Cortex REST API

您可以使用 Snowflake Cortex REST API 来调用您选择的 LLM 进行推理。您可以使用任何可以 HTTP POST 请求的编程语言发出请求。此功能可让您将最先进的 AI 功能引入您的应用程序。使用此 API 不需要仓库。

Cortex REST API 将生成的词元作为 服务器发送事件 (https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events) 流式传回给用户。

设置身份验证

要向 Cortex REST API 进行身份验证，可以使用 使用 Snowflake 对 Snowflake REST APIs 进行身份验证 中所述的方法。

小技巧

考虑为 Cortex REST API 请求创建专用用户。

设置授权

要发送 REST API 请求，必须向您的默认角色授予 SNOWFLAKE.CORTEX_USER 数据库角色，该数据库角色已获授予使用 LLM 函数的权限。在大多数情况下，用户已经拥有此权限，因为 SNOWFLAKE.CORTEX_USER 角色会自动授予给 PUBLIC 角色，且所有角色都会继承 PUBLIC。

如果 Snowflake 管理员希望选择个人用户，则其可能已经从 PUBLIC 撤销 SNOWFLAKE.CORTEX_USER，并且必须将此角色授予应该能够使用 Cortex REST API 的用户，如下所示。

GRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE my_role;
GRANT ROLE my_role TO USER my_user;
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重要

REST API 请求使用用户的默认角色，因此该角色必须具有必要的权限。 您可以使用以下方式更改用户的默认角色：ALTER USER ... SET DEFAULT_ROLE。

ALTER USER my_user SET DEFAULT_ROLE=my_role
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提交请求

您通过向 API 的 REST 端点发送 HTTP POST 请求，向 Cortex REST API 发出请求。 在请求中，必须设置 Authorization 标头以包含用于身份验证的令牌（例如，JSON Web 令牌 (JWT)、OAuth 令牌或 编程访问令牌）。有关详细信息，请参阅 使用 Snowflake 对 Snowflake REST APIs 进行身份验证。

请求主体是指定模型、提示或对话历史记录和选项的 JSON 对象。有关信息，请参阅本主题中的 API 参考部分。

可以使用 Python API 发出 REST 请求。要安装 Python API，请使用以下命令：

pip install snowflake-ml-python
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从 1.6.1 版本开始，Python API 包含在 snowflake-ml-python 包内。

成本注意事项

Snowflake Cortex REST API 请求会产生计算成本，具体取决于处理的词元数。请参阅 Snowflake 服务使用表，以了解每个函数的每百万个词元消耗的 credit 成本。词元是 Snowflake Cortex LLM 函数处理的最小文本单位，大约等于四个字符的文本。原始输入或输出文本与词元的等价性可能因模型而异。

COMPLETE 函数根据输入提示生成新文本。输入和输出词元都会产生计算成本。 如果您使用 COMPLETE 来提供对话或聊天用户体验，系统会处理所有先前的提示和响应，以生成每个新的响应，并产生相应的成本。

COMPLETE 函数

可以使用 REST API 提示 AI_COMPLETE 函数。

从 /api/v2/cortex/inference:complete 端点调用 COMPLETE 函数。其形式如下：

POST https://<account_identifier>.snowflakecomputing.cn/api/v2/cortex/inference:complete

其中，account_identifier 是您用来访问 Snowsight 的 账户标识符。

模型可用性

下表显示了 Cortex LLM REST API 中可用的不同函数。

COMPLETE 函数模型
（模型）
AWS US 西部 2
（俄勒冈）
AWS US 东部 1
（弗吉尼亚北部）
AWS 欧洲中部 1
（法兰克福）
AWS 欧洲西部 1
（爱尔兰）
AWS AP 东南部 2
（悉尼）
AWS AP 东北部 1
（东京）
Azure 东部 US 2
（弗吉尼亚）
Azure 西欧
（荷兰）
AWS
（跨区域）
(claude-4-sonnet)

✔
(claude-4-opus)

处于预览版
(claude-3-7-sonnet)

✔
(claude-3-5-sonnet)

✔

✔

✔
COMPLETE
(llama4-maverick)

✔

✔
(llama3.1-8b)

✔

✔

✔

✔

✔

✔

✔
(llama3.1-70b)

✔

✔

✔

✔

✔

✔

✔

✔

✔
(llama3.1-405b)

✔

✔

✔

✔
(llama3.2-1b)

✔

✔
(llama3.2-3b)

✔

✔
(deepseek-r1)

✔

✔
(mistral-7b)

✔

✔

✔

✔

✔

✔

✔
(mistral-large)

✔

✔

✔

✔

✔

✔

✔
(mistral-large2)

