1. What this workflow does
This workflow helps you talk with an OpenAI assistant that can find information from many documents stored as vector data.
It fixes problems like strange characters and missing citation details in answers.
The result is clean, easy-to-check answers with correct file names for sources.
It saves lots of time by stopping the need to fix or search citations manually after a chat.
You get fast, reliable text with good references from your documents.
2. Who should use this workflow
This workflow is good for knowledge workers who ask AI about many files.
Especially if you want clear citations and not messy AI answers.
People using OpenAI assistants with vector search will find it most useful.
Users who want to save half an hour or more per day fixing AI outputs will get real benefits.
If you want a cleaner way to share AI-generated info with coworkers, this helps.
3. Tools and services used
- n8n: Automation tool to run the workflow.
- LangChain Chat Trigger node: Starts chat inside n8n.
- OpenAI Assistant with Vector Store node: Questions AI plus document passage search.
- HTTP Request nodes: To get full thread messages and file info.
- Split Out nodes: Break down messages and citations.
- Set and Aggregate nodes: Prepare and collect citation info.
- Code node: Formats final text replacing raw citations.
These combine to handle input questions, process citations, and output good text.
4. Beginner step-by-step: How to use this workflow in n8n
Download and import the workflow
- Download the workflow file using the Download button on this page.
- Open your n8n editor.
- Use the Import from File option.
- Select the downloaded workflow file to add it.
Configure API keys and IDs
- Add your OpenAI API Key to the OpenAI credentials inside n8n.
- Check the OpenAI Assistant with Vector Store node and update the assistantId to your AI assistant’s ID.
- Verify other IDs like threadId or file IDs if needed.
Test and activate
- Run the workflow with a test query to see if it returns answers with citations.
- Fix any errors by checking credentials or inputs.
- Once working, activate the workflow to let users access it via the chat button in n8n.
Now the workflow works in production to give clean referenced answers.
Consider using self-host n8n if running on your own server.
5. How the workflow works
Inputs
- User asks a question via the chat button in n8n.
- The LangChain Chat Trigger node receives the input.
Processing steps
- The OpenAI Assistant with Vector Store node uses the question to search vector documents and make an AI answer that includes citation references.
- An HTTP Request node fetches all thread messages to get complete citation texts.
- Multiple Split Out nodes break down the thread messages into separate messages, then into content parts, and finally isolate each citation.
- For every citation, an HTTP Request fetches the file name from OpenAI’s file API using the file ID.
- A Set node cleans and organizes citation details.
- An Aggregate node gathers all citation info into one array.
- A Code node finishes by replacing raw citations in the answer with filename-based references using Markdown.
Output
The final text has clean content with clearly formatted citations.
This helps users trust and use the AI output faster.
6. Inputs and outputs details
Inputs
- User chat messages containing questions.
- OpenAI assistant ID linked to a vector file store.
Outputs
- Answer text with replaced citations showing original filenames.
- Well-structured JSON with conversation and file metadata.
This structure makes it easy to copy, share, or convert answers for reports.
7. Edge cases and troubleshooting
- Error authenticating with OpenAI API
Cause: Wrong or expired API Key in n8n credentials.
Fix: Update API Key inside n8n and test again. - No citations retrieved
Cause: Missing threadId or incomplete API response.
Fix: Ensure HTTP Request gets full thread messages; check data passing. - Missing file info for citation
Cause: File deleted or missing in OpenAI file list.
Fix: The workflow continues on error but check file existence.
Always keep Split Out nodes set to always output data.
Run test queries to confirm all parts work before production.
8. Customization ideas
- Change citation style inside Code node to use clickable links instead of just filenames.
- Enable optional Markdown to HTML node to get web-ready output.
- Add more documents in the OpenAI vector store and update assistantId accordingly.
- Edit LangChain Chat Trigger node UI text or chat settings for user experience.
These adjustments help tailor the workflow to fit different use cases.
9. Deployment basics
Activate the workflow in n8n by switching it on.
Users will see a chat button in the interface to ask questions.
Check workflow runs and logs to catch and fix issues.
Update API keys or assistantId if versions change.
If you want more control and uptime, consider running self-host n8n on a server.
This gives better availability for growing team use.
10. Summary of results
✓ Saves time fixing AI answers by automating citation retrieval.
✓ Gives cleaner, human-readable outputs with clear file references.
✓ Supports scalable document searches with vector data.
✓ Easy to import and configure in n8n.
✓ Improves trust and usability of AI-generated information.
