from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Define our WeatherForecast model
class WeatherForecast(BaseModel):
    temperature: float = Field(description="The temperature in Celsius")
    condition: str = Field(description="The weather condition (e.g., sunny, rainy, cloudy)")
    humidity: int = Field(description="The humidity percentage")
    wind_speed: float = Field(description="The wind speed in km/h")

# Set up the LLM
model = ChatOpenAI(model="gpt-4", temperature=0)

# Create the prompt template
prompt = ChatPromptTemplate.from_template(
    "Given the context below, provide a weather forecast for {city} tomorrow, respond in JSON with `temperature`, `condition`, `humidity`, and `wind_speed` keys\n\n{context}"
)

# Apply structured output to the LLM
structured_llm = model.with_structured_output(WeatherForecast, method="json_mode")

# Chain the prompt and structured LLM
weather_chain = prompt | structured_llm

# Generate a weather forecast
result = weather_chain.invoke({"city": "New York", "context": "The weather in New York will be sunny with a chance of rain."})

print(result)
# Output: WeatherForecast(temperature=22.5, condition='Partly cloudy', humidity=65, wind_speed=10.2)

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