Merge branch 'main' of https://github.com/anigasan/llama-stack

Integrating the Tavily Search functionality to be better integrated with the Llama 4 API

llama_stack/apis/tools/rag_tool.py

@@ -87,6 +87,20 @@ class RAGQueryGenerator(Enum):
     custom = "custom"
 
 
+@json_schema_type
+class RAGSearchMode(Enum):
+    """
+    Search modes for RAG query retrieval:
+    - VECTOR: Uses vector similarity search for semantic matching
+    - KEYWORD: Uses keyword-based search for exact matching
+    - HYBRID: Combines both vector and keyword search for better results
+    """
+
+    VECTOR = "vector"
+    KEYWORD = "keyword"
+    HYBRID = "hybrid"
+
+
 @json_schema_type
 class DefaultRAGQueryGeneratorConfig(BaseModel):
     type: Literal["default"] = "default"
@@ -128,7 +142,7 @@ class RAGQueryConfig(BaseModel):
     max_tokens_in_context: int = 4096
     max_chunks: int = 5
     chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n"
-    mode: str | None = None
+    mode: RAGSearchMode | None = RAGSearchMode.VECTOR
     ranker: Ranker | None = Field(default=None)  # Only used for hybrid mode
 
     @field_validator("chunk_template")

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -19,6 +19,7 @@ class VectorDB(Resource):
 
     embedding_model: str
     embedding_dimension: int
+    vector_db_name: str | None = None
 
     @property
     def vector_db_id(self) -> str:
@@ -70,6 +71,7 @@ class VectorDBs(Protocol):
         embedding_model: str,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
+        vector_db_name: str | None = None,
         provider_vector_db_id: str | None = None,
     ) -> VectorDB:
         """Register a vector database.
@@ -78,6 +80,7 @@ class VectorDBs(Protocol):
         :param embedding_model: The embedding model to use.
         :param embedding_dimension: The dimension of the embedding model.
         :param provider_id: The identifier of the provider.
+        :param vector_db_name: The name of the vector database.
         :param provider_vector_db_id: The identifier of the vector database in the provider.
         :returns: A VectorDB.
         """

llama_stack/apis/vector_io/vector_io.py

@@ -346,7 +346,6 @@ class VectorIO(Protocol):
         embedding_model: str | None = None,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         """Creates a vector store.
 
@@ -358,7 +357,6 @@ class VectorIO(Protocol):
         :param embedding_model: The embedding model to use for this vector store.
         :param embedding_dimension: The dimension of the embedding vectors (default: 384).
         :param provider_id: The ID of the provider to use for this vector store.
-        :param provider_vector_db_id: The provider-specific vector database ID.
         :returns: A VectorStoreObject representing the created vector store.
         """
         ...

llama_stack/distribution/configure.py

@@ -17,7 +17,7 @@ from llama_stack.distribution.distribution import (
     builtin_automatically_routed_apis,
     get_provider_registry,
 )
-from llama_stack.distribution.stack import replace_env_vars
+from llama_stack.distribution.stack import cast_image_name_to_string, replace_env_vars
 from llama_stack.distribution.utils.config_dirs import EXTERNAL_PROVIDERS_DIR
 from llama_stack.distribution.utils.dynamic import instantiate_class_type
 from llama_stack.distribution.utils.prompt_for_config import prompt_for_config
@@ -164,7 +164,8 @@ def upgrade_from_routing_table(
 def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfig:
     version = config_dict.get("version", None)
     if version == LLAMA_STACK_RUN_CONFIG_VERSION:
-        return StackRunConfig(**replace_env_vars(config_dict))
+        processed_config_dict = replace_env_vars(config_dict)
+        return StackRunConfig(**cast_image_name_to_string(processed_config_dict))
 
     if "routing_table" in config_dict:
         logger.info("Upgrading config...")
@@ -175,4 +176,5 @@ def parse_and_maybe_upgrade_config(config_dict: dict[str, Any]) -> StackRunConfi
     if not config_dict.get("external_providers_dir", None):
         config_dict["external_providers_dir"] = EXTERNAL_PROVIDERS_DIR
 
-    return StackRunConfig(**replace_env_vars(config_dict))
+    processed_config_dict = replace_env_vars(config_dict)
+    return StackRunConfig(**cast_image_name_to_string(processed_config_dict))

llama_stack/distribution/resolver.py

@@ -200,7 +200,7 @@ def validate_and_prepare_providers(
         specs = {}
         for provider in providers:
             if not provider.provider_id or provider.provider_id == "__disabled__":
-                logger.warning(f"Provider `{provider.provider_type}` for API `{api}` is disabled")
+                logger.debug(f"Provider `{provider.provider_type}` for API `{api}` is disabled")
                 continue
 
             validate_provider(provider, api, provider_registry)

llama_stack/distribution/routers/vector_io.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import asyncio
+import uuid
 from typing import Any
 
 from llama_stack.apis.common.content_types import (
@@ -81,6 +82,7 @@ class VectorIORouter(VectorIO):
         embedding_model: str,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
+        vector_db_name: str | None = None,
         provider_vector_db_id: str | None = None,
     ) -> None:
         logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
@@ -89,6 +91,7 @@ class VectorIORouter(VectorIO):
             embedding_model,
             embedding_dimension,
             provider_id,
+            vector_db_name,
             provider_vector_db_id,
         )
 
@@ -123,7 +126,6 @@ class VectorIORouter(VectorIO):
         embedding_model: str | None = None,
         embedding_dimension: int | None = None,
         provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
 
@@ -135,17 +137,17 @@ class VectorIORouter(VectorIO):
             embedding_model, embedding_dimension = embedding_model_info
             logger.info(f"No embedding model specified, using first available: {embedding_model}")
 
-        vector_db_id = name
+        vector_db_id = f"vs_{uuid.uuid4()}"
         registered_vector_db = await self.routing_table.register_vector_db(
-            vector_db_id,
-            embedding_model,
-            embedding_dimension,
-            provider_id,
-            provider_vector_db_id,
+            vector_db_id=vector_db_id,
+            embedding_model=embedding_model,
+            embedding_dimension=embedding_dimension,
+            provider_id=provider_id,
+            provider_vector_db_id=vector_db_id,
+            vector_db_name=name,
         )
-
         return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store(
-            vector_db_id,
+            name=name,
             file_ids=file_ids,
             expires_after=expires_after,
             chunking_strategy=chunking_strategy,

llama_stack/distribution/routing_tables/vector_dbs.py

@@ -36,6 +36,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
         provider_vector_db_id: str | None = None,
+        vector_db_name: str | None = None,
     ) -> VectorDB:
         if provider_vector_db_id is None:
             provider_vector_db_id = vector_db_id
@@ -62,6 +63,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
             "provider_resource_id": provider_vector_db_id,
             "embedding_model": embedding_model,
             "embedding_dimension": model.metadata["embedding_dimension"],
+            "vector_db_name": vector_db_name,
         }
         vector_db = TypeAdapter(VectorDBWithOwner).validate_python(vector_db_data)
         await self.register_object(vector_db)

llama_stack/distribution/server/server.py

@@ -47,6 +47,7 @@ from llama_stack.distribution.server.routes import (
     initialize_route_impls,
 )
 from llama_stack.distribution.stack import (
+    cast_image_name_to_string,
     construct_stack,
     replace_env_vars,
     validate_env_pair,
@@ -439,7 +440,7 @@ def main(args: argparse.Namespace | None = None):
                     logger.error(f"Error: {str(e)}")
                     sys.exit(1)
         config = replace_env_vars(config_contents)
-        config = StackRunConfig(**config)
+        config = StackRunConfig(**cast_image_name_to_string(config))
 
     # now that the logger is initialized, print the line about which type of config we are using.
     logger.info(log_line)

llama_stack/distribution/stack.py

@@ -267,6 +267,13 @@ def _convert_string_to_proper_type(value: str) -> Any:
     return value
 
 
+def cast_image_name_to_string(config_dict: dict[str, Any]) -> dict[str, Any]:
+    """Ensure that any value for a key 'image_name' in a config_dict is a string"""
+    if "image_name" in config_dict and config_dict["image_name"] is not None:
+        config_dict["image_name"] = str(config_dict["image_name"])
+    return config_dict
+
+
 def validate_env_pair(env_pair: str) -> tuple[str, str]:
     """Validate and split an environment variable key-value pair."""
     try:

llama_stack/providers/inline/files/localfs/files.py

@@ -51,6 +51,9 @@ class LocalfsFilesImpl(Files):
             },
         )
 
+    async def shutdown(self) -> None:
+        pass
+
     def _generate_file_id(self) -> str:
         """Generate a unique file ID for OpenAI API."""
         return f"file-{uuid.uuid4().hex}"

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -181,8 +181,8 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
             )
             self.cache[vector_db.identifier] = index
 
-        # Load existing OpenAI vector stores using the mixin method
-        self.openai_vector_stores = await self._load_openai_vector_stores()
+        # Load existing OpenAI vector stores into the in-memory cache
+        await self.initialize_openai_vector_stores()
 
     async def shutdown(self) -> None:
         # Cleanup if needed
@@ -261,42 +261,10 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
 
         return await index.query_chunks(query, params)
 
-    # OpenAI Vector Store Mixin abstract method implementations
-    async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
-        """Save vector store metadata to kvstore."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.set(key=key, value=json.dumps(store_info))
-
-    async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]:
-        """Load all vector store metadata from kvstore."""
-        assert self.kvstore is not None
-        start_key = OPENAI_VECTOR_STORES_PREFIX
-        end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff"
-        stored_openai_stores = await self.kvstore.values_in_range(start_key, end_key)
-
-        stores = {}
-        for store_data in stored_openai_stores:
-            store_info = json.loads(store_data)
-            stores[store_info["id"]] = store_info
-        return stores
-
-    async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
-        """Update vector store metadata in kvstore."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.set(key=key, value=json.dumps(store_info))
-
-    async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None:
-        """Delete vector store metadata from kvstore."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.delete(key)
-
     async def _save_openai_vector_store_file(
         self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
     ) -> None:
-        """Save vector store file metadata to kvstore."""
+        """Save vector store file data to kvstore."""
         assert self.kvstore is not None
         key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
         await self.kvstore.set(key=key, value=json.dumps(file_info))
@@ -324,7 +292,16 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
         await self.kvstore.set(key=key, value=json.dumps(file_info))
 
     async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
-        """Delete vector store file metadata from kvstore."""
+        """Delete vector store data from kvstore."""
         assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}"
-        await self.kvstore.delete(key)
+
+        keys_to_delete = [
+            f"{OPENAI_VECTOR_STORES_FILES_PREFIX}{store_id}:{file_id}",
+            f"{OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX}{store_id}:{file_id}",
+        ]
+        for key in keys_to_delete:
+            try:
+                await self.kvstore.delete(key)
+            except Exception as e:
+                logger.warning(f"Failed to delete key {key}: {e}")
+                continue

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -7,6 +7,7 @@
 import asyncio
 import json
 import logging
+import re
 import sqlite3
 import struct
 from typing import Any
@@ -117,6 +118,10 @@ def _rrf_rerank(
     return rrf_scores
 
 
+def _make_sql_identifier(name: str) -> str:
+    return re.sub(r"[^a-zA-Z0-9_]", "_", name)
+
+
 class SQLiteVecIndex(EmbeddingIndex):
     """
     An index implementation that stores embeddings in a SQLite virtual table using sqlite-vec.
@@ -130,9 +135,9 @@ class SQLiteVecIndex(EmbeddingIndex):
         self.dimension = dimension
         self.db_path = db_path
         self.bank_id = bank_id
-        self.metadata_table = f"chunks_{bank_id}".replace("-", "_")
-        self.vector_table = f"vec_chunks_{bank_id}".replace("-", "_")
-        self.fts_table = f"fts_chunks_{bank_id}".replace("-", "_")
+        self.metadata_table = _make_sql_identifier(f"chunks_{bank_id}")
+        self.vector_table = _make_sql_identifier(f"vec_chunks_{bank_id}")
+        self.fts_table = _make_sql_identifier(f"fts_chunks_{bank_id}")
         self.kvstore = kvstore
 
