* fix(proxy): strip LiteLLM policy tracking from OpenAI batch metadata
Batch create was failing with `Invalid type for 'metadata.applied_policies':
expected a string, but got an array instead` whenever a policy attachment
matched the request. The policy engine helpers wrote `applied_policies`,
`applied_guardrails`, and `policy_sources` into `data["metadata"]`
unconditionally, and `/v1/batches` forwarded that dict straight to OpenAI,
which only accepts string values.
- Route proxy-internal tracking into `litellm_metadata` for batch/file
routes via a shared `_get_or_create_proxy_metadata_bucket` helper.
- Sanitize `data["metadata"]` in `create_batch` to drop known internal
keys and non-string values before building the OpenAI request.
- Cover both behaviors with unit + endpoint tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(proxy): merge metadata buckets for batch policy response headers
Ensure get_logging_caching_headers reads both metadata and litellm_metadata so policy/guardrail headers are emitted on batch routes with user metadata, and log dropped non-string OpenAI metadata at debug level.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
- Change status codes from 400 to 500 for team metadata misconfig errors
(callers can't fix admin-set config, 400 is misleading)
- Add anchor value validation to batch endpoint (matching files endpoint)
- Coerce seconds to int to handle string values from metadata
- Add error-path tests: missing keys, invalid anchor, status code assertions
- Add happy-path test: team injects expiry when caller sends nothing
