pgvector is open-source vector similarity search for Postgres. After connecting with postgres run CREATE EXTENSION IF NOT EXISTS vector;
to create the vector extension.
Usage
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 a 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"})
Config
Here’s the parameters available for configuring pgvector:
Parameter | Description | Default Value |
---|
dbname | The name of the | 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 |
Responses are generated using AI and may contain mistakes.