Build and Update RAG System with Google Drive, Qdrant, and Gemini Chat

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

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.

Google Drive Integration:
Specify the Google Drive folder ID in the "Get files" node.
Ensure Google Drive OAuth credentials are configured.

OpenAI and Gemini Keys:
Add OpenAI API credentials for embeddings (used in "Embeddings OpenAI" nodes).
Configure Google Gemini credentials for the chat model.

Single-File Update:
Set the file_id in the "Edit Fields3" node to target a specific Google Drive file for updates.

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