> ## Documentation Index > Fetch the complete documentation index at: https://docs.runflow.ai/llms.txt > Use this file to discover all available pages before exploring further. # Knowledge Base Management > Manage vector stores for RAG (Retrieval Augmented Generation) with rf kb The `rf kb` command provides complete knowledge base (vector store) management for RAG applications. Upload documents, perform semantic search, check processing status, and manage your knowledge bases. ## Commands Overview | Command | Description | | -------------------------------- | -------------------------------------------- | | `rf kb list` | List all knowledge bases | | `rf kb create ` | Create new knowledge base | | `rf kb upload ` | Upload documents (async, with live progress) | | `rf kb jobs ` | List ingestion jobs and their progress | | `rf kb status ` | Check processing status | | `rf kb docs ` | List documents in KB | | `rf kb remove-doc ` | Remove specific document | | `rf kb search ` | Semantic search | | `rf kb delete ` | Delete knowledge base | ## Listing Knowledge Bases ```bash theme={null} # List all knowledge bases rf kb list # Output: # Knowledge Bases: # • support-docs (100 documents) # • product-manual (45 documents) # • faq-knowledge (12 documents) ``` ## Creating Knowledge Base ### Interactive Mode ```bash theme={null} 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 ``` ### Non-Interactive Mode Perfect for scripts and automation: ```bash theme={null} # 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 ``` ### Options | Option | Alias | Description | | ---------------------- | ----- | --------------------------- | | `--embedding ` | `-e` | Embedding config ID or name | | `--yes` | `-y` | Skip confirmations | ## Uploading Documents ### Upload Single File ```bash theme={null} # 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 ``` Since CLI `0.3.22`, uploads use the platform's **async ingestion pipeline**: the file is accepted immediately and embedded in the background with batched embeddings and checkpoint resume; the CLI shows live chunk progress until the job completes. Large files (50k+ row CSV catalogs) no longer time out. Against an older self-hosted API without the async endpoint, the CLI falls back to the legacy synchronous upload automatically. ### Upload Directory ```bash theme={null} # 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 ``` ### Supported File Types * **PDF** (`.pdf`) * **Text** (`.txt`) * **Markdown** (`.md`) * **Word Documents** (`.docx`) * **CSV** (`.csv`) — ingested one document per row (great for product catalogs) * **JSON** (`.json`) Files are automatically parsed, chunked, and embedded for semantic search. ## Ingestion Jobs Every upload creates an ingestion job you can inspect at any time: ```bash theme={null} 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... ``` Jobs survive worker restarts: ingestion resumes from the last checkpoint instead of starting over. ## Checking Processing Status ```bash theme={null} # 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 ``` **Statuses:** * **Processing** - Documents being embedded * **Ready** - All documents processed * **Error** - Some documents failed ## Listing Documents ```bash theme={null} # 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) ``` ## Removing Documents ```bash theme={null} # 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 ``` Removing a document is permanent and cannot be undone! ## Semantic Search Test your knowledge base with semantic search: ```bash theme={null} # 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... ``` **Search Features:** * Semantic similarity (not just keyword matching) * Ranked by relevance score (0-1) * Shows source document * Returns top relevant chunks ## Deleting Knowledge Base ```bash theme={null} # 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 ``` Deleting a knowledge base removes all documents and embeddings permanently! ## Complete Workflow Example ```bash theme={null} # 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 ``` ## Using with RAG Agents ### Creating RAG Agent with Knowledge Base ```bash theme={null} # 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 ``` ### Agent Configuration Your agent code will reference the knowledge base: ```typescript theme={null} 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', }); ``` ## Automation & Scripts ### Bulk Upload Script ```bash theme={null} #!/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 $2}')" != "Ready" ]; do echo "Processing..." sleep 5 done echo "Knowledge base ready!" ``` ### Multi-KB Setup ```bash theme={null} #!/bin/bash # Setup multiple knowledge bases kb_configs=( "support-docs:./support-files" "product-manual:./manuals" "legal-docs:./legal" ) for config in "${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 ``` ## Best Practices ### 1. Organize Documents by Domain ```bash theme={null} # 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 ``` ### 2. Keep Documents Updated ```bash theme={null} # Remove old versions rf kb remove-doc support-docs doc_old_manual --yes # Upload new versions rf kb upload support-docs ./manual-v2.pdf ``` ### 3. Test Before Production ```bash theme={null} # 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 ``` ### 4. Choose Appropriate Embedding Model ```bash theme={null} # 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" ``` ### 5. Monitor Processing Status ```bash theme={null} # After large uploads, monitor status rf kb upload docs ./large-dataset --yes rf kb status docs # Wait for processing to complete before using ``` ## Document Processing ### How It Works 1. **Upload** - File sent to server 2. **Parse** - Content extracted (text from PDF, DOCX, etc.) 3. **Chunk** - Split into manageable pieces (\~500 tokens) 4. **Embed** - Generate vector embeddings 5. **Index** - Store in vector database 6. **Ready** - Available for search ### Processing Time * Small files (\<1 MB): \~5-10 seconds * Medium files (1-10 MB): \~30-60 seconds * Large files (>10 MB): \~2-5 minutes * Directories: Depends on total size and file count ### Chunking Strategy Documents are automatically chunked with: * **Chunk size**: \~500 tokens (\~375 words) * **Overlap**: 50 tokens (for context continuity) * **Smart splitting**: Respects paragraph boundaries ## Troubleshooting ### KB Already Exists ```bash theme={null} rf kb create existing-kb # Error: Knowledge base 'existing-kb' already exists ``` **Solution:** Use a different name or delete the existing KB: ```bash theme={null} rf kb delete existing-kb --yes rf kb create existing-kb --embedding "OpenAI Small" ``` ### Upload Failed ```bash theme={null} rf kb upload docs ./file.pdf # Error: Failed to upload file ``` **Possible causes:** * File format not supported * File too large (>50 MB) * Network connection issues **Solution:** * Check file format (PDF, TXT, MD, DOCX only) * Split large files * Retry upload ### Documents Stuck in Processing ```bash theme={null} rf kb status docs # Status: Processing (stuck for 30+ minutes) ``` **Solution:** * Check document format and content * Contact support if issue persists * Remove and re-upload problematic documents ### Poor Search Results If search quality is low: 1. **Add more documents** - More context improves results 2. **Use larger embedding model** - Better semantic understanding 3. **Improve document quality** - Clear, well-structured content 4. **Test queries** - Refine search terms ```bash theme={null} # 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" ``` ## Advanced Usage ### Environment-Specific KBs ```bash theme={null} # 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 ``` ### Backup and Restore ```bash theme={null} # 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) ``` ## Next Steps Create agent with knowledge base Test KB integration locally Create RAG-specific prompts Learn more about RAG concepts