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# %%
import os
import utils
utils.load_env()
os.environ['LANGCHAIN_TRACING_V2'] = "true"
# %%
from langchain_core.messages import HumanMessage
# for llm model
from langchain_openai import ChatOpenAI
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from tools import find_place_from_text, nearby_search
from typing import Dict, List, Tuple
from langchain.agents import (
AgentExecutor,
)
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools.convert_to_openai import format_tool_to_openai_function
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
# Bind the tools to the model
tools = [find_place_from_text, nearby_search] # Include both tools if needed
llm = ChatOpenAI(model="gpt-4o-mini")
llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
def _format_chat_history(chat_history: List[Tuple[str, str]]):
buffer = []
for human, ai in chat_history:
buffer.append(HumanMessage(content=human))
buffer.append(AIMessage(content=ai))
return buffer
meta = utils.load_agent_meta()[0]
prompt = ChatPromptTemplate.from_messages(
[
("system", meta['prompt']),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
agent = (
{
"input": lambda x: x["input"],
"chat_history": lambda x: _format_chat_history(x["chat_history"]),
"agent_scratchpad": lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
),
}
| prompt
| llm_with_tools
| OpenAIFunctionsAgentOutputParser()
)
class AgentInput(BaseModel):
input: str
chat_history: List[Tuple[str, str]] = Field(
..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}}
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
input_type=AgentInput
)
# %%
def submitUserMessage(user_input: str) -> str:
responce = agent_executor.invoke({"input": user_input, "chat_history": []})
return responce["output"]
|