Index local PDF, Markdown, and plain text documents into structured Markdown for semantic search.
npx skills add https://github.com/malue-ai/dazee-small --skill raglite
Please help me install this Agent Skill. Skill: raglite Source: https://github.com/malue-ai/dazee-small/tree/main/skills/library/raglite Command: npx skills add https://github.com/malue-ai/dazee-small --skill raglite
This Skill addresses the need for fast, local knowledge retrieval from documents without relying on external services or complex vector databases.
Use the raglite skill to index the document located at '/path/to/my/report.pdf'.
High-intent search queries and answers about installing and using this skill.
To create a local RAG cache, you index PDF, Markdown, and plain text files to distill their content into structured Markdown. This builds a local retrieval index for rapid semantic search and question answering without external databases.
Yes, you can perform knowledge retrieval on private documents without an external vector database by using a local RAG approach. It distills files into structured Markdown and builds a local index for rapid retrieval.
The best way to index company reports for local question answering is to use a RAG approach that supports PDF and Markdown files. It distills the content into structured Markdown to enable efficient semantic search.
Yes, local RAG supports plain text and Markdown file formats, along with PDF files. It indexes these formats and distills their content into structured Markdown for efficient retrieval and question answering.
You use a local document index instead of a cloud service to keep knowledge retrieval fast and private without relying on external services. It builds a local RAG cache from your files for secure, offline question answering.
AissenLiuExtract text from PDFs and scanned documents via web_extract, pymupdf, or marker-pdf.
alsk1992Search local Markdown notes with BM25, vector, and hybrid modes.
revfactoryRead, parse, and convert HWP/HWPX documents to Markdown or other formats.
handsomelong922Store and retrieve embeddings with metadata using a vector database.