✔

✔

✔

✔

✔

✔

✔

✔

✔
(snowflake-llama-3.3-70b)

✔

✔

您还可以在任何支持的区域，使用任何 微调 模型。

API 参考

POST /api/v2/cortex/inference:complete

使用指定的大型语言模型完成提示或对话。请求主体是包含实参的 JSON 对象。

此端点对应于 :doc:`COMPLETE </sql-reference/functions/complete-snowflake-cortex>`SQL 函数。

必要标头

Authorization: Bearer token

对请求的授权。token 是 JSON Web 令牌 (JWT)、OAuth 令牌或 编程访问令牌。有关详细信息，请参阅 使用 Snowflake 对 Snowflake REST APIs 进行身份验证。

Content-Type: application/json

指定请求的主体是 JSON 格式。

Accept: application/json, text/event-stream

指定响应将包含 JSON（错误情况）或服务器发送的事件。

可选标头
X-Snowflake-Authorization-Token-Type: type

定义授权令牌的类型。

如果您省略了 X-Snowflake-Authorization-Token-Type 标头，Snowflake 将通过检查令牌来确定令牌类型。

即使此标头可选，您也可以选择指定此标头。您可以将标头设置为以下值之一：

  • ``KEYPAIR_JWT``（适用于密钥对身份验证）

  • ``OAUTH``（适用于 OAuth）

  • ``PROGRAMMATIC_ACCESS_TOKEN``（适用于 编程访问令牌）

必要 JSON 实参

实参

类型

描述

model

字符串

要使用的模型的标识符（请参阅 选择模型）。有关可能的值，请参阅 模型可用性。

或者，您可以使用任何 database.schema.model 格式的 微调 模型的完全限定名称。

备注

claude-3-5-sonnet 不支持 COMPLETE 结构化输出。

messages

数组

用于生成补全的提示或对话历史记录。按时间顺序排列的，代表对话的对象数组。每个对象必须包含 content 密钥或 content_list 密钥。它也可能包含 role 密钥。

  • content：包含系统消息、用户提示或模型先前响应的字符串。

  • role：表示消息角色的字符串，可以是以下之一：'system'、'user' 或 'assistant'。

请参阅 :ref:` COMPLETE 角色表 <label-sql-functions-complete-cortex-role-content>`，了解有关这些角色的更详细描述。

对于由单个用户消息组成的提示，role 可以省略；然后假定为 user。
可选 JSON 实参

实参

类型

默认值

描述

guardrails

对象

null

一个对象，用于控制是否对请求使用 Cortex Guard 限制。该对象包含两个字段：

  • enabled：一个布尔值，用于控制是否对请求使用限制。如果此字段不存在或不是 true，则不使用限制。

  • response_when_unsafe：一个字符串，用于确定当 Cortex Guard 模型判定响应不安全时的处理方式。返回的是该字符串而非实际生成内容。默认：“由 Cortex Guard 过滤的响应”

