Google has introduced a new tool called File Search, designed to simplify how developers and enterprises integrate private, internal data into AI applications powered by models like Gemini. The core challenge with large language models is their reliance solely on training data, leaving them unaware of up-to-date or proprietary information such as company documentation, codebases, or recent research. File Search addresses this by enabling seamless retrieval of relevant content from uploaded documents and grounding the model’s responses in that data—essentially delivering a complete, managed Retrieval-Augmented Generation (RAG) pipeline. Unlike traditional RAG setups that require manual steps like chunking text, generating embeddings, storing vectors, and orchestrating retrieval, File Search handles all of this automatically. It’s not a separate service or API but a built-in tool within the Gemini API, making it easy to add intelligent, data-aware responses to applications with minimal code. At the heart of the system is a powerful vector search engine powered by the gemini-embedding-001 model. This allows semantic search—understanding meaning rather than just matching keywords—so users can find relevant information even when their query uses different phrasing than the document. One of the standout features is automatic citation generation. Every response includes metadata indicating which parts of which files were used to generate the answer. This enhances trust, transparency, and verifiability, which is essential for enterprise and production use. The tool supports a wide range of file formats out of the box, including PDF, DOCX, TXT, JSON, and various code and configuration files. This eliminates the need for pre-processing or conversion, allowing teams to quickly build knowledge bases from existing documents. Costs are kept low. Storing documents and querying them is free. You only pay for the initial embedding of your document content, which can be as low as $0.15 per million tokens, depending on the model used. To use it, developers create a file search store, upload documents, and then call the Gemini model with the File Search tool enabled. The model automatically retrieves relevant context from the uploaded files and generates accurate, cited responses. For example, uploading a 180-page Samsung phone user manual allows the model to answer questions like which phone models it applies to, or how to set automatic screen timeout—accurately referencing the correct pages. The system also supports multiple files through simple loops, with clear limits: each file can be up to 100 MB, and total storage capacity depends on the user tier, ranging from 1 GB (free) to 1 TB (tier 3). Users can also control how files are split into chunks using the chunking_config option, setting max tokens per chunk and overlap between them—giving fine-grained control over retrieval quality. Compared to other Google tools like Context Grounding and LangExtract, File Search is the only one that permanently stores document embeddings. This means you don’t need to re-upload files for every query, making it ideal for long-term knowledge bases. In contrast, Context Grounding and LangExtract are more focused on real-time fact-checking and structured data extraction, respectively, and don’t maintain persistent data storage. File Search also includes a cleanup feature. While raw file content is automatically deleted after 48 hours, the embeddings remain until manually removed. Developers can delete entire stores programmatically when no longer needed. In summary, Google’s File Search tool represents a major step forward in simplifying RAG implementation. It removes the complexity of building and maintaining data pipelines, offering a fully managed, cost-effective, and easy-to-integrate solution for grounding AI responses in private data. With strong support for multiple formats, automatic citations, and seamless integration into the Gemini API, it’s a powerful addition for developers building intelligent, reliable AI applications.