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chainlit.py
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"""
Callbacks for Chainlit integration.
"""
import json
import logging
import textwrap
from typing import Any, Callable, Dict, List, Literal, Optional, no_type_check
from langroid.exceptions import LangroidImportError
from langroid.pydantic_v1 import BaseSettings
try:
import chainlit as cl
except ImportError:
raise LangroidImportError("chainlit", "chainlit")
from chainlit import run_sync
from chainlit.config import config
from chainlit.logger import logger
import langroid as lr
import langroid.language_models as lm
from langroid.utils.configuration import settings
from langroid.utils.constants import NO_ANSWER
# Attempt to reconfigure the root logger to your desired settings
log_level = logging.INFO if settings.debug else logging.WARNING
logger.setLevel(log_level)
logging.basicConfig(level=log_level)
logging.getLogger().setLevel(log_level)
USER_TIMEOUT = 60_000
SYSTEM = "System 🖥️"
LLM = "LLM 🧠"
AGENT = "Agent <>"
YOU = "You 😃"
ERROR = "Error 🚫"
@no_type_check
async def ask_helper(func, **kwargs):
res = await func(**kwargs).send()
while not res:
res = await func(**kwargs).send()
return res
@no_type_check
async def setup_llm() -> None:
"""From the session `llm_settings`, create new LLMConfig and LLM objects,
save them in session state."""
llm_settings = cl.user_session.get("llm_settings", {})
model = llm_settings.get("chat_model")
context_length = llm_settings.get("context_length", 16_000)
temperature = llm_settings.get("temperature", 0.2)
timeout = llm_settings.get("timeout", 90)
logger.info(f"Using model: {model}")
llm_config = lm.OpenAIGPTConfig(
chat_model=model or lm.OpenAIChatModel.GPT4o,
# or, other possibilities for example:
# "litellm/ollama_chat/mistral"
# "litellm/ollama_chat/mistral:7b-instruct-v0.2-q8_0"
# "litellm/ollama/llama2"
# "local/localhost:8000/v1"
# "local/localhost:8000"
chat_context_length=context_length, # adjust based on model
temperature=temperature,
timeout=timeout,
)
llm = lm.OpenAIGPT(llm_config)
cl.user_session.set("llm_config", llm_config)
cl.user_session.set("llm", llm)
@no_type_check
async def update_llm(new_settings: Dict[str, Any]) -> None:
"""Update LLMConfig and LLM from settings, and save in session state."""
cl.user_session.set("llm_settings", new_settings)
await inform_llm_settings()
await setup_llm()
async def make_llm_settings_widgets(
config: lm.OpenAIGPTConfig | None = None,
) -> None:
config = config or lm.OpenAIGPTConfig()
await cl.ChatSettings(
[
cl.input_widget.TextInput(
id="chat_model",
label="Model Name (Default GPT-4o)",
initial="",
placeholder="E.g. ollama/mistral or " "local/localhost:8000/v1",
),
cl.input_widget.NumberInput(
id="context_length",
label="Chat Context Length",
initial=config.chat_context_length,
placeholder="E.g. 16000",
),
cl.input_widget.Slider(
id="temperature",
label="LLM temperature",
min=0.0,
max=1.0,
step=0.1,
initial=config.temperature,
tooltip="Adjust based on model",
),
cl.input_widget.Slider(
id="timeout",
label="Timeout (seconds)",
min=10,
max=200,
step=10,
initial=config.timeout,
tooltip="Timeout for LLM response, in seconds.",
),
]
).send() # type: ignore
@no_type_check
async def inform_llm_settings() -> None:
llm_settings: Dict[str, Any] = cl.user_session.get("llm_settings", {})
settings_dict = dict(
model=llm_settings.get("chat_model"),
context_length=llm_settings.get("context_length"),
temperature=llm_settings.get("temperature"),
timeout=llm_settings.get("timeout"),
)
await cl.Message(
author=SYSTEM,
content="LLM settings updated",
elements=[
cl.Text(
name="settings",
display="side",
content=json.dumps(settings_dict, indent=4),
language="json",
)
],
).send()
async def add_instructions(
title: str = "Instructions",
content: str = "Enter your question/response in the dialog box below.",
display: Literal["side", "inline", "page"] = "inline",
) -> None:
await cl.Message(
author="",
content=title if display == "side" else "",
elements=[
cl.Text(
name=title,
content=content,
display=display,
)
],
).send()
async def add_image(
path: str,
name: str,
display: Literal["side", "inline", "page"] = "inline",
) -> None:
await cl.Message(
author="",
content=name if display == "side" else "",
elements=[
cl.Image(
name=name,
path=path,
display=display,
)
],
).send()
async def get_text_files(
message: cl.Message,
extensions: List[str] = [".txt", ".pdf", ".doc", ".docx"],
) -> Dict[str, str]:
"""Get dict (file_name -> file_path) from files uploaded in chat msg"""
files = [file for file in message.elements if file.path.endswith(tuple(extensions))]
return {file.name: file.path for file in files}
def wrap_text_preserving_structure(text: str, width: int = 90) -> str:
"""Wrap text preserving paragraph breaks. Typically used to
format an agent_response output, which may have long lines
with no newlines or paragraph breaks."""
