What file types does Google File Search support?

It supports over 100 file types including PDF, DOCX, XLSX, CSV, Markdown, and various programming languages like Python, Java, and JavaScript.

Does this skill handle API key security?

Yes, all templates and examples follow strict security protocols, requiring environment variables and .gitignore configuration to prevent accidental exposure of Google AI API keys.

Can I filter search results by metadata?

Absolutely. The skill provides templates for AIP-160 compliant filter syntax, allowing you to narrow searches by author, date, category, or custom numeric ranges.

Which Gemini models are compatible with this RAG skill?

This skill is optimized for Gemini 2.5 Pro (for complex reasoning) and Gemini 2.5 Flash (for speed), both of which support integrated file search and grounding metadata.

How does this skill help reduce AI hallucinations?

By implementing grounding citations, the skill forces the model to reference specific source document chunks, providing a transparent path to verify AI-generated claims.

Google File Search RAG

Name: Google File Search RAG
Author: vanman2024

byvanman2024

데이터 과학 및 ML

Implements managed Retrieval-Augmented Generation (RAG) using Google File Search and Gemini models for high-accuracy document retrieval and grounding.

소개

This skill provides a comprehensive toolkit for developers building RAG pipelines within the Google AI ecosystem. It includes production-ready templates and scripts for managing persistent file search stores, configuring intelligent document chunking, and implementing semantic search with metadata filtering. By leveraging Gemini 2.5 models and grounding citations, the skill ensures AI responses are accurate, verifiable, and backed by your specific document corpus, making it ideal for building enterprise-grade knowledge bases and intelligent search assistants.

주요 기능

Seamless integration with Gemini 2.5 Pro and Flash models for AI grounding
0 GitHub stars
Automated document chunking and embedding generation for 100+ file types
Grounding citation extraction to provide transparent source attribution
Metadata-based filtering and semantic search for high-precision retrieval
Persistent store management including indexing, deletion, and quota monitoring

사용 사례

Developing technical documentation search engines for large software projects
Implementing automated fact-checking systems using proprietary datasets
Building an internal enterprise knowledge base with verifiable citations

소개

주요 기능

Seamless integration with Gemini 2.5 Pro and Flash models for AI grounding
0 GitHub stars
Automated document chunking and embedding generation for 100+ file types
Grounding citation extraction to provide transparent source attribution
Metadata-based filtering and semantic search for high-precision retrieval
Persistent store management including indexing, deletion, and quota monitoring

사용 사례

Developing technical documentation search engines for large software projects
Implementing automated fact-checking systems using proprietary datasets
Building an internal enterprise knowledge base with verifiable citations