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ai-rag-pipeline

Name: ai-rag-pipeline
Rating: 4.2 (5 reviews)
Author: inference.sh

inference-sh/ai-rag-pipeline · Agent Skill

by inference.sh · single

Published Jul 5, 2026 · Last updated Jul 6, 2026

Build RAG pipelines that combine web search and LLMs for research, fact-checking, and grounded responses using the inference.sh CLI.

SKILL.md

Install the belt CLI skill: npx skills add belt-sh/cli

AI RAG Pipeline

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

Quick Start

Requires inference.sh CLI (belt). Install instructions

belt login

# Simple RAG: Search + LLM
SEARCH=$(belt app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}')
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Based on this research, summarize the key trends: $SEARCH\"
}"

What is RAG?

RAG combines:

Retrieval: Fetch relevant information from external sources
Augmentation: Add retrieved context to the prompt
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

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

# 2. Generate grounded response
belt 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

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

# Combine and analyze
belt 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

# 1. Extract content from specific URLs
CONTENT=$(belt app run tavily/extract --input '{
  "urls": [
    "https://example.com/research-paper",
    "https://example.com/industry-report"
  ]
}')

# 2. Analyze extracted content
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Analyze these documents and extract key insights:

$CONTENT

Provide:
1. Key findings
2. Data points
3. Recommendations\"
}"

Fact-Checking Pipeline

# Claim to verify
CLAIM="AI will replace 50% of jobs by 2030"

# 1. Search for evidence
EVIDENCE=$(belt app run tavily/search-assistant --input "{
  \"query\": \"$CLAIM evidence studies research\"
}")

# 2. Verify claim
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Fact-check this claim: '$CLAIM'

Based on the following evidence:
$EVIDENCE

Provide:
1. Verdict (True/False/Partially True/Unverified)
2. Supporting evidence
3. Contradicting evidence
4. Sources\"
}"

Research Report Generator

TOPIC="Impact of generative AI on creative industries"

# 1. Initial research
OVERVIEW=$(belt app run tavily/search-assistant --input "{\"query\": \"$TOPIC overview\"}")
STATISTICS=$(belt app run exa/search --input "{\"query\": \"$TOPIC statistics data\"}")
OPINIONS=$(belt app run tavily/search-assistant --input "{\"query\": \"$TOPIC expert opinions\"}")

# 2. Generate comprehensive report
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Generate a comprehensive research report on: $TOPIC

Research Data:
== Overview ==
$OVERVIEW

== Statistics ==
$STATISTICS

== Expert Opinions ==
$OPINIONS

Format as a professional report with:
- Executive Summary
- Key Findings
- Data Analysis
- Expert Perspectives
- Conclusion
- Sources\"
}"

Quick Answer with Sources

# Use Exa Answer for direct factual questions
belt app run exa/answer --input '{
  "question": "What is the current market cap of NVIDIA?"
}'

Best Practices

1. Query Optimization

# Bad: Too vague
"AI news"

# Good: Specific and contextual
"latest developments in large language models January 2024"

2. Context Management

# Summarize long search results before sending to LLM
SEARCH=$(belt app run tavily/search-assistant --input '{"query": "..."}')

# If too long, summarize first
SUMMARY=$(belt app run openrouter/claude-haiku-45 --input "{
  \"prompt\": \"Summarize these search results in bullet points: $SEARCH\"
}")

# Then use summary for analysis
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Based on this research summary, provide insights: $SUMMARY\"
}"

3. Source Attribution

Always ask the LLM to cite sources:

belt app run openrouter/claude-sonnet-45 --input '{
  "prompt": "... Always cite sources in [Source Name](URL) format."
}'

4. Iterative Research

# First pass: broad search
INITIAL=$(belt app run tavily/search-assistant --input '{"query": "topic overview"}')

# Second pass: dive deeper based on findings
DEEP=$(belt app run tavily/search-assistant --input '{"query": "specific aspect from initial search"}')

Pipeline Templates

Agent Research Tool

#!/bin/bash
# research.sh - Reusable research function

research() {
  local query="$1"

  # Search
  local results=$(belt app run tavily/search-assistant --input "{\"query\": \"$query\"}")

  # Analyze
  belt app run openrouter/claude-haiku-45 --input "{
    \"prompt\": \"Summarize: $results\"
  }"
}

research "your query here"

Related Skills

# Web search tools
npx skills add inference-sh/skills@web-search

# LLM models
npx skills add inference-sh/skills@llm-models

# Content pipelines
npx skills add inference-sh/skills@ai-content-pipeline

# Full platform skill
npx skills add inference-sh/skills@infsh-cli

Browse all apps: belt app store

Documentation

Adding Tools to Agents - Agent tool integration
Building a Research Agent - Full guide

---
name: ai-rag-pipeline
description: Build RAG pipelines that combine web search and LLMs for research, fact-checking, and grounded responses using the inference.sh CLI.
---

> **Install the belt CLI skill:** `npx skills add belt-sh/cli`

# 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

> Requires inference.sh CLI (`belt`). [Install instructions](https://raw.githubusercontent.com/inference-sh/skills/refs/heads/main/cli-install.md)

```bash
belt login

# Simple RAG: Search + LLM
SEARCH=$(belt app run tavily/search-assistant --input '{"query": "latest AI developments 2024"}')
belt 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 |
|------|--------|----------|
| 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=$(belt app run tavily/search-assistant --input '{
  "query": "What are the latest breakthroughs in quantum computing 2024?"
}')

# 2. Generate grounded response
belt 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=$(belt app run tavily/search-assistant --input '{"query": "electric vehicle market trends 2024"}')
EXA=$(belt app run exa/search --input '{"query": "EV market analysis latest reports"}')

# Combine and analyze
belt 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

```bash
# 1. Extract content from specific URLs
CONTENT=$(belt app run tavily/extract --input '{
  "urls": [
    "https://example.com/research-paper",
    "https://example.com/industry-report"
  ]
}')

# 2. Analyze extracted content
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Analyze these documents and extract key insights:

$CONTENT

Provide:
1. Key findings
2. Data points
3. Recommendations\"
}"
```

### Fact-Checking Pipeline

```bash
# Claim to verify
CLAIM="AI will replace 50% of jobs by 2030"

# 1. Search for evidence
EVIDENCE=$(belt app run tavily/search-assistant --input "{
  \"query\": \"$CLAIM evidence studies research\"
}")

# 2. Verify claim
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Fact-check this claim: '$CLAIM'

Based on the following evidence:
$EVIDENCE

Provide:
1. Verdict (True/False/Partially True/Unverified)
2. Supporting evidence
3. Contradicting evidence
4. Sources\"
}"
```

### Research Report Generator

```bash
TOPIC="Impact of generative AI on creative industries"

# 1. Initial research
OVERVIEW=$(belt app run tavily/search-assistant --input "{\"query\": \"$TOPIC overview\"}")
STATISTICS=$(belt app run exa/search --input "{\"query\": \"$TOPIC statistics data\"}")
OPINIONS=$(belt app run tavily/search-assistant --input "{\"query\": \"$TOPIC expert opinions\"}")

# 2. Generate comprehensive report
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Generate a comprehensive research report on: $TOPIC

Research Data:
== Overview ==
$OVERVIEW

== Statistics ==
$STATISTICS

== Expert Opinions ==
$OPINIONS

Format as a professional report with:
- Executive Summary
- Key Findings
- Data Analysis
- Expert Perspectives
- Conclusion
- Sources\"
}"
```

### Quick Answer with Sources

```bash
# Use Exa Answer for direct factual questions
belt app run exa/answer --input '{
  "question": "What is the current market cap of NVIDIA?"
}'
```

## Best Practices

### 1. Query Optimization

```bash
# Bad: Too vague
"AI news"

# Good: Specific and contextual
"latest developments in large language models January 2024"
```

### 2. Context Management

```bash
# Summarize long search results before sending to LLM
SEARCH=$(belt app run tavily/search-assistant --input '{"query": "..."}')

# If too long, summarize first
SUMMARY=$(belt app run openrouter/claude-haiku-45 --input "{
  \"prompt\": \"Summarize these search results in bullet points: $SEARCH\"
}")

# Then use summary for analysis
belt app run openrouter/claude-sonnet-45 --input "{
  \"prompt\": \"Based on this research summary, provide insights: $SUMMARY\"
}"
```

### 3. Source Attribution

Always ask the LLM to cite sources:

```bash
belt app run openrouter/claude-sonnet-45 --input '{
  "prompt": "... Always cite sources in [Source Name](URL) format."
}'
```

### 4. Iterative Research

```bash
# First pass: broad search
INITIAL=$(belt app run tavily/search-assistant --input '{"query": "topic overview"}')

# Second pass: dive deeper based on findings
DEEP=$(belt app run tavily/search-assistant --input '{"query": "specific aspect from initial search"}')
```

## Pipeline Templates

### Agent Research Tool

```bash
#!/bin/bash
# research.sh - Reusable research function

research() {
  local query="$1"

  # Search
  local results=$(belt app run tavily/search-assistant --input "{\"query\": \"$query\"}")

  # Analyze
  belt app run openrouter/claude-haiku-45 --input "{
    \"prompt\": \"Summarize: $results\"
  }"
}

research "your query here"
```

## Related Skills

```bash
# Web search tools
npx skills add inference-sh/skills@web-search

# LLM models
npx skills add inference-sh/skills@llm-models

# Content pipelines
npx skills add inference-sh/skills@ai-content-pipeline

# Full platform skill
npx skills add inference-sh/skills@infsh-cli
```

Browse all apps: `belt app store`

## Documentation

- [Adding Tools to Agents](https://inference.sh/docs/agents/adding-tools) - Agent tool integration
- [Building a Research Agent](https://inference.sh/blog/guides/research-agent) - Full guide

inference-sh/skills/tree/main/tools/llm/ai-rag-pipeline commit · 601a457f7e

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Frequently asked questions

How do I install the ai-rag-pipeline skill?

Run npx skillmds add inference-sh/ai-rag-pipeline in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the ai-rag-pipeline skill do?

Build RAG pipelines that combine web search and LLMs for research, fact-checking, and grounded responses using the inference.sh CLI. It is listed under AI & ML, DevOps & Infra, Research & Search, RAG & Embeddings, Web Research on SkillMD.

Is ai-rag-pipeline safe to use?

SkillMD's automated safety review verdict for this skill is CAUTION. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with ai-rag-pipeline?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is ai-rag-pipeline free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published ai-rag-pipeline?

inference.sh (@inference-sh) published this skill. Their other Agent Skills are listed on their SkillMD profile.