"Gemini File Search API Tutorial: Zero-Infrastructure RAG"
"The Gemini File Search API gives you production RAG without a vector database: upload documents to a managed store, query them with the file_search tool, and get answers with citations. This tutorial walks through the full pipeline in Python with real, current code."
Short answer: Gemini File Search is Google's managed RAG API. You create a File Search store, upload your documents, then pass the store as a file_search tool to generate_content — and the model answers from your documents, with citations. No chunker to write, no embeddings to generate, no vector database to run, and indexing costs $0.15 per million tokens with free storage. This gemini file search api tutorial walks through the entire pipeline in Python, with the current SDK: creating the store, uploading and indexing documents, querying with metadata filters, and reading citations from every answer. What you'll build A File Search store holding your document collection An upload script that indexes PDFs and text files (with progress polling) A Q&A loop that answers questions grounded in your documents Metadata filtering so queries can target document subsets Citation extraction so every answer shows its sources A cost estimate for indexing a real corpus Prerequisites: Python 3.10+, pip install google-genai==2.25.0 , and a free API key from Google AI Studio. File Search stores: your hosted vector index A File Search store is a persistent container for your document embeddings. Files uploaded through the plain Files API expire after 48 hours; content imported into a File Search store stays until you delete it. Store names are globally scoped to your project. # google-genai==2.25.0 from google import genai client = genai.Client() # reads GEMINI_API_KEY from the environment store = client.file_search_stores.create( config={"display_name": "support-docs"} ) print(store.name) # fileSearchStores/abc123... — save this # Lost the ID? List your stores to recover it for s in client.file_search_stores.list(config={"page_size": 20}): print(s.display_name, "->", s.name)…