Create UI

UI

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+import streamlit as st
+import bs4
+from langchain import hub
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.vectorstores import Chroma
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+
+#### INDEXING ####
+
+# Load Documents
+st.sidebar.write("Loading and processing documents...")
+loader = WebBaseLoader(
+    web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
+    bs_kwargs=dict(
+        parse_only=bs4.SoupStrainer(
+            class_=("post-content", "post-title", "post-header")
+        )
+    ),
+)
+docs = loader.load()
+
+# Split
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(docs)
+
+# Embed
+vectorstore = Chroma.from_documents(documents=splits, 
+                                    embedding=OpenAIEmbeddings())
+retriever = vectorstore.as_retriever()
+
+#### RETRIEVAL and GENERATION ####
+
+# Prompt
+prompt = hub.pull("rlm/rag-prompt")
+
+# LLM
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
+
+# Post-processing
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+# Chain
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+) 
+
+# Streamlit UI
+st.title("RAG-powered Q&A with Streamlit")
+st.write("Ask a question based on the indexed document:")
+
+# User input
+user_query = st.text_input("Enter your question:")
+
+if user_query:
+    with st.spinner("Fetching response..."):
+        response = rag_chain.invoke(user_query)
+    st.subheader("Response:")
+    st.write(response)