max_tokens

整数

4096

一个介于 1 和 4096（含 1 和 4096）之间的值，用于控制要输出的最大令牌数。词元达到此数量后，输出将被截断。

top_p

数字

1.0

从 0 到 1（含）的值，通过限制模型输出的可能词元集来控制语言模型的多样性。

temperature

数字

0.0

从 0 到 1（含）的值，通过影响每个步骤中选择哪个可能的词元来控制语言模型输出的随机
工具配置

重要

工具使用仅支持 Claude 3.5 Sonnet。

tool_spec 字段包含一系列可用的工具。下表显示可用字段：

工具规格

字段

类型

描述

tool_spec.type

字符串

工具的类型。类型和名称的组合是唯一的标识符。

tool_spec.name

字符串

工具名称。类型和名称的组合是唯一的标识符。

tool_spec.description

字符串

对所考虑使用工具的描述。

tool_spec.input_schema

对象

工具输入的 JSON 架构。

tool_spec.input_schema.type

字符串

输入架构对象的类型。

tool_spec.input_schema.properties

对象

每个输入参数的定义。

tool_spec.input_schema.required

数组

所需输入参数的列表。

tool_choice 字段配置工具选择行为。它有以下字段：

工具选择

字段

类型

描述

type

字符串

选择工具的方式。

有效值：

  • "auto"：模型决定是否调用您提供的工具。

  • "required"：使用一个或多个工具。

  • "tool"：使用您指定的工具。

name

数组

正在使用的工具的名称。仅在 type 为 "tool" 时有效。

tool_use 表示模型请求使用特定工具。它包含工具标识符和执行的输入参数。它有以下字段：

工具使用

字段

类型

描述

type

字符串

将其标识为工具使用请求。

tool_use

对象

工具使用请求详细信息的容器。

每个对象包含以下键：

工具使用请求键

字段

类型

描述

必填

tool_use_id

字符串

此工具使用请求的唯一标识符。

是

name

字符串

正在使用的工具的名称。

是

input

对象

正在使用的工具的名称。

是
工具结果

表示工具执行的结果。包含工具执行的输入参数和输出结果。

工具结果

字段

类型

描述

type

字符串

将其标识为工具结果。

tool_results

字符串

此工具使用请求的唯一标识符。

每个结果可以包含以下键：

工具结果键

字段

类型

描述

必填

tool_use_id

字符串

用于将此结果与对应工具使用请求相关联的唯一标识符。

是

name

字符串

运行的工具的名称。它必须与 tools 数组中的工具名称匹配。

是

content

数组

工具执行生成的内容元素的数组。

是

id

字符串

工具执行实例的可选标识符。

否

status

字符串

工具执行的状态指示器。

否

Results

对象

采用任意结构的附加结果数据。将 Additional properties 设置为 True。

是
输出

使用服务器发送事件 (SSEs) 生成词元后发送。每个 SSE 事件均使用 message 类型并包含具有以下结构的 JSON 对象。

键

值类型

描述

id

字符串

请求的唯一 ID，请求响应中发送的所有事件具有相同的值。

created

数字

生成响应时的 UNIX 时间戳（自 1970 年 1 月 1 日午夜以来的秒数）。

model

字符串

模型使用的标识符。

choices

数组

模型的响应。每个响应都是一个对象，包含 delta 键，其值是一个对象，该对象的 content 键包含模型生成的输出。如果响应被 Cortex Guard 阻止，则对象包含一个 finish_reason 键，其值为 "prohibited_content"。目前，该数组始终包含一个选项。

'usage

对象

此请求的词元使用情况，包括：

  • prompt_tokens：提示中的令牌数。

  • completion_tokens：完成中的词元数。

  • guard_tokens：Cortex Guard 处理消耗的词元数。如果未启用限制，则此字段不存在。

  • total_tokens：提示和完成中的词元总数。
状态代码

Snowflake Cortex LLM REST API 使用以下 HTTP 状态代码来指示成功完成或各种错误条件。

200 OK

请求成功完成。响应的主体包含模型的输出。

400 invalid options object

可选实参有无效值。

400 unknown model model_name

指定的模型不存在。

400 schema validation failed

与不正确的响应架构结构相关的错误。请修正架构并重试。

400 max tokens of count exceeded

请求超出了模型支持的最大词元数（请参阅 模型限制）。

400 all requests were throttled by remote service

由于使用率很高，请求已受到限流。请稍后再试。

402 budget exceeded

超出了模型消耗预算。

403 Not Authorized

账户未启用 REST API，或调用用户的默认角色没有 snowflake.cortex_user 数据库角色。

429 too many requests

由于已超出使用量配额，请求遭到拒绝。请稍后重试请求。

503 inference timed out

请求耗时太长。

基本示例

以下示例使用 curl 发出 COMPLETE 请求。 使用此命令中的适当值替换 token、prompt 和 account_identifier。

curl -X POST \
    -H "Authorization: Bearer <token>" \
    -H 'Content-Type: application/json' \
    -H 'Accept: application/json, text/event-stream' \
    -d '{
    "model": "mistral-large",
    "messages": [
        {
            "content": "<prompt>"
        }
    ],
    "top_p": 0,
    "temperature": 0
    }' \
https://<account_identifier>.snowflakecomputing.cn/api/v2/cortex/inference:complete
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输出

data: {
data:  "id": "65c5e2ac-529b-461e-8a8c-f80655e6bd3f",
data:  "created": 1723493954,
data:  "model": "mistral-7b",
data:  "choices": [
data:    {
data:      "delta": {
data:        "content": "Cor"
data:        }
data:      }
data:     ],
data:  "usage": {
data:    "prompt_tokens": 57,
data:    "completion_tokens": 1,
data:    "total_tokens": 58
data:  }
data: }

data: {
data:  "id": "65c5e2ac-529b-461e-8a8c-f80655e6bd3f",
data:  "created": 1723493954,
data:  "model": "mistral-7b",
data:  "choices": [
data:    {
data:      "delta": {
data:        "content": "tex"
data:        }
data:      }
data:     ],
data:  "usage": {
data:    "prompt_tokens": 57,
data:    "completion_tokens": 2,
data:    "total_tokens": 59
data:  }
data: }

