Advanced RAG Pipeline for Scientific Research
A deep dive into building robust, production-ready Retrieval-Augmented Generation systems for complex, multi-document analysis.
The Challenge
Unlocking Insights from Dense Scientific Literature
Standard RAG pipelines often fail when confronted with the unique structure of scientific PDFs—complex layouts, tables, multi-column text, and dense citations. This project demonstrates three distinct architectural approaches to overcome these challenges, showcasing a journey from a simple demo to a production-ready system.
Core Architectural Components
Building Blocks of an Advanced RAG System
Layout-Aware Parsing
Using tools like `pdfplumber` to respect document structure.
Hierarchical Chunking
Parent-child strategies to maintain semantic context.
Hybrid Retrieval
Combining lexical (BM25) and semantic (FAISS) search.
Cross-Encoder Reranking
Using advanced models to re-rank retrieved results for maximum relevance.
The Three Approaches
From Notebooks to Full-Stack Applications
1. The Research Approach
Google Colab Demonstration
A foundational notebook environment ideal for rapid prototyping and demonstrating core retrieval concepts. This approach focuses on the logic of parsing, chunking, and querying in an isolated, easy-to-follow format.
2. The Full-Stack Approach
Separated Frontend & Backend
This approach simulates a real-world production application with a distinct frontend (hosted on Netlify) and backend. It showcases the ability to build a user-facing product around a complex AI service.
Note: Click "Ingest Demo Papers" before querying.
3. The Rapid-Deployment Approach
Streamlit Application
Streamlit allows for incredibly fast development of interactive data applications. This demo highlights how a powerful RAG system can be quickly wrapped in a user-friendly interface for internal use or public-facing demos.
Note: Requires a Gemini API key to use.
Conclusion
Versatility in AI/ML Solutioning
This project demonstrates the ability to tackle a single, complex problem from multiple angles—from foundational research to full-stack productization. It highlights adaptability in choosing the right tools and architecture for the specific context, whether it's for a research paper, a customer-facing product, or a quick internal tool.