This article provides a complete, practical walkthrough of the Splitout Code Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automating RAG Citations with OpenAI Assistant and n8n: A No-Code Workflow for File-Based Source Referencing
   
   Meta Description:
   Learn how to build an automated system in n8n that uses OpenAI’s Assistant and file retrieval (RAG) to generate citations and source output from uploaded documents — complete with Markdown formatting and optional HTML conversion.
   
   Keywords:
   n8n, OpenAI Assistant, RAG, Retrieval-Augmented Generation, vector store, file retrieval, AI citations, markdown citation, HTML output, automation workflow, LangChain, no-code tool, content formatting, OpenAI API, document assistant
   
   Third-Party APIs Used:
   
   - OpenAI API (v1 endpoints for assistants, threads, and file retrieval)
   - n8n Nodes: Langchain (OpenAI Assistant, chat trigger, buffer memory)
   - Optional: Markdown conversion node (within n8n)
   
   —
   
   📄 Article:
   
   Automating File-Based Citations from Chat with OpenAI in n8n
   
   Retrieval-Augmented Generation (RAG) is quickly gaining popularity in the AI community for its ability to enhance model outputs with contextual accuracy sourced from documents. But with great power comes great complexity — especially when it’s time to map retrieved content back to its original source files. Fortunately, using n8n, a powerful workflow automation platform, developers and non-developers alike can bridge this gap using OpenAI's Assistant API and a vector store system.
   
   In this article, we explore a workflow titled “Make OpenAI Citation for File Retrieval RAG” that automates the process of retrieving a user's query from a GPT-powered assistant, parsing the citations and related sources used in the answer, and formatting them cleanly in Markdown (or optionally, HTML). This no-code/low-code approach eliminates the need for manual formatting and allows dynamic chat-based citation generation within any n8n instance.
   
   🎯 Use Case
   
   This workflow is ideal for anyone building a documentation assistant, chatbot, or report generation tool powered by OpenAI’s Assistant. When your assistant retrieves information from uploaded files in its vector store, this workflow ensures that accompanying citations such as (1), (2), or (filename.pdf) are dynamically injected into the output — making the content more transparent and verifiable.
   
   💡 Bonus Use Case: A chat button inside n8n is added via a chatTrigger node, allowing users to test interactions without leaving the automation platform.
   
   🔧 Workflow Summary
   
   Here's an overview of how the workflow operates:
   
   1. Initiate Conversation
      - A chat trigger node (“Create a simple Trigger to have the Chat button within N8N”) starts the workflow by capturing user queries submitted inside the n8n ecosystem.
   
   2. Ask the Assistant
      - The assistant node (“OpenAI Assistant with Vector Store”) sends the question to OpenAI, which runs context-aware retrieval via a preconfigured assistant (with access to a vector store of uploaded files).
   
   3. Fetch Assistant Thread
      - The workflow retrieves all messages in the thread via the OpenAI Threads API using a standard HTTP request node. This step is essential because the assistant does not always return annotated citations in a single response. Each response can contain helpful citations embedded in annotations.
   
   4. Parse and Split Content
      - Multiple splitOut nodes deconstruct the assistant response:
        - Messages are split.
        - Message contents are split.
        - Citations (annotations) are split from individual messages.
   
   5. Look Up File Metadata
      - For every citation, a subsequent HTTP request fetches the corresponding file name using its ID from OpenAI’s file endpoint:
        https://api.openai.com/v1/files/{{file_id}}
   
   6. Normalize Data
      - Using a set node, the workflow reduces each file citation into a structured object containing:
        - File ID
        - Filename
        - Annotated text snippet to be substituted with a reference.
   
   7. Combine All Citations
      - The aggregate node collects all citations to be processed in bulk and passed into the next stage.
   
   8. Format Final Output
      - A JavaScript code node modifies the assistant’s original response, replacing each annotated snippet with a more human-readable reference (e.g., _(source.pdf)_ in Markdown). 
      - The result is a beautifully templated message suitable for reports, documentation, or display in GUI interfaces.
   
   9. Optional: HTML Conversion
      - A disabled Markdown node is included for developers who want to convert the final Markdown result into HTML for web rendering or email formatting.
   
   📘 Key Features
   
   - Eliminates malformed character outputs in citations
   - Applies dynamic file-based referencing in Markdown or HTML
   - Automatically retrieves all relevant citations, even hidden ones
   - Fully extensible with additional transformations (e.g., inline links, footnotes)
   
   🔐 Setup Required
   
   To run this workflow, you’ll need:
   
   - A valid OpenAI API Key
   - An Assistant configured in OpenAI’s platform with vector store enabled
   - Uploaded files into the assistant’s vector storage
   - An n8n instance with LangChain-node support (for memory and assistant execution)
   - (Optional) Enable Markdown-to-HTML formatting if needed
   
   🧠 Extensibility Tips
   
   - You can customize citation formatting (e.g., turn _(source.pdf)_ into [source.pdf](#link)).
   - Add logging steps to track which files are cited most often.
   - Use the memory buffer node to maintain conversation context in multi-turn dialogue systems.
   
   📎 Conclusion
   
   Building a RAG-powered citation assistant doesn't need to be complicated. Thanks to OpenAI's APIs and n8n’s expressive no-code environment, automating tasks like citation sourcing, content formatting, and AI-driven document querying becomes achievable even by non-developers.
   
   Whether you're creating a chat-based document research assistant or streamlining AI-generated reports with verifiable sources, this workflow is a powerful building block in your AI automation toolkit.
   
   🔗 Connect with the original creator: Davi Saranszky Mesquita  
   LinkedIn: https://www.linkedin.com/in/mesquitadavi/
   
   — end —

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.