ai-rag-pipeline

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# AI RAG Pipeline

Build RAG (Retrieval Augmented Generation) pipelines via [inference.sh](https://inference.sh) CLI.

![AI RAG Pipeline](https://cloud.inference.sh/app/files/u/4mg21r6ta37mpaz6ktzwtt8krr/01kgndqjxd780zm2j3rmada6y8.jpeg)

## 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\"
}"
```

> **Install note:** The [install script](https://cli.inference.sh) 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](https://dist.inference.sh/cli/checksums.txt) 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

| 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 |
|------|--------|----------|
| Tavily Extract | `tavily/extract` | Clean content from URLs |
| Exa Extract | `exa/extract` | Analyze web content |

### LLM Tools

| Model | App ID | Best For |
|-------|--------|----------|
| Claude Sonnet 4.5 | `openrouter/claude-sonnet-45` | Complex analysis |
| Claude Haiku 4.5 | `openrouter/claude-haiku-45` | Fast processing |
| GPT-4o | `openrouter/gpt-4o` | General purpose |
| Gemini 2.5 Pro | `openrouter/gemini-25-pro` | Long context |

## Pipeline Examples

### Basic RAG Pipeline

```bash
# 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

```bash
# Search multiple sources
TAVILY=$(infsh app run tavily/search-assistant --input '{"query": "electric vehicle market trends 2024"}')
EXA=$(infsh app run exa/search --input '{"query": "EV market analysis latest reports"}')

# Combine and analyze
infsh app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Analyze these research results and identify common themes and contradictions.

Source 1 (Tavily):
$TAVILY

Source 2 (Exa):
$EXA

Provide a balanced analysis with sources.\"
}"
```

### URL Content Analysis

From concisegjh/openakita, Apache-2.0.

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