跳至主要內容

OCI 資料科學模型部署端點

OCI 資料科學 是一個完全託管且無伺服器的平台，供資料科學團隊在 Oracle Cloud Infrastructure 中建構、訓練和管理機器學習模型。

如需最新的更新、範例和實驗性功能，請參閱 ADS LangChain 整合。

本筆記本介紹如何使用託管在 OCI 資料科學模型部署上的 LLM。

對於驗證，oracle-ads 函式庫用於自動載入調用端點所需的憑證。

!pip3 install oracle-ads

先決條件​

部署模型​

您可以使用 OCI 資料科學模型部署上的 AI 快速操作輕鬆部署、微調和評估基礎模型。 如需其他部署範例，請造訪 Oracle GitHub 範例儲存庫。

政策​

請確保您擁有存取 OCI 資料科學模型部署端點所需的 政策。

設定​

部署模型後，您必須設定呼叫所需的以下參數

  • endpoint：已部署模型的模型 HTTP 端點，例如 https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict。

驗證​

您可以透過 ads 或環境變數設定驗證。 當您在 OCI 資料科學筆記本會話中工作時，您可以利用資源主體來存取其他 OCI 資源。 查看這裡以查看更多選項。

範例​

import ads
from langchain_community.llms import OCIModelDeploymentLLM

# Set authentication through ads
# Use resource principal are operating within a
# OCI service that has resource principal based
# authentication configured
ads.set_auth("resource_principal")

# Create an instance of OCI Model Deployment Endpoint
# Replace the endpoint uri and model name with your own
# Using generic class as entry point, you will be able
# to pass model parameters through model_kwargs during
# instantiation.
llm = OCIModelDeploymentLLM(
    endpoint="https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict",
    model="odsc-llm",
)

# Run the LLM
llm.invoke("Who is the first president of United States?")
import ads
from langchain_community.llms import OCIModelDeploymentVLLM

# Set authentication through ads
# Use resource principal are operating within a
# OCI service that has resource principal based
# authentication configured
ads.set_auth("resource_principal")

# Create an instance of OCI Model Deployment Endpoint
# Replace the endpoint uri and model name with your own
# Using framework specific class as entry point, you will
# be able to pass model parameters in constructor.
llm = OCIModelDeploymentVLLM(
    endpoint="https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict",
)

# Run the LLM
llm.invoke("Who is the first president of United States?")
import os

from langchain_community.llms import OCIModelDeploymentTGI

# Set authentication through environment variables
# Use API Key setup when you are working from a local
# workstation or on platform which does not support
# resource principals.
os.environ["OCI_IAM_TYPE"] = "api_key"
os.environ["OCI_CONFIG_PROFILE"] = "default"
os.environ["OCI_CONFIG_LOCATION"] = "~/.oci"

# Set endpoint through environment variables
# Replace the endpoint uri with your own
os.environ["OCI_LLM_ENDPOINT"] = (
    "https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict"
)

# Create an instance of OCI Model Deployment Endpoint
# Using framework specific class as entry point, you will
# be able to pass model parameters in constructor.
llm = OCIModelDeploymentTGI()

# Run the LLM
llm.invoke("Who is the first president of United States?")

非同步呼叫​

await llm.ainvoke("Tell me a joke.")

串流呼叫​

for chunk in llm.stream("Tell me a joke."):
    print(chunk, end="", flush=True)

API 參考​

有關所有功能和配置的完整詳細資訊，請參閱每個類別的 API 參考文檔

此頁面是否對您有幫助？