Update lab/app.py

lab/app.py

@@ -3,7 +3,8 @@ import pandas as pd
 import os
 from dotenv import load_dotenv
 from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
-from llama_index.readers.file import PagedCSVReader
+from llama_index.core.readers.base import BaseReader
+from llama_index.readers.file.paged_csv.base import PagedCSVReader
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.llms.openai import OpenAI
 from llama_index.vector_stores.faiss import FaissVectorStore
@@ -18,6 +19,7 @@ import faiss
 
 # Load environment variables
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+#os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
 
 # Global settings for LlamaIndex
 EMBED_DIMENSION = 512
@@ -25,7 +27,7 @@ Settings.llm = OpenAI(model="gpt-3.5-turbo")
 Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small", dimensions=EMBED_DIMENSION)
 
 # Streamlit app
-st.title("Streamlit App with LangChain and LlamaIndex")
+st.title("Chat w CSV Files - LangChain Vs LlamaIndex ")
 
 # File uploader
 uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
@@ -108,7 +110,7 @@ if uploaded_file:
 
         # Create a query engine
         llama_index = VectorStoreIndex(nodes)
-        query_engine = llama_index.as_query_engine(similarity_top_k=2)
+        query_engine = llama_index.as_query_engine(similarity_top_k=3)
 
         # Query input for LlamaIndex
         query = st.text_input("Ask a question about your data (LlamaIndex):")
@@ -117,4 +119,4 @@ if uploaded_file:
             st.write(f"Answer: {response.response}")
 
     # Cleanup temporary file
-    os.remove(temp_file_path)
+    os.remove(temp_file_path)