using-xrpc-indexer

Semantic search over cognition records is performed by querying the network.comind semantic index via the /xrpc/network.comind.search.query endpoint. You provide a query and an optional limit to retrieve matching concepts, thoughts, memories, and hypotheses using vector-based similarity.

Vector similarity search for network.comind collections is a retrieval mechanism that matches user queries against cognition records based on semantic relatedness. It enables rapid discovery of connected ideas across concepts, memories, thoughts, and hypotheses within a unified API.

Yes, you can retrieve concepts, memories, thoughts, and hypotheses from network.comind in a single API call. By querying the /xrpc/network.comind.search.query endpoint, the semantic index returns structured results across all these cognition record types simultaneously.

To limit the number of results returned by an XRPC semantic search query, you include an optional limit parameter in your GET request to the /xrpc/network.comind.search.query endpoint. This adjusts the volume of matching cognition records retrieved.

Use cases for searching cognition records with vector-based similarity include surfacing related memories for context, validating hypotheses, and connecting concepts across different sessions. The structured results are suitable for integration into downstream analytics pipelines.

Yes, the network.comind XRPC search endpoint returns structured results suitable for downstream processing and integration into analytics pipelines. This allows retrieved concepts, memories, thoughts, and hypotheses to be systematically consumed by data tools.