Empower Your AI with Custom Data: RAG Workflow Automation
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Seamlessly integrate your custom documents into AI-powered applications. This workflow automates the process of loading, embedding, and retrieving information from your files, enabling your AI to answer questions with context directly from your data.
About This Workflow
Unlock the power of Retrieval Augmented Generation (RAG) with this intuitive n8n workflow. This solution allows you to effortlessly transform your documents (PDFs, CSVs) into an AI's knowledge base. It first processes your uploaded files, creating embeddings for efficient search. Then, it sets up an AI agent capable of understanding and answering questions based on the content of your documents. This is perfect for building intelligent chatbots, Q&A systems, or augmenting existing AI models with your proprietary information, ensuring the AI's responses are accurate and relevant to your specific data.
Key Features
- Easy File Upload: Drag and drop your PDF and CSV files directly into the workflow to initiate data ingestion.
- Automated Data Processing: Leverages LangChain's robust data loaders and embedding models (OpenAI) for efficient transformation.
- In-Memory Vector Storage: Stores your data's embeddings in an accessible in-memory vector store for quick retrieval.
- AI Agent Integration: Powers an AI agent that can access your custom knowledge base to provide contextual answers.
- Real-time Chat Interaction: Responds to chat messages, acting as a conversational interface to your data.
How To Use
- Execute the 'Load Data' Flow: Click the 'Execute Workflow' button to start the data ingestion process.
- Upload Your Files: Use the 'Upload your file here' node to upload your PDF or CSV documents.
- Run the 'Retriever' Flow: Click the 'Open Chat' button to activate the AI agent.
- Ask Questions: In the chat interface, ask questions related to the content of the files you uploaded.
- Configure Embeddings (Optional): Ensure the 'Embeddings OpenAI' node is configured with your OpenAI API credentials for embedding generation. The workflow uses the same embedding node for both insertion and retrieval to maintain consistency.
Apps Used
Workflow JSON
{
"id": "96b383e6-f94a-4c3a-a3e3-0ab97d9b4997",
"name": "Empower Your AI with Custom Data: RAG Workflow Automation",
"nodes": 20,
"category": "Marketing",
"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: 96b383e6-f94a...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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