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Move converstion statemachine to it's own module
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@heiko-braun
heiko-braun committed Jan 12, 2024
1 parent 1dba589 commit b827457
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Showing 2 changed files with 150 additions and 205 deletions.
143 changes: 143 additions & 0 deletions core/slack.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,143 @@
import os
from core.agent import agent_executor, agent_llm

from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
AgentTokenBufferMemory,
)

from abc import ABC, abstractmethod
from statemachine import State
from statemachine import StateMachine

# --

class StatusStrategy(ABC):
@abstractmethod
def print(self, message) -> str:
pass

@abstractmethod
def set_visible(self, is_visible):
pass

@abstractmethod
def set_tagline(self, tagline):
pass

class SlackStatus(StatusStrategy):

def __init__(self, slack_client, channel, thread_ts):
self.client = slack_client
self.channel = channel
self.thread_ts = thread_ts
super().__init__()

def print(self, message):
slack_response = self.client.chat_postMessage(
channel=self.channel,
thread_ts=self.thread_ts,
text=f"{message}"
)

def set_visible(self, is_visible):
pass

def set_tagline(self, tagline):
slack_response = self.client.chat_postMessage(
channel=self.channel,
thread_ts=self.thread_ts,
text=f"{tagline}"
)

class Conversation(StateMachine):
"Conversation state machine"

greeting = State(initial=True)
running = State()
lookup = State()
answered = State()
retired = State(final=True)

listening = greeting.to(answered)
inquire = answered.to(running)
resolved = running.to(answered)
retire = answered.to(retired)

request_docs = running.to(lookup)
docs_supplied = lookup.to(running)

def __init__(self, slack_client, channel, thread_ts):

self.client = slack_client
self.feedback = SlackStatus(slack_client=slack_client, channel=channel, thread_ts=thread_ts)

# the slack thread representing this conversation
self.channel = channel
self.thread_ts = thread_ts

# internal states
self.prompt_text = None
self.thread = None
self.run = None

# the main interface towards the LLM
self.agent = agent_executor

# keeps track of previous messages
self.memory = AgentTokenBufferMemory(llm=agent_llm)

# interim, runtime states
self.response_handle = None

super().__init__()

def on_enter_greeting(self):
# mimic the first LLM response to get things started
self.response_handle = {
"output": "How can I help you?"
}
self.feedback.print("New Thread: " + str(self.thread_ts))

# starting a thinking loop
def on_enter_running(self):

print("running ..")

self.feedback.set_tagline("Thinking ...")

# request chat completion
self.response_handle = self.agent(
{"input": self.prompt_text, "history": self.memory.buffer},
callbacks=[],
include_run_info=True,
)

self.memory.save_context({"input": self.prompt_text}, self.response_handle)

self.resolved()

# the assistant has resolved the question
def on_enter_answered(self):

print("answered ..")
response_content = self.response_handle["output"]

# question complete, show answer
slack_response = self.client.chat_postMessage(
channel=self.channel,
thread_ts=self.thread_ts,
text=f"{response_content}")


self.feedback.set_visible(False)

def on_exit_answered(self, text=None):
print("exit_answered ..")
self.prompt_text = text

# display status widget
self.feedback.set_visible(True)

def on_enter_retired(self):
self.feedback.set_tagline("This conversation is retired and cannot be activated anymore.")

212 changes: 7 additions & 205 deletions slack_bot.py
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Original file line number Diff line number Diff line change
@@ -1,21 +1,14 @@
import os
from openai import OpenAI
from slack_bolt.adapter.socket_mode import SocketModeHandler
from slack_sdk import WebClient
from slack_bolt import App, Ack, Respond
import os
from core.agent import agent_executor, agent_llm
import signal
import sys
import time

from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
AgentTokenBufferMemory,
)
from slack_bolt.adapter.socket_mode import SocketModeHandler
from slack_sdk import WebClient
from slack_bolt import App, Ack, Respond

from abc import ABC, abstractmethod
from statemachine import State
from statemachine import StateMachine
from core.agent import agent_executor, agent_llm
from core.slack import Conversation

# --

Expand All @@ -24,199 +17,10 @@
client = WebClient(os.environ['SLACK_BOT_TOKEN'])
socket = SocketModeHandler(app, os.environ['SLACK_APP_TOKEN'])

starter_message = "How can I help you?"

