Generative AI and RAG Model Project

Overview

This project integrates a Generative AI model with a Retrieval-Augmented Generation (RAG) framework to enhance information retrieval and generation. The system combines the capabilities of a large language model (LLM) with a vector database to provide accurate, context-aware responses by retrieving relevant documents and augmenting the generated output with factual information.

Features

• Generative AI: Utilizes an advanced LLM (Large Language Model) for text generation, providing coherent and contextually relevant responses.
• Retrieval-Augmented Generation (RAG): Enhances accuracy by retrieving relevant documents before generating responses.
• Vector Database Integration: Stores and retrieves relevant chunks of information efficiently using embeddings.
• Embeddings Model: Converts text into numerical vectors for similarity-based search.
• Contextual Re-Ranking: Ensures retrieved documents are ranked based on their relevance to the query.
• Streamlit UI (Optional): A simple web-based interface for user interaction.
• API Support: Allows easy integration with external applications via FastAPI.
• Scalability: Supports large datasets and scalable model deployment.

Architecture

The architecture follows a step-by-step pipeline for optimal response generation:

1. User Query: The user submits a query through the interface or API.
2. Query Preprocessing: The system normalizes and embeds the query using a pre-trained embeddings model (e.g., OpenAI’s text-embedding-ada-002).
3. Retrieval Step: The system searches a vector database (e.g., FAISS, ChromaDB, Pinecone) for semantically similar documents.
4. Ranking & Filtering: Retrieved documents are ranked based on their semantic similarity to the query.
5. Augmentation Step: The top-ranked documents are appended to the user’s query before being sent to the LLM.
6. Generation Step: The LLM generates a response using both the user’s query and the retrieved documents.
7. Response Postprocessing: The generated response is refined, checked for hallucinations, and formatted before being sent back to the user.
8. Response Delivery: The final, factually augmented response is displayed to the user through the UI or API.

Tech Stack

• Programming Language: Python 3.11
• Frameworks & Libraries:
  • Language Model: OpenAI GPT-4, Hugging Face Transformers, or Llama 2
  • Embeddings & Vector Search: OpenAI text-embedding-ada-002, SentenceTransformers, FAISS, ChromaDB, or Pinecone
  • RAG Framework: LangChain for retrieval-augmented workflows
  • Web UI: Streamlit for interactive user experience
  • API Backend: FastAPI for scalable API deployment
  • Database: PostgreSQL, SQLite, or cloud-based vector databases
  • Environment Management: Python venv or Conda for dependency isolation

Installation

# Clone the repository
git clone <repository_url>
cd <project_directory>

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Usage

# Run the Streamlit app
streamlit run app.py

# Run FastAPI server (if applicable)
uvicorn main:app --reload

Configuration

1. Set up API keys for OpenAI / Hugging Face in a .env file:

   OPENAI_API_KEY=your_api_key_here
   

2. Define database connection settings if using a remote vector database.
3. Modify embeddings model and retrieval parameters in config.py to optimize search performance.

Example Workflow

from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# Load embeddings and vector database
embedding_model = OpenAIEmbeddings(model="text-embedding-ada-002")
vector_db = FAISS.load_local("vector_store", embedding_model)

# Initialize RAG pipeline
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(model_name="gpt-4"), 
    retriever=vector_db.as_retriever()
)

# Process user query
query = "What are the symptoms of lung cancer?"
response = qa_chain.run(query)
print(response)

Future Enhancements

• Multi-Document Retrieval: Implement advanced ranking techniques like BM25 + Dense Retrieval.
• Fine-Tuned LLMs: Train models for domain-specific knowledge.
• Query Expansion: Improve retrieval with query reformulation techniques.
• Hybrid Search: Combine keyword-based and semantic search for better performance.
• Fact-Checking Mechanism: Reduce hallucinations by cross-referencing multiple sources.
• Multi-Turn Conversation Support: Enhance context retention over multiple interactions.

License

This project is licensed under the MIT License.