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E-commerce recommender chatbot with RAG using LlamaIndex and Mistral-7B

Discover the benefits, challenges, and best practices for creating personalized and efficient shopping experiences with chatbots, leveraging innovative solutions like LlamaIndex and the Mistral-7B model for enhanced customer engagement and loyalty.
Usha Rengaraju
Created on January 18|Last edited on December 20
Providing personalized and seamless customer experiences is a cornerstone of modern e-commerce success. Customers expect their favorite brands to anticipate their needs, offer tailored recommendations, and provide instant assistance—all while maintaining a smooth shopping journey. Enter e-commerce recommendation chatbots, powered by advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), which revolutionize customer interactions and drive engagement.
In this article, we’ll walk through building a sophisticated e-commerce chatbot using the Mistral-7B model and LlamaIndex. We’ll cover benefits, challenges, and best practices—and dive into a tutorial to help you deploy your own chatbot.
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Table of Contents

E-Commerce Recommendation ChatbotsBenefits of E-Commerce Recommendation Chatbots with LLMsDevelopment challenges and considerationsBest practices for developing e-commerce recommendation chatbotsUnderstanding LlamaIndex, Mistral-7B and RAGWhat can LlamaIndex do for you?Mistral modelWhat makes Mistral tick?Understanding RAG (Retrieval-Augmented Generation)Key Components of RAGSteps to Build an e-commerce recommendation chatbot with RAGStep 1: Define your recommendation goalsStep 2: Data collectionStep 3: Implement the retrieval moduleStep 4: Select a generative modelStep 5: Build the integration layerStep 6: Train and fine-tuneStep 7: User interaction designStep 8: Incorporate contextual understandingStep 9: Implement multilingual supportStep 10: Continuous monitoring and improvementTutorial: Building our e-commerce recommendation chatbotUsing Weights & Biases to supercharge developmentStep-by-step tutorial: Build your chatbotThe datasetThe CodeResultsReferences


Before we dive in, I first want to cover some details about best practices and the tools selected. If you want to jump straight to the tutorial, click here.
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E-Commerce Recommendation Chatbots

E-commerce recommendation chatbots are intelligent virtual assistants designed to analyze user preferences, shopping behaviors, and past interactions. Using advanced AI models, these chatbots deliver personalized product recommendations in real time, creating a seamless and interactive shopping experience.

Benefits of E-Commerce Recommendation Chatbots with LLMs

Personalization

LLMs excel at understanding user preferences and behaviors. Leveraging this capability, e-commerce recommendation chatbots can deliver highly personalized product recommendations tailored to individual customers, significantly enhancing their shopping experience.

Natural Language Understanding

LLMs possess a high degree of natural language understanding, allowing chatbots to comprehend user queries and requests conversationally. This leads to more engaging and user-friendly interactions, fostering a sense of ease and comfort for customers.

Improved eustomer Engagement

Chatbots powered by LLMs can engage users in dynamic and meaningful conversations. By understanding context and providing relevant recommendations, these chatbots keep users engaged throughout the shopping journey, increasing the likelihood of successful conversions.

24/7 Availability

E-commerce recommendation chatbots can operate round the clock, offering assistance and recommendations at any time. This 24/7 availability enhances customer service and ensures that users receive immediate responses to their queries.
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Development challenges and considerations

Building an effective chatbot is no easy feat. Developers must address:
  • Data Privacy and Security: Ensuring customer data is protected during interactions.
  • Fine-Tuning Models: Adapting pre-trained models like Mistral-7B to specific e-commerce needs.
  • Seamless Integration: Connecting the chatbot with existing e-commerce platforms.

Best practices for developing e-commerce recommendation chatbots

  • User Onboarding: Implement a user-friendly onboarding process to gather initial information about the user's preferences and shopping history. This data will serve as the foundation for delivering personalized recommendations.
  • Contextual Understanding: Train the chatbot to understand and maintain context throughout the conversation. This ensures that recommendations are relevant and aligned with the user's evolving needs and preferences.
  • Multilingual Support: Consider incorporating multilingual support to cater to a diverse customer base. LLMs are capable of understanding and generating text in multiple languages, contributing to a more inclusive user experience.
  • Continuous Monitoring and Improvement: Regularly monitor the chatbot's performance and gather user feedback to identify areas for improvement. Implement an iterative development approach, incorporating user insights to enhance the chatbot's recommendation capabilities over time.

