Splits the OpenAI-family gate into per-param predicates so AZURE and
AZURE_OPENAI configs now receive prompt_cache_key for backend routing
affinity (Microsoft auto-caches GPT-4o+ deployments at >=1024 tokens;
the key clusters same-prefix requests on the same GPU pool and raises
hit rate on turn 2+). prompt_cache_retention stays opted out for Azure
because litellm 1.83.14's Azure transformer would drop it silently;
revisit when Azure's supported params list is updated.
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- Added a new function `_warm_agent_jit_caches` to pre-warm agent caches at startup, reducing cold invocation costs.
- Updated the `SurfSenseContextSchema` to include per-invocation fields for better state management during agent execution.
- Introduced caching mechanisms in various tools to ensure fresh database sessions are used, improving performance and reliability.
- Enhanced middleware to support new context features and improve error handling during connector and document type discovery.
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- Updated `litellm` dependency version from `1.83.4` to `1.83.7`.
- Adjusted `aiohttp` version from `3.13.5` to `3.13.4` in the lock file.
- Implemented `apply_litellm_prompt_caching` in `chat_deepagent.py` to improve prompt caching.
- Added model name resolution logic in `chat_deepagent.py` to ensure correct provider-variant dispatch.
- Enhanced `llm_config.py` to configure prompt caching for various LLM providers.
- Updated tests to verify correct model name forwarding and prompt caching behavior.
