolationError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if "Client error '400 Bad Request'" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message="AnthropicError - {}".format(error_str), - model=model, - llm_provider="anthropic", - ) - if hasattr(original_exception, "status_code"): - verbose_logger.debug( - f"status_code: {original_exception.status_code}" - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AnthropicException - {error_str}", - llm_provider="anthropic", - model=model, - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException - {error_str}", - model=model, - llm_provider="anthropic", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AnthropicException - {error_str}", - llm_provider="anthropic", - model=model, - ) - elif ( - original_exception.status_code == 500 - or original_exception.status_code == 529 - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"AnthropicException - {error_str}. Handle with `litellm.InternalServerError`.", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"AnthropicException BadGatewayError - {error_str}", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise litellm.ServiceUnavailableError( - message=f"AnthropicException - {error_str}. Handle with `litellm.ServiceUnavailableError`.", - llm_provider="anthropic", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"AnthropicException Timeout - {error_str}", - model=model, - llm_provider="anthropic", - exception_status_code=original_exception.status_code, - ) - elif custom_llm_provider == "replicate": - if "Incorrect authentication token" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "input is too long" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif exception_type == "ModelError": - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {error_str}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif "Request was throttled" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {error_str}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 400 - or original_exception.status_code == 413 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"ReplicateException - {original_exception.message}", - model=model, - llm_provider="replicate", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise UnprocessableEntityError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ReplicateException - {original_exception.message}", - llm_provider="replicate", - model=model, - response=getattr(original_exception, "response", None), - ) - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"ReplicateException - {str(original_exception)}", - llm_provider="replicate", + _map_anthropic_exception( model=model, - request=httpx.Request( - method="POST", - url="https://api.replicate.com/v1/deployments", - ), + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) + elif custom_llm_provider == "replicate": + _map_replicate_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, ) elif custom_llm_provider in litellm._openai_like_providers: - if "authorization denied for" in error_str: - exception_mapping_worked = True - - # Predibase returns the raw API Key in the response - this block ensures it's not returned in the exception - if ( - error_str is not None - and isinstance(error_str, str) - and "bearer" in error_str.lower() - ): - # only keep the first 10 chars after the occurnence of "bearer" - _bearer_token_start_index = error_str.lower().find("bearer") - error_str = error_str[: _bearer_token_start_index + 14] - error_str += "XXXXXXX" + '"' - - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif "token_quota_reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=f"{custom_llm_provider.capitalize()}Exception: Rate Limit Errror - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "The server received an invalid response from an upstream server." - in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif "model_no_support_for_function" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif ( - original_exception.status_code == 401 - or original_exception.status_code == 403 - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - original_exception.status_code == 422 - or original_exception.status_code == 424 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_openai_like_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "bedrock": - if ( - "too many tokens" in error_str - or "expected maxLength:" in error_str - or "Input is too long" in error_str - or "prompt is too long" in error_str - or "prompt: length: 1.." in error_str - or "Too many input tokens" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"BedrockException: Context Window Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif ( - "Conversation blocks and tool result blocks cannot be provided in the same turn." - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}\n. Enable 'litellm.modify_params=True' (for PROXY do: `litellm_settings::modify_params: True`) to insert a dummy assistant message and fix this error.", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "Malformed input request" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "A conversation must start with a user message." in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {error_str}\n. Pass in default user message via `completion(..,user_continue_message=)` or enable `litellm.modify_params=True`.\nFor Proxy: do via `litellm_settings::modify_params: True` or user_continue_message under `litellm_params`", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "Unable to locate credentials" in error_str - or "The security token included in the request is invalid" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException Invalid Authentication - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif "AccessDeniedException" in error_str: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"BedrockException PermissionDeniedError - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "throttlingException" in error_str - or "ThrottlingException" in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException: Rate Limit Error - {error_str}", - model=model, - llm_provider="bedrock", - response=getattr(original_exception, "response", None), - ) - elif ( - "Connect timeout on endpoint URL" in error_str - or "timed out" in error_str - ): - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException: Timeout Error - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif "Could not process image" in error_str: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"BedrockException - {error_str}", - model=model, - llm_provider="bedrock", - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"BedrockException - {original_exception.message}", - llm_provider="bedrock", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"BedrockException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_bedrock_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "sagemaker" or custom_llm_provider == "sagemaker_chat" ): - if "Unable to locate credentials" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"litellm.BadRequestError: SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif ( - "Input validation error: `best_of` must be > 0 and <= 2" - in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message="SagemakerException - the value of 'n' must be > 0 and <= 2 for sagemaker endpoints", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif ( - "`inputs` tokens + `max_new_tokens` must be <=" in error_str - or "instance type with more CPU capacity or memory" in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"SagemakerException - {error_str}", - model=model, - llm_provider="sagemaker", - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=httpx.Response( - status_code=500, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"SagemakerException - {original_exception.message}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - original_exception.status_code == 422 - or original_exception.status_code == 424 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"SagemakerException - {original_exception.message}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) + _map_sagemaker_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == LlmProviders.VERTEX_AI or custom_llm_provider == LlmProviders.VERTEX_AI_BETA or custom_llm_provider == LlmProviders.GEMINI ): - if ( - "Vertex AI API has not been used in project" in error_str - or "Unable to find your project" in error_str - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - litellm_debug_info=extra_information, - ) - if "400 Request payload size exceeds" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - ) - elif ExceptionCheckers.is_error_str_context_window_exceeded(error_str): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"ContextWindowExceededError: {custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif ( - "None Unknown Error." in error_str - or "Content has no parts." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=500, - content=str(original_exception), - request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore - ), - litellm_debug_info=extra_information, - ) - elif "API key not valid." in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif "403" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=httpx.Response( - status_code=403, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - litellm_debug_info=extra_information, - ) - elif ( - "The response was blocked." in error_str - or "Output blocked by content filtering policy" - in error_str # anthropic on vertex ai - ): - exception_mapping_worked = True - raise ContentPolicyViolationError( - message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "429 Quota exceeded" in error_str - or "Quota exceeded for" in error_str - or "Resource exhausted" in error_str - or "IndexError: list index out of range" in error_str - or "429 Unable to submit request because the service is temporarily out of capacity." - in error_str - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - isinstance(getattr(original_exception, "status_code", None), int) - and 500 <= original_exception.status_code < 600 - and _get_body_error_code(error_str) == 429 - ): - # upstream gateway wraps a 429 inside a 5xx envelope - # e.g. HTTP 500/503 with {"error":{"code":429,...}}. - # Scoped to 5xx so HTTP 400/401 with body code:429 - # still maps to BadRequestError / AuthenticationError. - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - elif ( - "500 Internal Server Error" in error_str - or "The model is overloaded." in error_str - ): - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=400, - request=httpx.Request( - method="POST", - url="https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 403: - exception_mapping_worked = True - raise PermissionDeniedError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=httpx.Response( - status_code=403, - request=httpx.Request( - method="POST", - url="https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - - if original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=429, - request=httpx.Request( - method="POST", - url=" https://cloud.google.com/vertex-ai/", - ), - ), - ) - if original_exception.status_code == 500: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - response=httpx.Response( - status_code=500, - content=str(original_exception), - request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore - ), - ) - if original_exception.status_code == 502: - exception_mapping_worked = True - raise APIConnectionError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) - if original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"{custom_llm_provider.capitalize()}Exception - {error_str}", - llm_provider=custom_llm_provider, - model=model, - ) + _map_vertex_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "cloudflare": - if "Authentication error" in error_str: - exception_mapping_worked = True - raise AuthenticationError( - message=f"Cloudflare Exception - {original_exception.message}", - llm_provider="cloudflare", - model=model, - response=getattr(original_exception, "response", None), - ) - if "must have required property" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"Cloudflare Exception - {original_exception.message}", - llm_provider="cloudflare", - model=model, - response=getattr(original_exception, "response", None), - ) + _map_cloudflare_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "cohere" or custom_llm_provider == "cohere_chat" ): # Cohere - if ( - "invalid api token" in error_str - or "No API key provided." in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "invalid type: parameter" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "too many tokens" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"CohereException - {original_exception.message}", - model=model, - llm_provider="cohere", - response=getattr(original_exception, "response", None), - ) - elif "internal server error" in error_str.lower(): - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {error_str}", - model=model, - llm_provider="cohere", - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - if ( - original_exception.status_code == 400 - or original_exception.status_code == 498 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "CohereConnectionError" in exception_type - ): # cohere seems to fire these errors when we load test it (1k+ messages / min) - exception_mapping_worked = True - raise RateLimitError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "invalid type:" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Unexpected server error" in error_str: - exception_mapping_worked = True - raise InternalServerError( - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - if hasattr(original_exception, "status_code"): - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"CohereException - {original_exception.message}", - llm_provider="cohere", - model=model, - request=getattr(original_exception, "request", None), - ) - raise original_exception + _map_cohere_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "huggingface": - if "length limit exceeded" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=error_str, - model=model, - llm_provider="huggingface", - response=getattr(original_exception, "response", None), - ) - elif "A valid user token is required" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=error_str, - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Rate limit reached" in error_str: - exception_mapping_worked = True - raise RateLimitError( - message=error_str, - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"HuggingfaceException - {original_exception.message}", - model=model, - llm_provider="huggingface", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"HuggingfaceException - {original_exception.message}", - model=model, - llm_provider="huggingface", - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"HuggingfaceException - {original_exception.message}", - llm_provider="huggingface", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_huggingface_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "ai21": - if hasattr(original_exception, "message"): - if "Prompt has too many tokens" in original_exception.message: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - if "Bad or missing API token." in original_exception.message: - exception_mapping_worked = True - raise BadRequestError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - ) - if original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"AI21Exception - {original_exception.message}", - model=model, - llm_provider="ai21", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"AI21Exception - {original_exception.message}", - llm_provider="ai21", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_ai21_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "nlp_cloud": - if "detail" in error_str: - if "Input text length should not exceed" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - elif "value is not a valid" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=500, - message=f"NLPCloudException - {error_str}", - model=model, - llm_provider="nlp_cloud", - request=getattr(original_exception, "request", None), - ) - if hasattr( - original_exception, "status_code" - ): # https://docs.nlpcloud.com/?shell#errors - if ( - original_exception.status_code == 400 - or original_exception.status_code == 406 - or original_exception.status_code == 413 - or original_exception.status_code == 422 - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 401 - or original_exception.status_code == 403 - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 522 - or original_exception.status_code == 524 - ): - exception_mapping_worked = True - raise Timeout( - message=f"NLPCloudException - {original_exception.message}", - model=model, - llm_provider="nlp_cloud", - ) - elif ( - original_exception.status_code == 429 - or original_exception.status_code == 402 - ): - exception_mapping_worked = True - raise RateLimitError( - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - original_exception.status_code == 500 - or original_exception.status_code == 503 - ): - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - request=getattr(original_exception, "request", None), - ) - elif ( - original_exception.status_code == 504 - or original_exception.status_code == 520 - ): - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"NLPCloudException - {original_exception.message}", - model=model, - llm_provider="nlp_cloud", - response=getattr(original_exception, "response", None), - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"NLPCloudException - {original_exception.message}", - llm_provider="nlp_cloud", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_nlp_cloud_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "together_ai": - try: - error_response = json.loads(error_str) - except Exception: - error_response = {"error": error_str} - if ( - "error" in error_response - and "`inputs` tokens + `max_new_tokens` must be <=" - in error_response["error"] - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif ( - "error" in error_response - and "invalid private key" in error_response["error"] - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"TogetherAIException - {error_response['error']}", - llm_provider="together_ai", - model=model, - response=getattr(original_exception, "response", None), - ) - elif ( - "error" in error_response - and "INVALID_ARGUMENT" in error_response["error"] - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif "A timeout occurred" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {error_str}", - model=model, - llm_provider="together_ai", - ) - elif ( - "error" in error_response - and "API key doesn't match expected format." - in error_response["error"] - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif ( - "error_type" in error_response - and error_response["error_type"] == "validation" - ): - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - model=model, - llm_provider="together_ai", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"TogetherAIException - {error_response['error']}", - model=model, - llm_provider="together_ai", - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 524: - exception_mapping_worked = True - raise Timeout( - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"TogetherAIException - {original_exception.message}", - llm_provider="together_ai", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_together_ai_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "aleph_alpha": - if ( - "This is longer than the model's maximum context length" - in error_str - ): - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "InvalidToken" in error_str or "No token provided" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif hasattr(original_exception, "status_code"): - verbose_logger.debug( - f"status code: {original_exception.status_code}" - ) - if original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - ) - elif original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 500: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"AlephAlphaException - {original_exception.message}", - llm_provider="aleph_alpha", - model=model, - response=getattr(original_exception, "response", None), - ) - raise original_exception - raise original_exception + _map_aleph_alpha_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif ( custom_llm_provider == "ollama" or custom_llm_provider == "ollama_chat" ): - if isinstance(original_exception, dict): - error_str = original_exception.get("error", "") - else: - error_str = str(original_exception) - if "no such file or directory" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"OllamaException: Invalid Model/Model not loaded - {original_exception}", - model=model, - llm_provider="ollama", - response=getattr(original_exception, "response", None), - ) - elif "Failed to establish a new connection" in error_str: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Invalid response object from API" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - response=getattr(original_exception, "response", None), - ) - elif "Read timed out" in error_str: - exception_mapping_worked = True - raise Timeout( - message=f"OllamaException: {original_exception}", - llm_provider="ollama", - model=model, - ) + _map_ollama_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "vllm": - if hasattr(original_exception, "status_code"): - if original_exception.status_code == 0: - exception_mapping_worked = True - raise APIConnectionError( - message=f"VLLMException - {original_exception.message}", - llm_provider="vllm", - model=model, - request=getattr(original_exception, "request", None), - ) + _map_vllm_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) elif custom_llm_provider == "azure" or custom_llm_provider == "azure_text": - message = get_error_message(error_obj=original_exception) - if message is None: - if hasattr(original_exception, "message"): - message = original_exception.message - else: - message = str(original_exception) - - # Azure OpenAI (especially Images) often nests error details under - # body["error"]. Detect content policy violations using the structured - # payload in addition to string matching. - azure_error_code: Optional[str] = None - try: - body_dict = getattr(original_exception, "body", None) or {} - if isinstance(body_dict, dict): - if isinstance(body_dict.get("error"), dict): - azure_error_code = body_dict["error"].get("code") # type: ignore[index] - # Also check inner_error for - # ResponsibleAIPolicyViolation which indicates a - # content policy violation even when the top-level - # code is generic (e.g. "invalid_request_error"). - if azure_error_code != "content_policy_violation": - _inner = body_dict["error"].get( - "inner_error" - ) or body_dict[ # type: ignore[index] - "error" - ].get( - "innererror" - ) # type: ignore[index] - if ( - isinstance(_inner, dict) - and _inner.get("code") - == "ResponsibleAIPolicyViolation" - ): - azure_error_code = "content_policy_violation" - else: - azure_error_code = body_dict.get("code") - except Exception: - azure_error_code = None - - if "Internal server error" in error_str: - exception_mapping_worked = True - raise litellm.InternalServerError( - message=f"AzureException Internal server error - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "This model's maximum context length is" in error_str: - exception_mapping_worked = True - raise ContextWindowExceededError( - message=f"AzureException ContextWindowExceededError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "DeploymentNotFound" in error_str: - exception_mapping_worked = True - raise NotFoundError( - message=f"AzureException NotFoundError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif ( - azure_error_code == "content_policy_violation" - or ExceptionCheckers.is_azure_content_policy_violation_error( - error_str - ) - ): - exception_mapping_worked = True - from litellm.llms.azure.exception_mapping import ( - AzureOpenAIExceptionMapping, - ) - - raise AzureOpenAIExceptionMapping.create_content_policy_violation_error( - message=message, - model=model, - extra_information=extra_information, - original_exception=original_exception, - ) - elif ( - azure_error_code == "invalid_encrypted_content" - or "could not be verified" in error_str - ): - exception_mapping_worked = True - helpful_message = ( - f"AzureException - {message}\n\n" - "This error occurs when load balancing Responses API across deployments with different API keys.\n" - " Encrypted content is tied to the organization that created it and cannot be decrypted by other organizations.\n\n" - " Solution: Enable 'encrypted_content_affinity' to route follow-up requests to the correct deployment:\n\n" - " router_settings:\n" - " enable_pre_call_checks: true\n" - " optional_pre_call_checks:\n" - " - encrypted_content_affinity\n\n" - " Learn more: https://docs.litellm.ai/docs/response_api#encrypted-content-affinity-multi-region-load-balancing" - ) - raise BadRequestError( - message=helpful_message, - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif "invalid_request_error" in error_str: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException BadRequestError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif ( - "The api_key client option must be set either by passing api_key to the client or by setting" - in error_str - ): - exception_mapping_worked = True - raise AuthenticationError( - message=f"{exception_provider} AuthenticationError - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif "Connection error" in error_str: - exception_mapping_worked = True - raise APIConnectionError( - message=f"{exception_provider} APIConnectionError - {message}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - ) - elif hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - body=getattr(original_exception, "body", None), - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AzureException AuthenticationError - {message}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"AzureException Timeout - {message}", - model=model, - litellm_debug_info=extra_information, - llm_provider="azure", - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"AzureException BadRequestError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"AzureException RateLimitError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 502: - exception_mapping_worked = True - raise BadGatewayError( - message=f"AzureException BadGatewayError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"AzureException ServiceUnavailableError - {message}", - model=model, - llm_provider="azure", - litellm_debug_info=extra_information, - response=getattr(original_exception, "response", None), - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"AzureException Timeout - {message}", - model=model, - litellm_debug_info=extra_information, - llm_provider="azure", - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"AzureException APIError - {message}", - llm_provider="azure", - litellm_debug_info=extra_information, - model=model, - request=httpx.Request( - method="POST", url="https://openai.com/" - ), - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"{exception_provider} APIConnectionError - {message}\n{_redact_string(traceback.format_exc())}", - llm_provider="azure", - model=model, - litellm_debug_info=extra_information, - request=httpx.Request(method="POST", url="https://openai.com/"), - ) + _map_azure_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) if custom_llm_provider == "openrouter": - if hasattr(original_exception, "status_code"): - exception_mapping_worked = True - if original_exception.status_code == 400: - exception_mapping_worked = True - raise BadRequestError( - message=f"{exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 401: - exception_mapping_worked = True - raise AuthenticationError( - message=f"AuthenticationError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 404: - exception_mapping_worked = True - raise NotFoundError( - message=f"NotFoundError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 408: - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 422: - exception_mapping_worked = True - raise BadRequestError( - message=f"BadRequestError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 429: - exception_mapping_worked = True - raise RateLimitError( - message=f"RateLimitError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 503: - exception_mapping_worked = True - raise ServiceUnavailableError( - message=f"ServiceUnavailableError: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - response=getattr(original_exception, "response", None), - litellm_debug_info=extra_information, - ) - elif original_exception.status_code == 504: # gateway timeout error - exception_mapping_worked = True - raise Timeout( - message=f"Timeout Error: {exception_provider} - {error_str}", - model=model, - llm_provider=custom_llm_provider, - litellm_debug_info=extra_information, - exception_status_code=original_exception.status_code, - ) - else: - exception_mapping_worked = True - raise APIError( - status_code=original_exception.status_code, - message=f"APIError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - request=getattr(original_exception, "request", None), - litellm_debug_info=extra_information, - ) - else: - # if no status code then it is an APIConnectionError: https://github.com/openai/openai-python#handling-errors - raise APIConnectionError( - message=f"APIConnectionError: {exception_provider} - {error_str}", - llm_provider=custom_llm_provider, - model=model, - litellm_debug_info=extra_information, - request=httpx.Request( - method="POST", url="https://api.openai.com/v1/" - ), - ) + _map_openrouter_exception( + model=model, + original_exception=mappable_exception, + custom_llm_provider=custom_llm_provider, + error_str=error_str, + exception_type=exception_type, + exception_provider=exception_provider, + extra_information=extra_information, + ) if ( "BadRequestError.__init__() missing 1 required positional argument: 'param'" in str(original_exception) diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py index d3330c3dcec..5f474b0800e 100644 --- a/litellm/litellm_core_utils/streaming_handler.py +++ b/litellm/litellm_core_utils/streaming_handler.py @@ -6,6 +6,7 @@ import logging import threading import time import traceback +from dataclasses import dataclass from typing import ( Any, AsyncIterator, @@ -97,6 +98,19 @@ def print_verbose(print_statement): pass +@dataclass(frozen=True, slots=True) +class _ProviderChunkParsed: + response_obj: dict[str, Any] + + +@dataclass(frozen=True, slots=True) +class _ProviderChunkEarlyReturn: + value: Any + + +_ProviderChunkResult = Union[_ProviderChunkParsed, _ProviderChunkEarlyReturn] + + class CustomStreamWrapper: def __init__( self, @@ -1145,381 +1159,392 @@ class CustomStreamWrapper: del model_response.choices[0].delta.reasoning_content return + def _dispatch_provider_chunk( + self, + chunk: Any, + model_response: ModelResponseStream, + completion_obj: dict[str, Any], + ) -> _ProviderChunkResult: + response_obj: dict[str, Any] = {} + if ( + isinstance(chunk, ModelResponseStream) + and self.custom_llm_provider is not None + and self.custom_llm_provider in litellm._custom_providers + ): + _has_content = bool( + chunk.choices + and chunk.choices[0].delta is not None + and ( + chunk.choices[0].delta.content or chunk.choices[0].delta.tool_calls + ) + ) + if self.received_finish_reason is not None: + if not _has_content: + raise StopIteration + if chunk.choices and chunk.choices[0].finish_reason: + self.received_finish_reason = chunk.choices[0].finish_reason + if not _has_content: + return _ProviderChunkEarlyReturn(None) + # Strip finish_reason from the content chunk so it appears + # only on the trailing empty-delta chunk (OpenAI spec). + # finish_reason_handler() will emit the proper terminal chunk. + chunk.choices[0].finish_reason = None # type: ignore[assignment] + return _ProviderChunkEarlyReturn(chunk) + + if ( + isinstance(chunk, dict) + and generic_chunk_has_all_required_fields( + chunk=chunk + ) # check if chunk is a generic streaming chunk + ) or ( + self.custom_llm_provider + and self.custom_llm_provider in litellm._custom_providers + ): + if self.received_finish_reason is not None: + _chunk_has_content = isinstance(chunk, dict) and ( + bool(chunk.get("text", "")) + or chunk.get("tool_use") is not None + # Usage-only final chunks are valid and needed to surface + # finish_reason/usage to downstream translators. + or chunk.get("usage") is not None + ) + if not _chunk_has_content and ( + not isinstance(chunk, dict) + or "provider_specific_fields" not in chunk + ): + raise StopIteration + anthropic_response_obj: GChunk = cast(GChunk, chunk) + completion_obj["content"] = anthropic_response_obj["text"] + if anthropic_response_obj["is_finished"]: + self.received_finish_reason = anthropic_response_obj["finish_reason"] + + if anthropic_response_obj["finish_reason"]: + self.intermittent_finish_reason = anthropic_response_obj[ + "finish_reason" + ] + + if anthropic_response_obj["usage"] is not None: + setattr( + model_response, + "usage", + litellm.Usage(**anthropic_response_obj["usage"]), + ) + + if ( + "tool_use" in anthropic_response_obj + and anthropic_response_obj["tool_use"] is not None + ): + completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]] + + if ( + "provider_specific_fields" in anthropic_response_obj + and anthropic_response_obj["provider_specific_fields"] is not None + ): + for key, value in anthropic_response_obj[ + "provider_specific_fields" + ].items(): + setattr(model_response, key, value) + + response_obj = cast(dict[str, Any], anthropic_response_obj) + elif self.model == "replicate" or self.custom_llm_provider == "replicate": + response_obj = self.handle_replicate_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "predibase": + response_obj = self.handle_predibase_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif ( + self.custom_llm_provider and self.custom_llm_provider == "baseten" + ): # baseten doesn't provide streaming + completion_obj["content"] = self.handle_baseten_chunk(chunk) + elif ( + self.custom_llm_provider and self.custom_llm_provider == "ai21" + ): # ai21 doesn't provide streaming + response_obj = self.handle_ai21_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": + response_obj = self.handle_maritalk_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider and self.custom_llm_provider == "vllm": + completion_obj["content"] = chunk[0].outputs[0].text + elif ( + self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" + ): # aleph alpha doesn't provide streaming + response_obj = self.handle_aleph_alpha_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "nlp_cloud": + try: + response_obj = self.handle_nlp_cloud_chunk(chunk) + completion_obj["content"] = response_obj["text"] + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + except Exception as e: + if self.received_finish_reason: + raise e + else: + if self.sent_first_chunk is False: + raise Exception("An unknown error occurred with the stream") + self.received_finish_reason = "stop" + elif self.custom_llm_provider == "vertex_ai" and not isinstance( + chunk, ModelResponseStream + ): + chunk = cast(Any, chunk) + import proto # type: ignore + + if hasattr(chunk, "candidates") is True: + try: + try: + completion_obj["content"] = chunk.text # type: ignore + except Exception as e: + original_exception = e + if "Part has no text." in str(e): + ## check for function calling + function_call = ( + chunk.candidates[0].content.parts[0].function_call # type: ignore + ) + + args_dict = {} + + # Check if it's a RepeatedComposite instance + for key, val in function_call.args.items(): + if isinstance( + val, + proto.marshal.collections.repeated.RepeatedComposite, # type: ignore + ): + # If so, convert to list + args_dict[key] = [v for v in val] + else: + args_dict[key] = val + + try: + args_str = json.dumps(args_dict) + except Exception as e: + raise e + _delta_obj = litellm.utils.Delta( + content=None, + tool_calls=[ + { + "id": f"call_{str(uuid.uuid4())}", + "function": { + "arguments": args_str, + "name": function_call.name, + }, + "type": "function", + } + ], + ) + _streaming_response = StreamingChoices(delta=_delta_obj) + _model_response = ModelResponseStream() + _model_response.choices = [_streaming_response] + response_obj = {"original_chunk": _model_response} + else: + raise original_exception + if ( + hasattr(chunk.candidates[0], "finish_reason") # type: ignore + and chunk.candidates[0].finish_reason.name # type: ignore + != "FINISH_REASON_UNSPECIFIED" + ): # every non-final chunk in vertex ai has this + self.received_finish_reason = map_finish_reason( # type: ignore + chunk.candidates[0].finish_reason.name + ) + except Exception: + if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore + raise Exception( + f"The response was blocked by VertexAI. {str(chunk)}" + ) + else: + completion_obj["content"] = str(chunk) + elif self.custom_llm_provider == "petals": + if self.completion_stream is None or len(self.completion_stream) == 0: + if self.received_finish_reason is not None: + raise StopIteration + else: + self.received_finish_reason = "stop" + chunk_size = 30 + stream = cast(Any, self.completion_stream) + new_chunk = stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = stream[chunk_size:] + elif self.custom_llm_provider == "palm": + # fake streaming + response_obj = {} + if self.completion_stream is None or len(self.completion_stream) == 0: + if self.received_finish_reason is not None: + raise StopIteration + else: + self.received_finish_reason = "stop" + chunk_size = 30 + stream = cast(Any, self.completion_stream) + new_chunk = stream[:chunk_size] + completion_obj["content"] = new_chunk + self.completion_stream = stream[chunk_size:] + elif self.custom_llm_provider == "triton": + response_obj = self.handle_triton_stream(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "text-completion-openai": + response_obj = self.handle_openai_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + if response_obj["usage"] is not None: + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].prompt_tokens, + completion_tokens=response_obj["usage"].completion_tokens, + total_tokens=response_obj["usage"].total_tokens, + ), + ) + elif self.custom_llm_provider == "text-completion-codestral": + if not isinstance(chunk, str): + raise ValueError(f"chunk is not a string: {chunk}") + response_obj = cast( + dict[str, Any], + litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), + ) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + if "usage" in response_obj is not None: + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].prompt_tokens, + completion_tokens=response_obj["usage"].completion_tokens, + total_tokens=response_obj["usage"].total_tokens, + ), + ) + elif self.custom_llm_provider == "azure_text": + response_obj = self.handle_azure_text_completion_chunk(chunk) + completion_obj["content"] = response_obj["text"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + elif self.custom_llm_provider == "cached_response": + chunk = cast(ModelResponseStream, chunk) + response_obj = { + "text": chunk.choices[0].delta.content, + "is_finished": True, + "finish_reason": chunk.choices[0].finish_reason, + "original_chunk": chunk, + "tool_calls": ( + chunk.choices[0].delta.tool_calls + if hasattr(chunk.choices[0].delta, "tool_calls") + else None + ), + } + + completion_obj["content"] = response_obj["text"] + if response_obj["tool_calls"] is not None: + completion_obj["tool_calls"] = response_obj["tool_calls"] + print_verbose(f"completion obj content: {completion_obj['content']}") + if hasattr(chunk, "id"): + model_response.id = chunk.id + self.response_id = chunk.id + if hasattr(chunk, "system_fingerprint"): + self.system_fingerprint = chunk.system_fingerprint + if response_obj["is_finished"]: + self.received_finish_reason = response_obj["finish_reason"] + else: # openai / azure chat model + if self.custom_llm_provider in [ + LlmProviders.AZURE.value, + LlmProviders.AZURE_AI.value, + ]: + if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): + # for azure, we need to pass the model from the original chunk + self.model = getattr(chunk, "model", self.model) + response_obj = self.handle_openai_chat_completion_chunk(chunk) + if response_obj is None: + return _ProviderChunkEarlyReturn(None) + completion_obj["content"] = response_obj["text"] + self.intermittent_finish_reason = response_obj.get("finish_reason", None) + if response_obj["is_finished"]: + if response_obj["finish_reason"] == "error": + raise Exception( + "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format( + self.custom_llm_provider, response_obj + ) + ) + self.received_finish_reason = response_obj["finish_reason"] + if response_obj.get("original_chunk", None) is not None: + if hasattr(response_obj["original_chunk"], "id"): + model_response = self.set_model_id( + response_obj["original_chunk"].id, model_response + ) + if hasattr(response_obj["original_chunk"], "system_fingerprint"): + model_response.system_fingerprint = response_obj[ + "original_chunk" + ].system_fingerprint + self.system_fingerprint = response_obj[ + "original_chunk" + ].system_fingerprint + if response_obj["logprobs"] is not None: + model_response.choices[0].logprobs = response_obj["logprobs"] + + if response_obj["usage"] is not None: + if isinstance(response_obj["usage"], dict): + setattr( + model_response, + "usage", + litellm.Usage( + prompt_tokens=response_obj["usage"].get( + "prompt_tokens", None + ) + or None, + completion_tokens=response_obj["usage"].get( + "completion_tokens", None + ) + or None, + total_tokens=response_obj["usage"].get("total_tokens", None) + or None, + ), + ) + elif isinstance(response_obj["usage"], Usage): + setattr( + model_response, + "usage", + response_obj["usage"], + ) + elif isinstance(response_obj["usage"], BaseModel): + setattr( + model_response, + "usage", + litellm.Usage(**response_obj["usage"].model_dump()), + ) + return _ProviderChunkParsed(response_obj) + def chunk_creator(self, chunk: Any): # type: ignore if hasattr(chunk, "id"): self.response_id = chunk.id model_response = self.model_response_creator() - response_obj: Dict[str, Any] = {} + response_obj: dict[str, Any] = {} try: # return this for all models - completion_obj: Dict[str, Any] = {"content": ""} - from litellm.types.utils import GenericStreamingChunk as GChunk - - if ( - isinstance(chunk, ModelResponseStream) - and self.custom_llm_provider is not None - and self.custom_llm_provider in litellm._custom_providers - ): - _has_content = bool( - chunk.choices - and chunk.choices[0].delta is not None - and ( - chunk.choices[0].delta.content - or chunk.choices[0].delta.tool_calls - ) - ) - if self.received_finish_reason is not None: - if not _has_content: - raise StopIteration - if chunk.choices and chunk.choices[0].finish_reason: - self.received_finish_reason = chunk.choices[0].finish_reason - if not _has_content: - return None - # Strip finish_reason from the content chunk so it appears - # only on the trailing empty-delta chunk (OpenAI spec). - # finish_reason_handler() will emit the proper terminal chunk. - chunk.choices[0].finish_reason = None # type: ignore[assignment] - return chunk - - if ( - isinstance(chunk, dict) - and generic_chunk_has_all_required_fields( - chunk=chunk - ) # check if chunk is a generic streaming chunk - ) or ( - self.custom_llm_provider - and self.custom_llm_provider in litellm._custom_providers - ): - if self.received_finish_reason is not None: - _chunk_has_content = isinstance(chunk, dict) and ( - bool(chunk.get("text", "")) - or chunk.get("tool_use") is not None - # Usage-only final chunks are valid and needed to surface - # finish_reason/usage to downstream translators. - or chunk.get("usage") is not None - ) - if not _chunk_has_content and ( - not isinstance(chunk, dict) - or "provider_specific_fields" not in chunk - ): - raise StopIteration - anthropic_response_obj: GChunk = cast(GChunk, chunk) - completion_obj["content"] = anthropic_response_obj["text"] - if anthropic_response_obj["is_finished"]: - self.received_finish_reason = anthropic_response_obj[ - "finish_reason" - ] - - if anthropic_response_obj["finish_reason"]: - self.intermittent_finish_reason = anthropic_response_obj[ - "finish_reason" - ] - - if anthropic_response_obj["usage"] is not None: - setattr( - model_response, - "usage", - litellm.Usage(**anthropic_response_obj["usage"]), - ) - - if ( - "tool_use" in anthropic_response_obj - and anthropic_response_obj["tool_use"] is not None - ): - completion_obj["tool_calls"] = [anthropic_response_obj["tool_use"]] - - if ( - "provider_specific_fields" in anthropic_response_obj - and anthropic_response_obj["provider_specific_fields"] is not None - ): - for key, value in anthropic_response_obj[ - "provider_specific_fields" - ].items(): - setattr(model_response, key, value) - - response_obj = cast(Dict[str, Any], anthropic_response_obj) - elif self.model == "replicate" or self.custom_llm_provider == "replicate": - response_obj = self.handle_replicate_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "predibase": - response_obj = self.handle_predibase_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif ( - self.custom_llm_provider and self.custom_llm_provider == "baseten" - ): # baseten doesn't provide streaming - completion_obj["content"] = self.handle_baseten_chunk(chunk) - elif ( - self.custom_llm_provider and self.custom_llm_provider == "ai21" - ): # ai21 doesn't provide streaming - response_obj = self.handle_ai21_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "maritalk": - response_obj = self.handle_maritalk_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider and self.custom_llm_provider == "vllm": - completion_obj["content"] = chunk[0].outputs[0].text - elif ( - self.custom_llm_provider and self.custom_llm_provider == "aleph_alpha" - ): # aleph alpha doesn't provide streaming - response_obj = self.handle_aleph_alpha_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "nlp_cloud": - try: - response_obj = self.handle_nlp_cloud_chunk(chunk) - completion_obj["content"] = response_obj["text"] - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - except Exception as e: - if self.received_finish_reason: - raise e - else: - if self.sent_first_chunk is False: - raise Exception("An unknown error occurred with the stream") - self.received_finish_reason = "stop" - elif self.custom_llm_provider == "vertex_ai" and not isinstance( - chunk, ModelResponseStream - ): - import proto # type: ignore - - if hasattr(chunk, "candidates") is True: - try: - try: - completion_obj["content"] = chunk.text # type: ignore - except Exception as e: - original_exception = e - if "Part has no text." in