使用自訂代理程式

事前準備

本教學課程假設您已詳閱並按照下列教學課程的指示操作：

取得代理程式的執行個體

如要查詢代理程式，首先需要代理程式的執行個體。您可以建立新的代理程式執行個體，或取得現有的代理程式執行個體。

如要取得與特定資源 ID 相對應的服務專員：

Vertex AI SDK for Python

請執行下列程式碼：

import vertexai

client = vertexai.Client(  # For service interactions via client.agent_engines
    project="PROJECT_ID",
    location="LOCATION",
)

agent = client.agent_engines.get(name="projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

print(agent)

其中

要求

請執行下列程式碼：

from google import auth as google_auth
from google.auth.transport import requests as google_requests
import requests

def get_identity_token():
    credentials, _ = google_auth.default()
    auth_request = google_requests.Request()
    credentials.refresh(auth_request)
    return credentials.token

response = requests.get(
f"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID",
    headers={
        "Content-Type": "application/json; charset=utf-8",
        "Authorization": f"Bearer {get_identity_token()}",
    },
)

REST 

curl \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID

使用 Python 適用的 Vertex AI SDK 時，agent 物件會對應至 AgentEngine 類別，其中包含下列項目：

  • 包含已部署代理程式相關資訊的 agent.api_resource。 您也可以呼叫 agent.operation_schemas()，傳回代理程式支援的作業清單。詳情請參閱「支援的作業」。
  • agent.api_client，可進行同步服務互動
  • agent.async_api_client，可進行非同步服務互動

本節其餘部分假設您有名為 agent 的執行個體。

列出支援的作業

在本機開發代理程式時，您可以存取並瞭解代理程式支援的作業。如要使用已部署的代理程式，您可以列舉代理程式支援的作業：

Vertex AI SDK for Python

請執行下列程式碼：

print(agent.operation_schemas())

要求

請執行下列程式碼：

import json

json.loads(response.content).get("spec").get("classMethods")

REST

以 spec.class_methods 表示，來自 curl 要求的相關回應。

每項作業的結構定義都是字典，其中記錄了可呼叫的代理程式方法資訊。支援的作業集取決於您用來開發代理程式的架構：

舉例來說，以下是 LangchainAgent 的 query 作業結構定義：

{'api_mode': '',
 'name': 'query',
 'description': """Queries the Agent with the given input and config.
    Args:
        input (Union[str, Mapping[str, Any]]):
            Required. The input to be passed to the Agent.
        config (langchain_core.runnables.RunnableConfig):
            Optional. The config (if any) to be used for invoking the Agent.
    Returns:
        The output of querying the Agent with the given input and config.
""",            '        ',
 'parameters': {'$defs': {'RunnableConfig': {'description': 'Configuration for a Runnable.',
                                             'properties': {'configurable': {...},
                                                            'run_id': {...},
                                                            'run_name': {...},
                                                            ...},
                                             'type': 'object'}},
                'properties': {'config': {'nullable': True},
                               'input': {'anyOf': [{'type': 'string'}, {'type': 'object'}]}},
                'required': ['input'],
                'type': 'object'}}

其中

  • name 是作業的名稱 (即名為 query 的作業的 agent.query)。
  • api_mode 是作業的 API 模式 ("" 代表同步，"stream" 代表串流)。
  • description 是根據方法 docstring 的作業說明。
  • parameters 是 OpenAPI 結構定義格式的輸入引數結構定義。

使用支援的作業查詢代理程式

如果是自訂代理程式，您可以使用開發代理程式時定義的任何查詢或串流作業：

請注意，部分架構僅支援特定查詢或串流作業：

架構 支援的查詢作業
代理開發套件 async_stream_query
LangChain query、stream_query
LangGraph query、stream_query
AG2 query
LlamaIndex query

查詢代理程式

使用 query 作業查詢代理程式：

Vertex AI SDK for Python 

agent.query(input="What is the exchange rate from US dollars to Swedish Krona today?")

