What authentication methods are supported for Gemini?

The tool supports both the Gemini Developer API using an API Key (recommended for development) and Google Cloud Vertex AI (recommended for production environments).

Does this skill support local code repositories?

Yes, it includes specialized Code-RAG support allowing you to upload and query local source code using natural language with glob pattern support.

What is the primary benefit of using this Gemini File Search tool?

It provides a fully managed, zero-infrastructure RAG system that automatically handles document ingestion, chunking, embedding generation, and semantic retrieval via Google's Gemini API.

How does the upload caching system work?

The tool uses mtime-based optimization to perform O(1) checks, automatically skipping unchanged files during the upload process to maximize efficiency.

Can I track the usage costs of my AI queries?

Yes, the tool includes a --show-cost flag that provides detailed token usage and estimated USD cost calculations for every RAG query performed.

Gemini File Search Tool

Name: Gemini File Search Tool
Author: dnvriend

bydnvriend

数据科学与机器学习

Manages Google Gemini File Search stores and implements production-ready RAG systems for documents and codebases.

关于

The Gemini File Search Tool skill provides a comprehensive interface for Google's fully managed Retrieval-Augmented Generation (RAG) system. It eliminates the complexity of vector databases, embeddings, and retrieval infrastructure by providing a production-ready interface to Gemini File Search. Developers can use this skill to automate document ingestion, perform semantic code searches, and execute natural language queries with source citations, making it ideal for building intelligent documentation systems and AI-powered internal search tools.

主要功能

Built-in Code-RAG support for semantic search across codebases
0 GitHub stars
Intelligent document uploading with mtime-based caching and glob support
Automated store management for Gemini File Search infrastructure
Natural language RAG queries with source citations and cost estimation
Native integration with both Gemini Developer API and Vertex AI

使用场景

Implementing Code-RAG systems for natural language exploration of large source repositories
Building semantic documentation search engines for technical manuals and PDF libraries
Automating document ingestion pipelines for AI-powered knowledge management tools

关于

主要功能

Built-in Code-RAG support for semantic search across codebases
0 GitHub stars
Intelligent document uploading with mtime-based caching and glob support
Automated store management for Gemini File Search infrastructure
Natural language RAG queries with source citations and cost estimation
Native integration with both Gemini Developer API and Vertex AI

使用场景

Implementing Code-RAG systems for natural language exploration of large source repositories
Building semantic documentation search engines for technical manuals and PDF libraries
Automating document ingestion pipelines for AI-powered knowledge management tools