FileSearch

The term “File Search” refers to several technologies depending on the context, but it most prominently refers to the fully managed Retrieval-Augmented Generation (RAG) tools provided by major AI platforms like Google and OpenAI. The primary implementations of File Search include: 1. AI Developer Tools (Managed RAG)

Instead of building a complex data pipeline from scratch, developers use a File Search API to let Large Language Models (LLMs) securely access and search external documents.

Google Gemini File Search API: A completely hosted RAG solution within the Gemini ecosystem. You upload files (like PDFs, DOCX, or code) to a FileSearchStore. Google automatically handles the chunking, embedding generation, and vector indexing. When a user queries the model via generateContent(), Gemini performs a semantic search, answers the question, and provides full, verifiable citations. As detailed in the Google Innovation Blog, query-time embeddings and storage are free, while initial document indexing costs $0.15 per 1 million tokens.

OpenAI Assistants File Search: An automated tool inside the OpenAI Assistants and Responses API. It uses a hybrid approach combining semantic vector search and keyword search to pull relevant context from custom “vector stores” to dramatically reduce model hallucinations. 2. Operating System & Local Utilities

“File Search” also refers to high-speed local desktop indexing programs designed to bypass slow default OS search bars:

File Search – generateContent API – Google AI for Developers