6 minutes to read - Mar 30, 2023

E-commerce chatbot build with Redis, Langchain and ChatGPT: a step-by-step tutorial

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E-commerce chatbot build with Redis, Langchain and ChatGPT: a step-by-step tutorial
In today's fast-paced digital world, e-commerce has become a significant part of our daily lives. With the vast number of products available online, it can be challenging for users to find the right product that suits their needs. To address this issue, we can use AI-powered chatbots to help users find the perfect product for their needs by leveraging natural language processing techniques.
Table of Contents
1Let’s start!
2Prerequisites
3Get the dataset CSV file
4Install the required Python packages
5Loading and Preprocessing the Data
6Creating the Redis Index and Loading Vectors
7Connect to our Redis DB
8Creating the Chatbot
9Go from user input string to product keywords
10Query our data
11Create the chatbot
12Conclusion

In this tutorial, we will walk you through the process of building an e-commerce chatbot that utilizes Amazon product embeddings, the ChatGPT API (gpt-3.5-turbo) and Langchain to create a seamless and engaging user experience. Our chatbot will take user input, find relevant products from a dataset, and present the information in a friendly and detailed manner. This not only enhances the user experience but also makes the process of finding products much more enjoyable.

Let’s start!

We will begin by loading and preprocessing the product data, followed by creating a Redis index and loading vectors into the index. Then, we will use Langchain to create an LLM chain and a prompt template for generating comma-separated product keywords based on the user input. Next, we will query the product embeddings in Redis using the generated keywords and retrieve the top results. Finally, we will present the retrieved products to the user in a nice and engaging way, allowing them to ask follow-up questions.

By the end of this tutorial, you will have a better understanding of how to build an CLI based e-commerce chatbot that can query Amazon product embeddings and generate user-friendly responses using Langchain. This will not only help improve the overall user experience in the e-commerce space but also pave the way for more advanced and personalized chatbot solutions in the future. So, let's get started and build our very own ecommerce chatbot!

Here is a quick example of how a conversation with our chatbot might look like:

Prerequisites

Get the dataset CSV file

Get the dataset CSV file from here.

Install the required Python packages

Before we start, make sure you have the following Python packages installed:

redis

pandas

sentence-transformers

openai

langchain

You can install them using the following commands:

Loading and Preprocessing the Data

First, we need to load the product data from a CSV file and truncate long text fields. We will use the first 1000 products with non-empty item keywords for our chatbot.

Creating the Redis Index and Loading Vectors

Now, we will create a function to load vectors into the Redis index and a function to create a flat index. We will use these functions later to index our product data.

Connect to our Redis DB

Next, we will create the Redis connection and load the vectors into the Redis index.

Creating the Chatbot

We will use the ChatGPT API (gpt-3.5-turbo) in combination with Langchain to create a response to our questions. If you want to dive deep and learn more about how to integrate the ChatGPT API to your other projects, we have dedicated tutorials for this.

Go from user input string to product keywords

We will use Langchain to create an LLM chain for our chatbot. First, we will create a prompt template to generate comma-separated product keywords from the user input.

Now, let's use our chain.

Query our data

We will then use the generated keywords to query the product embeddings in Redis and retrieve the top 3 results.

Create the chatbot

Finally, we will create another LLM chain to generate a nice response from the retrieved products and present it to the user. The user is also able to ask follow up questions. Note, that we added a memory to the chain to keep track of the chat history.

from langchain.memory import ConversationBufferMemory

template = """You are a chatbot. Be kind, detailed and nice. Present the given queried search result in a nice way as answer to the user input. dont ask questions back! just take the given context

{chat_history}

Human: {user_msg}

Chatbot:"""

prompt = PromptTemplate(

    input_variables=["chat_history", "user_msg"],

    template=template

)

memory = ConversationBufferMemory(memory_key="chat_history")

llm_chain = LLMChain(

    llm=OpenAI(model_name="gpt-3.5-turbo", temperature=0.8, openai_api_key="sk-9xxxxxxxxxxxxxxxxxxxx4"),

    prompt=prompt,

    verbose=False,

    memory=memory,

)

answer = llm_chain.predict(user_msg=f"{full_result_string} ---\n\n {userinput}")

print("Bot:", answer)

time.sleep(0.5)

while True:

    follow_up = input("Anything else you want to ask about this topic?")

    print("User:", follow_up)

    answer = llm_chain.predict(

        user_msg=follow_up

    )

    print("Bot:", answer)

    time.sleep(0.5)

Conclusion

In this tutorial, we have built an e-commerce chatbot that can query Amazon product embeddings using Redis and generate detailed and friendly responses with Langchain. We have demonstrated how to load and preprocess product data, create a Redis index, and load vectors into the index. We have also shown how to use Langchain to create an LLM chain for generating keywords and responses for user queries.

By utilizing the power of product embeddings and language models, our chatbot can efficiently search for relevant product recommendations and present them in an engaging and informative manner. This approach can be further extended to include more products, handle complex queries, and even provide personalized recommendations by incorporating user preferences.

We hope this tutorial has given you a good starting point for building your own e-commerce chatbot or implementing similar solutions in other domains. With the rapid advancements in AI technologies, there are endless possibilities for creating intelligent, engaging, and helpful chatbots that can improve user experience and drive business success.

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