when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. Regularization techniques help in reducing the complexity of the model by adding a penalty to the loss function. This penalty encourages the model to have smaller weights or fewer features, making it more generalized and less prone to overfitting. The goal is to find the right balance between fitting the training data well and being able to generalize well to unseen data.async for chunk in chat.astream_log(messages):    print(chunk)    RunLogPatch({'op': 'replace',      'path': '',      'value': {'final_output': None,                'id': '754c4143-2348-46c4-ad2b-3095913084c6',                'logs': {},                'streamed_output': []}})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content='')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content='The')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' purpose')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' of')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' model')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' regularization')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' is')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' to')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' prevent')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' a')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content='
Head to Integrations for documentation on built-in integrations with chat model providers.
Head to Integrations for documentation on built-in integrations with chat model providers. ->: when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. Regularization techniques help in reducing the complexity of the model by adding a penalty to the loss function. This penalty encourages the model to have smaller weights or fewer features, making it more generalized and less prone to overfitting. The goal is to find the right balance between fitting the training data well and being able to generalize well to unseen data.async for chunk in chat.astream_log(messages):    print(chunk)    RunLogPatch({'op': 'replace',      'path': '',      'value': {'final_output': None,                'id': '754c4143-2348-46c4-ad2b-3095913084c6',                'logs': {},                'streamed_output': []}})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content='')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content='The')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' purpose')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' of')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' model')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' regularization')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' is')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' to')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' prevent')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content=' a')})    RunLogPatch({'op': 'add',      'path': '/streamed_output/-',      'value': AIMessageChunk(content='