Update app.py

app.py

@@ -1,78 +1,118 @@
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_openai import OpenAIEmbeddings, ChatOpenAI
-from langchain_community.embeddings import OllamaEmbeddings
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-import os
-from langchain.retrievers.multi_query import MultiQueryRetriever
-from langchain_core.runnables import RunnablePassthrough
-from langchain_core.output_parsers import StrOutputParser
-from langchain.prompts import ChatPromptTemplate, PromptTemplate
-import streamlit as st
-
-os.environ["OPENAI_API_KEY"] =st.secrets["OPENAI_API_KEY"]
-
-llm = ChatOpenAI(model='gpt-4o', temperature=0.2)
-
-embeddings = OpenAIEmbeddings()
-
-vector_store = Chroma(embedding_function=embeddings, persist_directory="mining-rag")
-print("Vector store loaded successfully.")
-
-
-question=st.text_input('whats your question?')
-key=st.button('enter')
-
-if key:
-    QUERY_PROMPT = PromptTemplate(
-            input_variables=["question"],
-            template="""You are an AI language model assistant. Your task is to generate three
-            different versions of the given user question to retrieve relevant documents from
-            a vector database. By generating multiple perspectives on the user question, your
-            goal is to help the user overcome some of the limitations of the distance-based
-            similarity search. Provide these alternative questions separated by newlines.
-            Original question: {question}""",
-        )
-
-    retriever = MultiQueryRetriever.from_llm(
-            vector_store.as_retriever(),
-            llm,
-            prompt=QUERY_PROMPT
-        )
-
-    WRITER_SYSTEM_PROMPT = "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text."  # noqa: E501
-        # Report prompts from https://github.com/assafelovic/gpt-researcher/blob/master/gpt_researcher/master/prompts.py
-    RESEARCH_REPORT_TEMPLATE = """Information:
-        --------
-        {text}
-        --------
-        Using the above information, answer the following question or topic: "{question}" in a short manner-- \
-        The answer should focus on the answer to the question, should be well structured, informative, \
-        in depth, with facts and numbers if available and a minimum of 150 words and a maximum of 300 words.
-        You should strive to write the report using all relevant and necessary information provided.
-        You must write the report with markdown syntax.
-        You MUST determine your own concrete and valid opinion based on the given information. Do NOT deter to general and meaningless conclusions.
-        You must write the sources used in the context. if any article is used, mentioned in the end.
-        Please do your best, this is very important to my career."""  # noqa: E501
-
-    prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", WRITER_SYSTEM_PROMPT),
-                ("user", RESEARCH_REPORT_TEMPLATE),
-            ]
-        )
-
-    chain = (
-                {"text": retriever, "question": RunnablePassthrough()}
-                | prompt
-                | llm
-                | StrOutputParser()
-        )
-
-    answer = chain.invoke(
-            {
-                "question": question
-            }
-        )
-    st.write(answer)
-
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_openai import OpenAIEmbeddings, ChatOpenAI
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+import os
+from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain.prompts import ChatPromptTemplate, PromptTemplate
+import streamlit as st
+
+os.environ["OPENAI_API_KEY"] =st.secrets["OPENAI_API_KEY"]
+
+@st.cache_resource
+def load_resources():
+    llm = ChatOpenAI(model='gpt-4o', temperature=0.1)
+    embeddings = OpenAIEmbeddings()
+    vector_store = Chroma(embedding_function=embeddings, persist_directory="mining-rag")
+    print('vector store loaded')
+    return llm, vector_store
+
+llm, vector_store = load_resources()
+
+# Define the FAQ questions
+faq_questions = [
+    "What are the primary methods of mineral extraction used in the mining industry?",
+    "How do open-pit mining and underground mining differ in terms of process and environmental impact?",
+    "What are the most common minerals extracted through mining, and what are their primary uses?",
+    "How do mineral exploration techniques like geophysical surveys and drilling work?",
+    "What are the environmental impacts of mining, and how can they be mitigated?",
+    "What role does beneficiation play in the mining process, and what are some common beneficiation techniques?",
+    "What are tailings, and how are they managed in mining operations?",
+    "How does the mining industry ensure the safety of its workers?",
+    "What are the key regulations and standards governing the mining industry?",
+    "How is technology transforming the mining industry, particularly in terms of automation and data analysis?",
+    "What is the significance of sustainable mining practices, and what initiatives support sustainability in mining?",
+    "How do economic factors such as commodity prices influence mining operations?",
+    "What are the potential impacts of mining on local communities, and how are these addressed?",
+    "What is the role of reclamation and rehabilitation in the mining lifecycle?",
+    "How do companies assess the feasibility of a mining project?",
+    "What are some examples of innovative mining technologies or practices that are currently being developed?",
+    "What is artisanal and small-scale mining, and what are its challenges and opportunities?",
+    "How do geopolitical factors affect the global mining industry?",
+    "What are some of the ethical considerations in mining, particularly in relation to conflict minerals?",
+    "How can mining companies ensure they are adhering to best practices in environmental, social, and governance (ESG) criteria?"