active_conversations = []

class StatusStrategy(ABC):
@abstractmethod
def print(self, message) -> str:
pass

@abstractmethod
def set_visible(self, is_visible):
pass

@abstractmethod
def set_tagline(self, tagline):
pass

class SlackStatus(StatusStrategy):

def __init__(self, slack_client, channel, thread_ts):
self.client = slack_client
self.channel = channel
self.thread_ts = thread_ts

super().__init__()


def print(self, message):
slack_response = client.chat_postMessage(
channel=self.channel,
thread_ts=self.thread_ts,
text=f"{message}"
)

def set_visible(self, is_visible):
pass

def set_tagline(self, tagline):
slack_response = client.chat_postMessage(
channel=self.channel,
thread_ts=self.thread_ts,
text=f"{tagline}"
)

class Conversation(StateMachine):
"Conversation state machine"

greeting = State(initial=True)
running = State()
lookup = State()
answered = State()
retired = State(final=True)

listening = greeting.to(answered)
inquire = answered.to(running)
resolved = running.to(answered)
retire = answered.to(retired)

request_docs = running.to(lookup)
docs_supplied = lookup.to(running)

def __init__(self, slack_client, channel, thread_ts):

self.client = slack_client
self.feedback = SlackStatus(slack_client=slack_client, channel=channel, thread_ts=thread_ts)

# the slack thread representing this conversation
self.channel = channel
self.thread_ts = thread_ts

# internal states
self.prompt_text = None
self.thread = None
self.run = None

# the main interface towards the LLM
self.agent = agent_executor

# keeps track of previous messages
self.memory = AgentTokenBufferMemory(llm=agent_llm)

# interim, runtime states
self.response_handle = None

super().__init__()

def on_enter_greeting(self):
# mimic the first LLM response to get things started
self.response_handle = {
"output": "How can I help you?"
}
self.feedback.print("New Thread: " + str(self.thread_ts))


def on_enter_lookup(self):

self.feedback.set_tagline("Working ...")
self.feedback.print("Lookup additional information ...")

self.lookups_total = self.lookups_total +1

# take call arguments and invoke lookup
args = get_call_arguments(self.run)
outputs=[]

for a in args:
entity_args = a["call_arguments"]["entities"]
self.feedback.print("Keywords: " + ' | '.join(entity_args) )

keywords = ' '.join(entity_args)

# we may end up with no keywrods at all
if(len(keywords)==0 or keywords.isspace()):
outputs.append(
{
"tool_call_id": a["call_id"],
"output": "'No additional information found.'"
}
)
continue


#doc = fetch_pdf_pages(entities=keywords, feedback=self.feedback)
docs = fetch_and_rerank(
entities=keywords,
collections=["rhaetor.github.io_2", "rhaetor.github.io_components_2"],
feedback=self.feedback
)

response_content = []
response_content = [str(d.page_content) for d in docs]

outputs.append(
{
"tool_call_id": a["call_id"],
"output": "'"+(' '.join(response_content))+"'"
}
)

# submit lookup results (aka tool outputs)
self.run = self.openai_client.beta.threads.runs.submit_tool_outputs(
thread_id=self.thread.id,
run_id=self.run.id,
tool_outputs=outputs
)

self.docs_supplied()
self.feedback.print("Processing new information ...")

# starting a thinking loop
def on_enter_running(self):

print("running ..")

self.feedback.set_tagline("Thinking ...")

# request chat completion
self.response_handle = self.agent(
{"input": self.prompt_text, "history": self.memory.buffer},
callbacks=[],
include_run_info=True,
)

memory.save_context({"input": self.prompt_text}, self.response_handle)

self.resolved()

# the assistant has resolved the question
def on_enter_answered(self):

print("answered ..")
response_content = self.response_handle["output"]

# question complete, show answer
slack_response = self.client.chat_postMessage(
channel=self.channel,
thread_ts=self.thread_ts,
text=f"{response_content}")


self.feedback.set_visible(False)

def on_exit_answered(self, text=None):
print("exit_answered ..")
self.prompt_text = text

# display status widget
self.feedback.set_visible(True)

def on_enter_retired(self):
self.feedback.set_tagline("This conversation is retired and cannot be activated anymore.")

def graceful_shutdown(signum, frame):
# makre sure conversation are retried when bot stops
def graceful_shutdown(signum, frame):
print("Shutdown bot ...")

# retire all active conversations
Expand All @@ -227,8 +31,6 @@ def graceful_shutdown(signum, frame):
socket.close()
sys.exit(0)

memory = AgentTokenBufferMemory(llm=agent_llm)

def find_conversation(thread_ts):

for ref in active_conversations:
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