     @classmethod
@@ -148,14 +153,14 @@ class SQLiteVecIndex(EmbeddingIndex):
             try:
                 # Create the table to store chunk metadata.
                 cur.execute(f"""
-                    CREATE TABLE IF NOT EXISTS {self.metadata_table} (
+                    CREATE TABLE IF NOT EXISTS [{self.metadata_table}] (
                         id TEXT PRIMARY KEY,
                         chunk TEXT
                     );
                 """)
                 # Create the virtual table for embeddings.
                 cur.execute(f"""
-                    CREATE VIRTUAL TABLE IF NOT EXISTS {self.vector_table}
+                    CREATE VIRTUAL TABLE IF NOT EXISTS [{self.vector_table}]
                     USING vec0(embedding FLOAT[{self.dimension}], id TEXT);
                 """)
                 connection.commit()
@@ -163,7 +168,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                 # based on query. Implementation of the change on client side will allow passing the search_mode option
                 # during initialization to make it easier to create the table that is required.
                 cur.execute(f"""
-                            CREATE VIRTUAL TABLE IF NOT EXISTS {self.fts_table}
+                            CREATE VIRTUAL TABLE IF NOT EXISTS [{self.fts_table}]
                             USING fts5(id, content);
                         """)
                 connection.commit()
@@ -178,9 +183,9 @@ class SQLiteVecIndex(EmbeddingIndex):
             connection = _create_sqlite_connection(self.db_path)
             cur = connection.cursor()
             try:
-                cur.execute(f"DROP TABLE IF EXISTS {self.metadata_table};")
-                cur.execute(f"DROP TABLE IF EXISTS {self.vector_table};")
-                cur.execute(f"DROP TABLE IF EXISTS {self.fts_table};")
+                cur.execute(f"DROP TABLE IF EXISTS [{self.metadata_table}];")
+                cur.execute(f"DROP TABLE IF EXISTS [{self.vector_table}];")
+                cur.execute(f"DROP TABLE IF EXISTS [{self.fts_table}];")
                 connection.commit()
             finally:
                 cur.close()
@@ -212,7 +217,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                     metadata_data = [(chunk.chunk_id, chunk.model_dump_json()) for chunk in batch_chunks]
                     cur.executemany(
                         f"""
-                        INSERT INTO {self.metadata_table} (id, chunk)
+                        INSERT INTO [{self.metadata_table}] (id, chunk)
                         VALUES (?, ?)
                         ON CONFLICT(id) DO UPDATE SET chunk = excluded.chunk;
                         """,
@@ -230,7 +235,7 @@ class SQLiteVecIndex(EmbeddingIndex):
                         for chunk, emb in zip(batch_chunks, batch_embeddings, strict=True)
                     ]
                     cur.executemany(
-                        f"INSERT INTO {self.vector_table} (id, embedding) VALUES (?, ?);",
+                        f"INSERT INTO [{self.vector_table}] (id, embedding) VALUES (?, ?);",
                         embedding_data,
                     )
 
@@ -238,13 +243,13 @@ class SQLiteVecIndex(EmbeddingIndex):
                     fts_data = [(chunk.chunk_id, chunk.content) for chunk in batch_chunks]
                     # DELETE existing entries with same IDs (FTS5 doesn't support ON CONFLICT)
                     cur.executemany(
-                        f"DELETE FROM {self.fts_table} WHERE id = ?;",
+                        f"DELETE FROM [{self.fts_table}] WHERE id = ?;",
                         [(row[0],) for row in fts_data],
                     )
 
                     # INSERT new entries
                     cur.executemany(
-                        f"INSERT INTO {self.fts_table} (id, content) VALUES (?, ?);",
+                        f"INSERT INTO [{self.fts_table}] (id, content) VALUES (?, ?);",
                         fts_data,
                     )
 
@@ -280,8 +285,8 @@ class SQLiteVecIndex(EmbeddingIndex):
                 emb_blob = serialize_vector(emb_list)
                 query_sql = f"""
                     SELECT m.id, m.chunk, v.distance
-                    FROM {self.vector_table} AS v
-                    JOIN {self.metadata_table} AS m ON m.id = v.id
+                    FROM [{self.vector_table}] AS v
+                    JOIN [{self.metadata_table}] AS m ON m.id = v.id
                     WHERE v.embedding MATCH ? AND k = ?
                     ORDER BY v.distance;
                 """
@@ -322,9 +327,9 @@ class SQLiteVecIndex(EmbeddingIndex):
             cur = connection.cursor()
             try:
                 query_sql = f"""
-                    SELECT DISTINCT m.id, m.chunk, bm25({self.fts_table}) AS score
-                    FROM {self.fts_table} AS f
-                    JOIN {self.metadata_table} AS m ON m.id = f.id
+                    SELECT DISTINCT m.id, m.chunk, bm25([{self.fts_table}]) AS score
+                    FROM [{self.fts_table}] AS f
+                    JOIN [{self.metadata_table}] AS m ON m.id = f.id
                     WHERE f.content MATCH ?
                     ORDER BY score ASC
                     LIMIT ?;
@@ -452,8 +457,8 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
             )
             self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api)
 
-        # load any existing OpenAI vector stores
-        self.openai_vector_stores = await self._load_openai_vector_stores()
+        # Load existing OpenAI vector stores into the in-memory cache
+        await self.initialize_openai_vector_stores()
 
     async def shutdown(self) -> None:
         # nothing to do since we don't maintain a persistent connection
@@ -501,41 +506,6 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
         await self.cache[vector_db_id].index.delete()
         del self.cache[vector_db_id]
 
-    # OpenAI Vector Store Mixin abstract method implementations
-    async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
-        """Save vector store metadata to SQLite database."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.set(key=key, value=json.dumps(store_info))
-        self.openai_vector_stores[store_id] = store_info
-
-    async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]:
-        """Load all vector store metadata from SQLite database."""
-        assert self.kvstore is not None
-        start_key = OPENAI_VECTOR_STORES_PREFIX
-        end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff"
-        stored_openai_stores = await self.kvstore.values_in_range(start_key, end_key)
-        stores = {}
-        for store_data in stored_openai_stores:
-            store_info = json.loads(store_data)
-            stores[store_info["id"]] = store_info
-        return stores
-
-    async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
-        """Update vector store metadata in SQLite database."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.set(key=key, value=json.dumps(store_info))
-        self.openai_vector_stores[store_id] = store_info
-
-    async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None:
-        """Delete vector store metadata from SQLite database."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.delete(key)
-        if store_id in self.openai_vector_stores:
-            del self.openai_vector_stores[store_id]
-
     async def _save_openai_vector_store_file(
         self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
     ) -> None:

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -3,16 +3,17 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import logging
 
-from llama_stack.providers.remote.inference.llama_openai_compat.config import (
-    LlamaCompatConfig,
-)
-from llama_stack.providers.utils.inference.litellm_openai_mixin import (
-    LiteLLMOpenAIMixin,
-)
+from llama_api_client import AsyncLlamaAPIClient, NotFoundError
+
+from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
+from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 
 from .models import MODEL_ENTRIES
 
+logger = logging.getLogger(__name__)
+
 
 class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin):
     _config: LlamaCompatConfig
@@ -27,8 +28,32 @@ class LlamaCompatInferenceAdapter(LiteLLMOpenAIMixin):
         )
         self.config = config
 
+    async def check_model_availability(self, model: str) -> bool:
+        """
+        Check if a specific model is available from Llama API.
+
+        :param model: The model identifier to check.
+        :return: True if the model is available dynamically, False otherwise.
+        """
+        try:
+            llama_api_client = self._get_llama_api_client()
+            retrieved_model = await llama_api_client.models.retrieve(model)
+            logger.info(f"Model {retrieved_model.id} is available from Llama API")
+            return True
+
+        except NotFoundError:
+            logger.error(f"Model {model} is not available from Llama API")
+            return False
+
+        except Exception as e:
+            logger.error(f"Failed to check model availability from Llama API: {e}")
+            return False
+
     async def initialize(self):
         await super().initialize()
 
     async def shutdown(self):
         await super().shutdown()
+
+    def _get_llama_api_client(self) -> AsyncLlamaAPIClient:
+        return AsyncLlamaAPIClient(api_key=self.get_api_key(), base_url=self.config.openai_compat_api_base)

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -7,7 +7,6 @@
 import logging
 import warnings
 from collections.abc import AsyncIterator
-from functools import lru_cache
 from typing import Any
 
 from openai import APIConnectionError, AsyncOpenAI, BadRequestError
@@ -93,41 +92,21 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
 
         self._config = config
 
-    @lru_cache  # noqa: B019
-    def _get_client(self, provider_model_id: str) -> AsyncOpenAI:
+    @property
+    def _client(self) -> AsyncOpenAI:
         """
-        For hosted models, https://integrate.api.nvidia.com/v1 is the primary base_url. However,
-        some models are hosted on different URLs. This function returns the appropriate client
-        for the given provider_model_id.
+        Returns an OpenAI client for the configured NVIDIA API endpoint.
 
-        This relies on lru_cache and self._default_client to avoid creating a new client for each request
-        or for each model that is hosted on https://integrate.api.nvidia.com/v1.
-
-        :param provider_model_id: The provider model ID
         :return: An OpenAI client
         """
 
-        @lru_cache  # noqa: B019
-        def _get_client_for_base_url(base_url: str) -> AsyncOpenAI:
-            """
-            Maintain a single OpenAI client per base_url.
-            """
-            return AsyncOpenAI(
-                base_url=base_url,
-                api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
-                timeout=self._config.timeout,
-            )
-
-        special_model_urls = {
-            "meta/llama-3.2-11b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-11b-vision-instruct",
-            "meta/llama-3.2-90b-vision-instruct": "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct",
-        }
-
         base_url = f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
 
-        if _is_nvidia_hosted(self._config) and provider_model_id in special_model_urls:
-            base_url = special_model_urls[provider_model_id]
-        return _get_client_for_base_url(base_url)
+        return AsyncOpenAI(
+            base_url=base_url,
+            api_key=(self._config.api_key.get_secret_value() if self._config.api_key else "NO KEY"),
+            timeout=self._config.timeout,
+        )
 
     async def _get_provider_model_id(self, model_id: str) -> str:
         if not self.model_store:
@@ -169,7 +148,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            response = await self._get_client(provider_model_id).completions.create(**request)
+            response = await self._client.completions.create(**request)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 
@@ -222,7 +201,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
             extra_body["input_type"] = task_type_options[task_type]
 
         try:
-            response = await self._get_client(provider_model_id).embeddings.create(
+            response = await self._client.embeddings.create(
                 model=provider_model_id,
                 input=input,
                 extra_body=extra_body,
@@ -283,7 +262,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            response = await self._get_client(provider_model_id).chat.completions.create(**request)
+            response = await self._client.chat.completions.create(**request)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 
@@ -339,7 +318,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            return await self._get_client(provider_model_id).completions.create(**params)
+            return await self._client.completions.create(**params)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 
@@ -398,7 +377,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         )
 
         try:
-            return await self._get_client(provider_model_id).chat.completions.create(**params)
+            return await self._client.chat.completions.create(**params)
         except APIConnectionError as e:
             raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
 

llama_stack/providers/remote/inference/ollama/models.py

@@ -12,6 +12,19 @@ from llama_stack.providers.utils.inference.model_registry import (
     build_model_entry,
 )
 
