strictjson

A Strict JSON Framework for LLM Outputs

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Strict JSON is a framework designed to handle JSON outputs with complex structures, fixing issues that standard json.loads() cannot resolve. It provides functionalities for parsing LLM outputs into dictionaries, supporting various data types, type forcing, and error correction. The tool allows easy integration with OpenAI JSON Mode and offers community support through tutorials and discussions. Users can download the package via pip, set up API keys, and import functions for usage. The tool works by extracting JSON values using regex, matching output values to literals, and ensuring all JSON fields are output by LLM with optional type checking. It also supports LLM-based checks for type enforcement and error correction loops.

README:

Strict JSON v5.1.1

[UPDATE]: For Agentic Framework, do check out TaskGen (the official Agentic Framework building on StrictJSON). This will make the StrictJSON repo neater and this github will focus on using StrictJSON for LLM Output Parsing

https://github.com/simbianai/taskgen

A Strict JSON Framework for LLM Outputs, that fixes problems that json.loads() cannot solve

Works for JSON outputs with multiple ' or " or { or } or \ or unmatched braces/brackets that may break a json.loads()

Base Functionalities (see Tutorial.ipynb)

Ensures LLM outputs into a dictionary based on a JSON format (HUGE: Nested lists and dictionaries now supported)
Supports int, float, str, dict, list, array, code, Dict[], List[], Enum[], bool type forcing with LLM-based error correction, as well as LLM-based error correction using type: ensure <restriction>, and (advanced) custom user checks using custom_checks
Easy construction of LLM-based functions using Function (Note: renamed from strict_function to keep in line with naming convention of capitalised class groups. strict_function still works for legacy support.)
Easy integration with OpenAI JSON Mode by setting openai_json_mode = True
Exposing of llm variable for strict_json and Function for easy use of self-defined LLMs
AsyncFunction and strict_json_async for async (and faster) processing

Tutorials and Community Support

Created: 7 Apr 2023
Collaborators welcome
Video tutorial (Ask Me Anything): https://www.youtube.com/watch?v=L4aytve5v1Q
Video tutorial: https://www.youtube.com/watch?v=IjTUKAciTCg
Discussion Channel (my discord - John's AI Group): discord.gg/bzp87AHJy5

How do I use this?

Download package via command line pip install strictjson
Import the required functions from strictjson
Set up the relevant API Keys for your LLM if needed. Refer to Tutorial.ipynb for how to do it for Jupyter Notebooks.

How does it work?

Extract JSON values as a string using a special regex (add delimiters to key to make ###key###) to split keys and values. (New!) Also works for nested datatypes by splitting recursively.
Uses ast.literal_eval to best match the extracted output value to a literal (e.g. int, string, dict).
Ensures that all JSON fields are output by LLM, with optional type checking, if not it will feed in error message to LLM to iteratively correct its generation (default: 3 tries)

Features:

1. Basic Generation

system_prompt: Write in whatever you want the LLM to become. "You are a <purpose in life>"
user_prompt: The user input. Later, when we use it as a function, this is the function input
output_format: JSON of output variables in a dictionary, with the key as the output key, and the value as the output description
- The output keys will be preserved exactly, while the LLM will generate content to match the description of the value as best as possible
llm: The llm you want to use. Takes in system_prompt and user_prompt and outputs the LLM-generated string

Example LLM Definition

def llm(system_prompt: str, user_prompt: str) -> str:
    ''' Here, we use OpenAI for illustration, you can change it to your own LLM '''
    # ensure your LLM imports are all within this function
    from openai import OpenAI
    
    # define your own LLM here
    client = OpenAI()
    response = client.chat.completions.create(
        model='gpt-4o-mini',
        temperature = 0,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]
    )
    return response.choices[0].message.content

Example Usage

res = strict_json(system_prompt = 'You are a classifier',
                    user_prompt = 'It is a beautiful and sunny day',
                    output_format = {'Sentiment': 'Type of Sentiment',
                                    'Adjectives': 'Array of adjectives',
                                    'Words': 'Number of words'},
                    llm = llm)
                                    
print(res)

Example Output

{'Sentiment': 'Positive', 'Adjectives': ['beautiful', 'sunny'], 'Words': 7}

2. Advanced Generation

More advanced demonstration involving code that would typically break json.loads()

