295 lines
9.3 KiB
Python
295 lines
9.3 KiB
Python
import json
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import logging
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from typing import List, Optional
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from pydantic import BaseModel
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try:
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import psycopg2
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from psycopg2.extras import execute_values
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except ImportError:
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raise ImportError("The 'psycopg2' library is required. Please install it using 'pip install psycopg2'.")
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from mem0.vector_stores.base import VectorStoreBase
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logger = logging.getLogger(__name__)
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class OutputData(BaseModel):
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id: Optional[str]
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score: Optional[float]
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payload: Optional[dict]
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class PGVector(VectorStoreBase):
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def __init__(
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self,
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dbname,
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collection_name,
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embedding_model_dims,
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user,
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password,
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host,
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port,
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diskann,
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hnsw,
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):
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"""
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Initialize the PGVector database.
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Args:
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dbname (str): Database name
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collection_name (str): Collection name
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embedding_model_dims (int): Dimension of the embedding vector
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user (str): Database user
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password (str): Database password
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host (str, optional): Database host
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port (int, optional): Database port
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diskann (bool, optional): Use DiskANN for faster search
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hnsw (bool, optional): Use HNSW for faster search
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"""
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self.collection_name = collection_name
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self.use_diskann = diskann
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self.use_hnsw = hnsw
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self.embedding_model_dims = embedding_model_dims
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self.conn = psycopg2.connect(dbname=dbname, user=user, password=password, host=host, port=port)
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self.cur = self.conn.cursor()
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collections = self.list_cols()
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if collection_name not in collections:
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self.create_col(embedding_model_dims)
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def create_col(self, embedding_model_dims):
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"""
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Create a new collection (table in PostgreSQL).
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Will also initialize vector search index if specified.
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Args:
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embedding_model_dims (int): Dimension of the embedding vector.
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"""
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self.cur.execute("CREATE EXTENSION IF NOT EXISTS vector")
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self.cur.execute(
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f"""
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CREATE TABLE IF NOT EXISTS {self.collection_name} (
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id UUID PRIMARY KEY,
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vector vector({embedding_model_dims}),
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payload JSONB
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);
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"""
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)
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if self.use_diskann and embedding_model_dims < 2000:
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# Check if vectorscale extension is installed
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self.cur.execute("SELECT * FROM pg_extension WHERE extname = 'vectorscale'")
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if self.cur.fetchone():
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# Create DiskANN index if extension is installed for faster search
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self.cur.execute(
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f"""
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CREATE INDEX IF NOT EXISTS {self.collection_name}_diskann_idx
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ON {self.collection_name}
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USING diskann (vector);
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"""
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)
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elif self.use_hnsw:
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self.cur.execute(
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f"""
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CREATE INDEX IF NOT EXISTS {self.collection_name}_hnsw_idx
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ON {self.collection_name}
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USING hnsw (vector vector_cosine_ops)
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"""
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)
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self.conn.commit()
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def insert(self, vectors, payloads=None, ids=None):
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"""
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Insert vectors into a collection.
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Args:
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vectors (List[List[float]]): List of vectors to insert.
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payloads (List[Dict], optional): List of payloads corresponding to vectors.
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ids (List[str], optional): List of IDs corresponding to vectors.
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"""
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logger.info(f"Inserting {len(vectors)} vectors into collection {self.collection_name}")
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json_payloads = [json.dumps(payload) for payload in payloads]
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data = [(id, vector, payload) for id, vector, payload in zip(ids, vectors, json_payloads)]
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execute_values(
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self.cur,
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f"INSERT INTO {self.collection_name} (id, vector, payload) VALUES %s",
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data,
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)
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self.conn.commit()
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def search(self, query, vectors, limit=5, filters=None):
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"""
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Search for similar vectors.
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Args:
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query (str): Query.
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vectors (List[float]): Query vector.
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limit (int, optional): Number of results to return. Defaults to 5.
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filters (Dict, optional): Filters to apply to the search. Defaults to None.
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Returns:
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list: Search results.
