n8n templatesn8n templates

Make OpenAI Citation for File Retrieval RAG

This workflow combines OpenAI assistants with vector storage technology to implement a document retrieval and question-answering function. It can accurately extract relevant content from a document library and generate text with citations. It supports Markdown formatting and HTML conversion, enhancing the readability and professionalism of the output content while ensuring the reliability of the generated information. This makes it suitable for various scenarios such as intelligent Q&A, content creation, enterprise knowledge management, and educational research.

Workflow Diagram
Make OpenAI Citation for File Retrieval RAG Workflow diagram

Workflow Name

Make OpenAI Citation for File Retrieval RAG

Key Features and Highlights

This workflow integrates the OpenAI Assistant with vector storage technology to enable Retrieval-Augmented Generation (RAG) for file-based question answering. It accurately retrieves relevant content from a document repository and automatically generates output text with properly formatted citations. The output supports Markdown formatting and optionally can be converted to HTML, enhancing readability and professional presentation of the content.

Core Problem Addressed

When generating text, the OpenAI Assistant may produce anomalous characters or inaccurate citations. This workflow accesses OpenAI’s file vector storage to retrieve source references and dynamically inserts formatted citations, ensuring the generated content includes reliable source attribution and thereby improving the credibility and professionalism of the information.

Use Cases

  • Intelligent Q&A systems requiring automatic citation and source annotation
  • Content creation or document writing with auto-generated, cited text
  • Enterprise internal knowledge base search and response
  • Literature citation assistance in education and research fields
  • Any scenario combining document retrieval with natural language generation

Main Workflow Steps

  1. Trigger: Create a chat button trigger within n8n to start the workflow.
  2. Invoke OpenAI Assistant: Use the OpenAI Assistant with vector storage integration to perform file retrieval Q&A.
  3. Retrieve Full Conversation Thread: Obtain all messages via HTTP requests to ensure citation completeness.
  4. Split Messages and Citation Content: Parse the conversation thread multiple times to separate messages, text, and citation details for easier processing.
  5. Get Filename by File ID: Call the OpenAI File API to retrieve the specific names of referenced files.
  6. Unified Output Formatting: Use regular expressions to replace and format citations and text, generating Markdown-formatted output with embedded references.
  7. Optional Markdown to HTML Conversion: Support conversion of formatted Markdown content into HTML for web display.
  8. Aggregation: Consolidate all citations and text into a single, coherent output.

Systems and Services Involved

  • OpenAI API (Assistant, File Management, Thread Message Interfaces)
  • n8n Automation Platform (including HTTP Request nodes, Code nodes, Markdown conversion nodes, etc.)

Target Users and Value

  • Developers and Automation Engineers: Quickly build OpenAI-based file retrieval Q&A systems.
  • Content Creators and Editors: Produce text with precise citations to enhance content quality.
  • Enterprise Knowledge Managers: Enable intelligent search and citation within internal knowledge bases for easy information traceability.
  • Educators and Researchers: Assist in generating professional content with literature citations, reducing manual annotation workload.

Designed by Davi Saranszky Mesquita, this workflow offers an efficient and customizable citation formatting solution, making it an ideal tool for intelligent document retrieval and trustworthy question answering.