# Knowledge Base Browser

A powerful Gradio custom component for retrieval-augmented generation (RAG) applications. This component provides an intuitive interface for searching through documents with AI-powered semantic search capabilities.

## Features

- **Semantic Search**: AI-powered meaning-based search using OpenAI embeddings
- **Keyword Search**: Traditional text matching for precise queries
- **Hybrid Search**: Combines semantic and keyword approaches
- **Source Type Filtering**: Filter by PDF, web pages, academic papers, or code repositories
- **Citation Tracking**: Built-in citation management and export functionality
- **Agent Integration**: Designed for both human users and AI agents
- **LlamaIndex Integration**: Uses LlamaIndex with FAISS vector store for efficient retrieval
- **Responsive UI**: Modern, accessible interface with expandable result cards

## Installation

```bash
pip install gradio_kb_browser
```

For development installation:

```bash
git clone <repository-url>
cd kb_browser
pip install -e .
```

## Quick Start

### Basic Usage

```python
import gradio as gr
from kb_browser import KnowledgeBrowser

# Create the component
kb_browser = KnowledgeBrowser(
    index_path="./documents",  # Path to your document directory
    search_type="semantic",    # Default search type
    max_results=10            # Maximum results to return
)

# Use in a Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Document Search")
    
    query = gr.Textbox(label="Search Query")
    search_btn = gr.Button("Search")
    
    results = gr.JSON(label="Results")
    
    def search_documents(query_text):
        return kb_browser.search(query_text)
    
    search_btn.click(
        fn=search_documents,
        inputs=query,
        outputs=results
    )

demo.launch()
```

### Agent Integration

```python
from kb_browser import KnowledgeBrowser

# Initialize component for agent use
kb_browser = KnowledgeBrowser()

# Agent can search and get structured results
def agent_research(question):
    results = kb_browser.search(
        query=question,
        search_type="semantic",
        max_results=5
    )
    
    # Process results for agent response
    citations = []
    for doc in results["results"]:
        citations.append({
            "title": doc["title"],
            "source": doc["source"],
            "relevance": doc["relevance_score"],
            "snippet": doc["snippet"]
        })
    
    return citations
```

## Configuration

### Environment Variables

Set your OpenAI API key for semantic search:

```bash
export OPENAI_API_KEY="your-api-key-here"
```

### Component Parameters

- `query`: Initial search query string
- `results`: Pre-loaded search results
- `index_path`: Path to document directory (default: "./data")
- `search_type`: Search method - "semantic", "keyword", or "hybrid"
- `max_results`: Maximum number of results to return
- `label`: Component label for UI
- `visible`: Whether component is visible
- `elem_classes`: CSS classes for styling

## Document Formats

The component supports various document formats:

- **PDF Files**: Automatically parsed and indexed
- **Text Files**: Plain text documents
- **Markdown**: Documentation and notes
- **JSON**: Structured data documents

## Search Types

### Semantic Search
Uses OpenAI embeddings to understand meaning and context. Best for:
- Conceptual queries
- Finding related topics
- Cross-domain searches

### Keyword Search
Traditional text matching. Best for:
- Exact phrase searches
- Technical terms
- Specific names or identifiers

### Hybrid Search
Combines both approaches for comprehensive results.

## API Reference

### KnowledgeBrowser Class

#### Methods

- `search(query, search_type, max_results)`: Perform search and return results
- `preprocess(payload)`: Preprocess component input
- `postprocess(value)`: Postprocess component output
- `api_info()`: Get API schema information

#### Events

- `submit`: Triggered when search is performed
- `select`: Triggered when document is selected
- `change`: Triggered when component state changes

## Example Applications

### Research Assistant

```python
import gradio as gr
from kb_browser import KnowledgeBrowser

def create_research_app():
    kb_browser = KnowledgeBrowser(index_path="./research_papers")
    
    with gr.Blocks() as app:
        gr.Markdown("# Research Assistant")
        
        question = gr.Textbox(label="Research Question")
        search_btn = gr.Button("Search Literature")
        
        results_display = gr.HTML()
        citations = gr.State([])
        
        def research_query(question_text):
            results = kb_browser.search(question_text, max_results=5)
            
            html = "<div class='research-results'>"
            for doc in results["results"]:
                html += f"""
                <div class='paper'>
                    <h3>{doc['title']}</h3>
                    <p><strong>Source:</strong> {doc['source']}</p>
                    <p><strong>Relevance:</strong> {doc['relevance_score']:.0%}</p>
                    <p>{doc['snippet']}</p>
                </div>
                """
            html += "</div>"
            
            return html
        
        search_btn.click(research_query, question, results_display)
    
    return app
```

### Customer Support

```python
def create_support_app():
    kb_browser = KnowledgeBrowser(index_path="./support_docs")
    
    with gr.Blocks() as app:
        gr.Markdown("# Customer Support Assistant")
        
        issue = gr.Textbox(label="Describe your issue")
        help_btn = gr.Button("Find Solutions")
        
        solutions = gr.HTML()
        
        def find_solutions(issue_text):
            results = kb_browser.search(issue_text, search_type="hybrid")
            
            html = "<div class='solutions'>"
            for doc in results["results"][:3]:
                html += f"""
                <div class='solution'>
                    <h4>{doc['title']}</h4>
                    <p>{doc['snippet']}</p>
                    <a href="{doc.get('url', '#')}" target="_blank">View Full Article</a>
                </div>
                """
            html += "</div>"
            
            return html
        
        help_btn.click(find_solutions, issue, solutions)
    
    return app
```

## Development

### Running Tests

```bash
pip install pytest
pytest test_kb_browser.py -v
```

### Building the Component

```bash
pip install build
python -m build
```

### Publishing

```bash
gradio cc publish kb_browser --name "KnowledgeBaseBrowser"
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

## License

MIT License - see LICENSE file for details.

## Support

For issues and questions:
- GitHub Issues: [Create an issue](https://github.com/gradio-app/gradio/issues)
- Documentation: [Gradio Docs](https://gradio.app/docs)
- Community: [Gradio Discord](https://discord.gg/gradio)