from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
import requests
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from tenacity import (
before_sleep_log,
retry,
stop_after_attempt,
wait_exponential,
)
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),
)
[docs]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):
"""MiniMax embedding model integration.
Setup:
To use, you should have the environment variable ``MINIMAX_GROUP_ID`` and
``MINIMAX_API_KEY`` set with your API token.
.. code-block:: bash
export MINIMAX_API_KEY="your-api-key"
export MINIMAX_GROUP_ID="your-group-id"
Key init args — completion params:
model: Optional[str]
Name of ZhipuAI model to use.
api_key: Optional[str]
Automatically inferred from env var `MINIMAX_GROUP_ID` if not provided.
group_id: Optional[str]
Automatically inferred from env var `MINIMAX_GROUP_ID` if not provided.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_community.embeddings import MiniMaxEmbeddings
embed = MiniMaxEmbeddings(
model="embo-01",
# api_key="...",
# group_id="...",
# other
)
Embed single text:
.. code-block:: python
input_text = "The meaning of life is 42"
embed.embed_query(input_text)
.. code-block:: python
[0.03016241, 0.03617699, 0.0017198119, -0.002061239, -0.00029994643, -0.0061320597, -0.0043635326, ...]
Embed multiple text:
.. code-block:: python
input_texts = ["This is a test query1.", "This is a test query2."]
embed.embed_documents(input_texts)
.. code-block:: python
[
[-0.0021588828, -0.007608119, 0.029349545, -0.0038194496, 0.008031177, -0.004529633, -0.020150753, ...],
[ -0.00023150232, -0.011122423, 0.016930554, 0.0083089275, 0.012633711, 0.019683322, -0.005971041, ...]
]
""" # noqa: E501
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] = Field(default=None, alias="group_id")
"""Group ID for MiniMax API."""
minimax_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
"""API Key for MiniMax API."""
class Config:
allow_population_by_field_name = True
extra = "forbid"
@pre_init
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", "group_id"], "MINIMAX_GROUP_ID"
)
minimax_api_key = convert_to_secret_str(
get_from_dict_or_env(
values, ["minimax_api_key", "api_key"], "MINIMAX_API_KEY"
)
)
values["minimax_group_id"] = minimax_group_id
values["minimax_api_key"] = minimax_api_key
return values
[docs] 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.get_secret_value()}", # type: ignore[union-attr]
"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]
