How is your experience on Google File Searh Tool?

Gemini API
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Hi everyone,

I’ve been experimenting with the Google File Search Tool and I’m curious whether others have explored its capabilities more deeply. I’m considering integrating it into my RAG workflow, and I’d love to better understand its limitations and strengths.

Parsing

  • How strong is the parsing layer in practice?
  • In my tests, readable PDFs work reasonably well, but any documents with complex layouts, charts, or images containing non-selectable text seem to be ignored entirely. Is this expected, or are there known workarounds?

Retrieval

  • How effective is retrieval when dealing with named entities?
  • In particular, does the search engine handle entity variation or ambiguity well, or does it require exact matching?

Would appreciate insights, best practices, or real-world experiences from anyone who has tried this at scale.

Thanks!

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Hi @jackgurae , Welcome to the AI Forum!!!

Thanks for reaching out to us. The Gemini API enables Retrieval Augmented Generation (“RAG”) through the File Search tool. Here are some capabilities of File Search tool:

  • Parsing: The digital parser, which is used by default, extracts machine-readable text from documents. It detects text blocks, but not document elements such as tables, lists, and headings. The layout parser is recommended when you have rich content and structural elements like layouts, sections, paragraphs, tables, images, and lists to be extracted from documents. Additionally, for scanned PDFs or PDFs with text inside images, you can turn on the OCR parser to improve PDF indexing.

  • Retrieval: File Search uses semantic search technique to find information relevant to the user prompt and understands the meaning and context of your query. It does not require exact matching. When you import a file, it’s converted into numerical representations called embeddings, which capture the semantic meaning of the text.