---
title: "Get AI agent default configuration"
method: GET
path: "/ai_agent_default"
tags: ["AI"]
---

# Get AI agent default configuration

`GET /ai_agent_default`

Get the AI agent default config.

## Query parameters

- `mode` 'ask' | 'text_gen' | 'extract' | 'extract_structured', required
- `language` string
- `model` string

## Response `200`

A successful response including the default agent configuration.
This response can be one of the following four objects:
* AI agent for questions
* AI agent for text generation
* AI agent for freeform metadata extraction
* AI agent for structured metadata extraction.
The response depends on the agent configuration requested in this endpoint.

- union — Can be one of the following objects: * AI agent for questions * AI agent for text generation * AI agent for freeform metadata extraction * AI agent for structured metadata extraction.
  - object — The AI agent used to handle queries.
    - `type` 'ai_agent_ask', required — The type of AI agent used to handle queries.
    - `long_text` AiAgentLongTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
      - `embeddings` object
        - `model` string — The model used for the AI agent for calculating embeddings.
        - `strategy` object
          - `id` string — The strategy used for the AI agent for calculating embeddings.
          - `num_tokens_per_chunk` integer — The number of tokens per chunk.
    - `basic_text` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
    - `spreadsheet` AiAgentSpreadsheetTool — The AI agent tool used to handle spreadsheets and tabular data.
      - `model` string — The model used for the AI agent for spreadsheets. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
    - `long_text_multi` AiAgentLongTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
      - `embeddings` object
        - `model` string — The model used for the AI agent for calculating embeddings.
        - `strategy` object
          - `id` string — The strategy used for the AI agent for calculating embeddings.
          - `num_tokens_per_chunk` integer — The number of tokens per chunk.
    - `basic_text_multi` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
    - `basic_image` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
    - `basic_image_multi` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
  - object — The AI agent used for generating text.
    - `type` 'ai_agent_text_gen', required — The type of AI agent used for generating text.
    - `basic_gen` AiAgentBasicGenTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages aim at helping the LLM understand its role and what it is supposed to do. The input for `{current_date}` is optional, depending on the use.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When using the `prompt_template` parameter, you **must include** input for `{user_question}`. Inputs for `{current_date}` and `{content}` are optional, depending on the use.
      - `embeddings` object
        - `model` string — The model used for the AI agent for calculating embeddings.
        - `strategy` object
          - `id` string — The strategy used for the AI agent for calculating embeddings.
          - `num_tokens_per_chunk` integer — The number of tokens per chunk.
      - `content_template` string — How the content should be included in a request to the LLM. Input for `{content}` is optional, depending on the use.
  - object — The AI agent to be used for extraction.
    - `type` 'ai_agent_extract', required — The type of AI agent to be used for extraction.
    - `long_text` AiAgentLongTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
      - `embeddings` object
        - `model` string — The model used for the AI agent for calculating embeddings.
        - `strategy` object
          - `id` string — The strategy used for the AI agent for calculating embeddings.
          - `num_tokens_per_chunk` integer — The number of tokens per chunk.
    - `basic_text` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
    - `basic_image` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
  - object — The AI agent to be used for structured extraction.
    - `type` 'ai_agent_extract_structured', required — The type of AI agent to be used for extraction.
    - `long_text` AiAgentLongTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
      - `embeddings` object
        - `model` string — The model used for the AI agent for calculating embeddings.
        - `strategy` object
          - `id` string — The strategy used for the AI agent for calculating embeddings.
          - `num_tokens_per_chunk` integer — The number of tokens per chunk.
    - `basic_text` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.
    - `basic_image` AiAgentBasicTextTool — AI agent processor used to handle basic text.
      - `model` string — The model used for the AI agent for basic text. For specific model values, see the [available models list](https://developer.box.com/guides/box-ai/ai-models).
      - `num_tokens_for_completion` integer — The number of tokens for completion.
      - `llm_endpoint_params` union — The parameters for the LLM endpoint specific to a model.
        - object — AI LLM endpoint params OpenAI object.
          - `type` 'openai_params', required — The type of the AI LLM endpoint params object for OpenAI. This parameter is **required**.
          - `temperature` number, nullable — 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.
          - `top_p` number, nullable — 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.
          - `frequency_penalty` number, nullable — A 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.
          - `presence_penalty` number, nullable — A 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.
          - `stop` string, nullable — Up to 4 sequences where the API will stop generating further tokens.
        - object — AI LLM endpoint params Google object.
          - `type` 'google_params', required — The type of the AI LLM endpoint params object for Google. This parameter is **required**.
          - `temperature` number, nullable — The temperature is used for sampling during response generation, which occurs when `top-P` and `top-K` are applied. Temperature controls the degree of randomness in the token selection.
          - `top_p` number, nullable — `Top-P` changes how the model selects tokens for output. Tokens are selected from the most (see `top-K`) to least probable until the sum of their probabilities equals the `top-P` value.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
        - object — AI LLM endpoint params AWS object.
          - `type` 'aws_params', required — The type of the AI LLM endpoint params object for AWS. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
        - object — AI LLM endpoint params IBM object.
          - `type` 'ibm_params', required — The type of the AI LLM endpoint params object for IBM. This parameter is **required**.
          - `temperature` number, nullable — What sampling temperature to use, between 0 and 1. 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.
          - `top_p` number, nullable — 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.
          - `top_k` number, nullable — `Top-K` changes how the model selects tokens for output. A low `top-K` means the next selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a high `top-K` means that the next token is selected from among the three most probable tokens by using temperature.
      - `system_message` string — System messages try to help the LLM "understand" its role and what it is supposed to do.
      - `prompt_template` string — The prompt template contains contextual information of the request and the user prompt. When passing `prompt_template` parameters, you **must include** inputs for `{user_question}` and `{content}`. `{current_date}` is optional, depending on the use.

## Other responses

- `500` — An unexpected server error.
- `default` — An unexpected error.

---

[API](https://skmtc.net/box/apis/platform-api.md) · [All operations](https://skmtc.net/box/apis/platform-api/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/box/platform-api/revisions/ba8f087e1a4d/schema)