removed readme

README.md

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-# 🧠 Knowledge Management RAG System
-
-A powerful, local-first Retrieval-Augmented Generation (RAG) system designed to manage your personal knowledge base. Built with a modern client-server architecture, it allows you to upload documents, persist them in a vector database, and chat with your data using Google's Gemini models.
-
-![Python](https://img.shields.io/badge/Python-3.10%2B-blue)
-![FastAPI](https://img.shields.io/badge/FastAPI-0.109-009688)
-![Streamlit](https://img.shields.io/badge/Streamlit-1.31-FF4B4B)
-![LangChain](https://img.shields.io/badge/LangChain-0.1-green)
-
-## ✨ Key Features
-
--   **📄 Document Ingestion**: Seamlessly upload PDF, DOCX, and TXT files.
--   **🤖 Advanced Parsing**: Powered by [Docling](https://github.com/DS4SD/docling) for high-fidelity document parsing and chunking.
--   **🧠 Smart Retrieval**: Uses `sentence-transformers/all-MiniLM-L6-v2` embeddings stored in a local ChromaDB instance.
--   **💬 Context-Aware Chat**: Chat interface powered by Google Gemini 2.5 Flash Lite.
--   **STORAGE**: Uses ChromaDB for vector storage and **SQLite** for state management.
--   **⚡ High Performance**: Optimized architecture with model caching (LRU) to prevent redundant reloading.
--   **🧹 Management**: View and delete uploaded documents directly from the UI.
-
-## 🛠️ Architecture
-
-The project follows a clean segregation of duties:
-
-```
-/
-├── 📁 app/                 # FastAPI Backend
-│   ├── main.py             # API Entry point & Dependency Injection
-│   └── 📁 services/        # Core Business Logic
-│       ├── document_ingester.py  # Docling + ChromaDB ingestion
-│       ├── retriever.py          # Semantic Search Logic
-│       └── generation.py         # Gemini LLM Interface
-├── 📁 ui/                  # Streamlit Frontend
-│   ├── Home.py             # Landing Page
-│   └── 📁 pages/           # Chat & Document Management Modules
-├── 📁 data/                # Persistent Storage
-│   ├── 📁 chroma_db/       # Vector Database
-│   ├── 📁 sqlite_db/       # State Management (Metadata)
-│   └── 📁 uploads/         # Raw Files
-└── requirements.txt        # Dependencies
-```
-
-## 🚀 Getting Started
-
-### Prerequisites
-
--   Python 3.10 or higher
--   A Google AI Studio API Key
-
-### Installation
-
-1.  **Clone the repository**
-    ```bash
-    git clone https://github.com/yourusername/rag-knowledge-management.git
-    cd rag-knowledge-management
-    ```
-
-2.  **Create a virtual environment**
-    ```bash
-    python -m venv .venv
-    # Windows
-    .venv\Scripts\activate
-    # Mac/Linux
-    source .venv/bin/activate
-    ```
-
-3.  **Install dependencies**
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-4.  **Configure Environment**
-    Create a `.env` file in the root directory:
-    ```env
-    GOOGLE_API_KEY=your_google_api_key_here
-    API_URL=http://localhost:8000/
-    DATA_DIR=data/
-    ```
-
-### Running the Application
-
-You will need two terminal windows:
-
-**Terminal 1: Backend (API)**
-```bash
-uvicorn app.main:app --reload --port 8000
-```
-
-**Terminal 2: Frontend (UI)**
-```bash
-streamlit run ui/Home.py
-```
-
-## 📚 Usage Guide
-
-1.  **Upload Info**: Go to the **Documents** page. Upload your PDFs or text files. The system will parse and vectorise them automatically.
-2.  **Verify**: Check the file list to ensure your documents are indexed.
-3.  **Chat**: Switch to the **Chat** page. Ask questions like "Summarize the document I just uploaded" or specific details contained in your files.
-
-## 🔮 Roadmap
-
--   [ ] Multiple chat history
--   [ ] Docker & Docker Compose support
-
----
-*Built with ❤️ by logan*