Build a Complete RAG System with Automatic Source Citations
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Automate the creation of a sophisticated Retrieval Augmented Generation (RAG) system that provides accurate answers and automatically cites its sources. This workflow leverages Qdrant for vector storage and OpenAI for embeddings, ensuring robust data retrieval and AI-powered responses.
About This Workflow
This n8n workflow empowers you to build a powerful Retrieval Augmented Generation (RAG) system capable of answering questions based on your provided documents, complete with automatic source citations. By integrating LangChain, OpenAI, and Qdrant, the system efficiently processes, indexes, and retrieves information. The workflow begins by creating a Qdrant collection and then processes documents using OpenAI embeddings. These embeddings are stored in Qdrant, enabling precise similarity searches. When a chat message is received, the system retrieves relevant information from Qdrant, feeds it to a Google Gemini chat model, and generates an answer while pinpointing the exact sources used. This ensures transparency and trustworthiness in your AI applications.
Key Features
- Automated RAG Pipeline: Streamline the entire process from document ingestion to AI-powered question answering.
- Qdrant Vector Store Integration: Efficiently store and retrieve vector embeddings for rapid data access.
- OpenAI Embeddings: Utilize state-of-the-art models for high-quality text embeddings.
- Automatic Source Citation: Enhance answer credibility by automatically referencing the origin of information.
- Flexible Document Processing: Supports binary data loading and recursive text splitting for optimal chunking.
How To Use
- Set up Credentials: Ensure you have valid credentials configured for OpenAI and Qdrant (Hetzner) within n8n.
- Configure Qdrant Collection: The
Create collectionnode needs to be correctly configured with your Qdrant API endpoint and the desired collection name (COLLECTION). You may need to manually create the collection initially if the automatic creation fails. - Document Ingestion: Connect your document source to the
Get filenode (assuming it's present in the full workflow). TheDefault Data Loader1will then process these documents. - Embeddings and Indexing: The
Embeddings OpenAI1andQdrant Vector Storenodes handle the embedding of your document content and its insertion into the Qdrant collection. - Question Answering Setup: The
When chat message receivedtrigger initiates the RAG process. TheQuestion and Answer Chainnode, along withGoogle Gemini Chat ModelandVector Store Retriever, are responsible for processing the query and retrieving answers. - Vector Store Configuration: The
Qdrant Vector Store1andEmbeddings OpenAInodes are crucial for retrieving relevant information from your indexed data during the query phase.
Apps Used
Workflow JSON
{
"id": "08e08126-98c3-4124-902e-5eaaebbba449",
"name": "Build a Complete RAG System with Automatic Source Citations",
"nodes": 20,
"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: 08e08126-98c3...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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