Programming Tutorials

  You have built a robust pipeline using Gemini 1.5 Pro or Flash. The prompts function correctly in isolation. However, as soon as you scale up your throughput or increase the prompt complexity, your logs flood with this error: 429 Resource has been exhausted (e.g. check quota). This is the single most common bottleneck for teams moving Generative AI from prototype to production on Google Cloud Platform (GCP). While the error message suggests you simply ran out of "resources," the mechanics behind it are more nuanced. This guide provides a root cause analysis of Vertex AI quotas and details a production-grade implementation in Python to handle rate limiting and retries effectively. The Root Cause: RPM vs. TPM The primary reason developers hit  429  errors with Gemini isn't just the number of API calls; it is the  Token  density of those calls. Vertex AI enforces two distinct quotas simultaneously: Requests Per Minute (RPM):  The number of API calls you make...

  You have built a Retrieval-Augmented Generation (RAG) pipeline. You are using a high-end vector database, a state-of-the-art embedding model, and GPT-4 with a massive 128k context window. You query your system with a question you   know   the answer to. The relevant chunk is retrieved successfully by the vector store. Yet, the LLM hallucinates or responds with a polite "I don't know." This is the silent killer of RAG performance: the  "Lost in the Middle"  phenomenon. It is not an issue with your embeddings; it is a fundamental architectural limitation of how Large Language Models (LLMs) process sequential context. This article details why this happens at the attention layer and provides a production-ready solution using Python and LlamaIndex. The Root Cause: The U-Shaped Performance Curve To fix the problem, we must understand the attention mechanism failure. In 2023, researchers (Liu et al.) identified a U-shaped performance curve in LLMs regarding context r...