> ## Documentation Index > Fetch the complete documentation index at: https://docs.praison.ai/llms.txt > Use this file to discover all available pages before exploring further. # Knowledge CLI > Manage RAG/vector store knowledge bases with advanced retrieval strategies The `knowledge` command manages document knowledge bases with support for multiple vector stores, retrieval strategies, rerankers, and query modes. ## Quick Start ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # List knowledge sources praisonai knowledge list ``` List knowledge example ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Add a document praisonai knowledge add document.pdf # Query knowledge base with RAG praisonai knowledge query "API authentication" # Query with advanced options praisonai knowledge query "How to authenticate?" --retrieval fusion --reranker llm ``` ## Commands ### Add Documents ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Add a single file praisonai knowledge add document.pdf # Add a directory praisonai knowledge add ./docs/ # Add a URL praisonai knowledge add https://example.com/docs.html # Add with glob pattern praisonai knowledge add "*.pdf" ``` **Expected Output:** ``` 📚 Adding documents to knowledge base... ✅ Added: document.pdf • Pages: 45 • Chunks: 128 • Vectors: 128 Knowledge base updated successfully! ┌─────────────────────┬──────────────┐ │ Metric │ Value │ ├─────────────────────┼──────────────┤ │ Total Documents │ 12 │ │ Total Chunks │ 1,456 │ │ Storage Size │ 24.5 MB │ └─────────────────────┴──────────────┘ ``` ### Query Knowledge Base ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Basic query praisonai knowledge query "How to authenticate API requests" # Query with specific vector store praisonai knowledge query "authentication" --vector-store chroma # Query with fusion retrieval (multi-query) praisonai knowledge query "How to authenticate?" --retrieval fusion # Query with LLM reranking praisonai knowledge query "API auth" --reranker llm # Query with sub-question decomposition praisonai knowledge query "What is Python and how to install it?" --query-mode sub_question # Full advanced query praisonai knowledge query "authentication flow" \ --vector-store chroma \ --retrieval fusion \ --reranker llm \ --index-type hybrid \ --query-mode sub_question ``` **Expected Output:** ``` 🔍 Querying knowledge base... Found 5 relevant results: 1. [score: 0.95] api-docs.pdf (page 23) "Authentication is handled via Bearer tokens. Include the token in the Authorization header: Authorization: Bearer " 2. [score: 0.87] security-guide.md (section 3.2) "All API requests must be authenticated. Unauthenticated requests will receive a 401 Unauthorized response." 3. [score: 0.82] quickstart.txt (line 45) "To get started, first obtain an API key from the dashboard..." ``` ### List Documents ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai knowledge list ``` **Expected Output:** ``` 📚 Knowledge Base Contents: ┌────┬─────────────────────┬──────────┬────────┬─────────────────────┐ │ # │ Document │ Type │ Chunks │ Added │ ├────┼─────────────────────┼──────────┼────────┼─────────────────────┤ │ 1 │ api-docs.pdf │ PDF │ 234 │ 2024-12-15 10:30 │ │ 2 │ security-guide.md │ Markdown │ 45 │ 2024-12-15 10:32 │ │ 3 │ quickstart.txt │ Text │ 12 │ 2024-12-15 10:33 │ │ 4 │ faq.md │ Markdown │ 28 │ 2024-12-16 09:15 │ │ 5 │ architecture.pdf │ PDF │ 156 │ 2024-12-16 14:20 │ └────┴─────────────────────┴──────────┴────────┴─────────────────────┘ Total: 5 documents, 475 chunks ``` ### Show Knowledge Base Info ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai knowledge info ``` **Expected Output:** ``` 📊 Knowledge Base Information: ┌─────────────────────────┬────────────────────────────┐ │ Property │ Value │ ├─────────────────────────┼────────────────────────────┤ │ Location │ .praison/knowledge/ │ │ Vector Store │ ChromaDB │ │ Embedding Model │ text-embedding-3-small │ │ Total Documents │ 5 │ │ Total Chunks │ 475 │ │ Total Vectors │ 475 │ │ Storage Size │ 12.3 MB │ │ Last Updated │ 2024-12-16 14:20:00 │ └─────────────────────────┴────────────────────────────┘ ``` ### Clear Knowledge Base ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Clear all documents praisonai knowledge clear ``` **Expected Output:** ``` ⚠️ This will delete all documents from the knowledge base. Are you sure? (y/N): y 🗑️ Clearing knowledge base... ✅ Knowledge base cleared successfully! ``` ### Show Statistics ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai knowledge stats ``` **Expected Output:** ``` 📊 Knowledge Base Statistics: workspace: /path/to/project vector_store: chroma retrieval_strategy: basic reranker: none index_type: vector query_mode: default document_count: 5 ``` ### Export Knowledge Base ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Export to default timestamped file praisonai knowledge export # Export to specific file praisonai knowledge export backup.json # Export to specific path praisonai knowledge export /path/to/knowledge_backup.json ``` **Expected Output:** ``` ✅ Exported 12 documents to knowledge_export_20241226_103045.json ``` ### Import Knowledge Base ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Import from JSON file praisonai knowledge import backup.json # Import from specific path praisonai knowledge import /path/to/knowledge_backup.json ``` **Expected Output:** ``` ✅ Imported 12 documents from backup.json ``` ### Help ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai knowledge help ``` **Expected Output:** ``` Knowledge Commands: praisonai knowledge add - Add document(s) to knowledge base praisonai knowledge query - Query knowledge base with RAG praisonai knowledge list - List indexed documents praisonai knowledge clear - Clear knowledge base praisonai knowledge stats - Show knowledge base statistics praisonai knowledge export - Export knowledge base to JSON file praisonai knowledge import - Import knowledge base from JSON file Options: --workspace - Workspace directory (default: current) --vector-store - Vector store: chroma, pinecone, qdrant, weaviate, memory --retrieval - Retrieval: basic, fusion, recursive, auto_merge --reranker - Reranker: simple, llm, cross_encoder, cohere --index-type - Index: vector, keyword, hybrid --query-mode - Query: default, sub_question, summarize --session - Session ID for persistence --db - Database path for persistence ``` ## Advanced Options Reference ### Vector Stores | Store | Description | Requirements | | ---------- | ----------------------------------- | ----------------------------- | | `memory` | In-memory (default, no persistence) | None | | `chroma` | ChromaDB local vector store | `pip install chromadb` | | `pinecone` | Pinecone cloud vector store | `PINECONE_API_KEY` | | `qdrant` | Qdrant vector database | `pip install qdrant-client` | | `weaviate` | Weaviate vector database | `pip install weaviate-client` | ### Retrieval Strategies | Strategy | Description | | ------------ | ----------------------------------------- | | `basic` | Simple vector similarity search (default) | | `fusion` | Multi-query with Reciprocal Rank Fusion | | `recursive` | Depth-limited recursive retrieval | | `auto_merge` | Merges adjacent chunks from same document | ### Rerankers | Reranker | Description | Requirements | | --------------- | ------------------------------- | ----------------------------------- | | `simple` | Keyword-based scoring (default) | None | | `llm` | LLM-based relevance scoring | `OPENAI_API_KEY` | | `cross_encoder` | Cross-encoder model | `pip install sentence-transformers` | | `cohere` | Cohere Rerank API | `COHERE_API_KEY` | ### Index Types | Type | Description | | --------- | --------------------------------- | | `vector` | Vector similarity index (default) | | `keyword` | BM25 keyword index | | `hybrid` | Combined vector + keyword | ### Query Modes | Mode | Description | | -------------- | ---------------------------- | | `default` | Standard RAG query | | `sub_question` | Decomposes complex questions | | `summarize` | Summarizes retrieved context | ## Supported File Types | Type | Extensions | Description | | -------- | ------------------------- | ---------------------------------- | | PDF | `.pdf` | PDF documents with text extraction | | Markdown | `.md`, `.mdx` | Markdown files | | Text | `.txt` | Plain text files | | Code | `.py`, `.js`, `.ts`, etc. | Source code files | | HTML | `.html`, `.htm` | Web pages | | JSON | `.json` | JSON documents | | CSV | `.csv` | Tabular data | | URL | `http://`, `https://` | Web pages | ## Use Cases ### Documentation Search ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Add project documentation praisonai knowledge add ./docs/ # Query documentation praisonai "How do I configure the database?" --knowledge ``` ### Code Understanding ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Add codebase praisonai knowledge add ./src/ # Ask about code praisonai "Explain the authentication flow" --knowledge ``` ### Research Assistant ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Add research papers praisonai knowledge add ./papers/ # Query research praisonai knowledge search "machine learning optimization techniques" ``` ## Using Knowledge with Agents Once documents are added, agents can automatically query the knowledge base: ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Enable knowledge for agent praisonai "Answer based on the documentation" --knowledge ``` **Expected Output:** ``` 📚 Knowledge base enabled (5 documents) ╭─ Agent Info ─────────────────────────────────────────────────────────────────╮ │ 👤 Agent: DirectAgent │ │ Role: Assistant │ │ 📚 Knowledge: 5 documents, 475 chunks │ ╰──────────────────────────────────────────────────────────────────────────────╯ ╭────────────────────────────────── Response ──────────────────────────────────╮ │ Based on the documentation, here's how to configure the database: │ │ │ │ 1. Set the DATABASE_URL environment variable... │ │ [Response based on knowledge base content] │ ╰──────────────────────────────────────────────────────────────────────────────╯ 📖 Sources: • api-docs.pdf (page 12) • quickstart.txt (section 2) ``` ## Best Practices Organize documents by topic in separate directories for better search relevance. Large documents are automatically chunked. Very large knowledge bases may increase response latency. Use well-structured documents with clear headings Keep knowledge base updated with latest documentation Use specific search terms for better results Group related documents together ## Related * [Knowledge Concept](/concepts/knowledge) * [RAG Features](/features/rag) * [Fast Context CLI](/docs/cli/fast-context)