Make OpenAI Citation for File Retrieval RAG
This workflow integrates an intelligent assistant and vector storage, aiming to achieve smart Q&A after document retrieval and automatically add literature citations to the retrieved content. Users can format the output results as Markdown or HTML, facilitating the generation of professional documents with dynamic citation numbers, thereby enhancing the credibility and traceability of the information. It is suitable for fields such as research, education, and law, addressing issues of missing citations and strange characters in answers, and helping users efficiently generate standardized documents.
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Workflow Name
Make OpenAI Citation for File Retrieval RAG
Key Features and Highlights
This workflow integrates the OpenAI assistant with vector storage to enable intelligent Q&A based on file retrieval, automatically adding citations and source annotations to the retrieved content. It supports formatting output results in Markdown or HTML, facilitating the generation of professional documents with dynamic citation numbering (e.g., Citation 1, 2, 3), thereby enhancing the credibility and traceability of information.
Core Problems Addressed
- Resolves issues of strange characters and missing citations when the OpenAI assistant generates answers;
- Enables precise retrieval from vector-stored files with automatic attachment of correct bibliographic references;
- Unifies citation management across multiple message threads to avoid omissions and confusion;
- Provides flexible output formats to meet diverse presentation requirements.
Application Scenarios
- Research, education, legal, and information service sectors requiring intelligent Q&A and content generation based on extensive file repositories;
- Content creators and technical teams needing automated generation of reports, documents, or web content with standardized citations;
- Any organization or individual aiming to improve the accuracy and trustworthiness of AI assistant responses.
Main Workflow Steps
- Trigger and Conversation Initiation: Start the interactive dialogue via the built-in chat trigger on the n8n platform.
- Invoke OpenAI Assistant: Use the integrated OpenAI assistant and its vector storage for file retrieval-based Q&A.
- Retrieve Complete Message Thread Content: Obtain all content from the OpenAI message thread through HTTP requests to ensure citation completeness.
- Split Messages and Citations: Separate message content from corresponding citation annotations.
- File Name Retrieval: Fetch the corresponding file name by file ID using the OpenAI API.
- Data Aggregation and Organization: Aggregate the split citations and text for unified management.
- Format Output: Replace citation content with file name–inclusive formats using custom code blocks, optionally converting Markdown to HTML.
- Cache and Memory Management: Maintain conversation context through window buffer memory nodes.
Involved Systems or Services
- OpenAI API: Provides intelligent Q&A and file retrieval capabilities.
- n8n Platform: Serves as the automation workflow execution environment, orchestrating node operations.
- HTTP Request Nodes: Call OpenAI’s file and message thread API endpoints.
- Markdown/HTML Formatting: Supports dynamic output format conversion.
Target Users and Value Proposition
- Researchers and academics seeking to automate bibliographic citation processes;
- Content editors and technical writing teams aiming to improve document generation efficiency and accuracy;
- Enterprise knowledge managers enhancing internal knowledge base quality through automated citations;
- AI product developers striving to boost the professionalism and user trust of intelligent assistants.
This workflow, designed by Davi Saranszky Mesquita, offers high customizability, allowing users to adjust output formats as needed and flexibly apply it to various file retrieval and citation scenarios.
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