Add Chroma and PGVector adapters (#56)

Co-authored-by: Ashwin Bharambe <ashwin@meta.com>

llama_toolchain/memory/adapters/chroma/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_toolchain.core.datatypes import RemoteProviderConfig
+
+
+async def get_adapter_impl(config: RemoteProviderConfig, _deps):
+    from .chroma import ChromaMemoryAdapter
+
+    impl = ChromaMemoryAdapter(config.url)
+    await impl.initialize()
+    return impl

llama_toolchain/memory/adapters/chroma/chroma.py

@@ -0,0 +1,165 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import json
+import uuid
+from typing import List
+from urllib.parse import urlparse
+
+import chromadb
+from numpy.typing import NDArray
+
+from llama_toolchain.memory.api import *  # noqa: F403
+
+
+from llama_toolchain.memory.common.vector_store import BankWithIndex, EmbeddingIndex
+
+
+class ChromaIndex(EmbeddingIndex):
+    def __init__(self, client: chromadb.AsyncHttpClient, collection):
+        self.client = client
+        self.collection = collection
+
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        assert len(chunks) == len(
+            embeddings
+        ), f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
+
+        for i, chunk in enumerate(chunks):
+            print(f"Adding chunk #{i} tokens={chunk.token_count}")
+
+        await self.collection.add(
+            documents=[chunk.json() for chunk in chunks],
+            embeddings=embeddings,
+            ids=[f"{c.document_id}:chunk-{i}" for i, c in enumerate(chunks)],
+        )
+
+    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
+        results = await self.collection.query(
+            query_embeddings=[embedding.tolist()],
+            n_results=k,
+            include=["documents", "distances"],
+        )
+        distances = results["distances"][0]
+        documents = results["documents"][0]
+
+        chunks = []
+        scores = []
+        for dist, doc in zip(distances, documents):
+            try:
+                doc = json.loads(doc)
+                chunk = Chunk(**doc)
+            except Exception:
+                import traceback
+
+                traceback.print_exc()
+                print(f"Failed to parse document: {doc}")
+                continue
+
+            chunks.append(chunk)
+            scores.append(1.0 / float(dist))
+
+        return QueryDocumentsResponse(chunks=chunks, scores=scores)
+
+
+class ChromaMemoryAdapter(Memory):
+    def __init__(self, url: str) -> None:
+        print(f"Initializing ChromaMemoryAdapter with url: {url}")
+        url = url.rstrip("/")
+        parsed = urlparse(url)
+
+        if parsed.path and parsed.path != "/":
+            raise ValueError("URL should not contain a path")
+
+        self.host = parsed.hostname
+        self.port = parsed.port
+
+        self.client = None
+        self.cache = {}
+
+    async def initialize(self) -> None:
+        try:
+            print(f"Connecting to Chroma server at: {self.host}:{self.port}")
+            self.client = await chromadb.AsyncHttpClient(host=self.host, port=self.port)
+        except Exception as e:
+            import traceback
+
+            traceback.print_exc()
+            raise RuntimeError("Could not connect to Chroma server") from e
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def create_memory_bank(
+        self,
+        name: str,
+        config: MemoryBankConfig,
+        url: Optional[URL] = None,
+    ) -> MemoryBank:
+        bank_id = str(uuid.uuid4())
+        bank = MemoryBank(
+            bank_id=bank_id,
+            name=name,
+            config=config,
+            url=url,
+        )
+        collection = await self.client.create_collection(
+            name=bank_id,
+            metadata={"bank": bank.json()},
+        )
+        bank_index = BankWithIndex(
+            bank=bank, index=ChromaIndex(self.client, collection)
+        )
+        self.cache[bank_id] = bank_index
+        return bank
+
+    async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
+        bank_index = await self._get_and_cache_bank_index(bank_id)
+        if bank_index is None:
+            return None
+        return bank_index.bank
+
+    async def _get_and_cache_bank_index(self, bank_id: str) -> Optional[BankWithIndex]:
+        if bank_id in self.cache:
+            return self.cache[bank_id]
+
+        collections = await self.client.list_collections()
+        for collection in collections:
+            if collection.name == bank_id:
+                print(collection.metadata)
+                bank = MemoryBank(**json.loads(collection.metadata["bank"]))
+                index = BankWithIndex(
+                    bank=bank,
+                    index=ChromaIndex(self.client, collection),
+                )
+                self.cache[bank_id] = index
+                return index
+
+        return None
+
+    async def insert_documents(
+        self,
+        bank_id: str,
+        documents: List[MemoryBankDocument],
+        ttl_seconds: Optional[int] = None,
+    ) -> None:
+        index = await self._get_and_cache_bank_index(bank_id)
+        if not index:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        await index.insert_documents(documents)
+
+    async def query_documents(
+        self,
+        bank_id: str,
+        query: InterleavedTextMedia,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryDocumentsResponse:
+        index = await self._get_and_cache_bank_index(bank_id)
+        if not index:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        return await index.query_documents(query, params)

