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POST
/
chat
/
completions
Creates a completion for the chat message
curl --request POST \
  --url https://api.hicap.ai/v2/openai/chat/completions \
  --header 'Content-Type: application/json' \
  --header 'api-key: <api-key>' \
  --data '
{
  "messages": [
    {
      "content": "<string>",
      "role": "user",
      "name": "developer"
    }
  ],
  "model": "gpt-5",
  "temperature": 1,
  "top_p": 1,
  "stream": false,
  "stop": "<string>",
  "max_completion_tokens": 123,
  "presence_penalty": 0,
  "frequency_penalty": 0,
  "logit_bias": {},
  "store": true,
  "metadata": {},
  "user": "user-1234",
  "data_sources": [
    {
      "type": "azure_search"
    }
  ],
  "reasoning_effort": "medium",
  "logprobs": false,
  "top_logprobs": 10,
  "n": 1,
  "modalities": [
    "text"
  ],
  "prediction": {
    "type": "content",
    "content": "<string>"
  },
  "audio": {
    "voice": "alloy",
    "format": "wav"
  },
  "response_format": {
    "type": "text"
  },
  "seed": 0,
  "stream_options": null,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "<string>",
        "description": "<string>",
        "parameters": {},
        "strict": false
      }
    }
  ],
  "tool_choice": "none",
  "function_call": "none",
  "functions": [
    {
      "name": "<string>",
      "description": "<string>",
      "parameters": {}
    }
  ],
  "user_security_context": {
    "application_name": "<string>",
    "end_user_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "end_user_tenant_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
    "source_ip": "<string>"
  }
}
'
{
  "id": "<string>",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 123,
      "message": {
        "role": "assistant",
        "refusal": "<string>",
        "content": "<string>",
        "tool_calls": [
          {
            "id": "<string>",
            "type": "function",
            "function": {
              "name": "<string>",
              "arguments": "<string>"
            }
          }
        ],
        "function_call": {
          "name": "<string>",
          "arguments": "<string>"
        },
        "audio": {
          "id": "<string>",
          "expires_at": 123,
          "data": "<string>",
          "transcript": "<string>"
        },
        "context": {
          "citations": [
            {
              "content": "<string>",
              "title": "<string>",
              "url": "<string>",
              "filepath": "<string>",
              "chunk_id": "<string>",
              "rerank_score": 123
            }
          ],
          "intent": "<string>",
          "all_retrieved_documents": [
            {
              "content": "<string>",
              "search_queries": [
                "<string>"
              ],
              "data_source_index": 123,
              "original_search_score": 123,
              "title": "<string>",
              "url": "<string>",
              "filepath": "<string>",
              "chunk_id": "<string>",
              "rerank_score": 123,
              "filter_reason": "score"
            }
          ]
        }
      },
      "logprobs": {
        "content": [
          {
            "token": "<string>",
            "logprob": 123,
            "bytes": [
              123
            ],
            "top_logprobs": [
              {
                "token": "<string>",
                "logprob": 123,
                "bytes": [
                  123
                ]
              }
            ]
          }
        ],
        "refusal": [
          {
            "token": "<string>",
            "logprob": 123,
            "bytes": [
              123
            ],
            "top_logprobs": [
              {
                "token": "<string>",
                "logprob": 123,
                "bytes": [
                  123
                ]
              }
            ]
          }
        ]
      },
      "content_filter_results": {
        "sexual": {
          "filtered": true,
          "severity": "safe"
        },
        "violence": {
          "filtered": true,
          "severity": "safe"
        },
        "hate": {
          "filtered": true,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": true,
          "severity": "safe"
        },
        "profanity": {
          "filtered": true,
          "detected": true
        },
        "custom_blocklists": {
          "filtered": true,
          "details": [
            {
              "filtered": true,
              "id": "<string>"
            }
          ]
        },
        "error": {
          "code": "<string>",
          "message": "<string>"
        },
        "protected_material_text": {
          "filtered": true,
          "detected": true
        },
        "protected_material_code": {
          "filtered": true,
          "detected": true,
          "citation": {
            "URL": "<string>",
            "license": "<string>"
          }
        },
        "ungrounded_material": {
          "filtered": true,
          "detected": true,
          "details": [
            {
              "completion_start_offset": 123,
              "completion_end_offset": 123
            }
          ]
        }
      }
    }
  ],
  "created": 123,
  "model": "<string>",
  "object": "chat.completion",
  "prompt_filter_results": [
    {
      "prompt_index": 123,
      "content_filter_results": {
        "sexual": {
          "filtered": true,
          "severity": "safe"
        },
        "violence": {
          "filtered": true,
          "severity": "safe"
        },
        "hate": {
          "filtered": true,
          "severity": "safe"
        },
        "self_harm": {
          "filtered": true,
          "severity": "safe"
        },
        "profanity": {
          "filtered": true,
          "detected": true
        },
        "custom_blocklists": {
          "filtered": true,
          "details": [
            {
              "filtered": true,
              "id": "<string>"
            }
          ]
        },
        "error": {
          "code": "<string>",
          "message": "<string>"
        },
        "jailbreak": {
          "filtered": true,
          "detected": true
        },
        "indirect_attack": {
          "filtered": true,
          "detected": true
        }
      }
    }
  ],
  "system_fingerprint": "<string>",
  "usage": {
    "prompt_tokens": 123,
    "completion_tokens": 123,
    "total_tokens": 123,
    "prompt_tokens_details": {
      "audio_tokens": 123,
      "cached_tokens": 123
    },
    "completion_tokens_details": {
      "accepted_prediction_tokens": 123,
      "audio_tokens": 123,
      "reasoning_tokens": 123,
      "rejected_prediction_tokens": 123
    }
  }
}

Authorizations

​
api-key
string
header
required

Your Hicap API key.

