rag▌

Build RAG pipelines combining web search and LLMs for grounded, sourced AI responses. \n \n Integrates multiple search tools (Tavily, Exa) and LLM providers (Claude, GPT-4, Gemini via OpenRouter) via the inference.sh CLI \n Supports three core patterns: simple search-plus-answer, multi-source research aggregation, and URL content extraction with analysis \n Includes ready-to-use examples for fact-checking, research reports, and iterative deep-dive queries with built-in source attribution \n Best

Comprehensive patterns for building production RAG systems. Each category has individual rule files in rules/ loaded on-demand.

Complete RAG pipeline for document ingestion, embedding, retrieval, and LLM-powered response generation. \n \n Supports multiple document loaders (PDF, web pages, directories) and persistent vector stores (Chroma, FAISS, Pinecone) with configurable chunk size and overlap for optimal context preservation \n Includes similarity search, MMR (Maximal Marginal Relevance) retrieval, and metadata filtering to balance relevance and diversity in results \n Works with OpenAI embeddings and integrates seam