> For the complete documentation index, see [llms.txt](https://utca.knowledgator.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://utca.knowledgator.com/predictors/openaichatgptpredictor.md).

# OpenAIChatGPTPredictor

Basic ChatGPT predictor

This predictor is using OpenAI ChatGPT API:

{% embed url="<https://platform.openai.com/docs/introduction>" %}

Subclass of [**Predictor**](/predictors/predictor.md)**.**

## Module: [implementation](/framework-structure.md#implementation).predictors

## Methods and properties

Main methods and properties

***

### <mark style="color:blue;">\_\_init\_\_</mark>

#### Arguments:

* <mark style="color:orange;">**chat\_cfg**</mark>**&#x20;(**[**ChatGPTConfig**](#chatgptconfig)**):** Chat configuration.
* <mark style="color:orange;">**openai\_client**</mark>**&#x20;(Optional\[OpenAI], optional):** OpenAI client that will be used. If equals to None, default OpenAI client will be used. Defaults to None.
* <mark style="color:orange;">**input\_class**</mark>**&#x20;(Type\[**[**Input**](/core/schemas.md#input)**], optional):** Class for input validation. Defaults to [**ChatGPTInput**](#chatgptinput).
* <mark style="color:orange;">**output\_class**</mark>**&#x20;(Type\[**[**Output**](/core/schemas.md#output)**], optional):** Class for output validation. Defaults to [**ChatCompletionOutput**](#chatcompletionoutput)**.**
* <mark style="color:orange;">**name**</mark>**&#x20;(Optional\[str], optional):** Name for identification. If equals to None, class name will be used. Defaults to None.

***

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## <mark style="color:green;">ChatGPTConfig</mark>

Prebuild configuration that describes default parameters for ChatGPT API. Subclass of [**Config**](/core/schemas.md#config).

***

### <mark style="color:blue;">\_\_init\_\_</mark>

#### Arguments:

* <mark style="color:orange;">**model**</mark>: ID of the model to use. See the [model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API.
* <mark style="color:orange;">**frequency\_penalty**</mark>: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
* <mark style="color:orange;">**function\_call**</mark>: Deprecated in favor of `tool_choice`.\
  \
  Controls which (if any) function is called by the model. `none` means the model will not call a function and instead generates a message. `auto` means 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.\
  \
  `none` is the default when no functions are present. `auto` is the default if functions are present.
* <mark style="color:orange;">**functions**</mark>: Deprecated in favor of `tools`. A list of functions the model may generate JSON inputs for.
* <mark style="color:orange;">**logit\_bias**</mark>: 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.
* <mark style="color:orange;">**logprobs**</mark>: Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the `content` of `message`.
* <mark style="color:orange;">**max\_tokens**</mark>: 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
* <mark style="color:orange;">**n**</mark>: 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 `n` as `1` to minimize costs.
* <mark style="color:orange;">**presence\_penalty**</mark>: 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.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
* <mark style="color:orange;">**response\_format**</mark>: An object specifying the format that the model must output. Compatible with [GPT-4 Turbo](https://platform.openai.com/docs/models/gpt-4-and-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.

{% hint style="warning" %}
**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.
{% endhint %}

* <mark style="color:orange;">**seed**</mark>: This feature is in Beta. If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same `seed` and parameters should return the same result. Determinism is not guaranteed, and you should refer to the `system_fingerprint` response parameter to monitor changes in the backend.
* <mark style="color:orange;">**stop**</mark>: 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](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
* <mark style="color:orange;">**stream\_options**</mark>: Options for streaming response. Only set this when you set `stream: true`.
* <mark style="color:orange;">**temperature**</mark>: 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_p` but not both.
* <mark style="color:orange;">**tool\_choice**</mark>: Controls which (if any) tool is called by the model. `none` means the model will not call any tool and instead generates a message. `auto` means the model can pick between generating a message or calling one or more tools. `required` means 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.\
  \
  `none` is the default when no tools are present. `auto` is the default if tools are present.
* <mark style="color:orange;">**tools**</mark>: 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.
* <mark style="color:orange;">**top\_logprobs**</mark>: 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. `logprobs` must be set to `true` if this parameter is used.
* <mark style="color:orange;">**top\_p**</mark>: 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 `temperature` but not both.
* <mark style="color:orange;">**user**</mark>: A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
* <mark style="color:orange;">**extra\_headers**</mark>: Send extra headers
* <mark style="color:orange;">**extra\_query**</mark>: Add additional query parameters to the request
* <mark style="color:orange;">**extra\_body**</mark>: Add additional JSON properties to the request
* <mark style="color:orange;">**timeout**</mark>: Override the client-level default timeout for this request, in seconds

***

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## <mark style="color:green;">ChatGPTInput</mark>

Subclass of[ **IOModel**](/core/schemas.md#iomodel).

***

### <mark style="color:blue;">\_\_init\_\_</mark>

#### Arguments:

* <mark style="color:orange;">**messages**</mark>**&#x20;(Iterable\[ChatCompletionMessageParam]):** A list of messages comprising the conversation so far.

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## <mark style="color:green;">ChatCompletionOutput</mark>

Subclass of[ **IOModel**](/core/schemas.md#iomodel) and **ChatCompletion**.

