Chatbot Implementations with Langchain + Streamlit

Langchain is a powerful framework designed to streamline the development of applications using Language Models (LLMs).
It provides a comprehensive integration of various components, simplifying the process of assembling them to create robust applications.

💬 Sample chatbot use cases

Here are a few examples of chatbot implementations using Langchain and Streamlit:

• Appointment Chatbot
  A chatbot that allow you to interact with your Google Calendar. This function requires Google Calendar API credential setup.

• Basic Chatbot
  Engage in interactive conversations with the LLM.

• Context aware chatbot
  A chatbot that remembers previous conversations and provides responses accordingly.

• Chatbot with Internet Access
  An internet-enabled chatbot capable of answering user queries about recent events.

• Chat with your documents
  Empower the chatbot with the ability to access custom documents, enabling it to provide answers to user queries based on the referenced information.

🖥️ Running locally

Prepare a .env file to pass the API keys

OPENAI_API_KEY="sk-xxxxxx"
TAVILY_API_KEY="tvly-xxxxxx" (Optional)

Enable Interaction with Google API in appointment chatbot (Optional) Setup your Google Developer Account for Google Calendar, download the credentials json to the root folder and name it credentials.json

# Run main streamlit app
$ streamlit run Home.py

💁 Contributing

Planning to add more chatbot examples over time. PRs are welcome.