skill-gemini-file-search-tool

# When to use
- Managing Gemini File Search stores and documents
- Uploading documents for RAG queries (including codebases)
- Querying stores with natural language
- Building Code-RAG systems for semantic code search

# Gemini File Search Tool Skill

## Purpose

Comprehensive guide for managing Google's fully managed RAG (Retrieval-Augmented Generation) system using the `gemini-file-search-tool` CLI. This tool eliminates the complexity of vector databases, embeddings, and retrieval infrastructure by providing a production-ready interface to Gemini File Search.

## When to Use This Skill

**Use this skill when:**
- Creating and managing Gemini File Search stores
- Uploading documents for semantic search and RAG queries
- Building Code-RAG systems (semantic code search with natural language)
- Querying document stores with natural language
- Managing upload caches and operation status
- Implementing document search in AI applications

**Do NOT use this skill for:**
- General document processing (use document-skills instead)
- File system operations (use Read/Write tools)
- Cloud infrastructure management (use AWS/GCP CLIs)

## CLI Tool: gemini-file-search-tool

Production-ready CLI and Python library for Google's Gemini File Search API, a fully managed RAG system that automatically handles document ingestion, chunking, embedding generation, and semantic retrieval with zero infrastructure overhead.

### Installation

```bash
# Clone repository
git clone https://github.com/dnvriend/gemini-file-search-tool.git
cd gemini-file-search-tool

# Install globally with uv
uv tool install .

# Verify installation
gemini-file-search-tool --help
```

### Prerequisites

**Authentication (Choose one):**

**Option 1: Gemini Developer API (Recommended for development)**
```bash
export GEMINI_API_KEY="your-api-key-here"
# Or
export GOOGLE_API_KEY="your-api-key-here"
```
Get API key from: https://aistudio.google.com/apikey

**Option 2: Vertex AI (For production)**
```bash
export GOOGLE_