Create app.py

app.py

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+
+import streamlit as st
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from pinecone import Pinecone
+from langchain.vectorstores import Pinecone as LangchainPinecone
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_community.chat_message_histories import StreamlitChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+from langchain.chains import create_history_aware_retriever
+import time
+import os
+
+
+# Embedding setup
+model_name = "BAAI/bge-small-en"
+model_kwargs = {"device": "cpu"}
+encode_kwargs = {"normalize_embeddings": True}
+embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
+)
+
+# Pinecone setup
+pc = Pinecone(api_key="pcsk_5yLpy7_7DWbGm2s2HTf1NCbo4zFB8KLEZFLT54q3poTUoEFMbf1B9ShUZqpsT7EPnE3Pjw")
+text_field = "text"
+index_name = "contentengine"
+index = pc.Index(index_name)
+vectorstore = LangchainPinecone(index, embeddings.embed_query, text_field)
+
+# Retriever setup
+retriever = vectorstore.as_retriever(
+    search_type="similarity_score_threshold",
+    search_kwargs={"k": 1, "score_threshold": 0.5},
+)
+
+
+
+llm =  ChatGroq(model="llama3-8b-8192", api_key='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', max_tokens=4096)
+
+
+# Retriever prompt setup
+retriever_prompt = """
+Given a chat history and the latest user question which might reference context in the chat history,
+formulate a standalone question which can be understood without the chat history.
+Do NOT answer the question, just reformulate it if needed and otherwise return it as is.
+
+Chat History:
+{chat_history}
+
+User Question: {input}
+
+Standalone question:
+"""
+contextualize_q_prompt = ChatPromptTemplate.from_messages([
+    ("system", retriever_prompt),
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("human", "{input}"),
+])
+
+history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+
+from langchain_core.prompts import PromptTemplate
+
+template = """
+Context: This Content Engine is designed to analyze and compare key information across multiple Form 10-K filings for major companies, specifically Alphabet, Tesla, and Uber. The system uses Retrieval-Augmented Generation (RAG) to retrieve and summarize insights, highlight differences, and answer user queries on various financial and operational topics, such as risk factors, revenue, and business models.
+
+Chat History: {chat_history}
+Context: {context}
+Human: {input}
+
+Answer:
+"""
+
+# Define the PromptTemplate with specified input variables
+custom_rag_prompt = PromptTemplate(template=template, input_variables=["chat_history", "context", "input"])
+
+question_answering_chain = create_stuff_documents_chain(llm, custom_rag_prompt)
+rag_chain = create_retrieval_chain(history_aware_retriever, question_answering_chain)
+
+# ======================================================= Streamlit UI =======================================================
+
+st.title("Chat with Content Engine")
+
+# Initialize chat history
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = StreamlitChatMessageHistory(key="chat_messages")
+
+# Message history setup
+def get_chat_history():
+    return st.session_state.chat_history
+
+# Conversational_rag_chain to use the Streamlit chat history
+conversational_rag_chain = RunnableWithMessageHistory(
+    rag_chain,
+    get_chat_history,
+    input_messages_key="input",
+    history_messages_key="chat_history",
+    output_messages_key="answer"
+)
+
+# Function to interact with the chatbot
+def chat_with_bot(query: str) -> str:
+    result = conversational_rag_chain.invoke(
+        {"input": query},
+        config={
+            "configurable": {"session_id": "streamlit_session"}
+        },
+    )
+    return result["answer"]
+
+# Display chat messages from history
+for message in st.session_state.chat_history.messages:
+    with st.chat_message(message.type):
+        st.markdown(message.content)
+
+# Accept user input
+if user_input := st.chat_input("Enter your question here..."):
+
+    # Display user message in chat message container
+    with st.chat_message("human"):
+        st.markdown(user_input)
+
+    # Display assistant response in chat message container
+    with st.chat_message("ai"):
+        with st.spinner("Thinking..."):
+            response = chat_with_bot(user_input)
+            message_placeholder = st.empty()
+            full_response = "⚠️ **_Reminder: Please double-check information._** \n\n"
+            for chunk in response:
+                full_response += chunk
+                time.sleep(0.01)
+                message_placeholder.markdown(full_response + ":white_circle:", unsafe_allow_html=True)
+