ai-rag-pipelinelisted

# AI RAG Pipeline Build RAG (Retrieval Augmented Generation) pipelines via [inference.sh](https://inference.sh) CLI. ## Quick Start ```bash curl -fsSL https://cli.inference.sh | sh && 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 | App ID | Best For | |------|--------|----------| | Tavily Search | `tavily/search-assistant` | AI-powered search with answers | | Exa Search | `exa/search` | Neural search, semantic matching | | Exa Answer | `exa/answer` | Direct factual answers | ### Extraction Tools | Tool | App ID | Best For | |------|--------|---------