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from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
class openai_chain():
def __init__(self, inp_dir='output_reports/reports_1/faiss_index') -> None:
self.inp_dir = inp_dir
pass
def get_response(self, query, k=3, type="map_reduce", model_name="gpt-3.5-turbo"):
# Initialize OPENAI embeddings
embedding = OpenAIEmbeddings()
# Load Database for required PDF
db = FAISS.load_local(self.inp_dir, embedding)
# Get relevant docs
docs = db.similarity_search(query, k=k)
# Create Chain
chain = load_qa_chain(ChatOpenAI(model=model_name), chain_type=type)
# Get Response
response = chain.run(input_documents=docs, question=query)
return response
def get_response_from_drive(self, query, database, k=3, type="stuff", model_name="gpt-3.5-turbo"):
# Get relevant docs
docs = database.similarity_search(query, k=k)
# Create chain
chain = load_qa_chain(ChatOpenAI(model=model_name), chain_type=type)
#Get Response
response = chain.run(input_documents=docs, question=query)
return response |