first

.devcontainer/devcontainer.json

@@ -0,0 +1,46 @@
+// For format details, see https://aka.ms/devcontainer.json. For config options, see the README at:
+// https://github.com/microsoft/vscode-dev-containers/tree/v0.209.6/containers/python-3
+{
+    "image": "mcr.microsoft.com/devcontainers/python:0-3.11-bullseye",
+    "customizations": {
+        "codespaces": {
+          "openFiles": [
+            "README.md",
+            "streamlit_agent/mrkl_demo.py"
+          ]
+        },
+        "vscode": {
+          "settings": {},
+          "extensions": [
+            "ms-python.python",
+            "ms-python.vscode-pylance"
+          ]
+        }
+      },
+    // Use 'forwardPorts' to make a list of ports inside the container available locally.
+    "forwardPorts": [
+        8501
+    ],
+    // Use 'postCreateCommand' to run commands after the container is created.
+    // Install app dependencies.
+    "postCreateCommand": "pip3 install --user .",
+    // Use 'postAttachCommand' to run commands after a tool has attached to the container.
+    // Start the app.
+    "postAttachCommand": {
+        "server": "streamlit run streamlit_agent/mrkl_demo.py --server.enableCORS false --server.enableXsrfProtection false"
+    },
+    "portsAttributes": {
+        "8501": {
+            "label": "Application",
+            "onAutoForward": "openPreview"
+        }
+    },
+    // Comment out connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
+    "remoteUser": "vscode",
+    "features": {
+        // Optional features for development - increase container boot time!
+        // "ghcr.io/devcontainers-contrib/features/coverage-py:2": {},
+        // "git": "latest",
+        // "github-cli": "latest"
+    }
+}

.gitignore

@@ -0,0 +1,167 @@
+.vs/
+.vscode/
+.idea/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+docs/docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+notebooks/
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+.venvs
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# macOS display setting files
+.DS_Store
+
+# Wandb directory
+wandb/
+
+# asdf tool versions
+.tool-versions
+/.ruff_cache/
+
+*.pkl
+*.bin
+
+# integration test artifacts
+data_map*
+\[('_type', 'fake'), ('stop', None)]
+
+# Replit files
+*replit*
+
+node_modules
+docs/.yarn/
+docs/node_modules/
+docs/.docusaurus/
+docs/.cache-loader/
+docs/_dist
+docs/api_reference/_build
+docs/docs_skeleton/build
+docs/docs_skeleton/node_modules
+docs/docs_skeleton/yarn.lock

.pre-commit-config.yaml

@@ -0,0 +1,17 @@
+repos:
+  -   repo: https://github.com/pre-commit/pre-commit-hooks
+      rev: v4.4.0
+      hooks:
+        -   id: check-yaml
+        -   id: end-of-file-fixer
+        -   id: trailing-whitespace
+  -   repo: https://github.com/psf/black
+      rev: 23.3.0
+      hooks:
+        - id: black
+          # It is recommended to specify the latest version of Python
+          # supported by your project here, or alternatively use
+          # pre-commit's default_language_version, see
+          # https://pre-commit.com/#top_level-default_language_version
+          language_version: python3.11
+          args: ["--line-length", "100"]

LICENSE

@@ -0,0 +1,202 @@
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+

README.md

@@ -5,7 +5,7 @@ colorFrom: gray
 colorTo: pink
 sdk: streamlit
 sdk_version: 1.21.0
-app_file: app.py
+app_file: streamlit_agent\mrkl_demo.py
 pinned: false
 ---
 

README_.md

@@ -0,0 +1,40 @@
+# 🦜️🔗 LangChain 🤝 Streamlit agent examples
+
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/streamlit-agent?quickstart=1)
+
+This repository contains reference implementations of various LangChain agents as Streamlit apps including:
+
+- `basic_streaming.py`: How to do streaming with a simple app using `langchain.chat_models.ChatOpenAI`
+- `mrkl_demo.py`: An agent that replicates the [MRKL demo](https://python.langchain.com/docs/modules/agents/how_to/mrkl)
+- `minimal_agent.py`: A minimal agent with search (requires setting `OPENAI_API_KEY` env to run)
+- `search_and_chat.py`: A search-enabled chatbot that remembers chat history
+
+Apps feature LangChain 🤝 Streamlit integrations such as the
+[Callback integration](https://python.langchain.com/docs/modules/callbacks/integrations/streamlit).
+
+## Setup
+
+This project uses [Poetry](https://python-poetry.org/) for dependency management.
+
+```shell
+# Create Python environment
+$ poetry install
+
+# Install git pre-commit hooks
+$ poetry shell
+$ pre-commit install
+```
+
+## Running
+
+```shell
+# Run mrkl_demo.py or another app the same way
+$ streamlit run streamlit_agent/mrkl_demo.py
+```
+
+## Contributing
+
+We plan to add more agent examples over time - PRs welcome
+
+- [ ] Chat QA over docs
+- [ ] SQL agent

