How can I bypass ChatModel OpenAI processing to deliver a prepared code tool response directly?

Below is one approach that others have used to bypass further ChatModel processing when using a prepared phrase from the Code node:

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1. Branch Your Workflow Early

After your initial vector search, add a conditional (IF) node that checks whether the search returned any result. For example:

• Branch A: When a result is found, continue with your standard AI workflow.
• Branch B: When no result is found (i.e., vector search unsuccessful), trigger the Code tool to output your predefined phrase.

By explicitly splitting the paths, you ensure that the output from the Code tool isn’t “picked up” later by the ChatModel.

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2. Route the Predefined Response Directly

In the branch where no vector result is found, follow these steps:

• Call your Code tool: This node outputs your prepared phrase.
• Direct output node: Connect the Code tool output directly to the Chat Trigger’s final response or a dedicated response node.
  Make sure you do not pass this branch’s data through any Agent or ChatModel node. This way the phrase is sent “as is” to the user.

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3. Adjust Agent / Chat Model Settings (Optional)

If your workflow uses components like an Agent node that determines which tool to use, you can modify its logic to check for a flag or marker in the incoming data. This flag could indicate that the response is coming from the Code tool. Then the Agent can forward that response directly rather than sending it to ChatModel OpenAI for further processing.

For example, in your Code tool, you might add a property to your JSON output such as:

JSCopied
return [{ json: { response: "Your predefined phrase", bypassProcessing: true } }];

Then in the Agent or right before sending the final response, check for bypassProcessing and if true, send the response directly.

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Summary

To send the Code tool output directly without further processing by ChatModel OpenAI:

1. Use a conditional node to split your workflow based on whether vector storage returns results.
2. In the “no result” branch, route the output from the Code tool directly to the chat response node.
3. Optionally, mark the response (via a JSON flag) so that any common processing nodes (like the Agent) recognize it should be passed through unmodified.

By branching your workflow and ensuring the preconfigured response bypasses the ChatModel chain, you can deliver the exact prepared phrase to the user.

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This setup ensures that when your system relies on a predefined phrase, it reaches the user without unintended modifications by ChatModel OpenAI.