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Llm

LLM

A class for running a Language Model Chain.

Source code in write_the/llm.py 
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class LLM:
    """
    A class for running a Language Model Chain.
    """
    def __init__(self, prompt: PromptTemplate, temperature=0, model_name="gpt-3.5-turbo-instruct"):
        """
        Initializes the LLM class.

        Args:
          prompt (PromptTemplate): The prompt template to use.
          temperature (int, optional): The temperature to use for the model. Defaults to 0.
          model_name (str, optional): The name of the model to use. Defaults to "gpt-3.5-turbo-instruct".

        Side Effects:
          Sets the class attributes.

        Raises:
          KeyError: If the model_name is not found in the models dictionary.
        """
        self.prompt = prompt
        self.prompt_size = self.number_of_tokens(prompt.template)
        self.temperature = temperature
        self.model_name = model_name
        try:
            self.max_tokens = int(models[model_name]["context_window"])
        except KeyError:
            self.max_tokens = 4096
            if model_name.startswith('gpt-4'):
                self.max_tokens = 8192
            elif model_name.startswith('gpt-3'):
                self.max_tokens = 4096

    async def run(self, code, **kwargs):
        """
        Runs the Language Model Chain asynchronously.

        Args:
          code (str): The code to use for the chain.
          **kwargs (dict): Additional keyword arguments.

        Returns:
          str: The generated text.
        """
        if "-instruct" in self.model_name:
            llm = OpenAI(
                temperature=self.temperature, max_tokens=-1, model_name=self.model_name
            )
        else:
            llm = ChatOpenAI(
                temperature=self.temperature, model_name=self.model_name
            )
        chain = LLMChain(llm=llm, prompt=self.prompt)
        return await chain.apredict(code=code, **kwargs)

    def number_of_tokens(self, text):
        """
        Counts the number of tokens in a given text.

        Args:
          text (str): The text to count tokens for.

        Returns:
          int: The number of tokens in the text.
        """
        encoding = tiktoken.encoding_for_model("gpt-4")
        return len(encoding.encode(text))

__init__(prompt, temperature=0, model_name='gpt-3.5-turbo-instruct')

Initializes the LLM class.

Parameters:

Name Type Description Default
prompt PromptTemplate

The prompt template to use.

 required
temperature int

The temperature to use for the model. Defaults to 0.

 0
model_name str

The name of the model to use. Defaults to "gpt-3.5-turbo-instruct".

 'gpt-3.5-turbo-instruct'
Side Effects

Sets the class attributes.

Raises:

Type Description
KeyError

If the model_name is not found in the models dictionary.
Source code in write_the/llm.py 
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def __init__(self, prompt: PromptTemplate, temperature=0, model_name="gpt-3.5-turbo-instruct"):
    """
    Initializes the LLM class.

    Args:
      prompt (PromptTemplate): The prompt template to use.
      temperature (int, optional): The temperature to use for the model. Defaults to 0.
      model_name (str, optional): The name of the model to use. Defaults to "gpt-3.5-turbo-instruct".

    Side Effects:
      Sets the class attributes.

    Raises:
      KeyError: If the model_name is not found in the models dictionary.
    """
    self.prompt = prompt
    self.prompt_size = self.number_of_tokens(prompt.template)
    self.temperature = temperature
    self.model_name = model_name
    try:
        self.max_tokens = int(models[model_name]["context_window"])
    except KeyError:
        self.max_tokens = 4096
        if model_name.startswith('gpt-4'):
            self.max_tokens = 8192
        elif model_name.startswith('gpt-3'):
            self.max_tokens = 4096

number_of_tokens(text)

Counts the number of tokens in a given text.

Parameters:

Name Type Description Default
text str

The text to count tokens for.

 required

Returns:

Name Type Description
int

The number of tokens in the text.
Source code in write_the/llm.py 
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def number_of_tokens(self, text):
    """
    Counts the number of tokens in a given text.

    Args:
      text (str): The text to count tokens for.

    Returns:
      int: The number of tokens in the text.
    """
    encoding = tiktoken.encoding_for_model("gpt-4")
    return len(encoding.encode(text))

run(code, **kwargs) async

Runs the Language Model Chain asynchronously.

Parameters:

Name Type Description Default
code str

The code to use for the chain.

 required
**kwargs dict

Additional keyword arguments.

 {}

Returns:

Name Type Description
str

The generated text.
Source code in write_the/llm.py 
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async def run(self, code, **kwargs):
    """
    Runs the Language Model Chain asynchronously.

    Args:
      code (str): The code to use for the chain.
      **kwargs (dict): Additional keyword arguments.

    Returns:
      str: The generated text.
    """
    if "-instruct" in self.model_name:
        llm = OpenAI(
            temperature=self.temperature, max_tokens=-1, model_name=self.model_name
        )
    else:
        llm = ChatOpenAI(
            temperature=self.temperature, model_name=self.model_name
        )
    chain = LLMChain(llm=llm, prompt=self.prompt)
    return await chain.apredict(code=code, **kwargs)