工具调用思维链示例

以下示例使用 curl 在思维链过程中发出 COMPLETE 请求。在这种情况下，工具用于获取 CA 旧金山的天气信息。

请求

curl -X POST \
    -H "Authorization: Bearer <token>" \
    -H 'Content-Type: application/json' \
    -H 'Accept: application/json, text/event-stream' \
    -d '{
    "model": "claude-3-5-sonnet",
    "messages": [
      {
        "role": "user",
        "content": "What is the weather like in San Francisco?"
      }
    ],
    "tools": [
      {
        "tool_spec": {
          "type": "generic",
          "name": "get_weather",
          "input_schema": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
              }
            },
            "required": [
              "location"
            ]
          }
        }
      }
    ],
    "max_tokens": 4096,
    "top_p": 1,
    "stream": true
    }' \
https://<account_identifier>.snowflakecomputing.cn/api/v2/cortex/inference:complete
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响应

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content":"I","content_list":[{"text":"I","type":"text"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content":"'ll","content_list":[{"text":"'ll","type":"text"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content":" help you","content_list":[{"text":" help you","type":"text"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content":" check the","content_list":[{"text":" check the","type":"text"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content":" weather in","content_list":[{"text":" weather in","type":"text"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content":" San Francisco.","content_list":[{"text":" San Francisco.","type":"text"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content_list":[{"name":"get_weather","tool_use_id":"tooluse_Iwuh-FEeTC-Iefsxu2ueKQ"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content_list":[{"input":"{\"location\""}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content_list":[{"input":": \"San"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content_list":[{"input":" Francisco"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{"content_list":[{"input":", CA\"}"}]}}],"usage":{}}

data: {"id":"78fe5630-95b1-4960-ac2b-7eb85536b08e","model":"claude-3-5-sonnet","choices":[{"delta":{}}],"usage":{"prompt_tokens":390,"completion_tokens":53,"total_tokens":443}}

用户端执行 get_weather 工具并将结果提供给模型。

后续请求

curl -X POST \
    -H "Authorization: Bearer <token>" \
    -H 'Content-Type: application/json' \
    -H 'Accept: application/json, text/event-stream' \
    -d '{
    "model": "claude-3-5-sonnet",
    "messages": [
      {
        "role": "user",
        "content": "What is the weather like in San Francisco?"
      },
      {
        "role": "assistant",
        "content": "I'll help you check the weather in San Francisco.",
        "content_list": [
          {
            "type": "tool_use",
            "tool_use": {
              "tool_use_id": "tooluse_Iwuh-FEeTC-Iefsxu2ueKQ",
              "name": "get_weather",
              "input": {
                "location": "San Francisco, CA"
              }
            }
          }
        ]
      },
      {
        "role": "user",
        "content_list": [
          {
            "type": "tool_results",
            "tool_results": {
              "tool_use_id": "tooluse_Iwuh-FEeTC-Iefsxu2ueKQ",
              "name": "get_weather",
              "content": [
                {
                  "type": "text",
                  "text": "\"temperature\": \"69 fahrenheit\""
                }
              ]
            }
          }
        ]
      }
    ],
    "tools": [
      {
        "tool_spec": {
          "type": "generic",
          "name": "get_weather",
          "input_schema": {
            "type": "object",
            "properties": {
              "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
              }
            },
            "required": [
              "location"
            ]
          }
        }
      }
    ],
    "max_tokens": 4096,
    "top_p": 1,
    "stream": true
    }' \
https://<account_identifier>.snowflakecomputing.cn/api/v2/cortex/inference:complete
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最终响应

data: {"id":"07ffa851-4c47-4cea-9b7e-017a4cddc21d","model":"claude-3-5-sonnet","choices":[{"delta":{"content":"\n\nBase","content_list":[{"type":"text","text":"\n\nBase"}]}}],"usage":{}}

data: {"id":"07ffa851-4c47-4cea-9b7e-017a4cddc21d","model":"claude-3-5-sonnet","choices":[{"delta":{"content":"d on the weather data,","content_list":[{"type":"text","text":"d on the weather data,"}]}}],"usage":{}}

data: {"id":"07ffa851-4c47-4cea-9b7e-017a4cddc21d","model":"claude-3-5-sonnet","choices":[{"delta":{"content":" it's currently 69 ","content_list":[{"type":"text","text":" it's currently 69 "}]}}],"usage":{}}

data: {"id":"07ffa851-4c47-4cea-9b7e-017a4cddc21d","model":"claude-3-5-sonnet","choices":[{"delta":{"content":"degrees Fahrenheit in San Francisco,","content_list":[{"type":"text","text":"degrees Fahrenheit in San Francisco,"}]}}],"usage":{}}

data: {"id":"07ffa851-4c47-4cea-9b7e-017a4cddc21d","model":"claude-3-5-sonnet","choices":[{"delta":{"content":" CA.","content_list":[{"type":"text","text":" CA."}]}}],"usage":{}}

data: {"id":"07ffa851-4c47-4cea-9b7e-017a4cddc21d","model":"claude-3-5-sonnet","choices":[{"delta":{}}],"usage":{"prompt_tokens":466,"completion_tokens":26,"total_tokens":492}}

