Welcome to ChatBot! 🚀🤖

This documentation provides an overview of a chatbot implementation using the OpenAI API key, Langchain, and Chainlit libraries. It includes information about the code structure and usage.Langchain library, which enables seamless interaction with the OpenAI API. It also utilizes the Chainlit library for defining prompt templates and managing conversations.

References:

• chainlit Documentation
• API Reference-OpenAI API Key

Features

• Information Retrieval: The chatbot can answer questions and provide information on a wide range of topics.
• Recommendations: It can offer suggestions and recommendations based on user preferences and input.
• Conversational Engagement: The chatbot is capable of engaging in conversations, providing thoughtful responses to user messages.
• Assistance: It can assist users with various tasks and provide guidance as needed.

Demo

Limitations:

• Dependency on OpenAI API: The chatbot relies on the OpenAI API for generating responses.

• Knowledge Boundaries: My knowledge is based on the information available up until September 2021. I may not be aware of recent events, developments, or emerging trends beyond that point

Feedback and Improvements:

Your feedback is valuable in helping us improve the ChatBot's performance and user experience. If you have any suggestions, encounter any issues, or want to report a bug, please let us know. Your input will be greatly appreciated! Thank you for choosing ChatBot! Let's get started, and feel free to ask me anything!

Code Usage

To use the chatbot implemented by this code, follow these steps:-

Step 1: Create a Python file

Create a new Python file named app.py in your project directory. This file will contain the main logic for your LLM application.

​Install all necessary libraries

pip install -r requirements.txt

Step 2: Write the Application Logic

In app.py, import the necessary packages and define a factory function decorated with langchain_factory that returns any LangChain instance. In this tutorial, we are going to use LLMChain to keep it simple. Here’s the basic structure of the script:

import os
from langchain import PromptTemplate, OpenAI, LLMChain
import chainlit as cl

os.environ["OPENAI_API_KEY"] = "YOUR_OPEN_AI_API_KEY"

template = """Question: {question}

Answer: Let's think step by step."""

@cl.langchain_factory(use_async=True)
def factory():
    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)

    return llm_chain

This function sets up an instance of LLMChain with a custom PromptTemplate. The LLMChain is responsible for generating responses based on the input provided by users.

Behind the scenes, Chainlit takes care of:

• Websocket connections
• Instantiating one LangChain instance per user session
• Pass the right callback handler to the LangChain instance
• Running the LangChain instance on user input
• Sending the output of the LangChain instance back to the user

Step 3: Run the Application

To start your LLM app, open a terminal and navigate to the directory containing app.py Then run the following command:

chainlit run app.py -w

The -w flag tells Chainlit to enable auto-reloading, so you don’t need to restart the server every time you make changes to your application. Your chatbot UI should now be accessible at http://localhost:8000

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