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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.

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Usage

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"})

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Config

Here are the parameters available for configuring Baidu VectorDB:
ParameterDescriptionDefault Value
endpointEndpoint URL for your Baidu VectorDB instanceRequired
accountBaidu VectorDB account nameroot
api_keyAPI key for accessing Baidu VectorDBRequired
database_nameName of the databasemem0
table_nameName of the tablemem0_table
embedding_model_dimsDimensions of the embedding model1536
metric_typeDistance metric for similarity searchL2

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Distance Metrics

The following distance metrics are supported:
  • L2: Euclidean distance (default)
  • IP: Inner product
  • COSINE: Cosine similarity

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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