google-gemini-file-search

You can build a document Q&A knowledge base without a vector database by using Google Gemini's File Search API to handle semantic search and automatic chunking internally. This managed RAG approach eliminates the need for custom embedding pipelines, directly processing uploaded documents to provide natural language query responses.

Semantic search using Google Gemini supports over 100 file formats for ingestion. You can upload diverse document types directly into the system to build your knowledge base, and the API automatically processes these varied formats for retrieval without requiring manual conversion or preprocessing.

No, you do not need custom embedding code to create a RAG system with Gemini File Search. The API provides a fully managed Retrieval-Augmented Generation environment that automatically handles document chunking and embedding generation internally, bypassing the need for separate infrastructure or manual vectorization logic.

To get accurate citations for answers generated from internal documentation, the Gemini File Search API provides built-in grounding metadata. When users ask questions in natural language, the system returns responses with precise source citations, linking directly back to the original uploaded documents for verification.

The best way to query internal company policies using natural language is by uploading your policy documents to a managed RAG system. Google Gemini's File Search API processes the files, allowing employees to ask questions conversationally and receive grounded answers with direct citations to the source text.