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litellm/proxy_server_config.yaml
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Ishaan Jaff 9761ba7c7a [Bug Fix] Responses api session management for streaming responses (#13396) 
* fix proxy config

* fix(responses api): fix streaming ID consistency and tool format handling (#12640)

* fix(responses): ensure streaming chunk IDs use consistent encoding format

Fixes streaming ID inconsistency where streaming responses used raw provider IDs
while non-streaming responses used properly encoded IDs with provider context.

Changes:
- Updated LiteLLMCompletionStreamingIterator to accept provider context
- Added _encode_chunk_id() method using same logic as non-streaming responses
- Modified chunk transformation to encode all streaming item_ids with resp_ prefix
- Updated handlers to pass custom_llm_provider and litellm_metadata to streaming iterator

Impact:
- Streaming chunk IDs now format: resp_<base64_encoded_provider_context>
- Enables session continuity when using streaming response IDs as previous_response_id
- Allows provider detection and load balancing with streaming responses
- Maintains backward compatibility with existing streaming functionality

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix(types): add explicit Optional[str] type annotation for model_id

This resolves MyPy type checking error where model_id could be None
but wasn't explicitly typed as Optional[str].

* fix(types): handle None case for litellm_metadata access

Prevents 'Item None has no attribute get' error by checking for None
before accessing litellm_metadata dictionary.

* test: add comprehensive tests for streaming ID consistency

Adds unit and E2E tests to verify streaming chunk IDs are properly encoded
with consistent format across streaming responses.

## Tests Added

### Unit Test (test_reasoning_content_transformation.py)
- `test_streaming_chunk_id_encoding()`: Validates the `_encode_chunk_id()` method
  correctly encodes chunk IDs with `resp_` prefix and provider context

### E2E Tests (test_e2e_openai_responses_api.py)
- `test_streaming_id_consistency_across_chunks()`: Tests that all streaming chunk IDs
  are properly encoded across multiple chunks in a real streaming response
- `test_streaming_response_id_as_previous_response_id()`: Tests the core use case -
  using streaming response IDs for session continuity with `previous_response_id`

## Key Testing Approach
- Uses **Gemini** (non-OpenAI model) to test the transformation logic rather than
  OpenAI passthrough, since the streaming ID consistency issue occurs when LiteLLM
  transforms responses rather than just passing through to native OpenAI responses API
- Tests validate that streaming chunk IDs now use same encoding as non-streaming responses
- Verifies session continuity works with streaming responses

Addresses @ishaan-jaff's request for unit tests covering the streaming ID consistency fix.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fix(lint): remove unused imports in transformation.py

Removes unused imports to fix CI linting errors:
- GenericResponseOutputItem
- OutputFunctionToolCall

* test: remove E2E tests from openai_endpoints_tests

Remove streaming ID consistency E2E tests as requested by @ishaan-jaff.
Keep only the mock/unit test in test_reasoning_content_transformation.py

* revert: remove streaming chunk ID encoding to original behavior

This reverts the streaming chunk ID encoding changes to understand the original issue better.
Original behavior was:
- Streaming chunks: raw provider IDs
- Streaming final response: raw IDs (PROBLEM!)
- Non-streaming final response: encoded IDs (correct)

The real issue: streaming final response IDs were not encoded, breaking session continuity.

* fix(responses): encode streaming final response IDs to match OpenAI behavior

Fixes streaming ID inconsistency to match OpenAI's Responses API behavior:
- Streaming chunks: raw message IDs (like OpenAI's msg_xxx)
- Final response: encoded IDs (like OpenAI's resp_xxx)

This enables session continuity by ensuring streaming final response IDs
have the same encoded format as non-streaming responses, allowing them
to be used as previous_response_id in follow-up requests.

