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---
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title: Chat-with-Multiple-Documents-Using-Streamlit-and-Watsonx
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emoji: 😻
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colorFrom: purple
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colorTo: pink
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sdk: streamlit
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sdk_version: 1.40.0
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app_file: app.py
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pinned: false
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---
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# Multi-Document Retrieval with Watsonx
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**A Streamlit-powered app for querying multiple document types using Watsonx and LangChain.**
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This project allows users to upload various file formats (PDFs, DOCX, CSV, JSON, YAML, HTML, etc.) and retrieve contextually accurate responses using Watsonx LLM models and LangChain. The app provides a seamless interface to perform retrieval-augmented generation (RAG) from uploaded documents.
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---
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## Features
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- **File Support**: Supports multiple file formats such as PDFs, Word documents, PowerPoint presentations, CSV, JSON, YAML, HTML, and plain text.
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- **Watsonx LLM Integration**: Utilize IBM Watsonx's LLM models for querying and generating answers.
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- **Embeddings**: Uses `HuggingFace` embeddings for document indexing.
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- **RAG (Retrieval Augmented Generation)**: Combines document-based retrieval with LLMs for accurate responses.
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- **Streamlit Interface**: Provides an intuitive user experience.
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---
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## Installation
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Follow these steps to clone and run the project locally:
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### Prerequisites
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1. **Python 3.8+** installed on your system.
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2. Install `pip` (Python package manager).
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3. An IBM Watsonx API key and Project ID.
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4. Install Git if not already installed.
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### Clone the Repository
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```bash
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git clone https://github.com/Abd-al-RahmanH/Multi-Doc-Retrieval-Watsonx.git
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cd Multi-Doc-Retrieval-Watsonx
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```
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### Install Dependencies
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1. Create a virtual environment (optional but recommended):
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```bash
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python -m venv env
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source env/bin/activate # On Windows: .\env\Scripts\activate
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```
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2. Install required Python packages:
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```bash
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pip install -r requirements.txt
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```
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### Set Environment Variables
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Create a `.env` file in the project directory with the following keys:
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```env
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WATSONX_API_KEY=<your_watsonx_api_key>
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WATSONX_PROJECT_ID=<your_watsonx_project_id>
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```
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### Run the App
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1. Start the Streamlit app by running:
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```bash
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streamlit run app.py
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```
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2. Open the URL displayed in your terminal (usually [http://localhost:8501](http://localhost:8501)) to access the app.
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---
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## How to Use
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1. **Upload Documents**: Drag and drop supported files (e.g., PDFs, DOCX, JSON) in the app sidebar.
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2. **Select Model and Parameters**: Choose a Watsonx model and configure settings like output tokens and decoding methods.
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3. **Ask Questions**: Enter queries in the chat input to retrieve answers based on the uploaded document.
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---
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## Project Structure
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```plaintext
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Multi-Doc-Retrieval-Watsonx/
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├── app.py # Main application file
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├── requirements.txt # Python dependencies
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├── README.md # Project documentation
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└── .env # Environment variables (not included in repo, create manually)
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```
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---
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## Dependencies
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- **Streamlit**: For building the user interface.
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- **LangChain**: For document retrieval and RAG implementation.
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- **HuggingFace Transformers**: For embedding and vector representation.
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- **Watsonx Foundation Models**: For querying and text generation.
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- **Various Python Libraries**: For file handling, including `pandas`, `python-docx`, `python-pptx`, and more.
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---
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## Contributing
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We welcome contributions! If you'd like to improve this project:
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1. Fork the repository.
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2. Create a feature branch: `git checkout -b feature-name`.
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3. Commit your changes: `git commit -m 'Add a new feature'`.
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4. Push to the branch: `git push origin feature-name`.
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5. Open a Pull Request.
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---
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## License
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
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---
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