using-xrpc-indexer

To perform semantic search over cognition records, query the network.comind search API to retrieve concepts, thoughts, memories, and hypotheses based on vector similarity to your target text.

Vector similarity lookup for thoughts and memories is a method to surface related cognition records by matching your query against an indexed dataset using semantic distance via the XRPC protocol.

Retrieve related concepts from the comind index by sending a query to the similarity endpoint, which matches your input against stored hypotheses and memories to return structured results.

Yes, the XRPC indexer supports retrieving index statistics by exposing dedicated endpoints that return operational data and metrics alongside the search and similarity lookup functions.

You can integrate semantic search results into downstream reasoning workflows because the API returns structured data from the cognition index, ready for immediate processing in your applications.

A limitation of using XRPC for semantic search is that retrieval is strictly bound to the network.comind index, meaning it only surfaces concepts, thoughts, memories, and hypotheses available within that specific dataset.