ChatDatabricks

Databricks Lakehouse 平台将数据、分析和 AI 统一在一个平台上。

本笔记本提供了 Databricks 聊天模型入门的快速概述。有关所有 ChatDatabricks 功能和配置的详细文档，请参阅API 参考。

概述

ChatDatabricks 类封装了托管在 Databricks 模型服务上的聊天模型端点。此示例笔记本展示了如何封装您的服务端点并在您的 LangChain 应用程序中将其用作聊天模型。

集成细节

类	包	本地	可序列化	包下载	包最新
ChatDatabricks	databricks-langchain	❌	beta

模型功能

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
✅	✅	✅	❌	❌	❌	✅	✅	✅	❌

支持的方法

ChatDatabricks 支持 ChatModel 的所有方法，包括异步 API。

端点要求

ChatDatabricks 封装的服务端点必须具有与 OpenAI 兼容的聊天输入/输出格式（参考）。只要输入格式兼容，ChatDatabricks 就可以用于托管在Databricks 模型服务上的任何端点类型

基础模型 - 精选的先进基础模型列表，例如 DRBX、Llama3、Mixtral-8x7B 等。这些端点可在您的 Databricks 工作区中直接使用，无需任何设置。
自定义模型 - 您还可以通过 MLflow 将自定义模型部署到服务端点，并选择您喜欢的框架，例如 LangChain、Pytorch、Transformers 等。
外部模型 - Databricks 端点可以代理托管在 Databricks 外部的模型，例如 OpenAI GPT4 等专有模型服务。

设置

要访问 Databricks 模型，您需要创建一个 Databricks 帐户，设置凭据（仅当您在 Databricks 工作区外部时），并安装所需的软件包。

凭据（仅当您在 Databricks 外部时）

如果您在 Databricks 内部运行 LangChain 应用程序，则可以跳过此步骤。

否则，您需要手动将 Databricks 工作区主机名和个人访问令牌分别设置为 DATABRICKS_HOST 和 DATABRICKS_TOKEN 环境变量。有关如何获取访问令牌，请参阅身份验证文档。

import getpass
import os

os.environ["DATABRICKS_HOST"] = "https://your-workspace.cloud.databricks.com"
if "DATABRICKS_TOKEN" not in os.environ:
    os.environ["DATABRICKS_TOKEN"] = getpass.getpass(
        "Enter your Databricks access token: "
    )

Enter your Databricks access token:  ········

安装

LangChain Databricks 集成位于 databricks-langchain 包中。

%pip install -qU databricks-langchain

我们首先演示如何使用 ChatDatabricks 查询作为基础模型端点托管的 DBRX-instruct 模型。

对于其他类型的端点，设置端点本身的方式有一些差异，但是，一旦端点准备就绪，如何使用 ChatDatabricks 查询它没有任何区别。有关其他类型端点的示例，请参阅本笔记本的底部。

实例化

from databricks_langchain import ChatDatabricks

chat_model = ChatDatabricks(
    endpoint="databricks-dbrx-instruct",
    temperature=0.1,
    max_tokens=256,
    # See https://python.langchain.ac.cn/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html for other supported parameters
)

调用

chat_model.invoke("What is MLflow?")

AIMessage(content='MLflow is an open-source platform for managing end-to-end machine learning workflows. It was introduced by Databricks in 2018. MLflow provides tools for tracking experiments, packaging and sharing code, and deploying models. It is designed to work with any machine learning library and can be used in a variety of environments, including local machines, virtual machines, and cloud-based clusters. MLflow aims to streamline the machine learning development lifecycle, making it easier for data scientists and engineers to collaborate and deploy models into production.', response_metadata={'prompt_tokens': 229, 'completion_tokens': 104, 'total_tokens': 333}, id='run-d3fb4d06-3e10-4471-83c9-c282cc62b74d-0')

# You can also pass a list of messages
messages = [
    ("system", "You are a chatbot that can answer questions about Databricks."),
    ("user", "What is Databricks Model Serving?"),
]
chat_model.invoke(messages)

AIMessage(content='Databricks Model Serving is a feature of the Databricks platform that allows data scientists and engineers to easily deploy machine learning models into production. With Model Serving, you can host, manage, and serve machine learning models as APIs, making it easy to integrate them into applications and business processes. It supports a variety of popular machine learning frameworks, including TensorFlow, PyTorch, and scikit-learn, and provides tools for monitoring and managing the performance of deployed models. Model Serving is designed to be scalable, secure, and easy to use, making it a great choice for organizations that want to quickly and efficiently deploy machine learning models into production.', response_metadata={'prompt_tokens': 35, 'completion_tokens': 130, 'total_tokens': 165}, id='run-b3feea21-223e-4105-8627-41d647d5ccab-0')

链接

与其他聊天模型类似，ChatDatabricks 可以用作复杂链的一部分。

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a chatbot that can answer questions about {topic}.",
        ),
        ("user", "{question}"),
    ]
)

chain = prompt | chat_model
chain.invoke(
    {
        "topic": "Databricks",
        "question": "What is Unity Catalog?",
    }
)

