Semantic Python

Python, but == can understand meaning.

Semantic Python is not a model like Laya or Jev. It is an opt-in Python value layer that turns backend inference into inspectable decisions for normal Python control flow. Only Semantic(...) values can invoke a model.

Compare Jev, Laya, JevLang, and Semantic Python

Install

python -m pip install 'semantic-python[openai]'
export OPENAI_API_KEY="..."

Demo

Use natural-language intent in ordinary loops and async code:

functions · if · try/except · for · while · comprehensions · generators · classes · with · async/await

import asyncio

from semantic_python import (
    OpenAIBackend,
    Semantic,
    UncertainDecisionError,
    configure,
)

STOP = "the user wants to stop"

configure(
    backend=OpenAIBackend(model="gpt-6-luna"),
    true_threshold=0.85,
    false_threshold=0.15,
    uncertainty="raise",
)


def decide(text: str):
    return Semantic(text) == STOP


messages = [
    "Continue with the next ticket.",
    "Save the work and close this session.",
]

for text in messages:
    decision = decide(text)
    print(f"stop={decision.value} p={decision.probability:.2f} model={decision.model}")

    try:
        if decision:
            print("Stop requested")
            break
    except UncertainDecisionError:
        print("Send to human review")


async def decide_async(text: str):
    return await asyncio.to_thread(decide, text)


async_result = asyncio.run(decide_async(text))
opposite = Semantic(text) != STOP

print("async cache hit:", async_result.cached)
print("negated:", opposite.value, opposite.probability)

The comparison returns a bool-coercible Decision, so if and loops work normally while probability, model provenance, caching, and replay remain available. != negates the same judgment instead of issuing a second inference.

More Python patterns

These examples continue with the configured backend and STOP proposition from the main demo.

while

queue = iter(["Keep going", "Not yet", "Finished for today"])
message = Semantic(next(queue))

while message != STOP:
    print("Processing:", message.value)
    message = Semantic(next(queue))

Comprehensions and generators

texts = ["Continue", "Pause here", "That is all for today"]
decisions = {text: decide(text) for text in texts}

stop_requests = [text for text, decision in decisions.items() if decision.value]

probabilities = (decision.probability for decision in decisions.values())
print(stop_requests, list(probabilities))

Classes

from dataclasses import dataclass


@dataclass(frozen=True, slots=True)
class IntentRule:
    proposition: str

    def evaluate(self, text: str):
        return Semantic(text) == self.proposition


stop_rule = IntentRule("the user wants to stop")
decision = stop_rule.evaluate("Please close the session")
print(decision.value, decision.probability)

Context managers and offline tests

from semantic_python import FakeBackend, configuration

state = "I'm done"
fixtures = {(state, STOP): (True, 0.99)}

with configuration(backend=FakeBackend(fixtures), cache=False):
    assert Semantic(state) == STOP

Backends

Application code stays the same when the configured backend changes.

Backend	Install	Purpose
OpenAIBackend	semantic-python[openai]	Hosted inference
LayaBackend	semantic-python[laya]	Local inference
FakeBackend	Included	Deterministic offline tests

OpenAI receives the wrapped state and proposition. Laya may download model checkpoints on first use. FakeBackend performs no network requests.

Jev is a hosted decision model/API, Laya is an open-weights decision engine, and Semantic Python is the Python value layer around explicitly selected backends. Jev integration is roadmap research and is not currently implemented.

Semantics

Expression	Result
plain_string == other	Ordinary Python equality
Semantic(text) == proposition	Inspectable semantic Decision
Semantic(text) != proposition	Negated cached judgment
semantic is other	Ordinary Python identity
Semantic(...) == Semantic(...)	Rejected as ambiguous
hash(Semantic(...))	Rejected; inference is never used for hashing

Probabilities at or above 0.85 resolve true, probabilities at or below 0.15 resolve false, and the default policy raises UncertainDecisionError between those thresholds. Backend failures are raised rather than converted to False.

Examples

• OpenAI demo
• Python control-flow demo
• Stop loop
• Ticket routing
• Uncertainty and escalation

The offline examples run without credentials:

python -m pip install -e .
python examples/stop_loop.py
python examples/ticket_routing.py
python examples/escalation.py

Safety

• Model probability is an estimate, not truth or authorization.
• Hosted inference can transmit sensitive text and incur cost.
• Consequential actions should use conservative thresholds and human review.

See semantics, backends, and uncertainty for the complete behavior.

MIT licensed. Experimental and pre-alpha.