kgmodule-utils

kgmodule-utils — Shared graph store, semantic index, pipeline base, and snapshot infrastructure for the KGModule SDK.

Author: Eric G. Suchanek, PhD

Flux-Frontiers, Liberty TWP, OH

Overview

kgmodule-utils is the shared SDK layer for the Flux-Frontiers knowledge-graph ecosystem. It provides everything a domain KG module needs — from type abstractions and SQLite graph storage through pluggable vector indexing (sqlite-vec by default, LanceDB optional) and a full build/query/pack pipeline — so domain authors implement only what is specific to their source domain.

Every KGModule implementation — PyCodeKG, DocKG, and others — subclasses KGModule from here and implements exactly three methods: make_extractor(), kind(), and analyze().

Features

• kg_utils.specs — NodeSpec, EdgeSpec, BuildStats, QueryResult, SnippetPack dataclasses
• kg_utils.extractor — KGExtractor ABC: extract(), node_kinds(), edge_kinds(), coverage_metric()
• kg_utils.store — GraphStore: SQLite-backed node/edge store with BFS expansion, symbol resolution, caller lookup, and provenance recording
• kg_utils.semantic — SemanticIndex, SentenceTransformerEmbedder, SeedHit, model registry, resolve_model_path()
• kg_utils.vector_backend — VectorBackend protocol with SqliteVecBackend (default, exact recall) and LanceDBBackend (legacy); make_backend(), resolve_backend_name()
• kg_utils.pipeline — KGModule: full build → query → pack pipeline base with hybrid semantic + lexical reranking and snippet extraction
• kg_utils.embedder — get_embedder(), wrap_embedder(), load_sentence_transformer() factory functions
• kg_utils.embed — Embedder protocol, DEFAULT_MODEL, KNOWN_MODELS, resolve_model_path()
• kg_utils.snapshots — Snapshot, SnapshotManager, SnapshotManifest for temporal metric tracking
• kg_utils.synthesis — Unified text + image synthesis: oMLX, Ollama, and OpenAI text backends; mflux-local, mflux-serve, and DALL-E image backends; all env-var configurable
• kg_utils.viz — Shared interactive-HTML graph rendering (viz extra): build_graph_html(), select_nodes(), GraphTheme, TooltipSpec — one renderer for code, document, and metabolic graphs, with domain differences supplied as data
• kg_utils.analysis — Read persisted centrality back out of SQLite: load_scores(), available_metrics(), ScoreSet (raw score, dense rank, percentile, range scaling); stdlib only

Installation

Requirements: Python ≥ 3.12, < 3.14

Core only (stdlib, no optional deps)

pip install kgmodule-utils

With semantic search (sqlite-vec + sentence-transformers)

pip install 'kgmodule-utils[semantic]'

With text + image synthesis (oMLX / Ollama / OpenAI / mflux-serve)

pip install 'kgmodule-utils[synthesis]'

With local mflux image generation (Apple Silicon, includes synthesis)

pip install 'kgmodule-utils[synthesis-mflux]'

With interactive graph rendering (pyvis / vis-network)

pip install 'kgmodule-utils[viz]'

In a Poetry project

[tool.poetry.dependencies]
kgmodule-utils = { version = ">=0.4.0", extras = ["semantic", "synthesis"] }

Quick Start

Build a domain KG module

from collections.abc import Iterator
from pathlib import Path

from kg_utils.extractor import KGExtractor
from kg_utils.pipeline import KGModule
from kg_utils.specs import EdgeSpec, NodeSpec


class MyExtractor(KGExtractor):
    def node_kinds(self) -> list[str]:
        return ["document", "section"]

    def edge_kinds(self) -> list[str]:
        return ["CONTAINS"]

    def meaningful_node_kinds(self) -> list[str]:
        return ["section"]

    def extract(self) -> Iterator[NodeSpec | EdgeSpec]:
        for doc in self.repo_path.glob("**/*.md"):
            doc_id = f"document:{doc}"
            yield NodeSpec(node_id=doc_id, kind="document",
                           name=doc.stem, qualname=doc.stem,
                           source_path=str(doc))
            # … yield sections and CONTAINS edges


class MyKG(KGModule):
    _default_dir = ".mykg"

    def make_extractor(self) -> KGExtractor:
        return MyExtractor(self.repo_root)

    def kind(self) -> str:
        return "my"

    def analyze(self) -> str:
        s = self.stats()
        return f"# MyKG\nnodes={s['total_nodes']}"


# Build and query
kg = MyKG("/path/to/repo")
kg.build(wipe=True)

result = kg.query("authentication flow", k=8, hop=1)
pack   = kg.pack("error handling", max_nodes=10)
print(pack.to_markdown())

Track metrics over time

from kg_utils.snapshots import SnapshotManager

mgr = SnapshotManager(".mykg/snapshots", package_name="my-kg")

snapshot = mgr.capture(
    version="1.0.0",
    branch="main",
    graph_stats_dict=kg.stats(),
)
mgr.save_snapshot(snapshot)

snaps = mgr.list_snapshots(limit=5)
delta = mgr.diff_snapshots(snaps[-1]["key"], snaps[0]["key"])

