Build semantic search indexes from Markdown, Python, and structured data files.
npx skills add https://github.com/tya5/reyn --skill index-docs-tya5
Please help me install this Agent Skill. Skill: index_docs Source: https://github.com/tya5/reyn/tree/main/src/reyn/stdlib/skills/index_docs Command: npx skills add https://github.com/tya5/reyn --skill index-docs-tya5
Facilitates building fast, accurate, and scalable semantic search indexes over large document collections.
Use the index_docs skill to process files matching 'docs/**/*.md' for fast vector search and retrieval.
High-intent search queries and answers about installing and using this skill.
To create a semantic search index from Markdown, you process files matching patterns like 'docs/**/*.md', applying chunking strategies to split content at headings or sentences before generating vector embeddings for rapid retrieval.
Indexing Python scripts for knowledge base retrieval involves splitting the code into optimal chunks and generating vector embeddings, ensuring fast and relevant content retrieval for your applications without manual tagging.
Yes, you can index structured data files for vector search. The system supports diverse document formats, optimizing content split points to enhance retrieval relevance across large-scale document collections.
Content chunking strategy directly affects semantic search relevance by determining optimal split points, such as at headings or sentences, which ensures that vector embeddings accurately represent distinct sections of your documents.
Building a document indexing pipeline requires strategies for splitting content and managing large-scale preprocessing to generate vector embeddings, ensuring efficient workflow for your knowledge applications.
lucifertrjClean, tokenize, and normalize English text with NLTK for NLP pipelines.
rukikotooQuery the econometrics RAG knowledge base to retrieve relevant document chunks.
SignmanalStore and query vector embeddings with metadata filtering for RAG applications.
ccfCombine vector similarity with keyword matching for hybrid retrieval.
