Weaviate is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.
Installation
pip install weaviate weaviate-client
Usage
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "sk-xx"
config = {
"vector_store": {
"provider": "weaviate",
"config": {
"collection_name": "test",
"cluster_url": "http://localhost:8080",
"auth_client_secret": None,
}
}
}
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 movie? 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
Let’s see the available parameters for the weaviate config:
| Parameter | Description | Default Value |
|---|
collection_name | The name of the collection to store the vectors | mem0 |
embedding_model_dims | Dimensions of the embedding model | 1536 |
cluster_url | URL for the Weaviate server | None |
auth_client_secret | API key for Weaviate authentication | None |
