Evaluate and Enhance Your Chatbots with TruLens: A Comprehensive Guide

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, using LangChain and TruLens. Our focus will be on tracking the bot's responses to monitor key moderation metrics such as hate speech and malicious content, while optimizing overall performance and cost.

What is TruLens?

TruLens is a powerful suite of evaluation tools designed for monitoring and improving the performance of LLM-based applications. By assessing the quality of inputs, outputs, and internal processes, TruLens provides built-in feedback mechanisms for groundedness, relevance, and moderation assessments. Additionally, it accommodates custom evaluation needs and offers essential instrumentation for various LLM applications, including:

• Question Answering
• Retrieval-augmented Generation
• Agent-based Solutions

This capability allows users to monitor diverse usage metrics and metadata, delivering valuable insights into model performance.

Prerequisites

To follow along with this tutorial, you will need:

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

Setting Up

Let’s begin by creating a virtual environment in a new folder and installing the necessary libraries. Streamlit simplifies secure storage by providing file-based secrets management for easy access to your application’s API keys.

Follow these steps to incorporate your OpenAI API key and HuggingFace Access Token in Streamlit secrets. Create a .streamlit/secrets.toml file in your project directory and insert the following lines, substituting with your keys:

[openai]
api_key = "YOUR_OPENAI_API_KEY"

[huggingface]
access_token = "YOUR_HUGGINGFACE_ACCESS_TOKEN"

With that done, we are ready to start building!

Building the Chatbot

Create a chatbot.py file and open it. Start by importing the necessary libraries and loading the environment variables.

Chain Building

Next, we will build our LLM chain using a simple prompt, which we can later enhance based on our evaluation results.

Integrating TruLens

After setting up your LLM chain, use TruLens for evaluation and tracking. TruLens provides out-of-the-box Feedback Functions and an extensible framework for LLM evaluation.

A Feedback Function scores the output of an LLM application by analyzing generated text and metadata. In this setup, we will track the relevance of the bot’s answers and evaluate for hate speech, violence, self-harm, or malicious responses.

Building the Chatbot UI with Streamlit

We will leverage Streamlit's chat elements, including st.chat_message, st.chat_input, and st.session_state, to create a ChatGPT-like user interface.

Finally, initialize TruLens’ dashboard at the end of the file:

if __name__ == '__main__':
    TruLens.run()  # Starting the TruLens dashboard

Running the Chatbot

Run the chatbot using the following command in your terminal:

streamlit run chatbot.py

A new tab should open in your browser at http://localhost:8501/, and you can also access TruLens’ dashboard at http://192.168.0.7:8502/.

Evaluation and Improvement

Once the chatbot is running, assess its output using TruLens Eval and make modifications to enhance performance. For instance, change your prompt template to be more engaging for users.

By refining your prompts, you can achieve significantly improved moderation scores and more thoughtful responses. Experimenting with different models, such as switching from chatgpt-3.5-turbo to gpt-4, and adjusting the app_id, can also yield different outcomes.

Conclusion

In this tutorial, we successfully built a chatbot with contextual memory integrated with TruLens for comprehensive evaluation. TruLens allows continuous monitoring and improvement of LLM applications by comparing various configurations and model performances.

Assessing the impact of specific chain configurations on response quality, cost, and latency is crucial in developing LLM applications. The combination of TruLens and LangChain forms an effective toolkit for building reliable chatbots that effectively manage context, while enabling robust evaluation processes.

For deployment, consider uploading your application to GitHub and connecting the repository to the Streamlit platform.

Thank you for following this tutorial!