跳到主要内容
Open In ColabOpen on GitHub

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,请参阅 API 参考

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 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 快速操作 轻松地在 OCI Data Science 模型部署服务 上部署 LLM。您可以选择使用流行的推理框架(如 vLLM 或 TGI)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

类别本地可序列化JS 支持包下载量最新包版本
ChatOCIModelDeploymentlangchain-community测试版PyPI - DownloadsPyPI - Version

模型特性

工具调用结构化输出JSON 模式图片输入音频输入视频输入逐令牌流式传输原生异步令牌使用量对数概率
依赖依赖依赖依赖依赖依赖

某些模型功能,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 Data Science 模型部署上的 AI 快速操作 轻松部署基础模型。有关更多部署示例,请访问 Oracle GitHub 示例仓库

策略

请确保拥有访问 OCI Data Science 模型部署端点所需的 策略

凭证

您可以通过 Oracle ADS 设置身份验证。当您在 OCI Data Science Notebook 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配置凭据。例如,使用特定配置文件进行 API 密钥认证

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"

请查阅 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` 和所需的依赖项。

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

实例化

您可以实例化通用的 `ChatOCIModelDeployment` 或特定于框架的类,如 `ChatOCIModelDeploymentVLLM`。

  • 当您需要通用入口点来部署模型时,请使用 `ChatOCIModelDeployment`。您可以在此类的实例化过程中通过 `model_kwargs` 传递模型参数。这允许灵活性和易于配置,而无需依赖于特定于框架的详细信息。
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 ...
},
)
  • 使用特定于框架的类,如 `ChatOCIModelDeploymentVLLM`:这适用于您正在使用特定框架(例如 `vLLM`)并需要直接通过构造函数传递模型参数的情况,从而简化了设置过程。
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 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",
}
)
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