Ai rag pipeline skills inference sh ai rag pipeline

Skill bg-szy/TOP-SKILLS/skills/marketplace/ai-rag-pipeline__skills-inference-sh-ai-rag-pipeline

全球最大的 Claude Code 技能聚合库 · 收录 3900+ 来自 12+ 来源的技能，提供在线搜索与趋势分析看板 / The world's largest Claude Code skill aggregation hub — 3900+ skills from 12+ sources with online search and trend dashboard

Install

npx -y skills add bg-szy/TOP-SKILLS --skill ai-rag-pipeline__skills-inference-sh-ai-rag-pipeline

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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

SKILL.md

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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\"
}"

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=$(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

# 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

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

# 2. Analyze extracted content
infsh 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=$(infsh app run tavily/search-assistant --input "{
  \"query\": \"$CLAIM evidence studies research\"
}")

# 2. Verify claim
infsh 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=$(infsh app run tavily/search-assistant --input "{\"query\": \"$TOPIC overview\"}")
STATISTICS=$(infsh app run exa/search --input "{\"query\": \"$TOPIC statistics data\"}")
OPINIONS=$(infsh app run tavily/search-assistant --input "{\"query\": \"$TOPIC expert opinions\"}")

# 2. Generate comprehensive report
infsh 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
infsh 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=$(infsh app run tavily/search-assistant --input '{"query": "..."}')

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

# Then use summary for analysis
infsh 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:

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

4. Iterative Research

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

# Second pass: dive deeper based on findings
DEEP=$(infsh 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=$(infsh app run tavily/search-assistant --input "{\"query\": \"$query\"}")

  # Analyze
  infsh 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@inference-sh

Browse all apps: infsh app list

Documentation

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