🧠 Inquiro — Research Assistant API

Inquiro is an intelligent research assistant built with FastAPI, designed to simplify the management, summarization, and understanding of academic papers. With seamless integration of LLMs (Gemini) and vector databases (Qdrant), Inquiro allows you to:

• Upload and organize research papers by project
• Automatically generate intelligent summaries
• Chat with the collection of papers content for deeper understanding
• Translate or explain selection text in paper.
• Facilitate notes taking.
• Manage data with MongoDB and Qdrant

---

Features

• File Upload: Upload and validate PDF research papers by project.
• Auto Summarization: Extract content and summarize using LLMs.
• Paper Chat: Ask questions and receive answers from paper content.
• Markdown Editing: View, edit, and update summaries in .md format.
• Modular Architecture: Clean separation of routes, controllers, models.
• Translator: Translate the selection text.
• MongoDB Integration: Store project, paper, and summary metadata.
• Qdrant Vector DB: Efficient document embedding and retrieval.

---

Project Structure

backend/
├── main.py # FastAPI app entry
├── routes/ # API endpoints 
├── controllers/ # Business logic
├── models/ # DB models and schemas
├── AI/
│ ├── LLM/ # LLM providers (Gemini)
│ └── VectorDB/ # Vector DB (Qdrant)
├── utils/ # Utilities for PDFs, paths, enums
└── config/ # App settings and environment

Tech Stack

• Backend: FastAPI
• Database: MongoDB (via Motor)
• LLMs: OpenAI / Cohere / Gemini
• Vector DB: Qdrant
• PDF Processing: PyMuPDF
• Async File Handling: Aiofiles

---

🤝 Contributions

PRs are welcome! If you want to contribute or report a bug, please open an issue or submit a pull request.

This Project is Under Active Developing