# BasePrompt > `BasePrompt` is the base class for all prompts. Currently we support building prompts to instruct LLM by calling LLM API service of OpenAI (GPT-3, ChatGPT), Anthropic (Claude) and Cohere (Command) or by requesting locally deployed LLM like Llama2, ChatGLM2, etc. We will support more available LLM products in the future. > You can also easily inherit this base class to customize your own prompt class. Just override the `build_prompt` method and `parse_response` method. #### **Constructor** `__init__(self)` **Parameters** None. **Example** ```python from easyinstruct import BasePrompt prompts = BasePrompt() ``` #### **build\_prompt** `build_prompt(self, prompt: str)` **Description** Build a prompt from a given string input. **Parameters** * `prompt` (str): The prompt string. **Example** ```python prompts.build_prompt("Give me three names of cats.") ``` #### **get\_openai\_result** ```python get_openai_result( self, engine = "gpt-3.5-turbo", system_message: Optional[str] = "You are a helpful assistant.", temperature: Optional[float] = 0, max_tokens: Optional[int] = 64, top_p: Optional[float] = 1.0, n: Optional[int] = 1, frequency_penalty: Optional[float] = 0.0, presence_penalty: Optional[float] = 0.0 ) ``` **Description** Get the response from OpenAI API. **Parameters** * `engine` (str): The OpenAI engine to use for the API call. Defaults to "gpt-3.5-turbo". Available engines include "text-davinci-003", "text-davinci-002", "gpt-3.5-turbo", "gpt-3.5-turbo-0301", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k", "gpt-3.5-turbo-16k-0613", "gpt-4", "gpt-4-0613", "gpt-4-0314". * `system_message` (str): System messages provided to ChatGPT. Defaults to "You are a helpful assistant.". * `temperature` (float): What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. Defaults to 0. * `max_tokens` (int): The maximum number of tokens to generate in the completion. Defaults to 1024. * `top_p` (float): An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top\_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. Defaults to 1.0. * `n` (int): The number of completions to generate for each prompt. Defaults to 1. * `frequency_penalty` (float): Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. Defaults to 0.0. * `presence_penalty` (float): Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. Defaults to 0.0. **Example** ```python prompts.get_openai_result(engine = "gpt-3.5-turbo") ``` #### **get\_anthropic\_result** ```python get_anthropic_result( self, engine = "claude-2", max_tokens_to_sample: Optional[int] = 1024, stop_sequences: List[str] = [anthropic.HUMAN_PROMPT], temperature: Optional[float] = 1, top_k: Optional[int] = -1, top_p: Optional[float] = -1 ) ``` **Description** Get the response from Anthropic API. **Parameters** * `engine` (str): The Anthropic engine to use for the API call. Defaults to "claude-2". Available engines include "claude-2", "claude-2.0", "claude-instant-1", "claude-instant-1.2". * `max_token_to_sample` (int): A maximum number of tokens to generate before stopping. Defaults to 1024. * `stop_sequences` (List\[str]): A list of strings upon which to stop generating. You probably want `["\n\nHuman:"]`, as that's the cue for the next turn in the dialog agent. * `temperature` (float): Amount of randomness injected into the response. Ranges from 0 to 1. Use temp closer to 0 for analytical / multiple choice, and temp closer to 1 for creative and generative tasks. Defaults to 1. * `top_k` (int): Only sample from the top K options for each subsequent token. Used to remove "long tail" low probability responses. Defaults to -1, which disables it. * `top_p` (float): Does nucleus sampling, in which we compute the cumulative distribution over all the options for each subsequent token in decreasing probability order and cut it off once it reaches a particular probability specified by `top_p`. Defaults to -1, which disables it. Note that you should either alter `temperature` or `top_p`, but not both. **Example** ```python prompts.get_anthropic_result(engine="claude-2") ``` #### **get\_cohere\_result** ```python get_cohere_result( self, engine = "command", max_tokens: Optional[int] = 1024, temperature: Optional[float] = 0.75, k: Optional[int] = 0, p: Optional[float] = 0.75, frequency_penalty: Optional[float] = 0.0, presence_penalty: Optional[float] = 0.0, ) ``` **Description** Get the response from Anthropic API. **Parameters** * `engine` (str): The Cohere engine to use for the API call. Defaults to "command". Available engines include "command", "command-nightly", "command-light", "command-light-nightly". * `max_tokens` (int): The maximum number of tokens the model will generate as part of the response. Defaults to 1024. * `temperature` (float): A non-negative float that tunes the degree of randomness in generation. Lower temperatures mean less random generations. See [Temperature](https://docs.cohere.com/temperature-wiki) for more details. Defaults to 0.75. * `k` (int): Ensures only the top `k` most likely tokens are considered for generation at each step. Defaults to 0, min value of 0, max value of 500. * `p` (float): Ensures that only the most likely tokens, with total probability mass of `p`, are considered for generation at each step. If both `k` and `p` are enabled, `p` acts after `k`.\ Defaults to 0.75, min value of 0.01, max value of 0.99. * `frequency_penalty` (float): Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation. * `presence_penalty` (float): Can be used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies. Defaults to 0.0, min value of 0.0, max value of 1.0. **Example** ```python prompts.get_cohere_result(engine="command") ``` #### **get\_engine\_result** ```python get_engine_result( self, engine: BaseEngine, **kwargs, ) ``` **Description** Get the response from locally deployed LLM engine. **Parameters** * `engine` (BaseEngine): Instance of BaseEngine and all its subclasses. See [engines](https://zjunlp.gitbook.io/easyinstruct/documentations/engines "mention") for more details. **Example** ```python engine = Llama2Engine() prompts.get_engine_result(engine = engine) ``` #### **parse\_response** Implemented in subclasses.