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from pydantic import BaseModel, field_validator
from typing import Optional
import os
from llmdantic import LLMdantic, LLMdanticConfig
from sambanova.langchain_wrappers import SambaNovaFastAPI
from dotenv import load_dotenv
from llmdantic import LLMdanticResult
current_dir = os.getcwd()
utils_dir = os.path.abspath(os.path.join(current_dir, '..'))
load_dotenv(os.path.join(utils_dir, '.env'), override=True)
# load_dotenv('.env', override=True)
class Catergories_Classify_Input(BaseModel):
text: str
class Catergories_Classify_Output(BaseModel):
result: str
@field_validator("result")
def catergory_result_must_not_be_empty(cls, v) -> bool:
"""Category result must not be empty"""
if not v.strip():
raise ValueError("Category result must not be empty")
return v
class Evaluator:
def __init__(self, llm : Optional[str], prompt: str):
self.llm = SambaNovaFastAPI(model=llm, fastapi_url = "https://fast-api.snova.ai/v1/chat/completions" , fastapi_api_key = "dHVhbmFuaC5uay4xOF9fZ21haWwuY29tOlRWbG9yQkxhNUY=")
self.prompt = prompt
self.config = LLMdanticConfig(
objective=self.prompt,
inp_schema=Catergories_Classify_Input,
out_schema=Catergories_Classify_Output,
retries=5,
)
self.llmdantic = LLMdantic(llm=self.llm, config=self.config)
def classify_text(self, text: str) -> Optional[Catergories_Classify_Output]:
data = Catergories_Classify_Input(text=text)
result: LLMdanticResult = self.llmdantic.invoke(data)
return result.output |