A local-first document indexing and retrieval server that lets you ingest, search, and manage private documents using semantic and keyword search via MCP tools for AI assistants.

• Ingest files (ingest_file): Add local files (PDF, DOCX, TXT, Markdown) into the vector database; supports re-ingestion to update existing entries, and optional VLM visual captioning for PDF figures ('fast' or 'quality' profiles).

• Ingest raw content (ingest_data): Index text, HTML, or Markdown strings directly without needing a file on disk — useful for web pages, clipboard content, or notes.

• Search documents (query_documents): Perform hybrid semantic + keyword search across all ingested content, with configurable result limits (1–20).

• Read chunk neighbors (read_chunk_neighbors): Expand a search result by retrieving surrounding chunks (before/after) from the same document for additional context.

• Delete indexed content (delete_file): Remove a previously ingested file or data source from the vector database by file path or source identifier.

• List files (list_files): View all files in the configured base directory, their ingestion status, and any other ingested items.

• Check system status (status): Get an overview including total documents, chunks, database size, and current configuration.

1. Click on "Install Server".

2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.

3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@local-Rag find documents about customer churn analysis"

That's it! The server will respond to your query, and you can continue using it as needed.

Here is a step-by-step guide with screenshots.