+]
+
+def get_answer(question):
+    QUERY_PROMPT = PromptTemplate(
+        input_variables=["question"],
+        template="""You are an AI language model assistant. Your task is to generate three
+        different versions of the given user question to retrieve relevant documents from
+        a vector database. By generating multiple perspectives on the user question, your
+        goal is to help the user overcome some of the limitations of the distance-based
+        similarity search. Provide these alternative questions separated by newlines.
+        Original question: {question}""",
+    )
+
+    retriever = MultiQueryRetriever.from_llm(
+        vector_store.as_retriever(),
+        llm,
+        prompt=QUERY_PROMPT
+    )
+
+    WRITER_SYSTEM_PROMPT = "You are an AI critical thinker research assistant. Your sole purpose is to write well written, critically acclaimed, objective and structured reports on given text."
+    RESEARCH_REPORT_TEMPLATE = """Information:
+    --------
+    {text}
+    --------
+    Using the above information, answer the following question or topic: "{question}" in a short manner-- \
+    The answer should focus on the answer to the question, should be well structured, informative, \
+    in depth, with facts and numbers if available and a minimum of 150 words and a maximum of 300 words.
+    You should strive to write the report using all relevant and necessary information provided.
+    You must write the report with markdown syntax.
+    You MUST determine your own concrete and valid opinion based on the given information. Do NOT deter to general and meaningless conclusions.
+    You must write the sources used in the context. if any article is used, mentioned in the end.
+    Please do your best, this is very important to my career."""
+
+    prompt = ChatPromptTemplate.from_messages(
+        [
+            ("system", WRITER_SYSTEM_PROMPT),
+            ("user", RESEARCH_REPORT_TEMPLATE),
+        ]
+    )
+
+    chain = (
+        {"text": retriever, "question": RunnablePassthrough()}
+        | prompt
+        | llm
+        | StrOutputParser()
+    )
+
+    answer = chain.invoke({"question": question})
+    return answer
+
+# Streamlit UI
+st.title('Mining Minerals Expert RAG App')
+
+# Sidebar with FAQ Section
+st.sidebar.subheader("Frequently Asked Questions")
+faq_expander = st.sidebar.expander("FAQs")
+with faq_expander:
+    for i, faq in enumerate(faq_questions):
+        if st.sidebar.button(f"Q{i+1}: {faq}"):
+            answer = get_answer(faq)
+            st.sidebar.write(f"**Answer to Q{i+1}:** {answer}")
+
+# Custom Question Section
+st.subheader("Ask Your Own Question")
+question = st.text_input('Write your question below:')
+if st.button('Enter'):
+    if question:
+        answer = get_answer(question)
+        st.write(f"**Answer:** {answer}")
+    else:
+        st.write("Please enter a question.")
+