https://github.com/bbs1412/qna_chatbot
Mini Project which uses Streamlit for UI and Langchain Ecosystem to implement LLM application.
https://github.com/bbs1412/qna_chatbot
chatbot groq langchain ollama openai python qanda streamlit
Last synced: 2 months ago
JSON representation
Mini Project which uses Streamlit for UI and Langchain Ecosystem to implement LLM application.
- Host: GitHub
- URL: https://github.com/bbs1412/qna_chatbot
- Owner: Bbs1412
- License: mit
- Created: 2025-04-07T17:39:15.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-08T08:16:39.000Z (about 1 year ago)
- Last Synced: 2025-04-10T00:58:35.678Z (about 1 year ago)
- Topics: chatbot, groq, langchain, ollama, openai, python, qanda, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 8.79 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# QnA ChatBot using Langchain 🔗 and Streamlit
A mini project that showcases a QnA-style LLM application built with Streamlit for UI and the Langchain ecosystem for LLM orchestration.
## Index:
- [Project Details](#-project-details)
- [Aim](#aim)
- [Features](#features)
- [Tech Stack](#tech-stack)
- [Steps to run](#-steps-to-run)
- [Future Improvements](#-future-improvements)
- [Contributions](#-contributions)
- [License](#-license)
- [Contact](#-contact)
## 🎯 Project Details:
### Aim:
To provide an interactive chatbot interface that leverages the power of large language models (LLMs), allowing seamless provider switching and real-time response handling.
It also serves as a way to apply my learnings from the Langchain ecosystem into a practical and useful project.
### Features:
+ 🔄 Switch between multiple LLM providers:
- OpenAI
- Groq
- Google
- Ollama (Local LLM)
+ 🧠 Dynamically lists available models based on the selected provider.
+ 🔎 Supports LangSmith tracing for better observability and debugging.
- 🛠️ **Built using LangChain tools:**
- `ChatPromptTemplate`
- `ChatMessageHistory` + History Trimmer
- LangChain Expression Language (LCEL)
- `RunnablePassthrough`, `RunnableWithMessageHistory`
- `StringOutputParser`
- 🚧 Change LLM provider and model **on the fly** with zero restarts.
### Screenshot:
- Choosing a provider:
- Dynamic model listing:
- Chatbot interface:
### Tech Stack:
- **Frontend:** Streamlit
- **LLM Orchestration:** LangChain
## 🚀 Steps to run:
1. **Clone the repository:**
```bash
git clone --depth 1 https://github.com/Bbs1412/QnA_ChatBot
```
1. **Set up virtual environment:**
```bash
cd QnA_ChatBot
python -m venv venv
venv\Scripts\activate
# or
source venv/bin/activate
pip install -r requirements.txt
```
1. **Set the environment variables *(Optional)*:**
- Either create a `.env` file in the root directory:
```env
OPENAI_API_KEY=your_openai_api_key
GROQ_API_KEY=your_groq_api_key
GEMINI_API_KEY=your_gemini_api_key
```
- Or use Streamlit's config (`.streamlit/secrets.toml`):
```toml
[OpenAI]
API_KEY = "your_openai_api_key"
[Groq]
API_KEY = "your_groq_api_key"
[Google]
API_KEY = "your_google_api_key"
```
1. Run the app:
```bash
streamlit run app.py
```
## 📈 Future Improvements:
- Add sliders to let users control `temperature`, `max output tokens` and `context size` dynamically.
- Ensure these parameters are only passed to the LLM when explicitly set by the user.
- This avoids overriding the default values defined by some providers.
- (e.g., certain Ollama models), which differ from standard defaults like `1`.
- Add `Google` provider.
## 🤝 Contributions:
Any contributions or suggestions are welcome!
## 📜 License:
[](LICENSE)
- This project is licensed under the `MIT License`.
- See the [LICENSE](LICENSE) file for details.
## 📧 Contact:
- **Email -** [bhushanbsongire@gmail.com](mailto:bhushanbsongire@gmail.com)