openclaw-surreal-search

Graph and vector search over a large knowledge base is facilitated by a SurrealDB-backed workflow that unifies relational context, embedding-based similarity, and temporal provenance to enable fast, contextual retrieval.

Tracing provenance when querying corpus data is achieved by leveraging a local SurrealDB index that organizes ingested data with transparent references. This allows you to answer complex queries like 'who did X, where?' with fully traceable results.

Setting up a local SurrealDB index for knowledge base ingestion involves completing an end-to-end workflow setup including local database installation, data ingestion, and interactive querying. This creates a local index over your corpus data and references.

Yes, you can perform time-aware searches across your corpus data because the SurrealDB workflow combines temporal context with graph relationships and vector similarity. This time-aware provenance ensures precise results for complex queries.

The graph and vector search workflow does not require external database dependencies because it applies a local SurrealDB index over your corpus data. It supports a complete end-to-end setup including local database installation and data ingestion.