Developers Can Build Lean RAG Pipelines Using Go and Gemini File Search API

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A new approach to Retrieval-Augmented Generation (RAG) eliminates the need for dedicated vector databases by leveraging Google's Gemini File Search API. The tool allows the model to handle chunking, embedding, and retrieval internally across unstructured files such as PDFs, DOCX, and TXT formats. Paired with Go's concurrency model and type safety, developers can build production-grade RAG pipelines without managing vector indexing, schema migrations, or additional infrastructure. The method reduces ingestion latency and simplifies architecture by treating the uploaded file itself as the source of truth. However, the approach is best suited for unstructured document retrieval and is not a replacement for structured databases requiring complex joins or precise numeric filtering.

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