Hold the resolved config in a process-memory TTL cache so the
request-handling path doesn't run litellm_proxymodeltable.find_first
on every vector-store call.
The vector store create/update path previously called
``_resolve_embedding_config`` against the admin-configured router/DB
model and persisted the resolved ``litellm_embedding_config`` dict
(``api_key`` / ``api_base`` / ``api_version``) into the
``litellm_managedvectorstorestable.litellm_params`` column. Because the
resolver expanded ``os.environ/...`` references via ``get_secret``, the
DB row carried cleartext provider credentials, and the
``/vector_store/{new,info,update,list}`` responses returned them to any
authenticated caller who could supply a known admin model name.
Move the auto-resolve out of ``create_vector_store_in_db`` and out of
the update path. Persist only the user-supplied ``litellm_embedding_model``
reference. Resolve at request-handling time inside
``_update_request_data_with_litellm_managed_vector_store_registry`` so
the resolved config lives in the per-request ``data`` dict and is
garbage-collected after the response. Legacy rows that were created by
an earlier proxy version and already carry a resolved
``litellm_embedding_config`` skip the re-resolution and pass through
unchanged so embedding calls keep working.
The ``new_vector_store`` response now also runs the existing
``_redact_sensitive_litellm_params`` masker (already used by ``info``,
``update``, and ``list``), defending against caller-supplied cleartext
on the create path and against legacy rows whose persisted credentials
are still in the database.
Existing tests that asserted the old write-time-resolve behaviour are
updated to assert the new persistence shape (no embedding config
stored, just the model reference). Two new tests cover the use-time
path: one asserting fresh resolution happens when a row carries only
the model reference, the other asserting legacy rows with persisted
config skip re-resolution and continue to work.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three issues surfaced in review of the previous commit:
1. **Veria — Medium**: ``litellm_params`` carries a nested
``litellm_embedding_config`` dict (auto-resolved from the model
registry on create / update) which itself holds ``api_key`` /
``aws_*`` / ``vertex_credentials``. The previous redactor only
inspected top-level keys, so the nested values passed through
unredacted. Recurse into nested dicts.
2. **Greptile — P2**: when ``litellm_params`` is a JSON-serialized
string (the in-memory registry occasionally stores it that way), the
previous redactor silently no-op'd via the ``isinstance(..., dict)``
guard and echoed the raw payload back. Now: parse, redact, re-serialize.
If the string is not valid JSON, replace it with the redaction
sentinel rather than echo it.
3. **mypy** flagged ``_redact_sensitive_litellm_params``'s
``Optional[Dict[str, Any]]`` signature as incompatible with the
``object``-typed call site. Widened to ``Any -> Any`` to reflect the
actual contract (the function now handles dict / str / None / other).
Also fixes a related test regression in
``test_remove_sensitive_info_from_deployment_with_excluded_keys``: the
``"credentials"`` plural addition to ``SensitiveDataMasker`` defaults
caused the first call (without ``excluded_keys``) to mutate the input
dict's ``litellm_credentials_name`` to a masked value. The second call
(with ``excluded_keys``) then saw the already-masked value rather than
the original. Construct fresh input for each call.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two architectural extensions to the credential-redaction in the previous
commit:
1. ``/vector_store/update`` had two gaps:
- No per-store access control. Any authenticated principal that
passed the premium-feature gate could mutate *any* vector store,
including stores belonging to other teams.
- The response returned the full DB row including ``litellm_params``,
so the caller could read another team's persisted provider
credentials by submitting a no-op metadata change.
Mirror the access-control check ``/vector_store/info`` already
performs (``_check_vector_store_access`` against the existing row),
redact ``litellm_params`` in the response, and add an
``except HTTPException: raise`` guard so the 403/404 responses don't
get rewritten as 500 by the catch-all.
2. ``SensitiveDataMasker``'s default ``sensitive_patterns`` set used
segment-exact matching, so ``credential`` matched ``vertex_credential``
but not ``vertex_credentials`` (the actual Vertex field name). The
previous commit worked around this with a per-call extension; this
commit puts the plural in the upstream defaults so every caller
(Redis config dump, MCP debug headers, cache routes, ...) gets the
correct behavior. The local override in
``vector_store_endpoints/management_endpoints.py`` is removed.
