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Merge branch 'BerriAI:main' into LangfuseUsageDetails

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Fabrício Ceschin 2025-09-22 09:43:33 -04:00 • committed by GitHub
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57
.circleci/config.yml
View file

@ -1050,6 +1050,51 @@ jobs:
ls
python -m pytest -vv tests/test_litellm --cov=litellm --cov-report=xml -x -s -v --junitxml=test-results/junit-litellm.xml --durations=10 -n 8
no_output_timeout: 120m
- run:
name: Rename the coverage files
command: |
mv coverage.xml litellm_mapped_tests_coverage.xml
mv .coverage litellm_mapped_tests_coverage
# Store test results
- store_test_results:
path: test-results
- persist_to_workspace:
root: .
paths:
- litellm_mapped_tests_coverage.xml
- litellm_mapped_tests_coverage
litellm_mapped_enterprise_tests:
docker:
- image: cimg/python:3.11
auth:
username: ${DOCKERHUB_USERNAME}
password: ${DOCKERHUB_PASSWORD}
working_directory: ~/project
steps:
- checkout
- setup_google_dns
- run:
name: Install Dependencies
command: |
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
pip install "pytest-mock==3.12.0"
pip install "pytest==7.3.1"
pip install "pytest-retry==1.6.3"
pip install "pytest-cov==5.0.0"
pip install "pytest-asyncio==0.21.1"
pip install "respx==0.22.0"
pip install "hypercorn==0.17.3"
pip install "pydantic==2.10.2"
pip install "mcp==1.10.1"
pip install "requests-mock>=1.12.1"
pip install "responses==0.25.7"
pip install "pytest-xdist==3.6.1"
pip install "semantic_router==0.1.10"
pip install "fastapi-offline==1.7.3"
- setup_litellm_enterprise_pip
- run:
name: Run enterprise tests
command: |
@ -1779,8 +1824,8 @@ jobs:
docker run -d \
-p 4000:4000 \
-e DATABASE_URL=postgresql://postgres:postgres@host.docker.internal:5432/circle_test \
-e AZURE_API_KEY=$AZURE_BATCHES_API_KEY \
-e AZURE_API_BASE=$AZURE_BATCHES_API_BASE \
-e AZURE_API_KEY=$AZURE_API_KEY \
-e AZURE_API_BASE=$AZURE_API_BASE \
-e AZURE_API_VERSION="2024-05-01-preview" \
-e REDIS_HOST=$REDIS_HOST \
-e REDIS_PASSWORD=$REDIS_PASSWORD \
@ -3175,6 +3220,12 @@ workflows:
only:
- main
- /litellm_.*/
- litellm_mapped_enterprise_tests:
filters:
branches:
only:
- main
- /litellm_.*/
- litellm_mapped_tests:
filters:
branches:
@ -3219,6 +3270,7 @@ workflows:
- guardrails_testing
- llm_responses_api_testing
- litellm_mapped_tests
- litellm_mapped_enterprise_tests
- batches_testing
- litellm_utils_testing
- pass_through_unit_testing
@ -3279,6 +3331,7 @@ workflows:
- google_generate_content_endpoint_testing
- llm_responses_api_testing
- litellm_mapped_tests
- litellm_mapped_enterprise_tests
- batches_testing
- litellm_utils_testing
- pass_through_unit_testing

3
Dockerfile
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@ -41,9 +41,6 @@ RUN pip uninstall jwt -y
RUN pip uninstall PyJWT -y
RUN pip install PyJWT==2.9.0 --no-cache-dir
# Build Admin UI
RUN chmod +x docker/build_admin_ui.sh && ./docker/build_admin_ui.sh
# Runtime stage
FROM $LITELLM_RUNTIME_IMAGE AS runtime

2
README.md
View file

@ -37,7 +37,7 @@ LiteLLM manages:
- Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
- Set Budgets & Rate limits per project, api key, model [LiteLLM Proxy Server (LLM Gateway)](https://docs.litellm.ai/docs/simple_proxy)
[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#openai-proxy---docs) <br>
[**Jump to LiteLLM Proxy (LLM Gateway) Docs**](https://github.com/BerriAI/litellm?tab=readme-ov-file#litellm-proxy-server-llm-gateway---docs) <br>
[**Jump to Supported LLM Providers**](https://github.com/BerriAI/litellm?tab=readme-ov-file#supported-providers-docs)
🚨 **Stable Release:** Use docker images with the `-stable` tag. These have undergone 12 hour load tests, before being published. [More information about the release cycle here](https://docs.litellm.ai/docs/proxy/release_cycle)

134
cookbook/misc/RELEASE_NOTES_GENERATION_INSTRUCTIONS.md
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@ -4,10 +4,10 @@ This document provides comprehensive instructions for AI agents to generate rele
## Required Inputs
1. **Release Version** (e.g., `v1.76.3-stable`)
1. **Release Version** (e.g., `v1.77.3-stable`)
2. **PR Diff/Changelog** - List of PRs with titles and contributors
3. **Previous Version Commit Hash** - To compare model pricing changes
4. **Reference Release Notes** - Previous release notes to follow style/format
4. **Reference Release Notes** - Use recent stable releases (v1.76.3-stable, v1.77.2-stable) as templates for consistent formatting
## Step-by-Step Process
@ -26,12 +26,12 @@ git diff <previous_commit_hash> HEAD -- model_prices_and_context_window.json
### 2. Release Notes Structure
Follow this exact structure based on `docs/my-website/release_notes/v1.76.1-stable/index.md`:
Follow this exact structure based on recent stable releases (v1.76.3-stable, v1.77.2-stable):
```markdown
---
title: "v1.76.X-stable - [Key Theme]"
slug: "v1-76-X"
title: "v1.77.X-stable - [Key Theme]"
slug: "v1-77-X"
date: YYYY-MM-DDTHH:mm:ss
authors: [standard author block]
hide_table_of_contents: false
@ -43,23 +43,42 @@ hide_table_of_contents: false
## Key Highlights
[3-5 bullet points of major features]
## Major Changes
[Critical changes users need to know]
## Performance Improvements
[Performance-related changes]
## New Models / Updated Models
[Detailed model tables and provider updates]
#### New Model Support
[Model pricing table]
#### Features
[Provider-specific features organized by provider]
### Bug Fixes
[Provider-specific bug fixes organized by provider]
#### New Provider Support
[New provider integrations]
## LLM API Endpoints
[API-related features and fixes]
#### Features
[API-specific features organized by API type]
#### Bugs
[General bug fixes]
## Management Endpoints / UI
[Admin interface and management changes]
#### Features
[UI and management features]
#### Bugs
[Management-related bug fixes]
## Logging / Guardrail Integrations
[Observability and security features]
#### Features
[Organized by integration provider with proper doc links]
#### Guardrails
[Guardrail-specific features and fixes]
#### New Integration
[Major new integrations]
## Performance / Loadbalancing / Reliability improvements
[Infrastructure improvements]
@ -86,21 +105,27 @@ hide_table_of_contents: false
**New Models/Updated Models:**
- Extract from model_prices_and_context_window.json diff
- Create tables with: Provider, Model, Context Window, Input Cost, Output Cost, Features
- Group by provider
- Note pricing corrections
- Highlight deprecated models
- **Structure:**
- `#### New Model Support` - pricing table
- `#### Features` - organized by provider with documentation links
- `### Bug Fixes` - provider-specific bug fixes
- `#### New Provider Support` - major new provider integrations
- Group by provider with proper doc links: `**[Provider Name](../../docs/providers/[provider])**`
- Use bullet points under each provider for multiple features
- Separate features from bug fixes clearly
**Provider Features:**
- Group by provider (Gemini, OpenAI, Anthropic, etc.)
- Link to provider docs: `../../docs/providers/[provider_name]`
- Separate features from bug fixes
**API Endpoints:**
- Images API
- Video Generation (if applicable)
- Responses API
- Passthrough endpoints
- General chat completions
**LLM API Endpoints:**
- **Structure:**
- `#### Features` - organized by API type (Responses API, Batch API, etc.)
- `#### Bugs` - general bug fixes under **General** category
- **API Categories:**
- Responses API
- Batch API
- CountTokens API
- Images API
- Video Generation (if applicable)
- General (miscellaneous improvements)
- Use proper documentation links for each API type
**UI/Management:**
- Authentication changes
@ -108,11 +133,19 @@ hide_table_of_contents: false
- Team management
- Key management
**Integrations:**
- Logging providers (Datadog, Braintrust, etc.)
- Guardrails
- Cost tracking
- Observability
**Logging / Guardrail Integrations:**
- **Structure:**
- `#### Features` - organized by integration provider with proper doc links
- `#### Guardrails` - guardrail-specific features and fixes
- `#### New Integration` - major new integrations
- **Integration Categories:**
- **[DataDog](../../docs/proxy/logging#datadog)** - group all DataDog-related changes
- **[Langfuse](../../docs/proxy/logging#langfuse)** - Langfuse-specific features
- **[Prometheus](../../docs/proxy/logging#prometheus)** - monitoring improvements
- **[PostHog](../../docs/observability/posthog)** - observability integration
- Other logging providers with proper doc links
- Use bullet points under each provider for multiple features
- Separate logging features from guardrails clearly
### 4. Documentation Linking Strategy
@ -211,10 +244,41 @@ This release has a known issue...
:::
```
**Provider Features:**
**Provider Features (New Models / Updated Models section):**
```markdown
#### Features
- **[Provider Name](../../docs/providers/provider)**
- Feature description - [PR #XXXXX](link)
- Another feature description - [PR #YYYYY](link)
```
**API Features (LLM API Endpoints section):**
```markdown
#### Features
- **[API Name](../../docs/api_path)**
- Feature description - [PR #XXXXX](link)
- Another feature - [PR #YYYYY](link)
- **General**
- Miscellaneous improvements - [PR #ZZZZZ](link)
```
**Integration Features (Logging / Guardrail Integrations section):**
```markdown
#### Features
- **[Integration Name](../../docs/proxy/logging#integration)**
- Feature description - [PR #XXXXX](link)
- Bug fix description - [PR #YYYYY](link)
```
**Bug Fixes Pattern:**
```markdown
### Bug Fixes
- **[Provider/Component Name](../../docs/providers/provider)**
- Bug fix description - [PR #XXXXX](link)
```
### 10. Missing Documentation Check

50
docs/my-website/docs/completion/provider_specific_params.md
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@ -433,4 +433,54 @@ curl -X POST 'http://0.0.0.0:4000/chat/completions' \
],
"adapater_id": "my-special-adapter-id" # 👈 PROVIDER-SPECIFIC PARAM
}'
## Provider-Specific Metadata Parameters
| Provider | Parameter | Use Case |
|----------|-----------|----------|
| **AWS Bedrock** | `requestMetadata` | Cost attribution, logging |
| **Gemini/Vertex AI** | `labels` | Resource labeling |
| **Anthropic** | `metadata` | User identification |
<Tabs>
<TabItem value="bedrock" label="AWS Bedrock">
```python
import litellm
response = litellm.completion(
model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
messages=[{"role": "user", "content": "Hello!"}],
requestMetadata={"cost_center": "engineering"}
)
```
</TabItem>
<TabItem value="gemini" label="Gemini/Vertex AI">
```python
import litellm
response = litellm.completion(
model="vertex_ai/gemini-pro",
messages=[{"role": "user", "content": "Hello!"}],
labels={"environment": "production"}
)
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```python
import litellm
response = litellm.completion(
model="anthropic/claude-3-sonnet-20240229",
messages=[{"role": "user", "content": "Hello!"}],
metadata={"user_id": "user123"}
)
```
</TabItem>
</Tabs>
```

92
docs/my-website/docs/providers/bedrock.md
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@ -308,6 +308,65 @@ print(response)
</TabItem>
</Tabs>
## Usage - Request Metadata
Attach metadata to Bedrock requests for logging and cost attribution.
<Tabs>
<TabItem value="sdk" label="SDK">
```python
import os
from litellm import completion
os.environ["AWS_ACCESS_KEY_ID"] = ""
os.environ["AWS_SECRET_ACCESS_KEY"] = ""
os.environ["AWS_REGION_NAME"] = ""
response = completion(
model="bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0",
messages=[{"role": "user", "content": "Hello, how are you?"}],
requestMetadata={
"cost_center": "engineering",
"user_id": "user123"
}
)
```
</TabItem>
<TabItem value="proxy" label="PROXY">
**Set on yaml**
```yaml
model_list:
- model_name: bedrock-claude-v1
litellm_params:
model: bedrock/anthropic.claude-3-5-sonnet-20240620-v1:0
requestMetadata:
cost_center: "engineering"
```
**Set on request**
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
response = client.chat.completions.create(
model="bedrock-claude-v1",
messages=[{"role": "user", "content": "Hello"}],
extra_body={
"requestMetadata": {"cost_center": "engineering"}
}
)
```
</TabItem>
</Tabs>
## Usage - Function Calling / Tool calling
LiteLLM supports tool calling via Bedrock's Converse and Invoke API's.
@ -1954,6 +2013,39 @@ curl -L -X POST 'http://0.0.0.0:4000/v1/images/generations' \
</TabItem>
</Tabs>
### Using Inference Profiles with Image Generation
For AWS Bedrock Application Inference Profiles with image generation, use the `model_id` parameter to specify the inference profile ARN:
<Tabs>
<TabItem value="sdk" label="SDK">
```python
from litellm import image_generation
response = image_generation(
model="bedrock/amazon.nova-canvas-v1:0",
model_id="arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0",
prompt="A cute baby sea otter"
)
print(f"response: {response}")
```
</TabItem>
<TabItem value="proxy" label="PROXY">
```yaml
model_list:
- model_name: nova-canvas-inference-profile
litellm_params:
model: bedrock/amazon.nova-canvas-v1:0
model_id: arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0
aws_region_name: "eu-west-1"
```
</TabItem>
</Tabs>
## Supported AWS Bedrock Image Generation Models
| Model Name | Function Call |

144
docs/my-website/docs/providers/vertex.md
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@ -2509,150 +2509,6 @@ print("response from proxy", response)
</TabItem>
</Tabs>
## **Batch APIs**
Just add the following Vertex env vars to your environment.
```bash
# GCS Bucket settings, used to store batch prediction files in
export GCS_BUCKET_NAME = "litellm-testing-bucket" # the bucket you want to store batch prediction files in
export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file
# Vertex /batch endpoint settings, used for LLM API requests
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file
export VERTEXAI_LOCATION="us-central1" # can be any vertex location
export VERTEXAI_PROJECT="my-test-project"
```
### Usage
#### 1. Create a file of batch requests for vertex
LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**
Each `body` in the file should be an **OpenAI API request**
Create a file called `vertex_batch_completions.jsonl` in the current working directory, the `model` should be the Vertex AI model name
```
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-1.5-flash-001", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
```
#### 2. Upload a File of batch requests
For `vertex_ai` litellm will upload the file to the provided `GCS_BUCKET_NAME`
```python
import os
oai_client = OpenAI(
api_key="sk-1234", # litellm proxy API key
base_url="http://localhost:4000" # litellm proxy base url
)
file_name = "vertex_batch_completions.jsonl" #
_current_dir = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(_current_dir, file_name)
file_obj = oai_client.files.create(
file=open(file_path, "rb"),
purpose="batch",
extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use vertex_ai for this file upload
)
```
**Expected Response**
```json
{
"id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a",
"bytes": 416,
"created_at": 1733392026,
"filename": "litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a",
"object": "file",
"purpose": "batch",
"status": "uploaded",
"status_details": null
}
```
#### 3. Create a batch
```python
batch_input_file_id = file_obj.id # use `file_obj` from step 2
create_batch_response = oai_client.batches.create(
completion_window="24h",
endpoint="/v1/chat/completions",
input_file_id=batch_input_file_id, # example input_file_id = "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/c2b1b785-252b-448c-b180-033c4c63b3ce"
extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request
)
```
**Expected Response**
```json
{
"id": "3814889423749775360",
"completion_window": "24hrs",
"created_at": 1733392026,
"endpoint": "",
"input_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/d3f198cd-c0d1-436d-9b1e-28e3f282997a",
"object": "batch",
"status": "validating",
"cancelled_at": null,
"cancelling_at": null,
"completed_at": null,
"error_file_id": null,
"errors": null,
"expired_at": null,
"expires_at": null,
"failed_at": null,
"finalizing_at": null,
"in_progress_at": null,
"metadata": null,
"output_file_id": "gs://litellm-testing-bucket/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001",
"request_counts": null
}
```
#### 4. Retrieve a batch
```python
retrieved_batch = oai_client.batches.retrieve(
batch_id=create_batch_response.id,
extra_body={"custom_llm_provider": "vertex_ai"}, # tell litellm to use `vertex_ai` for this batch request
)
```
**Expected Response**
```json
{
"id": "3814889423749775360",
"completion_window": "24hrs",
"created_at": 1736500100,
"endpoint": "",
"input_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001/7b2e47f5-3dd4-436d-920f-f9155bbdc952",
"object": "batch",
"status": "completed",
"cancelled_at": null,
"cancelling_at": null,
"completed_at": null,
"error_file_id": null,
"errors": null,
"expired_at": null,
"expires_at": null,
"failed_at": null,
"finalizing_at": null,
"in_progress_at": null,
"metadata": null,
"output_file_id": "gs://example-bucket-1-litellm/litellm-vertex-files/publishers/google/models/gemini-1.5-flash-001",
"request_counts": null
}
```
## **Fine Tuning APIs**

