sourceryforge 0.1.1

Schema-first LLM extraction framework with entity grounding and deterministic post-processing

Sourcery: Schema-First Document Extraction (LangExtract Alternative on BlackGeorge)

Sourcery is a schema-first LLM extraction framework for turning unstructured documents into typed, grounded entities and claims.

It is built on BlackGeorge runtime primitives (Desk, Flow, Worker, Workforce, RunStore, EventBus) and is designed as a clean-break alternative to LangExtract.

What Is Sourcery

Sourcery is for people building:

• document AI pipelines,
• compliance and legal extraction systems,
• financial filing intelligence,
• contract and policy analyzers,
• review workflows with human approval.

Core idea:

1. Define extraction contracts in Pydantic v2.
2. Run deterministic chunked extraction with LLM structured output.
3. Align results to source offsets.
4. Reconcile at document-level into canonical claims.
5. Review/export via JSONL + HTML reviewer.

Why Sourcery vs LangExtract

Sourcery is optimized for type safety + runtime reliability + deterministic post-processing.

• Pydantic contracts are first-class (EntitySpec.attributes_model).
• BlackGeorge-native runtime orchestration (no custom provider router stack).
• Deterministic alignment statuses (exact, fuzzy, partial, unresolved).
• Deterministic merge behavior across passes.
• Typed error taxonomy for provider/runtime/pipeline/ingestion failures.
• Run replay seam via BlackGeorge run store.
• Built-in reviewer UI (search/filter/approve/export).
• Document-level reconciliation support (Workforce + Blackboard + resolver worker).

BlackGeorge Relationship

Sourcery is an application layer on top of BlackGeorge.

• Sourcery handles extraction domain logic.
• BlackGeorge handles model execution, workflow orchestration, events, pause/resume, and run storage.

This means BlackGeorge is a hard runtime dependency in this project.

Features

• Schema-first extraction with Pydantic models.
• Ingestion adapters: text, file, PDF, HTML, URL, OCR image.
• Deterministic chunking and alignment.
• Multi-pass extraction with stop-when-no-new-results.
• Cross-chunk refinement and document-level reconciliation.
• Session-based refinement mode.
• Reviewer HTML UI + export to JSONL/CSV.
• Run tracing and replay.

Install

uv sync --extra dev --extra ingest

PyPI distribution name: sourceryforge
Python import path: sourcery

pip install sourceryforge (uv add sourceryforge)

If you want Sourcery vs LangExtract benchmark tooling:

uv sync --extra benchmark

Set your provider key (example):

export DEEPSEEK_API_KEY="..."

Set RuntimeConfig.model to a provider/model route supported by your BlackGeorge runtime setup.

Reproducible Benchmark

Run the benchmark from this repo root:

uv run sourcery-benchmark --text-types english,japanese,french,spanish --max-chars 4500 --max-passes 2 --sourcery-model deepseek/deepseek-chat

Run it from any directory:

uv run --project /path/to/sourcery sourcery-benchmark --text-types english

Or run the compatibility wrapper:

uv run benchmark_compare.py --text-types english

Output JSON is written to benchmark_results/ and includes:

• run settings,
• tokenization throughput table,
• per-language Sourcery and LangExtract extraction metrics,
• aggregate summary for both frameworks.

Benchmark Port Scope

This is based on LangExtract benchmarks/benchmark.py behavior, but it is not a byte-for-byte clone.

• Ported: Gutenberg text sampling flow, per-language extraction runs, retry behavior, timing, grounded/unresolved metrics, JSON output artifacts.

Quickstart

from pydantic import BaseModel
import sourcery
from sourcery.contracts import (
    EntitySchemaSet,
    EntitySpec,
    ExtractRequest,
    ExtractionExample,
    ExtractionTask,
    ExampleExtraction,
    RuntimeConfig,
)

class PersonAttrs(BaseModel):
    role: str | None = None

request = ExtractRequest(
    documents="Alice is the CEO of Acme.",
    task=ExtractionTask(
        instructions="Extract people.",
        schema=EntitySchemaSet(
            entities=[EntitySpec(name="person", attributes_model=PersonAttrs)]
        ),
        examples=[
            ExtractionExample(
                text="Bob is the CTO.",
                extractions=[
                    ExampleExtraction(entity="person", text="Bob", attributes={"role": "CTO"})
                ],
            )
        ],
    ),
    runtime=RuntimeConfig(model="deepseek/deepseek-chat"),
)

result = sourcery.extract(request)
print(result.metrics.model_dump(mode="json"))

More examples: CODE_EXAMPLES.md Full usage and API guide: USAGE.md Notebook workflows: examples/notebooks/sourcery_quickstart.ipynb, examples/notebooks/sourcery_pdf_workflow.ipynb

Project Structure

• sourcery/contracts: public types and contracts.
• sourcery/pipeline: chunking, prompt compiler, aligner, merger.
• sourcery/runtime: engine + BlackGeorge runtime integration.
• sourcery/ingest: document loaders and adapters.
• sourcery/io: JSONL, visualization, reviewer UI.
• sourcery/observability: run trace collection.

Validation

uv run --extra dev pytest -q
uv run --extra dev ruff check sourcery tests
uv run --extra dev mypy sourcery

Documentation Site

Build and serve project docs with MkDocs:

uv run --extra docs mkdocs serve
uv run --extra docs mkdocs build --strict

Common Use Cases

• Regulatory compliance extraction.
• SEC filing and earnings-call intelligence.
• Contract clause extraction and renewal tracking.
• Policy change monitoring.
• Research paper benchmark extraction.
• Incident/postmortem structure mining.

License

Licensed under the MIT License. See LICENSE.