Update app.py

app.py

@@ -10,7 +10,7 @@ from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
 import traceback
 from transformers import pipeline
-from langchain.embeddings import HuggingFaceEmbeddings
+from langchain_community.embeddings import HuggingFaceEmbeddings
 
 # Load environment variables
 load_dotenv()
@@ -44,18 +44,14 @@ def get_text_chunks(text):
         return []
     return chunks
 
+# Function to create an in-memory FAISS vector store
 # Function to create an in-memory FAISS vector store
 def get_vector_store(text_chunks):
     try:
-        # Create a pipeline for feature extraction with the specified model
-        feature_extractor = pipeline("feature-extraction", model="jinaai/jina-embeddings-v2-base-code", trust_remote_code=True)
+        # Create an instance of HuggingFaceEmbeddings with the specified model
+        embedding_function = HuggingFaceEmbeddings(model_name="jinaai/jina-embeddings-v2-base-code")
 
-        # Define a function to generate embeddings using the pipeline
-        def embedding_function(text):
-            # The pipeline returns nested lists, so we flatten it by taking the first item.
-            return feature_extractor(text)[0]
-        
-        # Using FAISS to create vector store with the new embeddings function
+        # Using FAISS to create vector store with the embeddings function
         vector_store = FAISS.from_texts(text_chunks, embedding=embedding_function)
         return vector_store
     except Exception as e: