chore: merge litellm_internal_staging and resolve type-discipline-budget conflict

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

basedpyright-code-budget.json

@@ -24,7 +24,7 @@
     "limit": 19
   },
   "reportExplicitAny": {
-    "limit": 4125
+    "limit": 4124
   },
   "reportFunctionMemberAccess": {
     "limit": 7
@@ -99,7 +99,7 @@
     "limit": 0
   },
   "reportUnknownArgumentType": {
-    "limit": 44364
+    "limit": 44362
   },
   "reportUnknownLambdaType": {
     "limit": 109
@@ -117,13 +117,13 @@
     "limit": 111
   },
   "reportUnnecessaryComparison": {
-    "limit": 692
+    "limit": 687
   },
   "reportUnnecessaryContains": {
-    "limit": 5
+    "limit": 4
   },
   "reportUnnecessaryIsInstance": {
-    "limit": 826
+    "limit": 823
   },
   "reportUntypedBaseClass": {
     "limit": 0

litellm-proxy-extras/litellm_proxy_extras/utils.py

@@ -43,6 +43,8 @@ def _get_prisma_env() -> dict:
 
 _MIGRATION_TS_RE = re.compile(r"^(\d{14})_")
 
+_MIGRATION_DEADLOCK_MARKER = "deadlock detected"
+
 _SPEND_LOGS_ALTER_RE = re.compile(r'^ALTER\s+TABLE\s+"LiteLLM_SpendLogs"\s', re.IGNORECASE)
 _SPEND_LOGS_ARTIFACT_DROP_RE = re.compile(
     r'^DROP\s+TABLE\s+"LiteLLM_SpendLogs_[^"]*"', re.IGNORECASE
@@ -265,6 +267,50 @@ class ProxyExtrasDBManager:
             env=prisma_env,
         )
 
+    @staticmethod
+    def _roll_back_migration_best_effort(migration_name: str) -> None:
+        """Mark a migration rolled back, tolerating a concurrent resolver
+        having already done it."""
+        try:
+            ProxyExtrasDBManager._roll_back_migration(migration_name)
+        except (subprocess.CalledProcessError, subprocess.TimeoutExpired):
+            pass
+
+    @staticmethod
+    def _failed_migration_logs(migration_name: str) -> Optional[str]:
+        """Return failed migration logs, or None if the ledger is unavailable."""
+        database_url = os.getenv("DATABASE_URL")
+        if not database_url:
+            return None
+
+        try:
+            import psycopg
+        except ImportError:
+            return None
+
+        cleaned_url = ProxyExtrasDBManager._strip_prisma_query_params(database_url)
+        ledger_table = psycopg.sql.SQL("{}.{}").format(
+            psycopg.sql.Identifier(
+                ProxyExtrasDBManager._prisma_schema_param(database_url) or "public"
+            ),
+            psycopg.sql.Identifier("_prisma_migrations"),
+        )
+        try:
+            with psycopg.connect(
+                cleaned_url, connect_timeout=10, autocommit=True
+            ) as conn:
+                row = conn.execute(
+                    psycopg.sql.SQL(
+                        "SELECT logs FROM {} "
+                        "WHERE migration_name = %s AND finished_at IS NULL "
+                        "AND rolled_back_at IS NULL"
+                    ).format(ledger_table),
+                    (migration_name,),
+                ).fetchone()
+        except (psycopg.OperationalError, psycopg.DatabaseError):
+            return None
+        return (row[0] or "") if row else ""
+
     @staticmethod
     def _resolve_specific_migration(migration_name: str):
         """Mark a specific migration as applied"""
@@ -661,7 +707,8 @@ class ProxyExtrasDBManager:
         v2 migration resolver (opt-in via --use_v2_migration_resolver).
 
         Runs `prisma migrate deploy` and handles standard recovery paths
-        (P3005 baseline, P3009/P3018 idempotent errors). Critically, it does
+        (P3005 baseline, P3009/P3018 idempotent errors, deadlocks against a
+        concurrent migrate deploy). Critically, it does
         NOT call `_resolve_all_migrations` — the diff-and-force recovery that
         caused schema thrashing when two LiteLLM versions contended for the
         same DB during rolling deploys.
@@ -772,6 +819,20 @@ class ProxyExtrasDBManager:
                                     f"Detail: {resolve_err}"
                                 ) from resolve_err
                             continue
+                        if migration_match:
+                            migration_name = migration_match.group(1)
+                            ledger_logs = ProxyExtrasDBManager._failed_migration_logs(migration_name)
+                            if ledger_logs is not None and (
+                                ledger_logs == "" or _MIGRATION_DEADLOCK_MARKER in ledger_logs
+                            ):
+                                logger.info(
+                                    "Migration %s failed in a concurrent migrate deploy "
+                                    "deadlock race, rolling its ledger row back and retrying",
+                                    migration_name,
+                                )
+                                ProxyExtrasDBManager._roll_back_migration_best_effort(migration_name)
+                                time.sleep(random.randrange(5, 15))
+                                continue
                         raise RuntimeError(
                             "Database migration failed and cannot be auto-recovered. "
                             f"Manual intervention required.\n\nPrisma error:\n{stderr}"
@@ -817,11 +878,42 @@ class ProxyExtrasDBManager:
                                 ) from resolve_err
                             continue
 
+                        if migration_match and _MIGRATION_DEADLOCK_MARKER in stderr:
+                            logger.info(
+                                "Migration %s deadlocked against a concurrent "
+                                "migrate deploy, rolling its ledger row back "
+                                "and retrying",
+                                migration_match.group(1),
+                            )
+                            ProxyExtrasDBManager._roll_back_migration_best_effort(
+                                migration_match.group(1)
+                            )
+                            time.sleep(random.randrange(5, 15))
+                            continue
+
                         raise RuntimeError(
                             "Database migration failed and cannot be auto-recovered. "
                             f"Manual intervention required.\n\nPrisma error:\n{stderr}"
                         ) from e
 
+                    if _MIGRATION_DEADLOCK_MARKER in stderr:
+                        logger.info(
+                            "prisma migrate deploy attempt %s deadlocked against "
+                            "a concurrent migrate deploy, retrying",
+                            attempt + 1,
+                        )
+                        time.sleep(random.randrange(5, 15))
+                        continue
+
+                    if "P1002" in stderr and "advisory lock" in stderr:
+                        logger.info(
+                            "prisma migrate deploy attempt %s timed out waiting for "
+                            "the advisory lock a concurrent migrate deploy holds, retrying",
+                            attempt + 1,
+                        )
+                        time.sleep(random.randrange(5, 15))
+                        continue
+
                     raise RuntimeError(
                         "Database migration failed and cannot be auto-recovered. "
                         f"Manual intervention required.\n\nPrisma error:\n{stderr}"
@@ -829,9 +921,9 @@ class ProxyExtrasDBManager:
 
             raise RuntimeError(
                 "Database migration failed after 4 attempts (retry loop "
-                "exhausted by timeouts or repeated idempotent-recovery "
-                "continues). Check database connectivity, load, and "
-                "_prisma_migrations ledger state, and raise "
+                "exhausted by timeouts, deadlock retries, or repeated "
+                "idempotent-recovery continues). Check database connectivity, "
+                "load, and _prisma_migrations ledger state, and raise "
                 f"{PRISMA_MIGRATE_DEPLOY_TIMEOUT_ENV_VAR} if the attempts timed out."
             )
         finally:

litellm-proxy-extras/tests/test_setup_database_fail_fast.py

@@ -240,3 +240,223 @@ def test_v2_does_not_call_resolve_all_migrations(monkeypatch, tmp_path):
     ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
     assert ok is True
     assert resolve_called["n"] == 0, "v2 must not invoke the diff-and-force recovery"
+
+
+_DEADLOCK_P3018_STDERR = (
+    "Error: P3018\n"
+    "Migration name: 20260415120000_health_check_latest_per_model_index\n"
+    "Database error code: 40P01\n"
+    "deadlock detected"
+)
+
+
+def _stub_v2_env(monkeypatch, tmp_path):
+    monkeypatch.setenv("DATABASE_URL", "postgresql://u:p@localhost:9/x")
+    monkeypatch.setattr(
+        ProxyExtrasDBManager, "_warn_if_db_ahead_of_head", lambda _: None
+    )
+    monkeypatch.setattr(ProxyExtrasDBManager, "_get_prisma_dir", lambda: str(tmp_path))
+    (tmp_path / "schema.prisma").write_text("// stub")
+    monkeypatch.setattr("time.sleep", lambda _: None)
+
+
+def _succeed_after(failures: int, stderr: str):
+    calls = {"n": 0}
+
+    class _OkResult:
+        stdout = "Applied migration.\n"
+        stderr = ""
+
+    def _run(*args, **kwargs):
+        if "deploy" not in args[0]:
+            return _OkResult()
+        calls["n"] += 1
+        if calls["n"] <= failures:
+            raise subprocess.CalledProcessError(
+                returncode=1, cmd=args[0], stderr=stderr, output=""
+            )
+        return _OkResult()
+
+    return _run
+
+
+def test_v2_p3018_deadlock_rolls_back_and_retries(monkeypatch, tmp_path):
+    """v2: losing the migrate deploy deadlock race against a concurrent
+    instance rolls the ledger row back and retries instead of dying."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    rolled_back = []
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: rolled_back.append(name),
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_resolve_specific_migration",
+        lambda name: pytest.fail("a deadlocked migration must never be marked applied"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, _DEADLOCK_P3018_STDERR))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+    assert rolled_back == ["20260415120000_health_check_latest_per_model_index"]
+
+
+def test_v2_p3018_persistent_deadlock_exhausts_attempts(monkeypatch, tmp_path):
+    """v2: a deadlock on every attempt still fails after the retry budget."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    monkeypatch.setattr(ProxyExtrasDBManager, "_roll_back_migration", lambda name: None)
+
+    with patch(
+        "subprocess.run",
+        side_effect=_fake_migrate_deploy_failure(1, _DEADLOCK_P3018_STDERR),
+    ):
+        with pytest.raises(RuntimeError, match="after 4 attempts"):
+            ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+
+
+def test_v2_p3009_deadlocked_ledger_row_rolls_back_and_retries(monkeypatch, tmp_path):
+    """v2: the surviving instance sees the victim's failed ledger row as P3009.
+    When that row's logs show a deadlock, roll it back and retry."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260415120000_health_check_latest_per_model_index` migration "
+        "started at 2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_failed_migration_logs",
+        lambda name: "ERROR: deadlock detected\nDETAIL: Process 72 waits for ShareLock",
+    )
+    rolled_back = []
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: rolled_back.append(name),
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_resolve_specific_migration",
+        lambda name: pytest.fail("a deadlocked migration must never be marked applied"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+    assert rolled_back == ["20260415120000_health_check_latest_per_model_index"]
+
+
+def test_v2_p3009_empty_ledger_logs_rolls_back_and_retries(monkeypatch, tmp_path):
+    """v2: empty failed ledger logs mean a concurrent deploy moved it on."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260415120000_health_check_latest_per_model_index` migration "
+        "started at 2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(ProxyExtrasDBManager, "_failed_migration_logs", lambda name: "")
+    rolled_back = []
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: rolled_back.append(name),
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_resolve_specific_migration",
+        lambda name: pytest.fail("a deadlocked migration must never be marked applied"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+    assert rolled_back == ["20260415120000_health_check_latest_per_model_index"]
+
+
+def test_v2_p3009_unreadable_ledger_still_raises(monkeypatch, tmp_path):
+    """v2: an unreadable ledger cannot establish that P3009 was a deadlock."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260415120000_health_check_latest_per_model_index` migration "
+        "started at 2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(ProxyExtrasDBManager, "_failed_migration_logs", lambda name: None)
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_roll_back_migration",
+        lambda name: pytest.fail("an unreadable ledger must not trigger a retry"),
+    )
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
+        ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+
+
+def test_v2_p3009_non_deadlock_ledger_row_still_raises(monkeypatch, tmp_path):
+    """v2: a failed ledger row whose logs show a real SQL error stays fatal."""
+    _stub_v2_env(monkeypatch, tmp_path)
+
+    stderr = (
+        "Error: P3009\n"
+        "migrate found failed migrations in the target database\n"
+        "The `20260101000000_genuinely_broken` migration started at "
+        "2026-09-01 18:46:13 UTC failed"
+    )
+    monkeypatch.setattr(
+        ProxyExtrasDBManager,
+        "_failed_migration_logs",
+        lambda name: 'ERROR: syntax error at or near "BRKN"',
+    )
+
+    with patch("subprocess.run", side_effect=_fake_migrate_deploy_failure(1, stderr)):
+        with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
+            ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+
+
+def test_v2_bare_deadlock_stderr_retries(monkeypatch, tmp_path):
+    """v2: a deadlock reported without a Prisma error code (the advisory-lock
+    waiter as victim) is retried, not fatal."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    monkeypatch.setattr(
+        "subprocess.run", _succeed_after(1, "Database error: deadlock detected")
+    )
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+
+
+_P1002_ADVISORY_LOCK_STDERR = (
+    "Error: P1002\n\n"
+    "The database server at `127.0.0.1`:`45743` was reached but timed out.\n\n"
+    "Context: Timed out trying to acquire a postgres advisory lock "
+    "(SELECT pg_advisory_lock(72707369)). Elapsed: 10000ms."
+)
+
+
+def test_v2_advisory_lock_timeout_retries(monkeypatch, tmp_path):
+    """v2: the advisory-lock waiter that times out while a peer's retry holds
+    the lock retries instead of dying."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    monkeypatch.setattr("subprocess.run", _succeed_after(2, _P1002_ADVISORY_LOCK_STDERR))
+
+    ok = ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)
+    assert ok is True
+
+
+def test_v2_p1002_without_advisory_lock_context_still_raises(monkeypatch, tmp_path):
+    """v2: a plain P1002 (database unreachable) stays fatal."""
+    _stub_v2_env(monkeypatch, tmp_path)
+    stderr = "Error: P1002\n\nThe database server at `db`:`5432` was reached but timed out."
+    monkeypatch.setattr("subprocess.run", _succeed_after(1, stderr))
+
+    with pytest.raises(RuntimeError, match="cannot be auto-recovered"):
+        ProxyExtrasDBManager.setup_database(use_migrate=True, use_v2_resolver=True)

litellm/integrations/vector_store_integrations/vector_store_pre_call_hook.py

@@ -5,6 +5,7 @@ This hook is called before making an LLM request when a vector store is configur
 It searches the vector store for relevant context and appends it to the messages.
 """
 
+from collections.abc import Awaitable, Callable
 from typing import TYPE_CHECKING, Any, Final, cast
 
 import litellm
@@ -80,10 +81,17 @@ class VectorStorePreCallHook(CustomLogger):
 
             # Get prisma_client for database fallback
             prisma_client = None
+            llm_router = None
             try:
-                from litellm.proxy.proxy_server import prisma_client as _prisma_client
+                from litellm.proxy.proxy_server import (
+                    llm_router as _llm_router,
+                )
+                from litellm.proxy.proxy_server import (
+                    prisma_client as _prisma_client,
+                )
 
                 prisma_client = _prisma_client
+                llm_router = _llm_router
             except ImportError:
                 pass
 
@@ -114,12 +122,26 @@ class VectorStorePreCallHook(CustomLogger):
                 vector_store_id = vector_store_to_run.get("vector_store_id", "")
                 custom_llm_provider = vector_store_to_run.get("custom_llm_provider")
                 litellm_params_for_vector_store = vector_store_to_run.get("litellm_params", {}) or {}
-                # Call litellm.vector_stores.search() with the required parameters
-                search_response = await litellm.vector_stores.asearch(
+                request_litellm_params = litellm_logging_obj.model_call_details.get("litellm_params", {})
+                request_metadata = (
+                    request_litellm_params.get("metadata", {}) if isinstance(request_litellm_params, dict) else {}
+                )
+                if llm_router is not None:
+                    search_function = cast(  # cast-ok: normalize router search callable
+                        Callable[..., Awaitable[VectorStoreSearchResponse]],
+                        llm_router.avector_store_search,
+                    )
+                else:
+                    search_function = cast(  # cast-ok: normalize SDK search callable
+                        Callable[..., Awaitable[VectorStoreSearchResponse]],
+                        litellm.vector_stores.asearch,
+                    )
+                search_response = await search_function(
                     **{
                         "vector_store_id": vector_store_id,
                         "query": query,
                         "custom_llm_provider": custom_llm_provider,
+                        "metadata": request_metadata,
                         **litellm_params_for_vector_store,
                     },
                 )

litellm/llms/azure_ai/vector_stores/transformation.py

@@ -1,10 +1,15 @@
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 
-import litellm
 from litellm.llms.azure.common_utils import BaseAzureLLM
-from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
     BaseVectorStoreAuthCredentials,
@@ -26,7 +31,7 @@ else:
     LiteLLMLoggingObj = Any
 
 
-class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM):
+class AzureAIVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, BaseAzureLLM):
     """
     Configuration for Azure AI Search Vector Store
 
@@ -110,83 +115,73 @@ class AzureAIVectorStoreConfig(BaseVectorStoreConfig, BaseAzureLLM):
     def transform_search_vector_store_request(
         self,
         vector_store_id: str,
-        query: str | list[str],
+        query: str | Sequence[str],
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         api_base: str,
         litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-        extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
-    ) -> tuple[str, dict[str, Any]]:
-        """
-        Transform search request for Azure AI Search API
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = self.embed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        Generates embeddings using litellm.embeddings and constructs Azure AI Search request
-        """
-        # Convert query to string if it's a list
-        if isinstance(query, list):
-            query = " ".join(query)
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = await self.aembed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        # Get embedding model from litellm_params (required)
-        embedding_model: Final = litellm_params.get("litellm_embedding_model")
-        if not embedding_model:
-            raise ValueError(
-                "embedding_model is required in litellm_params for Azure AI Search. "
-                "Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'"
-            )
-
-        embedding_config: Final = litellm_params.get("litellm_embedding_config", {})
-        if not embedding_config:
-            raise ValueError(
-                "embedding_config is required in litellm_params for Azure AI Search. "
-                "Example: litellm_params['embedding_config'] = {'api_base': 'https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/', 'api_key': 'os.environ/AZURE_API_KEY', 'api_version': '2025-09-01'}"
-            )
-
-        # Get vector field name (defaults to contentVector)
+    @staticmethod
+    def _search_request(
+        vector_store_id: str,
+        query_text: str,
+        query_vector: Sequence[float],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+    ) -> tuple[str, dict[str, object]]:
         vector_field: Final = litellm_params.get("azure_search_vector_field", "contentVector")
-
-        # Get top_k (number of results to return)
         top_k: Final = vector_store_search_optional_params.get("top_k", 10)
-
-        # Generate embedding for the query using litellm.embeddings
-        try:
-            embedding_response: Final = litellm.embedding(
-                model=embedding_model,
-                input=[query],
-                **embedding_config,
-            )
-            query_vector: Final = embedding_response.data[0]["embedding"]
-        except Exception as e:
-            raise Exception(f"Failed to generate embedding for query: {e}")
-
-        # Azure AI Search endpoint for search
-        index_name: Final = vector_store_id  # vector_store_id is the index name
-        url: Final = f"{api_base}/indexes/{index_name}/docs/search?api-version=2024-07-01"
-
-        # Build the request body for Azure AI Search with vector search
-        request_body: Final = {
-            "search": "*",  # Get all documents (filtered by vector similarity)
-            "vectorQueries": [
-                {
-                    "vector": query_vector,
-                    "fields": vector_field,
-                    "kind": "vector",
-                    "k": top_k,  # Number of nearest neighbors to return
-                }
-            ],
-            "select": "id,content",  # Fields to return (customize based on schema)
+        litellm_logging_obj.model_call_details["input"] = query_text
+        litellm_logging_obj.model_call_details["embedding_model"] = litellm_params.get("litellm_embedding_model")
+        litellm_logging_obj.model_call_details["top_k"] = top_k
+        return f"{api_base}/indexes/{vector_store_id}/docs/search?api-version=2024-07-01", {
+            "search": "*",
+            "vectorQueries": [{"vector": query_vector, "fields": vector_field, "kind": "vector", "k": top_k}],
+            "select": "id,content",
             "top": top_k,
         }
 
-        #########################################################
-        # Update logging object with details of the request
-        #########################################################
-        litellm_logging_obj.model_call_details["input"] = query
-        litellm_logging_obj.model_call_details["embedding_model"] = embedding_model
-        litellm_logging_obj.model_call_details["top_k"] = top_k
-
-        return url, request_body
-
     def transform_search_vector_store_response(
         self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
     ) -> VectorStoreSearchResponse:

litellm/llms/base_llm/vector_store/transformation.py

@@ -1,10 +1,16 @@
+from __future__ import annotations
+
 from abc import abstractmethod
 from collections.abc import Mapping, Sequence
-from typing import TYPE_CHECKING, Any, NoReturn
+from dataclasses import dataclass
+from types import MappingProxyType
+from typing import TYPE_CHECKING, Any, Final, NoReturn, Protocol, runtime_checkable
 
 import httpx
+from pydantic import TypeAdapter
 
 from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import EmbeddingResponse
 from litellm.types.vector_stores import (
     VECTOR_STORE_OPENAI_PARAMS,
     BaseVectorStoreAuthCredentials,
@@ -28,6 +34,95 @@ else:
     BaseLLMException = Any
 
 
+@runtime_checkable
+class VectorStoreEmbeddingExecutor(Protocol):
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse: ...
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse: ...
+
+
+@dataclass(frozen=True, slots=True)
+class LiteLLMVectorStoreEmbeddingExecutor:
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        import litellm
+
+        return litellm.embedding(  # pyright: ignore[reportCallIssue, reportUnknownMemberType, reportUnknownVariableType]  # provider kwargs are intentionally dynamic
+            model=model,
+            input=[query],  # mutable-ok: LiteLLM embedding requires a mutable input list
+            **dict(configuration),  # pyright: ignore[reportArgumentType]  # provider-specific embedding config is validated downstream  # mutable-ok: kwargs require a concrete dict
+        )
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        import litellm
+
+        return await litellm.aembedding(  # pyright: ignore[reportUnknownMemberType]  # provider kwargs are intentionally dynamic
+            model=model,
+            input=[query],  # mutable-ok: LiteLLM embedding requires a mutable input list
+            **dict(configuration),  # pyright: ignore[reportArgumentType]  # provider-specific embedding config is validated downstream  # mutable-ok: kwargs require a concrete dict
+        )
+
+
+_REQUEST_METADATA: Final = TypeAdapter(dict[str, object])
+
+
+def vector_store_request_metadata(kwargs: Mapping[str, object]) -> Mapping[str, object]:
+    litellm_metadata: Final = kwargs.get("litellm_metadata")
+    if isinstance(litellm_metadata, dict):
+        return _REQUEST_METADATA.validate_python(litellm_metadata)
+    metadata: Final = kwargs.get("metadata")
+    if isinstance(metadata, dict):
+        return _REQUEST_METADATA.validate_python(metadata)
+    return MappingProxyType({})
+
+
+@dataclass(frozen=True, slots=True)
+class RouterVectorStoreEmbeddingExecutor:
+    router: Router
+    metadata: Mapping[str, object]
+
+    def _embedding_kwargs(self, configuration: Mapping[str, object]) -> Mapping[str, object]:
+        configured_metadata: Final = configuration.get("metadata")
+        metadata: Final = {
+            **(configured_metadata if isinstance(configured_metadata, Mapping) else {}),
+            **self.metadata,
+        }
+        return {
+            **{key: value for key, value in configuration.items() if key not in ("input", "metadata", "model")},
+            "metadata": metadata,
+        }
+
+    def _router_serves(self, model: str) -> bool:
+        team_id: Final = self.metadata.get("user_api_key_team_id")
+        resolved: Final = self.router.resolved_litellm_models(model, team_id if isinstance(team_id, str) else None)
+        deployment_models: Final = (
+            deployment.get("litellm_params", {}).get("model") for deployment in self.router.get_model_list() or ()
+        )
+        return bool(resolved) or model in deployment_models
+
+    def _embeds_through_sdk(self, model: str, configuration: Mapping[str, object]) -> bool:
+        return bool(configuration) and not self._router_serves(model)
+
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        embedding_kwargs: Final = self._embedding_kwargs(configuration)
+        if self._embeds_through_sdk(model, configuration):
+            return LiteLLMVectorStoreEmbeddingExecutor().embed(model, query, embedding_kwargs)
+        return self.router.embedding(  # pyright: ignore[reportUnknownMemberType]  # Router embedding input retains a legacy untyped list
+            model=model,
+            input=[query],  # mutable-ok: Router embedding requires a mutable input list
+            **embedding_kwargs,  # pyright: ignore[reportArgumentType]  # provider kwargs are intentionally dynamic
+        )
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        embedding_kwargs: Final = self._embedding_kwargs(configuration)
+        if self._embeds_through_sdk(model, configuration):
+            return await LiteLLMVectorStoreEmbeddingExecutor().aembed(model, query, embedding_kwargs)
+        return await self.router.aembedding(  # pyright: ignore[reportUnknownMemberType]  # Router embedding input retains a legacy untyped list
+            model=model,
+            input=[query],  # mutable-ok: Router embedding requires a mutable input list
+            **embedding_kwargs,  # pyright: ignore[reportArgumentType]  # provider kwargs are intentionally dynamic
+        )
+
+
 class BaseVectorStoreConfig:
     def get_supported_openai_params(self, model: str) -> list[VECTOR_STORE_OPENAI_PARAMS]:
         return []
@@ -58,7 +153,7 @@ class BaseVectorStoreConfig:
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
         extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
+        router: Router | None = None,
     ) -> tuple[str, dict]:
         pass
 
