Research LLM System

An integrated research assistant built with retrieval, structured data pipelines, and OpenAlex ingestion.

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Architecture Overview

research-llm/
├── rag/        # Retrieval-Augmented Generation system
├── database/   # Data ingestion, processing, and storage
├── openalex/   # OpenAlex data pipeline

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Modules

RAG System

Core retrieval and question answering engine.

• Semantic search
• Graph-based retrieval
• Streamlit interface
• Session memory

📂 Location: /rag 📖 Details: RAG README

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Database Pipeline

Handles ingestion, preprocessing, and enrichment of research data.

• PDF ingestion
• Data cleaning and normalization
• Model training utilities
• Pipeline orchestration

📂 Location: /database 📖 Details: Database README

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OpenAlex Integration

Fetches and processes academic metadata from OpenAlex.

• Paper metadata retrieval
• Download pipelines
• Chunking and normalization
• Graph + vector ingestion

📂 Location: /openalex 📖 Details: OpenAlex README

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Workflow

1. OpenAlex → fetch research papers
2. Database → clean, structure, enrich
3. RAG → retrieve + answer queries

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Quick Start

# Clone repo
git clone https://github.com/AryanApte1408/research-llm.git
cd research-llm

Navigate to a module:

cd rag
# or
cd database
# or
cd openalex

Follow instructions in each module’s README.

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Tech Stack

• Python
• ChromaDB
• Neo4j
• Streamlit
• OpenAlex API

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Notes

Each module is independently runnable but designed to work together as a pipeline.

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