This repository contains a Retrieval-Augmented Generation (RAG) chatbot built using LangChain and ChromaDB. The application is implemented in the app.py file and is deployed as a web application using Streamlit.
This project was developed as a hackathon submission to address a real-world problem faced by the Department of Technical Education, Government of Rajasthan. During the admission process, numerous engineering and polytechnic institutes receive a high volume of inquiries from students, parents, and other stakeholders. These queries range from admissions and fee structures to scholarships and placement opportunities.
The traditional methods of handling these inquiries—through phone, email, or in-person visits demand significant manpower. This project aims to streamline the process by creating an AI-powered chatbot that serves as a virtual assistant, available 24/7 to provide instant responses to a wide range of questions. This solution not only enhances accessibility to important information but also reduces the workload on college staff.
- Efficient Information Retrieval: The chatbot rapidly accesses and provides accurate information on topics such as admissions, fees, and scholarships using advanced NLP.
- Enhanced User Experience: The chatbot interface is intuitive and capable of understanding natural language, making it user-friendly.
- Reduced Workload: By automating responses to frequently asked questions, the chatbot reduces the need for human intervention, allowing staff to focus on more complex queries.
- Data Insights: The chatbot gathers valuable data from user interactions, helping the department optimize its services.
- Python 3.6 or later
- Streamlit
- LangChain
- ChromaDB
All required Python packages are listed in the requirements.txt file.
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Clone the repository (if applicable):
git clone https://github.com/Kunal-sharan/SSH_24_Techtronix.git cd SSH_24_Techtronix -
Install the required packages:
pip install -r requirements.txt
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Run the Streamlit application:
streamlit run app.py
This will start the Streamlit server, and you can interact with the RAG Chatbot by navigating to http://localhost:8501 in your web browser.
- LangChain: This is used to manage the logic of the chatbot, combining language models with external data sources.
- ChromaDB: This is a vector database that stores and retrieves relevant documents or information to augment the language model's responses.
- GROQ/Llama: This is the actual llm used which utilises the query response from the vector database and returned embeddings to give a smooth and effective response.
The chatbot leverages these tools to retrieve information from ChromaDB and use it to generate accurate and contextually relevant responses.
- app.py: The main script containing the Streamlit app and chatbot logic.
- requirements.txt: Lists the Python dependencies required to run the application.
To deploy the app on a hosting platform such as Streamlit Cloud, follow their documentation and ensure your repository contains both the app.py and requirements.txt files.
If you'd like to contribute to this project, feel free to fork the repository, make your changes, and submit a pull request.
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This is a Next.js project bootstrapped with create-next-app.
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun devOpen http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.
This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.
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- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
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The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details.
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