action openai_create_answer {
  label: "Answers the specified question using the provided documents and examples.\n\nThe endpoint first [searches](/docs/api-reference/searches) over provided documents or files to find relevant context. The relevant context is combined with the provided examples and question to create the prompt for [completion](/docs/api-reference/completions).\n"
  provider: openai
  method: POST
  path: "/answers"
  encoding: json
  input: {
    type: "object"
    required: ["examples", "examples_context", "model", "question"]
    properties: {
      documents: {
        type: "array"
        description: "List of documents from which the answer for the input `question` should be derived. If this is an empty list, the question will be answered based on the question-answer examples.\n\nYou should specify either `documents` or a `file`, but not both.\n"
        items: {
          type: "string"
        }
      }
      examples: {
        type: "array"
        description: "List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like. We recommend adding 2 to 3 examples."
        items: {
          type: "array"
          items: {
            type: "string"
          }
        }
      }
      examples_context: {
        type: "string"
        description: "A text snippet containing the contextual information used to generate the answers for the `examples` you provide."
      }
      expand: {
        type: "array"
        description: "If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support `completion` and `file` objects for expansion."
        items: {
          type: "object"
        }
      }
      file: {
        type: "string"
        description: "The ID of an uploaded file that contains documents to search over. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose.\n\nYou should specify either `documents` or a `file`, but not both.\n"
      }
      logit_bias: {
        type: "object"
        description: "Modify the likelihood of specified tokens appearing in the completion.\n\nAccepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. 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.\n\nAs an example, you can pass `{\"50256\": -100}` to prevent the <|endoftext|> token from being generated.\n"
      }
      logprobs: {
        type: "integer"
        description: "Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. For example, if `logprobs` is 5, the API will return a list of the 5 most likely tokens. The API will always return the `logprob` of the sampled token, so there may be up to `logprobs+1` elements in the response.\n\nThe maximum value for `logprobs` is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case.\n\nWhen `logprobs` is set, `completion` will be automatically added into `expand` to get the logprobs.\n"
      }
      max_rerank: {
        type: "integer"
        description: "The maximum number of documents to be ranked by [Search](/docs/api-reference/searches/create) when using `file`. Setting it to a higher value leads to improved accuracy but with increased latency and cost."
      }
      max_tokens: {
        type: "integer"
        description: "The maximum number of tokens allowed for the generated answer"
      }
      model: {
        type: "string"
        description: "ID of the model to use for completion. You can select one of `ada`, `babbage`, `curie`, or `davinci`."
      }
      n: {
        type: "integer"
        description: "How many answers to generate for each question."
      }
      question: {
        type: "string"
        description: "Question to get answered."
      }
      return_metadata: {
        type: "boolean"
        description: "A special boolean flag for showing metadata. If set to `true`, each document entry in the returned JSON will contain a \"metadata\" field.\n\nThis flag only takes effect when `file` is set.\n"
      }
      return_prompt: {
        type: "boolean"
        description: "If set to `true`, the returned JSON will include a \"prompt\" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes."
      }
      search_model: {
        type: "string"
        description: "ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of `ada`, `babbage`, `curie`, or `davinci`."
      }
      stop: {
        description: "Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.\n"
        type: "object"
      }
      temperature: {
        type: "number"
        description: "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."
      }
      user: {
        type: "string"
        description: "A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).\n"
      }
    }
    additionalProperties: false
  }
  output: {
    type: "object"
    properties: {
      answers: {
        type: "array"
        items: {
          type: "string"
        }
      }
      completion: {
        type: "string"
      }
      model: {
        type: "string"
      }
      object: {
        type: "string"
      }
      search_model: {
        type: "string"
      }
      selected_documents: {
        type: "array"
        items: {
          type: "object"
          properties: {
            document: {
              type: "integer"
            }
            text: {
              type: "string"
            }
          }
        }
      }
    }
  }
}