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gaik-project/gaik-toolkit

v0.2.0MIT

Skills for building document pipelines with the gaik Python toolkit: choosing a document parser that preserves table structure, designing extraction schemas that stay inside provider limits, and building hybrid pgvector search whose failures are visible instead of silent.

searching-documents

Builds and debugs retrieval with the gaik toolkit — PgVectorStore, Ranker, FinnishTextProcessor, RelevanceGate — as hybrid search: pgvector similarity plus Postgres full-text, fused by rank, and the same design in plain SQL for a TypeScript app. Use whenever gaik is used for search or RAG retrieval, whenever Finnish text is indexed for full-text search, when adding semantic or hybrid search over document chunks, and when deciding whether a search found anything relevant at all. Also use when search misbehaves without an error: a sentence query gets no keyword hits, a word visibly in the text is not found, hybrid returns what vector-only returns, gibberish fills a page of results, or a similarity threshold never filters anything; and when measuring retrieval with Hit@K or MRR, or judging whether a reranker, synonyms, or query translation helped.

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