Changes:
- Add custom_llm_provider and litellm_metadata to LiteLLMCompletionStreamingIterator
- Update handlers to pass provider context to streaming iterator
- Apply _update_responses_api_response_id_with_model_id to final streaming response
- Keep streaming chunks as raw IDs to match OpenAI format

Impact:
- Session continuity works with streaming responses
- Load balancing can detect provider from streaming final response IDs
- Format matches OpenAI's Responses API exactly

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

* test: update unit test to match correct OpenAI-compatible behavior

Updates the unit test to verify streaming chunk IDs are raw (not encoded)
to match OpenAI's responses API format:
- Streaming chunks: raw message IDs (like msg_xxx)
- Final response: encoded IDs (like resp_xxx)

This reflects the correct behavior implemented in the fix.

---------

Co-authored-by: Claude <noreply@anthropic.com>

* cleanup

* TestBaseResponsesAPIStreamingIterator

---------

Co-authored-by: Javier de la Torre <jatorre@carto.com>
Co-authored-by: Claude <noreply@anthropic.com>
2025-08-07 20:13:24 -07:00

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model_list:
- model_name: gpt-3.5-turbo-end-user-test
litellm_params:
model: gpt-3.5-turbo
region_name: "eu"
model_info:
id: "1"
- model_name: gpt-3.5-turbo-end-user-test
litellm_params:
model: azure/gpt-4o-new-test
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env. See https://docs.litellm.ai/docs/simple_proxy#load-api-keys-from-vault
- model_name: gpt-3.5-turbo
litellm_params:
model: azure/gpt-4o-new-test
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env. See https://docs.litellm.ai/docs/simple_proxy#load-api-keys-from-vault
- model_name: gpt-3.5-turbo-large
litellm_params:
model: "gpt-3.5-turbo-1106"
api_key: os.environ/OPENAI_API_KEY
rpm: 480
timeout: 300
stream_timeout: 60
- model_name: gpt-4
litellm_params:
model: azure/gpt-4o-new-test
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env. See https://docs.litellm.ai/docs/simple_proxy#load-api-keys-from-vault
rpm: 480
timeout: 300
stream_timeout: 60
- model_name: sagemaker-completion-model
litellm_params:
model: sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4
input_cost_per_second: 0.000420
- model_name: text-embedding-ada-002
litellm_params:
model: azure/azure-embedding-model
api_key: os.environ/AZURE_API_KEY
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
model_info:
mode: embedding
base_model: text-embedding-ada-002
- model_name: dall-e-2 # some tests use dall-e-2 which is now deprecated, alias to dall-e-3
litellm_params:
model: openai/dall-e-3
- model_name: openai-dall-e-3
litellm_params:
model: dall-e-3
- model_name: fake-openai-endpoint
litellm_params:
model: openai/fake
api_key: fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
- model_name: fake-openai-endpoint-2
litellm_params:
model: openai/my-fake-model
api_key: my-fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
stream_timeout: 0.001
rpm: 1
- model_name: fake-openai-endpoint-3
litellm_params:
model: openai/my-fake-model
api_key: my-fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
stream_timeout: 0.001
rpm: 1000
- model_name: fake-openai-endpoint-4
litellm_params:
model: openai/my-fake-model
api_key: my-fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
num_retries: 50
- model_name: fake-openai-endpoint-3
litellm_params:
model: openai/my-fake-model-2
api_key: my-fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
stream_timeout: 0.001
rpm: 1000
- model_name: bad-model
litellm_params:
model: openai/bad-model
api_key: os.environ/OPENAI_API_KEY
api_base: https://exampleopenaiendpoint-production.up.railway.app/
mock_timeout: True
timeout: 60
rpm: 1000
model_info:
health_check_timeout: 1
- model_name: good-model
litellm_params:
model: openai/bad-model
