Skill: okaris/ai-rag-pipeline (version 0.1.5)

--- name: ai-rag-pipeline description: "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" allowed-tools: Bash(infsh *) --- # 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): $EXA Provide a balanced analysis with sources.\" }" ``` ### URL Content Analysis ```bash # 1. Extract content from specific URLs CONTENT=$(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: $CONTENT Provide: 1. Key findings 2. Data points 3. Recommendations\" }" ``` ### Fact-Checking Pipeline ```bash # Claim to verify CLAIM="AI will replace 50% of jobs by 2030" # 1. Search for evidence EVIDENCE=$(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: $EVIDENCE Provide: 1. Verdict (True/False/Partially True/Unverified) 2. Supporting evidence 3. Contradicting evidence 4. Sources\" }" ``` ### Research Report Generator ```bash TOPIC="Impact of generative AI on creative industries" # 1. Initial research OVERVIEW=$(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 == $OPINIONS Format as a professional report with: - Executive Summary - Key Findings - Data Analysis - Expert Perspectives - Conclusion - Sources\" }" ``` ### Quick Answer with Sources ```bash # Use Exa Answer for direct factual questions infsh app run exa/answer --input '{ "question": "What is the current market cap of NVIDIA?" }' ``` ## Best Practices ### 1. Query Optimization ```bash # Bad: Too vague "AI news" # Good: Specific and contextual "latest developments in large language models January 2024" ``` ### 2. Context Management ```bash # Summarize long search results before sending to LLM SEARCH=$(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: $SUMMARY\" }" ``` ### 3. Source Attribution Always ask the LLM to cite sources: ```bash infsh app run openrouter/claude-sonnet-45 --input '{ "prompt": "... Always cite sources in [Source Name](URL) format." }' ``` ### 4. Iterative Research ```bash # First pass: broad search INITIAL=$(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 ```bash #!/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 ```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@inference-sh ``` Browse all apps: `infsh app list` ## 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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: suspicious | SkillSpector: clean (LOW)