langcore-dspy 1.1.0

DSPy-powered prompt optimization plugin for LangCore — auto-optimize extraction prompts with MIPROv2 and GEPA

LangCore DSPy Optimizer

A plugin for LangCore that uses DSPy to automatically optimize extraction prompts and few-shot examples. Inspired by LangStruct's DSPy integration.

Note: This is a third-party plugin for LangCore. For the main LangCore library, visit google/langcore.

Installation

Install from source:

git clone <repo-url>
cd langcore-dspy
pip install -e .

Features at a Glance

Feature	langcore-dspy	LangStruct
MIPROv2 optimizer	✅ Fast, general-purpose	✅
GEPA optimizer	✅ Reflective, feedback-driven (falls back to BootstrapFewShot)	✅
Optimizer aliases	✅ mipro, mipro_v2, gepa	❌
Persist optimized configs	✅ save() / load() to directory	✅
Evaluation (precision/recall/F1)	✅ evaluate() with per-document details	⚠️ Basic metrics
LangCore integration	✅ Native optimized_config parameter	❌ (separate pipeline)
Any LLM backend	✅ Via DSPy's LM abstraction	✅

Quick Start

1. Optimize Your Extraction Prompt

from langcore_dspy import DSPyOptimizer
import langcore as lx

# Prepare training data
examples = [
    lx.data.ExampleData(
        text="Invoice INV-001 for $500 due Jan 1, 2024",
        extractions=[
            lx.data.Extraction("invoice", "INV-001",
                               attributes={"amount": "500", "due": "2024-01-01"})
        ],
    )
]

train_texts = [
    "Invoice INV-002 totalling $1,200 payable by March 15, 2024",
    "Bill INV-003: $750, due date April 30, 2024",
]

expected_results = [
    [lx.data.Extraction("invoice", "INV-002",
                        attributes={"amount": "1200", "due": "2024-03-15"})],
    [lx.data.Extraction("invoice", "INV-003",
                        attributes={"amount": "750", "due": "2024-04-30"})],
]

# Run optimization
optimizer = DSPyOptimizer(model_id="openai/gpt-4o-mini")
config = optimizer.optimize(
    prompt_description="Extract invoice details: number, amount, due date.",
    examples=examples,
    train_texts=train_texts,
    expected_results=expected_results,
    optimizer="miprov2",
)

print(f"Optimized prompt: {config.prompt_description}")
print(f"Metadata: {config.metadata}")

2. Save & Load Optimized Configs

# Save to disk
config.save("./optimized_invoice_extractor")

# Load later
from langcore_dspy import OptimizedConfig
config = OptimizedConfig.load("./optimized_invoice_extractor")

The saved directory contains:

• config.json — optimized prompt description and metadata
• examples.json — curated few-shot examples

3. Use in Extraction

Pass the optimized config directly to lx.extract():

result = lx.extract(
    text_or_documents="Invoice INV-100 for $2,300 due June 1, 2024",
    model_id="gemini-2.5-flash",
    optimized_config=config,
)

When optimized_config is provided, it overrides prompt_description and examples with the optimized values.

4. Evaluate Performance

Measure extraction quality on a held-out test set:

metrics = config.evaluate(
    test_texts=["Invoice INV-200 for $900 due July 1, 2024"],
    expected_results=[
        [lx.data.Extraction("invoice", "INV-200",
                            attributes={"amount": "900", "due": "2024-07-01"})]
    ],
    extract_fn=lambda text: lx.extract(
        text_or_documents=text,
        model_id="gemini-2.5-flash",
        optimized_config=config,
    ),
    model_id="gemini-2.5-flash",
)

print(f"Precision: {metrics['precision']:.2f}")
print(f"Recall:    {metrics['recall']:.2f}")
print(f"F1:        {metrics['f1']:.2f}")

Supported Optimizers

Optimizer	Key	Aliases	Description
MIPROv2	miprov2	mipro, mipro_v2	Fast, general-purpose prompt optimization. Recommended default.
GEPA	gepa	—	Reflective optimizer with feedback-driven refinement. Falls back to BootstrapFewShot if dspy.GEPA is unavailable.

API Reference

DSPyOptimizer

DSPyOptimizer(model_id: str, api_key: str | None = None, **lm_kwargs)

• model_id — DSPy-compatible model identifier (e.g., "openai/gpt-4o-mini", "gemini/gemini-2.5-flash")
• api_key — Optional API key for the model provider
• **lm_kwargs — Additional keyword arguments forwarded to dspy.LM()

optimize()

optimizer.optimize(
    prompt_description: str,
    examples: list[ExampleData],
    train_texts: list[str],
    expected_results: list[list[Extraction]],
    optimizer: str = "miprov2",
    num_candidates: int = 7,
    max_bootstrapped_demos: int = 3,
    max_labeled_demos: int = 4,
) -> OptimizedConfig

OptimizedConfig

@dataclasses.dataclass
class OptimizedConfig:
    prompt_description: str
    examples: list[ExampleData]
    metadata: dict

• save(path) — persist to directory
• load(path) — classmethod, restore from directory
• evaluate(test_texts, expected_results, extract_fn, model_id) — compute precision/recall/F1

Requirements

• Python ≥ 3.10
• langcore ≥ 1.2.0
• dspy ≥ 2.6.0

License

Apache License 2.0 — see LICENSE for details.