Create Database
AI-generatedSummary
Create and initialize a MongoDB database for LightRAG by setting up required collections and vector indexes with specified embedding dimensions.
Inputs
- newDatabaseName (required) — Name of the MongoDB database to create and initialize for LightRAG.
- embeddingDimensions (required) — Embedding vector dimensions used when creating the LightRAG vector indexes.
Output shape
a single JSON object summarizing the database initialization status, including database name, created collections, embedding dimensions, and initialization status.
The operation completes the setup by creating collections ('lightrag_entities', 'lightrag_relations', 'lightrag_chunks', 'lightrag_doc_status') and their associated vector and traditional indexes tailored for the specified embedding dimension.
Examples
Example 1: Initialize a new MongoDB database named 'KnowledgeGraphDB' with 3072 embedding dimensions.
Provide 'KnowledgeGraphDB' as the Database Name and set Embedding Dimensions to 3072. Upon execution, the node creates necessary collections and vector indexes to support LightRAG workflows.