+SAFETY_MODELS_ENTRIES = [
+    # The Llama Guard models don't have their full fp16 versions
+    # so we are going to alias their default version to the canonical SKU
+    build_hf_repo_model_entry(
+        "llama-guard3:8b",
+        CoreModelId.llama_guard_3_8b.value,
+    ),
+    build_hf_repo_model_entry(
+        "llama-guard3:1b",
+        CoreModelId.llama_guard_3_1b.value,
+    ),
+]
+
 MODEL_ENTRIES = [
     build_hf_repo_model_entry(
         "llama3.1:8b-instruct-fp16",
@@ -73,16 +86,6 @@ MODEL_ENTRIES = [
         "llama3.3:70b",
         CoreModelId.llama3_3_70b_instruct.value,
     ),
-    # The Llama Guard models don't have their full fp16 versions
-    # so we are going to alias their default version to the canonical SKU
-    build_hf_repo_model_entry(
-        "llama-guard3:8b",
-        CoreModelId.llama_guard_3_8b.value,
-    ),
-    build_hf_repo_model_entry(
-        "llama-guard3:1b",
-        CoreModelId.llama_guard_3_1b.value,
-    ),
     ProviderModelEntry(
         provider_model_id="all-minilm:l6-v2",
         aliases=["all-minilm"],
@@ -100,4 +103,4 @@ MODEL_ENTRIES = [
             "context_length": 8192,
         },
     ),
-]
+] + SAFETY_MODELS_ENTRIES

llama_stack/providers/remote/inference/openai/openai.py

@@ -8,7 +8,7 @@ import logging
 from collections.abc import AsyncIterator
 from typing import Any
 
-from openai import AsyncOpenAI
+from openai import AsyncOpenAI, NotFoundError
 
 from llama_stack.apis.inference import (
     OpenAIChatCompletion,
@@ -60,6 +60,27 @@ class OpenAIInferenceAdapter(LiteLLMOpenAIMixin):
         # litellm specific model names, an abstraction leak.
         self.is_openai_compat = True
 
+    async def check_model_availability(self, model: str) -> bool:
+        """
+        Check if a specific model is available from OpenAI.
+
+        :param model: The model identifier to check.
+        :return: True if the model is available dynamically, False otherwise.
+        """
+        try:
+            openai_client = self._get_openai_client()
+            retrieved_model = await openai_client.models.retrieve(model)
+            logger.info(f"Model {retrieved_model.id} is available from OpenAI")
+            return True
+
+        except NotFoundError:
+            logger.error(f"Model {model} is not available from OpenAI")
+            return False
+
+        except Exception as e:
+            logger.error(f"Failed to check model availability from OpenAI: {e}")
+            return False
+
     async def initialize(self) -> None:
         await super().initialize()
 

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -217,7 +217,6 @@ class ChromaVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         embedding_model: str | None = None,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in Chroma")
 

llama_stack/providers/remote/vector_io/milvus/config.py

@@ -8,7 +8,7 @@ from typing import Any
 
 from pydantic import BaseModel, ConfigDict, Field
 
-from llama_stack.providers.utils.kvstore.config import KVStoreConfig
+from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -17,7 +17,7 @@ class MilvusVectorIOConfig(BaseModel):
     uri: str = Field(description="The URI of the Milvus server")
     token: str | None = Field(description="The token of the Milvus server")
     consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong")
-    kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
+    kvstore: KVStoreConfig = Field(description="Config for KV store backend")
 
     # This configuration allows additional fields to be passed through to the underlying Milvus client.
     # See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general.
@@ -25,4 +25,11 @@ class MilvusVectorIOConfig(BaseModel):
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
-        return {"uri": "${env.MILVUS_ENDPOINT}", "token": "${env.MILVUS_TOKEN}"}
+        return {
+            "uri": "${env.MILVUS_ENDPOINT}",
+            "token": "${env.MILVUS_TOKEN}",
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="milvus_remote_registry.db",
+            ),
+        }

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -12,7 +12,7 @@ import re
 from typing import Any
 
 from numpy.typing import NDArray
-from pymilvus import DataType, MilvusClient
+from pymilvus import DataType, Function, FunctionType, MilvusClient
 
 from llama_stack.apis.files.files import Files
 from llama_stack.apis.inference import Inference, InterleavedContent
@@ -74,12 +74,66 @@ class MilvusIndex(EmbeddingIndex):
         assert len(chunks) == len(embeddings), (
             f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
         )
+
         if not await asyncio.to_thread(self.client.has_collection, self.collection_name):
+            logger.info(f"Creating new collection {self.collection_name} with nullable sparse field")
+            # Create schema for vector search
+            schema = self.client.create_schema()
+            schema.add_field(
+                field_name="chunk_id",
+                datatype=DataType.VARCHAR,
+                is_primary=True,
+                max_length=100,
+            )
+            schema.add_field(
+                field_name="content",
+                datatype=DataType.VARCHAR,
+                max_length=65535,
+                enable_analyzer=True,  # Enable text analysis for BM25
+            )
+            schema.add_field(
+                field_name="vector",
+                datatype=DataType.FLOAT_VECTOR,
+                dim=len(embeddings[0]),
+            )
+            schema.add_field(
+                field_name="chunk_content",
+                datatype=DataType.JSON,
+            )
+            # Add sparse vector field for BM25 (required by the function)
+            schema.add_field(
+                field_name="sparse",
+                datatype=DataType.SPARSE_FLOAT_VECTOR,
+            )
+
+            # Create indexes
+            index_params = self.client.prepare_index_params()
+            index_params.add_index(
+                field_name="vector",
+                index_type="FLAT",
+                metric_type="COSINE",
+            )
+            # Add index for sparse field (required by BM25 function)
+            index_params.add_index(
+                field_name="sparse",
+                index_type="SPARSE_INVERTED_INDEX",
+                metric_type="BM25",
+            )
+
+            # Add BM25 function for full-text search
+            bm25_function = Function(
+                name="text_bm25_emb",
+                input_field_names=["content"],
+                output_field_names=["sparse"],
+                function_type=FunctionType.BM25,
+            )
+            schema.add_function(bm25_function)
+
             await asyncio.to_thread(
                 self.client.create_collection,
                 self.collection_name,
-                dimension=len(embeddings[0]),
-                auto_id=True,
+                schema=schema,
+                index_params=index_params,
                 consistency_level=self.consistency_level,
             )
 
@@ -88,8 +142,10 @@ class MilvusIndex(EmbeddingIndex):
             data.append(
                 {
                     "chunk_id": chunk.chunk_id,
+                    "content": chunk.content,
                     "vector": embedding,
                     "chunk_content": chunk.model_dump(),
+                    # sparse field will be handled by BM25 function automatically
                 }
             )
         try:
@@ -107,6 +163,7 @@ class MilvusIndex(EmbeddingIndex):
             self.client.search,
             collection_name=self.collection_name,
             data=[embedding],
+            anns_field="vector",
             limit=k,
             output_fields=["*"],
             search_params={"params": {"radius": score_threshold}},
@@ -121,7 +178,64 @@ class MilvusIndex(EmbeddingIndex):
         k: int,
         score_threshold: float,
     ) -> QueryChunksResponse:
-        raise NotImplementedError("Keyword search is not supported in Milvus")
+        """
+        Perform BM25-based keyword search using Milvus's built-in full-text search.
+        """
+        try:
+            # Use Milvus's built-in BM25 search
+            search_res = await asyncio.to_thread(
+                self.client.search,
+                collection_name=self.collection_name,
+                data=[query_string],  # Raw text query
+                anns_field="sparse",  # Use sparse field for BM25
+                output_fields=["chunk_content"],  # Output the chunk content
+                limit=k,
+                search_params={
+                    "params": {
+                        "drop_ratio_search": 0.2,  # Ignore low-importance terms
+                    }
+                },
+            )
+
+            chunks = []
+            scores = []
+            for res in search_res[0]:
+                chunk = Chunk(**res["entity"]["chunk_content"])
+                chunks.append(chunk)
+                scores.append(res["distance"])  # BM25 score from Milvus
+
+            # Filter by score threshold
+            filtered_chunks = [chunk for chunk, score in zip(chunks, scores, strict=False) if score >= score_threshold]
+            filtered_scores = [score for score in scores if score >= score_threshold]
+
+            return QueryChunksResponse(chunks=filtered_chunks, scores=filtered_scores)
+
+        except Exception as e:
+            logger.error(f"Error performing BM25 search: {e}")
+            # Fallback to simple text search
+            return await self._fallback_keyword_search(query_string, k, score_threshold)
+
+    async def _fallback_keyword_search(
+        self,
+        query_string: str,
+        k: int,
+        score_threshold: float,
+    ) -> QueryChunksResponse:
+        """
+        Fallback to simple text search when BM25 search is not available.
+        """
+        # Simple text search using content field
+        search_res = await asyncio.to_thread(
+            self.client.query,
+            collection_name=self.collection_name,
+            filter='content like "%{content}%"',
+            filter_params={"content": query_string},
+            output_fields=["*"],
+            limit=k,
+        )
+        chunks = [Chunk(**res["chunk_content"]) for res in search_res]
+        scores = [1.0] * len(chunks)  # Simple binary score for text search
+        return QueryChunksResponse(chunks=chunks, scores=scores)
 
     async def query_hybrid(
         self,
@@ -179,7 +293,8 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
             uri = os.path.expanduser(self.config.db_path)
             self.client = MilvusClient(uri=uri)
 
-        self.openai_vector_stores = await self._load_openai_vector_stores()
+        # Load existing OpenAI vector stores into the in-memory cache
+        await self.initialize_openai_vector_stores()
 
     async def shutdown(self) -> None:
         self.client.close()
@@ -246,38 +361,16 @@ class MilvusVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
         if not index:
             raise ValueError(f"Vector DB {vector_db_id} not found")
 
+        if params and params.get("mode") == "keyword":
+            # Check if this is inline Milvus (Milvus-Lite)
+            if hasattr(self.config, "db_path"):
+                raise NotImplementedError(
+                    "Keyword search is not supported in Milvus-Lite. "
+                    "Please use a remote Milvus server for keyword search functionality."
+                )
+
         return await index.query_chunks(query, params)
 
-    async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
-        """Save vector store metadata to persistent storage."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.set(key=key, value=json.dumps(store_info))
-        self.openai_vector_stores[store_id] = store_info
-
-    async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
-        """Update vector store metadata in persistent storage."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.set(key=key, value=json.dumps(store_info))
-        self.openai_vector_stores[store_id] = store_info
-
-    async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None:
-        """Delete vector store metadata from persistent storage."""
-        assert self.kvstore is not None
-        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
-        await self.kvstore.delete(key)
-        if store_id in self.openai_vector_stores:
-            del self.openai_vector_stores[store_id]
-
-    async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]:
-        """Load all vector store metadata from persistent storage."""
-        assert self.kvstore is not None
-        start_key = OPENAI_VECTOR_STORES_PREFIX
-        end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff"
-        stored = await self.kvstore.values_in_range(start_key, end_key)
-        return {json.loads(s)["id"]: json.loads(s) for s in stored}
-
     async def _save_openai_vector_store_file(
         self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
     ) -> None:

llama_stack/providers/remote/vector_io/pgvector/config.py

@@ -8,6 +8,10 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
 from llama_stack.schema_utils import json_schema_type
 
 
@@ -18,10 +22,12 @@ class PGVectorVectorIOConfig(BaseModel):
     db: str | None = Field(default="postgres")
     user: str | None = Field(default="postgres")
     password: str | None = Field(default="mysecretpassword")
+    kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
 
     @classmethod
     def sample_run_config(
         cls,
+        __distro_dir__: str,
         host: str = "${env.PGVECTOR_HOST:=localhost}",
         port: int = "${env.PGVECTOR_PORT:=5432}",
         db: str = "${env.PGVECTOR_DB}",
@@ -29,4 +35,14 @@ class PGVectorVectorIOConfig(BaseModel):
         password: str = "${env.PGVECTOR_PASSWORD}",
         **kwargs: Any,
     ) -> dict[str, Any]:
-        return {"host": host, "port": port, "db": db, "user": user, "password": password}
+        return {
+            "host": host,
+            "port": port,
+            "db": db,
+            "user": user,
+            "password": password,
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="pgvector_registry.db",
+            ),
+        }

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -13,24 +13,18 @@ from psycopg2 import sql
 from psycopg2.extras import Json, execute_values
 from pydantic import BaseModel, TypeAdapter
 
+from llama_stack.apis.files.files import Files
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
     QueryChunksResponse,
-    SearchRankingOptions,
     VectorIO,
-    VectorStoreChunkingStrategy,
-    VectorStoreDeleteResponse,
-    VectorStoreFileContentsResponse,
-    VectorStoreFileObject,
-    VectorStoreFileStatus,
-    VectorStoreListFilesResponse,
-    VectorStoreListResponse,
-    VectorStoreObject,
-    VectorStoreSearchResponsePage,
 )
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.utils.kvstore import kvstore_impl
+from llama_stack.providers.utils.kvstore.api import KVStore
+from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIVectorStoreMixin
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
@@ -40,6 +34,13 @@ from .config import PGVectorVectorIOConfig
 
 log = logging.getLogger(__name__)
 
+VERSION = "v3"
+VECTOR_DBS_PREFIX = f"vector_dbs:pgvector:{VERSION}::"
+VECTOR_INDEX_PREFIX = f"vector_index:pgvector:{VERSION}::"
+OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:pgvector:{VERSION}::"
+OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:pgvector:{VERSION}::"
+OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:pgvector:{VERSION}::"
+
 
 def check_extension_version(cur):
     cur.execute("SELECT extversion FROM pg_extension WHERE extname = 'vector'")
@@ -69,7 +70,7 @@ def load_models(cur, cls):
 
 
 class PGVectorIndex(EmbeddingIndex):
-    def __init__(self, vector_db: VectorDB, dimension: int, conn):
+    def __init__(self, vector_db: VectorDB, dimension: int, conn, kvstore: KVStore | None = None):
         self.conn = conn
         with conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
             # Sanitize the table name by replacing hyphens with underscores
@@ -77,6 +78,7 @@ class PGVectorIndex(EmbeddingIndex):
             # when created with patterns like "test-vector-db-{uuid4()}"
             sanitized_identifier = vector_db.identifier.replace("-", "_")
             self.table_name = f"vector_store_{sanitized_identifier}"
+            self.kvstore = kvstore
 
             cur.execute(
                 f"""
@@ -158,15 +160,28 @@ class PGVectorIndex(EmbeddingIndex):
             cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")
 
 
-class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: PGVectorVectorIOConfig, inference_api: Api.inference) -> None:
+class PGVectorVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
+    def __init__(
+        self,
+        config: PGVectorVectorIOConfig,
+        inference_api: Api.inference,
+        files_api: Files | None = None,
+    ) -> None:
         self.config = config
         self.inference_api = inference_api
         self.conn = None
         self.cache = {}
+        self.files_api = files_api
+        self.kvstore: KVStore | None = None
+        self.vector_db_store = None
+        self.openai_vector_store: dict[str, dict[str, Any]] = {}
+        self.metadatadata_collection_name = "openai_vector_stores_metadata"
 
     async def initialize(self) -> None:
         log.info(f"Initializing PGVector memory adapter with config: {self.config}")
+        self.kvstore = await kvstore_impl(self.config.kvstore)
+        await self.initialize_openai_vector_stores()
+
         try:
             self.conn = psycopg2.connect(
                 host=self.config.host,
@@ -201,14 +216,28 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
             log.info("Connection to PGVector database server closed")
 
     async def register_vector_db(self, vector_db: VectorDB) -> None:
+        # Persist vector DB metadata in the KV store
+        assert self.kvstore is not None
+        # Upsert model metadata in Postgres
         upsert_models(self.conn, [(vector_db.identifier, vector_db)])
 
-        index = PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn)
-        self.cache[vector_db.identifier] = VectorDBWithIndex(vector_db, index, self.inference_api)
+        # Create and cache the PGVector index table for the vector DB
+        index = VectorDBWithIndex(
+            vector_db,
+            index=PGVectorIndex(vector_db, vector_db.embedding_dimension, self.conn, kvstore=self.kvstore),
+            inference_api=self.inference_api,
+        )
+        self.cache[vector_db.identifier] = index
 
     async def unregister_vector_db(self, vector_db_id: str) -> None:
-        await self.cache[vector_db_id].index.delete()
-        del self.cache[vector_db_id]
+        # Remove provider index and cache
+        if vector_db_id in self.cache:
+            await self.cache[vector_db_id].index.delete()
+            del self.cache[vector_db_id]
+
+        # Delete vector DB metadata from KV store
+        assert self.kvstore is not None
+        await self.kvstore.delete(key=f"{VECTOR_DBS_PREFIX}{vector_db_id}")
 
     async def insert_chunks(
         self,
@@ -237,107 +266,124 @@ class PGVectorVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         self.cache[vector_db_id] = VectorDBWithIndex(vector_db, index, self.inference_api)
         return self.cache[vector_db_id]
 
-    async def openai_create_vector_store(
-        self,
-        name: str,
-        file_ids: list[str] | None = None,
-        expires_after: dict[str, Any] | None = None,
-        chunking_strategy: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-        embedding_model: str | None = None,
-        embedding_dimension: int | None = 384,
-        provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
-    ) -> VectorStoreObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
+    # OpenAI Vector Stores File operations are not supported in PGVector
+    async def _save_openai_vector_store_file(
+        self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
+    ) -> None:
+        """Save vector store file metadata to Postgres database."""
+        if self.conn is None:
+            raise RuntimeError("PostgreSQL connection is not initialized")
+        try:
+            with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
+                cur.execute(
+                    """
+                    CREATE TABLE IF NOT EXISTS openai_vector_store_files (
+                        store_id TEXT,
+                        file_id TEXT,
+                        metadata JSONB,
+                        PRIMARY KEY (store_id, file_id)
+                    )
+                    """
+                )
+                cur.execute(
+                    """
+                    CREATE TABLE IF NOT EXISTS openai_vector_store_files_contents (
+                        store_id TEXT,
+                        file_id TEXT,
+                        contents JSONB,
+                        PRIMARY KEY (store_id, file_id)
+                    )
+                    """
+                )
+                # Insert file metadata
+                files_query = sql.SQL(
+                    """
+                    INSERT INTO openai_vector_store_files (store_id, file_id, metadata)
+                    VALUES %s
+                    ON CONFLICT (store_id, file_id) DO UPDATE SET metadata = EXCLUDED.metadata
+                    """
+                )
+                files_values = [(store_id, file_id, Json(file_info))]
+                execute_values(cur, files_query, files_values, template="(%s, %s, %s)")
+                # Insert file contents
+                contents_query = sql.SQL(
+                    """
+                    INSERT INTO openai_vector_store_files_contents (store_id, file_id, contents)
+                    VALUES %s
+                    ON CONFLICT (store_id, file_id) DO UPDATE SET contents = EXCLUDED.contents
+                    """
+                )
+                contents_values = [(store_id, file_id, Json(file_contents))]
+                execute_values(cur, contents_query, contents_values, template="(%s, %s, %s)")
+        except Exception as e:
+            log.error(f"Error saving openai vector store file {file_id} for store {store_id}: {e}")
+            raise
 
-    async def openai_list_vector_stores(
-        self,
-        limit: int | None = 20,
-        order: str | None = "desc",
-        after: str | None = None,
-        before: str | None = None,
-    ) -> VectorStoreListResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
+    async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
+        """Load vector store file metadata from Postgres database."""
+        if self.conn is None:
+            raise RuntimeError("PostgreSQL connection is not initialized")
+        try:
+            with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
+                cur.execute(
+                    "SELECT metadata FROM openai_vector_store_files WHERE store_id = %s AND file_id = %s",
+                    (store_id, file_id),
+                )
+                row = cur.fetchone()
+                return row[0] if row and row[0] is not None else {}
+        except Exception as e:
+            log.error(f"Error loading openai vector store file {file_id} for store {store_id}: {e}")
+            return {}
 
-    async def openai_retrieve_vector_store(
-        self,
-        vector_store_id: str,
-    ) -> VectorStoreObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
+    async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
+        """Load vector store file contents from Postgres database."""
+        if self.conn is None:
+            raise RuntimeError("PostgreSQL connection is not initialized")
+        try:
+            with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
+                cur.execute(
+                    "SELECT contents FROM openai_vector_store_files_contents WHERE store_id = %s AND file_id = %s",
+                    (store_id, file_id),
+                )
+                row = cur.fetchone()
+                return row[0] if row and row[0] is not None else []
+        except Exception as e:
+            log.error(f"Error loading openai vector store file contents for {file_id} in store {store_id}: {e}")
+            return []
 
-    async def openai_update_vector_store(
-        self,
-        vector_store_id: str,
-        name: str | None = None,
-        expires_after: dict[str, Any] | None = None,
-        metadata: dict[str, Any] | None = None,
-    ) -> VectorStoreObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
+    async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
+        """Update vector store file metadata in Postgres database."""
+        if self.conn is None:
+            raise RuntimeError("PostgreSQL connection is not initialized")
+        try:
+            with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
+                query = sql.SQL(
+                    """
+                    INSERT INTO openai_vector_store_files (store_id, file_id, metadata)
+                    VALUES %s
+                    ON CONFLICT (store_id, file_id) DO UPDATE SET metadata = EXCLUDED.metadata
+                    """
+                )
+                values = [(store_id, file_id, Json(file_info))]
+                execute_values(cur, query, values, template="(%s, %s, %s)")
+        except Exception as e:
+            log.error(f"Error updating openai vector store file {file_id} for store {store_id}: {e}")
+            raise
 
-    async def openai_delete_vector_store(
-        self,
-        vector_store_id: str,
-    ) -> VectorStoreDeleteResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_search_vector_store(
-        self,
-        vector_store_id: str,
-        query: str | list[str],
-        filters: dict[str, Any] | None = None,
-        max_num_results: int | None = 10,
-        ranking_options: SearchRankingOptions | None = None,
-        rewrite_query: bool | None = False,
-        search_mode: str | None = "vector",
-    ) -> VectorStoreSearchResponsePage:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_attach_file_to_vector_store(
-        self,
-        vector_store_id: str,
-        file_id: str,
-        attributes: dict[str, Any] | None = None,
-        chunking_strategy: VectorStoreChunkingStrategy | None = None,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_list_files_in_vector_store(
-        self,
-        vector_store_id: str,
-        limit: int | None = 20,
-        order: str | None = "desc",
-        after: str | None = None,
-        before: str | None = None,
-        filter: VectorStoreFileStatus | None = None,
-    ) -> VectorStoreListFilesResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_retrieve_vector_store_file(
-        self,
-        vector_store_id: str,
-        file_id: str,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_retrieve_vector_store_file_contents(
-        self,
-        vector_store_id: str,
-        file_id: str,
-    ) -> VectorStoreFileContentsResponse:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_update_vector_store_file(
-        self,
-        vector_store_id: str,
-        file_id: str,
-        attributes: dict[str, Any] | None = None,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
-
-    async def openai_delete_vector_store_file(
-        self,
-        vector_store_id: str,
-        file_id: str,
-    ) -> VectorStoreFileObject:
-        raise NotImplementedError("OpenAI Vector Stores API is not supported in PGVector")
+    async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
+        """Delete vector store file metadata from Postgres database."""
+        if self.conn is None:
+            raise RuntimeError("PostgreSQL connection is not initialized")
+        try:
+            with self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor) as cur:
+                cur.execute(
+                    "DELETE FROM openai_vector_store_files WHERE store_id = %s AND file_id = %s",
+                    (store_id, file_id),
+                )
+                cur.execute(
+                    "DELETE FROM openai_vector_store_files_contents WHERE store_id = %s AND file_id = %s",
+                    (store_id, file_id),
+                )
+        except Exception as e:
+            log.error(f"Error deleting openai vector store file {file_id} for store {store_id}: {e}")
+            raise