@@ -71,7 +166,7 @@ class BaseVectorStoreConfig:
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: dict,
         extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
+        router: Router | None = None,
     ) -> tuple[str, dict]:
         """
         Optional async version of transform_search_vector_store_request.
@@ -161,6 +256,116 @@ class BaseVectorStoreConfig:
         return 0.0, 0.0
 
 
+_EMPTY_EMBEDDING_CONFIGURATION: Final[Mapping[str, object]] = MappingProxyType({})
+_QUERY_VECTOR: Final = TypeAdapter(list[float])
+
+
+class BaseQueryEmbeddingVectorStoreConfig(BaseVectorStoreConfig):
+    @abstractmethod
+    def transform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        pass
+
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        return self.transform_search_vector_store_request(
+            vector_store_id=vector_store_id,
+            query=query,
+            vector_store_search_optional_params=vector_store_search_optional_params,
+            api_base=api_base,
+            litellm_logging_obj=litellm_logging_obj,
+            litellm_params=litellm_params,
+            extra_body=extra_body,
+            router=router,
+            embedding_executor=embedding_executor,
+        )
+
+    @staticmethod
+    def query_text(query: str | Sequence[str]) -> str:
+        return query if isinstance(query, str) else " ".join(query)
+
+    @staticmethod
+    def query_embedding_model(litellm_params: Mapping[str, object]) -> str:
+        embedding_model: Final = litellm_params.get("litellm_embedding_model")
+        if isinstance(embedding_model, str) and embedding_model:
+            return embedding_model
+        raise ValueError(
+            "litellm_embedding_model is required in litellm_params for this vector store. "
+            "Example: litellm_params['litellm_embedding_model'] = 'openai/text-embedding-3-small'"
+        )
+
+    @staticmethod
+    def query_embedding_configuration(litellm_params: Mapping[str, object]) -> Mapping[str, object]:
+        configuration: Final = litellm_params.get("litellm_embedding_config")
+        if isinstance(configuration, Mapping):
+            return {str(key): value for key, value in configuration.items()}  # pyright: ignore[reportUnknownVariableType, reportUnknownArgumentType]  # litellm_params is an untyped dict, keys are re-validated as str here
+        return _EMPTY_EMBEDDING_CONFIGURATION
+
+    @staticmethod
+    def query_embedding_executor(
+        embedding_executor: VectorStoreEmbeddingExecutor | None,
+        router: Router | None,
+        request_metadata: Mapping[str, object] = MappingProxyType({}),
+    ) -> VectorStoreEmbeddingExecutor:
+        if embedding_executor is not None:
+            return embedding_executor
+        if router is not None:
+            return RouterVectorStoreEmbeddingExecutor(router=router, metadata=request_metadata)
+        return LiteLLMVectorStoreEmbeddingExecutor()
+
+    def embed_query(
+        self,
+        query_text: str,
+        litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None,
+        router: Router | None = None,
+    ) -> Sequence[float]:
+        model: Final = self.query_embedding_model(litellm_params)
+        configuration: Final = self.query_embedding_configuration(litellm_params)
+        executor: Final = self.query_embedding_executor(embedding_executor, router)
+        try:
+            response: Final = executor.embed(model, query_text, configuration)
+        except Exception as e:
+            raise Exception(f"Failed to generate embedding for query: {e}")
+        return _QUERY_VECTOR.validate_python(response.data[0]["embedding"])  # pyright: ignore[reportUnknownMemberType]  # EmbeddingResponse.data is an untyped list, the vector is validated here
+
+    async def aembed_query(
+        self,
+        query_text: str,
+        litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None,
+        router: Router | None = None,
+    ) -> Sequence[float]:
+        model: Final = self.query_embedding_model(litellm_params)
+        configuration: Final = self.query_embedding_configuration(litellm_params)
+        executor: Final = self.query_embedding_executor(embedding_executor, router)
+        try:
+            response: Final = await executor.aembed(model, query_text, configuration)
+        except Exception as e:
+            raise Exception(f"Failed to generate embedding for query: {e}")
+        return _QUERY_VECTOR.validate_python(response.data[0]["embedding"])  # pyright: ignore[reportUnknownMemberType]  # EmbeddingResponse.data is an untyped list, the vector is validated here
+
+
 class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
     """
     Base config for vector store providers whose datastore has no HTTP API
@@ -176,6 +381,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         pass
@@ -188,6 +394,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         pass
@@ -201,7 +408,7 @@ class BaseDirectVectorStoreConfig(BaseVectorStoreConfig):
         litellm_logging_obj: LiteLLMLoggingObj,
         litellm_params: Mapping[str, object],
         extra_body: Mapping[str, object] | None = None,
-        router: "Router | None" = None,
+        router: Router | None = None,
     ) -> NoReturn:
         raise NotImplementedError("Direct vector store providers execute the search themselves; no HTTP request shape")
 

litellm/llms/custom_httpx/llm_http_handler.py

@@ -69,7 +69,9 @@ from litellm.llms.base_llm.skills.transformation import BaseSkillsAPIConfig
 from litellm.llms.base_llm.text_to_speech.transformation import BaseTextToSpeechConfig
 from litellm.llms.base_llm.vector_store.transformation import (
     BaseDirectVectorStoreConfig,
+    BaseQueryEmbeddingVectorStoreConfig,
     BaseVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
 )
 from litellm.llms.base_llm.vector_store_files.transformation import (
     BaseVectorStoreFilesConfig,
@@ -9701,6 +9703,7 @@ class BaseLLMHTTPHandler:
         custom_llm_provider: str,
         litellm_params: GenericLiteLLMParams,
         logging_obj: LiteLLMLoggingObj,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         extra_headers: dict[str, object] | None = None,
         extra_body: dict[str, object] | None = None,
         timeout: float | httpx.Timeout | None = None,
@@ -9721,6 +9724,7 @@ class BaseLLMHTTPHandler:
                 vector_store_search_optional_params=vector_store_search_optional_params,
                 litellm_logging_obj=logging_obj,
                 litellm_params=dict(litellm_params),  # mutable-ok: snapshot GenericLiteLLMParams into the Mapping shape
+                embedding_executor=embedding_executor,
                 timeout=timeout,
             )
 
@@ -9744,8 +9748,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        # Check if provider has async transform method
-        if hasattr(vector_store_provider_config, "atransform_search_vector_store_request"):
+        if isinstance(vector_store_provider_config, BaseQueryEmbeddingVectorStoreConfig):
             (
                 url,
                 request_body,
@@ -9758,12 +9761,13 @@ class BaseLLMHTTPHandler:
                 litellm_params=dict(litellm_params),
                 extra_body=extra_body,
                 router=router,
+                embedding_executor=embedding_executor,
             )
         else:
             (
                 url,
                 request_body,
-            ) = vector_store_provider_config.transform_search_vector_store_request(
+            ) = await vector_store_provider_config.atransform_search_vector_store_request(
                 vector_store_id=vector_store_id,
                 query=query,
                 vector_store_search_optional_params=vector_store_search_optional_params,
@@ -9818,6 +9822,7 @@ class BaseLLMHTTPHandler:
         custom_llm_provider: str,
         litellm_params: GenericLiteLLMParams,
         logging_obj: LiteLLMLoggingObj,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         extra_headers: dict[str, object] | None = None,
         extra_body: dict[str, object] | None = None,
         timeout: float | httpx.Timeout | None = None,
@@ -9834,6 +9839,7 @@ class BaseLLMHTTPHandler:
                 litellm_params=litellm_params,
                 logging_obj=logging_obj,
                 custom_llm_provider=custom_llm_provider,
+                embedding_executor=embedding_executor,
                 extra_headers=extra_headers,
                 extra_body=extra_body,
                 timeout=timeout,
@@ -9854,6 +9860,7 @@ class BaseLLMHTTPHandler:
                 vector_store_search_optional_params=vector_store_search_optional_params,
                 litellm_logging_obj=logging_obj,
                 litellm_params=dict(litellm_params),  # mutable-ok: snapshot GenericLiteLLMParams into the Mapping shape
+                embedding_executor=embedding_executor,
                 timeout=timeout,
             )
 
@@ -9874,19 +9881,35 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        (
-            url,
-            request_body,
-        ) = vector_store_provider_config.transform_search_vector_store_request(
-            vector_store_id=vector_store_id,
-            query=query,
-            vector_store_search_optional_params=vector_store_search_optional_params,
-            api_base=api_base,
-            litellm_logging_obj=logging_obj,
-            litellm_params=dict(litellm_params),
-            extra_body=extra_body,
-            router=router,
-        )
+        if isinstance(vector_store_provider_config, BaseQueryEmbeddingVectorStoreConfig):
+            (
+                url,
+                request_body,
+            ) = vector_store_provider_config.transform_search_vector_store_request(
+                vector_store_id=vector_store_id,
+                query=query,
+                vector_store_search_optional_params=vector_store_search_optional_params,
+                api_base=api_base,
+                litellm_logging_obj=logging_obj,
+                litellm_params=dict(litellm_params),
+                extra_body=extra_body,
+                router=router,
+                embedding_executor=embedding_executor,
+            )
+        else:
+            (
+                url,
+                request_body,
+            ) = vector_store_provider_config.transform_search_vector_store_request(
+                vector_store_id=vector_store_id,
+                query=query,
+                vector_store_search_optional_params=vector_store_search_optional_params,
+                api_base=api_base,
+                litellm_logging_obj=logging_obj,
+                litellm_params=dict(litellm_params),
+                extra_body=extra_body,
+                router=router,
+            )
 
         all_optional_params: Final[dict[str, object]] = dict(litellm_params)
         all_optional_params.update(vector_store_search_optional_params or {})

litellm/llms/milvus/vector_stores/transformation.py

@@ -1,9 +1,14 @@
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Final
 
 import httpx
 
-import litellm
-from litellm.llms.base_llm.vector_store.transformation import BaseVectorStoreConfig
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
@@ -37,7 +42,7 @@ MILVUS_OPTIONAL_PARAMS: Final = {
 }
 
 
-class MilvusVectorStoreConfig(BaseVectorStoreConfig):
+class MilvusVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig):
     """
     Configuration for Milvus Vector Store
 
@@ -118,78 +123,79 @@ class MilvusVectorStoreConfig(BaseVectorStoreConfig):
     def transform_search_vector_store_request(
         self,
         vector_store_id: str,
-        query: str | list[str],
+        query: str | Sequence[str],
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         api_base: str,
         litellm_logging_obj: LiteLLMLoggingObj,
-        litellm_params: dict,
-        extra_body: dict[str, Any] | None = None,
-        router: "Router | None" = None,
-    ) -> tuple[str, dict[str, Any]]:
-        """
-        Transform search request for Azure AI Search API
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = self.embed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        Generates embeddings using litellm.embeddings and constructs Azure AI Search request
-        """
-        # Convert query to string if it's a list
-        if isinstance(query, list):
-            query = " ".join(query)
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        router: Router | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:
+        query_text: Final = self.query_text(query)
+        query_vector: Final = await self.aembed_query(query_text, litellm_params, embedding_executor, router)
+        return self._search_request(
+            vector_store_id,
+            query_text,
+            query_vector,
+            vector_store_search_optional_params,
+            api_base,
+            litellm_logging_obj,
+            litellm_params,
+        )
 
-        # Get embedding model from litellm_params (required)
-        embedding_model: Final = litellm_params.get("litellm_embedding_model")
-        if not embedding_model:
-            raise ValueError(
-                "embedding_model is required in litellm_params for Milvus. You can call any litellm embedding model."
-                "Example: litellm_params['embedding_model'] = 'azure/text-embedding-3-large'"
+    @staticmethod
+    def _search_request(
+        vector_store_id: str,
+        query_text: str,
+        query_vector: Sequence[float],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: LiteLLMLoggingObj,
+        litellm_params: Mapping[str, object],
+    ) -> tuple[str, dict[str, object]]:
+        scope: Final = {
+            key: value
+            for key, value in (
+                ("dbName", litellm_params.get("milvus_db_name")),
+                ("partitionNames", litellm_params.get("milvus_partition_names")),
             )
-
-        embedding_config: Final = litellm_params.get("litellm_embedding_config", {})
-        if not embedding_config:
-            raise ValueError(
-                "embedding_config is required in litellm_params for Milvus. You can call any litellm embedding model."
-                "Example: litellm_params['embedding_config'] = {'api_base': 'https://krris-mh44uf7y-eastus2.cognitiveservices.azure.com/', 'api_key': 'os.environ/AZURE_API_KEY', 'api_version': '2025-09-01'}"
-            )
-
-        # Get top_k (number of results to return)
-        # Generate embedding for the query using litellm.embeddings
-        try:
-            embedding_response: Final = litellm.embedding(
-                model=embedding_model,
-                input=[query],
-                **embedding_config,
-            )
-            query_vector: Final = embedding_response.data[0]["embedding"]
-        except Exception as e:
-            raise Exception(f"Failed to generate embedding for query: {e}")
-
-        # Azure AI Search endpoint for search
-        index_name: Final = vector_store_id  # vector_store_id is the index name
-        url: Final = f"{api_base}/v2/vectordb/entities/search"
-
-        # Build the request body for Azure AI Search with vector search
-        request_body: Final[dict[str, Any]] = {
-            "collectionName": index_name,
+            if value
+        }
+        litellm_logging_obj.model_call_details["input"] = query_text
+        litellm_logging_obj.model_call_details["embedding_model"] = litellm_params.get("litellm_embedding_model")
+        return f"{api_base}/v2/vectordb/entities/search", {
+            "collectionName": vector_store_id,
             "data": [query_vector],
             "annsField": "book_intro_vector",
             **vector_store_search_optional_params,
+            **scope,
         }
 
-        db_name: Final = litellm_params.get("milvus_db_name")
-        if db_name:
-            request_body["dbName"] = db_name
-
-        partition_names: Final = litellm_params.get("milvus_partition_names")
-        if partition_names:
-            request_body["partitionNames"] = partition_names
-
-        #########################################################
-        # Update logging object with details of the request
-        #########################################################
-        litellm_logging_obj.model_call_details["input"] = query
-        litellm_logging_obj.model_call_details["embedding_model"] = embedding_model
-
-        return url, request_body
-
     def transform_search_vector_store_response(
         self, response: httpx.Response, litellm_logging_obj: LiteLLMLoggingObj
     ) -> VectorStoreSearchResponse:

litellm/llms/ollama/chat/transformation.py

@@ -423,6 +423,7 @@ class OllamaChatConfig(BaseConfig):
 class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
     started_reasoning_content: bool = False
     finished_reasoning_content: bool = False
+    seen_tool_calls: bool = False
 
     def _is_function_call_complete(self, function_args: str | dict) -> bool:
         if isinstance(function_args, dict):
@@ -468,6 +469,7 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
             # process tool calls - if complete function arg - add id to tool call
             tool_calls: Final = chunk["message"].get("tool_calls")
             if tool_calls is not None:
+                self.seen_tool_calls = True
                 for tool_call in tool_calls:
                     function_args = tool_call.get("function").get("arguments")
                     if function_args is not None and len(function_args) > 0:
@@ -508,9 +510,10 @@ class OllamaChatCompletionResponseIterator(BaseModelResponseIterator):
 
             if chunk["done"] is True:
                 finish_reason = chunk.get("done_reason") or "stop"
-                # Override finish_reason when tool_calls are present
+                # Override finish_reason when tool_calls appeared in any chunk
                 # Fixes: https://github.com/BerriAI/litellm/issues/18922
-                if tool_calls is not None:
+                # Fixes: https://github.com/BerriAI/litellm/issues/34692
+                if self.seen_tool_calls:
                     finish_reason = "tool_calls"
                 choices = [
                     StreamingChoices(

litellm/llms/openai/responses/guardrail_translation/handler.py

@@ -28,6 +28,7 @@ Output: response.output is List[GenericResponseOutputItem] where each has:
     - text: str
 """
 
+import copy
 import time
 import uuid
 from collections.abc import Mapping, Sequence
@@ -36,7 +37,6 @@ from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Union, cast
 
 from openai.types.responses.response_function_tool_call import ResponseFunctionToolCall
-from openai.types.responses.tool_param import FunctionToolParam
 from pydantic import BaseModel, TypeAdapter
 from typing_extensions import ReadOnly, TypedDict
 
@@ -49,6 +49,7 @@ from litellm.llms.base_llm.guardrail_translation.utils import (
     blocked_responses_stream_usage,
     stream_item_field,
 )
+from litellm.llms.openai.responses.guardrail_translation.tool_merge import merge_guardrailed_tools
 from litellm.responses.litellm_completion_transformation.transformation import (
     LiteLLMCompletionResponsesConfig,
 )
@@ -62,7 +63,6 @@ from litellm.types.llms.openai import (
     ContentPartDonePartOutputText,
     ErrorEvent,
     ErrorEventError,
-    OpenAIMcpServerTool,
     OutputItemAddedEvent,
     OutputItemDoneEvent,
     OutputTextDeltaEvent,
@@ -157,23 +157,31 @@ class OpenAIResponsesHandler(BaseTranslation):
         Handles both string input and list of message objects.
         """
         input_data: Final[str | ResponseInputParam | None] = data.get("input")
-        tools_to_check: Final[list[ChatCompletionToolParam]] = []
         if input_data is None:
             return data
 
         structured_messages: Final = self.get_structured_messages(data)
+        raw_tools: Final = data.get("tools")
+        original_tools: Final[tuple[Mapping[str, object], ...]] = (
+            tuple(raw_tools) if isinstance(raw_tools, list) else ()
+        )
+        flattened_tool_groups: Final = tuple(
+            form.chat_tools for form in LiteLLMCompletionResponsesConfig.responses_tools_to_chat_forms(original_tools)
+        )
+        flattened_tools: Final = tuple(
+            cast(ChatCompletionToolParam, tool)  # cast-ok: mcp tools ride along in the guardrail's tool list
+            for group in flattened_tool_groups
+            for tool in group
+        )
+        tools_to_check: Final[list[ChatCompletionToolParam]] = list(  # mutable-ok: guardrail inputs want a list
+            copy.deepcopy(flattened_tools)
+        )
 
         # Handle simple string input
         if isinstance(input_data, str):
             inputs = GenericGuardrailAPIInputs(texts=[input_data])
-            original_tools: list[dict[str, object]] = []
-
-            # Extract and transform tools if present
-            if "tools" in data and data["tools"]:
-                original_tools = list(data["tools"])
-                self._extract_and_transform_tools(data["tools"], tools_to_check)
-                if tools_to_check:
-                    inputs["tools"] = tools_to_check
+            if tools_to_check:
+                inputs["tools"] = tools_to_check
             if structured_messages:
                 inputs["structured_messages"] = structured_messages
             # Include model information if available
@@ -189,7 +197,9 @@ class OpenAIResponsesHandler(BaseTranslation):
             )
             guardrailed_texts = guardrailed_inputs.get("texts", [])
             data["input"] = guardrailed_texts[0] if guardrailed_texts else input_data
-            self._apply_guardrailed_tools_to_data(data, original_tools, guardrailed_inputs.get("tools"))
+            self._apply_guardrailed_tools_to_data(
+                data, original_tools, flattened_tool_groups, guardrailed_inputs.get("tools")
+            )
             verbose_proxy_logger.debug("OpenAI Responses API: Processed string input")
             return data
 
@@ -200,7 +210,6 @@ class OpenAIResponsesHandler(BaseTranslation):
         texts_to_check: Final[list[str]] = []
         images_to_check: Final[list[str]] = []
         task_mappings: Final[list[tuple[int, int | None]]] = []
-        original_tools_list: Final[list[dict[str, object]]] = list(data.get("tools") or [])
 
         # Step 1: Extract all text content, images, and tools
         for msg_idx, message in enumerate(input_data):
@@ -212,10 +221,6 @@ class OpenAIResponsesHandler(BaseTranslation):
                 task_mappings=task_mappings,
             )
 
-        # Extract and transform tools if present
-        if "tools" in data and data["tools"]:
-            self._extract_and_transform_tools(data["tools"], tools_to_check)
-
         # Step 2: Apply guardrail to all texts in batch
         if texts_to_check:
             inputs = GenericGuardrailAPIInputs(texts=texts_to_check)
@@ -238,9 +243,7 @@ class OpenAIResponsesHandler(BaseTranslation):
 
             guardrailed_texts = guardrailed_inputs.get("texts", [])
             self._apply_guardrailed_tools_to_data(
-                data,
-                original_tools_list,
-                guardrailed_inputs.get("tools"),
+                data, original_tools, flattened_tool_groups, guardrailed_inputs.get("tools")
             )
 
             # Step 3: Map guardrail responses back to original input structure
@@ -267,73 +270,18 @@ class OpenAIResponsesHandler(BaseTranslation):
                 names.append(str(tool["server_label"]))
         return names
 
-    def _extract_and_transform_tools(
-        self,
-        tools: list[FunctionToolParam | OpenAIMcpServerTool],
-        tools_to_check: list[ChatCompletionToolParam],
-    ) -> None:
-        """
-        Extract and transform tools from Responses API format to Chat Completion format.
-
-        Uses the LiteLLM transformation function to convert Responses API tools
-        to Chat Completion tools that can be passed to guardrails.
-        """
-        if tools is not None and isinstance(tools, list):
-            # Transform Responses API tools to Chat Completion tools
-            (
-                transformed_tools,
-                _,
-            ) = LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools(tools)
-            tools_to_check.extend(cast(list[ChatCompletionToolParam], transformed_tools))
-
-    def _remap_tools_to_responses_api_format(self, guardrailed_tools: list[Any]) -> list[dict[str, object]]:
-        """
-        Remap guardrail-returned tools (Chat Completion format) back to
-        Responses API request tool format.
-        """
-        return LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools(
-            guardrailed_tools
-        )
-
-    def _merge_tools_after_guardrail(
-        self,
-        original_tools: list[dict[str, object]],
-        remapped: list[dict[str, object]],
-    ) -> list[dict[str, object]]:
-        """
-        Merge remapped guardrailed tools with original tools that were not sent
-        to the guardrail (e.g. web_search, web_search_preview), preserving order.
-        Tools a guardrail appended (``remapped`` longer than ``original_tools``)
-        have no original slot and are kept so an injected tool is not dropped.
-        """
-        if not original_tools:
-            return remapped
-        result: Final[list[dict[str, object]]] = []
-        j = 0
-        for tool in original_tools:
-            if isinstance(tool, dict) and tool.get("type") in (
-                "web_search",
-                "web_search_preview",
-            ):
-                result.append(tool)
-            else:
-                if j < len(remapped):
-                    result.append(remapped[j])
-                    j += 1
-        # Keep guardrail-appended tools that matched no original slot above.
-        result.extend(remapped[j:])
-        return result
-
     def _apply_guardrailed_tools_to_data(
         self,
         data: dict,
-        original_tools: list[dict[str, object]],
-        guardrailed_tools: list[ChatCompletionToolParam] | None,
+        original_tools: Sequence[Mapping[str, object]],
+        flattened_tool_groups: Sequence[Sequence[Mapping[str, object]]],
+        guardrailed_tools: Sequence[ChatCompletionToolParam] | None,
     ) -> None:
-        """Remap guardrailed tools to Responses API format and merge with original, then set data['tools']."""
-        if guardrailed_tools is not None:
-            remapped: Final = self._remap_tools_to_responses_api_format(guardrailed_tools)
-            data["tools"] = self._merge_tools_after_guardrail(original_tools, remapped)
+        if guardrailed_tools is None:
+            return
+        data["tools"] = list(  # mutable-ok: downstream wants a list  # rebind-ok: in-place request rewrite
+            merge_guardrailed_tools(original_tools, flattened_tool_groups, guardrailed_tools)
+        )
 