llama_toolchain/memory/adapters/pgvector/__init__.py

@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .config import PGVectorConfig
+
+
+async def get_adapter_impl(config: PGVectorConfig, _deps):
+    from .pgvector import PGVectorMemoryAdapter
+
+    impl = PGVectorMemoryAdapter(config)
+    await impl.initialize()
+    return impl

llama_toolchain/memory/adapters/pgvector/config.py

@@ -0,0 +1,17 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel, Field
+
+
+@json_schema_type
+class PGVectorConfig(BaseModel):
+    host: str = Field(default="localhost")
+    port: int = Field(default=5432)
+    db: str
+    user: str
+    password: str

llama_toolchain/memory/adapters/pgvector/pgvector.py

@@ -0,0 +1,234 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import uuid
+
+from typing import List, Tuple
+
+import psycopg2
+from numpy.typing import NDArray
+from psycopg2 import sql
+from psycopg2.extras import execute_values, Json
+from pydantic import BaseModel
+from llama_toolchain.memory.api import *  # noqa: F403
+
+
+from llama_toolchain.memory.common.vector_store import (
+    ALL_MINILM_L6_V2_DIMENSION,
+    BankWithIndex,
+    EmbeddingIndex,
+)
+
+from .config import PGVectorConfig
+
+
+def check_extension_version(cur):
+    cur.execute("SELECT extversion FROM pg_extension WHERE extname = 'vector'")
+    result = cur.fetchone()
+    return result[0] if result else None
+
+
+def upsert_models(cur, keys_models: List[Tuple[str, BaseModel]]):
+    query = sql.SQL(
+        """
+        INSERT INTO metadata_store (key, data)
+        VALUES %s
+        ON CONFLICT (key) DO UPDATE
+        SET data = EXCLUDED.data
+    """
+    )
+
+    values = [(key, Json(model.dict())) for key, model in keys_models]
+    execute_values(cur, query, values, template="(%s, %s)")
+
+
+def load_models(cur, keys: List[str], cls):
+    query = "SELECT key, data FROM metadata_store"
+    if keys:
+        placeholders = ",".join(["%s"] * len(keys))
+        query += f" WHERE key IN ({placeholders})"
+        cur.execute(query, keys)
+    else:
+        cur.execute(query)
+
+    rows = cur.fetchall()
+    return [cls(**row["data"]) for row in rows]
+
+
+class PGVectorIndex(EmbeddingIndex):
+    def __init__(self, bank: MemoryBank, dimension: int, cursor):
+        self.cursor = cursor
+        self.table_name = f"vector_store_{bank.name}"
+
+        self.cursor.execute(
+            f"""
+            CREATE TABLE IF NOT EXISTS {self.table_name} (
+                id TEXT PRIMARY KEY,
+                document JSONB,
+                embedding vector({dimension})
+            )
+        """
+        )
+
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        assert len(chunks) == len(
+            embeddings
+        ), f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
+
+        values = []
+        for i, chunk in enumerate(chunks):
+            print(f"Adding chunk #{i} tokens={chunk.token_count}")
+            values.append(
+                (
+                    f"{chunk.document_id}:chunk-{i}",
+                    Json(chunk.dict()),
+                    embeddings[i].tolist(),
+                )
+            )
+
+        query = sql.SQL(
+            f"""
+        INSERT INTO {self.table_name} (id, document, embedding)
+        VALUES %s
+        ON CONFLICT (id) DO UPDATE SET embedding = EXCLUDED.embedding, document = EXCLUDED.document
+    """
+        )
+        execute_values(self.cursor, query, values, template="(%s, %s, %s::vector)")
+
+    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
+        self.cursor.execute(
+            f"""
+        SELECT document, embedding <-> %s::vector AS distance
+        FROM {self.table_name}
+        ORDER BY distance
+        LIMIT %s
+    """,
+            (embedding.tolist(), k),
+        )