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -214,7 +214,6 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
         embedding_model: str | None = None,
         embedding_dimension: int | None = 384,
         provider_id: str | None = None,
-        provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         raise NotImplementedError("OpenAI Vector Stores API is not supported in Qdrant")
 

llama_stack/providers/remote/vector_io/weaviate/config.py

@@ -6,15 +6,26 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
 
 
 class WeaviateRequestProviderData(BaseModel):
     weaviate_api_key: str
     weaviate_cluster_url: str
+    kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
 
 
 class WeaviateVectorIOConfig(BaseModel):
     @classmethod
-    def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]:
-        return {}
+    def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
+        return {
+            "kvstore": SqliteKVStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="weaviate_registry.db",
+            ),
+        }

llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -14,10 +14,13 @@ from weaviate.classes.init import Auth
 from weaviate.classes.query import Filter
 
 from llama_stack.apis.common.content_types import InterleavedContent
+from llama_stack.apis.files.files import Files
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.utils.kvstore import kvstore_impl
+from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.vector_store import (
     EmbeddingIndex,
     VectorDBWithIndex,
@@ -27,11 +30,19 @@ from .config import WeaviateRequestProviderData, WeaviateVectorIOConfig
 
 log = logging.getLogger(__name__)
 
+VERSION = "v3"
+VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::"
+VECTOR_INDEX_PREFIX = f"vector_index:weaviate:{VERSION}::"
+OPENAI_VECTOR_STORES_PREFIX = f"openai_vector_stores:weaviate:{VERSION}::"
+OPENAI_VECTOR_STORES_FILES_PREFIX = f"openai_vector_stores_files:weaviate:{VERSION}::"
+OPENAI_VECTOR_STORES_FILES_CONTENTS_PREFIX = f"openai_vector_stores_files_contents:weaviate:{VERSION}::"
+
 
 class WeaviateIndex(EmbeddingIndex):
-    def __init__(self, client: weaviate.Client, collection_name: str):
+    def __init__(self, client: weaviate.Client, collection_name: str, kvstore: KVStore | None = None):
         self.client = client
         self.collection_name = collection_name
+        self.kvstore = kvstore
 
     async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray):
         assert len(chunks) == len(embeddings), (
@@ -109,11 +120,21 @@ class WeaviateVectorIOAdapter(
     NeedsRequestProviderData,
     VectorDBsProtocolPrivate,
 ):
-    def __init__(self, config: WeaviateVectorIOConfig, inference_api: Api.inference) -> None:
+    def __init__(
+        self,
+        config: WeaviateVectorIOConfig,
+        inference_api: Api.inference,
+        files_api: Files | None,
+    ) -> None:
         self.config = config
         self.inference_api = inference_api
         self.client_cache = {}
         self.cache = {}
+        self.files_api = files_api
+        self.kvstore: KVStore | None = None
+        self.vector_db_store = None
+        self.openai_vector_stores: dict[str, dict[str, Any]] = {}
+        self.metadata_collection_name = "openai_vector_stores_metadata"
 
     def _get_client(self) -> weaviate.Client:
         provider_data = self.get_request_provider_data()
@@ -132,7 +153,26 @@ class WeaviateVectorIOAdapter(
         return client
 
     async def initialize(self) -> None:
-        pass
+        """Set up KV store and load existing vector DBs and OpenAI vector stores."""
+        # Initialize KV store for metadata
+        self.kvstore = await kvstore_impl(self.config.kvstore)
+
+        # Load existing vector DB definitions
+        start_key = VECTOR_DBS_PREFIX
+        end_key = f"{VECTOR_DBS_PREFIX}\xff"
+        stored = await self.kvstore.values_in_range(start_key, end_key)
+        for raw in stored:
+            vector_db = VectorDB.model_validate_json(raw)
+            client = self._get_client()
+            idx = WeaviateIndex(client=client, collection_name=vector_db.identifier, kvstore=self.kvstore)
+            self.cache[vector_db.identifier] = VectorDBWithIndex(
+                vector_db=vector_db,
+                index=idx,
+                inference_api=self.inference_api,
+            )
+
+        # Load OpenAI vector stores metadata into cache
+        await self.initialize_openai_vector_stores()
 
     async def shutdown(self) -> None:
         for client in self.client_cache.values():
@@ -206,3 +246,21 @@ class WeaviateVectorIOAdapter(
             raise ValueError(f"Vector DB {vector_db_id} not found")
 
         return await index.query_chunks(query, params)
+
+    # OpenAI Vector Stores File operations are not supported in Weaviate
+    async def _save_openai_vector_store_file(
+        self, store_id: str, file_id: str, file_info: dict[str, Any], file_contents: list[dict[str, Any]]
+    ) -> None:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate")
+
+    async def _load_openai_vector_store_file(self, store_id: str, file_id: str) -> dict[str, Any]:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate")
+
+    async def _load_openai_vector_store_file_contents(self, store_id: str, file_id: str) -> list[dict[str, Any]]:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate")
+
+    async def _update_openai_vector_store_file(self, store_id: str, file_id: str, file_info: dict[str, Any]) -> None:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate")
+
+    async def _delete_openai_vector_store_file_from_storage(self, store_id: str, file_id: str) -> None:
+        raise NotImplementedError("OpenAI Vector Stores API is not supported in Weaviate")

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -13,7 +13,6 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
 )
-from llama_stack.apis.common.errors import UnsupportedModelError
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -39,7 +38,6 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
-from llama_stack.apis.models import Model
 from llama_stack.distribution.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
@@ -90,12 +88,6 @@ class LiteLLMOpenAIMixin(
     async def shutdown(self):
         pass
 
-    async def register_model(self, model: Model) -> Model:
-        model_id = self.get_provider_model_id(model.provider_resource_id)
-        if model_id is None:
-            raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys())
-        return model
-
     def get_litellm_model_name(self, model_id: str) -> str:
         # users may be using openai/ prefix in their model names. the openai/models.py did this by default.
         # model_id.startswith("openai/") is for backwards compatibility.

llama_stack/providers/utils/inference/model_registry.py

@@ -83,9 +83,37 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
     def get_llama_model(self, provider_model_id: str) -> str | None:
         return self.provider_id_to_llama_model_map.get(provider_model_id, None)
 
+    async def check_model_availability(self, model: str) -> bool:
+        """
+        Check if a specific model is available from the provider (non-static check).
+
+        This is for subclassing purposes, so providers can check if a specific
+        model is currently available for use through dynamic means (e.g., API calls).
+
+        This method should NOT check statically configured model entries in
+        `self.alias_to_provider_id_map` - that is handled separately in register_model.
+
+        Default implementation returns False (no dynamic models available).
+
+        :param model: The model identifier to check.
+        :return: True if the model is available dynamically, False otherwise.
+        """
+        return False
+
     async def register_model(self, model: Model) -> Model:
-        if not (supported_model_id := self.get_provider_model_id(model.provider_resource_id)):
-            raise UnsupportedModelError(model.provider_resource_id, self.alias_to_provider_id_map.keys())
+        # Check if model is supported in static configuration
+        supported_model_id = self.get_provider_model_id(model.provider_resource_id)
+
+        # If not found in static config, check if it's available dynamically from provider
+        if not supported_model_id:
+            if await self.check_model_availability(model.provider_resource_id):
+                supported_model_id = model.provider_resource_id
+            else:
+                # note: we cannot provide a complete list of supported models without
+                #       getting a complete list from the provider, so we return "..."
+                all_supported_models = [*self.alias_to_provider_id_map.keys(), "..."]
+                raise UnsupportedModelError(model.provider_resource_id, all_supported_models)
+
         provider_resource_id = self.get_provider_model_id(model.model_id)
         if model.model_type == ModelType.embedding:
             # embedding models are always registered by their provider model id and does not need to be mapped to a llama model
@@ -114,6 +142,7 @@ class ModelRegistryHelper(ModelsProtocolPrivate):
                         ALL_HUGGINGFACE_REPOS_TO_MODEL_DESCRIPTOR[llama_model]
                     )
 
+        # Register the model alias, ensuring it maps to the correct provider model id
         self.alias_to_provider_id_map[model.model_id] = supported_model_id
 
         return model

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import asyncio
+import json
 import logging
 import mimetypes
 import time
@@ -35,6 +36,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreSearchResponse,
     VectorStoreSearchResponsePage,
 )
+from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, make_overlapped_chunks
 
 logger = logging.getLogger(__name__)
@@ -59,26 +61,45 @@ class OpenAIVectorStoreMixin(ABC):
     # These should be provided by the implementing class
     openai_vector_stores: dict[str, dict[str, Any]]
     files_api: Files | None
+    # KV store for persisting OpenAI vector store metadata
+    kvstore: KVStore | None
 
-    @abstractmethod
     async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
         """Save vector store metadata to persistent storage."""
-        pass
+        assert self.kvstore is not None
+        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
+        await self.kvstore.set(key=key, value=json.dumps(store_info))
+        # update in-memory cache
+        self.openai_vector_stores[store_id] = store_info
 
-    @abstractmethod
     async def _load_openai_vector_stores(self) -> dict[str, dict[str, Any]]:
         """Load all vector store metadata from persistent storage."""
-        pass
+        assert self.kvstore is not None
+        start_key = OPENAI_VECTOR_STORES_PREFIX
+        end_key = f"{OPENAI_VECTOR_STORES_PREFIX}\xff"
+        stored_data = await self.kvstore.values_in_range(start_key, end_key)
+
+        stores: dict[str, dict[str, Any]] = {}
+        for item in stored_data:
+            info = json.loads(item)
+            stores[info["id"]] = info
+        return stores
 
-    @abstractmethod
     async def _update_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
         """Update vector store metadata in persistent storage."""
-        pass
+        assert self.kvstore is not None
+        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
+        await self.kvstore.set(key=key, value=json.dumps(store_info))
+        # update in-memory cache
+        self.openai_vector_stores[store_id] = store_info
 
-    @abstractmethod
     async def _delete_openai_vector_store_from_storage(self, store_id: str) -> None:
         """Delete vector store metadata from persistent storage."""
-        pass
+        assert self.kvstore is not None
+        key = f"{OPENAI_VECTOR_STORES_PREFIX}{store_id}"
+        await self.kvstore.delete(key)
+        # remove from in-memory cache
+        self.openai_vector_stores.pop(store_id, None)
 
     @abstractmethod
     async def _save_openai_vector_store_file(
@@ -117,6 +138,10 @@ class OpenAIVectorStoreMixin(ABC):
         """Unregister a vector database (provider-specific implementation)."""
         pass
 
