示例：集成 Open AI 模型

以下文档提供了一个示例配置和模型适配器，用于与 Open AI 模型的自定义连接。

有关逐步指南，请参阅我们的文档：如何创建模型适配器和如何创建到外部托管模型的连接。

示例 Open AI 模型适配器

要在 Foundry 中使用此示例，请在代码库应用程序中使用模型适配器库发布并标记一个模型适配器。

该模型适配器配置了与我们在 Azure 托管的 Open AI 实例的连接。此配置已通过 Python 3.8.17、pandas 1.5.3 和 openai 1.1.0 测试。构建 OpenAI 客户端需要以下五个输入：

base_url - 以 base_url 提供
api_type - 在连接配置中提供
api_version - 在连接配置中提供
engine - 在连接配置中提供
api_key - 以 resolved_credentials 提供

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import palantir_models as pm
import models_api.models_api_executable as executable_api

from typing import Optional

import openai
import logging

logger = logging.getLogger(__name__)


class OpenAIModelAdapter(pm.ExternalModelAdapter):
  def __init__(self, base_url, api_type, api_version, engine, api_key):
    # 定义用于补全的引擎
    self.engine = engine

    # 设置 OpenAI 变量
    openai.api_type = api_type
    openai.api_key = api_key
    openai.api_base = base_url
    openai.api_version = api_version

  @classmethod
  def init_external(cls, external_context) -> "pm.ExternalModelAdapter":
    # 从 external_context 中初始化参数
    base_url = external_context.base_url
    api_type = external_context.connection_config["api_type"]
    api_version = external_context.connection_config["api_version"]
    engine = external_context.connection_config["engine"]
    api_key = external_context.resolved_credentials["api_key"]
    return cls(
      base_url,
      api_type,
      api_version,
      engine,
      api_key
    )

  @classmethod
  def api(cls):
    # 定义输入输出数据格式
    inputs = {"df_in": pm.Pandas(columns=[("prompt", str)])}
    outputs = {"df_out": pm.Pandas(columns=[("prompt", str), ("prediction", str)])}
    return inputs, outputs

  def predict(self, df_in):
    # 执行预测操作
    predictions = []
    for _, row in df_in.iterrows():
      messages = [{"role": "user", "content": row['prompt']}]
      try:
        response = openai.ChatCompletion.create(
          engine=self.engine,
          messages=messages,
        )
      except openai.error.Timeout as e:
        logger.error(f"OpenAI API request timed out: {e}")
        raise e
      except openai.error.APIError as e:
        logger.error(f"OpenAI API returned an API Error: {e}")
        raise e
      except openai.error.APIConnectionError as e:
        logger.error(f"OpenAI API request failed to connect: {e}")
        raise e
      except openai.error.InvalidRequestError as e:
        logger.error(f"OpenAI API request was invalid: {e}")
        raise e
      except openai.error.AuthenticationError as e:
        logger.error(f"OpenAI API request was not authorized: {e}")
        raise e
      except openai.error.PermissionError as e:
        logger.error(f"OpenAI API request was not permitted: {e}")
        raise e
      except openai.error.RateLimitError as e:
        logger.error(f"OpenAI API request exceeded rate limit: {e}")
        raise e
      predictions.append(response.choices[0].message.content)
    df_in['prediction'] = predictions
    return df_in