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