Sohbet tamamlamaları

Kimlik doğrulama

Her isteğin bir Bearer belirtecine (Airforce API anahtarınız) ihtiyacı vardır. Anthropic x-api-key başlık da kabul edilir /v1/messages SDK uyumluluğu için.

Authorization: Bearer sk-air-YOUR_API_KEY
# alt for /v1/messages:
x-api-key: sk-air-YOUR_API_KEY

POST /v1/chat/completions

OpenAI uyumlu Sohbet Tamamlamaları. Yetkili ile çalışır openai Geçersiz kılarak SDK base_url ile https://api.airforce/v1.

Talep gövdesi

Temel örnek

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.1-chat",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"}
    ],
    "max_tokens": 200,
    "temperature": 0.7
  }'

Tepki şekli

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1710000000,
  "model": "gpt-5.1-chat",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "The capital of France is Paris."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 8,
    "total_tokens": 28
  }
}

Muhakeme ve düşünme

Reasoning/thinking is a cross-model feature for every model ID with supports_reasoning: true — Claude, OpenAI o-series/GPT-5, Gemini, Qwen, DeepSeek, and others. You send the same canonical parameters; Airforce maps them to each provider's native shape. This is not a DeepSeek-only API.

Truth source: check supports_reasoning: true bir model üzerinde GET /v1/models (or GET /api/models/{id}/allowed-params). Prefer that flag over guessing from the model name.

Kanonik parametreler

What differs by family (mapping only)

Parameters are the same everywhere. Only how we map them (and how hard "off" is) differs:

• Claude — Thinking on/off + budget; often also reasoning_effort via the gateway.
• OpenAI (o1/o3, GPT-5) — Mainly reasoning_effort. A full "thinking off" is often not available — you control how strongly the model reasons, not always whether it reasons at all.
• Gemini — thinking_config / budget mapped internally.
• Qwen / Xiaomi / Alibaba — thinking + enable_thinking-style controls.
• DeepSeek (generic) — Hybrid on/off is especially clear: thinking: { type: enabled|disabled } plus optional reasoning_effort.
• Resellers / other — Often generic passthrough of the same canonical fields.

Controlling where the trace appears

An optional reasoning object on the request decides what happens to the thinking trace. It is consumed by Airforce and never forwarded upstream.

"reasoning": { "format": "separate", "exclude": false }

Muhakeme çabası (OpenAI tarzı)

Primary control for o-series and GPT-5: how much the model may reason. Same canonical field as on every other supports_reasoning model — OpenAI is included, but behaviour is not 1:1 with DeepSeek's hard on/off.

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "o3-mini",
    "messages": [{"role": "user", "content": "Prove the Pythagorean theorem."}],
    "reasoning_effort": "high"
  }'

Genişletilmiş düşünme (Anthropic tarzı)

Budget-based thinking for Claude (and gateways that accept the Anthropic shape). You can still send reasoning_effort; we map when the channel supports it.

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4.6",
    "messages": [{"role": "user", "content": "Plan a 7-day Italy trip."}],
    "thinking": {"type": "enabled", "budget_tokens": 4000}
  }'

Hybrid thinking (e.g. DeepSeek V3.2/V4)

Example of a hybrid model family with a clear Thinking / Non-Thinking switch — not a separate protocol. deepseek-v3.2, deepseek-v4-flash and deepseek-v4-pro accept the same canonical fields as every other supports_reasoning model. Toggle thinking and optionally set effort in one request:

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [{"role": "user", "content": "Solve this step by step: integrate x^2 * e^x."}],
    "thinking": {"type": "enabled"},
    "reasoning_effort": "high"
  }'

Turn thinking off (faster, cheaper when you only need the final answer) — this hard off is clearer on hybrid models than on many OpenAI o-series profiles:

"thinking": {"type": "disabled"}
// or simply: "thinking": "off"

Native docs for this family often list effort levels such as "high" and "max". We accept the full low…max scale and map unsupported levels to the nearest value that reaches the model. Prefer the hybrid IDs above over retired deepseek-chat / deepseek-reasoner names when you need an explicit on/off switch.

Muhakeme izinin kendisi şu şekilde görünür: choices[0].message.reasoning (OpenAI şekli) veya thinking bloke etmek content (Anthropic biçimi). Muhakeme belirteçleri faturalandırılır ve raporlanır usage.completion_tokens_details.reasoning_tokens.

Bu completion_tokens_details.reasoning_tokens dökümü yalnızca üst akış sağlayıcısı bunu raporladığında mevcuttur. Stream edilen bir yanıtta iz, chunk başına delta.reasoning_content üzerinde gelir.

Görüş ve görüntü girişi

Şunlara sahip modeller: supports_vision: true İçerik blokları olarak gömülü görselleri kabul edin. Ya genel bir URL ya da bir base64 veri URL'si çalışır; boyut sınırları yukarı akış modeline bağlıdır.

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.1-chat",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "What is in this image?"},
        {"type": "image_url", "image_url": {"url": "https://example.com/cat.jpg"}}
      ]
    }]
  }'

Araç çağırma

Şunlara sahip modeller: supports_tools: true tanımladığınız işlevleri çağırabilir. Model bir döndürür tool_calls sıralamak; aramayı çalıştırırsınız ve ardından sonucu geri gönderirsiniz. tool mesaj.

Rica etmek

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.1-chat",
    "messages": [{"role": "user", "content": "What is the weather in Paris?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {"type": "string", "description": "City name"}
          },
          "required": ["location"]
        }
      }
    }],
    "tool_choice": "auto"
  }'

Araç çağrısıyla yanıt

{
  "id": "chatcmpl-abc123",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": null,
      "tool_calls": [{
        "id": "call_1",
        "type": "function",
        "function": {
          "name": "get_weather",
          "arguments": "{\"location\":\"Paris\"}"
        }
      }]
    },
    "finish_reason": "tool_calls"
  }]
}

Araç sonucunun takibi

{
  "model": "gpt-5.1-chat",
  "messages": [
    {"role": "user", "content": "What is the weather in Paris?"},
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [{
        "id": "call_1",
        "type": "function",
        "function": {"name": "get_weather", "arguments": "{\"location\":\"Paris\"}"}
      }]
    },
    {"role": "tool", "tool_call_id": "call_1", "content": "{\"temp_c\": 14, \"sky\": \"cloudy\"}"}
  ]
}

Assistant prefill

End your messages array with an assistant message that already contains some text, and the model continues from it instead of starting a fresh turn. This is a reliable way to force a response to begin a specific way — a leading "{" for JSON, a chosen language, or a fixed prefix. The same trick works on /v1/messages. Providers that reject native prefill are handled automatically: the gateway retries once with a compatible rewrite, so you do not have to special-case them.

{
  "model": "claude-sonnet-4.6",
  "messages": [
    {"role": "user", "content": "List three primary colors as a JSON array."},
    {"role": "assistant", "content": "["}
  ]
}

Structured outputs

Set response_format to make the model return JSON. Two modes are supported:

• { "type": "json_object" } — the response is a single valid JSON value.
• { "type": "json_schema", "json_schema": { "name", "schema", "strict" } } — the model is steered to produce JSON that matches your JSON Schema.

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.1-chat",
    "messages": [{"role": "user", "content": "Extract the city and country: I live in Paris, France."}],
    "response_format": {
      "type": "json_schema",
      "json_schema": {
        "name": "location",
        "schema": {
          "type": "object",
          "properties": { "city": {"type": "string"}, "country": {"type": "string"} },
          "required": ["city", "country"]
        }
      }
    }
  }'

Reliability: even when a model wraps its answer in prose or a markdown code fence, Airforce extracts the JSON payload so you always receive parseable content. If no valid JSON can be recovered, the original text is returned unchanged — so the guarantee never makes a response worse. This applies to non-streamed responses; streamed responses are passed through unchanged.

