---
title: "Chat"
method: POST
path: "/chat/completions"
tags: ["Chat"]
---

# Chat

`POST /chat/completions`

## Request

- Base URL: `https://gateway.obiguard.ai/v1`
- URL: `https://gateway.obiguard.ai/v1/chat/completions`
- Auth: API key in header `x-obiguard-api-key`

## Request body

- CreateChatCompletionRequest
  - `messages` ChatCompletionRequestMessage[], required: A list of messages comprising the conversation so far. [Example Python code](https://cookbook.openai.com/examples/how_to_format_inputs_to_chatgpt_models).
    - union
      - object
        - `content` string, required: The contents of the system message.
        - `role` 'system', required: The role of the messages author, in this case `system`.
        - `name` string: An optional name for the participant. Provides the model information to differentiate between participants of the same role.
      - object: New role by OpenAI for select models. Must be explicitly used for models that support it. When used with incompatible models or providers, Obiguard automatically converts it to a system role.
        - `content` string, required: The contents of the Developer message.
        - `role` 'developer', required: The role of the messages author, in this case `Developer`.
        - `name` string: An optional name for the participant. Provides the model information to differentiate between participants of the same role.
      - object
        - `content` union, required: The contents of the user message.
          - string: The text contents of the message.
          - ChatCompletionRequestMessageContentPart[]: An array of content parts with a defined type, each can be of type `text` or `image_url` when passing in images. You can pass multiple images by adding multiple `image_url` content parts. Image input is only supported when using the `gpt-4-visual-preview` model.
            - union
              - …
        - `role` 'user', required: The role of the messages author, in this case `user`.
        - `name` string: An optional name for the participant. Provides the model information to differentiate between participants of the same role.
      - object
        - `content` string, nullable: The contents of the assistant message. Required unless `tool_calls` or `function_call` is specified.
        - `role` 'assistant', required: The role of the messages author, in this case `assistant`.
        - `name` string: An optional name for the participant. Provides the model information to differentiate between participants of the same role.
        - `tool_calls` ChatCompletionMessageToolCall[]: The tool calls generated by the model, such as function calls.
          - `id` string, required: The ID of the tool call.
          - `type` 'function', required: The type of the tool. Currently, only `function` is supported.
          - `function` object, required: The function that the model called.
            - `name` string, required: The name of the function to call.
            - `arguments` string, required: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
        - `function_call` object, nullable: Deprecated and replaced by `tool_calls`. The name and arguments of a function that should be called, as generated by the model.
          - `arguments` string, required: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
          - `name` string, required: The name of the function to call.
      - object
        - `role` 'tool', required: The role of the messages author, in this case `tool`.
        - `content` string, required: The contents of the tool message.
        - `tool_call_id` string, required: Tool call that this message is responding to.
      - object
        - `role` 'function', required: The role of the messages author, in this case `function`.
        - `content` string, nullable, required: The contents of the function message.
        - `name` string, required: The name of the function to call.
  - `model` union, required: ID of the model to use. See the [model endpoint compatibility](https://platform.openai.com/docs/models/model-endpoint-compatibility) table for details on which models work with the Chat API.
    - string
    - 'gpt-4o' | 'gpt-4o-2024-05-13' | 'gpt-4-turbo' | 'gpt-4-turbo-2024-04-09' | 'gpt-4-0125-preview' | 'gpt-4-turbo-preview' | 'gpt-4-1106-preview' | 'gpt-4-vision-preview' | 'gpt-4' | 'gpt-4-0314' | 'gpt-4-0613' | 'gpt-4-32k' | 'gpt-4-32k-0314' | 'gpt-4-32k-0613' | 'gpt-3.5-turbo' | 'gpt-3.5-turbo-16k' | 'gpt-3.5-turbo-0301' | 'gpt-3.5-turbo-0613' | 'gpt-3.5-turbo-1106' | 'gpt-3.5-turbo-0125' | 'gpt-3.5-turbo-16k-0613'
  - `frequency_penalty` number, nullable: Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
  - `logit_bias` object, nullable: Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
  - `logprobs` boolean, nullable: 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, nullable: 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.
  - `max_tokens` integer, nullable: The maximum number of [tokens](https://platform.openai.com/tokenizer?view=bpe) that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
  - `n` integer, nullable: 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.
  - `presence_penalty` number, nullable: Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. [See more information about frequency and presence penalties.](https://platform.openai.com/docs/guides/text-generation/parameter-details)
  - `response_format` union: An object specifying the format that the model must output. Setting to `{ "type": "json_schema", "json_schema": {...} }`enables Structured Outputs which ensures the model will match your supplied JSON schema. Works across all the providers that support this functionality. [OpenAI & Azure OpenAI](/integrations/llms/openai/structured-outputs), [Gemini & Vertex AI](/integrations/llms/vertex-ai/controlled-generations). Setting to `{ "type": "json_object" }` enables the older JSON mode, which ensures the message the model generates is valid JSON. Using `json_schema` is preferred for models that support it.
