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import os
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain_core.messages import HumanMessage, ToolMessage
from langgraph.graph import END
from .prompt import VERIFICATION_PROMPT, SYSTEM_PROMPT
load_dotenv()
MODEL = os.getenv("MODEL")
@tool
def invalid(field:str, value:str, counter=1):
"""
Call this tool if the user's response is not valid for one of the fields you are verifying.
"""
if counter >= 2:
return f"The user's response for {field} with value {value} is not valid. Politely end the conversation and after ask them to call the support number."
else:
return f"The user's response for {field} with value {value} is not valid. Indicate to the user that it does not match our records. Please ask the user one more time."
@tool
def completed(**kwargs):
"""
Call this tool when verification is complete and successful.
"""
return "The verification is complete. Moving on to medical questions."
tools_by_name = {
"invalid": invalid,
"completed": completed
}
def verification_route(state):
if not state["messages"]:
return END
last_message = state["messages"][-1]
if last_message.tool_calls:
return "verification_tool_node"
else:
return END
class VerificationAgent:
def __init__(self):
self.prompt = ChatPromptTemplate.from_messages([
("system", SYSTEM_PROMPT),
("system", VERIFICATION_PROMPT),
("system", "Fields:{fields}"),
("system", "Values:{values}"),
MessagesPlaceholder(variable_name="messages")
])
self.llm = ChatOpenAI(model=MODEL, temperature=0, streaming=True)
self.chain = self.prompt | self.llm.bind_tools([invalid, completed])
def __call__(self, state):
result = self.chain.invoke(state)
if not state.get("counter") or not result.tool_calls:
state["counter"] = 0
return {**state, "messages": [result]}
def process_tool(state):
last_message = state["messages"][-1]
state["counter"] = state.get("counter")+1
#print('LAST MESSAGE**********************', last_message)
messages = []
for tool_call in last_message.tool_calls:
if tool_call["name"] == "invalid":
#print('TOOL CALL**********************', tools_by_name[tool_call["name"]].invoke({**tool_call["args"], "counter": state["counter"]}))
message = tools_by_name[tool_call["name"]].invoke({**tool_call["args"], "counter": state["counter"]})
if state["counter"] >= 2:
state["counter"] = 0
messages.append(ToolMessage(name=tool_call["name"], tool_call_id=tool_call["id"], content=message))
else:
state["counter"] += 1
messages.append(ToolMessage(name=tool_call["name"], tool_call_id=tool_call["id"], content=f"The user's response for {tool_call['args']['field']} is not valid. Indicate to the user that it does not match our records. Please ask the user one more time."))
elif tool_call["name"] == "completed":
state["next"]+=1
print("COMPLETED!!!!!", state["next"])
messages.append(ToolMessage(name=tool_call["name"], tool_call_id=tool_call["id"], content="Verification complete. Prompt the user that we are moving on to medical questions. Do not end with a farwell. Mention that during the next stage the patient can ask any questions they have."))
else:
messages.append(ToolMessage(name=tool_call["name"], tool_call_id=tool_call["id"], content=""))
return {**state, "messages": messages}
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