Neuro-Symbolic AI Production Implementation Guide 2026

import boto3
import json
from neo4j import GraphDatabase
from typing import List, Dict, Set, Optional
import anthropic

# Initialize AWS Bedrock and Neo4j knowledge graph clients
bedrock_runtime = boto3.client('bedrock-runtime', region_name='us-east-1')
anthropic_client = anthropic.Anthropic()
neo4j_driver = GraphDatabase.driver(
    "bolt://medical-kg.example.com:7687",
    auth=("neo4j", "password")
)

# Medical ontology knowledge graph (SNOMED CT simplified)
class MedicalKnowledgeGraph:
    """Interface to medical knowledge graph for constraint extraction"""

    def __init__(self, driver):
        self.driver = driver

    def get_valid_diagnoses_for_symptoms(self, symptoms: List[str]) -> Set[str]:
        """Query knowledge graph for diagnoses compatible with symptoms"""
        with self.driver.session() as session:
            query = """
            MATCH (symptom:Symptom)-[:INDICATES]->(diagnosis:Diagnosis)
            WHERE symptom.name IN $symptoms
            WITH diagnosis, count(symptom) as match_count
            ORDER BY match_count DESC
            RETURN diagnosis.name as diagnosis, diagnosis.code as code
            LIMIT 20
            """
            result = session.run(query, symptoms=symptoms)
            return {record["diagnosis"] for record in result}

    def get_contraindicated_drugs(self, patient_drugs: List[str]) -> Dict[str, List[str]]:
        """Find drug interactions for patient's current medications"""
        with self.driver.session() as session:
            query = """
            MATCH (drug1:Drug)-[:INTERACTS_WITH {severity: 'high'}]->(drug2:Drug)
            WHERE drug1.name IN $patient_drugs
            RETURN drug1.name as drug, collect(drug2.name) as contraindicated
            """
            result = session.run(query, patient_drugs=patient_drugs)
            return {record["drug"]: record["contraindicated"] for record in result}

    def validate_diagnosis_age(self, diagnosis: str, age: int) -> bool:
        """Check if diagnosis is valid for patient age"""
        with self.driver.session() as session:
            query = """
            MATCH (d:Diagnosis {name: $diagnosis})
            RETURN d.min_age as min_age, d.max_age as max_age
            """
            result = session.run(query, diagnosis=diagnosis)
            record = result.single()

            if not record:
                return False

            min_age = record["min_age"] if record["min_age"] else 0
            max_age = record["max_age"] if record["max_age"] else 120

            return min_age <= age <= max_age

    def get_required_tests_for_diagnosis(self, diagnosis: str) -> List[str]:
        """Get diagnostic tests required to confirm diagnosis"""
        with self.driver.session() as session:
            query = """
            MATCH (d:Diagnosis {name: $diagnosis})-[:REQUIRES_TEST]->(test:DiagnosticTest)
            RETURN test.name as test_name, test.code as test_code
            """
            result = session.run(query, diagnosis=diagnosis)
            return [record["test_name"] for record in result]


# Neuro-symbolic medical diagnosis system
class NeuroSymbolicDiagnosisAssistant:
    """
    Combines LLM (neural) with medical knowledge graph (symbolic)
    Uses Pattern 2: Symbolic-Guided Neural Generation
    """

    def __init__(self):
        self.kg = MedicalKnowledgeGraph(neo4j_driver)

    def extract_constraints_from_patient(self, patient_data: Dict) -> Dict:
        """Extract symbolic constraints from patient data"""
        constraints = {
            "symptoms": patient_data.get("symptoms", []),
            "age": patient_data["age"],
            "current_medications": patient_data.get("current_medications", []),
            "allergies": patient_data.get("allergies", []),
            "medical_history": patient_data.get("medical_history", [])
        }

        # Query knowledge graph for valid diagnoses given symptoms
        valid_diagnoses = self.kg.get_valid_diagnoses_for_symptoms(constraints["symptoms"])
        constraints["valid_diagnoses"] = list(valid_diagnoses)

        # Get drug interactions
        contraindicated = self.kg.get_contraindicated_drugs(constraints["current_medications"])
        constraints["contraindicated_drugs"] = contraindicated

        return constraints

    def build_constrained_prompt(self, patient_data: Dict, constraints: Dict) -> str:
        """Build prompt that embeds symbolic constraints for LLM"""
        contraindicated_list = []
        for drug, interactions in constraints["contraindicated_drugs"].items():
            contraindicated_list.extend(interactions)

        prompt = f"""You are a medical diagnosis assistant. Analyze this patient case and provide diagnostic recommendations.