pyproject.toml

@@ -0,0 +1,25 @@
+[tool.poetry]
+name = "streamlit-agent"
+version = "0.1.0"
+description = ""
+authors = ["Tim Conkling <[email protected]>", "Joshua Carroll <[email protected]>"]
+license = "Apache 2.0"
+readme = "README.md"
+packages = [{include = "streamlit_agent"}]
+
+[tool.poetry.dependencies]
+python = "^3.11"
+langchain = ">=0.0.216"
+streamlit = ">=1.24.0"
+openai = "^0.27.8"
+duckduckgo-search = "^3.8.3"
+
+
+[tool.poetry.group.dev.dependencies]
+black = "^23.3.0"
+mypy = "^1.4.1"
+pre-commit = "^3.3.3"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

streamlit_agent/basic_streaming.py

@@ -0,0 +1,38 @@
+from langchain.callbacks.base import BaseCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain.schema import ChatMessage
+import streamlit as st
+
+
+class StreamHandler(BaseCallbackHandler):
+    def __init__(self, container, initial_text=""):
+        self.container = container
+        self.text = initial_text
+
+    def on_llm_new_token(self, token: str, **kwargs) -> None:
+        self.text += token
+        self.container.markdown(self.text)
+
+
+with st.sidebar:
+    openai_api_key = st.text_input("OpenAI API Key", type="password")
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [ChatMessage(role="assistant", content="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():
+    st.session_state.messages.append(ChatMessage(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()
+
+    with st.chat_message("assistant"):
+        stream_handler = StreamHandler(st.empty())
+        llm = ChatOpenAI(openai_api_key=openai_api_key, streaming=True, callbacks=[stream_handler])
+        response = llm(st.session_state.messages)
+        st.session_state.messages.append(ChatMessage(role="assistant", content=response.content))