+        results = self.cursor.fetchall()
+
+        chunks = []
+        scores = []
+        for doc, dist in results:
+            chunks.append(Chunk(**doc))
+            scores.append(1.0 / float(dist))
+
+        return QueryDocumentsResponse(chunks=chunks, scores=scores)
+
+
+class PGVectorMemoryAdapter(Memory):
+    def __init__(self, config: PGVectorConfig) -> None:
+        print(f"Initializing PGVectorMemoryAdapter -> {config.host}:{config.port}")
+        self.config = config
+        self.cursor = None
+        self.conn = None
+        self.cache = {}
+
+    async def initialize(self) -> None:
+        try:
+            self.conn = psycopg2.connect(
+                host=self.config.host,
+                port=self.config.port,
+                database=self.config.db,
+                user=self.config.user,
+                password=self.config.password,
+            )
+            self.cursor = self.conn.cursor()
+
+            version = check_extension_version(self.cursor)
+            if version:
+                print(f"Vector extension version: {version}")
+            else:
+                raise RuntimeError("Vector extension is not installed.")
+
+            self.cursor.execute(
+                """
+                CREATE TABLE IF NOT EXISTS metadata_store (
+                    key TEXT PRIMARY KEY,
+                    data JSONB
+                )
+            """
+            )
+        except Exception as e:
+            import traceback
+
+            traceback.print_exc()
+            raise RuntimeError("Could not connect to PGVector database server") from e
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def create_memory_bank(
+        self,
+        name: str,
+        config: MemoryBankConfig,
+        url: Optional[URL] = None,
+    ) -> MemoryBank:
+        bank_id = str(uuid.uuid4())
+        bank = MemoryBank(
+            bank_id=bank_id,
+            name=name,
+            config=config,
+            url=url,
+        )
+        upsert_models(
+            self.cursor,
+            [
+                (bank.bank_id, bank),
+            ],
+        )
+        index = BankWithIndex(
+            bank=bank,
+            index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
+        )
+        self.cache[bank_id] = index
+        return bank
+
+    async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
+        bank_index = await self._get_and_cache_bank_index(bank_id)
+        if bank_index is None:
+            return None
+        return bank_index.bank
+
+    async def _get_and_cache_bank_index(self, bank_id: str) -> Optional[BankWithIndex]:
+        if bank_id in self.cache:
+            return self.cache[bank_id]
+
+        banks = load_models(self.cursor, [bank_id], MemoryBank)
+        if not banks:
+            return None
+
+        bank = banks[0]
+        index = BankWithIndex(
+            bank=bank,
+            index=PGVectorIndex(bank, ALL_MINILM_L6_V2_DIMENSION, self.cursor),
+        )
+        self.cache[bank_id] = index
+        return index
+
+    async def insert_documents(
+        self,
+        bank_id: str,
+        documents: List[MemoryBankDocument],
+        ttl_seconds: Optional[int] = None,
+    ) -> None:
+        index = await self._get_and_cache_bank_index(bank_id)
+        if not index:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        await index.insert_documents(documents)
+
+    async def query_documents(
+        self,
+        bank_id: str,
+        query: InterleavedTextMedia,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryDocumentsResponse:
+        index = await self._get_and_cache_bank_index(bank_id)
+        if not index:
+            raise ValueError(f"Bank {bank_id} not found")
+
+        return await index.query_documents(query, params)

