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ChatOCIModelDeployment

这将帮助您开始使用OCIModelDeployment 聊天模型。有关所有ChatOCIModelDeployment功能和配置的详细文档，请前往API参考。

OCI Data Science 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。您可以使用 AI Quick Actions 轻松地在 OCI Data Science 模型部署服务 上部署 LLMs。您可以选择使用流行的推理框架（如 vLLM 或 TGI）来部署模型。默认情况下，模型部署端点模拟 OpenAI API 协议。

有关最新更新、示例和实验性功能，请参阅 ADS LangChain 集成。

概述​

集成详情​

类包本地可序列化JS支持包下载包最新
ChatOCIModelDeploymentlangchain-community❌测试版❌PyPI - VersionPyPI - Version

模型特性​

工具调用结构化输出JSON模式图像输入音频输入视频输入令牌级流式传输原生异步令牌使用Logprobs
依赖依赖依赖依赖依赖依赖✅✅✅✅

一些模型功能，包括工具调用、结构化输出、JSON模式和多模态输入，取决于部署的模型。

设置​

要使用 ChatOCIModelDeployment，您需要部署一个带有聊天完成端点的聊天模型，并安装 langchain-community、langchain-openai 和 oracle-ads 集成包。

您可以使用OCI数据科学模型部署中的AI快速操作轻松部署基础模型。如需更多部署示例，请访问Oracle GitHub示例库。

策略​

确保拥有访问OCI数据科学模型部署端点所需的策略。

凭证​

您可以通过Oracle ADS设置认证。当您在OCI数据科学笔记本会话中工作时，您可以利用资源主体访问其他OCI资源。

import ads

# 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")

或者，您可以使用以下环境变量来配置凭据。例如，要使用特定配置文件的API密钥：

import os

# 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"

查看 Oracle ADS 文档 以查看更多选项。

安装​

LangChain OCIModelDeployment 集成位于 langchain-community 包中。以下命令将安装 langchain-community 及其所需的依赖项。

%pip install -qU langchain-community langchain-openai oracle-ads

实例化​

您可以使用通用的ChatOCIModelDeployment或特定框架的类如ChatOCIModelDeploymentVLLM来实例化模型。

  • 当您需要一个通用的入口点来部署模型时，使用ChatOCIModelDeployment。在实例化此类时，您可以通过model_kwargs传递模型参数。这提供了灵活性和配置的简便性，而无需依赖特定框架的细节。
from langchain_community.chat_models import ChatOCIModelDeployment

# Create an instance of OCI Model Deployment Endpoint
# Replace the endpoint uri with your own
# Using generic class as entry point, you will be able
# to pass model parameters through model_kwargs during
# instantiation.
chat = ChatOCIModelDeployment(
    endpoint="https://modeldeployment.<region>.oci.customer-oci.com/<ocid>/predict",
    streaming=True,
    max_retries=1,
    model_kwargs={
        "temperature": 0.2,
        "max_tokens": 512,
    },  # other model params...
    default_headers={
        "route": "/v1/chat/completions",
        # other request headers ...
    },
)
  • 使用特定框架的类，如ChatOCIModelDeploymentVLLM：这适用于当你使用特定框架（例如vLLM）并且需要通过构造函数直接传递模型参数时，简化设置过程。
from langchain_community.chat_models import ChatOCIModelDeploymentVLLM

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

调用​

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]

ai_msg = chat.invoke(messages)
ai_msg
AIMessage(content="J'adore programmer.", response_metadata={'token_usage': {'prompt_tokens': 44, 'total_tokens': 52, 'completion_tokens': 8}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-ca145168-efa9-414c-9dd1-21d10766fdd3-0')
print(ai_msg.content)

J'adore programmer.

链式调用​

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | chat
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)
API Reference:ChatPromptTemplate
AIMessage(content='Ich liebe Programmierung.', response_metadata={'token_usage': {'prompt_tokens': 38, 'total_tokens': 48, 'completion_tokens': 10}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-5dd936b0-b97e-490e-9869-2ad3dd524234-0')

异步调用​

from langchain_community.chat_models import ChatOCIModelDeployment

system = "You are a helpful translator that translates {input_language} to {output_language}."
human = "{text}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])

chat = ChatOCIModelDeployment(
    endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict"
)
chain = prompt | chat

await chain.ainvoke(
    {
        "input_language": "English",
        "output_language": "Chinese",
        "text": "I love programming",
    }
)
AIMessage(content='我喜欢编程', response_metadata={'token_usage': {'prompt_tokens': 37, 'total_tokens': 50, 'completion_tokens': 13}, 'model_name': 'odsc-llm', 'system_fingerprint': '', 'finish_reason': 'stop'}, id='run-a2dc9393-f269-41a4-b908-b1d8a92cf827-0')

流式调用​

import os
import sys

from langchain_community.chat_models import ChatOCIModelDeployment
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [("human", "List out the 5 states in the United State.")]
)

chat = ChatOCIModelDeployment(
    endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict"
)

chain = prompt | chat

for chunk in chain.stream({}):
    sys.stdout.write(chunk.content)
    sys.stdout.flush()


1. California
2. Texas
3. Florida
4. New York
5. Illinois

结构化输出​

from langchain_community.chat_models import ChatOCIModelDeployment
from pydantic import BaseModel


class Joke(BaseModel):
    """A setup to a joke and the punchline."""

    setup: str
    punchline: str


chat = ChatOCIModelDeployment(
    endpoint="https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict",
)
structured_llm = chat.with_structured_output(Joke, method="json_mode")
output = structured_llm.invoke(
    "Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys"
)

output.dict()
{'setup': 'Why did the cat get stuck in the tree?',
 'punchline': 'Because it was chasing its tail!'}

API参考​

有关所有功能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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