langchain/libs/partners/mistralai/langchain_mistralai/embeddings.py

import asyncio
import logging
import warnings
from typing import Dict, Iterable, List, Optional

import httpx
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import (
    BaseModel,
    Extra,
    Field,
    SecretStr,
    root_validator,
)
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from tokenizers import Tokenizer  # type: ignore

logger = logging.getLogger(__name__)

MAX_TOKENS = 16_000
"""A batching parameter for the Mistral API. This is NOT the maximum number of tokens
accepted by the embedding model for each document/chunk, but rather the maximum number 
of tokens that can be sent in a single request to the Mistral API (across multiple
documents/chunks)"""


class DummyTokenizer:
    """Dummy tokenizer for when tokenizer cannot be accessed (e.g., via Huggingface)"""

    def encode_batch(self, texts: List[str]) -> List[List[str]]:
        return [list(text) for text in texts]


class MistralAIEmbeddings(BaseModel, Embeddings):
    """MistralAI embedding models.

    To use, set the environment variable `MISTRAL_API_KEY` is set with your API key or
    pass it as a named parameter to the constructor.

    Example:
        .. code-block:: python

            from langchain_mistralai import MistralAIEmbeddings

            mistral = MistralAIEmbeddings(
                model="mistral-embed",
                api_key="my-api-key"
            )
    """

    client: httpx.Client = Field(default=None)  #: :meta private:
    async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
    mistral_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
    endpoint: str = "https://api.mistral.ai/v1/"
    max_retries: int = 5
    timeout: int = 120
    max_concurrent_requests: int = 64
    tokenizer: Tokenizer = Field(default=None)

    model: str = "mistral-embed"

    class Config:
        extra = Extra.forbid
        arbitrary_types_allowed = True
        allow_population_by_field_name = True

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate configuration."""

        values["mistral_api_key"] = convert_to_secret_str(
            get_from_dict_or_env(
                values, "mistral_api_key", "MISTRAL_API_KEY", default=""
            )
        )
        api_key_str = values["mistral_api_key"].get_secret_value()
        # todo: handle retries
        values["client"] = httpx.Client(
            base_url=values["endpoint"],
            headers={
                "Content-Type": "application/json",
                "Accept": "application/json",
                "Authorization": f"Bearer {api_key_str}",
            },
            timeout=values["timeout"],
        )
        # todo: handle retries and max_concurrency
        values["async_client"] = httpx.AsyncClient(
            base_url=values["endpoint"],
            headers={
                "Content-Type": "application/json",
                "Accept": "application/json",
                "Authorization": f"Bearer {api_key_str}",
            },
            timeout=values["timeout"],
        )
        if values["tokenizer"] is None:
            try:
                values["tokenizer"] = Tokenizer.from_pretrained(
                    "mistralai/Mixtral-8x7B-v0.1"
                )
            except IOError:  # huggingface_hub GatedRepoError
                warnings.warn(
                    "Could not download mistral tokenizer from Huggingface for "
                    "calculating batch sizes. Set a Huggingface token via the "
                    "HF_TOKEN environment variable to download the real tokenizer. "
                    "Falling back to a dummy tokenizer that uses `len()`."
                )
                values["tokenizer"] = DummyTokenizer()
        return values

    def _get_batches(self, texts: List[str]) -> Iterable[List[str]]:
        """Split a list of texts into batches of less than 16k tokens
        for Mistral API."""
        batch: List[str] = []
        batch_tokens = 0

        text_token_lengths = [
            len(encoded) for encoded in self.tokenizer.encode_batch(texts)
        ]

        for text, text_tokens in zip(texts, text_token_lengths):
            if batch_tokens + text_tokens > MAX_TOKENS:
                if len(batch) > 0:
                    # edge case where first batch exceeds max tokens
                    # should not yield an empty batch.
                    yield batch
                batch = [text]
                batch_tokens = text_tokens
            else:
                batch.append(text)
                batch_tokens += text_tokens
        if batch:
            yield batch

    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed a list of document texts.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        try:
            batch_responses = (
                self.client.post(
                    url="/embeddings",
                    json=dict(
                        model=self.model,
                        input=batch,
                    ),
                )
                for batch in self._get_batches(texts)
            )
            return [
                list(map(float, embedding_obj["embedding"]))
                for response in batch_responses
                for embedding_obj in response.json()["data"]
            ]
        except Exception as e:
            logger.error(f"An error occurred with MistralAI: {e}")
            raise

    async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed a list of document texts.

        Args:
            texts: The list of texts to embed.

        Returns:
            List of embeddings, one for each text.
        """
        try:
            batch_responses = await asyncio.gather(
                *[
                    self.async_client.post(
                        url="/embeddings",
                        json=dict(
                            model=self.model,
                            input=batch,
                        ),
                    )
                    for batch in self._get_batches(texts)
                ]
            )
            return [
                list(map(float, embedding_obj["embedding"]))
                for response in batch_responses
                for embedding_obj in response.json()["data"]
            ]
        except Exception as e:
            logger.error(f"An error occurred with MistralAI: {e}")
            raise

    def embed_query(self, text: str) -> List[float]:
        """Embed a single query text.

        Args:
            text: The text to embed.

        Returns:
            Embedding for the text.
        """
        return self.embed_documents([text])[0]

    async def aembed_query(self, text: str) -> List[float]:
        """Embed a single query text.

        Args:
            text: The text to embed.

        Returns:
            Embedding for the text.
        """
        return (await self.aembed_documents([text]))[0]