https://github.com/laavanjan/search-llm-deploy-streamlit
A powerful Streamlit application 🤖 that combines the capabilities of Large Language Models (LLMs) with web search 🌐 and academic research tools
https://github.com/laavanjan/search-llm-deploy-streamlit
agent ai arxiv-api chatbot duckduckgo-api groq-api langchain langsmith-tracing nlp search-engine streamlit-webapp wikipedia-api
Last synced: about 2 months ago
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A powerful Streamlit application 🤖 that combines the capabilities of Large Language Models (LLMs) with web search 🌐 and academic research tools
- Host: GitHub
- URL: https://github.com/laavanjan/search-llm-deploy-streamlit
- Owner: laavanjan
- License: gpl-3.0
- Created: 2025-02-23T04:38:13.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-02T16:21:16.000Z (about 2 months ago)
- Last Synced: 2025-03-02T16:33:37.963Z (about 2 months ago)
- Topics: agent, ai, arxiv-api, chatbot, duckduckgo-api, groq-api, langchain, langsmith-tracing, nlp, search-engine, streamlit-webapp, wikipedia-api
- Language: Python
- Homepage: https://search-llm-deploy-app-i9pedlvcogtb2wpnaeypde.streamlit.app/
- Size: 391 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🔍 Search-LLM-deploy-streamlit 🚀
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://www.python.org/downloads/release/python-390/)
[](https://streamlit.io/)
[](https://langchain.readthedocs.io/en/latest/index.html)
[](https://smith.langchain.com/)
[](YOUR_STREAMLIT_APP_URL)
**Search-LLM-deploy-streamlit** is a powerful Streamlit application 🤖 that combines the capabilities of Large Language Models (LLMs) with web search 🌐 and academic research tools 📚. It allows users to ask questions and receive comprehensive answers 💡, leveraging Groq's fast inference ⚡, DuckDuckGo search 🦆, Arxiv for academic papers 🔬, and Wikipedia for general knowledge 📖. This project demonstrates how to deploy an intelligent search agent using Streamlit and LangChain, with robust LangSmith tracing 🕵️♂️ for debugging and performance analysis.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Working Examples](#working-examples)
- [Live Demo](#live-demo)
- [Installation](#installation)
- [Usage](#usage)
- [How It Works](#how-it-works)
- [Technologies Used](#technologies-used)
- [LangSmith Tracing](#langsmith-tracing)
- [Environment Variables](#environment-variables)
- [License](#license)
- [Author](#author)
- [Contributing](#contributing)
- [Contact](#contact)
## Overview
This application provides an interactive chatbot interface 💬 for users to ask questions and receive answers based on information retrieved from the web 🌐, academic papers 📚, and Wikipedia 📖. It utilizes LangChain's agent capabilities to orchestrate the tools and Groq's LLM for generating responses 🧠. The integration of LangSmith enables detailed tracing 🕵️♂️ and debugging of the agent's actions, making it easier to understand and improve the application's performance 📈.
## Features
- **Interactive Chatbot Interface:** User-friendly Streamlit interface 💬 for conversational interaction.
- **Web Search Integration:** DuckDuckGo search 🦆 for real-time information retrieval 🌐.
- **Academic Paper Search:** Arxiv API 🔬 for searching and summarizing research papers 📚.
- **Wikipedia Integration:** Access to general knowledge 📖 through Wikipedia API.
- **Groq LLM Integration:** Utilizes Groq's "qwen-2.5-32b" model for fast and accurate responses ⚡.
- **LangSmith Tracing:** Comprehensive tracing 🕵️♂️ of agent actions for debugging and performance analysis 📈.
- **Streaming Responses:** Real-time feedback and response generation using `StreamlitCallbackHandler` 🔄.
- **Session State Management:** Maintains conversation history 📜 for a seamless user experience.