Body

application/json
​
messages
(Instruction message (developer semantics) · object | System message · object | User message · object | Assistant message · object | Tool message · object | Function message · object)[]
required

A list of messages comprising the conversation so far. Example Python code.

Minimum array length: 1

Instructional message that encodes developer semantics in this API. Use role=user and set name=developer to distinguish from ordinary user messages.

​
model
string | null
default:gpt-5

Model name to use

Example:

"gpt-5"

​
temperature
number | null
default:1

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.

Required range: 0 <= x <= 2
Example:

1

​
top_p
number | null
default:1

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.

Required range: 0 <= x <= 1
Example:

1

​
stream
boolean | null
default:false

If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Example Python code.

​
stop

Up to 4 sequences where the API will stop generating further tokens.

​
max_completion_tokens
integer | null

An upper bound for the number of tokens that can be generated for a completion, including visible output tokens and reasoning tokens. This is only supported in o1 series models. Will expand the support to other models in future API release.

​
presence_penalty
number
default:0

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.

Required range: -2 <= x <= 2
​
frequency_penalty
number
default:0

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.

Required range: -2 <= x <= 2
​
logit_bias
object

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.

​
store
boolean | null

Whether or not to store the output of this chat completion request for use in our model distillation or evaluation products.

​
metadata
object

Developer-defined tags and values used for filtering completions in the stored completions dashboard.

​
user
string

A unique identifier representing your end-user, which can help to monitor and detect abuse.

Example:

"user-1234"

​
data_sources
object[]

The configuration entries for Azure OpenAI chat extensions that use them. This additional specification is only compatible with Azure OpenAI.

​
reasoning_effort
enum<string>
default:medium

o1 models only

Constrains effort on reasoning for reasoning models.

Currently supported values are low, medium, and high. Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning in a response.

Available options:
low,
medium,
high
​
logprobs
boolean | null
default:false

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.

​
top_logprobs
integer | null

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.

Required range: 0 <= x <= 20
​
n
integer | null
default:1

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.

Required range: 1 <= x <= 128
Example:

1

​
modalities
enum<string>[]

Output types that you would like the model to generate for this request. Most models are capable of generating text, which is the default:

["text"]

The gpt-4o-audio-preview model can also be used to generate audio. To request that this model generate both text and audio responses, you can use:

["text", "audio"]

Available options:
text,
audio
​
prediction
Static Content · object

Configuration for a Predicted Output, which can greatly improve response times when large parts of the model response are known ahead of time. This is most common when you are regenerating a file with only minor changes to most of the content.

​
audio
object

Parameters for audio output. Required when audio output is requested with modalities: ["audio"]. Learn more.

​
response_format
object

An object specifying the format that the model must output. Compatible with GPT-4o, GPT-4o mini, GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.

Setting to { "type": "json_schema", "json_schema": {...} } enables Structured Outputs which guarantees the model will match your supplied JSON schema.

Setting to { "type": "json_object" } enables JSON mode, which guarantees the message the model generates is valid JSON.

Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.

​
seed
integer | null

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.

Required range: -9223372036854776000 <= x <= 9223372036854776000
​
stream_options
object

Options for streaming response. Only set this when you set stream: true.

​
tools
object[]

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.

​
tool_choice

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.

Available options:
none,
auto,
required
​
function_call
deprecated

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.

Available options:
none,
auto
​
functions
object[]
deprecated

Deprecated in favor of tools.

A list of functions the model may generate JSON inputs for.

Required array length: 1 - 128 elements
​
user_security_context
object

User security context contains several parameters that describe the AI application itself, and the end user that interacts with the AI application. These fields assist your security operations teams to investigate and mitigate security incidents by providing a comprehensive approach to protecting your AI applications. Learn more about protecting AI applications using Microsoft Defender for Cloud.

Response

OK

Represents a chat completion response returned by model, based on the provided input.

​
id
string
required

A unique identifier for the chat completion.

​
choices
object[]
required

A list of chat completion choices. Can be more than one if n is greater than 1.

​
created
integer
required

The Unix timestamp (in seconds) of when the chat completion was created.

​
model
string
required

The model used for the chat completion.

​
object
enum<string>
required

The object type, which is always chat.completion.

Available options:
chat.completion
​
prompt_filter_results
object[]

Content filtering results for zero or more prompts in the request. In a streaming request, results for different prompts may arrive at different times or in different orders.

​
system_fingerprint
string

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

​
usage
object

Usage statistics for the completion request.