Litellm oss staging 01 29 2026 #19975
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…hropic (#19896) * fix(vertex_ai): convert image URLs to base64 in tool messages for Anthropic Fixes #19891 Vertex AI Anthropic models don't support URL sources for images. LiteLLM already converted image URLs to base64 for user messages, but not for tool messages (role='tool'). This caused errors when using ToolOutputImage with image_url in tool outputs. Changes: - Add force_base64 parameter to convert_to_anthropic_tool_result() - Pass force_base64 to create_anthropic_image_param() for tool message images - Calculate force_base64 in anthropic_messages_pt() based on llm_provider - Add unit tests for tool message image handling * chore: remove extra comment from test file header
* fix(proxy_server): pass search_tools to Router during DB-triggered initialization * fix search tools from db * add missing statement to handle from db * fix import issues to pass lint errors
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…9654) Fixes #19478 The stream_chunk_builder function was not handling image chunks from models like gemini-2.5-flash-image. When streaming responses were reconstructed (e.g., for caching), images in delta.images were lost. This adds handling for image_chunks similar to how audio, annotations, and other delta fields are handled.
… list (#19952) The /health/services endpoint rejected datadog_llm_observability as an unknown service, even though it was registered in the core callback registry and __init__.py. Added it to both the Literal type hint and the hardcoded validation list in the health endpoint.
* fix(proxy): prevent provider-prefixed model leaks Proxy clients should not see LiteLLM internal provider prefixes (e.g. hosted_vllm/...) in the OpenAI-compatible response model field. This patch sanitizes the client-facing model name for both: - Non-streaming responses returned from base_process_llm_request - Streaming SSE chunks emitted by async_data_generator Adds regression tests covering vLLM-style hosted_vllm routing for both streaming and non-streaming paths. * chore(lint): suppress PLR0915 in proxy handler Ruff started flagging ProxyBaseLLMRequestProcessing.base_process_llm_request() for too many statements after the hotpatch changes. Add an explicit '# noqa: PLR0915' on the function definition to avoid a large refactor in a hotpatch. * refactor(proxy): make model restamp explicit Replace silent try/except/pass and type ignores with explicit model restamping. - Logs an error when the downstream response model differs from the client-requested model - Overwrites the OpenAI `model` field to the client-requested value to avoid leaking internal provider-prefixed identifiers - Applies the same behavior to streaming chunks, logging the mismatch only once per stream * chore(lint): drop PLR0915 suppression The model restamping bugfix made `base_process_llm_request()` slightly exceed Ruff's PLR0915 (too-many-statements) threshold, requiring a `# noqa` suppression. Collapse consecutive `hidden_params` extractions into tuple unpacking so the function falls back under the lint limit and remove the suppression. No functional change intended; this keeps the proxy model-field bugfix intact while aligning with project linting rules. * chore(proxy): log model mismatches as warnings These model-restamping logs are intentionally verbose: a mismatch is a useful signal that an internal provider/deployment identifier may be leaking into the public OpenAI response `model` field. - Downgrade model mismatch logs from error -> warning - Keep error logs only for cases where the proxy cannot read/override the model * fix(proxy): preserve client model for streaming aliasing Pre-call processing can rewrite request_data['model'] via model alias maps.\n\nOur streaming SSE generator was using the rewritten value when restamping chunk.model, which caused the public 'model' field to differ between streaming and non-streaming responses for alias-based requests.\n\nStash the original client model in request_data as _litellm_client_requested_model after the model has been routed, and prefer it when overriding the outgoing chunk model. Add a regression test for the alias-mapping case. * chore(lint): satisfy PLR0915 in streaming generator Ruff started flagging async_data_generator() for too many statements after adding model restamping logic.\n\nExtract the client-model selection + chunk restamping into small helpers to keep behavior unchanged while meeting the project's PLR0915 threshold.
…verify (#19893) * fix(hosted_vllm): route through base_llm_http_handler to support ssl_verify The hosted_vllm provider was falling through to the OpenAI catch-all path which doesn't pass ssl_verify to the HTTP client. This adds an explicit elif branch that routes hosted_vllm through base_llm_http_handler.completion() which properly passes ssl_verify to the httpx client. - Add explicit hosted_vllm branch in main.py completion() - Add ssl_verify tests for sync and async completion - Update existing audio_url test to mock httpx instead of OpenAI client * feat(hosted_vllm): add embedding support with ssl_verify - Add HostedVLLMEmbeddingConfig for embedding transformations - Register hosted_vllm embedding config in utils.py - Add lazy import for embedding transformation module - Add unit test for ssl_verify parameter handling
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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