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