paragraphs = text.split("\n\n") # Split the text into paragraphs
wrapped_text = []
for para in paragraphs:
if para.strip(): # If the paragraph is not just whitespace
# Wrap this paragraph and add it to the result
wrapped_paragraph = textwrap.fill(para, width=width)
wrapped_text.append(wrapped_paragraph)
else:
# Preserve paragraph breaks
wrapped_text.append("")
return "\n\n".join(wrapped_text)
class ChainlitCallbackConfig(BaseSettings):
user_has_agent_name: bool = True # show agent name in front of "YOU" ?
show_subtask_response: bool = True # show sub-task response as a step?
class ChainlitAgentCallbacks:
"""Inject Chainlit callbacks into a Langroid Agent"""
last_step: Optional[cl.Step] = None # used to display sub-steps under this
curr_step: Optional[cl.Step] = None # used to update an initiated step
stream: Optional[cl.Step] = None # pushed into openai_gpt.py to stream tokens
parent_agent: Optional[lr.Agent] = None # used to get parent id, for step nesting
def __init__(
self,
agent: lr.Agent,
msg: cl.Message = None,
config: ChainlitCallbackConfig = ChainlitCallbackConfig(),
):
"""Add callbacks to the agent, and save the initial message,
so we can alter the display of the first user message.
"""
agent.callbacks.start_llm_stream = self.start_llm_stream
agent.callbacks.cancel_llm_stream = self.cancel_llm_stream
agent.callbacks.finish_llm_stream = self.finish_llm_stream
agent.callbacks.show_llm_response = self.show_llm_response
agent.callbacks.show_agent_response = self.show_agent_response
agent.callbacks.get_user_response = self.get_user_response
agent.callbacks.get_last_step = self.get_last_step
agent.callbacks.set_parent_agent = self.set_parent_agent
agent.callbacks.show_error_message = self.show_error_message
agent.callbacks.show_start_response = self.show_start_response
self.config = config
self.agent: lr.Agent = agent
if msg is not None:
self.show_first_user_message(msg)
def _get_parent_id(self) -> str | None:
"""Get step id under which we need to nest the current step:
This should be the parent Agent's last_step.
"""
if self.parent_agent is None:
logger.info(f"No parent agent found for {self.agent.config.name}")
return None
logger.info(
f"Parent agent found for {self.agent.config.name} = "
f"{self.parent_agent.config.name}"
)
last_step = self.parent_agent.callbacks.get_last_step()
if last_step is None:
logger.info(f"No last step found for {self.parent_agent.config.name}")
return None
logger.info(
f"Last step found for {self.parent_agent.config.name} = {last_step.id}"
)
return last_step.id # type: ignore
def set_parent_agent(self, parent: lr.Agent) -> None:
self.parent_agent = parent
def get_last_step(self) -> Optional[cl.Step]:
return self.last_step
def start_llm_stream(self) -> Callable[[str], None]:
"""Returns a streaming fn that can be passed to the LLM class"""
self.stream = cl.Step(
id=self.curr_step.id if self.curr_step is not None else None,
name=self._entity_name("llm"),
type="llm",
parent_id=self._get_parent_id(),
)
self.last_step = self.stream
self.curr_step = None
logger.info(
f"""
Starting LLM stream for {self.agent.config.name}
id = {self.stream.id}
under parent {self._get_parent_id()}
"""
)
run_sync(self.stream.send()) # type: ignore
def stream_token(t: str) -> None:
if self.stream is None:
raise ValueError("Stream not initialized")
run_sync(self.stream.stream_token(t))
return stream_token
def cancel_llm_stream(self) -> None:
"""Called when cached response found."""
