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44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import os
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from dotenv import load_dotenv
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from AmazScraper.pydantic_class import _Response
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from AmazScraper.class_creator import create_class
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from langchain_openai import ChatOpenAI
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from langchain.prompts import PromptTemplate
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from langchain_core.pydantic_v1 import Field
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from langchain.output_parsers import PydanticOutputParser
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load_dotenv()
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MY_ENV_VAR = os.getenv('API_KEY')
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class Generator:
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def __init__(self, values:list, temperature_param:float = 0, model_name:str = "gpt-3.5-turbo"):
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"""
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Initializes the Generator object.
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Parameters:
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- values (list): A list of values used for class creation.
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- temperature_param (float): A parameter controlling the randomness of the language model's output.
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- model_name (str): The name of the language model to be used (default: "gpt-3.5-turbo").
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"""
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create_class(values)
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self.parser = PydanticOutputParser(pydantic_object=_Response)
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self.prompt = PromptTemplate(
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template="Answer the user query.\n{format_instructions}\n{query}\n",
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input_variables=["query"],
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partial_variables={"format_instructions": self.parser.get_format_instructions()},
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)
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self.model = ChatOpenAI(openai_api_key=MY_ENV_VAR, temperature=temperature_param, model=model_name)
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self.chain = self.prompt | self.model | self.parser
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def invocation(self, query_info):
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try:
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result = self.chain.invoke({"query": query_info})
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print(result)
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return result
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except Exception as e:
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print(f"Error: {e}") |