gemini-file-search-tool
Table of Contents
About
gemini-file-search-tool is a production-ready CLI and Python library for Google's Gemini File Search API, a fully managed Retrieval-Augmented Generation (RAG) system that eliminates the operational complexity of vector databases, embeddings, and retrieval infrastructure.
What is Gemini File Search?
Gemini File Search is Google's fully managed RAG solution that automatically handles document ingestion, chunking, embedding generation, and semantic retrieval. As announced in Google's developer blog, it provides enterprise-grade document search capabilities with zero infrastructure overhead, allowing developers to focus on building intelligent applications rather than managing search infrastructure.
Why This Tool?
This tool provides a CLI-first interface to the Gemini File Search API, designed specifically for integration with AI agents, automation workflows, and human operators:
- Agent-Friendly Design: CLIs provide structured commands with built-in documentation and rich error messages that AI agents can parse and act upon in ReAct (Reasoning and Acting) loops, making them superior to standalone scripts for agentic workflows
- Composable Architecture: JSON output to stdout and logs to stderr enable seamless piping and integration with other tools, perfect for complex automation pipelines
- Reusable Building Blocks: Commands can be composed into larger workflows, used in skills for Claude Code, or integrated into custom automation without modification
- Dual-Mode Operation: Functions as both a CLI tool for command-line operations and a Python library for programmatic integration
- Production Quality: Type-safe, thoroughly tested, with comprehensive error handling that provides actionable feedback for both humans and agents
Whether you're building AI-powered document search, integrating RAG into agentic workflows, or automating knowledge base operations, this tool provides the foundation for reliable, maintainable solutions.
Use Cases
- 📚 Knowledge Base Management: Index documentation, research papers, wikis, and technical specifications into searchable stores for instant retrieval
- 💻 Code-RAG (Retrieval-Augmented Generation for Code): Upload entire codebases to enable semantic code search and natural language querying. Ask questions like "how does authentication work?", "where is error handling implemented?", or "explain the database architecture". Perfect for onboarding, code reviews, architecture discovery, and building AI coding assistants.
- 🔍 Semantic Search: Query your document stores with natural language questions and receive contextually relevant answers with automatic citation
- 🎯 RAG Applications: Build production-ready retrieval-augmented generation systems with JSON-formatted responses including grounding metadata and source attribution
Code-RAG Example
Upload a codebase and query it with natural language:
# Upload your entire codebase
gemini-file-search-tool upload "src/**/*.py" --store "my-project-code" -v
# Query with natural language
gemini-file-search-tool query "How does the authentication system work?" \
--store "my-project-code" --show-cost -v
# Ask architectural questions
gemini-file-search-tool query "What design patterns are used in this codebase?" \
--store "my-project-code" --query-model pro
# Find implementations
gemini-file-search-tool query "Where is error handling for API calls implemented?" \
--store "my-project-code"
