Update app.py

app.py

@@ -9,6 +9,7 @@ from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.vector_stores.faiss import FaissVectorStore
 from llama_index.core.ingestion import IngestionPipeline
 from langchain_community.vectorstores import FAISS as LangChainFAISS
+from langchain_community.docstore.in_memory import InMemoryDocstore
 from langchain.chains import create_retrieval_chain
 from langchain.chains.combine_documents import create_stuff_documents_chain
 from langchain_core.prompts import ChatPromptTemplate
@@ -43,16 +44,17 @@ if uploaded_file:
             data.to_csv(temp_file.name, index=False, encoding="utf-8")
             temp_file.flush()
 
-        # Verify the temporary file
+        # Debugging: Verify the temporary file (Display partially)
         st.write("Temporary file path:", temp_file_path)
         with open(temp_file_path, "r") as f:
-            st.write("Temporary file content:")
-            st.text(f.read())
+            content = f.read()
+        st.write("Partial file content (first 500 characters):")
+        st.text(content[:500])
 
         # Tabs for LangChain and LlamaIndex
         tab1, tab2 = st.tabs(["LangChain", "LlamaIndex"])
 
-        # LangChain Tab with Custom Loader
+        # LangChain Tab with Proper FAISS Initialization
         with tab1:
             st.subheader("LangChain Query")
             try:
@@ -68,11 +70,15 @@ if uploaded_file:
                 if documents:
                     st.text(documents[0]["page_content"])
 
-                # Create FAISS VectorStore
+                # Create FAISS VectorStore with proper arguments
                 langchain_index = faiss.IndexFlatL2(EMBED_DIMENSION)
+                docstore = InMemoryDocstore()  # Create an in-memory docstore
+                index_to_docstore_id = {}  # Mapping of index to document ID
                 langchain_vector_store = LangChainFAISS(
                     embedding_function=OpenAIEmbeddings(),
                     index=langchain_index,
+                    docstore=docstore,
+                    index_to_docstore_id=index_to_docstore_id,
                 )
                 langchain_vector_store.add_documents(documents)