llama_toolchain/memory/common/vector_store.py

@@ -0,0 +1,120 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+import httpx
+import numpy as np
+from numpy.typing import NDArray
+
+from llama_models.llama3.api.datatypes import *  # noqa: F403
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_toolchain.memory.api import *  # noqa: F403
+
+
+ALL_MINILM_L6_V2_DIMENSION = 384
+
+EMBEDDING_MODEL = None
+
+
+def get_embedding_model() -> "SentenceTransformer":
+    global EMBEDDING_MODEL
+
+    if EMBEDDING_MODEL is None:
+        print("Loading sentence transformer")
+
+        from sentence_transformers import SentenceTransformer
+
+        EMBEDDING_MODEL = SentenceTransformer("all-MiniLM-L6-v2")
+
+    return EMBEDDING_MODEL
+
+
+async def content_from_doc(doc: MemoryBankDocument) -> str:
+    if isinstance(doc.content, URL):
+        async with httpx.AsyncClient() as client:
+            r = await client.get(doc.content.uri)
+            return r.text
+
+    return interleaved_text_media_as_str(doc.content)
+
+
+def make_overlapped_chunks(
+    document_id: str, text: str, window_len: int, overlap_len: int
+) -> List[Chunk]:
+    tokenizer = Tokenizer.get_instance()
+    tokens = tokenizer.encode(text, bos=False, eos=False)
+
+    chunks = []
+    for i in range(0, len(tokens), window_len - overlap_len):
+        toks = tokens[i : i + window_len]
+        chunk = tokenizer.decode(toks)
+        chunks.append(
+            Chunk(content=chunk, token_count=len(toks), document_id=document_id)
+        )
+
+    return chunks
+
+
+class EmbeddingIndex(ABC):
+    @abstractmethod
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
+        raise NotImplementedError()
+
+
+@dataclass
+class BankWithIndex:
+    bank: MemoryBank
+    index: EmbeddingIndex
+
+    async def insert_documents(
+        self,
+        documents: List[MemoryBankDocument],
+    ) -> None:
+        model = get_embedding_model()
+        for doc in documents:
+            content = await content_from_doc(doc)
+            chunks = make_overlapped_chunks(
+                doc.document_id,
+                content,
+                self.bank.config.chunk_size_in_tokens,
+                self.bank.config.overlap_size_in_tokens
+                or (self.bank.config.chunk_size_in_tokens // 4),
+            )
+            embeddings = model.encode([x.content for x in chunks]).astype(np.float32)
+
+            await self.index.add_chunks(chunks, embeddings)
+
+    async def query_documents(
+        self,
+        query: InterleavedTextMedia,
+        params: Optional[Dict[str, Any]] = None,
+    ) -> QueryDocumentsResponse:
+        if params is None:
+            params = {}
+        k = params.get("max_chunks", 3)
+
+        def _process(c) -> str:
+            if isinstance(c, str):
+                return c
+            else:
+                return "<media>"
+
+        if isinstance(query, list):
+            query_str = " ".join([_process(c) for c in query])
+        else:
+            query_str = _process(query)
+
+        model = get_embedding_model()
+        query_vector = model.encode([query_str])[0].astype(np.float32)
+        return await self.index.query(query_vector, k)

llama_toolchain/memory/meta_reference/faiss/faiss.py

@@ -5,108 +5,45 @@
 # the root directory of this source tree.
 
 import uuid
-from dataclasses import dataclass, field
+
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional
 
 import faiss
-import httpx
 import numpy as np
+from numpy.typing import NDArray
 
 from llama_models.llama3.api.datatypes import *  # noqa: F403
-from llama_models.llama3.api.tokenizer import Tokenizer
 
 from llama_toolchain.memory.api import *  # noqa: F403
+from llama_toolchain.memory.common.vector_store import (
+    ALL_MINILM_L6_V2_DIMENSION,
+    BankWithIndex,
+    EmbeddingIndex,
+)
 from .config import FaissImplConfig
 
