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Overview

Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.

Qdrant

Chroma

pgvector

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Qdrant

Qdrant is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.

To use Qdrant you can do like this:

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "test",
            "host": "localhost",
            "port": 6333,
        }
    }
}

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

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Chroma

Chroma is an AI-native open-source vector database that simplifies building LLM apps by providing tools for storing, embedding, and searching embeddings with a focus on simplicity and speed.

To use ChromaDB you can do like this:

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "chroma",
        "config": {
            "collection_name": "test",
            "path": "db",
        }
    }
}

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

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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.

Here’s how to use it:

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"})

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Common issues

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Using model with different dimensions

If you are using customized model, which is having different dimensions other than 1536 for example 768, you may encounter below error:

ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)

you could add "embedding_model_dims": 768, to the config of the vector_store to overcome this issue.