mosaicml.py

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

import requests
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_dict_or_env
from pydantic import BaseModel, ConfigDict, model_validator


class MosaicMLInstructorEmbeddings(BaseModel, Embeddings):
    """MosaicML embedding service.

    To use, you should have the
    environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
    it as a named parameter to the constructor.

    Example:
        .. 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"
    )
    """Endpoint URL to use."""
    embed_instruction: str = "Represent the document for retrieval: "
    """Instruction used to embed documents."""
    query_instruction: str = (
        "Represent the question for retrieving supporting documents: "
    )
    """Instruction used to embed the query."""
    retry_sleep: float = 1.0
    """How long to try sleeping for if a rate limit is encountered"""

    mosaicml_api_token: Optional[str] = None

    model_config = ConfigDict(
        extra="forbid",
    )

    @model_validator(mode="before")
    @classmethod
    def validate_environment(cls, values: Dict) -> Any:
        """Validate that api key and python package exists in environment."""
        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]:
        """Get the identifying parameters."""
        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]]:
        """Embed documents using a MosaicML deployed instructor embedding model.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        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]:
        """Embed a query using a MosaicML deployed instructor embedding model.

        Args:
            text: The text to embed.

        Returns:
            Embeddings for the text.
        """
        instruction_pair = (self.query_instruction, text)
        embedding = self._embed([instruction_pair])[0]
        return embedding