"Gemini Embeddings Semantic Search in Python, No Vector DB"
"Semantic search usually means standing up a vector database. It doesn't have to: the Gemini embeddings API plus in-memory cosine similarity gives you meaning-based search over your documents in under 60 lines of Python. This guide shows the full pipeline."
Search your documentation by *meaning*, not keywords. Ask "how do I get my money back" and find the refund policy — even though those words never appear in it. The usual advice says you need a vector database for this. You don't — not at first. This gemini embeddings semantic search python guide builds a working semantic search engine with the Gemini embeddings API and plain in-memory cosine similarity: no Pinecone bill, no pgvector setup, no infrastructure to babysit. You'll go from a text file to ranked, meaning-based results in about 60 lines of code, and you'll know exactly when (and whether) a vector database is worth it. What you'll learn How to generate embeddings with gemini-embedding-001 and the current Python SDK The task types that make retrieval actually work ( RETRIEVAL_DOCUMENT vs RETRIEVAL_QUERY ) Batch embedding for indexing a whole corpus efficiently Cosine similarity ranking with numpy — your "vector database" in 10 lines A chunking strategy that keeps retrieval accurate Exactly when to graduate from in-memory search to a real vector store Prerequisites: Python 3.10+, pip install google-genai==2.25.0 numpy , and a Gemini API key. Embed your documents with gemini-embedding-001 gemini-embedding-001 is Google's text embedding model: 3072 dimensions by default, with Matryoshka-style truncation if you want fewer. One call embeds a batch: # google-genai==2.25.0 from google import genai from google.genai import types client = genai.Client() # reads GEMINI_API_KEY from the environment docs = [ "Refunds are issued within 30 days of purchase to the original payment method.", "To reset your password, open Settings > Security > Reset password.", "Annual plans renew automatically unless cancelled 7 days before renewal.", ] result =…