Creating an Interactive Chatbot With Streamlit Frontend, LangChain Orchestration, and Bedrock/Claude LLM Backend

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In the current landscape of AI-driven applications, chatbots are one of the most popular implementations. Chatbots are revolutionizing customer service, learning platforms, and personal assistants by leveraging powerful large language models (LLMs). By combining the strengths of LangChain for conversation orchestration, Bedrock or Claude as the LLM backend, and Streamlit for an interactive frontend, you can build a chatbot that is robust, scalable, and easy to deploy.

This article walks you through creating a chatbot with these tools. We’ll cover:

  • Setting up the frontend using Streamlit
  • Orchestrating the conversation with LangChain
  • Integrating Amazon Bedrock or Claude as the LLM backend
  • Sample code implementation
  • Testing and deploying the chatbot

Setting Up Streamlit as the Frontend

Streamlit is an open-source Python library that makes it simple to create custom web apps for machine learning and data science. Its minimalist API allows you to rapidly build and deploy applications. To begin with, we’ll create a simple Streamlit interface that captures user input and displays responses.

Installing Dependencies

First, install the required libraries for this project:

bash
pip install streamlit langchain boto3 amazon-bedrock
  • streamlit – for building the web interface.
  • langchain – for orchestrating conversations.
  • boto3 and amazon-bedrock – for interacting with AWS Bedrock or Claude LLM APIs.

Creating the Chat Interface

The Streamlit interface will include a text input box for user input, a button to submit the text, and a section to display the chatbot’s response. Here’s a simple implementation:

python

import streamlit as st

# Streamlit App
st.title(“Interactive Chatbot”)
st.write(“Welcome to the chatbot powered by LangChain and Amazon Bedrock/Claude”)

# Create an input box for user input
user_input = st.text_input(“You:”, “”)

# Create a button for submission
if st.button(“Send”):
if user_input:
# Process the user input and show response
st.write(“User:”, user_input)
st.write(“Chatbot:”, “Thinking…”) # Placeholder for the response

This basic Streamlit interface allows users to input their queries and sends it for processing.

LangChain for Conversation Orchestration

LangChain is a framework that helps developers build robust conversational AI applications by providing a variety of modules to integrate with different LLM backends and other services. For our chatbot, we will use LangChain to manage the conversation flow between the frontend (Streamlit) and the backend (Claude/Bedrock LLM).

Integrating LangChain

LangChain simplifies working with LLMs by allowing you to define chains of operations, including message routing, context management, and formatting.

Here’s a simple setup for LangChain in our chatbot:

python
from langchain.llms import Bedrock
from langchain.chains import SimpleChain# Setup the LLM connection (in this case Bedrock or Claude)
def get_llm_response(prompt):
# Initialize Bedrock API Client
llm = Bedrock(
access_key=‘AWS_ACCESS_KEY’,
secret_key=‘AWS_SECRET_KEY’,
region=‘us-east-1’, # Example region
service=‘claude’ # Using Claude as the LLM backend
)# Send the prompt to the LLM
response = llm.generate(prompt)
return response# Setup LangChain logic
def create_chain():
# This is a simple chain that takes a prompt and returns the LLM response
def simple_chain(input_text):
response = get_llm_response(input_text)
return responsereturn SimpleChain(simple_chain)

This function sets up a chain where a prompt is sent to Claude/Bedrock, and the response is returned. You can further customize the chain to handle more complex conversations, including multi-turn dialogues, context management, and error handling.

Connecting to Amazon Bedrock/Claude

Amazon Bedrock provides access to some of the most powerful LLMs, including Claude, which is a model designed for conversations. You can use the AWS SDK (boto3) to interact with Bedrock and Claude. Let’s look at how to set this up in the chatbot.

Configuring Bedrock in Python

Before connecting to Bedrock, ensure you have AWS credentials configured. You can use the boto3 library for this:

bash
pip install boto3 amazon-bedrock

Here’s how you can authenticate and interact with Bedrock’s Claude service:

python

import boto3

def get_bedrock_response(prompt):
# Initialize Boto3 client for Bedrock
client = boto3.client(‘bedrock’, region_name=‘us-east-1’)

# Define the parameters for generating a response
parameters = {
“text”: prompt,
“max_tokens”: 100
}

# Call the Bedrock Claude model
response = client.invoke_model(
modelId=‘claude-v2’,
body=parameters
)

# Parse and return the response
response_body = response[‘body’]
return response_body[‘generated_text’]

This code snippet connects to the Bedrock API, sends a prompt to Claude, and returns the generated response. You can replace claude-v2 with other available models in Bedrock depending on your needs.

Orchestrating the Full Chatbot

Now that we have the frontend, conversation orchestration, and LLM integration ready, let’s combine everything into a complete chatbot application.

python
import streamlit as st
from langchain.llms import Bedrock# Streamlit Chatbot Application
st.title(“Interactive Chatbot”)# Input Box for User Query
user_input = st.text_input(“You:”)if st.button(“Send”):
if user_input:
# Show user input
st.write(“User:”, user_input)# Get response from LLM using LangChain
def get_llm_response(prompt):
llm = Bedrock(
access_key=‘AWS_ACCESS_KEY’,
secret_key=‘AWS_SECRET_KEY’,
region=‘us-east-1’,
service=‘claude’
)
response = llm.generate(prompt)
return response# Fetch and display response
chatbot_response = get_llm_response(user_input)
st.write(“Chatbot:”, chatbot_response)

This is a working chatbot that sends user input to Claude via Bedrock and returns the chatbot’s response.

Testing and Deploying the Chatbot

Once your chatbot is functional, the next step is to test and deploy it. Streamlit makes it incredibly easy to deploy applications directly from your local machine to a server.

Running the Chatbot Locally

To run the chatbot locally, execute the following command in your terminal:

bash
streamlit run app.py

This will launch a local Streamlit app that you can access in your browser. Input a message into the text box, and you should see a response generated by Claude or the Bedrock-powered model.

Deploying to Streamlit Cloud

Streamlit also offers an easy-to-use cloud hosting solution. You can push your chatbot code to GitHub and connect your repository to Streamlit Cloud to deploy it.

  1. Push the code to a GitHub repository.
  2. Go to Streamlit Cloud, create an account, and link your GitHub repository.
  3. Deploy the app in a few clicks. You will get a shareable link to your live chatbot.

Conclusion

Building an interactive chatbot using Streamlit, LangChain, and Amazon Bedrock or Claude offers a powerful yet simple way to harness LLMs for real-time conversations. Streamlit provides an intuitive frontend, LangChain orchestrates the conversation flow, and Claude, as the LLM backend, generates coherent and context-aware responses.

This architecture is highly scalable and can be expanded by adding more features, such as context tracking, sentiment analysis, and even multi-language support. With the ease of deployment via Streamlit and the robustness of Bedrock’s LLMs, this chatbot framework can serve various use cases, from customer service to education.

The modularity provided by LangChain also ensures that you can swap out components without rewriting the entire system. Whether you’re using Claude or any other LLM, the overall architecture remains flexible and adaptable.

By combining these technologies, developers can build chatbots that are not only intelligent but also highly interactive, meeting the increasing demand for sophisticated conversational agents.