264
docs/my-website/docs/providers/vertex_batch.md Normal file
View file

@ -0,0 +1,264 @@
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
## **Batch APIs**
Just add the following Vertex env vars to your environment.
```bash
# GCS Bucket settings, used to store batch prediction files in
export GCS_BUCKET_NAME="my-batch-bucket" # the bucket you want to store batch prediction files in
export GCS_PATH_SERVICE_ACCOUNT="/path/to/service_account.json" # path to your service account json file
# Vertex /batch endpoint settings, used for LLM API requests
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service_account.json" # path to your service account json file
export VERTEXAI_LOCATION="us-central1" # can be any vertex location
export VERTEXAI_PROJECT="my-project"
```
### Usage
Follow this complete workflow: create JSONL file → upload file → create batch → retrieve batch status → get file content
#### 1. Create a JSONL file of batch requests
LiteLLM expects the file to follow the **[OpenAI batches files format](https://platform.openai.com/docs/guides/batch)**.
Each `body` in the file should be an **OpenAI API request**.
Create a file called `batch_requests.jsonl` with your requests:
```jsonl
{"custom_id": "request-1", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are a helpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
{"custom_id": "request-2", "method": "POST", "url": "/v1/chat/completions", "body": {"model": "gemini-2.5-flash-lite", "messages": [{"role": "system", "content": "You are an unhelpful assistant."},{"role": "user", "content": "Hello world!"}],"max_tokens": 10}}
```
#### 2. Upload the file
Upload your JSONL file. For `vertex_ai`, the file will be stored in your configured GCS bucket provided by `GCS_BUCKET_NAME`.
<Tabs>
<TabItem value="python" label="Python">
```python showLineNumbers title="upload_file.py"
from openai import OpenAI
oai_client = OpenAI(
api_key="sk-1234", # litellm proxy API key
base_url="http://localhost:4000" # litellm proxy base url
)
file_obj = oai_client.files.create(
file=open("batch_requests.jsonl", "rb"),
purpose="batch",
extra_body={"custom_llm_provider": "vertex_ai"}
)
print(f"File uploaded with ID: {file_obj.id}")
```
</TabItem>
<TabItem value="curl" label="Curl">
```bash showLineNumbers title="Upload File"
curl --request POST \
--url http://localhost:4000/v1/files \
--header 'Content-Type: multipart/form-data' \
--form purpose=batch \
--form file=@batch_requests.jsonl \
--form custom_llm_provider=vertex_ai
```
</TabItem>
</Tabs>
**Expected Response:**
```json
{
"id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
"bytes": 416,
"created_at": 1758303684,
"filename": "litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
"object": "file",
"purpose": "batch",
"status": "uploaded",
"expires_at": null,
"status_details": null
}
```
#### 3. Create a batch
Create a batch job using the uploaded file ID.
<Tabs>
<TabItem value="python" label="Python">
```python showLineNumbers title="create_batch.py"
batch_input_file_id = file_obj.id # from step 2
create_batch_response = oai_client.batches.create(
completion_window="24h",
endpoint="/v1/chat/completions",
input_file_id=batch_input_file_id, # e.g. "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd"
extra_body={"custom_llm_provider": "vertex_ai"}
)
print(f"Batch created with ID: {create_batch_response.id}")
```
</TabItem>
<TabItem value="curl" label="Curl">
```bash showLineNumbers title="Create Batch Request"
curl --request POST \
--url http://localhost:4000/v1/batches \
--header 'Content-Type: application/json' \
--data '{
"input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
"endpoint": "/v1/chat/completions",
"completion_window": "24h",
"custom_llm_provider": "vertex_ai"
}'
```
</TabItem>
</Tabs>
**Expected Response:**
```json
{
"id": "7814463557919047680",
"completion_window": "24hrs",
"created_at": 1758328011,
"endpoint": "",
"input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
"object": "batch",
"status": "validating",
"cancelled_at": null,
"cancelling_at": null,
"completed_at": null,
"error_file_id": null,
"errors": null,
"expired_at": null,
"expires_at": null,
"failed_at": null,
"finalizing_at": null,
"in_progress_at": null,
"metadata": null,
"output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite",
"request_counts": null,
"usage": null
}
```
#### 4. Retrieve batch status
Check the status of your batch job. The batch will progress through states: `validating` → `in_progress` → `completed`.
<Tabs>
<TabItem value="python" label="Python">
```python showLineNumbers title="retrieve_batch.py"
retrieved_batch = oai_client.batches.retrieve(
batch_id=create_batch_response.id, # Created batch id, e.g. 7814463557919047680
extra_body={"custom_llm_provider": "vertex_ai"}
)
print(f"Batch status: {retrieved_batch.status}")
if retrieved_batch.status == "completed":
print(f"Output file: {retrieved_batch.output_file_id}")
```
</TabItem>
<TabItem value="curl" label="Curl">
```bash showLineNumbers title="Retrieve Batch Status"
curl --request GET \
--url 'http://localhost:4000/batches/7814463557919047680?provider=vertex_ai' \
--header 'Authorization: Bearer sk-1234'
```
</TabItem>
</Tabs>
**Expected Response (when completed):**
```json
{
"id": "7814463557919047680",
"completion_window": "24hrs",
"created_at": 1758328011,
"endpoint": "",
"input_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/abc123-def4-5678-9012-34567890abcd",
"object": "batch",
"status": "completed",
"cancelled_at": null,
"cancelling_at": null,
"completed_at": null,
"error_file_id": null,
"errors": null,
"expired_at": null,
"expires_at": null,
"failed_at": null,
"finalizing_at": null,
"in_progress_at": null,
"metadata": null,
"output_file_id": "gs://my-batch-bucket/litellm-vertex-files/publishers/google/models/gemini-2.5-flash-lite/prediction-model-2025-09-19T21:26:51.569037Z/predictions.jsonl",
"request_counts": null,
"usage": null
}
```
#### 5. Get file content
Once the batch is completed, retrieve the results using the `output_file_id` from the batch response.
**Important:** The `output_file_id` must be URL encoded when used in the request path.
<Tabs>
<TabItem value="python" label="Python">
```python showLineNumbers title="get_file_content.py"
import urllib.parse
import json
output_file_id = retrieved_batch.output_file_id
# URL encode the file ID
encoded_file_id = urllib.parse.quote_plus(output_file_id)
# Get file content
file_content = oai_client.files.content(
file_id=encoded_file_id,
extra_body={"custom_llm_provider": "vertex_ai"}
)
# Process the results
for line in file_content.text.strip().split('\n'):
result = json.loads(line)
print(f"Request: {result['request']}")
print(f"Response: {result['response']}")
print("---")
```
</TabItem>
<TabItem value="curl" label="Curl">
```bash showLineNumbers title="Get File Content"
# Note: The file ID must be URL encoded
curl --request GET \
--url 'http://localhost:4000/files/gs%253A%252F%252Fmy-batch-bucket%252Flitellm-vertex-files%252Fpublishers%252Fgoogle%252Fmodels%252Fgemini-2.5-flash-lite%252Fprediction-model-2025-09-19T21%253A26%253A51.569037Z%252Fpredictions.jsonl/content?provider=vertex_ai' \
--header 'Authorization: Bearer sk-1234'
```
</TabItem>
</Tabs>
**Expected Response:**
The response contains JSONL format with one result per line:
```jsonl
{"status":"","processed_time":"2025-09-19T21:29:47.352+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are a helpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.48079710006713866,"content":{"parts":[{"text":"Hello there! It's nice to meet you"}],"role":"model"},"finishReason":"MAX_TOKENS"}],"createTime":"2025-09-19T21:29:47.484619Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaIvKHdvshMIP_aOtuAg","usageMetadata":{"candidatesTokenCount":10,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":10}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":19,"trafficType":"ON_DEMAND"}}}
{"status":"","processed_time":"2025-09-19T21:29:47.358+00:00","request":{"contents":[{"parts":[{"text":"Hello world!"}],"role":"user"}],"generationConfig":{"max_output_tokens":10},"system_instruction":{"parts":[{"text":"You are an unhelpful assistant."}]}},"response":{"candidates":[{"avgLogprobs":-0.6168075137668185,"content":{"parts":[{"text":"I am unable to assist with this request."}],"role":"model"},"finishReason":"STOP"}],"createTime":"2025-09-19T21:29:47.470889Z","modelVersion":"gemini-2.5-flash-lite","responseId":"S8vNaOneHISShMIP28nA8QQ","usageMetadata":{"candidatesTokenCount":9,"candidatesTokensDetails":[{"modality":"TEXT","tokenCount":9}],"promptTokenCount":9,"promptTokensDetails":[{"modality":"TEXT","tokenCount":9}],"totalTokenCount":18,"trafficType":"ON_DEMAND"}}}
```

1
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@ -13,6 +13,7 @@ To start using Litellm, run the following commands in a shell:
```bash
# Get the code
curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/docker-compose.yml
curl -O https://raw.githubusercontent.com/BerriAI/litellm/main/prometheus.yml
# Add the master key - you can change this after setup
echo 'LITELLM_MASTER_KEY="sk-1234"' > .env

241
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@ -0,0 +1,241 @@
# Dynamic TPM/RPM Allocation
Prevent projects from gobbling too much tpm/rpm.
Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125)
## Quick Start Usage
1. Setup config.yaml
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: my-fake-model
litellm_params:
model: gpt-3.5-turbo
api_key: my-fake-key
mock_response: hello-world
tpm: 60
litellm_settings:
callbacks: ["dynamic_rate_limiter_v3"]
general_settings:
master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```python showLineNumbers title="test.py"
"""
- Run 2 concurrent teams calling same model
- model has 60 TPM
- Mock response returns 30 total tokens / request
- Each team will only be able to make 1 request per minute
"""
import requests
from openai import OpenAI, RateLimitError
def create_key(api_key: str, base_url: str):
response = requests.post(
url="{}/key/generate".format(base_url),
json={},
headers={
"Authorization": "Bearer {}".format(api_key)
}
)
_response = response.json()
return _response["key"]
key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# call proxy with key 1 - works
openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000")
response = openai_client_1.chat.completions.with_raw_response.create(
model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
)
print("Headers for call 1 - {}".format(response.headers))
_response = response.parse()
print("Total tokens for call - {}".format(_response.usage.total_tokens))
# call proxy with key 2 - works
openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000")
response = openai_client_2.chat.completions.with_raw_response.create(
model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
)
print("Headers for call 2 - {}".format(response.headers))
_response = response.parse()
print("Total tokens for call - {}".format(_response.usage.total_tokens))
# call proxy with key 2 - fails
try:
openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}])
raise Exception("This should have failed!")
except RateLimitError as e:
print("This was rate limited b/c - {}".format(str(e)))
```
**Expected Response**
```
This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key=<hashed_token> over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}}
```
## [BETA] Set Priority / Reserve Quota
Reserve TPM/RPM capacity for different environments or use cases. This ensures critical production workloads always have guaranteed capacity, while development or lower-priority tasks use remaining quota.
**Use Cases:**
- Production vs Development environments
- Real-time applications vs batch processing
- Critical services vs experimental features
:::tip
Reserving TPM/RPM on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it.
:::
### How Priority Reservation Works
Priority reservation allocates a percentage of your model's total TPM/RPM to specific priority levels. Keys with higher priority get guaranteed access to their reserved quota first.
**Example Scenario:**
- Model has 10 RPM total capacity
- Priority reservation: `{"prod": 0.9, "dev": 0.1}`
- Result: Production keys get 9 RPM guaranteed, Development keys get 1 RPM guaranteed
### Configuration
#### 1. Setup config.yaml
```yaml showLineNumbers title="config.yaml"
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: "gpt-3.5-turbo"
api_key: os.environ/OPENAI_API_KEY
rpm: 10 # Total model capacity
litellm_settings:
callbacks: ["dynamic_rate_limiter_v3"]
priority_reservation:
"prod": 0.9 # 90% reserved for production (9 RPM)
"dev": 0.1 # 10% reserved for development (1 RPM)
general_settings:
master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
database_url: postgres://.. # OR set `DATABASE_URL=".."` in your.env
```
**Configuration Details:**
`priority_reservation`: Dict[str, float]
- **Key (str)**: Priority level name (can be any string like "prod", "dev", "critical", etc.)
- **Value (float)**: Percentage of total TPM/RPM to reserve (0.0 to 1.0)
- **Note**: Values should sum to 1.0 or less
**Start Proxy**
```bash
litellm --config /path/to/config.yaml
```
#### 2. Create Keys with Priority Levels
**Production Key:**
```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"metadata": {"priority": "prod"}
}'
```
**Development Key:**
```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
"metadata": {"priority": "dev"}
}'
```
**Expected Response for both:**
```json
{
"key": "sk-...",
"metadata": {"priority": "prod"}, // or "dev"
...
}
```
#### 3. Test Priority Allocation
**Test Production Key (should get 9 RPM):**
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-prod-key' \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Hello from prod"}]
}'
```
**Test Development Key (should get 1 RPM):**
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-dev-key' \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Hello from dev"}]
}'
```
### Expected Behavior
With the configuration above:
1. **Production keys** can make up to 9 requests per minute
2. **Development keys** can make up to 1 request per minute
3. Production requests are never blocked by development usage
**Rate Limit Error Example:**
```json
{
"error": {
"message": "Key=sk-dev-... over available RPM=0. Model RPM=10, Reserved RPM for priority 'dev'=1, Active keys=1",
"type": "rate_limit_exceeded",
"code": 429
}
}
```
### Demo Video
This video walks through setting up dynamic rate limiting with priority reservation and locust tests to validate the behavior.
<iframe width="840" height="500" src="https://www.loom.com/embed/1b54b93139ee415d959402cc0629f3f7
" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>

185
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View file

@ -178,188 +178,3 @@ Expect to see this metric on prometheus to track the Remaining Budget for the te
```shell
litellm_remaining_team_budget_metric{team_alias="QA Prod Bot",team_id="de35b29e-6ca8-4f47-b804-2b79d07aa99a"} 9.699999999999992e-06
```
### Dynamic TPM/RPM Allocation
Prevent projects from gobbling too much tpm/rpm.
Dynamically allocate TPM/RPM quota to api keys, based on active keys in that minute. [**See Code**](https://github.com/BerriAI/litellm/blob/9bffa9a48e610cc6886fc2dce5c1815aeae2ad46/litellm/proxy/hooks/dynamic_rate_limiter.py#L125)
1. Setup config.yaml
```yaml
model_list:
- model_name: my-fake-model
litellm_params:
model: gpt-3.5-turbo
api_key: my-fake-key
mock_response: hello-world
tpm: 60
litellm_settings:
callbacks: ["dynamic_rate_limiter"]
general_settings:
master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```python
"""
- Run 2 concurrent teams calling same model
- model has 60 TPM
- Mock response returns 30 total tokens / request
- Each team will only be able to make 1 request per minute
"""
import requests
from openai import OpenAI, RateLimitError
def create_key(api_key: str, base_url: str):
response = requests.post(
url="{}/key/generate".format(base_url),
json={},
headers={
"Authorization": "Bearer {}".format(api_key)
}
)
_response = response.json()
return _response["key"]
key_1 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
key_2 = create_key(api_key="sk-1234", base_url="http://0.0.0.0:4000")
# call proxy with key 1 - works
openai_client_1 = OpenAI(api_key=key_1, base_url="http://0.0.0.0:4000")
response = openai_client_1.chat.completions.with_raw_response.create(
model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
)
print("Headers for call 1 - {}".format(response.headers))
_response = response.parse()
print("Total tokens for call - {}".format(_response.usage.total_tokens))
# call proxy with key 2 - works
openai_client_2 = OpenAI(api_key=key_2, base_url="http://0.0.0.0:4000")
response = openai_client_2.chat.completions.with_raw_response.create(
model="my-fake-model", messages=[{"role": "user", "content": "Hello world!"}],
)
print("Headers for call 2 - {}".format(response.headers))
_response = response.parse()
print("Total tokens for call - {}".format(_response.usage.total_tokens))
# call proxy with key 2 - fails
try:
openai_client_2.chat.completions.with_raw_response.create(model="my-fake-model", messages=[{"role": "user", "content": "Hey, how's it going?"}])
raise Exception("This should have failed!")
except RateLimitError as e:
print("This was rate limited b/c - {}".format(str(e)))
```
**Expected Response**
```
This was rate limited b/c - Error code: 429 - {'error': {'message': {'error': 'Key=<hashed_token> over available TPM=0. Model TPM=0, Active keys=2'}, 'type': 'None', 'param': 'None', 'code': 429}}
```
#### ✨ [BETA] Set Priority / Reserve Quota
Reserve tpm/rpm capacity for projects in prod.
:::tip
Reserving tpm/rpm on keys based on priority is a premium feature. Please [get an enterprise license](./enterprise.md) for it.
:::
1. Setup config.yaml
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: "gpt-3.5-turbo"
api_key: os.environ/OPENAI_API_KEY
rpm: 100
litellm_settings:
callbacks: ["dynamic_rate_limiter"]
priority_reservation: {"dev": 0, "prod": 1}
general_settings:
master_key: sk-1234 # OR set `LITELLM_MASTER_KEY=".."` in your .env
database_url: postgres://.. # OR set `DATABASE_URL=".."` in your .env
```
priority_reservation:
- Dict[str, float]
- str: can be any string
- float: from 0 to 1. Specify the % of tpm/rpm to reserve for keys of this priority.
**Start Proxy**
```
litellm --config /path/to/config.yaml
```
2. Create a key with that priority
```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer <your-master-key>' \
-H 'Content-Type: application/json' \
-D '{
"metadata": {"priority": "dev"} # 👈 KEY CHANGE
}'
```
**Expected Response**
```
{
...
"key": "sk-.."
}
```
3. Test it!
```bash
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: sk-...' \ # 👈 key from step 2.
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}'
```
**Expected Response**
```
Key=... over available RPM=0. Model RPM=100, Active keys=None
```

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@ -0,0 +1,328 @@
# SDK Header Support
LiteLLM SDK provides comprehensive support for passing additional headers with API requests. This is essential for enterprise environments using API gateways, service meshes, and multi-tenant architectures.
## Overview
Headers can be passed to LiteLLM in three ways, with the following priority order:
1. **Request-specific headers** (highest priority)
2. **extra_headers parameter**
3. **Global litellm.headers** (lowest priority)
When the same header key is specified in multiple places, the higher priority value will be used.
## Usage Methods
### 1. Global Headers (litellm.headers)
Set headers that will be included in all API requests:
```python
import litellm
# Set global headers for all requests
litellm.headers = {
"X-API-Gateway-Key": "your-gateway-key",
"X-Company-ID": "acme-corp",
"X-Environment": "production"
}
# Now all completion calls will include these headers
response = litellm.completion(
model="claude-3-5-sonnet-latest",
messages=[{"role": "user", "content": "Hello"}]
)
```
### 2. Per-Request Headers (extra_headers)
Pass headers for specific requests using the `extra_headers` parameter:
```python
import litellm
response = litellm.completion(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}],
extra_headers={
"X-Request-ID": "req-12345",
"X-Tenant-ID": "tenant-abc",
"X-Custom-Auth": "bearer-token-xyz"
}
)
```
### 3. Request Headers (headers parameter)
Use the `headers` parameter for the highest priority header control:
```python
import litellm
response = litellm.completion(
model="claude-3-5-sonnet-latest",
messages=[{"role": "user", "content": "Hello"}],
headers={
"X-Priority-Header": "high-priority-value",
"Authorization": "Bearer custom-token"
}
)
```
### 4. Combining All Methods
You can combine all three methods. Headers will be merged with the priority order:
```python
import litellm
# Global headers (lowest priority)
litellm.headers = {
"X-Company-ID": "acme-corp",
"X-Shared-Header": "global-value"
}
response = litellm.completion(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}],
extra_headers={
"X-Request-ID": "req-12345",
"X-Shared-Header": "extra-value" # Overrides global
},
headers={
"X-Priority-Header": "important",
"X-Shared-Header": "request-value" # Overrides both global and extra
}
)
# Final headers sent to API:
# {
# "X-Company-ID": "acme-corp", # From global
# "X-Request-ID": "req-12345", # From extra_headers
# "X-Priority-Header": "important", # From headers
# "X-Shared-Header": "request-value" # From headers (highest priority)
# }
```
## Enterprise Use Cases
### API Gateway Integration (Apigee, Kong, AWS API Gateway)
```python
import litellm
# Set up headers for API gateway routing and authentication
litellm.headers = {
"X-API-Gateway-Key": "your-gateway-key",
"X-Route-Version": "v2"
}
# Per-tenant requests
response = litellm.completion(
model="claude-3-5-sonnet-latest",
messages=[{"role": "user", "content": "Analyze this data"}],
extra_headers={
"X-Tenant-ID": "tenant-123",
"X-Department": "engineering"
}
)
```
### Service Mesh (Istio, Linkerd)
```python
import litellm
response = litellm.completion(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}],
extra_headers={
"X-Trace-ID": "trace-abc-123",
"X-Service-Name": "ai-service",
"X-Version": "1.2.3"
}
)
```
### Multi-Tenant SaaS Applications
```python
import litellm
def make_ai_request(user_id, tenant_id, content):
return litellm.completion(
model="claude-3-5-sonnet-latest",
messages=[{"role": "user", "content": content}],
extra_headers={
"X-User-ID": user_id,
"X-Tenant-ID": tenant_id,
"X-Request-Time": str(int(time.time()))
}
)
# Usage
response = make_ai_request("user-456", "tenant-org-1", "Help me write code")
```
### Request Tracing and Debugging
```python
import litellm
import uuid
def traced_completion(model, messages, **kwargs):
trace_id = str(uuid.uuid4())
return litellm.completion(
model=model,
messages=messages,
extra_headers={
"X-Trace-ID": trace_id,
"X-Debug-Mode": "true",
"X-Source-Service": "my-app"
},
**kwargs
)
# Usage
response = traced_completion(
model="gpt-4",
messages=[{"role": "user", "content": "Debug this issue"}]
)
```
### Custom Authentication
```python
import litellm
def get_custom_auth_token():
# Your custom authentication logic
return "custom-auth-token"
response = litellm.completion(
model="claude-3-5-sonnet-latest",
messages=[{"role": "user", "content": "Hello"}],
headers={
"X-Custom-Auth": get_custom_auth_token(),
"X-Auth-Type": "custom"
}
)
```
## Provider Support
Headers are supported across all LiteLLM providers including:
- **OpenAI** (GPT models)
- **Anthropic** (Claude models)
- **Cohere**
- **Hugging Face**
- **Custom providers**
- **Azure OpenAI**
- **AWS Bedrock**
- **Google Vertex AI**
Each provider will receive your custom headers along with their required authentication and API-specific headers.
## Best Practices
### 1. Use Meaningful Header Names
```python
# Good
extra_headers = {
"X-Request-ID": "req-12345",
"X-Tenant-ID": "org-456"
}
# Avoid
extra_headers = {
"custom1": "value1",
"h2": "value2"
}
```
### 2. Include Tracing Information
```python
extra_headers = {
"X-Trace-ID": trace_id,
"X-Span-ID": span_id,
"X-Service-Name": "ai-service"
}
```
### 3. Handle Sensitive Information Carefully
```python
# Don't log sensitive headers
import os
if os.getenv("ENVIRONMENT") != "production":
extra_headers["X-Debug-User"] = user_id
```
### 4. Use Environment-Specific Headers
```python
import os
environment = os.getenv("ENVIRONMENT", "development")
litellm.headers = {
"X-Environment": environment,
"X-Service-Version": os.getenv("SERVICE_VERSION", "unknown")
}
```
## Troubleshooting
### Headers Not Being Passed
If your headers aren't reaching the API:
1. **Check Header Names**: Ensure header names don't conflict with provider-specific headers
2. **Verify Priority**: Remember that `headers` > `extra_headers` > `litellm.headers`
3. **Test with Logging**: Enable verbose logging to see what headers are being sent
```python
import litellm
# Enable debug logging
litellm.set_verbose = True
response = litellm.completion(
model="gpt-4",
messages=[{"role": "user", "content": "test"}],
extra_headers={"X-Debug": "test"}
)
```
### Gateway or Proxy Issues
If using API gateways or proxies:
1. **Check Gateway Requirements**: Verify required headers for your gateway
2. **Test Direct vs Gateway**: Compare direct API calls vs gateway calls
3. **Validate Header Format**: Some gateways have header format requirements
## Security Considerations
1. **Don't Log Sensitive Headers**: Avoid logging authentication tokens or personal data
2. **Use HTTPS**: Always use secure connections when passing sensitive headers
3. **Validate Header Values**: Sanitize user-provided header values
4. **Rotate Keys**: Regularly rotate any API keys passed in headers
```python
import litellm
import re
def safe_header_value(value):
# Remove potentially dangerous characters
return re.sub(r'[^\w\-.]', '', str(value))
response = litellm.completion(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}],
extra_headers={
"X-User-ID": safe_header_value(user_id)
}
)
```

2
docs/my-website/release_notes/v1.76.0-stable/index.md
View file

@ -1,5 +1,5 @@
---
title: "[PRE-RELEASE]v1.76.0-stable - RPS Improvements"
title: "v1.76.0-stable - RPS Improvements"
slug: "v1-76-0"
date: 2025-08-23T10:00:00
authors:

11
docs/my-website/release_notes/v1.77.2-stable/index.md
View file

@ -1,5 +1,5 @@
---
title: "[Pre-Release] v1.77.2-stable - Bedrock Batches API"
title: "v1.77.2-stable - Bedrock Batches API"
slug: "v1-77-2"
date: 2025-09-13T10:00:00
authors:
@ -21,12 +21,6 @@ import TabItem from '@theme/TabItem';
## Deploy this version
:::info
This release is not yet live.
:::
<Tabs>
<TabItem value="docker" label="Docker">
@ -34,7 +28,7 @@ This release is not yet live.
docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
ghcr.io/berriai/litellm:main-v1.77.2.rc.2
ghcr.io/berriai/litellm:main-v1.77.2-stable
```
</TabItem>
@ -42,6 +36,7 @@ ghcr.io/berriai/litellm:main-v1.77.2.rc.2
<TabItem value="pip" label="Pip">
``` showLineNumbers title="pip install litellm"
pip install litellm==1.77.2.post1
```
</TabItem>

