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import os
import src.constants as constants_utils
import src.langchain_utils as langchain_utils
import src.weather as weather_utils
import src.mandi_price as mandi_utils
import src.translator as translator_utils
import src.web_crawler as web_crawler_utils
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(
format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO, datefmt="%Y-%m-%d %H:%M:%S"
)
import warnings
warnings.filterwarnings('ignore')
class KKMS_KSSW:
def __init__(self):
self.index_type = constants_utils.INDEX_TYPE
self.load_from_existing_index_store = constants_utils.LOAD_FROM_EXISTING_INDEX_STORE
# Instantiate langchain_utils class object
self.langchain_utils_obj = langchain_utils.LANGCHAIN_UTILS(
index_type=self.index_type,
load_from_existing_index_store=self.load_from_existing_index_store
)
# Instantiate Mandi Price utils class object
self.mandi_utils_obj = mandi_utils.MANDI_PRICE()
# Instantiate Weather class object
self.weather_utils_obj = weather_utils.WEATHER()
# Instantiate translator_utils class object
self.translator_utils_obj = translator_utils.TRANSLATOR()
# Initialize index (vector store)
def load_create_index(self):
logger.info(f"Load/Create index")
self.langchain_utils_obj.load_create_index()
# Upload data and update the index
def upload_data(
self,
doc_type,
files_or_urls,
index_category
):
logger.info(f"Uploading data")
self.langchain_utils_obj.upload_data(
doc_type=doc_type,
files_or_urls=files_or_urls,
index_category=index_category
)
# Define query on index to retrieve the most relevant top K documents from the vector store
def query(
self,
question,
question_category
):
'''
Args:
mode: can be any of [default, embedding]
response_mode: can be any of [default, compact, tree_summarize]
'''
logger.info(f"Querying from index/vector store")
return self.langchain_utils_obj.query(
question=question,
question_category=question_category
)
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