MongoDB
MongoDB is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "sk-xx"
config = {
"vector_store": {
"provider": "mongodb",
"config": {
"db_name": "mem0-db",
"collection_name": "mem0-collection",
"mongo_uri":"mongodb://username:password@localhost:27017"
}
}
}
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": "Iām 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"})
Here are the parameters available for configuring MongoDB:
| Parameter | Description | Default Value |
|---|
| db_name | Name of the MongoDB database | "mem0_db" |
| collection_name | Name of the MongoDB collection | "mem0_collection" |
| embedding_model_dims | Dimensions of the embedding vectors | 1536 |
| mongo_uri | The mongo URI connection string | mongodb://username:password@localhost:27017 |
Note: If Mongo_uri is not provided it will default to mongodb://username:password@localhost:27017.