258
docs/my-website/release_notes/v1.77.3-stable/index.md Normal file
View file

@ -0,0 +1,258 @@
---
title: "[Preview] v1.77.3-stable - Priority Based Rate Limiting"
slug: "v1-77-3"
date: 2025-09-21T10:00:00
authors:
- name: Krrish Dholakia
title: CEO, LiteLLM
url: https://www.linkedin.com/in/krish-d/
image_url: https://pbs.twimg.com/profile_images/1298587542745358340/DZv3Oj-h_400x400.jpg
- name: Ishaan Jaff
title: CTO, LiteLLM
url: https://www.linkedin.com/in/reffajnaahsi/
image_url: https://pbs.twimg.com/profile_images/1613813310264340481/lz54oEiB_400x400.jpg
hide_table_of_contents: false
---
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
## Deploy this version
<Tabs>
<TabItem value="docker" label="Docker">
``` showLineNumbers title="docker run litellm"
docker run \
-e STORE_MODEL_IN_DB=True \
-p 4000:4000 \
ghcr.io/berriai/litellm:main-v1.77.3.rc.1
```
</TabItem>
<TabItem value="pip" label="Pip">
``` showLineNumbers title="pip install litellm"
pip install litellm==1.77.3
```
</TabItem>
</Tabs>
---
## Key Highlights
- **+550 RPS Performance Improvements** - Optimizations in request handling and object initialization.
- **Priority Quota Reservation** - Proxy admins can now reserve TPM/RPM capacity for specific keys.
## Priority Quota Reservation
This release adds support for priority quota reservation. This allows **Proxy Admins** to reserve TPM/RPM capacity for keys based on metadata priority levels, ensuring critical production workloads get guaranteed access regardless of development traffic volume.
Get started [here](../../docs/proxy/dynamic_rate_limit#priority-quota-reservation)
<iframe width="700" height="500" src="https://www.loom.com/embed/1b54b93139ee415d959402cc0629f3f7" frameborder="0" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>
## New Models / Updated Models
#### New Model Support
| Provider | Model | Context Window | Input ($/1M tokens) | Output ($/1M tokens) | Features |
| -------- | ----- | -------------- | ------------------- | -------------------- | -------- |
| SambaNova | `sambanova/deepseek-v3.1` | 128K | $0.90 | $0.90 | Chat completions |
| SambaNova | `sambanova/gpt-oss-120b` | 128K | $0.72 | $0.72 | Chat completions |
| OVHCloud | Various models | Varies | Contact provider | Contact provider | Chat completions |
| CompactifAI | Various models | Varies | Contact provider | Contact provider | Chat completions |
| TwelveLabs | `twelvelabs/marengo-embed-2.7` | 32K | $0.12 | $0.00 | Embeddings |
#### Features
- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)**
- New provider support with comprehensive model catalog - [PR #14494](https://github.com/BerriAI/litellm/pull/14494)
- **[CompactifAI](../../docs/providers/compactifai)**
- New provider integration - [PR #14532](https://github.com/BerriAI/litellm/pull/14532)
- **[SambaNova](../../docs/providers/sambanova)**
- Added DeepSeek v3.1 and GPT-OSS-120B models - [PR #14500](https://github.com/BerriAI/litellm/pull/14500)
- **[Bedrock](../../docs/providers/bedrock)**
- Cross-region inference profile cost calculation - [PR #14566](https://github.com/BerriAI/litellm/pull/14566)
- AWS external ID parameter support for authentication - [PR #14582](https://github.com/BerriAI/litellm/pull/14582)
- CountTokens API implementation - [PR #14557](https://github.com/BerriAI/litellm/pull/14557)
- Titan V2 encoding_format parameter support - [PR #14687](https://github.com/BerriAI/litellm/pull/14687)
- Nova Canvas image generation inference profiles - [PR #14578](https://github.com/BerriAI/litellm/pull/14578)
- Bedrock Batches API - batch processing support with file upload and request transformation - [PR #14618](https://github.com/BerriAI/litellm/pull/14618)
- Bedrock Twelve Labs embedding provider support - [PR #14697](https://github.com/BerriAI/litellm/pull/14697)
- **[Vertex AI](../../docs/providers/vertex)**
- Gemini labels field provider-aware filtering - [PR #14563](https://github.com/BerriAI/litellm/pull/14563)
- Gemini Batch API support - [PR #14733](https://github.com/BerriAI/litellm/pull/14733)
- **[Volcengine](../../docs/providers/volcengine)**
- Fixed thinking parameters when disabled - [PR #14569](https://github.com/BerriAI/litellm/pull/14569)
- **[Cohere](../../docs/providers/cohere)**
- Handle Generate API deprecation, default to chat endpoints - [PR #14676](https://github.com/BerriAI/litellm/pull/14676)
- **[TwelveLabs](../../docs/providers/twelvelabs)**
- Added Marengo Embed 2.7 embedding support - [PR #14674](https://github.com/BerriAI/litellm/pull/14674)
### Bug Fixes
- **[Bedrock](../../docs/providers/bedrock)**
- Empty arguments handling in tool call invocation - [PR #14583](https://github.com/BerriAI/litellm/pull/14583)
- **[Vertex AI](../../docs/providers/vertex)**
- Avoid deepcopy crash with non-pickleables in Gemini/Vertex - [PR #14418](https://github.com/BerriAI/litellm/pull/14418)
- **[XAI](../../docs/providers/xai)**
- Fix unsupported stop parameter for grok-code models - [PR #14565](https://github.com/BerriAI/litellm/pull/14565)
- **[Gemini](../../docs/providers/gemini)**
- Updated error message for Gemini API - [PR #14589](https://github.com/BerriAI/litellm/pull/14589)
- Fixed 2.5 Flash Image Preview model routing - [PR #14715](https://github.com/BerriAI/litellm/pull/14715)
- API key passing for token counting endpoints - [PR #14744](https://github.com/BerriAI/litellm/pull/14744)
#### New Provider Support
- **[OVHCloud AI Endpoints](../../docs/providers/ovhcloud)**
- Complete provider integration with model catalog and authentication - [PR #14494](https://github.com/BerriAI/litellm/pull/14494)
- **[CompactifAI](../../docs/providers/compactifai)**
- New provider support with documentation - [PR #14532](https://github.com/BerriAI/litellm/pull/14532)
---
## LLM API Endpoints
#### Features
- **[/responses](../../docs/response_api)**
- Added cancel endpoint support for non-admin users - [PR #14594](https://github.com/BerriAI/litellm/pull/14594)
- Improved response session handling and cold storage configuration with s3 - [PR #14534](https://github.com/BerriAI/litellm/pull/14534)
- Added OpenAI & Azure /responses/cancel endpoint support - [PR #14561](https://github.com/BerriAI/litellm/pull/14561)
- **General**
- Enhanced rate limit error messages with details - [PR #14736](https://github.com/BerriAI/litellm/pull/14736)
- Middle-truncation for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637)
#### Bugs
- **[/chat/completions](../../docs/completion/input)**
- Fixed completion chat ID handling - [PR #14548](https://github.com/BerriAI/litellm/pull/14548)
- Prevent AttributeError for _get_tags_from_request_kwargs - [PR #14735](https://github.com/BerriAI/litellm/pull/14735)
- **[/responses](../../docs/response_api)**
- Fixed cost calculation - [PR #14675](https://github.com/BerriAI/litellm/pull/14675)
- **General**
- Rate limiter AttributeError fix - [PR #14609](https://github.com/BerriAI/litellm/pull/14609)
---
## Spend Tracking, Budgets and Rate Limiting
- **Responses API Cost Calculation** fix - [PR #14675](https://github.com/BerriAI/litellm/pull/14675)
- **Anthropic Cache Token Pricing** - Separate 1-hour vs 5-minute cache creation costs - [PR #14620](https://github.com/BerriAI/litellm/pull/14620), [PR #14652](https://github.com/BerriAI/litellm/pull/14652)
- **Indochina Time Timezone** support for budget resets - [PR #14666](https://github.com/BerriAI/litellm/pull/14666)
- **Soft Budget Alert Cache Issues** - Resolved soft budget alert cache issues - [PR #14491](https://github.com/BerriAI/litellm/pull/14491)
- **Dynamic Rate Limiter v3** - Priority routing improvements - [PR #14734](https://github.com/BerriAI/litellm/pull/14734)
- **Enhanced Rate Limit Errors** - More detailed error messages - [PR #14736](https://github.com/BerriAI/litellm/pull/14736)
---
## Management Endpoints / UI
#### Features
- **Team Member Service Account Keys** - Allow team members to view keys they create - [PR #14619](https://github.com/BerriAI/litellm/pull/14619)
- **Default Budget for JWT Teams** - Auto-assign budgets to generated teams - [PR #14514](https://github.com/BerriAI/litellm/pull/14514)
- **SSO Access Control Groups** - Enhanced token info endpoint integration - [PR #14738](https://github.com/BerriAI/litellm/pull/14738)
- **Health Test Connect Protection** - Restrict access based on model creation permissions - [PR #14650](https://github.com/BerriAI/litellm/pull/14650)
- **Amazon Bedrock Guardrail Info View** - Enhanced logging visualization - [PR #14696](https://github.com/BerriAI/litellm/pull/14696)
#### Bug Fixes
- **SCIM v2** - Fix group PUSH and PUT operations for non-existent members - [PR #14581](https://github.com/BerriAI/litellm/pull/14581)
- **Guardrail View/Edit/Delete** behavior fixes - [PR #14622](https://github.com/BerriAI/litellm/pull/14622)
- **In-Memory Guardrail** update failures - [PR #14653](https://github.com/BerriAI/litellm/pull/14653)
---
## Logging / Guardrail Integrations
#### Features
- **[DataDog](../../docs/proxy/logging#datadog)**
- Enhanced spend tracking metrics - [PR #14555](https://github.com/BerriAI/litellm/pull/14555)
- Stream support with is_streamed_request parameter - [PR #14673](https://github.com/BerriAI/litellm/pull/14673)
- Fixed tool calls metadata passing - [PR #14531](https://github.com/BerriAI/litellm/pull/14531)
- **[Langfuse](../../docs/proxy/logging#langfuse)**
- Added logging support for Responses API - [PR #14597](https://github.com/BerriAI/litellm/pull/14597)
- **[Langsmith](../../docs/proxy/logging#langsmith)**
- Langsmith Sampling Rate - Key/Team-level tracing configuration - [PR #14740](https://github.com/BerriAI/litellm/pull/14740)
- **[Prometheus](../../docs/proxy/logging#prometheus)**
- Multi-worker support improvements - [PR #14530](https://github.com/BerriAI/litellm/pull/14530)
- User email labels in monitoring - [PR #14520](https://github.com/BerriAI/litellm/pull/14520)
- **[Opik](../../docs/proxy/logging#opik)**
- Fixed timezone issue - [PR #14708](https://github.com/BerriAI/litellm/pull/14708)
### Bug Fixes
- **[S3](../../docs/proxy/logging#s3-buckets)**
- Fixed 404 error when using s3_endpoint_url - [PR #14559](https://github.com/BerriAI/litellm/pull/14559)
#### Guardrails
- **Tool Permission Guardrail** - Fine-grained tool access control - [PR #14519](https://github.com/BerriAI/litellm/pull/14519)
- **Bedrock Guardrails** - Selective guarding support with runtime endpoint configuration - [PR #14575](https://github.com/BerriAI/litellm/pull/14575), [PR #14650](https://github.com/BerriAI/litellm/pull/14650)
- **Default Last Message** in guardrails - [PR #14640](https://github.com/BerriAI/litellm/pull/14640)
- **AWS exceptions handling despite 200 response** - [PR #14658](https://github.com/BerriAI/litellm/pull/14658)
#### New Integration
- **[PostHog](../../docs/observability/posthog)** - Complete observability integration for LiteLLM usage tracking and analytics - [PR #14610](https://github.com/BerriAI/litellm/pull/14610)
---
## MCP Gateway
- **MCP Server Alias Parsing** - Multi-part URL path support - [PR #14558](https://github.com/BerriAI/litellm/pull/14558)
- **MCP Filter Recomputation** - After server deletion - [PR #14542](https://github.com/BerriAI/litellm/pull/14542)
- **MCP Gateway Tools List** improvements - [PR #14695](https://github.com/BerriAI/litellm/pull/14695)
---
## Performance / Loadbalancing / Reliability improvements
- **+500 RPS Performance Boost** when sending the `user` field - [PR #14616](https://github.com/BerriAI/litellm/pull/14616)
- **+50 RPS** by removing iscoroutine from hot path - [PR #14649](https://github.com/BerriAI/litellm/pull/14649)
- **7% reduction** in __init__ overhead - [PR #14689](https://github.com/BerriAI/litellm/pull/14689)
- **Generic Object Pool** implementation for better resource management - [PR #14702](https://github.com/BerriAI/litellm/pull/14702)
---
## General Proxy Improvements
- **Middle-Truncation** for spend log payloads - [PR #14637](https://github.com/BerriAI/litellm/pull/14637)
#### Security
- **Security Update** - Bump aiohttp==3.12.14, fix CVE-2025-53643 - [PR #14638](https://github.com/BerriAI/litellm/pull/14638)
---
## New Contributors
* @luisfucros made their first contribution in [PR #14500](https://github.com/BerriAI/litellm/pull/14500)
* @hanakannzashi made their first contribution in [PR #14548](https://github.com/BerriAI/litellm/pull/14548)
* @eliasto made their first contribution in [PR #14494](https://github.com/BerriAI/litellm/pull/14494)
* @Rasmusafj made their first contribution in [PR #14491](https://github.com/BerriAI/litellm/pull/14491)
* @LingXuanYin made their first contribution in [PR #14569](https://github.com/BerriAI/litellm/pull/14569)
* @ronaldpereira made their first contribution in [PR #14613](https://github.com/BerriAI/litellm/pull/14613)
* @hula-la made their first contribution in [PR #14534](https://github.com/BerriAI/litellm/pull/14534)
* @carlos-marchal-ph made their first contribution in [PR #14610](https://github.com/BerriAI/litellm/pull/14610)
* @akraines made their first contribution in [PR #14637](https://github.com/BerriAI/litellm/pull/14637)
* @mrFranklin made their first contribution in [PR #14708](https://github.com/BerriAI/litellm/pull/14708)
* @tcx4c70 made their first contribution in [PR #14675](https://github.com/BerriAI/litellm/pull/14675)
* @michaeltansg made their first contribution in [PR #14666](https://github.com/BerriAI/litellm/pull/14666)
* @tosi29 made their first contribution in [PR #14725](https://github.com/BerriAI/litellm/pull/14725)
* @gmdfalk made their first contribution in [PR #14735](https://github.com/BerriAI/litellm/pull/14735)
* @FelipeRodriguesGare made their first contribution in [PR #14733](https://github.com/BerriAI/litellm/pull/14733)
* @mritunjaysharma394 made their first contribution in [PR #14678](https://github.com/BerriAI/litellm/pull/14678)
---
## **[Full Changelog](https://github.com/BerriAI/litellm/compare/v1.77.2.rc.1...v1.77.3.rc.1)**

4
docs/my-website/sidebars.js
View file

@ -201,7 +201,7 @@ const sidebars = {
{
type: "category",
label: "Budgets + Rate Limits",
items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/customers"],
items: ["proxy/users", "proxy/temporary_budget_increase", "proxy/rate_limit_tiers", "proxy/team_budgets", "proxy/dynamic_rate_limit", "proxy/customers"],
},
{
type: "link",
@ -392,6 +392,7 @@ const sidebars = {
"providers/vertex",
"providers/vertex_partner",
"providers/vertex_image",
"providers/vertex_batch",
]
},
{
@ -523,6 +524,7 @@ const sidebars = {
"completion/batching",
"completion/mock_requests",
"completion/reliable_completions",
"proxy/veo_video_generation",
]
},

195
examples/sdk_headers_example.py Normal file
View file

@ -0,0 +1,195 @@
#!/usr/bin/env python3
"""
Example demonstrating LiteLLM SDK header support for enterprise environments.
This example shows how to use additional headers with API gateways, service meshes,
and multi-tenant architectures.
"""
import litellm
import os
from typing import Dict, Any
def example_global_headers():
"""Example: Set global headers for all requests"""
print("=== Global Headers Example ===")
# Set global headers that will be included in all API requests
litellm.headers = {
"X-API-Gateway-Key": "your-gateway-key-here",
"X-Company-ID": "acme-corp",
"X-Environment": "production"
}
print("Global headers set:", litellm.headers)
# These headers will now be included in all completion calls
# (Note: This example doesn't actually make API calls)
print("Global headers will be included in all subsequent completion() calls")
def example_per_request_headers():
"""Example: Using extra_headers for specific requests"""
print("\n=== Per-Request Headers Example ===")
headers_to_send = {
"X-Request-ID": "req-12345",
"X-Tenant-ID": "tenant-abc",
"X-Custom-Auth": "bearer-token-xyz"
}
print("Per-request headers:", headers_to_send)
# Example of how you would use extra_headers in a real call
# response = litellm.completion(
# model="claude-3-5-sonnet-latest",
# messages=[{"role": "user", "content": "Hello"}],
# extra_headers=headers_to_send
# )
def example_header_priority():
"""Example: Demonstrating header priority and merging"""
print("\n=== Header Priority Example ===")
# Set global headers
litellm.headers = {
"X-Company-ID": "acme-corp",
"X-Shared-Header": "global-value"
}
# Headers that would be sent in a request
extra_headers = {
"X-Request-ID": "req-12345",
"X-Shared-Header": "extra-value" # Overrides global
}
request_headers = {
"X-Priority-Header": "important",
"X-Shared-Header": "request-value" # Overrides both global and extra
}
print("Global headers:", litellm.headers)
print("Extra headers:", extra_headers)
print("Request headers:", request_headers)
print("\nFinal headers would be:")
print(" X-Company-ID: acme-corp (from global)")
print(" X-Request-ID: req-12345 (from extra)")
print(" X-Priority-Header: important (from request)")
print(" X-Shared-Header: request-value (request wins - highest priority)")
def example_enterprise_api_gateway():
"""Example: Enterprise API Gateway scenario"""
print("\n=== Enterprise API Gateway Example ===")
# Simulate enterprise environment with Apigee or similar
gateway_config = {
"X-API-Gateway-Key": os.getenv("API_GATEWAY_KEY", "demo-key"),
"X-Route-Version": "v2",
"X-Rate-Limit-Group": "premium"
}
# Set gateway headers globally
litellm.headers = gateway_config
print("Gateway headers configured:", gateway_config)
# Function to make tenant-specific requests
def make_tenant_request(tenant_id: str, user_id: str, content: str) -> Dict[str, Any]:
"""Make an AI request with tenant-specific headers"""
tenant_headers = {
"X-Tenant-ID": tenant_id,
"X-User-ID": user_id,
"X-Request-Time": "2024-01-01T00:00:00Z",
"X-Service-Name": "ai-assistant"
}
print(f"Making request for tenant {tenant_id}, user {user_id}")
print("Tenant-specific headers:", tenant_headers)
# In a real scenario, this would make the actual API call:
# return litellm.completion(
# model="claude-3-5-sonnet-latest",
# messages=[{"role": "user", "content": content}],
# extra_headers=tenant_headers
# )
# For demo purposes, return mock data
return {"mock": "response", "headers_used": {**gateway_config, **tenant_headers}}
# Example usage
result = make_tenant_request("tenant-123", "user-456", "Analyze this data")
print("Response:", result)
def example_service_mesh():
"""Example: Service mesh integration (Istio, Linkerd)"""
print("\n=== Service Mesh Example ===")
service_mesh_headers = {
"X-Trace-ID": "trace-abc-123",
"X-Span-ID": "span-def-456",
"X-Service-Name": "ai-service",
"X-Version": "1.2.3",
"X-Cluster": "prod-us-west-2"
}
print("Service mesh headers:", service_mesh_headers)
# Example of using these headers for distributed tracing
# response = litellm.completion(
# model="gpt-4",
# messages=[{"role": "user", "content": "Hello"}],
# extra_headers=service_mesh_headers
# )
def example_debugging_and_monitoring():
"""Example: Request debugging and monitoring"""
print("\n=== Debugging and Monitoring Example ===")
import uuid
import time
# Generate unique identifiers for request tracking
trace_id = str(uuid.uuid4())
request_id = f"req-{int(time.time())}"
debug_headers = {
"X-Trace-ID": trace_id,
"X-Request-ID": request_id,
"X-Debug-Mode": "true",
"X-Source-Service": "customer-support-bot",
"X-Request-Priority": "high"
}
print("Debug headers:", debug_headers)
print(f"Trace ID: {trace_id}")
print(f"Request ID: {request_id}")
# These headers help with:
# 1. Distributed tracing across services
# 2. Request correlation in logs
# 3. Debug mode enablement
# 4. Priority-based routing
if __name__ == "__main__":
print("LiteLLM SDK Header Support Examples")
print("=" * 50)
example_global_headers()
example_per_request_headers()
example_header_priority()
example_enterprise_api_gateway()
example_service_mesh()
example_debugging_and_monitoring()
print("\n" + "=" * 50)
print("All examples completed!")
print("\nTo use in your application:")
print("1. Set litellm.headers for global headers")
print("2. Use extra_headers parameter for request-specific headers")
print("3. Use headers parameter for highest priority headers")
print("4. Headers are merged with priority: headers > extra_headers > litellm.headers")

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litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19-py3-none-any.whl vendored Normal file
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litellm-proxy-extras/dist/litellm_proxy_extras-0.2.19.tar.gz vendored Normal file
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4
litellm-proxy-extras/pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm-proxy-extras"
version = "0.2.18"
version = "0.2.19"
description = "Additional files for the LiteLLM Proxy. Reduces the size of the main litellm package."
authors = ["BerriAI"]
readme = "README.md"
@ -22,7 +22,7 @@ requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
version = "0.2.18"
version = "0.2.19"
version_files = [
"pyproject.toml:version",
"../requirements.txt:litellm-proxy-extras==",

1
litellm/__init__.py
View file

@ -117,6 +117,7 @@ _custom_logger_compatible_callbacks_literal = Literal[
"logfire",
"literalai",
"dynamic_rate_limiter",
"dynamic_rate_limiter_v3",
"langsmith",
"prometheus",
"otel",

4
litellm/experimental_mcp_client/client.py
View file

@ -186,7 +186,9 @@ class MCPClient:
def _get_auth_headers(self) -> dict:
"""Generate authentication headers based on auth type."""
headers = {}
headers = {
"MCP-Protocol-Version": "2025-06-18"
}
if self._mcp_auth_value:
if self.auth_type == MCPAuth.bearer_token:

28
litellm/files/main.py
View file

@ -731,7 +731,7 @@ def file_list(
async def afile_content(
file_id: str,
custom_llm_provider: Literal["openai", "azure"] = "openai",
custom_llm_provider: Literal["openai", "azure", "vertex_ai"] = "openai",
extra_headers: Optional[Dict[str, str]] = None,
extra_body: Optional[Dict[str, str]] = None,
**kwargs,
@ -887,6 +887,32 @@ def file_content(
client=client,
litellm_params=litellm_params_dict,
)
elif custom_llm_provider == "vertex_ai":
api_base = optional_params.api_base or ""
vertex_ai_project = (
optional_params.vertex_project
or litellm.vertex_project
or get_secret_str("VERTEXAI_PROJECT")
)
vertex_ai_location = (
optional_params.vertex_location
or litellm.vertex_location
or get_secret_str("VERTEXAI_LOCATION")
)
vertex_credentials = optional_params.vertex_credentials or get_secret_str(
"VERTEXAI_CREDENTIALS"
)
response = vertex_ai_files_instance.file_content(
_is_async=_is_async,
file_content_request=_file_content_request,
api_base=api_base,
vertex_credentials=vertex_credentials,
vertex_project=vertex_ai_project,
vertex_location=vertex_ai_location,
timeout=timeout,
max_retries=optional_params.max_retries,
)
else:
raise litellm.exceptions.BadRequestError(
message="LiteLLM doesn't support {} for 'custom_llm_provider'. Supported providers are 'openai', 'azure', 'vertex_ai'.".format(

2
litellm/litellm_core_utils/custom_logger_registry.py
View file

@ -47,6 +47,7 @@ from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook i
VectorStorePreCallHook,
)
from litellm.proxy.hooks.dynamic_rate_limiter import _PROXY_DynamicRateLimitHandler
from litellm.proxy.hooks.dynamic_rate_limiter_v3 import _PROXY_DynamicRateLimitHandlerV3
class CustomLoggerRegistry:
@ -86,6 +87,7 @@ class CustomLoggerRegistry:
"s3_v2": S3Logger,
"aws_sqs": SQSLogger,
"dynamic_rate_limiter": _PROXY_DynamicRateLimitHandler,
"dynamic_rate_limiter_v3": _PROXY_DynamicRateLimitHandlerV3,
"vector_store_pre_call_hook": VectorStorePreCallHook,
"dotprompt": DotpromptManager,
"cloudzero": CloudZeroLogger,

160
litellm/litellm_core_utils/exception_mapping_utils.py
View file

@ -6,6 +6,7 @@ import httpx
import litellm
from litellm._logging import verbose_logger
from litellm.types.utils import LlmProviders
from ..exceptions import (
APIConnectionError,
@ -762,7 +763,7 @@ def exception_type( # type: ignore # noqa: PLR0915
error_str += "XXXXXXX" + '"'
raise AuthenticationError(
message=f"{custom_llm_provider}Exception: Authentication Error - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception: Authentication Error - {error_str}",
llm_provider=custom_llm_provider,
model=model,
response=getattr(original_exception, "response", None),
@ -771,14 +772,14 @@ def exception_type( # type: ignore # noqa: PLR0915
elif "model's maximum context limit" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
message=f"{custom_llm_provider}Exception: Context Window Error - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception: Context Window Error - {error_str}",
model=model,
llm_provider=custom_llm_provider,
)
elif "token_quota_reached" in error_str:
exception_mapping_worked = True
raise RateLimitError(
message=f"{custom_llm_provider}Exception: Rate Limit Errror - {error_str}",
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),
@ -789,14 +790,14 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise litellm.InternalServerError(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
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}Exception - Use 'watsonx_text' route instead. IBM WatsonX does not support `/text/chat` endpoint. - {error_str}",
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,
)
@ -804,7 +805,7 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 500:
exception_mapping_worked = True
raise litellm.InternalServerError(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
llm_provider=custom_llm_provider,
model=model,
)
@ -814,28 +815,28 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise AuthenticationError(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
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}Exception - {original_exception.message}",
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}Exception - {original_exception.message}",
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}Exception - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@ -846,7 +847,7 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise BadRequestError(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@ -854,7 +855,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif original_exception.status_code == 429:
exception_mapping_worked = True
raise RateLimitError(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@ -862,7 +863,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif original_exception.status_code == 503:
exception_mapping_worked = True
raise ServiceUnavailableError(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@ -870,7 +871,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif original_exception.status_code == 504: # gateway timeout error
exception_mapping_worked = True
raise Timeout(
message=f"{custom_llm_provider}Exception - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@ -1168,9 +1169,9 @@ def exception_type( # type: ignore # noqa: PLR0915
exception_status_code=original_exception.status_code,
)
elif (
custom_llm_provider == "vertex_ai"
or custom_llm_provider == "vertex_ai_beta"
or custom_llm_provider == "gemini"
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
@ -1178,9 +1179,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise BadRequestError(
message=f"litellm.BadRequestError: VertexAIException - {error_str}",
message=f"litellm.BadRequestError: {custom_llm_provider}Exception - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=400,
request=httpx.Request(
@ -1193,7 +1194,7 @@ def exception_type( # type: ignore # noqa: PLR0915
if "400 Request payload size exceeds" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
message=f"VertexException - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
model=model,
llm_provider=custom_llm_provider,
)
@ -1203,9 +1204,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise litellm.InternalServerError(
message=f"litellm.InternalServerError: VertexAIException - {error_str}",
message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=500,
content=str(original_exception),
@ -1216,7 +1217,7 @@ def exception_type( # type: ignore # noqa: PLR0915
elif "API key not valid." in error_str:
exception_mapping_worked = True
raise AuthenticationError(
message=f"{custom_llm_provider}Exception - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
@ -1224,9 +1225,9 @@ def exception_type( # type: ignore # noqa: PLR0915
elif "403" in error_str:
exception_mapping_worked = True
raise BadRequestError(
message=f"VertexAIException BadRequestError - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
response=httpx.Response(
status_code=403,
request=httpx.Request(
@ -1243,9 +1244,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise ContentPolicyViolationError(
message=f"VertexAIException ContentPolicyViolationError - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception ContentPolicyViolationError - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=400,
@ -1264,9 +1265,9 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise RateLimitError(
message=f"litellm.RateLimitError: VertexAIException - {error_str}",
message=f"litellm.RateLimitError: {custom_llm_provider}Exception - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=429,
@ -1282,18 +1283,18 @@ def exception_type( # type: ignore # noqa: PLR0915
):
exception_mapping_worked = True
raise litellm.InternalServerError(
message=f"litellm.InternalServerError: VertexAIException - {error_str}",
message=f"litellm.InternalServerError: {custom_llm_provider}Exception - {error_str}",
model=model,
llm_provider="vertex_ai",
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"VertexAIException BadRequestError - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception BadRequestError - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=400,
@ -1306,21 +1307,35 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 401:
exception_mapping_worked = True
raise AuthenticationError(
message=f"VertexAIException - {original_exception.message}",
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"VertexAIException - {original_exception.message}",
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"VertexAIException - {original_exception.message}",
message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
@ -1328,9 +1343,9 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 429:
exception_mapping_worked = True
raise RateLimitError(
message=f"litellm.RateLimitError: VertexAIException - {error_str}",
message=f"litellm.RateLimitError: {custom_llm_provider.capitalize()}Exception - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=429,
@ -1343,9 +1358,9 @@ def exception_type( # type: ignore # noqa: PLR0915
if original_exception.status_code == 500:
exception_mapping_worked = True
raise litellm.InternalServerError(
message=f"VertexAIException InternalServerError - {error_str}",
message=f"{custom_llm_provider.capitalize()}Exception InternalServerError - {error_str}",
model=model,
llm_provider="vertex_ai",
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
response=httpx.Response(
status_code=500,
@ -1353,71 +1368,20 @@ def exception_type( # type: ignore # noqa: PLR0915
request=httpx.Request(method="completion", url="https://github.com/BerriAI/litellm"), # type: ignore
),
)
if original_exception.status_code == 503:
if original_exception.status_code == 502:
exception_mapping_worked = True
raise ServiceUnavailableError(
message=f"VertexAIException - {original_exception.message}",
raise APIConnectionError(
message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
)
elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
if "503 Getting metadata" in error_str:
# auth errors look like this
# 503 Getting metadata from plugin failed with error: Reauthentication is needed. Please run `gcloud auth application-default login` to reauthenticate.
exception_mapping_worked = True
raise BadRequestError(
message="GeminiException - Invalid api key",
model=model,
llm_provider="palm",
response=getattr(original_exception, "response", None),
)
if (
"504 Deadline expired before operation could complete." in error_str
or "504 Deadline Exceeded" in error_str
):
exception_mapping_worked = True
raise Timeout(
message=f"GeminiException - {original_exception.message}",
model=model,
llm_provider="palm",
exception_status_code=original_exception.status_code,
)
if "400 Request payload size exceeds" in error_str:
exception_mapping_worked = True
raise ContextWindowExceededError(
message=f"GeminiException - {error_str}",
model=model,
llm_provider="palm",
response=getattr(original_exception, "response", None),
)
if (
"500 An internal error has occurred." in error_str
or "list index out of range" in error_str
):
exception_mapping_worked = True
raise APIError(
status_code=getattr(original_exception, "status_code", 500),
message=f"GeminiException - {original_exception.message}",
llm_provider="palm",
model=model,
request=httpx.Response(
status_code=429,
request=httpx.Request(
method="POST",
url=" https://cloud.google.com/vertex-ai/",
),
),
)
if hasattr(original_exception, "status_code"):
if original_exception.status_code == 400:
if original_exception.status_code == 503:
exception_mapping_worked = True
raise BadRequestError(
message=f"GeminiException - {error_str}",
raise ServiceUnavailableError(
message=f"{custom_llm_provider.capitalize()}Exception - {error_str}",
llm_provider=custom_llm_provider,
model=model,
llm_provider="palm",
response=getattr(original_exception, "response", None),
)
# Dailed: Error occurred: 400 Request payload size exceeds the limit: 20000 bytes
elif custom_llm_provider == "cloudflare":
if "Authentication error" in error_str:
exception_mapping_worked = True

32
litellm/litellm_core_utils/litellm_logging.py
View file

@ -3444,6 +3444,30 @@ def _init_custom_logger_compatible_class( # noqa: PLR0915
dynamic_rate_limiter_obj.update_variables(llm_router=llm_router)
_in_memory_loggers.append(dynamic_rate_limiter_obj)
return dynamic_rate_limiter_obj # type: ignore
elif logging_integration == "dynamic_rate_limiter_v3":
from litellm.proxy.hooks.dynamic_rate_limiter_v3 import (
_PROXY_DynamicRateLimitHandlerV3,
)
for callback in _in_memory_loggers:
if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3):
return callback # type: ignore
if internal_usage_cache is None:
raise Exception(
"Internal Error: Cache cannot be empty - internal_usage_cache={}".format(
internal_usage_cache
)
)
dynamic_rate_limiter_obj_v3 = _PROXY_DynamicRateLimitHandlerV3(
internal_usage_cache=internal_usage_cache
)
if llm_router is not None and isinstance(llm_router, litellm.Router):
dynamic_rate_limiter_obj_v3.update_variables(llm_router=llm_router)
_in_memory_loggers.append(dynamic_rate_limiter_obj_v3)
return dynamic_rate_limiter_obj_v3 # type: ignore
elif logging_integration == "langtrace":
if "LANGTRACE_API_KEY" not in os.environ:
raise ValueError("LANGTRACE_API_KEY not found in environment variables")
@ -3707,6 +3731,14 @@ def get_custom_logger_compatible_class( # noqa: PLR0915
for callback in _in_memory_loggers:
if isinstance(callback, _PROXY_DynamicRateLimitHandler):
return callback # type: ignore
elif logging_integration == "dynamic_rate_limiter_v3":
from litellm.proxy.hooks.dynamic_rate_limiter_v3 import (
_PROXY_DynamicRateLimitHandlerV3,
)
for callback in _in_memory_loggers:
if isinstance(callback, _PROXY_DynamicRateLimitHandlerV3):
return callback # type: ignore
elif logging_integration == "langtrace":
from litellm.integrations.opentelemetry import OpenTelemetry

158
litellm/llms/bedrock/chat/converse_transformation.py
View file

@ -175,6 +175,77 @@ class AmazonConverseConfig(BaseConfig):
and v is not None
}
def _validate_request_metadata(self, metadata: dict) -> None:
"""
Validate requestMetadata according to AWS Bedrock Converse API constraints.
Constraints:
- Maximum of 16 items
- Keys: 1-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{1,256}
- Values: 0-256 characters, pattern [a-zA-Z0-9\\s:_@$#=/+,-.]{0,256}
"""
import re
if not isinstance(metadata, dict):
raise litellm.exceptions.BadRequestError(
message="requestMetadata must be a dictionary",
model="bedrock",
llm_provider="bedrock",
)
if len(metadata) > 16:
raise litellm.exceptions.BadRequestError(
message="requestMetadata can contain a maximum of 16 items",
model="bedrock",
llm_provider="bedrock",
)
key_pattern = re.compile(r'^[a-zA-Z0-9\s:_@$#=/+,.-]{1,256}$')
value_pattern = re.compile(r'^[a-zA-Z0-9\s:_@$#=/+,.-]{0,256}$')
for key, value in metadata.items():
if not isinstance(key, str):
raise litellm.exceptions.BadRequestError(
message="requestMetadata keys must be strings",
model="bedrock",
llm_provider="bedrock",
)
if not isinstance(value, str):
raise litellm.exceptions.BadRequestError(
message="requestMetadata values must be strings",
model="bedrock",
llm_provider="bedrock",
)
if len(key) == 0 or len(key) > 256:
raise litellm.exceptions.BadRequestError(
message="requestMetadata key length must be 1-256 characters",
model="bedrock",
llm_provider="bedrock",
)
if len(value) > 256:
raise litellm.exceptions.BadRequestError(
message="requestMetadata value length must be 0-256 characters",
model="bedrock",
llm_provider="bedrock",
)
if not key_pattern.match(key):
raise litellm.exceptions.BadRequestError(
message=f"requestMetadata key '{key}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]",
model="bedrock",
llm_provider="bedrock",
)
if not value_pattern.match(value):
raise litellm.exceptions.BadRequestError(
message=f"requestMetadata value '{value}' contains invalid characters. Allowed: [a-zA-Z0-9\\s:_@$#=/+,.-]",
model="bedrock",
llm_provider="bedrock",
)
def get_supported_openai_params(self, model: str) -> List[str]:
from litellm.utils import supports_function_calling
@ -188,6 +259,7 @@ class AmazonConverseConfig(BaseConfig):
"top_p",
"extra_headers",
"response_format",
"requestMetadata",
]
if (
@ -497,6 +569,10 @@ class AmazonConverseConfig(BaseConfig):
optional_params["thinking"] = AnthropicConfig._map_reasoning_effort(
value
)
if param == "requestMetadata":
if value is not None and isinstance(value, dict):
self._validate_request_metadata(value) # type: ignore
optional_params["requestMetadata"] = value
# Only update thinking tokens for non-GPT-OSS models
if "gpt-oss" not in model:
@ -686,34 +762,8 @@ class AmazonConverseConfig(BaseConfig):
return {}
def _transform_request_helper(
self,
model: str,
system_content_blocks: List[SystemContentBlock],
optional_params: dict,
messages: Optional[List[AllMessageValues]] = None,
headers: Optional[dict] = None,
) -> CommonRequestObject:
## VALIDATE REQUEST
"""
Bedrock doesn't support tool calling without `tools=` param specified.
"""
if (
"tools" not in optional_params
and messages is not None
and has_tool_call_blocks(messages)
):
if litellm.modify_params:
optional_params["tools"] = add_dummy_tool(
custom_llm_provider="bedrock_converse"
)
else:
raise litellm.UnsupportedParamsError(
message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
model="",
llm_provider="bedrock",
)
def _prepare_request_params(self, optional_params: dict, model: str) -> tuple[dict, dict, dict]:
"""Prepare and separate request parameters."""
inference_params = copy.deepcopy(optional_params)
supported_converse_params = list(
AmazonConverseConfig.__annotations__.keys()
@ -727,6 +777,11 @@ class AmazonConverseConfig(BaseConfig):
)
inference_params.pop("json_mode", None) # used for handling json_schema
# Extract requestMetadata before processing other parameters
request_metadata = inference_params.pop("requestMetadata", None)
if request_metadata is not None:
self._validate_request_metadata(request_metadata)
# keep supported params in 'inference_params', and set all model-specific params in 'additional_request_params'
additional_request_params = {
k: v for k, v in inference_params.items() if k not in total_supported_params
@ -740,9 +795,10 @@ class AmazonConverseConfig(BaseConfig):
self._handle_top_k_value(model, inference_params)
)
original_tools = inference_params.pop("tools", [])
return inference_params, additional_request_params, request_metadata
# Initialize bedrock_tools
def _process_tools_and_beta(self, original_tools: list, model: str, headers: Optional[dict], additional_request_params: dict) -> tuple[List[ToolBlock], list]:
"""Process tools and collect anthropic_beta values."""
bedrock_tools: List[ToolBlock] = []
# Collect anthropic_beta values from user headers
@ -784,6 +840,44 @@ class AmazonConverseConfig(BaseConfig):
seen.add(beta)
additional_request_params["anthropic_beta"] = unique_betas
return bedrock_tools, anthropic_beta_list
def _transform_request_helper(
self,
model: str,
system_content_blocks: List[SystemContentBlock],
optional_params: dict,
messages: Optional[List[AllMessageValues]] = None,
headers: Optional[dict] = None,
) -> CommonRequestObject:
## VALIDATE REQUEST
"""
Bedrock doesn't support tool calling without `tools=` param specified.
"""
if (
"tools" not in optional_params
and messages is not None
and has_tool_call_blocks(messages)
):
if litellm.modify_params:
optional_params["tools"] = add_dummy_tool(
custom_llm_provider="bedrock_converse"
)
else:
raise litellm.UnsupportedParamsError(
message="Bedrock doesn't support tool calling without `tools=` param specified. Pass `tools=` param OR set `litellm.modify_params = True` // `litellm_settings::modify_params: True` to add dummy tool to the request.",
model="",
llm_provider="bedrock",
)
# Prepare and separate parameters
inference_params, additional_request_params, request_metadata = self._prepare_request_params(optional_params, model)
original_tools = inference_params.pop("tools", [])
# Process tools and collect beta values
bedrock_tools, anthropic_beta_list = self._process_tools_and_beta(original_tools, model, headers, additional_request_params)
bedrock_tool_config: Optional[ToolConfigBlock] = None
if len(bedrock_tools) > 0:
tool_choice_values: ToolChoiceValuesBlock = inference_params.pop(
@ -813,6 +907,10 @@ class AmazonConverseConfig(BaseConfig):
if bedrock_tool_config is not None:
data["toolConfig"] = bedrock_tool_config
# Request Metadata (top-level field)
if request_metadata is not None:
data["requestMetadata"] = request_metadata
return data
async def _async_transform_request(

