Installation
Usage
Config
Here are the parameters available for configuring Baidu VectorDB:
For the TypeScript OSS SDK, use the camelCase equivalents:
databaseNametableNameembeddingModelDimsmetricType
endpoint, account, apiKey, databaseName, tableName, and embeddingModelDims are required unless you inject a prebuilt client. metricType defaults to L2, matching the Python SDK.
Distance Metrics
The following distance metrics are supported:L2: Euclidean distance (default)IP: Inner productCOSINE: 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
textLemmatized column so keywordSearch() runs against a real full-text index. Mem0 lemmatizes the query before it reaches the vector store, so only the lemmatized form of each memory is indexed. If you point tableName at a table created before this index existed, keywordSearch() returns null and search falls back to vector similarity alone; recreate the table to enable it.