ChatOCI 模型部署

这将帮助您开始使用OCIModelDeployment 聊天模型。要查看所有ChatOCIModelDeployment功能和配置的详细文档，请访问API参考。

OCI数据科学是Oracle云基础设施上的一个完全托管且无服务器平台，用于数据科学团队构建、训练和管理机器学习模型。您可以使用AI快速操作轻松将LLM部署到OCI数据科学模型部署服务中。您可以选择使用vLLM或TGI等流行的推理框架进行部署。默认情况下，模型部署端点模仿OpenAI API协议。

对于最新更新、示例和实验功能，请参见ADS LangChain 维护集成。

概览

集成细节

Class	包	本地	序列化	JS支持	Package downloads	Package 最新版本
ChatOCIModelDeployment	langchain-community	❌	beta	❌

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	Token级流式传输	原生异步	Token 使用	对数概率
depends	depends	depends	depends	depends	depends	✅	✅	✅	✅

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

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。如需查看其他部署示例，请访问Oracle GitHub代码库。

Policies

确保拥有访问OCI数据科学模型部署端点所需的策略。

Credentials

您可以使用Oracle ADS进行身份验证。当您在OCI数据科学笔记本会话中工作时，可以利用资源主原则访问其他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")

Alternatively, you can configure the credentials using the following environment variables. For example, to use API key with specific profile:

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 文档以获取更多选项。

安装

The LangChain OCIModelDeployment 整合部分位于 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

Instantiation

您可以使用通用的ChatOCIModelDeployment实例化模型，或者使用特定框架的类如ChatOCIModelDeploymentVLLM。

使用ChatOCIModelDeployment作为部署模型时的通用入口点。在类的实例化过程中，可以通过model_kwargs传递模型参数，这使得配置更加灵活且简便，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 ...
    },
)

API 参考:ChatOCI 模型部署

使用特定框架的类（如ChatOCIModelDeploymentVLLM）：当您正在处理一个具体的框架（例如vLLM），并且需要直接通过构造函数传递模型参数以简化设置过程时，这非常适用。

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",
)

API 参考:ChatOCIModelDeploymentVLLM

Invocation

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 参考: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",
    }
)

API 参考:ChatOCI 模型部署

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')

Streaming calls

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()

API 参考:ChatOCIModelDeployment |ChatPromptTemplate

California
Texas
Florida
New York
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()

API 参考:ChatOCI 模型部署

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

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:

聊天模型概念指南
聊天模型使用指南

概览​

集成细节​

模型特性​

设置​

Policies​

Credentials​

安装​

Instantiation​

Invocation​

链式调用​

异步调用​

Streaming calls​

结构化输出​

API 参考​

相关​

概览

集成细节

模型特性

设置

Policies

Credentials

安装

Instantiation

Invocation

链式调用

异步调用

Streaming calls

结构化输出

API 参考

相关