File size: 1,087 Bytes
243b4bc 24efe34 243b4bc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | from src.constants import VECTOR_DB_PATH, EMBEDDING_MODEL
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from src.utils.asyncHandler import asyncHandler
import logging
import pandas as pd
class LoadVectorDB:
def __init__(self):
self.vector_db_path=VECTOR_DB_PATH
def initiate(self,k:int=5):
embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
vector_db=FAISS.load_local(self.vector_db_path, embeddings=embeddings, allow_dangerous_deserialization=True)
retriver=vector_db.as_retriever(search_kwargs={"k": k})
return retriver
class Connect_data:
def __init__(self,data_path:str):
self.data_path:str=data_path
self.data =pd.read_csv(self.data_path)
@asyncHandler
async def load_data(self)->pd.DataFrame:
logging.info("Entered in the connect db")
data = self.data
logging.info("Exited from the connect db")
data = data.sample(n=4480, random_state=42)
return data
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