Documentation Index
Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt
Use this file to discover all available pages before exploring further.
推論設定
reasoning パラメータを使用して推論動作を設定できます:
curl -X POST https://aihubmix.com/v1/responses \
-H "Authorization: Bearer YOUR_AIHUBMIX_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5",
"input": "Plan a week-long trip to the US for me.",
"reasoning": {
"effort": "high"
},
"max_output_tokens": 5000
}'
推論強度
effort パラメータは、モデルが推論に投入する計算リソースの量を制御し、本質的に推論の強度レベルを決定します。
| 推論レベル | 説明 |
|---|---|
| minimal | 最小限の計算による基本的な推論 |
| low | 単純な質問に適した軽量推論 |
| medium | 中程度の複雑な問題に適したバランスの取れた推論 |
| high | 複雑な問題に適した深い推論 |
会話で推論を使用する
推論機能はマルチターンダイアログでも利用できます:import requests
url = "https://aihubmix.com/v1/responses"
headers = {
"Authorization": "Bearer YOUR_AIHUBMIX_API_KEY",
"Content-Type": "application/json",
}
data = {
"model": "kimi-k2.5",
"input": [
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": "What is your favorite animal?",
}
],
},
{
"type": "message",
"role": "assistant",
"id": "msg_123",
"status": "completed",
"content": [
{
"type": "output_text",
"text": "I don't have a favorite animal.",
"annotations": []
}
],
},
{
"type": "message",
"role": "user",
"content": [
{
"type": "input_text",
"text": "Why is the sky blue?",
}
],
},
],
"reasoning": {
"effort": "high"
},
"max_output_tokens": 5000,
}
response = requests.post(url, headers=headers, json=data)
print(response.status_code)
print(response.json())
推論情報を含むレスポンス
推論が有効になっている場合、API は推論データを含む結果を返します:{
"id": "resp_051e00420efb9e150069aff6a18418819591abb7ce5f8487ed",
"object": "response",
"created_at": 1773139617,
"status": "completed",
"background": false,
"completed_at": 1773139621,
"content_filters": [
{
"blocked": false,
"source_type": "completion",
"content_filter_raw": [],
"content_filter_results": {},
"content_filter_offsets": {
"start_offset": 0,
"end_offset": 1147,
"check_offset": 0
}
}
],
"error": null,
"frequency_penalty": 0.0,
"incomplete_details": null,
"instructions": null,
"max_output_tokens": 5000,
"max_tool_calls": null,
"model": "gpt-54",
"output": [
{
"id": "rs_051e00420efb9e150069aff6a32f948195996db3ff98314ef2",
"type": "reasoning",
"summary": []
},
{
"id": "msg_051e00420efb9e150069aff6a33d808195825716a666d8ba8b",
"type": "message",
"status": "completed",
"role": "assistant",
"content": [
{
"type": "output_text",
"annotations": [],
"logprobs": [],
"text": "The sky looks blue because of how sunlight interacts with Earth’s atmosphere.\n\n1. **Sunlight isn’t just “white”**\nSunlight is made of many colors (red, orange, yellow, green, blue, violet), each with different wavelengths.\n\n2. **Air scatters short wavelengths more**\nAs sunlight passes through the atmosphere, it hits gas molecules and tiny particles.\n- Shorter wavelengths (blue, violet) are scattered in all directions much more than longer wavelengths (red, orange).\n- This effect is called **Rayleigh scattering**.\n\n3. **We see more blue than violet**\n- Our eyes are more sensitive to blue than to violet.\n- Some violet light is also absorbed higher in the atmosphere.\nSo the scattered light we perceive is mostly blue.\n\n4. **Why sunsets are red/orange**\nAt sunrise and sunset, sunlight passes through much more atmosphere.\n- Most of the blue light gets scattered out of the direct path.\n- The remaining light reaching your eyes from the Sun is richer in reds and oranges."
}
]
}
],
"parallel_tool_calls": true,
"presence_penalty": 0.0,
"previous_response_id": null,
"prompt_cache_key": null,
"prompt_cache_retention": null,
"reasoning": {
"effort": "high",
"summary": null
},
"safety_identifier": null,
"service_tier": "default",
"store": true,
"temperature": 1.0,
"text": {
"format": {
"type": "text"
},
"verbosity": "medium"
},
"tool_choice": "auto",
"tools": [],
"top_logprobs": 0,
"top_p": 1.0,
"truncation": "disabled",
"usage": {
"input_tokens": 35,
"input_tokens_details": {
"cached_tokens": 0
},
"output_tokens": 267,
"output_tokens_details": {
"reasoning_tokens": 29
},
"total_tokens": 302
},
"user": null,
"metadata": {}
}
使用上の推奨事項
- 適切な推論強度レベルを選択する:複雑な問題には
high、単純なタスクにはlowを使用します。 - トークン使用量を考慮する:推論はトークン消費を増加させます。
- ストリーミングを活用する:長い推論チェーンの場合、ストリーミングはユーザー体験を向上させます。
- コンテキストを提供する:効果的な推論を可能にするために、モデルに十分なコンテキストを与えてください。
最終更新日:2026-06-01