str(e): - ## check for function calling - function_call = ( - chunk.candidates[0].content.parts[0].function_call # type: ignore - ) - - args_dict = {} - - # Check if it's a RepeatedComposite instance - for key, val in function_call.args.items(): - if isinstance( - val, - proto.marshal.collections.repeated.RepeatedComposite, # type: ignore - ): - # If so, convert to list - args_dict[key] = [v for v in val] - else: - args_dict[key] = val - - try: - args_str = json.dumps(args_dict) - except Exception as e: - raise e - _delta_obj = litellm.utils.Delta( - content=None, - tool_calls=[ - { - "id": f"call_{str(uuid.uuid4())}", - "function": { - "arguments": args_str, - "name": function_call.name, - }, - "type": "function", - } - ], - ) - _streaming_response = StreamingChoices(delta=_delta_obj) - _model_response = ModelResponseStream() - _model_response.choices = [_streaming_response] - response_obj = {"original_chunk": _model_response} - else: - raise original_exception - if ( - hasattr(chunk.candidates[0], "finish_reason") # type: ignore - and chunk.candidates[0].finish_reason.name # type: ignore - != "FINISH_REASON_UNSPECIFIED" - ): # every non-final chunk in vertex ai has this - self.received_finish_reason = map_finish_reason( # type: ignore - chunk.candidates[0].finish_reason.name - ) - except Exception: - if chunk.candidates[0].finish_reason.name == "SAFETY": # type: ignore - raise Exception( - f"The response was blocked by VertexAI. {str(chunk)}" - ) - else: - completion_obj["content"] = str(chunk) - elif self.custom_llm_provider == "petals": - if self.completion_stream is None or len(self.completion_stream) == 0: - if self.received_finish_reason is not None: - raise StopIteration - else: - self.received_finish_reason = "stop" - chunk_size = 30 - new_chunk = self.completion_stream[:chunk_size] # type: ignore[index] - completion_obj["content"] = new_chunk - self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index] - elif self.custom_llm_provider == "palm": - # fake streaming - response_obj = {} - if self.completion_stream is None or len(self.completion_stream) == 0: - if self.received_finish_reason is not None: - raise StopIteration - else: - self.received_finish_reason = "stop" - chunk_size = 30 - new_chunk = self.completion_stream[:chunk_size] # type: ignore[index] - completion_obj["content"] = new_chunk - self.completion_stream = self.completion_stream[chunk_size:] # type: ignore[index] - elif self.custom_llm_provider == "triton": - response_obj = self.handle_triton_stream(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "text-completion-openai": - response_obj = self.handle_openai_text_completion_chunk(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - if response_obj["usage"] is not None: - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].prompt_tokens, - completion_tokens=response_obj["usage"].completion_tokens, - total_tokens=response_obj["usage"].total_tokens, - ), - ) - elif self.custom_llm_provider == "text-completion-codestral": - if not isinstance(chunk, str): - raise ValueError(f"chunk is not a string: {chunk}") - response_obj = cast( - Dict[str, Any], - litellm.CodestralTextCompletionConfig()._chunk_parser(chunk), - ) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - if "usage" in response_obj is not None: - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].prompt_tokens, - completion_tokens=response_obj["usage"].completion_tokens, - total_tokens=response_obj["usage"].total_tokens, - ), - ) - elif self.custom_llm_provider == "azure_text": - response_obj = self.handle_azure_text_completion_chunk(chunk) - completion_obj["content"] = response_obj["text"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - elif self.custom_llm_provider == "cached_response": - chunk = cast(ModelResponseStream, chunk) - response_obj = { - "text": chunk.choices[0].delta.content, - "is_finished": True, - "finish_reason": chunk.choices[0].finish_reason, - "original_chunk": chunk, - "tool_calls": ( - chunk.choices[0].delta.tool_calls - if hasattr(chunk.choices[0].delta, "tool_calls") - else None - ), - } - - completion_obj["content"] = response_obj["text"] - if response_obj["tool_calls"] is not None: - completion_obj["tool_calls"] = response_obj["tool_calls"] - print_verbose(f"completion obj content: {completion_obj['content']}") - if hasattr(chunk, "id"): - model_response.id = chunk.id - self.response_id = chunk.id - if hasattr(chunk, "system_fingerprint"): - self.system_fingerprint = chunk.system_fingerprint - if response_obj["is_finished"]: - self.received_finish_reason = response_obj["finish_reason"] - else: # openai / azure chat model - if self.custom_llm_provider in [ - LlmProviders.AZURE.value, - LlmProviders.AZURE_AI.value, - ]: - if isinstance(chunk, BaseModel) and hasattr(chunk, "model"): - # for azure, we need to pass the model from the original chunk - self.model = getattr(chunk, "model", self.model) - response_obj = self.handle_openai_chat_completion_chunk(chunk) - if response_obj is None: - return - completion_obj["content"] = response_obj["text"] - self.intermittent_finish_reason = response_obj.get( - "finish_reason", None - ) - if response_obj["is_finished"]: - if response_obj["finish_reason"] == "error": - raise Exception( - "{} raised a streaming error - finish_reason: error, no content string given. Received Chunk={}".format( - self.custom_llm_provider, response_obj - ) - ) - self.received_finish_reason = response_obj["finish_reason"] - if response_obj.get("original_chunk", None) is not None: - if hasattr(response_obj["original_chunk"], "id"): - model_response = self.set_model_id( - response_obj["original_chunk"].id, model_response - ) - if hasattr(response_obj["original_chunk"], "system_fingerprint"): - model_response.system_fingerprint = response_obj[ - "original_chunk" - ].system_fingerprint - self.system_fingerprint = response_obj[ - "original_chunk" - ].system_fingerprint - if response_obj["logprobs"] is not None: - model_response.choices[0].logprobs = response_obj["logprobs"] - - if response_obj["usage"] is not None: - if isinstance(response_obj["usage"], dict): - setattr( - model_response, - "usage", - litellm.Usage( - prompt_tokens=response_obj["usage"].get( - "prompt_tokens", None - ) - or None, - completion_tokens=response_obj["usage"].get( - "completion_tokens", None - ) - or None, - total_tokens=response_obj["usage"].get( - "total_tokens", None - ) - or None, - ), - ) - elif isinstance(response_obj["usage"], Usage): - setattr( - model_response, - "usage", - response_obj["usage"], - ) - elif isinstance(response_obj["usage"], BaseModel): - setattr( - model_response, - "usage", - litellm.Usage(**response_obj["usage"].model_dump()), - ) + completion_obj: dict[str, Any] = {"content": ""} + dispatch_result = self._dispatch_provider_chunk( + chunk=chunk, + model_response=model_response, + completion_obj=completion_obj, + ) + if isinstance(dispatch_result, _ProviderChunkEarlyReturn): + return dispatch_result.value + response_obj = dispatch_result.response_obj model_response.model = self.model ## FUNCTION CALL PARSING diff --git a/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py b/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py index c7c110ff3e3..8714939f025 100644 --- a/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py +++ b/litellm/llms/anthropic/experimental_pass_through/messages/interceptors/advisor.py @@ -84,8 +84,14 @@ class AdvisorOrchestrationHandler(MessagesInterceptor): ) # Optional routing overrides for the advisor sub-call (e.g. proxy routing). # If not set in the tool definition, litellm resolves from env vars. - advisor_api_key: Optional[str] = advisor_tool.get("api_key") - advisor_api_base: Optional[str] = advisor_tool.get("api_base") + # The advisor tool is caller-controlled; only honor a client-supplied + # api_base/api_key when the proxy has enabled clientside credentials, + # otherwise let litellm resolve from server config. + advisor_api_key: Optional[str] = None + advisor_api_base: Optional[str] = None + if _allow_client_side_advisor_credentials(): + advisor_api_key = advisor_tool.get("api_key") + advisor_api_base = advisor_tool.get("api_base") # Build the synthetic tool definition the provider will receive. synthetic_advisor_tool = _make_synthetic_advisor_tool() @@ -181,6 +187,20 @@ class AdvisorOrchestrationHandler(MessagesInterceptor): # --------------------------------------------------------------------------- +def _allow_client_side_advisor_credentials() -> bool: + """Whether a caller-supplied advisor api_base/api_key may be honored. + + Gated on the proxy's ``allow_client_side_credentials`` opt-in. When the + interceptor runs outside the proxy (SDK use), there is no admin boundary + to protect, so client-supplied routing is allowed. + """ + try: + from litellm.proxy.proxy_server import general_settings + except (ImportError, ModuleNotFoundError): + return True + return general_settings.get("allow_client_side_credentials") is True + + def _make_synthetic_advisor_tool() -> Dict: """Build a regular tool definition the executor provider can understand.""" return { diff --git a/litellm/llms/bedrock/base_aws_llm.py b/litellm/llms/bedrock/base_aws_llm.py index 2c9ea187912..c31462a735b 100644 --- a/litellm/llms/bedrock/base_aws_llm.py +++ b/litellm/llms/bedrock/base_aws_llm.py @@ -10,7 +10,6 @@ from typing import ( Callable, ClassVar, Dict, - List, Literal, Optional, Tuple, @@ -210,32 +209,11 @@ class BaseAWSLLM: """ Return a boto3.Credentials object """ - ## CHECK IS 'os.environ/' passed in - params_to_check: List[Optional[str]] = [ - aws_access_key_id, - aws_secret_access_key, - aws_session_token, - aws_region_name, - aws_session_name, - aws_profile_name, - aws_role_name, - aws_web_identity_token, - aws_sts_endpoint, - aws_external_id, - ] - - # Iterate over parameters and update if needed - for i, param in enumerate(params_to_check): - if param and param.startswith("os.environ/"): - _v = get_secret(param) - if _v is not None and isinstance(_v, str): - params_to_check[i] = _v - elif param is None: # check if uppercase value in env - key = self.aws_authentication_params[i] - if key.upper() in os.environ: - params_to_check[i] = os.getenv(key.upper()) - - # Assign updated values back to parameters + # Only config-sourced credentials are expanded against the environment. + # os.environ/ references in the model config are resolved at load time, + # so any reference still present at this point is caller-supplied input and is + # left as-is rather than expanded into a process environment variable. Each + # unset param falls back to its matching fixed AWS_* ambient env var. ( aws_access_key_id, aws_secret_access_key, @@ -247,7 +225,21 @@ class BaseAWSLLM: aws_web_identity_token, aws_sts_endpoint, aws_external_id, - ) = params_to_check + ) = tuple( + value if value is not None else os.getenv(env_var) + for value, env_var in ( + (aws_access_key_id, "AWS_ACCESS_KEY_ID"), + (aws_secret_access_key, "AWS_SECRET_ACCESS_KEY"), + (aws_session_token, "AWS_SESSION_TOKEN"), + (aws_region_name, "AWS_REGION_NAME"), + (aws_session_name, "AWS_SESSION_NAME"), + (aws_profile_name, "AWS_PROFILE_NAME"), + (aws_role_name, "AWS_ROLE_NAME"), + (aws_web_identity_token, "AWS_WEB_IDENTITY_TOKEN"), + (aws_sts_endpoint, "AWS_STS_ENDPOINT"), + (aws_external_id, "AWS_EXTERNAL_ID"), + ) + ) verbose_logger.debug( "in get credentials\n" @@ -845,6 +837,20 @@ class BaseAWSLLM: f"IN Web Identity Token: {aws_web_identity_token} | Role Name: {aws_role_name} | Session Name: {aws_session_name}" ) + # get_secret() expands environment-variable references (an os.environ/ + # prefix, or a bare name matching an environment variable). Config-sourced + # references are expanded at load time, so such a reference reaching here is + # caller-supplied input; reject it rather than expanding a process-environment + # value for use as the token. + if ( + aws_web_identity_token.startswith("os.environ/") + or aws_web_identity_token in os.environ + ): + raise AwsAuthError( + message="Invalid web identity token reference.", + status_code=400, + ) + oidc_token = get_secret(aws_web_identity_token) if oidc_token is None: diff --git a/litellm/llms/custom_httpx/llm_http_handler.py b/litellm/llms/custom_httpx/llm_http_handler.py index 8ac5b47c6e7..790bd0519d7 100644 --- a/litellm/llms/custom_httpx/llm_http_handler.py +++ b/litellm/llms/custom_httpx/llm_http_handler.py @@ -2407,11 +2407,17 @@ class BaseLLMHTTPHandler: provider_config=responses_api_provider_config, ) - return responses_api_provider_config.transform_response_api_response( - model=model, - raw_response=response, - logging_obj=logging_obj, + initial_response = ( + responses_api_provider_config.transform_response_api_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) ) + # Responses agentic interception (e.g. code interpreter) runs the follow-up + # loop via the async hook, so it is async-only for now; the sync path returns + # the initial response unchanged. + return initial_response async def async_response_api_handler( self, @@ -2570,12 +2576,44 @@ class BaseLLMHTTPHandler: provider_config=responses_api_provider_config, ) - return responses_api_provider_config.transform_response_api_response( - model=model, - raw_response=response, - logging_obj=logging_obj, + initial_response = ( + responses_api_provider_config.transform_response_api_response( + model=model, + raw_response=response, + logging_obj=logging_obj, + ) ) + final_response = await self._call_agentic_completion_hooks( + response=initial_response, + model=model, + messages=( + input + if isinstance(input, list) + else [{"role": "user", "content": input}] + ), + anthropic_messages_provider_config=responses_api_provider_config, + anthropic_messages_optional_request_params=response_api_optional_request_params, + logging_obj=logging_obj, + stream=False, + custom_llm_provider=custom_llm_provider, + kwargs=dict(litellm_params), + api_surface="responses", + ) + + result = final_response if final_response is not None else initial_response + if litellm_params.get( + "_code_interpreter_interception_converted_stream" + ) and not litellm_params.get("_agentic_loop_depth"): + return self._wrap_responses_response_as_fake_stream( + result=result, + model=model, + responses_api_provider_config=responses_api_provider_config, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + ) + return result + async def async_delete_response_api_handler( self, response_id: str, @@ -4875,6 +4913,132 @@ class BaseLLMHTTPHandler: return response + async def _execute_responses_agentic_plan( + self, + plan: AgenticLoopPlan, + model: str, + response_api_optional_request_params: dict, + logging_obj: "LiteLLMLoggingObj", + kwargs: dict, + depth: int, + max_loops: int, + fingerprints: list[str], + fingerprint: str, + callback: Any | None = None, + ) -> Any: + patch = plan.request_patch or AgenticLoopRequestPatch() + if patch.messages is None: + raise ValueError("Agentic loop plan missing patched responses input") + + optional_params = dict(response_api_optional_request_params) + optional_params.update(patch.optional_params) + if patch.tools is not None: + optional_params["tools"] = patch.tools + optional_params = { + k: v + for k, v in optional_params.items() + if k != "stream" and k != "_code_interpreter_interception_converted_stream" + } + + internal_keys = {"litellm_logging_obj"} + kwargs_for_followup = { + k: v + for k, v in kwargs.items() + if not k.startswith("_websearch_interception") + and not k.startswith("_compression_interception") + and k != "_code_interpreter_interception_converted_stream" + and k not in internal_keys + and k not in optional_params + } + kwargs_for_followup.update(patch.kwargs) + kwargs_for_followup["_agentic_loop_depth"] = depth + 1 + kwargs_for_followup["max_agentic_loops"] = max_loops + kwargs_for_followup["_agentic_loop_fingerprints"] = fingerprints + [fingerprint] + + try: + response = await litellm.aresponses( + model=patch.model or model, + input=patch.messages, + **optional_params, + **kwargs_for_followup, + ) + + if callback is not None: + try: + response = await callback.async_post_agentic_loop_response_hook( + response=response, plan=plan, kwargs=kwargs + ) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_post_agentic_loop_response_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + return response + finally: + if callback is not None: + await self._run_agentic_loop_cleanup( + callback=callback, + plan=plan, + kwargs=kwargs, + logging_obj=logging_obj, + model=model, + ) + + @staticmethod + async def _run_agentic_loop_cleanup( + callback: Any, + plan: AgenticLoopPlan, + kwargs: dict, + logging_obj: "LiteLLMLoggingObj", + model: str, + ) -> None: + try: + await callback.async_agentic_loop_cleanup_hook(plan=plan, kwargs=kwargs) + except Exception as e: + _call_id = getattr(logging_obj, "litellm_call_id", "unknown") + verbose_logger.exception( + "LiteLLM.AgenticHookError: Exception in " + "async_agentic_loop_cleanup_hook [call_id=%s model=%s]: %s", + _call_id, + model, + str(e), + ) + + def _wrap_responses_response_as_fake_stream( + self, + result: Any, + model: str, + responses_api_provider_config: Any, + logging_obj: "LiteLLMLoggingObj", + custom_llm_provider: str, + ) -> Any: + """ + Wrap a completed responses result as a synthetic stream. + + Used when an interceptor forced stream=False to run the agentic loop on + the non-streaming path, but the caller originally asked for streaming. + """ + import httpx + + from litellm.responses.streaming_iterator import ( + MockResponsesAPIStreamingIterator, + ) + + payload = result.model_dump() if hasattr(result, "model_dump") else result + raw_response = httpx.Response(status_code=200, json=payload) + return MockResponsesAPIStreamingIterator( + response=raw_response, + model=model, + responses_api_provider_config=responses_api_provider_config, + logging_obj=logging_obj, + custom_llm_provider=custom_llm_provider, + ) + async def _execute_chat_completion_agentic_plan( self, plan: AgenticLoopPlan, @@ -4940,6 +5104,7 @@ class BaseLLMHTTPHandler: stream: bool, custom_llm_provider: str, kwargs: Dict, + api_surface: str = "anthropic_messages", ) -> Optional[Any]: """ Call agentic completion hooks for all custom loggers (Anthropic Messages API). @@ -5046,6 +5211,20 @@ class BaseLLMHTTPHandler: if not plan.run_agentic_loop: continue + if api_surface == "responses": + return await self._execute_responses_agentic_plan( + plan=plan, + model=model, + response_api_optional_request_params=anthropic_messages_optional_request_params, + logging_obj=logging_obj, + kwargs=kwargs_with_provider, + depth=depth, + max_loops=max_loops, + fingerprints=fingerprints, + fingerprint=fingerprint, + callback=callback, + ) + return await self._execute_anthropic_agentic_plan( plan=plan, model=model, @@ -5083,7 +5262,7 @@ class BaseLLMHTTPHandler: else False ) - if websearch_converted_stream: + if api_surface == "anthropic_messages" and websearch_converted_stream: from typing import cast from litellm._logging import verbose_logger diff --git a/litellm/llms/e2b/sandbox/transformation.py b/litellm/llms/e2b/sandbox/transformation.py index 1ce28bc55fb..c279fab22ab 100644 --- a/litellm/llms/e2b/sandbox/transformation.py +++ b/litellm/llms/e2b/sandbox/transformation.py @@ -51,11 +51,13 @@ class E2BSandboxConfig(BaseSandboxConfig): timeout: int | None = None, allow_internet_access: bool = True, api_key: str | None = None, + api_base: str | None = None, metadata: dict | None = None, client: AsyncHTTPHandler | None = None, **kwargs, ) -> ContainerHandle: key = self.validate_environment(api_key=api_key) + base = api_base or E2B_API_BASE body = { "templateID": template or E2B_DEFAULT_TEMPLATE, "timeout": timeout if timeout is not None else DEFAULT_SANDBOX_TIMEOUT, @@ -68,7 +70,7 @@ class E2BSandboxConfig(BaseSandboxConfig): response = cast( httpx.Response, await self._http(client).post( - url=f"{E2B_API_BASE}/sandboxes", + url=f"{base}/sandboxes", headers={"X-API-Key": key, "Content-Type": "application/json"}, json=body, ), @@ -84,6 +86,7 @@ class E2BSandboxConfig(BaseSandboxConfig): "envd_access_token": data.get("envdAccessToken"), "traffic_access_token": data.get("trafficAccessToken"), "api_key": key, + "api_base": base, } return handle @@ -130,6 +133,7 @@ class E2BSandboxConfig(BaseSandboxConfig): *, container: Union[ContainerHandle, str], api_key: str | None = None, + api_base: str | None = None, client: AsyncHTTPHandler | None = None, **kwargs, ) -> bool: @@ -139,11 +143,12 @@ class E2BSandboxConfig(BaseSandboxConfig): or handle._hidden_params.get("api_key") or self.validate_environment() ) + base = api_base or handle._hidden_params.get("api_base") or E2B_API_BASE try: response = cast( httpx.Response, await self._http(client).delete( - url=f"{E2B_API_BASE}/sandboxes/{handle.id}", + url=f"{base}/sandboxes/{handle.id}", headers={"X-API-Key": key}, ), ) diff --git a/litellm/llms/fireworks_ai/chat/transformation.py b/litellm/llms/fireworks_ai/chat/transformation.py index 341c2fc7350..7e4395959b9 100644 --- a/litellm/llms/fireworks_ai/chat/transformation.py +++ b/litellm/llms/fireworks_ai/chat/transformation.py @@ -1,5 +1,15 @@ import json -from typing import Any, List, Literal, Optional, Tuple, Union, cast +from typing import ( + Any, + AsyncIterator, + Iterator, + List, + Literal, + Optional, + Tuple, + Union, + cast, +) import httpx @@ -15,7 +25,6 @@ from litellm.litellm_core_utils.llm_response_utils.get_headers import ( from litellm.secret_managers.main import get_secret_str from litellm.types.llms.openai import ( AllMessageValues, - ChatCompletionImageObject, ChatCompletionToolParam, OpenAIChatCompletionToolParam, ) @@ -25,6 +34,7 @@ from litellm.types.utils import ( Function, Message, ModelResponse, + ModelResponseStream, ProviderSpecificModelInfo, ) from litellm.utils import ( @@ -34,10 +44,34 @@ from litellm.utils import ( supports_tool_choice, ) -from ...openai.chat.gpt_transformation import OpenAIGPTConfig +from ...openai.chat.gpt_transformation import ( + OpenAIChatCompletionStreamingHandler, + OpenAIGPTConfig, +) from ..common_utils import FireworksAIException +def _extract_fireworks_hidden_params(payload: dict) -> dict: + """ + Collect Fireworks-specific response fields (perf_metrics, prompt_token_ids, + per-choice raw_output and token_ids) from a non-streaming completion payload + or a single streaming chunk, so the same data lands in ``_hidden_params`` on + both response paths. + """ + choices = [c for c in (payload.get("choices") or []) if isinstance(c, dict)] + top_level = { + f"fireworks_{field}": payload[field] + for field in ("perf_metrics", "prompt_token_ids") + if field in payload + } + per_choice = { + f"fireworks_{dest}": [c[field] for c in choices if field in c] + for field, dest in (("raw_output", "raw_outputs"), ("token_ids", "token_ids")) + if any(field in c for c in choices) + } + return {**top_level, **per_choice} + + class FireworksAIConfig(OpenAIGPTConfig): """ Reference: https://docs.fireworks.ai/api-reference/post-chatcompletions @@ -60,8 +94,7 @@ class FireworksAIConfig(OpenAIGPTConfig): logprobs: Optional[int] = None reasoning_effort: Optional[str] = None - # Non OpenAI parameters - Fireworks AI only params - prompt_truncate_length: Optional[int] = None + prompt_truncate_len: Optional[int] = None context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None def __init__( @@ -80,7 +113,7 @@ class FireworksAIConfig(OpenAIGPTConfig): user: Optional[str] = None, logprobs: Optional[int] = None, reasoning_effort: Optional[str] = None, - prompt_truncate_length: Optional[int] = None, + prompt_truncate_len: Optional[int] = None, context_length_exceeded_behavior: Optional[Literal["error", "truncate"]] = None, ) -> None: locals_ = locals().copy() @@ -108,8 +141,30 @@ class FireworksAIConfig(OpenAIGPTConfig): "response_format", "user", "logprobs", - "prompt_truncate_length", + "prompt_truncate_len", "context_length_exceeded_behavior", + "seed", + "top_logprobs", + "min_p", + "typical_p", + "repetition_penalty", + "mirostat_target", + "mirostat_lr", + "logit_bias", + "echo", + "echo_last", + "ignore_eos", + "prompt_cache_key", + "prompt_cache_isolation_key", + "raw_output", + "perf_metrics_in_response", + "return_token_ids", + "safe_tokenization", + "service_tier", + "speculation", + "prediction", + "stream_options", + "sampling_mask", ] # Only add tools for models that support function calling @@ -133,9 +188,11 @@ class FireworksAIConfig(OpenAIGPTConfig): if supports_tool_choice(model=model, custom_llm_provider="fireworks_ai"): supported_params.append("tool_choice") - # Only add reasoning_effort for models that support it + # Only add reasoning params for models that support it if supports_reasoning(model=model, custom_llm_provider="fireworks_ai"): supported_params.append("reasoning_effort") + supported_params.append("reasoning_history") + supported_params.append("thinking") return supported_params @@ -151,6 +208,18 @@ class FireworksAIConfig(OpenAIGPTConfig): param == "tools" and value is not None for param, value in non_default_params.items() ) + if ( + non_default_params.get("thinking") is not None + and non_default_params.get("reasoning_effort") is not None + ): + raise litellm.BadRequestError( + message=( + "Fireworks AI chat completions does not support specifying both " + "`thinking` and `reasoning_effort` in the same request." + ), + model=model, + llm_provider="fireworks_ai", + ) for param, value in non_default_params.items(): if param == "tool_choice": @@ -174,40 +243,19 @@ class FireworksAIConfig(OpenAIGPTConfig): optional_params["response_format"] = value elif param == "max_completion_tokens": optional_params["max_tokens"] = value + elif param == "reasoning_effort": + if value is True: + optional_params["reasoning_effort"] = "medium" + elif value is False: + optional_params["reasoning_effort"] = "none" + else: + optional_params["reasoning_effort"] = value elif param in supported_openai_params: if value is not None: optional_params[param] = value return optional_params - def _add_transform_inline_image_block( - self, - content: ChatCompletionImageObject, - model: str, - disable_add_transform_inline_image_block: Optional[bool], - ) -> ChatCompletionImageObject: - """ - Add transform_inline to the image_url (allows non-vision models to parse documents/images/etc.) - - ignore if model is a vision model - - ignore if user has disabled this feature - """ - if ( - "vision" in model or disable_add_transform_inline_image_block - ): # allow user to toggle this feature. - return content - if isinstance(content["image_url"], str): - # Skip base64 data URLs — appending #transform=inline corrupts the - # base64 payload and causes an "Incorrect padding" decode error on - # the Fireworks side. Data URLs are already inlined by definition. - # Lower-case before checking: URI schemes are case-insensitive (RFC 3986). - if not content["image_url"].lower().startswith("data:"): - content["image_url"] = f"{content['image_url']}#transform=inline" - elif isinstance(content["image_url"], dict): - url = content["image_url"]["url"] - if not url.lower().startswith("data:"): - content["image_url"]["url"] = f"{url}#transform=inline" - return content - def _transform_tools( self, tools: List[OpenAIChatCompletionToolParam] ) -> List[OpenAIChatCompletionToolParam]: @@ -225,36 +273,46 @@ class FireworksAIConfig(OpenAIGPTConfig): self, messages: List[AllMessageValues], model: str, litellm_params: dict ) -> List[AllMessageValues]: """ - Add 'transform=inline' to the url of the image_url + Strip fields not permitted by FireworksAI from messages. """ from litellm.litellm_core_utils.prompt_templates.common_utils import ( filter_value_from_dict, - migrate_file_to_image_url, ) - disable_add_transform_inline_image_block = cast( - Optional[bool], - litellm_params.get("disable_add_transform_inline_image_block") - or litellm.disable_add_transform_inline_image_block, + supports_vision_value = self._get_model_cost_capability_exact( + model=model, capability="supports_vision" ) - ## For any 'file' message type with pdf content, move to 'image_url' message type - for message in messages: - if message["role"] == "user": - _message_content = message.get("content") - if _message_content is not None and isinstance(_message_content, list): - for idx, content in enumerate(_message_content): - if content["type"] == "file": - _message_content[idx] = migrate_file_to_image_url(content) for message in messages: if message["role"] == "user": _message_content = message.get("content") if _message_content is not None and isinstance(_message_content, list): for content in _message_content: - if content["type"] == "image_url": - content = self._add_transform_inline_image_block( - content=content, + if not isinstance(content, dict): + continue + if content.get("type") == "file": + raise litellm.BadRequestError( + message=( + "Fireworks AI chat completions does not support " + "file content blocks. For PDFs, convert pages to " + "images and send image_url blocks to a Fireworks " + "vision model, or extract text before calling a " + "text-only model." + ), model=model, - disable_add_transform_inline_image_block=disable_add_transform_inline_image_block, + llm_provider="fireworks_ai", + ) + if ( + content.get("type") == "image_url" + and supports_vision_value is False + ): + raise litellm.BadRequestError( + message=( + f"Fireworks AI model {model} does not support " + "image inputs. Use a Fireworks vision model or " + "remove image_url content blocks." + ), + model=model, + llm_provider="fireworks_ai", ) filter_value_from_dict(cast(dict, message), "cache_control") # Remove fields not permitted by FireworksAI (additionalProperties: false @@ -317,43 +375,55 @@ class FireworksAIConfig(OpenAIGPTConfig): return True return ("-" + key_short + "-") in short_name - def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]: + @staticmethod + def _short_model_name(model: str) -> str: short_name = model if short_name.startswith("fireworks_ai/"): short_name = short_name[len("fireworks_ai/") :] if short_name.startswith("accounts/fireworks/models/"): short_name = short_name[len("accounts/fireworks/models/") :] + return short_name - candidate_keys = [ + def _get_model_cost_capability_exact( + self, model: str, capability: str + ) -> Optional[bool]: + short_name = self._short_model_name(model) + candidate_keys = ( model, f"fireworks_ai/{short_name}", f"fireworks_ai/accounts/fireworks/models/{short_name}", - ] - + ) for candidate_key in candidate_keys: model_info = litellm.model_cost.get(candidate_key) if model_info is not None and model_info.get(capability) is not None: return cast(Optional[bool], model_info.get(capability)) + return None - # Fallback: preserve historical substring matching for model name - # variants (e.g. fine-tuned or regionally-suffixed versions of a - # known model). Pick the *longest* matching entry so a more specific - # known model (e.g. "qwen3-8b-instruct") wins over a less specific - # one (e.g. "qwen3-8b") when the query model is more specific still. - # Use hyphen-aligned matching to avoid false positives where a short - # known model name is an unrelated substring of a longer one. - best_match_short: Optional[str] = None - best_match_value: Optional[bool] = None - for key_short, model_info in self._get_fireworks_index(): - if model_info.get(capability) is None: - continue - if not self._matches_on_hyphen_boundary(short_name, key_short): - continue - if best_match_short is None or len(key_short) > len(best_match_short): - best_match_short = key_short - best_match_value = cast(Optional[bool], model_info.get(capability)) + def _get_model_cost_capability(self, model: str, capability: str) -> Optional[bool]: + exact = self._get_model_cost_capability_exact( + model=model, capability=capability + ) + if exact is not None: + return exact - return best_match_value + # Fallback: substring matching for model name variants (e.g. fine-tuned + # or regionally-suffixed versions of a known model). Pick the *longest* + # matching entry so a more specific known model (e.g. "qwen3-8b-instruct") + # wins over a less specific one (e.g. "qwen3-8b"). Hyphen-aligned matching + # avoids false positives where a short known name is an unrelated + # substring of a longer one. This stays a soft signal: capability-gated + # hard rejections use the exact lookup so a fuzzy match never blocks a + # custom deployment. + short_name = self._short_model_name(model) + matches = [ + (key_short, cast(Optional[bool], model_info.get(capability))) + for key_short, model_info in self._get_fireworks_index() + if model_info.get(capability) is not None + and self._matches_on_hyphen_boundary(short_name, key_short) + ] + if not matches: + return None + return max(matches, key=lambda match: len(match[0]))[1] def get_provider_info(self, model: str) -> ProviderSpecificModelInfo: supports_function_calling_value = self._get_model_cost_capability( @@ -362,12 +432,16 @@ class FireworksAIConfig(OpenAIGPTConfig): supports_reasoning_value = self._get_model_cost_capability( model=model, capability="supports_reasoning" ) + supports_vision_value = self._get_model_cost_capability( + model=model, capability="supports_vision" + ) + supports_pdf_input_value = self._get_model_cost_capability( + model=model, capability="supports_pdf_input" + ) provider_specific_model_info: ProviderSpecificModelInfo = { "supports_function_calling": True, "supports_prompt_caching": True, # https://docs.fireworks.ai/guides/prompt-caching - "supports_pdf_input": True, # via document inlining - "supports_vision": True, # via document inlining } if supports_function_calling_value is not None: @@ -381,6 +455,14 @@ class FireworksAIConfig(OpenAIGPTConfig): supports_reasoning_value ) + if supports_vision_value is not None: + provider_specific_model_info["supports_vision"] = supports_vision_value + + if supports_pdf_input_value is not None: + provider_specific_model_info["supports_pdf_input"] = ( + supports_pdf_input_value + ) + return provider_specific_model_info def transform_request( @@ -402,6 +484,15 @@ class FireworksAIConfig(OpenAIGPTConfig): if "tools" in optional_params and optional_params["tools"] is not None: tools = self._transform_tools(tools=optional_params["tools"]) optional_params["tools"] = tools + if optional_params.get("stream"): + stream_options = optional_params.get("stream_options") + if stream_options is None: + optional_params["stream_options"] = {"include_usage": True} + elif stream_options.get("include_usage") is not False: + optional_params["stream_options"] = { + **stream_options, + "include_usage": True, + } return super().transform_request( model=model, messages=messages, @@ -494,10 +585,25 @@ class FireworksAIConfig(OpenAIGPTConfig): ) ) - response._hidden_params = {"additional_headers": additional_headers} + response._hidden_params = { + "additional_headers": additional_headers, + **_extract_fireworks_hidden_params(completion_response), + } return response + def get_model_response_iterator( + self, + streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse], + sync_stream: bool, + json_mode: Optional[bool] = False, + ) -> Any: + return FireworksAIChatCompletionStreamingHandler( + streaming_response=streaming_response, + sync_stream=sync_stream, + json_mode=json_mode, + ) + def _get_openai_compatible_provider_info( self, api_base: Optional[str], api_key: Optional[str] ) -> Tuple[Optional[str], Optional[str]]: @@ -554,3 +660,15 @@ class FireworksAIConfig(OpenAIGPTConfig): or get_secret_str("FIREWORKSAI_API_KEY") or get_secret_str("FIREWORKS_AI_TOKEN") ) + + +class FireworksAIChatCompletionStreamingHandler(OpenAIChatCompletionStreamingHandler): + def chunk_parser(self, chunk: dict) -> ModelResponseStream: + parsed = super().chunk_parser(chunk) + fireworks_fields = _extract_fireworks_hidden_params(chunk) + if fireworks_fields: + parsed.provider_specific_fields = { + **(getattr(parsed, "provider_specific_fields", None) or {}), + **fireworks_fields, + } + return parsed diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json index 7a5f8b9e1e3..5c962cf8440 100644 --- a/litellm/model_prices_and_context_window_backup.json +++ b/litellm/model_prices_and_context_window_backup.json @@ -15011,7 +15011,7 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": false + "supports_vision": true }, "fireworks_ai/accounts/fireworks/models/mixtral-8x22b-instruct-hf": { "input_cost_per_token": 1.2e-06, @@ -15314,7 +15314,7 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": false + "supports_vision": true }, "fireworks_ai/qwen3p7-plus": { "cache_read_input_token_cost": 8e-08, diff --git a/litellm/proxy/_experimental/out/404.html b/litellm/proxy/_experimental/out/404.html index d96cf497e18..6d246c81652 100644 --- a/litellm/proxy/_experimental/out/404.html +++ b/litellm/proxy/_experimental/out/404.html @@ -1 +1 @@ -404: This page could not be found.LiteLLM Dashboard

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12:I[897367,["/litellm-asset-prefix/_next/static/chunks/d96012bcfc98706a.js","/litellm-asset-prefix/_next/static/chunks/dbca964212122d58.js"],"OutletBoundary"] diff --git a/litellm/proxy/_types.py b/litellm/proxy/_types.py index 8e2ec423cde..e856e5e3cdb 100644 --- a/litellm/proxy/_types.py +++ b/litellm/proxy/_types.py @@ -2142,6 +2142,20 @@ class UserHeaderMapping(LiteLLMPydanticObjectBase): UserMCPManagementMode = Literal["restricted", "view_all"] +class PluginConfig(LiteLLMPydanticObjectBase): + """A single external service registered as an embeddable UI plugin.""" + + name: str = Field(description="unique plugin identifier (kebab-case)") + display_name: str | None = Field( + None, description="human-readable label shown in the UI view switcher" + ) + url: str = Field(description="base URL of the plugin service") + plugin_key: str | None = Field( + None, + description="plugin's own credential, injected as Bearer auth only on /plugin-proxy//* reverse-proxy calls", + ) + + class ConfigGeneralSettings(LiteLLMPydanticObjectBase): """ Documents all the fields supported by `general_settings` in config.yaml @@ -2150,6 +2164,9 @@ class ConfigGeneralSettings(LiteLLMPydanticObjectBase): completion_model: Optional[str] = Field( None, description="proxy level default model for all chat completion calls" ) + plugins: list[PluginConfig] | None = Field( + None, description="external services registered as embeddable UI plugins" + ) key_management_system: Optional[KeyManagementSystem] = Field( None, description="key manager to load keys from / decrypt keys with" ) @@ -3808,6 +3825,28 @@ class SpecialHeaders(enum.Enum): mcp_servers = "x-mcp-servers" mcp_access_groups = "x-mcp-access-groups" + @classmethod + def litellm_credential_header_names(cls) -> "frozenset[str]": + """Lowercased header names user_api_key_auth accepts as a litellm key. + + Every header here authenticates the caller, so any code that forwards a + request onward (e.g. the plugin reverse proxy) must strip all of them to + avoid leaking the caller's litellm credential downstream. The static + custom-key header (general_settings.litellm_key_header_name) is runtime + config and must be added on top of this set by the caller. + """ + return frozenset( + header.value.lower() + for header in ( + cls.openai_authorization, + cls.azure_authorization, + cls.anthropic_authorization, + cls.google_ai_studio_authorization, + cls.azure_apim_authorization, + cls.custom_litellm_api_key, + ) + ) + class LitellmDataForBackendLLMCall(TypedDict, total=False): headers: dict diff --git a/litellm/proxy/auth/auth_checks.py b/litellm/proxy/auth/auth_checks.py index 6ddf2cfeb20..05d532c8e21 100644 --- a/litellm/proxy/auth/auth_checks.py +++ b/litellm/proxy/auth/auth_checks.py @@ -699,6 +699,11 @@ async def common_checks( if valid_token is not None: from litellm.proxy.litellm_pre_call_utils import LiteLLMProxyRequestSetup + LiteLLMProxyRequestSetup.pre_seed_litellm_metadata_for_route( + request_data=request_body, + route=route, + ) + LiteLLMProxyRequestSetup.apply_key_tags_pre_auth( request_data=request_body, user_api_key_dict=valid_token, diff --git a/litellm/proxy/auth/auth_method.py b/litellm/proxy/auth/auth_method.py new file mode 100644 index 00000000000..a604eb563e6 --- /dev/null +++ b/litellm/proxy/auth/auth_method.py @@ -0,0 +1,13 @@ +from __future__ import annotations + +from enum import Enum + + +class AuthMethod(str, Enum): + API_KEY = "api_key" + HTTP_BASIC = "http_basic" + BEARER_JWT = "bearer_jwt" + OAUTH2_INTROSPECTION = "oauth2_introspection" + OIDC = "oidc" + SAML = "saml" + MUTUAL_TLS = "mutual_tls" diff --git a/litellm/proxy/auth/auth_utils.py b/litellm/proxy/auth/auth_utils.py index 94b2ed84f20..3a2f2221ee3 100644 --- a/litellm/proxy/auth/auth_utils.py +++ b/litellm/proxy/auth/auth_utils.py @@ -285,6 +285,8 @@ _BANNED_REQUEST_BODY_PARAMS: Tuple[str, ...] = ( "s3_endpoint_url", "sagemaker_base_url", "deployment_url", + # SDK-only field; also rejected outright in is_request_body_safe. + "model_list", # Observability credentials, hosts, and project identifiers: derived # from the canonical ``_supported_callback_params`` allowlist so new # integrations are covered automatically. Sorted for stable iteration @@ -365,6 +367,10 @@ def is_request_body_safe( ``litellm_embedding_config.api_base`` (VERIA-6) without exposing a recursion-depth DoS surface. """ + if "model_list" in request_body: + raise ValueError( + "Rejected Request: model_list is not allowed in the request body." + ) _check_banned_params(request_body, general_settings, llm_router, model) for nested_key in _NESTED_CONFIG_KEYS: nested = _coerce_metadata_to_dict(request_body.get(nested_key)) diff --git a/litellm/proxy/auth/network.py b/litellm/proxy/auth/network.py new file mode 100644 index 00000000000..4eb6f1dcec2 --- /dev/null +++ b/litellm/proxy/auth/network.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import ipaddress +from typing import Any, Union + +from fastapi import Request +from pydantic import BaseModel, Field + +from litellm._logging import verbose_proxy_logger + +TrustedProxyNetwork = Union[ipaddress.IPv4Network, ipaddress.IPv6Network] + + +class NetworkContext(BaseModel): + client_ip: str | None = None + host: str | None = None + via_trusted_proxy: bool = False + + +class TrustedProxyConfig(BaseModel): + use_forwarded_for: bool = False + trusted_proxy_cidrs: list[str] = Field(default_factory=list) + + +def normalize_cidr_ranges( + configured_ranges: Any, *, setting_name: str = "trusted_proxy_cidrs" +) -> list[str]: + if not configured_ranges: + return [] + if isinstance(configured_ranges, str): + return [r.strip() for r in configured_ranges.split(",") if r.strip()] + if isinstance(configured_ranges, (list, tuple, set)): + return [str(r).strip() for r in configured_ranges if str(r).strip()] + verbose_proxy_logger.warning( + "Invalid %s value: expected a list of CIDR ranges, got %s", + setting_name, + type(configured_ranges).__name__, + ) + return [] + + +def parse_trusted_proxy_ranges( + configured_ranges: Any, *, setting_name: str = "trusted_proxy_cidrs" +) -> list[TrustedProxyNetwork]: + networks: list[TrustedProxyNetwork] = [] + for cidr in normalize_cidr_ranges(configured_ranges, setting_name=setting_name): + try: + networks.append(ipaddress.ip_network(cidr, strict=False)) + except ValueError: + verbose_proxy_logger.warning( + "Invalid CIDR in %s: %s, skipping", setting_name, cidr + ) + return networks + + +def ip_in_networks(client_ip: str | None, networks: list[TrustedProxyNetwork]) -> bool: + if not client_ip or not networks: + return False + try: + addr = ipaddress.ip_address(client_ip.strip()) + except ValueError: + return False + return any(addr in network for network in networks) + + +def _is_valid_ip(value: str) -> bool: + try: + ipaddress.ip_address(value) + return True + except ValueError: + return False + + +def resolve_client_ip( + request: Request, config: TrustedProxyConfig +) -> tuple[str | None, bool]: + """Resolve the real client IP, trusting X-Forwarded-For only when the direct + peer is itself a configured trusted proxy. Walks the header right-to-left and + returns the first hop that is not a trusted proxy, so a forged left-most entry + cannot spoof the client.""" + peer = request.client.host if request.client else None + networks = parse_trusted_proxy_ranges(config.trusted_proxy_cidrs) + if not config.use_forwarded_for or not ip_in_networks(peer, networks): + return peer, False + forwarded = request.headers.get("x-forwarded-for", "") + hops = [h.strip() for h in forwarded.split(",") if h.strip()] + for hop in reversed(hops): + if _is_valid_ip(hop) and not ip_in_networks(hop, networks): + return hop, True + return peer, True + + +def resolve_network_context( + request: Request, config: TrustedProxyConfig +) -> NetworkContext: + ip, via_proxy = resolve_client_ip(request, config) + return NetworkContext( + client_ip=ip, + host=request.headers.get("host"), + via_trusted_proxy=via_proxy, + ) diff --git a/litellm/proxy/auth/resolvers/__init__.py b/litellm/proxy/auth/resolvers/__init__.py new file mode 100644 index 00000000000..d6bfb335c09 --- /dev/null +++ b/litellm/proxy/auth/resolvers/__init__.py @@ -0,0 +1,33 @@ +from litellm.proxy.auth.resolvers.exceptions import ( + IdentityResolutionError, + KeyNotFoundError, + KeyNotInCacheError, + NoDatabaseConnectionError, + PrincipalMissingSourceKeyError, +) +from litellm.proxy.auth.resolvers.models import ( + CredentialRef, + EndUserIdentity, + OrganizationIdentity, + Principal, + PrincipalType, + ProjectIdentity, + TeamIdentity, + UserIdentity, +) + +__all__ = [ + "CredentialRef", + "EndUserIdentity", + "IdentityResolutionError", + "KeyNotFoundError", + "KeyNotInCacheError", + "NoDatabaseConnectionError", + "OrganizationIdentity", + "Principal", + "PrincipalMissingSourceKeyError", + "PrincipalType", + "ProjectIdentity", + "TeamIdentity", + "UserIdentity", +] diff --git a/litellm/proxy/auth/resolvers/exceptions.py b/litellm/proxy/auth/resolvers/exceptions.py new file mode 100644 index 00000000000..dd953e66659 --- /dev/null +++ b/litellm/proxy/auth/resolvers/exceptions.py @@ -0,0 +1,50 @@ +from __future__ import annotations + +from fastapi import status + +from litellm.proxy._types import ProxyErrorTypes, ProxyException + + +class IdentityResolutionError(Exception): + """Base for every failure raised while resolving a caller's identity.""" + + +class NoDatabaseConnectionError(IdentityResolutionError): + def __init__(self) -> None: + super().__init__( + "No DB Connected. See - https://docs.litellm.ai/docs/proxy/virtual_keys" + ) + + +class KeyNotInCacheError(IdentityResolutionError): + def __init__(self, hashed_token: str) -> None: + super().__init__( + f"Key doesn't exist in cache + check_cache_only=True. key={hashed_token}." + ) + + +class KeyNotFoundError(IdentityResolutionError, ProxyException): + """The token matched nothing in the cache or the verification token table. + + Also a ``ProxyException`` so the auth flow keeps mapping a missing key to the + OpenAI 401 contract unchanged while callers migrate onto the typed hierarchy. + """ + + def __init__(self, hashed_token: str) -> None: + ProxyException.