🧠 ResearchOS: Enterprise GraphRAG Platform

ResearchOS is an autonomous, agentic AI research operating system. It transforms raw scientific literature (e.g., ArXiv papers) into a highly structured, searchable knowledge graph using Multimodal RAG, Graph Reasoning, and LangGraph Autonomous Agents.

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📑 Table of Contents

• What is ResearchOS?
• System Architecture & Methodology
• Core Capabilities
• Tech Stack
• Getting Started (Docker)
• API Reference
• Automated Testing

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💡 What is ResearchOS?

Traditional Vector-RAG systems struggle with complex, multi-hop reasoning because they rely purely on statistical text proximity. ResearchOS solves this by implementing GraphRAG.

Instead of just embedding text, our LLM pipeline extracts factual entities and relationships from research papers and builds an explicit Knowledge Graph in Neo4j. When a user asks a complex question, our autonomous LangGraph agents traverse this graph to synthesize hallucination-free answers with precise citations.

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🔬 System Architecture & Methodology

graph LR
    User[User / Frontend] -->|HTTP| Nginx[Nginx Reverse Proxy]
    Nginx -->|/api/*| FastAPI[FastAPI Backend]
    
    subgraph "Agentic Backend"
        FastAPI <-->|Cypher/Bolt| Neo4j[(Neo4j Graph Database)]
        FastAPI <-->|API| LLM[HuggingFace / OpenAI]
        FastAPI <-->|Scrape| ArXiv[ArXiv API]
    end

The ResearchOS Pipeline:

1. Ingestion & Parsing: Research papers are fetched dynamically via the ArXiv API. The raw academic text is intelligently chunked with semantic overlap to preserve context.
2. Entity & Relationship Extraction: An LLM pipeline reads the chunks and extracts core scientific entities (e.g., Algorithms, Datasets, Metrics) and their directional relationships (e.g., YOLOv8 -> OUTPERFORMS -> RetinaNet).
3. Graph Construction (Neo4j): These entities are pushed to Neo4j. Unlike vector embeddings, Neo4j allows the system to traverse explicit, factual links between concepts.
4. Agentic Retrieval: When queried, LangGraph autonomous agents perform semantic graph traversal (GraphRAG), synthesize multi-hop relationships, and generate highly accurate answers.

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🚀 Core Capabilities

• Autonomous Agents: LangGraph agents capable of fetching, parsing, and reasoning over complex academic papers without human intervention.
• Hallucination Reduction: By anchoring LLM generation to strict Graph relationships, factual inaccuracies are heavily minimized.
• Containerized Microservices: Fully orchestrated tri-service architecture (Nginx Frontend + FastAPI Backend + Neo4j Database) using Docker Compose for infinite scalability.

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🛠️ Tech Stack

• Backend Framework: FastAPI, Python 3.10+
• AI & Reasoning: LangChain, LangGraph, HuggingFace Transformers
• Database: Neo4j (Graph Database)
• Infrastructure: Docker, Docker Compose, Nginx (Reverse Proxy)
• Testing & QA: Pytest, HTTPX

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⚙️ Getting Started (Docker)

The fastest and most reliable way to run ResearchOS is via Docker. This ensures all heavy ML dependencies and the Neo4j database are perfectly isolated.

1. Prerequisites

• Docker & Docker Desktop installed.

2. Environment Setup

Clone the repository and set up your environment variables:

cp .env.example .env
# Open .env and add your OpenAI API Key and Neo4j credentials

3. Spin up the Cluster

Run the Docker Compose cluster in detached mode:

docker-compose up -d --build

4. Access the Application

• Frontend UI: http://localhost
• API Swagger Docs: http://localhost:8000/docs
• Neo4j Browser: http://localhost:7474

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🌐 API Reference

ResearchOS provides a fully documented REST API.

(For full interactive documentation, start the server and navigate to /docs)

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🧪 Automated Testing

This project maintains a robust, mocked test suite to ensure API reliability. It uses FastAPI's TestClient to test endpoints without incurring external LLM API costs.

# Install testing dependencies
pip install pytest httpx

# Run the test suite
pytest tests/

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Built for the future of AI-driven research.