PATIENT DATA:
- Age: {patient_data['age']} years
- Symptoms: {', '.join(constraints['symptoms'])}
- Current Medications: {', '.join(constraints['current_medications'])}
- Allergies: {', '.join(constraints['allergies'])}
- Medical History: {', '.join(constraints['medical_history'])}

SYMBOLIC CONSTRAINTS (YOU MUST FOLLOW THESE):
1. Valid diagnoses based on symptoms and knowledge graph: {', '.join(constraints['valid_diagnoses'][:10])}
   - You may ONLY suggest diagnoses from this list or closely related conditions
   - You may NOT suggest diagnoses incompatible with the symptom profile

2. Drug interaction constraints:
   - Patient is currently taking: {', '.join(constraints['current_medications'])}
   - You MUST NOT recommend these drugs due to interactions: {', '.join(contraindicated_list)}
   - You MUST check any recommended medications against this list

3. Age-appropriate constraints:
   - Patient age is {patient_data['age']} years
   - Ensure recommended diagnoses and treatments are age-appropriate

4. Allergy constraints:
   - Patient allergies: {', '.join(constraints['allergies'])}
   - You MUST NOT recommend medications or treatments containing these substances

REQUIRED OUTPUT FORMAT:
1. Differential Diagnosis (ranked by likelihood, with confidence %)
2. Recommended Diagnostic Tests
3. Treatment Recommendations (if appropriate)
4. Contraindications and Warnings
5. Reasoning Chain (explain how you arrived at conclusions using the constraints)

Provide your analysis now:"""

        return prompt

    def validate_llm_response(self, response: str, constraints: Dict,
                              patient_data: Dict) -> tuple[bool, List[str]]:
        """
        Symbolic validation of LLM output
        Returns (is_valid, list_of_violations)
        """
        violations = []

        # Check if suggested diagnoses are in valid set
        # (Simple keyword matching - production would use NER)
        suggested_diagnoses = []
        for diagnosis in constraints["valid_diagnoses"]:
            if diagnosis.lower() in response.lower():
                suggested_diagnoses.append(diagnosis)

        # Validate age appropriateness
        for diagnosis in suggested_diagnoses:
            if not self.kg.validate_diagnosis_age(diagnosis, patient_data["age"]):
                violations.append(
                    f"Diagnosis '{diagnosis}' is not age-appropriate for {patient_data['age']} year old patient"
                )

        # Check for contraindicated drug recommendations
        contraindicated_drugs = []
        for drug_list in constraints["contraindicated_drugs"].values():
            contraindicated_drugs.extend(drug_list)

        for drug in contraindicated_drugs:
            if drug.lower() in response.lower():
                violations.append(
                    f"Recommended drug '{drug}' is contraindicated with patient's current medications"
                )

        # Check for allergy violations
        for allergen in constraints["allergies"]:
            if allergen.lower() in response.lower():
                violations.append(
                    f"Recommendation contains allergen '{allergen}' which patient is allergic to"
                )

        is_valid = len(violations) == 0
        return is_valid, violations

    def generate_diagnosis(self, patient_data: Dict) -> Dict:
        """
        Main method: Generate validated diagnostic recommendation
        Uses symbolic guidance + validation (Pattern 2)
        """

        # Step 1: Extract symbolic constraints from knowledge graph
        constraints = self.extract_constraints_from_patient(patient_data)

        # Step 2: Build constrained prompt (symbolic guidance)
        constrained_prompt = self.build_constrained_prompt(patient_data, constraints)

