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search-retrieval

search-retrieval

Use and troubleshoot Khoj semantic search, query filters, embeddings, reranking, and SearchResponse interpretation.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: search-retrieval description: "Use and troubleshoot Khoj semantic search, query filters, embeddings, reranking, and SearchResponse interpretation." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

Khoj Search Retrieval

Use this sub-skill when working on Khoj semantic search, /api/search, query filters, retrieval ranking, or search-result interpretation.

Route Map

  • Use references/search-api.md for /api/search parameters, SearchType values, result collation, dedupe, reranking, and SearchResponse fields.
  • Use references/query-filters.md for date, file, and word filter syntax; examples; defiltering; and edge cases.
  • Use references/embeddings-and-ranking.md for embedding models, cross-encoder reranking, max_distance, defaults, and performance tradeoffs.
  • Use references/troubleshooting.md for no results, excessive results, bad filters, stale indexes, model/backend failures, user/agent isolation, and reranker failures.
  • Use scripts/inspect_query_filters.py to safely inspect filter terms and defiltered query text without starting the Khoj server.

Boundaries

  • This sub-skill covers semantic search, /api/search, filters, embeddings, reranking, and SearchResponse interpretation.
  • Route content ingestion, sync, update/index rebuild implementation, and file parsing to content-indexing.
  • Route chat, /notes, conversation context construction, and agent response behavior to chat-agents.
  • Route server startup, auth, deployment, Docker, and API infrastructure setup to deployment-api.
  • Route maintainer test selection, development setup, and release scripts to development.

Evidence Basis

This guidance is distilled from Khoj source and tests for search routing, text retrieval, filters, embeddings, API response models, and documented search/filter/performance behavior. It is self-contained for future coding agents and does not require reading original repo docs or tests at runtime.

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