* noma support v2 api and images with during call
* supporting streams and images with texts
* Supporting text now
* annonymization works
* removing function
* fixing noma.py
* all old tests pass
* adding new tests
* removing changes
* Fixing application id headers
* fix whitespace
* deleting unused imports
* Add gemini api key in the custom api url
* Update tests
* Use api key n the header
* Use api key n the header
* fix mypy error
* fix mypy error
* fix test gemini auth
* fix(redis): handle float redis_version from AWS ElastiCache Valkey
AWS ElastiCache Valkey returns redis_version as a float (7.0) instead
of a string ('7.0.0'), causing AttributeError: 'float' object has no
attribute 'split' in async_lpop when parsing version for LPOP count.
Changes:
- Extract version parsing into _parse_redis_major_version() helper
- Add DEFAULT_REDIS_MAJOR_VERSION constant (replaces magic number)
- Support multiple version formats: string, float, int, malformed
- Add comprehensive test coverage for all version format edge cases
Fixes: 'LiteLLM Redis Cache LPOP: - Got exception from REDIS' error
during db_spend_update_job cronjobs
* refactor: move DEFAULT_REDIS_MAJOR_VERSION to constants.py
This commit fixes two bugs in Responses API streaming tests:
1. **Usage field naming bug**: Tests were using `input_tokens` and
`output_tokens` but the Usage object uses `prompt_tokens` and
`completion_tokens`.
2. **Missing cost in streaming usage**: When `include_cost_in_streaming_usage`
was enabled, the cost was calculated and added to ResponseAPIUsage, but was
lost during the transformation to the Usage object.
Changes:
- Updated test assertions to use correct field names (prompt_tokens, completion_tokens)
- Added cost preservation logic in FakeStreamerResponsesAPIIterator
- Modified _transform_response_api_usage_to_chat_usage() to preserve cost attribute
All streaming tests now pass successfully.
* add helper functions
* update generic_cost_per_token function
* add test
* formatting
* add examples in docstring for _calculate_tiered_cost
* Restore files to upstream/main version
* dashscope specific calculation
* improve for different costs
* remove _calculate_flat_cost function
* fix(anthropic-adapter): properly translate Anthropic image format to OpenAI
Fixed bug where images were stripped during Anthropic Messages API to Azure
OpenAI translation. Image source data was being stringified instead of having
fields properly extracted.
- Added _translate_anthropic_image_to_openai() helper method
- Support both base64 and URL image formats per Anthropic API spec
- Refactored user message and tool result image handling
* test(anthropic-adapter): add comprehensive image translation tests
Add 5 unit tests covering image translation from Anthropic to OpenAI format:
- User messages with base64 images
- User messages with URL images
- Tool results with base64 images
- Tool results with URL images
- Mixed content with multiple images
* Add v1 cut of container api
* fix lint errors
* Add proxy support to container apis & logging support (#16049)
* Add proxy support to container apis
* Add logging support
* Add cost tracking support for containers and documentation
* Add new constant documentation
* Add container cost in model map
* fix failing azure tests
* Update tests based on model map changes
* fix model map tests
* fix model map tests
* Container modeshould be container
* Container tests fix
* Merge branch 'main' into litellm_sameer_oct_staging_2
* Add Prometheus metric to track callback logging failures in S3 (#16102)
* Add proxy support to container apis
* Add logging support
* prometheus metric measures how often s3_v2 is failing
* remove not needed files
* remove not needed files
* remove not needed files
* fix mypy errors
* Use logging_callback_manager to get all the callbacks
---------
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* feat(llm_passthrough_endpoints.py): support milvus passthrough api
* fix(llm_passthrough_endpoints.py): move streaming request value to the top of the function
* docs: document new milvus vector store passthrough flow
* feat: change guardrail_information to list type to support displaying multiple guardrails
* fix: add missing commit and revert auto-format changes in utils.py
---------
Co-authored-by: Krish Dholakia <krrishdholakia@gmail.com>
* streaming support in langfuse otel
* Added testing for Langfuse Otel tracing in the response API
---------
Co-authored-by: eycjur <eycjur@example.com>
* Add v1 cut of container api
* fix lint errors
* Add proxy support to container apis & logging support (#16049)
* Add proxy support to container apis
* Add logging support
* Add cost tracking support for containers and documentation
* Add new constant documentation
* Add container cost in model map
* fix failing azure tests
* Update tests based on model map changes
* fix model map tests
* fix model map tests
* Container modeshould be container
* Container tests fix
* Merge branch 'main' into litellm_sameer_oct_staging_2
---------
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* fix model error for apis which don't need model
* fix print statments:
* fix mypy lint errors
---------
Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com>
* feat(vector_store_endpoints/endpoints.py): add new index_create endpoint
allows admin to create a virtual index, to do permission management for
* feat(key_management_endpoints.py): enable setting allowed_vector_store_indexes on keys
proxy admin can enable dev to create an index on a vector stor
* feat: initial commit adding vector store index passthrough logic to litellm
* feat: add vector store table
* fix(azure_ai/transformation.py): fix headers
* feat: track read/write endpoints by vector store integration
enables permissions by index to work
* fix: azure_ai/vector_stores/search
document the vector store endpoints correctly
ensures permission management works as expected
* fix(proxy/utils.py): improve error message
* docs(azure_ai_vector_stores_passthrough.md): document azure ai passthrough vector store support
* docs(create.md): document azure ai support via passthrough for vector store create
* fix: fix code qa errors
* fix: document new allowed_vector_store_indexes endpoint
* feat(milvus/): initial commit adding milvus vector store support to LiteLLM
allows querying milvus vector store through litellm
* feat(bedrock/vector_stores): support translating openai filters param to aws kb
adds filtering to aws kb
* feat(milvus/): add milvus vector store unified search support
allows calling milvus vector store in through chat completions
* docs(milvus_vector_stores.md): document new milvus vector search integration
* feat(pass_through_endpoints.py): support passing form data through to a passthrough endpoint
Closes LIT-1147
* fix: fix linting errors