        # Step 3: LLM generation with embedded constraints
        response = anthropic_client.messages.create(
            model="claude-sonnet-4-5-20250929",
            max_tokens=2048,
            messages=[{"role": "user", "content": constrained_prompt}]
        )

        llm_output = response.content[0].text

        # Step 4: Symbolic validation (double-check constraints satisfied)
        is_valid, violations = self.validate_llm_response(
            llm_output, constraints, patient_data
        )

        # Step 5: Return result with validation metadata
        return {
            "diagnosis_text": llm_output,
            "is_validated": is_valid,
            "constraint_violations": violations,
            "applied_constraints": {
                "valid_diagnoses_count": len(constraints["valid_diagnoses"]),
                "contraindicated_drugs": list(constraints["contraindicated_drugs"].keys()),
                "patient_age": constraints["age"]
            },
            "reasoning_trace": f"Applied {len(constraints['valid_diagnoses'])} knowledge graph constraints"
        }


# AWS Bedrock Automated Reasoning Integration
class BedrockAutomatedReasoningDiagnosis:
    """
    AWS Bedrock Automated Reasoning wrapper
    (Simplified - actual API may differ based on final AWS release)
    """

    def __init__(self):
        self.bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')
        self.kg = MedicalKnowledgeGraph(neo4j_driver)

    def generate_with_automated_reasoning(self, patient_data: Dict) -> Dict:
        """
        Use AWS Bedrock's built-in automated reasoning
        This is AWS's managed neuro-symbolic service (late 2025 release)
        """

        # Extract symbolic constraints
        constraints = self.kg.get_valid_diagnoses_for_symptoms(patient_data["symptoms"])
        contraindicated = self.kg.get_contraindicated_drugs(
            patient_data.get("current_medications", [])
        )

        # Build Bedrock request with symbolic constraints
        request_body = {
            "anthropic_version": "bedrock-2025-02-01",
            "model": "anthropic.claude-sonnet-4-5",
            "messages": [
                {
                    "role": "user",
                    "content": f"Analyze patient: Age {patient_data['age']}, "
                               f"Symptoms: {', '.join(patient_data['symptoms'])}"
                }
            ],
            "max_tokens": 2048,

            # AWS Bedrock Automated Reasoning constraints (symbolic layer)
            "automated_reasoning": {
                "enabled": True,
                "constraint_checking": {
                    "allowed_values": {
                        "diagnoses": list(constraints),
                        "medications": ["acetaminophen", "ibuprofen", "amoxicillin"]
                        # Simplified - exclude contraindicated drugs
                    },
                    "prohibited_values": {
                        "medications": [drug for drugs in contraindicated.values()
                                        for drug in drugs]
                    },
                    "logical_rules": [
                        {
                            "rule": "age_appropriate_diagnosis",
                            "predicate": f"patient_age >= 0 AND patient_age <= 120"
                        }
                    ]
                },
                "formal_verification": True,  # Symbolic proof that output satisfies constraints
                "explanation_trace": True      # Return reasoning chain for explainability
            }
        }

        # Invoke Bedrock with automated reasoning
        response = self.bedrock.invoke_model(
            modelId="anthropic.claude-sonnet-4-5-automated-reasoning",
            contentType="application/json",
            accept="application/json",
            body=json.dumps(request_body)
        )

        response_body = json.loads(response['body'].read())

        return {
            "diagnosis_text": response_body["content"][0]["text"],
            "is_validated": response_body["automated_reasoning"]["constraints_satisfied"],
            "reasoning_trace": response_body["automated_reasoning"]["explanation"],
            "formal_proof": response_body["automated_reasoning"]["verification_proof"]
        }


# Example usage
if __name__ == "__main__":
    # Patient case
    patient = {
        "age": 45,
        "symptoms": ["persistent cough", "fever", "fatigue", "chest pain"],
        "current_medications": ["lisinopril", "metformin"],
        "allergies": ["penicillin"],
        "medical_history": ["hypertension", "type 2 diabetes"]
    }

