47 lines
1.8 KiB
Plaintext
47 lines
1.8 KiB
Plaintext
[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)
|
||
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` | |