kartik-mem0
fbb8e71224
Each fix verified against mem0/vector_stores/ source and configs:
- baidu: table_name default mem0_table → mem0 (BaiduDBConfig)
- langchain: TS import mem0ai → mem0ai/oss; fix undefined LangchainVectorStore
variable to use MemoryVectorStore directly
- mongodb: collection_name default mem0_collection → mem0; mongo_uri default
corrected to mongodb://localhost:27017 (MongoDBConfig)
- neon: hnsw default False → True (PGVectorConfig.hnsw = True)
- neptune_analytics: extra name vector_stores → vector-stores (pyproject.toml)
- pgvector: diskann default True → False; hnsw default False → True
(PGVectorConfig)
- upstash-vector: move enable_embeddings inside config: {} block
(UpstashVectorConfig field, not top-level VectorStoreConfig)
- valkey: remove non-existent distance_metric row (not in ValkeyConfig);
fix extra name vector_stores → vector-stores
- vertex_ai: region is required (no default in GoogleMatchingEngineConfig)
- weaviate: install pip install weaviate weaviate-client → weaviate-client
(weaviate is not a separate package)
- overview: update TS supported vector DBs list to reflect actual
mem0-ts/src/oss/src/vector_stores/ files
69 lines
2.4 KiB
Plaintext
69 lines
2.4 KiB
Plaintext
---
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title: Baidu VectorDB (Mochow)
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description: "Use Baidu Mochow as an enterprise vector database in Mem0 for high-performance vector storage and retrieval."
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---
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[Baidu VectorDB](https://cloud.baidu.com/doc/VDB/index.html) 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
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```python
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import os
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from mem0 import Memory
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config = {
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"vector_store": {
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"provider": "baidu",
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"config": {
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"endpoint": "http://your-mochow-endpoint:8287",
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"account": "root",
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"api_key": "your-api-key",
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"database_name": "mem0",
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"table_name": "mem0_table",
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"embedding_model_dims": 1536,
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"metric_type": "COSINE"
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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### Config
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Here are the parameters available for configuring Baidu VectorDB:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `endpoint` | Endpoint URL for your Baidu VectorDB instance | Required |
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| `account` | Baidu VectorDB account name | `root` |
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| `api_key` | API key for accessing Baidu VectorDB | Required |
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| `database_name` | Name of the database | `mem0` |
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| `table_name` | Name of the table | `mem0` |
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `metric_type` | Distance metric for similarity search | `L2` |
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### Distance Metrics
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The following distance metrics are supported:
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- `L2`: Euclidean distance (default)
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- `IP`: Inner product
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- `COSINE`: Cosine similarity
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### Index Configuration
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The vector index is automatically configured with the following HNSW parameters:
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- `m`: 16 (number of connections per element)
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- `efconstruction`: 200 (size of the dynamic candidate list)
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- `auto_build`: true (automatically build index)
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- `auto_build_index_policy`: Incremental build with 10000 rows increment
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