* fix(databricks): split parallel tool calls so each tool message follows tool_calls
Databricks OpenAI-compatible serving (e.g. GPT models) 400s with "messages with
role 'tool' must be a response to a preceeding message with 'tool_calls'" when an
assistant turn makes parallel tool calls. LiteLLM faithfully sends one assistant
message holding all tool_calls followed by one 'tool' message per result, so every
result after the first is preceded by another 'tool' message rather than the
assistant tool_calls message, which Databricks rejects.
Re-emit each result immediately after an assistant message that carries only its
matching tool_call, turning assistant(tool_calls=[A, B]), tool(A), tool(B) into
assistant(tool_calls=[A]), tool(A), assistant(tool_calls=[B]), tool(B). The
rewrite is a no-op when the turn is already valid (single call), the group is
incomplete, or ids don't line up, so no tool call is ever dropped. Scoped to
non-Claude models, matching the existing OpenAI-shaped transformation path.
* style(databricks): use builtin list generics in parallel tool-call split
Switch the List[...] annotations introduced by _split_parallel_tool_calls
to lowercase list[...] so the UP006 strict-rule budget stays within its
ceiling.
- Add OAuth M2M (Machine-to-Machine) authentication via DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET
- Add Databricks SDK auto-auth with automatic credential discovery
- Add sensitive data redaction for secure logging (tokens, API keys, secrets)
- Add custom user_agent parameter for partner attribution in Databricks telemetry
- Support user_agent in LiteLLM Proxy via config.yaml litellm_params
- Add 49 mocked unit tests for all new functionality
- Add 13 E2E tests for real-world validation (skipped in CI)
- Update documentation with new features and examples