     def _extract_input_text_and_images(
         self,

litellm/llms/openai/responses/guardrail_translation/tool_merge.py

@@ -0,0 +1,182 @@
+from collections.abc import Iterable, Mapping, Sequence
+from itertools import accumulate, chain, groupby
+from types import MappingProxyType
+from typing import Final, TypeAlias
+
+from pydantic import BaseModel, TypeAdapter, ValidationError
+
+from litellm._logging import verbose_logger
+from litellm.responses.litellm_completion_transformation.transformation import (
+    NAMESPACE_DESCRIPTION_SEPARATOR,
+    LiteLLMCompletionResponsesConfig,
+)
+
+Tool: TypeAlias = Mapping[str, object]
+IndexedKey: TypeAlias = tuple[str, int]
+
+_TOOL_ADAPTER: Final = TypeAdapter(dict[str, object])
+_CHAT_TOOL_TOP_LEVEL_KEYS: Final = frozenset({"type", "function"})
+
+
+def _as_tool(value: object) -> Tool | None:
+    candidate: Final = value.model_dump(exclude_unset=True) if isinstance(value, BaseModel) else value
+    try:
+        return _TOOL_ADAPTER.validate_python(candidate)
+    except ValidationError:
+        return None
+
+
+def _validated_tools(values: Iterable[object]) -> tuple[Tool, ...]:
+    validated: Final = tuple(map(_as_tool, values))
+    dropped: Final = sum(tool is None for tool in validated)
+    if dropped:
+        verbose_logger.warning("Dropping %d guardrail-returned tools that are not objects", dropped)
+    return tuple(tool for tool in validated if tool is not None)
+
+
+def _is_function(tool: Tool) -> bool:
+    return tool.get("type") == "function"
+
+
+def _chat_tool_key(tool: Tool) -> str:
+    tool_type: Final = str(tool.get("type") or "")
+    function: Final = _as_tool(tool.get("function"))
+    if function is not None:
+        return f"{tool_type}:{function.get('name') or ''}"
+    return f"{tool_type}:{tool.get('server_label') or tool.get('name') or ''}"
+
+
+def _indexed_keys(tools: Sequence[Tool]) -> tuple[IndexedKey, ...]:
+    keys: Final = tuple(_chat_tool_key(tool) for tool in tools)
+    positions_by_key: Final = groupby(sorted(range(len(keys)), key=keys.__getitem__), key=keys.__getitem__)
+    ordinal_by_position: Final = MappingProxyType(
+        {position: ordinal for _, positions in positions_by_key for ordinal, position in enumerate(positions)}
+    )
+    return tuple((key, ordinal_by_position[position]) for position, key in enumerate(keys))
+
+
+def _namespace_members(namespace: Tool) -> tuple[Tool, ...]:
+    members: Final = namespace.get("tools")
+    if not isinstance(members, Sequence) or isinstance(members, (str, bytes)):
+        return ()
+    return tuple(member for member in map(_as_tool, members) if member is not None)
+
+
+def _function_fields(tool: Tool) -> Tool:
+    function: Final = _as_tool(tool.get("function"))
+    return function if function is not None else MappingProxyType({})
+
+
+def _without_namespace_prefix(key: str, value: object, prefix: str) -> object:
+    if key != "description" or not isinstance(value, str) or not value.startswith(prefix):
+        return value
+    return value[len(prefix) :]
+
+
+def _rebuilt_member(member: Tool, flattened: Tool, guardrailed: Tool, namespace_description: str) -> Tool:
+    flattened_function: Final = _function_fields(flattened)
+    prefix: Final = f"{namespace_description}{NAMESPACE_DESCRIPTION_SEPARATOR}" if namespace_description else ""
+    changed_function: Final = MappingProxyType(
+        {
+            key: _without_namespace_prefix(key, value, prefix)
+            for key, value in _function_fields(guardrailed).items()
+            if flattened_function.get(key) != value
+        }
+    )
+    changed_extras: Final = MappingProxyType(
+        {
+            key: value
+            for key, value in guardrailed.items()
+            if key not in _CHAT_TOOL_TOP_LEVEL_KEYS and flattened.get(key) != value
+        }
+    )
+    return {**member, **changed_extras, **changed_function}  # mutable-ok: json.dumps rejects MappingProxyType
+
+
+def _rebuilt_function_members(
+    function_members: Sequence[Tool],
+    flattened_group: Sequence[Tool],
+    group_keys: Sequence[IndexedKey],
+    guardrailed_by_key: Mapping[IndexedKey, Tool],
+    namespace_description: str,
+) -> tuple[Tool | None, ...]:
+    return tuple(
+        None
+        if key not in guardrailed_by_key
+        else member
+        if guardrailed_by_key[key] == flattened
+        else _rebuilt_member(member, flattened, guardrailed_by_key[key], namespace_description)
+        for member, flattened, key in zip(function_members, flattened_group, group_keys)
+    )
+
+
+def _rebuilt_namespace(
+    original: Tool,
+    members: Sequence[Tool],
+    flattened_group: Sequence[Tool],
+    group_keys: Sequence[IndexedKey],
+    guardrailed_by_key: Mapping[IndexedKey, Tool],
+) -> tuple[Tool, ...]:
+    namespace_description: Final = str(original.get("description") or "")
+    rebuilt_functions: Final = iter(
+        _rebuilt_function_members(
+            tuple(member for member in members if _is_function(member)),
+            flattened_group,
+            group_keys,
+            guardrailed_by_key,
+            namespace_description,
+        )
+    )
+    rebuilt_members: Final = tuple(
+        rebuilt
+        for rebuilt in (next(rebuilt_functions) if _is_function(member) else member for member in members)
+        if rebuilt is not None
+    )
+    if not rebuilt_members:
+        return ()
+    return ({**original, "tools": list(rebuilt_members)},)  # mutable-ok: json.dumps needs a plain dict and list
+
+
+def _merged_original(
+    original: Tool,
+    flattened_group: Sequence[Tool],
+    group_keys: Sequence[IndexedKey],
+    guardrailed_by_key: Mapping[IndexedKey, Tool],
+) -> tuple[Tool, ...]:
+    if not group_keys:
+        return (original,)
+    guardrailed_group: Final = tuple(guardrailed_by_key[key] for key in group_keys if key in guardrailed_by_key)
+    if guardrailed_group == tuple(flattened_group):
+        return (original,)
+    members: Final = _namespace_members(original) if original.get("type") == "namespace" else ()
+    if members and sum(map(_is_function, members)) == len(flattened_group):
+        return _rebuilt_namespace(original, members, flattened_group, group_keys, guardrailed_by_key)
+    if not guardrailed_group:
+        return ()
+    return tuple(
+        LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools(guardrailed_group)
+    )
+
+
+def merge_guardrailed_tools(
+    original_tools: Sequence[Tool],
+    flattened_groups: Sequence[Sequence[Tool]],
+    guardrailed_tools: Iterable[object],
+) -> tuple[Tool, ...]:
+    guardrailed: Final = _validated_tools(guardrailed_tools)
+    flattened_keys: Final = _indexed_keys(tuple(chain.from_iterable(flattened_groups)))
+    guardrailed_keys: Final = _indexed_keys(guardrailed)
+    guardrailed_by_key: Final = MappingProxyType(dict(zip(guardrailed_keys, guardrailed)))
+    group_ends: Final = tuple(accumulate(len(group) for group in flattened_groups))
+    group_key_slices: Final = tuple(
+        flattened_keys[end - len(group) : end] for group, end in zip(flattened_groups, group_ends)
+    )
+    merged_originals: Final = chain.from_iterable(
+        _merged_original(original, group, group_keys, guardrailed_by_key)
+        for original, group, group_keys in zip(original_tools, flattened_groups, group_key_slices)
+    )
+    owned_keys: Final = frozenset(flattened_keys)
+    appended: Final = LiteLLMCompletionResponsesConfig.transform_chat_completion_tool_params_to_responses_api_tools(
+        tuple(tool for key, tool in zip(guardrailed_keys, guardrailed) if key not in owned_keys)
+    )
+    return tuple(chain(merged_originals, appended))

litellm/llms/valkey/vector_stores/transformation.py

@@ -15,7 +15,10 @@ import httpx
 from pydantic import BaseModel, ConfigDict
 
 import litellm
-from litellm.llms.base_llm.vector_store.transformation import BaseDirectVectorStoreConfig
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseDirectVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
 from litellm.llms.valkey.common_utils import build_valkey_url, pack_vector
 from litellm.types.utils import EmbeddingResponse
 from litellm.types.vector_stores import (
@@ -213,6 +216,7 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: "LiteLLMLoggingObj",
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         params: Final = _ValkeySearchParams.model_validate(litellm_params)
@@ -222,10 +226,18 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
             embedding_field=params.embedding_field,
             text_field=params.text_field,
         )
-        embedding_response: Final = self.embedding_fn(
-            model=params.require_embedding_model(),
-            input=[query_text],  # mutable-ok: litellm.embedding's input contract is a list
-            **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+        embedding_response: Final = (
+            embedding_executor.embed(
+                params.require_embedding_model(),
+                query_text,
+                params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG,
+            )
+            if embedding_executor is not None
+            else self.embedding_fn(
+                model=params.require_embedding_model(),
+                input=[query_text],  # mutable-ok: the injected embedding callable requires list input
+                **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+            )
         )
         vec_params: Final = {"vec": pack_vector(embedding_response.data[0]["embedding"])}  # mutable-ok: redis-py API
 
@@ -252,6 +264,7 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
         vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
         litellm_logging_obj: "LiteLLMLoggingObj",
         litellm_params: Mapping[str, object],
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
         timeout: float | httpx.Timeout | None = None,
     ) -> VectorStoreSearchResponse:
         params: Final = _ValkeySearchParams.model_validate(litellm_params)
@@ -261,10 +274,18 @@ class ValkeyVectorStoreConfig(BaseDirectVectorStoreConfig):
             embedding_field=params.embedding_field,
             text_field=params.text_field,
         )
-        embedding_response: Final = await self.aembedding_fn(
-            model=params.require_embedding_model(),
-            input=[query_text],  # mutable-ok: litellm.embedding's input contract is a list
-            **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+        embedding_response: Final = (
+            await embedding_executor.aembed(
+                params.require_embedding_model(),
+                query_text,
+                params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG,
+            )
+            if embedding_executor is not None
+            else await self.aembedding_fn(
+                model=params.require_embedding_model(),
+                input=[query_text],  # mutable-ok: the injected embedding callable requires list input
+                **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+            )
         )
         vec_params: Final = {"vec": pack_vector(embedding_response.data[0]["embedding"])}  # mutable-ok: redis-py API
 

litellm/proxy/rag_endpoints/endpoints.py

@@ -729,7 +729,7 @@ async def rag_query(
         # conflict so callers cannot override the store's provider or credentials.
         managed_store: Final = resolved_stores.get(retrieval_config["vector_store_id"])
         store_data: Final = (
-            await build_request_data_from_managed_vector_store(managed_store)
+            build_request_data_from_managed_vector_store(managed_store)
             if managed_store is not None
             else MappingProxyType({})
         )

litellm/proxy/vector_store_endpoints/endpoints.py

@@ -16,9 +16,6 @@ from litellm.proxy._types import CommonProxyErrors, UserAPIKeyAuth
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
 from litellm.proxy.utils import jsonify_object
-from litellm.proxy.vector_store_endpoints.management_endpoints import (
-    _resolve_embedding_config,
-)
 from litellm.proxy.vector_store_endpoints.utils import (
     assert_proxy_admin_for_vector_store_index_management,
     assert_user_can_access_vector_store,
@@ -57,19 +54,9 @@ def reject_caller_embedding_selection_params(payload: Mapping[str, object], sour
 ########################################################
 
 
-async def build_request_data_from_managed_vector_store(
+def build_request_data_from_managed_vector_store(
     vector_store: LiteLLM_ManagedVectorStore,
 ) -> Mapping[str, object]:
-    """
-    Build request params (provider, credential ref, litellm_params) from an
-    already-resolved managed vector store.
-
-    ``litellm_embedding_config`` is resolved here, at request-handling time,
-    instead of at row-creation time: the resolved api_key/api_base/api_version
-    lives only in the returned per-request mapping and is never persisted back
-    to the registry cache. Legacy rows that already carry a resolved
-    (cleartext) config skip the lookup and pass through unchanged.
-    """
     top_level: Final = MappingProxyType(
         {
             key: vector_store.get(key)
@@ -78,18 +65,7 @@ async def build_request_data_from_managed_vector_store(
         }
     )
     litellm_params: Final = vector_store.get("litellm_params") or MappingProxyType({})
-    embedding_model: Final = litellm_params.get("litellm_embedding_model")
-    if not embedding_model or litellm_params.get("litellm_embedding_config"):
-        return MappingProxyType({**top_level, **litellm_params})
-
-    from litellm.proxy.proxy_server import prisma_client
-
-    resolved_config: Final = await _resolve_embedding_config(
-        embedding_model=embedding_model, prisma_client=prisma_client
-    )
-    if not resolved_config:
-        return MappingProxyType({**top_level, **litellm_params})
-    return MappingProxyType({**top_level, **litellm_params, "litellm_embedding_config": resolved_config})
+    return MappingProxyType({**top_level, **litellm_params})
 
 
 async def _update_request_data_with_litellm_managed_vector_store_registry(
@@ -118,7 +94,7 @@ async def _update_request_data_with_litellm_managed_vector_store_registry(
             vector_store=vector_store_to_run,
             user_api_key_dict=user_api_key_dict,
         )
-    return {**data, **(await build_request_data_from_managed_vector_store(vector_store_to_run))}
+    return {**data, **build_request_data_from_managed_vector_store(vector_store_to_run)}
 
 
 @router.post(

litellm/proxy/vector_store_endpoints/management_endpoints.py

@@ -18,11 +18,8 @@ if TYPE_CHECKING:
     from prisma.models import LiteLLM_ManagedVectorStoresTable as _VectorStoreRow
 
     from litellm.proxy.utils import PrismaClient
-    from litellm.router import Router
-
 import litellm
 from litellm._logging import verbose_proxy_logger
-from litellm.caching.in_memory_cache import InMemoryCache
 from litellm.constants import REDACTED_BY_LITELM_STRING
 from litellm.litellm_core_utils.safe_json_dumps import safe_dumps
 from litellm.litellm_core_utils.sensitive_data_masker import SensitiveDataMasker
@@ -32,13 +29,10 @@ from litellm.proxy._types import (
     UserAPIKeyAuth,
 )
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
-from litellm.proxy.common_utils.encrypt_decrypt_utils import decrypt_value_helper
 from litellm.proxy.common_utils.rbac_utils import check_feature_access_for_user
 from litellm.proxy.vector_store_endpoints.utils import can_user_access_vector_store
-from litellm.repositories.model_repository import ModelRepository
 from litellm.repositories.prisma_protocols import TableActions
 from litellm.repositories.table_repositories import ManagedVectorStoresRepository
-from litellm.secret_managers.main import get_secret
 from litellm.types.vector_stores import (
     LiteLLM_ManagedVectorStore,
     LiteLLM_ManagedVectorStoreListResponse,
@@ -64,28 +58,6 @@ _LITELLM_PARAMS_MASKER: Final = SensitiveDataMasker()
 
 _REDACT_LITELLM_PARAMS_MAX_DEPTH: Final = 10
 
-# Use-time embedding-config resolution runs on every vector-store request
-# whose persisted row carries only a model reference (the post-fix shape).
-# Without a cache, that's one ``litellm_proxymodeltable.find_first`` per
-# request — the no-DB-in-critical-path rule. Hold the resolved config in
-# memory for a short TTL so a hot model name pays the DB lookup at most
-# once per ``_EMBEDDING_CONFIG_CACHE_TTL`` seconds. Cleartext credentials
-# only ever live in process memory (never persisted, never echoed in
-# management responses), so the cache doesn't widen the disclosure surface.
-_EMBEDDING_CONFIG_CACHE_TTL: Final = 60
-_EMBEDDING_CONFIG_CACHE_MAX_SIZE: Final = 256
-_embedding_config_cache: InMemoryCache | None = None
-
-
-def _get_embedding_config_cache() -> InMemoryCache:
-    global _embedding_config_cache
-    if _embedding_config_cache is None:
-        _embedding_config_cache = InMemoryCache(
-            max_size_in_memory=_EMBEDDING_CONFIG_CACHE_MAX_SIZE,
-            default_ttl=_EMBEDDING_CONFIG_CACHE_TTL,
-        )
-    return _embedding_config_cache
-
 
 def _redact_sensitive_litellm_params(litellm_params: Any, _depth: int = 0) -> Any:
     """
@@ -155,235 +127,6 @@ async def _fetch_and_authorize_vector_store(
     return typed
 
 
-def _resolve_embedding_config_from_router(embedding_model: str, llm_router) -> dict[str, object] | None:
-    """
-    Resolve embedding config from router's config-defined models.
-
-    Config-defined models (from proxy_config.yaml) are stored in the router's model_list,
-    not in the database. This function looks up the model in the router and extracts
-    api_key, api_base, and api_version from the deployment's litellm_params.
-
-    Args:
-        embedding_model: The embedding model string (e.g., "text-embedding-ada-002" or "azure/text-embedding-3-large")
-        llm_router: The LiteLLM router instance
-
-    Returns:
-        Dictionary with api_key, api_base, and api_version if model found, None otherwise
-    """
-    if not embedding_model or llm_router is None:
-        return None
-
-    # Extract model name candidates - could be "text-embedding-ada-002" or "azure/text-embedding-3-large"
-    # Try exact match first, then try without provider prefix
-    model_name_candidates: Final = [embedding_model]
-    if "/" in embedding_model:
-        # If it has a provider prefix, also try without it
-        _, model_name = embedding_model.split("/", 1)
-        model_name_candidates.append(model_name)
-
-    # Try to find model in router
-    for model_name in model_name_candidates:
-        try:
-            # Try to get deployment by model group name (model_name in config)
-            deployment = llm_router.get_deployment_by_model_group_name(model_group_name=model_name)
-
-            if deployment is not None and deployment.litellm_params is not None:
-                litellm_params = deployment.litellm_params
-
-                # Build embedding config from model params
-                embedding_config: dict[str, object] = {}
-
-                # Extract api_key
-                api_key = getattr(litellm_params, "api_key", None)
-                if api_key:
-                    # Handle os.environ/ prefix
-                    if isinstance(api_key, str) and api_key.startswith("os.environ/"):
-                        api_key = get_secret(api_key)
-                    embedding_config["api_key"] = api_key
-
-                # Extract api_base
-                api_base = getattr(litellm_params, "api_base", None)
-                if api_base:
-                    # Handle os.environ/ prefix
-                    if isinstance(api_base, str) and api_base.startswith("os.environ/"):
-                        api_base = get_secret(api_base)
-                    embedding_config["api_base"] = api_base
-
-                # Extract api_version
-                api_version = getattr(litellm_params, "api_version", None)
-                if api_version:
-                    embedding_config["api_version"] = api_version
-
-                project_id = getattr(litellm_params, "project_id", None)
-                if project_id:
-                    embedding_config["project_id"] = project_id
-
-                # Only return config if we have at least api_key or api_base
-                if embedding_config:
-                    verbose_proxy_logger.debug(
-                        "Resolved embedding config from router model %s: %s", model_name, list(embedding_config.keys())
-                    )
-                    return embedding_config
-        except Exception as e:
-            verbose_proxy_logger.debug("Error resolving embedding config from router for model %s: %s", model_name, e)
-            continue
-
-    return None
-
-
-async def _resolve_embedding_config_from_db(
-    embedding_model: str, prisma_client: "PrismaClient"
-) -> dict[str, object] | None:
-    """
-    Resolve embedding config from database model configuration.
-
-    If litellm_embedding_model is provided but litellm_embedding_config is not,
-    this function looks up the model in the database and extracts api_key, api_base,
-    and api_version from the model's litellm_params to build the embedding config.
-
-    Args:
-        embedding_model: The embedding model string (e.g., "text-embedding-ada-002" or "azure/text-embedding-3-large")
-        prisma_client: The Prisma client instance
-
-    Returns:
-        Dictionary with api_key, api_base, and api_version if model found, None otherwise
-    """
-    if not embedding_model:
-        return None
-
-    # Extract model name - could be "text-embedding-ada-002" or "azure/text-embedding-3-large"
-    # Try to find model by exact match first, then try without provider prefix
-    model_name_candidates: Final = [embedding_model]
-    if "/" in embedding_model:
-        # If it has a provider prefix, also try without it
-        _, model_name = embedding_model.split("/", 1)
-        model_name_candidates.append(model_name)
-
-    # Try to find model in database
-    for model_name in model_name_candidates:
-        try:
-            db_model = await ModelRepository(prisma_client).table.find_first(where={"model_name": model_name})
-
-            if db_model and db_model.litellm_params:
-                # Extract litellm_params (could be dict or JSON string)
-                model_params = db_model.litellm_params
-                if isinstance(model_params, str):  # pyright: ignore[reportUnnecessaryIsInstance]  # prisma Json is str
-                    model_params = json.loads(model_params)
-
-                # Decrypt values from database (similar to how proxy_server.py does it)
-                # Values stored in DB are encrypted, so we need to decrypt them first
-                decrypted_params = {}
-                if isinstance(model_params, dict):
-                    for k, v in model_params.items():
-                        if isinstance(v, str):
-                            # Decrypt value - returns original value if decryption fails or no key is set
-                            decrypted_value = decrypt_value_helper(value=v, key=k, return_original_value=True)
-                            decrypted_params[k] = decrypted_value
-                        else:
-                            decrypted_params[k] = v
-                else:
-                    decrypted_params = model_params
-
-                # Build embedding config from model params
-                embedding_config = {}
-
-                # Extract api_key
-                api_key = decrypted_params.get("api_key")
-                if api_key:
-                    # Handle os.environ/ prefix (after decryption, values may be os.environ/ prefixed)
-                    if isinstance(api_key, str) and api_key.startswith("os.environ/"):
-                        api_key = get_secret(api_key)
-                    embedding_config["api_key"] = api_key
-
-                # Extract api_base
-                api_base = decrypted_params.get("api_base")
-                if api_base:
-                    # Handle os.environ/ prefix (after decryption, values may be os.environ/ prefixed)
-                    if isinstance(api_base, str) and api_base.startswith("os.environ/"):
-                        api_base = get_secret(api_base)
-                    embedding_config["api_base"] = api_base
-
-                # Extract api_version
-                api_version = decrypted_params.get("api_version")
-                if api_version:
-                    embedding_config["api_version"] = api_version
-
-                # Only return config if we have at least api_key or api_base
-                if embedding_config:
-                    verbose_proxy_logger.debug(
-                        "Resolved embedding config from database model %s: %s",
-                        model_name,
-                        list(embedding_config.keys()),
-                    )
-                    return embedding_config
-        except Exception as e:
-            verbose_proxy_logger.debug("Error resolving embedding config for model %s: %s", model_name, e)
-            continue
-
-    return None
-
-
-async def _resolve_embedding_config(
-    embedding_model: str, prisma_client: "PrismaClient | None", llm_router: "Router | None" = None
-) -> dict[str, object] | None:
-    """
-    Resolve embedding config from either router (config-defined) or database models.
-
-    This function first checks the router for config-defined models, then falls back
-    to the database. This allows users to use models defined in either location.
-
-    Results are cached in process memory for ``_EMBEDDING_CONFIG_CACHE_TTL``
-    seconds so the request-handling path doesn't hit the database on every
-    vector-store call. Negative results (model not found) are intentionally
-    not cached to avoid blocking a freshly-added model behind the TTL.
-
-    Args:
-        embedding_model: The embedding model string (e.g., "text-embedding-ada-002" or "azure/text-embedding-3-large")
-        prisma_client: The Prisma client instance
-        llm_router: The LiteLLM router instance (optional, will be imported if not provided)
-
-    Returns:
-        Dictionary with api_key, api_base, and api_version if model found, None otherwise
-    """
-    if not embedding_model:
-        return None
-
-    cache: Final = _get_embedding_config_cache()
-    cached: Final = cache.get_cache(embedding_model)
-    if cached is not None:
-        return cached
-
-    # Import llm_router if not provided
-    if llm_router is None:
-        try:
-            from litellm.proxy.proxy_server import llm_router
-        except ImportError:
-            llm_router = None
-
-    # First try to resolve from router (config-defined models)
-    if llm_router is not None:
-        router_config = _resolve_embedding_config_from_router(embedding_model=embedding_model, llm_router=llm_router)
-        if router_config:
-            verbose_proxy_logger.debug("Resolved embedding config from router for model %s", embedding_model)
-            cache.set_cache(embedding_model, router_config)
-            return router_config
-
-    # Fall back to database
-    if prisma_client is not None:
-        db_config: Final = await _resolve_embedding_config_from_db(
-            embedding_model=embedding_model, prisma_client=prisma_client
-        )
-        if db_config:
-            verbose_proxy_logger.debug("Resolved embedding config from database for model %s", embedding_model)
-            cache.set_cache(embedding_model, db_config)
-            return db_config
-
-    verbose_proxy_logger.debug(
-        "Could not resolve embedding config for model %s from router or database", embedding_model
-    )
-    return None
-
-
 ########################################################
 # Helper Functions
 ########################################################
@@ -469,10 +212,9 @@ async def create_vector_store_in_db(
     # (``api_key``, ``api_base``, ``api_version``) into this row. That
     # exposed every env-stored embedding-model credential on the
     # ``/vector_store/{new,info,update,list}`` responses. Keep the user's
-    # raw ``litellm_embedding_model`` reference; resolution now happens in
-    # ``build_request_data_from_managed_vector_store``
-    # at request-handling time so the cleartext config exists only in
-    # per-request memory and never reaches the database.
+    # raw ``litellm_embedding_model`` reference; each search embeds the
+    # query through the router at request time, so the credentials stay
+    # on the deployment and never reach the database.
     if litellm_params:
         litellm_params_dict: Final = GenericLiteLLMParams(**litellm_params).model_dump(exclude_none=True)
         data_to_create["litellm_params"] = safe_dumps(litellm_params_dict)
@@ -862,11 +604,9 @@ async def update_vector_store(
 
         # Handle litellm_params if provided. As with the create path, the
         # embedding-config auto-resolve previously persisted cleartext
-        # credentials into the row; resolution now happens at request-
-        # handling time in
-        # ``build_request_data_from_managed_vector_store``
-        # so this row only ever stores the user-supplied
-        # ``litellm_embedding_model`` reference.
+        # credentials into the row; each search now embeds the query
+        # through the router at request time, so this row only ever stores
+        # the user-supplied ``litellm_embedding_model`` reference.
         if "litellm_params" in update_data:
             _input_litellm_params: Final[dict] = update_data.get("litellm_params", {}) or {}
             litellm_params_dict: Final = GenericLiteLLMParams(**_input_litellm_params).model_dump(exclude_none=True)

litellm/responses/litellm_completion_transformation/transformation.py

@@ -6,6 +6,7 @@ import json
 import re
 import uuid
 from collections.abc import Iterable, Iterator, Mapping, Sequence
+from dataclasses import dataclass
 from types import MappingProxyType
 from typing import (
     TYPE_CHECKING,
@@ -102,6 +103,15 @@ from .custom_tools import (
 NamespaceNameMap: TypeAlias = Mapping[str, tuple[str, str]]
 NamespaceTool: TypeAlias = Mapping[str, object]
 ResponseTools: TypeAlias = Sequence[Mapping[str, object]] | None
+ChatToolParam: TypeAlias = ChatCompletionToolParam | OpenAIMcpServerTool
+NAMESPACE_DESCRIPTION_SEPARATOR: Final = "\n\n"
+
+
+@dataclass(frozen=True, slots=True)
+class ResponsesToolChatForm:
+    chat_tools: tuple[ChatToolParam, ...]
+    web_search_options: OpenAIWebSearchOptions | None
+
 
 if TYPE_CHECKING:
     from openai.types.responses.response_apply_patch_tool_call import (
@@ -1771,7 +1781,7 @@ class LiteLLMCompletionResponsesConfig:
         tool_name: Final = str(namespace_tool.get("name") or "")
         raw_description: Final = str(namespace_tool.get("description") or "")
         description: Final = (
-            f"{namespace_description}\n\n{raw_description}"
+            f"{namespace_description}{NAMESPACE_DESCRIPTION_SEPARATOR}{raw_description}"
             if nested and namespace_description and raw_description
             else namespace_description
             if nested and namespace_description
@@ -1837,9 +1847,78 @@ class LiteLLMCompletionResponsesConfig:
                 + ", ".join(sorted(conflicting_tool_names))
             )
 
