Build and query RAG pipelines over document collections with Codex.
npx skills add https://github.com/guideops/agentic-shared --skill rag-pipeline-guideops
Please help me install this Agent Skill. Skill: rag-pipeline Source: https://github.com/guideops/agentic-shared/tree/main/skills/rag-pipeline Command: npx skills add https://github.com/guideops/agentic-shared --skill rag-pipeline-guideops
This Skill addresses the challenge of efficiently building and querying RAG (Retrieval-Augmented Generation) pipelines, enabling users to create and manage indexes over document collections for semantic search and retrieval-augmented generation.
Build an index for your documents with /rag-pipeline index --path wiki/.
High-intent search queries and answers about installing and using this skill.
To build a RAG pipeline for semantic search, you create and manage indexes over your document collections, enabling efficient retrieval. This Skill automates indexing and querying to generate responses using Codex based on retrieved context.
Yes, you need an OpenAI API key to use retrieval-augmented generation with Codex. The Python environment requires this key for Codex integration to generate responses based on retrieved document context.
The best way to index an Obsidian vault for AI-driven document analysis is using a RAG pipeline. You can build and manage indexes for arbitrary document collections by running a command like `/rag-pipeline index --path wiki/`.
Retrieval-augmented generation works by performing semantic searches on indexed document collections to find relevant context. It then uses Codex to generate responses based on the retrieved document information.
Yes, you can use Python to automate document indexing and summarization for large corpora. This Skill supports building RAG pipelines that index, search, and generate summaries, saving researchers significant time and effort.
Limitations of using Codex for retrieval-augmented generation include dependencies on Python and an OpenAI API key. The effectiveness of generated responses is constrained by the quality of indexed documents and semantic search results.
Eng0AIIndex and retrieve document Q&A data using Supabase pgvector in LangChain pipelines.
nuno7lopesSearch the web with advanced filters and extract content from URLs.
lvndryAutomate Obsidian note creation, retrieval, and vault organization via obsidian-cli.
MSamiulHasnatIndex embeddings and perform context-aware similarity queries for document retrieval.