__init__( + self, + message="Authentication Error, Invalid proxy server token passed. key={}, not found in db. Create key via `/key/generate` call.".format( + hashed_token + ), + type=ProxyErrorTypes.token_not_found_in_db, + param="key", + code=status.HTTP_401_UNAUTHORIZED, + ) + + +class PrincipalMissingSourceKeyError(IdentityResolutionError): + def __init__(self) -> None: + super().__init__( + "Principal carries no source key; it was not produced by " + "IdentityStore.resolve" + ) diff --git a/litellm/proxy/auth/resolvers/models.py b/litellm/proxy/auth/resolvers/models.py new file mode 100644 index 00000000000..97e66b8fe67 --- /dev/null +++ b/litellm/proxy/auth/resolvers/models.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +from enum import Enum + +from pydantic import BaseModel, ConfigDict, Field + +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.auth_method import AuthMethod +from litellm.proxy.auth.network import NetworkContext +from litellm.proxy.auth.roles import Role, TeamRole + + +class PrincipalType(str, Enum): + HUMAN = "human" + SERVICE_ACCOUNT = "service_account" + + +class UserIdentity(BaseModel): + id: str + external_id: str | None = None + user_name: str | None = None + email: str | None = None + display_name: str | None = None + + +class OrganizationIdentity(BaseModel): + id: str + name: str | None = None + + +class TeamIdentity(BaseModel): + id: str + name: str | None = None + role: TeamRole = TeamRole.MEMBER + + +class ProjectIdentity(BaseModel): + id: str + name: str | None = None + + +class EndUserIdentity(BaseModel): + id: str + + +class CredentialRef(BaseModel): + key_id: str | None = None + token_id: str | None = None + + +class Principal(BaseModel): + """Normalized caller identity, resolved once per request at the auth seam. + + Frozen and constructed fresh per request, never cached or shared. The identity + fields carry no policy, budget, or rate-limit state. ``source_key`` is a + transitional carrier for the resolved key object so ``key_from_principal`` can + hand it to the request flow that still consumes ``UserAPIKeyAuth``; it is + excluded from serialization and repr and goes away once those consumers read + identity off the Principal directly. + """ + + model_config = ConfigDict(frozen=True) + + principal_type: PrincipalType + subject: str + issuer: str | None = None + audience: list[str] = Field(default_factory=list) + + user: UserIdentity | None = None + organization: OrganizationIdentity | None = None + teams: list[TeamIdentity] = Field(default_factory=list) + project: ProjectIdentity | None = None + end_user: EndUserIdentity | None = None + + roles: list[Role] = Field(default_factory=list) + scopes: list[str] = Field(default_factory=list) + + auth_method: AuthMethod + credential_ref: CredentialRef = Field(default_factory=CredentialRef) + network: NetworkContext = Field(default_factory=NetworkContext) + + source_key: UserAPIKeyAuth | None = Field(default=None, exclude=True, repr=False) diff --git a/litellm/proxy/auth/resolvers/store.py b/litellm/proxy/auth/resolvers/store.py new file mode 100644 index 00000000000..c43d4c84ca4 --- /dev/null +++ b/litellm/proxy/auth/resolvers/store.py @@ -0,0 +1,206 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Sequence + +from pydantic import BaseModel + +from litellm._logging import verbose_proxy_logger +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.auth_checks import ( + _cache_key_object, + _copy_user_api_key_auth_for_cache, + _fetch_key_object_from_db_with_reconnect, + get_object_permission, +) +from litellm.proxy.auth.resolvers.exceptions import ( + KeyNotFoundError, + KeyNotInCacheError, + NoDatabaseConnectionError, + PrincipalMissingSourceKeyError, +) +from litellm.proxy.auth.auth_method import AuthMethod +from litellm.proxy.auth.network import NetworkContext +from litellm.proxy.auth.resolvers.models import ( + CredentialRef, + EndUserIdentity, + OrganizationIdentity, + Principal, + PrincipalType, + ProjectIdentity, + TeamIdentity, + UserIdentity, +) +from litellm.proxy.auth.roles import TeamRole, map_role, team_role + +if TYPE_CHECKING: + from litellm.caching.caching import DualCache + from litellm.integrations.opentelemetry import Span + from litellm.proxy.utils import PrismaClient, ProxyLogging + + +class IdentityStore: + """The auth flow's resolver: one combined_view lookup, projected into a Principal. + + ``resolve`` does the lookup (cache, then DB via the shared lower-level helpers, + then write-back) and returns the per-caller Principal. The Principal carries the + source key object so ``key_from_principal`` can hand it back to the parts of the + request flow that still consume ``UserAPIKeyAuth`` (budget, rate limits, policy); + that carrier is a stopgap until those consumers read identity off the Principal. + The Prisma client, key cache, the request's tracing span / logging sink, and + whether this store may only read the cache are injected so the composition root + can build the store once the proxy DB is connected; the span and logging sink + are infra the DB call is instrumented with and ``check_cache_only`` is a store + mode, none of them inputs to resolving identity. ``auth_checks.get_key_object`` + stays as the legacy entrypoint for its other callers until they migrate onto + this store. + """ + + def __init__( + self, + prisma_client: PrismaClient | None, + cache: DualCache, + *, + parent_otel_span: Span | None = None, + proxy_logging_obj: ProxyLogging | None = None, + check_cache_only: bool = False, + ) -> None: + self._prisma = prisma_client + self._cache = cache + self._parent_otel_span = parent_otel_span + self._proxy_logging_obj = proxy_logging_obj + self._check_cache_only = check_cache_only + + async def resolve( + self, + hashed_token: str, + *, + auth_method: AuthMethod = AuthMethod.API_KEY, + network: NetworkContext | None = None, + ) -> Principal: + key = await self._resolve_key(hashed_token) + return self._principal_from_key( + key, + auth_method=auth_method, + network=network, + subject_fallback=key.token, + credential_ref=CredentialRef(token_id=key.token), + ) + + @staticmethod + def key_from_principal(principal: Principal) -> UserAPIKeyAuth: + """Hand back the resolved key object carried on the Principal. + + Stopgap for the request flow that still consumes ``UserAPIKeyAuth`` for + budget, rate-limit, and policy state. Only Principals produced by + ``resolve`` carry a source key. + """ + if principal.source_key is None: + raise PrincipalMissingSourceKeyError() + return principal.source_key + + async def _resolve_key(self, hashed_token: str) -> UserAPIKeyAuth: + if self._prisma is None: + raise NoDatabaseConnectionError() + + cached = await self._cache.async_get_cache( + key=hashed_token, model_type=UserAPIKeyAuth + ) + if cached is not None: + return _copy_user_api_key_auth_for_cache(user_api_key_obj=cached) + + if self._check_cache_only: + raise KeyNotInCacheError(hashed_token) + + from_db: BaseModel | None = await _fetch_key_object_from_db_with_reconnect( + hashed_token=hashed_token, + prisma_client=self._prisma, + parent_otel_span=self._parent_otel_span, + proxy_logging_obj=self._proxy_logging_obj, + ) + if from_db is None: + raise KeyNotFoundError(hashed_token) + + key = UserAPIKeyAuth(**from_db.model_dump(exclude_none=True)) + + if key.object_permission_id and not key.object_permission: + try: + key.object_permission = await get_object_permission( + object_permission_id=key.object_permission_id, + prisma_client=self._prisma, + user_api_key_cache=self._cache, + parent_otel_span=self._parent_otel_span, + proxy_logging_obj=self._proxy_logging_obj, + ) + except Exception as e: + verbose_proxy_logger.debug( + f"Failed to load object_permission for key with object_permission_id={key.object_permission_id}: {e}" + ) + + await _cache_key_object( + hashed_token=hashed_token, + user_api_key_obj=key, + user_api_key_cache=self._cache, + proxy_logging_obj=self._proxy_logging_obj, + ) + return key + + @staticmethod + def _principal_from_key( + key: UserAPIKeyAuth, + *, + auth_method: AuthMethod, + issuer: str | None = None, + subject_fallback: str | None = None, + scopes: Sequence[str] = (), + credential_ref: CredentialRef | None = None, + network: NetworkContext | None = None, + ) -> Principal: + """Project the identity slice off an already-resolved key object and carry + the key on the Principal so ``key_from_principal`` can recover it. + + Pure: issues no lookup. Both ``resolve`` and the auth seam call this so + identity is projected once off whichever key object they already hold. + """ + teams: list[TeamIdentity] = [] + if key.team_id is not None: + role = ( + team_role(key.team_member.role) if key.team_member else TeamRole.MEMBER + ) + teams.append(TeamIdentity(id=key.team_id, name=key.team_alias, role=role)) + organization = ( + OrganizationIdentity(id=key.org_id, name=key.organization_alias) + if key.org_id is not None + else None + ) + user = ( + UserIdentity(id=key.user_id, email=key.user_email) + if key.user_id is not None + else None + ) + project = ( + ProjectIdentity(id=key.project_id, name=key.project_alias) + if key.project_id is not None + else None + ) + end_user = ( + EndUserIdentity(id=key.end_user_id) if key.end_user_id is not None else None + ) + mapped = map_role(key.user_role) + return Principal( + principal_type=( + PrincipalType.HUMAN if key.user_id else PrincipalType.SERVICE_ACCOUNT + ), + subject=key.user_id or key.key_alias or subject_fallback or "", + issuer=issuer, + user=user, + organization=organization, + teams=teams, + project=project, + end_user=end_user, + roles=[mapped] if mapped else [], + scopes=list(scopes), + auth_method=auth_method, + credential_ref=credential_ref or CredentialRef(), + network=network or NetworkContext(), + source_key=key, + ) diff --git a/litellm/proxy/auth/roles.py b/litellm/proxy/auth/roles.py new file mode 100644 index 00000000000..efe56a8b6b2 --- /dev/null +++ b/litellm/proxy/auth/roles.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from enum import Enum + + +class Role(str, Enum): + PLATFORM_ADMIN = "platform_admin" + PLATFORM_VIEWER = "platform_viewer" + ORG_ADMIN = "org_admin" + ORG_VIEWER = "org_viewer" + TEAM_ADMIN = "team_admin" + TEAM_MEMBER = "team_member" + + +class TeamRole(str, Enum): + ADMIN = "admin" + MEMBER = "member" + + +_ROLE_MAP: dict[str, Role] = { + "proxy_admin": Role.PLATFORM_ADMIN, + "proxy_admin_viewer": Role.PLATFORM_VIEWER, + "org_admin": Role.ORG_ADMIN, +} + + +def map_role(value: str | None) -> Role | None: + """Map a LiteLLM ``user_role`` string to a platform Role.""" + if value is None: + return None + return _ROLE_MAP.get(value) + + +def team_role(role: str | None) -> TeamRole: + return TeamRole.ADMIN if role == "admin" else TeamRole.MEMBER diff --git a/litellm/proxy/auth/trusted_proxy_utils.py b/litellm/proxy/auth/trusted_proxy_utils.py index df7b3080f28..35bb79e7efe 100644 --- a/litellm/proxy/auth/trusted_proxy_utils.py +++ b/litellm/proxy/auth/trusted_proxy_utils.py @@ -1,12 +1,15 @@ -import ipaddress -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, Optional from fastapi import Request from litellm._logging import verbose_proxy_logger +from litellm.proxy.auth.network import ( + ip_in_networks, + normalize_cidr_ranges, + parse_trusted_proxy_ranges, +) TRUSTED_PROXY_RANGES_KEY = "trusted_proxy_ranges" -TrustedProxyNetwork = Union[ipaddress.IPv4Network, ipaddress.IPv6Network] def _get_proxy_general_settings() -> Dict[str, Any]: @@ -18,43 +21,20 @@ def _get_proxy_general_settings() -> Dict[str, Any]: return {} -def _normalize_cidr_ranges(configured_ranges: Any, *, setting_name: str) -> List[str]: - if not configured_ranges: - return [] - if isinstance(configured_ranges, str): - return [ - raw_range.strip() - for raw_range in configured_ranges.split(",") - if raw_range.strip() - ] - if isinstance(configured_ranges, (list, tuple, set)): - return [ - str(raw_range).strip() - for raw_range in configured_ranges - if str(raw_range).strip() - ] - verbose_proxy_logger.warning( - "Invalid %s value: expected a list of CIDR ranges, got %s", - setting_name, - type(configured_ranges).__name__, +def get_trusted_proxy_cidrs( + general_settings: dict[str, Any] | None = None, +) -> list[str]: + """Operator-configured trusted reverse-proxy CIDRs, normalized to strings. + + Empty when none are configured, in which case X-Forwarded-For must not be + trusted and only the direct peer is authoritative. + """ + if general_settings is None: + general_settings = _get_proxy_general_settings() + return normalize_cidr_ranges( + general_settings.get(TRUSTED_PROXY_RANGES_KEY), + setting_name=TRUSTED_PROXY_RANGES_KEY, ) - return [] - - -def parse_trusted_proxy_ranges( - configured_ranges: Any, - *, - setting_name: str = TRUSTED_PROXY_RANGES_KEY, -) -> List[TrustedProxyNetwork]: - networks: List[TrustedProxyNetwork] = [] - for cidr in _normalize_cidr_ranges(configured_ranges, setting_name=setting_name): - try: - networks.append(ipaddress.ip_network(cidr, strict=False)) - except ValueError: - verbose_proxy_logger.warning( - "Invalid CIDR in %s: %s, skipping", setting_name, cidr - ) - return networks def _get_direct_client_ip(request: Request) -> Optional[str]: @@ -65,18 +45,6 @@ def _get_direct_client_ip(request: Request) -> Optional[str]: return None -def _is_ip_in_networks( - client_ip: Optional[str], networks: List[TrustedProxyNetwork] -) -> bool: - if not client_ip or not networks: - return False - try: - addr = ipaddress.ip_address(client_ip.strip()) - except ValueError: - return False - return any(addr in network for network in networks) - - def require_trusted_proxy_request( *, request: Request, @@ -105,7 +73,7 @@ def require_trusted_proxy_request( ) direct_client_ip = _get_direct_client_ip(request) - if not _is_ip_in_networks(direct_client_ip, trusted_networks): + if not ip_in_networks(direct_client_ip, trusted_networks): verbose_proxy_logger.warning( "%s rejected identity headers from untrusted direct client IP %r", feature_name, diff --git a/litellm/proxy/auth/user_api_key_auth.py b/litellm/proxy/auth/user_api_key_auth.py index 00d98a04a78..e439f6a5998 100644 --- a/litellm/proxy/auth/user_api_key_auth.py +++ b/litellm/proxy/auth/user_api_key_auth.py @@ -42,7 +42,6 @@ from litellm.proxy.auth.auth_checks import ( common_checks, get_end_user_object, get_jwt_key_mapping_object, - get_key_object, get_project_object, get_team_object, get_user_object, @@ -63,7 +62,12 @@ from litellm.proxy.auth.auth_utils import ( from litellm.proxy.auth.handle_jwt import JWTAuthManager, JWTHandler from litellm.proxy.auth.oauth2_check import Oauth2Handler from litellm.proxy.auth.oauth2_proxy_hook import handle_oauth2_proxy_request +from litellm.proxy.auth.auth_method import AuthMethod +from litellm.proxy.auth.network import TrustedProxyConfig, resolve_network_context +from litellm.proxy.auth.resolvers import CredentialRef, Principal +from litellm.proxy.auth.resolvers.store import IdentityStore from litellm.proxy.auth.route_checks import RouteChecks +from litellm.proxy.auth.trusted_proxy_utils import get_trusted_proxy_cidrs from litellm.proxy.common_utils.cache_coordinator import EventDrivenCacheCoordinator from litellm.proxy.common_utils.http_parsing_utils import ( _read_request_body, @@ -758,12 +762,13 @@ async def _auto_register_jwt_mapping( claim_value, ) - auto_registered_key = await get_key_object( - hashed_token=token_hash, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=parent_otel_span, - proxy_logging_obj=proxy_logging_obj, + auto_registered_key = IdentityStore.key_from_principal( + await IdentityStore( + prisma_client, + user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ).resolve(hashed_token=token_hash) ) if auto_registered_key is not None: auto_registered_key.org_id = org_id @@ -865,12 +870,13 @@ async def _resolve_jwt_to_virtual_key( ) return None elif cached_mapping is not None: - return await get_key_object( - hashed_token=cached_mapping, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=parent_otel_span, - proxy_logging_obj=proxy_logging_obj, + return IdentityStore.key_from_principal( + await IdentityStore( + prisma_client, + user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ).resolve(hashed_token=cached_mapping) ) # Resolve the mapping from DB, or treat prisma_client=None as a definitive @@ -889,12 +895,13 @@ async def _resolve_jwt_to_virtual_key( value=token_hash, ttl=jwt_handler.litellm_jwtauth.virtual_key_mapping_cache_ttl, ) - return await get_key_object( - hashed_token=token_hash, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=parent_otel_span, - proxy_logging_obj=proxy_logging_obj, + return IdentityStore.key_from_principal( + await IdentityStore( + prisma_client, + user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ).resolve(hashed_token=token_hash) ) # No mapping found (DB miss or no DB) — apply no-match policy. @@ -1493,13 +1500,14 @@ async def _user_api_key_auth_builder( ## Check CACHE try: with tracer.trace("litellm.proxy.auth.get_key_object_check_cache"): - valid_token = await get_key_object( - hashed_token=hash_token(api_key), - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=parent_otel_span, - proxy_logging_obj=proxy_logging_obj, - check_cache_only=True, + valid_token = IdentityStore.key_from_principal( + await IdentityStore( + prisma_client, + user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + check_cache_only=True, + ).resolve(hashed_token=hash_token(api_key)) ) except Exception: verbose_logger.debug("api key not found in cache.") @@ -1679,12 +1687,13 @@ async def _user_api_key_auth_builder( try: with tracer.trace("litellm.proxy.auth.get_key_object_from_db"): - valid_token = await get_key_object( - hashed_token=api_key, - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=parent_otel_span, - proxy_logging_obj=proxy_logging_obj, + valid_token = IdentityStore.key_from_principal( + await IdentityStore( + prisma_client, + user_api_key_cache, + parent_otel_span=parent_otel_span, + proxy_logging_obj=proxy_logging_obj, + ).resolve(hashed_token=api_key) ) except ProxyException as e: if e.code == 401 or e.code == "401": @@ -2387,6 +2396,17 @@ async def _run_centralized_common_checks( llm_router=llm_router, ) + # Pin the metadata variable name (litellm_metadata vs metadata) before + # any tag merge runs. Without this, header tags from + # apply_client_tag_policy_pre_auth would land in `metadata` while the + # later seed in common_checks pushes key tags and the + # _tag_max_budget_check read into `litellm_metadata`, hiding header + # tags from per-tag budget enforcement on LITELLM_METADATA_ROUTES. + LiteLLMProxyRequestSetup.pre_seed_litellm_metadata_for_route( + request_data=request_data, + route=route, + ) + # Merge x-litellm-tags into request_data BEFORE common_checks runs. # _tag_max_budget_check inside common_checks only inspects request_data; # without this pre-merge, header-supplied tags bypass tag-budget @@ -2501,6 +2521,34 @@ def _should_skip_budget_checks( return False +def _resolve_request_principal( + request: Request, valid_token: UserAPIKeyAuth +) -> Principal: + """Project the resolved identity into one per-request Principal, off the key + object the builder already fetched, and stamp the request network context + onto it once. X-Forwarded-For is only trusted when the operator configured + ``trusted_proxy_ranges``; otherwise the direct peer is authoritative. + + credential_ref and a stable subject fallback are always set off the token so + the Principal can never be anonymous, even for a keyless service-account key + with no user or alias.""" + cidrs = get_trusted_proxy_cidrs() + network = resolve_network_context( + request, + TrustedProxyConfig(use_forwarded_for=bool(cidrs), trusted_proxy_cidrs=cidrs), + ) + auth_method = ( + AuthMethod.BEARER_JWT if valid_token.jwt_claims else AuthMethod.API_KEY + ) + return IdentityStore._principal_from_key( + valid_token, + auth_method=auth_method, + network=network, + subject_fallback=valid_token.token, + credential_ref=CredentialRef(token_id=valid_token.token), + ) + + @tracer.wrap() async def user_api_key_auth( request: Request, @@ -2615,6 +2663,22 @@ async def user_api_key_auth( model=request_data.get("model") if isinstance(request_data, dict) else None, ) user_api_key_auth_obj.request_route = normalize_request_route(route) + + # Resolve caller identity once, here at the seam, into a single per-request + # Principal projected off the key object the builder already fetched (no + # second lookup). Downstream consumers read identity off this instead of + # re-resolving it. Additive and defensive: a projection failure must never + # reject an already-authenticated request, so it is left unset on failure; + # any future consumer must treat a missing principal as deny, not allow. + try: + request.state.principal = _resolve_request_principal( + request, user_api_key_auth_obj + ) + except Exception as e: + verbose_proxy_logger.warning( + "Principal projection at auth seam failed (non-fatal): %s", e + ) + return user_api_key_auth_obj diff --git a/litellm/proxy/common_utils/callback_utils.py b/litellm/proxy/common_utils/callback_utils.py index 71dce163b78..d48499af6f0 100644 --- a/litellm/proxy/common_utils/callback_utils.py +++ b/litellm/proxy/common_utils/callback_utils.py @@ -71,6 +71,23 @@ def initialize_callbacks_on_proxy( imported_list.append(compression_interception_obj) continue + if ( + isinstance(callback, str) + and callback == "code_interpreter_interception" + ): + from litellm.integrations.code_interpreter_interception.handler import ( + CodeInterpreterInterceptionLogger, + ) + + code_interpreter_interception_obj = ( + CodeInterpreterInterceptionLogger.initialize_from_proxy_config( + litellm_settings=litellm_settings, + callback_specific_params=callback_specific_params, + ) + ) + imported_list.append(code_interpreter_interception_obj) + continue + # check if callback is a custom logger compatible callback if isinstance(callback, str): callback = LoggingCallbackManager._add_custom_callback_generic_api_str( diff --git a/litellm/proxy/example_config_yaml/code_interpreter_interception_config.yaml b/litellm/proxy/example_config_yaml/code_interpreter_interception_config.yaml new file mode 100644 index 00000000000..9c85f5c5140 --- /dev/null +++ b/litellm/proxy/example_config_yaml/code_interpreter_interception_config.yaml @@ -0,0 +1,17 @@ +model_list: + - model_name: gpt-5 + litellm_params: + model: openai/gpt-5 + +# Sandbox tools configuration +sandbox_tools: + - sandbox_tool_name: "my-e2b" + litellm_params: + sandbox_provider: "e2b" + api_key: os.environ/E2B_API_KEY + +litellm_settings: + callbacks: ["code_interpreter_interception"] + code_interpreter_interception_params: + enabled_providers: ["openai"] + sandbox_tool_name: "my-e2b" diff --git a/litellm/proxy/litellm_pre_call_utils.py b/litellm/proxy/litellm_pre_call_utils.py index 2c5937d9506..c0cdf84dfb6 100644 --- a/litellm/proxy/litellm_pre_call_utils.py +++ b/litellm/proxy/litellm_pre_call_utils.py @@ -108,6 +108,7 @@ def parse_cache_control(cache_control): LITELLM_METADATA_ROUTES = ( "batches", + "bedrock", "/v1/messages", "responses", "files", @@ -141,6 +142,20 @@ _UNTRUSTED_ROOT_CONTROL_FIELDS = ( "service_callback", "logger_fn", "litellm_disabled_callbacks", + # Agentic-loop control fields. These bound or drive an interceptor's agentic + # loop (web search, compression, code interpreter) and are server-controlled. + # A client-supplied value would forge loop depth/cycle state, mark an + # interception as active (triggering sandbox code execution without the + # native tool ever being present), force the completed response to be + # re-wrapped as a synthetic stream the caller never asked for, or raise the + # loop ceiling to drive many upstream model calls and sandbox executions + # from a single request. + "_agentic_loop_depth", + "_agentic_loop_fingerprints", + "_code_interpreter_interception_active", + "_code_interpreter_interception_converted_stream", + "_code_interpreter_interception_sandbox_key", + "max_agentic_loops", ) _UNTRUSTED_METADATA_CONTROL_FIELDS = ( @@ -1223,6 +1238,27 @@ class LiteLLMProxyRequestSetup: return tags + @staticmethod + def pre_seed_litellm_metadata_for_route( + request_data: dict, + route: str, + ) -> None: + """Pre-seed ``litellm_metadata`` for routes that track tags there. + + Routes in ``LITELLM_METADATA_ROUTES`` (e.g. Bedrock, ``/v1/messages``, + responses, batches, files) store request-scoped tag metadata in + ``litellm_metadata`` rather than the provider-facing ``metadata`` + field. ``get_metadata_variable_name_from_kwargs`` picks the target + based on whether ``litellm_metadata`` is present, so it must be + seeded BEFORE any tag merge runs; otherwise header tags from + ``apply_client_tag_policy_pre_auth`` land in ``metadata`` while + key tags from ``apply_key_tags_pre_auth`` and the read in + ``_tag_max_budget_check`` resolve to ``litellm_metadata``, leaving + header tags invisible to per-tag budget enforcement. + """ + if any(metadata_route in route for metadata_route in LITELLM_METADATA_ROUTES): + request_data.setdefault("litellm_metadata", {}) + @staticmethod def apply_key_tags_pre_auth( request_data: dict, @@ -1454,8 +1490,7 @@ async def add_litellm_data_to_request( _metadata_variable_name=_metadata_variable_name, ) - # Add headers to metadata for guardrails to access (fixes #17477) - # Guardrails use metadata["headers"] to access request headers (e.g., User-Agent) + # Expose request headers under the metadata field for guardrails (fixes #17477) if _metadata_variable_name in data and isinstance( data[_metadata_variable_name], dict ): diff --git a/litellm/proxy/management_endpoints/common_daily_activity.py b/litellm/proxy/management_endpoints/common_daily_activity.py index 341a8767db0..79882909c23 100644 --- a/litellm/proxy/management_endpoints/common_daily_activity.py +++ b/litellm/proxy/management_endpoints/common_daily_activity.py @@ -1,7 +1,7 @@ import asyncio from datetime import datetime from types import SimpleNamespace -from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union +from typing import Any, Awaitable, Callable, Dict, List, Optional, Set, Tuple, Union from fastapi import HTTPException, status @@ -887,8 +887,17 @@ async def get_daily_activity( exclude_entity_ids: Optional[List[str]] = None, metadata_metrics_func: Optional[Callable[[List[Any]], SpendMetrics]] = None, timezone_offset_minutes: Optional[int] = None, + resolve_entity_metadata: Optional[ + Callable[[list[Any]], Awaitable[dict[str, dict]]] + ] = None, ) -> SpendAnalyticsPaginatedResponse: - """Common function to get daily activity for any entity type.""" + """Common function to get daily activity for any entity type. + + ``resolve_entity_metadata`` lets a caller resolve entity metadata from the + rows actually on the page (e.g. user_id -> user_email) instead of fetching + the whole entity table upfront, which matters when the entity set is + unbounded. + """ if prisma_client is None: raise HTTPException( @@ -939,11 +948,18 @@ async def get_daily_activity( take=page_size, ) + resolved_entity_metadata = entity_metadata_field + if resolve_entity_metadata is not None: + resolved_entity_metadata = { + **(entity_metadata_field or {}), + **(await resolve_entity_metadata(daily_spend_data)), + } + aggregated = await _aggregate_spend_records( prisma_client=prisma_client, records=daily_spend_data, entity_id_field=entity_id_field, - entity_metadata_field=entity_metadata_field, + entity_metadata_field=resolved_entity_metadata, ) metadata_metrics = aggregated["totals"] diff --git a/litellm/proxy/management_endpoints/internal_user_endpoints.py b/litellm/proxy/management_endpoints/internal_user_endpoints.py index ba7013570fe..6d7f565fb85 100644 --- a/litellm/proxy/management_endpoints/internal_user_endpoints.py +++ b/litellm/proxy/management_endpoints/internal_user_endpoints.py @@ -57,6 +57,9 @@ from litellm.repositories.verification_token_repository import ( from litellm.types.proxy.management_endpoints.common_daily_activity import ( SpendAnalyticsPaginatedResponse, ) +from litellm.types.proxy.management_endpoints.scim_v2 import ( + SCIM_ENTERPRISE_METADATA_KEY, +) from litellm.types.proxy.management_endpoints.internal_user_endpoints import ( BulkUpdateUserRequest, BulkUpdateUserResponse, @@ -719,6 +722,17 @@ async def _get_user_info_teams( return team_list, teams_1 +def _redact_scim_enterprise_metadata( + metadata: Optional[Dict[str, Any]], +) -> Optional[Dict[str, Any]]: + """SCIM enterprise attributes are persisted in user metadata so reporting can + group on them, but they are directory-only fields that generic user-info + endpoints must not surface; SCIM clients read them through the SCIM endpoints.""" + if not isinstance(metadata, dict) or SCIM_ENTERPRISE_METADATA_KEY not in metadata: + return metadata + return {k: v for k, v in metadata.items() if k != SCIM_ENTERPRISE_METADATA_KEY} + + def _build_user_info_response( user_id: Optional[str], user_info: Optional[Any], @@ -739,6 +753,9 @@ def _build_user_info_response( ) if isinstance(_user_info, dict): _user_info.pop("password", None) + _user_info["metadata"] = _redact_scim_enterprise_metadata( + _user_info.get("metadata") + ) return UserInfoResponse( user_id=user_id, @@ -983,7 +1000,7 @@ async def user_info_v2( models=user_data.get("models") or [], budget_duration=user_data.get("budget_duration"), budget_reset_at=user_data.get("budget_reset_at"), - metadata=user_data.get("metadata"), + metadata=_redact_scim_enterprise_metadata(user_data.get("metadata")), created_at=user_data.get("created_at"), updated_at=user_data.get("updated_at"), sso_user_id=user_data.get("sso_user_id"), @@ -2098,9 +2115,13 @@ async def get_users( user_list: List[LiteLLM_UserTableWithKeyCount] = [] if users is not None: for user in users: + user_dump = user.model_dump() + user_dump["metadata"] = _redact_scim_enterprise_metadata( + user_dump.get("metadata") + ) user_list.append( LiteLLM_UserTableWithKeyCount( - **user.model_dump(), key_count=user_key_counts.get(user.user_id, 0) + **user_dump, key_count=user_key_counts.get(user.user_id, 0) ) ) else: @@ -2596,6 +2617,25 @@ async def ui_view_users( # Using shared metric helper implementations from common_daily_activity +async def _resolve_user_email_metadata( + prisma_client: "PrismaClient", records: list[Any] +) -> dict[str, dict]: + """Map each user_id on the page to its email/alias so the Usage dashboard can + label the 'Spend Per User' chart with the email instead of the raw UUID.""" + user_ids = { + record.user_id for record in records if getattr(record, "user_id", None) + } + if not user_ids: + return {} + users = await UserRepository(prisma_client).table.find_many( + where={"user_id": {"in": list(user_ids)}} + ) + return { + user.user_id: {"user_email": user.user_email, "user_alias": user.user_alias} + for user in users + } + + @router.get( "/user/daily/activity", tags=["Budget & Spend Tracking", "Internal User management"], @@ -2698,6 +2738,9 @@ async def get_user_daily_activity( page=page, page_size=page_size, timezone_offset_minutes=timezone, + resolve_entity_metadata=lambda records: _resolve_user_email_metadata( + prisma_client, records + ), ) except HTTPException: diff --git a/litellm/proxy/management_endpoints/scim/scim_transformations.py b/litellm/proxy/management_endpoints/scim/scim_transformations.py index d1e00f87b69..866f5baf3a4 100644 --- a/litellm/proxy/management_endpoints/scim/scim_transformations.py +++ b/litellm/proxy/management_endpoints/scim/scim_transformations.py @@ -50,8 +50,16 @@ class ScimTransformations: scim_active = metadata.get("scim_active") active = True if scim_active is None else bool(scim_active) + schemas = ["urn:ietf:params:scim:schemas:core:2.0:User"] + enterprise_user = None + if metadata.get(SCIM_ENTERPRISE_METADATA_KEY): + enterprise_user = SCIMEnterpriseUser.model_validate( + metadata[SCIM_ENTERPRISE_METADATA_KEY] + ) + schemas.append(SCIM_ENTERPRISE_USER_SCHEMA) + return SCIMUser( - schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + schemas=schemas, id=user.user_id, userName=ScimTransformations._get_scim_user_name(user), displayName=ScimTransformations._get_scim_user_name(user), @@ -62,6 +70,7 @@ class ScimTransformations: emails=emails, groups=groups, active=active, + enterprise_user=enterprise_user, meta={ "resourceType": "User", "created": user_created_at, diff --git a/litellm/proxy/management_endpoints/scim/scim_v2.py b/litellm/proxy/management_endpoints/scim/scim_v2.py index 0798d1a510d..d5da0372a8f 100644 --- a/litellm/proxy/management_endpoints/scim/scim_v2.py +++ b/litellm/proxy/management_endpoints/scim/scim_v2.py @@ -5,7 +5,7 @@ This is an enterprise feature and requires a premium license. """ import re -from typing import Any, Dict, List, Optional, Set, Tuple +from typing import Any, Dict, Iterable, List, Optional, Set, Tuple from fastapi import ( APIRouter, @@ -69,14 +69,21 @@ class UserProvisionerHelpers: @staticmethod async def handle_existing_user_by_email( - prisma_client, new_user_request: NewUserRequest + prisma_client, + new_user_request: NewUserRequest, + admin_group: Optional[str] = None, ) -> Optional[SCIMUser]: """ Check if a user with the given email already exists and update them if found. + When admin_group is configured the resolved global role on new_user_request + is persisted too, so re-upserting an existing email demotes a user who is no + longer in the admin group instead of leaving the stale role. + Args: prisma_client: Database client new_user_request: New user request data + admin_group: Configured SCIM admin group, or None to leave role untouched Returns: SCIMUser if user was updated, None if no existing user found @@ -100,6 +107,11 @@ class UserProvisionerHelpers: "user_alias": new_user_request.user_alias, "teams": new_user_request.teams, "metadata": safe_dumps(new_user_request.metadata), + **( + {"user_role": new_user_request.user_role} + if admin_group is not None + else {} + ), }, ) @@ -118,6 +130,7 @@ class ScimUserData(TypedDict): given_name: Optional[str] family_name: Optional[str] active: Optional[bool] + enterprise: Optional[SCIMEnterpriseUser] class GroupMemberExtractionResult(BaseModel): @@ -199,11 +212,15 @@ def _extract_scim_user_data(user: SCIMUser) -> ScimUserData: "given_name": user.name.givenName if user.name else None, "family_name": user.name.familyName if user.name else None, "active": user.active, + "enterprise": user.enterprise_user, } def _build_scim_metadata( - given_name: Optional[str], family_name: Optional[str], active: Optional[bool] = None + given_name: Optional[str], + family_name: Optional[str], + active: Optional[bool] = None, + enterprise: Optional[SCIMEnterpriseUser] = None, ) -> Dict[str, Any]: """Build metadata dictionary with SCIM data.""" metadata: Dict[str, Any] = { @@ -216,6 +233,11 @@ def _build_scim_metadata( if active is not None: metadata["scim_active"] = active + if enterprise is not None: + metadata[SCIM_ENTERPRISE_METADATA_KEY] = enterprise.model_dump( + by_alias=True, exclude_none=True + ) + return metadata @@ -244,6 +266,117 @@ async def _get_scim_upsert_user_setting() -> bool: return True +ScimUserRole = Literal[ + LitellmUserRoles.PROXY_ADMIN, + LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, + LitellmUserRoles.INTERNAL_USER, + LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, +] + + +def _default_scim_user_role() -> ScimUserRole: + """Non-admin default role for SCIM-provisioned users.""" + if litellm.default_internal_user_params: + configured_role = litellm.default_internal_user_params.get("user_role") + if configured_role is not None: + return configured_role + return LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +async def _get_scim_admin_group() -> Optional[str]: + """ + Get the scim_admin_group setting from litellm_settings. + + Returns the configured admin group identifier, or None when unset so callers + leave a user's global role untouched (default-safe). + """ + try: + from litellm.proxy.proxy_server import proxy_config + + config = await proxy_config.get_config() + litellm_settings = config.get("litellm_settings", {}) or {} + return litellm_settings.get("scim_admin_group") or None + except Exception as e: + verbose_proxy_logger.warning( + f"Error reading scim_admin_group setting, defaulting to None: {e}" + ) + return None + + +def _resolve_scim_user_role( + groups: list[SCIMUserGroup], + admin_group: Optional[str], + default_role: ScimUserRole, +) -> Optional[LitellmUserRoles]: + """ + Resolve a user's global proxy role from their SCIM groups. + + Returns None when no admin group is configured, signalling callers to leave + the role unchanged. Otherwise grants PROXY_ADMIN when any group matches the + admin group by value or display, and falls back to the non-admin default. + """ + if admin_group is None: + return None + for group in groups: + if group.value == admin_group or group.display == admin_group: + return LitellmUserRoles.PROXY_ADMIN + return default_role + + +async def _scim_groups_from_team_ids( + prisma_client: Any, team_ids: list[str] +) -> list[SCIMUserGroup]: + """ + Build SCIMUserGroup objects from team ids, populating display from each + team's alias so admin-group matching by display name works the same way it + does on PUT (where SCIM groups carry display names natively). + """ + teams = [ + await TeamRepository(prisma_client).table.find_unique( + where={"team_id": team_id} + ) + for team_id in team_ids + ] + return [ + SCIMUserGroup( + value=team_id, + display=team.team_alias if team is not None else None, + ) + for team_id, team in zip(team_ids, teams) + ] + + +async def _recompute_scim_member_roles( + prisma_client: Any, user_ids: Iterable[str] +) -> None: + """ + Recompute and persist each user's global proxy role from their resulting team + membership. No-op unless scim_admin_group is configured, so a SCIM group write + that drops a member from the admin group demotes them just like the user + endpoints do, and the role is left untouched when the feature is off. + """ + admin_group = await _get_scim_admin_group() + if admin_group is None: + return + + default_role = _default_scim_user_role() + for user_id in user_ids: + user = await UserRepository(prisma_client).table.find_unique( + where={"user_id": user_id} + ) + if user is None: + continue + resolved_role = _resolve_scim_user_role( + await _scim_groups_from_team_ids(prisma_client, user.teams or []), + admin_group, + default_role, + ) + await UserRepository(prisma_client).table.update( + where={"user_id": user_id}, + data={"user_role": resolved_role}, + ) + + async def _extract_group_member_ids(group: SCIMGroup) -> GroupMemberExtractionResult: """ Extract member IDs from SCIMGroup, validating that all users exist. @@ -999,19 +1132,16 @@ async def create_user( # Create user in database user_id = user.userName or str(uuid.uuid4()) metadata = _build_scim_metadata( - user_data["given_name"], user_data["family_name"] + user_data["given_name"], + user_data["family_name"], + enterprise=user_data["enterprise"], ) - default_role: Optional[ - Literal[ - LitellmUserRoles.PROXY_ADMIN, - LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, - LitellmUserRoles.INTERNAL_USER, - LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, - ] - ] = LitellmUserRoles.INTERNAL_USER_VIEW_ONLY - if litellm.default_internal_user_params: - default_role = litellm.default_internal_user_params.get("user_role") + default_role = _default_scim_user_role() + admin_group = await _get_scim_admin_group() + resolved_role = _resolve_scim_user_role( + user.groups or [], admin_group, default_role + ) new_user_request = NewUserRequest( user_id=user_id, @@ -1020,12 +1150,14 @@ async def create_user( teams=user_data["teams"], metadata=metadata, auto_create_key=False, - user_role=default_role, + user_role=resolved_role if admin_group is not None else default_role, ) # Check if user with email already exists and update if found existing_user_scim = await UserProvisionerHelpers.handle_existing_user_by_email( - prisma_client=prisma_client, new_user_request=new_user_request + prisma_client=prisma_client, + new_user_request=new_user_request, + admin_group=admin_group, ) if existing_user_scim: @@ -1088,6 +1220,7 @@ async def update_user( user_data["given_name"], user_data["family_name"], scim_active_for_metadata, + enterprise=user_data["enterprise"], ) await _handle_team_membership_changes( @@ -1104,6 +1237,12 @@ async def update_user( "metadata": safe_dumps(metadata), } + admin_group = await _get_scim_admin_group() + if admin_group is not None: + update_data["user_role"] = _resolve_scim_user_role( + user.groups or [], admin_group, _default_scim_user_role() + ) + updated_user = await UserRepository(prisma_client).table.update( where={"user_id": user_id}, data=update_data, @@ -1417,6 +1556,14 @@ async def patch_user( update_data["teams"] = list(final_team_set) + admin_group = await _get_scim_admin_group() + if admin_group is not None: + update_data["user_role"] = _resolve_scim_user_role( + await _scim_groups_from_team_ids(prisma_client, list(final_team_set)), + admin_group, + _default_scim_user_role(), + ) + # Serialize metadata to JSON string for Prisma to avoid GraphQL parsing issues if "metadata" in update_data and isinstance(update_data["metadata"], dict): from litellm.litellm_core_utils.safe_json_dumps import safe_dumps @@ -1599,6 +1746,8 @@ async def create_group( user_api_key_dict=UserAPIKeyAuth(user_role=LitellmUserRoles.PROXY_ADMIN), ) + await _recompute_scim_member_roles(prisma_client, member_result.all_member_ids) + scim_group = await ScimTransformations.transform_litellm_team_to_scim_group( created_team ) @@ -1665,6 +1814,19 @@ async def update_group( final_members=final_members, ) + # A rename can flip whether this group matches scim_admin_group by display + # name, so retained members must be re-resolved too, not just the ones whose + # membership changed. + alias_changed = existing_team.team_alias != group.displayName + await _recompute_scim_member_roles( + prisma_client, + ( + current_members | final_members + if alias_changed + else current_members ^ final_members + ), + ) + # Convert to SCIM format and return scim_group = await ScimTransformations.transform_litellm_team_to_scim_group( updated_team @@ -1691,8 +1853,10 @@ async def delete_group( prisma_client = await _get_prisma_client_or_raise_exception() existing_team = await _check_team_exists(group_id) + member_ids = await _get_team_member_user_ids_from_team(existing_team) + # For each member, remove this team from their teams list - for member_id in existing_team.members or []: + for member_id in member_ids: user = await UserRepository(prisma_client).table.find_unique( where={"user_id": member_id} ) @@ -1704,6 +1868,8 @@ async def delete_group( where={"user_id": member_id}, data={"teams": new_teams} ) + await _recompute_scim_member_roles(prisma_client, member_ids) + # Delete team await TeamRepository(prisma_client).table.delete(where={"team_id": group_id}) @@ -1903,6 +2069,20 @@ async def patch_group( # Handle user-team relationship changes await _handle_group_membership_changes(group_id, current_members, final_members) + # A rename can flip whether this group matches scim_admin_group by display + # name, so retained members must be re-resolved too, not just the ones whose + # membership changed. + new_alias = update_data.get("team_alias", existing_team.team_alias) + alias_changed = new_alias != existing_team.team_alias + await _recompute_scim_member_roles( + prisma_client, + ( + current_members | final_members + if alias_changed + else current_members ^ final_members + ), + ) + # Refresh team one more time to get final state after membership changes final_team = await TeamRepository(prisma_client).table.find_unique( where={"team_id": group_id} diff --git a/litellm/proxy/plugin_routes.py b/litellm/proxy/plugin_routes.py new file mode 100644 index 00000000000..6a94f78fbe7 --- /dev/null +++ b/litellm/proxy/plugin_routes.py @@ -0,0 +1,344 @@ +""" +Plugin proxy routes for litellm. + +Enables external services to register as plugins and be proxied through +the litellm proxy server. + +Config (in litellm config.yaml general_settings): + plugins: + - name: my-plugin + url: "http://localhost:3210" + display_name: "My Plugin" + plugin_key: "sk-..." # optional: plugin's own auth key + +Plugin iframe auth: + The UI calls GET /api/plugins/auth-token to receive a short-lived identity + claim ({user_id, user_role, plugin, exp}) encrypted with a per-plugin key + derived as HMAC-SHA256(LITELLM_SALT_KEY, plugin_name). The claim carries no + litellm bearer token, so a compromised plugin learns only the caller's + identity, never their credential. LITELLM_SALT_KEY itself is never shared + with plugins — each plugin holds only its own derived key. +""" + +import base64 +import hashlib +import hmac as _hmac +import json +import os +import time +from collections.abc import Mapping + +from cryptography.fernet import Fernet, InvalidToken +from fastapi import APIRouter, Depends, HTTPException, Request, Response + +from litellm.proxy._types import PluginConfig, SpecialHeaders, UserAPIKeyAuth +from litellm.proxy.auth.user_api_key_auth import user_api_key_auth +from litellm.llms.custom_httpx.http_handler import get_async_httpx_client +from litellm.types.llms.custom_http import httpxSpecialProvider + +router = APIRouter() + +# Hop-by-hop headers (RFC 7230) and the litellm session cookie — never forwarded +# to a plugin backend. Credential headers are added on top per-request from the +# canonical SpecialHeaders set so the plugin only ever authenticates via its own +# injected plugin_key. +_HOP_BY_HOP_STRIP = frozenset( + { + "host", + "connection", + "transfer-encoding", + "te", + "trailers", + "upgrade", + "cookie", + } +) + + +def _configured_key_header_names() -> frozenset[str]: + """The lowercased general_settings.litellm_key_header_name, if configured. + + Read live from the proxy module (not import-time) so a custom key header set + via config is honoured without a restart. Returns empty when unset. + """ + try: + from litellm.proxy import proxy_server + except Exception: + return frozenset() + general_settings = getattr(proxy_server, "general_settings", None) + if not isinstance(general_settings, dict): + return frozenset() + name: object = general_settings.get("litellm_key_header_name") + return frozenset({name.lower()}) if isinstance(name, str) and name else frozenset() + + +def _request_strip_headers() -> frozenset[str]: + """Headers to drop before forwarding a request to a plugin backend. + + Every header user_api_key_auth accepts as a litellm credential is stripped — + Authorization, x-api-key, API-Key, x-goog-api-key, Ocp-Apim-Subscription-Key, + x-litellm-api-key, and any configured custom key header — so a plugin can + never be