ResearchGPT is a full-stack AI-powered research intelligence platform designed to help researchers, students, and professionals analyze, compare, and interact with large collections of research papers through Retrieval-Augmented Generation (RAG).
Unlike traditional PDF chatbots that rely solely on conversation history, ResearchGPT combines semantic retrieval, project-isolated vector databases, persistent research workspaces, source attribution, and multi-session research chats to deliver accurate, grounded, and scalable research assistance.
The platform enables users to upload research papers, organize them into projects, perform semantic search, compare studies, generate literature reviews, identify research gaps, and conduct long-term research conversations with persistent context.
ResearchGPT was built to address one of the biggest challenges in academic and technical research:
Understanding, comparing, and extracting insights from large collections of research papers efficiently.
The platform combines modern AI technologies with scalable system architecture to provide:
- Semantic document retrieval
- Multi-document contextual understanding
- AI-powered literature review generation
- Comparative research analysis
- Persistent research workspaces
- Grounded conversational AI
- Source-aware responses
Instead of manually reading hundreds of pages, users can interact with their research corpus through an intelligent AI assistant.
ResearchGPT organizes research into isolated project workspaces.
Each project maintains:
- Uploaded PDFs
- Project-specific vector databases
- Research chat sessions
- Message history
- Citation history
This enables users to manage multiple research domains independently without cross-project contamination.
The platform supports uploading multiple research papers into a unified workspace.
Features:
- Multi-file PDF upload
- Automatic text extraction
- Intelligent chunking
- Metadata management
- Semantic indexing
- Project-level document isolation
Uploaded papers are automatically processed and converted into searchable vector representations.
ResearchGPT supports multiple chat sessions inside a single research workspace.
Features:
- Create multiple research conversations
- Persistent chat history
- Session-based organization
- Reload previous conversations
- Long-term research workflows
Unlike conventional LLM chats, conversations remain useful because answers are generated from retrieved research evidence rather than relying solely on conversation memory.
Users can ask natural language questions about uploaded papers.
Example Queries:
Compare the methodologies used in these papers
What are the key findings related to climate change?
Identify research gaps across all uploaded studies
Summarize the limitations discussed in these papers
The system retrieves relevant research evidence before generating responses.
ResearchGPT uses vector embeddings and semantic similarity search instead of traditional keyword matching.
Capabilities:
- Meaning-based search
- Context-aware retrieval
- Cross-document understanding
- Semantic chunk matching
- Research-focused question answering
This allows users to discover relevant information even when exact keywords are absent.
The platform is designed to compare multiple research papers simultaneously.
ResearchGPT can:
- Compare methodologies
- Analyze similarities and differences
- Detect trends across studies
- Generate comparative insights
- Evaluate findings across papers
This transforms the system from a simple chatbot into a research analysis engine.
ResearchGPT assists researchers in literature review workflows by:
- Summarizing multiple papers
- Extracting key findings
- Organizing research themes
- Highlighting major contributions
- Generating structured reviews
All outputs are grounded in retrieved research evidence.
The platform can identify:
- Unexplored topics
- Missing research directions
- Potential future work
- Weaknesses in existing studies
- Areas requiring further investigation
This helps researchers discover new opportunities efficiently.
Every AI response includes document-level source attribution.
Features:
- Source-aware responses
- Citation persistence
- Document tracking
- Multi-document evidence support
Users can always determine which paper contributed to a generated answer.
ResearchGPT follows a distributed full-stack architecture.
React Frontend
│
▼
Node.js + Express API Gateway
│
▼
PostgreSQL Database
│
▼
FastAPI AI Service
│
▼
PDF Processing
│
▼
Chunking Pipeline
│
▼
Embedding Generation
│
▼
FAISS Vector Database
│
▼
Semantic Retrieval
│
▼
Gemini LLM
│
▼
Grounded AI Response
A key design goal of ResearchGPT is complete project-level isolation.
Each project maintains its own:
- Uploaded documents
- FAISS index
- Metadata store
- Chat sessions
- Message history
Directory Structure:
data/
└── users/
└── {user_id}
└── projects/
└── {project_id}
├── uploads/
│ └── PDF files
│
└── faiss/
├── index.faiss
├── metadata.json
└── documents.json
This ensures retrieval remains isolated and accurate for every project.
ResearchGPT uses a custom RAG pipeline.
PDF Upload
↓
Text Extraction
↓
Chunk Generation
↓
Embedding Creation
↓
FAISS Indexing
↓
Semantic Retrieval
↓
Source Aggregation
↓
Context Construction
↓
Gemini Grounded Generation
↓
Source Attribution
The platform retrieves relevant evidence before generating responses, reducing hallucinations and improving answer reliability.
The retrieval system follows these stages:
User queries are enriched to improve retrieval quality.
Queries are converted into dense vector representations using Sentence Transformers.
Relevant chunks are retrieved from project-specific FAISS indexes.
Retrieved chunks are grouped by source document.
Relevant chunks from one or more papers are selected based on semantic relevance.
An optimized context window is built from selected chunks.
Gemini generates responses using retrieved evidence only.
Source metadata is attached to responses for transparency and traceability.
ResearchGPT uses PostgreSQL for metadata persistence.
Core Tables:
users
Stores user accounts and authentication information.
projects
Stores research workspaces.
documents
Stores uploaded document metadata.
chat_sessions
Stores research conversations within a project.
chat_messages
Stores user messages, AI responses, and source citations.
- React.js
- Vite
- Tailwind CSS
- Axios
- React Markdown
- Context API
- Node.js
- Express.js
- PostgreSQL
- JWT Authentication
- Multer
- FastAPI
- Python
- Uvicorn
- Sentence Transformers
- FAISS Vector Database
- Google Gemini
- Semantic Retrieval Pipeline
- pdfplumber
- PyPDF2
User and project-level isolation ensures secure and scalable document retrieval.
Projects, chats, messages, and citations remain available across sessions.
Each project maintains its own FAISS index to eliminate retrieval contamination.
Responses are generated using retrieved evidence rather than relying solely on LLM memory.
Every response can be traced back to contributing research papers.
Frontend, API gateway, AI services, retrieval systems, and storage layers are fully decoupled.
ResearchGPT can be used for:
- Academic Research
- Literature Review Generation
- Research Gap Analysis
- Comparative Study Analysis
- Technical Paper Understanding
- Knowledge Discovery
- Scientific Research Assistance
- Educational Research Platforms
Planned improvements include:
- Cross-Encoder Re-ranking
- Hybrid Search (FAISS + BM25)
- Query Classification
- Citation Preview Panels
- Document Deletion & Reindexing
- Chat Auto-Naming
- Research Export Tools
- Cloud Deployment
- Team Collaboration Workspaces
ResearchGPT aims to become an intelligent research intelligence platform that enables users to:
- Organize research efficiently
- Understand papers faster
- Compare studies intelligently
- Generate grounded insights
- Identify research opportunities
- Conduct long-term AI-assisted research
By combining semantic retrieval, project-isolated vector databases, persistent o an AI-powered research experience.workspaces, and grounded LLM reasoning, ResearchGPT transforms traditional research workflows int