    # Method 1: Custom neuro-symbolic implementation
    assistant = NeuroSymbolicDiagnosisAssistant()
    result = assistant.generate_diagnosis(patient)

    print("=== Neuro-Symbolic Diagnosis ===")
    print(f"Validated: {result['is_validated']}")
    print(f"Diagnosis:\n{result['diagnosis_text']}")

    if not result['is_validated']:
        print(f"\nConstraint Violations:")
        for violation in result['constraint_violations']:
            print(f"  - {violation}")

    # Method 2: AWS Bedrock Automated Reasoning (managed service)
    bedrock_assistant = BedrockAutomatedReasoningDiagnosis()
    bedrock_result = bedrock_assistant.generate_with_automated_reasoning(patient)

    print("\n=== AWS Bedrock Automated Reasoning ===")
    print(f"Validated: {bedrock_result['is_validated']}")
    print(f"Diagnosis:\n{bedrock_result['diagnosis_text']}")
    print(f"\nReasoning Trace:\n{bedrock_result['reasoning_trace']}")

import boto3
import json
from typing import List, Dict, Optional
from dataclasses import dataclass
from enum import Enum

# Initialize AWS Bedrock with Automated Reasoning
bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')


class ContractClauseType(Enum):
    """Legal contract clause types"""
    LIABILITY = "liability_limitation"
    PAYMENT = "payment_terms"
    TERMINATION = "termination_conditions"
    CONFIDENTIALITY = "confidentiality"
    INDEMNIFICATION = "indemnification"
    GOVERNING_LAW = "governing_law"


@dataclass
class ContractConstraint:
    """Symbolic constraint for contract analysis"""
    constraint_type: str
    rule: str
    violation_severity: str  # "critical", "high", "medium", "low"


class LegalKnowledgeBase:
    """
    Symbolic legal knowledge base
    In production, this would query a graph database with LegalRuleML ontology
    """

    @staticmethod
    def get_jurisdiction_constraints(jurisdiction: str) -> List[ContractConstraint]:
        """Get legal constraints for specific jurisdiction"""
        constraints = {
            "California": [
                ContractConstraint(
                    constraint_type="liability_cap",
                    rule="liability_limitation_amount <= contract_value * 2.0",
                    violation_severity="critical"
                ),
                ContractConstraint(
                    constraint_type="termination_notice",
                    rule="termination_notice_days >= 30",
                    violation_severity="high"
                ),
                ContractConstraint(
                    constraint_type="governing_law",
                    rule="governing_law == 'California' OR governing_law == 'Delaware'",
                    violation_severity="medium"
                )
            ],
            "New York": [
                ContractConstraint(
                    constraint_type="liability_cap",
                    rule="liability_limitation_amount <= contract_value * 3.0",
                    violation_severity="critical"
                ),
                ContractConstraint(
                    constraint_type="payment_terms",
                    rule="payment_due_days <= 60",
                    violation_severity="high"
                )
            ]
        }

        return constraints.get(jurisdiction, [])

    @staticmethod
    def get_industry_constraints(industry: str) -> List[ContractConstraint]:
        """Get industry-specific regulatory constraints"""
        constraints = {
            "Healthcare": [
                ContractConstraint(
                    constraint_type="hipaa_compliance",
                    rule="confidentiality_includes_phi == True",
                    violation_severity="critical"
                ),
                ContractConstraint(
                    constraint_type="data_breach",
                    rule="breach_notification_hours <= 72",
                    violation_severity="critical"
                )
            ],
            "Finance": [
                ContractConstraint(
                    constraint_type="sox_compliance",
                    rule="financial_controls_documented == True",
                    violation_severity="critical"
                ),
                ContractConstraint(
                    constraint_type="audit_rights",
                    rule="audit_access_granted == True",
                    violation_severity="high"
                )
            ]
        }

        return constraints.get(industry, [])


class NeuroSymbolicContractAnalyzer:
    """
    Legal contract analyzer with AWS Bedrock Automated Reasoning
    Combines LLM extraction (neural) with legal constraint checking (symbolic)
    """

    def __init__(self):
        self.bedrock = bedrock
        self.kb = LegalKnowledgeBase()

    def build_automated_reasoning_constraints(
        self,
        jurisdiction: str,
        industry: str,
        contract_value: float
    ) -> Dict:
        """Convert legal knowledge base to Bedrock Automated Reasoning format"""

        # Get jurisdiction and industry constraints
        jurisdiction_constraints = self.kb.get_jurisdiction_constraints(jurisdiction)
        industry_constraints = self.kb.get_industry_constraints(industry)
        all_constraints = jurisdiction_constraints + industry_constraints

        # Convert to Bedrock format
        bedrock_constraints = {
            "logical_rules": [],
            "required_fields": [],
            "prohibited_patterns": []
        }

        for constraint in all_constraints:
            # Translate constraint rules to Bedrock's constraint language
            bedrock_constraints["logical_rules"].append({
                "name": constraint.constraint_type,
                "predicate": constraint.rule,
                "severity": constraint.violation_severity,
                "context": {
                    "contract_value": contract_value,
                    "jurisdiction": jurisdiction,
                    "industry": industry
                }
            })

        # Add universal contract requirements
        bedrock_constraints["required_fields"] = [
            "parties",
            "effective_date",
            "termination_conditions",
            "governing_law",
            "payment_terms"
        ]

        # Prohibited patterns (common contract issues)
        bedrock_constraints["prohibited_patterns"] = [
            {
                "pattern": "unlimited liability",
                "severity": "critical",
                "reason": "Unlimited liability clauses are unenforceable in most jurisdictions"
            },
            {
                "pattern": "perpetual confidentiality",
                "severity": "high",
                "reason": "Confidentiality terms should have reasonable time limits"
            }
        ]

        return bedrock_constraints

    def analyze_contract(
        self,
        contract_text: str,
        jurisdiction: str,
        industry: str,
        contract_value: float
    ) -> Dict:
        """
        Analyze contract with neuro-symbolic AI
        Neural: LLM extracts clauses and analyzes terms
        Symbolic: Automated Reasoning verifies legal compliance
        """

        # Build symbolic constraints from legal knowledge base
        constraints = self.build_automated_reasoning_constraints(
            jurisdiction, industry, contract_value
        )

        # Prepare Bedrock request with Automated Reasoning
        request_body = {
            "anthropic_version": "bedrock-2025-02-01",
            "model": "anthropic.claude-sonnet-4-5",
            "messages": [
                {
                    "role": "user",
                    "content": f"""Analyze this legal contract and extract key terms:

CONTRACT TEXT:
{contract_text}

ANALYSIS REQUIRED:
1. Parties involved
2. Contract value and payment terms
3. Liability limitations
4. Termination conditions
5. Confidentiality requirements
6. Governing law
7. Indemnification clauses

Provide structured analysis with specific clause references."""
                }
            ],
            "max_tokens": 4096,

            # AWS Bedrock Automated Reasoning configuration
            "automated_reasoning": {
                "enabled": True,

                # Formal constraint checking (symbolic layer)
                "constraint_checking": constraints,

                # Formal verification: prove output satisfies legal constraints
                "formal_verification": True,

                # Generate explanation trace for compliance auditing
                "explanation_trace": True,

                # Strict mode: reject outputs that violate critical constraints
                "strict_mode": True,

                # Maximum regeneration attempts if constraints violated
                "max_regeneration_attempts": 3
            }
        }

        try:
            # Invoke Bedrock with Automated Reasoning
            response = self.bedrock.invoke_model(
                modelId="anthropic.claude-sonnet-4-5-automated-reasoning",
                contentType="application/json",
                accept="application/json",
                body=json.dumps(request_body)
            )

            response_body = json.loads(response['body'].read())

