Build an E-Commerce Chatbot with Redis, Langchain & ChatGPT

Why Use AI in Your E-Commerce Business?

In today's fast-paced digital world, e-commerce has become a significant aspect of our daily lives. With the vast number of products available online, users often find it challenging to identify the ideal products that meet their needs. This issue can be effectively addressed through the implementation of AI-powered chatbots, which leverage natural language processing techniques to assist users in locating suitable products.

Building an E-Commerce Chatbot

This tutorial will guide you through constructing an e-commerce chatbot utilizing Amazon product embeddings, the ChatGPT API (gpt-3.5-turbo), and Langchain, ultimately creating a seamless and engaging user experience. Our chatbot will take user input, extract relevant products from a dataset, and present that information in an engaging and descriptive manner. This not only enhances the user experience but also transforms the process of product discovery into an enjoyable activity.

Getting Started

Let’s begin by loading and preprocessing our product data, creating a Redis index, and loading vectors into that index. Following this, we will use Langchain to establish a large language model (LLM) chain and a prompt template for generating comma-separated product keywords based on user input. Next, we will query the product embeddings in Redis using the generated keywords and retrieve the top results.

Sample Conversation

Here's a glimpse of how a conversation with our chatbot might look:

User: I need a new laptop for gaming.

Chatbot: Sure! Here are some laptops that are great for gaming: 

1. ASUS ROG Zephyrus G14

2. Acer Predator Helios 300

3. Razer Blade 15

Prerequisites

Get the dataset CSV file: You can download the dataset CSV file from here.
Install the required Python packages: Ensure the following packages are installed:

redis
pandas
sentence-transformers
openai
langchain

You can install them using the following command:

pip install redis pandas sentence-transformers openai langchain

Loading and Preprocessing the Data

First, load the product data from the CSV file and truncate any overly long text fields. We will focus on the first 1000 products with non-empty item keywords for our chatbot.

Creating the Redis Index and Loading Vectors

Create a function to load vectors into the Redis index and establish a flat index. These functions will be utilized later to index our product data.

Connecting to Redis DB

Create your Redis connection and load the vectors you’ve prepared into the Redis index.

Creating the Chatbot

Utilize the ChatGPT API (gpt-3.5-turbo) alongside Langchain to formulate responses to user inquiries. If you wish to delve deeper into integrating the ChatGPT API into your projects, dedicated tutorials are available.

Generating Keywords from User Input

Using Langchain, create an LLM chain for our chatbot. Begin by drafting a prompt template that will generate comma-separated product keywords based on user input.

Querying Our Data

Once keywords are generated, utilize them to query the product embeddings stored in Redis and retrieve the top 3 results.

Finalizing the Chatbot

Create another LLM chain to craft a refined response based on the products retrieved, presenting this information to the user in an engaging manner. Users will also be able to ask follow-up questions. Optionally, a memory component can be added to maintain the chat history.

Conclusion

This tutorial has guided you through the process of building an e-commerce chatbot capable of querying Amazon product embeddings utilizing Redis and generating detailed and user-friendly responses with Langchain. By loading and preprocessing product data, creating a Redis index, and using Langchain to facilitate keyword and response generation for user queries, we've shown how to create an effective chatbot solution.

Utilizing product embeddings and language models enables the chatbot to efficiently find relevant product recommendations and present them engagingly. This approach can be enhanced to cover more products, tackle complex queries, and potentially offer personalized recommendations based on user preferences.

We hope this guide serves as a valuable starting point for constructing your own e-commerce chatbot or implementing similar solutions in various domains. With advancements in AI technologies ongoing, there are limitless opportunities to create intelligent, interactive, and helpful chatbots that enhance user experiences and facilitate business success.

You can find the complete source code for this tutorial on GitHub. Join our AI Hackathons to apply your newfound skills and build the future with AI!