🎓 Academia.AI — Multi-Agent System for Automated Research Synthesis

Academia.AI is a resilient, multi-agent RAG-powered system designed to ingest, analyze, and synthesize academic research papers. Built as the deliverable for Agentic AI Developer Certification — Module 3, this system demonstrates the transition from prototype to production-grade AI tooling.

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🚀 Key Capabilities

Academia.AI enhances standard RAG pipelines with engineering-grade reliability:

• 🔁 Self-Healing Agents: Automatic retry logic with exponential backoff for LLM timeouts.
• 🛡️ API Reliability Checks: A pre-processing HealthCheck agent validates connectivity before workflow execution.
• 📄 Structured Validation: Final report must comply with a strict Pydantic JSON schema.
• 🧪 Unit-Tested Components: Tested ingestion workflow and keyword extraction ensure stability.

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🛠 Architecture Overview

Academia.AI operates through a structured LangGraph workflow coordinated across three agents:

1. Agent: Document Ingestor

• Loads PDF/Text files.
• Splits content into semantic chunks.
• Builds an FAISS vector DB for efficient retrieval.

2. Agent: Thesis Extractor

• Uses NLP and keyword frequency scoring to extract hypotheses and contributions.
• Identifies core research boundaries and terminology.

3. Agent: Insight Synthesizer

• Retrieves relevant context from FAISS.
• Generates the final research synthesis using a retry-enabled LLM call.
• Ensures grounded output aligned with academic structure.

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💻 Installation & Setup

Prerequisites

• Python 3.10+
• OpenRouter API key

1️⃣ Clone the Repository

git clone https://github.com/maddiravi/academia-ai-production.git
cd academia-ai-production

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Configure Environment Variables

cp .env_example .env

Edit .env with your API key:

OPENROUTER_API_KEY=sk-or-v1-xxxx
OPENROUTER_API_BASE=https://openrouter.ai/api/v1

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🏃 Running the Application

Launch the Streamlit interface:

streamlit run app.py

Usage Steps

1. Upload a research paper (PDF or TXT)
2. Click Start Analysis
3. Watch agent logs update in real-time
4. Review generated executive summary and keyword insights

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🧪 Testing & Quality Assurance

Run the automated unittest suite:

python -m unittest tests.test_academia

Test Coverage Includes:

• ✔ DocumentIngestor workflow validation
• ✔ Keyword extraction accuracy
• ✔ Fault tolerance and retry path behavior

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📂 Project Structure

academia-ai-production/
├── agents/
│   ├── document_ingestor.py
│   ├── thesis_extractor.py
│   └── insight_synthesizer.py
├── tools/
│   ├── health_check.py
│   └── file_processor.py
├── tests/
│   └── test_academia.py
├── app.py
├── requirements.txt
└── README.md

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🛡 License

Distributed under the MIT License.
This project is open-source and free for academic and development use.

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Output:

🔖 Recommended Tags

#AI #MultiAgentSystems #RAG #LangGraph #ResearchAutomation #Python #LLM #FAISS #Streamlit #OpenSource #AgenticAI #AIDevelopment #AcademicTools #ML #DeepLearning #KnowledgeExtraction