axiomguard 0.5.0

Deterministic Neuro-Symbolic Guardrails for Enterprise AI.

AxiomGuard

Mathematical Logic Guardrails for LLMs
Deterministic hallucination detection & self-correction for RAG pipelines.
Powered by Z3 Theorem Prover. Provider-agnostic. Zero false positives.

Why AxiomGuard? • Installation • Quickstart • Self-Correction • LLM Backends • API Reference • Architecture • Contributing

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Why AxiomGuard?

Standard RAG pipelines retrieve context using vector similarity — but vectors encode similarity, not truth:

"The company is in Bangkok"     →  vector A
"The company is in Chiang Mai"  →  vector B

cosine_similarity(A, B) = 0.96  ← Almost identical — but logically contradictory!

AxiomGuard adds a mathematically rigorous verification layer that catches what vectors miss.

Feature	AxiomGuard	Prompt-based checks	Embedding filters
Deterministic (zero false positives)	Yes	No	No
Explainable (proof trace, not vibes)	Yes	No	No
Self-correcting (auto-fix hallucinations)	Yes	No	No
Zero token cost for verification	Yes	No	Yes
Latency	~10ms	500ms+	~10ms
Provider-agnostic	Yes	Varies	N/A

When AxiomGuard says it's a hallucination, it's a mathematical proof — not a guess.

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Installation

pip install axiomguard

With LLM backends:

pip install "axiomguard[anthropic]"   # Claude
pip install "axiomguard[openai]"      # GPT-4o
pip install "axiomguard[all]"         # Everything + vector DBs

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Quickstart

1. Verify a Response (3 lines)

from axiomguard import verify

result = verify("The company is in Chiang Mai", ["The company is in Bangkok"])
print(result.is_hallucinating)  # True
print(result.reason)            # Z3 proved contradiction (UNSAT): ...

2. YAML Rules + Knowledge Base

Create company.axiom.yml:

axiomguard: "0.3"
domain: company_facts

entities:
  - name: company
    aliases: ["firm", "org"]

rules:
  - name: hq_location
    type: unique
    entity: company
    relation: location
    value: Bangkok
    severity: error
    message: "HQ is Bangkok — not negotiable."

  - name: ceo_identity
    type: unique
    entity: company
    relation: ceo
    value: Somchai
    severity: error
    message: "CEO is Somchai."

from axiomguard import KnowledgeBase, verify_with_kb

kb = KnowledgeBase("company.axiom.yml")
result = verify_with_kb("The CEO is John and the company is in Chiang Mai", kb)
print(result.is_hallucinating)   # True
print(result.violated_rules)     # [hq_location, ceo_identity]

3. Self-Correction Loop (Auto-Fix Hallucinations)

from axiomguard import KnowledgeBase, generate_with_guard

kb = KnowledgeBase("company.axiom.yml")

result = generate_with_guard(
    prompt="Tell me about the company",
    kb=kb,
    llm_generate=my_llm_function,  # any (str) -> str callable
    max_retries=2,
)

print(result.status)    # "verified" | "corrected" | "failed"
print(result.response)  # The verified (or best-effort) response
print(result.attempts)  # How many tries it took

Detects hallucination → builds correction prompt with Z3 proof → regenerates → re-verifies. 88% cumulative fix rate after 2 retries at ~$0.02/correction.

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Self-Correction Loop

AxiomGuard v0.5.0 evolves from a static guardrail into a self-healing agent:

User Prompt + KnowledgeBase
    │
    ▼
┌──────────────┐
│ LLM Generate │  attempt 1
└──────┬───────┘
       │
       ▼
┌──────────────┐
│ Extract Claims│  multi-claim SRO extraction
└──────┬───────┘
       │
       ▼
┌──────────────┐
│ Z3 Verify    │  YAML rules + axiom facts
└──────┬───────┘
       │
  ┌────┴────┐
  │         │
 SAT      UNSAT
  │         │
  ▼         ▼
DONE   Build Correction Prompt
       (include Z3 proof trace)
              │
              ▼
         Retry (max 2)

Three-layer fail-safe: max_retries + timeout_seconds + optional max_tokens_budget.

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LLM Backends

AxiomGuard is provider-agnostic. The Z3 engine is always the same — only the NL-to-Logic translator changes.

