Rag Pipeline

RAG Pipeline Plugin

Google File Search RAG - Fully managed RAG using Google's File Search API.

Overview

This plugin provides a streamlined approach to building RAG systems using Google's managed File Search API. No vector database setup, no embedding management - just upload documents and search.

Why Google File Search?

• No Infrastructure - No vector DB to manage
• Automatic Embeddings - Built-in semantic understanding
• Automatic Chunking - Smart document splitting
• Native Gemini Integration - Seamless RAG with Gemini models
• Grounding Support - Built-in citations and source attribution

Features

• Store Management - Create and manage document stores
• Document Upload - Support for PDF, DOCX, HTML, Markdown, text, code (100+ types)
• Semantic Search - Automatic embeddings with File Search tool
• RAG Generation - Complete RAG with Gemini and citations
• Metadata Filtering - Filter search by document attributes

Commands

/rag-pipeline:build

Build a complete Google File Search RAG system with:

• Store creation and configuration
• Document upload scripts
• Search API integration
• RAG endpoint with citations

Agents

@google-file-search-ts

TypeScript/JavaScript specialist using @google/genai SDK:

• Store management (ai.fileSearchStores.create())
• Document upload (ai.fileSearchStores.uploadToFileSearchStore())
• Semantic search (ai.models.generateContent() with fileSearch tool)
• Next.js API route examples
• Citation extraction from grounding metadata

@google-file-search-py

Python specialist using google-genai SDK:

• Store management (client.file_search_stores.create())
• Document upload (client.file_search_stores.upload_to_file_search_store())
• Semantic search (client.models.generate_content() with FileSearch tool)
• FastAPI endpoint examples
• Citation extraction from grounding metadata

@document-processor

Multi-format document processing:

• PDF text extraction
• Word document parsing
• HTML content extraction
• Markdown processing
• Batch processing scripts

Skills

google-file-search

Templates and patterns for both TypeScript and Python:

• Complete client classes
• Store creation patterns
• Upload with chunking configuration
• Search with metadata filtering
• RAG pipelines with citations

document-parsers

Document parsing utilities

chunking-strategies

Chunking configuration for optimal retrieval

Quick Start

TypeScript/JavaScript

npm install @google/genai

import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY! });

// 1. Create store
const store = await ai.fileSearchStores.create({
  config: { displayName: 'my-docs' }
});

// 2. Upload document (uploads AND indexes)
let op = await ai.fileSearchStores.uploadToFileSearchStore({
  file: './document.pdf',
  fileSearchStoreName: store.name,
  config: { displayName: 'document.pdf' }
});

// Wait for indexing
while (!op.done) {
  await new Promise(r => setTimeout(r, 2000));
  op = await ai.operations.get({ operation: op });
}

// 3. Search with File Search tool
const response = await ai.models.generateContent({
  model: 'gemini-2.5-flash',
  contents: 'How does X work?',
  config: {
    tools: [{ fileSearch: { fileSearchStoreNames: [store.name] }}]
  }
});

console.log(response.text);

Python

pip install google-genai

import os
import time
from google import genai
from google.genai import types

client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))

# 1. Create store
store = client.file_search_stores.create(
    config={"display_name": "my-docs"}
)

# 2. Upload document (uploads AND indexes)
operation = client.file_search_stores.upload_to_file_search_store(
    file="./document.pdf",
    file_search_store_name=store.name,
    config={"display_name": "document.pdf"}
)

# Wait for indexing
while not operation.done:
    time.sleep(2)
    operation = client.operations.get(operation)

# 3. Search with File Search tool
response = client.models.generate_content(
    model="gemini-2.5-flash",
    contents="How does X work?",
    config=types.GenerateContentConfig(
        tools=[types.Tool(file_search=types.FileSearch(
            file_search_store_names=[store.name]
        ))]
    )
)

print(response.text)

Documentation

• Google File Search
• API Keys

Requirements

TypeScript/JavaScript

npm install @google/genai

Python

pip install google-genai

Environment Variables

# Required
GOOGLE_API_KEY=your_api_key_here

Supported Models

• gemini-2.5-flash - Fast responses (recommended)
• gemini-2.5-pro - Complex reasoning

Version

3.0.0 - Rebuilt with official SDK patterns for both TypeScript and Python

License

MIT