The SRAmetaDBB project, presented at BioHackathon Japan 2023, introduced an experimental JavaScript pipeline for creating SQLite databases from NCBI SRA (Sequence Read Archive) metadata dumps, with a vision for offline analysis and integration with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. While promising, the prototype faced significant challenges in performance, memory management, and production readiness when scaling to the full SRA dataset of over 45 million records. This paper presents SRAKE (SRA Knowledge Engine), a complete reimplementation in Go that not only addresses these limitations but extends the original vision with semantic search capabilities, quality control mechanisms, and multiple access interfaces. SRAKE achieves a 20-fold improvement in ingestion speed, maintains constant memory usage through zero-copy streaming, and provides standards-compliant interfaces following clig.dev guidelines. The platform introduces biomedical-specific semantic search using SapBERT embeddings via ONNX Runtime, implements comprehensive quality control thresholds for search results, and offers multiple access modalities including a CLI, REST API, MCP server for AI integration, and a simple web interface. Our development implementation demonstrates that SRAKE successfully transforms the experimental SRAmetaDBB concept into a production-ready platform, and seamless integration with modern AI workflows while maintaining the core vision of providing offline-capable, LLM-ready access to SRA metadata.