streamlit_agent/callbacks/capturing_callback_handler.py

@@ -0,0 +1,159 @@
+"""Callback Handler captures all callbacks in a session for future offline playback."""
+
+from __future__ import annotations
+
+import pickle
+import time
+from typing import Any, TypedDict
+
+from langchain.callbacks.base import BaseCallbackHandler
+
+
+# This is intentionally not an enum so that we avoid serializing a
+# custom class with pickle.
+class CallbackType:
+    ON_LLM_START = "on_llm_start"
+    ON_LLM_NEW_TOKEN = "on_llm_new_token"
+    ON_LLM_END = "on_llm_end"
+    ON_LLM_ERROR = "on_llm_error"
+    ON_TOOL_START = "on_tool_start"
+    ON_TOOL_END = "on_tool_end"
+    ON_TOOL_ERROR = "on_tool_error"
+    ON_TEXT = "on_text"
+    ON_CHAIN_START = "on_chain_start"
+    ON_CHAIN_END = "on_chain_end"
+    ON_CHAIN_ERROR = "on_chain_error"
+    ON_AGENT_ACTION = "on_agent_action"
+    ON_AGENT_FINISH = "on_agent_finish"
+
+
+# We use TypedDict, rather than NamedTuple, so that we avoid serializing a
+# custom class with pickle. All of this class's members should be basic Python types.
+class CallbackRecord(TypedDict):
+    callback_type: str
+    args: tuple[Any, ...]
+    kwargs: dict[str, Any]
+    time_delta: float  # Number of seconds between this record and the previous one
+
+
+def load_records_from_file(path: str) -> list[CallbackRecord]:
+    """Load the list of CallbackRecords from a pickle file at the given path."""
+    with open(path, "rb") as file:
+        records = pickle.load(file)
+
+    if not isinstance(records, list):
+        raise RuntimeError(f"Bad CallbackRecord data in {path}")
+    return records
+
+
+def playback_callbacks(
+    handlers: list[BaseCallbackHandler],
+    records_or_filename: list[CallbackRecord] | str,
+    max_pause_time: float,
+) -> str:
+    if isinstance(records_or_filename, list):
+        records = records_or_filename
+    else:
+        records = load_records_from_file(records_or_filename)
+
+    for record in records:
+        pause_time = min(record["time_delta"], max_pause_time)
+        if pause_time > 0:
+            time.sleep(pause_time)
+
+        for handler in handlers:
+            if record["callback_type"] == CallbackType.ON_LLM_START:
+                handler.on_llm_start(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_LLM_NEW_TOKEN:
+                handler.on_llm_new_token(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_LLM_END:
+                handler.on_llm_end(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_LLM_ERROR:
+                handler.on_llm_error(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_TOOL_START:
+                handler.on_tool_start(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_TOOL_END:
+                handler.on_tool_end(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_TOOL_ERROR:
+                handler.on_tool_error(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_TEXT:
+                handler.on_text(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_CHAIN_START:
+                handler.on_chain_start(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_CHAIN_END:
+                handler.on_chain_end(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_CHAIN_ERROR:
+                handler.on_chain_error(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_AGENT_ACTION:
+                handler.on_agent_action(*record["args"], **record["kwargs"])
+            elif record["callback_type"] == CallbackType.ON_AGENT_FINISH:
+                handler.on_agent_finish(*record["args"], **record["kwargs"])
+
+    # Return the agent's result
+    for record in records:
+        if record["callback_type"] == CallbackType.ON_AGENT_FINISH:
+            return record["args"][0][0]["output"]
+
+    return "[Missing Agent Result]"
+
+
+class CapturingCallbackHandler(BaseCallbackHandler):
+    def __init__(self) -> None:
+        self._records: list[CallbackRecord] = []
+        self._last_time: float | None = None
+
+    def dump_records_to_file(self, path: str) -> None:
+        """Write the list of CallbackRecords to a pickle file at the given path."""
+        with open(path, "wb") as file:
+            pickle.dump(self._records, file)
+
+    def _append_record(
+        self, type: str, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> None:
+        time_now = time.time()
+        time_delta = time_now - self._last_time if self._last_time is not None else 0
+        self._last_time = time_now
+        self._records.append(
+            CallbackRecord(
+                callback_type=type, args=args, kwargs=kwargs, time_delta=time_delta
+            )
+        )
+
+    def on_llm_start(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_LLM_START, args, kwargs)
+
+    def on_llm_new_token(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_LLM_NEW_TOKEN, args, kwargs)
+
+    def on_llm_end(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_LLM_END, args, kwargs)
+
+    def on_llm_error(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_LLM_ERROR, args, kwargs)
+
+    def on_tool_start(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_TOOL_START, args, kwargs)
+
+    def on_tool_end(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_TOOL_END, args, kwargs)
+
+    def on_tool_error(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_TOOL_ERROR, args, kwargs)
+
+    def on_text(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_TEXT, args, kwargs)
+
+    def on_chain_start(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_CHAIN_START, args, kwargs)
+
+    def on_chain_end(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_CHAIN_END, args, kwargs)
+
+    def on_chain_error(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_CHAIN_ERROR, args, kwargs)
+
+    def on_agent_action(self, *args: Any, **kwargs: Any) -> Any:
+        self._append_record(CallbackType.ON_AGENT_ACTION, args, kwargs)
+
+    def on_agent_finish(self, *args: Any, **kwargs: Any) -> None:
+        self._append_record(CallbackType.ON_AGENT_FINISH, args, kwargs)

streamlit_agent/clear_results.py

@@ -0,0 +1,32 @@
+import streamlit as st
+
+
+# A hack to "clear" the previous result when submitting a new prompt. This avoids
+# the "previous run's text is grayed-out but visible during rerun" Streamlit behavior.
+class DirtyState:
+    NOT_DIRTY = "NOT_DIRTY"
+    DIRTY = "DIRTY"
+    UNHANDLED_SUBMIT = "UNHANDLED_SUBMIT"
+
+
+def get_dirty_state() -> str:
+    return st.session_state.get("dirty_state", DirtyState.NOT_DIRTY)
+
+
+def set_dirty_state(state: str) -> None:
+    st.session_state["dirty_state"] = state
+
+
+def with_clear_container(submit_clicked: bool) -> bool:
+    if get_dirty_state() == DirtyState.DIRTY:
+        if submit_clicked:
+            set_dirty_state(DirtyState.UNHANDLED_SUBMIT)
+            st.experimental_rerun()
+        else:
+            set_dirty_state(DirtyState.NOT_DIRTY)
+
+    if submit_clicked or get_dirty_state() == DirtyState.UNHANDLED_SUBMIT:
+        set_dirty_state(DirtyState.DIRTY)
+        return True
+
+    return False

streamlit_agent/minimal_agent.py

@@ -0,0 +1,17 @@
+from langchain.llms import OpenAI
+from langchain.agents import AgentType, initialize_agent, load_tools
+from langchain.callbacks import StreamlitCallbackHandler
+import streamlit as st
+
+llm = OpenAI(temperature=0, streaming=True)
+tools = load_tools(["ddg-search"])
+agent = initialize_agent(
+    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+)
+
+if prompt := st.chat_input():
+    st.chat_message("user").write(prompt)
+    with st.chat_message("assistant"):
+        st_callback = StreamlitCallbackHandler(st.container())
+        response = agent.run(prompt, callbacks=[st_callback])
+        st.write(response)

