coreason-optimizer 0.2.0

coreason-optimizer

coreason-optimizer

Automated Prompt Engineering / LLM Compilation / DSPy Integration for CoReason-AI

coreason-optimizer is the "Compiler" for the CoReason Agentic Platform. It automates prompt engineering by treating prompts as trainable weights, optimizing them against ground-truth datasets to maximize performance metrics.

---

Installation

pip install coreason-optimizer

Features

• Automated Optimization: Rewrites instructions and selects examples to maximize a score, not human intuition.
• Model-Specific Compilation: Generates optimized prompts specifically tuned for target models (e.g., GPT-4, Claude 3.5).
• Continuous Learning: Re-runs optimization on recent logs to patch prompts against data drift.
• Mutate-Evaluate Loop: Systematic cycle of drafting, evaluating, diagnosing, mutating, and selecting prompts.
• Strategies: Includes BootstrapFewShot (mining successful traces) and MIPRO (Multi-prompt Instruction PRoposal Optimizer).
• Integration: Works seamlessly with coreason-construct, coreason-archive, and coreason-assay.

For full product requirements, see docs/product_requirements.md.

Usage

Here is how to initialize and use the library to compile an agent:

from coreason_optimizer import OptimizerConfig, PromptOptimizer
from coreason_optimizer.core.interfaces import Construct
from coreason_optimizer.data import Dataset

# 1. Configuration
config = OptimizerConfig(
    target_model="gpt-4o",
    metric="exact_match",
    max_rounds=10
)

# 2. Load Data
dataset = Dataset.from_csv("data/gold_set.csv")
train_set, val_set = dataset.split(test_size=0.2)

# 3. Load Agent (Construct)
# In a real scenario, this would be imported from your agent code
# from src.agents.analyst import analyst_agent
class MockAgent(Construct):
    inputs = ["question"]
    outputs = ["answer"]
    system_prompt = "You are a helpful assistant."
agent = MockAgent()

# 4. Compile
optimizer = PromptOptimizer(config=config)
optimized_manifest = optimizer.compile(
    agent=agent,
    trainset=train_set,
    valset=val_set
)

print(f"Optimization complete. New Score: {optimized_manifest.performance_metric}")
print(f"Optimized Instruction: {optimized_manifest.optimized_instruction}")