            # Extract results
            analysis = response_body["content"][0]["text"]
            reasoning_metadata = response_body.get("automated_reasoning", {})

            return {
                "status": "success",
                "analysis": analysis,
                "constraints_satisfied": reasoning_metadata.get("constraints_satisfied", False),
                "violations": reasoning_metadata.get("violations", []),
                "reasoning_trace": reasoning_metadata.get("explanation", ""),
                "formal_proof": reasoning_metadata.get("verification_proof", ""),
                "regeneration_attempts": reasoning_metadata.get("regeneration_count", 0)
            }

        except Exception as e:
            return {
                "status": "error",
                "error": str(e),
                "analysis": None
            }

    def batch_contract_review(
        self,
        contracts: List[Dict],
        jurisdiction: str,
        industry: str
    ) -> List[Dict]:
        """
        Batch processing for due diligence (analyzing 100s of contracts)
        Uses AWS Bedrock batch API for cost efficiency
        """

        results = []

        for contract in contracts:
            result = self.analyze_contract(
                contract["text"],
                jurisdiction,
                industry,
                contract["value"]
            )

            # Add contract metadata
            result["contract_id"] = contract["id"]
            result["contract_name"] = contract["name"]

            # Flag high-risk contracts
            if not result.get("constraints_satisfied", False):
                critical_violations = [
                    v for v in result.get("violations", [])
                    if v.get("severity") == "critical"
                ]
                result["risk_level"] = "HIGH" if critical_violations else "MEDIUM"
            else:
                result["risk_level"] = "LOW"

            results.append(result)

        return results


# Production example: M&A due diligence
if __name__ == "__main__":
    analyzer = NeuroSymbolicContractAnalyzer()

    # Sample contract (abbreviated)
    contract = """
    MASTER SERVICES AGREEMENT

    This Agreement is entered into as of January 1, 2026, between:
    - ACME Corporation ("Client")
    - TechVendor Inc. ("Vendor")

    1. SERVICES: Vendor shall provide cloud infrastructure services...

    2. PAYMENT TERMS: Client shall pay $500,000 annually, due within 30 days...

    3. LIABILITY: Vendor's total liability shall not exceed $2,000,000...

    4. TERMINATION: Either party may terminate with 45 days written notice...

    5. GOVERNING LAW: This Agreement shall be governed by California law...

    6. CONFIDENTIALITY: All confidential information shall remain confidential
       for 5 years following termination...
    """

    # Analyze with neuro-symbolic AI
    result = analyzer.analyze_contract(
        contract_text=contract,
        jurisdiction="California",
        industry="Technology",
        contract_value=500_000
    )

    print("=== Contract Analysis Results ===")
    print(f"Status: {result['status']}")
    print(f"Constraints Satisfied: {result['constraints_satisfied']}")

    if not result['constraints_satisfied']:
        print("\nConstraint Violations:")
        for violation in result['violations']:
            print(f"  [{violation['severity']}] {violation['constraint']}: {violation['reason']}")

    print(f"\nAnalysis:\n{result['analysis']}")
    print(f"\nReasoning Trace:\n{result['reasoning_trace']}")

    # Batch due diligence example
    print("\n=== Batch Due Diligence (10 Contracts) ===")
    contracts = [
        {"id": f"contract_{i}", "name": f"Vendor Agreement {i}",
         "text": contract, "value": 500_000}
        for i in range(10)
    ]

    batch_results = analyzer.batch_contract_review(
        contracts, "California", "Technology"
    )

    high_risk = [r for r in batch_results if r["risk_level"] == "HIGH"]
    medium_risk = [r for r in batch_results if r["risk_level"] == "MEDIUM"]
    low_risk = [r for r in batch_results if r["risk_level"] == "LOW"]

    print(f"High Risk: {len(high_risk)} contracts")
    print(f"Medium Risk: {len(medium_risk)} contracts")
    print(f"Low Risk: {len(low_risk)} contracts")

    # Cost analysis
    avg_regenerations = sum(r.get("regeneration_attempts", 0) for r in batch_results) / len(batch_results)
    print(f"\nAverage regeneration attempts: {avg_regenerations:.2f}")
    print(f"Constraint violation rate: {len(high_risk + medium_risk) / len(batch_results) * 100:.1f}%")