api_key: os.environ/OPENAI_API_KEY
api_base: https://exampleopenaiendpoint-production.up.railway.app/
rpm: 1000
model_info:
health_check_timeout: 1
- model_name: "*"
litellm_params:
model: openai/*
api_key: os.environ/OPENAI_API_KEY
# provider specific wildcard routing
- model_name: "anthropic/*"
litellm_params:
model: "anthropic/*"
api_key: os.environ/ANTHROPIC_API_KEY
- model_name: "bedrock/*"
litellm_params:
model: "bedrock/*"
- model_name: "groq/*"
litellm_params:
model: "groq/*"
api_key: os.environ/GROQ_API_KEY
- model_name: mistral-embed
litellm_params:
model: mistral/mistral-embed
- model_name: gpt-instruct # [PROD TEST] - tests if `/health` automatically infers this to be a text completion model
litellm_params:
model: text-completion-openai/gpt-3.5-turbo-instruct
- model_name: fake-openai-endpoint-5
litellm_params:
model: openai/my-fake-model
api_key: my-fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.app/
timeout: 1
- model_name: badly-configured-openai-endpoint
litellm_params:
model: openai/my-fake-model
api_key: my-fake-key
api_base: https://exampleopenaiendpoint-production.up.railway.appxxxx/
- model_name: gemini-1.5-flash
litellm_params:
model: gemini/gemini-1.5-flash
api_key: os.environ/GOOGLE_API_KEY
- model_name: gpt-4o
litellm_params:
model: gpt-4o
api_key: os.environ/OPENAI_API_KEY
litellm_settings:
# set_verbose: True # Uncomment this if you want to see verbose logs; not recommended in production
drop_params: True
# max_budget: 100
# budget_duration: 30d
num_retries: 5
request_timeout: 600
telemetry: False
context_window_fallbacks: [{"gpt-3.5-turbo": ["gpt-3.5-turbo-large"]}]
default_team_settings:
- team_id: team-1
success_callback: ["langfuse"]
failure_callback: ["langfuse"]
langfuse_public_key: os.environ/LANGFUSE_PROJECT1_PUBLIC # Project 1
langfuse_secret: os.environ/LANGFUSE_PROJECT1_SECRET # Project 1
- team_id: team-2
success_callback: ["langfuse"]
failure_callback: ["langfuse"]
langfuse_public_key: os.environ/LANGFUSE_PROJECT2_PUBLIC # Project 2
langfuse_secret: os.environ/LANGFUSE_PROJECT2_SECRET # Project 2
langfuse_host: https://us.cloud.langfuse.com
# For /fine_tuning/jobs endpoints
finetune_settings:
- custom_llm_provider: azure
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-03-15-preview"
- custom_llm_provider: openai
api_key: os.environ/OPENAI_API_KEY
# for /files endpoints
files_settings:
- custom_llm_provider: azure
api_base: os.environ/AZURE_API_BASE
api_key: os.environ/AZURE_API_KEY
api_version: "2023-03-15-preview"
- custom_llm_provider: openai
api_key: os.environ/OPENAI_API_KEY
router_settings:
routing_strategy: usage-based-routing-v2
redis_host: os.environ/REDIS_HOST
redis_password: os.environ/REDIS_PASSWORD
redis_port: os.environ/REDIS_PORT
enable_pre_call_checks: true
model_group_alias: {"my-special-fake-model-alias-name": "fake-openai-endpoint-3"}
general_settings:
master_key: sk-1234 # [OPTIONAL] Use to enforce auth on proxy. See - https://docs.litellm.ai/docs/proxy/virtual_keys
store_model_in_db: True
proxy_budget_rescheduler_min_time: 60
proxy_budget_rescheduler_max_time: 64
proxy_batch_write_at: 1
database_connection_pool_limit: 10
# database_url: "postgresql://<user>:<password>@<host>:<port>/<dbname>" # [OPTIONAL] use for token-based auth to proxy
pass_through_endpoints:
- path: "/v1/rerank" # route you want to add to LiteLLM Proxy Server
target: "https://api.cohere.com/v1/rerank" # URL this route should forward requests to
headers: # headers to forward to this URL
content-type: application/json # (Optional) Extra Headers to pass to this endpoint
accept: application/json
forward_headers: True
# environment_variables:
# settings for using redis caching
# REDIS_HOST: redis-16337.c322.us-east-1-2.ec2.cloud.redislabs.com
# REDIS_PORT: "16337"
# REDIS_PASSWORD:
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