***

### <mark style="color:blue;">\_\_init\_\_</mark>

#### Arguments:

* <mark style="color:orange;">**id**</mark>**&#x20;(str):** A unique identifier for the chat completion.
* <mark style="color:orange;">**choices**</mark>**&#x20;(List\[Choice]):** A list of chat completion choices. Can be more than one if n is \
  greater than 1. Each Choise includes:
  * <mark style="color:red;">**finish\_reason**</mark>**&#x20;(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.
  * <mark style="color:red;">**index**</mark>**&#x20;(int):** The index of the choice in the list of choices.
  * <mark style="color:red;">**message**</mark>**&#x20;(Dict\[str, Any]):** A chat completion message generated by the model. Expected keys:
    * <mark style="color:purple;">**"content"**</mark>**&#x20;(Optional\[str]):** The contents of the message;
    * <mark style="color:purple;">**"tool\_calls"**</mark>**&#x20;(List\[Dict\[str, Any]):** For each item expected keys:
      * <mark style="color:blue;">**"id"**</mark>**&#x20;(str):** The ID of the tool call;
      * <mark style="color:blue;">**"type"**</mark>**&#x20;(str):** The type of the tool. Currently, only `function` is supported.
      * <mark style="color:blue;">**"function"**</mark> (Dict\[str, Any]): The function that the model called. Expected keys:
        * <mark style="color:green;">**"name"**</mark> (str): The name of the function to call.
        * <mark style="color:green;">**"arguments"**</mark>**&#x20;(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.
      * <mark style="color:blue;">**role**</mark>**&#x20;(str):** The role of the author of this message.
      * <mark style="color:blue;">**logprobs**</mark>**&#x20;(Optional\[Dict\[str, Any]]):** Log probability information for the choice. Expected keys:
        * <mark style="color:green;">**"content"**</mark>**&#x20;(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.
* <mark style="color:orange;">**created**</mark>**&#x20;(int):** The Unix timestamp (in seconds) of when the chat completion was created.
* <mark style="color:orange;">**model**</mark>**&#x20;(str):** The model used for the chat completion.
* <mark style="color:orange;">**object**</mark>**&#x20;(Literal\['chat.completion']):** The object type, which is always chat.completion.
* <mark style="color:orange;">**system\_fingerprint**</mark>**&#x20;(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.
* <mark style="color:orange;">**usage**</mark>**&#x20;(Optional\[CompletionUsage]):** Usage statistics for the completion request. Expected keys:
  * <mark style="color:red;">**completion\_tokens**</mark>**&#x20;(int):** Number of tokens in the generated completion.
  * <mark style="color:red;">**prompt\_tokens**</mark>**&#x20;(int):** Number of tokens in the prompt.
  * <mark style="color:red;">**total\_tokens**</mark>**&#x20;(int):** Total number of tokens used in the request (prompt + completion).

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## <mark style="color:green;">ChatCompletionStreamOutput</mark>

Subclass of[ **IOModel**](/core/schemas.md#iomodel).

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### <mark style="color:blue;">\_\_init\_\_</mark>

#### Arguments:

* <mark style="color:orange;">**stream**</mark>**&#x20;(Iterable\[ChatCompletionChunk]):** For each item expected keys:
  * <mark style="color:red;">**id**</mark>**&#x20;(str):** A unique identifier for the chat completion.
  * <mark style="color:red;">**choices**</mark>**&#x20;(List\[Choice]):** A list of chat completion choices. Can be more than one if n is \
    greater than 1. Each Choise includes:
    * <mark style="color:purple;">**finish\_reason**</mark>**&#x20;(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.
    * <mark style="color:purple;">**index**</mark>**&#x20;(int):** A unique identifier for the chat completion. Each chunk has the same ID.
    * <mark style="color:purple;">**delta**</mark>**&#x20;(Dict\[str, Any]):** A chat completion delta generated by streamed model responses. Expected keys:
      * <mark style="color:blue;">**"content"**</mark>**&#x20;(Optional\[str]):** The contents of the message;
      * <mark style="color:blue;">**"tool\_calls"**</mark>**&#x20;(List\[Dict\[str, Any]):** For each item expected keys:
        * **"id"** **(str):** The ID of the tool call;
        * <mark style="color:green;">**"type"**</mark>**&#x20;(str):** The type of the tool. Currently, only `function` is supported.
        * <mark style="color:green;">**"function"**</mark> (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.
        * <mark style="color:green;">**role**</mark>**&#x20;(str):** The role of the author of this message.
        * <mark style="color:green;">**logprobs**</mark>**&#x20;(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.
  * <mark style="color:red;">**created**</mark>**&#x20;(int):** The Unix timestamp (in seconds) of when the chat completion was created.
  * <mark style="color:red;">**model**</mark>**&#x20;(str):** The model used for the chat completion.
  * <mark style="color:red;">**object**</mark>**&#x20;(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.
  * <mark style="color:red;">**usage**</mark>**&#x20;(Optional\[CompletionUsage]):** Usage statistics for the completion request. Expected keys:
    * <mark style="color:purple;">**completion\_tokens**</mark>**&#x20;(int):** Number of tokens in the generated completion.
    * <mark style="color:purple;">**prompt\_tokens**</mark>**&#x20;(int):** Number of tokens in the prompt.
    * <mark style="color:purple;">**total\_tokens**</mark>**&#x20;(int):** Total number of tokens used in the request (prompt + completion).

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