RAG Agent CLI

# Create a new knowledge base
praisonai-ts knowledge create my-kb

# Add documents
praisonai-ts knowledge add my-kb ./documents/

# Add from URL
praisonai-ts knowledge add my-kb https://example.com/doc.pdf

# Query the knowledge base
praisonai-ts knowledge query my-kb "What is PraisonAI?"

# Query with options
praisonai-ts knowledge query my-kb "question" --top-k 5 --min-score 0.7

# Run agent with knowledge base
praisonai-ts run \
  --instructions "Answer questions using the knowledge base" \
  --knowledge my-kb \
  --prompt "What is PraisonAI?"

# Interactive mode
praisonai-ts chat \
  --knowledge my-kb \
  --instructions "You are a helpful assistant"

# Add all PDFs from a directory
praisonai-ts knowledge add docs-kb ./docs/ --pattern "*.pdf"

# Add with metadata
praisonai-ts knowledge add docs-kb ./docs/ \
  --metadata '{"source": "documentation", "version": "1.0"}'

# Start interactive RAG chat
praisonai-ts chat \
  --knowledge docs-kb \
  --instructions "Answer questions about the documentation" \
  --model gpt-4o

# Export to JSON
praisonai-ts knowledge export docs-kb --output kb-export.json

# Import from JSON
praisonai-ts knowledge import new-kb --input kb-export.json