モデルレスポンスを作成する
CometAPI POST /v1/responses を使用して、組み込みツールと関数呼び出しを含むマルチモーダルなモデルレスポンスと会話履歴を作成します。
gpt-6-astra でテキスト、画像、ファイル、会話履歴を送信します。例では、Web検索、カスタム関数呼び出し、構造化出力、推論、ストリーミング(Streaming)も示します。
会話を継続する
テキストのみの会話では、ユーザーメッセージとアシスタントのテキスト返信を保持してから、次のユーザーメッセージを追加します。response.output_text は、レスポンスからのアシスタントテキストを結合するSDKの便利なプロパティです。関数呼び出しを含む会話では、以下で説明するように、ツール呼び出し項目と結果項目も必要です。
Web検索を使用する
情報を検索して出典の引用を含めるには、web_search ツールを追加します。
web_search_call 項目とメッセージのURLアノテーションを確認してください。ほかの組み込みツールには独自の設定が必要です。詳細については、 OpenAI ツールガイド.
カスタム関数を呼び出す
関数の引数を定義し、モデルが関数の呼び出しをリクエストできるようにします:function_call 出力項目として表示されます。その arguments フィールドは JSON エンコードされた文字列です。関数を実行してから、元の入力と、response-item の id フィールドを除いた返された出力項目から次のリクエストを構築します。関数の name、arguments、call_id は保持し、対応する function_call_output と結果を call_id に含む output 項目を追加します。
構造化出力をリクエストする
スキーマに従う JSON をリクエストするにはtext.format を使用します:
推論を設定する
モデルの推論の努力量を選択するには、reasoning.effort を設定します:
low を、推論の例には high を使用します。max_output_tokens には、推論トークン(Token)と可視出力トークン(Token)の両方が含まれます。
レスポンスをストリーミング(Streaming)する
サーバー送信イベントを受信するには、stream を true に設定します:
response.created、response.in_progress、response.completed などのライフサイクルイベントが含まれます。テキストは response.output_text.delta イベントで届きます。ツールイベントと推論イベントはリクエストに応じて異なるため、すべてのレスポンスが同じシーケンスを持つと仮定するのではなく、type によってイベントを処理してください。
承認
Bearer token authentication. Use your CometAPI key.
ボディ
Model ID to use for this request. See the Models page for current options.
"gpt-6-astra"
Text, image, or file inputs to the model, used to generate a response. Can be a simple string for text-only input, or an array of input items for multimodal content (images, files) and multi-turn conversations.
A system (or developer) message inserted into the model's context. When used with previous_response_id, instructions from the previous response are not carried over — this makes it easy to swap system messages between turns.
Whether to run the model response in the background. Background responses do not return output directly — you retrieve the result later via the response ID.
Context management configuration for this request. Controls how the model manages context when the conversation exceeds the context window.
The conversation this response belongs to. Items from the conversation are prepended to input for context. Input and output items are automatically added to the conversation after the response completes. Cannot be used with previous_response_id.
Additional output data to include in the response. Use this to request extra information that is not included by default.
web_search_call.action.sources, code_interpreter_call.outputs, computer_call_output.output.image_url, file_search_call.results, message.input_image.image_url, message.output_text.logprobs, reasoning.encrypted_content An upper bound for the number of tokens that can be generated for a response, including visible output tokens and reasoning tokens.
The maximum number of total calls to built-in tools that can be processed in a response. This limit applies across all built-in tool calls, not per individual tool. Any further tool call attempts by the model will be ignored.
Set of up to 16 key-value pairs that can be attached to the response. Useful for storing additional information in a structured format. Keys have a maximum length of 64 characters; values have a maximum length of 512 characters.
Whether to allow the model to run tool calls in parallel.
An ID for a stored response that is accessible to the selected model and request context. Cannot be combined with conversation. The conversation-history example sends prior input and output items explicitly.
Reference to a prompt template and its variables.
A key used to cache responses for similar requests, helping optimize cache hit rates. Replaces the deprecated user field for caching purposes.
Legacy prompt-cache retention setting. Refer to the selected model documentation for prompt_cache_options and supported retention settings.
in_memory, 24h Reasoning configuration for the selected model.
A stable identifier for your end-users, used to help detect policy violations. Should be a hashed username or email — do not send identifying information directly.
64Specifies the processing tier for the request. When set, the response will include the actual service_tier used.
auto: Uses the tier configured in project settings (default behavior).default: Standard pricing and performance.flex: Flexible processing with potential cost savings.priority: Priority processing with faster response times.
auto, default, flex, priority Request response storage for later retrieval, when supported by the selected model and request context.
If set to true, the response data will be streamed to the client as it is generated using server-sent events (SSE). Events include response.created, response.output_text.delta, response.completed, and more.
Options for streaming responses. Only set this when stream is true.
Sampling temperature. Omit this field for GPT-6 Astra.
0 <= x <= 2Configuration for text output. Use this to request structured JSON output via JSON mode or JSON Schema.
Controls how the model selects which tool(s) to call.
auto(default): The model decides whether and which tools to call.none: The model will not call any tools.required: The model must call at least one tool.- An object specifying a particular tool to use.
Tools available to the model. This page demonstrates web_search and custom function tools. Other tool types require their own configuration; see the official tool reference.
Number of token alternatives to include for models supporting log probabilities. Requires message.output_text.logprobs in include. Omit for GPT-6 Astra.
0 <= x <= 20Nucleus sampling threshold. Omit this field for GPT-6 Astra. For sampling overrides, adjust either top_p or temperature.
0 <= x <= 1The truncation strategy for handling inputs that exceed the model's context window.
auto: The model truncates the input by dropping items from the beginning of the conversation to fit.disabled(default): The request fails with a 400 error if the input exceeds the context window.
auto, disabled Deprecated. Use safety_identifier and prompt_cache_key instead. A stable identifier for your end-user.
レスポンス
The generated Response object.
Unique identifier for the response.
"resp_example"
The object type, always response.
response "response"
Unix timestamp (in seconds) of when the response was created.
1788763707
The status of the response.
completed, in_progress, failed, cancelled, queued, incomplete "completed"
Whether the response was run in the background.
false
Unix timestamp of when the response was completed, or null if still in progress.
1788763711
Error information if the response failed, or null on success.
Details about why the response is incomplete, if applicable.
The system instructions used for this response.
The maximum output token limit that was applied.
The model used for the response.
"gpt-6-astra"
An array of output items generated by the model. Each item can be a message, function call, or other output type.
Whether parallel tool calls were enabled.
The ID of the previous response, if this is a multi-turn conversation.
The reasoning configuration that was used.
The service tier actually used to process the request.
Whether the response was stored.
The temperature value used.
The text configuration used.
The tool choice setting used.
The tools that were available for this response.
The top_p value used.
The truncation strategy used.
Token usage statistics for this response.
The user identifier, if provided.
The metadata attached to this response.
Content filter results applied to the response, if any.
The frequency penalty applied to the request.
Maximum number of tool calls allowed, if set.
The presence penalty applied to the request.
Cache key for prompt caching, if applicable.
Prompt cache retention policy, if applicable.
Safety system identifier for the response, if applicable.
Number of top log probabilities returned per token position.