handed the caller's live litellm key (confused-deputy escalation). + """ + return ( + _HOP_BY_HOP_STRIP + | SpecialHeaders.litellm_credential_header_names() + | _configured_key_header_names() + ) + + +# Headers to strip from plugin RESPONSES before returning to the browser. +# httpx already decompresses and de-chunks the body, so forwarding the wire +# encoding headers causes clients to attempt double-decompression (garbage) or +# incorrect length checks. set-cookie is removed so plugins cannot overwrite +# litellm session cookies. +_RESPONSE_STRIP = { + "content-encoding", + "transfer-encoding", + "content-length", + "set-cookie", +} + + +def _safe_response_headers(raw: "Mapping[str, str]") -> dict[str, str]: + """Strip wire-encoding/cookie headers and force proxied responses inert. + + Plugin-controlled bytes are served from the litellm dashboard origin, so a + compromised plugin could return an HTML/JS document that executes with the + admin's session against same-origin management APIs. A sandbox CSP forces + the response into an opaque origin with scripts disabled, and nosniff stops + content-type confusion from re-enabling execution. Both are set last so a + plugin cannot override them with its own headers. + """ + return { + **{k: v for k, v in raw.items() if k.lower() not in _RESPONSE_STRIP}, + "content-security-policy": "sandbox", + "x-content-type-options": "nosniff", + } + + +# In-memory plugin registry — populated from general_settings at startup +_plugin_registry: dict[str, PluginConfig] = {} + + +# --------------------------------------------------------------------------- +# Key derivation — audience-scoped per plugin so compromising one plugin +# cannot be used to forge claims for another. LITELLM_SALT_KEY is NEVER +# shared with plugins; each plugin only receives a key derived from +# HMAC(LITELLM_SALT_KEY, plugin_name) which reveals nothing about the master. +# --------------------------------------------------------------------------- +def _plugin_fernet(plugin_name: str) -> Fernet: + """Return a Fernet cipher whose key is scoped to a specific plugin. + + Key material: HMAC-SHA256(LITELLM_SALT_KEY, plugin_name). + A plugin possessing its own key cannot derive the master salt or + forge claims intended for a different plugin. + """ + salt = os.getenv("LITELLM_SALT_KEY", "").encode() + derived = _hmac.new(salt, plugin_name.encode(), hashlib.sha256).digest() + return Fernet(base64.urlsafe_b64encode(derived)) + + +_CLAIM_TTL_SECONDS = 30 # identity claims expire after 30 s + + +def issue_plugin_session_claim( + plugin_name: str, user_id: str | None, user_role: str | None +) -> str: + """Issue a short-lived, audience-scoped identity claim for the plugin. + + The claim contains {user_id, user_role, plugin, exp}. Crucially it + contains NO litellm bearer token — the plugin can only derive the + caller's identity, not act as them against the proxy. + """ + claim = { + "plugin": plugin_name, + "user_id": user_id or "", + "user_role": user_role or "", + "exp": int(time.time()) + _CLAIM_TTL_SECONDS, + } + return _plugin_fernet(plugin_name).encrypt(json.dumps(claim).encode()).decode() + + +def verify_plugin_session_claim(plugin_name: str, ciphertext: str) -> dict: + """Verify and decode a plugin session claim. + + Raises ValueError if the HMAC is invalid, the audience is wrong, or + the claim is expired. Returns the decoded claim dict on success. + """ + try: + raw = _plugin_fernet(plugin_name).decrypt( + ciphertext.encode(), ttl=_CLAIM_TTL_SECONDS + ) + claim = json.loads(raw) + except (InvalidToken, Exception) as exc: + raise ValueError("Invalid, tampered, or expired plugin session claim") from exc + + if claim.get("plugin") != plugin_name: + raise ValueError("Plugin claim audience mismatch") + if int(claim.get("exp", 0)) < int(time.time()): + raise ValueError("Plugin session claim expired") + return claim + + +# --------------------------------------------------------------------------- +# Config +# --------------------------------------------------------------------------- +def register_plugins_from_config(general_settings: dict[str, object]) -> None: + """Replace the plugin registry from general_settings. + + Replaces (not merges) so plugins removed from config are immediately + unreachable without requiring a process restart. + """ + raw = general_settings.get("plugins") + entries: list[object] = raw if isinstance(raw, list) else [] + new_registry = { + p.name: p for p in (PluginConfig.model_validate(entry) for entry in entries) + } + _plugin_registry.clear() + _plugin_registry.update(new_registry) + + +# --------------------------------------------------------------------------- +# Routes +# --------------------------------------------------------------------------- +@router.get("/api/plugins", tags=["plugins"]) +async def list_plugins( + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +) -> list[dict[str, str]]: + """Return registered plugins for authenticated UI callers. + + plugin_key is never returned — the browser never needs it (the proxy injects + it server-side from the registry), and exposing it here would leak the + credential into React state and DevTools. Admin key management goes through + the redacted /config/field/info path instead. + """ + return [ + { + "name": plugin.name, + "display_name": plugin.display_name or plugin.name, + "url": plugin.url, + } + for plugin in _plugin_registry.values() + ] + + +@router.get("/api/plugins/auth-token", tags=["plugins"]) +async def plugin_auth_token( + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), + plugin_name: str = "litellm-platform-plugin", +) -> dict: + """Issue a short-lived, audience-scoped plugin session claim. + + The claim contains {user_id, user_role, plugin, exp}. It does NOT + contain the caller's litellm bearer token — a compromised plugin can + only learn the caller's identity, not impersonate them against the proxy. + + Encrypted with a key derived from HMAC(LITELLM_SALT_KEY, plugin_name), + so each plugin holds only its own key and cannot forge claims for others. + + Requires LITELLM_SALT_KEY to be set; returns 503 otherwise. + """ + if not os.getenv("LITELLM_SALT_KEY"): + raise HTTPException( + status_code=503, + detail="LITELLM_SALT_KEY is not configured; plugin iframe auth unavailable.", + ) + if plugin_name not in _plugin_registry: + raise HTTPException( + status_code=404, detail=f"Plugin '{plugin_name}' is not registered." + ) + user_id = getattr(user_api_key_dict, "user_id", None) + user_role = getattr(user_api_key_dict, "user_role", None) + return { + "session_claim": issue_plugin_session_claim(plugin_name, user_id, user_role) + } + + +@router.api_route( + "/plugin-proxy/{plugin_name}/{path:path}", + methods=["GET", "POST", "PUT", "DELETE", "PATCH", "OPTIONS", "HEAD"], + tags=["plugins"], + include_in_schema=False, +) +async def plugin_proxy( + plugin_name: str, + path: str, + request: Request, + user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth), +) -> Response: + """Authenticated reverse-proxy to a registered plugin backend. + + Restricted to proxy_admin callers — the shared plugin_key must not be + usable as a confused-deputy credential by regular users. Plugin UIs + talk to the plugin service directly via the iframe; this route is for + administrative and server-to-server access only. + + The caller's litellm credential is stripped and replaced with the + plugin's own plugin_key so plugins never receive a live litellm API key. + """ + if getattr(user_api_key_dict, "user_role", None) != "proxy_admin": + return Response( + content="Plugin proxy access requires proxy_admin role.", + status_code=403, + ) + + plugin = _plugin_registry.get(plugin_name) + if not plugin: + return Response( + content=f"Plugin '{plugin_name}' not registered", + status_code=404, + ) + + target_url = f"{plugin.url.rstrip('/')}/{path}" + query = request.url.query + if query: + target_url = f"{target_url}?{query}" + + body = await request.body() + + # Strip caller credentials and hop-by-hop headers from forwarded request + strip = _request_strip_headers() + forward_headers = { + k: v for k, v in request.headers.items() if k.lower() not in strip + } + + # Inject plugin's own credential as upstream auth (if configured) + plugin_key = plugin.plugin_key + if plugin_key: + forward_headers["authorization"] = f"Bearer {plugin_key}" + + # Forward caller identity so the plugin can enforce its own access control. + # The plugin MUST NOT trust these as credentials — they are informational. + # The plugin_key above is the only authentication mechanism. + user_id = getattr(user_api_key_dict, "user_id", None) + user_role = getattr(user_api_key_dict, "user_role", None) + if user_id: + forward_headers["x-litellm-user-id"] = str(user_id) + if user_role: + forward_headers["x-litellm-user-role"] = str(user_role) + + handler = get_async_httpx_client( + llm_provider=httpxSpecialProvider.PassThroughEndpoint + ) + try: + req = handler.client.build_request( + method=request.method, + url=target_url, + headers=forward_headers, + content=body, + ) + # Do not follow redirects — a redirect to an internal URL would allow + # the plugin to SSRF the proxy into fetching arbitrary internal services. + resp = await handler.client.send(req, follow_redirects=False) + except Exception: + return Response( + content=f"Cannot connect to plugin '{plugin_name}' at {plugin.url}", + status_code=502, + ) + + return Response( + content=resp.content, + status_code=resp.status_code, + headers=_safe_response_headers(resp.headers), + ) diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py index bc9aa5c9b7d..e4c14c17811 100644 --- a/litellm/proxy/proxy_server.py +++ b/litellm/proxy/proxy_server.py @@ -419,6 +419,10 @@ from litellm.proxy.management_endpoints.workflow_management_endpoints import ( ) from litellm.proxy.management_helpers.audit_logs import create_audit_log_for_update from litellm.proxy.memory.memory_endpoints import router as memory_router +from litellm.proxy.plugin_routes import ( + router as plugin_router, + register_plugins_from_config, +) from litellm.proxy.middleware.in_flight_requests_middleware import ( InFlightRequestsMiddleware, ) @@ -4502,6 +4506,8 @@ class ProxyConfig: load_from_azure_key_vault(use_azure_key_vault=use_azure_key_vault) ### ALERTING ### self._load_alerting_settings(general_settings=general_settings) + ### PLUGINS ### + register_plugins_from_config(general_settings) ### CONNECT TO DATABASE ### database_url = general_settings.get("database_url", None) if database_url and database_url.startswith("os.environ/"): @@ -4773,6 +4779,11 @@ class ProxyConfig: config ) + ## SANDBOX TOOLS SETTINGS + from litellm.sandbox.sandbox_tools import register_sandbox_tools + + register_sandbox_tools(config.get("sandbox_tools") or []) + ## /fine_tuning/jobs endpoints config finetuning_config = config.get("finetune_settings", None) set_fine_tuning_config(config=finetuning_config) @@ -5663,6 +5674,10 @@ class ProxyConfig: llm_router=llm_router, ) + if _general_settings is not None and "plugins" in _general_settings: + general_settings["plugins"] = _general_settings["plugins"] + register_plugins_from_config(general_settings) + async def _reschedule_spend_log_cleanup_job(self): """ Reschedule the spend log cleanup job based on current general_settings. @@ -11566,9 +11581,12 @@ async def _get_caller_byok_team_scope( LitellmUserRoles.PROXY_ADMIN_VIEW_ONLY, ): return None + key_team_scope: set[str] = ( + {user_api_key_dict.team_id} if user_api_key_dict.team_id else set() + ) user_id = user_api_key_dict.user_id if user_id is None: - return set() + return key_team_scope try: user_row = await UserRepository(prisma_client).table.find_unique( where={"user_id": user_id} @@ -11576,12 +11594,12 @@ async def _get_caller_byok_team_scope( except Exception: verbose_proxy_logger.exception( "Failed to look up caller teams while scoping BYOK search; " - "defaulting to no team access." + "defaulting to key team scope only." ) - return set() + return key_team_scope if user_row is None: - return set() - return set(user_row.teams or []) + return key_team_scope + return key_team_scope | set(user_row.teams or []) def _byok_row_outside_caller_teams( @@ -14931,6 +14949,41 @@ async def update_config( Keep it more precise, to prevent overwrite other values unintentially """ +_PLUGIN_KEY_REDACTED = "***" + + +def _preserve_redacted_plugin_keys(incoming: object, existing: object) -> object: + """Restore real plugin_key values the client never sees. + + /config/field/info redacts every plugin_key to ``"***"``, so an admin + editing a plugin posts that placeholder (or a blank, when the UI clears the + field) straight back. Treat a blank or redacted plugin_key as "keep the + stored credential" by sourcing it from the existing config; only a real, + non-redacted value replaces it, and a blank with no stored key drops the + field entirely instead of persisting the placeholder. + """ + if not isinstance(incoming, list): + return incoming + + stored_keys = { + p["name"]: p["plugin_key"] + for p in (existing if isinstance(existing, list) else []) + if isinstance(p, dict) and p.get("name") and p.get("plugin_key") + } + + def resolve(plugin: object) -> object: + if not isinstance(plugin, dict): + return plugin + key = plugin.get("plugin_key") + if key not in (None, "", _PLUGIN_KEY_REDACTED): + return plugin + name = plugin.get("name") + if name in stored_keys: + return {**plugin, "plugin_key": stored_keys[name]} + return {k: v for k, v in plugin.items() if k != "plugin_key"} + + return [resolve(p) for p in incoming] + @router.post( "/config/field/update", @@ -14997,7 +15050,13 @@ async def update_config_general_settings( ## update db - general_settings[data.field_name] = data.field_value + field_value = data.field_value + if data.field_name == "plugins": + field_value = _preserve_redacted_plugin_keys( + field_value, general_settings.get("plugins") + ) + + general_settings[data.field_name] = field_value response = await ConfigRepository(prisma_client).table.upsert( where={"param_name": "general_settings"}, @@ -15008,6 +15067,9 @@ async def update_config_general_settings( ) await invalidate_config_param("general_settings") + if data.field_name == "plugins": + register_plugins_from_config(general_settings) + return response @@ -15119,6 +15181,15 @@ async def get_config_general_settings( general_settings[field_name], user_api_key_dict.user_role == LitellmUserRoles.PROXY_ADMIN, ) + if field_name == "plugins" and isinstance(field_value, list): + field_value = [ + ( + {k: ("***" if k == "plugin_key" else v) for k, v in p.items()} + if isinstance(p, dict) + else p + ) + for p in field_value + ] return ConfigFieldInfo(field_name=field_name, field_value=field_value) else: raise HTTPException( @@ -16453,6 +16524,7 @@ app.include_router(model_access_group_management_router) app.include_router(tag_management_router) app.include_router(workflow_management_router) app.include_router(memory_router) +app.include_router(plugin_router) app.include_router(cost_tracking_settings_router) app.include_router(router_settings_router) app.include_router(fallback_management_router) diff --git a/litellm/sandbox/main.py b/litellm/sandbox/main.py index a3f5f3b4665..45d3bffb4f9 100644 --- a/litellm/sandbox/main.py +++ b/litellm/sandbox/main.py @@ -70,6 +70,7 @@ async def acreate_sandbox( timeout: int | None = None, allow_internet_access: bool = True, api_key: str | None = None, + api_base: str | None = None, **kwargs, ) -> ContainerHandle: _update_logging(kwargs, provider, "create_sandbox") @@ -78,6 +79,7 @@ async def acreate_sandbox( timeout=timeout, allow_internet_access=allow_internet_access, api_key=api_key, + api_base=api_base, **_forward_kwargs(kwargs), ) @@ -104,12 +106,14 @@ async def adelete_sandbox( provider: str, container: Union[ContainerHandle, str], api_key: str | None = None, + api_base: str | None = None, **kwargs, ) -> bool: _update_logging(kwargs, provider, "delete_sandbox") return await _get_config(provider).adelete_sandbox( container=container, api_key=api_key, + api_base=api_base, **_forward_kwargs(kwargs), ) @@ -121,6 +125,7 @@ async def acode_interpreter_tool( template: str | None = None, timeout: int | None = None, api_key: str | None = None, + api_base: str | None = None, **kwargs, ) -> CodeExecutionResult: _update_logging(kwargs, provider, "code_interpreter_tool") @@ -128,7 +133,11 @@ async def acode_interpreter_tool( forwarded = _forward_kwargs(kwargs) container = await config.acreate_sandbox( - template=template, timeout=timeout, api_key=api_key, **forwarded + template=template, + timeout=timeout, + api_key=api_key, + api_base=api_base, + **forwarded, ) try: return await config.arun_code( @@ -137,7 +146,7 @@ async def acode_interpreter_tool( finally: try: await config.adelete_sandbox( - container=container, api_key=api_key, **forwarded + container=container, api_key=api_key, api_base=api_base, **forwarded ) except Exception as e: litellm._logging.verbose_logger.debug( diff --git a/litellm/sandbox/sandbox_tools.py b/litellm/sandbox/sandbox_tools.py new file mode 100644 index 00000000000..f4a6678f629 --- /dev/null +++ b/litellm/sandbox/sandbox_tools.py @@ -0,0 +1,60 @@ +""" +Registry for sandbox tools configured via the proxy's top-level `sandbox_tools`. + +A sandbox tool maps a name to a sandbox provider plus its credentials, so the +code interpreter interceptor can resolve a tool by name to provider/key/base. +""" + +from collections.abc import Iterator + +from litellm._logging import verbose_logger + +_SANDBOX_TOOL_REGISTRY: dict[str, dict] = {} + + +def _resolve_secret_value(value: str | None) -> str | None: + if not isinstance(value, str): + return None + if value.startswith("os.environ/"): + from litellm.secret_managers.main import get_secret_str + + return get_secret_str(value) + return value + + +def _iter_valid_tools(tools: list[dict]) -> Iterator[tuple[str, dict]]: + for tool in tools: + if not isinstance(tool, dict): + verbose_logger.warning("sandbox_tools: skipping non-dict entry %r", tool) + continue + name = tool.get("sandbox_tool_name") + if not name: + verbose_logger.warning( + "sandbox_tools: skipping entry missing 'sandbox_tool_name': %r", tool + ) + continue + params = tool.get("litellm_params") or {} + provider = params.get("sandbox_provider") + if not provider: + verbose_logger.warning( + "sandbox_tools: skipping entry missing 'sandbox_provider': %r", tool + ) + continue + yield name, { + "sandbox_provider": provider, + "api_key": _resolve_secret_value(params.get("api_key")), + "api_base": _resolve_secret_value(params.get("api_base")), + } + + +def register_sandbox_tools(tools: list[dict]) -> None: + global _SANDBOX_TOOL_REGISTRY + _SANDBOX_TOOL_REGISTRY = dict(_iter_valid_tools(tools)) + + +def resolve_sandbox_tool(name: str) -> dict | None: + return _SANDBOX_TOOL_REGISTRY.get(name) + + +def clear_sandbox_tools() -> None: + register_sandbox_tools([]) diff --git a/litellm/types/integrations/code_interpreter_interception.py b/litellm/types/integrations/code_interpreter_interception.py new file mode 100644 index 00000000000..2669c59db37 --- /dev/null +++ b/litellm/types/integrations/code_interpreter_interception.py @@ -0,0 +1,22 @@ +""" +Type definitions for Code Interpreter Interception integration. +""" + +from typing import List, TypedDict + + +class CodeInterpreterInterceptionConfig(TypedDict, total=False): + """ + Configuration parameters for CodeInterpreterInterceptionLogger. + + Used in proxy_config.yaml under litellm_settings: + litellm_settings: + code_interpreter_interception_params: + enabled: true + enabled_providers: ["openai"] + sandbox_tool_name: "my_e2b_sandbox" + """ + + enabled: bool + enabled_providers: List[str] + sandbox_tool_name: str diff --git a/litellm/types/mcp.py b/litellm/types/mcp.py index 21d4da82041..94e4c68f5e2 100644 --- a/litellm/types/mcp.py +++ b/litellm/types/mcp.py @@ -69,7 +69,6 @@ class MCPPublicServer(BaseModel): name: str alias: Optional[str] = None server_name: Optional[str] = None - url: Optional[str] = None transport: MCPTransportType spec_path: Optional[str] = None auth_type: Optional[MCPAuthType] = None diff --git a/litellm/types/proxy/management_endpoints/scim_v2.py b/litellm/types/proxy/management_endpoints/scim_v2.py index c5fdc66154f..6270d2d0925 100644 --- a/litellm/types/proxy/management_endpoints/scim_v2.py +++ b/litellm/types/proxy/management_endpoints/scim_v2.py @@ -1,7 +1,20 @@ from typing import Any, Dict, List, Literal, Optional, Union from fastapi import HTTPException -from pydantic import BaseModel, ConfigDict, EmailStr, field_validator +from pydantic import ( + BaseModel, + ConfigDict, + EmailStr, + Field, + field_validator, + model_serializer, +) +from pydantic_core.core_schema import SerializerFunctionWrapHandler + +SCIM_ENTERPRISE_USER_SCHEMA = ( + "urn:ietf:params:scim:schemas:extension:enterprise:2.0:User" +) +SCIM_ENTERPRISE_METADATA_KEY = "scim_enterprise" class LiteLLM_UserScimMetadata(BaseModel): @@ -42,13 +55,49 @@ class SCIMUserGroup(BaseModel): type: Optional[str] = "direct" # direct or indirect +class SCIMUserManager(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + value: Optional[str] = None + displayName: Optional[str] = None + ref: Optional[str] = Field(default=None, alias="$ref") + + +class SCIMEnterpriseUser(BaseModel): + model_config = ConfigDict(populate_by_name=True) + + employeeNumber: Optional[str] = None + costCenter: Optional[str] = None + organization: Optional[str] = None + division: Optional[str] = None + department: Optional[str] = None + manager: Optional[SCIMUserManager] = None + + class SCIMUser(SCIMResource): + model_config = ConfigDict(populate_by_name=True) + userName: Optional[str] = None name: Optional[SCIMUserName] = None displayName: Optional[str] = None active: bool = True emails: Optional[List[SCIMUserEmail]] = None groups: Optional[List[SCIMUserGroup]] = None + enterprise_user: Optional[SCIMEnterpriseUser] = Field( + default=None, + alias=SCIM_ENTERPRISE_USER_SCHEMA, + serialization_alias=SCIM_ENTERPRISE_USER_SCHEMA, + ) + + @model_serializer(mode="wrap") + def _omit_absent_enterprise( + self, handler: SerializerFunctionWrapHandler + ) -> Dict[str, Any]: + dumped = handler(self) + if self.enterprise_user is None: + dumped.pop(SCIM_ENTERPRISE_USER_SCHEMA, None) + dumped.pop("enterprise_user", None) + return dumped class SCIMMember(BaseModel): diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json index 47b7190185e..3c50dde9277 100644 --- a/model_prices_and_context_window.json +++ b/model_prices_and_context_window.json @@ -15019,7 +15019,7 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": false + "supports_vision": true }, "fireworks_ai/accounts/fireworks/models/mixtral-8x22b-instruct-hf": { "input_cost_per_token": 1.2e-06, @@ -15322,7 +15322,7 @@ "supports_reasoning": true, "supports_response_schema": true, "supports_tool_choice": true, - "supports_vision": false + "supports_vision": true }, "fireworks_ai/qwen3p7-plus": { "cache_read_input_token_cost": 8e-08, diff --git a/scripts/ruff_strict_gate.py b/scripts/ruff_strict_gate.py index 9c406b8482b..5951a1215ed 100644 --- a/scripts/ruff_strict_gate.py +++ b/scripts/ruff_strict_gate.py @@ -5,13 +5,9 @@ Each rule has a hard ceiling (baseline + slack) in ruff-strict-budget.json. The gate counts each rule across the whole tree and fails when a rule is both over its ceiling and higher than the base it merges into, so a change is blamed for the violations it adds, never for drift that already exists in the base. - -The base is the merge-base of the current branch with --base; this matches CI, -which checks out the PR head sha and runs the gate against the PR's base sha. """ import argparse -import contextlib import json import re import shutil @@ -19,7 +15,6 @@ import subprocess import sys import tempfile from collections import Counter -from collections.abc import Iterator from pathlib import Path from typing import NamedTuple @@ -45,12 +40,6 @@ class Breach(NamedTuple): added: int -class GateInputs(NamedTuple): - head: list[Violation] - base: dict[str, int] - changed: dict[str, set[int]] - - def _run(cmd: list, cwd: Path = REPO_ROOT) -> str: proc = subprocess.run(cmd, cwd=cwd, capture_output=True, text=True) if proc.returncode not in (0, 1): @@ -67,14 +56,14 @@ def _ruff_json(cwd: Path, config: Path) -> list: return json.loads(raw or "[]") -def collect_violations(root: Path, config: Path) -> list: +def head_violations() -> list: out = [] - for item in _ruff_json(root, config): + for item in _ruff_json(REPO_ROOT, STRICT_CONFIG): name = Path(item["filename"]) rel = ( - (name if name.is_absolute() else root / name) + (name if name.is_absolute() else REPO_ROOT / name) .resolve() - .relative_to(root) + .relative_to(REPO_ROOT) .as_posix() ) out.append(Violation(rel, item["location"]["row"], item["code"])) @@ -85,29 +74,27 @@ def count_by_rule(violations: list) -> dict: return dict(Counter(v.code for v in violations)) -@contextlib.contextmanager -def _temp_worktree(ref: str) -> Iterator[Path]: - parent = Path(tempfile.mkdtemp(prefix="ruff_wt_")) +def base_counts(ref: str) -> dict: + parent = Path(tempfile.mkdtemp(prefix="ruff_base_")) worktree = parent / "wt" try: _run(["git", "worktree", "add", "--detach", str(worktree), ref]) - yield worktree + shutil.copy(STRICT_CONFIG, worktree / "ruff-strict.toml") + items = _ruff_json(worktree, worktree / "ruff-strict.toml") + return dict(Counter(item["code"] for item in items)) finally: - subprocess.run( - ["git", "worktree", "remove", "--force", str(worktree)], - cwd=REPO_ROOT, - capture_output=True, - text=True, - ) + _run(["git", "worktree", "remove", "--force", str(worktree)]) shutil.rmtree(parent, ignore_errors=True) -def base_counts(ref: str) -> dict: - with _temp_worktree(ref) as worktree: - shutil.copy(STRICT_CONFIG, worktree / "ruff-strict.toml") - return count_by_rule( - collect_violations(worktree, worktree / "ruff-strict.toml") - ) +def evaluate(head: dict, base: dict, budget: dict) -> list: + breaches = [] + for rule, spec in budget.items(): + cap = spec["baseline"] + spec["slack"] + total = head.get(rule, 0) + if total > cap and total > base.get(rule, 0): + breaches.append(Breach(rule, total, cap, total - base.get(rule, 0))) + return sorted(breaches) def parse_changed_lines(diff_text: str) -> dict: @@ -123,31 +110,24 @@ def parse_changed_lines(diff_text: str) -> dict: return changed -def evaluate(head: dict, base: dict, budget: dict) -> list: - breaches = [] - for rule, spec in budget.items(): - cap = spec["baseline"] + spec["slack"] - total = head.get(rule, 0) - if total > cap and total > base.get(rule, 0): - breaches.append(Breach(rule, total, cap, total - base.get(rule, 0))) - return sorted(breaches) - - def introduced(violations: list, changed: dict) -> list: return [v for v in violations if v.line in changed.get(v.file, set())] -def gather(base: str) -> GateInputs: +def cmd_check(base: str) -> None: + budget = json.loads(BUDGET_PATH.read_text()) + head = head_violations() base_point = _run(["git", "merge-base", base, "HEAD"]).strip() or base - diff = _run(["git", "diff", base_point, "--unified=0", "--no-color", "--", TARGET]) - return GateInputs( - collect_violations(REPO_ROOT, STRICT_CONFIG), - base_counts(base_point), - parse_changed_lines(diff), + breaches = evaluate(count_by_rule(head), base_counts(base_point), budget) + if not breaches: + print(f"OK: every strict rule is within its codebase ceiling (base {base})") + return + new = introduced( + head, + parse_changed_lines( + _run(["git", "diff", base_point, "--unified=0", "--no-color", "--", TARGET]) + ), ) - - -def report(breaches: list, new: list, base: str) -> None: print(f"FAIL: strict-rule totals exceed their ceiling (base {base}):") for breach in breaches: print( @@ -158,24 +138,12 @@ def report(breaches: list, new: list, base: str) -> None: print( "Reduce the new violations or remove an equal number elsewhere; the ceiling is baseline + slack in ruff-strict-budget.json." ) - summary = "; ".join(f"{b.rule} {b.total}/{b.cap} (+{b.added})" for b in breaches) - print(f"BREACHED RULES: {summary}") - - -def cmd_check(base: str) -> None: - budget = json.loads(BUDGET_PATH.read_text()) - inputs = gather(base) - breaches = evaluate(count_by_rule(inputs.head), inputs.base, budget) - if not breaches: - print(f"OK: every strict rule is within its codebase ceiling (base {base})") - return - report(breaches, introduced(inputs.head, inputs.changed), base) raise SystemExit(1) def cmd_update() -> None: budget = json.loads(BUDGET_PATH.read_text()) - head = count_by_rule(collect_violations(REPO_ROOT, STRICT_CONFIG)) + head = count_by_rule(head_violations()) for rule in budget: budget[rule]["baseline"] = head.get(rule, 0) BUDGET_PATH.write_text(json.dumps(budget, indent=2, sort_keys=True) + "\n") diff --git a/tests/enterprise/litellm_enterprise/proxy/auth/test_user_api_key_auth.py b/tests/enterprise/litellm_enterprise/proxy/auth/test_user_api_key_auth.py index a45df5df008..fd674afd85a 100644 --- a/tests/enterprise/litellm_enterprise/proxy/auth/test_user_api_key_auth.py +++ b/tests/enterprise/litellm_enterprise/proxy/auth/test_user_api_key_auth.py @@ -4,6 +4,8 @@ import pytest from fastapi import Request from litellm_enterprise.proxy.auth.user_api_key_auth import enterprise_custom_auth +from litellm.proxy._types import UserAPIKeyAuth + @pytest.mark.asyncio async def test_enterprise_custom_auth_none_user_auth(): @@ -49,16 +51,19 @@ async def test_enterprise_custom_auth_returns_string(): mock_user_auth = AsyncMock(return_value="sk-test-key") request = MagicMock(spec=Request) - with patch( - "litellm.proxy.auth.user_api_key_auth.enterprise_custom_auth", mock_user_auth - ), patch("litellm.proxy.proxy_server.master_key", "sk-1234"), patch( - "litellm.proxy.proxy_server.prisma_client", MagicMock() + with ( + patch( + "litellm.proxy.auth.user_api_key_auth.enterprise_custom_auth", + mock_user_auth, + ), + patch("litellm.proxy.proxy_server.master_key", "sk-1234"), + patch("litellm.proxy.proxy_server.prisma_client", MagicMock()), ): # Verify the key is correctly handled in _user_api_key_auth_builder with patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object" + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key" ) as mock_get_key_object: - mock_get_key_object.return_value = MagicMock( + mock_get_key_object.return_value = UserAPIKeyAuth( token="sk-test-key", user_role="internal_user", team_id=None, @@ -82,9 +87,7 @@ async def test_enterprise_custom_auth_returns_string(): except Exception as e: print("error:", e) - # Verify get_key_object was called with the correct key + # Verify the key lookup was called with the correct hashed key mock_get_key_object.assert_called_once() - # The key should be hashed before being passed to get_key_object - assert mock_get_key_object.call_args[1]["hashed_token"] == hash_token( - "sk-test-key" - ) + # The key should be hashed before being passed to the resolver + assert mock_get_key_object.call_args[0][0] == hash_token("sk-test-key") diff --git a/tests/litellm_utils_tests/test_utils.py b/tests/litellm_utils_tests/test_utils.py index d64633413a0..697c3837602 100644 --- a/tests/litellm_utils_tests/test_utils.py +++ b/tests/litellm_utils_tests/test_utils.py @@ -1588,18 +1588,21 @@ def test_token_counter_with_image_url_with_detail_high(): assert _tokens == DEFAULT_IMAGE_TOKEN_COUNT + 7 -def test_fireworks_ai_document_inlining(): +def test_fireworks_ai_vision_capability_from_cost_map(monkeypatch): """ - With document inlining, all fireworks ai models are now: - - supports_pdf - - supports_vision + Fireworks deprecated document inlining on 2025-06-30, so vision/PDF support is + no longer hardcoded to True for every Fireworks model. Capabilities are read + from the model cost map: unmapped models no longer advertise vision or PDF + support, while mapped VLMs still do. """ + monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True") + monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url="")) from litellm.utils import supports_pdf_input, supports_vision - litellm._turn_on_debug() + assert supports_vision("fireworks_ai/llama-3.1-8b-instruct") is False + assert supports_pdf_input("fireworks_ai/llama-3.1-8b-instruct") is False - assert supports_pdf_input("fireworks_ai/llama-3.1-8b-instruct") is True - assert supports_vision("fireworks_ai/llama-3.1-8b-instruct") is True + assert supports_vision("fireworks_ai/minimax-m3") is True def test_logprobs_type(): diff --git a/tests/llm_translation/test_fireworks_ai_translation.py b/tests/llm_translation/test_fireworks_ai_translation.py index 4e5bef16b8c..204f4d9e31b 100644 --- a/tests/llm_translation/test_fireworks_ai_translation.py +++ b/tests/llm_translation/test_fireworks_ai_translation.py @@ -9,7 +9,6 @@ sys.path.insert( import litellm from litellm import transcription from litellm.llms.fireworks_ai.chat.transformation import FireworksAIConfig -from base_llm_unit_tests import BaseLLMChatTest from base_audio_transcription_unit_tests import BaseLLMAudioTranscriptionTest fireworks = FireworksAIConfig() @@ -146,11 +145,8 @@ class TestFireworksAIAudioTranscription(BaseLLMAudioTranscriptionTest): ) def test_document_inlining_example(disable_add_transform_inline_image_block): """ - Document inlining appends ``#transform=inline`` to image/PDF URLs in the - outgoing request unless explicitly disabled. Assert the transform on the - serialized payload rather than making a live Fireworks call — the live - call only proved the model responded and broke whenever Fireworks rotated - its serverless model catalog. + Fireworks document inlining has been removed from the platform. LiteLLM + must not append ``#transform=inline`` regardless of the legacy disable flag. """ from unittest.mock import patch @@ -163,7 +159,7 @@ def test_document_inlining_example(disable_add_transform_inline_image_block): with patch.object(client, "post") as mock_post: try: completion( - model="fireworks_ai/accounts/fireworks/models/deepseek-v3p1", + model="fireworks_ai/accounts/fireworks/models/minimax-m3", messages=[ { "role": "user", @@ -182,89 +178,80 @@ def test_document_inlining_example(disable_add_transform_inline_image_block): disable_add_transform_inline_image_block=disable_add_transform_inline_image_block, client=client, ) - except Exception as e: - print(e) + except Exception: + pass mock_post.assert_called_once() json_data = json.loads(mock_post.call_args.kwargs["data"]) sent_url = json_data["messages"][0]["content"][0]["image_url"]["url"] - if disable_add_transform_inline_image_block is True: - assert sent_url == pdf_url - assert "#transform=inline" not in sent_url - else: - assert sent_url == pdf_url + "#transform=inline" + assert sent_url == pdf_url + assert "#transform=inline" not in sent_url @pytest.mark.parametrize( - "content, model, expected_url", + "content, expected_url", [ ( {"image_url": "http://example.com/image.png"}, - "gpt-4", - "http://example.com/image.png#transform=inline", + "http://example.com/image.png", ), ( {"image_url": {"url": "http://example.com/image.png"}}, - "gpt-4", - {"url": "http://example.com/image.png#transform=inline"}, + {"url": "http://example.com/image.png"}, ), - ( - {"image_url": "http://example.com/image.png"}, - "vision-gpt", - "http://example.com/image.png", - ), - # data: URLs must never have #transform=inline appended — doing so - # corrupts the base64 payload (fixes #23583). - # URI schemes are case-insensitive (RFC 3986) so check all variants. ( {"image_url": "data:image/png;base64,iVBORw0KGgo="}, - "gpt-4", "data:image/png;base64,iVBORw0KGgo=", ), ( {"image_url": {"url": "data:image/jpeg;base64,/9j/4AAQ=="}}, - "gpt-4", {"url": "data:image/jpeg;base64,/9j/4AAQ=="}, ), ( {"image_url": "Data:image/png;base64,iVBORw0KGgo="}, - "gpt-4", "Data:image/png;base64,iVBORw0KGgo=", ), ], ) -def test_transform_inline(content, model, expected_url): +def test_transform_inline_no_longer_added(content, expected_url): + image_block = {"type": "image_url", **content} + messages = [{"role": "user", "content": [image_block]}] - result = litellm.FireworksAIConfig()._add_transform_inline_image_block( - content=content, model=model, disable_add_transform_inline_image_block=False + result = litellm.FireworksAIConfig()._transform_messages_helper( + messages=messages, + model="accounts/fireworks/models/minimax-m3", + litellm_params={}, ) + result_image_block = result[0]["content"][0] if isinstance(expected_url, str): - assert result["image_url"] == expected_url + assert result_image_block["image_url"] == expected_url else: - assert result["image_url"]["url"] == expected_url["url"] + assert result_image_block["image_url"]["url"] == expected_url["url"] @pytest.mark.parametrize( - "model, is_disabled, expected_url", - [ - ("gpt-4", True, "http://example.com/image.png"), - ("vision-gpt", False, "http://example.com/image.png"), - ("gpt-4", False, "http://example.com/image.png#transform=inline"), - ], + "is_disabled", + [True, False], ) -def test_global_disable_flag(model, is_disabled, expected_url): - content = {"image_url": "http://example.com/image.png"} - result = litellm.FireworksAIConfig()._add_transform_inline_image_block( - content=content, - model=model, - disable_add_transform_inline_image_block=is_disabled, +def test_global_disable_flag_no_longer_adds_transform_inline(is_disabled): + url = "http://example.com/image.png" + litellm.disable_add_transform_inline_image_block = is_disabled + messages = [ + { + "role": "user", + "content": [{"type": "image_url", "image_url": url}], + } + ] + result = litellm.FireworksAIConfig()._transform_messages_helper( + messages=messages, + model="accounts/fireworks/models/minimax-m3", + litellm_params={}, ) - assert result["image_url"] == expected_url + assert result[0]["content"][0]["image_url"] == url litellm.disable_add_transform_inline_image_block = False # Reset for other tests def test_global_disable_flag_with_transform_messages_helper(monkeypatch): - from openai import OpenAI from unittest.mock import patch from litellm import completion from litellm.llms.custom_httpx.http_handler import HTTPHandler @@ -279,7 +266,7 @@ def test_global_disable_flag_with_transform_messages_helper(monkeypatch): ) as mock_post: try: completion( - model="fireworks_ai/accounts/fireworks/models/deepseek-v3p1", + model="fireworks_ai/accounts/fireworks/models/minimax-m3", messages=[ { "role": "user", @@ -296,11 +283,10 @@ def test_global_disable_flag_with_transform_messages_helper(monkeypatch): ], client=client, ) - except Exception as e: - print(e) + except Exception: + pass mock_post.assert_called_once() - print(mock_post.call_args.kwargs) json_data = json.loads(mock_post.call_args.kwargs["data"]) assert ( "#transform=inline" diff --git a/tests/proxy_unit_tests/test_jwt_key_mapping.py b/tests/proxy_unit_tests/test_jwt_key_mapping.py index 61c24183964..65d075b7f99 100644 --- a/tests/proxy_unit_tests/test_jwt_key_mapping.py +++ b/tests/proxy_unit_tests/test_jwt_key_mapping.py @@ -60,7 +60,8 @@ async def test_jwt_to_virtual_key_mapping_resolution(): # Use patch to mock get_key_object in the module where it's used with patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", new_callable=AsyncMock + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", + new_callable=AsyncMock, ) as mock_get_key: mock_get_key.return_value = mock_key_obj @@ -105,7 +106,8 @@ async def test_jwt_to_virtual_key_mapping_no_mapping(): # Mock get_key_object just in case with patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", new_callable=AsyncMock + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", + new_callable=AsyncMock, ): user_api_key_cache = DualCache() @@ -481,7 +483,8 @@ async def test_reject_behavior_raises_403_on_no_mapping(): user_api_key_cache = DualCache() with patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", new_callable=AsyncMock + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", + new_callable=AsyncMock, ): with pytest.raises(HTTPException) as exc_info: await _resolve_jwt_to_virtual_key( @@ -519,7 +522,8 @@ async def test_reject_behavior_caches_sentinel_after_db_miss(): user_api_key_cache = DualCache() with patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", new_callable=AsyncMock + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", + new_callable=AsyncMock, ): # First call — DB miss, should raise 403 and write sentinel with pytest.raises(HTTPException) as exc_info: @@ -578,7 +582,8 @@ async def test_reject_behavior_raises_403_on_cached_no_mapping(): await user_api_key_cache.async_set_cache(cache_key, "__NO_MAPPING__") with patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", new_callable=AsyncMock + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", + new_callable=AsyncMock, ): with pytest.raises(HTTPException) as exc_info: await _resolve_jwt_to_virtual_key( @@ -671,7 +676,7 @@ async def test_auto_register_creates_key_and_mapping_when_helper_invoked(): with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, ) as mock_get_key, patch( @@ -803,7 +808,7 @@ async def test_auto_register_race_condition_unique_conflict(): with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, ) as mock_get_key, patch( @@ -833,13 +838,7 @@ async def test_auto_register_race_condition_unique_conflict(): # Cache should hold the winner's token, not the loser's cached = await user_api_key_cache.async_get_cache("jwt_key_mapping:sub:user-42") assert cached == "winner_token_hash" - mock_get_key.assert_called_once_with( - hashed_token="winner_token_hash", - prisma_client=prisma_client, - user_api_key_cache=user_api_key_cache, - parent_otel_span=None, - proxy_logging_obj=None, - ) + mock_get_key.assert_called_once_with("winner_token_hash") # ────────────────────────────────────────────── @@ -1093,7 +1092,7 @@ async def test_auto_register_race_conflict_tolerates_delete_failure(): with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, ) as mock_get_key, patch( @@ -1249,7 +1248,7 @@ async def test_auto_register_helper_stamps_validated_identity_context(): with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, ) as mock_get_key, patch( diff --git a/tests/test_litellm/integrations/code_interpreter_interception/__init__.py b/tests/test_litellm/integrations/code_interpreter_interception/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tests/test_litellm/integrations/code_interpreter_interception/test_handler.py b/tests/test_litellm/integrations/code_interpreter_interception/test_handler.py new file mode 100644 index 00000000000..f33814b86df --- /dev/null +++ b/tests/test_litellm/integrations/code_interpreter_interception/test_handler.py @@ -0,0 +1,982 @@ +""" +Unit tests for CodeInterpreterInterceptionLogger. + +All sandbox dependencies are injected (dependency injection, no monkeypatch): +a FakeSandbox stands in for the real e2b config and records how it is called. +""" + +import time + +import pytest + +from litellm.integrations.code_interpreter_interception.handler import ( + CodeInterpreterInterceptionLogger, + LITELLM_CODE_EXECUTION_TOOL_NAME, +) +from litellm.llms.base_llm.sandbox.transformation import CodeExecutionResult +from litellm.types.utils import CallTypes + +_ACTIVE_KEY = "_code_interpreter_interception_active" +_SANDBOX_KEY = "_code_interpreter_interception_sandbox_key" + + +class FakeHandle: + def __init__(self, sandbox_id="sbx_fake"): + self.id = sandbox_id + + +class FakeSandbox: + """Records acreate_sandbox / arun_code / adelete_sandbox calls.""" + + def __init__(self, stdout="42"): + self.stdout = stdout + self.create_calls = [] + self.run_calls = [] + self.delete_calls = [] + + async def acreate_sandbox(self, **kwargs): + self.create_calls.append(kwargs) + return FakeHandle() + + async def arun_code(self, *, container, code, **kwargs): + self.run_calls.append({"container": container, "code": code}) + return CodeExecutionResult(stdout=self.stdout) + + async def adelete_sandbox(self, *, container, **kwargs): + self.delete_calls.append({"container": container}) + return True + + +class FakeLogging: + def __init__(self, litellm_call_id="k1"): + self.litellm_call_id = litellm_call_id + self.model_call_details = {} + + +def _function_call_item(call_id="c1", name=LITELLM_CODE_EXECUTION_TOOL_NAME): + return { + "type": "function_call", + "call_id": call_id, + "name": name, + "arguments": '{"code":"print(40 + 2)"}', + } + + +class FakeResponse: + def __init__(self, output): + self.output = output + + +def _iter_messages(plan): + patch = plan.request_patch + assert patch is not None, "plan.request_patch must be set" + assert patch.messages is not None, "plan.request_patch.messages must be set" + return patch.messages + + +@pytest.mark.asyncio +async def test_build_plan_runs_code_and_feeds_output_back(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + response = FakeResponse(output=[_function_call_item()]) + + plan = await logger.async_build_agentic_loop_plan( + tools={ + "tool_calls": [ + { + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"print(40 + 2)"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=response, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id="k1"), + stream=False, + kwargs={"litellm_call_id": "k1", _SANDBOX_KEY: "sbxkey1"}, + ) + + assert sandbox.run_calls, "sandbox.arun_code must be invoked" + assert sandbox.run_calls[0]["code"] == "print(40 + 2)" + + messages = _iter_messages(plan) + outputs = [ + m + for m in messages + if isinstance(m, dict) and m.get("type") == "function_call_output" + ] + assert outputs, "expected a function_call_output item appended" + output_item = next(m for m in outputs if m.get("call_id") == "c1") + assert "42" in str(output_item["output"]) + + +@pytest.mark.asyncio +async def test_pre_call_converts_code_interpreter_tool(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "custom_llm_provider": "openai", + } + + result = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert result is not None + tools = result["tools"] + assert not any( + t.get("type") == "code_interpreter" for t in tools + ), "code_interpreter tool must be removed" + names = [t.get("name") or (t.get("function") or {}).get("name") for t in tools] + assert LITELLM_CODE_EXECUTION_TOOL_NAME in names + + +@pytest.mark.asyncio +@pytest.mark.parametrize( + "tool_choice", + [ + {"type": "code_interpreter"}, + {"type": "hosted_tool", "name": "code_interpreter"}, + ], +) +async def test_pre_call_rewrites_forced_code_interpreter_tool_choice(tool_choice): + """A forced tool_choice targeting the native code_interpreter tool must be + rewritten to the generated function tool; otherwise the outbound request + references a tool that no longer exists and the provider rejects it.""" + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "tool_choice": tool_choice, + "custom_llm_provider": "openai", + } + + result = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert result is not None + assert result["tool_choice"] == { + "type": "function", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + } + + +@pytest.mark.asyncio +async def test_pre_call_leaves_unrelated_tool_choice_untouched(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "tool_choice": "auto", + "custom_llm_provider": "openai", + } + + result = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert result is not None + assert result["tool_choice"] == "auto" + + +@pytest.mark.asyncio +async def test_pre_call_noop_on_non_responses(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "custom_llm_provider": "openai", + } + + result = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.acompletion) + + assert result is None + + +@pytest.mark.asyncio +async def test_should_run_detects_only_matching_function_call(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + + active_kwargs = {"_code_interpreter_interception_active": True} + match = FakeResponse( + output=[_function_call_item(name=LITELLM_CODE_EXECUTION_TOOL_NAME)] + ) + should_run, payload = await logger.async_should_run_agentic_loop( + response=match, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + tools=[], + stream=False, + custom_llm_provider="openai", + kwargs=active_kwargs, + ) + assert should_run is True + assert payload.get("tool_calls") + + no_match = FakeResponse(output=[_function_call_item(name="something_else")]) + should_run2, payload2 = await logger.async_should_run_agentic_loop( + response=no_match, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + tools=[], + stream=False, + custom_llm_provider="openai", + kwargs=active_kwargs, + ) + assert should_run2 is False + assert payload2 == {} + + +@pytest.mark.asyncio +async def test_container_reused_within_request_via_server_sandbox_key(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + response = FakeResponse(output=[_function_call_item()]) + + common = dict( + tools={ + "tool_calls": [ + { + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"print(40 + 2)"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=response, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + stream=False, + ) + + await logger.async_build_agentic_loop_plan( + logging_obj=FakeLogging(litellm_call_id="k1"), + kwargs={"litellm_call_id": "k1", _SANDBOX_KEY: "server-nonce-1"}, + **common, + ) + await logger.async_build_agentic_loop_plan( + logging_obj=FakeLogging(litellm_call_id="k1"), + kwargs={"litellm_call_id": "k1", _SANDBOX_KEY: "server-nonce-1"}, + **common, + ) + + assert ( + len(sandbox.create_calls) == 1 + ), "the sandbox is reused across loop iterations sharing one server sandbox key" + + +@pytest.mark.asyncio +async def test_colliding_caller_call_id_does_not_share_sandbox(): + """Two requests with the same caller-controlled litellm_call_id but distinct + server-minted sandbox keys must NOT share a container; otherwise one user's + code could read another in-flight request's sandbox state.""" + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + common = dict( + tools={ + "tool_calls": [ + { + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"print(1)"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=FakeResponse(output=[_function_call_item()]), + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + stream=False, + ) + + await logger.async_build_agentic_loop_plan( + logging_obj=FakeLogging(litellm_call_id="shared"), + kwargs={"litellm_call_id": "shared", _SANDBOX_KEY: "nonce-A"}, + **common, + ) + await logger.async_build_agentic_loop_plan( + logging_obj=FakeLogging(litellm_call_id="shared"), + kwargs={"litellm_call_id": "shared", _SANDBOX_KEY: "nonce-B"}, + **common, + ) + + assert ( + len(sandbox.create_calls) == 2 + ), "distinct server sandbox keys must isolate sandboxes despite a colliding call id" + + +@pytest.mark.asyncio +async def test_pre_call_mints_server_sandbox_key(): + """The interceptor mints a server-side sandbox key (not derived from the + caller-controlled call id) when it activates.""" + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "custom_llm_provider": "openai", + "litellm_call_id": "caller-supplied", + _SANDBOX_KEY: "caller-forged", + } + + result = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert result is not None + assert result[_SANDBOX_KEY] not in ("caller-forged", "caller-supplied") + assert len(result[_SANDBOX_KEY]) >= 16 + + +@pytest.mark.asyncio +async def test_build_plan_records_code_interpreter_call_metadata(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + plan = await logger.async_build_agentic_loop_plan( + tools={ + "tool_calls": [ + { + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"print(40 + 2)"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=FakeResponse(output=[_function_call_item()]), + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id="k1"), + stream=False, + kwargs={"litellm_call_id": "k1", _SANDBOX_KEY: "sbxkey1"}, + ) + + calls = plan.metadata["code_interpreter_calls"] + assert calls, "build_plan must record a code_interpreter_call for re-injection" + assert calls[0]["code"] == "print(40 + 2)" + assert calls[0]["container_id"] == "sbx_fake" + assert calls[0]["type"] == "code_interpreter_call" + assert calls[0]["status"] == "completed" + assert calls[0]["outputs"] == [{"type": "logs", "logs": "42"}], ( + "outputs must be an OpenAI-shaped logs array (not None) so clients that " + "iterate over code_interpreter_call.outputs do not break" + ) + + +@pytest.mark.asyncio +async def test_build_plan_outputs_empty_array_when_no_stdout(): + """No stdout must still yield an iteration-safe empty array, never None.""" + sandbox = FakeSandbox(stdout="") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + plan = await logger.async_build_agentic_loop_plan( + tools={ + "tool_calls": [ + { + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"pass"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=FakeResponse(output=[_function_call_item()]), + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id="k1"), + stream=False, + kwargs={"litellm_call_id": "k1", _SANDBOX_KEY: "sbxkey1"}, + ) + + assert plan.metadata["code_interpreter_calls"][0]["outputs"] == [] + + +@pytest.mark.asyncio +async def test_post_hook_injects_code_interpreter_call_matching_openai_shape(): + from litellm.types.integrations.custom_logger import AgenticLoopPlan + + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + ci_item = { + "id": "ci_x", + "type": "code_interpreter_call", + "status": "completed", + "code": "print(1)", + "container_id": "sbx_fake", + "outputs": [{"type": "logs", "logs": "1"}], + } + plan = AgenticLoopPlan( + run_agentic_loop=True, + metadata={"code_interpreter_calls": [ci_item]}, + ) + response = FakeResponse(output=[{"type": "message", "content": []}]) + + out = await logger.async_post_agentic_loop_response_hook( + response=response, plan=plan, kwargs={} + ) + + types = [item.get("type") for item in out.output] + assert types == ["code_interpreter_call", "message"], ( + "code_interpreter_call must be re-injected before the message, matching " + "OpenAI's native output ordering" + ) + assert set(out.output[0].keys()) == { + "id", + "type", + "status", + "code", + "container_id", + "outputs", + }, "injected item must match OpenAI's code_interpreter_call keys exactly" + + +@pytest.mark.asyncio +async def test_post_hook_noop_without_recorded_calls(): + from litellm.types.integrations.custom_logger import AgenticLoopPlan + + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + response = FakeResponse(output=[{"type": "message", "content": []}]) + out = await logger.async_post_agentic_loop_response_hook( + response=response, plan=AgenticLoopPlan(run_agentic_loop=True), kwargs={} + ) + assert [item.get("type") for item in out.output] == ["message"] + + +@pytest.mark.asyncio +async def test_pre_call_forces_non_stream_for_loop(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "custom_llm_provider": "openai", + "stream": True, + } + + out = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert out is not None + assert out["stream"] is False, "loop requires a non-streaming upstream call" + assert out["_code_interpreter_interception_converted_stream"] is True, ( + "the converted-stream flag must be set so the final response is wrapped " + "back into a stream for the caller" + ) + + +async def _build_plan(logger, sandbox, call_id="k1", provider="openai"): + return await logger.async_build_agentic_loop_plan( + tools={ + "tool_calls": [ + { + "call_id": "c1", + "name": LITELLM_CODE_EXECUTION_TOOL_NAME, + "arguments": '{"code":"print(40 + 2)"}', + } + ] + }, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=FakeResponse(output=[_function_call_item()]), + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id=call_id), + stream=False, + kwargs={"litellm_call_id": call_id, _SANDBOX_KEY: "sbxkey1"}, + ) + + +@pytest.mark.asyncio +async def test_gate_refuses_without_server_active_marker(): + """A forged litellm_code_execution call must not trigger the loop unless the + pre-call hook actually converted a native code_interpreter tool.""" + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + forged = FakeResponse( + output=[_function_call_item(name=LITELLM_CODE_EXECUTION_TOOL_NAME)] + ) + + should_run, payload = await logger.async_should_run_agentic_loop( + response=forged, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + tools=[], + stream=False, + custom_llm_provider="openai", + kwargs={}, + ) + + assert should_run is False + assert payload == {} + + +@pytest.mark.asyncio +async def test_gate_rechecks_provider_scope(): + """enabled_providers must be re-enforced at the gate, not only in pre-call.""" + logger = CodeInterpreterInterceptionLogger( + sandbox_config=FakeSandbox(), enabled_providers=["openai"] + ) + response = FakeResponse( + output=[_function_call_item(name=LITELLM_CODE_EXECUTION_TOOL_NAME)] + ) + + should_run, _ = await logger.async_should_run_agentic_loop( + response=response, + model="claude-x", + messages=[{"role": "user", "content": "x"}], + tools=[], + stream=False, + custom_llm_provider="anthropic", + kwargs={_ACTIVE_KEY: True}, + ) + + assert should_run is False + + +@pytest.mark.asyncio +async def test_pre_call_strips_client_forged_marker_on_initial_request(): + """A client cannot pre-set the active marker on the original request.""" + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "web_search"}], + "custom_llm_provider": "openai", + _ACTIVE_KEY: True, + } + + await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert _ACTIVE_KEY not in kwargs, ( + "no native code_interpreter tool was present, so a client-supplied " + "active marker must be cleared" + ) + + +@pytest.mark.asyncio +async def test_pre_call_preserves_marker_on_server_followup(): + """On a server-driven followup (depth>0) the marker is trusted and kept.""" + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + kwargs = { + "tools": [{"type": "function", "name": LITELLM_CODE_EXECUTION_TOOL_NAME}], + "custom_llm_provider": "openai", + "_agentic_loop_depth": 1, + _ACTIVE_KEY: True, + } + + await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert kwargs.get(_ACTIVE_KEY) is True, ( + "the server-set marker must survive followup requests so multi-round " + "code execution keeps working" + ) + + +@pytest.mark.asyncio +async def test_sandbox_deleted_after_loop_completes(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + + plan = await _build_plan(logger, sandbox, call_id="k1") + assert sandbox.create_calls, "sandbox must be created during the loop" + assert ( + not sandbox.delete_calls + ), "sandbox must outlive the loop until the final hook" + + await logger.async_post_agentic_loop_response_hook( + response=FakeResponse(output=[{"type": "message", "content": []}]), + plan=plan, + kwargs={}, + ) + + assert len(sandbox.delete_calls) == 1, ( + "the sandbox must be deleted once the final response is assembled, " + "otherwise it keeps running and billing" + ) + assert "sbxkey1" not in logger._container_cache + + +@pytest.mark.asyncio +async def test_post_hook_delete_is_idempotent_across_loop_levels(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + plan = await _build_plan(logger, sandbox, call_id="k1") + response = FakeResponse(output=[{"type": "message", "content": []}]) + + await logger.async_post_agentic_loop_response_hook( + response=response, plan=plan, kwargs={} + ) + await logger.async_post_agentic_loop_response_hook( + response=response, plan=plan, kwargs={} + ) + + assert len(sandbox.delete_calls) == 1, ( + "deleting an already-removed container must be a no-op so unwinding " + "loop levels do not double-delete" + ) + + +@pytest.mark.asyncio +async def test_build_plan_deletes_sandbox_when_execution_raises(): + """If sandbox execution raises before a plan is built (e.g. E2B aborts + output over its cap), the cached sandbox must be deleted before re-raising, + otherwise a caller can leak paid containers until the prune TTL.""" + + class RaisingSandbox(FakeSandbox): + async def arun_code(self, *, container, code, **kwargs): + raise ValueError("output exceeded cap") + + sandbox = RaisingSandbox() + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + + with pytest.raises(ValueError, match="exceeded cap"): + await _build_plan(logger, sandbox, call_id="k1") + + assert len(sandbox.create_calls) == 1, "the sandbox must have been created" + assert len(sandbox.delete_calls) == 1, ( + "a build failure must delete the cached sandbox so it does not keep " + "running and billing" + ) + assert "sbxkey1" not in logger._container_cache + + +@pytest.mark.asyncio +async def test_cleanup_hook_deletes_sandbox(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + plan = await _build_plan(logger, sandbox, call_id="k1") + + await logger.async_agentic_loop_cleanup_hook(plan=plan, kwargs={}) + + assert len(sandbox.delete_calls) == 1, ( + "the cleanup hook must delete the sandbox so a rerun failure cannot " + "leak a running container" + ) + assert "sbxkey1" not in logger._container_cache + + +@pytest.mark.asyncio +async def test_cleanup_hook_is_idempotent_with_post_hook(): + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + plan = await _build_plan(logger, sandbox, call_id="k1") + + await logger.async_post_agentic_loop_response_hook( + response=FakeResponse(output=[{"type": "message", "content": []}]), + plan=plan, + kwargs={}, + ) + await logger.async_agentic_loop_cleanup_hook(plan=plan, kwargs={}) + + assert len(sandbox.delete_calls) == 1, ( + "cleanup running in finally after the success-path post hook already " + "deleted the sandbox must not double-delete" + ) + + +@pytest.mark.asyncio +async def test_responses_plan_cleans_up_sandbox_when_followup_raises(): + """If the agentic rerun fails, _execute_responses_agentic_plan must still + invoke the cleanup hook so the sandbox is not left running.""" + import litellm + from litellm.integrations.custom_logger import CustomLogger + from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler + from litellm.types.integrations.custom_logger import ( + AgenticLoopPlan, + AgenticLoopRequestPatch, + ) + + cleanup_calls = [] + + class CleanupCallback(CustomLogger): + async def async_post_agentic_loop_response_hook(self, response, plan, kwargs): + return response + + async def async_agentic_loop_cleanup_hook(self, plan, kwargs): + cleanup_calls.append(plan) + + plan = AgenticLoopPlan( + run_agentic_loop=True, + request_patch=AgenticLoopRequestPatch( + model="gpt-5", messages=[{"role": "user", "content": "x"}] + ), + metadata={"sandbox_key": "sbxkey1"}, + ) + + original = litellm.aresponses + + async def _boom(*args, **kwargs): + raise RuntimeError("upstream blew up") + + litellm.aresponses = _boom + try: + with pytest.raises(RuntimeError, match="upstream blew up"): + await BaseLLMHTTPHandler()._execute_responses_agentic_plan( + plan=plan, + model="gpt-5", + response_api_optional_request_params={}, + logging_obj=FakeLogging(litellm_call_id="k1"), + kwargs={}, + depth=0, + max_loops=3, + fingerprints=[], + fingerprint="fp", + callback=CleanupCallback(), + ) + finally: + litellm.aresponses = original + + assert cleanup_calls == [plan], ( + "cleanup hook must run in finally even when the rerun raises, otherwise " + "the sandbox keeps running until the prune TTL" + ) + + +@pytest.mark.asyncio +async def test_run_code_does_not_re_resolve_registry(monkeypatch): + """Params resolved once at create time must be reused for running code, so a + registry clear between create and run cannot turn into a create-then-fail.""" + import litellm + from litellm.sandbox import sandbox_tools + + sandbox_tools.register_sandbox_tools( + [ + { + "sandbox_tool_name": "e2b_default", + "litellm_params": {"sandbox_provider": "e2b", "api_key": "sk-x"}, + } + ] + ) + + create_kwargs = {} + run_kwargs = {} + + async def fake_acreate_sandbox(**kwargs): + create_kwargs.update(kwargs) + return FakeHandle() + + async def fake_arun_code(**kwargs): + run_kwargs.update(kwargs) + return CodeExecutionResult(stdout="ok") + + monkeypatch.setattr(litellm, "acreate_sandbox", fake_acreate_sandbox) + monkeypatch.setattr(litellm, "arun_code", fake_arun_code) + + logger = CodeInterpreterInterceptionLogger(sandbox_tool_name="e2b_default") + try: + container, params = await logger._get_or_create_container(cache_key="k1") + assert params is not None and params["sandbox_provider"] == "e2b" + + sandbox_tools.clear_sandbox_tools() + + stdout = await logger._run_tool_call( + container=container, params=params, arguments='{"code":"print(1)"}' + ) + finally: + sandbox_tools.clear_sandbox_tools() + + assert stdout == "ok", "run must succeed using the params captured at create time" + assert run_kwargs["provider"] == "e2b" + + +@pytest.mark.asyncio +async def test_run_tool_call_surfaces_execution_error(): + """A sandbox execution error must be fed back to the model as a labelled + string, not raised, so the agentic loop can react to it.""" + + class ErroringSandbox(FakeSandbox): + async def arun_code(self, *, container, code, **kwargs): + self.run_calls.append({"container": container, "code": code}) + return CodeExecutionResult( + stdout="", error={"name": "ValueError", "value": "boom"} + ) + + sandbox = ErroringSandbox() + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + container = await logger._create_container() + + stdout = await logger._run_tool_call( + container=container[0], params=None, arguments='{"code":"raise ValueError(1)"}' + ) + + assert stdout == "[execution error] boom" + + +@pytest.mark.asyncio +async def test_run_tool_call_reports_unparseable_arguments(): + """Malformed tool arguments must produce a parse error string the model can + see rather than crashing the interceptor.""" + sandbox = FakeSandbox() + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + container = await logger._create_container() + + stdout = await logger._run_tool_call( + container=container[0], params=None, arguments="not-json" + ) + + assert stdout == "[invalid tool arguments: could not parse code]" + assert not sandbox.run_calls, "code must not run when arguments cannot be parsed" + + +@pytest.mark.asyncio +async def test_pre_call_skips_provider_outside_scope(): + """enabled_providers must filter the pre-call conversion so a request to an + out-of-scope provider is left untouched.""" + logger = CodeInterpreterInterceptionLogger( + sandbox_config=FakeSandbox(), enabled_providers=["openai"] + ) + kwargs = { + "tools": [{"type": "code_interpreter", "container": {"type": "auto"}}], + "custom_llm_provider": "anthropic", + } + + result = await logger.async_pre_call_deployment_hook(kwargs, CallTypes.aresponses) + + assert result is None + assert kwargs["tools"][0]["type"] == "code_interpreter", "tool must be untouched" + assert _ACTIVE_KEY not in kwargs + + +@pytest.mark.asyncio +async def test_resolve_provider_falls_back_to_model_lookup(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + + assert logger._resolve_provider({"custom_llm_provider": "openai"}) == "openai" + assert logger._resolve_provider({"model": "gpt-5"}) == "openai" + assert logger._resolve_provider({"model": 123}) is None + assert logger._resolve_provider({"model": "no-such-provider-xyz"}) is None + + +@pytest.mark.asyncio +async def test_create_container_without_sandbox_raises(): + """The registry path must raise a clear error when no sandbox is resolvable + instead of silently creating nothing.""" + logger = CodeInterpreterInterceptionLogger(sandbox_tool_name="missing") + + with pytest.raises(ValueError, match="no sandbox available"): + await logger._create_container() + + +@pytest.mark.asyncio +async def test_run_code_without_params_raises(): + logger = CodeInterpreterInterceptionLogger(sandbox_tool_name="missing") + + with pytest.raises(ValueError, match="no sandbox available to run code"): + await logger._run_code(container=FakeHandle(), params=None, code="print(1)") + + +@pytest.mark.asyncio +async def test_delete_container_swallows_errors(): + """A delete failure must not propagate; the request already succeeded.""" + + class FailingDeleteSandbox(FakeSandbox): + async def adelete_sandbox(self, *, container, **kwargs): + raise RuntimeError("e2b unreachable") + + sandbox = FailingDeleteSandbox() + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + container, params = await logger._create_container() + + await logger._delete_container(container=container, params=params) + + +@pytest.mark.asyncio +async def test_prune_expired_cache_deletes_underlying_container(): + """Expired cache entries must have their sandbox deleted, not just dropped, + otherwise an orphaned sandbox keeps running.""" + import litellm.integrations.code_interpreter_interception.handler as handler_mod + + sandbox = FakeSandbox() + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + container, params = await logger._create_container() + logger._container_cache["old"] = ( + container, + params, + time.time() - handler_mod._CACHE_TTL_SECONDS - 1, + ) + + await logger._prune_expired_cache() + + assert "old" not in logger._container_cache + assert len(sandbox.delete_calls) == 1, "expired sandbox must be deleted" + + +@pytest.mark.asyncio +async def test_normalize_messages_handles_str_and_unknown(): + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + + assert logger._normalize_messages("hi") == [{"role": "user", "content": "hi"}] + assert logger._normalize_messages([{"role": "user"}]) == [{"role": "user"}] + assert logger._normalize_messages(42) == [] + + +def test_from_config_yaml_reads_fields(): + cfg = { + "enabled": False, + "enabled_providers": ["openai"], + "sandbox_tool_name": "e2b_default", + } + logger = CodeInterpreterInterceptionLogger.from_config_yaml(cfg) + + assert logger.enabled is False + assert logger.enabled_providers == ["openai"] + assert logger.sandbox_tool_name == "e2b_default" + + +def test_initialize_from_proxy_config_prefers_litellm_settings(): + logger = CodeInterpreterInterceptionLogger.initialize_from_proxy_config( + litellm_settings={ + "code_interpreter_interception_params": { + "enabled_providers": ["openai"], + "sandbox_tool_name": "e2b_default", + } + }, + callback_specific_params={}, + ) + + assert logger.enabled_providers == ["openai"] + assert logger.sandbox_tool_name == "e2b_default" + + +@pytest.mark.asyncio +async def test_build_plan_handles_dict_shaped_response(): + """A responses payload delivered as a plain dict (not an object) must flow + through detection, execution, and re-injection the same as the typed form.""" + sandbox = FakeSandbox(stdout="42") + logger = CodeInterpreterInterceptionLogger(sandbox_config=sandbox) + dict_response = {"output": [_function_call_item()]} + + plan = await logger.async_build_agentic_loop_plan( + tools={"tool_calls": logger._extract_code_execution_tool_calls(dict_response)}, + model="gpt-5", + messages=[{"role": "user", "content": "x"}], + response=dict_response, + anthropic_messages_provider_config=None, + anthropic_messages_optional_request_params={"tools": []}, + logging_obj=FakeLogging(litellm_call_id="k1"), + stream=False, + kwargs={"litellm_call_id": "k1", _SANDBOX_KEY: "sbxkey1"}, + ) + + assert sandbox.run_calls, "code must run for a dict-shaped response" + assert plan.metadata["code_interpreter_calls"][0]["code"] == "print(40 + 2)" + + out = await logger.async_post_agentic_loop_response_hook( + response={"output": [{"type": "message", "content": []}]}, + plan=plan, + kwargs={}, + ) + + assert [item.get("type") for item in out["output"]] == [ + "code_interpreter_call", + "message", + ], "the dict-shaped response must get the code_interpreter_call re-injected" + + +@pytest.mark.asyncio +async def test_extract_tool_calls_reads_object_attributes(): + """Detection must work when output items are objects with attributes, not + only dicts.""" + + class Item: + def __init__(self): + self.type = "function_call" + self.name = LITELLM_CODE_EXECUTION_TOOL_NAME + self.call_id = "c9" + self.arguments = '{"code":"print(1)"}' + + logger = CodeInterpreterInterceptionLogger(sandbox_config=FakeSandbox()) + calls = logger._extract_code_execution_tool_calls(FakeResponse(output=[Item()])) + + assert len(calls) == 1 + assert calls[0]["call_id"] == "c9" + assert calls[0]["arguments"] == '{"code":"print(1)"}' diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_mount.py b/tests/test_litellm/integrations/otel/test_otel_v2_mount.py index 956d8c53cee..7240d49d022 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_mount.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_mount.py @@ -35,6 +35,13 @@ from litellm.integrations.otel.mount import ( # noqa: E402 ) +@pytest.fixture(autouse=True) +def _clear_otel_v2_flag_cache(): + is_otel_v2_enabled.cache_clear() + yield + is_otel_v2_enabled.cache_clear() + + class _FakeSpan: """Minimal recording span capturing what the hook writes.""" @@ -70,8 +77,10 @@ def _instrumented_app(): def test_gate_toggles_with_env(monkeypatch): """The startup mount is guarded by this flag.""" monkeypatch.delenv("LITELLM_OTEL_V2", raising=False) + is_otel_v2_enabled.cache_clear() assert is_otel_v2_enabled() is False monkeypatch.setenv("LITELLM_OTEL_V2", "1") + is_otel_v2_enabled.cache_clear() assert is_otel_v2_enabled() is True diff --git a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py index 3447f5bdb7e..4bb26a70b02 100644 --- a/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py +++ b/tests/test_litellm/integrations/otel/test_otel_v2_sources_of_truth.py @@ -1,6 +1,8 @@ """Tests for the OTel v2 sources of truth: span registry, semconv keys, config, and the typed StandardLoggingPayload adapter. These need no OTel SDK.""" +import pytest + from litellm.integrations.otel import ( BAGGAGE_PROMOTED_KEYS, DB, @@ -28,6 +30,13 @@ from litellm.integrations.otel.model.spans import ( ) +@pytest.fixture(autouse=True) +def _clear_otel_v2_flag_cache(): + is_otel_v2_enabled.cache_clear() + yield + is_otel_v2_enabled.cache_clear() + + def _sample_payload(**overrides): payload = { "call_type": "acompletion", @@ -561,11 +570,37 @@ def test_capture_message_content_normalizer_only_touches_strings(): def test_v2_flag_is_off_by_default(monkeypatch): monkeypatch.delenv("LITELLM_OTEL_V2", raising=False) + is_otel_v2_enabled.cache_clear() assert is_otel_v2_enabled() is False monkeypatch.setenv("LITELLM_OTEL_V2", "true") + is_otel_v2_enabled.cache_clear() assert is_otel_v2_enabled() is True +def test_v2_flag_resolved_once_not_per_call(monkeypatch): + """Regression for LIT-3895: ``is_otel_v2_enabled`` sits on the proxy hot path + (auth, logging-callback setup). Building the pydantic-settings model on every + call re-scanned the environment at ~28us a pop and dropped throughput, so the + flag must be resolved once and cached rather than reconstructed per call.""" + from litellm.integrations.otel.model import config as config_mod + + constructions = 0 + real_flag = config_mod._OTelV2Flag + + def _counting_flag(*args, **kwargs): + nonlocal constructions + constructions += 1 + return real_flag(*args, **kwargs) + + monkeypatch.setattr(config_mod, "_OTelV2Flag", _counting_flag) + config_mod.is_otel_v2_enabled.cache_clear() + + for _ in range(50): + config_mod.is_otel_v2_enabled() + + assert constructions == 1 + + def test_config_from_env(monkeypatch): for var in ( "OTEL_EXPORTER", diff --git a/tests/test_litellm/interactions/test_openapi_compliance.py b/tests/test_litellm/interactions/test_openapi_compliance.py index aededaaca77..44c0b6e5b02 100644 --- a/tests/test_litellm/interactions/test_openapi_compliance.py +++ b/tests/test_litellm/interactions/test_openapi_compliance.py @@ -156,7 +156,10 @@ class TestResponseCompliance: # The response is the dedicated `Interaction` schema. Google moved the # output-only fields (notably the `steps` array, formerly `outputs`) # off `CreateModelInteractionParams` and onto `Interaction`; the request - # schema no longer carries `steps`. Keep this aligned with the live spec. + # schema no longer carries `steps`. Google later moved `role` off + # `Interaction` onto the per-turn `Turn` schema (asserted in + # test_turn_schema), so it is no longer a top-level output field here. + # Keep this aligned with the live spec. schema = spec_dict["components"]["schemas"]["Interaction"] # Output fields (readOnly). @@ -165,7 +168,6 @@ class TestResponseCompliance: "status", "created", "updated", - "role", "steps", "usage", ] diff --git a/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py b/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py index 35c02184a51..53960847fdc 100644 --- a/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py +++ b/tests/test_litellm/litellm_core_utils/test_exception_mapping_utils.py @@ -1,6 +1,7 @@ import os import sys +import httpx import pytest import litellm @@ -550,3 +551,78 @@ class TestExtractAndRaiseLitellmException: ) assert result is None + + +class ModelError(Exception): + """Mimics replicate's SDK exception, whose mapping keys on the class name.""" + + +class CohereConnectionError(Exception): + """Mimics cohere's SDK exception, whose mapping keys on the class name.""" + + +def test_replicate_model_error_maps_to_bad_request(): + """The replicate branch keys on ``type(original_exception).__name__ == + "ModelError"`` rather than on the error string. The dispatch now lives in a + per-provider helper, so this class-name value has to be threaded into the + helper; if it is not, the bare name ``exception_type`` resolves to the + module-level function and the comparison is always False, silently mismapping + to APIConnectionError.""" + original_exception = ModelError("the deployed model failed to return a prediction") + + with pytest.raises(litellm.BadRequestError) as excinfo: + exception_type( + model="replicate/meta/llama-2-70b-chat", + original_exception=original_exception, + custom_llm_provider="replicate", + ) + + assert excinfo.value.llm_provider == "replicate" + + +def test_cohere_connection_error_maps_to_rate_limit(): + """The cohere branch keys on ``"CohereConnectionError" in + type(original_exception).__name__``. With the dispatch extracted into a helper + the class-name value must be passed in; otherwise ``in`` runs against the + module-level ``exception_type`` function object and raises TypeError, which the + outer handler swallows into a generic APIConnectionError.""" + original_exception = CohereConnectionError("connection reset by peer") + original_exception.message = "connection reset by peer" + + with pytest.raises(litellm.RateLimitError) as excinfo: + exception_type( + model="command-r", + original_exception=original_exception, + custom_llm_provider="cohere", + ) + + assert excinfo.value.llm_provider == "cohere" + + +class ReplicateError(Exception): + """Mimics a replicate HTTP error carrying a status_code and response.""" + + def __init__(self, message: str, status_code: int) -> None: + super().__init__(message) + self.message = message + self.status_code = status_code + self.response = httpx.Response( + status_code=status_code, + request=httpx.Request("POST", "https://api.replicate.com/v1/predictions"), + ) + + +def test_replicate_422_maps_to_unprocessable_entity(): + """The replicate status-code ladder carried two identical ``status_code == 422`` + branches; the second was unreachable dead code. After dropping the duplicate the + surviving branch must still map 422 to UnprocessableEntityError.""" + original_exception = ReplicateError("validation failed for the input", 422) + + with pytest.raises(litellm.UnprocessableEntityError) as excinfo: + exception_type( + model="replicate/meta/llama-2-70b-chat", + original_exception=original_exception, + custom_llm_provider="replicate", + ) + + assert excinfo.value.llm_provider == "replicate" diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py index e95cd656cc4..d7c54fe6707 100644 --- a/tests/test_litellm/litellm_core_utils/test_streaming_handler.py +++ b/tests/test_litellm/litellm_core_utils/test_streaming_handler.py @@ -18,6 +18,8 @@ from litellm.litellm_core_utils.litellm_logging import Logging from litellm.litellm_core_utils.streaming_handler import ( AUDIO_ATTRIBUTE, CustomStreamWrapper, + _ProviderChunkEarlyReturn, + _ProviderChunkParsed, ) from litellm.types.utils import ( CompletionTokensDetailsWrapper, @@ -1801,6 +1803,344 @@ def test_usage_only_chunk_not_dropped_when_finish_reason_already_set( assert result.usage is not None +def _run_dispatch(wrapper: CustomStreamWrapper, chunk): + model_response = wrapper.model_response_creator() + completion_obj = {"content": ""} + result = wrapper._dispatch_provider_chunk( + chunk=chunk, + model_response=model_response, + completion_obj=completion_obj, + ) + return result, model_response, completion_obj + + +def test_dispatch_vllm_extracts_output_text( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """vllm chunks expose text at chunk[0].outputs[0].text; the dispatch must + surface that as the content and report a parsed result.""" + initialized_custom_stream_wrapper.custom_llm_provider = "vllm" + + class _Output: + text = "hello from vllm" + + class _VLLMChunk: + outputs = [_Output()] + + result, _, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, [_VLLMChunk()] + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "hello from vllm" + + +def test_dispatch_petals_slices_completion_stream( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """petals fakes streaming by slicing 30 chars off the buffered completion + stream each call, leaving the remainder for the next chunk.""" + initialized_custom_stream_wrapper.custom_llm_provider = "petals" + initialized_custom_stream_wrapper.completion_stream = "A" * 50 + + result, _, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, chunk=None + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "A" * 30 + assert initialized_custom_stream_wrapper.completion_stream == "A" * 20 + assert initialized_custom_stream_wrapper.received_finish_reason is None + + +def test_dispatch_petals_empty_stream_sets_stop( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """An exhausted petals stream marks the turn finished with a stop reason.""" + initialized_custom_stream_wrapper.custom_llm_provider = "petals" + initialized_custom_stream_wrapper.completion_stream = "" + + result, _, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, chunk=None + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_petals_empty_stream_after_finish_raises( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """Once petals has already finished, an empty stream signals end-of-iteration.""" + initialized_custom_stream_wrapper.custom_llm_provider = "petals" + initialized_custom_stream_wrapper.completion_stream = "" + initialized_custom_stream_wrapper.received_finish_reason = "stop" + + with pytest.raises(StopIteration): + _run_dispatch(initialized_custom_stream_wrapper, chunk=None) + + +def test_dispatch_palm_slices_completion_stream( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """palm uses the same fake-streaming slice strategy as petals.""" + initialized_custom_stream_wrapper.custom_llm_provider = "palm" + initialized_custom_stream_wrapper.completion_stream = "B" * 40 + + result, _, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, chunk=None + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "B" * 30 + assert initialized_custom_stream_wrapper.completion_stream == "B" * 10 + + +def test_dispatch_cached_response_extracts_delta( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """cached_response replays a stored ModelResponseStream; the dispatch lifts + its delta content, finish_reason and id back onto the live response.""" + initialized_custom_stream_wrapper.custom_llm_provider = "cached_response" + chunk = ModelResponseStream( + id="chatcmpl-cache-1", + choices=[ + StreamingChoices( + index=0, + delta=Delta(content="cached text"), + finish_reason="stop", + ) + ], + ) + + result, model_response, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, chunk + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "cached text" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + assert model_response.id == "chatcmpl-cache-1" + assert initialized_custom_stream_wrapper.response_id == "chatcmpl-cache-1" + + +def test_dispatch_vertex_ai_legacy_text_and_finish_reason( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """Legacy vertex_ai chunks (non-ModelResponseStream) expose .text and a + candidate finish_reason enum that must be normalised to an OpenAI reason.""" + initialized_custom_stream_wrapper.custom_llm_provider = "vertex_ai" + + class _FinishReason: + name = "STOP" + + class _Candidate: + finish_reason = _FinishReason() + + class _VertexChunk: + candidates = [_Candidate()] + text = "vertex content" + + result, _, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, _VertexChunk() + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "vertex content" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_vertex_ai_legacy_without_candidates_stringifies_chunk( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """A legacy vertex_ai chunk with no candidates falls back to str(chunk).""" + initialized_custom_stream_wrapper.custom_llm_provider = "vertex_ai" + + class _RawChunk: + def __str__(self) -> str: + return "raw vertex blob" + + result, _, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, _RawChunk() + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "raw vertex blob" + + +def test_dispatch_vertex_ai_legacy_function_call( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """A legacy vertex_ai chunk whose part has no text but carries a + function_call is converted into an OpenAI tool-call delta.""" + initialized_custom_stream_wrapper.custom_llm_provider = "vertex_ai" + + class _FunctionCall: + name = "get_weather" + args = {"location": "SF"} + + class _Part: + function_call = _FunctionCall() + + class _Content: + parts = [_Part()] + + class _FinishReason: + name = "STOP" + + class _Candidate: + content = _Content() + finish_reason = _FinishReason() + + class _VertexFunctionChunk: + candidates = [_Candidate()] + + @property + def text(self): + raise RuntimeError("Part has no text.") + + result, _, _ = _run_dispatch( + initialized_custom_stream_wrapper, _VertexFunctionChunk() + ) + + assert isinstance(result, _ProviderChunkParsed) + tool_calls = result.response_obj["original_chunk"].choices[0].delta.tool_calls + assert tool_calls[0].function.name == "get_weather" + assert json.loads(tool_calls[0].function.arguments) == {"location": "SF"} + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_custom_provider_returns_chunk_early( + monkeypatch: pytest.MonkeyPatch, + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """A registered custom provider passes its already-OpenAI-shaped chunk + straight through as an early return rather than re-parsing it.""" + monkeypatch.setattr(litellm, "_custom_providers", ["my-custom-llm"]) + initialized_custom_stream_wrapper.custom_llm_provider = "my-custom-llm" + chunk = ModelResponseStream( + choices=[ + StreamingChoices(index=0, delta=Delta(content="hi"), finish_reason=None) + ] + ) + + result, _, _ = _run_dispatch(initialized_custom_stream_wrapper, chunk) + + assert isinstance(result, _ProviderChunkEarlyReturn) + assert result.value is chunk + + +def test_dispatch_custom_provider_finish_only_returns_none_early( + monkeypatch: pytest.MonkeyPatch, + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """A custom-provider chunk that carries only a finish_reason (no content) + records the reason and returns None so no empty delta is emitted.""" + monkeypatch.setattr(litellm, "_custom_providers", ["my-custom-llm"]) + initialized_custom_stream_wrapper.custom_llm_provider = "my-custom-llm" + chunk = ModelResponseStream( + choices=[ + StreamingChoices(index=0, delta=Delta(content=None), finish_reason="stop") + ] + ) + + result, _, _ = _run_dispatch(initialized_custom_stream_wrapper, chunk) + + assert isinstance(result, _ProviderChunkEarlyReturn) + assert result.value is None + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_text_completion_codestral_parses_chunk( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """text-completion-codestral streams raw SSE JSON strings that the dispatch + routes through CodestralTextCompletionConfig to extract content/finish.""" + initialized_custom_stream_wrapper.custom_llm_provider = "text-completion-codestral" + chunk = json.dumps( + {"choices": [{"delta": {"content": "codestral text"}, "finish_reason": "stop"}]} + ) + + result, _, completion_obj = _run_dispatch(initialized_custom_stream_wrapper, chunk) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "codestral text" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_text_completion_codestral_requires_string( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """The codestral branch only knows how to parse raw strings; anything else + is a programming error and must surface loudly.""" + initialized_custom_stream_wrapper.custom_llm_provider = "text-completion-codestral" + + with pytest.raises(ValueError): + _run_dispatch(initialized_custom_stream_wrapper, {"not": "a string"}) + + +def test_dispatch_triton_stream( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """triton stream chunks arrive as dicts keyed by text_output/stop_reason.""" + initialized_custom_stream_wrapper.custom_llm_provider = "triton" + chunk = {"text_output": "triton text", "is_finished": True, "stop_reason": "stop"} + + result, _, completion_obj = _run_dispatch(initialized_custom_stream_wrapper, chunk) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "triton text" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_ai21_decodes_completion( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """ai21 does fake streaming over a single byte-encoded JSON completion.""" + initialized_custom_stream_wrapper.custom_llm_provider = "ai21" + chunk = json.dumps({"completions": [{"data": {"text": "ai21 text"}}]}).encode( + "utf-8" + ) + + result, _, completion_obj = _run_dispatch(initialized_custom_stream_wrapper, chunk) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "ai21 text" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + + +def test_dispatch_text_completion_openai_with_usage( + initialized_custom_stream_wrapper: CustomStreamWrapper, +): + """text-completion-openai chunks expose choices[].text and an optional usage + block that the dispatch lifts onto the model response.""" + initialized_custom_stream_wrapper.custom_llm_provider = "text-completion-openai" + + class _Choice: + text = "oai text" + finish_reason = "stop" + + class _Usage: + prompt_tokens = 5 + completion_tokens = 3 + total_tokens = 8 + + class _TextChunk: + choices = [_Choice()] + usage = _Usage() + + result, model_response, completion_obj = _run_dispatch( + initialized_custom_stream_wrapper, _TextChunk() + ) + + assert isinstance(result, _ProviderChunkParsed) + assert completion_obj["content"] == "oai text" + assert initialized_custom_stream_wrapper.received_finish_reason == "stop" + assert model_response.usage.prompt_tokens == 5 + assert model_response.usage.total_tokens == 8 + + @pytest.mark.asyncio async def test_custom_stream_wrapper_anext_does_not_block_event_loop_for_sync_iterators( logging_obj: Logging, diff --git a/tests/test_litellm/llms/anthropic/messages/test_advisor_orchestration.py b/tests/test_litellm/llms/anthropic/messages/test_advisor_orchestration.py index 2cb7b4db3d4..31047d30970 100644 --- a/tests/test_litellm/llms/anthropic/messages/test_advisor_orchestration.py +++ b/tests/test_litellm/llms/anthropic/messages/test_advisor_orchestration.py @@ -516,3 +516,217 @@ async def test_max_uses_none_falls_back_to_default(): ) assert str(_c.ADVISOR_MAX_USES) in str(exc_info.value) + + +# --------------------------------------------------------------------------- +# 12. Defense-in-depth: client-supplied advisor api_base/api_key are dropped +# unless the proxy admin opted into clientside credentials +# --------------------------------------------------------------------------- + + +ADVISOR_TOOL_WITH_CREDS = { + "type": "advisor_20260301", + "name": "advisor", + "model": "claude-opus-4-6", + "api_base": "https://other.example", + "api_key": "sk-other", +} + + +async def _run_advisor_and_capture_subcall_kwargs(): + """Run one advisor turn and return the kwargs of the advisor sub-call.""" + from litellm.llms.anthropic.experimental_pass_through.messages.interceptors.advisor import ( + AdvisorOrchestrationHandler, + ) + + advisor_tool_use_resp = _make_advisor_tool_use_response(tool_id="toolu_01") + advisor_advice_resp = _make_text_response("advice", model="claude-opus-4-6") + final_resp = _make_text_response("final answer") + + captured = {} + call_count = 0 + + async def mock_call(model, messages, tools, stream, max_tokens, **kwargs): + nonlocal call_count + call_count += 1 + if call_count == 1: + return advisor_tool_use_resp + if call_count == 2: + # The advisor sub-call — capture its routing kwargs. + captured["api_key"] = kwargs.get("api_key") + captured["api_base"] = kwargs.get("api_base") + return advisor_advice_resp + return final_resp + + with patch( + "litellm.llms.anthropic.experimental_pass_through.messages.interceptors.advisor._call_messages_handler", + side_effect=mock_call, + ): + h = AdvisorOrchestrationHandler() + await h.handle( + model="openai/gpt-4o-mini", + messages=MESSAGES, + tools=[ADVISOR_TOOL_WITH_CREDS], + stream=False, + max_tokens=512, + custom_llm_provider="openai", + ) + return captured + + +@pytest.mark.asyncio +async def test_advisor_creds_dropped_when_proxy_opt_in_disabled(): + """On the proxy without opt-in, the caller's advisor api_base/api_key must + NOT reach the sub-call (would redirect it / leak the server key).""" + with patch( + "litellm.llms.anthropic.experimental_pass_through.messages.interceptors.advisor._allow_client_side_advisor_credentials", + return_value=False, + ): + captured = await _run_advisor_and_capture_subcall_kwargs() + assert captured["api_key"] is None + assert captured["api_base"] is None + + +@pytest.mark.asyncio +async def test_advisor_creds_honored_when_proxy_opt_in_enabled(): + """With the admin opt-in, the documented clientside routing still works.""" + with patch( + "litellm.llms.anthropic.experimental_pass_through.messages.interceptors.advisor._allow_client_side_advisor_credentials", + return_value=True, + ): + captured = await _run_advisor_and_capture_subcall_kwargs() + assert captured["api_key"] == "sk-other" + assert captured["api_base"] == "https://other.example" + + +# --------------------------------------------------------------------------- +# 13. The proxy gate itself: _allow_client_side_advisor_credentials() and the +# full handle() driven by the real proxy general_settings flag. +# --------------------------------------------------------------------------- + + +def _fake_proxy_server(general_settings: Dict): + """A stand-in litellm.proxy.proxy_server module exposing general_settings. + + The real proxy_server pulls in heavy optional deps that may be absent in a + unit-test environment, so the gate's + ``from litellm.proxy.proxy_server import general_settings`` is satisfied by + injecting this lightweight module into sys.modules. + """ + import types + + module = types.ModuleType("litellm.proxy.proxy_server") + module.general_settings = general_settings # type: ignore[attr-defined] + return module + + +def test_allow_client_side_advisor_credentials_reads_proxy_flag(): + """The gate mirrors the proxy's allow_client_side_credentials opt-in.""" + import sys + + from litellm.llms.anthropic.experimental_pass_through.messages.interceptors.advisor import ( + _allow_client_side_advisor_credentials, + ) + + cases = ( + ({"allow_client_side_credentials": True}, True), + ({"allow_client_side_credentials": False}, False), + # Flag absent entirely -> default deny on the proxy. + ({}, False), + ) + for settings, expected in cases: + with patch.dict( + sys.modules, + {"litellm.proxy.proxy_server": _fake_proxy_server(settings)}, + ): + assert _allow_client_side_advisor_credentials() is expected + + +def test_allow_client_side_advisor_credentials_defaults_true_outside_proxy(): + """Outside the proxy (proxy_server import unavailable), there is no admin + boundary, so the gate permits client-supplied routing.""" + import builtins + import sys + + from litellm.llms.anthropic.experimental_pass_through.messages.interceptors.advisor import ( + _allow_client_side_advisor_credentials, + ) + + real_import = builtins.