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from langchain_anthropic import ChatAnthropic
from langchain_openai.chat_models import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.runnables import Runnable, RunnableConfig
from langgraph.checkpoint.sqlite import SqliteSaver
from erp_core.state_definer import State
import time
from datetime import datetime
import getpass
class Assistant:
"""
Assistant class to handle the conversation with the user.
"""
def __init__(self, runnable: Runnable):
self.runnable = runnable
def __call__(self, state: State, config: RunnableConfig):
while True:
result = self.runnable.invoke(state)
if not result.tool_calls and (
not result.content
or isinstance(result.content, list)
and not result.content[0].get("text")
):
messages = state["messages"] + [("user", "Respond with a real output.")]
state = {**state, "messages": messages}
messages = state["messages"] + [("user", "Respond with a real output.")]
state = {**state, "messages": messages}
else:
break
return {"messages": result}
class CompleteOrEscalate(BaseModel):
"""A tool to mark the current task as completed and/or to escalate control of the dialog to the main assistant,
who can re-route the dialog based on the user's needs."""
cancel: bool = True
reason: str
class Config:
schema_extra = {
"example": {
"cancel": True,
"reason": "User changed their mind about the current task.",
},
"example 2": {
"cancel": True,
"reason": "I have fully completed the task.",
},
"example 3": {
"cancel": False,
"reason": "I need to search the user's emails or calendar for more information.",
},
} |