有了函式呼叫功能，您就能定義自訂函式，並讓 LLM 呼叫這些函式，藉此擷取即時資訊或與外部系統 (例如 SQL 資料庫或客戶服務工具) 互動。
如要進一步瞭解函式呼叫的概念資訊，請參閱「函式呼叫簡介」。
使用函式呼叫
下列範例說明如何使用函式呼叫。
Python
在試用這個範例之前，請先按照Python使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。 詳情請參閱 Vertex AI Python API 參考說明文件。
如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。 詳情請參閱「為本機開發環境設定驗證」。
執行這個範例前，請務必設定 OPENAI_BASE_URL 環境變數。
詳情請參閱「驗證和憑證」。
from openai import OpenAI client = OpenAI() response = client.chat.completions.create( model="MODEL", messages=[ {"role": "user", "content": "CONTENT"} ], tools=[ { "type": "function", "function": { "name": "FUNCTION_NAME", "description": "FUNCTION_DESCRIPTION", "parameters": PARAMETERS_OBJECT, } } ], tool_choice="auto", )
- MODEL：要使用的模型名稱，例如
qwen/qwen3-next-80b-a3b-instruct-maas。 - CONTENT：要傳送至模型的使用者提示。
- FUNCTION_NAME：要呼叫的函式名稱。
- FUNCTION_DESCRIPTION：函式的說明。
- PARAMETERS_OBJECT：定義函式參數的字典，例如：
{"type": "object", "properties": {"location": {"type": "string", "description": "The city and state"}}, "required": ["location"]}
REST
設定環境後，即可使用 REST 測試文字提示。下列範例會將要求傳送至發布商模型端點。
使用任何要求資料之前，請先替換以下項目：
- PROJECT_ID：您的 Google Cloud 專案 ID。
- LOCATION：支援開放式模型的區域。
- MODEL：要使用的模型名稱，例如
qwen/qwen3-next-80b-a3b-instruct-maas。 - CONTENT：要傳送至模型的使用者提示。
- FUNCTION_NAME：要呼叫的函式名稱。
- FUNCTION_DESCRIPTION：函式的說明。
- PARAMETERS_OBJECT：定義函式參數的 JSON 結構定義物件，例如：
{"type": "object", "properties": {"location": {"type": "string", "description": "The city and state"}}, "required": ["location"]}
HTTP 方法和網址：
POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/openapi/chat/completions
JSON 要求主體：
{
"model": "MODEL",
"messages": [
{
"role": "user",
"content": "CONTENT"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "FUNCTION_NAME",
"description": "FUNCTION_DESCRIPTION",
"parameters": PARAMETERS_OBJECT
}
}
],
"tool_choice": "auto"
}
如要傳送要求，請選擇以下其中一個選項：
curl
將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：
curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/openapi/chat/completions"
PowerShell
將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：
$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }
Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/endpoints/openapi/chat/completions" | Select-Object -Expand Content
您應該會收到執行成功的狀態碼 (2xx) 和空白回應。
範例
以下是使用 get_current_weather 函式擷取氣象資訊後，您可能會看到的完整輸出內容。
Python
from openai import OpenAI client = OpenAI() response = client.chat.completions.create( model="qwen/qwen3-next-80b-a3b-instruct-maas", messages=[ { "role": "user", "content": "Which city has a higher temperature, Boston or new Delhi and by how much in F?" }, { "role": "assistant", "content": "I'll check the current temperatures for Boston and New Delhi in Fahrenheit and compare them. I'll call the weather function for both cities.", "tool_calls": [{"function":{"arguments":"{\"location\":\"Boston, MA\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"},{"function":{"arguments":"{\"location\":\"New Delhi, India\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"}] }, { "role": "tool", "content": "The temperature in Boston is 75 degrees Fahrenheit.", "tool_call_id": "get_current_weather" }, { "role": "tool", "content": "The temperature in New Delhi is 50 degrees Fahrenheit.", "tool_call_id": "get_current_weather" } ], tools=[ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], tool_choice="auto" )
curl
curl -X POST \ -H "Authorization: Bearer $(gcloud auth print-access-token)" \ -H "Content-Type: application/json" \ https://us-central1-aiplatform.googleapis.com/v1/projects/sample-project/locations/us-central1/endpoints/openapi/chat/completions -d \ '{ "model": "qwen/qwen3-next-80b-a3b-instruct-maas", "messages": [ { "role": "user", "content": "Which city has a higher temperature, Boston or new Delhi and by how much in F?" }, { "role": "assistant", "content": "I'll check the current temperatures for Boston and New Delhi in Fahrenheit and compare them. I'll call the weather function for both cities.", "tool_calls": [{"function":{"arguments":"{\"location\":\"Boston, MA\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"},{"function":{"arguments":"{\"location\":\"New Delhi, India\",\"unit\":\"fahrenheit\"}","name":"get_current_weather"},"id":"get_current_weather","type":"function"}] }, { "role": "tool", "content": "The temperature in Boston is 75 degrees Fahrenheit.", "tool_call_id": "get_current_weather" }, { "role": "tool", "content": "The temperature in New Delhi is 50 degrees Fahrenheit.", "tool_call_id": "get_current_weather" } ], "tools": [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" }, "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] } }, "required": ["location"] } } } ], "tool_choice": "auto" }'
{ "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "Based on the current weather data:\n\n- **Boston, MA**: 75°F \n- **New Delhi, India**: 50°F \n\n**Comparison**: \nBoston is **25°F warmer** than New Delhi. \n\n**Answer**: \nBoston has a higher temperature than New Delhi by 25 degrees Fahrenheit.", "role": "assistant" } } ], "created": 1750450289, "id": "2025-06-20|13:11:29.240295-07|6.230.75.101|-987540014", "model": "qwen/qwen3-next-80b-a3b-instruct-maas", "object": "chat.completion", "system_fingerprint": "", "usage": { "completion_tokens": 66, "prompt_tokens": 217, "total_tokens": 283 } }
特定模型適用的指引
以下各節提供函式呼叫的特定模型指南。
DeepSeek
如果您使用系統提示，DeepSeek 模型在函式呼叫方面的表現不佳。為獲得最佳成效，使用 DeepSeek 模型搭配函式呼叫時，請省略系統提示。
Llama
meta/llama3-405b-instruct-maas 不支援 tool_choice = 'required'。
OpenAI
使用 openai/gpt-oss-120b-instruct-maas 和 openai/gpt-oss-20b-instruct-maas 時，請將工具定義放在系統提示中，以獲得最佳成效。例如：
{"messages": [ {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required:\n..."}, {"role": "user", "content": "What's the weather like in Boston?"}, ... ]}
這些模型不支援 tool_choice = 'required' 或具名工具呼叫。
Qwen
明確將 tool_choice 設為 auto 或 none 時，Qwen 模型成效最佳。如果未設定 tool_choice，模型可能無法正常運作。