    - object: Default response format. Used to generate text responses.
      - `type` 'text', required: The type of response format being defined. Always `text`.
    - object: JSON Schema response format. Used to generate structured JSON responses. Learn more about [Structured Outputs](/integrations/llms/openai/structured-outputs).
      - `type` 'json_schema', required: The type of response format being defined. Always `json_schema`.
      - `json_schema` object, required: Structured Outputs configuration options, including a JSON Schema.
        - `description` string: A description of what the response format is for, used by the model to determine how to respond in the format.
        - `name` string, required: The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
        - `schema` ResponseFormatJsonSchemaSchema: The schema for the response format, described as a JSON Schema object. Learn how to build JSON schemas [here](https://json-schema.org/).
        - `strict` boolean, nullable: Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the `schema` field. Only a subset of JSON Schema is supported when `strict` is `true`.
    - object: JSON object response format. An older method of generating JSON responses. Using `json_schema` is recommended for models that support it. Note that the model will not generate JSON without a system or user message instructing it to do so.
      - `type` 'json_object', required: The type of response format being defined. Always `json_object`.
  - `seed` integer, nullable: 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.
  - `stop` union: Up to 4 sequences where the API will stop generating further tokens.
    - string, nullable
    - string[]
  - `stream` boolean, nullable: If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-UShttps://platform.openai.com/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
  - `stream_options` ChatCompletionStreamOptions, nullable: Options for streaming response. Only set this when you set `stream: true`.
    - `include_usage` boolean: If set, an additional chunk will be streamed before the `data: [DONE]` message. The `usage` field on this chunk shows the token usage statistics for the entire request, and the `choices` field will always be an empty array. All other chunks will also include a `usage` field, but with a null value.
  - `thinking` object, nullable: View the thinking/reasoning tokens as part of your response. Thinking models produce a long internal chain of thought before generating a response. Supported only for specific Claude models on Anthropic, Google Vertex AI, and AWS Bedrock. Requires setting `strict_openai_compliance = false` in your API call.
    - `type` 'enabled' | 'disabled', required: Enables or disables the thinking mode capability.
    - `budget_tokens` integer: The maximum number of tokens to allocate for the thinking process. A higher token budget allows for more thorough reasoning but may increase overall response time.
  - `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.
  - `tools` ChatCompletionTool[]: 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.
    - `type` 'function', required: The type of the tool. Currently, only `function` is supported.
    - `function` FunctionObject, required
      - `description` string: A description of what the function does, used by the model to choose when and how to call the function.
      - `name` string, required: The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
      - `parameters` FunctionParameters: The parameters the functions accepts, described as a JSON Schema object. See the [guide](https://platform.openai.com/docs/guides/function-calling) for examples, and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format. Omitting `parameters` defines a function with an empty parameter list.
  - `tool_choice` union: 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.
    - 'none' | 'auto' | 'required': `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.
    - object: Specifies a tool the model should use. Use to force the model to call a specific function.
      - `type` 'function', required: The type of the tool. Currently, only `function` is supported.
      - `function` object, required
        - `name` string, required: The name of the function to call.
  - `parallel_tool_calls` boolean: Whether to enable [parallel function calling](https://platform.openai.com/docs/guides/function-calling/parallel-function-calling) during tool use.
  - `user` string: A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids).
  - `function_call` union: 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.
    - 'none' | 'auto': `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.
    - object: Specifying a particular function via `{"name": "my_function"}` forces the model to call that function.
      - `name` string, required: The name of the function to call.
  - `functions` ChatCompletionFunctions[]: Deprecated in favor of `tools`. A list of functions the model may generate JSON inputs for.
    - `description` string: A description of what the function does, used by the model to choose when and how to call the function.
    - `name` string, required: The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
    - `parameters` FunctionParameters: The parameters the functions accepts, described as a JSON Schema object. See the [guide](https://platform.openai.com/docs/guides/function-calling) for examples, and the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format. Omitting `parameters` defines a function with an empty parameter list.

