[pgvector](https://github.com/pgvector/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

```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)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
```

### Config

Here's 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` |