from langchain.chat_models import init_chat_model
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.prompts import ChatPromptTemplate
from pydantic import BaseModel, Field

from env_utils import DEEPSEEK_API_KEY, DEEPSEEK_BASE_URL
from langchain.agents import create_agent,AgentState
from langchain.agents.middleware import SummarizationMiddleware, before_model, after_model
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.prebuilt import ToolRuntime
from langgraph.prebuilt import ToolRuntime
from langgraph.types import Command

from init_llm import deepseek_llm

# 大模型使用工具前，需要通过“bind_tools()”方法将工具与模型绑定，让模型能够识别可用工具，绑定后，模型会根据输入内容自动判断是否需要调用工具：

from langchain.tools import tool
from langchain_core.messages import HumanMessage

from init_llm import deepseek_llm


@tool
def get_weather(location: str) -> str:
    """获取指定位置的天气"""
    return f" {location}的天气是晴朗的。"

# 1.模型绑定工具
model_with_tools = deepseek_llm.bind_tools([get_weather])

messages = []
human_message = HumanMessage(content="北京的天气")
#human_message = HumanMessage(content="海水为什么是咸的？")
messages.append(human_message)
print("messages:" ,messages)
# 2. 模型生成调用工具请求
response = model_with_tools.invoke(messages)

print("response", response)

messages.append(response)
print("messages:" ,messages)

#3.开发者根据模型的响应，调用工具并获取结果
for tool_call in response.tool_calls:
    if tool_call['name'] == 'get_weather':
        # 调用工具并获取结果
        tool_result = get_weather.invoke(tool_call)
        messages.append(tool_result)

#4. 模型根据工具调用结果生成最终响应
print("messages:" ,messages)
final_response = model_with_tools.invoke(messages)
print("final_response", final_response)
print(final_response.content)

messages: [

HumanMessage(content='北京的天气', additional_kwargs={}, response_metadata={}), 

AIMessage(content='好的，我来查询一下北京的天气情况。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 53, 'prompt_tokens': 273, 'total_tokens': 326, 'completion_tokens_details': None, 'prompt_tokens_details': {'audio_tokens': None, 'cached_tokens': 256}, 'prompt_cache_hit_tokens': 256, 'prompt_cache_miss_tokens': 17}, 'model_provider': 'deepseek', 'model_name': 'deepseek-v4-flash', 'system_fingerprint': 'fp_8b330d02d0_prod0820_fp8_kvcache_20260402', 'id': '1d839e43-1d75-4cf8-9133-ce6565ca44f2', 'finish_reason': 'tool_calls', 'logprobs': None}, id='lc_run--019e2ee5-21dd-77f0-baf6-2bb25846662e-0', tool_calls=[{'name': 'get_weather', 'args': {'location': '北京'}, 'id': 'call_00_VEykwaI7WVp2hz6hkIj01205', 'type': 'tool_call'}], invalid_tool_calls=[], usage_metadata={'input_tokens': 273, 'output_tokens': 53, 'total_tokens': 326, 'input_token_details': {'cache_read': 256}, 'output_token_details': {}}), 

ToolMessage(content=' 北京的天气是晴朗的。', name='get_weather', tool_call_id='call_00_VEykwaI7WVp2hz6hkIj01205')]