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git-raka

folder-index

by git-raka

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    • A
      license
      A
      quality
      C
      maintenance
      Enables AI agents to search a local folder of Markdown, AsciiDoc, and text files through a SQLite FTS5 keyword index, returning the most relevant sections with file paths, line ranges, and heading breadcrumbs instead of grepping through a repo. It also exposes tools to list indexed documents and refresh the index incrementally, all offline with no dependencies, network calls, or embeddings.
      3
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables agents to run full-text search inside Word, PowerPoint, Excel, PDF, and plain-text files by extracting their contents into a single local SQLite FTS5 index. Exposes tools to search documents with ranked highlighted snippets and human-readable locators, list indexed files, and incrementally build or refresh the index.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Turns a local folder of documents into a queryable index that an LLM agent can pull from on demand, keeping only a compact table of contents in context. Exposes five tools (index, search, read, grep, neighbours) over a single offline SQLite FTS5 store so the model retrieves exact, citable sections instead of being handed whole files.
      25 PyPI
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI assistants to run incremental SQLite FTS5 full-text search over explicitly configured local workspace roots, covering Markdown, code, PDF, DOCX, XLSX, CSV and HTML with BM25 ranking, Chinese bigrams, and phrase, prefix, path, type and date filters. Exposes search, safe root-confined reads with line/page/sheet locations, recent-file listing, and reindexing as MCP tools so assistants can locate and quote source content without network or account access.
      MIT
    • A
      license
      A
      quality
      A
      maintenance
      Enables local AI agents to perform hybrid search (BM25 + vector) over privately indexed local files, including notes, PDFs, DOCX, source code, and OCR'd images. All indexing and retrieval stay on the machine with read-only, whitelist-scoped access.
      3
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Indexes local documents (PDF, Word, Markdown, text) into a SQLite database for AI agents to search and retrieve bounded, source-located passages. Runs fully locally with optional OCR, preserving privacy.
      5
      MIT