Pydantic

from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
 

from langchain.output_parsers import PydanticOutputParser
from pydantic import BaseModel, Field, validator
from typing import List
 

model_name = 'text-davinci-003'
temperature = 0.0
model = OpenAI(model_name=model_name, temperature=temperature)
 

# Define your desired data structure.
class Joke(BaseModel):
    setup: str = Field(description="question to set up a joke")
    punchline: str = Field(description="answer to resolve the joke")
 
    # You can add custom validation logic easily with Pydantic.
    @validator('setup')
    def question_ends_with_question_mark(cls, field):
        if field[-1] != '?':
            raise ValueError("Badly formed question!")
        return field
 
# And a query intented to prompt a language model to populate the data structure.
joke_query = "Tell me a joke."
 
# Set up a parser + inject instructions into the prompt template.
parser = PydanticOutputParser(pydantic_object=Joke)
 
prompt = PromptTemplate(
    template="Answer the user query.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()}
)
 
_input = prompt.format_prompt(query=joke_query)
 
output = model(_input.to_string())
 
parser.parse(output)
 

Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')
 

# Here's another example, but with a compound typed field.
class Actor(BaseModel):
    name: str = Field(description="name of an actor")
    film_names: List[str] = Field(description="list of names of films they starred in")
 
actor_query = "Generate the filmography for a random actor."
 
parser = PydanticOutputParser(pydantic_object=Actor)
 
prompt = PromptTemplate(
    template="Answer the user query.\n{format_instructions}\n{query}\n",
    input_variables=["query"],
    partial_variables={"format_instructions": parser.get_format_instructions()}
)
 
_input = prompt.format_prompt(query=actor_query)
 
output = model(_input.to_string())
 
parser.parse(output)
 

Actor(name='Tom Hanks', film_names=['Forrest Gump', 'Saving Private Ryan', 'The Green Mile', 'Cast Away', 'Toy Story'])