Create app.py

app.py

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+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+from langchain.chat_models import ChatOpenAI
+import os
+
+# Streamlit app title
+st.title("Question Answering with the Constitution of Pakistan")
+
+# Load the PDF
+pdf_path = "The Constitution of the Islamic Republic of Pakistan.pdf"
+
+# Load data only once to optimize
+@st.cache_data
+def load_pdf_data(pdf_path):
+    loader = PyPDFLoader(pdf_path)
+    docs = loader.load()
+    return docs
+
+docs = load_pdf_data(pdf_path)
+
+# Split documents
+@st.cache_data
+def split_docs(docs):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)
+    return text_splitter.split_documents(docs)
+
+splits = split_docs(docs)
+
+# Load OpenAI embeddings
+openai_api_key = st.secrets["openai_api_key"]  # Keeping API key secret in Streamlit
+embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+# Vectorstore setup (Chroma)
+persist_directory = 'docs/chroma/'
+vectordb = Chroma.from_documents(documents=splits, embedding=embedding, persist_directory=persist_directory)
+
+# Define LLM and chain
+llm_name = "gpt-3.5-turbo"
+llm = ChatOpenAI(model_name=llm_name, temperature=0, openai_api_key=openai_api_key)
+
+# Custom PromptTemplate
+template = """Use the following pieces of context to answer the question at the end. 
+If you don't know the answer, just say that you don't know, don't try to make up an answer. 
+Use three sentences maximum. Keep the answer as concise as possible. 
+Always say "thanks for asking!" at the end of the answer.
+
+{context}
+
+Question: {question}
+Helpful Answer:"""
+QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
+
+# Build the QA chain with restrictions
+qa_chain = RetrievalQA.from_chain_type(
+    llm,
+    retriever=vectordb.as_retriever(),
+    return_source_documents=True,
+    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
+)
+
+# Streamlit user input
+question = st.text_input("Ask a question about the Constitution of Pakistan:")
+
+if st.button("Get Answer"):
+    if question:
+        with st.spinner('Generating answer...'):
+            result = qa_chain({"query": question})
+            st.write(result["result"])  # Display the concise answer
+
+            # Display source documents
+            st.subheader("Source Document:")
+            for doc in result["source_documents"]:
+                st.write(doc.page_content)  # Show the content of the source document
+    else:
+        st.error("Please ask a question.")