semantix-ai 0.1.5.post2

A Semantic Type System for AI outputs — validate intent, not just shape.

Semantix

A Semantic Type System for AI Outputs

Define what your LLM output should mean, not just what shape it has.

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The Problem

You validate your LLM outputs with Pydantic — great, the JSON is well-formed. But the model just returned a "polite decline" that says "I'd rather gouge my eyes out." It passes your type checks. It fails the vibe check.

Pydantic validates shape. Semantix validates meaning.

from semantix import Intent, validate_intent

class ProfessionalDecline(Intent):
    """The text must politely decline an invitation without being rude or aggressive."""

@validate_intent
def decline_invite(event: str) -> ProfessionalDecline:
    return call_my_llm(event)   # returns a plain string

result = decline_invite("the company retreat")
# ✓ result is a ProfessionalDecline instance — validated by a judge
# ✗ raises SemanticIntentError if the output is rude, off-topic, etc.

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Installation

# Core (bring your own judge)
pip install semantix-ai

# With OpenAI judge (GPT-4o-mini — accurate, needs API key)
pip install "semantix-ai[openai]"

# With embedding judge (sentence-transformers — fast, runs locally)
pip install "semantix-ai[embeddings]"

# With NLI judge (cross-encoder entailment — accurate, runs locally)
pip install "semantix-ai[nli]"

# Everything
pip install "semantix-ai[all]"

Note: The package name on PyPI is semantix-ai. The import is from semantix import ....

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Quick Start

1. Define an Intent

An Intent is a class whose docstring describes a semantic contract:

from semantix import Intent

class PositiveSentiment(Intent):
    """The text must express a clearly positive, optimistic, or encouraging sentiment."""
    threshold = 0.85  # optional — default is 0.8

2. Decorate your LLM call

from semantix import validate_intent

@validate_intent
def encourage(name: str) -> PositiveSentiment:
    return openai_client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": f"Encourage {name}"}],
    ).choices[0].message.content

3. Handle failures

from semantix import SemanticIntentError

try:
    result = encourage("Alice")
    print(result.text)  # the validated string
except SemanticIntentError as e:
    print(f"Failed: {e.intent_name} (score={e.score})")

That's it. Three steps. Your LLM output is now semantically typed.

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Why Not Just Use Guardrails / NeMo / Instructor?

	Semantix	Guardrails AI	NeMo Guardrails	Instructor
Validates meaning	✅ Intent docstrings	❌ Schema-focused	✅ Dialogue rails	❌ Schema-focused
Zero required deps	✅ Core is dependency-free	❌ Heavy dependency tree	❌ Heavy dependency tree	❌ Requires Pydantic
Works with any LLM	✅ Decorator on any function	⚠️ LLM-specific wrappers	⚠️ Config-driven	⚠️ Patched clients
Pluggable judges	✅ LLM / Embedding / NLI / Custom	❌ Fixed validators	❌ Fixed approach	❌ Fixed approach
Lines of code to validate	~5	~20+	~30+ (YAML config)	~10
Composable	✅ A & B, A | B	❌	❌	❌

Semantix is not a replacement for structural validation — use Pydantic for that. Semantix is the next layer: after you know the shape is right, verify the meaning is right too.

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Universal Agent Support (MCP)

Semantix ships with a built-in MCP server so any AI agent can run semantic intent checks as a tool — no code changes required.

pip install "semantix-ai[mcp,nli]"
mcp run semantix/mcp/server.py

The verify_text_intent tool accepts any text and intent description, returns a confidence score, and provides structured correction suggestions when validation fails — enabling cross-agent self-healing.

Add to Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "semantix-verify": {
      "command": "mcp",
      "args": ["run", "semantix/mcp/server.py"],
      "cwd": "/path/to/your/semantix-ai"
    }
  }
}

Claude can then call verify_text_intent to validate any text against a semantic requirement before responding.

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Zero-Latency Infrastructure (NEW in v0.1.5)

Quantized Inference

Semantix ships a quantized NLI judge that runs INT8 ONNX inference — no PyTorch, no GPU, ~50% faster:

pip install "semantix-ai[turbo]"

from semantix import validate_intent

# Automatically uses QuantizedNLIJudge when onnxruntime is installed
@validate_intent
def review(text: str) -> LegalCompliance:
    return call_llm(text)

Total dependency footprint: ~25MB (onnxruntime + tokenizers) vs ~500MB+ for PyTorch.

Forensic Analysis on Failure

When validation fails, the ForensicJudge identifies exactly which tokens caused the contradiction:

from semantix import ForensicJudge, QuantizedNLIJudge

judge = ForensicJudge(QuantizedNLIJudge())

@validate_intent(judge=judge)
def review(text: str) -> LegalCompliance:
    return call_llm(text)

# On failure, Verdict.reason contains:
# ## Breach Report
# **Score:** 0.0823
# ### Token Attribution
# **indemnify** (0.72), **forfeit** (0.58), **waive** (0.41)
# ### Summary
# Intent failed. High contradiction detected. Suspect Tokens: [indemnify, forfeit, waive]

Immutable Audit Trail

Every validation is logged as a hash-chained JSON-LD certificate:

from semantix.audit.engine import AuditEngine

engine = AuditEngine()  # singleton
engine.verify_chain()   # True if no tampering
engine.flush(Path("audit.jsonl"))

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Features

Swappable Judges

Choose the right speed/accuracy tradeoff for your use case:

from semantix import EmbeddingJudge, LLMJudge, NLIJudge, CachingJudge

# Default — NLI entailment with softmax calibration (no API key, runs locally)
# Uses correct entailment index + softmax for true 0–1 probability scores
@validate_intent(judge=NLIJudge())
def default_fn(x: str) -> MyIntent: ...

