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

{
  "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

# 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