Build a RAG system by uploading PDFs to the Google Gemini File Search Store

This workflow implements a Retrieval-Augmented Generation (RAG) system using Google Gemini's File Search API. It allows users to upload files to a dedicated search store and then ask questions about their content in a chat interface.

The system automatically retrieves relevant information from the uploaded files to provide accurate, context-aware answers.

Key Advantages

1. ✅ Seamless Integration of File Upload + AI Context

The workflow automates the entire lifecycle:

• Upload file
• Index file
• Retrieve content for AI chat

Everything happens inside one n8n automation, without manual actions.

2. ✅ Automatic Retrieval for Every User Query

The AI agent is instructed to always query the Search Store.
This ensures:

• More accurate answers
• Context-aware responses
• Ability to reference the exact content the user has uploaded

Perfect for knowledge bases, documentation Q&A, internal tools, and support.

3. ✅ Reusable Search Store for Multiple Sessions

Once created, the Search Store can be reused:

• Multiple files can be imported
• Many queries can leverage the same indexed data

A sustainable foundation for scalable RAG operations.

4. ✅ Visual and Modular Workflow Design

Thanks to n8n’s node-based flow:

• Each step is clearly separated
• Easy to debug
• Easy to expand (e.g., adding authentication, connecting to a database, notifications, etc.)

5. ✅ Supports Both Form Submission and Chat Messages

The workflow is built with two entry points:

• A form for uploading files
• A chat-triggered entry point for RAG conversations

Meaning the system can be embedded in multiple user interfaces.

6. ✅ Compliant and Efficient Interaction With Gemini APIs

Your workflow respects the structure of Gemini’s File Search API:

• /fileSearchStores (create store)
• upload endpoint
• importFile endpoint
• generateContent with file search tools

This ensures compatibility and future expandability.

7. ✅ Memory-Aware Conversations

With the Memory Buffer node, the chat session preserves context across messages—providing a more natural and sophisticated conversational experience.

How it Works

STEP 1 - Create a new Search Store

Triggered manually via the “Execute workflow” node, this step sends a request to the Gemini API to create a FileSearch Store, which acts as a private vector index for your documents.

• The store name is then saved using a Set node.
• This store will later be used for file import and retrieval.

STEP 2 - Upload and import a file into the Search Store

When the form is submitted (through the Form Trigger), the workflow:

1. Accepts a file upload via the form.
2. Uploads the file to Gemini using the /upload endpoint.
3. Imports the uploaded file into the Search Store, making it searchable.

This step ensures content is stored, chunked, and indexed so the AI model can retrieve relevant sections later.

STEP 3 - RAG-enabled Chat with Google Gemini

When a chat message is received:

• The workflow loads the Search Store identifier.
• A LangChain Agent is used along with the Google Gemini Chat Model.
• The model is configured to always use the SearchStore tool, so every user query is enriched by a search inside the indexed files.
• The system retrieves relevant chunks from your documents and uses them as context for generating more accurate responses.

This creates a fully functioning RAG chatbot powered by Gemini.

Set up Steps

Before activating this workflow, you must complete the following configuration:

1. Google Gemini API Credentials: Ensure you have a valid Google AI Studio API key. This key must be entered in all HTTP Request nodes (Create Store, Upload File, Import to Store, and SearchStore).

2. Configure the Search Store:

   • Manually trigger the "Create Store" node once via the "Execute Workflow" button. This will call the Gemini API to create a new File Search Store and return its resource name (e.g., fileSearchStores/my-store-12345).
   • Copy this resource name and update the "Get Store" and "Get Store1" Set nodes. Replace the placeholder value fileSearchStores/my-store-XXX in both nodes with the actual name of your newly created store.

3. Deploy Triggers: For production use, you should activate the workflow. This will generate public URLs for the "On form submission" node (for file uploads) and the "When chat message received" node (for the chat interface). These URLs can be embedded in your applications (e.g., a website or dashboard).

Once these steps are complete, the workflow is ready. Users can start uploading files via the form and then ask questions about them in the chat.

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