langcore-dspy 1.1.2

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

LangCore DSPy

Procider plugin for LangCore — automatically optimize extraction prompts and few-shot examples using DSPy.

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Overview

langcore-dspy is a plugin for LangCore that uses DSPy's optimization framework to automatically refine extraction prompts and curate few-shot examples. Given training data, it searches for the best prompt description and example set to maximize extraction precision and recall — then produces a portable OptimizedConfig you can save, load, and pass directly to lx.extract().

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Features

• MIPROv2 optimizer — fast, general-purpose prompt optimization that explores candidate prompts and selects the best performer
• GEPA optimizer — reflective, feedback-driven optimization with falling back to BootstrapFewShot when dspy.GEPA is unavailable
• Optimizer aliases — use mipro, mipro_v2, or miprov2 interchangeably; gepa for the reflective optimizer
• Persist & load configs — save optimized configurations to disk (config.json + examples.json) and reload them later
• Built-in evaluation — measure precision, recall, and F1 on held-out test sets with per-document detail
• Native LangCore integration — pass optimized_config directly to lx.extract(), which overrides prompt_description and examples
• Any LLM backend — works with any model supported by DSPy's LM abstraction (OpenAI, Google, Anthropic, etc.)

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Installation

pip install langcore-dspy

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Quick Start

1. Optimize Your Extraction Prompt

from langcore_dspy import DSPyOptimizer
import langcore as lx

# Prepare few-shot examples to guide optimization
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"})
        ],
    )
]

# Training data the optimizer will use to evaluate candidates
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 LangCore Extraction

Pass the optimized config directly to lx.extract() — it overrides prompt_description and examples with the optimized values:

import langcore as lx
from langcore_dspy import OptimizedConfig

config = OptimizedConfig.load("./optimized_invoice_extractor")

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,
)

print(result)

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}")

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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.

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API Reference

DSPyOptimizer

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

Parameter	Description
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

Method	Description
save(path)	Persist to a directory (config.json + examples.json)
load(path)	Class method — restore from a saved directory
evaluate(test_texts, expected_results, extract_fn, model_id)	Compute precision, recall, and F1 on a test set

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Composing with Other Plugins

langcore-dspy produces an OptimizedConfig that works with any LangCore provider stack:

import langcore as lx
from langcore_dspy import OptimizedConfig
from langcore_audit import AuditLanguageModel, LoggingSink
from langcore_guardrails import GuardrailLanguageModel, SchemaValidator, OnFailAction

# Load optimized prompt + examples
config = OptimizedConfig.load("./optimized_invoice_extractor")

# Build provider stack
llm = lx.factory.create_model(
    lx.factory.ModelConfig(model_id="litellm/gpt-4o", provider="LiteLLMLanguageModel")
)
guarded = GuardrailLanguageModel(
    model_id="guardrails/gpt-4o", inner=llm,
    validators=[SchemaValidator(Invoice, on_fail=OnFailAction.REASK)],
)
audited = AuditLanguageModel(
    model_id="audit/gpt-4o", inner=guarded,
    sinks=[LoggingSink()],
)

# Extract with optimized config + full provider stack
result = lx.extract(
    text_or_documents="Invoice INV-500 for $8,200 due Dec 31, 2025",
    model=audited,
    optimized_config=config,
)

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Development

pip install -e ".[dev]"
pytest

Requirements

• Python ≥ 3.12
• langcore
• dspy ≥ 2.6.0

License

Apache License 2.0 — see LICENSE for details.