improvements

README.md

@@ -1,161 +0,0 @@
-# SmartDoc AI
-
-SmartDoc AI is an advanced document analysis and question answering system, designed for source-grounded Q&A over complex business and scientific reports�especially where key evidence lives in tables and charts.
-
----
-
-## ?? Personal Research Update
-
-**SmartDoc AI � Document Q&A + Selective Chart Understanding**
-
-I�ve been developing SmartDoc AI as a technical experiment to improve question answering over complex business/scientific reports�especially where key evidence lives in tables and charts.
-
-### Technical highlights:
-
-- **Multi-format ingestion:** PDF, DOCX, TXT, Markdown
-- **LLM-assisted query decomposition:** breaks complex prompts into clearer sub-questions for retrieval + answering
-- **Selective chart pipeline (cost-aware):**
-  - Local OpenCV heuristics flag pages that likely contain charts
-  - Gemini Vision is invoked only for chart pages to generate structured chart analysis (reduces unnecessary vision calls)
-- **Table extraction + robust PDF parsing:** pdfplumber strategies for bordered and borderless tables
-- **Parallelized processing:** concurrent PDF parsing + chart detection; batch chart analysis where enabled
-- **Hybrid retrieval:** BM25 + vector search combined via an ensemble retriever
-- **Multi-agent answering:** answer drafting + verification pass, with retrieved context available for inspection (page/source metadata)
-
-**Runtime note:** Large PDFs (many pages/charts) can take minutes depending on DPI, chart volume, and available memory/CPU (HF Spaces limits can be a factor).
-
----
-
-## ?? Demo Videos
-
-- [SmartDoc AI technical demo #1](https://youtu.be/uVU_sLiJU4w)
-- [SmartDoc AI technical demo #2](https://youtu.be/c8CF7-OaKmQ)
-- [SmartDoc AI technical demo #3](https://youtu.be/P17SZSQJ6Wc)
-
----
-
-## Repository
-?? https://github.com/TilanTAB/Intelligent-Document-Analysis-SmartDoc-AI
-
----
-
-## Use Cases
-
-- Source-grounded Q&A for business/research documents
-- Automated extraction and summarization from tables/charts
-
-If you�re interested in architecture tradeoffs (cost, latency, memory limits, retrieval quality), feel free to connect.
-
----
-
-## Features
-
-- **Multi-format Document Support**: PDF, DOCX, TXT, and Markdown
-- **Smart Chunking**: Configurable chunk size and overlap for optimal retrieval
-- **Intelligent Caching**: Speeds up repeated queries
-- **Chart Extraction**: Detects and analyzes charts using OpenCV and Gemini Vision
-- **Hybrid Search**: Combines keyword and vector search for best results
-- **Multi-Agent Workflow**: Relevance checking, research, and answer verification
-- **Production Ready**: Structured logging, environment-based config, and test suite
-- **Efficient**: Local chart detection saves up to 95% on API costs
-
----
-
-## Quick Start
-
-### Prerequisites
-- Python 3.11 or higher
-- Google API Key for Gemini models ([Get one here](https://ai.google.dev/))
-
-### Installation
-
-1. Clone the repository:
-```bash
-git clone https://github.com/TilanTAB/Intelligent-Document-Analysis-SmartDoc-AI.git
-cd Intelligent-Document-Analysis-SmartDoc-AI
-```
-
-2. Activate the virtual environment:
-```bash
-# Windows PowerShell
-.\activate_venv.ps1
-# Windows Command Prompt
-activate_venv.bat
-# Or manually:
-.\venv\Scripts\Activate.ps1
-```
-
-3. Install dependencies (if needed):
-```bash
-pip install -r requirements.txt
-```
-
-4. Configure environment variables:
-```bash
-cp .env.template .env
-# Edit .env and set your API key
-GOOGLE_API_KEY=your_api_key_here
-```
-
-5. (Optional) Verify installation:
-```bash
-python verify_environment.py
-```
-
-6. Run the application:
-```bash
-python main.py
-```
-
-7. Open your browser to [http://localhost:7860](http://localhost:7860)
-
----
-
-## Configuration
-
-All settings can be configured via environment variables or the `.env` file. Key options include:
-- `GOOGLE_API_KEY`: Your Gemini API key (required)
-- `CHUNK_SIZE`, `CHUNK_OVERLAP`: Document chunking
-- `ENABLE_CHART_EXTRACTION`: Enable/disable chart detection
-- `CHART_USE_LOCAL_DETECTION`: Use OpenCV for free chart detection
-- `CHART_ENABLE_BATCH_ANALYSIS`: Batch process charts for speed
-- `CHART_GEMINI_BATCH_SIZE`: Number of charts per Gemini API call
-- `LOG_LEVEL`: Logging verbosity
-- `GRADIO_SERVER_PORT`: Web interface port
-
----
-
-## Project Structure
-
-- `intelligence/` - Multi-agent system (relevance, research, verification)
-- `configuration/` - App settings and logging
-- `content_analyzer/` - Document and chart processing
-- `search_engine/` - Hybrid retriever logic
-- `core/` - Utilities and diagnostics
-- `tests/` - Test suite
-- `main.py` - Application entry point
-
----
-
-## Troubleshooting
-
-- **API Key Not Found**: Set `GOOGLE_API_KEY` in your `.env` file.
-- **Python 3.13 Issues**: Use Python 3.11 or 3.12 for best compatibility.
-- **Chart Detection Slow**: Lower `CHART_DPI` or `CHART_MAX_IMAGE_SIZE` in `.env`.
-- **ChromaDB Lock Issues**: Stop all instances and remove lock files in `vector_store/`.
-
----
-
-## Contributing
-
-Contributions are welcome! Please fork the repository, create a feature branch, and submit a pull request with a clear description.
-
----
-
-## License
-
-This project is licensed under the MIT License.
-
----
-
-SmartDoc AI is actively maintained and designed for real-world document analysis and Q&A. For updates and support, visit the [GitHub repository](https://github.com/TilanTAB/Intelligent-Document-Analysis-SmartDoc-AI).