parsec-llm 0.2.1

Lightweight orchestration toolkit to generate, validate, repair and enforce structured output from LLMs

parsec

⚡ Lightweight orchestration toolkit to generate, validate, repair and enforce structured output from large language models (LLMs). The project provides a provider-agnostic adapter interface, validators (JSON/Pydantic), prompt template management with versioning, caching, dataset collection, and an enforcement engine that retries and repairs LLM output until it conforms to a schema.

This repository contains:

• Adapter abstractions for OpenAI, Anthropic, and Google Gemini.
• Validation and repair utilities for JSON and Pydantic schemas.
• An EnforcementEngine that generates, validates, repairs, and retries.
• Prompt template system with versioning and YAML persistence.
• LRU caching to reduce redundant API calls and costs.
• Dataset collection for training and fine-tuning.
• Examples and comprehensive test suite.

Features

Core Enforcement

• Provider-agnostic adapters: OpenAI, Anthropic (Claude), Google Gemini
• Multiple validators: JSON Schema, Pydantic models
• Automatic repair: Schema-based heuristics fix common formatting issues
• Retry loop: Progressive feedback to model for iterative repair
• Dataset collection: Capture and export training data (JSONL, JSON, CSV)

Prompt Management

• Template system: Type-safe variable substitution with validation
• Version control: Semantic versioning (1.0.0, 2.0.0, etc.)
• YAML persistence: Save/load templates from files
• Template registry: Centralized management of all templates
• Template manager: One-line API for template + enforcement

Performance & Caching

• LRU cache: In-memory caching with TTL support
• Cost reduction: Avoid redundant API calls for identical requests
• Cache integration: Seamless integration with enforcement engine
• Statistics tracking: Monitor cache hits, misses, and hit rates

Installation

pip install parsec-llm

Or for development:

git clone https://github.com/olliekm/parsec.git
cd parsec
pip install -e ".[dev]"

Quick Start

Basic Usage

from parsec.models.adapters import OpenAIAdapter
from parsec.validators import JSONValidator
from parsec.enforcement import EnforcementEngine

# Set up components
adapter = OpenAIAdapter(api_key="your-api-key", model="gpt-4o-mini")
validator = JSONValidator()
engine = EnforcementEngine(adapter, validator, max_retries=3)

# Define your schema
schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"}
    },
    "required": ["name", "age"]
}

# Enforce structured output
result = await engine.enforce(
    "Extract: John Doe is 30 years old",
    schema
)

print(result.data)  # {"name": "John Doe", "age": 30}
print(result.success)  # True
print(result.retry_count)  # 0

With Caching

from parsec.cache import InMemoryCache

# Add cache to reduce redundant API calls
cache = InMemoryCache(max_size=100, default_ttl=3600)
engine = EnforcementEngine(adapter, validator, cache=cache)

# First call hits API
result1 = await engine.enforce(prompt, schema)

# Second identical call returns cached result (no API call!)
result2 = await engine.enforce(prompt, schema)

# Check cache performance
stats = cache.get_stats()
print(stats)  # {'hits': 1, 'misses': 1, 'hit_rate': '50.00%'}

With Prompt Templates

from parsec.prompts import PromptTemplate, TemplateRegistry, TemplateManager

# Create a reusable template
template = PromptTemplate(
    name="extract_person",
    template="Extract person info from: {text}\n\nReturn as JSON.",
    variables={"text": str},
    required=["text"]
)

# Register with version
registry = TemplateRegistry()
registry.register(template, "1.0.0")

# Use with enforcement
manager = TemplateManager(registry, engine)
result = await manager.enforce_with_template(
    template_name="extract_person",
    variables={"text": "John Doe, age 30"},
    schema=schema
)

# Save templates to file
registry.save_to_disk("templates.yaml")

# Load templates later
registry.load_from_disk("templates.yaml")

With Pydantic Models

from pydantic import BaseModel
from parsec.validators import PydanticValidator

class Person(BaseModel):
    name: str
    age: int
    email: str

validator = PydanticValidator()
engine = EnforcementEngine(adapter, validator)

result = await engine.enforce(
    "Extract: John Doe, 30 years old, [e-mail]",
    Person
)

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

Development Setup

Requirements: Python 3.9+

1. Install dependencies:

pip install -e ".[dev]"

2. Run tests:

poetry run pytest -q

3. Run the OpenAI example (requires OPENAI_API_KEY):

export OPENAI_API_KEY="sk-..."
export OPENAI_MODEL="gpt-4o-mini"  # optional
poetry run python examples/run_with_openai.py

The example demonstrates using OpenAIAdapter, JSONValidator and EnforcementEngine to extract structured data using a JSON schema.