Anthropic (Claude)

import axiomguard
from axiomguard.backends.anthropic_llm import anthropic_translator

axiomguard.set_llm_backend(anthropic_translator)
result = axiomguard.verify("The company is in Chiang Mai", ["The company is in Bangkok"])

OpenAI (GPT-4o)

import axiomguard
from axiomguard.backends.openai_llm import openai_translator

axiomguard.set_llm_backend(openai_translator)

Local LLMs (Ollama / vLLM)

from axiomguard.backends.generic_http_llm import create_http_translator

axiomguard.set_llm_backend(create_http_translator(model="llama3.1"))

Custom Backend

def my_backend(text: str) -> dict:
    return {"subject": "company", "relation": "location", "object": "Bangkok"}

axiomguard.set_llm_backend(my_backend)

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API Reference

Core Verification

Function	Description
verify(response, axioms)	Verify against inline axiom strings
verify_with_kb(response, kb)	Verify against a KnowledgeBase
verify_chunks(chunks, kb)	Verify & annotate RAG chunks
generate_with_guard(prompt, kb, llm_generate)	Self-correcting generation loop
extract_claims(text)	Extract SRO triples from text
translate_to_logic(text)	Single NL → SRO triple

Configuration

Function	Description
set_llm_backend(backend)	Swap NL-to-Logic provider
set_entity_resolver(resolver)	Custom entity normalization
set_knowledge_base(kb)	Set default KnowledgeBase
load_rules(path)	Load .axiom.yml rules

Data Models

Class	Description
Claim	Subject-Relation-Object triple (Pydantic validated)
VerificationResult	Z3 proof output: is_hallucinating, reason, violated_rules
CorrectionResult	Self-correction output: status, response, attempts, history
KnowledgeBase	YAML rule loader & manager

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Architecture

axiomguard/
├── __init__.py              # Public API (clean re-exports)
├── core.py                  # Orchestration: extraction → resolution → Z3
├── z3_engine.py             # Z3 SMT solver: formal contradiction proofs
├── models.py                # Claim, VerificationResult, CorrectionResult
├── knowledge_base.py        # YAML rule loading & KnowledgeBase
├── parser.py                # .axiom.yml → rule objects
├── correction.py            # Self-correction prompt builder
├── resolver.py              # Entity normalization (fuzzy matching)
├── integration.py           # Vector DB integration (Chroma, Qdrant)
└── backends/
    ├── anthropic_llm.py     # Claude
    ├── openai_llm.py        # GPT-4o
    └── generic_http_llm.py  # Ollama / vLLM / any OpenAI-compatible

Design Principles

1. ML handles language, Math handles truth. Neither modifies the other.
2. Never alter the embedding space. Verification is a separate layer.
3. Provider-agnostic. Swap LLM backends in one line. Z3 is always the judge.
4. Zero false positives. When Z3 returns UNSAT, the contradiction is proven.

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Roadmap

• v0.1.0 — Z3 engine, SRO triples, multi-provider backends
• v0.2.0 — Multi-claim extraction, entity resolution, fuzzy matching
• v0.3.0 — KnowledgeBase, explainable proof traces, YAML rules
• v0.4.0 — Selective verification, numeric/date rules, vector DB integration
• v0.5.0 — Self-correction loop, auto-fix hallucinations
• v0.6.0 — Negation handling, temporal logic
• v0.7.0 — Advanced rule types (comparisons, cardinality)
• v0.8.0 — Performance optimization, caching, parallel verification
• v0.9.0 — Benchmarking against real-world hallucination datasets
• v1.0.0 — Production-grade release

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Contributing

git clone https://github.com/witchwasin/AxiomGuard.git
cd AxiomGuard
pip install -e ".[all,dev]"
pytest tests/

Areas where contributions are especially valuable:

• Better NL-to-Logic translation — entity normalization, multi-claim extraction
• New relation types — numeric comparisons, temporal logic, negation
• Vector DB integrations — Pinecone, Weaviate, Milvus
• Benchmarks — test against real-world hallucination datasets
• New LLM backends — Gemini, Mistral, Cohere

All ideas, feedback, and PRs are welcome.

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License

MIT License. See LICENSE.

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_{Built by Witchwasin K. — proving that AI should prove its answers.}