Mastering Large Codebases with Retrieval Augmented Generation
Introduction to Understanding Codebases
Importance of navigating new code as a software developer.
Introduction of Retrieval Augmented Generation (RAG) technology.
Overview of RAG Technology
Explanation of RAG's two components: setup and runtime process.
Data ingestion, transformation, and indexing into embeddings.
Use Case: Google ADK
Using RAG to understand and query the Google ADK codebase.
Objective to parse and ask questions from the codebase.
Setting Up the RAG Environment
Using Vertex AI RAG engine for an end-to-end solution.
Cloning the repository and preparing data for ingestion.
Creating and Querying the RAG Corpus
Steps involved in creating a RAG corpus and ingesting files.
Querying the corpus and generating relevant responses.
Using Vertex AI Studio for Queries
Navigating to Vertex AI Studio to interact with the RAG model.
Demonstration of asking questions about the ADK with grounded responses.
Conclusion
Recap of setting up RAG for codebase understanding.
Encouragement to like and subscribe for more content.
RAG your Codebase with Vertex AI RAG Engine
RAG your Codebase with Vertex AI RAG Engine
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