user inference api to generate embeddings in vector store

llama_stack/providers/inline/memory/faiss/__init__.py

@@ -4,16 +4,19 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
 from .config import FaissImplConfig
 
 
-async def get_provider_impl(config: FaissImplConfig, _deps):
+async def get_provider_impl(config: FaissImplConfig, deps: Dict[Api, ProviderSpec]):
     from .faiss import FaissMemoryImpl
 
     assert isinstance(
         config, FaissImplConfig
     ), f"Unexpected config type: {type(config)}"
 
-    impl = FaissMemoryImpl(config)
+    impl = FaissMemoryImpl(config, deps[Api.inference])
     await impl.initialize()
     return impl

llama_stack/providers/inline/memory/faiss/faiss.py

@@ -19,13 +19,12 @@ from numpy.typing import NDArray
 from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 from llama_stack.apis.memory import *  # noqa: F403
-from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
+from llama_stack.providers.datatypes import Api, MemoryBanksProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
 
 from llama_stack.providers.utils.memory.vector_store import (
-    ALL_MINILM_L6_V2_DIMENSION,
-    BankWithIndex,
     EmbeddingIndex,
+    InferenceEmbeddingMixin,
 )
 
 from .config import FaissImplConfig
@@ -95,6 +94,15 @@ class FaissIndex(EmbeddingIndex):
         await self.kvstore.delete(f"faiss_index:v1::{self.bank_id}")
 
     async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        # Add dimension check
+        embedding_dim = (
+            embeddings.shape[1] if len(embeddings.shape) > 1 else embeddings.shape[0]
+        )
+        if embedding_dim != self.index.d:
+            raise ValueError(
+                f"Embedding dimension mismatch. Expected {self.index.d}, got {embedding_dim}"
+            )
+
         indexlen = len(self.id_by_index)
         for i, chunk in enumerate(chunks):
             self.chunk_by_index[indexlen + i] = chunk
@@ -123,9 +131,10 @@ class FaissIndex(EmbeddingIndex):
         return QueryDocumentsResponse(chunks=chunks, scores=scores)
 
 
-class FaissMemoryImpl(Memory, MemoryBanksProtocolPrivate):
+class FaissMemoryImpl(InferenceEmbeddingMixin, Memory, MemoryBanksProtocolPrivate):
-    def __init__(self, config: FaissImplConfig) -> None:
+    def __init__(self, config: FaissImplConfig, inference_api: Api.inference) -> None:
         self.config = config
+        self.inference_api = inference_api
         self.cache = {}
         self.kvstore = None
 
@@ -138,10 +147,10 @@ class FaissMemoryImpl(Memory, MemoryBanksProtocolPrivate):
 
         for bank_data in stored_banks:
             bank = VectorMemoryBank.model_validate_json(bank_data)
-            index = BankWithIndex(
+            index = self._create_bank_with_index(
-                bank=bank,
+                bank,
-                index=await FaissIndex.create(
+                await FaissIndex.create(
-                    ALL_MINILM_L6_V2_DIMENSION, self.kvstore, bank.identifier
+                    bank.embedding_dimension, self.kvstore, bank.identifier
                 ),
             )
             self.cache[bank.identifier] = index
@@ -166,13 +175,12 @@ class FaissMemoryImpl(Memory, MemoryBanksProtocolPrivate):
         )
 
         # Store in cache
-        index = BankWithIndex(
+        self.cache[memory_bank.identifier] = self._create_bank_with_index(
-            bank=memory_bank,
+            memory_bank,
-            index=await FaissIndex.create(
+            await FaissIndex.create(
-                ALL_MINILM_L6_V2_DIMENSION, self.kvstore, memory_bank.identifier
+                memory_bank.embedding_dimension, self.kvstore, memory_bank.identifier
             ),
         )
-        self.cache[memory_bank.identifier] = index
 
     async def list_memory_banks(self) -> List[MemoryBank]:
         return [i.bank for i in self.cache.values()]