self.last_step = None
if self.stream is not None:
run_sync(self.stream.remove()) # type: ignore
def finish_llm_stream(self, content: str, is_tool: bool = False) -> None:
"""Update the stream, and display entire response in the right language."""
if self.agent.llm is None or self.stream is None:
raise ValueError("LLM or stream not initialized")
if content == "":
run_sync(self.stream.remove()) # type: ignore
else:
run_sync(self.stream.update()) # type: ignore
stream_id = self.stream.id if content else None
step = cl.Step(
id=stream_id,
name=self._entity_name("llm", tool=is_tool),
type="llm",
parent_id=self._get_parent_id(),
language="json" if is_tool else None,
)
step.output = textwrap.dedent(content) or NO_ANSWER
logger.info(
f"""
Finish STREAM LLM response for {self.agent.config.name}
id = {step.id}
under parent {self._get_parent_id()}
"""
)
run_sync(step.update()) # type: ignore
def show_llm_response(
self,
content: str,
is_tool: bool = False,
cached: bool = False,
language: str | None = None,
) -> None:
"""Show non-streaming LLM response."""
step = cl.Step(
id=self.curr_step.id if self.curr_step is not None else None,
name=self._entity_name("llm", tool=is_tool, cached=cached),
type="llm",
parent_id=self._get_parent_id(),
language=language or ("json" if is_tool else None),
)
self.last_step = step
self.curr_step = None
step.output = textwrap.dedent(content) or NO_ANSWER
logger.info(
f"""
Showing NON-STREAM LLM response for {self.agent.config.name}
id = {step.id}
under parent {self._get_parent_id()}
"""
)
run_sync(step.send()) # type: ignore
def show_error_message(self, error: str) -> None:
"""Show error message as a step."""
step = cl.Step(
name=self.agent.config.name + f"({ERROR})",
type="run",
parent_id=self._get_parent_id(),
language="text",
)
self.last_step = step
step.output = error
run_sync(step.send())
def show_agent_response(self, content: str, language="text") -> None:
"""Show message from agent (typically tool handler).
Agent response can be considered as a "step"
between LLM response and user response
"""
step = cl.Step(
id=self.curr_step.id if self.curr_step is not None else None,
name=self._entity_name("agent"),
type="tool",
parent_id=self._get_parent_id(),
language=language,
)
if language == "text":
content = wrap_text_preserving_structure(content, width=90)
self.last_step = step
self.curr_step = None
step.output = content
logger.info(
f"""
Showing AGENT response for {self.agent.config.name}
id = {step.id}
under parent {self._get_parent_id()}
"""
)
run_sync(step.send()) # type: ignore
def show_start_response(self, entity: str) -> None:
"""When there's a potentially long-running process, start a step,
so that the UI displays a spinner while the process is running."""
if self.curr_step is not None:
run_sync(self.curr_step.remove()) # type: ignore
step = cl.Step(
name=self._entity_name(entity),
type="run",
parent_id=self._get_parent_id(),
language="text",
)
step.output = ""
self.last_step = step
self.curr_step = step
logger.info(
f"""
Showing START response for {self.agent.config.name} ({entity})
id = {step.id}
under parent {self._get_parent_id()}
"""
)
run_sync(step.send()) # type: ignore
def _entity_name(
self, entity: str, tool: bool = False, cached: bool = False
) -> str:
"""Construct name of entity to display as Author of a step"""
tool_indicator = " => 🛠️" if tool else ""
cached = "(cached)" if cached else ""
match entity:
case "llm":
model = self.agent.config.llm.chat_model
return (
self.agent.config.name + f"({LLM} {model} {tool_indicator}){cached}"
)
case "agent":
return self.agent.config.name + f"({AGENT})"
case "user":
if self.config.user_has_agent_name:
return self.agent.config.name + f"({YOU})"
else:
return YOU
case _:
return self.agent.config.name + f"({entity})"
def _get_user_response_buttons(self, prompt: str) -> str:
"""Not used. Save for future reference"""
res = run_sync(
ask_helper(
cl.AskActionMessage,
content="Continue, exit or say something?",
actions=[
cl.Action(
name="continue",
value="continue",
label="✅ Continue",
),
cl.Action(
name="feedback",
value="feedback",
label="💬 Say something",
),
cl.Action(name="exit", value="exit", label="🔚 Exit Conversation"),
],
)
)
if res.get("value") == "continue":
return ""
if res.get("value") == "exit":
return "x"
if res.get("value") == "feedback":
return self.get_user_response(prompt)
return "" # process the "feedback" case here
def get_user_response(self, prompt: str) -> str:
"""Ask for user response, wait for it, and return it,
as a cl.Step rather than as a cl.Message so we can nest it
under the parent step.
