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OCI 数据科学模型部署端点

OCI 数据科学是一个完全托管的无服务器平台，供数据科学团队在 Oracle 云基础设施中构建、训练和管理机器学习模型。

有关最新更新、示例和实验性功能，请参阅ADS LangChain 集成。

此笔记本介绍了如何使用托管在 OCI 数据科学模型部署上的 LLM。

对于身份验证，oracle-ads 库用于自动加载调用端点所需的凭据。

!pip3 install oracle-ads

先决条件​

部署模型​

您可以使用 OCI 数据科学模型部署上的 AI 快速操作轻松部署、微调和评估基础模型。 有关其他部署示例，请访问Oracle GitHub 示例存储库。

策略​

确保拥有访问 OCI 数据科学模型部署端点所需的策略。

设置​

部署模型后，您必须设置调用的以下必需参数

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

身份验证​

您可以通过 ads 或环境变量设置身份验证。 当您在 OCI 数据科学笔记本会话中工作时，可以利用资源主体来访问其他 OCI 资源。 请查看此处以查看更多选项。

示例​

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?")

异步调用​

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

流式调用​

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

API 参考​

有关所有特性和配置的全面详细信息，请参阅每个类的 API 参考文档。

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