+    async def initialize_openai_vector_stores(self) -> None:
+        """Load existing OpenAI vector stores into the in-memory cache."""
+        self.openai_vector_stores = await self._load_openai_vector_stores()
+
     @abstractmethod
     async def insert_chunks(
         self,
@@ -147,8 +172,9 @@ class OpenAIVectorStoreMixin(ABC):
         provider_vector_db_id: str | None = None,
     ) -> VectorStoreObject:
         """Creates a vector store."""
-        store_id = name or str(uuid.uuid4())
         created_at = int(time.time())
+        # Derive the canonical vector_db_id (allow override, else generate)
+        vector_db_id = provider_vector_db_id or f"vs_{uuid.uuid4()}"
 
         if provider_id is None:
             raise ValueError("Provider ID is required")
@@ -156,19 +182,19 @@ class OpenAIVectorStoreMixin(ABC):
         if embedding_model is None:
             raise ValueError("Embedding model is required")
 
-        # Use provided embedding dimension or default to 384
+        # Embedding dimension is required (defaulted to 384 if not provided)
         if embedding_dimension is None:
             raise ValueError("Embedding dimension is required")
 
-        provider_vector_db_id = provider_vector_db_id or store_id
+        # Register the VectorDB backing this vector store
         vector_db = VectorDB(
-            identifier=store_id,
+            identifier=vector_db_id,
             embedding_dimension=embedding_dimension,
             embedding_model=embedding_model,
             provider_id=provider_id,
-            provider_resource_id=provider_vector_db_id,
+            provider_resource_id=vector_db_id,
+            vector_db_name=name,
         )
-        # Register the vector DB
         await self.register_vector_db(vector_db)
 
         # Create OpenAI vector store metadata
@@ -182,11 +208,11 @@ class OpenAIVectorStoreMixin(ABC):
             in_progress=0,
             total=0,
         )
-        store_info = {
-            "id": store_id,
+        store_info: dict[str, Any] = {
+            "id": vector_db_id,
             "object": "vector_store",
             "created_at": created_at,
-            "name": store_id,
+            "name": name,
             "usage_bytes": 0,
             "file_counts": file_counts.model_dump(),
             "status": status,
@@ -206,18 +232,18 @@ class OpenAIVectorStoreMixin(ABC):
         store_info["metadata"] = metadata
 
         # Save to persistent storage (provider-specific)
-        await self._save_openai_vector_store(store_id, store_info)
+        await self._save_openai_vector_store(vector_db_id, store_info)
 
         # Store in memory cache
-        self.openai_vector_stores[store_id] = store_info
+        self.openai_vector_stores[vector_db_id] = store_info
 
         # Now that our vector store is created, attach any files that were provided
         file_ids = file_ids or []
-        tasks = [self.openai_attach_file_to_vector_store(store_id, file_id) for file_id in file_ids]
+        tasks = [self.openai_attach_file_to_vector_store(vector_db_id, file_id) for file_id in file_ids]
         await asyncio.gather(*tasks)
 
         # Get the updated store info and return it
-        store_info = self.openai_vector_stores[store_id]
+        store_info = self.openai_vector_stores[vector_db_id]
         return VectorStoreObject.model_validate(store_info)
 
     async def openai_list_vector_stores(

llama_stack/templates/nvidia/nvidia.py

@@ -68,7 +68,7 @@ def get_distribution_template() -> DistributionTemplate:
         ),
     ]
 
-    default_models = get_model_registry(available_models)
+    default_models, _ = get_model_registry(available_models)
     return DistributionTemplate(
         name="nvidia",
         distro_type="self_hosted",

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -128,6 +128,7 @@ def get_distribution_template() -> DistributionTemplate:
             provider_id="${env.ENABLE_PGVECTOR:+pgvector}",
             provider_type="remote::pgvector",
             config=PGVectorVectorIOConfig.sample_run_config(
+                f"~/.llama/distributions/{name}",
                 db="${env.PGVECTOR_DB:=}",
                 user="${env.PGVECTOR_USER:=}",
                 password="${env.PGVECTOR_PASSWORD:=}",
@@ -146,7 +147,8 @@ def get_distribution_template() -> DistributionTemplate:
         ),
     ]
 
-    default_models = get_model_registry(available_models) + [
+    models, _ = get_model_registry(available_models)
+    default_models = models + [
         ModelInput(
             model_id="meta-llama/Llama-3.3-70B-Instruct",
             provider_id="groq",

llama_stack/templates/open-benchmark/run.yaml

@@ -54,6 +54,9 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/pgvector_registry.db
   safety:
   - provider_id: llama-guard
     provider_type: inline::llama-guard

llama_stack/templates/starter/run.yaml

@@ -166,6 +166,9 @@ providers:
       db: ${env.PGVECTOR_DB:=}
       user: ${env.PGVECTOR_USER:=}
       password: ${env.PGVECTOR_PASSWORD:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db
   files:
   - provider_id: meta-reference-files
     provider_type: inline::localfs
@@ -1172,24 +1175,8 @@ models:
   provider_id: ${env.ENABLE_SENTENCE_TRANSFORMERS:=sentence-transformers}
   model_type: embedding
 shields:
-- shield_id: ${env.ENABLE_OLLAMA:=__disabled__}
-  provider_id: llama-guard
-  provider_shield_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.SAFETY_MODEL:=llama-guard3:1b}
-- shield_id: ${env.ENABLE_FIREWORKS:=__disabled__}
-  provider_id: llama-guard
-  provider_shield_id: ${env.ENABLE_FIREWORKS:=__disabled__}/${env.SAFETY_MODEL:=accounts/fireworks/models/llama-guard-3-8b}
-- shield_id: ${env.ENABLE_FIREWORKS:=__disabled__}
-  provider_id: llama-guard
-  provider_shield_id: ${env.ENABLE_FIREWORKS:=__disabled__}/${env.SAFETY_MODEL:=accounts/fireworks/models/llama-guard-3-11b-vision}
-- shield_id: ${env.ENABLE_TOGETHER:=__disabled__}
-  provider_id: llama-guard
-  provider_shield_id: ${env.ENABLE_TOGETHER:=__disabled__}/${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-8B}
-- shield_id: ${env.ENABLE_TOGETHER:=__disabled__}
-  provider_id: llama-guard
-  provider_shield_id: ${env.ENABLE_TOGETHER:=__disabled__}/${env.SAFETY_MODEL:=meta-llama/Llama-Guard-3-11B-Vision-Turbo}
-- shield_id: ${env.ENABLE_SAMBANOVA:=__disabled__}
-  provider_id: llama-guard
-  provider_shield_id: ${env.ENABLE_SAMBANOVA:=__disabled__}/${env.SAFETY_MODEL:=sambanova/Meta-Llama-Guard-3-8B}
+- shield_id: ${env.SAFETY_MODEL:=__disabled__}
+  provider_shield_id: ${env.ENABLE_OLLAMA:=__disabled__}/${env.SAFETY_MODEL:=__disabled__}
 vector_dbs: []
 datasets: []
 scoring_fns: []

llama_stack/templates/starter/starter.py

@@ -12,7 +12,6 @@ from llama_stack.distribution.datatypes import (
     ModelInput,
     Provider,
     ProviderSpec,
-    ShieldInput,
     ToolGroupInput,
 )
 from llama_stack.distribution.utils.dynamic import instantiate_class_type
@@ -32,75 +31,39 @@ from llama_stack.providers.registry.inference import available_providers
 from llama_stack.providers.remote.inference.anthropic.models import (
     MODEL_ENTRIES as ANTHROPIC_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.anthropic.models import (
-    SAFETY_MODELS_ENTRIES as ANTHROPIC_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.bedrock.models import (
     MODEL_ENTRIES as BEDROCK_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.bedrock.models import (
-    SAFETY_MODELS_ENTRIES as BEDROCK_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.cerebras.models import (
     MODEL_ENTRIES as CEREBRAS_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.cerebras.models import (
-    SAFETY_MODELS_ENTRIES as CEREBRAS_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.databricks.databricks import (
     MODEL_ENTRIES as DATABRICKS_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.databricks.databricks import (
-    SAFETY_MODELS_ENTRIES as DATABRICKS_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.fireworks.models import (
     MODEL_ENTRIES as FIREWORKS_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.fireworks.models import (
-    SAFETY_MODELS_ENTRIES as FIREWORKS_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.gemini.models import (
     MODEL_ENTRIES as GEMINI_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.gemini.models import (
-    SAFETY_MODELS_ENTRIES as GEMINI_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.groq.models import (
     MODEL_ENTRIES as GROQ_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.groq.models import (
-    SAFETY_MODELS_ENTRIES as GROQ_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.nvidia.models import (
     MODEL_ENTRIES as NVIDIA_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.nvidia.models import (
-    SAFETY_MODELS_ENTRIES as NVIDIA_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.openai.models import (
     MODEL_ENTRIES as OPENAI_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.openai.models import (
-    SAFETY_MODELS_ENTRIES as OPENAI_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.runpod.runpod import (
     MODEL_ENTRIES as RUNPOD_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.runpod.runpod import (
-    SAFETY_MODELS_ENTRIES as RUNPOD_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.sambanova.models import (
     MODEL_ENTRIES as SAMBANOVA_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.sambanova.models import (
-    SAFETY_MODELS_ENTRIES as SAMBANOVA_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.inference.together.models import (
     MODEL_ENTRIES as TOGETHER_MODEL_ENTRIES,
 )
-from llama_stack.providers.remote.inference.together.models import (
-    SAFETY_MODELS_ENTRIES as TOGETHER_SAFETY_MODELS_ENTRIES,
-)
 from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
 from llama_stack.providers.remote.vector_io.pgvector.config import (
     PGVectorVectorIOConfig,
@@ -111,6 +74,7 @@ from llama_stack.templates.template import (
     DistributionTemplate,
     RunConfigSettings,
     get_model_registry,
+    get_shield_registry,
 )
 
 
@@ -164,28 +128,13 @@ def _get_model_entries_for_provider(provider_type: str) -> list[ProviderModelEnt
 def _get_model_safety_entries_for_provider(provider_type: str) -> list[ProviderModelEntry]:
     """Get model entries for a specific provider type."""
     safety_model_entries_map = {
-        "openai": OPENAI_SAFETY_MODELS_ENTRIES,
-        "fireworks": FIREWORKS_SAFETY_MODELS_ENTRIES,
-        "together": TOGETHER_SAFETY_MODELS_ENTRIES,
-        "anthropic": ANTHROPIC_SAFETY_MODELS_ENTRIES,
-        "gemini": GEMINI_SAFETY_MODELS_ENTRIES,
-        "groq": GROQ_SAFETY_MODELS_ENTRIES,
-        "sambanova": SAMBANOVA_SAFETY_MODELS_ENTRIES,
-        "cerebras": CEREBRAS_SAFETY_MODELS_ENTRIES,
-        "bedrock": BEDROCK_SAFETY_MODELS_ENTRIES,
-        "databricks": DATABRICKS_SAFETY_MODELS_ENTRIES,
-        "nvidia": NVIDIA_SAFETY_MODELS_ENTRIES,
-        "runpod": RUNPOD_SAFETY_MODELS_ENTRIES,
-    }
-
-    # Special handling for providers with dynamic model entries
-    if provider_type == "ollama":
-        return [
+        "ollama": [
             ProviderModelEntry(
-                provider_model_id="llama-guard3:1b",
+                provider_model_id="${env.SAFETY_MODEL:=__disabled__}",
                 model_type=ModelType.llm,
             ),
-        ]
+        ],
+    }
 
     return safety_model_entries_map.get(provider_type, [])
 
@@ -246,28 +195,20 @@ def get_remote_inference_providers() -> tuple[list[Provider], dict[str, list[Pro
 