 
-async def content_from_doc(doc: MemoryBankDocument) -> str:
+class FaissIndex(EmbeddingIndex):
-    if isinstance(doc.content, URL):
+    id_by_index: Dict[int, str]
-        async with httpx.AsyncClient() as client:
+    chunk_by_index: Dict[int, str]
-            r = await client.get(doc.content.uri)
-            return r.text
 
-    return interleaved_text_media_as_str(doc.content)
+    def __init__(self, dimension: int):
+        self.index = faiss.IndexFlatL2(dimension)
+        self.id_by_index = {}
+        self.chunk_by_index = {}
 
-
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
-def make_overlapped_chunks(
-    text: str, window_len: int, overlap_len: int
-) -> List[Tuple[str, int]]:
-    tokenizer = Tokenizer.get_instance()
-    tokens = tokenizer.encode(text, bos=False, eos=False)
-
-    chunks = []
-    for i in range(0, len(tokens), window_len - overlap_len):
-        toks = tokens[i : i + window_len]
-        chunk = tokenizer.decode(toks)
-        chunks.append((chunk, len(toks)))
-
-    return chunks
-
-
-@dataclass
-class BankState:
-    bank: MemoryBank
-    index: Optional[faiss.IndexFlatL2] = None
-    doc_by_id: Dict[str, MemoryBankDocument] = field(default_factory=dict)
-    id_by_index: Dict[int, str] = field(default_factory=dict)
-    chunk_by_index: Dict[int, str] = field(default_factory=dict)
-
-    async def insert_documents(
-        self,
-        model: "SentenceTransformer",
-        documents: List[MemoryBankDocument],
-    ) -> None:
-        tokenizer = Tokenizer.get_instance()
-        chunk_size = self.bank.config.chunk_size_in_tokens
-
-        for doc in documents:
         indexlen = len(self.id_by_index)
-            self.doc_by_id[doc.document_id] = doc
-
-            content = await content_from_doc(doc)
-            chunks = make_overlapped_chunks(
-                content,
-                self.bank.config.chunk_size_in_tokens,
-                self.bank.config.overlap_size_in_tokens
-                or (self.bank.config.chunk_size_in_tokens // 4),
-            )
-            embeddings = model.encode([x[0] for x in chunks]).astype(np.float32)
-            await self._ensure_index(embeddings.shape[1])
-
-            self.index.add(embeddings)
         for i, chunk in enumerate(chunks):
-                self.chunk_by_index[indexlen + i] = Chunk(
+            self.chunk_by_index[indexlen + i] = chunk
-                    content=chunk[0],
+            print(f"Adding chunk #{indexlen + i} tokens={chunk.token_count}")
-                    token_count=chunk[1],
+            self.id_by_index[indexlen + i] = chunk.document_id
-                    document_id=doc.document_id,
-                )
-                print(f"Adding chunk #{indexlen + i} tokens={chunk[1]}")
-                self.id_by_index[indexlen + i] = doc.document_id
 
-    async def query_documents(
+        self.index.add(np.array(embeddings).astype(np.float32))
-        self,
-        model: "SentenceTransformer",
-        query: InterleavedTextMedia,
-        params: Optional[Dict[str, Any]] = None,
-    ) -> QueryDocumentsResponse:
-        if params is None:
-            params = {}
-        k = params.get("max_chunks", 3)
 
-        def _process(c) -> str:
+    async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
-            if isinstance(c, str):
-                return c
-            else:
-                return "<media>"
-
-        if isinstance(query, list):
-            query_str = " ".join([_process(c) for c in query])
-        else:
-            query_str = _process(query)
-
-        query_vector = model.encode([query_str])[0]
         distances, indices = self.index.search(
-            query_vector.reshape(1, -1).astype(np.float32), k
+            embedding.reshape(1, -1).astype(np.float32), k
         )
 
         chunks = []
@@ -119,17 +56,11 @@ class BankState:
 
         return QueryDocumentsResponse(chunks=chunks, scores=scores)
 
-    async def _ensure_index(self, dimension: int) -> faiss.IndexFlatL2:
-        if self.index is None:
-            self.index = faiss.IndexFlatL2(dimension)
-        return self.index
-
 
 class FaissMemoryImpl(Memory):
     def __init__(self, config: FaissImplConfig) -> None:
         self.config = config
-        self.model = None
+        self.cache = {}
-        self.states = {}
 
     async def initialize(self) -> None: ...
 
