Source code for langchain.embeddings.minimax
"""Wrapper around MiniMax APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from tenacity import (
    before_sleep_log,
    retry,
    stop_after_attempt,
    wait_exponential,
)
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _create_retry_decorator() -> Callable[[Any], Any]:
    """Returns a tenacity retry decorator."""
    multiplier = 1
    min_seconds = 1
    max_seconds = 4
    max_retries = 6
    return retry(
        reraise=True,
        stop=stop_after_attempt(max_retries),
        wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
        before_sleep=before_sleep_log(logger, logging.WARNING),
    )
def embed_with_retry(embeddings: MiniMaxEmbeddings, *args: Any, **kwargs: Any) -> Any:
    """Use tenacity to retry the completion call."""
    retry_decorator = _create_retry_decorator()
    @retry_decorator
    def _embed_with_retry(*args: Any, **kwargs: Any) -> Any:
        return embeddings.embed(*args, **kwargs)
    return _embed_with_retry(*args, **kwargs)
[docs]class MiniMaxEmbeddings(BaseModel, Embeddings):
    """Wrapper around MiniMax's embedding inference service.
    To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
    ``MINIMAX_API_KEY`` set with your API token, or pass it as a named parameter to
    the constructor.
    Example:
        .. code-block:: python
            from langchain.embeddings import MiniMaxEmbeddings
            embeddings = MiniMaxEmbeddings()
            query_text = "This is a test query."
            query_result = embeddings.embed_query(query_text)
            document_text = "This is a test document."
            document_result = embeddings.embed_documents([document_text])
    """
    endpoint_url: str = "https://api.minimax.chat/v1/embeddings"
    """Endpoint URL to use."""
    model: str = "embo-01"
    """Embeddings model name to use."""
    embed_type_db: str = "db"
    """For embed_documents"""
    embed_type_query: str = "query"
    """For embed_query"""
    minimax_group_id: Optional[str] = None
    """Group ID for MiniMax API."""
    minimax_api_key: Optional[str] = None
    """API Key for MiniMax API."""
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that group id and api key exists in environment."""
        minimax_group_id = get_from_dict_or_env(
            values, "minimax_group_id", "MINIMAX_GROUP_ID"
        )
        minimax_api_key = get_from_dict_or_env(
            values, "minimax_api_key", "MINIMAX_API_KEY"
        )
        values["minimax_group_id"] = minimax_group_id
        values["minimax_api_key"] = minimax_api_key
        return values
    def embed(
        self,
        texts: List[str],
        embed_type: str,
    ) -> List[List[float]]:
        payload = {
            "model": self.model,
            "type": embed_type,
            "texts": texts,
        }
        # HTTP headers for authorization
        headers = {
            "Authorization": f"Bearer {self.minimax_api_key}",
            "Content-Type": "application/json",
        }
        params = {
            "GroupId": self.minimax_group_id,
        }
        # send request
        response = requests.post(
            self.endpoint_url, params=params, headers=headers, json=payload
        )
        parsed_response = response.json()
        # check for errors
        if parsed_response["base_resp"]["status_code"] != 0:
            raise ValueError(
                f"MiniMax API returned an error: {parsed_response['base_resp']}"
            )
        embeddings = parsed_response["vectors"]
        return embeddings
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed documents using a MiniMax embedding endpoint.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        embeddings = embed_with_retry(self, texts=texts, embed_type=self.embed_type_db)
        return embeddings
[docs]    def embed_query(self, text: str) -> List[float]:
        """Embed a query using a MiniMax embedding endpoint.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        embeddings = embed_with_retry(
            self, texts=[text], embed_type=self.embed_type_query
        )
        return embeddings[0]