## Working Examples
| Image 1 | Image 2 | Image 3 |
|---------|---------|---------|
|  |  |  |
| What is Bayes theorem | Describe SriLanka | Describe University of Moratuwa |
| Image 4 | Image 5 | Image 6 |
|  |  |  |
| Who is the main character dragon ball | who wrote dragon ball | Explain the 3 laws of Newton |
## Live Demo
Try the live demo of the chatbot here:
[YOUR_STREAMLIT_APP_URL](https://search-llm-deploy-app-i9pedlvcogtb2wpnaeypde.streamlit.app/)
## Installation
1. **Clone the repository:**
```bash
git clone [https://github.com/laavanjan/Search-LLM-deploy-streamlit.git](https://www.google.com/search?q=https://github.com/laavanjan/Search-LLM-deploy-streamlit.git)
cd Search-LLM-deploy-streamlit
```
2. **Create a Conda virtual environment (recommended):**
```bash
conda create -n search-llm-env python=3.9 -y
conda activate search-llm-env
```
* Replace `search-llm-env` with your desired environment name if needed.
* Ensure you have Conda installed. If not, you can download it from [Anaconda's website](https://www.anaconda.com/products/distribution).
3. **Install dependencies:**
```bash
pip install -r requirements.txt
```
4. **Set up your `.env` file with the required API keys:**
```
GROQ_KEY=your_groq_api_key
LANGCHAIN_API_KEY=your_langchain_api_key
LANGCHAIN_PROJECT=your_langchain_project_name
```
## Usage
1. **Run the Streamlit app:**
```bash
streamlit run app.py
```
2. **Open the application in your web browser.**
3. **Enter your question in the chat input and press Enter.**
4. **View the generated response in the chat interface.**
## How It Works
1. The user enters a question in the chat input 💬.
2. The application uses LangChain's agent framework to determine the appropriate tools to use (DuckDuckGo 🦆, Arxiv 🔬, Wikipedia 📖).
3. The agent retrieves relevant information from the selected tools 🌐.
4. Groq's "qwen-2.5-32b" model processes the retrieved information and generates a response 🧠.
5. The response is displayed in the chat interface 🔄, with real-time streaming enabled by `StreamlitCallbackHandler`.
6. LangSmith traces the agent's actions 🕵️♂️, providing detailed insights into the process 📈.
## Technologies Used
- Python 🐍
- Streamlit 💧
- LangChain 🔗
- Groq (qwen-2.5-32b) ⚡
- DuckDuckGo Search API 🦆
- Arxiv API 🔬
- Wikipedia API 📖
- LangSmith 🕵️♂️
- dotenv 🔑
## LangSmith Tracing
This project utilizes LangSmith for comprehensive tracing 🕵️♂️ and debugging of the LangChain agent. To view the traces, ensure you have set the `LANGCHAIN_API_KEY` and `LANGCHAIN_PROJECT` environment variables.
**[Add LangSmith dashboard screenshot here! Replace with your image link or relative path.]**
## Environment Variables
- `GROQ_KEY`: Your Groq API key 🔑.
- `LANGCHAIN_API_KEY`: Your LangChain API key 🔑.
- `LANGCHAIN_PROJECT`: Your LangChain project name 🏷️.
## License
This project is licensed under the [GNU General Public License v3.0](https://www.gnu.org/licenses/gpl-3.0) - see the [LICENSE](LICENSE) file for details. 📝
## Author
- [laavanjan](https://github.com/laavanjan) 👤
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any bugs or feature requests. 🤝
1. Fork the repository. 🍴
2. Create your feature branch (`git checkout -b feature/YourFeature`). 🌿
3. Commit your changes (`git commit -am 'Add some feature'`). 💾
4. Push to the branch (`git push origin feature/YourFeature`). 🚀
5. Open a pull request. 📬
## Contact
- Email: [email protected] 📧
- LinkedIn: [www.linkedin.com/in/luckkumikanthan-laavanjan-3a0758170](www.linkedin.com/in/luckkumikanthan-laavanjan-3a0758170) 💼