RAG Transformer: Advanced Retrieval-Augmented Generation System

Table of Contents

• Project Overview
  • What is RAG Transformer?
  • Key Capabilities
  • How It Works
  • Technical Architecture
  • Unique Features
• Components
  • RAG Pipeline
  • Knowledge Graph
  • Model Integrations
• Prerequisites
• Installation Steps
• Usage
  • Basic RAG Pipeline
  • Advanced RAG Pipeline
  • Web Interface
  • API Server
  • Knowledge Graph Visualization
  • Docker Deployment
• API Documentation
• Troubleshooting
• Future Improvements
• Code of Conduct
• License

Project Overview

What is RAG Transformer?

RAG (Retrieval-Augmented Generation) Transformer is an intelligent AI assistant that combines advanced machine learning techniques to provide contextually rich, informative responses across multiple knowledge domains:

Key Capabilities:

• Intelligent Knowledge Retrieval: Searches through a diverse dataset of machine learning, science fiction, and cosmic content
• Context-Aware Responses: Generates answers that are not just accurate, but contextually relevant
• Multidomain Intelligence: Bridges insights from:
  1. Machine Learning Concepts
  2. Science Fiction Movies
  3. Cosmic and Astronomical Observations
• Advanced Prompting: Uses few-shot examples and domain-specific templates for better responses
• Knowledge Graph Visualization: Creates visual representations of relationships between concepts
• Web Interface: User-friendly Streamlit interface with analytics and knowledge base exploration
• API Server: FastAPI-based API for integration with other applications
• Multiple Model Support: Integrates with Hugging Face, OpenAI, and Anthropic models
• Deployment Options: Docker, AWS, and GCP deployment utilities

How It Works

The system uses a sophisticated multi-step process:

1. Query Processing: Parse and analyze the user query
2. Document Retrieval: Find relevant documents in the knowledge base
3. Context Preparation: Combine retrieved documents into a context
4. Response Generation: Generate a response using the context and query
5. Response Evaluation: Assess and improve the quality of the response

Technical Architecture

• Embedding Model: all-MiniLM-L6-v2
• Generation Models: google/flan-t5-small, OpenAI GPT, Anthropic Claude
• Indexing: FAISS semantic search
• Data Sources: TMDB and NASA APIs, Wikipedia, Web content
• Web Framework: Streamlit, FastAPI
• Deployment: Docker, AWS ECS, Google Cloud Run

Unique Features

• Interactive query interface
• Knowledge graph visualization
• Performance analytics
• Multi-model support
• Diverse knowledge base
• Real-time response generation
• Cloud deployment options

Components

RAG Pipeline

The RAG pipeline consists of the following steps:

1. Query Processing: Parse and analyze the user query
2. Document Retrieval: Find relevant documents in the knowledge base
3. Context Preparation: Combine retrieved documents into a context
4. Response Generation: Generate a response using the context and query
5. Response Evaluation: Assess and improve the quality of the response

Knowledge Graph

The knowledge graph visualizes relationships between concepts in the knowledge base:

1. Entity Extraction: Identify entities in the documents
2. Relation Extraction: Determine relationships between entities
3. Graph Construction: Create a graph of entities and relationships
4. Community Detection: Identify clusters of related entities
5. Visualization: Create a visual representation of the graph

Model Integrations

The system supports multiple language models:

1. Hugging Face Models: Local models for generation
2. OpenAI Models: GPT models via API
3. Anthropic Models: Claude models via API

Prerequisites

• Python 3.8+
• Git
• Terminal/Command Line
• Docker (optional, for containerized deployment)

Installation Steps

1. Clone Repository

git clone https://github.com/synthara-company/rag.git
cd rag

2. Create Virtual Environment

python3 -m venv venv
source venv/bin/activate  # On macOS/Linux
# venv\Scripts\activate   # On Windows

3. Install Dependencies

pip install -r requirements.txt

4. Set up API Keys

Create a .env file in the project root with the following content:

TMDB_API_KEY=your_tmdb_api_key
NASA_API_KEY=your_nasa_api_key
OPENAI_API_KEY=your_openai_api_key  # Optional
ANTHROPIC_API_KEY=your_anthropic_api_key  # Optional

5. Collect Datasets

python sci_fi_dataset_collector.py

Usage

Basic RAG Pipeline

python rag_pipeline.py

Advanced RAG Pipeline

python advanced_rag_pipeline.py

Web Interface

streamlit run advanced_web_interface.py

API Server

python api_server.py

Knowledge Graph Visualization

python knowledge_graph_visualizer.py

Docker Deployment

cd deployment
docker-compose up -d

API Documentation

Once the API server is running, access the API documentation at:

http://localhost:8000/docs

Troubleshooting

• Ensure all dependencies are installed
• Check Python version compatibility
• Verify API keys in .env file
• For Docker issues, check Docker logs and ensure ports are available
• For model loading issues, try using a smaller model by modifying the configuration

Future Improvements

• Integration with more powerful language models
• Enhanced knowledge graph visualization
• Support for multi-modal content (images, audio)
• User authentication and personalization
• Improved performance and scalability

Code of Conduct

Respect Others: Always treat others with kindness and respect. Do not engage in any form of bullying, harassment, or discrimination.

No Hate Speech: Do not post or share content that is offensive, hateful, or discriminatory towards any individual or group based on race, gender, religion, ethnicity, or sexual orientation.

No Trolling or Spamming: Do not post irrelevant or disruptive content. This includes spamming, posting off-topic content, or engaging in trolling activities that disrupt the community.

No Illegal Activities: Do not share, promote or engage in illegal activities. This includes sharing links to illegal downloads, discussing illegal activities, or selling illicit substances.

Keep It Clean: Do not post or share explicit, adult or overly violent content.

Be Relevant and Constructive: Make sure your posts are relevant to the current topic or thread. Constructive criticism is welcome, but offensive or mean-spirited comments are not.

Use Appropriate Language: Avoid swearing, offensive language, or sexually explicit language.

No Sharing Personal Information: Do not share your own or other members' personal information, such as addresses, phone numbers, or other confidential information.

Respect Intellectual Property: Do not post anything that infringes on anyone's intellectual property rights. Always credit the original source of the content.

No Promotion or Advertising: Do not advertise or promote commercial products or services without permission from the moderators.

Follow the Moderators: Listen to and respect decisions made by the moderators. They have the right to warn, kick, or ban users who violate the rules.

No Duplicate Accounts: Each user is allowed only one account.

License

This project is licensed under the MIT License - see the LICENSE file for details.