API 参考：ChatPromptTemplate

AIMessage(content="Unity Catalog is a new data catalog feature in Databricks that allows you to discover, manage, and govern all your data assets across your data landscape, including data lakes, data warehouses, and data marts. It provides a centralized repository for storing and managing metadata, data lineage, and access controls for all your data assets. Unity Catalog enables data teams to easily discover and access the data they need, while ensuring compliance with data privacy and security regulations. It is designed to work seamlessly with Databricks' Lakehouse platform, providing a unified experience for managing and analyzing all your data.", response_metadata={'prompt_tokens': 32, 'completion_tokens': 118, 'total_tokens': 150}, id='run-82d72624-f8df-4c0d-a976-919feec09a55-0')

调用（流式传输）

for chunk in chat_model.stream("How are you?"):
    print(chunk.content, end="|")

I|'m| an| AI| and| don|'t| have| feelings|,| but| I|'m| here| and| ready| to| assist| you|.| How| can| I| help| you| today|?||

异步调用

import asyncio

country = ["Japan", "Italy", "Australia"]
futures = [chat_model.ainvoke(f"Where is the capital of {c}?") for c in country]
await asyncio.gather(*futures)

工具调用

ChatDatabricks 支持与 OpenAI 兼容的工具调用 API，允许您描述工具及其参数，并让模型返回一个 JSON 对象，其中包含要调用的工具以及该工具的输入。工具调用对于构建工具使用链和代理，以及更普遍地从模型中获取结构化输出非常有用。

通过 ChatDatabricks.bind_tools，我们可以轻松地将 Pydantic 类、字典模式、LangChain 工具，甚至函数作为工具传递给模型。在底层，这些会被转换为与 OpenAI 兼容的工具模式，如下所示：

{
    "name": "...",
    "description": "...",
    "parameters": {...}  # JSONSchema
}

并在每次模型调用中传递。

from pydantic import BaseModel, Field


class GetWeather(BaseModel):
    """Get the current weather in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


class GetPopulation(BaseModel):
    """Get the current population in a given location"""

    location: str = Field(..., description="The city and state, e.g. San Francisco, CA")


llm_with_tools = chat_model.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke(
    "Which city is hotter today and which is bigger: LA or NY?"
)
print(ai_msg.tool_calls)

封装自定义模型端点

先决条件

一个 LLM 已被注册并通过 MLflow 部署到 Databricks 服务端点。该端点必须具有与 OpenAI 兼容的聊天输入/输出格式（参考）
您必须拥有该端点的 “可以查询”权限。

一旦端点准备就绪，其使用模式与基础模型相同。

chat_model_custom = ChatDatabricks(
    endpoint="YOUR_ENDPOINT_NAME",
    temperature=0.1,
    max_tokens=256,
)

chat_model_custom.invoke("How are you?")

封装外部模型

先决条件：创建代理端点

首先，创建一个新的 Databricks 服务端点，将请求代理到目标外部模型。对于代理外部模型，端点创建应该相当快。

这需要您在 Databricks 密钥管理器中注册您的 OpenAI API 密钥，如下所示：

# Replace `<scope>` with your scope
databricks secrets create-scope <scope>
databricks secrets put-secret <scope> openai-api-key --string-value $OPENAI_API_KEY

有关如何设置 Databricks CLI 和管理密钥的信息，请参阅 https://docs.databricks.com/en/security/secrets/secrets.html

from mlflow.deployments import get_deploy_client

client = get_deploy_client("databricks")

secret = "secrets/<scope>/openai-api-key"  # replace `<scope>` with your scope
endpoint_name = "my-chat"  # rename this if my-chat already exists
client.create_endpoint(
    name=endpoint_name,
    config={
        "served_entities": [
            {
                "name": "my-chat",
                "external_model": {
                    "name": "gpt-3.5-turbo",
                    "provider": "openai",
                    "task": "llm/v1/chat",
                    "openai_config": {
                        "openai_api_key": "{{" + secret + "}}",
                    },
                },
            }
        ],
    },
)

一旦端点状态变为“就绪”，您就可以像查询其他类型的端点一样查询该端点。

chat_model_external = ChatDatabricks(
    endpoint=endpoint_name,
    temperature=0.1,
    max_tokens=256,
)
chat_model_external.invoke("How to use Databricks?")

Databricks 上的函数调用

Databricks 函数调用与 OpenAI 兼容，并且仅在模型服务期间作为基础模型 API 的一部分可用。

有关支持的模型，请参阅 Databricks 函数调用简介。

llm = ChatDatabricks(endpoint="databricks-meta-llama-3-70b-instruct")
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
            },
        },
    }
]

# supported tool_choice values: "auto", "required", "none", function name in string format,
# or a dictionary as {"type": "function", "function": {"name": <<tool_name>>}}
model = llm.bind_tools(tools, tool_choice="auto")

messages = [{"role": "user", "content": "What is the current temperature of Chicago?"}]
print(model.invoke(messages))

有关如何在链中使用 UC 函数，请参阅 Databricks Unity Catalog。

API 参考

有关所有 ChatDatabricks 功能和配置的详细文档，请访问 API 参考： https://python.langchain.ac.cn/api_reference/databricks/chat_models/langchain_databricks.chat_models.ChatDatabricks.html

聊天模型概念指南
聊天模型操作指南

概述​

集成细节​

模型功能​

支持的方法​

端点要求​

设置​

凭据（仅当您在 Databricks 外部时）​

安装​

实例化​

调用​

链接​

调用（流式传输）​

异步调用​

工具调用​

封装自定义模型端点​

封装外部模型​

Databricks 上的函数调用​

API 参考​

相关内容​

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概述