llama_stack/providers/registry/memory.py

@@ -39,6 +39,7 @@ def available_providers() -> List[ProviderSpec]:
             module="llama_stack.providers.inline.memory.faiss",
             config_class="llama_stack.providers.inline.memory.faiss.FaissImplConfig",
             deprecation_warning="Please use the `inline::faiss` provider instead.",
+            api_dependencies=[Api.inference],
         ),
         InlineProviderSpec(
             api=Api.memory,
@@ -46,6 +47,7 @@ def available_providers() -> List[ProviderSpec]:
             pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
             module="llama_stack.providers.inline.memory.faiss",
             config_class="llama_stack.providers.inline.memory.faiss.FaissImplConfig",
+            api_dependencies=[Api.inference],
         ),
         remote_provider_spec(
             Api.memory,
@@ -55,6 +57,7 @@ def available_providers() -> List[ProviderSpec]:
                 module="llama_stack.providers.remote.memory.chroma",
                 config_class="llama_stack.distribution.datatypes.RemoteProviderConfig",
             ),
+            api_dependencies=[Api.inference],
         ),
         remote_provider_spec(
             Api.memory,
@@ -64,6 +67,7 @@ def available_providers() -> List[ProviderSpec]:
                 module="llama_stack.providers.remote.memory.pgvector",
                 config_class="llama_stack.providers.remote.memory.pgvector.PGVectorConfig",
             ),
+            api_dependencies=[Api.inference],
         ),
         remote_provider_spec(
             Api.memory,
@@ -74,6 +78,7 @@ def available_providers() -> List[ProviderSpec]:
                 config_class="llama_stack.providers.remote.memory.weaviate.WeaviateConfig",
                 provider_data_validator="llama_stack.providers.remote.memory.weaviate.WeaviateRequestProviderData",
             ),
+            api_dependencies=[Api.inference],
         ),
         remote_provider_spec(
             api=Api.memory,
@@ -83,6 +88,7 @@ def available_providers() -> List[ProviderSpec]:
                 module="llama_stack.providers.remote.memory.sample",
                 config_class="llama_stack.providers.remote.memory.sample.SampleConfig",
             ),
+            api_dependencies=[],
         ),
         remote_provider_spec(
             Api.memory,
@@ -92,5 +98,6 @@ def available_providers() -> List[ProviderSpec]:
                 module="llama_stack.providers.remote.memory.qdrant",
                 config_class="llama_stack.providers.remote.memory.qdrant.QdrantConfig",
             ),
+            api_dependencies=[Api.inference],
         ),
     ]

llama_stack/providers/remote/memory/chroma/__init__.py

@@ -4,12 +4,15 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Dict
+
 from llama_stack.distribution.datatypes import RemoteProviderConfig
+from llama_stack.providers.datatypes import Api, ProviderSpec
 
 
-async def get_adapter_impl(config: RemoteProviderConfig, _deps):
+async def get_adapter_impl(config: RemoteProviderConfig, deps: Dict[Api, ProviderSpec]):
     from .chroma import ChromaMemoryAdapter
 
-    impl = ChromaMemoryAdapter(config.url)
+    impl = ChromaMemoryAdapter(config.url, deps[Api.inference])
     await impl.initialize()
     return impl

llama_stack/providers/remote/memory/chroma/chroma.py

@@ -20,6 +20,7 @@ from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
 from llama_stack.providers.utils.memory.vector_store import (
     BankWithIndex,
     EmbeddingIndex,
+    InferenceEmbeddingMixin,
 )
 
 log = logging.getLogger(__name__)
@@ -71,8 +72,8 @@ class ChromaIndex(EmbeddingIndex):
         await self.client.delete_collection(self.collection.name)
 
 
-class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
+class ChromaMemoryAdapter(InferenceEmbeddingMixin, Memory, MemoryBanksProtocolPrivate):
-    def __init__(self, url: str) -> None:
+    def __init__(self, url: str, inference_api: Api.inference) -> None:
         log.info(f"Initializing ChromaMemoryAdapter with url: {url}")
         url = url.rstrip("/")
         parsed = urlparse(url)
@@ -82,6 +83,7 @@ class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
 
         self.host = parsed.hostname
         self.port = parsed.port
+        self.inference_api = inference_api
 
         self.client = None
         self.cache = {}
@@ -109,10 +111,9 @@ class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
             name=memory_bank.identifier,
             metadata={"bank": memory_bank.model_dump_json()},
         )
-        bank_index = BankWithIndex(
+        self.cache[memory_bank.identifier] = self._create_bank_with_index(
-            bank=memory_bank, index=ChromaIndex(self.client, collection)
+            memory_bank, ChromaIndex(self.client, collection)
         )
-        self.cache[memory_bank.identifier] = bank_index
 
     async def list_memory_banks(self) -> List[MemoryBank]:
         collections = await self.client.list_collections()
@@ -124,11 +125,10 @@ class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
                 log.exception(f"Failed to parse bank: {collection.metadata}")
                 continue
 
