AI RAG Pipeline

Build RAG (Retrieval Augmented Generation) pipelines via inference.sh CLI.

Quick Start

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\"
}"

Install note: The install script only detects your OS/architecture, downloads the matching binary from dist.inference.sh, and verifies its SHA-256 checksum. No elevated permissions or background processes. Manual install & verification available.

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

Extraction Tools

LLM Tools

Pipeline Examples

Basic RAG Pipeline

# 1. Search for information
SEARCH_RESULT=$(infsh app run tavily/search-assistant --input '{
  "query": "What are the latest breakthroughs in quantum computing 2024?"
}')

# 2. Generate grounded response
infsh app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"You are a research assistant. Based on the following search results, provide a comprehensive summary with citations.

Search Results:
$SEARCH_RESULT

Provide a well-structured summary with source citations.\"
}"

Multi-Source Research