__import__ + + def _blocked_import(name, *args, **kwargs): + if name == "litellm.proxy.proxy_server": + raise ImportError("proxy server unavailable") + return real_import(name, *args, **kwargs) + + with patch.dict(sys.modules): + sys.modules.pop("litellm.proxy.proxy_server", None) + with patch.object(builtins, "__import__", _blocked_import): + assert _allow_client_side_advisor_credentials() is True + + +def test_advisor_gate_propagates_non_import_errors(): + """Non-ImportError failures during the proxy module probe must not + default permissive. If the proxy is partially loaded and raises + RuntimeError, the gate should surface that rather than silently + returning True.""" + import sys + + from litellm.llms.anthropic.experimental_pass_through.messages.interceptors import ( + advisor, + ) + + original = sys.modules.get("litellm.proxy.proxy_server") + + class _Broken: + def __getattr__(self, _name): + raise RuntimeError("partial proxy boot") + + sys.modules["litellm.proxy.proxy_server"] = _Broken() + try: + with pytest.raises(RuntimeError, match="partial proxy boot"): + advisor._allow_client_side_advisor_credentials() + finally: + if original is None: + sys.modules.pop("litellm.proxy.proxy_server", None) + else: + sys.modules["litellm.proxy.proxy_server"] = original + + +@pytest.mark.asyncio +async def test_advisor_ignores_tool_credentials_when_clientside_disabled(): + """Driven by the real proxy flag (not a patched gate): with + allow_client_side_credentials False, the tool-supplied api_base/api_key must + not reach the advisor sub-call.""" + import sys + + with patch.dict( + sys.modules, + { + "litellm.proxy.proxy_server": _fake_proxy_server( + {"allow_client_side_credentials": False} + ) + }, + ): + captured = await _run_advisor_and_capture_subcall_kwargs() + assert captured["api_key"] is None + assert captured["api_base"] is None + + +@pytest.mark.asyncio +async def test_advisor_uses_tool_credentials_when_clientside_enabled(): + """Driven by the real proxy flag: with allow_client_side_credentials True, + the tool-supplied api_base/api_key flow through to the advisor sub-call.""" + import sys + + with patch.dict( + sys.modules, + { + "litellm.proxy.proxy_server": _fake_proxy_server( + {"allow_client_side_credentials": True} + ) + }, + ): + captured = await _run_advisor_and_capture_subcall_kwargs() + assert captured["api_key"] == "sk-other" + assert captured["api_base"] == "https://other.example" diff --git a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py index 3f91f6ac26e..2d5242d510f 100644 --- a/tests/test_litellm/llms/bedrock/test_base_aws_llm.py +++ b/tests/test_litellm/llms/bedrock/test_base_aws_llm.py @@ -163,6 +163,134 @@ def test_aws_profile_path_not_cached_in_iam_cache(): assert mock_profile.call_count == 2 +def test_get_credentials_does_not_expand_request_env_reference(): + """ + A parameter of the form os.environ/ reaching get_credentials is left as-is + rather than expanded against the process environment, so the downstream auth + helper only ever receives the literal value. + """ + env = _os_environ_without_aws_keys() + env["SERVER_ONLY_VALUE"] = "config-managed-value" + base = BaseAWSLLM() + with patch.dict(os.environ, env, clear=True), patch.object( + base, + "_auth_with_aws_profile", + return_value=(Credentials("ak", "sk", None), None), + ) as mock_profile: + base.get_credentials(aws_profile_name="os.environ/SERVER_ONLY_VALUE") + + assert mock_profile.call_args.args[0] == "os.environ/SERVER_ONLY_VALUE" + assert "config-managed-value" not in str(mock_profile.call_args) + + +def test_get_credentials_falls_back_to_ambient_aws_profile_name_env(): + """ + The fixed AWS_* ambient fallback keeps working: an unset aws_profile_name + resolves from the AWS_PROFILE_NAME environment variable. + """ + env = _os_environ_without_aws_keys() + env["AWS_PROFILE_NAME"] = "ambient-profile" + base = BaseAWSLLM() + with patch.dict(os.environ, env, clear=True), patch.object( + base, + "_auth_with_aws_profile", + return_value=(Credentials("ak", "sk", None), None), + ) as mock_profile: + base.get_credentials(aws_profile_name=None) + + assert mock_profile.call_args.args[0] == "ambient-profile" + + +def test_get_credentials_ambient_fallback_resolves_aws_external_id(): + """ + Each unset param falls back to its own AWS_* env var. Regression for an index + misalignment between the value list and the env-name list, which left + AWS_EXTERNAL_ID unresolved. + """ + env = _os_environ_without_aws_keys() + env["AWS_EXTERNAL_ID"] = "ext-from-env" + base = BaseAWSLLM() + with patch.dict(os.environ, env, clear=True), patch.object( + base, + "_auth_with_aws_role", + return_value=(Credentials("ak", "sk", "tok"), None), + ) as mock_role: + base.get_credentials( + aws_role_name="arn:aws:iam::123456789012:role/x", + aws_session_name="s", + ) + + assert mock_role.call_args.kwargs["aws_external_id"] == "ext-from-env" + + +def _capturing_sts_client(captured: Dict[str, Any]) -> MagicMock: + sts = MagicMock() + + def _assume(**params): + captured["WebIdentityToken"] = params.get("WebIdentityToken") + return { + "Credentials": { + "AccessKeyId": "AKIA", + "SecretAccessKey": "sk", + "SessionToken": "tok", + }, + "PackedPolicySize": 10, + } + + sts.assume_role_with_web_identity.side_effect = _assume + return sts + + +@pytest.mark.parametrize( + "token_ref", + ["os.environ/SERVER_ONLY_VALUE", "SERVER_ONLY_VALUE"], + ids=["os_environ_prefix", "bare_env_name"], +) +def test_web_identity_token_env_reference_not_expanded(token_ref): + """ + A web-identity token that is an environment-variable reference (an os.environ/ + prefix, or a bare name matching an env var) is rejected rather than expanded, so + the process-environment value is never used as the token. + """ + env = _os_environ_without_aws_keys() + env["SERVER_ONLY_VALUE"] = "server-only-value" + captured: Dict[str, Any] = {} + base = BaseAWSLLM() + with patch.dict(os.environ, env, clear=True), patch( + "boto3.client", side_effect=lambda *a, **k: _capturing_sts_client(captured) + ), patch("boto3.Session", return_value=MagicMock()): + with pytest.raises(AwsAuthError): + base.get_credentials( + aws_web_identity_token=token_ref, + aws_role_name="arn:aws:iam::123456789012:role/x", + aws_session_name="s", + aws_sts_endpoint="https://custom-sts.example", + ) + + assert "server-only-value" not in str(captured) + + +def test_web_identity_token_oidc_reference_still_resolved(): + """ + The env-reference guard does not over-reject: an oidc/ reference still flows to + get_secret (mocked to None here), surfacing the existing 401 rather than the 400 + used for rejected env-var references. + """ + base = BaseAWSLLM() + env = _os_environ_without_aws_keys() + with patch.dict(os.environ, env, clear=True), patch( + "litellm.llms.bedrock.base_aws_llm.get_secret", return_value=None + ): + with pytest.raises(AwsAuthError) as exc: + base.get_credentials( + aws_web_identity_token="oidc/circleci/", + aws_role_name="arn:aws:iam::123456789012:role/x", + aws_session_name="s", + ) + + assert exc.value.status_code == 401 + + def test_web_identity_path_not_cached_in_iam_cache(): base = BaseAWSLLM() with patch.object( diff --git a/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py b/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py index 683ec158f44..03e763a4161 100644 --- a/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py +++ b/tests/test_litellm/llms/fireworks_ai/chat/test_fireworks_ai_chat_transformation.py @@ -1,9 +1,8 @@ import json import os import sys -from unittest.mock import AsyncMock, MagicMock, patch +from unittest.mock import MagicMock, patch -import httpx import pytest import litellm @@ -12,10 +11,14 @@ sys.path.insert( 0, os.path.abspath("../../../../..") ) # Adds the parent directory to the system path -from litellm import get_model_info, supports_reasoning +from litellm import get_model_info, supports_reasoning, supports_vision from litellm.llms.fireworks_ai.chat.transformation import FireworksAIConfig -from litellm.types.llms.openai import ChatCompletionToolCallFunctionChunk -from litellm.types.utils import ChatCompletionMessageToolCall, Function, Message +from litellm.types.utils import ( + ChatCompletionMessageToolCall, + Function, + Message, + ModelResponse, +) @pytest.fixture(autouse=True) @@ -98,12 +101,12 @@ def test_supports_reasoning_effort(): for model in supported_models: assert ( - supports_reasoning(model=model, custom_llm_provider="fireworks_ai") == True + supports_reasoning(model=model, custom_llm_provider="fireworks_ai") is True ), f"{model} should support reasoning_effort" for model in unsupported_models: assert ( - supports_reasoning(model=model, custom_llm_provider="fireworks_ai") == False + supports_reasoning(model=model, custom_llm_provider="fireworks_ai") is False ), f"{model} should not support reasoning_effort" @@ -115,11 +118,13 @@ def test_get_supported_openai_params_reasoning_effort(): "fireworks_ai/accounts/fireworks/models/glm-5p1" ) assert "reasoning_effort" in supported_params + assert "thinking" in supported_params unsupported_params = config.get_supported_openai_params( "fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct" ) assert "reasoning_effort" not in unsupported_params + assert "thinking" not in unsupported_params def test_get_supported_openai_params_parallel_tool_calls(): @@ -181,41 +186,6 @@ def test_get_provider_info_omits_false_supports_reasoning(monkeypatch): assert "supports_reasoning" not in info -def test_add_transform_inline_image_block_skips_data_urls(): - """ - data: URLs must not have #transform=inline appended — doing so corrupts the - base64 payload and raises binascii.Error: Incorrect padding on the Fireworks side. - Regression test for https://github.com/BerriAI/litellm/issues/23583 - """ - config = FireworksAIConfig() - data_url = "data:image/jpeg;base64,/9j/4AAQSkZJRgAB" - - # str branch - str_content = {"type": "image_url", "image_url": data_url} - result = config._add_transform_inline_image_block( - str_content, model="gpt-4", disable_add_transform_inline_image_block=False - ) - assert result["image_url"] == data_url, "data URL must not be modified (str branch)" - - # dict branch - dict_content = {"type": "image_url", "image_url": {"url": data_url}} - result = config._add_transform_inline_image_block( - dict_content, model="gpt-4", disable_add_transform_inline_image_block=False - ) - assert ( - result["image_url"]["url"] == data_url - ), "data URL must not be modified (dict branch)" - - # regular https URL should still get the suffix - https_content = {"type": "image_url", "image_url": "https://example.com/image.jpg"} - result = config._add_transform_inline_image_block( - https_content, model="gpt-4", disable_add_transform_inline_image_block=False - ) - assert result["image_url"].endswith( - "#transform=inline" - ), "https URL should get #transform=inline" - - @pytest.mark.parametrize( "api_base, expected_url_prefix", [ @@ -582,3 +552,549 @@ def test_transform_request_routes_short_form_model_to_models_path(): headers={}, ) assert result["model"] == "accounts/fireworks/models/glm-5p2" + + +def _make_fireworks_raw_response(body: dict) -> MagicMock: + mock = MagicMock() + mock.status_code = 200 + mock.json.return_value = body + mock.text = json.dumps(body) + mock.headers = {} + return mock + + +_BASE_CHAT_COMPLETION_RESPONSE: dict = { + "id": "resp-test", + "object": "chat.completion", + "created": 1234567890, + "model": "glm-5p1", + "choices": [ + { + "index": 0, + "message": {"role": "assistant", "content": "Hello"}, + "finish_reason": "stop", + } + ], + "usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15}, +} + + +def _run_transform_response(response_body: dict) -> ModelResponse: + config = FireworksAIConfig() + raw_response = _make_fireworks_raw_response(response_body) + logging_obj = MagicMock() + return config.transform_response( + model="accounts/fireworks/models/glm-5p1", + raw_response=raw_response, + model_response=ModelResponse(), + logging_obj=logging_obj, + request_data={}, + messages=[{"role": "user", "content": "Hi"}], + optional_params={}, + litellm_params={}, + encoding=None, + api_key="test-key", + ) + + +_REASONING_MODEL = "fireworks_ai/accounts/fireworks/models/glm-5p1" +_NON_REASONING_MODEL = "fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct" + + +def test_get_supported_openai_params_includes_all_fireworks_params(): + config = FireworksAIConfig() + params = config.get_supported_openai_params(_REASONING_MODEL) + + required = [ + "seed", + "top_logprobs", + "min_p", + "typical_p", + "repetition_penalty", + "mirostat_target", + "mirostat_lr", + "logit_bias", + "echo", + "echo_last", + "ignore_eos", + "prompt_cache_key", + "prompt_cache_isolation_key", + "raw_output", + "perf_metrics_in_response", + "return_token_ids", + "safe_tokenization", + "service_tier", + "speculation", + "prediction", + "stream_options", + "sampling_mask", + "thinking", + "reasoning_history", + ] + missing = [p for p in required if p not in params] + assert missing == [], f"Missing params: {missing}" + + +def test_native_openai_params_flow_end_to_end_with_drop_params_false(): + """ + The OpenAI-native params Fireworks supports (seed, top_logprobs, logit_bias, + prompt_cache_key, service_tier, prediction) previously hit + ``UnsupportedParamsError`` with ``drop_params=False`` because they were absent + from ``get_supported_openai_params``. Listing them must let them survive the + ``get_optional_params`` gate and reach the request, not just appear in the + supported list. Asserting via ``get_optional_params`` (the real gate) rather + than ``map_openai_params`` catches a revert of the supported-params additions, + which a list-membership check would not. + """ + native = { + "seed": 42, + "top_logprobs": 3, + "logit_bias": {"1": 1}, + "prompt_cache_key": "cache-key", + "service_tier": "auto", + "prediction": {"type": "content", "content": "x"}, + } + optional_params = litellm.get_optional_params( + model="accounts/fireworks/models/llama-v3-70b-instruct", + custom_llm_provider="fireworks_ai", + drop_params=False, + **native, + ) + for key, value in native.items(): + assert optional_params.get(key) == value + + +def test_prompt_truncate_len_correct_name(): + config = FireworksAIConfig() + params = config.get_supported_openai_params(_REASONING_MODEL) + assert "prompt_truncate_len" in params + assert "prompt_truncate_length" not in params + + result = config.map_openai_params( + {"prompt_truncate_len": 4096}, + {}, + _REASONING_MODEL, + drop_params=False, + ) + assert result == {"prompt_truncate_len": 4096} + + +def test_stream_options_include_usage_auto_injected(): + config = FireworksAIConfig() + result = config.transform_request( + model="accounts/fireworks/models/glm-5p1", + messages=[{"role": "user", "content": "Hi"}], + optional_params={"stream": True}, + litellm_params={}, + headers={}, + ) + assert result["stream_options"] == {"include_usage": True} + + +def test_stream_options_not_injected_when_not_streaming(): + config = FireworksAIConfig() + result = config.transform_request( + model="accounts/fireworks/models/glm-5p1", + messages=[{"role": "user", "content": "Hi"}], + optional_params={}, + litellm_params={}, + headers={}, + ) + assert "stream_options" not in result + + +def test_stream_options_preserves_user_override(): + config = FireworksAIConfig() + result = config.transform_request( + model="accounts/fireworks/models/glm-5p1", + messages=[{"role": "user", "content": "Hi"}], + optional_params={"stream": True, "stream_options": {"include_usage": False}}, + litellm_params={}, + headers={}, + ) + assert result["stream_options"]["include_usage"] is False + + +def test_reasoning_history_in_supported_params(): + config = FireworksAIConfig() + reasoning_params = config.get_supported_openai_params(_REASONING_MODEL) + assert "reasoning_history" in reasoning_params + + non_reasoning_params = config.get_supported_openai_params(_NON_REASONING_MODEL) + assert "reasoning_history" not in non_reasoning_params + + +def test_thinking_param_passthrough(): + config = FireworksAIConfig() + thinking = {"type": "disabled"} + result = config.map_openai_params( + {"thinking": thinking}, + {}, + _REASONING_MODEL, + drop_params=False, + ) + assert result == {"thinking": thinking} + + +def test_thinking_and_reasoning_effort_conflict_rejected(): + config = FireworksAIConfig() + with pytest.raises( + litellm.BadRequestError, + match="does not support specifying both `thinking` and `reasoning_effort`", + ): + config.map_openai_params( + { + "thinking": {"type": "enabled", "budget_tokens": 4096}, + "reasoning_effort": "medium", + }, + {}, + _REASONING_MODEL, + drop_params=False, + ) + + +def test_minimax_m3_supports_vision_from_model_map(): + config = FireworksAIConfig() + + for model in [ + "fireworks_ai/accounts/fireworks/models/minimax-m3", + "fireworks_ai/minimax-m3", + ]: + assert supports_vision(model=model, custom_llm_provider="fireworks_ai") is True + assert config.get_provider_info(model)["supports_vision"] is True + + +def test_transform_messages_helper_rejects_file_blocks(): + config = FireworksAIConfig() + messages = [ + { + "role": "user", + "content": [ + { + "type": "file", + "file": { + "file_data": "data:application/pdf;base64,JVBERi0xLjQKJSVFT0YK", + "filename": "tiny.pdf", + }, + }, + {"type": "text", "text": "Describe this"}, + ], + } + ] + + with pytest.raises( + litellm.BadRequestError, + match="Fireworks AI chat completions does not support file content blocks", + ): + config._transform_messages_helper( + messages, model="accounts/fireworks/models/kimi-k2p6", litellm_params={} + ) + + +def test_transform_messages_helper_rejects_non_vision_image_inputs(): + config = FireworksAIConfig() + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this"}, + { + "type": "image_url", + "image_url": { + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE=" + }, + }, + ], + } + ] + + with pytest.raises(litellm.BadRequestError, match="does not support image inputs"): + config._transform_messages_helper( + messages, model="accounts/fireworks/models/glm-5p2", litellm_params={} + ) + + +def test_transform_messages_helper_allows_vision_image_inputs(): + config = FireworksAIConfig() + messages = [ + { + "role": "user", + "content": [ + {"type": "text", "text": "Describe this"}, + { + "type": "image_url", + "image_url": { + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE=" + }, + }, + ], + } + ] + + out = config._transform_messages_helper( + messages, model="accounts/fireworks/models/minimax-m3", litellm_params={} + ) + assert out == messages + + +def test_image_inputs_not_rejected_for_fuzzy_non_vision_match(): + """ + A custom/fine-tuned model id that hyphen-matches a known non-vision model + (glm-5p2 has supports_vision=False) must not inherit that False via the + substring fallback and hard-reject valid image_url blocks. The capability + gate for rejection uses an exact cost-map match; the fuzzy fallback stays a + soft signal only, so an unmapped vision-capable deployment is not blocked. + """ + config = FireworksAIConfig() + custom_model = "accounts/myorg/models/custom-glm-5p2" + + assert config._get_model_cost_capability(custom_model, "supports_vision") is False + assert ( + config._get_model_cost_capability_exact(custom_model, "supports_vision") is None + ) + + messages = [ + { + "role": "user", + "content": [ + { + "type": "image_url", + "image_url": { + "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAE=" + }, + }, + ], + } + ] + out = config._transform_messages_helper( + messages, model=custom_model, litellm_params={} + ) + assert out == messages + + +def test_transform_messages_helper_skips_non_dict_content(): + config = FireworksAIConfig() + messages = [ + { + "role": "user", + "content": ["just a string", {"type": "text", "text": "hello"}], + } + ] + + out = config._transform_messages_helper( + messages, model="accounts/fireworks/models/glm-5p2", litellm_params={} + ) + assert out == messages + + +def test_transform_messages_helper_no_transform_inline(): + config = FireworksAIConfig() + url = "https://example.com/image.jpg" + messages = [ + { + "role": "user", + "content": [{"type": "image_url", "image_url": url}], + } + ] + out = config._transform_messages_helper( + messages, model="accounts/fireworks/models/minimax-m3", litellm_params={} + ) + block = out[0]["content"][0] + assert block["image_url"] == url + assert "#transform=inline" not in block["image_url"] + + +def test_get_provider_info_vision_from_model_cost(monkeypatch): + config = FireworksAIConfig() + + vision_model = "fireworks_ai/test-vision-from-cost" + monkeypatch.setitem( + litellm.model_cost, + vision_model, + {"supports_vision": True, "supports_pdf_input": True}, + ) + info = config.get_provider_info(vision_model) + assert info["supports_vision"] is True + assert info["supports_pdf_input"] is True + + no_vision_model = "fireworks_ai/test-no-vision-from-cost" + monkeypatch.setitem(litellm.model_cost, no_vision_model, {}) + info_no_vision = config.get_provider_info(no_vision_model) + assert info_no_vision.get("supports_vision") is not True + assert "supports_pdf_input" not in info_no_vision + + +def test_reasoning_effort_boolean_true_to_medium(): + config = FireworksAIConfig() + result = config.map_openai_params( + {"reasoning_effort": True}, + {}, + _REASONING_MODEL, + drop_params=False, + ) + assert result["reasoning_effort"] == "medium" + + +def test_reasoning_effort_boolean_false_to_none(): + config = FireworksAIConfig() + result = config.map_openai_params( + {"reasoning_effort": False}, + {}, + _REASONING_MODEL, + drop_params=False, + ) + assert result["reasoning_effort"] == "none" + + +def test_reasoning_effort_string_passthrough(): + config = FireworksAIConfig() + result = config.map_openai_params( + {"reasoning_effort": "high"}, + {}, + _REASONING_MODEL, + drop_params=False, + ) + assert result["reasoning_effort"] == "high" + + +def test_reasoning_effort_integer_passthrough(): + config = FireworksAIConfig() + result = config.map_openai_params( + {"reasoning_effort": 1000}, + {}, + _REASONING_MODEL, + drop_params=False, + ) + assert result["reasoning_effort"] == 1000 + assert isinstance(result["reasoning_effort"], int) + + +def test_transform_response_captures_perf_metrics(): + body = { + **_BASE_CHAT_COMPLETION_RESPONSE, + "perf_metrics": {"prompt-tokens": 10}, + } + result = _run_transform_response(body) + assert result._hidden_params["fireworks_perf_metrics"] == {"prompt-tokens": 10} + + +def test_transform_response_captures_prompt_token_ids(): + body = { + **_BASE_CHAT_COMPLETION_RESPONSE, + "prompt_token_ids": [1, 2, 3], + } + result = _run_transform_response(body) + assert result._hidden_params["fireworks_prompt_token_ids"] == [1, 2, 3] + + +def test_transform_response_captures_raw_output(): + raw_output = { + "prompt_fragments": [], + "prompt_token_ids": [], + "completion": "test", + } + body = { + **_BASE_CHAT_COMPLETION_RESPONSE, + "choices": [ + { + **_BASE_CHAT_COMPLETION_RESPONSE["choices"][0], + "raw_output": raw_output, + } + ], + } + result = _run_transform_response(body) + assert result._hidden_params["fireworks_raw_outputs"] == [raw_output] + + +def test_transform_response_captures_token_ids(): + body = { + **_BASE_CHAT_COMPLETION_RESPONSE, + "choices": [ + { + **_BASE_CHAT_COMPLETION_RESPONSE["choices"][0], + "token_ids": [4, 5, 6], + } + ], + } + result = _run_transform_response(body) + assert result._hidden_params["fireworks_token_ids"] == [[4, 5, 6]] + + +def test_streaming_surfaces_fireworks_response_fields(): + """ + The Fireworks-specific response fields captured into _hidden_params for + non-streaming calls must also reach streamed responses. They ride the + streamed chunks' provider_specific_fields (litellm rebuilds each streamed + chunk, so per-chunk _hidden_params does not survive): per-choice + token_ids/raw_output on the content chunk, response-level + perf_metrics/prompt_token_ids on the final usage chunk. Driving the real + litellm.completion(stream=True) path also covers the get_model_response_iterator + wiring; dropping the Fireworks iterator would leave these fields unset. + """ + from litellm.llms.custom_httpx.http_handler import HTTPHandler + + model = "accounts/fireworks/models/llama-v3p1-8b-instruct" + raw_output = {"completion": "Hi"} + sse_lines = [ + "data: " + + json.dumps( + { + "id": "stream-1", + "object": "chat.completion.chunk", + "created": 1, + "model": model, + "choices": [ + { + "index": 0, + "delta": {"role": "assistant", "content": "Hi"}, + "token_ids": [123], + "raw_output": raw_output, + } + ], + } + ), + "data: " + + json.dumps( + { + "id": "stream-1", + "object": "chat.completion.chunk", + "created": 1, + "model": model, + "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}], + "usage": { + "prompt_tokens": 5, + "completion_tokens": 1, + "total_tokens": 6, + }, + "perf_metrics": {"prompt-tokens": 5}, + "prompt_token_ids": [1, 2, 3], + } + ), + "data: [DONE]", + ] + + raw_response = MagicMock() + raw_response.status_code = 200 + raw_response.headers = {} + raw_response.iter_lines = lambda: iter(sse_lines) + + client = HTTPHandler() + with patch.object(client, "post", return_value=raw_response): + stream = litellm.completion( + model=f"fireworks_ai/{model}", + messages=[{"role": "user", "content": "hi"}], + stream=True, + api_key="fw-test-key", + client=client, + ) + surfaced: dict = {} + for chunk in stream: + fields = getattr(chunk, "provider_specific_fields", None) or {} + surfaced.update( + {k: v for k, v in fields.items() if k.startswith("fireworks_")} + ) + + assert surfaced["fireworks_token_ids"] == [[123]] + assert surfaced["fireworks_raw_outputs"] == [raw_output] + assert surfaced["fireworks_perf_metrics"] == {"prompt-tokens": 5} + assert surfaced["fireworks_prompt_token_ids"] == [1, 2, 3] diff --git a/tests/test_litellm/proxy/auth/test_auth_checks.py b/tests/test_litellm/proxy/auth/test_auth_checks.py index 5ec5d12784f..cf8448c0383 100644 --- a/tests/test_litellm/proxy/auth/test_auth_checks.py +++ b/tests/test_litellm/proxy/auth/test_auth_checks.py @@ -1750,6 +1750,60 @@ async def test_reject_clientside_metadata_tags_allows_key_tags_without_client_ta assert request_body["metadata"]["tags"] == ["engineering"] +@pytest.mark.asyncio +@pytest.mark.parametrize( + "route", + [ + "/bedrock/model/us.anthropic.claude-sonnet-4-6/invoke", + "/v1/messages", + ], +) +async def test_common_checks_metadata_route_keeps_key_tags_out_of_provider_metadata( + route, +): + """GH#30629: on routes that track tags in litellm_metadata (bedrock, /v1/messages, + responses, ...) key-level tags must land in litellm_metadata, never in the + provider-facing metadata field (Bedrock rejects non-user_id metadata with HTTP 400). + The auth-time pre-seed keys off LITELLM_METADATA_ROUTES, so hardcoding a single route + or dropping the pre-seed makes apply_key_tags_pre_auth fall back to metadata; this + guards that regression. + """ + from fastapi import Request + + from litellm.proxy.auth.auth_checks import common_checks + + request_body = {"messages": [{"role": "user", "content": "test"}]} + + mock_request = MagicMock(spec=Request) + valid_token = UserAPIKeyAuth( + token="test-token", + metadata={"tags": ["engineering"]}, + ) + + with patch( + "litellm.proxy.auth.auth_checks.get_tag_objects_batch", + new_callable=AsyncMock, + return_value={}, + ): + result = await common_checks( + request_body=request_body, + team_object=None, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route=route, + llm_router=None, + proxy_logging_obj=MagicMock(), + valid_token=valid_token, + request=mock_request, + ) + + assert result is True + assert request_body["litellm_metadata"]["tags"] == ["engineering"] + assert "metadata" not in request_body + + @pytest.mark.asyncio async def test_virtual_key_soft_budget_check_with_user_obj(): """Test _virtual_key_soft_budget_check includes user_email when user_obj is provided""" @@ -2365,7 +2419,9 @@ async def test_virtual_key_budget_check_reads_from_spend_counter(): proxy_logging_obj = ProxyLogging(user_api_key_cache=None) proxy_logging_obj.budget_alerts = AsyncMock() - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:key:test-hashed-token": return 1.5 return fallback_spend @@ -2397,7 +2453,9 @@ async def test_virtual_key_budget_check_fallback_no_counter(): proxy_logging_obj.budget_alerts = AsyncMock() # get_current_spend returns fallback_spend when no counter exists - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): return fallback_spend with patch("litellm.proxy.proxy_server.get_current_spend", mock_get_current_spend): @@ -2424,7 +2482,9 @@ async def test_team_budget_check_reads_from_spend_counter(): proxy_logging_obj = ProxyLogging(user_api_key_cache=None) proxy_logging_obj.budget_alerts = AsyncMock() - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team:test-team": return 1.5 return fallback_spend @@ -2449,7 +2509,9 @@ async def test_end_user_budget_check_reads_from_spend_counter(): litellm_budget_table=LiteLLM_BudgetTable(max_budget=1.0), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:end_user:customer-1": return 1.5 return fallback_spend @@ -2475,7 +2537,9 @@ async def test_tag_budget_check_reads_from_spend_counter(): litellm_budget_table=LiteLLM_BudgetTable(max_budget=1.0), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:tag:paid-tag": return 1.5 return fallback_spend @@ -2523,7 +2587,9 @@ async def test_team_member_budget_check_reads_from_spend_counter(): proxy_logging_obj = ProxyLogging(user_api_key_cache=None) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return 1.5 return fallback_spend @@ -2756,7 +2822,9 @@ async def test_team_member_budget_check_falls_back_to_team_default_budget_id(): return_value=fake_budget_row ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return 70.0 return fallback_spend @@ -2853,7 +2921,9 @@ async def test_team_member_budget_check_per_member_override_wins_over_team_defau mocked_spend = 70.0 - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return mocked_spend return fallback_spend @@ -2943,7 +3013,9 @@ async def test_team_member_budget_check_null_clone_falls_back_to_team_default(): return_value=fake_default_row ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return 500.0 return fallback_spend @@ -3010,7 +3082,9 @@ async def test_team_member_budget_check_null_clone_with_null_default_skips_enfor return_value=fake_default_row ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return 1000.0 return fallback_spend @@ -3077,7 +3151,9 @@ async def test_team_member_budget_check_zero_team_default_treated_as_no_cap(): return_value=fake_default_row ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return 0.0 return fallback_spend @@ -3135,7 +3211,9 @@ async def test_team_member_budget_check_zero_per_member_row_still_blocks(): prisma_client = MagicMock() prisma_client.db.litellm_budgettable.find_unique = AsyncMock(return_value=None) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:team_member:test-user:test-team": return 0.0 return fallback_spend diff --git a/tests/test_litellm/proxy/auth/test_auth_utils.py b/tests/test_litellm/proxy/auth/test_auth_utils.py index e652c109987..cd8cf10d037 100644 --- a/tests/test_litellm/proxy/auth/test_auth_utils.py +++ b/tests/test_litellm/proxy/auth/test_auth_utils.py @@ -2160,3 +2160,48 @@ class TestGetRequestRouteTemplate: lambda self: (_ for _ in ()).throw(RuntimeError("boom")) ) assert get_request_route_template(req) is None + + +class TestIsRequestBodySafeBlocksModelList: + """model_list is an SDK-only field with no proxy API meaning; it must + be rejected from the request body regardless of any opt-in.""" + + def test_model_list_rejected_with_no_opt_in(self): + with pytest.raises(ValueError, match="model_list is not allowed"): + is_request_body_safe( + request_body={ + "model": "gpt-4", + "messages": [{"role": "user", "content": "hi"}], + "model_list": [{"model_name": "x", "litellm_params": {}}], + }, + general_settings={}, + llm_router=None, + model="gpt-4", + ) + + def test_model_list_rejected_even_with_proxy_wide_opt_in(self): + with pytest.raises(ValueError, match="model_list is not allowed"): + is_request_body_safe( + request_body={ + "model": "gpt-4", + "messages": [{"role": "user", "content": "hi"}], + "model_list": [], + }, + general_settings={"allow_client_side_credentials": True}, + llm_router=None, + model="gpt-4", + ) + + def test_normal_body_still_passes(self): + assert ( + is_request_body_safe( + request_body={ + "model": "gpt-4", + "messages": [{"role": "user", "content": "hi"}], + }, + general_settings={}, + llm_router=None, + model="gpt-4", + ) + is True + ) diff --git a/tests/test_litellm/proxy/auth/test_custom_auth_end_user_budget.py b/tests/test_litellm/proxy/auth/test_custom_auth_end_user_budget.py index e49f025df2e..3203878a1e0 100644 --- a/tests/test_litellm/proxy/auth/test_custom_auth_end_user_budget.py +++ b/tests/test_litellm/proxy/auth/test_custom_auth_end_user_budget.py @@ -106,7 +106,9 @@ async def test_custom_auth_enforces_end_user_budget_when_common_checks_skipped() litellm_budget_table=LiteLLM_BudgetTable(max_budget=1.0), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:end_user:customer-1": return 5.0 return fallback_spend diff --git a/tests/test_litellm/proxy/auth/test_handle_jwt.py b/tests/test_litellm/proxy/auth/test_handle_jwt.py index 63510086f95..b547ec877e2 100644 --- a/tests/test_litellm/proxy/auth/test_handle_jwt.py +++ b/tests/test_litellm/proxy/auth/test_handle_jwt.py @@ -1206,7 +1206,13 @@ async def test_auth_builder_returns_team_membership_object(): JWTAuthManager, "get_objects", new_callable=AsyncMock, - return_value=(user_object, None, None, mock_team_membership, user_object.user_id), + return_value=( + user_object, + None, + None, + mock_team_membership, + user_object.user_id, + ), ) as mock_get_objects, patch.object( JWTAuthManager, "map_user_to_teams", new_callable=AsyncMock @@ -3509,9 +3515,7 @@ def test_canonical_user_id_no_change_when_ids_match(): user_object = LiteLLM_UserTable(user_id=same, user_email=same) assert ( - JWTAuthManager._canonical_user_id_from_db( - user_id=same, user_object=user_object - ) + JWTAuthManager._canonical_user_id_from_db(user_id=same, user_object=user_object) == same ) @@ -3802,12 +3806,15 @@ async def test_get_objects_team_membership_uses_rebound_user_id(): user_id_jwt_field="email", user_id_upsert=True ) - with patch( - "litellm.proxy.auth.handle_jwt.get_user_object", - side_effect=fake_get_user_object, - ), patch( - "litellm.proxy.auth.handle_jwt.get_team_membership", - side_effect=fake_get_team_membership, + with ( + patch( + "litellm.proxy.auth.handle_jwt.get_user_object", + side_effect=fake_get_user_object, + ), + patch( + "litellm.proxy.auth.handle_jwt.get_team_membership", + side_effect=fake_get_team_membership, + ), ): ( user_object, diff --git a/tests/test_litellm/proxy/auth/test_model_checks.py b/tests/test_litellm/proxy/auth/test_model_checks.py index 8d686900ea6..02b1f698132 100644 --- a/tests/test_litellm/proxy/auth/test_model_checks.py +++ b/tests/test_litellm/proxy/auth/test_model_checks.py @@ -436,7 +436,9 @@ def test_wildcard_custom_prefix_keeps_org_segment_for_non_provider_first_segment result = get_known_models_from_wildcard( wildcard_model="my_hf/*", - litellm_params=LiteLLM_Params(model="huggingface/*", custom_llm_provider="huggingface"), + litellm_params=LiteLLM_Params( + model="huggingface/*", custom_llm_provider="huggingface" + ), ) assert result == ["my_hf/meta-llama/Llama-3-8B"] diff --git a/tests/test_litellm/proxy/auth/test_network.py b/tests/test_litellm/proxy/auth/test_network.py new file mode 100644 index 00000000000..b67723305e4 --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_network.py @@ -0,0 +1,104 @@ +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Tuple + +from fastapi import Request + +from litellm.proxy.auth.network import ( + TrustedProxyConfig, + resolve_client_ip, + resolve_network_context, +) + +TRUSTED = TrustedProxyConfig(use_forwarded_for=True, trusted_proxy_cidrs=["10.0.0.0/8"]) + + +def make_request( + *, + headers: Optional[Dict[str, str]] = None, + client: Optional[Tuple[str, int]] = ("203.0.113.7", 5555), +) -> Request: + raw_headers: List[Tuple[bytes, bytes]] = [ + (key.lower().encode(), value.encode()) for key, value in (headers or {}).items() + ] + scope: Dict[str, Any] = { + "type": "http", + "http_version": "1.1", + "method": "GET", + "path": "/", + "raw_path": b"/", + "query_string": b"", + "headers": raw_headers, + "client": client, + "server": ("testserver", 80), + "scheme": "http", + } + return Request(scope) + + +def test_xff_ignored_when_forwarding_disabled(): + config = TrustedProxyConfig( + use_forwarded_for=False, trusted_proxy_cidrs=["10.0.0.0/8"] + ) + request = make_request( + headers={"x-forwarded-for": "203.0.113.9"}, client=("10.0.0.1", 1) + ) + ip, via_proxy = resolve_client_ip(request, config) + assert ip == "10.0.0.1" + assert via_proxy is False + + +def test_xff_honored_from_trusted_peer(): + request = make_request( + headers={"x-forwarded-for": "203.0.113.9, 10.0.0.5"}, client=("10.0.0.1", 1) + ) + ip, via_proxy = resolve_client_ip(request, TRUSTED) + assert ip == "203.0.113.9" + assert via_proxy is True + + +def test_spoofed_xff_from_untrusted_peer_is_ignored(): + request = make_request( + headers={"x-forwarded-for": "203.0.113.9"}, client=("8.8.8.8", 1) + ) + ip, via_proxy = resolve_client_ip(request, TRUSTED) + assert ip == "8.8.8.8" + assert via_proxy is False + + +def test_right_to_left_parse_skips_chained_trusted_proxies(): + request = make_request( + headers={"x-forwarded-for": "198.51.100.4, 10.1.1.1, 10.0.0.9"}, + client=("10.0.0.1", 1), + ) + ip, via_proxy = resolve_client_ip(request, TRUSTED) + assert ip == "198.51.100.4" + assert via_proxy is True + + +def test_all_trusted_hops_fall_back_to_peer(): + request = make_request( + headers={"x-forwarded-for": "10.1.1.1, 10.0.0.9"}, client=("10.0.0.1", 1) + ) + ip, via_proxy = resolve_client_ip(request, TRUSTED) + assert ip == "10.0.0.1" + assert via_proxy is True + + +def test_invalid_xff_token_is_skipped(): + request = make_request( + headers={"x-forwarded-for": "not-an-ip, 203.0.113.50"}, client=("10.0.0.1", 1) + ) + ip, _ = resolve_client_ip(request, TRUSTED) + assert ip == "203.0.113.50" + + +def test_network_context_captures_host_and_proxy_flag(): + request = make_request( + headers={"x-forwarded-for": "203.0.113.9", "host": "proxy.litellm.ai"}, + client=("10.0.0.1", 1), + ) + ctx = resolve_network_context(request, TRUSTED) + assert ctx.client_ip == "203.0.113.9" + assert ctx.host == "proxy.litellm.ai" + assert ctx.via_trusted_proxy is True diff --git a/tests/test_litellm/proxy/auth/test_onboarding.py b/tests/test_litellm/proxy/auth/test_onboarding.py index c81f4cb7d66..d55a5472af1 100644 --- a/tests/test_litellm/proxy/auth/test_onboarding.py +++ b/tests/test_litellm/proxy/auth/test_onboarding.py @@ -18,7 +18,6 @@ from fastapi import HTTPException import litellm from litellm.proxy._types import InvitationClaim - # --------------------------------------------------------------------------- # Helpers # --------------------------------------------------------------------------- diff --git a/tests/test_litellm/proxy/auth/test_resolvers_exceptions.py b/tests/test_litellm/proxy/auth/test_resolvers_exceptions.py new file mode 100644 index 00000000000..f88b7e2be14 --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_resolvers_exceptions.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from litellm.proxy._types import ProxyErrorTypes, ProxyException +from litellm.proxy.auth.resolvers.exceptions import ( + IdentityResolutionError, + KeyNotFoundError, + KeyNotInCacheError, + NoDatabaseConnectionError, + PrincipalMissingSourceKeyError, +) + + +def test_all_resolution_errors_share_one_base(): + errors = [ + NoDatabaseConnectionError(), + KeyNotInCacheError("hashed"), + KeyNotFoundError("hashed"), + PrincipalMissingSourceKeyError(), + ] + assert all(isinstance(e, IdentityResolutionError) for e in errors) + + +def test_key_not_found_preserves_the_public_401_contract(): + # The auth seam catches ProxyException and rewrites the 401 message, so a + # missing key must keep mapping to that exact contract. + error = KeyNotFoundError("hashed-token") + + assert isinstance(error, ProxyException) + assert error.code == "401" + assert error.type == ProxyErrorTypes.token_not_found_in_db.value + assert error.param == "key" + + +def test_key_not_in_cache_names_the_token(): + assert "hashed-token" in str(KeyNotInCacheError("hashed-token")) diff --git a/tests/test_litellm/proxy/auth/test_resolvers_models.py b/tests/test_litellm/proxy/auth/test_resolvers_models.py new file mode 100644 index 00000000000..0fbe10b6f28 --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_resolvers_models.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +import pytest +from pydantic import ValidationError + +from litellm.proxy.auth.auth_method import AuthMethod +from litellm.proxy.auth.resolvers.models import ( + EndUserIdentity, + Principal, + PrincipalType, + ProjectIdentity, + TeamIdentity, + UserIdentity, +) +from litellm.proxy.auth.roles import Role, TeamRole + + +def _principal() -> Principal: + return Principal( + principal_type=PrincipalType.HUMAN, + subject="u1", + auth_method=AuthMethod.OIDC, + ) + + +def test_principal_is_frozen(): + principal = _principal() + with pytest.raises(ValidationError): + principal.subject = "mutated" + + +def test_principal_defaults_are_independent_instances(): + a = _principal() + b = _principal() + assert a.teams == [] and a.scopes == [] and a.audience == [] + assert a.teams is not b.teams + + +def test_principal_requires_identity_core_fields(): + with pytest.raises(ValidationError): + Principal(subject="u1") # missing principal_type + auth_method + + +def test_principal_roles_are_validated_against_role_enum(): + principal = Principal( + principal_type=PrincipalType.HUMAN, + subject="u1", + auth_method=AuthMethod.OIDC, + roles=["org_admin"], + ) + assert principal.roles == [Role.ORG_ADMIN] + assert isinstance(principal.roles[0], Role) + + with pytest.raises(ValidationError): + Principal( + principal_type=PrincipalType.HUMAN, + subject="u1", + auth_method=AuthMethod.OIDC, + roles=["not_a_real_role"], + ) + + +def test_principal_default_network_and_collections(): + principal = Principal( + principal_type=PrincipalType.SERVICE_ACCOUNT, + subject="svc", + auth_method=AuthMethod.MUTUAL_TLS, + ) + assert principal.teams == [] + assert principal.scopes == [] + assert principal.project is None + assert principal.end_user is None + assert principal.network.client_ip is None + assert principal.network.via_trusted_proxy is False + + +def test_team_identity_defaults_to_member_role(): + team = TeamIdentity(id="g1") + assert team.role == TeamRole.MEMBER + + +def test_user_identity_optional_fields_default_none(): + user = UserIdentity(id="u1") + assert user.email is None + assert user.external_id is None + + +def test_project_identity_name_is_optional(): + assert ProjectIdentity(id="p1").name is None + assert ProjectIdentity(id="p1", name="Acme").name == "Acme" + + +def test_end_user_identity_requires_id(): + with pytest.raises(ValidationError): + EndUserIdentity() diff --git a/tests/test_litellm/proxy/auth/test_resolvers_seam.py b/tests/test_litellm/proxy/auth/test_resolvers_seam.py new file mode 100644 index 00000000000..cb42d4d3d2d --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_resolvers_seam.py @@ -0,0 +1,96 @@ +from __future__ import annotations + +from typing import Any, Dict, List, Optional, Tuple + +from fastapi import Request + +from litellm.proxy._types import UserAPIKeyAuth +from litellm.proxy.auth.auth_method import AuthMethod +from litellm.proxy.auth.resolvers.models import PrincipalType +from litellm.proxy.auth.roles import Role +from litellm.proxy.auth.user_api_key_auth import _resolve_request_principal + + +def _request( + *, + headers: Optional[Dict[str, str]] = None, + client: Optional[Tuple[str, int]] = ("203.0.113.7", 5555), +) -> Request: + raw: List[Tuple[bytes, bytes]] = [ + (k.lower().encode(), v.encode()) for k, v in (headers or {}).items() + ] + scope: Dict[str, Any] = { + "type": "http", + "http_version": "1.1", + "method": "POST", + "path": "/v1/chat/completions", + "raw_path": b"/v1/chat/completions", + "query_string": b"", + "headers": raw, + "client": client, + "server": ("testserver", 80), + "scheme": "http", + } + return Request(scope) + + +def test_seam_projects_full_identity_from_key_object(): + token = UserAPIKeyAuth( + token="hashed-token", + user_id="u-1", + user_role="org_admin", + team_id="t-1", + team_alias="Eng", + org_id="o-1", + organization_alias="Acme", + end_user_id="cust-9", + ) + + principal = _resolve_request_principal(_request(), token) + + assert principal.principal_type == PrincipalType.HUMAN + assert principal.auth_method == AuthMethod.API_KEY + assert principal.user is not None and principal.user.id == "u-1" + assert principal.roles == [Role.ORG_ADMIN] + assert [t.id for t in principal.teams] == ["t-1"] + assert principal.teams[0].name == "Eng" + assert principal.organization is not None and principal.organization.id == "o-1" + assert principal.organization.name == "Acme" + assert principal.end_user is not None and principal.end_user.id == "cust-9" + # the key is always identifiable via credential_ref, even when other ids exist + assert principal.credential_ref.token_id == "hashed-token" + + +def test_seam_principal_is_never_anonymous_for_keyless_service_account(): + # no user_id and no key_alias -> subject must still identify the key + token = UserAPIKeyAuth(token="hashed-token") + + principal = _resolve_request_principal(_request(), token) + + assert principal.principal_type == PrincipalType.SERVICE_ACCOUNT + assert principal.user is None + assert principal.subject == "hashed-token" + assert principal.credential_ref.token_id == "hashed-token" + + +def test_seam_stamps_direct_peer_when_no_trusted_proxy_configured(): + token = UserAPIKeyAuth(token="hashed-token", user_id="u-1") + + # No trusted_proxy_ranges configured -> XFF is not trusted, direct peer wins. + principal = _resolve_request_principal( + _request(headers={"x-forwarded-for": "10.9.9.9"}, client=("203.0.113.7", 5555)), + token, + ) + + assert principal.network.client_ip == "203.0.113.7" + assert principal.network.via_trusted_proxy is False + + +def test_seam_detects_jwt_auth_method(): + token = UserAPIKeyAuth( + token="hashed-token", user_id="u-2", jwt_claims={"sub": "u-2"} + ) + + principal = _resolve_request_principal(_request(), token) + + assert principal.auth_method == AuthMethod.BEARER_JWT diff --git a/tests/test_litellm/proxy/auth/test_resolvers_store.py b/tests/test_litellm/proxy/auth/test_resolvers_store.py new file mode 100644 index 00000000000..5e644a084a5 --- /dev/null +++ b/tests/test_litellm/proxy/auth/test_resolvers_store.