https://github.com/phonhay103/web-search-chat-qa
Web search powered question answering systems
https://github.com/phonhay103/web-search-chat-qa
Last synced: 3 months ago
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Web search powered question answering systems
- Host: GitHub
- URL: https://github.com/phonhay103/web-search-chat-qa
- Owner: phonhay103
- License: mit
- Created: 2025-02-13T09:52:04.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-13T12:18:58.000Z (4 months ago)
- Last Synced: 2025-02-13T12:26:02.434Z (4 months ago)
- Language: Python
- Size: 76.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Search Q&A App
An interactive Q&A web application that combines web search, markdown scraping, and LLM responses.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Setup](#setup)
- [Configuration](#configuration)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Overview
This project enables users to perform comprehensive web searches and scrape markdown content. It leverages [Firecrawl](https://www.firecrawl.dev/) for searching and scraping, and integrates with Google’s generative AI models to generate contextual responses based on the scraped data.
## Features
- **Web Search & Scraping**: Query Firecrawl to retrieve markdown content.
- **Model Selection**: Choose from multiple LLM models to tailor responses.
- **Adjustable Temperature**: Fine-tune the randomness of LLM outputs.
- **Q&A Interface**: Pose questions based on the scraped content.
- **Chat History**: View the conversation history in an expandable panel.
- **Streamlit UI**: Enjoy a fast, user-friendly interface.
## Setup
1. **Requirements**: Ensure Python 3.12 is installed.
2. **Install Dependencies**:
- Using pip:
```
pip install -r requirements.txt
```
- Alternatively, using uv:
```
uv install -r requirements.txt
```
3. **Environment Variables**:
- Copy the example file:
```
cp .env.example .env
```
- Then update the following variables in `.env`:
- **FIRECRAWL_API_KEY**: Your Firecrawl API key.
- **GOOGLE_API_KEY**: Your Google API key.
## Configuration
- **LLM Models**: See the list of available models in `app.py` and modify as required.
- **Temperature Setting**: Adjust the slider in the UI for desired response variability.
- **Search Parameters**: Change the `top_k` value in the search form to control the number of results.
## Usage
Launch the app using:
```
streamlit run app.py
```
Then:
1. Enter a search query and set the number of results.
2. View scraped data and corresponding URLs.
3. Select the LLM model and set the temperature.
4. Ask a question based on the scraped data.
5. Review the response and access the chat history.
## Contributing
Contributions are welcome! Fork the repository and submit a pull request with detailed explanations of your changes.
## License
This project is licensed under the MIT License.