* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
chunk_parser built ModelResponseStream without passing usage, so the
cache_read_input_tokens and cache_creation_input_tokens that Databricks
returns for Anthropic models never reached the cost calculator. Every
streamed request was billed at the full input rate even when served
from cache.
ModelResponseStream already coerces a usage dict into Usage, which maps
those keys into prompt_tokens_details, so passing the chunk's usage
through is sufficient.
* 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
