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| title: SmartDoc AI | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: "6.2.0" | |
| app_file: main.py | |
| pinned: false | |
| # 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). | |