要求

from google import auth as google_auth
from google.auth.transport import requests as google_requests
import requests

def get_identity_token():
    credentials, _ = google_auth.default()
    auth_request = google_requests.Request()
    credentials.refresh(auth_request)
    return credentials.token

requests.post(
    f"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:query",
    headers={
        "Content-Type": "application/json; charset=utf-8",
        "Authorization": f"Bearer {get_identity_token()}",
    },
    data=json.dumps({
        "class_method": "query",
        "input": {
            "input": "What is the exchange rate from US dollars to Swedish Krona today?"
        }
    })
)

REST 

curl \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:query -d '{
  "class_method": "query",
  "input": {
    "input": "What is the exchange rate from US dollars to Swedish Krona today?"
  }
}'

查詢回應是類似本機應用程式測試輸出的字串：

{"input": "What is the exchange rate from US dollars to Swedish Krona today?",
 # ...
 "output": "For 1 US dollar you will get 10.7345 Swedish Krona."}

串流傳送服務專員的回覆

使用 stream_query 作業，從代理程式串流傳輸回覆：

Vertex AI SDK for Python 

agent = agent_engines.get("projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

for response in agent.stream_query(
    input="What is the exchange rate from US dollars to Swedish Krona today?"
):
    print(response)

要求

from google import auth as google_auth
from google.auth.transport import requests as google_requests
import requests

def get_identity_token():
    credentials, _ = google_auth.default()
    auth_request = google_requests.Request()
    credentials.refresh(auth_request)
    return credentials.token

requests.post(
    f"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:streamQuery",
    headers={
        "Content-Type": "application/json",
        "Authorization": f"Bearer {get_identity_token()}",
    },
    data=json.dumps({
        "class_method": "stream_query",
        "input": {
            "input": "What is the exchange rate from US dollars to Swedish Krona today?"
        },
    }),
    stream=True,
)

REST 

curl \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID:streamQuery?alt=sse -d '{
  "class_method": "stream_query",
  "input": {
    "input": "What is the exchange rate from US dollars to Swedish Krona today?"
  }
}'

Vertex AI Agent Engine 會以疊代生成的物件序列形式，串流傳輸回覆內容。舉例來說，一組三個回覆可能如下所示：

{'actions': [{'tool': 'get_exchange_rate', ...}]}  # first response
{'steps': [{'action': {'tool': 'get_exchange_rate', ...}}]}  # second response
{'output': 'The exchange rate is 11.0117 SEK per USD as of 2024-12-03.'}  # final response

非同步查詢代理程式

如果您在開發代理程式時定義了 async_query 作業，Python 適用的 Vertex AI SDK 支援代理程式的用戶端非同步查詢：

Vertex AI SDK for Python 

agent = agent_engines.get("projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

response = await agent.async_query(
    input="What is the exchange rate from US dollars to Swedish Krona today?"
)
print(response)

查詢回應是與本機測試輸出內容相同的字典：

{"input": "What is the exchange rate from US dollars to Swedish Krona today?",
 # ...
 "output": "For 1 US dollar you will get 10.7345 Swedish Krona."}

從代理程式非同步串流回應

如果您在開發代理程式時定義了 async_stream_query 作業，可以使用其中一項作業 (例如 async_stream_query) 從代理程式非同步串流回應：

Vertex AI SDK for Python 

agent = agent_engines.get("projects/PROJECT_ID/locations/LOCATION/reasoningEngines/RESOURCE_ID")

async for response in agent.async_stream_query(
    input="What is the exchange rate from US dollars to Swedish Krona today?"
):
    print(response)

async_stream_query 作業會在幕後呼叫相同的 streamQuery 端點，並以非同步方式串流回應，做為一系列反覆產生的物件。舉例來說，一組三個回覆可能如下所示：

{'actions': [{'tool': 'get_exchange_rate', ...}]}  # first response
{'steps': [{'action': {'tool': 'get_exchange_rate', ...}}]}  # second response
{'output': 'The exchange rate is 11.0117 SEK per USD as of 2024-12-03.'}  # final response

回覆內容應與本機測試期間產生的內容相同。

後續步驟