pgvector - Mem0 — ContextQMD

pgvector is an open-source vector similarity search extension for Postgres. After connecting to Postgres, run CREATE EXTENSION IF NOT EXISTS vector; to create the vector extension.

Usage

<CodeGroup> ```python Python import os from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = { "vector_store": { "provider": "pgvector", "config": { "user": "test", "password": "123", "host": "127.0.0.1", "port": "5432", } } }

m = Memory.from_config(config) messages = [ {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"}, {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."}, {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."}, {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."} ] m.add(messages, user_id="alice", metadata={"category": "movies"})


```typescript TypeScript
import { Memory } from 'mem0ai/oss';

const config = {
  vectorStore: {
    provider: 'pgvector',
    config: {
      collectionName: 'memories',
      embeddingModelDims: 1536,
      user: 'test',
      password: '123',
      host: '127.0.0.1',
      port: 5432,
      dbname: 'vector_store', // Optional; TypeScript OSS defaults to `vector_store` when omitted
      diskann: false, // Optional, requires pgvectorscale extension
      hnsw: false, // Optional, for HNSW indexing
    },
  },
};

const memory = new Memory(config);
const messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

</CodeGroup>

Config

Here are the parameters available for configuring pgvector:

Parameter	Description	Default Value
`dbname`	The name of the database	`postgres`
`collection_name`	The name of the collection	`mem0`
`embedding_model_dims`	Dimensions of the embedding model	`1536`
`user`	User name to connect to the database	`None`
`password`	Password to connect to the database	`None`
`host`	The host where the Postgres server is running	`None`
`port`	The port where the Postgres server is running	`None`
`diskann`	Whether to use diskann for vector similarity search (requires pgvectorscale)	`True`
`hnsw`	Whether to use hnsw for vector similarity search	`False`
`sslmode`	SSL mode for PostgreSQL connection (e.g., 'require', 'prefer', 'disable')	`None`
`connection_string`	PostgreSQL connection string (overrides individual connection parameters)	`None`
`connection_pool`	psycopg2 connection pool object (overrides connection string and individual parameters)	`None`

Note (TypeScript OSS): If you omit dbname, the TypeScript client uses the database name vector_store. Python defaults to postgres for dbname, as in the table above.

Note: The connection parameters have the following priority:

connection_pool (highest priority)
connection_string
Individual connection parameters (user, password, host, port, sslmode)