Enhance Customer Care with TruLens, MongoDB & LlamaIndex

Visionary Data: Leveraging TruLens with MongoDB & LlamaIndex

Hello everyone! I'm Sanchay Thalnerkar, an engineering student with a passion for creating in-depth and engaging tutorials. Today, we're going to build a Query and Feedback System for Customer Care. This system will help businesses efficiently manage customer queries and feedback, ensuring a smoother and more responsive customer service experience.

We'll be using a modern tech stack including TruLens, LlamaIndex, MongoDB Atlas, and Vector Index. Let's dive into what each component does and how they integrate into our project.

Step 1: Understanding the Tech Stack

TruLens

TruLens is a model interpretability library designed to provide insights into the inner workings of machine learning models. In our project, TruLens can be used to analyze how our model processes queries and feedback, helping us to understand and improve the decision-making process behind the scenes.

LlamaIndex

LlamaIndex is a high-performance vector search engine. It is built for efficiently searching through large volumes of data based on vector similarities. For our customer care system, LlamaIndex will allow us to quickly find similar customer queries and responses, enhancing the efficiency of our query handling.

MongoDB Atlas

MongoDB Atlas is a fully-managed cloud database service. It offers powerful, scalable storage solutions for modern applications. We will use MongoDB Atlas to store and manage customer queries and feedback, ensuring that data is easily accessible and secure.

Setting Up the Project Directory for the Query and Feedback System

Great, now that you understand the tech stack and have your environment ready, it's time to set up the project directory! Let’s start by creating the project directory and adding the necessary files for our Query and Feedback System. This setup will provide a structured foundation for developing our application.

Step 1: Create the Project Directory

First, you'll need to create a new directory for your project. Here's how you can do it:

1. Open your terminal or command prompt.
2. Navigate to the location where you want to create your project directory. This can be done with the cd command (change directory).
3. Create the directory:

mkdir CustomerCareSystem

4. Navigate into your new directory:

cd CustomerCareSystem

Step 2: Creating Project Files

In this section, we focus on the importance of modularization, detailed responsibilities of each file, and integrating TruLens into our feedback manager. Modularizing your code is not just about keeping it organized; it's about creating a maintainable, scalable, and collaborative environment. Let’s detail each component and explain how they work together to create a robust system.

Why Modularize?

Modularization in software development offers several key benefits:

• Clarity: Each module has a specific responsibility, which makes the system easier to understand at a glance.
• Isolation: Errors are contained within a module, which reduces the impact on the overall system.
• Extensibility: New functionality can be added with minimal modifications to existing code.
• Collaboration: Developers can work on separate modules simultaneously without interference.

Detailed File Structure and Responsibilities

Here's an overview of the main files and their responsibilities:

• config.py: Contains configuration settings.
• query_manager.py: Handles query-related operations.
• feedback_manager.py: Manages user feedback and integrates TruLens.
• setup.py: Manages dependencies and packaging.
• app.py: Entry point for the Flask application.
• data_manager.py: Interacts with MongoDB Atlas.
• eCommerce_FAQ_Chatbot_dataset.json: Stores sample queries and responses.

Integrating TruLens with FeedbackManager

The FeedbackManager class would use TruLens as follows:

1. Initialization: Incorporate TruLens during the initialization of the feedback manager.
2. Analysis: Use TruLens to analyze how the model processed the input that led to the response.
3. Reporting: Generate reports detailing the influence of various features.

Step 3: Open Your Project in an IDE

Open your preferred Integrated Development Environment (IDE) like PyCharm, Visual Studio Code, or any other that supports Python development.

1. Open the project folder you just created.

Step 4: Setting Up the Virtual Environment and Installing Dependencies

Now that we have our project structure in place, the next crucial step is to set up a virtual environment. This will isolate our project dependencies from the global Python environment.

Creating a Virtual Environment

1. Navigate to your project directory:

cd path/to/CustomerCareSystem

2. Create the virtual environment:

python -m venv venv

3. Activate the virtual environment:

.
v envin\activate  /* for Windows */

source venv/bin/activate  /* for macOS or Linux */

Installing Dependencies

With the virtual environment activated, install the libraries using pip. Here are the commands:

pip install flask pymongo trulens llama-index

Creating and Configuring the .env File

Create a new file named .env in the root of your project directory.

1. Add your OpenAI API Key:

OPENAI_API_KEY=your_openai_api_key_here

Configuring the Application with config.py

config.py initializes environment variables, API key management, and sets configurations for LlamaIndex.

Understanding data_manager.py

This script manages MongoDB connections, document management, and processes frequently asked questions from a JSON file.

Understanding the FeedbackManager Class

The FeedbackManager class integrates feedback mechanisms using TruLens to evaluate query handling efficiency.

Exploring the QueryManager Class

This class handles query operations using LlamaIndex, ensuring relevance and efficiency in response retrieval.

Setting Up MongoDB Atlas

1. Register or Log into MongoDB Atlas.
2. Create a new database cluster and configure it as necessary.
3. Retrieve your MongoDB connection URI and store it in your .env file as MONGO_URI.

Setting Up Vector Index in MongoDB Atlas

Creating a vector search index within your MongoDB collection is essential for enabling efficient document retrieval based on vector similarities.

Integrating Everything in app.py

This script manages the user interface and incorporates functionalities from the other modules, providing a seamless user experience.

Running the Application

Run your application using the command:

streamlit run app.py

System Walkthrough

Here's a showcase of the system's capabilities through a sequence of user interface snapshots.

1. System Initialization: Kick off the application.
2. Submitting Queries: User-friendly query input.
3. Query Response: Swift generation of responses.
4. Response Analysis: Understanding the decision-making process.
5. Dashboard Management: Efficient management tools.
6. App Leaderboard: Displays key performance metrics.
7. Evaluation Records: Detailed records from interactions.
8. Trace Details: Granular view of query handling.

With this modular and well-structured approach, you have a robust customer care system that adapts and grows with user needs. Don't forget to check out the code in the GitHub repository for full customization options!