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import streamlit as st
from langchain.agents import initialize_agent, AgentType
from langchain.callbacks import StreamlitCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from llm_helper import get_agent_chain, get_lc_oai_tools
with st.sidebar:
openai_api_key = st.secrets["OPENAI_API_KEY"]
"[Get an OpenAI API key](https://platform.openai.com/account/api-keys)"
"[View the source code](https://github.com/streamlit/llm-examples/blob/main/pages/2_Chat_with_search.py)"
"[](https://codespaces.new/streamlit/llm-examples?quickstart=1)"
st.title("🔎 LangChain - Chat with search")
"""
In this example, we're using `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an interactive Streamlit app.
Try more LangChain 🤝 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).
"""
if "messages" not in st.session_state:
st.session_state["messages"] = [
{"role": "assistant", "content": "Hi, I'm a chatbot who can search the web. How can I help you?"}
]
for msg in st.session_state.messages:
st.chat_message(msg["role"]).write(msg["content"])
if prompt := st.chat_input(placeholder="Who won the Women's U.S. Open in 2018?"):
st.session_state.messages.append({"role": "user", "content": prompt})
st.chat_message("user").write(prompt)
if not openai_api_key:
st.info("Please add your OpenAI API key to continue.")
st.stop()
llm = ChatOpenAI(model_name="gpt-3.5-turbo-1106", openai_api_key=openai_api_key, streaming=True)
lc_tools, _ = get_lc_oai_tools()
search_agent = initialize_agent(lc_tools, llm, agent=AgentType.OPENAI_FUNCTIONS, handle_parsing_errors=True, verbose=True)
agent_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant, use the search tool to answer the user's question and cite only the page number when you use information coming (like [p1]) from the source document. Always use the content from the source document to answer the user's question. If you need to compare multiple subjects, search them one by one."),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
search_agent.agent.prompt = agent_prompt
with st.chat_message("assistant"):
st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
response = search_agent.run(prompt, callbacks=[st_cb])
# search_agent = get_agent_chain(callbacks=[st_cb])
# response = search_agent.invoke({"input": prompt})
# response = response["output"]
st.session_state.messages.append({"role": "assistant", "content": response})
st.write(response)
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