ai-rag-pipeline

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\"
}"

[User Query] -> [Web Search] -> [LLM with Context] -> [Answer]

[Query] -> [Multiple Searches] -> [Aggregate] -> [LLM Analysis] -> [Report]

[URLs] -> [Content Extraction] -> [Chunking] -> [LLM Summary] -> [Output]

# 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.\"
}"

# 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.\"
}"

# 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\"
}"

# 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\"
}"

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\"
}"

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

# Bad: Too vague
"AI news"
 
# Good: Specific and contextual
"latest developments in large language models January 2024"

# 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\"
}"

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

# 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"}')

#!/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"

# 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