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OCI 数据科学模型部署端点

OCI 数据科学 是一个完全托管且无服务器的平台，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。

对于最新更新、示例和实验功能，请参见ADS LangChain 维护集成。

此笔记本介绍了如何使用托管在OCI Data Science 模型部署上的大语言模型（LLM）。

为了进行身份验证，使用 oracle-ads 库来自动加载调用端点所需的凭据。

!pip3 install oracle-ads

前置条件​

部署模型​

您可以使用 OCI 数据科学模型部署中的 AI 快捷操作 轻松部署、微调和评估基础模型。有关更多部署示例，请访问 Oracle GitHub 示例仓库。

Policies​

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，您必须设置调用所需的以下参数：

  • 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​

您可以通过广告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，可以利用资源主体访问其他OCI资源。请查看 此处 了解更多信息。

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.")

Streaming calls​

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

API 参考​

对于所有功能和配置的全面详情，请参阅每个类别的API参考文档: