from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# Define the response schemas
response_schemas = [
    ResponseSchema(name="recipe", description="the recipe for the dish requested by the user"),
    ResponseSchema(name="ingredients", description="list of ingredients required for the recipe, should be a detailed list"),
]

# Create the parser
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)

# Generate format instructions
format_instructions = output_parser.get_format_instructions()

# Create the prompt template
prompt = PromptTemplate(
    template="Provide the recipe for the dish requested.\n{format_instructions}\n{dish}",
    input_variables=["dish"],
    partial_variables={"format_instructions": format_instructions},
)

# Set up the LLM and chain
model = ChatOpenAI(model="gpt-4-0613", temperature=0)
chain = prompt | model | output_parser

# Generate recipe and ingredients
response = chain.invoke({"dish": "Spaghetti Bolognese"})

print(response)
# Output:
# {
#   "recipe": "To make Spaghetti Bolognese, cook minced beef with onions, garlic, tomatoes, and Italian herbs. Simmer until thickened and serve over cooked spaghetti.",
#   "ingredients": "Minced beef, onions, garlic, tomatoes, Italian herbs, spaghetti, olive oil, salt, pepper."
# }

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