Also updates ``test_excluded_keys_exact_match`` which relied on
``credentials`` *not* being a sensitive pattern to demonstrate
case-sensitive ``excluded_keys`` matching. The intent of the test
(case-sensitive match) is preserved; the assertion now reflects that
when ``excluded_keys`` fails to apply (wrong case), the field falls
through to standard pattern-based masking instead of being passed
through unchanged.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
/simplify pass:
- Remove the single-call-site ``_redact_vector_store`` wrapper. Inline
the two-line redaction at its only caller in ``list_vector_stores``;
``get_vector_store_info`` was already calling the inner helper directly.
- Inherit ``SensitiveDataMasker``'s default sensitive-key set instead of
duplicating the 12-element list, then add only the plural
``credentials`` extension. Won't drift if upstream defaults change.
- Trim the over-explained docstring on ``_redact_sensitive_litellm_params``
to a one-paragraph summary; the WHY (credential-leakage class) belongs
in the commit message, not in every consumer's IDE tooltip.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
``LiteLLM_ManagedVectorStore.litellm_params`` carries the upstream provider
credential — OpenAI ``api_key``, AWS ``aws_access_key_id`` /
``aws_secret_access_key``, GCP ``vertex_credentials``, etc. ``GET
/vector_store/list`` and ``POST /vector_store/info`` return these
verbatim to any authenticated principal. Because both routes are in
``openai_routes``, ``RouteChecks.is_llm_api_route`` short-circuits the
standard role gate, so even read-only users and narrowly-scoped keys can
read every stored credential.
Replace credential-bearing values with the ``REDACTED_BY_LITELM``
sentinel in both responses while preserving non-secret keys
(``api_base``, ``region``, ``model``, ``api_version``) so callers can
still see *which* upstream is configured. Detection reuses
``SensitiveDataMasker.is_sensitive_key`` with the default heuristics
plus the plural ``credentials`` pattern (covers Vertex's
``vertex_credentials`` field, which the singular ``credential`` pattern
misses on segment-exact matching).
Applied at:
- ``list_vector_stores`` (``GET /vector_store/list``,
``GET /v1/vector_store/list``)
- ``get_vector_store_info`` (``POST /vector_store/info``), both the
in-memory-registry path and the prisma-DB fallback
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): honor object_permission for managed vector store access
* perf(proxy): preload team object_permission on UserAPIKeyAuth
Populate team_object_permission during virtual-key and JWT auth when the
team is loaded, so can_user_access_vector_store uses it in memory first
and only falls back to get_object_permission by id when missing.
Made-with: Cursor
* Add support for vector store files endpoints (#16490)
* Add base code for vector store integration
* fix azure related tests and linting error
* fix mypy errors
* Add vector store files documentation
* fix mapped tests
* Add bytedance and ideogram support in fal ai (#16636)
* Add fal ai flux pro v1.1 support (#16578)
* Add fal ai flux pro v1.1 support
* Add tests and docs
---------
Co-authored-by: Ishaan Jaffer <ishaanjaffer0324@gmail.com>
* feat(vector_store_endpoints/endpoints.py): add new index_create endpoint
allows admin to create a virtual index, to do permission management for
* feat(key_management_endpoints.py): enable setting allowed_vector_store_indexes on keys
proxy admin can enable dev to create an index on a vector stor
* feat: initial commit adding vector store index passthrough logic to litellm
* feat: add vector store table
* fix(azure_ai/transformation.py): fix headers
* feat: track read/write endpoints by vector store integration
enables permissions by index to work
* fix: azure_ai/vector_stores/search
document the vector store endpoints correctly
ensures permission management works as expected
* fix(proxy/utils.py): improve error message
* docs(azure_ai_vector_stores_passthrough.md): document azure ai passthrough vector store support
* docs(create.md): document azure ai support via passthrough for vector store create
* fix: fix code qa errors
* fix: document new allowed_vector_store_indexes endpoint
user