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OCI Data Science 是一个完全托管的无服务器平台，供数据科学团队在 Oracle Cloud Infrastructure 上构建、训练和管理机器学习模型。
最新更新、示例和实验性功能，请参阅 ADS LangChain 集成。
本 notebook 介绍如何使用托管在 OCI Data Science 模型部署上的 LLM。 认证使用 oracle-ads 库自动加载调用端点所需的凭证。 
!pip3 install oracle-ads

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前提条件

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部署模型

您可以使用 OCI Data Science 模型部署上的 AI Quick Actions 轻松部署、微调和评估基础模型。更多部署示例，请访问 Oracle GitHub 示例仓库。

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策略

确保已配置访问 OCI Data Science 模型部署端点所需的策略。

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配置

部署模型后，需要设置以下必填参数：
  • endpoint：已部署模型的 HTTP 端点，例如 https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict。

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认证

可通过 ads 或环境变量进行认证。在 OCI Data Science Notebook Session 中工作时，可利用资源主体访问其他 OCI 资源。查看此处了解更多选项。

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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?")

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异步调用

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

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流式调用

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

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API 参考

有关所有功能和配置的详细信息，请参阅各类的 API 参考文档：
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