json-repair-llm 0.1.0

JSON repair using multiple backends: LLMs and FSM-based processing with Pydantic models

JSON Repair LLM

A Python package for repairing broken JSON using multiple backends: LLMs and FSM-based processing with Pydantic models.

Disclaimer

This package is not a replacement for a full-fledged JSON parser or parsers like json-repair. It is designed to assist in repairing broken JSON structures, that cloud LLMs might produce (some models even with proper prompting still might return broken JSON or JSON with extra-text).

The main purpose is to process output from cloud LLMs, where packages like outlines or xgrammar are not applicable.

Features

• Multiple backends: Choose between LLM-based repair, FSM-based repair, or both
• Pydantic integration: Validate and enforce schema compliance
• Slot filling: Extract structured information from unstructured text
• Flexible configuration: Use with different LLM models
• Low dependency mode: Use FSM processor with minimal dependencies

How It Works

1. LLM-based repair:
   • uses a large language model(HuggingFaceTB/SmolLM2-360M-Instruct) to repair broken JSON.
   • outlines helps to generate a grammatically correct JSON.
   • additionally, llm backend can be used as "slot filling" processor to extract structured data from unstructured text.
   • based on pydantic models, it can validate and enforce schema compliance.
2. FSM-based repair:
   • uses a very simplistic finite state machine (FSM) to parse and repair broken JSON (if json-repair was not successful).
   • based on pydantic models, it can validate and enforce schema compliance.

Installation

# Basic installation
pip install json-repair-llm

# With FSM capabilities
pip install json-repair-llm[fsm]

# With all dependencies (including flash-attention)
pip install json-repair-llm[full]

Flash Attention (Optional)

For better performance with LLM-based repair, you can install flash-attention:

pip install flash-attn --no-build-isolation

Usage

Basic Usage

from json_repair_llm import JsonRepairProcessor
from pydantic import BaseModel


# Define your data schema
class UserProfile(BaseModel):
    name: str
    age: int
    email: str


# Create processor with default LLM backend
processor = JsonRepairProcessor(UserProfile)

# Process broken JSON
broken_json = '{name": "John Doe"\, "age": 30, email: "[e-mail]"}'
result = processor(broken_json)

print(result.model_dump())
# {'name': 'John Doe', 'age': 30, 'email': '[e-mail]'}

Using Different Backends

# Use FSM backend (no LLM required)
processor_fsm = JsonRepairProcessor(UserProfile, backend="fsm")

# Use both backends (FSM first, then LLM if needed)
processor_all = JsonRepairProcessor(UserProfile, backend="all")

Slot Filling for Unstructured Text

# Extract structured data from plain text
processor = JsonRepairProcessor(UserProfile, backend="llm")
plain_text = """
Hello, my name is Jane Smith. I'm 28 years old.
You can contact me at [e-mail] for more information.
"""
result = processor(plain_text, use_slot_filling=True)
print(result.model_dump())
# {'name': 'Jane Smith', 'age': 28, 'email': '[e-mail]'}

Using a Custom LLM Model

processor = JsonRepairProcessor(
    UserProfile,
    backend="llm",
    model_name="gpt2",  # Replace with your preferred model
    prompt_template="Fix this JSON: {{ broken_json }}\nSchema: {{ schema }}\nFixed JSON:"
)

FSM vs LLM Backend

• FSM Backend:

  • Faster, no external API calls
  • Works without internet connection
  • Limited to simpler JSON repairs
  • No dependencies on ML libraries

• LLM Backend:

  • Better at handling complex repairs
  • Can extract structure from unstructured text
  • Higher accuracy for severely broken JSON
  • Requires ML libraries and models

License

MIT License