import os

LC_chat_w_search.py

@@ -0,0 +1,44 @@
+# LC_chat_w_search.py
+
+
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain.tools import DuckDuckGoSearchRun
+
+with st.sidebar:
+    openai_api_key = st.text_input("OpenAI API Key", key="langchain_search_api_key_openai", type="password")
+    "[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)"
+    "[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](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", openai_api_key=openai_api_key, streaming=True)
+    search = DuckDuckGoSearchRun(name="Search")
+    search_agent = initialize_agent([search], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
+        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
+        st.session_state.messages.append({"role": "assistant", "content": response})
+        st.write(response)

LC_streaming.py

@@ -0,0 +1,36 @@
+# LC_Streaming.py
+
+
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import HumanMessage
+import streamlit as st
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text="", display_method='markdown'):
+        self.container = container
+        self.text = initial_text
+        self.display_method = display_method
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token + "/"
+        display_function = getattr(self.container, self.display_method, None)
+        if display_function is not None:
+            display_function(self.text)
+        else:
+            raise ValueError(f"Invalid display_method: {self.display_method}")
+
+query = st.text_input("input your query", value="Tell me a joke")
+ask_button = st.button("ask")
+
+st.markdown("### streaming box")
+chat_box = st.empty()
+stream_handler = StreamHandler(chat_box, display_method='write')
+chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[stream_handler])
+
+st.markdown("### together box")
+
+if query and ask_button:
+    response = chat([HumanMessage(content=query)])
+    llm_response = response.content
+    st.markdown(llm_response)

app.py

@@ -1,9 +1,11 @@
 import streamlit as st
 from langchain.llms import OpenAI
+import os
+
 
 st.title('🦜🔗 Dev App')
 
-openai_api_key = os.environ["OPENAI_API_KEY"]#st.sidebar.text_input('OpenAI API Key')
+openai_api_key = os.environ["OPENAI_API_KEY"] #st.sidebar.text_input('OpenAI API Key')
 
 def generate_response(input_text):
   llm = OpenAI(temperature=0.0, openai_api_key=openai_api_key)