Réflexion pour les modèles ouverts

Certains modèles ouverts sont compatibles avec un mode "réflexion" qui leur permet de raisonner étape par étape avant de fournir une réponse finale. Cela est utile pour les tâches nécessitant une logique transparente, comme les preuves mathématiques, le débogage de code complexe ou la planification d'agents en plusieurs étapes.

Conseils spécifiques aux modèles

Les sections suivantes fournissent des conseils spécifiques aux modèles pour la réflexion.

DeepSeek R1 0528

Pour DeepSeek R1 0528, le raisonnement est entouré de tags <think></think> dans le champ content. Il n'y a pas de champ reasoning_content.

Exemple de requête :

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/endpoints/openapi/chat/completions -d '{
  "model": "deepseek-ai/deepseek-r1-0528-maas",
  "messages": [{
    "role": "user",
    "content": "Who are you?"
  }]
}'

Exemple de réponse :

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "<think>\nHmm, the user just asked \u201cWho are you?\u201d -
        a simple but fundamental question. \n\nFirst, let's consider the
        context. This is likely their first interaction, so they're probably
        testing the waters or genuinely curious about what I am. The phrasing is
        neutral - no urgency or frustration detected. \n\nI should keep my
        response warm and informative without being overwhelming. Since they
        didn't specify language preference, I'll default to English but note
        they might be multilingual. \n\nKey points to cover:\n- My identity
        (DeepSeek-R1)\n- My capabilities \n- My purpose (being helpful)\n- Tone:
        friendly with emojis to seem approachable\n- Ending with an open
        question to continue conversation\n\nThe smiley face feels appropriate
        here - establishes friendliness. Mentioning \u201cAI assistant\u201d
        upfront avoids confusion. Listing examples of what I can do gives
        concrete value. Ending with \u201cHow can I help?\u201d turns it into an
        active conversation starter. \n\nNot adding too much technical detail
        (like model specs) since that might overwhelm a first-time user. The
        \u201calways learning\u201d phrase subtly manages expectations about my
        limitations.\n</think>\nI'm DeepSeek-R1, your friendly AI assistant!
        \ud83d\ude0a \nI'm here to help you with questions, ideas, problems, or
        just a chat\u2014whether it's about homework, writing, coding, career
        advice, or fun trivia. I can read files (like PDFs, Word, Excel, etc.),
        browse the web for you (if enabled), and I'm always learning to be more
        helpful!\n\nSo\u2026 how can I help you today? \ud83d\udcac\u2728",
        "role": "assistant"
      }
    }
  ],
  "created": 1758229877,
  "id": "dXXMaKrsKqaQm9IPsLeTkAQ",
  "model": "deepseek-ai/deepseek-r1-0528-maas",
  "object": "chat.completion",
  "system_fingerprint": "",
  "usage": {
    "completion_tokens": 329,
    "prompt_tokens": 9,
    "total_tokens": 338
  }
}

DeepSeek v3.1

DeepSeek-V3.1 est compatible avec le raisonnement hybride, qui vous permet d'activer ou de désactiver le raisonnement. Par défaut, le raisonnement est désactivé. Pour l'activer, incluez "chat_template_kwargs": { "thinking": true } dans votre requête.

Lorsque le raisonnement est activé, le texte de réflexion apparaît au début du champ content, suivi de </think>, puis du texte de la réponse (par exemple, THINKING_TEXT</think>ANSWER_TEXT). Notez qu'il n'y a pas de balise <think> initiale.

Exemple de requête :

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://us-west2-aiplatform.googleapis.com/v1/projects/test-project/locations/us-west2/endpoints/openapi/chat/completions -d '{
  "model": "deepseek-ai/deepseek-v3.1-maas",
  "messages": [{
    "role": "user",
    "content": "What are the first 3 even prime numbers?"
  }],
  "chat_template_kwargs": {
    "thinking": true
  }
}'

Exemple de réponse :

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "matched_stop": 1,
      "message": {
        "content": "SNIPPED</think>The only even prime number is 2. By
        definition, a prime number is a natural number greater than 1 that has
        no positive divisors other than 1 and itself. Since all even numbers
        other than 2 are divisible by 2 and themselves, they have at least three
        divisors and are not prime. Therefore, there are no second or third even
        prime numbers. The concept of \"first three even prime numbers\" is not
        applicable beyond the first one.",
        "reasoning_content": null,
        "role": "assistant",
        "tool_calls": null
      }
    }
  ],
  "created": 1758229458,
  "id": "ynPMaIWPN-KeltsPh87s0Qg",
  "model": "deepseek-ai/deepseek-v3.1-maas",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 1525,
    "prompt_tokens": 17,
    "prompt_tokens_details": null,
    "total_tokens": 1542
  }
}

Qwen3-Next Thinking

Pour les modèles Qwen3-Next Thinking, les jetons de raisonnement se trouvent dans le champ reasoning_content et le texte de réponse normal se trouve dans le champ content.

Exemple de requête :

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://aiplatform.googleapis.com/v1/projects/test-project/locations/global/endpoints/openapi/chat/completions -d '{
  "model": "qwen/qwen3-next-80b-a3b-thinking-maas",
  "messages": [{
    "role": "user",
    "content": "Who are you?"
  }]
}'

Exemple de réponse :

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "matched_stop": 151645,
      "message": {
        "content": "Hello! I'm Qwen, a large language model developed by Alibaba
        Cloud. I can help answer questions, write stories, emails, scripts,
        perform logical reasoning, code, and more. How can I assist you today?
        \ud83d\ude0a",
        "reasoning_content": "Okay, the user asked, Who are you?
        I need to respond appropriately. First, I should state my name clearly:
        I am Qwen, a large language model developed by Alibaba Cloud. Then,
        maybe mention my capabilities, like answering questions, writing
        stories, emails, scripts, logical reasoning, programming, etc. Also,
        highlight that I can express views and play games. But keep it concise
        since the user might just want a brief intro.\n\nWait, the user might
        not know much about me, so I should explain what I can do in simple
        terms. Maybe add something about being trained on a large amount of
        data, so I can handle various topics. But don't get too technical. Also,
        maybe mention that I'm here to help. Should I ask if they have any
        specific questions? That might be good to invite further
        interaction.\n\nLet me check the structure. Start with a greeting,
        introduce myself, list key abilities, and offer assistance. Keep it
        friendly and approachable. Avoid jargon. Maybe say something like \"I'm
        Qwen, a large language model created by Alibaba Cloud. I can help with
        answering questions, writing, coding, and more. How can I assist you
        today?\" That seems good.\n\nWait, the original response in Chinese
        might be different, but the user asked in English, so the response
        should be in English. Let me confirm. The user's query is in English, so
        I should respond in English. Yes.\n\nAlso, check for any specific
        details. Maybe mention that I have 128K context length, but maybe that's
        too technical. Maybe just say \"I have a large context window\" or not
        necessary. For a basic introduction, maybe stick to the main points
        without too many specs. The user might not need the technical details
        right away.\n\nSo, the response should be: Hello! I'm Qwen, a large
        language model developed by Alibaba Cloud. I can help answer questions,
        write stories, emails, scripts, perform logical reasoning, code, and
        more. How can I assist you today? That's concise and covers the main
        points.\n\nWait, in the initial problem statement, the user's question
        is \"Who are you?\" So the standard response would be to introduce
        myself. Yes. Also, maybe add that I'm an AI language model, but the name
        Qwen is important. So the key elements are name, developer,
        capabilities, and offer help.\n\nYes. Let me make sure there's no
        mistake. Alibaba Cloud is the correct developer. Yes. So the response
        should be accurate. Alright, that seems good.\n",
        "role": "assistant",
        "tool_calls": null
      }
    }
  ],
  "created": 1758229652,
  "id": "knTMaJC0EJfM5OMP7I3xkAk",
  "metadata": {
    "weight_version": "default"
  },
  "model": "qwen/qwen3-next-80b-a3b-thinking-maas",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 573,
    "prompt_tokens": 14,
    "prompt_tokens_details": null,
    "reasoning_tokens": 0,
    "total_tokens": 587
  }
}

GPT OSS

Pour les modèles GPT OSS, le texte de réflexion se trouve dans le champ reasoning_content et le texte de réponse normal dans le champ content. Ces modèles sont également compatibles avec le paramètre reasoning_effort.

Exemple de requête :

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/endpoints/openapi/chat/completions -d '{
  "model": "openai/gpt-oss-120b-maas",
  "messages": [{
    "role": "user",
    "content": "Who are you?"
  }],
  "reasoning_effort": "high"
}'

Exemple de réponse :

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "matched_stop": 200002,
      "message": {
        "content": "I\u2019m ChatGPT, a large language model created by OpenAI
        (based on the GPT\u20114 architecture). I\u2019m here to help answer
        your questions, brainstorm ideas, explain concepts, and assist with a
        wide variety of topics. Let me know what you\u2019d like to talk
        about!",
        "reasoning_content": "The user asks: \"Who are you?\" It's a simple
        question. They want to know who the assistant is. According to policy,
        we should respond with a brief, friendly description: \"I am ChatGPT, a
        large language model trained by OpenAI.\" Possibly mention the version
        (GPT-4). Also mention we are here to assist. There's no request for
        disallowed content. So a straightforward answer. Possibly ask if they
        need help. Provide a short intro.\n\nThus answer: \"I am ChatGPT, a
        large language model trained by OpenAI. I'm here to help answer your
        questions...\"\n\nWe should avoid disallowed content. It's fine.\n\nThus
        final answer.",
        "role": "assistant",
        "tool_calls": null
      }
    }
  ],
  "created": 1758229799,
  "id": "JnXMaJq6HLGzquEP4OD18Qg",
  "metadata": {
    "weight_version": "default"
  },
  "model": "openai/gpt-oss-120b-maas",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 201,
    "prompt_tokens": 71,
    "prompt_tokens_details": null,
    "reasoning_tokens": 0,
    "total_tokens": 272
  }
}

Étapes suivantes