"""
return run_sync(self.ask_user_step(prompt=prompt, suppress_values=["c"]))
def show_user_response(self, message: str) -> None:
"""Show user response as a step."""
step = cl.Step(
id=cl.context.current_step.id,
name=self._entity_name("user"),
type="run",
parent_id=self._get_parent_id(),
)
step.output = message
logger.info(
f"""
Showing USER response for {self.agent.config.name}
id = {step.id}
under parent {self._get_parent_id()}
"""
)
run_sync(step.send())
def show_first_user_message(self, msg: cl.Message):
"""Show first user message as a step."""
step = cl.Step(
id=msg.id,
name=self._entity_name("user"),
type="run",
parent_id=self._get_parent_id(),
)
self.last_step = step
step.output = msg.content
run_sync(step.update())
async def ask_user_step(
self,
prompt: str,
timeout: int = USER_TIMEOUT,
suppress_values: List[str] = ["c"],
) -> str:
"""
Ask user for input, as a step nested under parent_id.
Rather than rely entirely on AskUserMessage (which doesn't let us
nest the question + answer under a step), we instead create fake
steps for the question and answer, and only rely on AskUserMessage
with an empty prompt to await user response.
Args:
prompt (str): Prompt to display to user
timeout (int): Timeout in seconds
suppress_values (List[str]): List of values to suppress from display
(e.g. "c" for continue)
Returns:
str: User response
"""
# save hide_cot status to restore later
# (We should probably use a ctx mgr for this)
hide_cot = config.ui.hide_cot
# force hide_cot to False so that the user question + response is visible
config.ui.hide_cot = False
if prompt != "":
# Create a question step to ask user
question_step = cl.Step(
name=f"{self.agent.config.name} (AskUser ❓)",
type="run",
parent_id=self._get_parent_id(),
)
question_step.output = prompt
await question_step.send() # type: ignore
# Use AskUserMessage to await user response,
# but with an empty prompt so the question is not visible,
# but still pauses for user input in the input box.
res = await cl.AskUserMessage(
content="",
timeout=timeout,
).send()
if res is None:
run_sync(
cl.Message(
content=f"Timed out after {USER_TIMEOUT} seconds. Exiting."
).send()
)
return "x"
# The above will try to display user response in res
# but we create fake step with same id as res and
# erase it using empty output so it's not displayed
step = cl.Step(
id=res["id"],
name="TempUserResponse",
type="run",
parent_id=self._get_parent_id(),
)
step.output = ""
await step.update() # type: ignore
# Finally, reproduce the user response at right nesting level
if res["output"] in suppress_values:
config.ui.hide_cot = hide_cot # restore original value
return ""
step = cl.Step(
name=self._entity_name(entity="user"),
type="run",
parent_id=self._get_parent_id(),
)
step.output = res["output"]
await step.send() # type: ignore
config.ui.hide_cot = hide_cot # restore original value
return res["output"]
class ChainlitTaskCallbacks(ChainlitAgentCallbacks):
"""
Recursively inject ChainlitAgentCallbacks into a Langroid Task's agent and
agents of sub-tasks.
"""
def __init__(
self,
task: lr.Task,
msg: cl.Message = None,
config: ChainlitCallbackConfig = ChainlitCallbackConfig(),
):
"""Inject callbacks recursively, ensuring msg is passed to the
top-level agent"""
super().__init__(task.agent, msg, config)
self._inject_callbacks(task)
self.task = task
if config.show_subtask_response:
self.task.callbacks.show_subtask_response = self.show_subtask_response
@classmethod
def _inject_callbacks(
cls, task: lr.Task, config: ChainlitCallbackConfig = ChainlitCallbackConfig()
) -> None:
# recursively apply ChainlitAgentCallbacks to agents of sub-tasks
for t in task.sub_tasks:
cls(t, config=config)
# ChainlitTaskCallbacks(t, config=config)
def show_subtask_response(
self, task: lr.Task, content: str, is_tool: bool = False
) -> None:
"""Show sub-task response as a step, nested at the right level."""
# The step should nest under the calling agent's last step
step = cl.Step(
name=self.task.agent.config.name + f"( ⏎ From {task.agent.config.name})",
type="run",
parent_id=self._get_parent_id(),
language="json" if is_tool else None,
)
step.output = content or NO_ANSWER
self.last_step = step
run_sync(step.send())