Comprehensive Guide to Building a Chatbot with Custom Data Sources Powered by LlamaIndex

Introduction

In today’s fast-paced digital landscape, providing instant, accurate information to users is crucial for businesses. A chatbot powered by custom data sources can significantly enhance user experience, offering immediate answers tailored to specific queries. Using LlamaIndex and Streamlit, you can create a sophisticated chatbot that taps into your unique datasets. This guide will walk you through the entire process, from setting up your environment to deploying your chatbot.

Small Business Use Case

Consider a small e-commerce business that specializes in eco-friendly products. Customers frequently ask about product details, sustainability certifications, and shipping policies. A chatbot powered by custom data sources can:

Enhance Customer Service: Provide instant answers to common questions, reducing the need for human customer service agents and improving response times.
Increase Engagement: Keep customers engaged by offering immediate support and product information, leading to higher satisfaction and potentially increased sales.
24/7 Availability: Operate around the clock, ensuring that customer queries are addressed even outside of business hours.
Tailored Information: Use data specific to the business, such as product catalogs and FAQs, to give accurate and relevant responses.

By implementing such a chatbot, the business can improve customer interaction, boost efficiency, and provide a seamless shopping experience.

Building a Chatbot with Custom Data Sources Powered by LlamaIndex

Prerequisites

Before diving into the creation of your chatbot, ensure you have the following:

OpenAI API Key: Necessary for accessing GPT-3.5.
Python Environment: Ensure Python is installed on your system.
Dependencies: Install the essential libraries (streamlit, openai, llama-index, nltk).

Step 1: Configure App Secrets

Create a secrets.toml file to securely store your OpenAI API key:

openai_key = "<your OpenAI API key here>"

If you are using Git, make sure to add this file to your .gitignore to prevent accidental exposure of your API key.

Step 2: Install Dependencies

For local development, run the following command to install the necessary libraries:

pip install streamlit openai llama-index nltk

For deployment on Streamlit Community Cloud, create a requirements.txt file with the following contents:

streamlit
openai
llama-index
nltk

Step 3: Build the App

3.1. Import Libraries

Begin by importing the required libraries in your Python script:

import streamlit as st
from llama_index import VectorStoreIndex, ServiceContext, Document
from llama_index.llms import OpenAI
import openai
from llama_index import SimpleDirectoryReader

3.2. Initialize Message History

Set up your OpenAI API key and initialize the chat message history:

openai.api_key = st.secrets["openai_key"]
st.header("Chat with the Streamlit Docs")

if "messages" not in st.session_state:
    st.session_state.messages = [
        {"role": "assistant", "content": "Ask me a question about Streamlit's open-source Python library!"}
    ]

3.3. Load and Index Data

Store your documents in a folder named data. Use LlamaIndex’s SimpleDirectoryReader to load and index these documents:

@st.cache_resource(show_spinner=False)
def load_data():
    reader = SimpleDirectoryReader(input_dir="./data", recursive=True)
    docs = reader.load_data()
    service_context = ServiceContext.from_defaults(
        llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5, system_prompt="You are an expert on the Streamlit Python library...")
    )
    index = VectorStoreIndex.from_documents(docs, service_context=service_context)
    return index

index = load_data()

3.4. Create the Chat Engine

Set up the chat engine using LlamaIndex’s CondenseQuestionChatEngine:

chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)

3.5. Prompt for User Input and Display Message History

Prompt the user for input and display the message history:

if prompt := st.chat_input("Your question"):
    st.session_state.messages.append({"role": "user", "content": prompt})

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

3.6. Pass Query to Chat Engine and Display Response

Generate a response from the chat engine and display it:

if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = chat_engine.chat(prompt)
            st.write(response.response)
            message = {"role": "assistant", "content": response.response}
            st.session_state.messages.append(message)

Running the App

Locally: Run streamlit run <your_script_name>.py to start the application locally.
On Streamlit Community Cloud: Deploy your app by following Streamlit’s deployment guidelines.

By following these steps, you’ll create a functional chatbot that leverages custom data sources to provide tailored responses. For more detailed information and examples, you can check the original article.

Want to jump right in?

Check out the app: LlamaIndex Chat with Docs

View the code: GitHub Repository

FAQs

How do I secure my OpenAI API key?

Store your API key in a secrets.toml file and add this file to your .gitignore to prevent it from being committed to your repository.

What data format is required for the documents?

Your documents should be stored in a format that can be read by LlamaIndex’s SimpleDirectoryReader, such as plain text or markdown files.

Can I use other language models with LlamaIndex?

Yes, LlamaIndex is designed to work with various language models, but this guide specifically uses OpenAI’s GPT-3.5.

Is it possible to customize the chatbot’s behavior?

Absolutely! You can modify the system prompt, temperature, and other parameters in the ServiceContext to tailor the chatbot’s responses to your needs.

How can I deploy my chatbot on Streamlit Community Cloud?

Follow Streamlit’s deployment guidelines to upload your application and necessary files, such as requirements.txt, to Streamlit Community Cloud.

What are the benefits of using LlamaIndex?

LlamaIndex simplifies the process of creating and managing an index of your documents, allowing for efficient querying and integration with language models for generating responses.

Conclusion

Building a chatbot with custom data sources powered by LlamaIndex and Streamlit is an excellent way for small business owners to leverage AI technology. This guide has covered all the necessary steps, from setting up your environment to deploying your chatbot. By following these instructions, you can create a powerful tool that enhances user engagement and provides immediate, accurate information.