jsonAI 0.12.2

No project description provided

jsonAI

jsonAI is a Python library for generating JSON objects based on a given schema using a pre-trained language model. It supports a wide range of data types, including numbers, integers, booleans, strings, datetime, date, time, UUID, and binary data.

The idea to create json structures with strong typed schemas is now possible, with any number of variable combinations.

Architecture Overview

The jsonAI library is structured into several key components to provide robust and flexible structured data generation:

• Jsonformer (in jsonAI/main.py): The main facade class that orchestrates the generation process. It takes the model, tokenizer, schema, and prompt, and coordinates the use of other components to produce the final output. It also handles output formatting and validation.
• TypeGenerator (in jsonAI/type_generator.py): Responsible for generating values for individual data types based on the schema and the current generation context (prompt).
• OutputFormatter (in jsonAI/output_formatter.py): Handles the conversion of the generated data structure (internal dictionary representation) into the desired output format (JSON, XML, YAML).
• SchemaValidator (in jsonAI/schema_validator.py): Provides functionality to validate the generated data structure against the provided JSON schema using the jsonschema library.

This modular architecture improves separation of concerns and makes the library more maintainable and extensible.

This currently supports a subset of JSON Schema. Below is a list of the supported schema types:

• number
• integer
• boolean
• string (descriptions also enabled to satisfy summary)
• datetime
• date
• time
• UUID
• binary data

combinations

• arrays
• enums
• complex object

Supported Output Formats

In addition to JSON, jsonAI now supports generating output in XML and YAML formats. You can specify the desired format using the output_format parameter in the Jsonformer constructor.

XML Output Example:

from transformers import AutoModelForCausalLM, AutoTokenizer
from jsonAI.main import Jsonformer

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

json_schema = {
    "type": "object",
    "properties": {
        "book": {
            "type": "object",
            "properties": {
                "title": {"type": "string"},
                "author": {"type": "string"},
                "year": {"type": "integer"}
            }
        }
    }
}

prompt = "Generate information about a book."

jsonformer = Jsonformer(
    model=model,
    tokenizer=tokenizer,
    json_schema=json_schema,
    prompt=prompt,
    output_format="xml"
)

generated_data = jsonformer()
print(generated_data)

YAML Output Example:

from transformers import AutoModelForCausalLM, AutoTokenizer
from jsonAI.main import Jsonformer

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

json_schema = {
    "type": "object",
    "properties": {
        "person": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "age": {"type": "integer"},
                "isStudent": {"type": "boolean"}
            }
        }
    }
}

prompt = "Generate information about a person."

jsonformer = Jsonformer(
    model=model,
    tokenizer=tokenizer,
    json_schema=json_schema,
    prompt=prompt,
    output_format="yaml"
)

generated_data = jsonformer()
print(generated_data)

Output Validation

You can enable schema validation for the generated output by setting the validate_output parameter to True. This requires the jsonschema library to be installed (pip install jsonschema).

from transformers import AutoModelForCausalLM, AutoTokenizer
from jsonAI.main import Jsonformer

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

json_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer", "minimum": 0}
    },
    "required": ["name", "age"]
}

prompt = "Generate a person's information."

# This will raise a jsonschema.exceptions.ValidationError if the output doesn't match the schema
jsonformer = Jsonformer(
    model=model,
    tokenizer=tokenizer,
    json_schema=json_schema,
    prompt=prompt,
    validate_output=True
)

generated_data = jsonformer()
print(generated_data)

Examples

We have included examples to demonstrate how to integrate jsonAI with other libraries and frameworks. You can find them in the examples/ directory.

FastAPI Integration Example

This example shows how to use jsonAI within a FastAPI web application to create an API endpoint that generates structured data based on user input.

To run the FastAPI example:

1. Install necessary dependencies:

   pip install fastapi uvicorn transformers torch jsonschema PyYAML
   

2. Navigate to the examples/ directory.
3. Run the server:

   uvicorn fastapi_example:app --reload
   

4. Send a POST request to http://127.0.0.1:8000/generate/ with a JSON body containing prompt and optionally json_schema, output_format, and validate_output. See the comments in examples/fastapi_example.py for more details.