# Fast — local cosine similarity (no API key needed)
@validate_intent(judge=EmbeddingJudge())
def fast_fn(x: str) -> MyIntent: ...

# Accurate — asks GPT-4o-mini for a 0–1 confidence score + reason
@validate_intent(judge=LLMJudge(model="gpt-4o-mini"))
def accurate_fn(x: str) -> MyIntent: ...

# Cached — wraps any judge with LRU cache
@validate_intent(judge=CachingJudge(NLIJudge(), maxsize=256))
def cached_fn(x: str) -> MyIntent: ...

Informed Self-Healing Retries

On failure, the decorator injects structured feedback so the LLM knows exactly what went wrong — zero boilerplate:

from typing import Optional
from semantix import validate_intent

@validate_intent(retries=2)
def decline(event: str, semantix_feedback: Optional[str] = None) -> ProfessionalDecline:
    prompt = f"Decline this invite: {event}"
    if semantix_feedback:
        prompt += f"\n\n{semantix_feedback}"
    return call_llm(prompt)

On the first call semantix_feedback is None. If validation fails, the next retry receives a Markdown report with the score, reason, requirement, and rejected output — so the LLM can self-correct.

Benchmark result: Self-healing improves reliability from 21.1% to 70.0% (+48.9%) across 3 intent categories.

The manual get_last_failure() API is also still available for custom feedback formatting.

Composite Intents

Combine multiple intents with & (all must pass) or | (any must pass):

from semantix import AllOf, AnyOf

# Operator syntax
PoliteAndPositive = ProfessionalDecline & PositiveSentiment

# Function syntax
FlexibleDecline = AnyOf(ProfessionalDecline, CasualDecline)

@validate_intent(judge=EmbeddingJudge())
def respond(msg: str) -> PoliteAndPositive: ...

Async Support

Works transparently with async def:

@validate_intent(judge=EmbeddingJudge())
async def encourage(name: str) -> PositiveSentiment:
    response = await async_openai_call(name)
    return response

Streaming

Validate once the full stream is assembled:

from semantix import StreamCollector

# Iterator wrapper — yields chunks through, validates at the end
sc = StreamCollector(ProfessionalDecline, judge=my_judge)
for chunk in sc.wrap(llm_stream()):
    print(chunk, end="")
result = sc.result()  # validated Intent or raises

# Async context manager
async with StreamCollector(ProfessionalDecline, judge=my_judge) as sc:
    async for chunk in llm_stream:
        sc.feed(chunk)
result = sc.result()

Observability

All validation events are emitted via Python's logging module:

import logging
logging.getLogger("semantix").setLevel(logging.DEBUG)

INFO  semantix.validation | intent=ProfessionalDecline passed=True score=0.92 latency_ms=45.23 attempt=1

Custom Judges

Implement the Judge interface to plug in any backend:

from semantix import Judge, Verdict

class MyCustomJudge(Judge):
    def evaluate(self, output: str, intent_description: str, threshold: float = 0.8) -> Verdict:
        score = my_scoring_function(output, intent_description)
        return Verdict(passed=score >= threshold, score=score, reason="Custom logic")

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API Reference

Symbol	Description
Intent	Base class — subclass with a docstring to define a semantic type
SemanticIntentError	Raised when validation fails (.output, .score, .intent_name)
@validate_intent	Decorator — validates return values against their Intent type hint
get_last_failure()	Returns the last SemanticIntentError in current context (for smart retries)
Judge	Abstract base — implement .evaluate() for custom backends
Verdict	Dataclass — .passed, .score, .reason
LLMJudge	OpenAI-based judge (accurate, needs API key)
EmbeddingJudge	Sentence-transformers cosine similarity judge (fast, local)
NLIJudge	Cross-encoder NLI entailment judge (softmax-calibrated, local, default)
CachingJudge	LRU cache wrapper for any judge
AllOf(*intents)	Composite — all intents must be satisfied
AnyOf(*intents)	Composite — at least one intent must be satisfied
QuantizedNLIJudge	INT8 ONNX NLI judge — fast, no PyTorch (needs onnxruntime)
ForensicJudge	Wrapper — token-level attribution Breach Report on failure
AuditEngine	Hash-chained JSON-LD audit trail singleton
StreamCollector	Validates streamed LLM output once fully assembled

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Project Structure

semantix/
├── __init__.py          # Public API
├── intent.py            # Intent base class + metaclass
├── exceptions.py        # SemanticIntentError
├── decorator.py         # @validate_intent (retries, self-healing)
├── composite.py         # AllOf / AnyOf combinators
├── observability.py     # Structured logging
├── streaming.py         # StreamCollector
├── audit/
│   ├── __init__.py      # Package marker
│   └── engine.py        # AuditEngine (JSON-LD + SHA-256 chain)
├── judges/
│   ├── __init__.py      # Judge ABC + Verdict
│   ├── embedding.py     # EmbeddingJudge
│   ├── llm.py           # LLMJudge (granular 0–1 scoring)
│   ├── nli.py           # NLIJudge (softmax + entailment mapping)
│   ├── quantized_nli.py # QuantizedNLIJudge (ONNX INT8)
│   ├── forensic.py      # ForensicJudge (token attribution)
│   └── caching.py       # CachingJudge
└── mcp/
    └── server.py        # MCP server (verify_text_intent tool)

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Development

git clone https://github.com/labrat-akhona/semantix-ai.git
cd semantix-ai

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
python -m pytest tests/ -v

# Lint
ruff check .

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Contributing

Contributions are welcome! Please open an issue first to discuss what you'd like to change.

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License

MIT — see LICENSE for details.

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Built by Akhona Eland in South Africa 🇿🇦