Code Structure

• src/parsec/core/ — Core abstractions and schemas
• src/parsec/models/ — LLM provider adapters (OpenAI, Anthropic, Gemini)
• src/parsec/validators/ — Validator implementations (JSON, Pydantic)
• src/parsec/enforcement/ — Enforcement and orchestration engine
• src/parsec/prompts/ — Prompt template system with versioning
• src/parsec/cache/ — Caching implementations (InMemoryCache)
• src/parsec/training/ — Dataset collection for fine-tuning
• src/parsec/utils/ — Utility functions (partial JSON parsing)
• examples/ — Working examples with real API calls
• tests/ — Comprehensive test suite with pytest

Examples

Check out the examples/ directory for complete working examples:

• basic_usage.py - Simple extraction with JSON schema
• prompt_template_example.py - Template system with versioning
• prompt_persistence_example.py - Save/load templates from YAML
• template_manager_example.py - TemplateManager integration
• template_manager_live_example.py - Live demo with real API calls
• streaming_example.py - Streaming support (experimental)

Run any example:

python3 examples/template_manager_live_example.py

Testing

Run the test suite with:

poetry run pytest -q

Advanced Features

Dataset Collection

Collect and export training data for fine-tuning:

from parsec.training import DatasetCollector

collector = DatasetCollector(
    output_path="./training_data",
    format="jsonl",  # or "json", "csv"
    auto_flush=True
)

engine = EnforcementEngine(adapter, validator, collector=collector)

# Data is automatically collected during enforcement
result = await engine.enforce(prompt, schema)

# Export collected data
collector.flush()  # Writes to disk

Template Versioning Workflow

# v1.0.0 - Initial template
template_v1 = PromptTemplate(
    name="extract_person",
    template="Extract: {text}",
    variables={"text": str},
    required=["text"]
)
registry.register(template_v1, "1.0.0")

# v2.0.0 - Improved with validation rules
template_v2 = PromptTemplate(
    name="extract_person",
    template="Extract: {text}\n\nValidation: {rules}",
    variables={"text": str, "rules": str},
    required=["text"],
    defaults={"rules": "Strict validation"}
)
registry.register(template_v2, "2.0.0")

# Use specific version
result = await manager.enforce_with_template(
    template_name="extract_person",
    version="2.0.0",  # Explicit version
    variables={"text": "John Doe, 30"}
)

# Or use latest automatically
result = await manager.enforce_with_template(
    template_name="extract_person",  # Gets v2.0.0
    variables={"text": "John Doe, 30"}
)

Multi-Provider Support

from parsec.models.adapters import OpenAIAdapter, AnthropicAdapter

# Switch between providers easily
openai_adapter = OpenAIAdapter(api_key=openai_key, model="gpt-4o-mini")
anthropic_adapter = AnthropicAdapter(api_key=anthropic_key, model="claude-3-5-sonnet-20241022")

# Same enforcement code works with any adapter
engine = EnforcementEngine(anthropic_adapter, validator)
result = await engine.enforce(prompt, schema)

Roadmap

• Core enforcement engine with retry logic
• Multiple LLM providers (OpenAI, Anthropic, Gemini)
• JSON and Pydantic validation
• LRU caching with TTL
• Prompt template system with versioning
• Dataset collection for training
• Streaming support for real-time output
• Batch processing with rate limiting
• Cost tracking and analytics
• A/B testing for prompt variants
• Output post-processing pipeline

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Notes

• Examples with real API calls will incur costs — use test/development API keys
• The framework is intentionally modular — extend adapters and validators as needed
• Template system supports version control via YAML files for team collaboration

License

This project is licensed under the MIT License - see the LICENSE file for details.

Copyright (c) 2025 Oliver Kwun-Morfitt