confident-ai--deepeval
wehub-resource-sync
298 行
10 KiB
Python
298 行
10 KiB
Python
from typing import Optional, Tuple, Union, Dict, List
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from openai import OpenAI, AsyncOpenAI
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from pydantic import BaseModel, SecretStr
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from deepeval.errors import DeepEvalError
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from deepeval.config.settings import get_settings
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from deepeval.models.retry_policy import (
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create_retry_decorator,
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sdk_retries_for,
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)
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from deepeval.models.llms.utils import trim_and_load_json
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from deepeval.models.utils import (
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require_costs,
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require_secret_api_key,
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EvaluationCost,
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)
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from deepeval.test_case import MLLMImage
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from deepeval.utils import check_if_multimodal, convert_to_multi_modal_array
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from deepeval.models import DeepEvalBaseLLM
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from deepeval.constants import ProviderSlug as PS
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from deepeval.models.llms.constants import KIMI_MODELS_DATA
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from deepeval.utils import require_param
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retry_kimi = create_retry_decorator(PS.KIMI)
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class KimiModel(DeepEvalBaseLLM):
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def __init__(
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self,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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temperature: Optional[float] = None,
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cost_per_input_token: Optional[float] = None,
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cost_per_output_token: Optional[float] = None,
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generation_kwargs: Optional[Dict] = None,
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**kwargs,
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):
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settings = get_settings()
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model = model or settings.MOONSHOT_MODEL_NAME
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if temperature is not None:
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temperature = float(temperature)
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elif settings.TEMPERATURE is not None:
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temperature = settings.TEMPERATURE
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else:
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temperature = 0.0
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cost_per_input_token = (
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cost_per_input_token
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if cost_per_input_token is not None
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else settings.MOONSHOT_COST_PER_INPUT_TOKEN
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)
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cost_per_output_token = (
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cost_per_output_token
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if cost_per_output_token is not None
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else settings.MOONSHOT_COST_PER_OUTPUT_TOKEN
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)
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if api_key is not None:
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# keep it secret, keep it safe from serializings, logging and alike
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self.api_key: Optional[SecretStr] = SecretStr(api_key)
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else:
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self.api_key = settings.MOONSHOT_API_KEY
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# validation
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model = require_param(
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model,
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provider_label="KimiModel",
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env_var_name="MOONSHOT_MODEL_NAME",
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param_hint="model",
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)
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if temperature < 0:
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raise DeepEvalError("Temperature must be >= 0.")
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self.model_data = KIMI_MODELS_DATA.get(model)
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self.temperature = temperature
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cost_per_input_token, cost_per_output_token = require_costs(
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self.model_data,
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model,
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"MOONSHOT_COST_PER_INPUT_TOKEN",
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"MOONSHOT_COST_PER_OUTPUT_TOKEN",
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cost_per_input_token,
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cost_per_output_token,
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)
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self.model_data.input_price = float(cost_per_input_token)
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self.model_data.output_price = float(cost_per_output_token)
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self.base_url = "https://api.moonshot.cn/v1"
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# Keep sanitized kwargs for client call to strip legacy keys
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self.kwargs = kwargs
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self.kwargs.pop("temperature", None)
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self.generation_kwargs = dict(generation_kwargs or {})
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self.generation_kwargs.pop("temperature", None)
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super().__init__(model)
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###############################################
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# Other generate functions
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###############################################
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@retry_kimi
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def generate(
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self, prompt: str, schema: Optional[BaseModel] = None
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) -> Tuple[Union[str, BaseModel], float]:
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if check_if_multimodal(prompt):
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prompt = convert_to_multi_modal_array(input=prompt)
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content = self.generate_content(prompt)
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else:
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content = [{"type": "text", "text": prompt}]
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client = self.load_model(async_mode=False)
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if schema and self.supports_json_mode() is True:
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completion = client.chat.completions.create(
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model=self.name,
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messages=[{"role": "user", "content": content}],
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response_format={"type": "json_object"},
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temperature=self.temperature,
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**self.generation_kwargs,
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)
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json_output = trim_and_load_json(
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completion.choices[0].message.content
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)
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cost = self.calculate_cost(
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completion.usage.prompt_tokens,
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completion.usage.completion_tokens,
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)
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return schema.model_validate(json_output), cost
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completion = client.chat.completions.create(
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model=self.name,
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messages=[{"role": "user", "content": content}],
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**self.generation_kwargs,
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)
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output = completion.choices[0].message.content
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cost = self.calculate_cost(
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completion.usage.prompt_tokens,
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completion.usage.completion_tokens,
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)
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if schema:
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json_output = trim_and_load_json(output)
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return schema.model_validate(json_output), cost
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else:
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return output, cost
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@retry_kimi
