Build private knowledge bases for AI-powered document Q&A. Supports PDF, Word, TXT, Markdown uploads with smart chunking and vector retrieval. Automatically...
---
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
> ๐ 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: โ
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...
โ
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
๐ 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/
โโโ documents/
โ โโโ doc_001/
โ โ โโโ original.pdf # Original document
โ โ โโโ metadata.json # Metadata
โ โโโ doc_002/
โ โ โโโ content.txt # Extracted text
โ โ โโโ metadata.json
โ โโโ ...
โโโ index/
โโโ 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
โ
Question Embedding
โ
Vector Similarity Matching
โ
Top-K Relevant Chunks
โ
Build Prompt Context
โ
LLM Generate Answer
โ
Add Citation
โ
Return Answer
```
## Usage Examples
### Example 1: Enterprise Knowledge Base Q&A
```
User: What is the company's remote work policy?
Agent: Searching knowledge base...
โ
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 |
๐ 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...
โ
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'
});
```
๐ 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...
โ
Upload complete!
| Document | Status | Knowledge Points |
|----------|--------|-------------------|
| Product Introduction.pdf | โ
Success | 15 |
| Technical Whitepaper.docx | โ
Success | 28 |
| FAQ.md | โ
Success | 42 |
๐ 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
- ๐ข **Enterprise Knowledge Management**: Employee handbooks, product docs, technical docs
- ๐ **Online Education**: Course materials, textbook Q&A
- ๐ **E-commerce Customer Service**: Product FAQ, shopping guides
- ๐ผ **Legal Compliance**: Contract terms, regulations interpretation
- ๐ฅ **Healthcare**: Health guides, medication instructions
## Technical Implementation
### Core Components
```
knowledge_qa/
โโโ uploader.py # Document upload module
โโโ parser.py # Document parsing module
โโโ chunker.py # Text chunking module
โโโ indexer.py # Vector indexing module
โโโ retriever.py # Retrieval module
โโโ 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
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