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DOCS: Fix typo/line break in python code (#13708)
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@ -26,16 +26,15 @@ As shown, the evaluator sees that the user is increasingly frustrated, indicatin
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The user feedback is inferred by custom `RunEvaluator`. This evaluator is called using the `EvaluatorCallbackHandler`, which run it in a separate thread to avoid interfering with the chat bot's runtime. You can use this custom evaluator on any compatible chat bot by calling the following function on your LangChain object:
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The user feedback is inferred by custom `RunEvaluator`. This evaluator is called using the `EvaluatorCallbackHandler`, which run it in a separate thread to avoid interfering with the chat bot's runtime. You can use this custom evaluator on any compatible chat bot by calling the following function on your LangChain object:
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```python
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```python
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my_chain
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my_chain.with_config(
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.with_config(
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callbacks=[
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callbacks=[
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EvaluatorCallbackHandler(
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EvaluatorCallbackHandler(
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evaluators=[
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evaluators=[
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ResponseEffectivenessEvaluator(evaluate_response_effectiveness)
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ResponseEffectivenessEvaluator(evaluate_response_effectiveness)
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]
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]
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)
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)
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],
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],
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)
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)
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```
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```
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The evaluator instructs an LLM, specifically `gpt-3.5-turbo`, to evaluate the AI's most recent chat message based on the user's followup response. It generates a score and accompanying reasoning that is converted to feedback in LangSmith, applied to the value provided as the `last_run_id`.
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The evaluator instructs an LLM, specifically `gpt-3.5-turbo`, to evaluate the AI's most recent chat message based on the user's followup response. It generates a score and accompanying reasoning that is converted to feedback in LangSmith, applied to the value provided as the `last_run_id`.
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