Knowledge Base Management

# List all knowledge bases
rf kb list

# Output:
# Knowledge Bases:
# • support-docs (100 documents)
# • product-manual (45 documents)
# • faq-knowledge (12 documents)

rf kb create support-docs
# → Select embedding configuration:
#   • OpenAI Small (text-embedding-3-small)
#   • OpenAI Large (text-embedding-3-large)
#   • Custom...
# ✓ Knowledge base 'support-docs' created

# Using embedding name
rf kb create support-docs --embedding "OpenAI Small"

# Using short form
rf kb create support-docs -e openai-small

# Using embedding config ID
rf kb create support-docs --embedding embedding-cfg-123

# Upload one file
rf kb upload support-docs ./manual.pdf
# ⠋ Processing manual.pdf (450/1200 chunks)
# ✓ manual.pdf: 1200 chunks indexed

# Supported formats
rf kb upload support-docs ./faq.txt
rf kb upload support-docs ./guide.md
rf kb upload support-docs ./doc.docx
rf kb upload support-docs ./catalog.csv

# Upload all files in directory (with confirmation)
rf kb upload support-docs ./docs
# ? Upload 15 files from ./docs? (y/N)

# Skip confirmation
rf kb upload support-docs ./docs --yes
# ✓ Uploaded 15 files

rf kb jobs support-docs

# Output:
# 📥 Ingestion jobs in "support-docs"
#
#   completed   manual.pdf        1200/1200 chunks  7/1/2026, 3:12:04 PM
#   processing  catalog.csv       31488/53068 chunks  7/1/2026, 3:20:11 PM
#   failed      broken-export.csv -  7/1/2026, 2:55:40 PM
#     Unsupported file type: application/octet-stream...

# Check if documents are processed
rf kb status support-docs

# Output:
# Knowledge Base: support-docs
# Status: Processing
# Documents: 15 total, 12 processed, 3 pending
# Embedding: OpenAI Small

# List all documents in KB
rf kb docs support-docs

# Output:
# Documents in support-docs:
# • doc_abc123 - manual.pdf (1.2 MB, 45 chunks)
# • doc_def456 - faq.txt (50 KB, 12 chunks)
# • doc_ghi789 - guide.md (200 KB, 28 chunks)

# Remove with confirmation
rf kb remove-doc support-docs doc_abc123
# ? Are you sure you want to remove document 'doc_abc123'? (y/N)

# Remove without confirmation
rf kb remove-doc support-docs doc_abc123 --yes
# ✓ Document removed

# Short form
rf kb remove-doc support-docs doc_abc123 -y

# Search for relevant content
rf kb search support-docs "how to reset password"

# Output:
# Search Results:
#
# [1] Score: 0.92
# Source: faq.txt
# Content: To reset your password, go to Settings > Security...
#
# [2] Score: 0.87
# Source: manual.pdf
# Content: Password Reset Procedure: 1. Click Forgot Password...
#
# [3] Score: 0.81
# Source: guide.md
# Content: User accounts can be recovered by...

# Delete with confirmation
rf kb delete support-docs
# ? Are you sure you want to delete knowledge base 'support-docs'? (y/N)

# Delete without confirmation
rf kb delete support-docs --yes
# ✓ Knowledge base deleted

# Short form
rf kb delete support-docs -y

# 1. Create knowledge base
rf kb create support-docs --embedding "OpenAI Small"

# 2. Upload documents
rf kb upload support-docs ./docs/manual.pdf
rf kb upload support-docs ./docs/faq.md
rf kb upload support-docs ./docs --yes

# 3. Check processing status
rf kb status support-docs
# Wait until status is "Ready"

# 4. Test semantic search
rf kb search support-docs "how to install"
rf kb search support-docs "pricing information"
rf kb search support-docs "troubleshooting errors"

# 5. List all documents
rf kb docs support-docs

# 6. Remove outdated document
rf kb remove-doc support-docs doc_old123 --yes

# 7. Use in your agent
cd my-agent/
rf test
# → Agent now has access to support-docs knowledge base

# 1. Create agent from RAG template
rf create --name support-bot --template rag-agent --yes

# 2. Create and populate knowledge base
rf kb create support-knowledge --embedding "OpenAI Small"
rf kb upload support-knowledge ./company-docs --yes

# 3. Test locally
cd support-bot/
rf test

# 4. Deploy
rf agents deploy

import { Agent } from '@runflow-ai/core';

const agent = new Agent({
  name: 'Support Bot',
  instructions: 'Use the knowledge base to answer questions',
  knowledgeBase: 'support-knowledge', // Reference your KB
  model: 'gpt-4',
});

#!/bin/bash

# Create KB
rf kb create product-docs --embedding "OpenAI Small"

# Upload all PDFs
find ./documents -name "*.pdf" -exec rf kb upload product-docs {} \;

# Wait for processing
while [ "$(rf kb status product-docs | grep 'Status:' | awk '{print $2}')" != "Ready" ]; do
  echo "Processing..."
  sleep 5
done

echo "Knowledge base ready!"

#!/bin/bash

# Setup multiple knowledge bases
kb_configs=(
  "support-docs:./support-files"
  "product-manual:./manuals"
  "legal-docs:./legal"
)

for config in "${kb_configs[@]}"; do
  kb_name="${config%%:*}"
  kb_dir="${config##*:}"
  
  rf kb create "$kb_name" --embedding "OpenAI Small"
  rf kb upload "$kb_name" "$kb_dir" --yes
done

# Create separate KBs for different topics
rf kb create customer-support
rf kb create product-documentation
rf kb create legal-compliance
rf kb create internal-wiki

# Remove old versions
rf kb remove-doc support-docs doc_old_manual --yes

# Upload new versions
rf kb upload support-docs ./manual-v2.pdf

# Always test search quality
rf kb search my-kb "common query 1"
rf kb search my-kb "common query 2"
rf kb search my-kb "edge case query"

# Refine documents if results are poor

# Small model - faster, cheaper, good for most use cases
rf kb create kb-small --embedding "OpenAI Small"

# Large model - more accurate, better for complex domains
rf kb create kb-large --embedding "OpenAI Large"

# After large uploads, monitor status
rf kb upload docs ./large-dataset --yes
rf kb status docs

# Wait for processing to complete before using

rf kb create existing-kb
# Error: Knowledge base 'existing-kb' already exists

rf kb delete existing-kb --yes
rf kb create existing-kb --embedding "OpenAI Small"

rf kb upload docs ./file.pdf
# Error: Failed to upload file

rf kb status docs
# Status: Processing (stuck for 30+ minutes)

# Try different embedding
rf kb create kb-improved --embedding "OpenAI Large"
rf kb upload kb-improved ./docs --yes
rf kb search kb-improved "test query"

# Development KB with test data
rf switch dev
rf kb create test-kb --embedding "OpenAI Small"
rf kb upload test-kb ./test-data --yes

# Production KB with real data
rf switch prod
rf kb create prod-kb --embedding "OpenAI Large"
rf kb upload prod-kb ./production-data --yes

# Document metadata can be exported via API
# (CLI backup feature coming soon)

# For now, keep source files backed up
cp -r ./original-docs ./backup-$(date +%Y%m%d)