结构化输出示例

此示例演示了使用 curl 检索遵循指定 JSON 架构的结构化响应。

curl --location --request POST 'https://<account_identifier>.snowflakecomputing.cn/api/v2/cortex/inference:complete' \
  --header 'Authorization: Bearer <token>' \
  --header 'Accept: application/json, text/event-stream' \
  --header 'Content-Type: application/json' \
  --data-raw '{
    "model": "mistral-large2",
    "messages": [{ "content": "Please prepare me a data set consisting of 3 people and their ages" }],
    "max_tokens": 1000,
    "response_format": {
      "type": "json",
      "schema": {
        "type": "object",
        "properties": {
          "people": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "name": {
                  "type": "string"
                },
                "age": {
                  "type": "number"
                }
              },
              "required": ["name", "age"]
            }
          }
        },
        "required": ["people"]
      }
    }
  }'
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Python API 示例请求

可以使用 Python API 向 COMPLETE 函数发出 REST 请求。在开始使用 API 之前，先创建一个会话。有关创建会话的更多信息，请参阅 为 Snowpark Python 创建会话。

以下 Python API 示例使用一条消息提示 mistral-7b 模型并打印响应。

from snowflake.snowpark import Session
from snowflake.cortex import Complete

session = Session.builder.configs(...).create()

stream = Complete(
  "mistral-7b",
  "What are unique features of the Snowflake SQL dialect?",
  session=session,
  stream=True)

for update in stream:
  print(update)
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备注

Python API 的流模式目前在存储过程和 Snowsight 中不起作用。

使用量配额

为了确保所有 Snowflake 客户的高性能标准，Snowflake Cortex LLM REST API 请求受使用配额的限制。超出配额的请求可能会被限制。Snowflake 可能会偶尔调整这些配额。以下表中的配额按账户应用，并且为每个模型单独应用。

下表显示了 COMPLETE 函数的使用配额。

COMPLETE 函数配额
模型
每分钟
处理的词元 (TPM)
每分钟的
请求 (RPM)
最大输出（词元）
claude-3-7-sonnet

300,000

30

4,096
claude-3-5-sonnet

300,000

30

4,096
deepseek-r1

100,000

50

4,096
llama3.1-8b

400,000

200

4,096
llama3.1-70b

200,000

100

4,096
llama3.1-405b

100,000

50

4,096
mistral-7b

400,000

200

4,096
mistral-large2

200,000

100

4,096

EMBED 函数

您可以向 /api/v2/cortex/inference:embed 端点发出请求，为您的文本创建嵌入。因此，请求采用以下形式：

POST https://<account_identifier>.snowflakecomputing.cn/api/v2/cortex/inference:embed

其中，account_identifier 是您用来访问 Snowsight 的 账户标识符。

模型可用性

下表显示了您可以使用 REST API 提示的 EMBED 函数模型。

EMBED 函数模型
模型
AWS US 西部 2
（俄勒冈）
AWS US 东部 1
（弗吉尼亚北部）
AWS 欧洲中部 1
（法兰克福）
AWS 欧洲西部 1
（爱尔兰）
AWS AP 东南部 2
（悉尼）
AWS AP 东北部 1
（东京）
Azure 东部 US 2
（弗吉尼亚）
Azure 西欧
（荷兰）
snowflake-arctic-embed-m-v1.5

✔

✔

✔

✔

✔

✔

✔

✔
snowflake-arctic-embed-m

✔

✔

✔

✔

✔

✔

✔

✔
e5-base-v2

✔

✔

✔

✔

✔

✔
snowflake-arctic-embed-l-v2.0

✔

✔

✔

✔

✔

✔

✔

✔

下表显示了每个模型可以返回的维度数。

EMBED 函数模型
模型
维度
维度
snowflake-arctic-embed-m-v1.5

768
snowflake-arctic-embed-m

768
e5-base-v2

768
snowflake-arctic-embed-l-v2.0

1024

API 参考

POST /api/v2/cortex/inference:embed

为您指定的文本创建嵌入。

必要标头

Authorization: Bearer token。

对请求的授权。token 是 JSON Web 令牌 (JWT)、OAuth 令牌或 编程访问令牌。有关详细信息，请参阅 使用 Snowflake 对 Snowflake REST APIs 进行身份验证。

