Automate RAG Document Updates with Qdrant and OpenAI Integration
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Streamline your Retrieval Augmented Generation (RAG) process by automating document updates in Qdrant. This workflow leverages OpenAI for embeddings and Qdrant for efficient vector storage, ensuring your RAG system always has the latest information.
About This Workflow
This n8n workflow provides a robust solution for managing and updating documents within a Retrieval Augmented Generation (RAG) system. By integrating with OpenAI's powerful embedding models and Qdrant's scalable vector database, you can ensure your RAG knowledge base remains current and accurate. The workflow handles the creation of Qdrant collections, generation of embeddings for new or updated documents, and efficient insertion into the vector store. It also includes functionality to delete outdated documents based on file IDs, simplifying data lifecycle management. Ideal for developers and data engineers looking to build and maintain sophisticated RAG applications.
Key Features
- Automated Document Updates: Seamlessly update your RAG knowledge base as your source documents change.
- OpenAI Embeddings Integration: Utilizes OpenAI's advanced models for high-quality vector representations of your text data.
- Qdrant Vector Store Management: Efficiently stores, retrieves, and manages vector data in Qdrant.
- Dynamic Collection Creation: Automatically sets up necessary Qdrant collections with specified vector configurations.
- Targeted Document Deletion: Removes obsolete document vectors using metadata filtering for cleaner data.
How To Use
- Configure Qdrant Collection Creation: In the
Create collectionnode (8f944f0e-51a0-470e-9735-1ed539522af), updateQDRANTURLandCOLLECTIONto match your Qdrant instance and desired collection name. Adjustsizeanddistanceas needed for your embeddings. - Set Up Credentials: Ensure your
OpenAi accountcredentials are correctly configured for theEmbeddings OpenAInodes (71115f33-f461-4942-9df1-e554e6432054and1c0d5b8c-e53e-47d3-aaac-ddccae80f280). Also, configure yourQdrantApi account (Hetzner)for theQdrant Vector Store(4e5539a5-0f3a-4de9-ba69-0eb7d9c00804) andDelete single file(dfa7b994-13b7-490f-82b7-5ae4ea1e2e7f) nodes. - Define Data Source: The workflow is designed to process binary data. Configure the
Default Data Loadernodes (81d855d2-a883-4e74-ae2a-1a4e722af4d7andf838f548-52dd-4aaf-aa97-9a8029018a1a) to point to your data source (e.g., a previous node that downloads files). - Configure Text Splitting: Adjust
chunkSizeandchunkOverlapin theRecursive Character Text Splitternodes (25c035aa-7a07-47cf-8878-44ac2eab0c3dand07bb3ae1-145f-4784-8409-d3bc73d5522c) to optimize how your documents are segmented for embedding. - Set Up File ID for Deletion: In the
Sticky Note(56e6534e-e191-4756-8bcf-d9ff8fc88b5f) andDelete single file(dfa7b994-13b7-490f-82b7-5ae4ea1e2e7f) nodes, specify how thefile_idis passed to the deletion endpoint. The current example uses{{$json.file_id}}, which assumes thefile_idis available in the JSON output of a preceding node. - Trigger the Workflow: Use the
When clicking ‘Test workflow’node (23afa2cc-7085-474f-aa9e-b110ef17208c) to manually initiate the process or integrate it with your preferred trigger mechanism.
Apps Used
Workflow JSON
{
"id": "fb47e60c-d756-43b0-a3d5-d168fb94b24c",
"name": "Automate RAG Document Updates with Qdrant and OpenAI Integration",
"nodes": 23,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: fb47e60c-d756...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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