Basic Usage

Examples

# Define the JSON schema
json_schema = {
    "type": "object",
    "properties": {
        "transaction_id": {"type": "uuid"},
        "store": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "location": {"type": "string"},
                "datetime": {"type": "datetime"}
            }
        },
        "customer": {
            "type": "object",
            "properties": {
                "customer_id": {"type": "uuid"},
                "name": {"type": "string"},
                "membership": {"type": "boolean"}
            }
        },
        "items": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "item_id": {"type": "uuid"},
                    "name": {"type": "string"},
                    "category": {"type": "string"},
                    "price": {"type": "number"},
                    "quantity": {"type": "integer"}
                }
            }
        },
        "total_amount": {"type": "number"},
        "payment_method": {"type": "string"},
        "transaction_date": {"type": "date"},
        "transaction_time": {"type": "time"},
        "receipt_binary": {"type": "binary"}
    }
}

# Define the prompt
prompt = "Generate a JSON object representing a transaction at a Starbucks coffee shop. The transaction includes details such as transaction ID, store information, customer information, items purchased, total amount, payment method, transaction date and time, and a binary receipt."

# Initialize Jsonformer
jsonformer = Jsonformer(
    model=model,
    tokenizer=tokenizer,
    json_schema=json_schema,
    prompt=prompt,
    debug=True,
    output_format="json", # Specify output format (e.g., "json", "xml", "yaml")
    validate_output=False # Enable/disable validation (requires jsonschema)
)

# Generate the data
generated_data = jsonformer()
print(generated_data)
# The highlight_values utility might be useful for debugging JSON output
# from jsonAI.format import highlight_values
# highlight_values(generated_data)

Example with various types

from transformers import AutoModelForCausalLM, AutoTokenizer
from jsonAI.main import Jsonformer

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
json_schema = {
    "type": "object",
    "properties": {
        "number": {"type": "number"},
        "integer": {"type": "integer"},
        "boolean": {"type": "boolean"},
        "string": {"type": "string"},
        "datetime": {"type": "datetime"},
        "date": {"type": "date"},
        "time": {"type": "time"},
        "uuid": {"type": "uuid"},
        "binary": {"type": "binary"},
    }
}
prompt = "Generate a JSON object with various data types"

jsonformer = Jsonformer(
    model=model,
    tokenizer=tokenizer,
    json_schema=json_schema,
    prompt=prompt,
    debug=True,
    output_format="json", # Specify output format
    validate_output=False # Enable/disable validation
)

generated_data = jsonformer()
print(generated_data)

Probabilistic Generation

jsonAI includes features for probabilistic structured generation, allowing you to extract probability distributions or weighted means for certain types.

Supported Probabilistic Types:

• p_enum: Returns a list of possible values and their probabilities for an enumeration.
• p_integer: Returns the probabilistic weighted mean for an integer range.

Example:

from transformers import AutoModelForCausalLM, AutoTokenizer
from jsonAI.main import Jsonformer

model_name = "databricks/dolly-v2-3b" # Note: Probabilistic features may work better with larger models
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

json_schema = {
    "type": "object",
    "properties": {
        # Get probability distribution for age within a range
        "age_probs": {"type": "p_enum", "values": [str(s) for s in range(10, 20)]},
        # Get probabilistic weighted mean for age within a range
        "age_wmean": {"type": "p_integer", "minimum": 10, "maximum": 20},
        # Get probability distribution for a boolean choice
        "is_student_probs": {"type": "p_enum", "values": ["true", "false"]},
        # Standard boolean generation
        "is_student": {"type": "boolean"},
        # Standard types also supported alongside probabilistic ones
        "name": {"type": "string", "maxLength": 4},
        "age": {"type": "integer"},
        "unit_time": {"type": "number"},
        "courses": {"type": "array", "items": {"type": "string"}},
        "trim": {"type": ["string", "null"]},
        "color": {
            "type": "enum",
            "values": ["red", "green", "blue", "brown", "white", "black"],
        },
    },
}

prompt = "Generate a young person's information based on the following schema:"
jsonformer = Jsonformer(model, tokenizer, json_schema, prompt, temperature=0)
generated_data = jsonformer()

print(generated_data)

Development

this is for colab

# autoreload your package
%load_ext autoreload
%autoreload 2

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

print("Loading model and tokenizer...")
model_name = "databricks/dolly-v2-3b"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    use_cache=True,
    torch_dtype=torch.float16,
    attn_implementation="eager",
).to("cuda:0")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True, use_cache=True)
print("Loaded model and tokenizer")

!git clone https://github.com/kishoretvk/jsonAI.git
%cd jsonAI

!pip install jaxtyping termcolor typeguard

from jsonAI.format import highlight_values
from jsonAI.main import Jsonformer

after the above stpes on colab try any example given

below refers to older code

prob_jsonformer: Probabilistic Structured JSON from Language Models.

This fork has been modified to include the token probabilities. This is not complaint with json schema, but it can be useful for efficient extracting of a range of possible values.

I've also merged some of the recent PR's for enum, integer, null, union. They are not yet included in the upstream Jsonformer. You can see them all below in this example:

# installing
pip install git+https://github.com/wassname/prob_jsonformer.git

Metrics

How well does it work? Well when I asked is Q: Please sample a number from the distribution [0, 20]: , assumming it should be a uniform distribution, this is how well it did:

Lower is better as it indicates a faithful sampling of the distribution. Time is in seconds.

method	KL_div_loss	time
method0: sampling	-3.09214	48.5044
method1: hindsight	-3.09214	0.683987
method3: generation tree	-3.09216	0.075112

KL_div_loss is the -1 * KL divergence between the true distribution and the generated distribution.

Example

from prob_jsonformer import Jsonformer
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "databricks/dolly-v2-3b"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

json_schema = {
    "type": "object",
    "properties": {
        # we can return the probability of each choice, even if they are multiple tokens
        "age_probs": {"type": "p_enum", "values": [str(s) for s in range(10, 20)]},
        # we can return the probabilistic weighted mean of a range
        "age_wmean": {"type": "p_integer", "minimum": 10, "maximum": 20},
        # the prob of true and false
        "is_student_probs": {"type": "p_enum", "values": ["true", "false"]},
        "is_student": {"type": "boolean"},
        # we've merged patches for enum, integer, null, union - currently mising from jsonformer
        "name": {"type": "string", "maxLength": 4},
        "age": {"type": "integer"},
        "unit_time": {"type": "number"},
        "courses": {"type": "array", "items": {"type": "string"}},
        "trim": {"type": ["string", "null"]},
        "color": {
            "type": "enum",
            "values": ["red", "green", "blue", "brown", "white", "black"],
        },
    },
}

prompt = "Generate a young person's information based on the following schema:"
jsonformer = Jsonformer(model, tokenizer, json_schema, prompt, temperature=0)
generated_data = jsonformer()

generated_data = {
    "age_probs": [
        {"prob": 0.62353515625, "choice": "10"},
        {"prob": 0.349609375, "choice": "12"},
        {"prob": 0.01123809814453125, "choice": "11"},
        {"prob": 0.00760650634765625, "choice": "16"},
        {"prob": 0.0025482177734375, "choice": "13"},
        {"prob": 0.0025081634521484375, "choice": "15"},
        {"prob": 0.0018062591552734375, "choice": "14"},
        {"prob": 0.00104522705078125, "choice": "18"},
        {"prob": 0.00011551380157470703, "choice": "17"},
        {"prob": 5.042552947998047e-05, "choice": "19"},
    ],
    "age_wmean": 15.544570922851562,
    "is_student_probs": [
        {"prob": 0.962890625, "choice": "true"},
        {"prob": 0.037322998046875, "choice": "false"},
    ],
    "is_student": False,
    "name": "John",
    "age": 17,
    "unit_time": 0.5,
    "courses": ["C++"],
    "trim": None,
    "color": "green",
}

The original README is included below.

ORIGINAL: Jsonformer: A Bulletproof Way to Generate Structured JSON from Language Models.

Problem: Getting models to output structured JSON is hard

Solution: Only generate the content tokens and fill in the fixed tokens

Generating structured JSON from language models is a challenging task. The generated JSON must be syntactically correct, and it must conform to a schema that specifies the structure of the JSON.

Current approaches to this problem are brittle and error-prone. They rely on prompt engineering, fine-tuning, and post-processing, but they still fail to generate syntactically correct JSON in many cases.

Jsonformer is a new approach to this problem. In structured data, many tokens are fixed and predictable. Jsonformer is a wrapper around Hugging Face models that fills in the fixed tokens during the generation process, and only delegates the generation of content tokens to the language model. This makes it more efficient and bulletproof than existing approaches.

This currently supports a subset of JSON Schema. Below is a list of the supported schema types:

• number
• boolean
• string
• array
• object

Example

from jsonformer import Jsonformer
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b")
tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b")

json_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "number"},
        "is_student": {"type": "boolean"},
        "courses": {
            "type": "array",
            "items": {"type": "string"}
        }
    }
}

prompt = "Generate a person's information based on the following schema:"
jsonformer = Jsonformer(model, tokenizer, json_schema, prompt)
generated_data = jsonformer()

print(generated_data)

Jsonformer works on complex schemas, even with tiny models. Here is an example of a schema with nested objects and arrays, generated by a 3B parameter model.

{"type": "object", "properties": {"car": {"type": "object", "properties": {"make": {"type": "string"}, "model": {"type": "string"}, "year": {"type": "number"}, "colors": {"type": "array", "items": {"type": "string"}}, "features": {"type": "object", "properties": {"audio": {"type": "object", "properties": {"brand": {"type": "string"}, "speakers": {"type": "number"}, "hasBluetooth": {"type": "boolean"}}}, "safety": {"type": "object", "properties": {"airbags": {"type": "number"}, "parkingSensors": {"type": "boolean"}, "laneAssist": {"type": "boolean"}}}, "performance": {"type": "object", "properties": {"engine": {"type": "string"}, "horsepower": {"type": "number"}, "topSpeed": {"type": "number"}}}}}}}, "owner": {"type": "object", "properties": {"firstName": {"type": "string"}, "lastName": {"type": "string"}, "age": {"type": "number"}}}}}

{
  car: {
    make: "audi",
    model: "model A8",
    year: 2016.0,
    colors: [
      "blue"
    ],
    features: {
      audio: {
        brand: "sony",
        speakers: 2.0,
        hasBluetooth: True
      },
      safety: {
        airbags: 2.0,
        parkingSensors: True,
        laneAssist: True
      },
      performance: {
        engine: "4.0",
        horsepower: 220.0,
        topSpeed: 220.0
      }
    }
  },
  owner: {
    firstName: "John",
    lastName: "Doe",
    age: 40.0
  }
}

Features

• Bulletproof JSON generation: Jsonformer ensures that the generated JSON is always syntactically correct and conforms to the specified schema.
• Efficiency: By generating only the content tokens and filling in the fixed tokens, Jsonformer is more efficient than generating a full JSON string and parsing it.
• Flexible and extendable: Jsonformer is built on top of the Hugging Face transformers library, making it compatible with any model that supports the Hugging Face interface.

Installation

pip install jsonformer

Development

Poetry is used for dependency management.

poetry install

poetry run python -m jsonformer.example

License

Jsonformer is released under the MIT License. You are free to use, modify, and distribute this software for any purpose, commercial or non-commercial, as long as the original copyright and license notice are included.