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async def a_generate(
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self, prompt: str, schema: Optional[BaseModel] = None
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) -> Tuple[Union[str, BaseModel], float]:
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if check_if_multimodal(prompt):
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prompt = convert_to_multi_modal_array(input=prompt)
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content = self.generate_content(prompt)
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else:
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content = [{"type": "text", "text": prompt}]
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client = self.load_model(async_mode=True)
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if schema and self.supports_json_mode() is True:
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completion = await client.chat.completions.create(
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model=self.name,
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messages=[{"role": "user", "content": content}],
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response_format={"type": "json_object"},
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temperature=self.temperature,
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**self.generation_kwargs,
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)
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json_output = trim_and_load_json(
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completion.choices[0].message.content
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)
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cost = self.calculate_cost(
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completion.usage.prompt_tokens,
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completion.usage.completion_tokens,
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)
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return schema.model_validate(json_output), cost
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completion = await client.chat.completions.create(
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model=self.name,
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messages=[{"role": "user", "content": content}],
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**self.generation_kwargs,
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)
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output = completion.choices[0].message.content
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cost = self.calculate_cost(
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completion.usage.prompt_tokens,
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completion.usage.completion_tokens,
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)
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if schema:
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json_output = trim_and_load_json(output)
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return schema.model_validate(json_output), cost
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else:
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return output, cost
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def generate_content(
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self, multimodal_input: List[Union[str, MLLMImage]] = []
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):
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content = []
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for element in multimodal_input:
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if isinstance(element, str):
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content.append({"type": "text", "text": element})
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elif isinstance(element, MLLMImage):
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if element.url and not element.local:
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content.append(
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{
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"type": "image_url",
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"image_url": {"url": element.url},
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}
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)
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else:
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element.ensure_images_loaded()
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data_uri = (
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f"data:{element.mimeType};base64,{element.dataBase64}"
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)
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content.append(
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{
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"type": "image_url",
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"image_url": {"url": data_uri},
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}
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)
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return content
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###############################################
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# Utilities
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###############################################
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def calculate_cost(self, input_tokens: int, output_tokens: int) -> float:
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if self.model_data.input_price and self.model_data.output_price:
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input_cost = input_tokens * self.model_data.input_price
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output_cost = output_tokens * self.model_data.output_price
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return EvaluationCost(
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input_cost + output_cost, input_tokens, output_tokens
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)
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###############################################
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# Capabilities
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###############################################
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def supports_log_probs(self) -> Union[bool, None]:
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return self.model_data.supports_log_probs
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def supports_temperature(self) -> Union[bool, None]:
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return self.model_data.supports_temperature
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def supports_multimodal(self) -> Union[bool, None]:
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return self.model_data.supports_multimodal
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def supports_structured_outputs(self) -> Union[bool, None]:
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return self.model_data.supports_structured_outputs
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def supports_json_mode(self) -> Union[bool, None]:
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return self.model_data.supports_json
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###############################################
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# Model
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###############################################
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def load_model(self, async_mode: bool = False):
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if not async_mode:
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return self._build_client(OpenAI)
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return self._build_client(AsyncOpenAI)
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def _client_kwargs(self) -> Dict:
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"""
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If Tenacity is managing retries, force OpenAI SDK retries off to avoid double retries.
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If the user opts into SDK retries for 'kimi' via DEEPEVAL_SDK_RETRY_PROVIDERS,
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leave their retry settings as is.
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"""
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kwargs = dict(self.kwargs or {})
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if not sdk_retries_for(PS.KIMI):
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kwargs["max_retries"] = 0
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return kwargs
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def _build_client(self, cls):
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api_key = require_secret_api_key(
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self.api_key,
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provider_label="Kimi",
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env_var_name="MOONSHOT_API_KEY",
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param_hint="`api_key` to KimiModel(...)",
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)
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kw = dict(
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api_key=api_key,
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base_url=self.base_url,
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**self._client_kwargs(),
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)
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try:
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return cls(**kw)
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except TypeError as e:
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# older OpenAI SDKs may not accept max_retries, in that case remove and retry once
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if "max_retries" in str(e):
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kw.pop("max_retries", None)
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return cls(**kw)
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raise
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def get_model_name(self):
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return f"{self.name} (KIMI)"
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