Understanding LlamaIndex, Mistral-7B and RAG

In this tutorial, we will be using Llamaindex to create a chatbot. LlamaIndex is an open-source framework that organizes and structures LLM knowledge, making it accessible and usable for building intelligent applications.
No more wading through mountains of text – LlamaIndex indexes and retrieves relevant information efficiently, like a well-trained alpaca fetching your favorite scarf.

What can LlamaIndex do for you?

  • Supercharge your search: Forget keyword fumbling. LlamaIndex understands the meaning of your queries and retrieves the most relevant information, even if it's hidden in complex text. Think of it as an alpaca finding a specific needle in a haystack (but much faster).
  • Build smarter chatbots: Chatbots powered by LlamaIndex go beyond scripted responses. They can engage in meaningful conversations, access real-world data, and even generate creative text formats.
  • Power next-level personalization: With LlamaIndex, your applications can tailor their responses to each user's specific needs and preferences. Imagine an alpaca learning your favorite food and bringing you a basket of it every morning – that's the level of personalization we're talking about.
  • Boost your productivity: Tired of repetitive tasks? LlamaIndex can automate tasks like data extraction and summarization.

Mistral model

In the grand arena of large language models (LLMs), size isn't everything. Enter Mistral, the nimble contender proving that brain over brawn reigns supreme. This 7.3 billion parameter marvel is shaking up the field with its punchy performance and charming efficiency. Think of it as the David to Goliath, slaying performance benchmarks with a slingshot of innovative tech.

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What makes Mistral tick?

  • Goldilocks Efficiency: While other models bulk up at 13B or 34B parameters, Mistral strikes the perfect balance. It packs an impressive punch for its size, outperforming even larger models on key tasks like reasoning and natural language processing (NLP). Think of it as a miniature athlete, effortlessly outrunning lumbering giants.
  • Innovation Under the Hood: Mistral boasts a unique architectural blend of grouped-query and sliding window attention mechanisms. This fancy footwork allows it to grasp information contextually and seamlessly navigate long sequences, like a master dancer effortlessly gliding through a complex routine.
  • Open-source Champion: Unlike some guarded models, Mistral embraces transparency. Its code is open-source, allowing anyone to peek under the hood and contribute to its evolution. This open-door policy fosters a thriving community of developers and researchers, accelerating its growth and potential.
  • Chatty Charmer: Forget robotic responses – Mistral can hold a decent conversation. Its chat-fine-tuned version excels at understanding conversational nuances and generating engaging dialogue, making it the perfect companion for chatbots and virtual assistants who don't want to put you to sleep.

Understanding RAG (Retrieval-Augmented Generation)

In the ever-evolving landscape of natural language processing (NLP), the emergence of powerful models like RAG (Retrieval-Augmented Generation) has opened new frontiers in text generation and comprehension. RAG combines the strengths of retrieval-based methods and generative models, offering a unique approach to understanding and producing human-like text.
RAG is a state-of-the-art NLP model that integrates the strengths of retrieval-based approaches with the creative capabilities of generative models. Developed to handle complex language tasks, RAG excels at information retrieval while maintaining the ability to generate coherent and contextually relevant responses.

Key Components of RAG

Retrieval Module

The retrieval module in RAG is responsible for efficiently searching through large amounts of data, such as documents or passages, to find relevant information. It employs advanced techniques like dense vector representations to capture semantic similarity and retrieve contextually relevant content.

Generative Module

The generative module is the creative aspect of RAG. It takes the retrieved information and synthesizes it into human-like responses. This module is typically based on powerful language models like GPT (Generative Pre-trained Transformer) or similar architectures.

