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import streamlit as st | |
import pandas as pd | |
import os | |
import traceback | |
from dotenv import load_dotenv | |
from llama_index.readers.file.paged_csv.base import PagedCSVReader | |
from llama_index.core import Settings, VectorStoreIndex | |
from llama_index.llms.openai import OpenAI | |
from llama_index.embeddings.openai import OpenAIEmbedding | |
from llama_index.vector_stores.faiss import FaissVectorStore | |
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 | |
from langchain_openai import OpenAIEmbeddings, ChatOpenAI | |
from langchain_core.documents import Document | |
import faiss | |
import tempfile | |
# Load environment variables | |
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") | |
# β Check OpenAI API Key | |
if not os.getenv("OPENAI_API_KEY"): | |
st.error("β οΈ OpenAI API Key is missing! Please check your .env file or environment variables.") | |
# β Ensure OpenAI Embeddings match FAISS dimensions | |
embedding_function = OpenAIEmbeddings() | |
test_vector = embedding_function.embed_query("test") | |
faiss_dimension = len(test_vector) | |
# β Update global settings for LlamaIndex | |
Settings.llm = OpenAI(model="gpt-4o") | |
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small", dimensions=faiss_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) | |
# Save the uploaded file to a temporary location | |
with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", mode="w", encoding="utf-8") as temp_file: | |
temp_file_path = temp_file.name | |
data.to_csv(temp_file.name, index=False, encoding="utf-8") | |
temp_file.flush() | |
# Tabs for LangChain and LlamaIndex | |
tab1, tab2 = st.tabs(["Chat w CSV using LangChain", "Chat w CSV using LlamaIndex"]) | |
# β LangChain Processing | |
with tab1: | |
st.subheader("LangChain Query") | |
try: | |
# β Store each row as a single document | |
st.write("Processing CSV with a custom loader...") | |
documents = [] | |
for _, row in data.iterrows(): | |
content = " | ".join([f"{col}: {row[col]}" for col in data.columns]) # β Store entire row as a document | |
doc = Document(page_content=content) | |
documents.append(doc) | |
# β Create FAISS VectorStore | |
st.write(f"β Initializing FAISS with dimension: {faiss_dimension}") | |
langchain_index = faiss.IndexFlatL2(faiss_dimension) | |
docstore = InMemoryDocstore() | |
index_to_docstore_id = {} | |
langchain_vector_store = LangChainFAISS( | |
embedding_function=embedding_function, | |
index=langchain_index, | |
docstore=docstore, | |
index_to_docstore_id=index_to_docstore_id, | |
) | |
# β Ensure documents are added correctly | |
try: | |
langchain_vector_store.add_documents(documents) | |
st.write("β Documents successfully added to FAISS VectorStore.") | |
except Exception as e: | |
st.error(f"Error adding documents to FAISS: {e}") | |
st.text(traceback.format_exc()) | |
# β Limit number of retrieved documents | |
retriever = langchain_vector_store.as_retriever(search_kwargs={"k": 15}) # Fetch 15 docs instead of 5 | |
# β Query Processing | |
query = st.text_input("Ask a question about your data (LangChain):") | |
if query: | |
try: | |
retrieved_docs = retriever.get_relevant_documents(query) | |
retrieved_context = "\n\n".join([doc.page_content for doc in retrieved_docs]) | |
retrieved_context = retrieved_context[:3000] | |
# β Show retrieved context for debugging | |
st.write("π **Retrieved Context Preview:**") | |
st.text(retrieved_context) | |
system_prompt = ( | |
"You are an assistant for question-answering tasks. " | |
"Use the following pieces of retrieved context to answer " | |
"the question. Keep the answer concise.\n\n" | |
f"{retrieved_context}" | |
) | |
# Simulate LangChain RAG Chain (update actual logic if necessary) | |
st.write("π Query processed successfully.") | |
st.write(f"**Sample Answer:** The answer to '{query}' depends on the retrieved context.") | |
except Exception as e: | |
error_message = traceback.format_exc() | |
st.error(f"Error processing query: {e}") | |
st.text(error_message) | |
except Exception as e: | |
error_message = traceback.format_exc() | |
st.error(f"Error processing with LangChain: {e}") | |
st.text(error_message) | |
except Exception as e: | |
error_message = traceback.format_exc() | |
st.error(f"Error reading uploaded file: {e}") | |
st.text(error_message) | |