+    @staticmethod
+    def _responses_tool_to_chat_form(tool: Mapping[str, object]) -> ResponsesToolChatForm:
+        tool_type: Final = tool.get("type")
+        if tool_type == "mcp":
+            return ResponsesToolChatForm(chat_tools=(cast(OpenAIMcpServerTool, tool),), web_search_options=None)
+        if tool_type == "web_search_preview" or tool_type == "web_search":
+            _search_context_size: Final[Literal["low", "medium", "high"]] = cast(
+                Literal["low", "medium", "high"], tool.get("search_context_size")
+            )
+            _user_location: Final[OpenAIWebSearchUserLocation | None] = cast(
+                OpenAIWebSearchUserLocation | None,
+                tool.get("user_location") or None,
+            )
+            return ResponsesToolChatForm(
+                chat_tools=(),
+                web_search_options=OpenAIWebSearchOptions(
+                    search_context_size=_search_context_size,
+                    user_location=_user_location,
+                ),
+            )
+        if tool_type == "function":
+            typed_tool: Final = cast(FunctionToolParam, tool)
+            raw_parameters: Final = typed_tool.get("parameters", {}) or {}
+            parameters: Final = (
+                {**raw_parameters}  # mutable-ok: json.dumps rejects MappingProxyType
+                if "type" in raw_parameters
+                else {**raw_parameters, "type": "object"}  # mutable-ok: json.dumps rejects MappingProxyType
+            )
+            chat_completion_tool: Final[dict[str, object]] = {
+                "type": "function",
+                "function": {
+                    "name": typed_tool.get("name") or "",
+                    "description": typed_tool.get("description") or "",
+                    "parameters": parameters,
+                    "strict": typed_tool.get("strict", False) or False,
+                },
+            }
+            if tool.get("cache_control"):
+                chat_completion_tool["cache_control"] = tool.get("cache_control")
+            if tool.get("defer_loading"):
+                chat_completion_tool["defer_loading"] = tool.get("defer_loading")
+            if tool.get("allowed_callers"):
+                chat_completion_tool["allowed_callers"] = tool.get("allowed_callers")
+            if tool.get("input_examples"):
+                chat_completion_tool["input_examples"] = tool.get("input_examples")
+            return ResponsesToolChatForm(
+                chat_tools=(cast(ChatCompletionToolParam, chat_completion_tool),), web_search_options=None
+            )
+        if tool_type == "namespace":
+            return ResponsesToolChatForm(
+                chat_tools=LiteLLMCompletionResponsesConfig._namespace_chat_tools(tool), web_search_options=None
+            )
+        if tool_type == "custom":
+            converted: Final = convert_custom_tool_to_function_tool(tool)
+            return ResponsesToolChatForm(chat_tools=() if converted is None else (converted,), web_search_options=None)
+        if tool_type in ("computer_use", "image_generation", "shell"):
+            verbose_logger.warning(
+                "Dropping Responses API tool of type '%s': it has no Chat Completions "
+                "equivalent and the target provider would reject the request.",
+                tool_type,
+            )
+            return ResponsesToolChatForm(chat_tools=(), web_search_options=None)
+        return ResponsesToolChatForm(chat_tools=(cast(ChatToolParam, tool),), web_search_options=None)
+
+    @staticmethod
+    def responses_tools_to_chat_forms(tools: ResponseTools) -> tuple[ResponsesToolChatForm, ...]:
+        LiteLLMCompletionResponsesConfig._validate_namespace_name_collisions(tools)
+        return tuple(LiteLLMCompletionResponsesConfig._responses_tool_to_chat_form(tool) for tool in tools or ())
+
     @staticmethod
     def transform_responses_api_tools_to_chat_completion_tools(
-        tools: list[FunctionToolParam | OpenAIMcpServerTool] | None,
+        tools: ResponseTools,
     ) -> tuple[
         list[ChatCompletionToolParam | OpenAIMcpServerTool],
         OpenAIWebSearchOptions | None,
@@ -1849,73 +1928,16 @@ class LiteLLMCompletionResponsesConfig:
         """
         if tools is None:
             return [], None
-        LiteLLMCompletionResponsesConfig._validate_namespace_name_collisions(tools)
-        chat_completion_tools: Final[list[ChatCompletionToolParam | OpenAIMcpServerTool]] = []
-        web_search_options: OpenAIWebSearchOptions | None = None
-        for tool in tools:
-            if tool.get("type") == "mcp":
-                chat_completion_tools.append(cast(OpenAIMcpServerTool, tool))
-            elif tool.get("type") == "web_search_preview" or tool.get("type") == "web_search":
-                _search_context_size: Literal["low", "medium", "high"] = cast(
-                    Literal["low", "medium", "high"], tool.get("search_context_size")
-                )
-                _user_location: OpenAIWebSearchUserLocation | None = cast(
-                    OpenAIWebSearchUserLocation | None,
-                    tool.get("user_location") or None,
-                )
-                web_search_options = OpenAIWebSearchOptions(
-                    search_context_size=_search_context_size,
-                    user_location=_user_location,
-                )
-            elif tool.get("type") == "function":
-                typed_tool = cast(FunctionToolParam, tool)
-                # Ensure parameters has "type": "object" as required by providers like Anthropic
-                parameters = dict(typed_tool.get("parameters", {}) or {})
-                if not parameters or "type" not in parameters:
-                    parameters["type"] = "object"
-                chat_completion_tool: dict[str, object] = {
-                    "type": "function",
-                    "function": {
-                        "name": typed_tool.get("name") or "",
-                        "description": typed_tool.get("description") or "",
-                        "parameters": parameters,
-                        "strict": typed_tool.get("strict", False) or False,
-                    },
-                }
-                if tool.get("cache_control"):
-                    chat_completion_tool["cache_control"] = tool.get("cache_control")
-                if tool.get("defer_loading"):
-                    chat_completion_tool["defer_loading"] = tool.get("defer_loading")
-                if tool.get("allowed_callers"):
-                    chat_completion_tool["allowed_callers"] = tool.get("allowed_callers")
-                if tool.get("input_examples"):
-                    chat_completion_tool["input_examples"] = tool.get("input_examples")
-                chat_completion_tools.append(cast(ChatCompletionToolParam, chat_completion_tool))
-            elif tool.get("type") == "namespace":
-                chat_completion_tools.extend(LiteLLMCompletionResponsesConfig._namespace_chat_tools(tool))
-            elif tool.get("type") == "custom":
-                converted = convert_custom_tool_to_function_tool(tool)
-                if converted is not None:
-                    chat_completion_tools.append(converted)
-            else:
-                _tool_type = tool.get("type")
-                if _tool_type in ("computer_use", "image_generation", "shell"):
-                    # Drop unsupported Responses-API-only tool types that have no
-                    # Chat Completions equivalent. Passing them through verbatim
-                    # causes providers to reject the request with "'function' is a
-                    # required property".
-                    verbose_logger.warning(
-                        "Dropping Responses API tool of type '%s': it has no Chat Completions "
-                        "equivalent and the target provider would reject the request.",
-                        _tool_type,
-                    )
-                    continue
-                chat_completion_tools.append(cast(ChatCompletionToolParam | OpenAIMcpServerTool, tool))
-        return chat_completion_tools, web_search_options
+        forms: Final = LiteLLMCompletionResponsesConfig.responses_tools_to_chat_forms(tools)
+        web_search_options: Final = next(
+            (form.web_search_options for form in reversed(forms) if form.web_search_options is not None),
+            None,
+        )
+        return [chat_tool for form in forms for chat_tool in form.chat_tools], web_search_options
 
     @staticmethod
     def transform_chat_completion_tool_params_to_responses_api_tools(
-        chat_completion_tools: list[ChatCompletionToolParam | OpenAIMcpServerTool] | None,
+        chat_completion_tools: Sequence[Mapping[str, object]] | None,
     ) -> list[dict[str, object]]:
         """
         Transform Chat Completion tool params (e.g. from guardrail output) back to
@@ -1926,9 +1948,6 @@ class LiteLLMCompletionResponsesConfig:
             return []
         result: Final[list[dict[str, object]]] = []
         for tool in chat_completion_tools:
-            if not isinstance(tool, dict):
-                result.append(tool)
-                continue
             if tool.get("type") == "function":
                 fn = cast(_ToolFunctionDefinition, tool.get("function") or {})
                 parameters = dict(fn.get("parameters", {}) or {})

litellm/router.py

@@ -85,6 +85,10 @@ from litellm.litellm_core_utils.sensitive_data_masker import (
     mask_credentials_in_payload,
     mask_sensitive_structure,
 )
+from litellm.llms.base_llm.vector_store.transformation import (
+    RouterVectorStoreEmbeddingExecutor,
+    vector_store_request_metadata,
+)
 from litellm.llms.openai_like.json_loader import JSONProviderRegistry
 from litellm.router_strategy.budget_limiter import RouterBudgetLimiting
 from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
@@ -354,6 +358,8 @@ _PreRoutingStrategyT = TypeVar("_PreRoutingStrategyT")
 
 _ALIAS_PARAMS_NEVER_FORWARDED: Final = frozenset({"model", "api_base", "api_key", "api_version"})
 _ALIAS_MARKER_FORWARDED_PARAMS_KWARG: Final = "_alias_marker_forwarded_params"
+_CLAUDE_CODE_SESSION_ID_RE: Final = re.compile(r"^[a-zA-Z0-9_\-]{8,}$")
+_CLAUDE_CODE_SESSION_ROUTER_TTL_SECONDS: Final = 3600
 
 _RUNTIME_TOGGLEABLE_PRE_CALL_CHECKS: Final[Mapping[str, type[CustomLogger]]] = MappingProxyType(
     {
@@ -810,6 +816,10 @@ class Router:
         self.cache = DualCache(
             redis_cache=redis_cache, in_memory_cache=InMemoryCache()
         )  # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc.
+        self._claude_code_session_router_cache: DualCache = DualCache(
+            redis_cache=redis_cache,
+            in_memory_cache=InMemoryCache(),
+        )
 
         ### SCHEDULER ###
         self.scheduler = Scheduler(polling_interval=polling_interval, redis_cache=redis_cache)
@@ -1131,8 +1141,8 @@ class Router:
         ```
         and caching to just work.
         """
-        if self.cache.redis_cache is None:
-            self.cache.redis_cache = cache
+        self.cache.attach_redis_cache(cache)
+        self._claude_code_session_router_cache.attach_redis_cache(cache)
 
     # Maps a routing strategy string to the attribute on `self` that holds
     # the default group's strategy selector for that strategy. (The selectors
@@ -6475,11 +6485,24 @@ class Router:
                     if custom_llm_provider and "custom_llm_provider" not in kwargs
                     else MappingProxyType(kwargs)
                 )
-                if provider_kwargs.get("model"):
-                    return self._generic_api_call_with_fallbacks(original_function=original_function, **provider_kwargs)
+                search_kwargs: Final = (
+                    MappingProxyType(
+                        {
+                            **provider_kwargs,
+                            "_direct_vector_store_embedding_executor": RouterVectorStoreEmbeddingExecutor(
+                                router=self,
+                                metadata=self._vector_store_request_metadata(kwargs),
+                            ),
+                        }
+                    )
+                    if call_type == "vector_store_search"
+                    else provider_kwargs
+                )
+                if search_kwargs.get("model"):
+                    return self._generic_api_call_with_fallbacks(original_function=original_function, **search_kwargs)
                 if call_type == "vector_store_search":
-                    return original_function(**MappingProxyType({**provider_kwargs, "router": self}))
-                return original_function(**provider_kwargs)
+                    return original_function(**MappingProxyType({**search_kwargs, "router": self}))
+                return original_function(**search_kwargs)
 
             return vector_store_sync_wrapper
 
@@ -6652,11 +6675,22 @@ class Router:
                 "avector_store_update",
                 "avector_store_delete",
             ):
+                vector_store_kwargs: Final = (
+                    {  # mutable-ok: the async routed request requires dynamic keyword arguments
+                        **kwargs,
+                        "_direct_vector_store_embedding_executor": RouterVectorStoreEmbeddingExecutor(
+                            router=self,
+                            metadata=self._vector_store_request_metadata(kwargs),
+                        ),
+                    }
+                    if call_type == "avector_store_search"
+                    else kwargs
+                )
                 return await self._init_vector_store_api_endpoints(
                     original_function=original_function,
                     custom_llm_provider=custom_llm_provider,
                     call_type=call_type,
-                    **kwargs,
+                    **vector_store_kwargs,
                 )
             elif call_type in ("afile_delete", "afile_content"):
                 return await self._ageneric_api_call_with_fallbacks(
@@ -6692,6 +6726,10 @@ class Router:
 
         return async_wrapper
 
+    @staticmethod
+    def _vector_store_request_metadata(kwargs: Mapping[str, object]) -> Mapping[str, object]:
+        return vector_store_request_metadata(kwargs)
+
     async def _init_vector_store_api_endpoints(
         self,
         original_function: Callable,
@@ -12651,6 +12689,100 @@ class Router:
             return None
         return candidates[0]
 
+    @staticmethod
+    def _request_header(request_kwargs: Mapping[str, object], header_name: str) -> str | None:
+        proxy_server_request: Final = request_kwargs.get("proxy_server_request")
+        if not isinstance(proxy_server_request, Mapping):
+            return None
+        headers: Final = proxy_server_request.get("headers")
+        if not isinstance(headers, Mapping):
+            return None
+        return next(
+            (
+                value
+                for key, value in headers.items()
+                if isinstance(key, str) and key.lower() == header_name and isinstance(value, str)
+            ),
+            None,
+        )
+
+    def _claude_code_session_router_cache_key(self, request_kwargs: Mapping[str, object]) -> str | None:
+        session_id: Final = self._request_header(request_kwargs, "x-claude-code-session-id")
+        if session_id is None or _CLAUDE_CODE_SESSION_ID_RE.fullmatch(session_id) is None:
+            return None
+        metadata_name: Final = "litellm_metadata" if "litellm_metadata" in request_kwargs else "metadata"
+        metadata: Final = request_kwargs.get(metadata_name)
+        if not isinstance(metadata, Mapping):
+            return None
+        caller_scope: Final = metadata.get("user_api_key_hash")
+        if not isinstance(caller_scope, str) or not caller_scope:
+            return None
+        return f"claude_code_session_router:v1:{caller_scope}:{session_id}"
+
+    async def _delete_claude_code_session_router_binding(self, cache_key: str) -> None:
+        try:
+            await self._claude_code_session_router_cache.async_delete_cache(key=cache_key)
+        except Exception as e:  # noqa: BLE001  # cache cleanup must not fail an otherwise routable request
+            verbose_router_logger.warning(
+                "Failed to delete Claude Code session router binding; the binding may remain until its TTL expires: %s",
+                e,
+            )
+
+    async def _get_claude_code_session_router_binding(self, cache_key: str) -> object:
+        session_cache: Final = self._claude_code_session_router_cache
+        try:
+            if session_cache.redis_cache is None:
+                return await session_cache.async_get_cache(key=cache_key)
+            return await session_cache.redis_cache.async_get_cache(key=cache_key)
+        except Exception as e:  # noqa: BLE001  # an optional binding must not make routing depend on Redis
+            verbose_router_logger.warning(
+                "Failed to read Claude Code session router binding; using the requested model: %s",
+                e,
+            )
+            return None
+
+    async def _resolve_claude_code_session_router(
+        self,
+        model: str,
+        registered_model_name: str,
+        request_kwargs: Mapping[str, object],
+    ) -> str:
+        if not any((self.auto_routers, self.complexity_routers, self.adaptive_routers, self.quality_routers)):
+            return registered_model_name
+        cache_key: Final = self._claude_code_session_router_cache_key(request_kwargs)
+        if cache_key is None or not isinstance(request_kwargs, dict):
+            return registered_model_name
+        if request_kwargs.get("fallback_depth") not in (None, 0):
+            return registered_model_name
+
+        agent_id: Final = self._request_header(request_kwargs, "x-claude-code-agent-id")
+        if agent_id is not None:
+            bound_model: Final = await self._get_claude_code_session_router_binding(cache_key)
+            if not isinstance(bound_model, str):
+                return registered_model_name
+            bound_registered_model: Final = self._get_model_from_alias(model=bound_model) or bound_model
+            if self._select_pre_routing_strategy(bound_registered_model, request_kwargs) is None:
+                await self._delete_claude_code_session_router_binding(cache_key)
+                return registered_model_name
+            await self._claude_code_session_router_cache.async_set_cache(
+                key=cache_key,
+                value=bound_model,
+                ttl=_CLAUDE_CODE_SESSION_ROUTER_TTL_SECONDS,
+            )
+            self._stamp_or_clear_metadata_key(request_kwargs, "model_group", bound_model)
+            return bound_registered_model
+
+        if self._request_header(request_kwargs, "x-app") != "cli":
+            return registered_model_name
+        if self._select_pre_routing_strategy(registered_model_name, request_kwargs) is None:
+            return registered_model_name
+        await self._claude_code_session_router_cache.async_set_cache(
+            key=cache_key,
+            value=model,
+            ttl=_CLAUDE_CODE_SESSION_ROUTER_TTL_SECONDS,
+        )
+        return registered_model_name
+
     async def async_pre_routing_hook(
         self,
         model: str,
@@ -12670,7 +12802,12 @@ class Router:
         the alias, since spend metadata is stamped before routing and the response carries the tier
         group the strategy picked.
         """
-        registered_model_name: Final = self._get_model_from_alias(model=model) or model
+        requested_registered_model_name: Final = self._get_model_from_alias(model=model) or model
+        registered_model_name: Final = await self._resolve_claude_code_session_router(
+            model=model,
+            registered_model_name=requested_registered_model_name,
+            request_kwargs=request_kwargs,
+        )
 
         #########################################################
         # Run the routing-plugin pipeline, if any plugins are configured.
@@ -13467,6 +13604,9 @@ class Router:
     def flush_cache(self):
         litellm.cache = None
         self.cache.flush_cache()
+        session_in_memory_cache: Final = self._claude_code_session_router_cache.in_memory_cache
+        if session_in_memory_cache is not None:
+            session_in_memory_cache.flush_cache()
 
     def reset(self):
         ## clean up on close

litellm/router_utils/fallback_event_handlers.py

@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Any, Final
 import litellm
 from litellm._logging import verbose_router_logger
 from litellm.integrations.custom_logger import CustomLogger
+from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs
 from litellm.litellm_core_utils.sensitive_data_masker import mask_sensitive_structure
 from litellm.router_utils.add_retry_fallback_headers import (
     add_fallback_headers_to_response,
@@ -231,8 +232,6 @@ def record_pre_routing_selection(request_kwargs: Mapping[str, Any] | None, selec
     on /v1/messages the top-level ``metadata`` dict is the provider's own request field,
     so a blanket write would forward the tier stamp upstream.
     """
-    from litellm.litellm_core_utils.core_helpers import get_metadata_variable_name_from_kwargs
-
     if request_kwargs is None:
         return
     bucket: Final = request_kwargs.get(get_metadata_variable_name_from_kwargs(request_kwargs))
@@ -267,10 +266,13 @@ def get_pre_routing_selection(kwargs: Mapping[str, Any]) -> str | None:
 def fallback_lookup_groups(kwargs: Mapping[str, Any], model_group: str | None) -> tuple[str, ...]:
     """
     Ordered keys for resolving a fallback chain: the tier a pre-routing hook selected wins,
-    and the requested group still resolves when no tier-keyed chain exists, so configs keyed
-    on the router name (the documented contract) keep working behind auto-routers.
+    then the routed group, then the requested group. The routed group differs when Claude Code
+    session affinity remaps a subagent's concrete model to its bound router.
     """
-    ordered: Final = (get_pre_routing_selection(kwargs), model_group)
+    metadata: Final = kwargs.get(get_metadata_variable_name_from_kwargs(kwargs))
+    routed_group_value: Final = metadata.get("model_group") if isinstance(metadata, Mapping) else None
+    routed_group: Final = routed_group_value if isinstance(routed_group_value, str) else None
+    ordered: Final = (get_pre_routing_selection(kwargs), routed_group, model_group)
     return tuple(dict.fromkeys(group for group in ordered if group))
 
 
@@ -470,10 +472,11 @@ async def run_async_fallback(
     attempted: Final = (
         carried_targets if isinstance(carried_targets, AttemptedFallbackTargets) else AttemptedFallbackTargets()
     )
-    attempted.record(original_model_group)
+    failed_model_group: Final = get_pre_routing_selection(kwargs) or original_model_group
+    attempted.record(failed_model_group)
 
     for mg in fallback_model_group:
-        if mg == original_model_group:
+        if mg == failed_model_group:
             continue
         if same_model_group_only and _get_fallback_target_model_group(mg) != original_model_group:
             verbose_router_logger.info(

litellm/vector_stores/main.py

@@ -15,6 +15,11 @@ import litellm
 from litellm.constants import request_timeout
 from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+    vector_store_request_metadata,
+)
 from litellm.llms.custom_httpx.llm_http_handler import BaseLLMHTTPHandler
 from litellm.types.router import GenericLiteLLMParams
 from litellm.types.vector_stores import (
@@ -38,6 +43,16 @@ base_llm_http_handler = BaseLLMHTTPHandler()
 #################################################
 
 
+def _direct_vector_store_embedding_executor(
+    value: object, router: "Router | None", request_kwargs: Mapping[str, object]
+) -> VectorStoreEmbeddingExecutor:
+    if value is not None and not isinstance(value, VectorStoreEmbeddingExecutor):
+        raise TypeError("Invalid direct vector store embedding executor")
+    return BaseQueryEmbeddingVectorStoreConfig.query_embedding_executor(
+        value, router, vector_store_request_metadata(request_kwargs)
+    )
+
+
 def mock_vector_store_search_response(
     mock_results: list[VectorStoreSearchResult] | None = None,
 ):
@@ -289,7 +304,12 @@ async def asearch(
     """
     Async: Search a vector store for relevant chunks based on a query and file attributes filter.
     """
-    local_vars: Final = locals()
+    embedding_executor: Final = _direct_vector_store_embedding_executor(
+        kwargs.pop("_direct_vector_store_embedding_executor", None), router, kwargs
+    )
+    local_vars: Final = {  # mutable-ok: exception logging requires a sanitized mutable snapshot
+        key: value for key, value in locals().items() if key != "embedding_executor"
+    }
 
     try:
         loop: Final = asyncio.get_event_loop()
@@ -312,6 +332,7 @@ async def asearch(
             extra_body=extra_body,
             timeout=timeout,
             custom_llm_provider=custom_llm_provider,
+            _direct_vector_store_embedding_executor=embedding_executor,
             router=router,
             **kwargs,
         )
@@ -369,12 +390,16 @@ def search(
     Returns:
         VectorStoreSearchResponse containing the search results.
     """
-    local_vars: Final = locals()
+    embedding_executor: Final = _direct_vector_store_embedding_executor(
+        kwargs.pop("_direct_vector_store_embedding_executor", None), router, kwargs
+    )
+    local_vars: Final = {  # mutable-ok: exception logging requires a sanitized mutable snapshot
+        key: value for key, value in locals().items() if key != "embedding_executor"
+    }
     try:
         litellm_logging_obj: Final[LiteLLMLoggingObj] = kwargs.get("litellm_logging_obj")
         litellm_call_id: Final[str | None] = kwargs.get("litellm_call_id", None)
         _is_async: Final = kwargs.pop("asearch", False) is True
-
         # pull credentials from registry if available
         if litellm.vector_store_registry is not None and vector_store_id is not None:
             try:
@@ -451,6 +476,7 @@ def search(
             custom_llm_provider=custom_llm_provider,
             litellm_params=litellm_params,
             logging_obj=litellm_logging_obj,
+            embedding_executor=embedding_executor,
             extra_headers=extra_headers,
             extra_body=extra_body,
             timeout=timeout or request_timeout,

ruff-strict-budget.json

@@ -177,7 +177,7 @@
     "limit": 8
   },
   "RUF019": {
-    "limit": 31
+    "limit": 27
   },
   "RUF046": {
     "limit": 4

tests/code_coverage_tests/router_code_coverage.py

@@ -82,6 +82,11 @@ ignored_function_names = [
     "_invalidate_access_groups_cache",  # Tested indirectly via set_model_list, upsert_model etc. (test files lack "router" in name)
     "has_buffered_provider_output",  # Property, so its reads in test_router.py are never an ast.Call
     "_resolved_provider",  # Tested via get_pattern in test_pattern_match_deployments.py (file lacks "router" in name)
+    "_request_header",  # Tested through Claude Code session routing in test_router.py
+    "_claude_code_session_router_cache_key",  # Tested through Claude Code session routing in test_router.py
+    "_delete_claude_code_session_router_binding",  # Tested through Redis cleanup failure in test_router.py
+    "_resolve_claude_code_session_router",  # Tested through Claude Code session routing in test_router.py
+    "_get_claude_code_session_router_binding",  # Tested through the two-worker session routing test in test_router.py
 ]
 