Example Usage

res = strict_json(system_prompt = 'You are a code generator, generating code to fulfil a task',
                    user_prompt = 'Given array p, output a function named func_sum to return its sum',
                    output_format = {'Elaboration': 'How you would do it',
                                     'C': 'Code',
                                    'Python': 'Code'},
                    llm = llm)
                                    
print(res)

Example Output

{'Elaboration': 'Use a loop to iterate through each element in the array and add it to a running total.',

'C': 'int func_sum(int p[], int size) {\n int sum = 0;\n for (int i = 0; i < size; i++) {\n sum += p[i];\n }\n return sum;\n}',

'Python': 'def func_sum(p):\n sum = 0\n for num in p:\n sum += num\n return sum'}

3. Type forcing output variables

Generally, strict_json will infer the data type automatically for you for the output fields
However, if you would like very specific data types, you can do data forcing using type: <data_type> at the last part of the output field description
<data_type> must be of the form int, float, str, dict, list, array, code, Dict[], List[], Array[], Enum[], bool for type checking to work
code removes all unicode escape characters that might interfere with normal code running
The Enum and List are not case sensitive, so enum and list works just as well
For Enum[list_of_category_names], it is best to give an "Other" category in case the LLM fails to classify correctly with the other options.
If list or List[] is not formatted correctly in LLM's output, we will correct it by asking the LLM to list out the elements line by line
For dict, we can further check whether keys are present using Dict[list_of_key_names]
Other types will first be forced by rule-based conversion, any further errors will be fed into LLM's error feedback mechanism
If <data_type> is not the specified data types, it can still be useful to shape the output for the LLM. However, no type checking will be done.
Note: LLM understands the word Array better than List since Array is the official JSON object type, so in the backend, any type with the word List will be converted to Array.

LLM-based checks

If you would like the LLM to ensure that the type is being met, use type: ensure <requirement>
This will run a LLM to check if the requirement is met. If requirement is not met, the LLM will generate what needs to be done to meet the requirement, which will be fed into the error-correcting loop of strict_json

Example Usage 1

res = strict_json(system_prompt = 'You are a classifier',
                    user_prompt = 'It is a beautiful and sunny day',
                    output_format = {'Sentiment': 'Type of Sentiment, type: Enum["Pos", "Neg", "Other"]',
                                    'Adjectives': 'Array of adjectives, type: List[str]',
                                    'Words': 'Number of words, type: int',
                                    'In English': 'Whether sentence is in English, type: bool'},
                  llm = llm)
                                    
print(res)

Example Output 1

{'Sentiment': 'Pos', 'Adjectives': ['beautiful', 'sunny'], 'Words': 7, 'In English': True}

Example Usage 2

res = strict_json(system_prompt = 'You are an expert at organising birthday parties',
                    user_prompt = 'Give me some information on how to organise a birthday',
                    output_format = {'Famous Quote about Age': 'type: ensure quote contains the word age',
                                    'Lucky draw numbers': '3 numbers from 1-50, type: List[int]',
                                    'Sample venues': 'Describe two venues, type: List[Dict["Venue", "Description"]]'},
                    llm = llm)

print(res)

Example Output 2

Using LLM to check "The secret of staying young is to live honestly, eat slowly, and lie about your age. - Lucille Ball" to see if it adheres to "quote contains the word age" Requirement Met: True

{'Famous Quote about Age': 'The secret of staying young is to live honestly, eat slowly, and lie about your age. - Lucille Ball', 'Lucky draw numbers': [7, 21, 35],

'Sample venues': [{'Venue': 'Beachside Resort', 'Description': 'A beautiful resort with stunning views of the beach. Perfect for a summer birthday party.'}, {'Venue': 'Indoor Trampoline Park', 'Description': 'An exciting venue with trampolines and fun activities. Ideal for an active and energetic birthday celebration.'}]}

4. Functions

Enhances strict_json() with a function-like interface for repeated use of modular LLM-based functions (or wraps external functions)
Use angle brackets <> to enclose input variable names. First input variable name to appear in fn_description will be first input variable and second to appear will be second input variable. For example, fn_description = 'Adds up two numbers, <var1> and <var2>' will result in a function with first input variable var1 and second input variable var2
(Optional) If you would like greater specificity in your function's input, you can describe the variable after the : in the input variable name, e.g. <var1: an integer from 10 to 30>. Here, var1 is the input variable and an integer from 10 to 30 is the description.
(Optional) If your description of the variable is one of int, float, str, dict, list, array, code, Dict[], List[], Array[], Enum[], bool, we will enforce type checking when generating the function inputs in get_next_subtask method of the Agent class. Example: <var1: int>. Refer to Section 3. Type Forcing Output Variables for details.
Inputs (primary):
- fn_description: String. Function description to describe process of transforming input variables to output variables. Variables must be enclosed in <> and listed in order of appearance in function input.
  - New feature: If external_fn is provided and no fn_description is provided, then we will automatically parse out the fn_description based on docstring of external_fn. The docstring should contain the names of all compulsory input variables
  - New feature: If external_fn is provided and no output_format is provided, then we will automatically derive the output_format from the function signature
- output_format: Dict. Dictionary containing output variables names and description for each variable.
Inputs (optional):
- examples - Dict or List[Dict]. Examples in Dictionary form with the input and output variables (list if more than one)
- external_fn - Python Function. If defined, instead of using LLM to process the function, we will run the external function. If there are multiple outputs of this function, we will map it to the keys of output_format in a one-to-one fashion
- fn_name - String. If provided, this will be the name of the function. Otherwise, if external_fn is provided, it will be the name of external_fn. Otherwise, we will use LLM to generate a function name from the fn_description
- kwargs - Dict. Additional arguments you would like to pass on to the strict_json function
Outputs: JSON of output variables in a dictionary (similar to strict_json)

Example Usage 1 (Description only)

# basic configuration with variable names (in order of appearance in fn_description)
fn = Function(fn_description = 'Output a sentence with <obj> and <entity> in the style of <emotion>', 
                     output_format = {'output': 'sentence'},
                     llm = llm)

# Use the function
fn('ball', 'dog', 'happy') #obj, entity, emotion

Example Output 1

{'output': 'The happy dog chased the ball.'}

Example Usage 2 (Examples only)

# Construct the function: infer pattern from just examples without description (here it is multiplication)
fn = Function(fn_description = 'Map <var1> and <var2> to output based on examples', 
                     output_format = {'output': 'final answer'}, 
                     examples = [{'var1': 3, 'var2': 2, 'output': 6}, 
                                 {'var1': 5, 'var2': 3, 'output': 15}, 
                                 {'var1': 7, 'var2': 4, 'output': 28}],
                     llm = llm)

# Use the function
fn(2, 10) #var1, var2

Example Output 2

{'output': 20}

Example Usage 3 (Description and Examples)

# Construct the function: description and examples with variable names
# variable names will be referenced in order of appearance in fn_description
fn = Function(fn_description = 'Output the sum and difference of <num1> and <num2>', 
                 output_format = {'sum': 'sum of two numbers', 
                                  'difference': 'absolute difference of two numbers'},
                 examples = {'num1': 2, 'num2': 4, 'sum': 6, 'difference': 2},
                 llm = llm)

# Use the function
fn(3, 4) #num1, num2

Example Output 3

{'sum': 7, 'difference': 1}

Example Usage 4 (External Function with automatic inference of fn_description and output_format - Preferred)

# Docstring should provide all input variables, otherwise we will add it in automatically
# We will ignore shared_variables, *args and **kwargs
# No need to define llm in Function for External Functions
from typing import List
def add_number_to_list(num1: int, num_list: List[int], *args, **kwargs) -> List[int]:
    '''Adds num1 to num_list'''
    num_list.append(num1)
    return num_list

fn = Function(external_fn = add_number_to_list)

# Show the processed function docstring
print(str(fn))

# Use the function
fn(3, [2, 4, 5])

Example Output 5

Description: Adds <num1: int> to <num_list: list>

Input: ['num1', 'num_list']

Output: {'num_list': 'Array of numbers'}

{'num_list': [2, 4, 5, 3]}

Example Usage 5 (External Function with manually defined fn_description and output_format - Legacy Approach)

def binary_to_decimal(x):
    return int(str(x), 2)

# an external function with a single output variable, with an expressive variable description
fn = Function(fn_description = 'Convert input <x: a binary number in base 2> to base 10', 
            output_format = {'output1': 'x in base 10'},
            external_fn = binary_to_decimal,
            llm = llm)

