Added documentation & Screenshot

Author: stacksapien

LICENSE

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+MIT License
+
+Copyright (c) 2023 [VISHAL VERMA (stacksapien)]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# QA with Your Documents
+
+This project provides an interactive Streamlit-based web application that allows users to upload PDF and CSV files, store their content in a vector database using LangChain and Chroma, and query the uploaded documents using OpenAI's LLMs (e.g., GPT-3.5-turbo). The app intelligently retrieves relevant information from the documents and provides citations for the sources.
+
+---
+
+## Features
+
+- **Upload and Process Documents**:
+  - Upload multiple PDF and CSV files.
+  - Extract content using LangChain's document loaders.
+- **Vector Database Storage**:
+
+  - Store document embeddings in a persistent Chroma vector database.
+
+- **Interactive Query System**:
+
+  - Ask questions about the uploaded documents.
+  - Retrieve answers along with source citations.
+
+- **Download Cited Files**:
+  - Easily download files cited in the query response.
+
+---
+
+## Technologies Used
+
+- **Streamlit**: For creating the web interface.
+- **LangChain**: For document processing and retrieval.
+- **Chroma**: As the vector database for storing embeddings.
+- **OpenAI API**: For LLM-based query answering.
+- **Python**: The core language for building the application.
+
+---
+
+## Installation
+
+### Prerequisites
+
+- Python 3.10 or later
+- OpenAI API Key
+
+### Steps
+
+1. **Clone the Repository**:
+
+   ```bash
+   git clone git@github.com:stacksapien/smart-doc-search.git
+   cd smart-doc-search
+   ```
+
+2. **Set Up a Virtual Environment**:
+
+   ```bash
+   python3 -m venv env
+   source env/bin/activate # On Windows: .\\env\\Scripts\\activate
+   ```
+
+3. **Install Dependencies**:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Configure Environment Variables**:
+   Create a file named `.env` in the root directory and add your OpenAI API key:
+
+   ```
+   OPENAI_API_KEY=your_openai_api_key
+   ```
+
+5. **Run the Application**:
+
+   ```bash
+   streamlit run app.py
+   ```
+
+6. **Access the App**:
+   Open your browser and navigate to:
+   ```
+   http://localhost:8501
+   ```
+
+---
+
+## Usage
+
+### Upload Files
+
+- Upload one or more PDF or CSV files using the file uploader.
+- Uploaded files are processed and stored in the `uploaded_files` directory.
+
+### Ask Questions
+
+- Enter your query in the text box provided.
+- The app retrieves relevant answers from the uploaded documents and displays the sources.
+
+### Download Cited Files
+
+- Files cited in the response are available for download.
+
+---
+
+## File Structure
+
+```
+smart-doc-search/
+│
+├── app.py # Main Streamlit application
+├── requirements.txt # List of Python dependencies
+├── .env # Environment variables (not included in Git)
+├── uploaded_files/ # Directory for storing uploaded files
+├── chromadb/ # Directory for persistent Chroma vector database
+└── README.md # Project documentation
+```
+
+---
+
+## Deployment
+
+### Deploy on AWS EC2
+
+1. Launch an Ubuntu EC2 instance and configure security groups to allow inbound traffic on ports 22 and 8501.
+2. SSH into the instance and set up Python, Streamlit, and the application as per the installation instructions.
+3. Use a process manager like `tmux` or `screen` to keep the app running.
+
+### Use Custom Domain
+
+- Configure a reverse proxy (e.g., Nginx) to serve the Streamlit app under your domain.
+- Enable HTTPS using Certbot for SSL certificates.
+
+---
+
+## Contributing
+
+Contributions are welcome! Please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch: `git checkout -b feature-name`
+3. Commit your changes: `git commit -m "Add feature-name"`
+4. Push to the branch: `git push origin feature-name`
+5. Submit a pull request.
+
+---
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+---
+
+## Acknowledgments
+
+- [LangChain](https://langchain.com)
+- [Streamlit](https://streamlit.io)
+- [Chroma](https://www.trychroma.com)
+- [OpenAI](https://openai.com)
+
+---
+
+## Issues
+
+If you encounter any issues or have feature requests, please [open an issue](https://github.com/stacksapien/smart-doc-search/issues).
+
+---
+
+## Screenshots
+
+### Upload Documents
+
+![Upload Documents](screenshots/1.png)
+
+### Query and Get Results
+
+![Query Results](screenshots/2.png)
+
+---
+
+## Author
+
+- **Vishal Verma** - [LinkedIn](https://www.linkedin.com/in/stacksapien)
+
+Feel free to reach out with any questions or feedback!