File size: 2,717 Bytes
3998131 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
# Module C: RAG-Based Letter Generation
This module generates professional, bias-free Nepali government letters by combining **Retrieval-Augmented Generation (RAG)** with **LLM-based adaptation**.
Instead of manually selecting templates, users simply describe their need (e.g., "I need a citizenship certificate for my child"), and the system:
1. **Retrieves** the most relevant official template from the vector database.
2. **Generates** a final letter by intelligently filling the template with user details.
## Features
- **Intent-Based Retrieval**: Finds the right template even if the user doesn't know the official name.
- **Smart Filling**: Extracts details (Name, Date, District) from natural language.
- **Modular Design**: Separate Indexer, Retriever, and Generator components.
## Setup
1. **Install Dependencies**:
```bash
pip install chromadb sentence-transformers mistralai python-dotenv
```
2. **Environment Variables**:
Create a `.env` file in the project root:
```env
MISTRAL_API_KEY=your_api_key_here
```
## Usage
### 1. Python API
```python
from module_c.interface import LetterGenerationAPI
api = LetterGenerationAPI()
# Describe your need in English or Nepali
description = "I need to apply for citizenship for my daughter Sita. I am Ram Sharma from Kathmandu."
result = api.generate_smart_letter(description)
if result['success']:
print(f"Used Template: {result['template_used']}")
print(result['letter'])
else:
print(f"Error: {result['error']}")
```
### 2. Interactive Flow (Handling Missing Info)
```python
# 1. Analyze Requirements
analysis = api.analyze_requirements("I need a citizenship certificate")
if analysis['success'] and analysis['missing_fields']:
print(f"Selected Template: {analysis['template_name']}")
print(f"Missing Fields: {analysis['missing_fields']}")
# 2. Provide Missing Info
additional_data = {
"Date": "2081-01-01",
"District": "Kathmandu",
"Applicant Name": "Ram Sharma"
}
result = api.generate_smart_letter(
"I need a citizenship certificate",
additional_data=additional_data
)
print(result['letter'])
```
### 3. Adding New Templates
1. Add your template text file (`.txt`) to `data/module-C/`.
2. Run the indexer to update the vector database:
```bash
python module_c/indexer.py
```
## Project Structure
- `interface.py`: Main entry point.
- `retriever.py`: Finds relevant templates using Vector DB.
- `generator.py`: Uses LLM to fill retrieved templates.
- `indexer.py`: Ingests templates into ChromaDB.
- `vector_db.py`: Manages ChromaDB connection.
- `config.py`: Configuration settings.
|