Custom Question Answering Chatbot using Langchain and Gemini AI

Custom Question Answering Chatbot using Langchain and Gemini LLM

• Introduction
• Project Overview
• Tools Used
• Getting Started
• Screenshot
• Use Case
• Future Improvements
• Contributors
• License

Introduction

This project implements a custom question answering chatbot using Langchain and Google Gemini Language Model (LLM). The chatbot is trained on industrial data from an online learning platform, consisting of questions and corresponding answers.

Project Overview

The project workflow involves the following steps:

1. Data Fine-Tuning: The Google Gemini LLM is fine-tuned with the industrial data, ensuring that the model can accurately answer questions based on the provided context.

2. Embedding and Vector Database: HuggingFace sentence embedding is utilized to convert questions and answers into vectors, which are stored in a vector database.

3. Retriever Implementation: A retriever component is developed to retrieve similar-looking vectors from the vector database based on the user's query.

4. Integration with Langchain RetrivalQA Chain: The components are integrated into a chain using Langchain RetrivalQA chain, which processes incoming queries and retrieves relevant answers.

5. User Interface: Streamlit is used to create a simple user interface, allowing users to input their questions and receive answers from the chatbot.

Tools Used

• Google Gemini LLM: Language model fine-tuned with industrial data.
• HuggingFace: Library used for sentence embedding.
• Langchain: Framework for building conversational AI systems.
• Streamlit: Library for building web-based user interfaces.

Getting Started

To run the project locally, follow these steps:

1. Clone the repository to your local machine.
2. Install the necessary dependencies listed in the requirements.txt file.
3. Run the Streamlit application by executing streamlit run app.py in your terminal.

Screenshot

Use Case

The custom question answering chatbot serves various purposes, including:

• Providing quick and accurate responses to user queries related to the topic covered by the industrial data.
• Enhancing user experience on online learning platforms by offering immediate assistance.
• Streamlining customer support processes by automating responses to frequently asked questions.

Future Improvements

• Incorporate additional pre-processing techniques to handle a wider range of user queries.
• Implement advanced language models for more accurate responses.
• Enhance the user interface with additional features for a better user experience.

Contributors

• Abhijit Maharana

License

This project is licensed under the MIT License.