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.

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Usage

import os
from zentry 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"})

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Config

Here’s the parameters available for configuring pgvector:
ParameterDescriptionDefault Value
dbnameThe name of thepostgres
collection_nameThe name of the collectionzentry
embedding_model_dimsDimensions of the embedding model1536
userUser name to connect to the databaseNone
passwordPassword to connect to the databaseNone
hostThe host where the Postgres server is runningNone
portThe port where the Postgres server is runningNone
diskannWhether to use diskann for vector similarity search (requires pgvectorscale)True
hnswWhether to use hnsw for vector similarity searchFalse