Enhance Your Chatbots with TruLens: A Complete Guide

Enhance Your LLM Applications with TruLens

In this tutorial, we will explore how to build and evaluate a contextual chatbot using Langchain and TruLens. The key focus will be on monitoring our bot's responses in order to assess critical moderation metrics—such as hate speech and maliciousness—while also optimizing performance and costs.

What is TruLens?

TruLens provides a robust set of tools designed for monitoring and refining the performance of LLM-based applications. Its evaluation capabilities allow users to measure the quality of inputs and outputs alongside internal processes. TruLens incorporates built-in feedback mechanisms for groundedness, relevance, and moderation assessment, while remaining flexible enough to accommodate custom evaluation needs.

Key Features:

• Essential instrumentation for various LLM applications, including question-answering and agent-based solutions.
• Comprehensive monitoring of usage metrics and metadata.
• Customizable feedback functions that analyze generated text and metadata.

Prerequisites

Before we begin, ensure you have the following ready:

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

Setting Up Your Environment

Let's create a virtual environment in a new folder:

mkdir my_chatbot
cd my_chatbot
conda create --name chatbot_env python=3.10  # Create a new conda environment
conda activate chatbot_env  # Activate the environment

Next, install the necessary libraries. Streamlit is an excellent choice for handling sensitive data securely:

pip install streamlit langchain trulens  # Install required libraries

You will need to manage your API keys securely using Streamlit's built-in file-based secrets management. Create a file named .streamlit/secrets.toml in your project directory and add your OpenAI API key and HuggingFace Access Token:

[general]
openai_api_key = "your_openai_api_key"
huggingface_api_key = "your_huggingface_key"

Building the Chatbot

Now, create a file called chatbot.py and start by importing the required libraries and loading the variables:

import streamlit as st
from langchain.llms import OpenAI
from trulens import TruChain
# Additional imports as needed

Chain Building

Build your LLM chain with a base prompt that can be improved over time:

llm_chain = OpenAI(temperature=0.5)

Integrating TruLens

Once your LLM chain is ready, use TruLens for evaluation:

trulens_chain = TruChain(llm_chain)

This setup allows you to track the relevance of responses and evaluate for any harmful outputs.

Creating the Chatbot UI

Utilize Streamlit's chat components to create an intuitive interface:

st.title("Chatbot with TruLens")
user_input = st.chat_input("Say something:")

if user_input:
    response = trulens_chain(user_input)
    st.chat_message("bot", response)

Launching Your Chatbot

To run your chatbot, execute the following command in your terminal:

streamlit run chatbot.py

A new browser tab will open at http://localhost:8501 displaying your chatbot. You can also view the TruLens dashboard at http://192.168.0.7:8502.

Evaluation and Improvement

Now that your chatbot is up and running, assess its performance using TruLens Eval:

Experiment with different prompt templates to gauge performance improvements. For instance:

prompt_template = "[New Template] ..."

Monitor moderation scores, as they will provide insight into the quality of the responses.

Testing Different Models

Ultimately, you can test various model configurations, such as switching from chatgpt-3.5-turbo to gpt-4 to see how it affects performance and costs:

model = "gpt-4"

This exploration helps in identifying the model best suited for your application.

Wrapping Up

We successfully built a chatbot integrated with TruLens, allowing for ongoing evaluation and enhancement of performance metrics. Understanding how specific configurations influence response quality, cost, and latency is essential for optimizing LLM applications.

With TruLens and Langchain, you have a powerful toolkit for creating reliable and efficient chatbots. For deployment, consider uploading your project to GitHub and connecting it with the Streamlit platform.

Thank you for following along!