## Response `200`

OK

- CreateChatCompletionResponse: 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.
    - `finish_reason` 'stop' | 'length' | 'tool_calls' | 'content_filter' | 'function_call', required: The reason the model stopped generating tokens. This will be `stop` if the model hit a natural stop point or a provided stop sequence, `length` if the maximum number of tokens specified in the request was reached, `content_filter` if content was omitted due to a flag from our content filters, `tool_calls` if the model called a tool, or `function_call` (deprecated) if the model called a function.
    - `index` integer, required: The index of the choice in the list of choices.
    - `message` ChatCompletionResponseMessage, required: A chat completion message generated by the model.
      - `content` string, nullable, required: The contents of the message.
      - `tool_calls` ChatCompletionMessageToolCall[]: The tool calls generated by the model, such as function calls.
        - `id` string, required: The ID of the tool call.
        - `type` 'function', required: The type of the tool. Currently, only `function` is supported.
        - `function` object, required: The function that the model called.
          - `name` string, required: The name of the function to call.
          - `arguments` string, required: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
      - `role` 'assistant', required: The role of the author of this message.
      - `function_call` object: Deprecated and replaced by `tool_calls`. The name and arguments of a function that should be called, as generated by the model.
        - `arguments` string, required: The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.
        - `name` string, required: The name of the function to call.
      - `content_blocks` ChatCompletionMessageContentBlock[], nullable: The content blocks of the message. This is only present for certain providers with strict-open-ai-compliance flag set to false
        - union: A block of content in a chat completion message.
          - object
            - `type` 'text', required: The type of the content part.
            - `text` string, required: The text content.
          - object
            - `type` 'thinking', required: The type of the content part.
            - `thinking` string, required: The thinking content.
          - object
            - `type` 'redacted_thinking', required: The type of the content part.
            - `data` string, required: The redacted thinking content.
    - `logprobs` object, nullable, required: Log probability information for the choice.
      - `content` ChatCompletionTokenLogprob[], nullable, required: A list of message content tokens with log probability information.
        - `token` string, required: The token.
        - `logprob` number, required: The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value `-9999.0` is used to signify that the token is very unlikely.
        - `bytes` integer[], nullable, required: A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be `null` if there is no bytes representation for the token.
        - `top_logprobs` object[], required: List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested `top_logprobs` returned.
          - `token` string, required: The token.
          - `logprob` number, required: The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value `-9999.0` is used to signify that the token is very unlikely.
          - `bytes` integer[], nullable, required: A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be `null` if there is no bytes representation for the token.
  - `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.
  - `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.
  - `object` 'chat.completion', required: The object type, which is always `chat.completion`.
  - `usage` CompletionUsage: Usage statistics for the completion request.
    - `completion_tokens` integer, required: Number of tokens in the generated completion.
    - `prompt_tokens` integer, required: Number of tokens in the prompt.
    - `total_tokens` integer, required: Total number of tokens used in the request (prompt + completion).

---

[API](https://skmtc.dev/obiguard/apis/obiguard-api.md) - [All operations](https://skmtc.dev/obiguard/apis/obiguard-api/llms.txt) - [OpenAPI document](https://skmtc.dev/obiguard/apis/obiguard-api/revisions/ddd03c93e108?raw)