Building Your First RAG App with Llama_Index, OpenAI GPT-4, and ChromaDB 😊, 🤖, 🎉

Retrieval-Augmented Generation (RAG) is an innovative approach that combines the strengths of information retrieval and generative models. In this blog, we’ll guide you through building your first RAG app using the Llama_Index framework, the OpenAI GPT-4 chat model, OpenAI’s embedding model, and ChromaDB for vector storage. This app will allow users to interact with it via the terminal, providing a conversational experience.

Prerequisites

Before we start, ensure you have:

• Python 3.7 or later
• OpenAI API Key: Sign up for an API key at OpenAI.
• Required Libraries: Install the necessary libraries using pip:

pip install llama_index openai chromadb

Step 1: Setting Up Your Environment

Create a new Python file, e.g., rag_chatbot.py, and begin by importing the necessary libraries:

import os
import openai
import llama_index as li
import chromadb
from chromadb.config import Settings

Step 2: Configure OpenAI and ChromaDB

Set up your OpenAI API key and initialize ChromaDB for storing embeddings:

# Set your OpenAI API key
openai.api_key = 'your_openai_api_key'

# Initialize ChromaDB
chroma_client = chromadb.Client(
  Settings(chroma_db_impl="duckdb+parquet", persist_directory="chroma_db")
)
collection = chroma_client.create_collection(name="document_vectors")

Step 3: Load Data and Index It

Create a function to load documents from a folder and index them using Llama_Index:

def load_and_index_data(folder_path):
    index = li.SimpleDocumentIndex()
    
    for filename in os.listdir(folder_path):
        if filename.endswith('.txt'):
            file_path = os.path.join(folder_path, filename)
            with open(file_path, 'r') as file:
                content = file.read()
                index.add_document(content)

                # Create embeddings using OpenAI's embedding model
                embedding_response = openai.Embedding.create(
                                input=content,
                                model="text-embedding-ada-002")
                vector = embedding_response['data'][0]['embedding']
                
                # Store vector in ChromaDB
                collection.add([content], [vector])

    return index

# Load and index data from a folder
data_index = load_and_index_data('path_to_your_text_files')

Step 4: Define the RAG Pipeline

Create a function to handle the RAG process, retrieving relevant documents and generating responses using GPT-4:

def rag_pipeline(user_query):
    # Retrieve relevant documents
    retrieved_docs = data_index.retrieve(user_query, top_k=3)

    # Prepare context for GPT-4
    context = "\n".join(retrieved_docs)
    
    # Generate a response with GPT-4
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "user", "content": context + "\n\n" + user_query}
        ]
    )
    
    return response['choices'][0]['message']['content']

Step 5: Create a Conversational Loop

Now, we’ll implement a loop that allows the user to input queries and receive responses from the bot:

def chat_with_bot():
    print("Welcome to the RAG Chatbot! Type 'exit' to stop the conversation.")
    
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'exit':
            print("Goodbye!")
            break
        
        response = rag_pipeline(user_input)
        print("Bot:", response)

# Start the chat loop
if __name__ == "__main__":
    chat_with_bot()

Step 6: Run Your RAG App

Run your Python script in the terminal:

python rag_chatbot.py

You should see a prompt welcoming you to the chatbot. You can enter your queries, and the bot will respond based on the indexed documents and the context provided.

Conclusion

Congratulations! You’ve successfully built your first RAG app using Llama_Index, OpenAI GPT-4, and ChromaDB. This application demonstrates how to combine retrieval and generation to create an intelligent conversational agent.

Feel free to expand on this project by adding more sophisticated retrieval methods, improving the user interface, or integrating additional features. The possibilities are endless, and with RAG, you can create powerful applications that leverage the best of both worlds: retrieval and generation. Happy coding!

#RAG #ChatGPT #llamaindex #GenAI #ChromaDB