Building a chatbot with advanced RAG (Retrieval Augmented Generation) and open-source LLM (Language Model) using LlamaIndex, Lang Chain, and Flask is an exciting project that can greatly enhance the conversational abilities of your chatbot. In this tutorial, we will walk you through the process of setting up the necessary tools and technologies, as well as implementing the RAG and LLM models to create a powerful and intelligent chatbot.
Step 1: Setting up the environment
Before we begin building the chatbot, we need to set up the necessary environment. Make sure you have Python installed on your system. You can download Python from https://www.python.org/downloads/.
Next, we need to install the required libraries and frameworks. We will be using Hugging Face’s Transformers library, Flask for building the web server, and LlamaIndex and Lang Chain for the RAG and LLM models. You can install these libraries using pip:
pip install transformers flask llmaindex lang_chainStep 2: Creating the Flask web server
Now that we have the necessary tools installed, we can start building the Flask web server that will serve as the backend for our chatbot. Create a new Python file, for example, app.py, and add the following code:
from flask import Flask
app = Flask(__name__)
@app.route('/')
def index():
return 'Hello, World!'
if __name__ == '__main__':
app.run()Save the file and run the Flask app by running the following command in the terminal:
python app.pyThis will start the Flask development server, and you should see the message ‘Hello, World!’ when you visit http://localhost:5000 in your web browser.
Step 3: Setting up the RAG and LLM models
Next, we need to load the RAG and LLM models using the LlamaIndex and Lang Chain libraries. Create a new Python file, for example, chatbot.py, and add the following code:
from llmaindex import get_rag_model
from lang_chain import get_llm_model
rag_model = get_rag_model()
llm_model = get_llm_model()Save the file and import the rag_model and llm_model variables in the Flask app by adding the following lines to app.py:
from chatbot import rag_model, llm_modelStep 4: Implementing the chatbot logic
Now that we have the RAG and LLM models loaded, we can implement the chatbot logic. Update the Flask app in app.py to include the chatbot logic:
from flask import request
@app.route('/chatbot', methods=['POST'])
def chatbot():
input_text = request.json['input']
completion = rag_model.completion(input_text, model=llm_model, max_length=100)
return {'response': completion['choices'][0]['text']}This code defines a route /chatbot that accepts POST requests with a JSON payload containing the input text. The chatbot will use the RAG and LLM models to generate a response and return it as a JSON object.
Step 5: Testing the chatbot
To test the chatbot, you can use a tool like Postman to send POST requests to the /chatbot endpoint with the input text. Alternatively, you can create a simple HTML form to interact with the chatbot. Create a new HTML file, for example, index.html, and add the following code:
<!DOCTYPE html>
<html>
<head>
<title>Chatbot</title>
</head>
<body>
<h1>Chatbot</h1>
<form id="chatbot-form" action="/chatbot" method="post">
<input type="text" name="input" id="input" />
<button type="submit">Send</button>
</form>
<div id="response"></div>
<script>
document.getElementById('chatbot-form').addEventListener('submit', async function (e) {
e.preventDefault();
const input = document.getElementById('input').value;
const response = await fetch('/chatbot', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ input }),
}).then(res => res.json());
document.getElementById('response').innerText = response.response;
});
</script>
</body>
</html>Save the file and open it in your web browser. You should see a simple form with an input field where you can type your message and a button to send it to the chatbot. The chatbot will generate a response using the RAG and LLM models and display it on the page.
Congratulations! You have successfully built a chatbot with advanced RAG and open-source LLM using LlamaIndex, Lang Chain, and Flask. Feel free to customize and enhance the chatbot further by adding more features and improving the models. Happy coding!
Hi, I have tried some chatbot with nemo guardrails but it is not working.
could you try and rectify please…