 
 # build a list of shields for all possible providers
-def get_shields_for_providers(providers: list[Provider]) -> list[ShieldInput]:
-    shields = []
+def get_safety_models_for_providers(providers: list[Provider]) -> dict[str, list[ProviderModelEntry]]:
+    available_models = {}
     for provider in providers:
         provider_type = provider.provider_type.split("::")[1]
         safety_model_entries = _get_model_safety_entries_for_provider(provider_type)
         if len(safety_model_entries) == 0:
             continue
-        if provider.provider_id:
-            shield_id = provider.provider_id
-        else:
-            raise ValueError(f"Provider {provider.provider_type} has no provider_id")
-        for safety_model_entry in safety_model_entries:
-            print(f"provider.provider_id: {provider.provider_id}")
-            print(f"safety_model_entry.provider_model_id: {safety_model_entry.provider_model_id}")
-            shields.append(
-                ShieldInput(
-                    provider_id="llama-guard",
-                    shield_id=shield_id,
-                    provider_shield_id=f"{provider.provider_id}/${{env.SAFETY_MODEL:={safety_model_entry.provider_model_id}}}",
-                )
-            )
-    return shields
+
+        env_var = f"ENABLE_{provider_type.upper().replace('-', '_').replace('::', '_')}"
+        provider_id = f"${{env.{env_var}:=__disabled__}}"
+
+        available_models[provider_id] = safety_model_entries
+
+    return available_models
 
 
 def get_distribution_template() -> DistributionTemplate:
@@ -300,6 +241,7 @@ def get_distribution_template() -> DistributionTemplate:
             provider_id="${env.ENABLE_PGVECTOR:=__disabled__}",
             provider_type="remote::pgvector",
             config=PGVectorVectorIOConfig.sample_run_config(
+                f"~/.llama/distributions/{name}",
                 db="${env.PGVECTOR_DB:=}",
                 user="${env.PGVECTOR_USER:=}",
                 password="${env.PGVECTOR_PASSWORD:=}",
@@ -307,8 +249,6 @@ def get_distribution_template() -> DistributionTemplate:
         ),
     ]
 
-    shields = get_shields_for_providers(remote_inference_providers)
-
     providers = {
         "inference": ([p.provider_type for p in remote_inference_providers] + ["inline::sentence-transformers"]),
         "vector_io": ([p.provider_type for p in vector_io_providers]),
@@ -361,7 +301,10 @@ def get_distribution_template() -> DistributionTemplate:
         },
     )
 
-    default_models = get_model_registry(available_models)
+    default_models, ids_conflict_in_models = get_model_registry(available_models)
+
+    available_safety_models = get_safety_models_for_providers(remote_inference_providers)
+    shields = get_shield_registry(available_safety_models, ids_conflict_in_models)
 
     return DistributionTemplate(
         name=name,

llama_stack/templates/template.py

@@ -37,7 +37,7 @@ from llama_stack.providers.utils.sqlstore.sqlstore import get_pip_packages as ge
 
 def get_model_registry(
     available_models: dict[str, list[ProviderModelEntry]],
-) -> list[ModelInput]:
+) -> tuple[list[ModelInput], bool]:
     models = []
 
     # check for conflicts in model ids
@@ -74,7 +74,50 @@ def get_model_registry(
                         metadata=entry.metadata,
                     )
                 )
-    return models
+    return models, ids_conflict
+
+
+def get_shield_registry(
+    available_safety_models: dict[str, list[ProviderModelEntry]],
+    ids_conflict_in_models: bool,
+) -> list[ShieldInput]:
+    shields = []
+
+    # check for conflicts in shield ids
+    all_ids = set()
+    ids_conflict = False
+
+    for _, entries in available_safety_models.items():
+        for entry in entries:
+            ids = [entry.provider_model_id] + entry.aliases
+            for model_id in ids:
+                if model_id in all_ids:
+                    ids_conflict = True
+                    rich.print(
+                        f"[yellow]Shield id {model_id} conflicts; all shield ids will be prefixed with provider id[/yellow]"
+                    )
+                    break
+            all_ids.update(ids)
+            if ids_conflict:
+                break
+        if ids_conflict:
+            break
+
+    for provider_id, entries in available_safety_models.items():
+        for entry in entries:
+            ids = [entry.provider_model_id] + entry.aliases
+            for model_id in ids:
+                identifier = f"{provider_id}/{model_id}" if ids_conflict and provider_id not in model_id else model_id
+                shields.append(
+                    ShieldInput(
+                        shield_id=identifier,
+                        provider_shield_id=f"{provider_id}/{entry.provider_model_id}"
+                        if ids_conflict_in_models
+                        else entry.provider_model_id,
+                    )
+                )
+
+    return shields
 
 
 class DefaultModel(BaseModel):

llama_stack/templates/watsonx/watsonx.py

@@ -69,7 +69,7 @@ def get_distribution_template() -> DistributionTemplate:
         },
     )
 
-    default_models = get_model_registry(available_models)
+    default_models, _ = get_model_registry(available_models)
     return DistributionTemplate(
         name="watsonx",
         distro_type="remote_hosted",

llama_stack/ui/app/logs/vector-stores/[id]/page.tsx

@@ -0,0 +1,82 @@
+"use client";
+
+import { useEffect, useState } from "react";
+import { useParams, useRouter } from "next/navigation";
+import { useAuthClient } from "@/hooks/use-auth-client";
+import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
+import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
+import { VectorStoreDetailView } from "@/components/vector-stores/vector-store-detail";
+
+export default function VectorStoreDetailPage() {
+  const params = useParams();
+  const id = params.id as string;
+  const client = useAuthClient();
+  const router = useRouter();
+
+  const [store, setStore] = useState<VectorStore | null>(null);
+  const [files, setFiles] = useState<VectorStoreFile[]>([]);
+  const [isLoadingStore, setIsLoadingStore] = useState(true);
+  const [isLoadingFiles, setIsLoadingFiles] = useState(true);
+  const [errorStore, setErrorStore] = useState<Error | null>(null);
+  const [errorFiles, setErrorFiles] = useState<Error | null>(null);
+
+  useEffect(() => {
+    if (!id) {
+      setErrorStore(new Error("Vector Store ID is missing."));
+      setIsLoadingStore(false);
+      return;
+    }
+    const fetchStore = async () => {
+      setIsLoadingStore(true);
+      setErrorStore(null);
+      try {
+        const response = await client.vectorStores.retrieve(id);
+        setStore(response as VectorStore);
+      } catch (err) {
+        setErrorStore(
+          err instanceof Error
+            ? err
+            : new Error("Failed to load vector store."),
+        );
+      } finally {
+        setIsLoadingStore(false);
+      }
+    };
+    fetchStore();
+  }, [id, client]);
+
+  useEffect(() => {
+    if (!id) {
+      setErrorFiles(new Error("Vector Store ID is missing."));
+      setIsLoadingFiles(false);
+      return;
+    }
+    const fetchFiles = async () => {
+      setIsLoadingFiles(true);
+      setErrorFiles(null);
+      try {
+        const result = await client.vectorStores.files.list(id as any);
+        setFiles((result as any).data);
+      } catch (err) {
+        setErrorFiles(
+          err instanceof Error ? err : new Error("Failed to load files."),
+        );
+      } finally {
+        setIsLoadingFiles(false);
+      }
+    };
+    fetchFiles();
+  }, [id]);
+
+  return (
+    <VectorStoreDetailView
+      store={store}
+      files={files}
+      isLoadingStore={isLoadingStore}
+      isLoadingFiles={isLoadingFiles}
+      errorStore={errorStore}
+      errorFiles={errorFiles}
+      id={id}
+    />
+  );
+}

llama_stack/ui/app/logs/vector-stores/layout.tsx

@@ -0,0 +1,16 @@
+"use client";
+
+import React from "react";
+import LogsLayout from "@/components/layout/logs-layout";
+
+export default function VectorStoresLayout({
+  children,
+}: {
+  children: React.ReactNode;
+}) {
+  return (
+    <LogsLayout sectionLabel="Vector Stores" basePath="/logs/vector-stores">
+      {children}
+    </LogsLayout>
+  );
+}

llama_stack/ui/app/logs/vector-stores/page.tsx

@@ -0,0 +1,121 @@
+"use client";
+
+import React from "react";
+import { useAuthClient } from "@/hooks/use-auth-client";
+import type {
+  ListVectorStoresResponse,
+  VectorStore,
+} from "llama-stack-client/resources/vector-stores/vector-stores";
+import { useRouter } from "next/navigation";
+import { usePagination } from "@/hooks/use-pagination";
+import {
+  Table,
+  TableBody,
+  TableCaption,
+  TableCell,
+  TableHead,
+  TableHeader,
+  TableRow,
+} from "@/components/ui/table";
+import { Skeleton } from "@/components/ui/skeleton";
+
+export default function VectorStoresPage() {
+  const client = useAuthClient();
+  const router = useRouter();
+  const {
+    data: stores,
+    status,
+    hasMore,
+    error,
+    loadMore,
+  } = usePagination<VectorStore>({
+    limit: 20,
+    order: "desc",
+    fetchFunction: async (client, params) => {
+      const response = await client.vectorStores.list({
+        after: params.after,
+        limit: params.limit,
+        order: params.order,
+      } as any);
+      return response as ListVectorStoresResponse;
+    },
+    errorMessagePrefix: "vector stores",
+  });
+
+  // Auto-load all pages for infinite scroll behavior (like Responses)
+  React.useEffect(() => {
+    if (status === "idle" && hasMore) {
+      loadMore();
+    }
+  }, [status, hasMore, loadMore]);
+
+  if (status === "loading") {
+    return (
+      <div className="space-y-2">
+        <Skeleton className="h-8 w-full" />
+        <Skeleton className="h-4 w-full" />
+        <Skeleton className="h-4 w-full" />
+      </div>
+    );
+  }
+
+  if (status === "error") {
+    return <div className="text-destructive">Error: {error?.message}</div>;
+  }
+
+  if (!stores || stores.length === 0) {
+    return <p>No vector stores found.</p>;
+  }
+
+  return (
+    <div className="overflow-auto flex-1 min-h-0">
+      <Table>
+        <TableHeader>
+          <TableRow>
+            <TableHead>ID</TableHead>
+            <TableHead>Name</TableHead>
+            <TableHead>Created</TableHead>
+            <TableHead>Completed</TableHead>
+            <TableHead>Cancelled</TableHead>
+            <TableHead>Failed</TableHead>
+            <TableHead>In Progress</TableHead>
+            <TableHead>Total</TableHead>
+            <TableHead>Usage Bytes</TableHead>
+            <TableHead>Provider ID</TableHead>
+            <TableHead>Provider Vector DB ID</TableHead>
+          </TableRow>
+        </TableHeader>
+        <TableBody>
+          {stores.map((store) => {
+            const fileCounts = store.file_counts;
+            const metadata = store.metadata || {};
+            const providerId = metadata.provider_id ?? "";
+            const providerDbId = metadata.provider_vector_db_id ?? "";
+
+            return (
+              <TableRow
+                key={store.id}
+                onClick={() => router.push(`/logs/vector-stores/${store.id}`)}
+                className="cursor-pointer hover:bg-muted/50"
+              >
+                <TableCell>{store.id}</TableCell>
+                <TableCell>{store.name}</TableCell>
+                <TableCell>
+                  {new Date(store.created_at * 1000).toLocaleString()}
+                </TableCell>
+                <TableCell>{fileCounts.completed}</TableCell>
+                <TableCell>{fileCounts.cancelled}</TableCell>
+                <TableCell>{fileCounts.failed}</TableCell>
+                <TableCell>{fileCounts.in_progress}</TableCell>
+                <TableCell>{fileCounts.total}</TableCell>
+                <TableCell>{store.usage_bytes}</TableCell>
+                <TableCell>{providerId}</TableCell>
+                <TableCell>{providerDbId}</TableCell>
+              </TableRow>
+            );
+          })}
+        </TableBody>
+      </Table>
+    </div>
+  );
+}

llama_stack/ui/components/layout/app-sidebar.tsx

@@ -1,6 +1,11 @@
 "use client";
 