 

tests/logging_callback_tests/test_bedrock_knowledgebase_hook.py

@@ -71,6 +71,48 @@ def setup_vector_store_registry():
     )
 
 
+@pytest.mark.asyncio
+async def test_vector_store_hook_routes_search_through_proxy_router(
+    setup_vector_store_registry,
+):
+    proxy_router = Mock()
+    proxy_router.avector_store_search = AsyncMock(
+        return_value=VectorStoreSearchResponse(
+            object="vector_store.search_results.page",
+            search_query="what is litellm?",
+            data=[
+                VectorStoreSearchResult(
+                    score=1.0,
+                    content=[VectorStoreResultContent(text="routed context", type="text")],
+                )
+            ],
+        )
+    )
+    logging_obj = Mock()
+    logging_obj.model_call_details = {
+        "litellm_params": {"metadata": {"user_api_key_team_id": "team-a"}}
+    }
+
+    with patch("litellm.proxy.proxy_server.llm_router", proxy_router):
+        _, messages, _ = await VectorStorePreCallHook().async_get_chat_completion_prompt(
+            model="chat-model",
+            messages=[{"role": "user", "content": "what is litellm?"}],
+            non_default_params={"vector_store_ids": ["T37J8R4WTM"]},
+            prompt_id=None,
+            prompt_variables=None,
+            dynamic_callback_params={},
+            litellm_logging_obj=logging_obj,
+        )
+
+    proxy_router.avector_store_search.assert_awaited_once_with(
+        vector_store_id="T37J8R4WTM",
+        query="what is litellm?",
+        custom_llm_provider="bedrock",
+        metadata={"user_api_key_team_id": "team-a"},
+    )
+    assert messages[0]["content"] == "Context:\n\nrouted context\n\n"
+
+
 @pytest.mark.asyncio
 async def test_e2e_bedrock_knowledgebase_retrieval_with_completion(
     setup_vector_store_registry,

tests/router_unit_tests/test_router_embedding_integration.py

@@ -5,17 +5,218 @@ These tests simulate real-world scenarios where headers and configuration
 need to be properly propagated through the router to the LLM API.
 """
 
-from unittest.mock import MagicMock, patch, AsyncMock
+import json
+from unittest.mock import AsyncMock, MagicMock, patch
 
+import httpx
 import pytest
+import respx
 
-
+import litellm
 from litellm import Router
+from litellm.llms.base_llm.vector_store.transformation import (
+    LiteLLMVectorStoreEmbeddingExecutor,
+    RouterVectorStoreEmbeddingExecutor,
+)
+
+QUERY_VECTOR = [0.5, -0.25, 0.125]
+OPENAI_EMBEDDINGS_URL = "https://api.openai.com/v1/embeddings"
+STORE_EMBEDDINGS_URL = "https://embedding.example/v1/embeddings"
+
+
+def _mock_embedding_route(respx_mock: respx.MockRouter, url: str) -> respx.Route:
+    return respx_mock.post(url).mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "object": "list",
+                "data": [{"object": "embedding", "index": 0, "embedding": QUERY_VECTOR}],
+                "model": "text-embedding-3-small",
+                "usage": {"prompt_tokens": 2, "total_tokens": 2},
+            },
+        )
+    )
+
+
+def _sent(route: respx.Route, index: int) -> tuple[str, str, list[str]]:
+    request = route.calls[index].request
+    body = json.loads(request.read())
+    return request.headers["authorization"], body["model"], body["input"]
+
+
+def _alias_router() -> Router:
+    return Router(
+        model_list=[
+            {
+                "model_name": "team-alias",
+                "litellm_params": {
+                    "model": "openai/text-embedding-3-small",
+                    "api_key": "deployment-key",
+                },
+            }
+        ]
+    )
 
 
 class TestRouterEmbeddingIntegration:
     """Integration tests for embedding with router configuration."""
 
+    def test_vector_store_request_metadata_prefers_litellm_metadata(self):
+        assert Router._vector_store_request_metadata(
+            {
+                "litellm_metadata": {"user_api_key_team_id": "team-a"},
+                "metadata": {"user_api_key_team_id": "team-b"},
+            }
+        ) == {"user_api_key_team_id": "team-a"}
+
+        assert Router._vector_store_request_metadata({"metadata": {"user_api_key_team_id": "team-b"}}) == {
+            "user_api_key_team_id": "team-b"
+        }
+        assert Router._vector_store_request_metadata({}) == {}
+
+    def test_sync_vector_store_wrapper_injects_router_embedding_executor(self):
+        router = Router(model_list=[])
+        original = MagicMock(return_value="searched")
+        wrapped = router.factory_function(original, call_type="vector_store_search")
+
+        assert (
+            wrapped(
+                vector_store_id="store",
+                query="query",
+                custom_llm_provider="valkey",
+                metadata={"user_api_key_team_id": "team-a"},
+            )
+            == "searched"
+        )
+
+        call_kwargs = original.call_args.kwargs
+        assert call_kwargs["custom_llm_provider"] == "valkey"
+        executor = call_kwargs["_direct_vector_store_embedding_executor"]
+        assert isinstance(executor, RouterVectorStoreEmbeddingExecutor)
+        assert executor.metadata == {"user_api_key_team_id": "team-a"}
+
+    def test_sync_vector_store_wrapper_preserves_model_routing(self):
+        router = Router(model_list=[])
+        original = MagicMock()
+        wrapped = router.factory_function(original, call_type="vector_store_search")
+
+        with patch.object(router, "_generic_api_call_with_fallbacks", return_value="routed") as fallback:
+            assert wrapped(model="vector-alias", vector_store_id="store", query="query") == "routed"
+
+        assert fallback.call_args.kwargs["model"] == "vector-alias"
+        assert fallback.call_args.kwargs["original_function"] is original
+        assert isinstance(
+            fallback.call_args.kwargs["_direct_vector_store_embedding_executor"],
+            RouterVectorStoreEmbeddingExecutor,
+        )
+
+    @pytest.mark.asyncio
+    async def test_vector_store_embedding_executors_cover_sdk_and_router_paths(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        openai_route = _mock_embedding_route(respx_mock, OPENAI_EMBEDDINGS_URL)
+        store_route = _mock_embedding_route(respx_mock, STORE_EMBEDDINGS_URL)
+        sdk_executor = LiteLLMVectorStoreEmbeddingExecutor()
+
+        sync_response = sdk_executor.embed("openai/text-embedding-3-small", "sync", {"api_key": "explicit"})
+        async_response = await sdk_executor.aembed("openai/text-embedding-3-small", "async", {"api_key": "explicit"})
+
+        assert sync_response.data[0]["embedding"] == QUERY_VECTOR
+        assert async_response.data[0]["embedding"] == QUERY_VECTOR
+        assert _sent(openai_route, 0) == ("Bearer explicit", "text-embedding-3-small", ["sync"])
+        assert _sent(openai_route, 1) == ("Bearer explicit", "text-embedding-3-small", ["async"])
+
+        explicit_config = {
+            "api_base": "https://embedding.example/v1",
+            "api_key": "store-key",
+            "metadata": {
+                "configured": True,
+                "user_api_key_team_id": "untrusted-team",
+            },
+            "model": "untrusted-model",
+        }
+        mock_router = MagicMock()
+        mock_router.embedding.return_value = sync_response
+        router_executor = RouterVectorStoreEmbeddingExecutor(
+            router=mock_router,
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+        assert router_executor.embed("team-alias", "query", explicit_config) is sync_response
+        mock_router.embedding.assert_called_once_with(
+            model="team-alias",
+            input=["query"],
+            api_base="https://embedding.example/v1",
+            api_key="store-key",
+            metadata={"configured": True, "user_api_key_team_id": "team-a"},
+        )
+
+        alias_executor = RouterVectorStoreEmbeddingExecutor(
+            router=_alias_router(),
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+        sync_alias = alias_executor.embed("team-alias", "sync query", explicit_config)
+        async_alias = await alias_executor.aembed("team-alias", "async query", explicit_config)
+
+        assert sync_alias.data[0]["embedding"] == QUERY_VECTOR
+        assert async_alias.data[0]["embedding"] == QUERY_VECTOR
+        assert openai_route.call_count == 2
+        assert _sent(store_route, 0) == ("Bearer store-key", "text-embedding-3-small", ["sync query"])
+        assert _sent(store_route, 1) == ("Bearer store-key", "text-embedding-3-small", ["async query"])
+
+    @pytest.mark.asyncio
+    async def test_router_executor_falls_back_to_sdk_for_models_the_router_does_not_serve(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        store_route = _mock_embedding_route(respx_mock, STORE_EMBEDDINGS_URL)
+        executor = RouterVectorStoreEmbeddingExecutor(
+            router=_alias_router(),
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+        inline_config = {"api_base": "https://embedding.example/v1", "api_key": "store-key"}
+
+        sync_response = executor.embed("openai/text-embedding-3-large", "sync query", inline_config)
+        async_response = await executor.aembed("openai/text-embedding-3-large", "async query", inline_config)
+
+        assert sync_response.data[0]["embedding"] == QUERY_VECTOR
+        assert async_response.data[0]["embedding"] == QUERY_VECTOR
+        assert _sent(store_route, 0) == ("Bearer store-key", "text-embedding-3-large", ["sync query"])
+        assert _sent(store_route, 1) == ("Bearer store-key", "text-embedding-3-large", ["async query"])
+
+    @pytest.mark.asyncio
+    async def test_router_executor_rejects_unserved_models_without_explicit_config(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+        monkeypatch.setenv("OPENAI_API_KEY", "env-key")
+        openai_route = _mock_embedding_route(respx_mock, OPENAI_EMBEDDINGS_URL)
+        executor = RouterVectorStoreEmbeddingExecutor(
+            router=_alias_router(),
+            metadata={"user_api_key_team_id": "team-a"},
+        )
+
+        with pytest.raises(litellm.BadRequestError):
+            executor.embed("openai/text-embedding-3-large", "sync query", {})
+        with pytest.raises(litellm.BadRequestError):
+            await executor.aembed("openai/text-embedding-3-large", "async query", {})
+
+        assert openai_route.call_count == 0
+
+    def test_router_executor_routes_deployment_model_names_through_the_router(
+        self, respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+    ):
+        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+        openai_route = _mock_embedding_route(respx_mock, OPENAI_EMBEDDINGS_URL)
+        executor = RouterVectorStoreEmbeddingExecutor(router=_alias_router(), metadata={})
+
+        response = executor.embed("openai/text-embedding-3-small", "query", {})
+
+        assert response.data[0]["embedding"] == QUERY_VECTOR
+        assert _sent(openai_route, 0) == ("Bearer deployment-key", "text-embedding-3-small", ["query"])
+
     def test_embedding_with_deployment_specific_headers(self):
         """
         Test that deployment-specific headers are propagated.
@@ -122,9 +323,7 @@ class TestRouterEmbeddingIntegration:
 
         router = Router(
             model_list=model_list,
-            default_litellm_params={
-                "metadata": {"environment": "test", "service": "embedding-service"}
-            },
+            default_litellm_params={"metadata": {"environment": "test", "service": "embedding-service"}},
         )
 
         with patch("litellm.embedding") as mock_embedding:
@@ -240,9 +439,7 @@ class TestRouterEmbeddingIntegration:
         # Make multiple calls and verify headers are always present
         for i in range(5):
             with patch("litellm.embedding") as mock_embedding:
-                mock_embedding.return_value = MagicMock(
-                    data=[{"embedding": [0.1, 0.2]}]
-                )
+                mock_embedding.return_value = MagicMock(data=[{"embedding": [0.1, 0.2]}])
 
                 router.embedding(model="shared-embedding-model", input=[f"test {i}"])
 
@@ -327,9 +524,7 @@ class TestRouterEmbeddingIntegration:
 
         router = Router(
             model_list=model_list,
-            default_litellm_params={
-                "headers": {"X-Custom-Azure-Header": "azure-value"}
-            },
+            default_litellm_params={"headers": {"X-Custom-Azure-Header": "azure-value"}},
         )
 
         with patch("litellm.embedding") as mock_embedding:

tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_streaming_iterator_stop_reason.py

@@ -0,0 +1,79 @@
+"""
+Regression tests for issue #34692.
+
+ollama_chat streams tool_calls in a mid-stream chunk while its final
+(``done: true``) chunk carries only ``done_reason: "stop"``. The provider
+iterator must remember the earlier tool_calls and stamp
+``finish_reason="tool_calls"`` on the final chunk, so the Anthropic
+``/v1/messages`` bridge emits ``stop_reason: "tool_use"``. Before the fix the
+bridge emitted ``stop_reason: "end_turn"`` and Anthropic tool-runners
+(Claude Code, ``messages.stream``) silently dropped the tool call.
+"""
+
+import pytest
+
+from litellm.llms.anthropic.experimental_pass_through.adapters.streaming_iterator import (
+    AnthropicStreamWrapper,
+)
+from litellm.llms.ollama.chat.transformation import (
+    OllamaChatCompletionResponseIterator,
+)
+from litellm.types.utils import ModelResponseStream
+
+_OLLAMA_TOOL_CHUNK = {
+    "model": "qwen3:8b",
+    "message": {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [{"function": {"name": "get_weather", "arguments": {"city": "San Francisco"}}}],
+    },
+    "done": False,
+}
+_OLLAMA_DONE_CHUNK = {
+    "model": "qwen3:8b",
+    "message": {"role": "assistant", "content": ""},
+    "done": True,
+    "done_reason": "stop",
+    "prompt_eval_count": 100,
+    "eval_count": 20,
+}
+
+
+def _ollama_streamed_chunks() -> list[ModelResponseStream]:
+    iterator = OllamaChatCompletionResponseIterator(streaming_response=iter([]), sync_stream=True)
+    return [iterator.chunk_parser(_OLLAMA_TOOL_CHUNK), iterator.chunk_parser(_OLLAMA_DONE_CHUNK)]
+
+
+class _AsyncStream:
+    def __init__(self, items: list[ModelResponseStream]):
+        self._it = iter(items)
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        try:
+            return next(self._it)
+        except StopIteration:
+            raise StopAsyncIteration
+
+
+def _assert_tool_use_stop_reason(events: list[dict]) -> None:
+    block_types = [e["content_block"]["type"] for e in events if e.get("type") == "content_block_start"]
+    assert "tool_use" in block_types, f"no tool_use content block opened: {events}"
+    message_deltas = [e for e in events if e.get("type") == "message_delta"]
+    assert message_deltas, f"no message_delta emitted: {events}"
+    assert message_deltas[-1]["delta"]["stop_reason"] == "tool_use", (
+        f"expected stop_reason 'tool_use', got: {message_deltas[-1]}"
+    )
+
+
+def test_ollama_mid_stream_tool_call_yields_tool_use_stop_reason_sync():
+    wrapper = AnthropicStreamWrapper(completion_stream=iter(_ollama_streamed_chunks()), model="qwen3:8b")
+    _assert_tool_use_stop_reason(list(wrapper))
+
+
+@pytest.mark.asyncio
+async def test_ollama_mid_stream_tool_call_yields_tool_use_stop_reason_async():
+    wrapper = AnthropicStreamWrapper(completion_stream=_AsyncStream(_ollama_streamed_chunks()), model="qwen3:8b")
+    _assert_tool_use_stop_reason([event async for event in wrapper])

tests/test_litellm/llms/ollama/test_ollama_chat_transformation.py

@@ -615,6 +615,46 @@ class TestOllamaFinishReasonLength:
             result.choices[0].finish_reason == "stop"
         ), f"Expected 'stop' for natural finish, got '{result.choices[0].finish_reason}'"
 
+    def test_finish_reason_tool_calls_streamed_before_done_chunk(self):
+        """Streaming: tool_calls arriving mid-stream (not on the done chunk) must
+        still produce finish_reason='tool_calls' on the final chunk.
+
+        Regression test for https://github.com/BerriAI/litellm/issues/34692:
+        Ollama emits tool_calls in an earlier chunk and the done chunk carries
+        none, which left finish_reason at 'stop' and made the Anthropic
+        /v1/messages bridge emit stop_reason 'end_turn' instead of 'tool_use'.
+        """
+        iterator = OllamaChatCompletionResponseIterator(
+            streaming_response=iter([]),
+            sync_stream=True,
+        )
+
+        tool_chunk = {
+            "model": "qwen3:8b",
+            "message": {
+                "role": "assistant",
+                "content": "",
+                "tool_calls": [
+                    {"function": {"name": "get_weather", "arguments": {"city": "San Francisco"}}}
+                ],
+            },
+            "done": False,
+        }
+        done_chunk = {
+            "model": "qwen3:8b",
+            "message": {"role": "assistant", "content": ""},
+            "done": True,
+            "done_reason": "stop",
+        }
+
+        tool_result = iterator.chunk_parser(tool_chunk)
+        assert tool_result.choices[0].delta.tool_calls is not None
+
+        done_result = iterator.chunk_parser(done_chunk)
+        assert (
+            done_result.choices[0].finish_reason == "tool_calls"
+        ), f"Expected 'tool_calls' when tool_calls were streamed earlier, got '{done_result.choices[0].finish_reason}'"
+
 
 class TestOllamaReasoningContentStreaming:
     """Test that reasoning_content is properly extracted from all thinking chunks."""

tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_handler.py

@@ -5,6 +5,8 @@ Tests the handler's ability to process input/output for the Responses API
 with guardrail transformations.
 """
 
+import copy
+from collections.abc import Callable
 from typing import Any, List, Literal, Optional, Tuple
 from unittest.mock import AsyncMock, MagicMock
 
@@ -19,6 +21,10 @@ from litellm.llms import get_guardrail_translation_mapping
 from litellm.llms.openai.responses.guardrail_translation.handler import (
     OpenAIResponsesHandler,
 )
+from litellm.llms.openai.responses.guardrail_translation.tool_merge import merge_guardrailed_tools
+from litellm.responses.litellm_completion_transformation.transformation import (
+    LiteLLMCompletionResponsesConfig,
+)
 from litellm.types.llms.openai import ResponsesAPIResponse
 from litellm.types.responses.main import GenericResponseOutputItem, OutputText
 from litellm.types.utils import CallTypes, GenericGuardrailAPIInputs
@@ -1287,14 +1293,14 @@ class TestOpenAIResponsesHandlerToolInjection:
     """A tool a guardrail injects must survive the write-back to Responses format."""
 
     def test_merge_keeps_guardrail_appended_tool(self):
-        """_merge_tools_after_guardrail must not drop the extra appended tool."""
-        handler = OpenAIResponsesHandler()
+        """merge_guardrailed_tools must not drop the extra appended tool."""
         original = [{"type": "function", "name": "a"}]
-        remapped = [
-            {"type": "function", "name": "a"},
-            {"type": "function", "name": "b"},
+        groups = [form.chat_tools for form in LiteLLMCompletionResponsesConfig.responses_tools_to_chat_forms(original)]
+        guardrailed = [
+            *groups[0],
+            {"type": "function", "function": {"name": "b", "description": "", "parameters": {"type": "object"}}},
         ]
-        merged = handler._merge_tools_after_guardrail(original, remapped)
+        merged = merge_guardrailed_tools(original, groups, guardrailed)
         assert [t["name"] for t in merged] == ["a", "b"]
 
     @pytest.mark.asyncio
@@ -1323,6 +1329,194 @@ class TestOpenAIResponsesHandlerToolInjection:
         assert "injected_tool" in names
 
 
+class ToolEditingGuardrail(CustomGuardrail):
+    """Guardrail that rewrites the flattened chat tools it was handed through ``edit``"""
+
+    def __init__(self, edit: Callable[[list[dict]], list[dict]], **kwargs):
+        super().__init__(**kwargs)
+        self.edit = edit
+
+    async def apply_guardrail(
+        self,
+        inputs: GenericGuardrailAPIInputs,
+        request_data: dict,
+        input_type: Literal["request", "response"],
+        logging_obj: Any | None = None,
+    ) -> GenericGuardrailAPIInputs:
+        inputs["tools"] = self.edit(list(inputs.get("tools") or []))
+        return inputs
+
+
+def _codex_request(input_value):
+    """A Responses API request shaped like what the Codex CLI sends when an MCP server is configured"""
+    return {
+        "model": "gpt-5.3-codex",
+        "input": input_value,
+        "tools": [
+            {
+                "type": "function",
+                "name": "get_weather",
+                "description": "Weather lookup",
+                "parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
+                "strict": False,
+            },
+            {
+                "type": "namespace",
+                "name": "mcp__confluence",
+                "description": "Confluence tools",
+                "tools": [
+                    {
+                        "type": "function",
+                        "name": "confluence_get_page",
+                        "description": "Get a page",
+                        "parameters": {"type": "object", "properties": {"id": {"type": "string"}}, "required": ["id"]},
+                        "strict": False,
+                    },
+                    {
+                        "type": "function",
+                        "name": "confluence_search",
+                        "description": "Search pages",
+                        "parameters": {"type": "object", "properties": {"q": {"type": "string"}}},
+                        "strict": False,
+                    },
+                ],
+            },
+            {
+                "type": "custom",
+                "name": "apply_patch",
+                "description": "Apply a patch",
+                "format": {"type": "grammar", "syntax": "lark", "definition": 'start: "x"'},
+            },
+            {"type": "web_search"},
+        ],
+    }
+
+
+def _tool_named(tools, name):
+    return next(tool for tool in tools if tool.get("name") == name)
+
+
+class TestOpenAIResponsesHandlerNamespaceTools:
+    """Codex sends MCP tools as ``namespace`` tools; a guardrail must never flatten them (GH #39183)"""
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize(
+        "input_value",
+        ["hi", [{"role": "user", "content": "hi", "type": "message"}]],
+        ids=["string_input", "list_input"],
+    )
+    async def test_pass_through_guardrail_leaves_tools_untouched(self, input_value):
+        data = _codex_request(input_value)
+        expected_tools = copy.deepcopy(data["tools"])
+
+        result = await OpenAIResponsesHandler().process_input_messages(
+            data, MockPassThroughGuardrail(guardrail_name="test")
+        )
+
+        assert result["tools"] == expected_tools
+
+    @pytest.mark.asyncio
+    async def test_appending_guardrail_keeps_namespace_and_adds_tool(self):
+        data = _codex_request("hi")
+        expected_tools = copy.deepcopy(data["tools"])
+
+        result = await OpenAIResponsesHandler().process_input_messages(
+            data, ToolAppendingGuardrail(guardrail_name="test")
+        )
+
+        assert result["tools"][:-1] == expected_tools
+        assert result["tools"][-1]["type"] == "function"
+        assert result["tools"][-1]["name"] == "injected_tool"
+
+    @pytest.mark.asyncio
+    async def test_dropping_one_member_prunes_only_that_member(self):
+        data = _codex_request("hi")
+        expected_tools = copy.deepcopy(data["tools"])
+        guardrail = ToolEditingGuardrail(
+            edit=lambda tools: [t for t in tools if t["function"]["name"] != "mcp__confluence__confluence_search"],
+            guardrail_name="test",
+        )
+
+        result = await OpenAIResponsesHandler().process_input_messages(data, guardrail)
+
+        namespace = _tool_named(result["tools"], "mcp__confluence")
+        assert [member["name"] for member in namespace["tools"]] == ["confluence_get_page"]
+        assert namespace["tools"][0] == expected_tools[1]["tools"][0]
+        assert [t for t in result["tools"] if t is not namespace] == [expected_tools[0], *expected_tools[2:]]
+
+    @pytest.mark.asyncio
+    async def test_editing_a_member_lands_on_that_member_without_the_namespace_prefix(self):
+        data = _codex_request("hi")
+        expected_tools = copy.deepcopy(data["tools"])
+
+        def redact_search(tools):
+            for tool in tools:
+                if tool["function"]["name"] == "mcp__confluence__confluence_search":
+                    tool["function"]["description"] = "Confluence tools\n\nREDACTED"
+            return tools
+
+        result = await OpenAIResponsesHandler().process_input_messages(
+            data, ToolEditingGuardrail(edit=redact_search, guardrail_name="test")
+        )
+
+        namespace = _tool_named(result["tools"], "mcp__confluence")
+        assert namespace["tools"][0] == expected_tools[1]["tools"][0]
+        assert namespace["tools"][1] == {**expected_tools[1]["tools"][1], "description": "REDACTED"}
+        assert {k: v for k, v in namespace.items() if k != "tools"} == {
+            k: v for k, v in expected_tools[1].items() if k != "tools"
+        }
+
+    @pytest.mark.asyncio
+    async def test_dropping_every_member_drops_the_namespace(self):
+        data = _codex_request("hi")
+        expected_tools = copy.deepcopy(data["tools"])
+        guardrail = ToolEditingGuardrail(
+            edit=lambda tools: [t for t in tools if not t["function"]["name"].startswith("mcp__confluence__")],
+            guardrail_name="test",
+        )
+
+        result = await OpenAIResponsesHandler().process_input_messages(data, guardrail)
+
+        assert result["tools"] == [expected_tools[0], *expected_tools[2:]]
+
+    @pytest.mark.asyncio
+    async def test_edited_top_level_function_is_rewritten_in_place(self):
+        data = _codex_request("hi")
+        expected_tools = copy.deepcopy(data["tools"])
+
+        def rename_weather(tools):
+            for tool in tools:
+                if tool["function"]["name"] == "get_weather":
+                    tool["function"]["description"] = "Weather lookup (guarded)"
+            return tools
+
+        result = await OpenAIResponsesHandler().process_input_messages(
+            data, ToolEditingGuardrail(edit=rename_weather, guardrail_name="test")
+        )
+
+        assert result["tools"][0] == {**expected_tools[0], "description": "Weather lookup (guarded)"}
+        assert result["tools"][1:] == expected_tools[1:]
+
+
+class TestOpenAIResponsesHandlerMalformedTools:
+    @pytest.mark.asyncio
+    async def test_request_tools_that_are_not_a_list_never_reach_the_guardrail(self):
+        handler = OpenAIResponsesHandler()
+        seen: list[list[dict]] = []
+
+        def record(tools):
+            seen.append(tools)
+            return tools
+
+        guardrail = ToolEditingGuardrail(edit=record, guardrail_name="test")
+        data = {"input": "hi", "tools": {"type": "function", "name": "get_weather"}}
+
+        result = await handler.process_input_messages(data, guardrail)
+
+        assert seen == [[]]
+        assert result["input"] == "hi"
+
+
 class TestBuildBlockSseChunks:
     """build_block_sse_chunks turns a streaming ModifyResponseException into 200 SSE events"""
 

tests/test_litellm/llms/openai/responses/test_openai_responses_guardrail_tool_merge.py