-            index = BankWithIndex(
+            self.cache[bank.identifier] = self._create_bank_with_index(
-                bank=bank,
+                bank,
-                index=ChromaIndex(self.client, collection),
+                ChromaIndex(self.client, collection),
             )
-            self.cache[bank.identifier] = index
 
         return [i.bank for i in self.cache.values()]
 
@@ -166,6 +166,6 @@ class ChromaMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
         collection = await self.client.get_collection(bank_id)
         if not collection:
             raise ValueError(f"Bank {bank_id} not found in Chroma")
-        index = BankWithIndex(bank=bank, index=ChromaIndex(self.client, collection))
+        index = self._create_bank_with_index(bank, ChromaIndex(self.client, collection))
         self.cache[bank_id] = index
         return index

llama_stack/providers/remote/memory/pgvector/__init__.py

@@ -4,12 +4,16 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
 from .config import PGVectorConfig
 
 
-async def get_adapter_impl(config: PGVectorConfig, _deps):
+async def get_adapter_impl(config: PGVectorConfig, deps: Dict[Api, ProviderSpec]):
     from .pgvector import PGVectorMemoryAdapter
 
-    impl = PGVectorMemoryAdapter(config)
+    impl = PGVectorMemoryAdapter(config, deps[Api.inference])
     await impl.initialize()
     return impl

llama_stack/providers/remote/memory/pgvector/pgvector.py

@@ -16,11 +16,12 @@ from pydantic import BaseModel, parse_obj_as
 
 from llama_stack.apis.memory import *  # noqa: F403
 
-from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
+from llama_stack.providers.datatypes import Api, MemoryBanksProtocolPrivate
+
 from llama_stack.providers.utils.memory.vector_store import (
-    ALL_MINILM_L6_V2_DIMENSION,
     BankWithIndex,
     EmbeddingIndex,
+    InferenceEmbeddingMixin,
 )
 
 from .config import PGVectorConfig
@@ -119,9 +120,12 @@ class PGVectorIndex(EmbeddingIndex):
         self.cursor.execute(f"DROP TABLE IF EXISTS {self.table_name}")
 
 
-class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
+class PGVectorMemoryAdapter(
-    def __init__(self, config: PGVectorConfig) -> None:
+    InferenceEmbeddingMixin, Memory, MemoryBanksProtocolPrivate
+):
+    def __init__(self, config: PGVectorConfig, inference_api: Api.inference) -> None:
         self.config = config
+        self.inference_api = inference_api
         self.cursor = None
         self.conn = None
         self.cache = {}
@@ -160,27 +164,17 @@ class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
     async def shutdown(self) -> None:
         pass
 
-    async def register_memory_bank(
+    async def register_memory_bank(self, memory_bank: MemoryBank) -> None:
-        self,
-        memory_bank: MemoryBank,
-    ) -> None:
         assert (
             memory_bank.memory_bank_type == MemoryBankType.vector.value
         ), f"Only vector banks are supported {memory_bank.memory_bank_type}"
 
-        upsert_models(
+        upsert_models(self.cursor, [(memory_bank.identifier, memory_bank)])
-            self.cursor,
+        index = PGVectorIndex(memory_bank, memory_bank.embedding_dimension, self.cursor)
-            [
+        self.cache[memory_bank.identifier] = self._create_bank_with_index(
-                (memory_bank.identifier, memory_bank),
+            memory_bank, index
-            ],
         )
 
-        index = BankWithIndex(
-            bank=memory_bank,
-            index=PGVectorIndex(memory_bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
-        )
-        self.cache[memory_bank.identifier] = index
-
     async def unregister_memory_bank(self, memory_bank_id: str) -> None:
         await self.cache[memory_bank_id].index.delete()
         del self.cache[memory_bank_id]
@@ -189,9 +183,9 @@ class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
         banks = load_models(self.cursor, VectorMemoryBank)
         for bank in banks:
             if bank.identifier not in self.cache:
-                index = BankWithIndex(
+                index = self._create_bank_with_index(
-                    bank=bank,
+                    bank,
-                    index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
+                    PGVectorIndex(bank, bank.embedding_dimension, self.cursor),
                 )
                 self.cache[bank.identifier] = index
         return banks
@@ -214,14 +208,13 @@ class PGVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
         index = await self._get_and_cache_bank_index(bank_id)
         return await index.query_documents(query, params)
 
