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Generate Structured Data

Guardrails provides several interfaces to help llms respond in valid JSON which can then be validated using Guardrails validators. In this cookbook, we'll demonstrate how each interface is used with examples.

Prerequisites​

First, create a Pydantic model and guard for the structured data. The model should describe the data structure to be returned. Pydantic Fields support Guardrails validators and are initialized at the same time as validators.

We've included here a sample prompt and data asking an LLM to produce some structured data.

Learn more about pydantic here.

from guardrails import Guard
from guardrails.hub import RegexMatch
from pydantic import BaseModel, Field
from typing import List

NAME_REGEX = "^[A-Z][a-z]+\s[A-Z][a-z]+$"

class Delivery(BaseModel):
    custome_name: str=Field(validators=[RegexMatch(regex=NAME_REGEX)], description="customer name")
    pickup_time: str=Field(description="date and time of pickup")
    pickup_location: str=Field(description="address of pickup")
    dropoff_time: str=Field(description="date and time of dropoff")
    dropoff_location: str=Field(description="address of dropoff")
    price: str = Field(description="price of delivery with currency symbol included")

class Schedule(BaseModel):
    deliveries: List[Delivery]

guard = Guard.for_pydantic(Schedule)
chat_history="""
nelson and murdock: i need a pickup 797 9th Avenue, manila envelope, June 3 10:00am with dropoff 10:30am Courthouse, 61 Center Street C/O frank james
operator: quote - $23.00
neslon and murdock: perfect, we accept the quote
operator: 797 9th ave, 10:00am pickup comfirmed
abc flowers: i need a pickup of a flowers from abc flowers at 21 3rd street at 11:00am on june 2 with a dropoff at 75th Ave at 5:30pm same day
operator: 21 3rd street flowers quote - $14.50
abc flowers: accepted
polk and wardell: i need a pickup of a bagels from Bakers Co at 331 5th street at 11:00am on june 3 with a dropoff at 75th Ave at 5:30pm same day
operator: 331 5th street bagels quote - $34.50
polk and wardell: accepted
"""

prompt = """
From the chat exchanges below extract a schedule of deliveries.
Chats:
${chat_history}
"""

messages = [{
  "role": "system",
  "content": "You are a helpful assistant."
}, {
  "role": "user",
  "content": prompt
}]

Options​

Function/tool calling structured response formatting​

For models that support openai tool/function calling(gpt-4o, gpt-4-turbo, or gpt-3.5-turbo).

tools = [] # an open ai compatible list of tools

response = guard(
    model="gpt-4o",
    messages=messages,
    prompt_params={"chat_history": chat_history},
    tools=guard.json_function_calling_tool(tools),
    tool_choice="required",
)

Prompt Updates​

For models that support JSON through prompt engineering and hinting (most models).

prompt+="""

${gr.complete_json_suffix_v3}
"""
response = guard(
    model="gpt-4o",
    messages=[messages[0],{
      "role": "user",
      "content": prompt
    }],
    prompt_params={"chat_history": chat_history},
)

Constrained decoding structured response formatting​

For Hugging Face models structured JSON output maybe returned utilizing constrained decoding. Constrained decoding is a technique that allows you to guide the model to generate a specific type of output, a little bit like JSON ad-libs. Learn more about constrained decoding here.

g = Guard.for_pydantic(NewFriends, output_formatter="jsonformer")

# JSONFormer is only compatible with HF Pipelines and HF Models:
from transformers import pipeline
pipe = pipeline("text-generation", "TinyLlama/TinyLlama-1.1B-Chat-v1.0")

# Inference is straightforward:
out = g(pipe, messages=[{"role":"user","content":prompt}]).validated_output

# `out` is a dict.  Format it as JSON for readability:
import json
print(json.dumps(out, indent=2))

JSON Mode​

For models that support JSON through an input argument(gpt-4o, gpt-4-turbo, or gpt-3.5-turbo)

messages[-1]["content"]+="""

${gr.complete_json_suffix_v3}
"""
response = guard(
    model="gpt-4o",
    messages=messages,
    prompt_params={"chat_history": chat_history},
    response_format={ "type": "json_object" }
)

Strict JSON Mode​

For models that support strict JSON mode,

response = guard(
    model="gpt-4o",
    messages=messages,
    prompt_params={"chat_history": chat_history},
    response_format=g.response_format_json_schema()
)