Content-Type: application/json

指定请求的主体是 JSON 格式。

Accept: application/json

指定响应包含 JSON。

可选标头
X-Snowflake-Authorization-Token-Type: type

定义授权令牌的类型。

如果您省略了 X-Snowflake-Authorization-Token-Type 标头，Snowflake 将通过检查令牌来确定令牌类型。

即使此标头可选，您也可以选择指定此标头。您可以将标头设置为以下值之一：

  • ``KEYPAIR_JWT``（适用于密钥对身份验证）

  • ``OAUTH``（适用于 OAuth）

  • ``PROGRAMMATIC_ACCESS_TOKEN``（适用于 编程访问令牌）

必要 JSON 实参

实参

类型

描述

text

数组

您要为其生成嵌入的文本字符串列表。该列表最多可包含 1280 个字符串，每个字符串的最长长度可达 4096 个字符。

model

字符串

用来创建嵌入的模型。
状态代码

Snowflake Cortex LLM REST API 使用以下 HTTP 状态代码来指示成功完成或各种错误条件。

200 OK

请求成功完成。响应的主体包含模型的输出。

400 invalid options object

可选实参有无效值。

400 unknown model model_name

指定的模型不存在。

400 schema validation failed

与不正确的响应架构结构相关的错误。请修正架构并重试。

400 max tokens of count exceeded

请求超出了模型支持的最大词元数（请参阅 模型限制）。

400 all requests were throttled by remote service

由于使用率很高，请求已受到限流。请稍后再试。

402 budget exceeded

超出了模型消耗预算。

403 Not Authorized

账户未启用 REST API，或调用用户的默认角色没有 snowflake.cortex_user 数据库角色。

429 too many requests

由于已超出使用量配额，请求遭到拒绝。请稍后重试请求。

503 embed timed out

请求耗时太长。

CURL 请求示例

以下示例使用 curl 向 e5-base-v2 模型发出 EMBED 请求。使用适当值替换此命令中的 token 和 account_identifier。

curl --location "<account_url>/api/v2/cortex/inference:embed" \
--header 'X-Snowflake-Authorization-Token-Type: KEYPAIR_JWT' \
--header 'Content-Type: application/json' \
--header 'Accept: application/json' \
--header "Authorization: Bearer <token>" \
--data '{
"text": ["foo", "bar"],
"model": "e5-base-v2"
}'
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输出