Integration Layer

RAG's unique strength lies in its ability to seamlessly integrate the retrieval and generative modules. The integration layer ensures that the retrieved information is effectively utilized to enhance the quality and relevance of the generated responses.
RAG, with its blend of retrieval-based techniques and generative capabilities, is particularly well-suited for building recommendation systems. It can efficiently retrieve relevant product information and seamlessly generate personalized recommendations, offering a powerful solution for enhancing the shopping experience.

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Steps to Build an e-commerce recommendation chatbot with RAG

Step 1: Define your recommendation goals

Clearly outline the goals of your recommendation chatbot. Are you focusing on suggesting products based on user preferences, upselling, cross-selling, or a combination of these? Defining clear objectives will guide the development process.

Step 2: Data collection

Gather a diverse and representative dataset of product information, user preferences, and historical purchasing data. Ensure that the dataset encompasses a wide range of products and reflects the preferences of your target audience.

Step 3: Implement the retrieval module

Develop a robust retrieval module that can efficiently search through your database. Use techniques like dense vector representations (e.g., embeddings) to capture semantic similarity and retrieve relevant product information based on user queries.

Step 4: Select a generative model

Choose a generative model for the creative aspect of your recommendation chatbot. RAG is compatible with various generative models; consider using a pre-trained language model like GPT (Generative Pre-trained Transformer) and fine-tune it on your specific e-commerce task.

Step 5: Build the integration layer

Create the integration layer that seamlessly combines the output of the retrieval module with the generative model. This layer ensures that the retrieved product information is effectively utilized to generate coherent and contextually relevant product recommendations.

Step 6: Train and fine-tune

Train your recommendation chatbot on the prepared dataset. Fine-tune both the retrieval and generative components to optimize performance for your specific e-commerce task. Pay attention to metrics such as recommendation accuracy, user engagement, and conversion rates.

Step 7: User interaction design

Design a user-friendly conversational interface for your chatbot. Consider the user experience (UX) to ensure smooth interactions. Allow users to ask questions, provide feedback, and navigate through product recommendations effortlessly.

Step 8: Incorporate contextual understanding

Train your chatbot to understand and maintain context throughout the conversation. Contextual understanding is crucial for generating recommendations that align with the evolving preferences and queries of the user.

Step 9: Implement multilingual support

Consider incorporating multilingual support to cater to a diverse user base. RAG's language capabilities can be harnessed to provide recommendations in different languages, enhancing the inclusivity of your e-commerce chatbot.

Step 10: Continuous monitoring and improvement

Regularly monitor the performance of your recommendation chatbot. Gather user feedback and leverage analytics to identify areas for improvement. Implement iterative updates to enhance the chatbot's recommendation capabilities over time.

Tutorial: Building our e-commerce recommendation chatbot

Now let's jump to making use of this technology and building an e-commerce recommender chatbot.

Using Weights & Biases to supercharge development

Weights & Biases offers a powerful platform for tracking, visualizing, and optimizing your chatbot’s performance.
By integrating Weights & Biases, you can:
  • Simplify Debugging: Gain clear insights into your chatbot’s behavior and identify issues faster.
  • Enhance Iterative Training: Monitor changes across experiments to fine-tune model performance effectively.
  • Collaborate Seamlessly: Share interactive dashboards with your team to streamline the development process.
By leveraging these capabilities, you ensure your chatbot delivers reliable and engaging experiences to customers.

Step-by-step tutorial: Build your chatbot

If you haven't already, you'll want to sign up for a free W&B account here.
Once you've done that you can install it: 
pip install wandb
Then login using
wandb login

The dataset

In this tutorial, we fine-tune the Mistral model using the cnn_dailymail dataset and use that model for the video summarization task.
Use the following code to log the dataset with W&B which is how we'll be able to easily visualize what's going on.
table = wandb.Table(data=df)
run.log({'data':table})



The Code

Following is the implementation.
from llama_index.embeddings import HuggingFaceEmbedding
from llama_index.prompts import PromptTemplate
import torch
from llama_index.llms import HuggingFaceLLM
from llama_index import VectorStoreIndex, ServiceContext, download_loader
import accelerate
from llama_index.memory import ChatMemoryBuffer


