stl-parser 1.8.2

Semantic Tension Language (STL) Parser - A lightweight parser for structured knowledge representation

STL Parser (v1.8.2)

A comprehensive Python toolkit for Semantic Tension Language (STL) — parse, build, validate, query, diff, stream, and repair structured knowledge.

Overview

stl-parser provides feature parity with the JSON ecosystem for STL documents:

Capability	Functions	JSON Equivalent
Parse & Serialize	parse(), to_json(), to_stl(), to_rdf()	json.loads() / json.dumps()
Build Programmatically	stl(), stl_doc(), StatementBuilder	Manual dict construction
Schema Validation	load_schema(), validate_against_schema()	JSON Schema
LLM Output Repair	clean(), repair(), validate_llm_output()	N/A (STL-unique)
Query & Filter	find(), filter_statements(), select(), stl_pointer()	jq / JSONPath
Diff & Patch	stl_diff(), stl_patch(), diff_to_text()	json-diff / json-patch
Streaming I/O	STLEmitter, STLReader, stream_parse()	NDJSON streaming
Graph Analysis	STLGraph, STLAnalyzer	N/A (STL-unique)
Confidence Decay	effective_confidence(), decay_report()	N/A (STL-unique)
CLI	10 commands (validate, convert, query, diff, ...)	jq CLI

Installation

From Source

git clone https://github.com/scos-lab/semantic-tension-language.git
cd semantic-tension-language/parser
pip install -e .

Development Installation

pip install -e ".[dev]"

Quick Start

Parse STL

from stl_parser import parse

result = parse('[Einstein] -> [Theory_Relativity] ::mod(confidence=0.98, rule="empirical")')
stmt = result.statements[0]
print(stmt.source.name)          # "Einstein"
print(stmt.modifiers.confidence)  # 0.98

Build Programmatically

from stl_parser import stl

stmt = stl("[Rain]", "[Flooding]").mod(confidence=0.85, rule="causal", strength=0.8).build()
print(str(stmt))  # [Rain] -> [Flooding] ::mod(confidence=0.85, rule="causal", strength=0.8)

Build a Multi-Statement Document

from stl_parser import stl, stl_doc

doc = stl_doc(
    stl("[Rain]", "[Flooding]").mod(rule="causal", confidence=0.85),
    stl("[Flooding]", "[Evacuation]").mod(rule="causal", confidence=0.90),
)
print(len(doc.statements))  # 2

Validate Against a Schema

from stl_parser import parse, load_schema, validate_against_schema

schema = load_schema("docs/schemas/causal.stl.schema")
result = parse('[Rain] -> [Flooding] ::mod(rule="causal", confidence=0.85, strength=0.8)')
validation = validate_against_schema(result, schema)
print(f"Valid: {validation.is_valid}")

Clean LLM Output

LLMs (especially small local models) generate imperfect STL. The 3-stage clean -> repair -> parse pipeline auto-fixes common issues:

from stl_parser import validate_llm_output

# Spaces in anchors, wrong arrow, missing ::, typo, value out of range — all fixed
result = validate_llm_output("[Big Problem] -> [Smart Solution] mod(confience=1.2, strenght=0.3)")
print(result.statements[0])
# [Big_Problem] -> [Smart_Solution] ::mod(confidence=1.0, strength=0.3)

print(f"Valid: {result.is_valid}, Repairs: {len(result.repairs)}")
# Valid: True, Repairs: 6

Auto-repair capabilities:

LLM Error	Example	Repaired To
Spaces in anchors	[Heavy Rain]	[Heavy_Rain]
Wrong arrow	=>, ➡, —>	->
Missing :: prefix	mod(...)	::mod(...)
Missing brackets	Rain -> [Flood]	[Rain] -> [Flood]
Modifier typos	confience, strenght	confidence, strength
Value out of range	confidence=1.5	confidence=1.0
Unquoted strings	rule=causal	rule="causal"
Code fences	```stl ... ```	Extracted content
Multi-line statements	Split across lines	Merged

This makes STL a reliable LLM-to-program communication protocol — small models (7B-14B) that struggle with JSON tool calling can generate STL naturally, and the parser handles the rest.

Query Statements

from stl_parser import parse, find, find_all, filter_statements, select

result = parse("""
[Rain] -> [Flooding] ::mod(rule="causal", confidence=0.85)
[Sun] -> [Evaporation] ::mod(rule="causal", confidence=0.90)
[Wind] -> [Erosion] ::mod(rule="causal", confidence=0.70)
""")

# Find first match ("source" resolves to source.name)
stmt = find(result, source="Rain")

# Filter returns a new ParseResult
high_conf = filter_statements(result, confidence__gte=0.85)
print(len(high_conf.statements))  # 2

# Extract a single field from all statements
names = select(result, field="source")   # ["Rain", "Sun", "Wind"]
confs = select(result, field="confidence")  # [0.85, 0.9, 0.7]

Diff & Patch

from stl_parser import parse, stl_diff, stl_patch

before = parse('[A] -> [B] ::mod(confidence=0.8)')
after  = parse('[A] -> [B] ::mod(confidence=0.9)')

diff = stl_diff(before, after)
print(diff.summary)  # {added: 0, removed: 0, modified: 1}

patched = stl_patch(before, diff)