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"""
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filter_conditions = []
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filter_params = []
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if filters:
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for k, v in filters.items():
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filter_conditions.append("payload->>%s = %s")
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filter_params.extend([k, str(v)])
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filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else ""
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self.cur.execute(
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f"""
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SELECT id, vector <=> %s::vector AS distance, payload
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FROM {self.collection_name}
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{filter_clause}
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ORDER BY distance
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LIMIT %s
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""",
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(vectors, *filter_params, limit),
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)
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results = self.cur.fetchall()
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return [OutputData(id=str(r[0]), score=float(r[1]), payload=r[2]) for r in results]
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def delete(self, vector_id):
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"""
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Delete a vector by ID.
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Args:
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vector_id (str): ID of the vector to delete.
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"""
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self.cur.execute(f"DELETE FROM {self.collection_name} WHERE id = %s", (vector_id,))
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self.conn.commit()
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def update(self, vector_id, vector=None, payload=None):
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"""
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Update a vector and its payload.
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Args:
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vector_id (str): ID of the vector to update.
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vector (List[float], optional): Updated vector.
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payload (Dict, optional): Updated payload.
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"""
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if vector:
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self.cur.execute(
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f"UPDATE {self.collection_name} SET vector = %s WHERE id = %s",
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(vector, vector_id),
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)
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if payload:
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self.cur.execute(
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f"UPDATE {self.collection_name} SET payload = %s WHERE id = %s",
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(psycopg2.extras.Json(payload), vector_id),
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)
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self.conn.commit()
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def get(self, vector_id) -> OutputData:
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"""
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Retrieve a vector by ID.
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Args:
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vector_id (str): ID of the vector to retrieve.
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Returns:
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OutputData: Retrieved vector.
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"""
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self.cur.execute(
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f"SELECT id, vector, payload FROM {self.collection_name} WHERE id = %s",
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(vector_id,),
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)
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result = self.cur.fetchone()
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if not result:
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return None
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return OutputData(id=str(result[0]), score=None, payload=result[2])
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def list_cols(self) -> List[str]:
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"""
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List all collections.
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Returns:
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List[str]: List of collection names.
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"""
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self.cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'")
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return [row[0] for row in self.cur.fetchall()]
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def delete_col(self):
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"""Delete a collection."""
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self.cur.execute(f"DROP TABLE IF EXISTS {self.collection_name}")
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self.conn.commit()
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def col_info(self):
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"""
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Get information about a collection.
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Returns:
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Dict[str, Any]: Collection information.
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"""
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self.cur.execute(
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f"""
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SELECT
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table_name,
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(SELECT COUNT(*) FROM {self.collection_name}) as row_count,
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(SELECT pg_size_pretty(pg_total_relation_size('{self.collection_name}'))) as total_size
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FROM information_schema.tables
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WHERE table_schema = 'public' AND table_name = %s
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""",
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(self.collection_name,),
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)
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result = self.cur.fetchone()
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return {"name": result[0], "count": result[1], "size": result[2]}
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def list(self, filters=None, limit=100):
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"""
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List all vectors in a collection.
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Args:
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filters (Dict, optional): Filters to apply to the list.
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limit (int, optional): Number of vectors to return. Defaults to 100.
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Returns:
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List[OutputData]: List of vectors.
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"""
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filter_conditions = []
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filter_params = []
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if filters:
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for k, v in filters.items():
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filter_conditions.append("payload->>%s = %s")
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filter_params.extend([k, str(v)])
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filter_clause = "WHERE " + " AND ".join(filter_conditions) if filter_conditions else ""
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query = f"""
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SELECT id, vector, payload
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FROM {self.collection_name}
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{filter_clause}
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LIMIT %s
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"""
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self.cur.execute(query, (*filter_params, limit))
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results = self.cur.fetchall()
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return [[OutputData(id=str(r[0]), score=None, payload=r[2]) for r in results]]
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def __del__(self):
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"""
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Close the database connection when the object is deleted.
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"""
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if hasattr(self, "cur"):
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self.cur.close()
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if hasattr(self, "conn"):
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self.conn.close()
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def reset(self):
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"""Reset the index by deleting and recreating it."""
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logger.warning(f"Resetting index {self.collection_name}...")
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self.delete_col()
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self.create_col(self.embedding_model_dims)
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