https://github.com/emineugurlu/doc-assistant
Enterprise-grade AI document analysis platform. Features automated summarization, semantic Q&A, and keyword search using FastAPI and Gemini AI. Built with a scalable micro-services architecture.
https://github.com/emineugurlu/doc-assistant
ai-summarization computer-engineering document-analysis fastapi full-stack gemini-api nlp python sqlite
Last synced: 4 days ago
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Enterprise-grade AI document analysis platform. Features automated summarization, semantic Q&A, and keyword search using FastAPI and Gemini AI. Built with a scalable micro-services architecture.
- Host: GitHub
- URL: https://github.com/emineugurlu/doc-assistant
- Owner: emineugurlu
- License: mit
- Created: 2026-04-13T11:27:45.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2026-05-28T14:27:28.000Z (21 days ago)
- Last Synced: 2026-05-28T16:16:20.873Z (21 days ago)
- Topics: ai-summarization, computer-engineering, document-analysis, fastapi, full-stack, gemini-api, nlp, python, sqlite
- Language: Python
- Homepage:
- Size: 142 KB
- Stars: 11
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📄🤖 Doc Assistant: Intelligent Document Analysis Ecosystem
> **"A high-performance, AI-driven platform designed to revolutionize document interaction. By leveraging the Gemini API and a robust FastAPI backend, Doc Assistant enables users to distill complex PDF/TXT data into actionable insights through automated summarization and semantic Q&A."**
**Doc Assistant** is a professional-grade analysis tool developed by **Emine Uğurlu**. It addresses the challenge of information overload by providing a scalable environment for instant document parsing, keyword search, and intelligent dialogue with static files.
---
## 🚀 Engineering & AI Excellence
This project showcases advanced backend orchestration and AI service integration:
* **Gemini AI Integration:** Implementation of sophisticated prompt engineering within `ai_service.py` to deliver high-context summaries and precise Q&A.
* **Asynchronous Backend Architecture:** Utilizing **FastAPI** to manage non-blocking I/O operations for seamless file uploads and real-time AI processing.
* **Document Parsing Engine:** Robust text extraction and chunking logic for PDF and TXT formats handled by a dedicated `file_processor.py`.
* **Relational Data Management:** Structured storage of document metadata and user interactions using **SQLite** with efficient CRUD operations.
* **Scalable Routing Layer:** Modular API design with separate routers for AI chat, search, and document management.
## ✨ Core Features
* 🧠 **Semantic Q&A:** Ask complex questions and receive context-aware answers directly from your documents.
* 📝 **Automated Summarization:** Instantly generate executive summaries for long-form PDF and TXT files.
* 🔍 **Precision Search:** Deep-file keyword search engine to locate critical information across your library.
* 🗂️ **Document Management:** Fully interactive dashboard to upload, view, and manage your analyzed documents.
## 📸 Interface Showcase
---
## 🛠️ Tech Stack
* **Backend:** FastAPI, Python, Pydantic.
* **AI Engine:** Google Gemini API.
* **Database:** SQLite.
* **Frontend:** HTML5, CSS3, JavaScript (Vanilla).
---
## ⚙️ Installation & Setup
1. **Clone the Repository:**
```bash
git clone https://github.com/emineugurlu/doc-assistant
cd doc-assistant
````
2.**Environment Configuration:**
Create a .env file and add your GEMINI_API_KEY.
3.**Install Dependencies:**
````bash
pip install -r requirements.txt
````
4.**Launch the Server:**
````bash
uvicorn main:app --reload
````
Developed by Emine Uğurlu - Computer Engineer
Empowering document intelligence through advanced engineering.