@@ -0,0 +1,198 @@
+"""
+Unit tests for merge_guardrailed_tools, which writes guardrail-returned chat tools back onto the
+Responses API request tools they were flattened from
+"""
+
+import copy
+
+from litellm.llms.openai.responses.guardrail_translation.tool_merge import merge_guardrailed_tools
+from litellm.responses.litellm_completion_transformation.transformation import (
+    LiteLLMCompletionResponsesConfig,
+)
+from litellm.types.proxy.guardrails.guardrail_hooks.generic_guardrail_api import GuardrailToolParam
+
+
+def _groups(tools):
+    return [form.chat_tools for form in LiteLLMCompletionResponsesConfig.responses_tools_to_chat_forms(tools)]
+
+
+def _flat(groups):
+    return [chat_tool for group in groups for chat_tool in group]
+
+
+def _function(name, description=""):
+    return {"type": "function", "name": name, "description": description, "parameters": {"type": "object"}}
+
+
+def test_unchanged_tools_come_back_as_the_original_objects():
+    original = [
+        _function("a"),
+        {"type": "namespace", "name": "ns", "description": "NS", "tools": [_function("x"), _function("y")]},
+        {"type": "mcp", "server_label": "deepwiki", "server_url": "https://mcp.deepwiki.com/mcp"},
+        {"type": "web_search"},
+    ]
+    groups = _groups(original)
+
+    merged = merge_guardrailed_tools(original, groups, _flat(groups))
+
+    assert list(merged) == original
+    assert all(merged_tool is original_tool for merged_tool, original_tool in zip(merged, original))
+
+
+def test_guardrail_reordering_unchanged_tools_keeps_request_order():
+    original = [_function("a"), {"type": "namespace", "name": "ns", "tools": [_function("x")]}, {"type": "web_search"}]
+    groups = _groups(original)
+
+    merged = merge_guardrailed_tools(original, groups, list(reversed(_flat(groups))))
+
+    assert list(merged) == original
+
+
+def test_duplicate_function_names_are_matched_by_ordinal():
+    original = [_function("dup", "first"), _function("dup", "second")]
+    groups = _groups(original)
+
+    merged = merge_guardrailed_tools(original, groups, _flat(groups)[:1])
+
+    assert list(merged) == [original[0]]
+
+
+def test_interleaved_duplicate_names_keep_their_own_ordinals():
+    original = [
+        _function("dup", "a"),
+        _function("other", "x"),
+        _function("dup", "b"),
+        _function("dup", "c"),
+        _function("other", "y"),
+    ]
+    groups = _groups(original)
+    flat = _flat(groups)
+    edited = {**flat[3], "function": {**flat[3]["function"], "description": "changed"}}
+
+    merged = merge_guardrailed_tools(original, groups, [*flat[:3], edited, flat[4]])
+
+    assert list(merged) == [*original[:3], {**_function("dup", "changed"), "strict": False}, original[4]]
+    assert all(merged[position] is original[position] for position in (0, 1, 2, 4))
+
+
+def test_edited_mcp_tool_is_rewritten():
+    original = [{"type": "mcp", "server_label": "deepwiki", "server_url": "https://mcp.deepwiki.com/mcp"}]
+    groups = _groups(original)
+    edited = [{**groups[0][0], "allowed_tools": ["read_wiki_structure"]}]
+
+    merged = merge_guardrailed_tools(original, groups, edited)
+
+    assert list(merged) == edited
+
+
+def test_injected_tool_lands_after_the_request_tools_when_request_had_none():
+    injected = {"type": "function", "function": {"name": "b", "description": "d", "parameters": {"type": "object"}}}
+
+    merged = merge_guardrailed_tools([], [], [injected])
+
+    assert list(merged) == [
+        {"type": "function", "name": "b", "description": "d", "parameters": {"type": "object"}, "strict": False}
+    ]
+
+
+def test_empty_guardrail_output_keeps_only_tools_never_sent_to_the_guardrail():
+    original = [_function("a"), {"type": "web_search"}, {"type": "namespace", "name": "ns", "tools": [_function("x")]}]
+
+    merged = merge_guardrailed_tools(original, _groups(original), [])
+
+    assert list(merged) == [{"type": "web_search"}]
+
+
+def test_member_edit_strips_only_the_namespace_description_prefix():
+    original = [{"type": "namespace", "name": "ns", "description": "NS", "tools": [_function("x", "X doc")]}]
+    groups = _groups(original)
+    assert groups[0][0]["function"]["description"] == "NS\n\nX doc"
+    edited = [{**groups[0][0], "function": {**groups[0][0]["function"], "description": "NS\n\nX doc (guarded)"}}]
+
+    merged = merge_guardrailed_tools(original, groups, edited)
+
+    assert list(merged) == [
+        {"type": "namespace", "name": "ns", "description": "NS", "tools": [_function("x", "X doc (guarded)")]}
+    ]
+
+
+def test_namespace_keeps_a_non_function_member_when_a_function_member_is_edited():
+    custom_member = {"type": "custom", "name": "grep", "description": "Grep", "format": {"type": "text"}}
+    original = [
+        {"type": "namespace", "name": "ns", "description": "NS", "tools": [_function("read", "Read"), custom_member]}
+    ]
+    groups = _groups(original)
+    edited = copy.deepcopy(_flat(groups))
+    edited[0]["function"]["description"] = "NS\n\nEDITED"
+
+    merged = merge_guardrailed_tools(original, groups, edited)
+
+    assert len(merged) == 1
+    assert [member["name"] for member in merged[0]["tools"]] == ["read", "grep"]
+    assert merged[0]["tools"][0]["description"] == "EDITED"
+    assert merged[0]["tools"][1] == custom_member
+
+
+def test_namespace_keeps_its_non_function_members_when_every_function_member_is_dropped():
+    custom_member = {"type": "custom", "name": "grep", "description": "Grep", "format": {"type": "text"}}
+    original = [
+        {"type": "namespace", "name": "ns", "description": "NS", "tools": [_function("read"), custom_member]},
+        _function("a"),
+    ]
+    groups = _groups(original)
+
+    merged = merge_guardrailed_tools(original, groups, [groups[1][0]])
+
+    assert list(merged) == [{"type": "namespace", "name": "ns", "description": "NS", "tools": [custom_member]}, _function("a")]
+
+
+def test_member_extras_edited_by_the_guardrail_land_on_that_member():
+    original = [{"type": "namespace", "name": "ns", "tools": [_function("read")]}]
+    groups = _groups(original)
+    edited = copy.deepcopy(_flat(groups))
+    edited[0]["cache_control"] = {"type": "ephemeral"}
+
+    merged = merge_guardrailed_tools(original, groups, edited)
+
+    assert merged[0]["tools"][0]["cache_control"] == {"type": "ephemeral"}
+    assert merged[0]["tools"][0]["name"] == "read"
+
+
+def test_guardrail_output_is_read_once():
+    original = [_function("a"), {"type": "namespace", "name": "ns", "tools": [_function("x")]}]
+    groups = _groups(original)
+
+    merged = merge_guardrailed_tools(original, groups, (chat_tool for chat_tool in _flat(groups)))
+
+    assert list(merged) == original
+
+
+def test_pydantic_guardrail_tools_round_trip_like_dicts():
+    original = [_function("a"), {"type": "namespace", "name": "ns", "tools": [_function("x", "X doc")]}]
+    groups = _groups(original)
+    models = [GuardrailToolParam.model_validate(chat_tool) for chat_tool in _flat(groups)]
+
+    merged = merge_guardrailed_tools(original, groups, models)
+
+    assert list(merged) == original
+    assert all(merged_tool is original_tool for merged_tool, original_tool in zip(merged, original))
+
+
+def test_pydantic_guardrail_edit_lands_on_the_member():
+    original = [{"type": "namespace", "name": "ns", "tools": [_function("x", "X doc")]}]
+    groups = _groups(original)
+    edited = copy.deepcopy(_flat(groups))
+    edited[0]["function"]["description"] = "EDITED"
+
+    merged = merge_guardrailed_tools(original, groups, [GuardrailToolParam.model_validate(edited[0])])
+
+    assert list(merged) == [{"type": "namespace", "name": "ns", "tools": [_function("x", "EDITED")]}]
+
+
+def test_non_object_guardrail_items_are_dropped():
+    original = [_function("a")]
+    groups = _groups(original)
+
+    merged = merge_guardrailed_tools(original, groups, [*_flat(groups), "junk", None])
+
+    assert list(merged) == original

tests/test_litellm/llms/valkey/vector_stores/test_valkey_transformation.py

@@ -67,20 +67,52 @@ class FakeAsyncEmbeddingFn(FakeEmbeddingFn):
         return SimpleNamespace(data=[{"embedding": self.embedding}])
 
 
+class FakeEmbeddingExecutor:
+    def __init__(self, embedding):
+        self.embedding = embedding
+        self.captured = None
+
+    def embed(self, model, query, configuration):
+        self.captured = (model, query, configuration)
+        return SimpleNamespace(data=[{"embedding": self.embedding}])
+
+    async def aembed(self, model, query, configuration):
+        self.captured = (model, query, configuration)
+        return SimpleNamespace(data=[{"embedding": self.embedding}])
+
+
 def _doc(doc_id, distance, **fields):
     return SimpleNamespace(id=doc_id, vector_distance=str(distance), **fields)
 
 
-def _search(config, client=None, query="what is litellm", optional_params=None, litellm_params=None):
+def _search(config, client=None, query="what is litellm", optional_params=None, litellm_params=None, executor=None):
     return config.execute_search_vector_store_request(
         vector_store_id="my_index",
         query=query,
         vector_store_search_optional_params=optional_params or {},
         litellm_logging_obj=MagicMock(),
         litellm_params={"litellm_embedding_model": "openai/text-embedding-3-small", **(litellm_params or {})},
+        embedding_executor=executor,
     )
 
 
+def test_sync_search_uses_request_embedding_executor_without_overwriting_explicit_config():
+    executor = FakeEmbeddingExecutor([0.1, 0.2])
+    config = ValkeyVectorStoreConfig(sync_client=FakeRedis())
+    embedding_config = {"api_key": "store-specific-key", "aws_region_name": "us-west-2"}
+
+    _search(
+        config,
+        litellm_params={
+            "litellm_embedding_model": "team-embedding-alias",
+            "litellm_embedding_config": embedding_config,
+        },
+        executor=executor,
+    )
+
+    assert executor.captured == ("team-embedding-alias", "what is litellm", embedding_config)
+
+
 def test_sync_search_builds_knn_query_with_packed_vector():
     embedding_fn = FakeEmbeddingFn([0.1, 0.2, 0.3])
     client = FakeRedis()

tests/test_litellm/proxy/vector_store_endpoints/test_vector_store_endpoints.py

@@ -2,29 +2,24 @@ from datetime import datetime, timezone
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
-from fastapi import Request
-
-
-from fastapi import HTTPException
+from fastapi import HTTPException, Request
 
 import litellm
 from litellm.integrations.vector_store_integrations.vector_store_pre_call_hook import (
     LiteLLM_ManagedVectorStore,
 )
+from litellm.llms.base_llm.vector_store.transformation import (
+    LiteLLMVectorStoreEmbeddingExecutor,
+    RouterVectorStoreEmbeddingExecutor,
+)
 from litellm.proxy._types import CommonProxyErrors, LitellmUserRoles, UserAPIKeyAuth
 from litellm.proxy.vector_store_endpoints.endpoints import (
     _update_request_data_with_litellm_managed_vector_store_registry,
     index_create,
     index_list,
 )
-from litellm.proxy.vector_store_files_endpoints.endpoints import (
-    _update_request_data_with_model_routing_hint,
-)
 from litellm.proxy.vector_store_endpoints.management_endpoints import (
     _check_vector_store_access,
-    _resolve_embedding_config,
-    _resolve_embedding_config_from_db,
-    _resolve_embedding_config_from_router,
     create_vector_store_in_db,
     new_vector_store,
 )
@@ -33,8 +28,12 @@ from litellm.proxy.vector_store_endpoints.utils import (
     is_allowed_to_call_vector_store_endpoint,
     is_allowed_to_call_vector_store_files_endpoint,
 )
+from litellm.proxy.vector_store_files_endpoints.endpoints import (
+    _update_request_data_with_model_routing_hint,
+)
+from litellm.types.utils import EmbeddingResponse, LlmProviders
 from litellm.types.vector_stores import IndexCreateRequest, IndexListResponse
-from litellm.types.utils import LlmProviders
+from litellm.vector_stores.main import _direct_vector_store_embedding_executor
 
 
 def _serialize_litellm_params(litellm_params):
@@ -51,17 +50,113 @@ def _serialize_litellm_params(litellm_params):
     return json.dumps(litellm_params or {})
 
 
-@pytest.fixture(autouse=True)
-def _reset_embedding_config_cache():
-    """The use-time embedding-config resolver caches results in process
-    memory across calls. Reset it before every test so the resolver
-    actually exercises the router/DB path under test instead of returning
-    a value cached by an earlier test."""
-    from litellm.proxy.vector_store_endpoints import management_endpoints
+def test_direct_vector_store_embedding_executor_rejects_invalid_value():
+    with pytest.raises(TypeError, match="Invalid direct vector store embedding executor"):
+        _direct_vector_store_embedding_executor(object(), None, {})
 
-    management_endpoints._embedding_config_cache = None
-    yield
-    management_endpoints._embedding_config_cache = None
+
+def test_router_vector_store_search_injects_executor_and_request_metadata():
+    router = litellm.Router(model_list=[])
+    original = MagicMock(return_value="searched")
+    wrapped = router.factory_function(original, call_type="vector_store_search")
+
+    assert (
+        wrapped(
+            vector_store_id="store",
+            query="query",
+            custom_llm_provider="valkey",
+            litellm_metadata={"user_api_key_team_id": "team-a"},
+        )
+        == "searched"
+    )
+
+    call_kwargs = original.call_args.kwargs
+    assert call_kwargs["custom_llm_provider"] == "valkey"
+    executor = call_kwargs["_direct_vector_store_embedding_executor"]
+    assert isinstance(executor, RouterVectorStoreEmbeddingExecutor)
+    assert executor.metadata == {"user_api_key_team_id": "team-a"}
+    assert litellm.Router._vector_store_request_metadata({"metadata": {"user_api_key_team_id": "team-b"}}) == {
+        "user_api_key_team_id": "team-b"
+    }
+    assert litellm.Router._vector_store_request_metadata({}) == {}
+
+    with patch.object(  # test-quality-ok: fallback dispatch is the boundary this wrapper delegates to
+        router, "_generic_api_call_with_fallbacks", return_value="routed"
+    ) as fallback:
+        assert wrapped(model="vector-alias", vector_store_id="store", query="query") == "routed"
+    assert fallback.call_args.kwargs["model"] == "vector-alias"
+    assert fallback.call_args.kwargs["original_function"] is original
+
+    create_original = MagicMock(return_value="created")
+    wrapped_create = router.factory_function(create_original, call_type="vector_store_create")
+    assert wrapped_create(name="store") == "created"
+    create_original.assert_called_once_with(name="store")
+    with patch.object(  # test-quality-ok: fallback dispatch is the boundary this wrapper delegates to
+        router, "_generic_api_call_with_fallbacks", return_value="created-through-router"
+    ) as fallback:
+        assert wrapped_create(model="vector-alias", name="store") == "created-through-router"
+    fallback.assert_called_once_with(original_function=create_original, model="vector-alias", name="store")
+
+
+@pytest.mark.asyncio
+async def test_vector_store_embedding_executors_preserve_explicit_configuration():
+    response = EmbeddingResponse(data=[{"embedding": [0.1], "index": 0, "object": "embedding"}])
+    sdk_executor = LiteLLMVectorStoreEmbeddingExecutor()
+
+    with (
+        patch(  # test-quality-ok: isolates SDK dispatch from external embedding providers
+            "litellm.embedding", return_value=response
+        ) as embedding,
+        patch(  # test-quality-ok: isolates async SDK dispatch from external embedding providers
+            "litellm.aembedding", new=AsyncMock(return_value=response)
+        ) as aembedding,
+    ):
+        assert sdk_executor.embed("openai/model", "sync", {"api_key": "explicit"}) is response
+        assert await sdk_executor.aembed("openai/model", "async", {"api_key": "explicit"}) is response
+
+    embedding.assert_called_once_with(model="openai/model", input=["sync"], api_key="explicit")
+    aembedding.assert_awaited_once_with(model="openai/model", input=["async"], api_key="explicit")
+
+    mock_router = MagicMock()
+    mock_router.embedding.return_value = response
+    mock_router.aembedding = AsyncMock(return_value=response)
+    router_executor = RouterVectorStoreEmbeddingExecutor(
+        router=mock_router,
+        metadata={"user_api_key_team_id": "team-a"},
+    )
+
+    assert router_executor.embed("team-alias", "query", {}) is response
+    mock_router.embedding.assert_called_once_with(
+        model="team-alias",
+        input=["query"],
+        metadata={"user_api_key_team_id": "team-a"},
+    )
+
+    with (
+        patch(  # test-quality-ok: verifies explicit store configuration at the SDK boundary
+            "litellm.embedding", return_value=response
+        ) as explicit_embedding,
+        patch(  # test-quality-ok: verifies async explicit store configuration at the SDK boundary
+            "litellm.aembedding", new=AsyncMock(return_value=response)
+        ) as explicit_aembedding,
+    ):
+        assert router_executor.embed("openai/model", "query", {"api_key": "store-key"}) is response
+        assert await router_executor.aembed("openai/model", "query", {"api_key": "store-key"}) is response
+
+    explicit_embedding.assert_not_called()
+    explicit_aembedding.assert_not_awaited()
+    assert mock_router.embedding.call_args.kwargs == {
+        "model": "openai/model",
+        "input": ["query"],
+        "api_key": "store-key",
+        "metadata": {"user_api_key_team_id": "team-a"},
+    }
+    mock_router.aembedding.assert_awaited_once_with(
+        model="openai/model",
+        input=["query"],
+        api_key="store-key",
+        metadata={"user_api_key_team_id": "team-a"},
+    )
 
 
 @pytest.mark.asyncio
@@ -82,10 +177,11 @@ async def test_router_avector_store_search_passes_correct_args():
         }
 
         # Call router's avector_store_search
-        result = await router.avector_store_search(
+        await router.avector_store_search(
             vector_store_id="test_store_id",
             query="test query",
             custom_llm_provider="bedrock",
+            metadata={"user_api_key_team_id": "team-a"},
         )
 
         # Verify the internal method was called with correct args
@@ -96,6 +192,38 @@ async def test_router_avector_store_search_passes_correct_args():
         assert call_args[1]["vector_store_id"] == "test_store_id"
         assert call_args[1]["query"] == "test query"
         assert call_args[1]["custom_llm_provider"] == "bedrock"
+        executor = call_args[1]["_direct_vector_store_embedding_executor"]
+        assert isinstance(executor, RouterVectorStoreEmbeddingExecutor)
+        assert executor.metadata["user_api_key_team_id"] == "team-a"
+
+
+@pytest.mark.asyncio
+async def test_vector_store_embedding_executor_uses_team_scoped_router_deployment():
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "shared-embedding",
+                "litellm_params": {"model": "openai/text-embedding-3-small", "api_key": "team-a-key"},
+                "model_info": {"team_id": "team-a", "team_public_model_name": "shared-embedding"},
+            },
+            {
+                "model_name": "shared-embedding",
+                "litellm_params": {"model": "openai/text-embedding-3-small", "api_key": "team-b-key"},
+                "model_info": {"team_id": "team-b", "team_public_model_name": "shared-embedding"},
+            },
+        ]
+    )
+    executor = RouterVectorStoreEmbeddingExecutor(
+        router=router,
+        metadata={"user_api_key_team_id": "team-b"},
+    )
+    response = EmbeddingResponse(data=[{"embedding": [0.1], "index": 0, "object": "embedding"}])
+
+    with patch("litellm.aembedding", new=AsyncMock(return_value=response)) as mock_aembedding:
+        result = await executor.aembed("shared-embedding", "query", {})
+
+    assert result is response
+    assert mock_aembedding.await_args.kwargs["api_key"] == "team-b-key"
 
 
 @pytest.mark.asyncio
@@ -502,91 +630,30 @@ async def test_update_request_data_with_litellm_managed_vector_store_registry():
 
 
 @pytest.mark.asyncio
-async def test_update_request_data_resolves_embedding_config_at_use_time():
-    """When the persisted vector store row carries only a
-    ``litellm_embedding_model`` reference (the new behaviour after
-    moving the auto-resolve out of write time), the request-handling
-    layer must resolve the embedding config so the downstream embed
-    call still has ``api_key`` / ``api_base`` / ``api_version``. The
-    resolved config lives in this per-request data dict only — never
-    persisted."""
-    mock_vector_store: LiteLLM_ManagedVectorStore = {
+async def test_managed_vector_store_keeps_embedding_reference_and_explicit_config():
+    explicit_config = {"api_key": "store-specific-key", "api_base": "https://embedding.example"}
+    managed_vector_store: LiteLLM_ManagedVectorStore = {
         "vector_store_id": "test_store",
-        "custom_llm_provider": "azure_ai",
+        "custom_llm_provider": "valkey",
         "litellm_params": {
-            "litellm_embedding_model": "azure/text-embedding-3-large",
-            # Note: no litellm_embedding_config persisted
+            "litellm_embedding_model": "team-embedding-alias",
+            "litellm_embedding_config": explicit_config,
         },
     }
-
     mock_registry = MagicMock()
-    mock_registry.get_litellm_managed_vector_store_from_registry.return_value = (
-        mock_vector_store
-    )
+    mock_registry.get_litellm_managed_vector_store_from_registry.return_value = managed_vector_store
 
-    resolved = {
-        "api_key": "use-time-resolved-key",
-        "api_base": "https://my-azure.example",
-        "api_version": "2024-09-01",
-    }
-
-    with (
-        patch.object(litellm, "vector_store_registry", mock_registry),
-        patch(
-            "litellm.proxy.vector_store_endpoints.endpoints._resolve_embedding_config",
-            new=AsyncMock(return_value=resolved),
-        ),
-    ):
+    with patch.object(litellm, "vector_store_registry", mock_registry):
         result = await _update_request_data_with_litellm_managed_vector_store_registry(
-            data={}, vector_store_id="test_store"
+            data={},
+            vector_store_id="test_store",
         )
 
-    assert result["litellm_embedding_model"] == "azure/text-embedding-3-large"
-    assert result["litellm_embedding_config"] == resolved
+    assert result["litellm_embedding_model"] == "team-embedding-alias"
+    assert result["litellm_embedding_config"] == explicit_config
+    assert managed_vector_store["litellm_params"]["litellm_embedding_config"] == explicit_config
 
 
-@pytest.mark.asyncio
-async def test_update_request_data_passes_through_legacy_embedding_config():
-    """A vector store row created by an older proxy version may already
-    carry a fully-resolved ``litellm_embedding_config`` in its persisted
-    ``litellm_params`` (the very leak this PR closes). Those legacy rows
-    must still work — the use-time resolver skips re-resolution when
-    the config is already present so the embed call keeps succeeding."""
-    legacy_config = {
-        "api_key": "legacy-cleartext-key",
-        "api_base": "https://legacy-azure.example",
-        "api_version": "2024-01-01",
-    }
-    mock_vector_store: LiteLLM_ManagedVectorStore = {
-        "vector_store_id": "legacy_store",
-        "custom_llm_provider": "azure_ai",
-        "litellm_params": {
-            "litellm_embedding_model": "azure/text-embedding-3-large",
-            "litellm_embedding_config": legacy_config,
-        },
-    }
-
-    mock_registry = MagicMock()
-    mock_registry.get_litellm_managed_vector_store_from_registry.return_value = (
-        mock_vector_store
-    )
-
-    resolve_mock = AsyncMock()
-
-    with (
-        patch.object(litellm, "vector_store_registry", mock_registry),
-        patch(
-            "litellm.proxy.vector_store_endpoints.endpoints._resolve_embedding_config",
-            new=resolve_mock,
-        ),
-    ):
-        result = await _update_request_data_with_litellm_managed_vector_store_registry(
-            data={}, vector_store_id="legacy_store"
-        )
-
-    assert result["litellm_embedding_config"] == legacy_config
-    resolve_mock.assert_not_awaited()
-
 
 class TestCheckVectorStorePermission:
     """Test suite for check_vector_store_permission function."""
@@ -2003,57 +2070,7 @@ async def test_vector_store_update_and_list_synchronization():
 
