Retrieval Augmented Generation (RAG) is a technique to ground Large Language Model (LLM) responses in external, up-to-date, or private data. By retrieving relevant information (often using vector search) and feeding it into the model's context, RAG enables more accurate and fact-based answers.
For normalized text embeddings, the choice between cosine similarity, dot product, and Euclidean distance is simpler than it appears, as all three produce identical search rankings, which simplifies building RAG systems.
At TEQNation 2024, I demonstrated how to build a complex LLM app using RAG and ReACT, grounded in over 20 million Hacker News comments, and explained how I did data scraping, indexing, and implementation with LangChain and Cloud Run.
Learn to build a Retrieval-Augmented Generation (RAG) app with LangChain and Gemini on Cloud Run, using a vector database to answer questions from Cloud Run release notes.
A technical overview of automated base image updates on Cloud Run.
Use these gcloud commands to configure a custom domain for a Cloud Run service with a managed SSL certificate and a HTTP-to-HTTPS redirect.
Learn how to set up a hook in Gemini CLI that uses Ruff to format and sort imports in your Python code every time it modifies a file.