以下是请求的输出：

{
  "object" : "list",
  "data" : [ {
    "object" : "embedding",
    "embedding" : [ [ -0.02102863, 0.0051381723, -0.0071509206, -0.032512695, 0.056507032, -0.0048937695, 0.04964661, 0.023239689, 0.012616035, 6.152943E-4, -0.02553793, 0.04697104, -0.07258329, 0.017351158, -0.039407425, 0.021243019, 0.035988644, -0.015490267, 0.05665567, -0.053865768, -0.04021352, -0.039481744, 0.008032414, -0.022284945, 0.014539813, 0.056415558, -0.009439873, 0.0452445, -0.070742406, -0.04087098, 0.018997658, 0.028213495, 0.030248754, -0.06257851, -0.078929186, 0.06901587, -0.01433257, -0.012313747, -0.0479315, -0.033473153, -0.0036935527, 0.00631302, 0.014592694, 0.009840421, -0.05247081, -0.025823781, -0.07126838, 0.062384125, -0.043775223, -0.06945036, -0.076539464, 0.050744276, 0.06811258, -0.010631514, -0.040602278, -9.8618606E-5, -0.023782805, -0.033667535, -0.0234455, -0.022579372, 0.024588903, 0.029179674, 0.034513652, -0.008181056, 0.014506939, 0.039761875, -0.015241577, 0.038847152, -0.03438216, -0.01464129, -0.03883572, 0.0032029606, -0.056404125, -0.04456989, -0.01811581, -0.03901295, 0.016362112, 0.060897704, 0.014445482, 0.045175895, 3.5731378E-4, -0.040259257, 0.0684899, 0.020189658, 0.018345919, -0.03804677, -0.0285508, 0.025762323, -0.03626306, 0.07249182, -0.018425958, -0.06349323, 0.018077219, 0.038018186, 0.062292654, -0.049417924, 0.024297336, -0.021676084, 0.024983378, 0.0071148323, -0.070502296, -0.04697104, -0.012957627, -0.005341126, -0.05370569, -0.022244927, 0.024060082, 0.042385988, -0.0156189, 0.033987686, -0.024211582, 0.008102447, 0.0043735206, 0.024837594, -0.03626878, 0.03845268, 0.030763287, -0.048011538, -0.01775921, 0.009250762, -0.0022539354, 0.062029675, 0.049315017, 0.08743611, 0.014078163, 0.019217765, -0.0033973393, 0.031495064, -0.039476026, -0.027496008, 0.041316908, 0.066477515, 0.023971466, -0.035908606, -0.022893809, -0.04403249, -0.03938455, -0.008456187, 0.006146154, 0.019652259, 0.0046407916, 0.007190225, 0.021315912, -0.021576036, 0.054014407, -0.03280998, -0.018031484, -0.030105831, -0.051750466, 0.007942371, 0.008939991, 0.033776157, 0.016630813, -0.026647031, -0.04227165, 0.0511216, 0.025389288, -0.00816319, -0.050767142, 0.008878533, 0.030734701, 0.03327878, 0.009108642, 0.0484689, -0.027779002, -0.04547318, 0.012650337, -0.011424036, -0.012454529, 0.043872416, -0.023902861, -0.06754088, 0.039395988, -0.009394137, -0.013557914, 0.054060146, 0.03977331, 0.01960938, 0.020804238, -0.016243484, -0.08142752, 0.05857659, 0.029253993, 0.030408831, -0.048834786, -0.042614672, 0.008380437, -0.033324514, 0.04100247, 0.01713391, -0.034262102, -0.062281217, 0.05965139, -0.044295475, 0.045255937, -0.03626306, 1.14161754E-4, 0.0087263165, 0.03139216, 0.05053846, 0.020355452, 0.01695954, 0.028790914, -0.07459568, -0.06803255, 0.0015907609, 0.041185413, -0.034239236, -0.0143690165, -0.0136236595, -0.01011341, -0.0054477844, 0.012941548, -0.037143484, 0.047319777, -0.044947214, 0.020506952, -0.039258778, -0.012000026, -0.021536015, 0.049909588, -0.0355713, -0.025071993, 0.041339774, 0.009808978, 0.045072988, 0.065974414, -0.02064702, 0.0044607054, -0.013032663, 1.0004786E-4, 0.04048222, -0.01808767, -0.027704682, -0.039378837, 0.037846677, -0.01080231, -0.00804099, 0.049658038, -0.04925785, -0.009008595, 0.03676616, -0.010243069, 0.043746643, -0.04403249, -0.033193022, 0.028302109, -0.035994362, -0.026950035, -0.03394195, -0.011519082, -0.028808065, 0.037440766, -0.019132009, 0.03737788, -0.045736164, 0.0033159165, 0.030523172, 0.074252665, 0.0112153655, 0.021767555, 0.02050052, 0.02876233, -0.052882437, 0.0010833754, -0.017059589, -0.021901906, -0.04230023, 0.061881028, 0.03359893, -0.002392573, 0.047531307, 0.015124377, 0.021558885, 0.04292339, -0.045421727, 0.038801417, -0.004391386, -0.019043395, -0.03198673, -0.009232273, 0.03895006, -0.06231552, -0.03994482, 0.027779002, -0.035965774, 0.0336218, -0.016790174, -0.024385951, -0.052013453, 0.006636746, 0.028728027, -0.07149705, -0.005236791, 0.02509629, -0.08283962, 0.024062939, 0.038264018, 0.0052610883, 0.019983845, -0.022567939, 0.036028665, 0.012551718, 0.039733294, 0.020849973, -0.06289866, -0.014504082, -0.014029568, -0.012900457, 0.046056315, -0.045210198, -0.029385487, 0.01857603, -0.015710996, 0.03486239, -0.03192384, -0.04491863, -0.026112491, -0.026069613, -0.03267277, -0.009650331, 0.01740261, 0.058107797, -0.015014325, 0.033264484, -0.007932008, -0.058942482, 0.016094483, -0.039453156, -0.049863853, -0.00993904, 0.0336904, 0.008465835, 0.054460336, -0.044101093, -0.011365437, -0.037966736, -0.04518733, -0.024280185, -0.012904745, 0.018640345, -0.011682732, 0.027887626, -0.0029442657, 0.06867285, -0.017228242, 0.009504546, -0.050618496, -0.012899027, -0.03216396, -0.028968142, 0.019423576, 0.011018128, -0.017585555, -0.02778472, -0.013630806, -0.007985963, 0.044478416, -0.01570537, -0.016562209, 0.04075092, -0.031900976, -0.014078163, -1.6436433E-4, 0.02021967, 0.018799707, -0.063424625, -0.024686094, 0.04659372, -0.055466533, -0.03190669, -0.021633206, -0.037772354, -0.019120127, -0.0011362578, -0.019701209, 0.03355891, 0.046868134, -0.03438216, -0.0099447565, -0.030340228, -0.13531044, -0.019526483, -0.012966203, -0.0062001087, 0.0069561847, 0.023239689, 0.025612252, 0.010147711, 0.002053125, 0.022387853, 0.04639362, 0.017559828, -0.017425478, -0.0066185235, -0.013880925, -0.04315207, -0.032404073, 0.05231074, -0.03317587, -0.012551718, 0.053808596, 0.05265376, 0.051396016, 0.02508057, -0.013882355, 0.018711807, -0.04357513, 0.07334937, -0.045518916, -0.016896654, -0.030197302, 0.073337935, -0.020974318, -0.032169674, 0.023462651, 0.07043369, -0.010009431, 0.0576047, -0.056061104, 0.031569388, -0.04271758, 0.023205386, -0.037778072, 0.006159375, 0.003181455, 0.008509785, 0.04234597, -0.040882416, 0.048520353, -0.053156856, -0.03995054, -0.0877677, 0.04227165, -0.012376278, -0.044489853, 0.00877777, -0.04128832, -0.027247319, -0.0066703334, 0.039230194, 0.034873825, 0.03382761, 0.044352643, -0.039698992, -0.03430784, -0.020772079, -0.065414146, 5.7384593E-4, 0.02876233, 0.0067003476, 0.04351796, 0.038366925, 0.03943029, -0.033650383, 0.037966736, -0.013735142, 0.05691866, 0.008862811, -0.037635144, 0.032421224, -0.013979544, -0.068787195, -0.018368786, -0.003927593, 0.009075055, -0.016353536, -0.029505543, 0.029848564, -0.035376925, -0.079306506, 0.0051510357, 0.013992407, 0.035622753, 0.006298013, 0.015807562, -0.033753287, 0.008042865, 0.0218333, -0.011912841, -0.055649478, 0.025718017, -0.02185617, -0.004469727, -0.02198766, -0.00784143, 0.034096308, 0.054448903, 0.0307004, -0.043952454, -0.017951444, 0.011615557, 0.054620415, -0.08550376, -0.050755706, -0.051350277, -0.03050602, 0.0114068845, 0.028590819, 0.008987871, 0.0056341235, 0.037475068, -0.030620363, -0.06979338, 0.007883101, 0.08535511, -0.023171084, 5.5740948E-5, -0.006744655, -0.005266805, -0.043815248, 0.015816137, -0.014817088, -0.042991996, 0.011708458, -0.05513495, -0.041299757, 0.027315922, -0.026052462, -0.04580477, -5.0309784E-4, 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    "index" : 1
  } ],
  "model" : "e5-base-v2",
  "usage" : {
    "total_tokens" : 6
  }
}

