📚 Document Chat Assistant 🤖

🌟 Project Overview

Revolutionize document interaction with our cutting-edge Document Chat Assistant! This intelligent application empowers users to upload, analyze, and explore multiple documents through an intuitive, AI-powered chat interface.

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✨ Key Features

Feature	Description	🚀 Highlights
🗂️ Multi-Document Upload	Upload PDF and TXT files seamlessly	Process multiple documents simultaneously
🧠 Smart Document Processing	Advanced document chunking and embedding	Uses state-of-the-art NLP techniques
💬 RAG-Powered Interaction	Context-aware response generation	Combines retrieval and language models
💾 Persistent Document Storage	Efficient embedding management	Utilizes Chroma for quick information retrieval
🤝 Interactive Chat Interface	Natural language document exploration	Ask complex questions, get precise answers
🔄 Flexible Reset Options	Manage chat and database	Easy reset for new document sets

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🚀 Getting Started

Prerequisites

• 🐍 Python 3.8+
• 📦 pip package manager

Installation

# Clone the repository
git clone https://github.com/yourusername/document-chat-assistant.git

# Navigate to project directory
cd document-chat-assistant

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

Running the Application

# Launch Streamlit application
streamlit run app.py

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🔍 How It Works

graph TD
    A[Upload Documents] --> B[Preprocess Documents]
    B --> C[Create Embeddings]
    C --> D[Store in Chroma]
    D --> E[User Query]
    E --> F[Retrieve Relevant Context]
    F --> G[Generate AI Response]
    G --> H[Display Answer]

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Code Breakdown

Document Upload and Processing

File Uploader:

uploaded_files = st.file_uploader(
    "Upload Documents", 
    type=["pdf", "txt"], 
    accept_multiple_files=True
)

Document Processing Workflow:

def process_documents(uploaded_files):
    documents = []
    for file in uploaded_files:
        # Use appropriate loader based on file type
        if file.type == "application/pdf":
            loader = PyPDFLoader(file)
        else:
            loader = TextLoader(file)
        
        # Split documents into chunks
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=500,
            chunk_overlap=50
        )
        document_chunks = text_splitter.split_documents(loader.load())
        
        # Create embeddings
        embeddings = HuggingFaceEmbeddings()
        vectorstore.add_documents(document_chunks)
        
    return documents

RAG Prompt Template

prompt_template = """
You are a helpful assistant. Answer the question based strictly on the provided context.
Think step by step and provide a detailed, accurate response.

Context:
{context}

Question: {question}
Helpful Answer:"""

Key Technologies

• 🧠 AI/ML:
  • LangChain
  • HuggingFace Embeddings
  • ChatGroq
• 🌐 Web Framework: Streamlit
• 💾 Vector Database: Chroma

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🤝 Contributing

Interested in improving the Document Chat Assistant? We welcome contributions!

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📜 License

Distributed under the MIT License. See LICENSE for more information.

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**Created with ❤️ by AI Enthusiasts **