knowledge-base-qa-assistant

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

Trigger Keywords

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
        

     
     📎 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

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)

Author Info

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