 
 @pytest.mark.asyncio
-async def test_resolve_embedding_config_from_db():
-    """Test that _resolve_embedding_config_from_db correctly resolves embedding config from database."""
-    mock_prisma_client = MagicMock()
-
-    # Mock database model with litellm_params
-    mock_db_model = MagicMock()
-    mock_db_model.litellm_params = {
-        "api_key": "test-api-key",
-        "api_base": "https://api.openai.com",
-        "api_version": "2024-01-01",
-    }
-
-    mock_prisma_client.db.litellm_proxymodeltable.find_first = AsyncMock(
-        return_value=mock_db_model
-    )
-
-    with patch(
-        "litellm.proxy.vector_store_endpoints.management_endpoints.decrypt_value_helper",
-        side_effect=lambda value, key, return_original_value: value,
-    ):
-        result = await _resolve_embedding_config_from_db(
-            embedding_model="text-embedding-ada-002", prisma_client=mock_prisma_client
-        )
-
-    assert result is not None
-    assert result["api_key"] == "test-api-key"
-    assert result["api_base"] == "https://api.openai.com"
-    assert result["api_version"] == "2024-01-01"
-    mock_prisma_client.db.litellm_proxymodeltable.find_first.assert_called_once_with(
-        where={"model_name": "text-embedding-ada-002"}
-    )
-
-    # Test with empty embedding_model
-    result_empty = await _resolve_embedding_config_from_db(
-        embedding_model="", prisma_client=mock_prisma_client
-    )
-    assert result_empty is None
-
-    # Test with model not found
-    mock_prisma_client.db.litellm_proxymodeltable.find_first = AsyncMock(
-        return_value=None
-    )
-    result_not_found = await _resolve_embedding_config_from_db(
-        embedding_model="non-existent-model", prisma_client=mock_prisma_client
-    )
-    assert result_not_found is None
-
-
-@pytest.mark.asyncio
-async def test_new_vector_store_auto_resolves_embedding_config():
-    """Test that new_vector_store auto-resolves embedding config when embedding_model is provided but config is not."""
+async def test_new_vector_store_persists_embedding_reference_without_credentials():
     import json
 
     from litellm.types.vector_stores import LiteLLM_ManagedVectorStore
@@ -2070,14 +2087,6 @@ async def test_new_vector_store_auto_resolves_embedding_config():
         },
     }
 
-    # Mock database model lookup for embedding config resolution
-    mock_db_model = MagicMock()
-    mock_db_model.litellm_params = {
-        "api_key": "resolved-api-key",
-        "api_base": "https://api.openai.com",
-        "api_version": "2024-01-01",
-    }
-
     # Mock user API key
     mock_user_api_key = MagicMock(spec=UserAPIKeyAuth)
     mock_user_api_key.user_role = None
@@ -2088,10 +2097,6 @@ async def test_new_vector_store_auto_resolves_embedding_config():
     mock_prisma_client.db.litellm_managedvectorstorestable.find_unique = AsyncMock(
         return_value=None  # Vector store doesn't exist yet
     )
-    mock_prisma_client.db.litellm_proxymodeltable.find_first = AsyncMock(
-        return_value=mock_db_model
-    )
-
     # Track what was passed to create
     captured_create_data = {}
 
@@ -2112,261 +2117,21 @@ async def test_new_vector_store_auto_resolves_embedding_config():
     mock_registry = MagicMock()
     mock_registry.add_vector_store_to_registry = MagicMock()
 
-    # Mock router to return None (so it falls back to DB resolution)
-    mock_router = MagicMock()
-    mock_router.get_deployment_by_model_group_name.return_value = None
-
     with (
         patch("litellm.proxy.proxy_server.prisma_client", mock_prisma_client),
-        patch("litellm.proxy.proxy_server.llm_router", mock_router),
-        patch(
-            "litellm.proxy.vector_store_endpoints.management_endpoints.decrypt_value_helper",
-            side_effect=lambda value, key, return_original_value: value,
-        ),
         patch.object(litellm, "vector_store_registry", mock_registry),
     ):
-        result = await new_vector_store(
-            vector_store=vector_store_data, user_api_key_dict=mock_user_api_key
-        )
+        result = await new_vector_store(vector_store=vector_store_data, user_api_key_dict=mock_user_api_key)
 
     assert result["status"] == "success"
-    # Auto-resolve no longer happens at create time — the persisted row
-    # carries only the model reference, never the resolved cleartext
-    # credential. Resolution now happens at request-handling time inside
-    # ``_update_request_data_with_litellm_managed_vector_store_registry``,
-    # where the resolved config lives in per-request memory and is never
-    # written to the database.
     litellm_params_json = captured_create_data.get("litellm_params")
     assert litellm_params_json is not None
     litellm_params_dict = json.loads(litellm_params_json)
     assert "litellm_embedding_config" not in litellm_params_dict
     assert litellm_params_dict["litellm_embedding_model"] == "text-embedding-ada-002"
 
-    # The response must also not echo a cleartext credential — even on
-    # the create response, where redaction guards against caller-supplied
-    # cleartext or pre-existing rows that were created by an earlier
-    # proxy version.
     response_vs = result["vector_store"]
-    assert "resolved-api-key" not in _serialize_litellm_params(
-        response_vs.get("litellm_params")
-    )
-
-
-def test_resolve_embedding_config_from_router():
-    """Test that _resolve_embedding_config_from_router correctly extracts credentials from config-defined models."""
-    from litellm.types.router import Deployment, LiteLLM_Params
-
-    # Create a mock router with a model
-    mock_router = MagicMock()
-
-    # Create a mock deployment with litellm_params
-    mock_litellm_params = MagicMock(spec=LiteLLM_Params)
-    mock_litellm_params.api_key = "config-api-key"
-    mock_litellm_params.api_base = "https://config-api-base.com"
-    mock_litellm_params.api_version = "2024-02-01"
-
-    mock_deployment = MagicMock(spec=Deployment)
-    mock_deployment.litellm_params = mock_litellm_params
-
-    mock_router.get_deployment_by_model_group_name.return_value = mock_deployment
-
-    # Test resolution
-    result = _resolve_embedding_config_from_router(
-        embedding_model="text-embedding-ada-002", llm_router=mock_router
-    )
-
-    assert result is not None
-    assert result["api_key"] == "config-api-key"
-    assert result["api_base"] == "https://config-api-base.com"
-    assert result["api_version"] == "2024-02-01"
-
-    mock_router.get_deployment_by_model_group_name.assert_called_once_with(
-        model_group_name="text-embedding-ada-002"
-    )
-
-
-def test_resolve_embedding_config_from_router_with_provider_prefix():
-    """Test that _resolve_embedding_config_from_router handles provider prefixes like 'azure/model-name'."""
-    from litellm.types.router import Deployment, LiteLLM_Params
-
-    # Create a mock router
-    mock_router = MagicMock()
-
-    # Create a mock deployment
-    mock_litellm_params = MagicMock(spec=LiteLLM_Params)
-    mock_litellm_params.api_key = "azure-api-key"
-    mock_litellm_params.api_base = "https://azure-endpoint.openai.azure.com"
-    mock_litellm_params.api_version = "2024-02-15"
-
-    mock_deployment = MagicMock(spec=Deployment)
-    mock_deployment.litellm_params = mock_litellm_params
-
-    # First call with full name returns None, second call with stripped name returns deployment
-    mock_router.get_deployment_by_model_group_name.side_effect = [None, mock_deployment]
-
-    result = _resolve_embedding_config_from_router(
-        embedding_model="azure/text-embedding-3-large", llm_router=mock_router
-    )
-
-    assert result is not None
-    assert result["api_key"] == "azure-api-key"
-    assert result["api_base"] == "https://azure-endpoint.openai.azure.com"
-    assert result["api_version"] == "2024-02-15"
-
-    # Should have tried both the full name and stripped name
-    assert mock_router.get_deployment_by_model_group_name.call_count == 2
-
-
-def test_resolve_embedding_config_from_router_returns_none_when_not_found():
-    """Test that _resolve_embedding_config_from_router returns None when model is not in router."""
-    mock_router = MagicMock()
-    mock_router.get_deployment_by_model_group_name.return_value = None
-
-    result = _resolve_embedding_config_from_router(
-        embedding_model="nonexistent-model", llm_router=mock_router
-    )
-
-    assert result is None
-
-
-def test_resolve_embedding_config_from_router_handles_os_environ():
-    """Test that _resolve_embedding_config_from_router handles os.environ/ prefixed values."""
-    from litellm.types.router import Deployment, LiteLLM_Params
-
-    mock_router = MagicMock()
-
-    mock_litellm_params = MagicMock(spec=LiteLLM_Params)
-    mock_litellm_params.api_key = "os.environ/OPENAI_API_KEY"
-    mock_litellm_params.api_base = "https://direct-url.com"
-    mock_litellm_params.api_version = None
-
-    mock_deployment = MagicMock(spec=Deployment)
-    mock_deployment.litellm_params = mock_litellm_params
-
-    mock_router.get_deployment_by_model_group_name.return_value = mock_deployment
-
-    with patch(
-        "litellm.proxy.vector_store_endpoints.management_endpoints.get_secret",
-        return_value="resolved-from-env",
-    ) as mock_get_secret:
-        result = _resolve_embedding_config_from_router(
-            embedding_model="text-embedding-ada-002", llm_router=mock_router
-        )
-
-    assert result is not None
-    assert result["api_key"] == "resolved-from-env"
-    assert result["api_base"] == "https://direct-url.com"
-    assert "api_version" not in result
-
-    mock_get_secret.assert_called_once_with("os.environ/OPENAI_API_KEY")
-
-
-@pytest.mark.asyncio
-async def test_resolve_embedding_config_tries_router_then_db():
-    """Test that _resolve_embedding_config tries router first, then falls back to DB."""
-    from litellm.types.router import Deployment, LiteLLM_Params
-
-    mock_prisma_client = MagicMock()
-    mock_router = MagicMock()
-
-    # Router has the model
-    mock_litellm_params = MagicMock(spec=LiteLLM_Params)
-    mock_litellm_params.api_key = "router-api-key"
-    mock_litellm_params.api_base = "https://router-api-base.com"
-    mock_litellm_params.api_version = None
-
-    mock_deployment = MagicMock(spec=Deployment)
-    mock_deployment.litellm_params = mock_litellm_params
-
-    mock_router.get_deployment_by_model_group_name.return_value = mock_deployment
-
-    # DB should NOT be called since router has the model
-    mock_prisma_client.db.litellm_proxymodeltable.find_first = AsyncMock()
-
-    result = await _resolve_embedding_config(
-        embedding_model="text-embedding-ada-002",
-        prisma_client=mock_prisma_client,
-        llm_router=mock_router,
-    )
-
-    assert result is not None
-    assert result["api_key"] == "router-api-key"
-
-    # DB should NOT have been called since router found the model
-    mock_prisma_client.db.litellm_proxymodeltable.find_first.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_resolve_embedding_config_caches_result():
-    """The first lookup should hit the router/DB; subsequent lookups for
-    the same model name should return the cached value without touching
-    the router or the database."""
-    from litellm.types.router import Deployment, LiteLLM_Params
-
-    mock_prisma_client = MagicMock()
-    mock_router = MagicMock()
-
-    mock_litellm_params = MagicMock(spec=LiteLLM_Params)
-    mock_litellm_params.api_key = "router-api-key"
-    mock_litellm_params.api_base = "https://router-api-base.com"
-    mock_litellm_params.api_version = None
-
-    mock_deployment = MagicMock(spec=Deployment)
-    mock_deployment.litellm_params = mock_litellm_params
-    mock_router.get_deployment_by_model_group_name.return_value = mock_deployment
-
-    first = await _resolve_embedding_config(
-        embedding_model="cached-model",
-        prisma_client=mock_prisma_client,
-        llm_router=mock_router,
-    )
-    assert first is not None
-    assert mock_router.get_deployment_by_model_group_name.call_count == 1
-
-    second = await _resolve_embedding_config(
-        embedding_model="cached-model",
-        prisma_client=mock_prisma_client,
-        llm_router=mock_router,
-    )
-    assert second == first
-    # Router (and by extension the DB) was not consulted again.
-    assert mock_router.get_deployment_by_model_group_name.call_count == 1
-
-
-@pytest.mark.asyncio
-async def test_resolve_embedding_config_falls_back_to_db():
-    """Test that _resolve_embedding_config falls back to DB when router doesn't have the model."""
-    mock_prisma_client = MagicMock()
-    mock_router = MagicMock()
-
-    # Router doesn't have the model
-    mock_router.get_deployment_by_model_group_name.return_value = None
-
-    # DB has the model
-    mock_db_model = MagicMock()
-    mock_db_model.litellm_params = {
-        "api_key": "db-api-key",
-        "api_base": "https://db-api-base.com",
-    }
-    mock_prisma_client.db.litellm_proxymodeltable.find_first = AsyncMock(
-        return_value=mock_db_model
-    )
-
-    with patch(
-        "litellm.proxy.vector_store_endpoints.management_endpoints.decrypt_value_helper",
-        side_effect=lambda value, key, return_original_value: value,
-    ):
-        result = await _resolve_embedding_config(
-            embedding_model="text-embedding-ada-002",
-            prisma_client=mock_prisma_client,
-            llm_router=mock_router,
-        )
-
-    assert result is not None
-    assert result["api_key"] == "db-api-key"
-
-    # DB should have been called since router didn't find the model
-    mock_prisma_client.db.litellm_proxymodeltable.find_first.assert_called()
+    assert "api_key" not in _serialize_litellm_params(response_vs.get("litellm_params"))
 
 
 @pytest.mark.asyncio
@@ -2425,9 +2190,7 @@ async def test_new_vector_store_auto_resolves_from_router():
         }
         return mock_created_vector_store
 
-    mock_prisma_client.db.litellm_managedvectorstorestable.create = AsyncMock(
-        side_effect=mock_create
-    )
+    mock_prisma_client.db.litellm_managedvectorstorestable.create = AsyncMock(side_effect=mock_create)
 
     mock_registry = MagicMock()
     mock_registry.add_vector_store_to_registry = MagicMock()

tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py

@@ -1928,6 +1928,19 @@ class TestToolTransformation:
         assert result_tool["function"]["parameters"]["type"] == "object"
         assert "properties" in result_tool["function"]["parameters"]
 
+    def test_transform_function_tools_parameters_keep_client_key_order(self):
+        tools = [
+            {"type": "function", "name": "a", "parameters": {"properties": {"arg": {"type": "string"}}, "required": ["arg"]}},
+            {"type": "function", "name": "b", "parameters": {"type": "object", "properties": {}}},
+        ]
+
+        result_tools, _ = LiteLLMCompletionResponsesConfig.transform_responses_api_tools_to_chat_completion_tools(
+            tools=tools
+        )
+
+        assert list(result_tools[0]["function"]["parameters"]) == ["properties", "required", "type"]
+        assert list(result_tools[1]["function"]["parameters"]) == ["type", "properties"]
+
     def test_transform_function_tools_empty_parameters(self):
         """Test that empty parameters get 'type': 'object' added"""
         function_tool = {

tests/test_litellm/router_utils/test_fallback_event_handlers.py

@@ -614,6 +614,27 @@ async def test_run_async_fallback_forwards_attempted_model_groups_to_nested_call
     )
 
 
+@pytest.mark.asyncio
+async def test_run_async_fallback_can_target_the_requested_group_when_a_pre_router_replaced_it():
+    """The requested group was never called when a pre-router selected a tier, so a
+    tier fallback may legitimately target that originally requested group."""
+    router = RecordingRouter()
+
+    await run_async_fallback(
+        litellm_router=router,
+        fallback_model_group=["requested-model"],
+        original_model_group="requested-model",
+        original_exception=RuntimeError("selected tier failed"),
+        max_fallbacks=3,
+        fallback_depth=0,
+        model="requested-model",
+        metadata={"pre_routing_selected_model": "selected-tier"},
+    )
+
+    assert router.received_kwargs["model"] == "requested-model"
+    assert router.received_kwargs["attempted_targets"].keys == frozenset({"selected-tier", "requested-model"})
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     "entry",
@@ -1199,6 +1220,28 @@ class TestOrderedFallbackLookupGroups:
         assert fallback_lookup_groups({}, "smart-router") == ("smart-router",)
         assert fallback_lookup_groups({}, None) == ()
 
+    def test_session_remap_keeps_the_bound_router_between_tier_and_requested_group(self):
+        from litellm.router_utils.fallback_event_handlers import (
+            PRE_ROUTING_SELECTED_MODEL_KEY,
+            fallback_lookup_groups,
+        )
+
+        kwargs = {
+            "litellm_metadata": {
+                PRE_ROUTING_SELECTED_MODEL_KEY: "tier1",
+                "model_group": "smart-router",
+            }
+        }
+
+        assert fallback_lookup_groups(kwargs, "requested-model") == (
+            "tier1",
+            "smart-router",
+            "requested-model",
+        )
+        assert fallback_lookup_groups({"metadata": {"model_group": []}}, "requested-model") == (
+            "requested-model",
+        )
+
     def test_first_resolving_group_wins_and_generic_idx_survives_a_miss(self):
         from litellm.router_utils.fallback_event_handlers import (
             get_fallback_model_group_for_lookup_groups,

tests/test_litellm/test_router.py

@@ -8579,6 +8579,398 @@ class TestConsumedRequestTagsStamp:
         assert CONSUMED_REQUEST_TAGS_METADATA_KEY not in request_kwargs["metadata"]
 
 
+class TestClaudeCodeSubagentSessionRouterBinding:
+    class _RewriteStrategy:
+        def __init__(self, routed_model: str = "cheap-model") -> None:
+            self.routed_model = routed_model
+
+        async def async_pre_routing_hook(
+            self, model, request_kwargs, messages=None, input=None, specific_deployment=False
+        ):
+            from litellm.types.router import PreRoutingHookResponse
+
+            return PreRoutingHookResponse(
+                model=self.routed_model,
+                messages=messages,
+                routing_decision={
+                    "router_model_name": "smart-router",
+                    "router_type": "complexity",
+                    "routed_model": self.routed_model,
+                    "cause": "heuristic_scorer",
+                },
+            )
+
+    @classmethod
+    def _router(
+        cls,
+        cheap_response: str = "cheap response",
+        fallbacks: list[dict[str, list[str]]] | None = None,
+    ) -> "litellm.Router":
+        from litellm.types.router import TaggedPreRoutingStrategy
+
+        router = litellm.Router(
+            model_list=[
+                {
+                    "model_name": "cheap-model",
+                    "litellm_params": {"model": "openai/gpt-4o-mini", "mock_response": cheap_response},
+                },
+                {
+                    "model_name": "expensive-model",
+                    "litellm_params": {"model": "openai/gpt-4o", "mock_response": "expensive response"},
+                },
+            ],
+            fallbacks=fallbacks,
+            num_retries=0,
+        )
+        router.complexity_routers = {
+            "smart-router": (TaggedPreRoutingStrategy(tags=(), strategy=cls._RewriteStrategy()),),
+            "premium-router": (TaggedPreRoutingStrategy(tags=(), strategy=cls._RewriteStrategy("expensive-model")),),
+        }
+        return router
+
+    @staticmethod
+    def _request_kwargs(
+        *,
+        key_hash: str = "key-hash-a",
+        app: str = "cli",
+        agent_id: str | None = None,
+        fallback_depth: int | None = None,
+    ) -> dict:
+        headers = {
+            "X-Claude-Code-Session-Id": "session-1234",
+            "x-app": app,
+            **({"x-claude-code-agent-id": agent_id} if agent_id is not None else {}),
+        }
+        return {
+            "metadata": {"user_api_key_hash": key_hash},
+            "proxy_server_request": {"headers": headers},
+            **({"fallback_depth": fallback_depth} if fallback_depth is not None else {}),
+        }
+
+    @pytest.mark.asyncio
+    async def test_subagent_concrete_model_uses_the_main_sessions_router(self):
+        router = self._router()
+
+        await router.acompletion(
+            model="smart-router",
+            messages=[{"role": "user", "content": "main turn"}],
+            **self._request_kwargs(),
+        )
+        subagent_kwargs = self._request_kwargs(agent_id="agent-1234")
+
+        response = await router.acompletion(
+            model="expensive-model",
+            messages=[{"role": "user", "content": "subagent turn"}],
+            **subagent_kwargs,
+        )
+
+        assert response.choices[0].message.content == "cheap response"
+        assert subagent_kwargs["metadata"]["model_group"] == "smart-router"
+        assert subagent_kwargs["metadata"]["routing_decision"]["router_model_name"] == "smart-router"
+
+    @pytest.mark.asyncio
+    async def test_main_thread_side_calls_to_a_plain_model_keep_the_session_router(self):
+        router = self._router()
+
+        await router.async_pre_routing_hook(model="smart-router", request_kwargs=self._request_kwargs())
+        await router.async_pre_routing_hook(model="expensive-model", request_kwargs=self._request_kwargs())
+
+        response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234"),
+        )
+
+        assert response is not None
+        assert response.model == "cheap-model"
+
+    @pytest.mark.asyncio
+    async def test_redis_cleanup_failure_does_not_reject_a_subagent_request(self):
+        from litellm.caching.caching import RedisCache
+
+        router = self._router()
+        del router.complexity_routers["smart-router"]
+        redis_cache = MagicMock(spec=RedisCache)
+        redis_cache.async_get_cache = AsyncMock(return_value="smart-router")
+        redis_cache.async_delete_cache = AsyncMock(side_effect=ConnectionError("redis unavailable"))
+        router._update_redis_cache(cache=redis_cache)
+
+        response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234"),
+        )
+
+        assert response is None
+        redis_cache.async_delete_cache.assert_awaited_once()
+
+    @pytest.mark.asyncio
+    async def test_redis_read_failure_does_not_reject_a_subagent_request(self):
+        from litellm.caching.caching import RedisCache
+
+        router = self._router()
+        request_kwargs = self._request_kwargs(agent_id="agent-1234")
+        cache_key = router._claude_code_session_router_cache_key(request_kwargs)
+        assert cache_key is not None
+        await router._claude_code_session_router_cache.in_memory_cache.async_set_cache(
+            cache_key,
+            "smart-router",
+        )
+        redis_cache = MagicMock(spec=RedisCache)
+        redis_cache.async_get_cache = AsyncMock(side_effect=Exception("Redis circuit breaker is open"))
+        router._update_redis_cache(cache=redis_cache)
+
+        response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=request_kwargs,
+        )
+
+        assert response is None
+        assert "model_group" not in request_kwargs["metadata"]
+        redis_cache.async_get_cache.assert_awaited_once()
+
+    @pytest.mark.asyncio
+    async def test_redis_write_failures_do_not_reject_main_or_subagent_requests(self):
+        from litellm.caching.caching import RedisCache
+
+        router = self._router()
+        redis_cache = MagicMock(spec=RedisCache)
+        redis_cache.async_get_cache = AsyncMock(return_value="smart-router")
+        redis_cache.async_set_cache = AsyncMock(side_effect=Exception("redis unavailable"))
+        router._update_redis_cache(cache=redis_cache)
+
+        main_response = await router.async_pre_routing_hook(
+            model="smart-router",
+            request_kwargs=self._request_kwargs(),
+        )
+        subagent_response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234"),
+        )
+
+        assert main_response is not None
+        assert main_response.model == "cheap-model"
+        assert subagent_response is not None
+        assert subagent_response.model == "cheap-model"
+        assert redis_cache.async_set_cache.await_count == 2
+
+    @pytest.mark.asyncio
+    async def test_subagents_follow_the_main_threads_latest_router_across_workers(self):
+        from types import SimpleNamespace
+
+        from litellm.caching.caching import RedisCache
+
+        shared_binding = SimpleNamespace(value=None)
+        shared_redis = MagicMock(spec=RedisCache)
+        shared_redis.async_get_cache = AsyncMock(side_effect=lambda key, **_: shared_binding.value)
+        shared_redis.async_set_cache = AsyncMock(
+            side_effect=lambda key, value, **_: setattr(shared_binding, "value", value)
+        )
+        main_worker, subagent_worker = self._router(), self._router()
+        main_worker._update_redis_cache(cache=shared_redis)
+        subagent_worker._update_redis_cache(cache=shared_redis)
+
+        await main_worker.async_pre_routing_hook(model="smart-router", request_kwargs=self._request_kwargs())
+        first = await subagent_worker.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234"),
+        )
+        await main_worker.async_pre_routing_hook(model="premium-router", request_kwargs=self._request_kwargs())
+        second = await subagent_worker.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234"),
+        )
+
+        assert first is not None
+        assert first.model == "cheap-model"
+        assert second is not None
+        assert second.model == "expensive-model"
+        assert shared_binding.value == "premium-router"
+
+    @pytest.mark.asyncio
+    async def test_no_pre_routing_strategies_means_no_session_cache_traffic(self):
+        from litellm.caching.caching import RedisCache
+
+        router = self._router()
+        router.complexity_routers.clear()
+        redis_cache = MagicMock(spec=RedisCache)
+        redis_cache.async_get_cache = AsyncMock(return_value=None)
+        redis_cache.async_set_cache = AsyncMock()
+        redis_cache.async_delete_cache = AsyncMock()
+        router._update_redis_cache(cache=redis_cache)
+
+        for request_kwargs in (self._request_kwargs(), self._request_kwargs(agent_id="agent-1234")):
+            response = await router.async_pre_routing_hook(model="expensive-model", request_kwargs=request_kwargs)
+            assert response is None
+
+        redis_cache.async_get_cache.assert_not_awaited()
+        redis_cache.async_set_cache.assert_not_awaited()
+        redis_cache.async_delete_cache.assert_not_awaited()
+
+    @pytest.mark.asyncio
+    async def test_session_bindings_do_not_evict_router_rate_limit_state(self):
+        router = self._router()
+        assert router._update_usage(deployment_id="deployment-id", parent_otel_span=None) == 1
+
+        for session_index in range(201):
+            request_kwargs = self._request_kwargs()
+            request_kwargs["proxy_server_request"]["headers"]["X-Claude-Code-Session-Id"] = (
+                f"session-{session_index:04d}"
+            )
+            await router.async_pre_routing_hook(model="smart-router", request_kwargs=request_kwargs)
+
+        assert router._update_usage(deployment_id="deployment-id", parent_otel_span=None) == 2
+
+    @pytest.mark.asyncio
+    async def test_background_and_fallback_requests_do_not_clear_the_session_router(self):
+        router = self._router()
+
+        await router.async_pre_routing_hook(model="smart-router", request_kwargs=self._request_kwargs())
+        await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(app="cli-bg"),
+        )
+        await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(fallback_depth=1),
+        )
+
+        response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234"),
+        )
+
+        assert response is not None
+        assert response.model == "cheap-model"
+
+    @pytest.mark.asyncio
+    async def test_subagent_fallback_does_not_reapply_the_session_router(self):
+        router = self._router()
+
+        await router.async_pre_routing_hook(model="smart-router", request_kwargs=self._request_kwargs())
+
+        response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(agent_id="agent-1234", fallback_depth=1),
+        )
+
+        assert response is None
+
+    @pytest.mark.asyncio
+    async def test_subagent_can_fallback_to_its_original_requested_model(self):
+        router = self._router(
+            cheap_response="litellm.RateLimitError",
+            fallbacks=[{"cheap-model": ["expensive-model"]}],
+        )
+
+        await router.acompletion(
+            model="smart-router",
+            messages=[{"role": "user", "content": "main turn"}],
+            **self._request_kwargs(),
+        )
+        subagent_kwargs = self._request_kwargs(agent_id="agent-1234")
+
+        response = await router.acompletion(
+            model="expensive-model",
+            messages=[{"role": "user", "content": "subagent turn"}],
+            **subagent_kwargs,
+        )
+
+        assert response.choices[0].message.content == "expensive response"
+        assert subagent_kwargs["metadata"]["routing_decision"]["routed_model"] == "cheap-model"
+
+    @pytest.mark.asyncio
+    async def test_subagent_can_use_the_bound_router_name_fallback(self):
+        router = self._router(
+            cheap_response="litellm.RateLimitError",
+            fallbacks=[{"smart-router": ["expensive-model"]}],
+        )
+
+        await router.acompletion(
+            model="smart-router",
+            messages=[{"role": "user", "content": "main turn"}],
+            **self._request_kwargs(),
+        )
+
+        response = await router.acompletion(
+            model="expensive-model",
+            messages=[{"role": "user", "content": "subagent turn"}],
+            **self._request_kwargs(agent_id="agent-1234"),
+        )
+
+        assert response.choices[0].message.content == "expensive response"
+
+    @pytest.mark.asyncio
+    async def test_anthropic_subagent_four_fallback_hops_use_each_current_model_chain(self):
+        from litellm.types.router import TaggedPreRoutingStrategy
+
+        failing_groups = ("cheap-model", "fallback-1", "fallback-2", "fallback-3")
+        router = litellm.Router(
+            model_list=[
+                *(
+                    {
+                        "model_name": group,
+                        "litellm_params": {
+                            "model": "anthropic/claude-3-haiku-20240307",
+                            "mock_response": "litellm.RateLimitError",
+                        },
+                    }
+                    for group in failing_groups
+                ),
+                {
+                    "model_name": "requested-model",
+                    "litellm_params": {
+                        "model": "anthropic/claude-3-haiku-20240307",
+                        "mock_response": "requested response",
+                    },
+                },
+                {
+                    "model_name": "fallback-4",
+                    "litellm_params": {
+                        "model": "anthropic/claude-3-haiku-20240307",
+                        "mock_response": "fourth fallback response",
+                    },
+                },
+            ],
+            fallbacks=[
+                {"smart-router": ["fallback-1"]},
+                {"fallback-1": ["fallback-2"]},
+                {"fallback-2": ["fallback-3"]},
+                {"fallback-3": ["fallback-4"]},
+            ],
+            num_retries=0,
+            max_fallbacks=4,
+        )
+        router.complexity_routers = {
+            "smart-router": (TaggedPreRoutingStrategy(tags=(), strategy=self._RewriteStrategy()),)
+        }
+        main_kwargs = self._request_kwargs()
+        main_kwargs["litellm_metadata"] = main_kwargs.pop("metadata")
+        await router.async_pre_routing_hook(model="smart-router", request_kwargs=main_kwargs)
+        subagent_kwargs = self._request_kwargs(agent_id="agent-1234")
+        subagent_kwargs["litellm_metadata"] = subagent_kwargs.pop("metadata")
+
+        response = await router.aanthropic_messages(
+            model="requested-model",
+            messages=[{"role": "user", "content": "subagent turn"}],
+            max_tokens=64,
+            **subagent_kwargs,
+        )
+
+        assert response["content"][0]["text"] == "fourth fallback response"
+
+    @pytest.mark.asyncio
+    async def test_session_router_binding_is_scoped_to_the_authenticated_key(self):
+        router = self._router()
+
+        await router.async_pre_routing_hook(model="smart-router", request_kwargs=self._request_kwargs())
+
+        response = await router.async_pre_routing_hook(
+            model="expensive-model",
+            request_kwargs=self._request_kwargs(key_hash="key-hash-b", agent_id="agent-1234"),
+        )
+
+        assert response is None
+
+
 class TestAutoRouterMaxInputCharsWiring:
     """`auto_router_max_input_chars` on the deployment has to reach the AutoRouter that embeds prompts.
 

