Source code for langchain_community.embeddings.mosaicml

from typing import Any, Dict, List, Mapping, Optional, Tuple

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.utils import get_from_dict_or_env


class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
    """MosaicML嵌入式服务。

要使用，您应该设置环境变量``MOSAICML_API_TOKEN``为您的API令牌，或将其作为命名参数传递给构造函数。

示例：
    .. code-block:: python

        from langchain_community.llms import MosaicMLInstructorEmbeddings
        endpoint_url = (
            "https://models.hosted-on.mosaicml.hosting/instructor-large/v1/predict"
        )
        mosaic_llm = MosaicMLInstructorEmbeddings(
            endpoint_url=endpoint_url,
            mosaicml_api_token="my-api-key"
        )
"""

    endpoint_url: str = (
        "https://models.hosted-on.mosaicml.hosting/instructor-xl/v1/predict"
    )
    """要使用的端点URL。"""
    embed_instruction: str = "Represent the document for retrieval: "
    """用于嵌入文档的指令。"""
    query_instruction: str = (
        "Represent the question for retrieving supporting documents: "
    )
    """用于嵌入查询的指令。"""
    retry_sleep: float = 1.0
    """遇到速率限制时尝试休眠的时间长度"""

    mosaicml_api_token: Optional[str] = None

    class Config:
        """此pydantic对象的配置。"""

        extra = Extra.forbid

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """验证环境中是否存在API密钥和Python包。"""
        mosaicml_api_token = get_from_dict_or_env(
            values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
        )
        values["mosaicml_api_token"] = mosaicml_api_token
        return values

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """获取识别参数。"""
        return {"endpoint_url": self.endpoint_url}

    def _embed(
        self, input: List[Tuple[str, str]], is_retry: bool = False
    ) -> List[List[float]]:
        payload = {"inputs": input}

        # HTTP headers for authorization
        headers = {
            "Authorization": f"{self.mosaicml_api_token}",
            "Content-Type": "application/json",
        }

        # send request
        try:
            response = requests.post(self.endpoint_url, headers=headers, json=payload)
        except requests.exceptions.RequestException as e:
            raise ValueError(f"Error raised by inference endpoint: {e}")

        try:
            if response.status_code == 429:
                if not is_retry:
                    import time

                    time.sleep(self.retry_sleep)

                    return self._embed(input, is_retry=True)

                raise ValueError(
                    f"Error raised by inference API: rate limit exceeded.\nResponse: "
                    f"{response.text}"
                )

            parsed_response = response.json()

            # The inference API has changed a couple of times, so we add some handling
            # to be robust to multiple response formats.
            if isinstance(parsed_response, dict):
                output_keys = ["data", "output", "outputs"]
                for key in output_keys:
                    if key in parsed_response:
                        output_item = parsed_response[key]
                        break
                else:
                    raise ValueError(
                        f"No key data or output in response: {parsed_response}"
                    )

                if isinstance(output_item, list) and isinstance(output_item[0], list):
                    embeddings = output_item
                else:
                    embeddings = [output_item]
            else:
                raise ValueError(f"Unexpected response type: {parsed_response}")

        except requests.exceptions.JSONDecodeError as e:
            raise ValueError(
                f"Error raised by inference API: {e}.\nResponse: {response.text}"
            )

        return embeddings

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """使用MosaicML部署的教师嵌入模型嵌入文档。

参数：
    texts：要嵌入的文本列表。

返回：
    每个文本的嵌入列表。
"""
        instruction_pairs = [(self.embed_instruction, text) for text in texts]
        embeddings = self._embed(instruction_pairs)
        return embeddings

    def embed_query(self, text: str) -> List[float]:
        """使用MosaicML部署的嵌入模型嵌入一个查询。

参数：
    text: 要嵌入的文本。

返回：
    文本的嵌入结果。
"""
        instruction_pair = (self.query_instruction, text)
        embedding = self._embed([instruction_pair])[0]
        return embedding