By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an engineering challenge and also exhibit undesirable traits.
To help solve this, Google released the File Search Tool on the Gemini API, a fully managed RAG system “that abstracts away the retrieval pipeline.” File Search removes much of the tool and application-gathering involved in setting up RAG pipelines, so engineers don’t need to stitch together things like storage solutions and embedding creators.
This tool competes directly with enterprise RAG products from OpenAI, AWS and Microsoft, which also aim to simplify RAG architecture. Google, though, claims its offering requires less orchestration and is more standalone.
“File Search provides a simple, integrated and scalable way to ground Gemini with your data, delivering responses that are more accurate, relevant and verifiable,” Google said in a blog post.
Enterprises can access some features of File Search, such as storage and embedding generation, for free at query time. Users will begin paying for embeddings when these files are indexed at a fixed rate of $0.15 per 1 million tokens.
Google’s Gemini Embedding model, which eventually became the top embedding model on the Massive Text Embedding Benchmark, powers File Search.
File Search and integrated experiences
Google said File Search works “by handling the complexities of RAG for you.”
File Search manages file storage, chunking strategies and embeddings. Developers can invoke File Search within the existing generateContent API, which Google said makes the tool easier to adopt.
File Search uses vector search to “understand the meaning and context of a user’s query.” Ideally, it will find the relevant information to answer a query from documents, even if the prompt contains inexact words.
The feature has built-in citations that point to the specific parts of a document it used to generate answers, and also supports a variety of file formats. These include PDF, Docx, txt, JSON and “many common programming language file types," Google says.
Continuous RAG experimentation
Enterprises may have already begun building out a RAG pipeline as they lay the groundwork for their AI agents to actually tap the correct data and make informed decisions.
Because RAG represents a key part of how enterprises maintain accuracy and tap into insights about their business, organizations must quickly have visibility into this pipeline. RAG can be an engineering pain because orchestrating multiple tools together can become complicated.
Building “traditional” RAG pipelines means organizations must assemble and fine-tune a file ingestion and parsing program, including chunking, embedding generation and updates. They must then contract a vector database like Pinecone, determine its retrieval logic, and fit it all within a model’s context window. Additionally, they can, if desired, add source citations.
File Search aims to streamline all of that, although competitor platforms offer similar features. OpenAI’s Assistants API allows developers to utilize a file search feature, guiding an agent to relevant documents for responses. AWS’s Bedrock unveiled a data automation managed service in December.
While File Search stands similarly to these other platforms, Google’s offering abstracts all, rather than just some, elements of the RAG pipeline creation.
Phaser Studio, the creator of AI-driven game generation platform Beam, said in Google’s blog that it used File Search to sift through its library of 3,000 files.
“File Search allows us to instantly surface the right material, whether that’s a code snippet for bullet patterns, genre templates or architectural guidance from our Phaser ‘brain’ corpus,” said Phaser CTO Richard Davey. “The result is ideas that once took days to prototype now become playable in minutes.”
Since the announcement, several users expressed interest in using the feature.
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