Enhance Your Chatbot Performance with TruLens and Langchain

Improve Your LLM Applications with TruLens

In this tutorial, we will explore how to build and evaluate a contextual chatbot, also known as a conversational LLM with memory, utilizing Langchain and TruLens effectively.

Introduction to TruLens: Evaluate and Track LLM Applications

TruLens is a robust suite of tools designed to monitor and enhance the performance of LLM-based applications. With its comprehensive evaluation capabilities, TruLens enables users to assess key metrics such as quality of inputs, outputs, and internal processes.

Some standout features include:

• Built-in feedback mechanisms for groundedness, relevance, and moderation.
• Support for custom evaluation needs.
• Instrumentation for various LLM applications, including question answering, retrieval-augmented generation, and agent-based solutions.

This instrumentation allows users to analyze diverse usage metrics and metadata, offering critical insights into the model's performance.

Prerequisites for Building the Chatbot

Before we dive into the development, ensure you have the following:

• Python 3.10+
• Conda (recommended)
• OpenAI API Key
• HuggingFace API Key

Setting Up Your Development Environment

Let's begin by creating a virtual environment inside a new folder. After that, install the necessary libraries:

conda create -n chatbot_env python=3.10
conda activate chatbot_env
pip install streamlit openai huggingface_hub

To keep sensitive data secure, Streamlit offers built-in file-based secrets management. Create a .streamlit/secrets.toml file in your project directory and include your OpenAI and HuggingFace keys:

[general]
openai_api_key = "YOUR_OPENAI_API_KEY"
huggingface_api_key = "YOUR_HUGGINGFACE_API_KEY"

Building the Chatbot

Start by creating a chatbot.py file. Import the necessary libraries and load the environment variables as follows:

import streamlit as st
import openai
from huggingface_hub import HfApi
# Load secrets

Chain Building

Build your LLM chain with a simple prompt that can be enhanced based on the evaluation results:

def create_llm_chain():
    # Define your prompt and chain structure here
    pass

Integrating TruLens for Evaluation

Once the LLM chain is established, use TruLens to evaluate and track performance:

from trulens import TruLens
trulens = TruLens(chain=create_llm_chain())

Here, TruLens will help monitor and evaluate metrics including relevance and moderation, effectively ensuring quality responses from the chatbot.

Creating a Chatbot UI with Streamlit

Leverage Streamlit's user-friendly chat elements to build an interactive UI:

st.title('Contextual Chatbot')
user_input = st.chat_input('Type your message here...')
if user_input:
    response = trulens.get_response(user_input)
    st.chat_message(response)

After setting up the UI, initialize TruLens' dashboard toward the end of your chatbot.py file.

Running the Chatbot

Run your chatbot using the command below, resulting in a new tab opening in your browser:

streamlit run chatbot.py

Access TruLens' dashboard through the specified IP address to evaluate performance metrics.

Evaluation and Improvement

Monitor the chatbot's output and consider enhancing the LLM chain's initial prompts:

prompt_template = "How can I assist you today?"

Adjust the model you are using to test its performance:

llm_model = "gpt-4"

Compare costs and performance as you experiment with different models, noting their unique advantages.

Conclusion

We have successfully built a contextual chatbot integrated with TruLens for ongoing evaluation. By leveraging tools like Langchain and TruLens, we can continuously monitor and improve the performance of LLM applications while ensuring optimal performance.

Consider deploying your application on GitHub and integrating it with the Streamlit platform for easy access and sharing.

Thank you for participating, and happy coding!