MAX_TEXT_LENGTH=1024  # Maximum num of text characters to use

def auto_truncate(val):

    """Truncate the given text."""

    return val[:MAX_TEXT_LENGTH]

# Load Product data and truncate long text fields

all_prods_df = pd.read_csv("product_data.csv", converters={

    'bullet_point': auto_truncate,

    'item_keywords': auto_truncate,

    'item_name': auto_truncate,

    'material': auto_truncate

})

all_prods_df['item_keywords'].replace('', None, inplace=True)
all_prods_df.dropna(subset=['item_keywords'], inplace=True)
all_prods_df.reset_index(drop=True, inplace=True)

all_prods_df['material'].replace('', None, inplace=True)
all_prods_df.dropna(subset=['material'], inplace=True)
all_prods_df.reset_index(drop=True, inplace=True)
# Num products to use (subset)
NUMBER_PRODUCTS = 2500

# Get the first 2500 products
product_metadata = (
    all_prods_df
     .head(NUMBER_PRODUCTS)
     .to_dict(orient='index')
)

product_metadata[0]
texts = [
    v['item_name'] for k, v in product_metadata.items()
]
metadatas = list(product_metadata.values())
index_name = "products"

query_wrapper_prompt = PromptTemplate(
    "Below is an instruction that describes a task. "
    "Write a response that appropriately completes the request.\n\n"
    "### Instruction:\n{query_str}\n\n### Response:"
)llm = HuggingFaceLLM(
    context_window=2048,
    max_new_tokens=256,
    generate_kwargs={"temperature": 0.25, "do_sample": False},
    query_wrapper_prompt=query_wrapper_prompt,
    tokenizer_name="mistralai/Mistral-7B-v0.1",
    model_name="mistralai/Mistral-7B-
v0.1",
    tokenizer_kwargs={"max_length": 2048},
    model_kwargs={"torch_dtype": torch.float16}
)

service_context = ServiceContext.from_defaults(chunk_size=512, llm=llm, embed_model="local")
JsonDataReader = download_loader("JsonDataReader")
loader = JsonDataReader()
documents = loader.load_data(product_metadata)

index = VectorStoreIndex.from_documents(
    documents, service_context=service_context
)
memory = ChatMemoryBuffer.from_defaults(token_limit=3900)

chat_engine = index.as_chat_engine(
    chat_mode="condense_plus_context",
    memory=memory,
    context_prompt=(
        "You are a friendly, conversational retail shopping assistant. Use the following context including product names, descriptions, and keywords to show the shopper whats available, help find what they want, and answer any questions"
        "It's ok if you don't know the answer."
        "Here are the relevant documents for the context:\n"
        "{context_str}"
        "\nInstruction: Use the previous chat history, or the context above, to interact and help the user."
    ),
    verbose=False,
)

response = chat_engine.stream_chat("gold-plated earrings")
for token in response.response_gen:
    print(token, end="")


Results

Now this ecommerce recommendation bot is ready. When it is given a prompt for any product, it gives all the details for the particular product. For example here we give it in input:
'gold plated earings'
The model returns:
Assistant: Absolutely! Here are some details about our gold-plated earrinngs
- Design: Our gold-plated earrings feature a delicate and elegant design that is perfect for any occasion. The earrings are made from high-quality gold plating, which gives them a shiny and luxurious finish.
- Material: Our gold-plated earrings are made from 14k gold, which means they are both durable and lightweight. The gold plating is carefully applied to the earrings to ensure that they remain shiny and glossy over time.
- Color: Our gold-plated earrings come in a variety of colors, including yellow, rose, and white gold. The color of the gold plating may vary slightly from piece to piece, but the overall effect is always beautiful and elegant.


References

Tags: Articles, LLM, Community Posts, Recommender Systems

Created with ❤️ on Weights & Biases.

https://wandb.ai/tensorgirl/ecommercerecommendation/reports/E-Commerce-Recommendation-Chatbot-with-Retrieval-Augmented-Generation-using-Llamaindex-and-Mistral-7B--Vmlldzo2NTU3MDYw
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