9
litellm/llms/bedrock/image/amazon_nova_canvas_transformation.py
View file

@ -7,12 +7,12 @@ from litellm.types.llms.bedrock import (
AmazonNovaCanvasColorGuidedGenerationParams,
AmazonNovaCanvasColorGuidedRequest,
AmazonNovaCanvasImageGenerationConfig,
AmazonNovaCanvasInpaintingParams,
AmazonNovaCanvasInpaintingRequest,
AmazonNovaCanvasRequestBase,
AmazonNovaCanvasTextToImageParams,
AmazonNovaCanvasTextToImageRequest,
AmazonNovaCanvasTextToImageResponse,
AmazonNovaCanvasInpaintingParams,
AmazonNovaCanvasInpaintingRequest,
)
from litellm.types.utils import ImageResponse
@ -67,6 +67,11 @@ class AmazonNovaCanvasConfig:
"""
task_type = optional_params.pop("taskType", "TEXT_IMAGE")
image_generation_config = optional_params.pop("imageGenerationConfig", {})
# Extract model_id parameter to prevent "extraneous key" error from Bedrock API
# Following the same pattern as chat completions and embeddings
unencoded_model_id = optional_params.pop("model_id", None) # noqa: F841
image_generation_config = {**image_generation_config, **optional_params}
if task_type == "TEXT_IMAGE":
text_to_image_params: Dict[str, Any] = image_generation_config.pop(

12
litellm/llms/bedrock/image/image_handler.py
View file

@ -233,7 +233,17 @@ class BedrockImageGeneration(BaseAWSLLM):
Returns:
dict: The request body to use for the Bedrock Image Generation API
"""
provider = model.split(".")[0]
# Use the existing ARN-aware provider detection method
bedrock_provider = self.get_bedrock_invoke_provider(model)
if bedrock_provider == "amazon" or bedrock_provider == "nova":
# Handle Amazon Nova Canvas models
provider = "amazon"
elif bedrock_provider == "stability":
provider = "stability"
else:
# Fallback to original logic for backward compatibility
provider = model.split(".")[0]
inference_params = copy.deepcopy(optional_params)
inference_params.pop(
"user", None

3
litellm/llms/gemini/google_genai/transformation.py
View file

@ -121,7 +121,8 @@ class GoogleGenAIConfig(BaseGoogleGenAIGenerateContentConfig, VertexLLM):
default_headers = {
"Content-Type": "application/json",
}
gemini_api_key = self._get_google_ai_studio_api_key(dict(litellm_params or {}))
# Use the passed api_key first, then fall back to litellm_params and environment
gemini_api_key = api_key or self._get_google_ai_studio_api_key(dict(litellm_params or {}))
if gemini_api_key is not None:
default_headers[self.XGOOGLE_API_KEY] = gemini_api_key
if headers is not None:

9
litellm/llms/vertex_ai/batches/transformation.py
View file

@ -114,7 +114,14 @@ class VertexAIBatchTransformation:
"""
Gets the output file id from the Vertex AI Batch response
"""
output_file_id: str = ""
output_file_id: str = (
response.get("outputInfo", OutputInfo()).get("gcsOutputDirectory", "")
+ "/predictions.jsonl"
)
if output_file_id != "/predictions.jsonl":
return output_file_id
output_config = response.get("outputConfig")
if output_config is None:
return output_file_id

143
litellm/llms/vertex_ai/files/handler.py
View file

@ -1,5 +1,6 @@
import asyncio
from typing import Any, Coroutine, Optional, Union
import urllib.parse
from typing import Any, Coroutine, Optional, Tuple, Union
import httpx
@ -9,7 +10,12 @@ from litellm.integrations.gcs_bucket.gcs_bucket_base import (
GCSLoggingConfig,
)
from litellm.llms.custom_httpx.http_handler import get_async_httpx_client
from litellm.types.llms.openai import CreateFileRequest, OpenAIFileObject
from litellm.types.llms.openai import (
CreateFileRequest,
FileContentRequest,
HttpxBinaryResponseContent,
OpenAIFileObject,
)
from litellm.types.llms.vertex_ai import VERTEX_CREDENTIALS_TYPES
from .transformation import VertexAIJsonlFilesTransformation
@ -105,3 +111,136 @@ class VertexAIFilesHandler(GCSBucketBase):
max_retries=max_retries,
)
)
def _extract_bucket_and_object_from_file_id(self, file_id: str) -> Tuple[str, str]:
"""
Extract bucket name and object path from URL-encoded file_id.
Expected format: gs%3A%2F%2Fbucket-name%2Fpath%2Fto%2Ffile
Which decodes to: gs://bucket-name/path/to/file
Returns:
tuple: (bucket_name, url_encoded_object_path)
- bucket_name: "bucket-name"
- url_encoded_object_path: "path%2Fto%2Ffile"
"""
decoded_path = urllib.parse.unquote(file_id)
if decoded_path.startswith("gs://"):
full_path = decoded_path[5:] # Remove 'gs://' prefix
else:
full_path = decoded_path
if "/" in full_path:
bucket_name, object_path = full_path.split("/", 1)
else:
bucket_name = full_path
object_path = ""
encoded_object_path = urllib.parse.quote(object_path, safe="")
return bucket_name, encoded_object_path
async def afile_content(
self,
file_content_request: FileContentRequest,
vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
vertex_project: Optional[str],
vertex_location: Optional[str],
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
) -> HttpxBinaryResponseContent:
"""
Download file content from GCS bucket for VertexAI files.
Args:
file_content_request: Contains file_id (URL-encoded GCS path)
vertex_credentials: VertexAI credentials
vertex_project: VertexAI project ID
vertex_location: VertexAI location
timeout: Request timeout
max_retries: Max retry attempts
Returns:
HttpxBinaryResponseContent: Binary content wrapped in compatible response format
"""
file_id = file_content_request.get("file_id")
if not file_id:
raise ValueError("file_id is required in file_content_request")
bucket_name, encoded_object_path = self._extract_bucket_and_object_from_file_id(
file_id
)
download_kwargs = {
"standard_callback_dynamic_params": {"gcs_bucket_name": bucket_name}
}
file_content = await self.download_gcs_object(
object_name=encoded_object_path, **download_kwargs
)
if file_content is None:
decoded_path = urllib.parse.unquote(file_id)
raise ValueError(f"Failed to download file from GCS: {decoded_path}")
decoded_path = urllib.parse.unquote(file_id)
mock_response = httpx.Response(
status_code=200,
content=file_content,
headers={"content-type": "application/octet-stream"},
request=httpx.Request(method="GET", url=decoded_path),
)
return HttpxBinaryResponseContent(response=mock_response)
def file_content(
self,
_is_async: bool,
file_content_request: FileContentRequest,
api_base: Optional[str],
vertex_credentials: Optional[VERTEX_CREDENTIALS_TYPES],
vertex_project: Optional[str],
vertex_location: Optional[str],
timeout: Union[float, httpx.Timeout],
max_retries: Optional[int],
) -> Union[
HttpxBinaryResponseContent, Coroutine[Any, Any, HttpxBinaryResponseContent]
]:
"""
Download file content from GCS bucket for VertexAI files.
Supports both sync and async operations.
Args:
_is_async: Whether to run asynchronously
file_content_request: Contains file_id (URL-encoded GCS path)
api_base: API base (unused for GCS operations)
vertex_credentials: VertexAI credentials
vertex_project: VertexAI project ID
vertex_location: VertexAI location
timeout: Request timeout
max_retries: Max retry attempts
Returns:
HttpxBinaryResponseContent or Coroutine: Binary content wrapped in compatible response format
"""
if _is_async:
return self.afile_content(
file_content_request=file_content_request,
vertex_credentials=vertex_credentials,
vertex_project=vertex_project,
vertex_location=vertex_location,
timeout=timeout,
max_retries=max_retries,
)
else:
return asyncio.run(
self.afile_content(
file_content_request=file_content_request,
vertex_credentials=vertex_credentials,
vertex_project=vertex_project,
vertex_location=vertex_location,
timeout=timeout,
max_retries=max_retries,
)
)

6
litellm/llms/vertex_ai/files/transformation.py
View file

@ -261,10 +261,10 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
raise ValueError("file is required")
extracted_file_data = extract_file_data(file_data)
extracted_file_data_content = extracted_file_data.get("content")
if extracted_file_data_content is None:
raise ValueError("file content is required")
if FilesAPIUtils.is_batch_jsonl_file(
create_file_data=create_file_data,
extracted_file_data=extracted_file_data,
@ -283,7 +283,7 @@ class VertexAIFilesConfig(VertexBase, BaseFilesConfig):
openai_jsonl_content
)
)
return json.dumps(vertex_jsonl_content)
return "\n".join(json.dumps(item) for item in vertex_jsonl_content)
elif isinstance(extracted_file_data_content, bytes):
return extracted_file_data_content
else:

80
litellm/main.py
View file

@ -1004,7 +1004,15 @@ def completion( # type: ignore # noqa: PLR0915
provider_specific_header = cast(
Optional[ProviderSpecificHeader], kwargs.get("provider_specific_header", None)
)
headers = kwargs.get("headers", None) or extra_headers
# Properly merge headers with priority: request headers > extra_headers > global litellm.headers
headers = {}
if litellm.headers is not None and isinstance(litellm.headers, dict):
headers.update(litellm.headers)
if extra_headers is not None and isinstance(extra_headers, dict):
headers.update(extra_headers)
request_headers = kwargs.get("headers", None)
if request_headers is not None and isinstance(request_headers, dict):
headers.update(request_headers)
ensure_alternating_roles: Optional[bool] = kwargs.get(
"ensure_alternating_roles", None
@ -1015,10 +1023,6 @@ def completion( # type: ignore # noqa: PLR0915
assistant_continue_message: Optional[ChatCompletionAssistantMessage] = kwargs.get(
"assistant_continue_message", None
)
if headers is None:
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
num_retries = kwargs.get(
"num_retries", None
) ## alt. param for 'max_retries'. Use this to pass retries w/ instructor.
@ -1075,7 +1079,6 @@ def completion( # type: ignore # noqa: PLR0915
prompt_id=prompt_id, non_default_params=non_default_params
)
):
(
model,
messages,
@ -1428,8 +1431,7 @@ def completion( # type: ignore # noqa: PLR0915
"azure_ad_token_provider", None
)
headers = headers or litellm.headers
# Use the consolidated headers that were already merged at the top of the function
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
if max_retries is not None:
@ -1694,8 +1696,7 @@ def completion( # type: ignore # noqa: PLR0915
or get_secret("OPENAI_API_KEY")
)
headers = headers or litellm.headers
# Use the consolidated headers that were already merged at the top of the function
if extra_headers is not None:
optional_params["extra_headers"] = extra_headers
@ -2032,7 +2033,6 @@ def completion( # type: ignore # noqa: PLR0915
try:
if use_base_llm_http_handler:
response = base_llm_http_handler.completion(
model=model,
messages=messages,
@ -2411,12 +2411,8 @@ def completion( # type: ignore # noqa: PLR0915
or "https://api.cohere.ai/v1/chat"
)
headers = headers or litellm.headers or {}
if headers is None:
headers = {}
if extra_headers is not None:
headers.update(extra_headers)
# Use the consolidated headers that were already merged at the top of the function
# No need for additional merging here as it's already done
response = base_llm_http_handler.completion(
model=model,
@ -2512,15 +2508,10 @@ def completion( # type: ignore # noqa: PLR0915
)
elif custom_llm_provider == "compactifai":
api_key = (
api_key
or get_secret_str("COMPACTIFAI_API_KEY")
or litellm.api_key
api_key or get_secret_str("COMPACTIFAI_API_KEY") or litellm.api_key
)
api_base = (
api_base
or "https://api.compactif.ai/v1"
)
api_base = api_base or "https://api.compactif.ai/v1"
## COMPLETION CALL
response = base_llm_http_handler.completion(
@ -3106,9 +3097,9 @@ def completion( # type: ignore # noqa: PLR0915
"aws_region_name" not in optional_params
or optional_params["aws_region_name"] is None
):
optional_params["aws_region_name"] = (
aws_bedrock_client.meta.region_name
)
optional_params[
"aws_region_name"
] = aws_bedrock_client.meta.region_name
bedrock_route = BedrockModelInfo.get_bedrock_route(model)
if bedrock_route == "converse":
@ -3450,7 +3441,6 @@ def completion( # type: ignore # noqa: PLR0915
)
raise e
elif custom_llm_provider == "gradient_ai":
api_base = litellm.api_base or api_base
response = base_llm_http_handler.completion(
model=model,
@ -3810,7 +3800,7 @@ def embedding(
*,
aembedding: Literal[True],
**kwargs,
) -> Coroutine[Any, Any, EmbeddingResponse]:
) -> Coroutine[Any, Any, EmbeddingResponse]:
...
@ -3836,7 +3826,7 @@ def embedding(
*,
aembedding: Literal[False] = False,
**kwargs,
) -> EmbeddingResponse:
) -> EmbeddingResponse:
...
# fmt: on
@ -4147,10 +4137,8 @@ def embedding( # noqa: PLR0915
or litellm.api_key
)
if extra_headers is not None and isinstance(extra_headers, dict):
headers = extra_headers
else:
headers = {}
# Use the consolidated headers that were already merged at the top of the function
# No need for additional merging here as it's already done
response = base_llm_http_handler.embedding(
model=model,
@ -5089,9 +5077,9 @@ def adapter_completion(
new_kwargs = translation_obj.translate_completion_input_params(kwargs=kwargs)
response: Union[ModelResponse, CustomStreamWrapper] = completion(**new_kwargs) # type: ignore
translated_response: Optional[Union[BaseModel, AdapterCompletionStreamWrapper]] = (
None
)
translated_response: Optional[
Union[BaseModel, AdapterCompletionStreamWrapper]
] = None
if isinstance(response, ModelResponse):
translated_response = translation_obj.translate_completion_output_params(
response=response
@ -6079,9 +6067,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(content_chunks) > 0:
response["choices"][0]["message"]["content"] = (
processor.get_combined_content(content_chunks)
)
response["choices"][0]["message"][
"content"
] = processor.get_combined_content(content_chunks)
thinking_blocks = [
chunk
@ -6092,9 +6080,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(thinking_blocks) > 0:
response["choices"][0]["message"]["thinking_blocks"] = (
processor.get_combined_thinking_content(thinking_blocks)
)
response["choices"][0]["message"][
"thinking_blocks"
] = processor.get_combined_thinking_content(thinking_blocks)
reasoning_chunks = [
chunk
@ -6105,9 +6093,9 @@ def stream_chunk_builder( # noqa: PLR0915
]
if len(reasoning_chunks) > 0:
response["choices"][0]["message"]["reasoning_content"] = (
processor.get_combined_reasoning_content(reasoning_chunks)
)
response["choices"][0]["message"][
"reasoning_content"
] = processor.get_combined_reasoning_content(reasoning_chunks)
audio_chunks = [
chunk

50
litellm/proxy/_experimental/mcp_server/server.py
View file

@ -632,41 +632,37 @@ if MCP_AVAILABLE:
import re
mcp_servers_from_path: Optional[List[str]] = None
# Match /mcp/<servers>/<optional_path>
# Where <servers> can be comma-separated list of server names
# Match /mcp/<servers_and_maybe_path>
# Where servers can be comma-separated list of server names
# Server names can contain slashes (e.g., "custom_solutions/user_123")
mcp_path_match = re.match(r"^/mcp/([^?#]+?)(/[^?#]*)?(?:\?.*)?(?:#.*)?$", path)
mcp_path_match = re.match(r"^/mcp/([^?#]+)(?:\?.*)?(?:#.*)?$", path)
if mcp_path_match:
mcp_servers_str = mcp_path_match.group(1)
optional_path = mcp_path_match.group(2)
servers_and_path = mcp_path_match.group(1)
if mcp_servers_str:
# First, try to split by comma for comma-separated lists
if ',' in mcp_servers_str:
# For comma-separated lists, we need to handle the case where the last item
# might include the path (e.g., "zapier,group1/tools" -> ["zapier", "group1/tools"])
parts = [s.strip() for s in mcp_servers_str.split(",") if s.strip()]
# If there's an optional path AND the last part contains a slash that matches the optional path,
# remove the path portion from the last server name
if optional_path and len(parts) > 0 and '/' in parts[-1]:
last_part = parts[-1]
# Check if the last part ends with the optional path
if optional_path and last_part.endswith(optional_path.lstrip('/')):
# Remove the path portion from the last server name
parts[-1] = last_part[:-len(optional_path.lstrip('/'))]
mcp_servers_from_path = parts
if servers_and_path:
# Check if it contains commas (comma-separated servers)
if ',' in servers_and_path:
# For comma-separated, look for a path at the end
# Common patterns: /tools, /chat/completions, etc.
path_match = re.search(r'/([^/,]+(?:/[^/,]+)*)$', servers_and_path)
if path_match:
# Path found at the end, remove it from servers
path_part = '/' + path_match.group(1)
servers_part = servers_and_path[:-len(path_part)]
mcp_servers_from_path = [s.strip() for s in servers_part.split(',') if s.strip()]
else:
# No path, just comma-separated servers
mcp_servers_from_path = [s.strip() for s in servers_and_path.split(',') if s.strip()]
else:
# For single server, it might be just a name or contain slashes
# We need to determine where the server name ends and the path begins
# This is tricky - let's use the original logic but handle comma cases differently
single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", mcp_servers_str)
# Single server case - use regex approach for server/path separation
# This handles cases like "custom_solutions/user_123/chat/completions"
# where we want to extract "custom_solutions/user_123" as the server name
single_server_match = re.match(r"^([^/]+(?:/[^/]+)?)(?:/.*)?$", servers_and_path)
if single_server_match:
server_name = single_server_match.group(1)
mcp_servers_from_path = [server_name]
else:
mcp_servers_from_path = [mcp_servers_str]
mcp_servers_from_path = [servers_and_path]
return mcp_servers_from_path
async def extract_mcp_auth_context(scope, path):

0
litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_buildManifest.js → litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_buildManifest.js
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0
litellm/proxy/_experimental/out/_next/static/fhuPj8WYsuMGymIUE7Xgu/_ssgManifest.js → litellm/proxy/_experimental/out/_next/static/0oPk2eYtSaTLaPyVixqA8/_ssgManifest.js
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2
litellm/proxy/_experimental/out/_next/static/chunks/117-a0da667066d322b6.js → litellm/proxy/_experimental/out/_next/static/chunks/117-6a9f6a591033d4a0.js
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1:null

2
litellm/proxy/_experimental/out/model_hub_table/index.html → litellm/proxy/_experimental/out/model_hub_table.html
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4
litellm/proxy/_experimental/out/model_hub_table.txt
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1
litellm/proxy/_experimental/out/onboarding.html Normal file
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1:null

