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from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
from operator import itemgetter
from prompt_templates import PromptTemplates
class RetrievalManager:
"""
RetrievalManager class.
This class represents a retrieval manager that processes questions using a retrieval-augmented QA chain and returns the response.
Attributes:
retriever (object): The retriever object used for retrieval.
chat_model (object): The ChatOpenAI object representing the OpenAI Chat model.
Methods:
notebook_QA(question):
Processes a question using the retrieval-augmented QA chain and returns the response.
"""
def __init__(self, retriever):
self.retriever = retriever
self.chat_model = ChatOpenAI(model="gpt-4-turbo", temperature=0.1)
self.prompts = PromptTemplates()
def notebook_QA(self, question):
"""
Processes a question using the retrieval-augmented QA chain and returns the response.
Parameters:
question (str): The question to be processed.
Returns:
str: The response generated by the retrieval-augmented QA chain.
"""
retrieval_augmented_qa_chain = (
{"context": itemgetter("question") | self.retriever, "question": itemgetter("question")}
| RunnablePassthrough.assign(context=itemgetter("context"))
| {"response": self.prompts.get_rag_qa_prompt() | self.chat_model, "context": itemgetter("context")}
)
response = retrieval_augmented_qa_chain.invoke({"question": question})
return response["response"].content
def get_RAG_QA_chain(self):
return (
{"context": itemgetter("question") | self.retriever, "question": itemgetter("question")}
| RunnablePassthrough.assign(context=itemgetter("context"))
| {"response": self.prompts.get_rag_qa_prompt() | self.chat_model, "context": itemgetter("context")}
)
|