Skill: huajianjiu000/knowledge-base-qa-assistant (version 1.0.0)

--- name: knowledge-base-qa-assistant version: 1.0.0 description: | Build private knowledge bases for AI-powered document Q&A. Supports PDF, Word, TXT, Markdown uploads with smart chunking and vector retrieval. Automatically cites sources in answers. Perfect for enterprise knowledge management and customer service knowledge bases. tags: ["knowledge-base", "rag", "document-qa", "vector-search", "enterprise", "content-management"] --- # Knowledge Base QA Assistant > 📚 Build a private knowledge base for AI-powered document Q&A ## Skill Overview This skill helps AI Agents build and manage private knowledge bases, supporting document uploads (PDF, Word, TXT, Markdown, etc.), then providing precise Q&A based on the knowledge base content. Ideal for enterprise knowledge management, product documentation Q&A, and customer service knowledge bases. ## Core Capabilities - **Multi-format Support**: PDF, Word, TXT, Markdown, Excel, PPT, and more - **Smart Chunking**: Automatically split long documents into semantically complete chunks - **Vector Retrieval**: Precise matching based on semantic similarity - **Source Citation**: Automatically cite reference sources in answers - **Batch Upload**: Support batch upload of multiple documents ## Trigger Keywords - `/knowledge-qa` - `/upload-document` - `/document-qa` - `/knowledge-base-manage` - `/doc-question` - `/rag-qa` ## How to Use ### Step 1: Build Knowledge Base User uploads documents to build knowledge base: ``` User: Please upload this product document to the knowledge base Agent: Please provide the document content or upload file User: [Upload PDF file] Agent: ✅ Document uploaded to knowledge base successfully! Document Name: Product Manual.pdf File Size: 2.5MB Pages: 45 Status: Indexed, ready for Q&A Knowledge Points Extracted: 23 Knowledge Chunks: 12 ``` ### Step 2: Knowledge Base Q&A ``` User: What payment methods does the product support? Agent: Searching knowledge base... ✅ Found relevant information! Answer: The product supports the following payment methods: 1. **Online Payment** - PayPal - Stripe - Credit Card (Visa, MasterCard supported) 2. **Offline Payment** - Bank Transfer - Wire Transfer 📎 Reference: Product Manual Section 3.2, Published: 2024-01-15 ``` ## Knowledge Base Commands | Command | Function | |---------|----------| | `/knowledge-list` | View all documents in knowledge base | | `/delete-doc [filename]` | Delete specified document | | `/knowledge-stats` | View knowledge base statistics | | `/clear-knowledge` | Clear all documents (requires confirmation) | ## Knowledge Base Structure ### Document Storage ``` knowledge_base/ ├── documents/ │ ├── doc_001/ │ │ ├── original.pdf # Original document │ │ └── metadata.json # Metadata │ ├── doc_002/ │ │ ├── content.txt # Extracted text │ │ └── metadata.json │ └── ... └── index/ └── vector_index.json # Vector index ``` ### Metadata Format ```json { "doc_id": "doc_001", "filename": "Product Manual.pdf", "upload_time": "2024-01-20T10:30:00Z", "file_size": "2.5MB", "page_count": 45, "chunk_count": 12, "status": "indexed", "tags": ["product", "user-guide", "features"] } ``` ## Retrieval Flow ``` User Question ↓ Question Embedding ↓ Vector Similarity Matching ↓ Top-K Relevant Chunks ↓ Build Prompt Context ↓ LLM Generate Answer ↓ Add Citation ↓ Return Answer ``` ## Usage Examples ### Example 1: Enterprise Knowledge Base Q&A ``` User: What is the company's remote work policy? Agent: Searching knowledge base... ✅ Found relevant information! From Employee Handbook 2024, Chapter 4, Section 2: **Remote Work Policy:** | Employee Type | Remote Days | Requirements | |---------------|-------------|--------------| | Full-time | 3 days/week | Manager approval | | Part-time | 2 days/week | Core hours required | | Contract | Case-by-case | HR consultation | 📎 Reference: Employee Handbook 2024, Chapter 4, Section 2 ``` ### Example 2: Technical Documentation Q&A ``` User: How to configure OAuth2.0 login? Agent: Searching technical docs... ✅ Found configuration guide! **OAuth2.0 Configuration Steps:** 1. **Register Application** Register app with OAuth provider, get client_id and client_secret 2. **Configure Callback URL** Set auth callback URL: https://your-app.com/callback 3. **Implement Auth Flow** ```javascript const oauth = new OAuth2({ clientId: 'your_client_id', clientSecret: 'your_client_secret', redirectUri: 'https://your-app.com/callback' }); ``` 📎 Reference: Developer Documentation Section 5.2 ``` ### Example 3: Batch Document Upload ``` User: I want to upload these 3 documents to the knowledge base: 1. Product Introduction.pdf 2. Technical Whitepaper.docx 3. FAQ.md Agent: Batch uploading... ✅ Upload complete! | Document | Status | Knowledge Points | |----------|--------|-------------------| | Product Introduction.pdf | ✅ Success | 15 | | Technical Whitepaper.docx | ✅ Success | 28 | | FAQ.md | ✅ Success | 42 | 📚 Knowledge Base Stats: - Total Documents: 3 - Total Knowledge Points: 85 - Knowledge Chunks: 12 ``` ## Configuration Options ### Retrieval Parameters | Parameter | Default | Description | |-----------|--------|-------------| | top_k | 5 | Number of relevant chunks to return | | similarity_threshold | 0.7 | Similarity threshold | | max_tokens | 2000 | Maximum answer tokens | | include_source | true | Whether to include source citation | ### Chunking Strategies | Strategy | Use Case | |----------|----------| | Fixed Length | General scenarios | | Semantic Chunking | Maintain semantic integrity | | Paragraph Chunking | Split by natural paragraphs | ## Notes 1. **Document Quality**: Ensure documents are clear and well-formatted before upload 2. **Privacy Protection**: Be careful when uploading sensitive documents 3. **Knowledge Updates**: Re-upload documents when updated to refresh index 4. **Size Limit**: Single upload recommended not exceeding 50MB 5. **Index Delay**: Indexing takes ~1-5 minutes after upload ## Use Cases - 🏢 **Enterprise Knowledge Management**: Employee handbooks, product docs, technical docs - 📖 **Online Education**: Course materials, textbook Q&A - 🛒 **E-commerce Customer Service**: Product FAQ, shopping guides - 💼 **Legal Compliance**: Contract terms, regulations interpretation - 🏥 **Healthcare**: Health guides, medication instructions ## Technical Implementation ### Core Components ``` knowledge_qa/ ├── uploader.py # Document upload module ├── parser.py # Document parsing module ├── chunker.py # Text chunking module ├── indexer.py # Vector indexing module ├── retriever.py # Retrieval module └── generator.py # Answer generation module ``` ### API Usage Example ```python # 1. Upload document result = upload_document(file_path, knowledge_base_id) # 2. Retrieve relevant knowledge chunks = retrieve(query, top_k=5, threshold=0.7) # 3. Generate answer answer = generate_answer(question, context_chunks) ``` ## Changelog ### v1.0.0 (2024-01-20) - Initial release - Support for PDF, Word, TXT, Markdown formats - Vector retrieval and RAG Q&A implemented - Source citation support ## Author Info - Author: AI Agent Helper - Version: 1.0.0 - Framework: OpenClaw


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)