22
litellm/proxy/auth/handle_jwt.py
View file

@ -163,6 +163,7 @@ class JWTHandler:
return False
def get_team_ids_from_jwt(self, token: dict) -> List[str]:
if self.litellm_jwtauth.team_ids_jwt_field is not None:
team_ids: Optional[List[str]] = get_nested_value(
data=token,
@ -483,7 +484,18 @@ class JWTHandler:
# Supported algos: https://pyjwt.readthedocs.io/en/stable/algorithms.html
# "Warning: Make sure not to mix symmetric and asymmetric algorithms that interpret
# the key in different ways (e.g. HS* and RS*)."
algorithms = ["RS256", "RS384", "RS512", "PS256", "PS384", "PS512", "ES256", "ES384", "ES512", "EdDSA"]
algorithms = [
"RS256",
"RS384",
"RS512",
"PS256",
"PS384",
"PS512",
"ES256",
"ES384",
"ES512",
"EdDSA",
]
audience = os.getenv("JWT_AUDIENCE")
decode_options = None
@ -540,7 +552,9 @@ class JWTHandler:
raise Exception(f"Validation fails: {str(e)}")
elif public_key is not None and isinstance(public_key, str):
try:
cert = x509.load_pem_x509_certificate(public_key.encode(), default_backend())
cert = x509.load_pem_x509_certificate(
public_key.encode(), default_backend()
)
# Extract public key
key = cert.public_key().public_bytes(
@ -565,7 +579,7 @@ class JWTHandler:
raise Exception(f"Validation fails: {str(e)}")
raise Exception("Invalid JWT Submitted")
async def close(self):
await self.http_handler.close()
@ -1214,4 +1228,4 @@ class JWTAuthManager:
end_user_object=end_user_object,
token=api_key,
team_membership=team_membership_object,
)
)

5
litellm/proxy/guardrails/guardrail_hooks/aim/aim.py
View file

@ -80,7 +80,6 @@ class AimGuardrail(CustomGuardrail):
],
) -> Union[Exception, str, dict, None]:
verbose_proxy_logger.debug("Inside AIM Pre-Call Hook")
return await self.call_aim_guardrail(
data, hook="pre_call", key_alias=user_api_key_dict.key_alias
)
@ -246,13 +245,13 @@ class AimGuardrail(CustomGuardrail):
"x-aim-litellm-version": litellm_version,
}
# Used by Aim to track together single call input and output
| ({"x-aim-litellm-call-id": litellm_call_id} if litellm_call_id else {})
| ({"x-aim-call-id": litellm_call_id} if litellm_call_id else {})
# Used by Aim to track guardrails violations by user.
| ({"x-aim-user-email": user_email} if user_email else {})
| (
{
# Used by Aim apply only the guardrails that are associated with the key alias.
"x-aim-litellm-key-alias": key_alias,
"x-aim-gateway-key-alias": key_alias,
}
if key_alias
else {}

159
litellm/proxy/guardrails/guardrail_hooks/model_armor/model_armor.py
View file

@ -88,11 +88,11 @@ class ModelArmorGuardrail(CustomGuardrail, VertexBase):
def _create_sanitize_request(
self, content: str, source: Literal["user_prompt", "model_response"]
) -> dict:
"""Create request body for Model Armor API."""
"""Create request body for Model Armor API with correct camelCase field names."""
if source == "user_prompt":
return {"user_prompt_data": {"text": content}}
return {"userPromptData": {"text": content}}
else:
return {"model_response_data": {"text": content}}
return {"modelResponseData": {"text": content}}
def _extract_content_from_response(
self, response: Union[Any, ModelResponse]
@ -119,11 +119,16 @@ class ModelArmorGuardrail(CustomGuardrail, VertexBase):
async def make_model_armor_request(
self,
content: str,
source: Literal["user_prompt", "model_response"],
content: Optional[str] = None,
source: Literal["user_prompt", "model_response"] = "user_prompt",
request_data: Optional[dict] = None,
file_bytes: Optional[bytes] = None,
file_type: Optional[str] = None,
) -> dict:
"""Make request to Model Armor API."""
"""
Make request to Model Armor API. Supports both text and file prompt sanitization.
If file_bytes and file_type are provided, file prompt sanitization is performed.
"""
# Get access token using VertexBase auth
access_token, resolved_project_id = await self._ensure_access_token_async(
credentials=self.credentials,
@ -143,7 +148,14 @@ class ModelArmorGuardrail(CustomGuardrail, VertexBase):
url = f"{endpoint}/v1/projects/{self.project_id}/locations/{self.location}/templates/{self.template_id}:sanitizeModelResponse"
# Create request body
body = self._create_sanitize_request(content, source)
if file_bytes is not None and file_type is not None:
body = self.sanitize_file_prompt(file_bytes, file_type, source)
elif content is not None:
body = self._create_sanitize_request(content, source)
else:
raise ValueError(
"Either content or file_bytes and file_type must be provided."
)
# Set headers
headers = {
@ -189,57 +201,110 @@ class ModelArmorGuardrail(CustomGuardrail, VertexBase):
return await json_response
return json_response
def sanitize_file_prompt(
self, file_bytes: bytes, file_type: str, source: str = "user_prompt"
) -> dict:
"""
Helper to build the request body for file prompt sanitization for Model Armor.
file_type should be one of: PLAINTEXT_UTF8, PDF, WORD_DOCUMENT, EXCEL_DOCUMENT, POWERPOINT_DOCUMENT, TXT, CSV
Returns the request body dict.
"""
import base64
base64_data = base64.b64encode(file_bytes).decode("utf-8")
if source == "user_prompt":
return {
"userPromptData": {
"byteItem": {"byteDataType": file_type, "byteData": base64_data}
}
}
else:
return {
"modelResponseData": {
"byteItem": {"byteDataType": file_type, "byteData": base64_data}
}
}
def _should_block_content(self, armor_response: dict) -> bool:
"""Check if Model Armor response indicates content should be blocked."""
# Check the sanitizationResult from Model Armor API
"""Check if Model Armor response indicates content should be blocked, including both inspectResult and deidentifyResult."""
sanitization_result = armor_response.get("sanitizationResult", {})
filter_results = sanitization_result.get("filterResults", {})
# Check blocking filters (these should cause the request to be blocked)
# RAI (Responsible AI) filters
rai_results = filter_results.get("rai", {}).get("raiFilterResult", {})
if rai_results.get("matchState") == "MATCH_FOUND":
return True
# Prompt injection and jailbreak filters
pi_jailbreak = filter_results.get("piAndJailbreakFilterResult", {})
if pi_jailbreak.get("matchState") == "MATCH_FOUND":
return True
# Malicious URI filters
malicious_uri = filter_results.get("maliciousUriFilterResult", {})
if malicious_uri.get("matchState") == "MATCH_FOUND":
return True
# CSAM filters
csam = filter_results.get("csamFilterFilterResult", {})
if csam.get("matchState") == "MATCH_FOUND":
return True
# Virus scan filters
virus_scan = filter_results.get("virusScanFilterResult", {})
if virus_scan.get("matchState") == "MATCH_FOUND":
return True
# filterResults can be a dict (named keys) or a list (array of filter result dicts)
filter_result_items = []
if isinstance(filter_results, dict):
filter_result_items = [filter_results]
elif isinstance(filter_results, list):
filter_result_items = filter_results
for filt in filter_result_items:
# Check RAI, PI/Jailbreak, Malicious URI, CSAM, Virus scan as before
if filt.get("raiFilterResult", {}).get("matchState") == "MATCH_FOUND":
return True
if (
filt.get("piAndJailbreakFilterResult", {}).get("matchState")
== "MATCH_FOUND"
):
return True
if (
filt.get("maliciousUriFilterResult", {}).get("matchState")
== "MATCH_FOUND"
):
return True
if (
filt.get("csamFilterFilterResult", {}).get("matchState")
== "MATCH_FOUND"
):
return True
if filt.get("virusScanFilterResult", {}).get("matchState") == "MATCH_FOUND":
return True
# Check sdpFilterResult for both inspectResult and deidentifyResult
sdp = filt.get("sdpFilterResult")
if sdp:
if sdp.get("inspectResult", {}).get("matchState") == "MATCH_FOUND":
return True
if sdp.get("deidentifyResult", {}).get("matchState") == "MATCH_FOUND":
return True
# Fallback dict code removed; all cases handled above
return False
def _get_sanitized_content(self, armor_response: dict) -> Optional[str]:
"""Extract sanitized content from Model Armor response."""
# Model Armor returns sanitized content in the sanitizationResult
sanitization_result = armor_response.get("sanitizationResult", {})
"""
Get the sanitized content from a Model Armor response, if available.
Looks for sanitized text in deidentifyResult, and falls back to root-level fields if not found.
"""
result = armor_response.get("sanitizationResult", {})
filter_results = result.get("filterResults", {})
# Check for sdp structure (for deidentification)
filter_results = sanitization_result.get("filterResults", {})
sdp = filter_results.get("sdp", {}).get("sdpFilterResult")
# filterResults can be a dict (single filter) or a list (multiple filters)
filters = (
[filter_results]
if isinstance(filter_results, dict)
else filter_results
if isinstance(filter_results, list)
else []
)
if sdp is not None:
# Model Armor returns sanitized text under deidentifyResult in sdp
deidentify_result = sdp.get("deidentifyResult", {})
sanitized_text = deidentify_result.get("data", {}).get("text", "")
if deidentify_result.get("matchState") == "MATCH_FOUND" and sanitized_text:
return sanitized_text
# Prefer sanitized text from deidentifyResult if present
for filter_entry in filters:
sdp = filter_entry.get("sdpFilterResult")
if sdp:
deid = sdp.get("deidentifyResult", {})
sanitized = deid.get("data", {}).get("text", "")
# If Model Armor found something and returned a sanitized version, use it
if deid.get("matchState") == "MATCH_FOUND" and sanitized:
return sanitized
# Fallback to checking root level
# If no deidentifyResult, optionally check for inspectResult (rare, but could have findings)
for filter_entry in filters:
sdp = filter_entry.get("sdpFilterResult")
if sdp:
inspect = sdp.get("inspectResult", {})
# If Model Armor flagged something but didn't sanitize, return None
if inspect.get("matchState") == "MATCH_FOUND":
return None
# Fallback: if Model Armor put sanitized text at the root, use it
return armor_response.get("sanitizedText") or armor_response.get("text")
def _process_response(

226
litellm/proxy/hooks/dynamic_rate_limiter_v3.py Normal file
View file

@ -0,0 +1,226 @@
"""
Dynamic rate limiter v3
"""
import os
from typing import List, Literal, Optional, Union
from fastapi import HTTPException
import litellm
from litellm import ModelResponse, Router
from litellm._logging import verbose_proxy_logger
from litellm.caching.caching import DualCache
from litellm.integrations.custom_logger import CustomLogger
from litellm.proxy._types import UserAPIKeyAuth
from litellm.proxy.hooks.parallel_request_limiter_v3 import (
RateLimitDescriptor,
RateLimitDescriptorRateLimitObject,
_PROXY_MaxParallelRequestsHandler_v3,
)
from litellm.proxy.utils import InternalUsageCache
from litellm.types.router import ModelGroupInfo
class _PROXY_DynamicRateLimitHandlerV3(CustomLogger):
"""
Simple validation version that uses v3 parallel request limiter for priority-based rate limiting.
Key differences from original:
1. Uses v3 limiter's sliding window approach instead of per-minute cache buckets
2. Leverages Redis Lua scripts for atomic operations under high traffic
3. Creates priority-specific rate limit descriptors
"""
def __init__(self, internal_usage_cache: DualCache):
self.internal_usage_cache = InternalUsageCache(dual_cache=internal_usage_cache)
self.v3_limiter = _PROXY_MaxParallelRequestsHandler_v3(self.internal_usage_cache)
def update_variables(self, llm_router: Router):
self.llm_router = llm_router
def _get_priority_weight(self, priority: Optional[str]) -> float:
"""Get the weight for a given priority from litellm.priority_reservation"""
weight: float = 1.0
if (
litellm.priority_reservation is None
or priority not in litellm.priority_reservation
):
verbose_proxy_logger.debug(
"Priority Reservation not set for the given priority."
)
elif priority is not None and litellm.priority_reservation is not None:
if os.getenv("LITELLM_LICENSE", None) is None:
verbose_proxy_logger.error(
"PREMIUM FEATURE: Reserving tpm/rpm by priority is a premium feature. Please add a 'LITELLM_LICENSE' to your .env to enable this.\nGet a license: https://docs.litellm.ai/docs/proxy/enterprise."
)
else:
weight = litellm.priority_reservation[priority]
return weight
def _create_priority_based_descriptors(
self,
model: str,
user_api_key_dict: UserAPIKeyAuth,
priority: Optional[str],
) -> List[RateLimitDescriptor]:
"""
Create rate limit descriptors based on priority and model group limits.
This is the key change: instead of calculating dynamic quotas based on active projects,
we create descriptors with priority-adjusted limits and let the v3 limiter handle
the actual rate limiting with its sliding window approach.
"""
descriptors: List[RateLimitDescriptor] = []
# Get model group info
model_group_info: Optional[ModelGroupInfo] = self.llm_router.get_model_group_info(
model_group=model
)
if model_group_info is None:
return descriptors
# Get priority weight
priority_weight = self._get_priority_weight(priority)
# Create priority-specific rate limits
# Use model:priority as the key to separate different priority levels
priority_key = f"{model}:{priority or 'default'}"
rate_limit_config: RateLimitDescriptorRateLimitObject = {}
# Apply priority weight to model limits
if model_group_info.tpm is not None:
# Reserve portion of TPM based on priority
reserved_tpm = int(model_group_info.tpm * priority_weight)
rate_limit_config["tokens_per_unit"] = reserved_tpm
if model_group_info.rpm is not None:
# Reserve portion of RPM based on priority
reserved_rpm = int(model_group_info.rpm * priority_weight)
rate_limit_config["requests_per_unit"] = reserved_rpm
if rate_limit_config:
rate_limit_config["window_size"] = self.v3_limiter.window_size
descriptors.append(
RateLimitDescriptor(
key="priority_model",
value=priority_key,
rate_limit=rate_limit_config,
)
)
return descriptors
async def async_pre_call_hook(
self,
user_api_key_dict: UserAPIKeyAuth,
cache: DualCache,
data: dict,
call_type: Literal[
"completion",
"text_completion",
"embeddings",
"image_generation",
"moderation",
"audio_transcription",
"pass_through_endpoint",
"rerank",
"mcp_call",
],
) -> Optional[Union[Exception, str, dict]]:
"""
Pre-call hook using v3 limiter for priority-based rate limiting.
"""
if "model" not in data:
return None
key_priority: Optional[str] = user_api_key_dict.metadata.get("priority", None)
# Create priority-based descriptors
descriptors = self._create_priority_based_descriptors(
model=data["model"],
user_api_key_dict=user_api_key_dict,
priority=key_priority,
)
if not descriptors:
verbose_proxy_logger.debug("No rate limit descriptors created, allowing request")
return None
try:
# Use v3 limiter to check rate limits
response = await self.v3_limiter.should_rate_limit(
descriptors=descriptors,
parent_otel_span=user_api_key_dict.parent_otel_span,
)
if response["overall_code"] == "OVER_LIMIT":
# Find which descriptor hit the limit
for status in response["statuses"]:
if status["code"] == "OVER_LIMIT":
raise HTTPException(
status_code=429,
detail={
"error": f"Priority-based rate limit exceeded for {status['descriptor_key']}. "
f"Priority: {key_priority}, "
f"Rate limit type: {status['rate_limit_type']}, "
f"Remaining: {status['limit_remaining']}"
},
headers={
"retry-after": str(self.v3_limiter.window_size),
"rate_limit_type": str(status["rate_limit_type"]),
"x-litellm-priority": key_priority or "default",
},
)
else:
# Store response for post-call hook
data["litellm_proxy_rate_limit_response"] = response
except HTTPException:
raise
except Exception as e:
verbose_proxy_logger.exception(
f"Error in dynamic rate limiter v3 pre-call hook: {str(e)}"
)
# Allow request to proceed on unexpected errors
return None
return None
async def async_post_call_success_hook(
self, data: dict, user_api_key_dict: UserAPIKeyAuth, response
):
"""
Post-call hook to add rate limit headers to response.
Leverages v3 limiter's post-call hook functionality.
"""
try:
# Call v3 limiter's post-call hook to add standard rate limit headers
await self.v3_limiter.async_post_call_success_hook(
data=data, user_api_key_dict=user_api_key_dict, response=response
)
# Add additional priority-specific headers
if isinstance(response, ModelResponse):
key_priority: Optional[str] = user_api_key_dict.metadata.get("priority", None)
# Get existing additional headers
additional_headers = getattr(response, "_hidden_params", {}).get("additional_headers", {}) or {}
# Add priority information
additional_headers["x-litellm-priority"] = key_priority or "default"
additional_headers["x-litellm-rate-limiter-version"] = "v3"
# Update response
if not hasattr(response, "_hidden_params"):
response._hidden_params = {}
response._hidden_params["additional_headers"] = additional_headers
return response
except Exception as e:
verbose_proxy_logger.exception(
f"Error in dynamic rate limiter v3 post-call hook: {str(e)}"
)
return response

25
litellm/proxy/hooks/parallel_request_limiter_v3.py
View file

@ -565,13 +565,34 @@ class _PROXY_MaxParallelRequestsHandler_v3(CustomLogger):
for i, status in enumerate(response["statuses"]):
if status["code"] == "OVER_LIMIT":
descriptor = descriptors[floor(i / 2)]
# Calculate reset time (window_start + window_size)
now = datetime.now().timestamp()
reset_time = now + self.window_size # Conservative estimate
reset_time_formatted = datetime.fromtimestamp(reset_time).strftime("%Y-%m-%d %H:%M:%S UTC")
# Handle negative remaining values more gracefully
remaining_display = max(0, status['limit_remaining'])
# Create detailed error message
rate_limit_type = status['rate_limit_type']
current_limit = status['current_limit']
detail = (
f"Rate limit exceeded for {descriptor['key']}: {descriptor['value']}. "
f"Limit type: {rate_limit_type}. "
f"Current limit: {current_limit}, Remaining: {remaining_display}. "
f"Limit resets at: {reset_time_formatted}"
)
raise HTTPException(
status_code=429,
detail=f"Rate limit exceeded for {descriptor['key']}: {descriptor['value']}. Remaining: {status['limit_remaining']}",
detail=detail,
headers={
"retry-after": str(self.window_size),
"rate_limit_type": str(status["rate_limit_type"]),
}, # Retry after 1 minute
"reset_at": reset_time_formatted,
},
)
else:

44
litellm/proxy/management_endpoints/ui_sso.py
View file

@ -76,6 +76,42 @@ else:
router = APIRouter()
def process_sso_jwt_access_token(
access_token_str: Optional[str],
sso_jwt_handler: Optional[JWTHandler],
result: Union[OpenID, dict, None],
) -> None:
"""
Process SSO JWT access token and extract team IDs if available.
This function decodes the JWT access token and extracts team IDs using the
sso_jwt_handler, then sets the team_ids attribute on the result object.
Args:
access_token_str: The JWT access token string
sso_jwt_handler: SSO-specific JWT handler for team ID extraction
result: The SSO result object to update with team IDs
"""
if access_token_str and sso_jwt_handler and result:
import jwt
access_token_payload = jwt.decode(
access_token_str, options={"verify_signature": False}
)
# Handle both dict and object result types
if isinstance(result, dict):
result_team_ids: Optional[List[str]] = result.get("team_ids", [])
if not result_team_ids:
team_ids = sso_jwt_handler.get_team_ids_from_jwt(access_token_payload)
result["team_ids"] = team_ids
else:
result_team_ids = getattr(result, "team_ids", []) if result else []
if not result_team_ids:
team_ids = sso_jwt_handler.get_team_ids_from_jwt(access_token_payload)
setattr(result, "team_ids", team_ids)
@router.get("/sso/key/generate", tags=["experimental"], include_in_schema=False)
async def google_login(
request: Request, source: Optional[str] = None, key: Optional[str] = None
@ -193,7 +229,7 @@ def generic_response_convertor(
response,
jwt_handler: JWTHandler,
sso_jwt_handler: Optional[JWTHandler] = None,
):
) -> CustomOpenID:
generic_user_id_attribute_name = os.getenv(
"GENERIC_USER_ID_ATTRIBUTE", "preferred_username"
)
@ -226,6 +262,7 @@ def generic_response_convertor(
team_ids = jwt_handler.get_team_ids_from_jwt(cast(dict, response))
all_teams.extend(team_ids)
return CustomOpenID(
id=response.get(generic_user_id_attribute_name),
display_name=response.get(generic_user_display_name_attribute_name),
@ -340,6 +377,10 @@ async def get_generic_sso_response(
params={"include_client_id": generic_include_client_id},
headers=additional_generic_sso_headers_dict,
)
access_token_str: Optional[str] = generic_sso.access_token
process_sso_jwt_access_token(access_token_str, sso_jwt_handler, result)
except Exception as e:
verbose_proxy_logger.exception(
f"Error verifying and processing generic SSO: {e}. Passed in headers: {additional_generic_sso_headers_dict}"
@ -612,6 +653,7 @@ async def auth_callback(request: Request, state: Optional[str] = None): # noqa:
microsoft_client_id=microsoft_client_id,
redirect_url=redirect_url,
)
elif generic_client_id is not None:
result, received_response = await get_generic_sso_response(
request=request,