Akış

Ayarlamak stream: true Kısmi tamamlamaları Sunucu Tarafından Gönderilen Olaylar olarak almak için. Her olay, akışsız yanıtla aynı şekle sahip bir JSON öbeğidir; ancak message şununla değiştirilir: delta. Akış şununla bitiyor: data: [DONE].

curl https://api.airforce/v1/chat/completions \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-5.1-chat",
    "messages": [{"role": "user", "content": "Write a haiku about Berlin."}],
    "stream": true,
    "stream_options": {"include_usage": true}
  }'

Tel formatı

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.1-chat","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.1-chat","choices":[{"index":0,"delta":{"content":"Cold "},"finish_reason":null}]}

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.1-chat","choices":[{"index":0,"delta":{"content":"stone "},"finish_reason":null}]}

…

data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","created":1710000000,"model":"gpt-5.1-chat","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":12,"completion_tokens":17,"total_tokens":29}}

data: [DONE]

Reliability & smart routing

Every model ID resolves to a pool of upstream providers behind the scenes. If the first one errors or times out, the request is automatically retried against the next provider for the same model, in order, before any failure is returned — you do not configure or trigger this. The model field in the response always reports the variant that actually answered. This is independent of the optional models / fallbacks array, which adds your own cross-model candidates on top: first the primary model exhausts its own provider chain, then each fallback model exhausts its chain.

POST /v1/messages

Anthropic uyumlu Mesajlar API'si. Resmi @anthropic-ai/sdk ayarlayarak baseURL ile https://api.airforce. OpenAI/Google/etc'ye iletir. Claude olmayan modeller için şeffaf bir şekilde.

Talep gövdesi

Örnek

curl https://api.airforce/v1/messages \
  -H "x-api-key: sk-air-YOUR_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4.6",
    "max_tokens": 256,
    "system": "You are a helpful assistant.",
    "messages": [
      {"role": "user", "content": "Hello, Claude!"}
    ]
  }'

Tepki şekli

Etkinliklerin akışı

Anthropic SSE, tek seferlik JSON parçaları yerine adlandırılmış olayları kullanır. Her olayın hem bir event: isim ve bir data: JSON yükü.

event: message_start
data: {"type":"message_start","message":{"id":"msg_01","role":"assistant","content":[],"model":"claude-sonnet-4.6","stop_reason":null,"usage":{"input_tokens":12,"output_tokens":1}}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"output_tokens":17}}

event: message_stop
data: {"type":"message_stop"}

POST /v1/messages/count_tokens

Anthropic-compatible token counting. Send the same system / messages / tools you would pass to /v1/messages and get an input-token estimate back without running the model — nothing is billed.

curl https://api.airforce/v1/messages/count_tokens \
  -H "x-api-key: sk-air-YOUR_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4.6",
    "system": "You are a helpful assistant.",
    "messages": [{"role": "user", "content": "Hello, Claude!"}]
  }'

# → {"input_tokens": 34}

The count is a fast character-based estimate (about 4 characters per token) over system, messages and tools — close enough for context-budget checks, not an exact tokenizer run.

İstemi önbelleğe alma

Açık /v1/messages Claude modellerinde, bir öneki ileterek önbelleğe alınmış olarak işaretleyin system önbelleğe alınmış segmentin taşındığı bir blok dizisi olarak cache_control: { type: "ephemeral" }. Aynı önekle başlayan sonraki istekler, daha ucuz önbellek okuma hızından ücret alır. Şunlara sahip modeller: supports_caching: true içinde /v1/models bunu destekleyin.

