ai-rag-pipeline

--- name: ai-rag-pipeline description: "Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline" allowed-tools: Bash(infsh *) --- # AI RAG Pipeline Build RAG (Retrieval Augmented Generation) pipelines via [inference.sh](https://inference.sh) CLI.  ## Quick Start > Requires inference.sh CLI (`infsh`). Get installation instructions: `npx skills add inference-sh/skills@agent-tools` ```bash infsh login # Simple RAG: Search + LLM SEARCH=$(infsh app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}') infsh app run openrouter/claude-sonnet-45 --input "{ \"prompt\": \"Based on this research, summarize the key trends: $SEARCH\" }" ``` ## What is RAG? RAG combines: 1. **Retrieval**: Fetch relevant information from external sources 2. **Augmentation**: Add retrieved context to the prompt 3. **Generation**: LLM generates response using the context This produces more accurate, up-to-date, and verifiable AI responses. ## RAG Pipeline Patterns ### Pattern 1: Simple Search + Answer ``` [User Query] -> [Web Search] -> [LLM with Context] -> [Answer] ``` ### Pattern 2: Multi-Source Research ``` [Query] -> [Multiple Searches] -> [Aggregate] -> [LLM Analysis] -> [Report] ``` ### Pattern 3: Extract + Process ``` [URLs] -> [Content Extraction] -> [Chunking] -> [LLM Summary] -> [Output] ``` ## Available Tools ### Search Tools | Tool [... prompt truncated for preview ...]