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docs: update StreamlitCallbackHandler example (#16970)

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- **Description:** docs: update StreamlitCallbackHandler example.
  - **Issue:** None
  - **Dependencies:** None

I have updated the example for StreamlitCallbackHandler in the
documentation bellow.
https://python.langchain.com/docs/integrations/callbacks/streamlit

Previously, the example used `initialize_agent`, which has been
deprecated, so I've updated it to use `create_react_agent` instead. Many
langchain users are likely searching examples of combining
`create_react_agent` or `openai_tools_agent_chain` with
StreamlitCallbackHandler. I'm sure this update will be really helpful
for them!

Unfortunately, writing unit tests for this example is difficult, so I
have not written any tests. I have run this code in a standalone Python
script file and ensured it runs correctly.
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os1ma 2024-02-06 11:20:59 +09:00 committed by GitHub
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docs/docs/integrations/callbacks/streamlit.md
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@ -28,7 +28,7 @@ You can run `streamlit hello` to load a sample app and validate your install suc
To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output. To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output.
```python ```python
from langchain.callbacks import StreamlitCallbackHandler from langchain_community.callbacks import StreamlitCallbackHandler
import streamlit as st import streamlit as st
st_callback = StreamlitCallbackHandler(st.container()) st_callback = StreamlitCallbackHandler(st.container())
@ -44,23 +44,26 @@ agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `a
thoughts and actions live in your app. thoughts and actions live in your app.
```python ```python
from langchain_openai import OpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_community.callbacks import StreamlitCallbackHandler
import streamlit as st import streamlit as st
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent, load_tools
from langchain_community.callbacks import StreamlitCallbackHandler
from langchain_openai import OpenAI
llm = OpenAI(temperature=0, streaming=True) llm = OpenAI(temperature=0, streaming=True)
tools = load_tools(["ddg-search"]) tools = load_tools(["ddg-search"])
agent = initialize_agent( prompt = hub.pull("hwchase17/react")
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True agent = create_react_agent(llm, tools, prompt)
) agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
if prompt := st.chat_input(): if prompt := st.chat_input():
st.chat_message("user").write(prompt) st.chat_message("user").write(prompt)
with st.chat_message("assistant"): with st.chat_message("assistant"):
st_callback = StreamlitCallbackHandler(st.container()) st_callback = StreamlitCallbackHandler(st.container())
response = agent.run(prompt, callbacks=[st_callback]) response = agent_executor.invoke(
st.write(response) {"input": prompt}, {"callbacks": [st_callback]}
)
st.write(response["output"])
``` ```
**Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully. **Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully.