Research Alignment Agent

An AI-powered system for analyzing academic papers, detecting alignment or contradictions, and maintaining research consistency over time.

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Overview

Academic research often involves referencing multiple papers across different timelines, making it difficult to ensure consistency and alignment between sources.

This project aims to build a research alignment agent that helps users analyze academic papers, compare new research with previously referenced work, and detect agreements or contradictions.

The system allows users to upload drafts, topics, and reference papers, while maintaining a long-term contextual memory. When new research is introduced, it evaluates whether it aligns with or contradicts earlier work—even if those references were added weeks earlier.

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Core Features

• Upload research papers, drafts, and topics
• Generate embeddings and maintain long-term memory
• Detect alignment or contradiction between research papers
• Summarize academic documents
• Answer relevance-based queries
• Suggest references based on highlighted sections
• Context-aware reasoning across time

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Architecture Overview

The system consists of the following components:

• Input Module Handles user uploads (PDFs, drafts, topics)

• Embedding Engine Converts documents into vector representations

• Vector Database (Memory Layer) Stores embeddings for long-term contextual retrieval

• Alignment Engine Compares new inputs with stored research and detects alignment or contradiction

• LLM Module Generates summaries, explanations, and answers queries

• Query Interface / API Layer Enables interaction with the system

Data Flow

1. User uploads documents or drafts
2. Documents are processed and converted into embeddings
3. Embeddings are stored in a vector database
4. New documents are compared with existing memory
5. Alignment or contradiction is detected
6. Results are returned with summaries and explanations

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Tech Stack

Layer	Technology Used
Backend	Python (FastAPI)
LLM	OpenAI API
Embeddings	OpenAI / SentenceTransformers
Database	FAISS / Pinecone
DevOps	Docker, GitHub Actions
Other Tools	LangChain / LlamaIndex

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Project Structure

src/
├── agent/          # Core agent logic
├── embeddings/     # Embedding + retrieval logic
├── memory/         # Vector database handling
├── alignment/      # Alignment & contradiction detection
├── api/            # FastAPI routes
└── utils/          # Helper functions

tests/              # Test cases
docs/               # Documentation (optional)

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⚙️ Setup Instructions

1. Clone the Repository

git clone <repository-url>
cd Research-Agent

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment Variables

cp .env.example .env

Update .env with your credentials.

4. Run the Project

uvicorn src.api.main:app --reload

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Docker Setup

Build Image

docker build -t research-agent .

Run Container

docker run -p 8000:8000 research-agent

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Git Hooks Setup

This repository uses custom Git hooks to enforce commit standards and workflow discipline.

After cloning the repository, run:

git config core.hooksPath .githooks

This enables:

• Commit message validation
• Pre-push checks

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Environment Variables

Variable Name	Description
OPENAI_API_KEY	API key for LLM access
VECTOR_DB_KEY	Vector database API key (if remote)
MODEL_NAME	Model used for embeddings/LLM
PORT	Application port

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Example Use Case

1. User uploads a draft paper and reference materials
2. System stores and embeds key ideas
3. Weeks later, user uploads a new research paper
4. System detects contradictions with previously cited work
5. User receives a detailed explanation and summary
6. User highlights a section → system suggests relevant references

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Deployment

• Containerized using Docker
• Can be deployed on AWS / GCP / Azure
• CI/CD handled via GitHub Actions

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Testing

pytest

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Project Status

🟢 In Development

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Contributing

Please refer to CONTRIBUTING.md for contribution guidelines.

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License

This project is licensed under the terms of the MIT License.