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import os |
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import streamlit as st |
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from langchain.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings |
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from langchain.vectorstores.faiss import FAISS |
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from huggingface_hub import snapshot_download |
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from langchain.callbacks import StreamlitCallbackHandler |
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from langchain.agents import OpenAIFunctionsAgent, AgentExecutor |
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from langchain.agents.agent_toolkits import create_retriever_tool |
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from langchain.agents.openai_functions_agent.agent_token_buffer_memory import ( |
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AgentTokenBufferMemory, |
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) |
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from langchain.chat_models import ChatOpenAI |
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from langchain.schema import SystemMessage, AIMessage, HumanMessage |
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from langchain.prompts import MessagesPlaceholder |
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from langsmith import Client |
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client = Client() |
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st.set_page_config( |
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page_title="Investor Education ChatChain", |
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page_icon="📖", |
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layout="wide", |
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initial_sidebar_state="collapsed", |
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) |
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api_key = os.environ["OPENAI_API_KEY"] |
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site_options = {'US': 'vanguard_embeddings_US', |
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'AUS': 'vanguard-embeddings'} |
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site_options_list = list(site_options.keys()) |
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site_radio = st.radio( |
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"Which Vanguard website location would you want to chat to?", |
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('US', 'AUS')) |
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@st.cache_data |
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def load_vectorstore(site): |
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'''load embeddings and vectorstore''' |
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emb = HuggingFaceEmbeddings(model_name="all-mpnet-base-v2") |
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vectorstore = FAISS.load_local(site_options[site], emb,allow_dangerous_deserialization=True) |
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return vectorstore.as_retriever(search_kwargs={"k": 4}) |
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tool = create_retriever_tool( |
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load_vectorstore(site_radio), |
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"search_vaguard_website", |
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"Searches and returns documents regarding the Vanguard website across US and AUS locations. The websites provide investment related information to the user") |
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tools = [tool] |
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llm = ChatOpenAI(temperature=0, streaming=True, model="gpt-4o") |
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message = SystemMessage( |
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content=( |
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"You are a helpful chatbot who is tasked with answering questions about investments using informationn that has been scraped from a website to answer the users question accurately." |
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"Do not use any information not provided in the website context." |
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"Unless otherwise explicitly stated, it is probably fair to assume that questions are about the CFA program and materials. " |
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"If there is any ambiguity, you probably assume they are about that." |
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) |
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) |
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prompt = OpenAIFunctionsAgent.create_prompt( |
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system_message=message, |
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extra_prompt_messages=[MessagesPlaceholder(variable_name="history")], |
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) |
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agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt) |
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agent_executor = AgentExecutor( |
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agent=agent, |
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tools=tools, |
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verbose=True, |
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return_intermediate_steps=True, |
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) |
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memory = AgentTokenBufferMemory(llm=llm) |
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starter_message = "Ask me anything about information on the Vanguard US/AUS websites!" |
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if "messages" not in st.session_state or st.sidebar.button("Clear message history"): |
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st.session_state["messages"] = [AIMessage(content=starter_message)] |
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def send_feedback(run_id, score): |
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client.create_feedback(run_id, "user_score", score=score) |
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for msg in st.session_state.messages: |
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if isinstance(msg, AIMessage): |
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st.chat_message("assistant").write(msg.content) |
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elif isinstance(msg, HumanMessage): |
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st.chat_message("user").write(msg.content) |
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memory.chat_memory.add_message(msg) |
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if prompt := st.chat_input(placeholder=starter_message): |
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st.chat_message("user").write(prompt) |
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with st.chat_message("assistant"): |
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st_callback = StreamlitCallbackHandler(st.container()) |
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response = agent_executor( |
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{"input": prompt, "history": st.session_state.messages}, |
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callbacks=[st_callback], |
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include_run_info=True, |
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) |
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st.session_state.messages.append(AIMessage(content=response["output"])) |
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st.write(response["output"]) |
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memory.save_context({"input": prompt}, response) |
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st.session_state["messages"] = memory.buffer |
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run_id = response["__run"].run_id |
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col_blank, col_text, col1, col2 = st.columns([10, 2, 1, 1]) |
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with col_text: |
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st.text("Feedback:") |
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