+        self.inference_api = inference_api
+
     async def _get_and_cache_bank_index(self, bank_id: str) -> BankWithIndex:
         if bank_id in self.cache:
             return self.cache[bank_id]
 
         bank = await self.memory_bank_store.get_memory_bank(bank_id)
-        index = BankWithIndex(
+        index = PGVectorIndex(bank, bank.embedding_dimension, self.cursor)
-            bank=bank,
+        self.cache[bank_id] = self._create_bank_with_index(bank, index)
-            index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
+        return self.cache[bank_id]
-        )
-        self.cache[bank_id] = index
-        return index

llama_stack/providers/remote/memory/qdrant/__init__.py

@@ -4,12 +4,16 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
 from .config import QdrantConfig
 
 
-async def get_adapter_impl(config: QdrantConfig, _deps):
+async def get_adapter_impl(config: QdrantConfig, deps: Dict[Api, ProviderSpec]):
     from .qdrant import QdrantVectorMemoryAdapter
 
-    impl = QdrantVectorMemoryAdapter(config)
+    impl = QdrantVectorMemoryAdapter(config, deps[Api.inference])
     await impl.initialize()
     return impl

llama_stack/providers/remote/memory/qdrant/qdrant.py

@@ -21,6 +21,7 @@ from llama_stack.providers.remote.memory.qdrant.config import QdrantConfig
 from llama_stack.providers.utils.memory.vector_store import (
     BankWithIndex,
     EmbeddingIndex,
+    InferenceEmbeddingMixin,
 )
 
 log = logging.getLogger(__name__)
@@ -100,11 +101,14 @@ class QdrantIndex(EmbeddingIndex):
         return QueryDocumentsResponse(chunks=chunks, scores=scores)
 
 
-class QdrantVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
+class QdrantVectorMemoryAdapter(
-    def __init__(self, config: QdrantConfig) -> None:
+    InferenceEmbeddingMixin, Memory, MemoryBanksProtocolPrivate
+):
+    def __init__(self, config: QdrantConfig, inference_api: Api.inference) -> None:
         self.config = config
         self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
         self.cache = {}
+        self.inference_api = inference_api
 
     async def initialize(self) -> None:
         pass
@@ -120,7 +124,7 @@ class QdrantVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
             memory_bank.memory_bank_type == MemoryBankType.vector
         ), f"Only vector banks are supported {memory_bank.memory_bank_type}"
 
-        index = BankWithIndex(
+        index = self._create_bank_with_index(
             bank=memory_bank,
             index=QdrantIndex(self.client, memory_bank.identifier),
         )
@@ -140,7 +144,7 @@ class QdrantVectorMemoryAdapter(Memory, MemoryBanksProtocolPrivate):
         if not bank:
             raise ValueError(f"Bank {bank_id} not found")
 
-        index = BankWithIndex(
+        index = self._create_bank_with_index(
             bank=bank,
             index=QdrantIndex(client=self.client, collection_name=bank_id),
         )

llama_stack/providers/remote/memory/weaviate/__init__.py

@@ -4,12 +4,16 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
 from .config import WeaviateConfig, WeaviateRequestProviderData  # noqa: F401
 
 
-async def get_adapter_impl(config: WeaviateConfig, _deps):
+async def get_adapter_impl(config: WeaviateConfig, deps: Dict[Api, ProviderSpec]):
     from .weaviate import WeaviateMemoryAdapter
 
-    impl = WeaviateMemoryAdapter(config)
+    impl = WeaviateMemoryAdapter(config, deps[Api.inference])
     await impl.initialize()
     return impl

llama_stack/providers/remote/memory/weaviate/weaviate.py

@@ -19,6 +19,7 @@ from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
 from llama_stack.providers.utils.memory.vector_store import (
     BankWithIndex,
     EmbeddingIndex,
+    InferenceEmbeddingMixin,
 )
 
 from .config import WeaviateConfig, WeaviateRequestProviderData
@@ -82,10 +83,14 @@ class WeaviateIndex(EmbeddingIndex):
 
 
 class WeaviateMemoryAdapter(
-    Memory, NeedsRequestProviderData, MemoryBanksProtocolPrivate
+    InferenceEmbeddingMixin,
+    Memory,
+    NeedsRequestProviderData,
+    MemoryBanksProtocolPrivate,
 ):
-    def __init__(self, config: WeaviateConfig) -> None:
+    def __init__(self, config: WeaviateConfig, inference_api: Api.inference) -> None:
         self.config = config
+        self.inference_api = inference_api
         self.client_cache = {}
         self.cache = {}
 