streamlit_agent/mrkl_demo.py

@@ -0,0 +1,102 @@
+from pathlib import Path
+
+import streamlit as st
+
+from langchain import SQLDatabase
+from langchain.agents import AgentType
+from langchain.agents import initialize_agent, Tool
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chains import LLMMathChain, SQLDatabaseChain
+from langchain.llms import OpenAI
+from langchain.utilities import DuckDuckGoSearchAPIWrapper
+
+from streamlit_agent.callbacks.capturing_callback_handler import playback_callbacks
+from streamlit_agent.clear_results import with_clear_container
+
+DB_PATH = (Path(__file__).parent / "Chinook.db").absolute()
+
+SAVED_SESSIONS = {
+    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?": "leo.pickle",
+    "What is the full name of the artist who recently released an album called "
+    "'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs "
+    "are in the FooBar database?": "alanis.pickle",
+}
+
+st.set_page_config(
+    page_title="MRKL", page_icon="🦜", layout="wide", initial_sidebar_state="collapsed"
+)
+
+"# 🦜🔗 MRKL"
+
+# Setup credentials in Streamlit
+user_openai_api_key = st.sidebar.text_input(
+    "OpenAI API Key", type="password", help="Set this to run your own custom questions."
+)
+
+if user_openai_api_key:
+    openai_api_key = user_openai_api_key
+    enable_custom = True
+else:
+    openai_api_key = "not_supplied"
+    enable_custom = False
+
+# Tools setup
+llm = OpenAI(temperature=0, openai_api_key=openai_api_key, streaming=True)
+search = DuckDuckGoSearchAPIWrapper()
+llm_math_chain = LLMMathChain.from_llm(llm)
+db = SQLDatabase.from_uri(f"sqlite:///{DB_PATH}")
+db_chain = SQLDatabaseChain.from_llm(llm, db)
+tools = [
+    Tool(
+        name="Search",
+        func=search.run,
+        description="useful for when you need to answer questions about current events. You should ask targeted questions",
+    ),
+    Tool(
+        name="Calculator",
+        func=llm_math_chain.run,
+        description="useful for when you need to answer questions about math",
+    ),
+    Tool(
+        name="FooBar DB",
+        func=db_chain.run,
+        description="useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context",
+    ),
+]
+
+# Initialize agent
+mrkl = initialize_agent(
+    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
+)
+
+with st.form(key="form"):
+    if not enable_custom:
+        "Ask one of the sample questions, or enter your API Key in the sidebar to ask your own custom questions."
+    prefilled = st.selectbox("Sample questions", sorted(SAVED_SESSIONS.keys())) or ""
+    mrkl_input = ""
+
+    if enable_custom:
+        user_input = st.text_input("Or, ask your own question")
+    if not mrkl_input:
+        user_input = prefilled
+    submit_clicked = st.form_submit_button("Submit Question")
+
+output_container = st.empty()
+if with_clear_container(submit_clicked):
+    output_container = output_container.container()
+    output_container.chat_message("user").write(user_input)
+
+    answer_container = output_container.chat_message("assistant", avatar="🦜")
+    st_callback = StreamlitCallbackHandler(answer_container)
+
+    # If we've saved this question, play it back instead of actually running LangChain
+    # (so that we don't exhaust our API calls unnecessarily)
+    if user_input in SAVED_SESSIONS:
+        session_name = SAVED_SESSIONS[user_input]
+        session_path = Path(__file__).parent / "runs" / session_name
+        print(f"Playing saved session: {session_path}")
+        answer = playback_callbacks([st_callback], str(session_path), max_pause_time=2)
+    else:
+        answer = mrkl.run(user_input, callbacks=[st_callback])
+
+    answer_container.write(answer)

streamlit_agent/runs/alanis.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a9e28abe936e327d1dbebcab8416f7d46a875ce9a0daf06073ecfd0b1c09414
+size 60689

streamlit_agent/runs/leo.pickle

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f2c0ae12968c6715c16d9af53cad58df80816406b0822fe19efc23748bf6ace
+size 28543

streamlit_agent/search_and_chat.py

@@ -0,0 +1,36 @@
+from langchain.agents import initialize_agent, AgentType
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chat_models import ChatOpenAI
+from langchain.tools import DuckDuckGoSearchRun
+import streamlit as st
+
+st.set_page_config(page_title="LangChain: Chat with search", page_icon="🦜")
+st.title("🦜 LangChain: Chat with search")
+
+openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [{"role": "assistant", "content": "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_agent = initialize_agent(
+        tools=[DuckDuckGoSearchRun(name="Search")],
+        llm=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)