Update src/helper.py

src/helper.py

@@ -1,30 +1,29 @@
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from sentence_transformers import SentenceTransformer
-
-
-#Extract Data From the PDF File
-def load_pdf_file(data):
-    loader= DirectoryLoader(data,
-                            glob="*.pdf",
-                            loader_cls=PyPDFLoader)
-
-    documents=loader.load()
-
-    return documents
-
-
-
-#Split the Data into Text Chunks
-def text_split(extracted_data):
-    text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-    text_chunks=text_splitter.split_documents(extracted_data)
-    return text_chunks
-
-
-
-#Download the Embeddings from HuggingFace 
-def download_hugging_face_embeddings():
-    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')  #this model return 384 dimensions
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from sentence_transformers import SentenceTransformer
+from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+#Extract Data From the PDF File
+def load_pdf_file(data):
+    loader= DirectoryLoader(data,
+                            glob="*.pdf",
+                            loader_cls=PyPDFLoader)
+
+    documents=loader.load()
+
+    return documents
+
+
+
+#Split the Data into Text Chunks
+def text_split(extracted_data):
+    text_splitter=RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+    text_chunks=text_splitter.split_documents(extracted_data)
+    return text_chunks
+
+
+
+#Download the Embeddings from HuggingFace 
+def download_hugging_face_embeddings():
+    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')  #this model return 384 dimensions
     return embeddings