{
  "model": "claude-sonnet-4.6",
  "max_tokens": 1024,
  "system": [
    {"type": "text", "text": "You are a senior staff engineer at Airforce."},
    {
      "type": "text",
      "text": "<repository-snapshot>...</repository-snapshot>",
      "cache_control": {"type": "ephemeral"}
    }
  ],
  "messages": [
    {"role": "user", "content": "Where is rate limiting enforced?"}
  ]
}

Cache sayıları yanıtta nasıl raporlanır

Cache token sayıları her formatın yerel biçiminde geçirilir, böylece SDK'lar (openai, @anthropic-ai/sdk, @google/genai) bunları özel kod olmadan okur. Değer sıfır olduğunda alanlar atlanır, cache'lenmemiş yanıtları yalın tutar.

/v1/chat/completions (OpenAI şekli)

"usage": {
  "prompt_tokens": 2104,
  "completion_tokens": 147,
  "total_tokens": 2251,
  "prompt_tokens_details": { "cached_tokens": 1980 },
  "cache_creation_input_tokens": 124,
  "cache_creation": {
    "ephemeral_5m_input_tokens": 124,
    "ephemeral_1h_input_tokens": 0
  }
}

/v1/messages (Anthropic şekli)

"usage": {
  "input_tokens": 2104,
  "output_tokens": 147,
  "cache_read_input_tokens": 1980,
  "cache_creation_input_tokens": 124,
  "cache_creation": {
    "ephemeral_5m_input_tokens": 124,
    "ephemeral_1h_input_tokens": 0
  }
}

/v1beta/.../generateContent (Gemini şekli)

"usageMetadata": {
  "promptTokenCount": 2104,
  "candidatesTokenCount": 147,
  "totalTokenCount": 2251,
  "cachedContentTokenCount": 1980
}

Önbellekleme nerede geçerli

Açık cache_control işaretleri Claude modelleri için /v1/messages ve /v1/chat/completions üzerinde dikkate alınır — bunları system veya message içerik bloklarına koyun. Diğer birçok sağlayıcı (OpenAI ailesi, DeepSeek, Gemini) otomatik önbellekler: işaret göndermezsiniz ve yeterince uzun bir önek yeniden kullanıldığında yanıtta yalnızca cached_tokens görürsünüz.

Önbellek süresi: 5 dakika veya 1 saat

Önbelleğe alınmış bir önek varsayılan olarak 5 dakika yaşar ve her isabet sayacı yeniler. Daha uzun yaşayan bir önek için işarete ttl: "1h" ekleyin. Yanıt her TTL’yi cache_creation altında ayrı ayrı bildirir.

"cache_control": { "type": "ephemeral", "ttl": "1h" }

Örnek: önce yazma, sonra okuma

Tam olarak aynı isteği iki kez gönderin (yukarıdaki önbellek örneği). Öneki ilk gören çağrı tek seferlik bir önbellek yazması öder; TTL içindeki özdeş çağrılar çok daha ucuz önbellek okumasını öder.

İlk çağrı — önbellek yazma (usage alıntısı):

"usage": {
  "input_tokens": 2104,
  "output_tokens": 12,
  "cache_creation_input_tokens": 1980,
  "cache_read_input_tokens": 0
}

TTL içinde ikinci özdeş çağrı — önbellek okuma:

"usage": {
  "input_tokens": 2104,
  "output_tokens": 12,
  "cache_creation_input_tokens": 0,
  "cache_read_input_tokens": 1980
}

Sınırlar ve maliyet

• Claude, önbelleğe alınabilir minimum bir önek gerektirir (yaklaşık 1024 token; bazı modellerde daha fazla). Daha kısa önekler basitçe önbelleğe alınmaz.
• İstek başına en fazla 4 önbellek kırılma noktası ve önbelleğe alınan önek çağrılar arasında bayt bayt aynı olmalıdır — tek karakterlik bir değişiklik bile önbelleği ıskalar.
• Önbellek yazmaları normal girdiden daha pahalıdır (5m ≈ 1,25×, 1h ≈ 2×); okumalar çok daha ucuzdur (≈ 0,1×). Her modelin önbellek fiyatlarını fiyatlandırma sayfasında görün.