@@ -135,11 +140,10 @@ class WeaviateMemoryAdapter(
                 ],
             )
 
-        index = BankWithIndex(
+        self.cache[memory_bank.identifier] = self._create_bank_with_index(
-            bank=memory_bank,
+            memory_bank,
-            index=WeaviateIndex(client=client, collection_name=memory_bank.identifier),
+            WeaviateIndex(client=client, collection_name=memory_bank.identifier),
         )
-        self.cache[memory_bank.identifier] = index
 
     async def list_memory_banks(self) -> List[MemoryBank]:
         # TODO: right now the Llama Stack is the source of truth for these banks. That is
@@ -160,7 +164,7 @@ class WeaviateMemoryAdapter(
         if not client.collections.exists(bank.identifier):
             raise ValueError(f"Collection with name `{bank.identifier}` not found")
 
-        index = BankWithIndex(
+        index = self._create_bank_with_index(
             bank=bank,
             index=WeaviateIndex(client=client, collection_name=bank_id),
         )

llama_stack/providers/utils/memory/vector_store.py

@@ -22,28 +22,10 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_stack.apis.memory import *  # noqa: F403
+from llama_stack.providers.datatypes import Api
 
 log = logging.getLogger(__name__)
 
-ALL_MINILM_L6_V2_DIMENSION = 384
-
-EMBEDDING_MODELS = {}
-
-
-def get_embedding_model(model: str) -> "SentenceTransformer":
-    global EMBEDDING_MODELS
-
-    loaded_model = EMBEDDING_MODELS.get(model)
-    if loaded_model is not None:
-        return loaded_model
-
-    log.info(f"Loading sentence transformer for {model}...")
-    from sentence_transformers import SentenceTransformer
-
-    loaded_model = SentenceTransformer(model)
-    EMBEDDING_MODELS[model] = loaded_model
-    return loaded_model
-
 
 def parse_pdf(data: bytes) -> str:
     # For PDF and DOC/DOCX files, we can't reliably convert to string
@@ -166,12 +148,12 @@ class EmbeddingIndex(ABC):
 class BankWithIndex:
     bank: VectorMemoryBank
     index: EmbeddingIndex
+    inference_api: Api.inference
 
     async def insert_documents(
         self,
         documents: List[MemoryBankDocument],
     ) -> None:
-        model = get_embedding_model(self.bank.embedding_model)
         for doc in documents:
             content = await content_from_doc(doc)
             chunks = make_overlapped_chunks(
@@ -183,7 +165,10 @@ class BankWithIndex:
             )
             if not chunks:
                 continue
-            embeddings = model.encode([x.content for x in chunks]).astype(np.float32)
+            embeddings_response = await self.inference_api.embeddings(
+                self.bank.embedding_model, [x.content for x in chunks]
+            )
+            embeddings = np.array(embeddings_response.embeddings)
 
             await self.index.add_chunks(chunks, embeddings)
 
@@ -208,6 +193,25 @@ class BankWithIndex:
         else:
             query_str = _process(query)
 
-        model = get_embedding_model(self.bank.embedding_model)
+        embeddings_response = await self.inference_api.embeddings(
-        query_vector = model.encode([query_str])[0].astype(np.float32)
+            self.bank.embedding_model, [query_str]
+        )
+        query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
         return await self.index.query(query_vector, k, score_threshold)
+
+
+class InferenceEmbeddingMixin:
+    inference_api: Api.inference
+
+    def __init__(self, inference_api: Api.inference):
+        self.inference_api = inference_api
+
+    def _create_bank_with_index(
+        self, bank: VectorMemoryBank, index: EmbeddingIndex
+    ) -> BankWithIndex:
+
+        return BankWithIndex(
+            bank=bank,
+            index=index,
+            inference_api=self.inference_api,
+        )