@@ -153,14 +84,15 @@ class FaissMemoryImpl(Memory):
             config=config,
             url=url,
         )
-        state = BankState(bank=bank)
+        index = BankWithIndex(bank=bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION))
-        self.states[bank_id] = state
+        self.cache[bank_id] = index
         return bank
 
     async def get_memory_bank(self, bank_id: str) -> Optional[MemoryBank]:
-        if bank_id not in self.states:
+        index = self.cache.get(bank_id)
+        if index is None:
             return None
-        return self.states[bank_id].bank
+        return index.bank
 
     async def insert_documents(
         self,
@@ -168,10 +100,11 @@ class FaissMemoryImpl(Memory):
         documents: List[MemoryBankDocument],
         ttl_seconds: Optional[int] = None,
     ) -> None:
-        assert bank_id in self.states, f"Bank {bank_id} not found"
+        index = self.cache.get(bank_id)
-        state = self.states[bank_id]
+        if index is None:
+            raise ValueError(f"Bank {bank_id} not found")
 
-        await state.insert_documents(self.get_model(), documents)
+        await index.insert_documents(documents)
 
     async def query_documents(
         self,
@@ -179,16 +112,8 @@ class FaissMemoryImpl(Memory):
         query: InterleavedTextMedia,
         params: Optional[Dict[str, Any]] = None,
     ) -> QueryDocumentsResponse:
-        assert bank_id in self.states, f"Bank {bank_id} not found"
+        index = self.cache.get(bank_id)
-        state = self.states[bank_id]
+        if index is None:
+            raise ValueError(f"Bank {bank_id} not found")
 
-        return await state.query_documents(self.get_model(), query, params)
+        return await index.query_documents(query, params)
-
-    def get_model(self) -> "SentenceTransformer":
-        from sentence_transformers import SentenceTransformer
-
-        if self.model is None:
-            print("Loading sentence transformer")
-            self.model = SentenceTransformer("all-MiniLM-L6-v2")
-
-        return self.model

llama_toolchain/memory/providers.py

@@ -6,7 +6,12 @@
 
 from typing import List
 
-from llama_toolchain.core.datatypes import Api, InlineProviderSpec, ProviderSpec
+from llama_toolchain.core.datatypes import *  # noqa: F403
+
+EMBEDDING_DEPS = [
+    "blobfile",
+    "sentence-transformers",
+]
 
 
 def available_memory_providers() -> List[ProviderSpec]:
@@ -14,12 +19,25 @@ def available_memory_providers() -> List[ProviderSpec]:
         InlineProviderSpec(
             api=Api.memory,
             provider_id="meta-reference-faiss",
-            pip_packages=[
+            pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
-                "blobfile",
-                "faiss-cpu",
-                "sentence-transformers",
-            ],
             module="llama_toolchain.memory.meta_reference.faiss",
             config_class="llama_toolchain.memory.meta_reference.faiss.FaissImplConfig",
         ),
+        remote_provider_spec(
+            api=Api.memory,
+            adapter=AdapterSpec(
+                adapter_id="chromadb",
+                pip_packages=EMBEDDING_DEPS + ["chromadb-client"],
+                module="llama_toolchain.memory.adapters.chroma",
+            ),
+        ),
+        remote_provider_spec(
+            api=Api.memory,
+            adapter=AdapterSpec(
+                adapter_id="pgvector",
+                pip_packages=EMBEDDING_DEPS + ["psycopg2-binary"],
+                module="llama_toolchain.memory.adapters.pgvector",
+                config_class="llama_toolchain.memory.adapters.pgvector.PGVectorConfig",
+            ),
+        ),
     ]