POST /v1/responses

Durum bilgili konuşmalar için OpenAI Responses-API yüzeyi. Aynı Bearer/x-api-key kimlik doğrulaması. Cache sayıları input_tokens_details.cached_tokens (okuma) artı düz cache_creation_input_tokens + cache_creation.ephemeral_* (yazmalar) olarak görünür, /v1/chat/completions ile eşitlik için.

POST /v1beta/models/{model}:generateContent

Google Gemini-compatible endpoint. Works with the official @google/genai SDK and the Gemini CLI by pointing the base URL at https://api.airforce/v1beta. Any routed model works — requests are translated to and from the native Gemini shape, and the model is taken from the URL path (not the body).

Authentication

Pass your Airforce API key any of the three ways Google clients use:

# 1) query parameter (Google default)
?key=sk-air-YOUR_API_KEY

# 2) header
x-goog-api-key: sk-air-YOUR_API_KEY

# 3) bearer token
Authorization: Bearer sk-air-YOUR_API_KEY

Request body

Example

curl "https://api.airforce/v1beta/models/gemini-3.1-pro:generateContent" \
  -H "x-goog-api-key: sk-air-YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [
      {"role": "user", "parts": [{"text": "What is the capital of France?"}]}
    ],
    "systemInstruction": {"parts": [{"text": "You are a helpful assistant."}]},
    "generationConfig": {"temperature": 0.7, "maxOutputTokens": 256}
  }'

Response shape

{
  "candidates": [{
    "content": {
      "role": "model",
      "parts": [{"text": "The capital of France is Paris."}]
    },
    "finishReason": "STOP",
    "index": 0
  }],
  "usageMetadata": {
    "promptTokenCount": 16,
    "candidatesTokenCount": 8,
    "totalTokenCount": 24
  },
  "modelVersion": "gemini-3.1-pro"
}

POST /v1beta/models/{model}:streamGenerateContent

Streaming uses the :streamGenerateContent action and returns Server-Sent Events. Each data: line is a full Gemini-shaped chunk (not a delta object); the final chunk carries usageMetadata.

data: {"candidates":[{"content":{"role":"model","parts":[{"text":"The capital"}]},"index":0}],"modelVersion":"gemini-3.1-pro"}

data: {"candidates":[{"content":{"role":"model","parts":[{"text":" is Paris."}]},"index":0}],"modelVersion":"gemini-3.1-pro"}

data: {"candidates":[{"content":{"role":"model","parts":[]},"finishReason":"STOP","index":0}],"usageMetadata":{"promptTokenCount":16,"candidatesTokenCount":8,"totalTokenCount":24}}

List models

The catalog is also exposed in Gemini Model-resource shape so Google clients can enumerate models.

curl https://api.airforce/v1beta/models

Notes: the base URL is https://api.airforce/v1beta (or /v1), not Google's host. The model name comes from the URL path, not the request body. Only the first candidate is returned, and a subset of Gemini fields is translated — safetySettings and cachedContent are currently ignored. Billing, rate limits and smart routing apply exactly as on /v1/chat/completions.

Hatalar

Airforce, her iki uç nokta için standart HTTP durum kodlarını ve tek tip bir hata zarfını döndürür.

{
  "error": {
    "message": "The requested model does not exist or you do not have access to it.",
    "type": "model_not_found",
    "param": null,
    "code": "404"
  }
}

Açıklayıcı slug, type içindedir. code, HTTP durumunun string biçimidir (örn. "404") ve param, parametre aralığı doğrulama hataları dışında null'dur; bu hatalarda sorun çıkaran parametreyi adlandırır.

curl https://api.airforce/v1/models \
  -H "Authorization: Bearer sk-air-YOUR_API_KEY"