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +from typing import Dict, Optional + +import pytest + +from litellm.proxy._types import UserAPIKeyAuth, hash_token +from litellm.proxy.auth.resolvers.exceptions import ( + NoDatabaseConnectionError, + PrincipalMissingSourceKeyError, +) +from litellm.proxy.auth.auth_method import AuthMethod +from litellm.proxy.auth.resolvers.models import Principal, PrincipalType +from litellm.proxy.auth.resolvers.store import IdentityStore + + +class _FakeCache: + """Stands in for the DualCache get_key_object reads. It returns a cache hit + before the DB is touched, so seeding it exercises resolve without a database + (a non-None prisma client is still required; it is never reached on a hit).""" + + def __init__(self, entries: Optional[Dict[str, object]] = None) -> None: + self._entries = entries or {} + + async def async_get_cache(self, key, *args, **kwargs): + return self._entries.get(key) + + async def async_set_cache(self, *args, **kwargs): + return None + + +async def test_resolve_returns_a_principal_projected_from_the_looked_up_key(): + raw = "sk-live-abc" + key = UserAPIKeyAuth(token=hash_token(raw), user_id="u-1", team_id="t-1") + store = IdentityStore(object(), _FakeCache({hash_token(raw): key})) + + principal = await store.resolve(hashed_token=hash_token(raw)) + + assert isinstance(principal, Principal) + assert principal.principal_type == PrincipalType.HUMAN + assert principal.user is not None and principal.user.id == "u-1" + assert [t.id for t in principal.teams] == ["t-1"] + + +async def test_resolve_carries_the_key_for_key_from_principal(): + raw = "sk-live-abc" + key = UserAPIKeyAuth(token=hash_token(raw), user_id="u-1", team_id="t-1") + store = IdentityStore(object(), _FakeCache({hash_token(raw): key})) + + principal = await store.resolve(hashed_token=hash_token(raw)) + recovered = IdentityStore.key_from_principal(principal) + + assert recovered.user_id == "u-1" + assert recovered.team_id == "t-1" + + +def test_key_from_principal_raises_when_no_source_key_is_carried(): + bare = Principal( + principal_type=PrincipalType.SERVICE_ACCOUNT, + subject="svc", + auth_method=AuthMethod.API_KEY, + ) + with pytest.raises(PrincipalMissingSourceKeyError): + IdentityStore.key_from_principal(bare) + + +async def test_resolve_raises_without_a_db_connection(): + store = IdentityStore(None, _FakeCache()) + with pytest.raises(NoDatabaseConnectionError): + await store.resolve(hashed_token="missing") diff --git a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py index 80f12d4459f..7219ab58799 100644 --- a/tests/test_litellm/proxy/auth/test_user_api_key_auth.py +++ b/tests/test_litellm/proxy/auth/test_user_api_key_auth.py @@ -1106,7 +1106,7 @@ async def test_proxy_admin_expired_key_from_cache(): # Mock get_key_object to return expired token from cache with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, ) as mock_get_key_object, patch( @@ -1261,7 +1261,7 @@ async def test_scim_deactivated_user_key_is_rejected(): with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, return_value=valid_token, ), @@ -2484,7 +2484,7 @@ async def test_user_api_key_auth_builder_no_blocking_calls(): stack.enter_context(p) stack.enter_context( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, return_value=valid_token, ) @@ -2598,7 +2598,7 @@ async def test_team_metadata_refreshed_from_team_object_during_auth(): with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", new_callable=AsyncMock, return_value=valid_token, ), @@ -2689,6 +2689,67 @@ async def test_centralized_common_checks_runs_for_standard_auth(): setattr(_proxy_server_mod, k, v) +@pytest.mark.asyncio +@pytest.mark.parametrize( + "route", + [ + "/bedrock/model/us.anthropic.claude-sonnet-4-6/invoke", + "/v1/messages", + ], +) +async def test_centralized_common_checks_routes_header_tags_to_litellm_metadata(route): + """GH#30629: on LITELLM_METADATA_ROUTES the tag-budget read resolves to + litellm_metadata, so the litellm_metadata pre-seed must run before + apply_client_tag_policy_pre_auth merges x-litellm-tags. Otherwise header tags + land in metadata and silently escape _tag_max_budget_check. This guards the + pre-seed call site in _run_centralized_common_checks; dropping it routes header + tags back into metadata. + """ + import litellm.proxy.proxy_server as _proxy_server_mod + from fastapi import Request + from starlette.datastructures import URL + + token = UserAPIKeyAuth(api_key="sk-test", user_id="u1") + request = Request( + scope={ + "type": "http", + "method": "POST", + "headers": [(b"x-litellm-tags", b"tenant:acme")], + "query_string": b"", + } + ) + request._url = URL(url=route) + request_data: dict = {"model": "us.anthropic.claude-sonnet-4-6"} + + attrs = _proxy_attrs_for_centralized_checks(user_custom_auth=None) + originals = {a: getattr(_proxy_server_mod, a, None) for a in attrs} + try: + for k, v in attrs.items(): + setattr(_proxy_server_mod, k, v) + with ( + patch( + "litellm.proxy.auth.user_api_key_auth.common_checks", + new_callable=AsyncMock, + ), + patch( + "litellm.proxy.auth.user_api_key_auth._reserve_budget_after_common_checks", + new_callable=AsyncMock, + ), + ): + await _run_centralized_common_checks( + user_api_key_auth_obj=token, + request=request, + request_data=request_data, + route=route, + ) + finally: + for k, v in originals.items(): + setattr(_proxy_server_mod, k, v) + + assert request_data["litellm_metadata"]["tags"] == ["tenant:acme"] + assert "metadata" not in request_data + + @pytest.mark.asyncio async def test_centralized_common_checks_skipped_for_custom_auth_without_flag(): """Existing RPS guarantee: custom-auth deployments without @@ -3652,7 +3713,7 @@ async def _run_builder_with_key_lookup(get_key_object_mock): request._url = URL(url="/chat/completions") with ( patch( - "litellm.proxy.auth.user_api_key_auth.get_key_object", + "litellm.proxy.auth.resolvers.store.IdentityStore._resolve_key", get_key_object_mock, ), patch( diff --git a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py index 21d41e0992b..ad0e7010325 100644 --- a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py +++ b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_transformations.py @@ -13,7 +13,10 @@ from litellm.proxy.management_endpoints.scim.scim_transformations import ( ScimTransformations, ) from litellm.types.proxy.management_endpoints.scim_v2 import ( + SCIM_ENTERPRISE_USER_SCHEMA, + SCIMEnterpriseUser, SCIMPatchOperation, + SCIMUser, ) @@ -149,6 +152,77 @@ class TestScimTransformations: assert scim_user.name.givenName == "Test" assert scim_user.name.familyName == "User" + @pytest.mark.asyncio + async def test_transform_user_with_enterprise_metadata(self, mock_prisma_client): + mock_client, mock_find_unique = mock_prisma_client + mock_find_unique.return_value = None + + user = LiteLLM_UserTable( + user_id="user-ent", + user_email="ent@example.com", + user_alias=None, + teams=[], + created_at=None, + updated_at=None, + metadata={ + "scim_enterprise": {"costCenter": "CC-42", "department": "Platform"} + }, + ) + + with patch("litellm.proxy.proxy_server.prisma_client", mock_client): + scim_user = await ScimTransformations.transform_litellm_user_to_scim_user( + user + ) + + assert scim_user.enterprise_user is not None + assert scim_user.enterprise_user.costCenter == "CC-42" + assert scim_user.enterprise_user.department == "Platform" + assert SCIM_ENTERPRISE_USER_SCHEMA in scim_user.schemas + + @pytest.mark.asyncio + async def test_transform_user_without_enterprise_metadata_omits_schema( + self, mock_user, mock_prisma_client + ): + mock_client, mock_find_unique = mock_prisma_client + team1 = LiteLLM_TeamTable( + team_id="team-1", team_alias="Team One", members_with_roles=[] + ) + team2 = LiteLLM_TeamTable( + team_id="team-2", team_alias="Team Two", members_with_roles=[] + ) + mock_find_unique.side_effect = [team1, team2] + + with patch("litellm.proxy.proxy_server.prisma_client", mock_client): + scim_user = await ScimTransformations.transform_litellm_user_to_scim_user( + mock_user + ) + + assert scim_user.enterprise_user is None + assert SCIM_ENTERPRISE_USER_SCHEMA not in scim_user.schemas + + def test_scim_user_serialization_omits_absent_enterprise_urn(self): + without_enterprise = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + id="user-1", + userName="user@example.com", + ) + dumped = without_enterprise.model_dump(by_alias=True) + assert SCIM_ENTERPRISE_USER_SCHEMA not in dumped + assert "enterprise_user" not in dumped + assert SCIM_ENTERPRISE_USER_SCHEMA not in dumped["schemas"] + + with_enterprise = SCIMUser( + schemas=[ + "urn:ietf:params:scim:schemas:core:2.0:User", + SCIM_ENTERPRISE_USER_SCHEMA, + ], + id="user-2", + userName="ent@example.com", + enterprise_user=SCIMEnterpriseUser(costCenter="CC-42"), + ) + dumped_ent = with_enterprise.model_dump(by_alias=True) + assert dumped_ent[SCIM_ENTERPRISE_USER_SCHEMA]["costCenter"] == "CC-42" + @pytest.mark.asyncio async def test_transform_litellm_team_to_scim_group( self, mock_team, mock_prisma_client diff --git a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py index ad893012807..7f5aee51f51 100644 --- a/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/scim/test_scim_v2_endpoints.py @@ -15,15 +15,19 @@ from litellm.proxy.management_endpoints.scim.scim_v2 import ( _extract_group_member_ids, _handle_team_membership_changes, _process_group_patch_operations, + _recompute_scim_member_roles, create_group, create_user, + delete_group, get_users, get_service_provider_config, + patch_group, patch_user, update_group, update_user, ) from litellm.types.proxy.management_endpoints.scim_v2 import ( + SCIM_ENTERPRISE_USER_SCHEMA, SCIMGroup, SCIMMember, SCIMPatchOp, @@ -115,6 +119,59 @@ async def test_create_user_defaults_to_viewer(mocker, monkeypatch): assert called_args.user_role == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY +@pytest.mark.asyncio +async def test_create_user_ingests_enterprise_extension(mocker, monkeypatch): + """A SCIM create payload carrying the enterprise extension block should land + in the created user's metadata under scim_enterprise""" + + scim_user = SCIMUser.model_validate( + { + "schemas": [ + "urn:ietf:params:scim:schemas:core:2.0:User", + SCIM_ENTERPRISE_USER_SCHEMA, + ], + "userName": "ent-user", + "name": {"familyName": "User", "givenName": "Ent"}, + "emails": [{"value": "ent@example.com"}], + SCIM_ENTERPRISE_USER_SCHEMA: { + "costCenter": "CC-42", + "department": "Platform", + }, + } + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) + + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + + new_user_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.new_user", + AsyncMock(return_value=NewUserRequest(user_id="ent-user")), + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await create_user(user=scim_user) + + created_metadata = new_user_mock.call_args.kwargs["data"].metadata + assert created_metadata["scim_enterprise"] == { + "costCenter": "CC-42", + "department": "Platform", + } + + @pytest.mark.asyncio async def test_create_user_uses_default_internal_user_params_role(mocker, monkeypatch): """If role is set in default_internal_user_params, new user should use that role""" @@ -1720,3 +1777,1015 @@ async def test_process_group_patch_operations_with_flag_false_rejects( assert exc_info.value.status_code == 400 assert "does not exist" in str(exc_info.value.detail) assert "new-user-1" in str(exc_info.value.detail) + + +@pytest.mark.asyncio +async def test_create_user_grants_admin_when_in_scim_admin_group(mocker, monkeypatch): + """When scim_admin_group is configured and a created user's groups include it, + the user is provisioned as PROXY_ADMIN.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + scim_user = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="new-admin", + emails=[SCIMUserEmail(value="new-admin@example.com")], + groups=[SCIMUserGroup(value="litellm-admins", display="LiteLLM Admins")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + new_user_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.new_user", + AsyncMock(return_value=NewUserRequest(user_id="new-admin")), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await create_user(user=scim_user) + + called_args = new_user_mock.call_args.kwargs["data"] + assert called_args.user_role == LitellmUserRoles.PROXY_ADMIN + + +@pytest.mark.asyncio +async def test_create_user_keeps_default_when_not_in_scim_admin_group( + mocker, monkeypatch +): + """When scim_admin_group is configured but the user's groups don't include it, + the user keeps the non-admin default role.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + scim_user = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="regular-user", + emails=[SCIMUserEmail(value="regular@example.com")], + groups=[SCIMUserGroup(value="engineering", display="Engineering")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock(return_value=None) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + new_user_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.new_user", + AsyncMock(return_value=NewUserRequest(user_id="regular-user")), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await create_user(user=scim_user) + + called_args = new_user_mock.call_args.kwargs["data"] + assert called_args.user_role == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_update_user_demotes_admin_when_removed_from_scim_admin_group( + mocker, monkeypatch +): + """Core demotion test: a PUT whose new groups no longer include the configured + admin group must re-evaluate the role and write the non-admin default, so an + admin removed from the IdP group is demoted without re-login.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + existing_user = mocker.MagicMock() + existing_user.teams = ["litellm-admins"] + existing_user.metadata = {} + + updated_user = { + "user_id": "demote-me", + "user_email": "demote@example.com", + "user_alias": None, + "teams": ["engineering"], + "metadata": "{}", + } + + scim_user = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="demote-me", + emails=[SCIMUserEmail(value="demote@example.com")], + groups=[SCIMUserGroup(value="engineering", display="Engineering")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value=updated_user + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._check_user_exists", + AsyncMock(return_value=existing_user), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._handle_team_membership_changes", + AsyncMock(), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await update_user(user_id="demote-me", user=scim_user) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_update_user_does_not_force_role_when_scim_admin_group_unset( + mocker, monkeypatch +): + """When scim_admin_group is unset, PUT must not touch user_role (current + behavior preserved).""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + existing_user = mocker.MagicMock() + existing_user.teams = ["litellm-admins"] + existing_user.metadata = {} + + updated_user = { + "user_id": "no-touch", + "user_email": "no-touch@example.com", + "user_alias": None, + "teams": ["litellm-admins"], + "metadata": "{}", + } + + scim_user = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="no-touch", + emails=[SCIMUserEmail(value="no-touch@example.com")], + groups=[SCIMUserGroup(value="litellm-admins", display="LiteLLM Admins")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value=updated_user + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._check_user_exists", + AsyncMock(return_value=existing_user), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._handle_team_membership_changes", + AsyncMock(), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await update_user(user_id="no-touch", user=scim_user) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert "user_role" not in call_args[1]["data"] + + +@pytest.mark.asyncio +async def test_update_user_demotes_when_default_params_lack_user_role( + mocker, monkeypatch +): + """Regression: default_internal_user_params set without a user_role key must + still resolve to the non-admin default on demotion, not silently skip and + leave the user PROXY_ADMIN.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr( + "litellm.default_internal_user_params", {"max_budget": 10}, raising=False + ) + + existing_user = mocker.MagicMock() + existing_user.teams = ["litellm-admins"] + existing_user.metadata = {} + + updated_user = { + "user_id": "demote-me", + "user_email": "demote@example.com", + "user_alias": None, + "teams": ["engineering"], + "metadata": "{}", + } + + scim_user = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="demote-me", + emails=[SCIMUserEmail(value="demote@example.com")], + groups=[SCIMUserGroup(value="engineering", display="Engineering")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value=updated_user + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._check_user_exists", + AsyncMock(return_value=existing_user), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._handle_team_membership_changes", + AsyncMock(), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await update_user(user_id="demote-me", user=scim_user) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_patch_user_demotes_admin_when_removed_from_scim_admin_group( + mocker, monkeypatch +): + """PATCH that drops the admin team from the resulting team set must write the + non-admin default, mirroring the PUT demotion path.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + existing_user = mocker.MagicMock() + existing_user.teams = ["litellm-admins"] + existing_user.metadata = {} + + patch_ops = SCIMPatchOp( + schemas=["urn:ietf:params:scim:api:messages:2.0:PatchOp"], + Operations=[ + SCIMPatchOperation( + op="replace", path="groups", value=[{"value": "engineering"}] + ) + ], + ) + + updated_user = { + "user_id": "demote-me", + "user_alias": None, + "teams": ["engineering"], + "metadata": "{}", + } + + engineering_team = mocker.MagicMock() + engineering_team.team_alias = "Engineering" + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value=updated_user + ) + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=engineering_team + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._check_user_exists", + AsyncMock(return_value=existing_user), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._handle_team_membership_changes", + AsyncMock(), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock( + return_value=SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="demote-me", + ) + ), + ) + + await patch_user(user_id="demote-me", patch_ops=patch_ops) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_patch_user_grants_admin_by_team_display_name(mocker, monkeypatch): + """PATCH carries groups as team ids, so admin-group matching must fall back to + each team's display name; an admin group configured as a human-readable alias + grants PROXY_ADMIN even when the team id differs.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "LiteLLM Admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + existing_user = mocker.MagicMock() + existing_user.teams = [] + existing_user.metadata = {} + + patch_ops = SCIMPatchOp( + schemas=["urn:ietf:params:scim:api:messages:2.0:PatchOp"], + Operations=[ + SCIMPatchOperation( + op="replace", path="groups", value=[{"value": "team-abc-123"}] + ) + ], + ) + + updated_user = { + "user_id": "promote-me", + "user_alias": None, + "teams": ["team-abc-123"], + "metadata": "{}", + } + + admin_team = mocker.MagicMock() + admin_team.team_alias = "LiteLLM Admins" + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value=updated_user + ) + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=admin_team + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._check_user_exists", + AsyncMock(return_value=existing_user), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._handle_team_membership_changes", + AsyncMock(), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock( + return_value=SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="promote-me", + ) + ), + ) + + await patch_user(user_id="promote-me", patch_ops=patch_ops) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.PROXY_ADMIN + + +def _scim_admin_prisma(mocker, *, user_teams): + """Prisma double whose user resolves to user_teams and whose teams expose an + alias equal to their id, used by the role-recompute helper tests.""" + user = mocker.MagicMock() + user.user_id = "member-1" + user.teams = user_teams + + def _team_find_unique(where): + team = mocker.MagicMock() + team.team_alias = where["team_id"] + return team + + prisma = mocker.MagicMock() + prisma.db = mocker.MagicMock() + prisma.db.litellm_usertable = mocker.MagicMock() + prisma.db.litellm_usertable.find_unique = AsyncMock(return_value=user) + prisma.db.litellm_usertable.update = AsyncMock(return_value=user) + prisma.db.litellm_teamtable = mocker.MagicMock() + prisma.db.litellm_teamtable.find_unique = AsyncMock(side_effect=_team_find_unique) + return prisma + + +@pytest.mark.asyncio +async def test_recompute_scim_member_roles_demotes_when_not_in_admin_group( + mocker, monkeypatch +): + """The shared recompute helper writes the non-admin default for a member whose + resulting teams no longer include the configured admin group.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + prisma = _scim_admin_prisma(mocker, user_teams=["engineering"]) + + await _recompute_scim_member_roles(prisma, ["member-1"]) + + call_args = prisma.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_recompute_scim_member_roles_grants_when_in_admin_group( + mocker, monkeypatch +): + """The shared recompute helper grants PROXY_ADMIN when a member's resulting + teams include the configured admin group.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + prisma = _scim_admin_prisma(mocker, user_teams=["litellm-admins"]) + + await _recompute_scim_member_roles(prisma, ["member-1"]) + + call_args = prisma.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.PROXY_ADMIN + + +@pytest.mark.asyncio +async def test_recompute_scim_member_roles_noop_when_admin_group_unset( + mocker, monkeypatch +): + """With scim_admin_group unset the recompute helper must not touch any role, + preserving current behavior for SCIM group writes.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + + prisma = _scim_admin_prisma(mocker, user_teams=["litellm-admins"]) + + await _recompute_scim_member_roles(prisma, ["member-1"]) + + prisma.db.litellm_usertable.update.assert_not_called() + + +@pytest.mark.asyncio +async def test_update_group_recomputes_roles_for_changed_members(mocker): + """PUT /Groups must recompute the global role for every member whose + membership changed, so an admin dropped from the admin group is demoted.""" + from litellm.proxy._types import LiteLLM_TeamTable, Member + from litellm.proxy.management_endpoints.scim.scim_transformations import ( + ScimTransformations, + ) + + group_id = "test-team-123" + existing_team = LiteLLM_TeamTable( + team_id=group_id, + team_alias="Admins", + members=["user1", "user2"], + members_with_roles=[ + Member(user_id="user1", role="user"), + Member(user_id="user2", role="user"), + ], + metadata={}, + ) + scim_group_update = SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="Admins", + members=[SCIMMember(value="user2"), SCIMMember(value="user3")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_teamtable.update = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=mocker.MagicMock() + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.patch_team_membership", + AsyncMock(), + ) + recompute_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._recompute_scim_member_roles", + AsyncMock(), + ) + mocker.patch.object( + ScimTransformations, + "transform_litellm_team_to_scim_group", + AsyncMock(return_value=scim_group_update), + ) + + await update_group(group_id=group_id, group=scim_group_update) + + recompute_mock.assert_awaited_once() + assert set(recompute_mock.call_args[0][1]) == {"user1", "user3"} + + +@pytest.mark.asyncio +async def test_patch_group_recomputes_roles_for_changed_members(mocker): + """PATCH /Groups must recompute the global role for every member whose + membership changed, mirroring the PUT path.""" + from litellm.proxy._types import LiteLLM_TeamTable, Member + from litellm.proxy.management_endpoints.scim.scim_transformations import ( + ScimTransformations, + ) + + group_id = "test-team-123" + existing_team = LiteLLM_TeamTable( + team_id=group_id, + team_alias="Admins", + members=["user1", "user2"], + members_with_roles=[ + Member(user_id="user1", role="user"), + Member(user_id="user2", role="user"), + ], + metadata={}, + ) + patch_ops = SCIMPatchOp( + schemas=["urn:ietf:params:scim:api:messages:2.0:PatchOp"], + Operations=[ + SCIMPatchOperation(op="remove", path="members", value=[{"value": "user1"}]) + ], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_teamtable.update = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=mocker.MagicMock() + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.patch_team_membership", + AsyncMock(), + ) + recompute_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._recompute_scim_member_roles", + AsyncMock(), + ) + mocker.patch.object( + ScimTransformations, + "transform_litellm_team_to_scim_group", + AsyncMock( + return_value=SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="Admins", + ) + ), + ) + + await patch_group(group_id=group_id, patch_ops=patch_ops) + + recompute_mock.assert_awaited_once() + assert set(recompute_mock.call_args[0][1]) == {"user1"} + + +@pytest.mark.asyncio +async def test_delete_group_recomputes_roles_for_members(mocker): + """DELETE /Groups must recompute the global role for the team's members, so + deleting the admin group demotes everyone who was only admin through it.""" + from litellm.proxy._types import Member + + existing_team = mocker.MagicMock() + existing_team.members_with_roles = [ + Member(user_id="user1", role="user"), + Member(user_id="user2", role="user"), + ] + + member = mocker.MagicMock() + member.teams = ["test-team-123"] + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_teamtable.delete = AsyncMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=member) + mock_prisma_client.db.litellm_usertable.update = AsyncMock() + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + recompute_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._recompute_scim_member_roles", + AsyncMock(), + ) + + await delete_group(group_id="test-team-123") + + recompute_mock.assert_awaited_once() + assert list(recompute_mock.call_args[0][1]) == ["user1", "user2"] + + +@pytest.mark.asyncio +async def test_handle_existing_user_by_email_applies_role_when_admin_group_set(mocker): + """When admin_group is configured, re-upserting an existing email persists the + resolved role so a now-non-admin user can't keep a stale PROXY_ADMIN.""" + existing_user = mocker.MagicMock() + existing_user.user_id = "old-user-id" + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock( + return_value=existing_user + ) + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value={"user_id": "new-user-id"} + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=mocker.MagicMock()), + ) + + new_user_request = NewUserRequest( + user_id="new-user-id", + user_email="test@example.com", + teams=["engineering"], + metadata={}, + auto_create_key=False, + user_role=LitellmUserRoles.INTERNAL_USER_VIEW_ONLY, + ) + + await UserProvisionerHelpers.handle_existing_user_by_email( + prisma_client=mock_prisma_client, + new_user_request=new_user_request, + admin_group="litellm-admins", + ) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_handle_existing_user_by_email_leaves_role_when_admin_group_unset(mocker): + """With admin_group unset, the existing-email upsert must not write user_role, + preserving current behavior when the feature is off.""" + existing_user = mocker.MagicMock() + existing_user.user_id = "old-user-id" + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock( + return_value=existing_user + ) + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value={"user_id": "new-user-id"} + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=mocker.MagicMock()), + ) + + new_user_request = NewUserRequest( + user_id="new-user-id", + user_email="test@example.com", + teams=["engineering"], + metadata={}, + auto_create_key=False, + user_role=LitellmUserRoles.PROXY_ADMIN, + ) + + await UserProvisionerHelpers.handle_existing_user_by_email( + prisma_client=mock_prisma_client, + new_user_request=new_user_request, + ) + + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert "user_role" not in call_args[1]["data"] + + +@pytest.mark.asyncio +async def test_create_user_existing_email_upsert_demotes_when_admin_group_set( + mocker, monkeypatch +): + """End-to-end create wiring: a SCIM POST that upserts an existing email while + the user is not in the admin group must write the non-admin default, not leave + a stale PROXY_ADMIN.""" + from litellm.proxy.proxy_server import proxy_config + + async def mock_get_config(): + return {"litellm_settings": {"scim_admin_group": "litellm-admins"}} + + monkeypatch.setattr(proxy_config, "get_config", mock_get_config) + monkeypatch.setattr("litellm.default_internal_user_params", None, raising=False) + + scim_user = SCIMUser( + schemas=["urn:ietf:params:scim:schemas:core:2.0:User"], + userName="returning-user", + emails=[SCIMUserEmail(value="returning@example.com")], + groups=[SCIMUserGroup(value="engineering", display="Engineering")], + ) + + existing_user = mocker.MagicMock() + existing_user.user_id = "returning-user" + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock(return_value=None) + mock_prisma_client.db.litellm_usertable.find_first = AsyncMock( + return_value=existing_user + ) + mock_prisma_client.db.litellm_usertable.update = AsyncMock( + return_value={"user_id": "returning-user"} + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + new_user_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.new_user", + AsyncMock(return_value=NewUserRequest(user_id="returning-user")), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.ScimTransformations.transform_litellm_user_to_scim_user", + AsyncMock(return_value=scim_user), + ) + + await create_user(user=scim_user) + + new_user_mock.assert_not_called() + call_args = mock_prisma_client.db.litellm_usertable.update.call_args + assert call_args[1]["data"]["user_role"] == LitellmUserRoles.INTERNAL_USER_VIEW_ONLY + + +@pytest.mark.asyncio +async def test_create_group_recomputes_roles_for_members(mocker): + """POST /Groups must recompute the global role for the new team's members, so a + team created with the admin-group display name elevates its members.""" + from litellm.proxy.management_endpoints.scim.scim_transformations import ( + ScimTransformations, + ) + + group_id = "admin-team-1" + scim_group = SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="LiteLLM Admins", + members=[SCIMMember(value="user1"), SCIMMember(value="user2")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock(return_value=None) + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=mocker.MagicMock() + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.new_team", + AsyncMock(return_value=mocker.MagicMock()), + ) + recompute_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._recompute_scim_member_roles", + AsyncMock(), + ) + mocker.patch.object( + ScimTransformations, + "transform_litellm_team_to_scim_group", + AsyncMock(return_value=scim_group), + ) + + await create_group(group=scim_group) + + recompute_mock.assert_awaited_once() + assert set(recompute_mock.call_args[0][1]) == {"user1", "user2"} + + +@pytest.mark.asyncio +async def test_update_group_rename_recomputes_retained_members(mocker): + """A PUT that renames the group (alias changes) but leaves membership unchanged + must still recompute retained members, since a rename can flip whether the + group matches scim_admin_group by display name.""" + from litellm.proxy._types import LiteLLM_TeamTable, Member + from litellm.proxy.management_endpoints.scim.scim_transformations import ( + ScimTransformations, + ) + + group_id = "test-team-123" + existing_team = LiteLLM_TeamTable( + team_id=group_id, + team_alias="LiteLLM Admins", + members=["user1"], + members_with_roles=[Member(user_id="user1", role="user")], + metadata={}, + ) + scim_group_update = SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="Engineering", + members=[SCIMMember(value="user1")], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_teamtable.update = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=mocker.MagicMock() + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.patch_team_membership", + AsyncMock(), + ) + recompute_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._recompute_scim_member_roles", + AsyncMock(), + ) + mocker.patch.object( + ScimTransformations, + "transform_litellm_team_to_scim_group", + AsyncMock(return_value=scim_group_update), + ) + + await update_group(group_id=group_id, group=scim_group_update) + + recompute_mock.assert_awaited_once() + assert set(recompute_mock.call_args[0][1]) == {"user1"} + + +@pytest.mark.asyncio +async def test_patch_group_rename_recomputes_retained_members(mocker): + """A PATCH that renames the group (displayName op) but leaves membership + unchanged must still recompute retained members, mirroring the PUT path.""" + from litellm.proxy._types import LiteLLM_TeamTable, Member + from litellm.proxy.management_endpoints.scim.scim_transformations import ( + ScimTransformations, + ) + + group_id = "test-team-123" + existing_team = LiteLLM_TeamTable( + team_id=group_id, + team_alias="LiteLLM Admins", + members=["user1"], + members_with_roles=[Member(user_id="user1", role="user")], + metadata={}, + ) + patch_ops = SCIMPatchOp( + schemas=["urn:ietf:params:scim:api:messages:2.0:PatchOp"], + Operations=[ + SCIMPatchOperation(op="replace", path="displayName", value="Engineering") + ], + ) + + mock_prisma_client = mocker.MagicMock() + mock_prisma_client.db = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable = mocker.MagicMock() + mock_prisma_client.db.litellm_teamtable.find_unique = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_teamtable.update = AsyncMock( + return_value=existing_team + ) + mock_prisma_client.db.litellm_usertable = mocker.MagicMock() + mock_prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=mocker.MagicMock() + ) + + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._get_prisma_client_or_raise_exception", + AsyncMock(return_value=mock_prisma_client), + ) + mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2.patch_team_membership", + AsyncMock(), + ) + recompute_mock = mocker.patch( + "litellm.proxy.management_endpoints.scim.scim_v2._recompute_scim_member_roles", + AsyncMock(), + ) + mocker.patch.object( + ScimTransformations, + "transform_litellm_team_to_scim_group", + AsyncMock( + return_value=SCIMGroup( + schemas=["urn:ietf:params:scim:schemas:core:2.0:Group"], + id=group_id, + displayName="Engineering", + ) + ), + ) + + await patch_group(group_id=group_id, patch_ops=patch_ops) + + recompute_mock.assert_awaited_once() + assert set(recompute_mock.call_args[0][1]) == {"user1"} diff --git a/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py b/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py index 2d2c18bb46e..b882090e8f1 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py +++ b/tests/test_litellm/proxy/management_endpoints/test_common_daily_activity.py @@ -638,6 +638,84 @@ async def test_aggregated_activity_preserves_metadata_for_deleted_keys(): assert key_data.metrics.spend == 10.0 +def _daily_user_spend_record(*, user_id, api_key, spend): + """A LiteLLM_DailyUserSpend row as the per-user breakdown reads it.""" + return SimpleNamespace( + date="2024-01-01", + user_id=user_id, + api_key=api_key, + model="gpt-4", + model_group="gpt-4", + custom_llm_provider="openai", + mcp_namespaced_tool_name=None, + endpoint="/chat/completions", + spend=spend, + prompt_tokens=10, + completion_tokens=5, + cache_read_input_tokens=0, + cache_creation_input_tokens=0, + api_requests=1, + successful_requests=1, + failed_requests=0, + ) + + +@pytest.mark.asyncio +async def test_get_daily_activity_applies_resolve_entity_metadata_to_breakdown(): + """Regression for LIT-3889: the Spend Per User chart showed raw UUIDs. + + /user/daily/activity used to pass entity_metadata_field=None, so every + user entity in the breakdown carried empty metadata and the dashboard had + nothing to render but the user_id UUID. The page-scoped resolver must put + the resolved email/alias onto the entity metadata so the UI can label it, + while a spender with no email on file still falls back to the raw UUID. + """ + mock_prisma = MagicMock() + mock_prisma.db = MagicMock() + + records = [ + _daily_user_spend_record(user_id="user-with-email", api_key="key-1", spend=7.0), + _daily_user_spend_record(user_id="user-no-email", api_key="key-2", spend=3.0), + ] + + mock_table = MagicMock() + mock_table.count = AsyncMock(return_value=len(records)) + mock_table.find_many = AsyncMock(return_value=records) + mock_prisma.db.litellm_dailyuserspend = mock_table + mock_prisma.db.litellm_verificationtoken = MagicMock() + mock_prisma.db.litellm_verificationtoken.find_many = AsyncMock(return_value=[]) + + seen_user_ids = {} + + async def resolver(page_records): + seen_user_ids["ids"] = {r.user_id for r in page_records} + return {"user-with-email": {"user_email": "spender@example.com"}} + + result = await get_daily_activity( + prisma_client=mock_prisma, + table_name="litellm_dailyuserspend", + entity_id_field="user_id", + entity_id=None, + entity_metadata_field=None, + start_date="2024-01-01", + end_date="2024-01-01", + model=None, + api_key=None, + page=1, + page_size=1000, + resolve_entity_metadata=resolver, + ) + + # Resolver is driven by the user_ids actually on the page + assert seen_user_ids["ids"] == {"user-with-email", "user-no-email"} + + entities = result.results[0].breakdown.entities + # Email is on the entity metadata so the UI labels the chart with it + assert entities["user-with-email"].metadata["user_email"] == "spender@example.com" + # No email on file -> empty metadata -> UI falls back to the UUID + assert entities["user-no-email"].metadata == {} + + class TestAdjustDatesForTimezone: """ Regression tests for the timezone double-counting bug. @@ -758,6 +836,8 @@ class TestBuildAggregatedSqlQuery: ] assert "model = $4" in sql assert "api_key = $5" in sql + + @pytest.mark.asyncio async def test_get_daily_activity_aggregated_empty_result_set(): """Regression test for the empty-range 500. diff --git a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py index 627958cef93..b4602e0ad8b 100644 --- a/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py +++ b/tests/test_litellm/proxy/management_endpoints/test_internal_user_endpoints.py @@ -2,6 +2,7 @@ import json import os import sys from datetime import datetime, timezone +from types import SimpleNamespace import pytest from fastapi.testclient import TestClient @@ -20,6 +21,7 @@ from litellm.proxy._types import ( ) from litellm.proxy.management_endpoints.internal_user_endpoints import ( LiteLLM_UserTableWithKeyCount, + _resolve_user_email_metadata, _update_internal_user_params, get_user_key_counts, get_users, @@ -657,6 +659,56 @@ async def test_get_users_includes_timestamps(mocker): assert user_response.key_count == 0 +@pytest.mark.asyncio +async def test_get_users_redacts_scim_enterprise_metadata(mocker): + """ + /user/list must strip scim_enterprise from each user's metadata while leaving + the rest of the metadata intact, matching the user-info endpoints. + """ + mock_prisma_client = mocker.MagicMock() + + mock_user_row = mocker.MagicMock() + mock_user_row.user_id = "listed-user" + mock_user_row.model_dump.return_value = { + "user_id": "listed-user", + "user_email": "listed@example.com", + "user_role": "internal_user", + "metadata": { + "scim_metadata": {"givenName": "Jane", "familyName": "Doe"}, + "scim_enterprise": {"costCenter": "CC-42", "department": "Platform"}, + }, + } + + async def mock_find_many(*args, **kwargs): + return [mock_user_row] + + async def mock_count(*args, **kwargs): + return 1 + + mock_prisma_client.db.litellm_usertable.find_many = mock_find_many + mock_prisma_client.db.litellm_usertable.count = mock_count + mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + async def mock_get_user_key_counts(*args, **kwargs): + return {"listed-user": 0} + + mocker.patch( + "litellm.proxy.management_endpoints.internal_user_endpoints.get_user_key_counts", + mock_get_user_key_counts, + ) + + admin_key = UserAPIKeyAuth(user_id="admin", user_role=LitellmUserRoles.PROXY_ADMIN) + response = await get_users( + page=1, page_size=1, user_api_key_dict=admin_key, organization_ids=None + ) + + listed = response["users"][0] + assert listed.metadata == { + "scim_metadata": {"givenName": "Jane", "familyName": "Doe"} + } + assert "scim_enterprise" not in (listed.metadata or {}) + + def test_validate_sort_params(): """ Test that validate_sort_params returns None if sort_by is None @@ -2167,6 +2219,94 @@ async def test_user_info_v2_proxy_admin_can_query_any_user(mocker): assert response.metadata == {"team": "engineering"} +@pytest.mark.asyncio +async def test_user_info_v2_redacts_scim_enterprise_metadata(mocker): + """ + SCIM enterprise attributes are persisted in metadata for reporting, but + /v2/user/info must not surface them; the rest of metadata is preserved. + """ + from fastapi import Request + + from litellm.proxy._types import UserInfoV2Response + from litellm.proxy.management_endpoints.internal_user_endpoints import user_info_v2 + + mock_prisma_client = mocker.MagicMock() + + mock_user_row = mocker.MagicMock() + mock_user_row.model_dump.return_value = { + "user_id": "target-user-123", + "user_email": "target@example.com", + "metadata": { + "scim_metadata": {"givenName": "Jane", "familyName": "Doe"}, + "scim_enterprise": { + "costCenter": "CC-42", + "department": "Platform", + "employeeNumber": "E-1001", + }, + }, + "teams": ["team-1"], + } + + async def mock_find_unique(*args, **kwargs): + if kwargs.get("where", {}).get("user_id") == "target-user-123": + return mock_user_row + return None + + mock_prisma_client.db.litellm_usertable.find_unique = mocker.AsyncMock( + side_effect=mock_find_unique + ) + + mocker.patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client) + + mock_request = mocker.MagicMock(spec=Request) + + admin_key = UserAPIKeyAuth( + user_id="admin-user", user_role=LitellmUserRoles.PROXY_ADMIN + ) + + response = await user_info_v2( + request=mock_request, + user_id="target-user-123", + user_api_key_dict=admin_key, + ) + + assert isinstance(response, UserInfoV2Response) + assert response.metadata == { + "scim_metadata": {"givenName": "Jane", "familyName": "Doe"} + } + assert "scim_enterprise" not in (response.metadata or {}) + + +def test_build_user_info_response_redacts_scim_enterprise_metadata(): + """ + The shared /user/info builder strips scim_enterprise from the returned user row + while leaving every other metadata key intact. + """ + from litellm.proxy.management_endpoints.internal_user_endpoints import ( + _build_user_info_response, + ) + + user_row = { + "user_id": "target-user-123", + "metadata": { + "scim_metadata": {"givenName": "Jane"}, + "scim_enterprise": {"costCenter": "CC-42"}, + }, + } + + response = _build_user_info_response( + user_id="target-user-123", + user_info=user_row, + keys=None, + team_list=[], + teams_1=None, + ) + + assert response.user_info is not None + assert response.user_info["metadata"] == {"scim_metadata": {"givenName": "Jane"}} + assert "scim_enterprise" not in response.user_info["metadata"] + + @pytest.mark.asyncio async def test_user_info_v2_internal_user_can_query_self(mocker): """ @@ -2960,3 +3100,56 @@ async def test_ghsa_wvg4_proxy_admin_can_update_user_budget(mocker): user_request=user_request, user_api_key_dict=admin_caller ) assert result is not None + + +@pytest.mark.asyncio +async def test_resolve_user_email_metadata_maps_page_user_ids_to_email(mocker): + """Regression for LIT-3889. + + The Spend Per User chart rendered raw UUIDs because the per-user activity + breakdown carried no email. This resolver must turn the user_ids on the + page into {user_id: {user_email, user_alias}} so the chart can label each + spender, and it must only look up the user_ids actually present (not the + whole user table). + """ + + mock_prisma_client = mocker.MagicMock() + find_many = mocker.AsyncMock( + return_value=[ + SimpleNamespace( + user_id="u1", user_email="alice@example.com", user_alias="Alice" + ), + SimpleNamespace(user_id="u2", user_email=None, user_alias="bob-alias"), + ] + ) + mock_prisma_client.db.litellm_usertable.find_many = find_many + + records = [ + SimpleNamespace(user_id="u1"), + SimpleNamespace(user_id="u1"), # duplicate -> deduped + SimpleNamespace(user_id="u2"), + ] + + result = await _resolve_user_email_metadata(mock_prisma_client, records) + + assert result == { + "u1": {"user_email": "alice@example.com", "user_alias": "Alice"}, + "u2": {"user_email": None, "user_alias": "bob-alias"}, + } + where_arg = find_many.call_args.kwargs["where"] + assert set(where_arg["user_id"]["in"]) == {"u1", "u2"} + + +@pytest.mark.asyncio +async def test_resolve_user_email_metadata_skips_db_when_no_user_ids(mocker): + """No user_ids on the page (e.g. all spend is unattributed) means no query.""" + mock_prisma_client = mocker.MagicMock() + find_many = mocker.AsyncMock(return_value=[]) + mock_prisma_client.db.litellm_usertable.find_many = find_many + + records = [SimpleNamespace(user_id=None), SimpleNamespace(user_id="")] + + result = await _resolve_user_email_metadata(mock_prisma_client, records) + + assert result == {} + find_many.assert_not_called() diff --git a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py index 362f4986c62..379e219b29b 100644 --- a/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py +++ b/tests/test_litellm/proxy/pass_through_endpoints/test_pass_through_endpoints.py @@ -2682,15 +2682,19 @@ async def test_add_litellm_data_to_request_adds_headers_to_metadata(): version="1.0", ) - # Verify headers are added to metadata for guardrails - assert "metadata" in result, "metadata should be present in result" - assert "headers" in result["metadata"], "headers should be present in metadata" + # Verify headers are added to litellm_metadata for guardrails. + # Bedrock passthrough uses litellm_metadata to prevent key-level + # tags from leaking into the provider payload (GH#30629). + assert "litellm_metadata" in result, "litellm_metadata should be present in result" + assert ( + "headers" in result["litellm_metadata"] + ), "headers should be present in litellm_metadata" assert isinstance( - result["metadata"]["headers"], dict + result["litellm_metadata"]["headers"], dict ), "headers should be a dictionary" # Verify specific headers are accessible (important for guardrails) - headers = result["metadata"]["headers"] + headers = result["litellm_metadata"]["headers"] assert ( "user-agent" in headers or "User-Agent" in headers ), "User-Agent header should be accessible in metadata" diff --git a/tests/test_litellm/proxy/proxy_server/test_routes_model_info.py b/tests/test_litellm/proxy/proxy_server/test_routes_model_info.py index 017f4bd4368..3bf14c08d14 100644 --- a/tests/test_litellm/proxy/proxy_server/test_routes_model_info.py +++ b/tests/test_litellm/proxy/proxy_server/test_routes_model_info.py @@ -9,7 +9,7 @@ Pins (PR2): from __future__ import annotations -from unittest.mock import AsyncMock, MagicMock +from unittest.mock import MagicMock import pytest @@ -128,6 +128,104 @@ def test_v1_model_info_no_model_list_error(client, auth_as, null_router, path): assert "LLM Model List not loaded" in response.text +# --------------------------------------------------------------------------- +# GET /model/info — team BYOK scoping (issue #30983) +# --------------------------------------------------------------------------- + +_BYOK_TEAM_ID = "team-abc" +_BYOK_PUBLIC_NAME = "my-byok-gpt-4" +_BYOK_INTERNAL_NAME = f"model_name_{_BYOK_TEAM_ID}_0123456789abcdef" + + +@pytest.fixture +def byok_team_router(monkeypatch): + """Router holding one team-scoped BYOK deployment for team `team-abc`. + + Mirrors how a team's own-key BYOK model lives in the router: the routing + key is an internal mangled name while the public name lives in + `model_info.team_public_model_name`. + """ + byok_deployment = { + "model_name": _BYOK_INTERNAL_NAME, + "litellm_params": {"model": "openai/gpt-4"}, + "model_info": { + "id": "byok-deployment-id", + "db_model": True, + "team_id": _BYOK_TEAM_ID, + "team_public_model_name": _BYOK_PUBLIC_NAME, + }, + } + + router = MagicMock() + router.model_list = [byok_deployment] + router.get_model_list_from_model_alias = MagicMock(return_value=[]) + router.get_model_names = MagicMock(return_value=[]) + router.get_model_access_groups = MagicMock(return_value={}) + router.get_model_ids = MagicMock(return_value=[]) + + monkeypatch.setattr(proxy_server, "llm_router", router) + monkeypatch.setattr(proxy_server, "llm_model_list", [byok_deployment]) + monkeypatch.setattr(proxy_server, "user_model", None) + yield router + + +@pytest.mark.parametrize("path", ["/v1/model/info", "/model/info"]) +def test_model_info_team_key_sees_own_byok_model(client, auth_as, byok_team_router, mock_prisma, monkeypatch, path): + """Regression for #30983: a team key (user_id=None) must see its own + team's BYOK model under the public name. + + Before the fix `_get_caller_byok_team_scope` keyed only off the bound + user's team memberships, returned an empty set for a team key, and the + BYOK row was dropped -> `{"data": []}`. + """ + from litellm.proxy._types import LitellmUserRoles + + monkeypatch.setattr(proxy_server, "prisma_client", mock_prisma) + mock_prisma.db.litellm_usertable.find_unique.return_value = None + + with auth_as( + role=LitellmUserRoles.INTERNAL_USER, + user_id=None, + team_id=_BYOK_TEAM_ID, + team_models=[_BYOK_PUBLIC_NAME], + ): + response = client.get(path) + + assert response.status_code == 200 + data = response.json()["data"] + surfaced_names = [m.get("model_name") for m in data] + assert _BYOK_PUBLIC_NAME in surfaced_names + assert _BYOK_INTERNAL_NAME not in surfaced_names + + +@pytest.mark.parametrize("path", ["/v1/model/info", "/model/info"]) +def test_model_info_team_key_cannot_see_other_teams_byok_model( + client, auth_as, byok_team_router, mock_prisma, monkeypatch, path +): + """A team key for a different team must NOT see team-abc's BYOK row. + + Guards the fix from over-broadening into a cross-team metadata leak. + """ + from litellm.proxy._types import LitellmUserRoles + + monkeypatch.setattr(proxy_server, "prisma_client", mock_prisma) + mock_prisma.db.litellm_usertable.find_unique.return_value = None + + with auth_as( + role=LitellmUserRoles.INTERNAL_USER, + user_id=None, + team_id="other-team", + team_models=[_BYOK_PUBLIC_NAME], + ): + response = client.get(path) + + assert response.status_code == 200 + data = response.json()["data"] + surfaced_names = [m.get("model_name") for m in data] + assert _BYOK_PUBLIC_NAME not in surfaced_names + assert _BYOK_INTERNAL_NAME not in surfaced_names + + # --------------------------------------------------------------------------- # GET /model_group/info # --------------------------------------------------------------------------- diff --git a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py index 40d590132aa..34405f20727 100644 --- a/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py +++ b/tests/test_litellm/proxy/proxy_server/test_team_model_name_translation.py @@ -316,8 +316,10 @@ async def test_model_info_v1_unrestricted_key_hides_other_team_byok(monkeypatch) @pytest.mark.asyncio async def test_model_info_v1_service_key_hides_all_team_byok(monkeypatch): - """A key without a resolvable user (e.g. CI/service token) sees only - global deployments, never any team-scoped BYOK rows.""" + """A key with no resolvable user and no team (e.g. a CI/service token + created outside any team) sees only global deployments, never team-scoped + BYOK rows. A team-scoped key does see its own team's rows (issue #30983), + pinned by the /model/info route tests.""" team_row = _team_row() other_team_row = _other_team_row() global_row = { @@ -343,7 +345,7 @@ async def test_model_info_v1_service_key_hides_all_team_byok(monkeypatch): caller = UserAPIKeyAuth( user_id=None, user_role=LitellmUserRoles.INTERNAL_USER, - team_id="team-abc-123", + team_id=None, models=[], team_models=[], ) @@ -352,6 +354,106 @@ async def test_model_info_v1_service_key_hides_all_team_byok(monkeypatch): assert [m["model_info"]["id"] for m in resp["data"]] == ["global-id-1"] +@pytest.mark.asyncio +@pytest.mark.parametrize( + "find_unique", + [ + AsyncMock(return_value=MagicMock(teams=[])), + AsyncMock(return_value=None), + AsyncMock(side_effect=RuntimeError("db down")), + ], + ids=["user-not-in-team", "user-row-missing", "user-lookup-error"], +) +async def test_model_info_v1_team_key_sees_own_byok_regardless_of_user_lookup( + monkeypatch, find_unique +): + """A team-scoped key sees its own team's BYOK rows even when the bound user + is not a member of that team, has no DB row, or the lookup errors; the + key's team_id is authoritative (issue #30983). Other teams' rows stay + hidden.""" + global_row = { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + "model_info": {"id": "global-id-1", "db_model": False}, + } + router = MagicMock() + router.model_list = [_team_row(), _other_team_row(), global_row] + router.get_model_names.return_value = ["gpt-4"] + router.get_model_access_groups.return_value = {} + + prisma_client = MagicMock() + prisma_client.db.litellm_usertable.find_unique = find_unique + + async def _populate(**kwargs): + return kwargs["all_models"] + + monkeypatch.setattr(ps, "user_model", None) + monkeypatch.setattr(ps, "llm_model_list", router.model_list) + monkeypatch.setattr(ps, "llm_router", router) + monkeypatch.setattr(ps, "prisma_client", prisma_client) + monkeypatch.setattr(ps, "_populate_team_access_on_models", _populate) + monkeypatch.setattr( + ps, "_enrich_model_info_with_litellm_data", lambda model, **kw: model + ) + + caller = UserAPIKeyAuth( + user_id="user-1", + user_role=LitellmUserRoles.INTERNAL_USER, + team_id="team-abc-123", + models=[], + team_models=[], + ) + resp = await ps.model_info_v1(user_api_key_dict=caller, litellm_model_id=None) + + assert [m["model_info"]["id"] for m in resp["data"]] == ["byok-id-1", "global-id-1"] + + +@pytest.mark.asyncio +async def test_model_info_v1_user_team_membership_grants_byok(monkeypatch): + """A user's own team memberships still grant that team's BYOK rows, unioned + with any team the key itself is scoped to.""" + global_row = { + "model_name": "gpt-4", + "litellm_params": {"model": "gpt-4"}, + "model_info": {"id": "global-id-1", "db_model": False}, + } + router = MagicMock() + router.model_list = [_team_row(), _other_team_row(), global_row] + router.get_model_names.return_value = ["gpt-4"] + router.get_model_access_groups.return_value = {} + + prisma_client = MagicMock() + prisma_client.db.litellm_usertable.find_unique = AsyncMock( + return_value=MagicMock(teams=["team-other"]) + ) + + async def _populate(**kwargs): + return kwargs["all_models"] + + monkeypatch.setattr(ps, "user_model", None) + monkeypatch.setattr(ps, "llm_model_list", router.model_list) + monkeypatch.setattr(ps, "llm_router", router) + monkeypatch.setattr(ps, "prisma_client", prisma_client) + monkeypatch.setattr(ps, "_populate_team_access_on_models", _populate) + monkeypatch.setattr( + ps, "_enrich_model_info_with_litellm_data", lambda model, **kw: model + ) + + caller = UserAPIKeyAuth( + user_id="user-2", + user_role=LitellmUserRoles.INTERNAL_USER, + team_id=None, + models=[], + team_models=[], + ) + resp = await ps.model_info_v1(user_api_key_dict=caller, litellm_model_id=None) + + assert [m["model_info"]["id"] for m in resp["data"]] == [ + "byok-id-other", + "global-id-1", + ] + + @pytest.mark.asyncio async def test_model_info_v1_populates_access_via_team_ids(monkeypatch): """`/v1/model/info` must populate access_via_team_ids when the DB is connected.""" diff --git a/tests/test_litellm/proxy/public_endpoints/test_public_endpoints.py b/tests/test_litellm/proxy/public_endpoints/test_public_endpoints.py index 48d0b1deadd..1ca28e1c4e8 100644 --- a/tests/test_litellm/proxy/public_endpoints/test_public_endpoints.py +++ b/tests/test_litellm/proxy/public_endpoints/test_public_endpoints.py @@ -819,3 +819,44 @@ def test_public_mcp_hub_returns_empty_when_whitelist_unset(): assert response.status_code == 200 assert response.json() == [] app.dependency_overrides.clear() + + +def test_public_mcp_hub_does_not_expose_upstream_url(): + """Regression: /public/mcp_hub is unauthenticated, so the gateway-internal + upstream url must never appear in its response even when the server has one.""" + from litellm.types.mcp_server.mcp_server_manager import MCPServer + from litellm.proxy._types import MCPTransport + + app = FastAPI() + app.include_router(router) + app.dependency_overrides[user_api_key_auth] = lambda: MagicMock() + client = TestClient(app) + + secret_url = "https://internal-only.example.com/mcp" + server = MCPServer( + server_id="listed", + name="listed", + server_name="listed", + url=secret_url, + transport=MCPTransport.http, + available_on_public_internet=True, + ) + + mock_manager = MagicMock() + mock_manager.get_public_mcp_servers.return_value = [server] + + with ( + patch("litellm.public_mcp_servers", ["listed"]), + patch( + "litellm.proxy._experimental.mcp_server.mcp_server_manager.global_mcp_server_manager", + mock_manager, + ), + ): + response = client.get("/public/mcp_hub") + + assert response.status_code == 200 + data = response.json() + assert [item["server_id"] for item in data] == ["listed"] + assert all("url" not in item for item in data) + assert secret_url not in response.text + app.dependency_overrides.clear() diff --git a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py index 6b692180559..74f681bb97e 100644 --- a/tests/test_litellm/proxy/test_litellm_pre_call_utils.py +++ b/tests/test_litellm/proxy/test_litellm_pre_call_utils.py @@ -96,6 +96,20 @@ class TestGetMetadataVariableName: request = self._make_request("/v1/embeddings") assert _get_metadata_variable_name(request) == "metadata" + def test_returns_litellm_metadata_for_bedrock_invoke(self): + # GH#30629: bedrock passthrough must use litellm_metadata + # to prevent key-level tags from leaking into provider body + request = self._make_request( + "/bedrock/model/us.anthropic.claude-sonnet-4-6/invoke" + ) + assert _get_metadata_variable_name(request) == "litellm_metadata" + + def test_returns_litellm_metadata_for_bedrock_converse(self): + request = self._make_request( + "/bedrock/model/us.anthropic.claude-sonnet-4-6/converse" + ) + assert _get_metadata_variable_name(request) == "litellm_metadata" + def test_get_enforced_params_for_service_account_settings(): """ @@ -718,7 +732,18 @@ async def test_add_litellm_data_to_request_strips_user_control_fields(): @pytest.mark.asyncio @pytest.mark.parametrize( "control_field", - ["callbacks", "service_callback", "logger_fn", "litellm_disabled_callbacks"], + [ + "callbacks", + "service_callback", + "logger_fn", + "litellm_disabled_callbacks", + "_agentic_loop_depth", + "_agentic_loop_fingerprints", + "_code_interpreter_interception_active", + "_code_interpreter_interception_converted_stream", + "_code_interpreter_interception_sandbox_key", + "max_agentic_loops", + ], ) async def test_add_litellm_data_to_request_strips_callback_control_fields( control_field, @@ -741,12 +766,19 @@ async def test_add_litellm_data_to_request_strips_callback_control_fields( request_mock.client = MagicMock() request_mock.client.host = "127.0.0.1" - sample_value = ( - ["langfuse"] - if control_field - in ("callbacks", "service_callback", "litellm_disabled_callbacks") - else "module.func" - ) + sample_values = { + "callbacks": ["langfuse"], + "service_callback": ["langfuse"], + "litellm_disabled_callbacks": ["langfuse"], + "logger_fn": "module.func", + "_agentic_loop_depth": 5, + "_agentic_loop_fingerprints": ["forged"], + "_code_interpreter_interception_active": True, + "_code_interpreter_interception_converted_stream": True, + "_code_interpreter_interception_sandbox_key": "forged-key", + "max_agentic_loops": 9999, + } + sample_value = sample_values[control_field] updated = await add_litellm_data_to_request( data={ @@ -4150,7 +4182,9 @@ class TestApplyClientTagPolicyPreAuth: litellm_budget_table=LiteLLM_BudgetTable(max_budget=0.10), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:tag:paid": return 0.50 return fallback_spend @@ -4207,7 +4241,9 @@ class TestApplyClientTagPolicyPreAuth: litellm_budget_table=LiteLLM_BudgetTable(max_budget=0.10), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:tag:tenant:acme": return 0.50 return fallback_spend @@ -4234,6 +4270,99 @@ class TestApplyClientTagPolicyPreAuth: assert exc_info.value.current_cost == 0.50 assert exc_info.value.max_budget == 0.10 + @pytest.mark.asyncio + @pytest.mark.parametrize( + "route", + [ + "/bedrock/model/us.anthropic.claude-sonnet-4-6/invoke", + "/v1/messages", + ], + ) + async def test_header_tags_visible_to_tag_max_budget_check_on_metadata_route( + self, route + ): + """Regression: on LITELLM_METADATA_ROUTES (bedrock, /v1/messages, ...), + common_checks pre-seeds ``litellm_metadata`` and writes key tags there + before ``_tag_max_budget_check`` reads from the same key. The auth wrapper + calls ``apply_client_tag_policy_pre_auth`` first, so without an earlier + pre-seed header tags land in ``metadata`` and the budget check (now + resolving to ``litellm_metadata``) silently ignores them. This test mirrors + the actual auth-time call order and verifies that an over-budget + header-supplied tag still trips ``_tag_max_budget_check``. + """ + from litellm.proxy._types import LiteLLM_BudgetTable, LiteLLM_TagTable + from litellm.proxy.auth.auth_checks import common_checks + from litellm.proxy.utils import ProxyLogging + + request_mock = _build_request_mock_with_headers( + {"x-litellm-tags": "tenant:acme"} + ) + data = {"model": "us.anthropic.claude-sonnet-4-6"} + valid_token = UserAPIKeyAuth( + token="test-token", + api_key="hashed-key", + metadata={}, + team_metadata={}, + ) + + LiteLLMProxyRequestSetup.pre_seed_litellm_metadata_for_route( + request_data=data, + route=route, + ) + LiteLLMProxyRequestSetup.apply_client_tag_policy_pre_auth( + request=request_mock, + request_data=data, + user_api_key_dict=valid_token, + ) + + tag_object = LiteLLM_TagTable( + tag_name="tenant:acme", + spend=0.0, + litellm_budget_table=LiteLLM_BudgetTable(max_budget=0.10), + ) + + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): + if counter_key == "spend:tag:tenant:acme": + return 0.50 + return fallback_spend + + with ( + patch( + "litellm.proxy.proxy_server.prisma_client", + MagicMock(), + ), + patch( + "litellm.proxy.proxy_server.get_current_spend", + mock_get_current_spend, + ), + patch( + "litellm.proxy.auth.auth_checks.get_tag_objects_batch", + new_callable=AsyncMock, + return_value={"tenant:acme": tag_object}, + ), + ): + with pytest.raises(litellm.BudgetExceededError) as exc_info: + await common_checks( + request_body=data, + team_object=None, + user_object=None, + end_user_object=None, + global_proxy_spend=None, + general_settings={}, + route=route, + llm_router=None, + proxy_logging_obj=ProxyLogging(user_api_key_cache=None), + valid_token=valid_token, + request=request_mock, + ) + assert exc_info.value.current_cost == 0.50 + assert exc_info.value.max_budget == 0.10 + + assert "metadata" not in data + assert data["litellm_metadata"]["tags"] == ["tenant:acme"] + class TestApplyKeyTagsPreAuth: def test_merges_key_tags_into_metadata(self): @@ -4362,7 +4491,9 @@ class TestApplyKeyTagsPreAuth: litellm_budget_table=LiteLLM_BudgetTable(max_budget=0.10), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:tag:engineering": return 0.50 return fallback_spend @@ -4413,7 +4544,9 @@ class TestApplyKeyTagsPreAuth: litellm_budget_table=LiteLLM_BudgetTable(max_budget=0.10), ) - async def mock_get_current_spend(counter_key, fallback_spend, max_budget=None, **kwargs): + async def mock_get_current_spend( + counter_key, fallback_spend, max_budget=None, **kwargs + ): if counter_key == "spend:tag:engineering": return 0.05 return fallback_spend diff --git a/tests/test_litellm/proxy/test_plugin_routes.py b/tests/test_litellm/proxy/test_plugin_routes.py new file mode 100644 index 00000000000..52999447179 --- /dev/null +++ b/tests/test_litellm/proxy/test_plugin_routes.py @@ -0,0 +1,232 @@ +"""Regression tests for UI-registered embed plugins. + +Covers three bugs: +1. `general_settings.plugins` was not a field on ConfigGeneralSettings, so the + admin UI's POST /config/field/update with field_name="plugins" was rejected + with "Invalid field=plugins passed in." +2. The in-memory plugin registry only refreshed at startup, so a plugin added + via the UI did not appear in /api/plugins until a restart. +3. Plugins persisted to DB general_settings were not loaded on startup (the + registry only initialised from the YAML config), so UI-added plugins vanished + after a restart. +""" + +import asyncio +from unittest.mock import MagicMock + +from litellm.proxy._types import ( + ConfigGeneralSettings, + LitellmUserRoles, + PluginConfig, + UserAPIKeyAuth, +) +from litellm.proxy.plugin_routes import list_plugins, register_plugins_from_config + + +def _admin() -> UserAPIKeyAuth: + return UserAPIKeyAuth(api_key="sk-admin", user_role=LitellmUserRoles.PROXY_ADMIN) + + +def _non_admin() -> UserAPIKeyAuth: + return UserAPIKeyAuth(api_key="sk-user", user_role=LitellmUserRoles.INTERNAL_USER) + + +def test_plugins_is_a_valid_general_setting() -> None: + """The config-update endpoint gates on this exact membership check.""" + assert "plugins" in ConfigGeneralSettings.model_fields + + +def test_config_general_settings_parses_plugin_list() -> None: + """A list of plugin dicts (what the UI sends) coerces into PluginConfig.""" + settings = ConfigGeneralSettings.model_validate( + { + "plugins": [ + { + "name": "chat-ui", + "display_name": "Chat UI", + "url": "http://localhost:3300", + }, + { + "name": "agent-builder", + "url": "http://127.0.0.1:4010", + "plugin_key": "sk-secret", + }, + ] + } + ) + plugins = settings.plugins + assert plugins is not None + assert [p.name for p in plugins] == ["chat-ui", "agent-builder"] + assert isinstance(plugins[0], PluginConfig) + assert plugins[1].display_name is None + assert plugins[1].plugin_key == "sk-secret" + + +def test_registered_plugins_appear_in_list_without_restart() -> None: + """register_plugins_from_config makes UI-added plugins visible immediately, + and replaces (not merges) so removed plugins disappear.""" + register_plugins_from_config( + { + "plugins": [ + { + "name": "chat-ui", + "display_name": "Chat UI", + "url": "http://localhost:3300", + } + ] + } + ) + names = [p["name"] for p in asyncio.run(list_plugins(user_api_key_dict=_admin()))] + assert names == ["chat-ui"] + + register_plugins_from_config( + { + "plugins": [ + { + "name": "chat-ui", + "display_name": "Chat UI", + "url": "http://localhost:3300", + }, + { + "name": "agent-builder", + "display_name": "Agent Builder", + "url": "http://127.0.0.1:4010", + }, + ] + } + ) + names = sorted( + p["name"] for p in asyncio.run(list_plugins(user_api_key_dict=_admin())) + ) + assert names == ["agent-builder", "chat-ui"] + + # Removing a plugin from config drops it from the live list. + register_plugins_from_config({}) + assert asyncio.run(list_plugins(user_api_key_dict=_admin())) == [] + + +def test_plugin_key_is_never_returned_to_the_browser() -> None: + """plugin_key is a credential the UI never needs; /api/plugins must omit it + for every caller, admin included, so it never lands in browser state.""" + register_plugins_from_config( + { + "plugins": [ + { + "name": "p", + "display_name": "P", + "url": "http://localhost:9", + "plugin_key": "sk-secret", + } + ] + } + ) + + admin_entry = asyncio.run(list_plugins(user_api_key_dict=_admin()))[0] + user_entry = asyncio.run(list_plugins(user_api_key_dict=_non_admin()))[0] + + assert "plugin_key" not in admin_entry + assert "plugin_key" not in user_entry + assert admin_entry["url"] == "http://localhost:9" + + register_plugins_from_config({}) + + +def test_db_persisted_plugins_load_on_startup() -> None: + """Plugins saved to DB general_settings must register when the DB config is + merged at startup, not just when present in the YAML file.""" + from litellm.proxy.proxy_server import ProxyConfig + + register_plugins_from_config({}) # start empty (as if YAML had no plugins) + + ProxyConfig()._add_general_settings_from_db_config( + config_data={ + "general_settings": { + "plugins": [ + { + "name": "db-plugin", + "display_name": "DB Plugin", + "url": "http://localhost:5000", + } + ] + } + }, + general_settings={}, + proxy_logging_obj=MagicMock(), + ) + + names = [p["name"] for p in asyncio.run(list_plugins(user_api_key_dict=_admin()))] + assert names == ["db-plugin"] + + register_plugins_from_config({}) + + +def test_safe_response_headers_sandbox_and_strips_wire_headers() -> None: + """Proxied plugin responses must be inert and shed wire/cookie headers.""" + from litellm.proxy.plugin_routes import _safe_response_headers + + out = _safe_response_headers( + { + "content-type": "text/html", + "content-encoding": "gzip", + "content-length": "123", + "set-cookie": "session=abc", + "content-security-policy": "default-src *", + } + ) + + assert out["content-security-policy"] == "sandbox" + assert out["x-content-type-options"] == "nosniff" + assert out["content-type"] == "text/html" + for stripped in ("content-encoding", "content-length", "set-cookie"): + assert stripped not in out + + +def test_litellm_credential_header_names_covers_every_auth_header() -> None: + """The canonical strip set must list every header user_api_key_auth accepts + as a litellm key, so a new auth header can't silently start leaking.""" + from litellm.proxy._types import SpecialHeaders + + assert SpecialHeaders.litellm_credential_header_names() == { + "authorization", + "api-key", + "x-api-key", + "x-goog-api-key", + "ocp-apim-subscription-key", + "x-litellm-api-key", + } + + +def test_every_litellm_auth_header_is_stripped_before_forwarding() -> None: + """A plugin must never receive any header that authenticates against litellm, + only the hop-by-hop set and benign headers are forwarded.""" + from litellm.proxy.plugin_routes import _request_strip_headers + + strip = _request_strip_headers() + incoming = { + "Authorization": "Bearer sk-litellm", + "API-Key": "sk-litellm", + "X-Api-Key": "sk-litellm", + "X-Goog-Api-Key": "sk-litellm", + "Ocp-Apim-Subscription-Key": "sk-litellm", + "X-Litellm-Api-Key": "sk-litellm", + "Cookie": "litellm_session=abc", + "Accept": "application/json", + "X-Trace-Id": "t-1", + } + forwarded = {k: v for k, v in incoming.items() if k.lower() not in strip} + + assert forwarded == {"Accept": "application/json", "X-Trace-Id": "t-1"} + + +def test_configured_custom_key_header_is_stripped() -> None: + """A custom general_settings.litellm_key_header_name must also be stripped, + read live so config changes are honoured without a restart.""" + from litellm.proxy import proxy_server + from litellm.proxy.plugin_routes import _request_strip_headers + + original = getattr(proxy_server, "general_settings", None) + proxy_server.general_settings = {"litellm_key_header_name": "X-My-Tenant-Key"} + try: + assert "x-my-tenant-key" in _request_strip_headers() + finally: + proxy_server.general_settings = original diff --git a/tests/test_litellm/proxy/test_proxy_server.py b/tests/test_litellm/proxy/test_proxy_server.py index 54ea53b5342..a203fcc7ec0 100644 --- a/tests/test_litellm/proxy/test_proxy_server.py +++ b/tests/test_litellm/proxy/test_proxy_server.py @@ -1348,6 +1348,7 @@ async def test_apply_search_filter_scopes_byok_to_caller_teams(): non_admin = MagicMock(spec=UserAPIKeyAuth) non_admin.user_role = LitellmUserRoles.INTERNAL_USER non_admin.user_id = "user-mine" + non_admin.team_id = None filtered, total_count = await _apply_search_filter_to_models( all_models=[caller_team_byok, other_team_byok, public_model], @@ -1381,6 +1382,7 @@ async def test_apply_search_filter_scopes_byok_to_caller_teams(): admin = MagicMock(spec=UserAPIKeyAuth) admin.user_role = LitellmUserRoles.PROXY_ADMIN admin.user_id = "admin-1" + admin.team_id = None filtered_admin, _ = await _apply_search_filter_to_models( all_models=[caller_team_byok, other_team_byok, public_model], @@ -8328,6 +8330,42 @@ def test_get_config_list_includes_cancel_on_disconnect(monkeypatch): app.dependency_overrides.clear() +def test_preserve_redacted_plugin_keys_keeps_stored_credential(): + """A redacted or blank plugin_key on update must not overwrite the real key.""" + from litellm.proxy.proxy_server import _preserve_redacted_plugin_keys + + existing = [{"name": "p1", "url": "https://p1", "plugin_key": "sk-real-1"}] + + redacted = _preserve_redacted_plugin_keys( + [{"name": "p1", "url": "https://p1-new", "plugin_key": "***"}], existing + ) + assert redacted == [ + {"name": "p1", "url": "https://p1-new", "plugin_key": "sk-real-1"} + ] + + blanked = _preserve_redacted_plugin_keys( + [{"name": "p1", "url": "https://p1", "plugin_key": ""}], existing + ) + assert blanked[0]["plugin_key"] == "sk-real-1" + + +def test_preserve_redacted_plugin_keys_sets_new_and_drops_orphan_placeholder(): + """A real new key replaces; a placeholder with no stored key is dropped, never persisted.""" + from litellm.proxy.proxy_server import _preserve_redacted_plugin_keys + + existing = [{"name": "p1", "url": "https://p1", "plugin_key": "sk-real-1"}] + + rotated = _preserve_redacted_plugin_keys( + [{"name": "p1", "url": "https://p1", "plugin_key": "sk-new"}], existing + ) + assert rotated[0]["plugin_key"] == "sk-new" + + new_plugin = _preserve_redacted_plugin_keys( + [{"name": "p2", "url": "https://p2", "plugin_key": "***"}], existing + ) + assert "plugin_key" not in new_plugin[0] + + def _config_field_info_client(monkeypatch, user_role): import types from unittest.mock import AsyncMock, MagicMock diff --git a/tests/test_litellm/sandbox/test_e2b_sandbox.py b/tests/test_litellm/sandbox/test_e2b_sandbox.py index 948d39fac8f..cc5b12156a1 100644 --- a/tests/test_litellm/sandbox/test_e2b_sandbox.py +++ b/tests/test_litellm/sandbox/test_e2b_sandbox.py @@ -295,3 +295,24 @@ async def test_public_lifecycle_create_run_delete(): async def test_unsupported_provider_raises(): with pytest.raises(ValueError): await litellm.acreate_sandbox(provider="not-a-provider") + + +# ---------- api_base override ---------- + + +@pytest.mark.asyncio +async def test_create_uses_api_base_override(): + client = FakeHTTPClient() + await E2BSandboxConfig().acreate_sandbox( + api_base="http://my-sandbox:8080", api_key="k", client=client + ) + _, url, _, _ = client.calls[0] + assert url == "http://my-sandbox:8080/sandboxes" + + +@pytest.mark.asyncio +async def test_create_defaults_to_e2b_api_base(): + client = FakeHTTPClient() + await E2BSandboxConfig().acreate_sandbox(api_key="k", client=client) + _, url, _, _ = client.calls[0] + assert url == "https://api.e2b.app/sandboxes" diff --git a/tests/test_litellm/sandbox/test_sandbox_tools.py b/tests/test_litellm/sandbox/test_sandbox_tools.py new file mode 100644 index 00000000000..06136534b13 --- /dev/null +++ b/tests/test_litellm/sandbox/test_sandbox_tools.py @@ -0,0 +1,181 @@ +"""Unit tests for the sandbox-tool registry.""" + +from litellm.sandbox import sandbox_tools + + +def _reset(): + sandbox_tools.clear_sandbox_tools() + + +def test_register_resolves_provider_key_and_base(): + _reset() + sandbox_tools.register_sandbox_tools( + [ + { + "sandbox_tool_name": "e2b_default", + "litellm_params": { + "sandbox_provider": "e2b", + "api_key": "sk-literal", + "api_base": "https://sandbox.internal", + }, + } + ] + ) + + resolved = sandbox_tools.resolve_sandbox_tool("e2b_default") + assert resolved == { + "sandbox_provider": "e2b", + "api_key": "sk-literal", + "api_base": "https://sandbox.internal", + } + _reset() + + +def test_register_clears_stale_entries_on_reload(): + """A tool removed from the config must not survive a re-registration.""" + _reset() + sandbox_tools.register_sandbox_tools( + [ + { + "sandbox_tool_name": "old", + "litellm_params": {"sandbox_provider": "e2b"}, + } + ] + ) + assert sandbox_tools.resolve_sandbox_tool("old") is not None + + sandbox_tools.register_sandbox_tools( + [ + { + "sandbox_tool_name": "new", + "litellm_params": {"sandbox_provider": "e2b"}, + } + ] + ) + + assert sandbox_tools.resolve_sandbox_tool("new") is not None + assert ( + sandbox_tools.resolve_sandbox_tool("old") is None + ), "stale tool must be gone after the config is reloaded" + _reset() + + +def test_register_empty_list_clears_removed_tools(): + """Reloading a config with sandbox_tools removed (the proxy passes an empty + list) must drop previously registered credentials from the process.""" + _reset() + sandbox_tools.register_sandbox_tools( + [ + { + "sandbox_tool_name": "e2b_default", + "litellm_params": {"sandbox_provider": "e2b", "api_key": "sk-x"}, + } + ] + ) + assert sandbox_tools.resolve_sandbox_tool("e2b_default") is not None + + sandbox_tools.register_sandbox_tools([]) + + assert ( + sandbox_tools.resolve_sandbox_tool("e2b_default") is None + ), "removing sandbox_tools from config must clear stale credentials" + _reset() + + +def test_register_resolves_secret_from_env(monkeypatch): + _reset() + monkeypatch.setenv("MY_SANDBOX_KEY", "sk-from-env") + sandbox_tools.register_sandbox_tools( + [ + { + "sandbox_tool_name": "e2b_default", + "litellm_params": { + "sandbox_provider": "e2b", + "api_key": "os.environ/MY_SANDBOX_KEY", + }, + } + ] + ) + + resolved = sandbox_tools.resolve_sandbox_tool("e2b_default") + assert resolved is not None + assert resolved["api_key"] == "sk-from-env" + assert resolved["api_base"] is None + _reset() + + +def test_resolve_unknown_returns_none(): + _reset() + assert sandbox_tools.resolve_sandbox_tool("nope") is None + + +def test_register_skips_malformed_entries_without_crashing(): + """A single malformed entry (missing sandbox_tool_name, or not a dict) must + not crash registration during proxy startup/hot-reload; valid entries in the + same list must still register.""" + _reset() + sandbox_tools.register_sandbox_tools( + [ + {"litellm_params": {"sandbox_provider": "e2b"}}, # missing name + "not-a-dict", # wrong type + {"sandbox_tool_name": "", "litellm_params": {}}, # empty name + { + "sandbox_tool_name": "good", + "litellm_params": {"sandbox_provider": "e2b"}, + }, + ] + ) + + assert sandbox_tools.resolve_sandbox_tool("good") is not None + assert sandbox_tools.resolve_sandbox_tool("") is None + assert set(sandbox_tools._SANDBOX_TOOL_REGISTRY) == {"good"} + _reset() + + +def test_register_skips_entry_missing_sandbox_provider(): + """An entry with a name but no sandbox_provider must be skipped at + registration so it cannot later resolve and call acreate_sandbox(provider=None), + which fails with a cryptic runtime error instead of a clear startup warning.""" + _reset() + sandbox_tools.register_sandbox_tools( + [ + {"sandbox_tool_name": "no_provider", "litellm_params": {"api_key": "sk-x"}}, + { + "sandbox_tool_name": "null_provider", + "litellm_params": {"sandbox_provider": None}, + }, + { + "sandbox_tool_name": "good", + "litellm_params": {"sandbox_provider": "e2b"}, + }, + ] + ) + + assert sandbox_tools.resolve_sandbox_tool("no_provider") is None + assert sandbox_tools.resolve_sandbox_tool("null_provider") is None + assert set(sandbox_tools._SANDBOX_TOOL_REGISTRY) == {"good"} + _reset() + + +def test_register_swaps_registry_atomically(): + """register_sandbox_tools must replace the registry in one rebind so a + concurrent resolve never observes a half-populated or transiently empty + registry between clearing and repopulating.""" + _reset() + sandbox_tools.register_sandbox_tools( + [{"sandbox_tool_name": "a", "litellm_params": {"sandbox_provider": "e2b"}}] + ) + before = sandbox_tools._SANDBOX_TOOL_REGISTRY + + sandbox_tools.register_sandbox_tools( + [ + {"sandbox_tool_name": "b", "litellm_params": {"sandbox_provider": "e2b"}}, + {"sandbox_tool_name": "c", "litellm_params": {"sandbox_provider": "e2b"}}, + ] + ) + after = sandbox_tools._SANDBOX_TOOL_REGISTRY + + assert after is not before, "the registry must be replaced, not mutated in place" + assert set(after) == {"b", "c"} + assert "a" not in after + _reset() diff --git a/tests/test_litellm/test_ruff_strict_gate.py b/tests/test_litellm/test_ruff_strict_gate.py index 96852e3a8a5..22255f0555e 100644 --- a/tests/test_litellm/test_ruff_strict_gate.py +++ b/tests/test_litellm/test_ruff_strict_gate.py @@ -82,13 +82,3 @@ def test_introduced_keeps_only_violations_on_changed_lines(): @pytest.mark.parametrize("hunk", ["@@ -1 +1 @@", "@@ -1,0 +1,2 @@"]) def test_parse_changed_lines_handles_single_and_ranged_hunks(hunk): assert gate.parse_changed_lines(f"+++ b/litellm/a.py\n{hunk}\n")["litellm/a.py"] - - -def test_report_emits_breached_rules_as_final_line(capsys): - # CI surfaces only the tail of the log, so the breached-rule summary (rule, - # total/cap, added) must be the last line or it gets truncated away. - breaches = sorted([gate.Breach("UP045", 530, 529, 1), gate.Breach("ANN401", 12, 10, 2)]) - new = [gate.Violation("litellm/types/llms/bedrock.py", 16, "UP045")] - gate.report(breaches, new, "origin/litellm_internal_staging") - last = capsys.readouterr().out.strip().splitlines()[-1] - assert last == "BREACHED RULES: ANN401 12/10 (+2); UP045 530/529 (+1)" diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py index 44e0b55ee3b..d94a86d8e55 100644 --- a/tests/test_litellm/test_utils.py +++ b/tests/test_litellm/test_utils.py @@ -4292,7 +4292,7 @@ _FIREWORKS_MODELS = [ 6e-08, 512000, 512000, - False, + True, True, ), ( diff --git a/ui/litellm-dashboard/src/app/(dashboard)/agentControlPlaneView.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/agentControlPlaneView.test.tsx new file mode 100644 index 00000000000..26de1eed7b8 --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/agentControlPlaneView.test.tsx @@ -0,0 +1,83 @@ +import { describe, it, expect, vi } from "vitest"; +import { render, waitFor } from "@testing-library/react"; +import { AgentControlPlaneView } from "./layout"; + +const { getMock } = vi.hoisted(() => ({ getMock: vi.fn(() => Promise.resolve({ session_claim: "claim" })) })); + +const pluginModeValue = { + mode: "litellm-platform-plugin" as string, + setMode: vi.fn(), + plugins: [{ name: "litellm-platform-plugin", display_name: "Chat UI", url: "http://localhost:3300" }], + activePlugin: { name: "litellm-platform-plugin", display_name: "Chat UI", url: "http://localhost:3300" } as { + name: string; + display_name: string; + url: string; + } | null, +}; + +vi.mock("@/contexts/PluginModeContext", () => ({ usePluginMode: () => pluginModeValue })); +vi.mock("@/contexts/AuthContext", () => ({ useAuth: () => ({ accessToken: "sk-test-token" }) })); + +vi.mock("@/lib/http/client", () => ({ + createApiClient: () => ({ get: getMock }), +})); +vi.mock("@/components/networking", () => ({ getProxyBaseUrl: () => "" })); + +describe("AgentControlPlaneView iframe", () => { + it("embeds the plugin at its ROOT url, never a hardcoded subpath like /sessions", () => { + const { container } = render(); + const iframe = container.querySelector("iframe"); + + expect(iframe).not.toBeNull(); + const src = iframe!.getAttribute("src")!; + expect(src).toBe("http://localhost:3300/"); + expect(src).not.toContain("/sessions"); + // title comes from the plugin's display_name, not a hardcoded label + expect(iframe!.getAttribute("title")).toBe("Chat UI"); + }); + + it("does not double the slash when the plugin url has a trailing slash", () => { + pluginModeValue.activePlugin = { + name: "litellm-platform-plugin", + display_name: "Chat UI", + url: "http://localhost:3300/", + }; + const { container } = render(); + expect(container.querySelector("iframe")!.getAttribute("src")).toBe("http://localhost:3300/"); + pluginModeValue.activePlugin = { + name: "litellm-platform-plugin", + display_name: "Chat UI", + url: "http://localhost:3300", + }; + }); + + it("does not leak the raw token in the iframe src (token goes via encrypted postMessage)", () => { + const { container } = render(); + expect(container.querySelector("iframe")!.getAttribute("src")).not.toContain("token"); + }); + + it("does not delegate clipboard-read to the untrusted plugin iframe", () => { + const { container } = render(); + const allow = container.querySelector("iframe")!.getAttribute("allow") ?? ""; + + expect(allow).not.toContain("clipboard-read"); + expect(allow).toContain("clipboard-write"); + }); + + it("requests the auth-token claim scoped to the active plugin, not a hardcoded default", async () => { + getMock.mockClear(); + pluginModeValue.activePlugin = { name: "reports-plugin", display_name: "Reports", url: "http://localhost:3300" }; + render(); + + await waitFor(() => expect(getMock).toHaveBeenCalled()); + const [path, opts] = getMock.mock.calls[0]; + expect(path).toBe("/api/plugins/auth-token"); + expect(opts.query).toEqual({ plugin_name: "reports-plugin" }); + + pluginModeValue.activePlugin = { + name: "litellm-platform-plugin", + display_name: "Chat UI", + url: "http://localhost:3300", + }; + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.test.tsx b/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.test.tsx new file mode 100644 index 00000000000..3a3251ea76a --- /dev/null +++ b/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.test.tsx @@ -0,0 +1,63 @@ +import { render } from "@testing-library/react"; +import { describe, it, expect, vi } from "vitest"; + +const { userDashboardSpy } = vi.hoisted(() => ({ + userDashboardSpy: vi.fn((_props: Record) => null), +})); + +vi.mock("@/components/user_dashboard", () => ({ + default: (props: Record) => userDashboardSpy(props), +})); + +// AuthContext is still hydrating: userID has not been populated yet (the regression). +vi.mock("@/contexts/AuthContext", () => ({ + useAuth: () => ({ + userID: null, + userRole: "", + userEmail: null, + accessToken: null, + premiumUser: false, + setUserRole: vi.fn(), + setUserEmail: vi.fn(), + }), +})); + +// useAuthorized decodes the cookie synchronously, so identity is already available. +vi.mock("@/app/(dashboard)/hooks/useAuthorized", () => ({ + default: () => ({ + isLoading: false, + isAuthorized: true, + token: "jwt", + accessToken: "sk-access", + userId: "u-123", + userEmail: "admin@example.com", + userRole: "Admin", + premiumUser: false, + disabledPersonalKeyCreation: false, + showSSOBanner: false, + }), +})); + +vi.mock("@/app/(dashboard)/hooks/teams/useTeams", () => ({ + teamListCall: vi.fn(() => new Promise(() => {})), +})); + +vi.mock("@/components/organizations", () => ({ + fetchOrganizations: vi.fn(), +})); + +vi.mock("next/navigation", () => ({ + useSearchParams: () => new URLSearchParams(""), +})); + +import ApiKeysDashboard from "./ApiKeysDashboard"; + +describe("ApiKeysDashboard identity source", () => { + it("passes the useAuthorized userID through even while AuthContext.userID is still null", () => { + render(); + + expect(userDashboardSpy).toHaveBeenCalled(); + const props = userDashboardSpy.mock.calls[0][0]; + expect(props.userID).toBe("u-123"); + }); +}); diff --git a/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.tsx b/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.tsx index 9c8bdd5c56f..54f4bf41a21 100644 --- a/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.tsx +++ b/ui/litellm-dashboard/src/app/(dashboard)/api-keys/ApiKeysDashboard.tsx @@ -1,6 +1,7 @@ "use client"; import { teamListCall as v2TeamListCall } from "@/app/(dashboard)/hooks/teams/useTeams"; +import useAuthorized from "@/app/(dashboard)/hooks/useAuthorized"; import { KeyResponse, Team } from "@/components/key_team_helpers/key_list"; import { Organization } from "@/components/networking"; import { CreateKeyPrefillData } from "@/components/organisms/create_key_button"; @@ -11,7 +12,10 @@ import { useSearchParams } from "next/navigation"; import { useEffect, useMemo, useState } from "react"; export default function ApiKeysDashboard() { - const { userID, userRole, userEmail, accessToken, premiumUser, setUserRole, setUserEmail } = useAuth(); + // Identity comes from useAuthorized (synchronous cookie decode) so userID is set whenever the + // route is authorized; useAuth only supplies the backfill setters UserDashboard still expects. + const { userId: userID, userRole, userEmail, accessToken, premiumUser } = useAuthorized(); + const { setUserRole, setUserEmail } = useAuth(); const searchParams = useSearchParams()!; const [teams, setTeams] = useState(null); @@ -82,7 +86,7 @@ export default function ApiKeysDashboard() { getProxyBaseUrl() ?? "" }); + +// Wrapper so PluginModeProvider receives the live accessToken from auth context, +// which means plugin data refreshes on login/logout without stale cookie reads. +function PluginModeProviderWithAuth({ children }: { children: React.ReactNode }) { + const { accessToken } = useAuth(); + return {children}; +} + +export function AgentControlPlaneView() { + const { activePlugin } = usePluginMode(); + const activePluginName = activePlugin?.name; + const agentPlatformUrl = activePlugin?.url ?? ""; + const { accessToken } = useAuth(); + const iframeRef = useRef(null); + const [auth, setAuth] = useState<{ plugin: string; claim: string } | null>(null); + + // Fetch a short-lived identity claim scoped to the *active* plugin. The claim + // is encrypted under that plugin's own per-plugin key, so it must be requested + // per plugin and re-fetched when the user switches plugins. + useEffect(() => { + if (!accessToken || !activePluginName) return; + let cancelled = false; + pluginApiClient + .get("/api/plugins/auth-token", { accessToken, query: { plugin_name: activePluginName } }) + .then((data: { session_claim?: string }) => { + if (!cancelled && data?.session_claim) setAuth({ plugin: activePluginName, claim: data.session_claim }); + }) + .catch(() => {}); + return () => { + cancelled = true; + }; + }, [accessToken, activePluginName]); + + // Deliver the claim to the iframe via postMessage, but only while it was issued + // for the plugin currently mounted — never replay one plugin's claim to another. + // targetOrigin is the configured plugin URL — no other origin receives it. + useEffect(() => { + const iframe = iframeRef.current; + if (!iframe || !auth || auth.plugin !== activePluginName || !agentPlatformUrl) return; + const send = () => { + iframe.contentWindow?.postMessage({ type: "litellm-auth", session_claim: auth.claim }, agentPlatformUrl); + }; + // Cover both orderings: the iframe may have already fired `load` before the + // claim arrived (send now), or it may load/reload later (send on the event). + send(); + iframe.addEventListener("load", send); + return () => iframe.removeEventListener("load", send); + }, [auth, activePluginName, agentPlatformUrl]); + + if (!agentPlatformUrl) { + return ( +
+
+

Plugin

+

Configure the plugin URL in settings

+
+
+ ); + } + + // Embed the plugin at its root; the plugin renders its own full UI (incl. nav) inside. + return ( +