cassetteql

S3-native knowledge graphs with calibrated reasoning. Immutable cassette files, split Parquet indexes, a Dempster–Shafer reasoner that tells you when it doesn't know, a trained sheaf GNN prior, and Kan-based schema migration. The bundled SPLADE-tiny model (17 MB) means no GPU, no model download, no API keys.

  ┌──────────────────────────────────┐
  │  ▓▓      .inf  cassette      ▓▓  │  one cassette =
  │  ●                            ●  │  one ingest batch
  │  ╲╲─────────────────────────╱╱   │
  │   header  ·  records  ·  footer  │
  │     │         │           │      │
  │     │         │           └─ JSON: offsets + stats
  │     │         └─ gzip frames (range-addressable)
  │     └─ schema_ref · created_at
  └──────────────────────────────────┘
      immutable · content-addressed · S3-native

pip install cassetteql

Imports as cognition (same pattern as pillow/PIL):

from cognition.cassette import InfonStore, Query, Analyst

One-minute start

from cognition.cassette import InfonStore, Query

store = InfonStore("./data/chips", schema_path="schema.json")

# Delta ingest — idempotent, reports coverage diagnostics for free.
result = store.ingest(documents)
print(result["report"].summary())

# Calibrated single-claim verdict.
v = store.ask(Query().where(subject="toyota",
                             predicate="invest",
                             object="solid_state"))
print(v.label, v.mass.supports, v.mass.theta)   # SUPPORTS 0.53 0.29

# Multi-hop MCTS with retraction-aware chain mass.
v = store.connect("toyota", "catl")

# One tree walk resolves connectivity to many targets.
vs = store.any_of("toyota", {"catl", "lg", "samsung", "sk_hynix"})

Swap the root URI for s3://bucket/prefix and the same code runs against S3:

pip install 'cassetteql[s3]'
store = InfonStore("s3://acme/chips", schema_path="schema.json")

What makes it different

Optional extras

pip install 'cassetteql[s3]'       # S3 / GCS / Azure via fsspec
pip install 'cassetteql[agent]'    # Strands Analyst
pip install 'cassetteql[aws]'      # Lambda container deploy + S3
pip install 'cassetteql[all]'      # everything optional

Measured

Each row below is a reproducible probe — a standalone Python script that writes a temp store, runs the scenario, and asserts the result. Probes ship inside the source distribution.

Dependencies

17 MB SPLADE-tiny ships inside the wheel — one pip install, no follow-up download, no GPU.

License

Apache-2.0. The bundled SPLADE-tiny-msmarco model is also Apache-2.0.