Add Chroma and PGVector adapters (#56)

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

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)