# RAG Knowledge Corpus

This directory contains the knowledge corpus for the RAG (Retrieval-Augmented Generation) system. The corpus is organized as a structured knowledge base that can be searched and used to augment AI responses with relevant context.

## 📁 Directory Structure

```
corpus/
├── README.md              # This file
├── corpus_registry.txt    # Auto-generated registry of available topics
├── corpus_manager.sh      # Management script (in parent directory)
├── topic_template.md      # Template for new topics
├── .topic_keywords        # Topic keyword mappings
├── .file_processors       # File processing configurations
│
├── programming/           # Programming topics
│   ├── lil/              # Lil programming language
│   │   └── guide.md
│   └── algorithms.txt
│
├── science/              # Scientific topics
│   ├── physics.txt
│   └── biology.md
│
├── literature/           # Literary topics
├── general/              # General knowledge
└── examples/             # Example content
```

## 🔧 Management Tools

### Corpus Manager (`./corpus_manager.sh`)

The corpus manager provides utilities for managing the knowledge base:

```bash
# Update the corpus registry (run after adding new files)
./corpus_manager.sh update

# List all available topics
./corpus_manager.sh list

# Check if a topic exists
./corpus_manager.sh exists programming

# List files in a specific topic
./corpus_manager.sh files programming

# Create template files for a new topic
./corpus_manager.sh template newtopic

# Get corpus statistics
./corpus_manager.sh count science
```

### RAG Search (`./rag_search.sh`)

Search the corpus using efficient Unix tools:

```bash
# Search entire corpus
./rag_search.sh search "quantum physics"

# Search specific topic
./rag_search.sh search "lil programming" programming

# Get context around matches
./rag_search.sh context "variables" programming

# Extract relevant sections
./rag_search.sh extract "functions" programming

# Show corpus statistics
./rag_search.sh stats
```

## 📝 File Format Guidelines

### Supported Formats
- **`.txt`** - Plain text files
- **`.md`** - Markdown files (recommended)
- **`.html`** - HTML files

### Content Organization
1. **Use clear, descriptive headers** (`#`, `##`, `###`)
2. **Include examples and code blocks** where relevant
3. **Add cross-references** between related topics
4. **Use consistent formatting** and terminology
5. **Include practical applications** and use cases

### Markdown Template
```markdown
# Topic Name - Comprehensive Guide

## Introduction
[Brief overview of the topic]

## Core Concepts
### [Subtopic 1]
[Explanation and details]

### [Subtopic 2]
[Explanation and details]

## Examples
[Code examples, diagrams, practical applications]

## Best Practices
[Recommended approaches and common pitfalls]

## References
[Links to additional resources]
```

## ➕ Adding New Content

### Step 1: Create Topic Directory
```bash
# Create a new topic directory
mkdir -p corpus/newtopic

# Or use the template command
./corpus_manager.sh template newtopic
```

### Step 2: Add Content Files
```bash
# Create content files in your preferred format
vim corpus/newtopic/guide.md
vim corpus/newtopic/examples.txt
vim corpus/newtopic/reference.html
```

### Step 3: Update Registry
```bash
# Update the corpus registry to include new files
./corpus_manager.sh update

# Verify the topic is recognized
./corpus_manager.sh exists newtopic
./corpus_manager.sh files newtopic
```

### Step 4: Test Search
```bash
# Test that content is searchable
./rag_search.sh search "keyword" newtopic
./rag_search.sh context "concept" newtopic
```

## 🔍 Search Behavior

### Keyword Matching
- **Case-insensitive** search across all text files
- **Multi-word queries** supported
- **Partial matches** found within words
- **Context extraction** shows surrounding lines

### Topic Filtering
- **General search**: Searches entire corpus
- **Topic-specific**: Limited to specific directories
- **Hierarchical**: Supports subtopics (e.g., `science/physics`)

### Performance
- **Sub-second lookups** using Unix tools
- **Efficient grep/sed/awk** processing
- **Cached registry** for fast topic discovery
- **Minimal memory usage**

## 🔧 Advanced Configuration

### Custom Topic Keywords
Edit `corpus/.topic_keywords` to add custom topic detection:
```
newtopic|keyword1 keyword2 keyword3
```

### File Processors
Edit `corpus/.file_processors` to add support for new file types:
```
custom|processing_command
```

### Registry Customization
The `corpus_registry.txt` file can be manually edited for custom topic mappings:
```
topic|path/to/files|keywords|description
```

## 🎯 Integration with AI Systems

The corpus is designed to integrate with AI thinking mechanisms:

### Automatic RAG Detection
- **Query analysis** determines when corpus search is needed
- **Topic classification** matches queries to appropriate corpus sections
- **Confidence scoring** determines RAG vs direct response

### Context Injection
- **Relevant sections** extracted and formatted
- **Context length** managed to stay within token limits
- **Multiple sources** combined for comprehensive answers

### Fallback Strategy
- **Graceful degradation** when no relevant corpus found
- **Direct LLM response** when corpus search yields no results
- **Error handling** for missing or corrupted files

## 📊 Current Corpus Statistics

*Run `./rag_search.sh stats` to see current corpus statistics.*

## 🚀 Best Practices

1. **Keep files focused** - One topic per file when possible
2. **Use descriptive names** - File names should indicate content
3. **Regular updates** - Run `update` after adding new files
4. **Test searches** - Verify content is discoverable
5. **Cross-reference** - Link related topics when appropriate
6. **Version control** - Track changes to corpus files

## 🔄 Maintenance

### Regular Tasks
- Run `./corpus_manager.sh update` after adding files
- Test search functionality with new content
- Review and update outdated information
- Archive unused or deprecated topics

### Performance Monitoring
- Monitor search response times
- Check registry file size and complexity
- Validate file integrity periodically
- Clean up temporary search files

This corpus system provides a scalable, efficient foundation for knowledge-augmented AI responses while maintaining the flexibility to grow and adapt to new requirements.