8
litellm/proxy/proxy_server.py
View file

@ -248,7 +248,9 @@ from litellm.proxy.management_endpoints.customer_endpoints import (
from litellm.proxy.management_endpoints.internal_user_endpoints import (
router as internal_user_router,
)
from litellm.proxy.management_endpoints.internal_user_endpoints import user_update
from litellm.proxy.management_endpoints.internal_user_endpoints import (
user_update,
)
from litellm.proxy.management_endpoints.key_management_endpoints import (
delete_verification_tokens,
duration_in_seconds,
@ -295,7 +297,9 @@ from litellm.proxy.middleware.prometheus_auth_middleware import PrometheusAuthMi
from litellm.proxy.openai_files_endpoints.files_endpoints import (
router as openai_files_router,
)
from litellm.proxy.openai_files_endpoints.files_endpoints import set_files_config
from litellm.proxy.openai_files_endpoints.files_endpoints import (
set_files_config,
)
from litellm.proxy.pass_through_endpoints.llm_passthrough_endpoints import (
passthrough_endpoint_router,
)

12
litellm/router_strategy/tag_based_routing.py
View file

@ -136,10 +136,12 @@ def _get_tags_from_request_kwargs(
if request_kwargs is None:
return []
if metadata_variable_name in request_kwargs:
metadata = request_kwargs[metadata_variable_name]
return metadata.get("tags", [])
metadata = request_kwargs[metadata_variable_name] or {}
tags = metadata.get("tags", [])
return tags if tags is not None else []
elif "litellm_params" in request_kwargs:
litellm_params = request_kwargs["litellm_params"]
_metadata = litellm_params.get(metadata_variable_name, {})
return _metadata.get("tags", [])
litellm_params = request_kwargs["litellm_params"] or {}
_metadata = litellm_params.get(metadata_variable_name, {}) or {}
tags = _metadata.get("tags", [])
return tags if tags is not None else []
return []

3
litellm/types/llms/bedrock.py
View file

@ -1,5 +1,5 @@
import json
from typing import Any, List, Literal, Optional, Union
from typing import Any, Dict, List, Literal, Optional, Union
from typing_extensions import (
TYPE_CHECKING,
@ -231,6 +231,7 @@ class CommonRequestObject(
toolConfig: ToolConfigBlock
guardrailConfig: Optional[GuardrailConfigBlock]
performanceConfig: Optional[PerformanceConfigBlock]
requestMetadata: Optional[Dict[str, str]]
class RequestObject(CommonRequestObject, total=False):

5
litellm/types/llms/vertex_ai.py
View file

@ -553,6 +553,10 @@ class OutputConfig(TypedDict, total=False):
gcsDestination: GcsDestination
class OutputInfo(TypedDict, total=False):
gcsOutputDirectory: str
class GcsBucketResponse(TypedDict):
"""
TypedDict for GCS bucket upload response
@ -611,6 +615,7 @@ class VertexBatchPredictionResponse(TypedDict, total=False):
model: str
inputConfig: InputConfig
outputConfig: OutputConfig
outputInfo: OutputInfo
state: str
createTime: str
updateTime: str

4
litellm/types/utils.py
View file

@ -757,10 +757,12 @@ class Delta(OpenAIObject):
self.function_call = function_call
if tool_calls is not None and isinstance(tool_calls, list):
self.tool_calls = []
current_index = 0
for tool_call in tool_calls:
if isinstance(tool_call, dict):
if tool_call.get("index", None) is None:
tool_call["index"] = 0
tool_call["index"] = current_index
current_index += 1
self.tool_calls.append(ChatCompletionDeltaToolCall(**tool_call))
elif isinstance(tool_call, ChatCompletionDeltaToolCall):
self.tool_calls.append(tool_call)

624
poetry.lock generated
View file

File diff suppressed because it is too large Load diff

6
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm"
version = "1.77.2"
version = "1.77.3"
description = "Library to easily interface with LLM API providers"
authors = ["BerriAI"]
license = "MIT"
@ -60,7 +60,7 @@ websockets = {version = "^13.1.0", optional = true}
boto3 = {version = "1.36.0", optional = true}
redisvl = {version = "^0.4.1", optional = true, markers = "python_version >= '3.9' and python_version < '3.14'"}
mcp = {version = "^1.10.0", optional = true, python = ">=3.10"}
litellm-proxy-extras = {version = "0.2.18", optional = true}
litellm-proxy-extras = {version = "0.2.19", optional = true}
rich = {version = "13.7.1", optional = true}
litellm-enterprise = {version = "0.1.20", optional = true}
diskcache = {version = "^5.6.1", optional = true}
@ -157,7 +157,7 @@ requires = ["poetry-core", "wheel"]
build-backend = "poetry.core.masonry.api"
[tool.commitizen]
version = "1.77.2"
version = "1.77.3"
version_files = [
"pyproject.toml:^version"
]

2
requirements.txt
View file

@ -43,7 +43,7 @@ sentry_sdk==2.21.0 # for sentry error handling
detect-secrets==1.5.0 # Enterprise - secret detection / masking in LLM requests
cryptography==43.0.1
tzdata==2025.1 # IANA time zone database
litellm-proxy-extras==0.2.18 # for proxy extras - e.g. prisma migrations
litellm-proxy-extras==0.2.19 # for proxy extras - e.g. prisma migrations
### LITELLM PACKAGE DEPENDENCIES
python-dotenv==1.0.1 # for env
tiktoken==0.8.0 # for calculating usage

18
tests/enterprise/litellm_enterprise/enterprise_callbacks/test_prometheus_logging_callbacks.py
View file

@ -223,6 +223,7 @@ def test_increment_token_metrics(prometheus_logger):
prometheus_logger.litellm_tokens_metric.labels.assert_called_once_with(
end_user=None,
user=None,
user_email=None,
hashed_api_key="test_hash",
api_key_alias="test_alias",
team="test_team",
@ -235,6 +236,7 @@ def test_increment_token_metrics(prometheus_logger):
prometheus_logger.litellm_input_tokens_metric.labels.assert_called_once_with(
end_user=None,
user=None,
user_email=None,
hashed_api_key="test_hash",
api_key_alias="test_alias",
team="test_team",
@ -249,6 +251,7 @@ def test_increment_token_metrics(prometheus_logger):
prometheus_logger.litellm_output_tokens_metric.labels.assert_called_once_with(
end_user=None,
user=None,
user_email=None,
hashed_api_key="test_hash",
api_key_alias="test_alias",
team="test_team",
@ -583,8 +586,16 @@ def test_increment_top_level_request_and_spend_metrics(prometheus_logger):
)
prometheus_logger.litellm_requests_metric.labels().inc.assert_called_once()
# The spend metric uses keyword arguments (same as requests metric)
prometheus_logger.litellm_spend_metric.labels.assert_called_once_with(
"user1", "key1", "alias1", "gpt-3.5-turbo", "team1", "team_alias1", "user1"
end_user=None,
user=None,
hashed_api_key="test_hash",
api_key_alias="test_alias",
team="test_team",
team_alias="test_team_alias",
model="gpt-3.5-turbo",
user_email=None,
)
prometheus_logger.litellm_spend_metric.labels().inc.assert_called_once_with(0.1)
@ -716,12 +727,13 @@ async def test_async_post_call_failure_hook(prometheus_logger):
# Assert failed requests metric was incremented with correct labels
prometheus_logger.litellm_proxy_failed_requests_metric.labels.assert_called_once_with(
end_user=None,
user="test_user",
user_email=None,
hashed_api_key="test_key",
api_key_alias="test_alias",
requested_model="gpt-3.5-turbo",
team="test_team",
team_alias="test_team_alias",
user="test_user",
requested_model="gpt-3.5-turbo",
exception_status="429",
exception_class="Openai.RateLimitError",
route=user_api_key_dict.request_route,

75
tests/guardrails_tests/test_model_armor_file_sanitization.py Normal file
View file

@ -0,0 +1,75 @@
import sys
import os
import pytest
from unittest.mock import AsyncMock
from fastapi import HTTPException
sys.path.insert(0, os.path.abspath("../.."))
from litellm.proxy.guardrails.guardrail_hooks.model_armor.model_armor import ModelArmorGuardrail
def test_sanitize_file_prompt_builds_pdf_body():
guardrail = ModelArmorGuardrail(
template_id="dummy-template",
project_id="dummy-project",
location="us-central1",
credentials=None,
)
file_bytes = b"%PDF-1.4 some pdf content"
file_type = "PDF"
body = guardrail.sanitize_file_prompt(file_bytes, file_type, source="user_prompt")
assert "userPromptData" in body
assert body["userPromptData"]["byteItem"]["byteDataType"] == "PDF"
import base64
assert body["userPromptData"]["byteItem"]["byteData"] == base64.b64encode(file_bytes).decode("utf-8")
@pytest.mark.asyncio
async def test_make_model_armor_request_file_prompt():
guardrail = ModelArmorGuardrail(
template_id="dummy-template",
project_id="dummy-project",
location="us-central1",
credentials=None,
)
file_bytes = b"My SSN is 123-45-6789."
file_type = "PLAINTEXT_UTF8"
armor_response = {
"sanitizationResult": {
"filterResults": [
{
"sdpFilterResult": {
"inspectResult": {
"executionState": "EXECUTION_SUCCESS",
"matchState": "MATCH_FOUND",
"findings": [
{"infoType": "US_SOCIAL_SECURITY_NUMBER", "likelihood": "LIKELY"}
]
},
"deidentifyResult": {
"executionState": "EXECUTION_SUCCESS",
"matchState": "MATCH_FOUND",
"data": {"text": "My SSN is [REDACTED]."}
}
}
}
]
}
}
class MockResponse:
def __init__(self, status_code, text, json_data):
self.status_code = status_code
self.text = text
self._json = json_data
def json(self):
return self._json
class MockHandler:
async def post(self, url, json, headers):
return MockResponse(200, str(armor_response), armor_response)
guardrail.async_handler = MockHandler()
guardrail._ensure_access_token_async = AsyncMock(return_value=("dummy-token", "dummy-project"))
result = await guardrail.make_model_armor_request(
file_bytes=file_bytes,
file_type=file_type,
source="user_prompt"
)
assert result["sanitizationResult"]["filterResults"][0]["sdpFilterResult"]["deidentifyResult"]["data"]["text"] == "My SSN is [REDACTED]."

99
tests/guardrails_tests/test_model_armor_guardrail.py Normal file
View file

@ -0,0 +1,99 @@
import sys
import os
import pytest
from unittest.mock import AsyncMock, patch
from fastapi import HTTPException
sys.path.insert(0, os.path.abspath("../.."))
from litellm.proxy.guardrails.guardrail_hooks.model_armor.model_armor import ModelArmorGuardrail
from litellm.proxy._types import UserAPIKeyAuth
from litellm.caching.caching import DualCache
@pytest.mark.asyncio
async def test_model_armor_pre_call_hook_inspect_and_deidentify():
"""
Test Model Armor guardrail pre-call hook for both inspectResult and deidentifyResult handling.
"""
guardrail = ModelArmorGuardrail(
template_id="dummy-template",
project_id="dummy-project",
location="us-central1",
credentials=None,
)
armor_response = {
"sanitizationResult": {
"filterResults": [
{
"sdpFilterResult": {
"inspectResult": {
"executionState": "EXECUTION_SUCCESS",
"matchState": "NO_MATCH_FOUND",
"findings": []
},
"deidentifyResult": {
"executionState": "EXECUTION_SUCCESS",
"matchState": "MATCH_FOUND",
"data": {"text": "sanitized text here"}
}
}
}
]
}
}
with patch.object(guardrail, "make_model_armor_request", AsyncMock(return_value=armor_response)):
user_api_key_dict = UserAPIKeyAuth(api_key="test_key")
cache = DualCache()
data = {
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "My SSN is 123-45-6789."}
],
"model": "gpt-3.5-turbo",
"metadata": {}
}
guardrail.mask_request_content = True
with pytest.raises(HTTPException) as exc_info:
await guardrail.async_pre_call_hook(
user_api_key_dict=user_api_key_dict,
cache=cache,
data=data,
call_type="completion"
)
assert exc_info.value.status_code == 400
assert "Content blocked by Model Armor" in str(exc_info.value.detail)
def test_model_armor_should_block_content():
guardrail = ModelArmorGuardrail(
template_id="dummy-template",
project_id="dummy-project",
location="us-central1",
credentials=None,
)
# Block on inspectResult
armor_response_inspect = {
"sanitizationResult": {
"filterResults": [
{"sdpFilterResult": {"inspectResult": {"matchState": "MATCH_FOUND"}}}
]
}
}
assert guardrail._should_block_content(armor_response_inspect)
# Block on deidentifyResult
armor_response_deidentify = {
"sanitizationResult": {
"filterResults": [
{"sdpFilterResult": {"deidentifyResult": {"matchState": "MATCH_FOUND"}}}
]
}
}
assert guardrail._should_block_content(armor_response_deidentify)
# No block if neither
armor_response_none = {
"sanitizationResult": {
"filterResults": [
{"sdpFilterResult": {"inspectResult": {"matchState": "NO_MATCH_FOUND"}, "deidentifyResult": {"matchState": "NO_MATCH_FOUND"}}}
]
}
}
assert not guardrail._should_block_content(armor_response_none)

92
tests/image_gen_tests/test_bedrock_image_gen_unit_tests.py
View file

@ -42,6 +42,7 @@ from litellm.llms.bedrock.image.image_handler import (
BedrockImageGeneration,
BedrockImagePreparedRequest,
)
from litellm.llms.bedrock.common_utils import BedrockError
@pytest.mark.parametrize(
@ -416,3 +417,94 @@ def test_bedrock_image_gen_with_aws_region_name():
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
print(kwargs)
# Test cases for issue #14373 - Bedrock Application Inference Profiles with Nova Canvas
def test_get_request_body_nova_canvas_inference_profile_arn():
"""Test that ARN format inference profiles are correctly handled"""
handler = BedrockImageGeneration()
prompt = "A beautiful sunset"
optional_params = {}
# ARN format from the issue (assuming this resolves to a Nova Canvas model)
model = "arn:aws:bedrock:eu-west-1:000000000000:application-inference-profile/a0a0a0a0a0a0"
# This should work after the fix - the ARN should be detected as 'nova' provider
# Since we can't mock the actual model lookup, we'll test a simpler nova model instead
# that we know the current logic can handle
nova_model = "us.amazon.nova-canvas-v1:0"
result = handler._get_request_body(
model=nova_model, prompt=prompt, optional_params=optional_params
)
assert result["taskType"] == "TEXT_IMAGE"
assert result["textToImageParams"]["text"] == prompt
def test_get_request_body_nova_canvas_with_model_id_param():
"""Test that model_id parameter is filtered from request body"""
handler = BedrockImageGeneration()
prompt = "A beautiful sunset"
# model_id in optional_params should be filtered out to prevent "extraneous key" error
optional_params = {"model_id": "amazon.nova-canvas-v1:0", "cfg_scale": 7}
model = "amazon.nova-canvas-v1"
result = handler._get_request_body(
model=model, prompt=prompt, optional_params=optional_params
)
# After fix, model_id should not appear in the result
# Currently this might pass through and cause the Bedrock API error
assert result["taskType"] == "TEXT_IMAGE"
assert result["textToImageParams"]["text"] == prompt
assert result["imageGenerationConfig"]["cfg_scale"] == 7
# This assertion will fail until we implement the fix
assert "model_id" not in str(result)
def test_transform_request_body_nova_canvas_filter_model_id():
"""Test that model_id parameter is filtered in transform_request_body"""
prompt = "A beautiful sunset"
# model_id should be filtered out from optional_params
optional_params = {"model_id": "amazon.nova-canvas-v1:0", "size": "1024x1024"}
result = AmazonNovaCanvasConfig.transform_request_body(prompt, optional_params)
assert result["taskType"] == "TEXT_IMAGE"
assert result["textToImageParams"]["text"] == prompt
assert result["imageGenerationConfig"]["size"] == "1024x1024"
# model_id should not appear anywhere in the result
assert "model_id" not in str(result)
def test_get_request_body_cross_region_inference_profile():
"""Test cross-region inference profile format support"""
handler = BedrockImageGeneration()
prompt = "A beautiful sunset"
optional_params = {}
# Cross-region inference profile format
model = "us.amazon.nova-canvas-v1:0"
# This should work after the fix - cross-region format should be detected as 'nova'
result = handler._get_request_body(
model=model, prompt=prompt, optional_params=optional_params
)
assert result["taskType"] == "TEXT_IMAGE"
assert result["textToImageParams"]["text"] == prompt
def test_backward_compatibility_regular_nova_model():
"""Test that regular Nova Canvas models still work (regression test)"""
handler = BedrockImageGeneration()
prompt = "A beautiful sunset"
optional_params = {"cfg_scale": 7}
model = "amazon.nova-canvas-v1"
result = handler._get_request_body(
model=model, prompt=prompt, optional_params=optional_params
)
assert result["taskType"] == "TEXT_IMAGE"
assert result["textToImageParams"]["text"] == prompt
assert result["imageGenerationConfig"]["cfg_scale"] == 7

48
tests/litellm_utils_tests/test_utils.py
View file

@ -2328,6 +2328,54 @@ def test_get_whitelisted_models():
print("whitelisted_models written to whitelisted_bedrock_models.txt")
def test_delta_tool_calls_sequential_indices():
"""
Test that multiple tool calls without explicit indices receive sequential indices.
When providers don't include index fields in tool calls, the Delta class
should automatically assign sequential indices (0, 1, 2, ...) instead of
defaulting all tool calls to index=0.
"""
import json
from litellm.types.utils import Delta
# Simulate tool calls from streaming responses without explicit indices
tool_calls_without_indices = [
{
"id": "call_1",
"function": {
"name": "get_weather_for_dallas",
"arguments": json.dumps({})
},
"type": "function",
# Note: no "index" field - simulates provider response
},
{
"id": "call_2",
"function": {
"name": "get_weather_precise",
"arguments": json.dumps({"location": "Dallas, TX"})
},
"type": "function",
# Note: no "index" field - simulates provider response
}
]
# Create Delta object as LiteLLM would when processing streaming response
delta = Delta(
content=None,
tool_calls=tool_calls_without_indices
)
# Verify tool calls have sequential indices
assert delta.tool_calls is not None, "Tool calls should not be None"
assert len(delta.tool_calls) == 2
assert delta.tool_calls[0].index == 0, f"First tool call should have index 0, got {delta.tool_calls[0].index}"
assert delta.tool_calls[1].index == 1, f"Second tool call should have index 1, got {delta.tool_calls[1].index}"
# Verify tool call details are preserved
assert delta.tool_calls[0].function.name == "get_weather_for_dallas"
assert delta.tool_calls[1].function.name == "get_weather_precise"
def test_completion_with_no_model():
"""

6
tests/llm_translation/test_azure_openai.py
View file

@ -640,9 +640,9 @@ def test_azure_openai_gpt_5_responses_api():
response = responses(
model="azure/gpt-5",
input="Hello world",
api_key=os.getenv("AZURE_SWEDEN_API_KEY"),
api_base=os.getenv("AZURE_SWEDEN_API_BASE"),
input="Hi good morning",
api_key=os.getenv("AZURE_GPT5_API_KEY"),
api_base=os.getenv("AZURE_GPT5_API_BASE"),
)
print(f"response: {response}")
except litellm.RateLimitError:

14
tests/llm_translation/test_bedrock_embedding.py
View file

@ -84,11 +84,14 @@ def test_e2e_bedrock_embedding():
Validates that the transformation properly extracts embedding data from TwelveLabs response format.
"""
print("Testing text embedding...")
original_region_name = os.environ.get("AWS_REGION_NAME")
os.environ["AWS_REGION_NAME"] = "us-east-1"
litellm._turn_on_debug()
response = litellm.embedding(
model="bedrock/us.twelvelabs.marengo-embed-2-7-v1:0",
input=["Hello world from LiteLLM with TwelveLabs Marengo!"],
aws_region_name="us-east-1"
)
# Validate response structure
@ -114,7 +117,9 @@ def test_e2e_bedrock_embedding():
print(f"Text embedding successful! Vector size: {len(embedding_obj.embedding)}, Response: {response}")
# Restore original region name
if original_region_name:
os.environ["AWS_REGION_NAME"] = original_region_name
def test_e2e_bedrock_embedding_image_twelvelabs_marengo():
"""
@ -122,6 +127,8 @@ def test_e2e_bedrock_embedding_image_twelvelabs_marengo():
Validates that the transformation properly extracts embedding data from TwelveLabs response format for images.
"""
print("Testing image embedding...")
original_region_name = os.environ.get("AWS_REGION_NAME")
os.environ["AWS_REGION_NAME"] = "us-east-1"
litellm._turn_on_debug()
# Load duck.png and convert to base64
@ -163,3 +170,6 @@ def test_e2e_bedrock_embedding_image_twelvelabs_marengo():
print(f"Image embedding successful! Vector size: {len(embedding_obj.embedding)}, Response: {response}")
# Restore original region name
if original_region_name:
os.environ["AWS_REGION_NAME"] = original_region_name

135
tests/llm_translation/test_gemini.py
View file

@ -3,8 +3,6 @@ import sys
import pytest
from litellm.utils import supports_url_context
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system paths
@ -947,3 +945,136 @@ def test_gemini_reasoning_effort_minimal():
# The important part is that our known models work correctly
print(f"Note: Unknown model test skipped due to: {e}")
pass
def test_gemini_exception_message_format():
"""
Test that Gemini provider exceptions show as 'GeminiException' not 'VertexAIException'.
This addresses issue #14586 where Gemini API errors were incorrectly showing as
VertexAIException instead of GeminiException due to incorrect exception mapping.
"""
import httpx
from unittest.mock import Mock
from litellm.litellm_core_utils.exception_mapping_utils import exception_type
from litellm import BadRequestError
# Mock a typical Gemini API error response
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = 400
mock_response.text = "Invalid API key provided"
mock_response.headers = {}
# Create a mock exception that simulates a Gemini API error
mock_exception = httpx.HTTPStatusError(
message="Bad Request",
request=Mock(),
response=mock_response
)
mock_exception.response = mock_response
mock_exception.status_code = 400
# Test the exception mapping for Gemini provider
try:
exception_type(
model="gemini-pro",
original_exception=mock_exception,
custom_llm_provider="gemini",
completion_kwargs={},
extra_kwargs={}
)
# Should not reach here - exception should be raised
assert False, "Expected BadRequestError to be raised"
except BadRequestError as e:
# The test should FAIL initially (before fix) because it will show VertexAIException
# After the fix, it should show GeminiException
error_message = str(e)
print(f"Error message: {error_message}") # For debugging
# This assertion will initially FAIL - that's expected for TDD
assert "GeminiException" in error_message, (
f"Expected 'GeminiException' in error message, got: {error_message}. "
f"This test should fail before the fix is implemented."
)
assert "VertexAIException" not in error_message, (
f"Should not contain 'VertexAIException' in error message, got: {error_message}"
)
@pytest.mark.parametrize("status_code,expected_exception", [
(400, "BadRequestError"),
(401, "AuthenticationError"),
(403, "PermissionDeniedError"),
(404, "NotFoundError"),
(408, "Timeout"),
(429, "RateLimitError"),
(500, "InternalServerError"),
(502, "APIConnectionError"),
(503, "ServiceUnavailableError"),
])
def l(status_code, expected_exception):
"""
Test comprehensive Gemini error handling for all HTTP status codes.
This ensures that Gemini API errors of different types are properly mapped
to the correct LiteLLM exception types with GeminiException prefix.
"""
import httpx
from unittest.mock import Mock
from litellm.litellm_core_utils.exception_mapping_utils import exception_type
from litellm.exceptions import (
BadRequestError, AuthenticationError, PermissionDeniedError, NotFoundError,
Timeout, RateLimitError, InternalServerError, APIConnectionError, ServiceUnavailableError
)
# Mock the appropriate error response
mock_response = Mock(spec=httpx.Response)
mock_response.status_code = status_code
mock_response.text = f"API Error {status_code}"
mock_response.headers = {}
# Create a mock exception
mock_exception = httpx.HTTPStatusError(
message=f"HTTP {status_code}",
request=Mock(),
response=mock_response
)
mock_exception.response = mock_response
mock_exception.status_code = status_code
# Set message attribute for compatibility with exception mapping
mock_exception.message = f"HTTP {status_code}"
# Test the exception mapping
try:
exception_type(
model="gemini-pro",
original_exception=mock_exception,
custom_llm_provider="gemini",
completion_kwargs={},
extra_kwargs={}
)
assert False, f"Expected {expected_exception} to be raised for status {status_code}"
except Exception as e:
# Verify the correct exception type is raised
exception_classes = {
"BadRequestError": BadRequestError,
"AuthenticationError": AuthenticationError,
"PermissionDeniedError": PermissionDeniedError,
"NotFoundError": NotFoundError,
"Timeout": Timeout,
"RateLimitError": RateLimitError,
"InternalServerError": InternalServerError,
"APIConnectionError": APIConnectionError,
"ServiceUnavailableError": ServiceUnavailableError,
}
expected_class = exception_classes[expected_exception]
assert isinstance(e, expected_class), f"Expected {expected_exception}, got {type(e).__name__}"
# Verify the error message contains GeminiException
error_message = str(e)
assert "GeminiException" in error_message, (
f"Expected 'GeminiException' in error message for status {status_code}, got: {error_message}"
)
assert "VertexAIException" not in error_message, (
f"Should not contain 'VertexAIException' for status {status_code}, got: {error_message}"
)

2
tests/llm_translation/test_rerank.py
View file

@ -287,7 +287,7 @@ async def test_rerank_custom_callbacks():
top_n=3,
)
await asyncio.sleep(5)
await asyncio.sleep(8)
print("async re rank response: ", response)
assert custom_logger.kwargs is not None

14
tests/local_testing/test_aim_guardrails.py
View file

@ -209,6 +209,7 @@ async def test_post_call__with_anonymized_entities__it_deanonymizes_output():
"messages": [
{"role": "user", "content": "Hi my name id Brian"},
],
"litellm_call_id": "test-call-id",
}
with patch(
@ -217,6 +218,13 @@ async def test_post_call__with_anonymized_entities__it_deanonymizes_output():
def mock_post_detect_side_effect(url, *args, **kwargs):
request_body = kwargs.get("json", {})
request_headers = kwargs.get("headers", {})
assert (
request_headers["x-aim-call-id"] == "test-call-id"
), "Wrong header: x-aim-call-id"
assert (
request_headers["x-aim-gateway-key-alias"] == "test-key"
), "Wrong header: x-aim-gateway-key-alias"
if request_body["messages"][-1]["role"] == "user":
return response_with_detections
elif request_body["messages"][-1]["role"] == "assistant":
@ -229,7 +237,7 @@ async def test_post_call__with_anonymized_entities__it_deanonymizes_output():
data = await aim_guardrail.async_pre_call_hook(
data=data,
cache=DualCache(),
user_api_key_dict=UserAPIKeyAuth(),
user_api_key_dict=UserAPIKeyAuth(key_alias="test-key"),
call_type="completion",
)
assert data["messages"][0]["content"] == "Hi my name is [NAME_1]"
@ -249,7 +257,9 @@ async def test_post_call__with_anonymized_entities__it_deanonymizes_output():
)
result = await aim_guardrail.async_post_call_success_hook(
data=data, response=llm_response(), user_api_key_dict=UserAPIKeyAuth()
data=data,
response=llm_response(),
user_api_key_dict=UserAPIKeyAuth(key_alias="test-key"),
)
assert result["choices"][0]["message"]["content"] == "Hello Brian! How are you?"

3
tests/logging_callback_tests/test_unit_tests_init_callbacks.py
View file

@ -94,6 +94,9 @@ async def use_callback_in_llm_call(
if callback == "dynamic_rate_limiter":
# internal CustomLogger class that expects internal_usage_cache passed to it, it always fails when tested in this way
return
elif callback == "dynamic_rate_limiter_v3":
# internal CustomLogger class that expects internal_usage_cache passed to it, it always fails when tested in this way
return
elif callback == "argilla":
litellm.argilla_transformation_object = {}
elif callback == "openmeter":

78
tests/mcp_tests/test_mcp_client_unit.py
View file

@ -188,84 +188,6 @@ class TestMCPClientUnitTests:
name="test_tool", arguments={"arg1": "value1"}
)
def test_protocol_version_header_extraction(self):
"""Test that MCP protocol version header is correctly extracted from requests."""
from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
MCPRequestHandler,
)
# Mock scope with headers
mock_scope = {
"type": "http",
"method": "GET",
"path": "/test",
"headers": [
(b"authorization", b"Bearer test_token"),
(b"mcp-protocol-version", b"2025-06-18"),
(b"content-type", b"application/json"),
],
}
# Mock the user_api_key_auth function
with patch(
"litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp.user_api_key_auth"
) as mock_auth:
mock_auth.return_value = MagicMock()
# Call process_mcp_request
import asyncio
result = asyncio.run(MCPRequestHandler.process_mcp_request(mock_scope))
# Verify the protocol version is extracted
(
user_api_key_auth,
mcp_auth_header,
mcp_servers,
mcp_server_auth_headers,
mcp_protocol_version,
) = result
assert mcp_protocol_version == "2025-06-18"
def test_protocol_version_header_missing(self):
"""Test that MCP protocol version header is None when not provided."""
from litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp import (
MCPRequestHandler,
)
# Mock scope without protocol version header
mock_scope = {
"type": "http",
"method": "GET",
"path": "/test",
"headers": [
(b"authorization", b"Bearer test_token"),
(b"content-type", b"application/json"),
],
}
# Mock the user_api_key_auth function
with patch(
"litellm.proxy._experimental.mcp_server.auth.user_api_key_auth_mcp.user_api_key_auth"
) as mock_auth:
mock_auth.return_value = MagicMock()
# Call process_mcp_request
import asyncio
result = asyncio.run(MCPRequestHandler.process_mcp_request(mock_scope))
# Verify the protocol version is None
(
user_api_key_auth,
mcp_auth_header,
mcp_servers,
mcp_server_auth_headers,
mcp_protocol_version,
) = result
assert mcp_protocol_version is None
if __name__ == "__main__":

5
tests/mcp_tests/test_mcp_server.py
View file

@ -729,7 +729,6 @@ async def test_get_tools_from_mcp_servers():
from litellm.proxy._experimental.mcp_server.mcp_server_manager import (
MCPServer,
MCPTransport,
MCPSpecVersion,
)
# Mock data
@ -1791,7 +1790,6 @@ async def test_list_tool_rest_api_with_server_specific_auth():
assert (
call_args[0][1] == "Bearer zapier_token"
) # server_auth_header
assert call_args[0][2] == "2025-06-18" # mcp_protocol_version
@pytest.mark.asyncio
@ -1874,7 +1872,6 @@ async def test_list_tool_rest_api_with_default_auth():
assert (
call_args[0][1] == "Bearer default_token"
) # server_auth_header
assert call_args[0][2] == "2025-06-18" # mcp_protocol_version
@pytest.mark.asyncio
@ -1979,12 +1976,10 @@ async def test_list_tool_rest_api_all_servers_with_auth():
# First call should be for zapier server with zapier auth
assert calls[0][0][0] == mock_zapier_server # server
assert calls[0][0][1] == "Bearer zapier_token" # server_auth_header
assert calls[0][0][2] == "2025-06-18" # mcp_protocol_version
# Second call should be for slack server with slack auth
assert calls[1][0][0] == mock_slack_server # server
assert calls[1][0][1] == "Bearer slack_token" # server_auth_header
assert calls[1][0][2] == "2025-06-18" # mcp_protocol_version
@pytest.mark.asyncio

2
tests/store_model_in_db_tests/test_mcp_servers.py
View file

@ -12,8 +12,6 @@ from starlette import status
from litellm.constants import LITELLM_PROXY_ADMIN_NAME
from litellm.proxy._types import (
MCPSpecVersion,
MCPSpecVersionType,
MCPTransportType,
MCPTransport,
NewMCPServerRequest,

3
tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
View file

@ -226,7 +226,7 @@ def test_string_cost_values():
completion_tokens=500,
total_tokens=1500,
prompt_tokens_details=PromptTokensDetailsWrapper(
audio_tokens=100, cached_tokens=200, text_tokens=700, image_tokens=None
audio_tokens=100, cached_tokens=200, text_tokens=700, image_tokens=None, cache_creation_tokens=150
),
completion_tokens_details=CompletionTokensDetailsWrapper(
audio_tokens=50,
@ -235,7 +235,6 @@ def test_string_cost_values():
accepted_prediction_tokens=None,
rejected_prediction_tokens=None,
),
_cache_creation_input_tokens=150,
)
# Mock get_model_info to return our mock model info

670
tests/test_litellm/llms/bedrock/chat/test_converse_transformation.py
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tests/test_litellm/llms/vertex_ai/files/test_vertex_ai_files_handler.py Normal file
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@ -0,0 +1,270 @@
"""
Test Vertex AI files handler functionality
"""
import asyncio
import pytest
from unittest.mock import AsyncMock, patch
import httpx
from litellm.llms.vertex_ai.files.handler import VertexAIFilesHandler
from litellm.types.llms.openai import FileContentRequest, HttpxBinaryResponseContent
class TestVertexAIFilesHandler:
"""Test Vertex AI files handler"""
def setup_method(self):
"""Setup test method"""
self.handler = VertexAIFilesHandler()
def test_extract_bucket_and_object_from_file_id_standard_path(self):
"""Test extraction of bucket and object from URL-encoded file_id with standard path"""
# Sample file_id with nested folder structure
file_id = "gs%3A%2F%2Ftest-bucket%2Ftest-folder" "%2Fsub-folder%2Ftest-file.txt"
bucket_name, encoded_object_path = (
self.handler._extract_bucket_and_object_from_file_id(file_id)
)
# Verify bucket name extraction
assert bucket_name == "test-bucket"
# Verify object path encoding
expected_encoded_object = "test-folder%2Fsub-folder%2Ftest-file.txt"
assert encoded_object_path == expected_encoded_object
def test_extract_bucket_and_object_from_file_id_bucket_only(self):
"""Test extraction when only bucket name is provided"""
file_id = "gs%3A%2F%2Ftest-bucket"
bucket_name, encoded_object_path = (
self.handler._extract_bucket_and_object_from_file_id(file_id)
)
assert bucket_name == "test-bucket"
assert encoded_object_path == ""
def test_extract_bucket_and_object_from_file_id_simple_path(self):
"""Test extraction with simple path"""
file_id = "gs%3A%2F%2Ftest-bucket%2Ftest-file.txt"
bucket_name, encoded_object_path = (
self.handler._extract_bucket_and_object_from_file_id(file_id)
)
assert bucket_name == "test-bucket"
assert encoded_object_path == "test-file.txt"
def test_extract_bucket_and_object_from_file_id_no_gs_prefix(self):
"""Test extraction when gs:// prefix is missing"""
file_id = "test-bucket%2Ftest-file.txt"
bucket_name, encoded_object_path = (
self.handler._extract_bucket_and_object_from_file_id(file_id)
)
assert bucket_name == "test-bucket"
assert encoded_object_path == "test-file.txt"
@pytest.mark.asyncio
async def test_afile_content_success(self):
"""Test successful async file content retrieval"""
# Setup test data
file_id = "gs%3A%2F%2Ftest-bucket%2Ftest-file.txt"
expected_content = b"test file content"
file_content_request = FileContentRequest(
file_id=file_id, extra_headers=None, extra_body=None
)
# Mock the download_gcs_object method
with patch.object(
self.handler, "download_gcs_object", new_callable=AsyncMock
) as mock_download:
mock_download.return_value = expected_content
# Call the method
result = await self.handler.afile_content(
file_content_request=file_content_request,
vertex_credentials=None,
vertex_project="test-project",
vertex_location="us-central1",
timeout=60.0,
max_retries=3,
)
# Verify the result
assert isinstance(result, HttpxBinaryResponseContent)
assert hasattr(result, "response")
assert result.response.content == expected_content
assert result.response.status_code == 200
# Verify the download was called with correct parameters
mock_download.assert_called_once()
call_args = mock_download.call_args
assert call_args.kwargs["object_name"] == "test-file.txt"
assert "standard_callback_dynamic_params" in call_args.kwargs
assert (
call_args.kwargs["standard_callback_dynamic_params"]["gcs_bucket_name"]
== "test-bucket"
)
@pytest.mark.asyncio
async def test_afile_content_missing_file_id(self):
"""Test async file content retrieval with missing file_id"""
file_content_request = FileContentRequest(extra_headers=None, extra_body=None)
# Should raise ValueError for missing file_id
with pytest.raises(
ValueError, match="file_id is required in file_content_request"
):
await self.handler.afile_content(
file_content_request=file_content_request,
vertex_credentials=None,
vertex_project="test-project",
vertex_location="us-central1",
timeout=60.0,
max_retries=3,
)
@pytest.mark.asyncio
async def test_afile_content_download_failure(self):
"""Test async file content retrieval when download fails"""
file_id = "gs%3A%2F%2Ftest-bucket%2Ftest-file.txt"
file_content_request = FileContentRequest(
file_id=file_id, extra_headers=None, extra_body=None
)
# Mock download to return None (failure)
with patch.object(
self.handler, "download_gcs_object", new_callable=AsyncMock
) as mock_download:
mock_download.return_value = None
# Should raise ValueError for failed download
with pytest.raises(
ValueError,
match="Failed to download file from GCS: gs://test-bucket/test-file.txt",
):
await self.handler.afile_content(
file_content_request=file_content_request,
vertex_credentials=None,
vertex_project="test-project",
vertex_location="us-central1",
timeout=60.0,
max_retries=3,
)
def test_file_content_sync_success(self):
"""Test successful sync file content retrieval"""
file_id = "gs%3A%2F%2Ftest-bucket%2Ftest-file.txt"
expected_content = b"test file content"
file_content_request = FileContentRequest(
file_id=file_id, extra_headers=None, extra_body=None
)
# Create expected response
mock_response = httpx.Response(
status_code=200,
content=expected_content,
headers={"content-type": "application/octet-stream"},
request=httpx.Request(method="GET", url="gs://test-bucket/test-file.txt"),
)
expected_result = HttpxBinaryResponseContent(response=mock_response)
# Mock asyncio.run to return our expected result
with patch("asyncio.run") as mock_run:
mock_run.return_value = expected_result
result = self.handler.file_content(
_is_async=False,
file_content_request=file_content_request,
api_base="",
vertex_credentials=None,
vertex_project="test-project",
vertex_location="us-central1",
timeout=60.0,
max_retries=3,
)
# Verify the result
assert result == expected_result
# Verify asyncio.run was called (indicating sync execution)
mock_run.assert_called_once()
@pytest.mark.asyncio
async def test_file_content_async_mode(self):
"""Test async file content retrieval when _is_async=True"""
file_id = "gs%3A%2F%2Ftest-bucket%2Ftest-file.txt"
expected_content = b"test file content"
file_content_request = FileContentRequest(
file_id=file_id, extra_headers=None, extra_body=None
)
# Mock the afile_content method
with patch.object(
self.handler, "afile_content", new_callable=AsyncMock
) as mock_afile_content:
mock_response = httpx.Response(
status_code=200,
content=expected_content,
headers={"content-type": "application/octet-stream"},
request=httpx.Request(
method="GET", url="gs://test-bucket/test-file.txt"
),
)
mock_afile_content.return_value = HttpxBinaryResponseContent(
response=mock_response
)
# Call the method with _is_async=True
result = self.handler.file_content(
_is_async=True,
file_content_request=file_content_request,
api_base="",
vertex_credentials=None,
vertex_project="test-project",
vertex_location="us-central1",
timeout=60.0,
max_retries=3,
)
# Should return a coroutine since _is_async=True
assert asyncio.iscoroutine(result)
# Await the result
final_result = await result
assert isinstance(final_result, HttpxBinaryResponseContent)
assert final_result.response.content == expected_content
def test_httpx_response_compatibility(self):
"""Test that the created HttpxBinaryResponseContent is compatible with expected interface"""
# Test the mock response creation logic
expected_content = b"test file content"
decoded_path = "gs://test-bucket/test-file.txt"
mock_response = httpx.Response(
status_code=200,
content=expected_content,
headers={"content-type": "application/octet-stream"},
request=httpx.Request(method="GET", url=decoded_path),
)
result = HttpxBinaryResponseContent(response=mock_response)
# Verify the response properties
assert result.response.status_code == 200
assert result.response.content == expected_content
assert result.response.headers["content-type"] == "application/octet-stream"
# Verify it has the expected interface (matching OpenAI file content response)
assert hasattr(result, "response")
assert hasattr(result.response, "content")
assert hasattr(result.response, "status_code")
assert hasattr(result.response, "headers")

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