-import { MessageSquareText, MessagesSquare, MoveUpRight } from "lucide-react";
+import {
+  MessageSquareText,
+  MessagesSquare,
+  MoveUpRight,
+  Database,
+} from "lucide-react";
 import Link from "next/link";
 import { usePathname } from "next/navigation";
 import { cn } from "@/lib/utils";
@@ -28,6 +33,11 @@ const logItems = [
     url: "/logs/responses",
     icon: MessagesSquare,
   },
+  {
+    title: "Vector Stores",
+    url: "/logs/vector-stores",
+    icon: Database,
+  },
   {
     title: "Documentation",
     url: "https://llama-stack.readthedocs.io/en/latest/references/api_reference/index.html",
@@ -57,13 +67,13 @@ export function AppSidebar() {
                       className={cn(
                         "justify-start",
                         isActive &&
-                          "bg-gray-200 hover:bg-gray-200 text-primary hover:text-primary",
+                          "bg-gray-200 dark:bg-gray-700 hover:bg-gray-200 dark:hover:bg-gray-700 text-gray-900 dark:text-gray-100",
                       )}
                     >
                       <Link href={item.url}>
                         <item.icon
                           className={cn(
-                            isActive && "text-primary",
+                            isActive && "text-gray-900 dark:text-gray-100",
                             "mr-2 h-4 w-4",
                           )}
                         />

llama_stack/ui/components/layout/detail-layout.tsx

@@ -93,7 +93,9 @@ export function PropertyItem({
     >
       <strong>{label}:</strong>{" "}
       {typeof value === "string" || typeof value === "number" ? (
-        <span className="text-gray-900 font-medium">{value}</span>
+        <span className="text-gray-900 dark:text-gray-100 font-medium">
+          {value}
+        </span>
       ) : (
         value
       )}
@@ -112,7 +114,9 @@ export function PropertiesCard({ children }: PropertiesCardProps) {
         <CardTitle>Properties</CardTitle>
       </CardHeader>
       <CardContent>
-        <ul className="space-y-2 text-sm text-gray-600">{children}</ul>
+        <ul className="space-y-2 text-sm text-gray-600 dark:text-gray-400">
+          {children}
+        </ul>
       </CardContent>
     </Card>
   );

llama_stack/ui/components/ui/message-components.tsx

@@ -17,10 +17,10 @@ export const MessageBlock: React.FC<MessageBlockProps> = ({
 }) => {
   return (
     <div className={`mb-4 ${className}`}>
-      <p className="py-1 font-semibold text-gray-800 mb-1">
+      <p className="py-1 font-semibold text-muted-foreground mb-1">
         {label}
         {labelDetail && (
-          <span className="text-xs text-gray-500 font-normal ml-1">
+          <span className="text-xs text-muted-foreground font-normal ml-1">
             {labelDetail}
           </span>
         )}

llama_stack/ui/components/vector-stores/vector-store-detail.tsx

@@ -0,0 +1,128 @@
+"use client";
+
+import type { VectorStore } from "llama-stack-client/resources/vector-stores/vector-stores";
+import type { VectorStoreFile } from "llama-stack-client/resources/vector-stores/files";
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
+import { Skeleton } from "@/components/ui/skeleton";
+import {
+  DetailLoadingView,
+  DetailErrorView,
+  DetailNotFoundView,
+  DetailLayout,
+  PropertiesCard,
+  PropertyItem,
+} from "@/components/layout/detail-layout";
+import {
+  Table,
+  TableBody,
+  TableCaption,
+  TableCell,
+  TableHead,
+  TableHeader,
+  TableRow,
+} from "@/components/ui/table";
+
+interface VectorStoreDetailViewProps {
+  store: VectorStore | null;
+  files: VectorStoreFile[];
+  isLoadingStore: boolean;
+  isLoadingFiles: boolean;
+  errorStore: Error | null;
+  errorFiles: Error | null;
+  id: string;
+}
+
+export function VectorStoreDetailView({
+  store,
+  files,
+  isLoadingStore,
+  isLoadingFiles,
+  errorStore,
+  errorFiles,
+  id,
+}: VectorStoreDetailViewProps) {
+  const title = "Vector Store Details";
+
+  if (errorStore) {
+    return <DetailErrorView title={title} id={id} error={errorStore} />;
+  }
+  if (isLoadingStore) {
+    return <DetailLoadingView title={title} />;
+  }
+  if (!store) {
+    return <DetailNotFoundView title={title} id={id} />;
+  }
+
+  const mainContent = (
+    <>
+      <Card>
+        <CardHeader>
+          <CardTitle>Files</CardTitle>
+        </CardHeader>
+        <CardContent>
+          {isLoadingFiles ? (
+            <Skeleton className="h-4 w-full" />
+          ) : errorFiles ? (
+            <div className="text-destructive text-sm">
+              Error loading files: {errorFiles.message}
+            </div>
+          ) : files.length > 0 ? (
+            <Table>
+              <TableCaption>Files in this vector store</TableCaption>
+              <TableHeader>
+                <TableRow>
+                  <TableHead>ID</TableHead>
+                  <TableHead>Status</TableHead>
+                  <TableHead>Created</TableHead>
+                  <TableHead>Usage Bytes</TableHead>
+                </TableRow>
+              </TableHeader>
+              <TableBody>
+                {files.map((file) => (
+                  <TableRow key={file.id}>
+                    <TableCell>{file.id}</TableCell>
+                    <TableCell>{file.status}</TableCell>
+                    <TableCell>
+                      {new Date(file.created_at * 1000).toLocaleString()}
+                    </TableCell>
+                    <TableCell>{file.usage_bytes}</TableCell>
+                  </TableRow>
+                ))}
+              </TableBody>
+            </Table>
+          ) : (
+            <p className="text-gray-500 italic text-sm">
+              No files in this vector store.
+            </p>
+          )}
+        </CardContent>
+      </Card>
+    </>
+  );
+
+  const sidebar = (
+    <PropertiesCard>
+      <PropertyItem label="ID" value={store.id} />
+      <PropertyItem label="Name" value={store.name || ""} />
+      <PropertyItem
+        label="Created"
+        value={new Date(store.created_at * 1000).toLocaleString()}
+      />
+      <PropertyItem label="Status" value={store.status} />
+      <PropertyItem label="Total Files" value={store.file_counts.total} />
+      <PropertyItem label="Usage Bytes" value={store.usage_bytes} />
+      <PropertyItem
+        label="Provider ID"
+        value={(store.metadata.provider_id as string) || ""}
+      />
+      <PropertyItem
+        label="Provider DB ID"
+        value={(store.metadata.provider_vector_db_id as string) || ""}
+      />
+    </PropertiesCard>
+  );
+
+  return (
+    <DetailLayout title={title} mainContent={mainContent} sidebar={sidebar} />
+  );
+}

llama_stack/ui/package-lock.json

@@ -15,7 +15,7 @@
         "@radix-ui/react-tooltip": "^1.2.6",
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
-        "llama-stack-client": "0.2.13",
+        "llama-stack-client": "^0.2.14",
         "lucide-react": "^0.510.0",
         "next": "15.3.3",
         "next-auth": "^4.24.11",
@@ -676,406 +676,6 @@
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       }
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-      "cpu": [
-        "ppc64"
-      ],
-      "license": "MIT",
-      "optional": true,
-      "os": [
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-      ],
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-        "node": ">=18"
-      }
-    },
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-      "license": "MIT",
-      "optional": true,
-      "os": [
-        "win32"
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-      "engines": {
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@@ -5999,46 +5599,6 @@
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-      "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.25.5.tgz",
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-      "hasInstallScript": true,
-      "license": "MIT",
-      "bin": {
-        "esbuild": "bin/esbuild"
-      },
-      "engines": {
-        "node": ">=18"
-      },
-      "optionalDependencies": {
-        "@esbuild/aix-ppc64": "0.25.5",
-        "@esbuild/android-arm": "0.25.5",
-        "@esbuild/android-arm64": "0.25.5",
-        "@esbuild/android-x64": "0.25.5",
-        "@esbuild/darwin-arm64": "0.25.5",
-        "@esbuild/darwin-x64": "0.25.5",
-        "@esbuild/freebsd-arm64": "0.25.5",
-        "@esbuild/freebsd-x64": "0.25.5",
-        "@esbuild/linux-arm": "0.25.5",
-        "@esbuild/linux-arm64": "0.25.5",
-        "@esbuild/linux-ia32": "0.25.5",
-        "@esbuild/linux-loong64": "0.25.5",
-        "@esbuild/linux-mips64el": "0.25.5",
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     "node_modules/escalade": {
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@@ -6993,6 +6553,7 @@
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+      "dev": true,
       "hasInstallScript": true,
       "license": "MIT",
       "optional": true,
@@ -7154,6 +6715,7 @@
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+      "dev": true,
       "license": "MIT",
       "dependencies": {
         "resolve-pkg-maps": "^1.0.0"
@@ -9537,9 +9099,10 @@
       "license": "MIT"
     },
     "node_modules/llama-stack-client": {
-      "version": "0.2.13",
-      "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.13.tgz",
-      "integrity": "sha512-R1rTFLwgUimr+KjEUkzUvFL6vLASwS9qj3UDSVkJ5BmrKAs5GwVAMeL7yZaTBXGuPUVh124WSlC4d9H0FjWqLA==",
+      "version": "0.2.14",
+      "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.14.tgz",
+      "integrity": "sha512-bVU3JHp+EPEKR0Vb9vcd9ZyQj/72jSDuptKLwOXET9WrkphIQ8xuW5ueecMTgq8UEls3lwB3HiZM2cDOR9eDsQ==",
+      "license": "Apache-2.0",
       "dependencies": {
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         "@types/node-fetch": "^2.6.4",
@@ -9547,8 +9110,7 @@
         "agentkeepalive": "^4.2.1",
         "form-data-encoder": "1.7.2",
         "formdata-node": "^4.3.2",
-        "node-fetch": "^2.6.7",
-        "tsx": "^4.19.2"
+        "node-fetch": "^2.6.7"
       }
     },
     "node_modules/llama-stack-client/node_modules/@types/node": {
@@ -11148,6 +10710,7 @@
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/resolve-pkg-maps/-/resolve-pkg-maps-1.0.0.tgz",
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+      "dev": true,
       "license": "MIT",
       "funding": {
         "url": "https://github.com/privatenumber/resolve-pkg-maps?sponsor=1"
@@ -12198,25 +11761,6 @@
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-    "node_modules/tsx": {
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-      "resolved": "https://registry.npmjs.org/tsx/-/tsx-4.19.4.tgz",
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-      "license": "MIT",
-      "dependencies": {
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-        "get-tsconfig": "^4.7.5"
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-      "bin": {
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-      },
-      "engines": {
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-      "optionalDependencies": {
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       "resolved": "https://registry.npmjs.org/tw-animate-css/-/tw-animate-css-1.2.9.tgz",

llama_stack/ui/package.json

@@ -20,7 +20,7 @@
     "@radix-ui/react-tooltip": "^1.2.6",
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
-    "llama-stack-client": "0.2.13",
+    "llama-stack-client": "^0.2.15",
     "lucide-react": "^0.510.0",
     "next": "15.3.3",
     "next-auth": "^4.24.11",