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

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
本技能来自小葱技能站7w4.net。

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

```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

这个 Skill 的文档说明清晰完整，提供了多种文档格式支持、智能检索和源引用等功能，使用示例丰富。但作为 Skill 包缺少实际可运行的代码和配置文件，目前只是一个功能说明文档，无法直接使用。如需投入使用，需要补充完整的代码实现。