Cheap RAG in Go with Gemini File Search: no vector DB, two calls, one hosted store

7.6 relevance

Practical RAG tutorial in Go without vector DB, directly relevant to AI/ML workflows.

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Why It Matters

For a solutions architect interested in cost-effective AI integrations and cloud infrastructure, this shows how to leverage GCP's Gemini File Search to build a grounded, citation-based document retrieval tool without the overhead of vector databases or embedding pipelines.

Key Takeaways

Assess Gemini File Search for internal knowledge retrieval if you already use GCP and want a zero-maintenance, free-tier RAG alternative that uses two API calls and a single hosted store.

Cheap RAG in Go with Gemini File Search: no vector DB, two calls, one hosted store

Summary

A developer built a minimal RAG system in Go using Gemini File Search, eliminating vector databases by having Google handle chunking, embedding, and indexing with free storage and query-time embedding. The architecture uses two model calls, a SQLite backend via modernc.org/sqlite, and a REST client for Gemini; PDF-to-markdown conversion is handled by pymupdf4llm. The entire retrieval side runs on Gemini's free tier, with the store tied to the API key rather than a user account.

Author

Athreya aka Maneshwar