Refactoring RAG AI App Code Base for Future Enhancements : From Monolith...

Refactoring RAG AI App Code Base for Future Enhancements : From Monolith to Modular Mastery | Part16

Welcome back to Part 15 of our RAG tutorial series! In this episode, we transition our AI-powered Q&A application from a monolithic codebase to a clean, modular architecture, enhancing maintainability, scalability, and

 collaboration.

 

📚 What We've Built So Far:

📺 Parts 1-2: Introduced RAG concepts and set up the environment.

📺 Part 3: Implemented PDF data ingestion.

📺 Part 4: Developed a console-based RAG application.

📺 Parts 5-8: Transitioned to interactive web apps using Gradio.

📺 Parts 9-10: Enhanced retrieval with RERANK models.

📺 Part 11: Integrated web data sources.

📺 Parts 12-14: Connected relational databases like SQLite and PostgreSQL.

📺 Part15: Setup MongoDb with Knowledge base loaded

🔧 Why Refactor?

Our current single-file setup poses challenges:

  ➡️ Readability: Difficult to locate specific functionalities.

  ➡️ Maintainability: Changes in one section can inadvertently affect others.

  ➡️ Testability: Hard to isolate components for testing.

  ➡️ Collaboration: Multiple developers working on the same file can lead to conflicts.

  ➡️ Extensibility: Adding new features becomes cumbersome.

🏗️ Modular Architecture Overview: We'll restructure our project as follows:

rag_app/

├── main_app.py           # Gradio UI

├── config.py             # API keys and constants

├── services/             # Core utilities

│   ├── initializer.py

│   ├── embedding_utils.py

│   ├── summary_questions.py

├── ingestion/            # Data ingestion modules

│   ├── pdf_ingestion.py

│   ├── url_ingestion.py

│   ├── sqlite_ingestion.py

│   ├── postgres_ingestion.py

├── rag_query/            # Query handling logic

│   └── query_engine.py

✅ Benefits of Modularization:

👉 Maintainability: Easier to manage and update specific components.

👉 Scalability: Simplifies the addition of new features or data sources.

👉 Testability: Facilitates unit testing of individual modules.

👉 Collaboration: Enables multiple developers to work concurrently without conflicts.

🔄 RAG Series Playlist: [Link to your Full RAG Playlist Here]

Part1 (Theory & Concepts) 👉 https://youtu.be/E2YsOkIsihQ

Part2 (Env Setup) 👉 https://youtu.be/DO_crG2LdOo

Part3 (PDF to Vector DB) 👉 https://youtu.be/CJ9C00nj4X4

Part4 (Console RAG Code) 👉 https://youtu.be/bXo7Few4gbU

Part5 (Streamlit vs Gradio) 👉 https://youtu.be/KmRl1gjJq68

Part6 (Console App to Interactive Web App) 👉 https://www.youtube.com/watch?v=w_2ojb7YnKQ

Part7 (Upload PDF & Ask Question From Loaded PDF) 👉 https://www.youtube.com/watch?v=hrVfMRO2a_Q

Part8 (Summarization & Suggestion for 5 Questions) 👉 https://www.youtube.com/watch?v=jIMNOjAO2CE

Part9 (Fixing Vector Similarity Limitations with RERANK Models) 👉 https://www.youtube.com/watch?v=ZvKs6X97IOk

Part10 (RERANK Model Integration with Pinecone To Fine-Tuning RAG Retrieval Quality) 👉 https://www.youtube.com/watch?v=4EXzPbCdJYg

Part11 (Extract data from Web Search (URL) for RAG powered AI app ) 👉 https://www.youtube.com/watch?v=NVaFQlmUEks

Part12 (Integrate Knowledge from Relational DB along with PDF & URL data ) 👉 https://www.youtube.com/watch?v=uQkEWrSEV8Y

Part13 (POSTGRESQL - Setup & Load 733 Questions/Answers for 50 Topics of System Design) 👉 https://www.youtube.com/watch?v=2wOFrdjF_VA

Part14 (Integrate PostgreSQL Database as LLM Knowledge Base for system design concepts) 👉 https://www.youtube.com/watch?v=vsRen4HVGhg

Part15 (MongoDB - Setup & Load data) 👉 https://www.youtube.com/watch?v=FE_OI2EhZGM

Part16 (Refactored Code) 👉 https://www.youtube.com/watch?v=VVCpdtmkhf8