* feat(batches/): fix batch cost calculation - ensure it's accurate
use the correct cost value - prev. defaulting to non-batch cost
* feat(batch_utils.py): log batch models to spend logs + standard logging payload
makes it easy to understand how cost was calculated
* fix: fix stored payload for test
* test: fix test
* feat: initial commit - enable dev to see translated request
* feat(utils.py): expose new endpoint - `/utils/transform_request` to see the raw request sent by litellm
* feat(transform_request.tsx): allow user to see their transformed request
* refactor(litellm_logging.py): return raw request in 3 parts - api_base, headers, request body
easier to render each individually on UI vs. extracting from combined string
* feat: transform_request.tsx
working e2e raw request viewing
* fix(litellm_logging.py): fix transform viewing for bedrock models
* fix(litellm_logging.py): don't return sensitive headers in raw request headers
prevent accidental leak
* feat(transform_request.tsx): style improvements
* feat(key_management_endpoints.py): adding support for rotating master key
* feat(key_management_endpoints.py): support decryption-re-encryption of models in db, when master key rotated
* fix(user_api_key_auth.py): raise valid token is None error earlier
enables easier debugging with api key hash in error message
* feat(key_management_endpoints.py): rotate any env vars
* fix(key_management_endpoints.py): uncomment check
* fix: fix linting error
* fix(team_endpoints.py): ensure 404 raised when team not found
* fix(key_management_endpoints.py): fix adding tags to key when metadata is empty
* fix(key_management_endpoints.py): refactor set metadata field to use common function across keys + teams
reduces scope for errors + easier testing
* fix: fix linting error
* fix(core_helpers.py): handle litellm_metadata instead of 'metadata'
* feat(batches/): ensure batches logs are written to db
makes batches response dict compatible
* fix(cost_calculator.py): handle batch response being a dictionary
* fix(batches/main.py): modify retrieve endpoints to use @client decorator
enables logging to work on retrieve call
* fix(batches/main.py): fix retrieve batch response type to be 'dict' compatible
* fix(spend_tracking_utils.py): send unique uuid for retrieve batch call type
create batch and retrieve batch share the same id
* fix(spend_tracking_utils.py): prevent duplicate retrieve batch calls from being double counted
* refactor(batches/): refactor cost tracking for batches - do it on retrieve, and within the established litellm_logging pipeline
ensures cost is always logged to db
* fix: fix linting errors
* fix: fix linting error
* fix(common_utils.py): handle $id in response schema when calling vertex ai
Fixes issue where `$id` present in response_schema was not accepted by vertex ai
* test(test_vertex.py): add unit test to ensure $id stripped out of vertex schema
* test(test_router_tag_routing.py): add unit test for tag-based routing on embeddings
* fix(router.py): pass request kwargs on async embeddings to async_get_available_deployment function
* fix(router.py): require request kwargs to always be passed in
ensures tag-based routing always works, across endpoints
* feat(langfuse_prompt_management.py): support using prompt management per langfuse project with key/team based logging
* fix: fix linting error
* fix: fix test
* fix: fix test
* fix: fix test
* fix: fix linting error
* fix(route_llm_request.py): move to using common router, even for client-side credentials
ensures fallbacks / cooldown logic still works
* test(test_route_llm_request.py): add unit test for route request
* feat(router.py): generate unique model id when clientside credential passed in
Prevents cooldowns for api key 1 from impacting api key 2
* test(test_router.py): update testing to ensure original litellm params not mutated
* fix(router.py): upsert clientside call into llm router model list
enables cooldown logic to work accurately
* fix: fix linting error
* test(test_router_utils.py): add direct test for new util on router
