docs(vectordbs): correct vector store config defaults and imports
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
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@@ -42,8 +42,8 @@ Here are the parameters available for configuring MongoDB:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| db_name | Name of the MongoDB database | `"mem0_db"` |
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| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
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| collection_name | Name of the MongoDB collection | `"mem0"` |
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| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
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| mongo_uri | The MongoDB URI connection string | `mongodb://username:password@localhost:27017` |
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| mongo_uri | The MongoDB URI connection string | `mongodb://localhost:27017` |
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> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.
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> **Note**: If `mongo_uri` is not provided, it will default to `mongodb://localhost:27017`.
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