🌟 Multimodal Document Processing RAG with LangChain 🌟

This project is a Streamlit application for processing multimodal documents and querying a Milvus database. It leverages cutting-edge tools like LangChain, transformers, EasyOCR, and others for processing, storing, and querying text extracted from various file types. 🚀

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✨ Features

🗂️ Upload File Processing:

• Supports multiple file types: audio, video, image, text, csv, yaml, json, docx, and pdf.
• Extracts text content using:
  • 🔊 Audio: speech_recognition and pydub.
  • 🎥 Video: Custom extraction logic.
  • 🖼️ Image: EasyOCR.
  • 📄 Text/Logs/Documents: LangChain loaders.

🛠️ Milvus Integration:

• 🗃️ Stores processed document embeddings for similarity-based querying.
• 🧠 Utilizes HuggingFaceEmbeddings for generating vector representations.

🔍 Query Interface:

• Natural language query interface.
• Implements a Retrieval-Augmented Generation (RAG) pipeline for AI-driven responses.

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🛠️ Installation

🔧 Prerequisites

• Python 3.8+
• pip or conda package manager
• CUDA-compatible GPU (optional, for faster processing)

📥 Fork and Clone the Repository

1. Fork the repository: Navigate to RAG-Architecture GitHub Repository and click Fork.

2. Clone the forked repository:

   git clone https://github.com/<your-username>/RAG-Architecture.git
   cd RAG-Architecture
   

📦 Install Dependencies

pip install -r requirements.txt

🚀 Usage

🖥️ Start the Application

Run the Streamlit app:

streamlit run app.py

🔄 Application Modes

📤 Upload Files:

• Upload a file to process and store its content in Milvus.
• Displays extracted content and stores embeddings in the database.

❓ Query:

• Enter a question to search and retrieve relevant information from the Milvus database.
• Returns AI-generated responses using LangChain's RAG pipeline.

📁 File Structure

## 📁 **File Structure**

```bash
project/
│
├── app.py                      # 🎯 Main Streamlit application
├── requirements.txt            # 📦 Python dependencies
├── utils/                      # 🛠️ Utility modules
│   ├── audio_utils.py          # 🎵 Audio file processing
│   ├── video_utils.py          # 📹 Video file processing
│   ├── image_utils.py          # 🖼️ Image file processing
│   ├── document_loaders.py     # 📜 Document processing loaders
│   ├── milvus_client.py        # 🗄️ Initializes Milvus database
│
├── milvus_database.db          # 🗃️ Milvus database file (auto-created)
├── Dataset                     # 📂 Folder to store datasets
├── Images                      # 📁 Folder for storing images

🔑 Key Modules

app.py

🧩 Main application logic

• Handles file uploads, document processing, and querying.

utils/

• 🎵 Audio: Splits audio into chunks and transcribes text.
• 📹 Video: Processes video files to extract and analyze content.
• 🖼️ Image: Uses EasyOCR for extracting text.
• 📜 Logs/Documents: Processes CSV, YAML, JSON, and PDF files into structured LangChain documents.

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🛠️ Example Workflow

📤 Uploading a File

1. Select "Upload Files" mode.
2. Upload a file (e.g., example.pdf).
3. Process and store the file in the database.

❓ Querying the Database

1. Select "Query" mode.
2. Enter a natural language question.
3. Receive a concise, fact-based response.

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🌟 Future Improvements

• 🔍 Add more advanced query capabilities.
• 📂 Enhance support for additional file types and embeddings.
• ⚡ Improve scalability for larger datasets.

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📜 License This project is licensed under the MIT License.

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🙌 Acknowledgments

• 🌐 Streamlit for the interactive UI.
• 📚 LangChain and Milvus for document processing, retrieval and vector db.
• 🤖 Transformers for embedding generation.
• 🖼️ EasyOCR for image text extraction.
• 📹 Moviepy for video processing.