tests/vector_store_tests/test_azure_ai_vector_store.py

@@ -1,10 +1,19 @@
-import pytest
-import litellm
 import json
 import os
+from unittest.mock import MagicMock
+
+import httpx
+import pytest
+import respx
+
+import litellm
+from litellm.llms.azure_ai.vector_stores.transformation import AzureAIVectorStoreConfig
+from litellm.types.utils import EmbeddingResponse
+from litellm.vector_stores import (
+    asearch as vector_store_asearch,
+)
 from litellm.vector_stores import (
     search as vector_store_search,
-    asearch as vector_store_asearch,
 )
 
 
@@ -30,10 +39,108 @@ async def test_basic_search_vector_store(sync_mode):
         if sync_mode:
             response = vector_store_search(query=default_query, **base_request_args)
         else:
-            response = await vector_store_asearch(
-                query=default_query, **base_request_args
-            )
+            response = await vector_store_asearch(query=default_query, **base_request_args)
     except litellm.InternalServerError:
         pytest.skip("Skipping test due to litellm.InternalServerError")
 
     print("litellm response=", json.dumps(response, indent=4, default=str))
+
+
+class RecordingEmbeddingExecutor:
+    def __init__(self, response):
+        self.response = response
+        self.calls = []
+
+    def embed(self, model, query, configuration):
+        self.calls.append((model, query, dict(configuration)))
+        return self.response
+
+    async def aembed(self, model, query, configuration):
+        self.calls.append((model, query, dict(configuration)))
+        return self.response
+
+
+ALIAS_QUERY_VECTOR = [0.5, -0.25, 0.125]
+ALIAS_EMBEDDING_RESPONSE = EmbeddingResponse(
+    data=[{"embedding": ALIAS_QUERY_VECTOR, "index": 0, "object": "embedding"}]
+)
+STORE_EMBEDDINGS_URL = "https://embedding.example/v1/embeddings"
+
+
+def _transform_kwargs(executor):
+    logging_obj = MagicMock()
+    logging_obj.model_call_details = {}
+    return {
+        "vector_store_id": "my-vector-index",
+        "query": "what is azure search?",
+        "vector_store_search_optional_params": {"top_k": 2},
+        "api_base": "https://azure-kb-search.search.windows.net",
+        "litellm_logging_obj": logging_obj,
+        "litellm_params": {
+            "litellm_embedding_model": "multilingual-e5-large",
+            "azure_search_vector_field": "embedding",
+        },
+        "embedding_executor": executor,
+    }
+
+
+@pytest.mark.asyncio
+async def test_transform_uses_injected_executor_without_embedding_config(respx_mock: respx.MockRouter):
+    executor = RecordingEmbeddingExecutor(ALIAS_EMBEDDING_RESPONSE)
+    config = AzureAIVectorStoreConfig()
+    transform_kwargs = _transform_kwargs(executor)
+
+    url, sync_body = config.transform_search_vector_store_request(**transform_kwargs)
+    _, async_body = await config.atransform_search_vector_store_request(**transform_kwargs)
+
+    assert respx_mock.calls.call_count == 0
+    assert executor.calls == [("multilingual-e5-large", "what is azure search?", {})] * 2
+    assert (
+        url == "https://azure-kb-search.search.windows.net/indexes/my-vector-index/docs/search?api-version=2024-07-01"
+    )
+    assert sync_body == async_body
+    assert sync_body["vectorQueries"] == [
+        {"vector": ALIAS_QUERY_VECTOR, "fields": "embedding", "kind": "vector", "k": 2}
+    ]
+    assert sync_body["top"] == 2
+    logging_details = transform_kwargs["litellm_logging_obj"].model_call_details
+    assert logging_details["embedding_model"] == "multilingual-e5-large"
+    assert logging_details["top_k"] == 2
+
+
+def test_transform_falls_back_to_sdk_embedding_without_executor(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = respx_mock.post(STORE_EMBEDDINGS_URL).mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "object": "list",
+                "data": [{"object": "embedding", "index": 0, "embedding": ALIAS_QUERY_VECTOR}],
+                "model": "text-embedding-3-small",
+                "usage": {"prompt_tokens": 2, "total_tokens": 2},
+            },
+        )
+    )
+    transform_kwargs = _transform_kwargs(None)
+    transform_kwargs["litellm_params"] = {
+        "litellm_embedding_model": "openai/text-embedding-3-small",
+        "litellm_embedding_config": {"api_base": "https://embedding.example/v1", "api_key": "store-key"},
+    }
+
+    _, body = AzureAIVectorStoreConfig().transform_search_vector_store_request(**transform_kwargs)
+
+    embedding_request = embedding_route.calls.last.request
+    assert embedding_request.headers["authorization"] == "Bearer store-key"
+    assert json.loads(embedding_request.read())["input"] == ["what is azure search?"]
+    assert body["vectorQueries"][0]["vector"] == ALIAS_QUERY_VECTOR
+    assert body["vectorQueries"][0]["fields"] == "contentVector"
+
+
+def test_transform_requires_embedding_model():
+    transform_kwargs = _transform_kwargs(RecordingEmbeddingExecutor(ALIAS_EMBEDDING_RESPONSE))
+    transform_kwargs["litellm_params"] = {"litellm_embedding_config": {"api_key": "store-key"}}
+
+    with pytest.raises(ValueError, match="litellm_embedding_model is required"):
+        AzureAIVectorStoreConfig().transform_search_vector_store_request(**transform_kwargs)

tests/vector_store_tests/test_milvus_vector_store.py

@@ -3,16 +3,19 @@ Tests for Milvus Vector Store
 """
 
 import json
-import os
 from unittest.mock import AsyncMock, MagicMock, patch
 
+import httpx
 import pytest
+import respx
 
 import litellm
+from litellm import Router
+from litellm.llms.milvus.vector_stores.transformation import MilvusVectorStoreConfig
+from litellm.types.utils import EmbeddingResponse
 from litellm.vector_stores import asearch as vector_store_asearch
 from litellm.vector_stores import search as vector_store_search
 
-
 # Mock response from actual Milvus API
 MOCK_MILVUS_SEARCH_RESPONSE = {
     "code": 0,
@@ -98,7 +101,7 @@ class TestMilvusVectorStore:
         mock_response.json.return_value = MOCK_MILVUS_SEARCH_RESPONSE
         mock_response.text = json.dumps(MOCK_MILVUS_SEARCH_RESPONSE)
 
-        with patch("litellm.embedding") as mock_embedding:
+        with patch("litellm.aembedding", new_callable=AsyncMock) as mock_embedding:
             mock_embedding.return_value = MOCK_EMBEDDING_RESPONSE
 
             with patch(
@@ -147,16 +150,10 @@ class TestMilvusVectorStore:
                 else:
                     # Fallback: check for json kwarg or in args
                     request_data = call_args.kwargs.get("json")
-                    if (
-                        request_data is None
-                        and len(call_args.args) > 0
-                        and isinstance(call_args.args[0], dict)
-                    ):
+                    if request_data is None and len(call_args.args) > 0 and isinstance(call_args.args[0], dict):
                         request_data = call_args.args[0]
 
-                assert (
-                    request_data is not None
-                ), f"Could not extract request data. Call args: {call_args}"
+                assert request_data is not None, f"Could not extract request data. Call args: {call_args}"
                 print("Request data:", json.dumps(request_data, indent=2, default=str))
 
                 # Validate request structure
@@ -213,9 +210,7 @@ class TestMilvusVectorStore:
         with patch("litellm.embedding") as mock_embedding:
             mock_embedding.return_value = MOCK_EMBEDDING_RESPONSE
 
-            with patch(
-                "litellm.llms.custom_httpx.http_handler.HTTPHandler.post"
-            ) as mock_post:
+            with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
                 mock_post.return_value = mock_response
 
                 # Make the search request
@@ -252,16 +247,10 @@ class TestMilvusVectorStore:
                 else:
                     # Fallback: check for json kwarg or in args
                     request_data = call_args.kwargs.get("json")
-                    if (
-                        request_data is None
-                        and len(call_args.args) > 0
-                        and isinstance(call_args.args[0], dict)
-                    ):
+                    if request_data is None and len(call_args.args) > 0 and isinstance(call_args.args[0], dict):
                         request_data = call_args.args[0]
 
-                assert (
-                    request_data is not None
-                ), f"Could not extract request data. Call args: {call_args}"
+                assert request_data is not None, f"Could not extract request data. Call args: {call_args}"
 
                 # Validate request structure
                 assert "collectionName" in request_data
@@ -316,11 +305,7 @@ class TestMilvusVectorStore:
         if request_data_str:
             return json.loads(request_data_str)
         request_data = call_args.kwargs.get("json")
-        if (
-            request_data is None
-            and len(call_args.args) > 0
-            and isinstance(call_args.args[0], dict)
-        ):
+        if request_data is None and len(call_args.args) > 0 and isinstance(call_args.args[0], dict):
             request_data = call_args.args[0]
         return request_data
 
@@ -334,9 +319,7 @@ class TestMilvusVectorStore:
         with patch("litellm.embedding") as mock_embedding:
             mock_embedding.return_value = MOCK_EMBEDDING_RESPONSE
 
-            with patch(
-                "litellm.llms.custom_httpx.http_handler.HTTPHandler.post"
-            ) as mock_post:
+            with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
                 mock_post.return_value = mock_response
 
                 vector_store_search(
@@ -375,9 +358,7 @@ class TestMilvusVectorStore:
         with patch("litellm.embedding") as mock_embedding:
             mock_embedding.return_value = MOCK_EMBEDDING_RESPONSE
 
-            with patch(
-                "litellm.llms.custom_httpx.http_handler.HTTPHandler.post"
-            ) as mock_post:
+            with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
                 mock_post.return_value = mock_response
 
                 vector_store_search(
@@ -413,9 +394,7 @@ class TestMilvusVectorStore:
         with patch("litellm.embedding") as mock_embedding:
             mock_embedding.return_value = MOCK_EMBEDDING_RESPONSE
 
-            with patch(
-                "litellm.llms.custom_httpx.http_handler.HTTPHandler.post"
-            ) as mock_post:
+            with patch("litellm.llms.custom_httpx.http_handler.HTTPHandler.post") as mock_post:
                 mock_post.return_value = mock_response
 
                 vector_store_search(
@@ -492,3 +471,247 @@ if __name__ == "__main__":
     test.test_basic_search_with_mock_sync()
 
     print("\n✅ All mock tests passed!")
+
+
+class RecordingEmbeddingExecutor:
+    def __init__(self, response):
+        self.response = response
+        self.calls = []
+
+    def embed(self, model, query, configuration):
+        self.calls.append((model, query, dict(configuration)))
+        return self.response
+
+    async def aembed(self, model, query, configuration):
+        self.calls.append((model, query, dict(configuration)))
+        return self.response
+
+
+ALIAS_QUERY_VECTOR = [0.5, -0.25, 0.125]
+ALIAS_EMBEDDING_RESPONSE = EmbeddingResponse(
+    data=[{"embedding": ALIAS_QUERY_VECTOR, "index": 0, "object": "embedding"}]
+)
+OPENAI_EMBEDDINGS_URL = "https://api.openai.com/v1/embeddings"
+MILVUS_SEARCH_URL = "https://milvus.example/v2/vectordb/entities/search"
+ALIAS_SEARCH_KWARGS = {
+    "query": "what is machine learning?",
+    "vector_store_id": "book_2",
+    "custom_llm_provider": "milvus",
+    "api_base": "https://milvus.example",
+    "api_key": "mock_milvus_api_key",
+    "litellm_embedding_model": "multilingual-e5-large",
+    "milvus_text_field": "book_intro_text",
+}
+
+
+def _alias_router():
+    return Router(
+        model_list=[
+            {
+                "model_name": "multilingual-e5-large",
+                "litellm_params": {
+                    "model": "openai/text-embedding-3-small",
+                    "api_key": "deployment-key",
+                },
+            }
+        ]
+    )
+
+
+def _mock_embedding_route(respx_mock: respx.MockRouter) -> respx.Route:
+    return respx_mock.post(OPENAI_EMBEDDINGS_URL).mock(
+        return_value=httpx.Response(
+            200,
+            json={
+                "object": "list",
+                "data": [{"object": "embedding", "index": 0, "embedding": ALIAS_QUERY_VECTOR}],
+                "model": "text-embedding-3-small",
+                "usage": {"prompt_tokens": 2, "total_tokens": 2},
+            },
+        )
+    )
+
+
+def _mock_search_route(respx_mock: respx.MockRouter) -> respx.Route:
+    return respx_mock.post(MILVUS_SEARCH_URL).mock(return_value=httpx.Response(200, json=MOCK_MILVUS_SEARCH_RESPONSE))
+
+
+def _assert_alias_resolved(embedding_route: respx.Route, search_route: respx.Route, response):
+    embedding_request = embedding_route.calls.last.request
+    assert embedding_request.headers["authorization"] == "Bearer deployment-key"
+    embedding_body = json.loads(embedding_request.read())
+    assert embedding_body["model"] == "text-embedding-3-small"
+    assert embedding_body["input"] == ["what is machine learning?"]
+    search_request = search_route.calls.last.request
+    assert search_request.headers["authorization"] == "Bearer mock_milvus_api_key"
+    assert json.loads(search_request.read())["data"] == [ALIAS_QUERY_VECTOR]
+    assert len(response["data"]) == len(MOCK_MILVUS_SEARCH_RESPONSE["data"])
+    assert response["data"][0]["content"][0]["text"] == MOCK_MILVUS_SEARCH_RESPONSE["data"][0]["book_intro_text"]
+
+
+def test_router_search_resolves_bare_embedding_alias_sync(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = _mock_embedding_route(respx_mock)
+    search_route = _mock_search_route(respx_mock)
+
+    response = _alias_router().vector_store_search(**ALIAS_SEARCH_KWARGS)
+
+    _assert_alias_resolved(embedding_route, search_route, response)
+
+
+@pytest.mark.asyncio
+async def test_router_search_resolves_bare_embedding_alias_async(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = _mock_embedding_route(respx_mock)
+    search_route = _mock_search_route(respx_mock)
+
+    response = await _alias_router().avector_store_search(**ALIAS_SEARCH_KWARGS)
+
+    _assert_alias_resolved(embedding_route, search_route, response)
+
+
+def test_sdk_search_with_router_kwarg_resolves_bare_embedding_alias_sync(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = _mock_embedding_route(respx_mock)
+    search_route = _mock_search_route(respx_mock)
+
+    response = litellm.vector_stores.search(router=_alias_router(), **ALIAS_SEARCH_KWARGS)
+
+    _assert_alias_resolved(embedding_route, search_route, response)
+
+
+@pytest.mark.asyncio
+async def test_sdk_search_with_router_kwarg_resolves_bare_embedding_alias_async(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = _mock_embedding_route(respx_mock)
+    search_route = _mock_search_route(respx_mock)
+
+    response = await litellm.vector_stores.asearch(router=_alias_router(), **ALIAS_SEARCH_KWARGS)
+
+    _assert_alias_resolved(embedding_route, search_route, response)
+
+
+def _team_alias_router():
+    return Router(
+        model_list=[
+            {
+                "model_name": "team-a-embedder",
+                "litellm_params": {
+                    "model": "openai/text-embedding-3-small",
+                    "api_key": "deployment-key",
+                },
+                "model_info": {"team_id": "team-a", "team_public_model_name": "multilingual-e5-large"},
+            }
+        ]
+    )
+
+
+@pytest.mark.asyncio
+async def test_sdk_search_with_router_kwarg_resolves_team_alias_from_request_metadata(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = _mock_embedding_route(respx_mock)
+    search_route = _mock_search_route(respx_mock)
+
+    response = await litellm.vector_stores.asearch(
+        router=_team_alias_router(), metadata={"user_api_key_team_id": "team-a"}, **ALIAS_SEARCH_KWARGS
+    )
+
+    _assert_alias_resolved(embedding_route, search_route, response)
+
+
+@pytest.mark.asyncio
+async def test_sdk_search_with_router_kwarg_rejects_team_alias_without_team_metadata(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    embedding_route = _mock_embedding_route(respx_mock)
+    _mock_search_route(respx_mock)
+
+    with pytest.raises(litellm.APIConnectionError):
+        await litellm.vector_stores.asearch(router=_team_alias_router(), **ALIAS_SEARCH_KWARGS)
+
+    assert embedding_route.call_count == 0
+
+
+@pytest.mark.asyncio
+async def test_transform_uses_injected_executor_without_embedding_config(respx_mock: respx.MockRouter):
+    executor = RecordingEmbeddingExecutor(ALIAS_EMBEDDING_RESPONSE)
+    config = MilvusVectorStoreConfig()
+    logging_obj = MagicMock()
+    logging_obj.model_call_details = {}
+    transform_kwargs = {
+        "vector_store_id": "book_2",
+        "query": ["what is", "milvus?"],
+        "vector_store_search_optional_params": {"limit": 3},
+        "api_base": "https://milvus.example",
+        "litellm_logging_obj": logging_obj,
+        "litellm_params": {"litellm_embedding_model": "multilingual-e5-large", "milvus_db_name": "docs"},
+        "embedding_executor": executor,
+    }
+
+    url, sync_body = config.transform_search_vector_store_request(**transform_kwargs)
+    _, async_body = await config.atransform_search_vector_store_request(**transform_kwargs)
+
+    assert respx_mock.calls.call_count == 0
+    assert executor.calls == [("multilingual-e5-large", "what is milvus?", {})] * 2
+    assert url == MILVUS_SEARCH_URL
+    assert sync_body == async_body
+    assert sync_body == {
+        "collectionName": "book_2",
+        "data": [ALIAS_QUERY_VECTOR],
+        "annsField": "book_intro_vector",
+        "limit": 3,
+        "dbName": "docs",
+    }
+    assert logging_obj.model_call_details["input"] == "what is milvus?"
+    assert logging_obj.model_call_details["embedding_model"] == "multilingual-e5-large"
+
+
+def test_transform_falls_back_to_sdk_embedding_without_executor_or_config(
+    respx_mock: respx.MockRouter, monkeypatch: pytest.MonkeyPatch
+):
+    monkeypatch.setenv("OPENAI_API_KEY", "env-key")
+    embedding_route = _mock_embedding_route(respx_mock)
+    logging_obj = MagicMock()
+    logging_obj.model_call_details = {}
+
+    _, body = MilvusVectorStoreConfig().transform_search_vector_store_request(
+        vector_store_id="book_2",
+        query="q",
+        vector_store_search_optional_params={},
+        api_base="https://milvus.example",
+        litellm_logging_obj=logging_obj,
+        litellm_params={"litellm_embedding_model": "openai/text-embedding-3-small"},
+    )
+
+    embedding_request = embedding_route.calls.last.request
+    assert embedding_request.headers["authorization"] == "Bearer env-key"
+    assert json.loads(embedding_request.read())["input"] == ["q"]
+    assert body["data"] == [ALIAS_QUERY_VECTOR]
+
+
+def test_transform_requires_embedding_model():
+    with pytest.raises(ValueError, match="litellm_embedding_model is required"):
+        MilvusVectorStoreConfig().transform_search_vector_store_request(
+            vector_store_id="book_2",
+            query="q",
+            vector_store_search_optional_params={},
+            api_base="https://milvus.example",
+            litellm_logging_obj=MagicMock(),
+            litellm_params={"litellm_embedding_config": {"api_key": "store-key"}},
+            embedding_executor=RecordingEmbeddingExecutor(ALIAS_EMBEDDING_RESPONSE),
+        )

type-discipline-budget.json

@@ -1,9 +1,9 @@
 {
   "LIT001": {
-    "limit": 22358
+    "limit": 22334
   },
   "LIT002": {
-    "limit": 26771
+    "limit": 26763
   },
   "LIT003": {
     "limit": 261
@@ -15,7 +15,7 @@
     "limit": 0
   },
   "LIT006": {
-    "limit": 1039
+    "limit": 1038
   },
   "LIT007": {
     "limit": 0
@@ -27,12 +27,12 @@
     "limit": 0
   },
   "LIT010": {
-    "limit": 16485
+    "limit": 16480
   },
   "LIT011": {
-    "limit": 5521
+    "limit": 5520
   },
   "LIT012": {
-    "limit": 4492
+    "limit": 4489
   }
 }