Streaming I/O

from stl_parser import STLEmitter, stream_parse

# Write statements to a file
with STLEmitter("output.stl") as emitter:
    emitter.emit("[A]", "[B]", confidence=0.9, rule="causal")
    emitter.emit("[C]", "[D]", confidence=0.8, rule="logical")

# Stream-parse a file
for stmt in stream_parse("output.stl"):
    print(f"{stmt.source.name} -> {stmt.target.name}")

Confidence Decay

from stl_parser import parse, effective_confidence

# Statement with a timestamp
result = parse('[Fact_X] -> [Conclusion_Y] ::mod(confidence=0.95, timestamp="2020-01-01T00:00:00Z")')
stmt = result.statements[0]

# Compute decayed confidence (exponential half-life)
decayed = effective_confidence(stmt, half_life_days=365)
print(f"Original: {stmt.modifiers.confidence}, Decayed: {decayed:.4f}")

CLI Reference

The stl command provides 10 subcommands:

# Validate an STL file
stl validate input.stl

# Parse and output as JSON
stl parse input.stl --json

# Convert to JSON, RDF/Turtle, JSON-LD, N-Triples
stl convert input.stl --to json --output output.json
stl convert input.stl --to rdf --format turtle --output output.ttl

# Graph analysis (nodes, edges, centrality, cycles)
stl analyze input.stl

# Build a statement from CLI
stl build "[Rain]" "[Flooding]" --mod "rule=causal,confidence=0.85"

# Clean and repair LLM output
stl clean messy_output.txt --show-repairs
stl clean messy_output.txt --schema domain.stl.schema

# Validate against a domain schema
stl schema-validate input.stl --schema medical.stl.schema

# Query with filters and field selection
stl query input.stl --where "rule=causal,confidence__gte=0.8" --select "source.name,target.name"

# Semantic diff between two files
stl diff before.stl after.stl
stl diff before.stl after.stl --format json

# Apply a diff patch
stl patch base.stl changes.json --output patched.stl

Architecture

stl_parser/                    # 19 modules
│
├── Core Layer
│   ├── grammar.py             # Lark EBNF grammar definition
│   ├── parser.py              # parse(), parse_file() — core parser with multi-line merge
│   ├── models.py              # Pydantic v2 models (Anchor, Modifier, Statement, ParseResult)
│   ├── validator.py           # Structural and semantic validation
│   ├── errors.py              # Error hierarchy (E001-E969, W001-W099)
│   └── _utils.py              # Shared utilities (sanitize_anchor_name, etc.)
│
├── Serialization Layer
│   ├── serializer.py          # to_json(), to_dict(), to_stl(), to_rdf(), from_json()
│   ├── builder.py             # stl(), stl_doc(), StatementBuilder — programmatic construction
│   └── emitter.py             # STLEmitter — thread-safe streaming writer
│
├── Analysis Layer
│   ├── graph.py               # STLGraph — NetworkX-based graph construction
│   ├── analyzer.py            # STLAnalyzer — centrality, cycles, statistics
│   └── decay.py               # Confidence decay with configurable half-life
│
├── Query Layer
│   ├── query.py               # find(), filter_statements(), select(), stl_pointer()
│   ├── diff.py                # stl_diff(), stl_patch(), diff_to_text()
│   └── reader.py              # stream_parse(), STLReader — streaming input
│
├── Validation Layer
│   ├── schema.py              # load_schema(), validate_against_schema(), .stl.schema format
│   └── llm.py                 # clean(), repair(), validate_llm_output() — 3-stage pipeline
│
└── Interface Layer
    └── cli.py                 # Typer CLI with 10 commands

Schema Ecosystem

Six domain-specific schemas are included in docs/schemas/:

Schema	Domain	Key Constraints
tcm.stl.schema	Traditional Chinese Medicine	Unicode anchors, domain required
scientific.stl.schema	Scientific research	source required, confidence 0.3-1.0
causal.stl.schema	Causal inference	strength required, rule must be "causal"
historical.stl.schema	Historical knowledge	time + source required, multi-script
medical.stl.schema	Medical/clinical	Prefixed anchors (Symptom_, Drug_, ...)
legal.stl.schema	Legal reasoning	Prefixed anchors (Law_, Regulation_, ...)

Create custom schemas using docs/schemas/_template.stl.schema.

Development

Setup

git clone https://github.com/scos-lab/semantic-tension-language.git
cd semantic-tension-language/parser
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev]"

Run Tests

pytest tests/                          # Run all 530+ tests
pytest tests/ --cov=stl_parser         # With coverage report
pytest tests/test_builder.py           # Single module
pytest tests/ -k "test_query"          # By name pattern

Code Quality

black stl_parser/          # Format
ruff stl_parser/           # Lint
mypy stl_parser/           # Type check

Documentation

• STL Core Specification v1.0
• STL Core Specification Supplement
• Project Index — Module reference and architecture overview
• Schema Ecosystem — Domain-specific schema library

Contributing

We welcome contributions. See CONTRIBUTING.md for guidelines.

License

Apache License 2.0 — see LICENSE.

Citation

@software{stl_parser_2025,
  author = {SCOS-Lab},
  title = {STL Parser: A Comprehensive Toolkit for Semantic Tension Language},
  year = {2025},
  version = {1.8.2},
  url = {https://github.com/scos-lab/semantic-tension-language}
}