[View code on GitHub](https://github.com/creatorrr/turbo-chat/blob/master/turbo_chat/utils/tokens.py)

This code provides utility functions to work with OpenAI models in the `turbo-chat` project, specifically focusing on token counting and handling model-specific token limits. It imports necessary libraries, defines a dictionary of model token windows, and implements two functions: `get_max_tokens_length` and `count_tokens`.

The `MODEL_WINDOWS` dictionary maps OpenAI model names to their respective token limits. For example, "gpt-4" has a limit of 8,192 tokens, while "gpt-3.5-turbo" has a limit of 4,096 tokens.

The `get_max_tokens_length` function takes a model name as input and returns the maximum token length for that model. It iterates through the `MODEL_WINDOWS` dictionary and checks if the input model name starts with the model prefix. If it finds a match, it returns the corresponding token limit. If the model name is not found, it raises a `ValueError`.

```python
max_tokens = get_max_tokens_length("gpt-3.5-turbo")
```

The `count_tokens` function takes a list of messages and a `TurboModel` object as input and returns the total number of tokens in the messages. It first retrieves the appropriate encoding for the model using `tiktoken.encoding_for_model`. Then, it checks if the model is "gpt-3.5-turbo-0301" and calculates the token count accordingly. For other models, it simply sums up the tokens in the messages' content.

```python
messages = [{"content": "Hello, how are you?"}, {"content": "I'm fine, thank you!"}]
model = TurboModel("gpt-3.5-turbo")
total_tokens = count_tokens(messages, model)
```

These utility functions can be used in the larger `turbo-chat` project to ensure that the input and output tokens stay within the model's token limits, preventing errors and optimizing the usage of OpenAI API calls.
## Questions: 
 1. **Question**: What is the purpose of the `get_max_tokens_length` function and how does it determine the maximum token length for a given model?
   **Answer**: The `get_max_tokens_length` function returns the maximum token length for a given model by checking if the model name starts with any of the keys in the `MODEL_WINDOWS` dictionary. If a match is found, it returns the corresponding value as the maximum token length.

2. **Question**: How does the `count_tokens` function handle token counting differently for the "gpt-3.5-turbo-0301" model compared to other models?
   **Answer**: For the "gpt-3.5-turbo-0301" model, the `count_tokens` function calculates the number of tokens by iterating through each message and considering the role/name and content tokens, as well as additional tokens for message formatting. For other models, it simply sums up the tokens for the content of each message, without considering role/name or formatting tokens.

3. **Question**: What is the purpose of the `tiktoken` library in this code and how is it used in the `count_tokens` function?
   **Answer**: The `tiktoken` library is used to encode text into tokens for a specific model. In the `count_tokens` function, it is used to obtain the encoding for the given `TurboModel` and then encode the content of each message into tokens, which are then counted to determine the total number of tokens.