How can my n8n chatbot return complete data results from a Supabase database?

Below is a checklist of steps and suggestions to narrow down the issue and ensure that your chatbot returns the full list of items:

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1. Verify That Supabase Returns All Rows

• Default Row Limits:
Some Supabase queries may return a default number of rows (for example, if using PostgREST in the background).
• Action:
Double-check your Supabase query. If needed, specify a range (e.g. using .range(0, 2058)) to ensure all 2059 rows are returned. You could try testing the query separately (perhaps with a REST client) to see if all rows are retrieved.

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2. Inspect the n8n Workflow

• Data Passing:
Ensure that the node fetching the data from Supabase passes the complete dataset to subsequent nodes (be it a Code node or a Question and Answer Chain node).
• Field Naming:
Confirm that the incoming data includes the fields you expect (for example, if using the AI node, the default field name might be chatInput).

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3. Consider Prompt and OpenAI Token Limits

• Longer Responses:
If OpenAI’s Chat model is truncating its output, it might be due to the maximum token limit.
• Action:
Increase the “Maximum Number of Tokens” in your OpenAI node configuration. This allows the model to use more tokens for its response so that it can list all items.

• Prompt Clarity:
Review your prompt to ensure that you’re explicitly asking for a complete list. In some cases, restructuring the prompt or splitting the query into smaller parts (for example, filtering by sub-ranges of the dataset) might help.

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4. Check for Workflow Execution Settings

• EXECUTIONS_PROCESS:
Since you're not certain what your n8n EXECUTIONS_PROCESS setting is, verify that it isn’t causing partial execution or data loss.
• Action:
Review the execution environment details in your n8n settings or Docker logs to ensure that nothing is interfering with the complete processing of data.

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Summary

1. Supabase Query: Confirm you are fetching all rows (consider adding a range).
2. Data Flow: Ensure no node in your n8n workflow inadvertently limits or fails to pass along the complete dataset.
3. OpenAI Configuration: Increase max tokens in your OpenAI configuration and adjust your prompt to request a complete answer.
4. Execution Settings: Verify that your execution settings (n8n version and Docker configuration) are not imposing unforeseen limits.

By following these steps, you should be able to isolate whether the issue comes from the database query, the data processing in n8n, or the token-related response limitations of OpenAI’s model.

Hope this helps!