Baidu VectorDB (Mochow) - Mem0

Baidu VectorDB is an enterprise-level distributed vector database service developed by Baidu Intelligent Cloud. It is powered by Baidu's proprietary "Mochow" vector database kernel, providing high performance, availability, and security for vector search.

Usage

python

import os
from mem0 import Memory

config = {
    "vector_store": {
        "provider": "baidu",
        "config": {
            "endpoint": "http://your-mochow-endpoint:8287",
            "account": "root",
            "api_key": "your-api-key",
            "database_name": "mem0",
            "table_name": "mem0_table",
            "embedding_model_dims": 1536,
            "metric_type": "COSINE"
        }
    }
}

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

Here are the parameters available for configuring Baidu VectorDB:

Parameter	Description	Default Value
`endpoint`	Endpoint URL for your Baidu VectorDB instance	Required
`account`	Baidu VectorDB account name	`root`
`api_key`	API key for accessing Baidu VectorDB	Required
`database_name`	Name of the database	`mem0`
`table_name`	Name of the table	`mem0_table`
`embedding_model_dims`	Dimensions of the embedding model	`1536`
`metric_type`	Distance metric for similarity search	`L2`

Distance Metrics

The following distance metrics are supported:

L2: Euclidean distance (default)
IP: Inner product
COSINE: Cosine similarity

Index Configuration

The vector index is automatically configured with the following HNSW parameters:

m: 16 (number of connections per element)
efconstruction: 200 (size of the dynamic candidate list)
auto_build: true (automatically build index)
auto_build_index_policy: Incremental build with 10000 rows increment