Update llm.py

llm.py

@@ -6,9 +6,10 @@ from langchain_core.prompts import ChatPromptTemplate
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain.chains import create_retrieval_chain
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
-
-def load_and_process_pdf(pdf_path):
-    df = df.read_excel("chunk_metadata_template.xlsx", index=False)
+import pandas as pd
+from langchain.schema import Document
+def load_and_process_pdf():
+    df = pd.read_excel("content/chunk_metadata_template.xlsx")
     splits = []
     for i, row in df.iterrows():
         # Create a Document object for each row, including page_content and metadata
@@ -21,17 +22,17 @@ def load_and_process_pdf(pdf_path):
             }
         )
         # Append the Document object to the chunks list
-        chunks.append(chunk_with_metadata)
+        splits.append(chunk_with_metadata)
     return splits
 
-def create_vectorstore(splits):
+def create_vectorstore(splits = load_and_process_pdf()):
     model_name = "nomic-ai/nomic-embed-text-v1"
     model_kwargs = {
         'device': 'cpu',
         'trust_remote_code':True
         }
     encode_kwargs = {'normalize_embeddings': True}
-    hf = HuggingFaceBgeEmbeddings(
+    embeddings = HuggingFaceBgeEmbeddings(
         model_name=model_name,
         model_kwargs=model_kwargs,
         encode_kwargs=encode_kwargs)