Update app.py

app.py

@@ -31,7 +31,7 @@ from sklearn.metrics import silhouette_score
 from scipy.stats import spearmanr
 from functools import lru_cache
 from langchain.retrievers import MultiQueryRetriever
-from langchain_community.llms import HuggingFacePipeline
+from langchain_huggingfaces import HuggingFacePipeline
 from transformers import pipeline
 from sklearn.model_selection import ParameterGrid
 from tqdm import tqdm
@@ -197,9 +197,11 @@ def optimize_query(query, llm_model, chunks, embedding_model, vector_store_type,
     multi_query_retriever = MultiQueryRetriever.from_llm(
         retriever=temp_retriever,
         llm=llm
-    )
-    optimized_queries = multi_query_retriever.generate_queries(query)
-    return optimized_queries
+    ) 
+        # Use a NoOpRunManager as the run_manager
+    run_manager = NoOpRunManager()
+    optimized_queries = multi_query_retriever.generate_queries(query, run_manager=run_manager)
+    return optimized_queries 
     
 
 def create_custom_embedding(texts, model_type='word2vec', vector_size=100, window=5, min_count=1):