context-fabric 0.4.1

Graph-based corpus engine for annotated text with efficient traversal and search

Context-Fabric

A graph-based corpus engine for annotated text with efficient traversal and search.

Overview

Context-Fabric provides a powerful data model for working with annotated text corpora as graphs. It enables efficient navigation, feature lookup, and pattern-based search across large textual datasets.

Forked from Dirk Roorda's Text-Fabric.

Installation

pip install context-fabric

Quick Start

from cfabric.core import Fabric

# Load a dataset
CF = Fabric(locations='path/to/data')
api = CF.load('feature1 feature2')

# Navigate nodes
for node in api.N():
    print(api.F.feature1.v(node))

# Use locality
embedders = api.L.u(node)
embedded = api.L.d(node)

Core API

• N (Nodes) - Walk through nodes in canonical order
• F (Features) - Access node feature values
• E (Edges) - Access edge feature values
• L (Locality) - Navigate between related nodes
• T (Text) - Retrieve text representations
• S (Search) - Search using templates

Performance

Context-Fabric uses memory-mapped numpy arrays for dramatically faster loading and reduced memory consumption compared to Text-Fabric's pickle-based caching.

Benchmarks (BHSA Hebrew Bible corpus — 1.4M nodes, 109 features)

Metric	Text-Fabric	Context-Fabric	Improvement
Load Time	7.9s	0.7s	11x faster
Memory Usage	6.3 GB	305 MB	95% reduction
Compile Time	8s	91s	one-time cost
Cache Size	138 MB	859 MB	6x larger

The key insight: compilation happens once, loading happens every session. Context-Fabric trades one-time compile cost for dramatic runtime efficiency:

• Memory-mapped arrays: Data stays on disk, accessed on-demand
• Efficient sparse iteration: Uses numpy vectorized operations instead of Python loops
• Cached materialization: Dictionary views computed once per session

Parallel Worker Scaling

Memory-mapped arrays enable efficient parallel processing. Multiple workers share the same mmap'd data instead of each loading a full copy into RAM.

Spawn mode (cold start — each worker loads independently):

Metric	Text-Fabric	Context-Fabric	Savings
Total (4 workers)	7.7 GB	1.3 GB	84% less
Per worker	1.9 GB	315 MB	6x less

Fork mode (API scenario — pre-load then fork workers):

Metric	Text-Fabric	Context-Fabric	Savings
Total (4 workers)	6.3 GB	398 MB	94% less
Per worker	1.6 GB	99 MB	16x less

Memory measured as total RSS after loading from cache on BHSA corpus.

Run the benchmark yourself:

python benchmarks/compare_performance.py --source path/to/tf/data --workers 4

Testing

See TESTING.md for how to run tests.

Authors

• Cody Kingham
• Dirk Roorda

License

MIT License - see LICENSE for details.