每个嵌入都有一个索引，该索引与请求列表中的文本字符串相对应。索引从 0 开始，因此列表中第一个文本字符串的索引为 0，第二个文本字符串的索引为 1，依此类推。

在前面的示例中，“foo”对应于索引 0，“bar”对应于索引 1。“foo”的嵌入是嵌入列表中的第一个元素，“bar”的嵌入是嵌入列表中的第二个元素。

Python 请求示例

以下示例使用 Python API 向 e5-base-v2 模型发出 EMBED 请求。使用适当值替换此命令中的 token 和 account_identifier。

from snowflake.core import Root
from snowflake.snowpark.context import get_active_session

def embed_service():
    # Initialize Snowflake session and root
    session = get_active_session()
    root = Root(session)

    # Send embed_request request and process response
    response = root.cortex_embed_service.embed("e5-base-v2", ['foo', 'bar'])
    print(response)

if __name__ == "__main__":
    embed_service()
Copy

输出

以下是请求的输出：

{
  "object" : "list",
  "data" : [ {
    "object" : "embedding",
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    "index" : 1
  } ],
  "model" : "e5-base-v2",
  "usage" : {
    "total_tokens" : 6
  }
}

每个嵌入都有一个索引，该索引与请求列表中的文本字符串相对应。索引从 0 开始，因此列表中第一个文本字符串的索引为 0，第二个文本字符串的索引为 1，依此类推。

在前面的示例中，“foo”对应于索引 0，“bar”对应于索引 1。“foo”的嵌入是嵌入列表中的第一个元素，“bar”的嵌入是嵌入列表中的第二个元素。

使用量配额

下表显示了 EMBED 函数的使用配额。

EMBED 函数配额
模型
每分钟
处理的词元 (TPM)
每分钟的
请求 (RPM)
最大输出（词元）
snowflake-arctic-embed-m-v1.5

400,000

200

4,096
snowflake-arctic-embed-m

400,000

200

4,096
e5-base-v2

400,000

200

4,096
nv-embed-qa-4

400,000

200

4,096
multilingual-e5-large

400,000

200

4,096
voyage-multilingual-2

400,000

200

4,096
语言: 中文