OpenAIChatGPTPredictor
Basic ChatGPT predictor
This predictor is using OpenAI ChatGPT API:
Subclass of Predictor.
Module: implementation.predictors
Methods and properties
Main methods and properties
__init__
Arguments:
chat_cfg (ChatGPTConfig): Chat configuration.
openai_client (Optional[OpenAI], optional): OpenAI client that will be used. If equals to None, default OpenAI client will be used. Defaults to None.
input_class (Type[Input], optional): Class for input validation. Defaults to ChatGPTInput.
output_class (Type[Output], optional): Class for output validation. Defaults to ChatCompletionOutput.
name (Optional[str], optional): Name for identification. If equals to None, class name will be used. Defaults to None.
ChatGPTConfig
Prebuild configuration that describes default parameters for ChatGPT API. Subclass of Config.
__init__
Arguments:
model: ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
frequency_penalty: 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. See more information about frequency and presence penalties.
function_call: Deprecated in favor of
tool_choice. Controls which (if any) function is called by the model.nonemeans the model will not call a function and instead generates a message.automeans the model can pick between generating a message or calling a function. Specifying a particular function via{"name": "my_function"}forces the model to call that function.noneis the default when no functions are present.autois the default if functions are present.functions: Deprecated in favor of
tools. A list of functions the model may generate JSON inputs for.logit_bias: Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
logprobs: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the
contentofmessage.max_tokens: The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. Example Python code for counting tokens.
n: How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep
nas1to minimize costs.presence_penalty: 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. See more information about frequency and presence penalties.
response_format: An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models newer than
gpt-3.5-turbo-1106. Setting to{ "type": "json_object" }enables JSON mode, which guarantees the message the model generates is valid JSON.
Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
seed: This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same
seedand parameters should return the same result. Determinism is not guaranteed, and you should refer to thesystem_fingerprintresponse parameter to monitor changes in the backend.stop: Up to 4 sequences where the API will stop generating further tokens.
stream: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a
data: [DONE]message. Example Python code.stream_options: Options for streaming response. Only set this when you set
stream: true.temperature: 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. We generally recommend altering this or
top_pbut not both.tool_choice: Controls which (if any) tool is called by the model.
nonemeans the model will not call any tool and instead generates a message.automeans the model can pick between generating a message or calling one or more tools.requiredmeans the model must call one or more tools. Specifying a particular tool via{"type": "function", "function": {"name": "my_function"}}forces the model to call that tool.noneis the default when no tools are present.autois the default if tools are present.tools: A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.
top_logprobs: An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability.
logprobsmust be set totrueif this parameter is used.top_p: 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. We generally recommend altering this or
temperaturebut not both.user: A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.
extra_headers: Send extra headers
extra_query: Add additional query parameters to the request
extra_body: Add additional JSON properties to the request
timeout: Override the client-level default timeout for this request, in seconds
ChatGPTInput
Subclass of IOModel.
__init__
Arguments:
messages (Iterable[ChatCompletionMessageParam]): A list of messages comprising the conversation so far.
ChatCompletionOutput
Subclass of IOModel and ChatCompletion.
__init__
Arguments:
id (str): A unique identifier for the chat completion.
choices (List[Choice]): A list of chat completion choices. Can be more than one if n is greater than 1. Each Choise includes:
finish_reason (str): The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from our content filters, tool_calls if the model called a tool, or function_call (deprecated) if the model called a function.
index (int): The index of the choice in the list of choices.
message (Dict[str, Any]): A chat completion message generated by the model. Expected keys:
"content" (Optional[str]): The contents of the message;
"tool_calls" (List[Dict[str, Any]): For each item expected keys:
"id" (str): The ID of the tool call;
"type" (str): The type of the tool. Currently, only
functionis supported."function" (Dict[str, Any]): The function that the model called. Expected keys:
"name" (str): The name of the function to call.
"arguments" (str): The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
role (str): The role of the author of this message.
logprobs (Optional[Dict[str, Any]]): Log probability information for the choice. Expected keys:
"content" (Optional[List[str, Any]]): For each item expected keys:
"token" (str): The token.
"logprob" (float): The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.
"bytes" (Optional[List[int]]): A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be None if there is no bytes representation for the token.
"top_logprobs" (List[Dict[str, Any]]): List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested top_logprobs returned. For each item expected keys:
"token" (str): The token.
"logprob" (float): The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.
"bytes" (Optional[List[int]]): A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be None if there is no bytes representation for the token.
created (int): The Unix timestamp (in seconds) of when the chat completion was created.
model (str): The model used for the chat completion.
object (Literal['chat.completion']): The object type, which is always chat.completion.
system_fingerprint (Optional[str]): This fingerprint represents the backend configuration that the model runs with.
Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
usage (Optional[CompletionUsage]): Usage statistics for the completion request. Expected keys:
completion_tokens (int): Number of tokens in the generated completion.
prompt_tokens (int): Number of tokens in the prompt.
total_tokens (int): Total number of tokens used in the request (prompt + completion).
ChatCompletionStreamOutput
Subclass of IOModel.
__init__
Arguments:
stream (Iterable[ChatCompletionChunk]): For each item expected keys:
id (str): A unique identifier for the chat completion.
choices (List[Choice]): A list of chat completion choices. Can be more than one if n is greater than 1. Each Choise includes:
finish_reason (str): The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from our content filters, tool_calls if the model called a tool, or function_call (deprecated) if the model called a function.
index (int): A unique identifier for the chat completion. Each chunk has the same ID.
delta (Dict[str, Any]): A chat completion delta generated by streamed model responses. Expected keys:
"content" (Optional[str]): The contents of the message;
"tool_calls" (List[Dict[str, Any]): For each item expected keys:
"id" (str): The ID of the tool call;
"type" (str): The type of the tool. Currently, only
functionis supported."function" (Dict[str, Any]): The function that the model called. Expected keys:
"name" (str): The name of the function to call.
"arguments" (str): The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
role (str): The role of the author of this message.
logprobs (Optional[Dict[str, Any]]): Log probability information for the choice. Expected keys:
"content" (Optional[List[str, Any]]): For each item expected keys:
"token" (str): The token.
"logprob" (float): The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.
"bytes" (Optional[List[int]]): A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be None if there is no bytes representation for the token.
"top_logprobs" (List[Dict[str, Any]]): List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested top_logprobs returned. For each item expected keys:
"token" (str): The token.
"logprob" (float): The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.
"bytes" (Optional[List[int]]): A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be None if there is no bytes representation for the token.
created (int): The Unix timestamp (in seconds) of when the chat completion was created.
model (str): The model used for the chat completion.
object (Literal['chat.completion']): The object type, which is always chat.completion.
system_fingerprint (Optional[str]): This fingerprint represents the backend configuration that the model runs with.
Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
usage (Optional[CompletionUsage]): Usage statistics for the completion request. Expected keys:
completion_tokens (int): Number of tokens in the generated completion.
prompt_tokens (int): Number of tokens in the prompt.
total_tokens (int): Total number of tokens used in the request (prompt + completion).
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