Create test.py

test.py

@@ -0,0 +1,145 @@
+import streamlit as st
+import pandas as pd
+import io
+import os
+from dotenv import load_dotenv
+from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
+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
+from llama_index.core.ingestion import IngestionPipeline
+from langchain_community.document_loaders.csv_loader import CSVLoader
+from langchain_community.vectorstores import FAISS as LangChainFAISS
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+import faiss
+import tempfile
+
+# Load environment variables
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+# Global settings for LlamaIndex
+EMBED_DIMENSION = 512
+Settings.llm = OpenAI(model="gpt-3.5-turbo")
+Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small", dimensions=EMBED_DIMENSION)
+
+# Streamlit app
+st.title("Chat with CSV Files - LangChain vs LlamaIndex")
+
+# File uploader
+uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+if uploaded_file:
+    try:
+        # Read and preview CSV data using pandas
+        data = pd.read_csv(uploaded_file)
+        st.write("Preview of uploaded data:")
+        st.dataframe(data)
+
+        # Tabs
+        tab1, tab2 = st.tabs(["Chat w CSV using LangChain", "Chat w CSV using LlamaIndex"])
+
+        # LangChain Tab
+        with tab1:
+            st.subheader("LangChain Query")
+            try:
+                # Save the uploaded file to a temporary file for LangChain
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w") as temp_file:
+                    # Write the DataFrame to the temp file
+                    data.to_csv(temp_file.name, index=False)
+                    temp_file_path = temp_file.name
+
+                # Use CSVLoader with the temporary file path
+                loader = CSVLoader(file_path=temp_file_path)
+                docs = loader.load_and_split()
+
+                # Preview the first document
+                if docs:
+                    st.write("Preview of a document chunk (LangChain):")
+                    st.text(docs[0].page_content)
+
+                # LangChain FAISS VectorStore
+                langchain_index = faiss.IndexFlatL2(EMBED_DIMENSION)
+                langchain_vector_store = LangChainFAISS(
+                    embedding_function=OpenAIEmbeddings(),
+                    index=langchain_index,
+                )
+                langchain_vector_store.add_documents(docs)
+
+                # LangChain Retrieval Chain
+                retriever = langchain_vector_store.as_retriever()
+                system_prompt = (
+                    "You are an assistant for question-answering tasks. "
+                    "Use the following pieces of retrieved context to answer "
+                    "the question. If you don't know the answer, say that you "
+                    "don't know. Use three sentences maximum and keep the "
+                    "answer concise.\n\n{context}"
+                )
+                prompt = ChatPromptTemplate.from_messages(
+                    [("system", system_prompt), ("human", "{input}")]
+                )
+                question_answer_chain = create_stuff_documents_chain(ChatOpenAI(), prompt)
+                langchain_rag_chain = create_retrieval_chain(retriever, question_answer_chain)
+
+                # Query input for LangChain
+                query = st.text_input("Ask a question about your data (LangChain):")
+                if query:
+                    answer = langchain_rag_chain.invoke({"input": query})
+                    st.write(f"Answer: {answer['answer']}")
+
+            except Exception as e:
+                st.error(f"Error processing with LangChain: {e}")
+            finally:
+                # Clean up the temporary file
+                if 'temp_file_path' in locals() and os.path.exists(temp_file_path):
+                    os.remove(temp_file_path)
+
+        # LlamaIndex Tab
+        with tab2:
+            st.subheader("LlamaIndex Query")
+            try:
+                # Save uploaded file content to a temporary CSV file for LlamaIndex
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w") as temp_file:
+                    data.to_csv(temp_file.name, index=False)
+                    temp_file_path = temp_file.name
+
+                # Use PagedCSVReader for LlamaIndex
+                csv_reader = PagedCSVReader()
+                reader = SimpleDirectoryReader(
+                    input_files=[temp_file_path],
+                    file_extractor={".csv": csv_reader},
+                )
+                docs = reader.load_data()
+
+                # Preview the first document
+                if docs:
+                    st.write("Preview of a document chunk (LlamaIndex):")
+                    st.text(docs[0].text)
+
+                # Initialize FAISS Vector Store
+                llama_faiss_index = faiss.IndexFlatL2(EMBED_DIMENSION)
+                llama_vector_store = FaissVectorStore(faiss_index=llama_faiss_index)
+
+                # Create the ingestion pipeline and process the data
+                pipeline = IngestionPipeline(vector_store=llama_vector_store, documents=docs)
+                nodes = pipeline.run()
+
+                # Create a query engine
+                llama_index = VectorStoreIndex(nodes)
+                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):")
+                if query:
+                    response = query_engine.query(query)
+                    st.write(f"Answer: {response.response}")
+            except Exception as e:
+                st.error(f"Error processing with LlamaIndex: {e}")
+            finally:
+                # Clean up the temporary file
+                if 'temp_file_path' in locals() and os.path.exists(temp_file_path):
+                    os.remove(temp_file_path)
+    except Exception as e:
+        st.error(f"Error reading uploaded file: {e}")