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Build a RAG system with automatic citations using Qdrant, Gemini & OpenAI

DavideDavide
2661 views
2/3/2026
File MonitoringEmail NotificationGoogle DriveAutomationSendGrid
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This workflow implements a Retrieval-Augmented Generation (RAG) system that:

  • Stores vectorized documents in Qdrant,
  • Retrieves relevant content based on user input,
  • Generates AI answers using Google Gemini,
  • Automatically cites the document sources (from Google Drive).

Workflow Steps

  1. Create Qdrant Collection A REST API node creates a new collection in Qdrant with specified vector size (1536) and cosine similarity.

  2. Load Files from Google Drive The workflow lists all files in a Google Drive folder, downloads them as plain text, and loops through each.

  3. Text Preprocessing & Embedding

    • Documents are split into chunks (500 characters, with 50-character overlap).
    • Embeddings are created using OpenAI embeddings (text-embedding-3-small assumed).
    • Metadata (file name and ID) is attached to each chunk.

  4. Store in Qdrant All vectors, along with metadata, are inserted into the Qdrant collection.

  5. Chat Input & Retrieval

    • When a chat message is received, the question is embedded and matched against Qdrant.
    • Top 5 relevant document chunks are retrieved.
    • A Gemini model is used to generate the answer based on those sources.

  6. Source Aggregation & Response

    • File IDs and names are deduplicated.

    • The AI response is combined with a list of cited documents (filenames).

    • Final output:

      AI Response

Sources: ["Document1", "Document2"]

Main Advantages

  • End-to-end Automation: From document ingestion to chat response generation, fully automated with no manual steps.
  • Scalable Knowledge Base: Easy to expand by simply adding files to the Google Drive folder.
  • Traceable Responses: Each answer includes its source files, increasing transparency and trustworthiness.
  • Modular Design: Each step (embedding, storage, retrieval, response) is isolated and reusable.
  • Multi-provider AI: Combines OpenAI (for embeddings) and Google Gemini (for chat), optimizing performance and flexibility.
  • Secure & Customizable: Uses API credentials and configurable chunk size, collection name, etc.

How It Works

  1. Document Processing & Vectorization

    • The workflow retrieves documents from a specified Google Drive folder.
    • Each file is downloaded, split into chunks (using a recursive text splitter), and converted into embeddings via OpenAI.
    • The embeddings, along with metadata (file ID and name), are stored in a Qdrant vector database under the collection negozio-emporio-verde.

  2. Query Handling & Response Generation

    • When a user submits a chat message, the workflow:
      • Embeds the query using OpenAI.
      • Retrieves the top 5 relevant document chunks from Qdrant.
      • Uses Google Gemini to generate a response based on the retrieved context.
      • Aggregates and deduplicates the source file names from the retrieved chunks.
    • The final output includes both the AI-generated response and a list of source documents (e.g., Sources: ["FAQ.pdf", "Policy.txt"]).

Set Up Steps

  1. Configure Qdrant Collection

    • Replace QDRANTURL and COLLECTION in the "Create collection" HTTP node to initialize the Qdrant collection with:
      • Vector size: 1536 (OpenAI embedding dimension).
      • Distance metric: Cosine.
    • Ensure the "Clear collection" node is configured to reset the collection if needed.

  2. Google Drive & OpenAI Integration

    • Link the Google Drive node to the target folder (Test Negozio in this example).
    • Verify OpenAI and Google Gemini API credentials are correctly set in their respective nodes.

  3. Metadata & Output Customization

    • Adjust the "Aggregate" and "Response" nodes if additional metadata fields are needed.
    • Modify the "Output" node to format the response (e.g., changing Sources: {{...}} to match your preferred style).

  4. Testing

    • Trigger the workflow manually to test document ingestion.
    • Use the chat interface to verify responses include accurate source attribution.

Note: Replace placeholder values (e.g., QDRANTURL) with actual endpoints before deployment.

Need help customizing?

Contact me for consulting and support or add me on Linkedin.

n8n RAG System with Automatic Citations using Qdrant, Gemini, and OpenAI

This n8n workflow demonstrates how to build a Retrieval Augmented Generation (RAG) system capable of answering questions based on provided documents and automatically generating citations for the answers. It leverages Google Drive for document ingestion, Qdrant as a vector store, and integrates both Google Gemini and OpenAI for language model capabilities and embeddings.

What it does

This workflow automates the following steps:

  1. Triggers on Manual Execution or Chat Message: The workflow can be initiated manually for testing or by receiving a chat message (e.g., from a user query).
  2. Loads Documents from Google Drive: It fetches documents (e.g., PDFs, text files) from a specified folder in Google Drive.
  3. Splits Documents into Chunks: The Recursive Character Text Splitter intelligently breaks down the loaded documents into smaller, manageable chunks to optimize for vector embedding and retrieval.
  4. Generates Embeddings (OpenAI): It uses OpenAI's embedding model to convert the text chunks into numerical vector representations.
  5. Stores Vectors in Qdrant: These generated vectors are then stored in a Qdrant vector database, creating a searchable index of your document content.
  6. Retrieves Relevant Information: When a query is received, the Vector Store Retriever searches Qdrant for document chunks whose embeddings are most similar to the query's embedding.
  7. Generates Answers with Citations: The Question and Answer Chain (LangChain) takes the retrieved relevant document chunks and the user's query to generate a comprehensive answer. It is configured to automatically include citations from the source documents.
  8. Utilizes Google Gemini Chat Model: The workflow uses the Google Gemini chat model for the language generation part of the RAG system.
  9. Aggregates Output: The results, including the generated answer and citations, are aggregated for a clear and structured output.
  10. Includes a Wait Node (Optional): A Wait node is included, potentially for rate limiting or to simulate processing time, which can be adjusted or removed.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance (self-hosted or cloud).
  • Google Drive Account: With access to the documents you wish to use for your RAG system.
  • Google Gemini API Key: For the Google Gemini Chat Model.
  • OpenAI API Key: For the Embeddings OpenAI node.
  • Qdrant Instance: A running Qdrant vector database instance (local or cloud). You'll need its API URL and potentially an API key.
  • LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Google Drive: Set up a Google Drive credential to access your documents.
    • Google Gemini: Configure a Google Gemini credential with your API key.
    • OpenAI: Set up an OpenAI credential with your API key.
    • Qdrant: Configure a Qdrant credential with your Qdrant instance URL and API key (if required).
  3. Configure Google Drive Node (58):
    • Specify the "Folder ID" where your source documents are located.
    • Adjust the "File Type" if you are using specific document formats (e.g., application/pdf, text/plain).
  4. Configure Qdrant Vector Store Node (1248):
    • Provide your Qdrant "Collection Name".
    • Ensure the "Qdrant API" credential is correctly selected.
  5. Configure Embeddings OpenAI Node (1141):
    • Ensure the "OpenAI API" credential is correctly selected.
  6. Configure Google Gemini Chat Model Node (1262):
    • Ensure the "Google Gemini API" credential is correctly selected.
  7. Test the Workflow:
    • You can manually execute the workflow using the Manual Trigger node to test document ingestion and vector storage.
    • To test the RAG query, use the Chat Trigger node and provide a sample question.
  8. Activate the Workflow: Once configured and tested, activate the workflow to enable it to respond to chat messages or other triggers you might connect.

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