# Use the function
fn(10) #x

Example Output 4

{'output1': 2}

5. Integrating with OpenAI JSON Mode

If you want to use the OpenAI JSON Mode, you can simply add in openai_json_mode = True and set model = 'gpt-4-1106-preview' or model = 'gpt-3.5-turbo-1106' in strict_json or Function
We will set model to gpt-3.5-turbo-1106 by default if you provide an invalid model
This does not work with the llm variable
Note that type checking does not work with OpenAI JSON Mode

Example Usage

res = strict_json(system_prompt = 'You are a classifier',
                    user_prompt = 'It is a beautiful and sunny day',
                    output_format = {'Sentiment': 'Type of Sentiment',
                                    'Adjectives': 'Array of adjectives',
                                    'Words': 'Number of words'},
                    model = 'gpt-3.5-turbo-1106' # Set the model
                    openai_json_mode = True) # Toggle this to True
                                    
print(res)

Example Output

{'Sentiment': 'positive', 'Adjectives': ['beautiful', 'sunny'], 'Words': 6}

6. Nested Outputs

StrictJSON supports nested outputs like nested lists and dictionaries

Example Input

res = strict_json(system_prompt = 'You are a classifier',
                    user_prompt = 'It is a beautiful and sunny day',
                    output_format = {'Sentiment': ['Type of Sentiment', 
                                                   'Strength of Sentiment, type: Enum[1, 2, 3, 4, 5]'],
                                    'Adjectives': "Name and Description as separate keys, type: List[Dict['Name', 'Description']]",
                                    'Words': {
                                        'Number of words': 'Word count', 
                                        'Language': {
                                              'English': 'Whether it is English, type: bool',
                                              'Chinese': 'Whether it is Chinese, type: bool'
                                                  },
                                        'Proper Words': 'Whether the words are proper in the native language, type: bool'
                                        }
                                    },
                 llm = llm)

print(res)

Example Output

{'Sentiment': ['Positive', 3],

'Adjectives': [{'Name': 'beautiful', 'Description': 'pleasing to the senses'}, {'Name': 'sunny', 'Description': 'filled with sunshine'}],

'Words':

{'Number of words': 6,

'Language': {'English': True, 'Chinese': False},

'Proper Words': True}

}

7. Return as JSON

By default, strict_json returns a Python Dictionary
If needed to parse as JSON, simply set return_as_json=True
By default, this is set to False in order to return a Python Dictionry

8. Async Mode

AsyncFunction and strict_json_async
- These are the async equivalents of Function and strict_json
- You will need to define an LLM that can operate in async mode
- Everything is the same as the sync version of the functions, except you use the await keyword when calling AsyncFunction and strict_json_async
Using Async can help do parallel processes simulataneously, resulting in a much faster workflow

Example LLM in Async Mode

async def llm_async(system_prompt: str, user_prompt: str):
    ''' Here, we use OpenAI for illustration, you can change it to your own LLM '''
    # ensure your LLM imports are all within this function
    from openai import AsyncOpenAI
    
    # define your own LLM here
    client = AsyncOpenAI()
    response = await client.chat.completions.create(
        model='gpt-4o-mini',
        temperature = 0,
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]
    )
    return response.choices[0].message.content

Example Input (strict_json_async)

res = await strict_json_async(system_prompt = 'You are a classifier',
                    user_prompt = 'It is a beautiful and sunny day',
                    output_format = {'Sentiment': 'Type of Sentiment',
                                    'Adjectives': 'Array of adjectives',
                                    'Words': 'Number of words'},
                                     llm = llm_async) # set this to your own LLM

print(res)

Example Output

{'Sentiment': 'Positive', 'Adjectives': ['beautiful', 'sunny'], 'Words': 7}

Example Input (AsyncFunction)

fn =  AsyncFunction(fn_description = 'Output a sentence with <obj> and <entity> in the style of <emotion>', 
                     output_format = {'output': 'sentence'},
                     llm = llm_async) # set this to your own LLM

res = await fn('ball', 'dog', 'happy') #obj, entity, emotion

print(res)

Example Output

{'output': 'The dog happily chased the ball.'}

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PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

github

: 1.8k

tabby

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

github

: 21.5k

spear

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

github

: 224

Magick

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

github

: 675