google-gemini-file-search

You can build a RAG knowledge base without a manual vector database by using Google Gemini's managed File Search API. It automates vector database setup, chunking, and embeddings to provide fully managed document Q&A and semantic search capabilities.

Google Gemini's File Search API supports over 100 file formats for document Q&A, including PDFs, Word, Excel, and code files. This broad format support allows you to ingest diverse documents directly into your searchable knowledge base.

You get automatic citations for document search answers by using the grounding metadata provided by Google Gemini's File Search API. This feature automatically cites the source documents directly within the generated Q&A responses.

A fully managed RAG system using Google Gemini's File Search API handles document chunking and embeddings automatically. It eliminates manual configuration by processing your ingested files and preparing them for semantic search internally.

The best way to create an internal company wiki with natural language questions is to use a managed RAG system like Google Gemini's File Search API. It lets employees ask questions and retrieves answers directly from company policy documents with source citations.

Yes, you can use Google Gemini's managed File Search API for semantic search across code files. It supports over 100 file formats including code, automatically chunking and embedding them to enable accurate semantic search and Q&A.