API Reference

kg_utils.specs

kg_utils.extractor

kg_utils.store

kg_utils.semantic

kg_utils.pipeline

kg_utils.snapshots

kg_utils.synthesis

Full reference: docs/synthesis.md

kg_utils.viz

Requires the viz extra.

kg_utils.analysis

Project Structure

KG_utils/
├── pyproject.toml
├── docs/
│   └── synthesis.md          # Synthesis sub-package reference
├── src/
│   └── kg_utils/
│       ├── __init__.py
│       ├── specs.py          # NodeSpec, EdgeSpec, BuildStats, QueryResult, SnippetPack
│       ├── extractor.py      # KGExtractor ABC
│       ├── store.py          # GraphStore (SQLite)
│       ├── semantic.py       # SemanticIndex, SentenceTransformerEmbedder, SeedHit
│       ├── vector_backend.py # VectorBackend protocol, SqliteVecBackend (default), LanceDBBackend
│       ├── pipeline.py       # KGModule concrete base class
│       ├── module.py         # Re-export shim
│       ├── embed.py          # Embedder protocol, model registry
│       ├── embedder.py       # SentenceTransformerEmbedder factory functions
│       ├── corpus_embedder.py # CorpusEmbedder / EmbeddingCache: multi-worker corpus embedding
│       ├── retrieval/        # Serialize + enrich KG hits (hit_to_dict, attach_content_by_sqlite)
│       │   ├── __init__.py
│       │   └── hits.py
│       ├── worker/           # RunPod /runsync client (WorkerClient, decode/handle helpers)
│       │   ├── __init__.py
│       │   ├── client.py
│       │   └── ops.py
│       ├── snapshots/
│       │   ├── __init__.py
│       │   ├── models.py     # Snapshot, SnapshotManifest, PruneResult
│       │   └── manager.py    # SnapshotManager
│       ├── synthesis/
│       │   ├── __init__.py   # Public API + factory functions
│       │   ├── _config.py    # TextBackend, ImageBackend, TextConfig, ImageConfig, env factories
│       │   ├── _text.py      # TextSynthesizer
│       │   ├── _image.py     # ImageSynthesizer
│       │   └── factory.py    # Per-request backend override helpers
│       ├── viz/              # Shared graph rendering (viz extra)
│       │   ├── __init__.py   # build_graph_html, select_nodes, GraphTheme, TooltipSpec
│       │   ├── graph_html.py # Interactive HTML renderer (vis-network inlined)
│       │   ├── theme.py      # GraphTheme, KindStyle, with_alpha
│       │   └── tooltip.py    # TooltipSpec, TooltipRow
│       └── analysis/
│           ├── __init__.py   # load_scores, available_metrics, ScoreSet
│           └── scores.py     # Read persisted centrality out of SQLite
└── tests/
    ├── test_store.py               # GraphStore unit tests
    ├── test_pipeline_utils.py      # Pipeline utility function tests
    ├── test_pipeline_module.py     # End-to-end integration tests (--integration)
    ├── test_types.py               # Spec dataclass and KGExtractor tests
    ├── test_snapshots.py           # Snapshot lifecycle tests
    ├── test_integration.py         # Cross-module integration tests
    ├── test_synthesis_config.py    # Config defaults and env-var priority chains (44 tests)
    ├── test_synthesis_text.py      # TextSynthesizer with mocked openai client (38 tests)
    └── test_synthesis_image.py     # ImageSynthesizer with mocked backends (34 tests)

Development

git clone https://github.com/Flux-Frontiers/KG_utils.git
cd KG_utils
poetry install --with dev

Run the fast test suite (no model downloads):

poetry run pytest -m "not integration"

Run all tests including semantic/integration (requires [semantic] extra):

poetry run pytest

Citation

If you use kgmodule-utils in research or a project, please cite it:

APA

Suchanek, E. G. (2026). kgmodule-utils: Shared SDK for the KGModule Knowledge-Graph Ecosystem (Version 0.9.0) [Software]. Flux-Frontiers. https://doi.org/10.5281/zenodo.21387077

BibTeX

@software{suchanek_kgmodule_utils,
  author    = {Suchanek, Eric G.},
  title     = {{kgmodule-utils}: Shared SDK for the KGModule Knowledge-Graph Ecosystem},
  version   = {0.9.0},
  year      = {2026},
  publisher = {Flux-Frontiers},
  url       = {https://github.com/Flux-Frontiers/KG_utils},
  doi       = {10.5281/zenodo.21387077},
}

Citation metadata is also available in CITATION.cff.

License

Elastic License 2.0 — see LICENSE.

Free to use, modify, and distribute. You may not offer the software as a hosted or managed service to third parties. Commercial use internally is permitted.