📖 Build and update RAG system with Google Drive, Qdrant, and Gemini Chat

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Description

This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It enables full or incremental updates to documents in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering.

Here is a clear and professional description in English of the n8n workflow “Create a RAG with Qdrant and update single files”, including its benefits:

Benefits

• Efficient RAG Setup Seamlessly integrates OpenAI, Qdrant, and Google Drive to create a scalable RAG pipeline.

• Single File Update You can replace the vector representation of a single file without reprocessing the entire collection—ideal for maintaining document freshness.

• Flexible File Source Works with Google Drive, allowing document management and updates from a familiar interface.

How It Works

This workflow is designed to create a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as a document source. It consists of four main phases:

• Collection Setup:

  • Creates or clears a Qdrant collection to store vectorized documents.
  • Configures the collection with cosine distance metrics and other parameters.

• Document Processing:

  • Retrieves files from a specified Google Drive folder.
  • Downloads and processes each file (text extraction, chunking, and embedding using OpenAI).
  • Stores the embeddings in Qdrant for vector search.

• Single-File Update:

  • Allows updating or deleting a specific file in the Qdrant collection by referencing its Google Drive ID.
  • Re-embeds the file and updates the vector store.

• RAG Querying:

  • Uses a chat trigger to receive user questions.
  • Retrieves relevant documents from Qdrant using vector similarity.
  • Generates answers using Google Gemini as the language model.

Set Up Steps

1. Configure Qdrant:

   • Replace QDRANTURL and COLLECTION in the “Create collection” and “Clear collection” HTTP nodes.
   • Ensure Qdrant API credentials are correctly set in the credentials section.

2. Google Drive Integration:

   • Specify the Google Drive folder ID in the “Get files” node.
   • Ensure Google Drive OAuth credentials are configured.

3. OpenAI and Gemini Keys:

   • Add OpenAI API credentials for embeddings (used in “Embeddings OpenAI” nodes).
   • Configure Google Gemini credentials for the chat model.

4. Single-File Update:

   • Set the file_id in the “Edit Fields3” node to target a specific Google Drive file for updates.

5. Testing:

   • Trigger the workflow manually to populate the Qdrant collection.
   • Use the chat interface to test RAG responses.

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🔗 Nodes Used

HTTP Request, Google Drive, Question and Answer Chain, Embeddings OpenAI, Vector Store Retriever, Recursive Character Text Splitter

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup