openai 3.4.0 -> 3.5.0: +81 strings, -552 strings, 37 interesting

[model_id] (1)
  + py
        with client.chat.completions.stream(
            model="gpt-4o-2024-08-06",
            messages=[...],
        ) as stream:
            for event in stream:
                if event.type == "content.delta":
                    print(event.delta, flush=True, end="")

[feature_flag] (4)
  + py
        connection = client.beta.realtime.connect(...).enter()
        # ...
        connection.close()
  + py
        connection = client.beta.responses.connect(...).enter()
        # ...
        connection.close()
  + py
    connection = client.beta.realtime.connect(...).enter()
    # ...
    connection.close()
  + py
    connection = client.beta.responses.connect(...).enter()
    # ...
    connection.close()

[sentence] (37)
  + py
        with client.chat.completions.stream(
            model="gpt-4o-2024-08-06",
            messages=[...],
        ) as stream:
            for event in stream:
                if event.type == "content.delta":
                    print(event.delta, flush=True, end="")
  + py
        connection = client.beta.realtime.connect(...).enter()
        # ...
        connection.close()
  + py
        connection = client.beta.responses.connect(...).enter()
        # ...
        connection.close()
  + py
    connection = client.beta.realtime.connect(...).enter()
    # ...
    connection.close()
  + py
    connection = client.beta.responses.connect(...).enter()
    # ...
    connection.close()
  + - Based on your data it seems like you're trying to fine-tune a model for {ft_type}
- For classification, we recommend you try one of the faster and cheaper models, such as `ada`
- For classification, you can estimate the expected model performance by keeping a held out dataset, which is not used fo
  + - There are {len(long_indexes)} examples that are very long. These are rows: {long_indexes}
For conditional generation, and for classification the examples shouldn't

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