Semantica Core Features: A Complete Guide to Graph-Native AI Infrastructure

Semantica is a graph-native infrastructure layer that sits between LLM/agent stacks and enterprise data, built around a Context Graph with full provenance tracking, deterministic reasoning engines, and regulator-ready auditability.

Semantica provides a comprehensive knowledge graph infrastructure designed for deterministic, explainable AI systems. Its modular architecture in semantica-agi/semantica centers on the Context Graph—a bi-temporal, queryable knowledge structure where every entity, relationship, decision, and fact carries complete provenance. This guide breaks down the core features that make Semantica suitable for high-stakes enterprise deployments requiring auditability and transparency.

Context Graphs: The Foundation of Semantica

The Context Graph is Semantica's first-class data structure, implemented in [semantica/context/context_graph.py](https://github.com/semantica-agi/semantica/blob/main/semantica/context/context_graph.py). Unlike traditional vector databases, every node in this graph—whether an entity, decision, or fact—exposes its origin and evolution through time.

Key capabilities include:

• Bi-temporal snapshots: Query the graph at any point in time
• Causal traversals: Follow CAUSED, INFLUENCED, and PRECEDENT_FOR relationships
• Native decision nodes: Decisions are first-class citizens, not metadata

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Record a decision with full provenance

decision_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, and existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)

Decision Intelligence with Causal Tracking

Semantica's Decision Intelligence module treats decisions as queryable graph nodes. According to the source code in semantica/context/context_graph.py, methods like record_decision(), add_causal_relationship(), trace_decision_chain(), and find_similar_decisions() enable:

• Causal attribution: Link decisions to their antecedents and consequences
• Pattern recognition: Surface similar historical decisions for case-based reasoning
• Audit trails: Every decision carries immutable reasoning and confidence scores

# Add entities and causal relationships

graph.add_node("acme_corp", "Organization", name="Acme Corp", industry="SaaS")
graph.add_node("alice_chen", "Person", name="Alice Chen", role="CTO")

graph.add_edge(
    "alice_chen", "acme_corp",
    edge_type="works_for",
    since="2019-03-01"
)

# Multi-hop traversal for impact analysis

neighbors = graph.get_neighbors("acme_corp", hops=2)

Deterministic Reasoning Engines

The semantica/reasoning/ directory houses multiple inference engines that produce explainable, reproducible results without black-box LLM calls:

Engine	Use Case	Implementation
Forward-chaining	Rule-based derivation from facts	Production rule system
Rete network	High-performance pattern matching	Network-based rule evaluation
Datalog	Declarative logical inference	Recursive query evaluation
SPARQL	RDF graph querying	Standards-compliant endpoint

from semantica.reasoning import ReteEngine, Rule, RuleType, Fact

engine = ReteEngine()
engine.build_network([
    Rule(
        rule_id="aml_flag",
        name="Flag high-risk transactions",
        conditions=[
            {"field": "amount", "operator": ">", "value": 10_000},
            {"field": "country", "operator": "in", "value": ["IR", "KP", "SY"]},
        ],
        conclusion="flag_for_compliance_review",
        rule_type=RuleType.IMPLICATION,
    ),
])

engine.add_fact(Fact("tx_001", "transaction", [{"amount": 15_000, "country": "IR"}]))
matches = engine.match_patterns()  # → [{'rule': 'aml_flag', ...}]

AI Governance and Ontology Management

The semantica/ontology/ module provides enterprise-grade knowledge management:

• OWL generation: Automatic ontology creation from graph schemas
• SHACL validation: Constraint checking for data quality
• SKOS vocabularies: Controlled term management for consistent labeling
• Policy enforcement: Graph-level rule validation before write operations

Full Auditability with W3C PROV-O

Every fact in Semantica links to its source via W3C PROV-O provenance standards. The semantica/provenance/ module ensures:

• Lineage tracking: Who created what, when, and from which source
• Export formats: JSON, CSV, and RDF for regulatory submissions
• Immutable records: Tamper-evident provenance chains suitable for compliance audits

End-to-End Knowledge Pipeline

Semantica implements a comprehensive pipeline spanning files 66-70 of the README, with each stage as a standalone, importable module:

Sources → Ingest → Parse → Normalize → Split → Extract → 
  Conflict Detection → Deduplication → Knowledge Graph → 
  [Ontology · Reasoning · Provenance · Decisions] → 
  Vector Store + Polyglot Graph Store → Export / Visualize / API

Pipeline Stages

Stage	Directory	Purpose
Ingestion	semantica/ingest/*	Multi-source data acquisition
Normalization	semantica/normalize/*	Text, date, number canonicalization
Splitting/Chunking	semantica/split/*	Entity-aware segmentation for GraphRAG
Extraction	semantica/semantic_extract/*	NER, relations, events, triplets
Conflict Detection	semantica/conflicts/*	Contradiction identification
Deduplication	semantica/deduplication/*	Merge with provenance preservation
KG Construction	semantica/kg/*	Unified graph assembly

Enterprise Data Platform Connectors

Native ingestion modules in semantica/ingest/ support:

• Databricks (databricks_ingestor.py): Unity Catalog integration
• Snowflake: Direct warehouse connectivity
• SAP OData: ERP system extraction
• Generic sources: Files, databases, streams, web pages, Git repositories

Polyglot Graph Storage

The storage layer avoids vendor lock-in through a registry pattern in [semantica/vector_store/registry.py](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/registry.py):

RDF Triple Stores: Oxigraph, Blazegraph, Apache Jena, RDF4J
Labeled Property Graphs: Neo4j, FalkorDB, Apache AGE, AWS Neptune

Graph Analytics and Visualization

The [semantica/kg/graph_analyzer.py](https://github.com/semantica-agi/semantica/blob/main/semantica/kg/graph_analyzer.py) module provides:

• Centrality analysis (PageRank, betweenness)
• Community detection (Louvain, label propagation)
• Link prediction for knowledge completion
• Bi-temporal analytics for trend analysis

from semantica.visualization import KGVisualizer

kg = graph.to_kg_dict()
viz = KGVisualizer(layout="force", color_scheme="default")
viz.visualize_network(kg, output="interactive", file_path="kg.html")

# Open kg.html for interactive exploration

Hybrid Vector Search with Explanations

The semantica/vector_store/ abstraction supports FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, and SQLite:

from semantica.vector_store import VectorStore, HybridSearch

vs = VectorStore(backend="faiss", dimension=1536)
vs.store_decision(
    scenario="Personal loan A-7291, $85k income, 31% DTI, 3yr employment",
    outcome="approved",
    confidence=0.94,
    category="loan_underwriting",
)

# Dense semantic search

results = vs.search(query="personal loan approval with low DTI", limit=5)

# Hybrid (dense + sparse) ranking

hs = HybridSearch(vector_store=vs)
hybrid_hits = hs.search("high-risk transactions 2024")

# Explain retrieval decisions

explanation = vs.explain_decision(results[0]["id"])

Drop-In Integrations for Agent Frameworks

Semantica provides first-class adapters for popular AI frameworks:

Integration	Location	Capability
Agno	semantica/integrations/agno/	Multi-agent shared context
CrewAI	Integration layer	Crew coordination over shared graph
LangChain	Integration layer	Tool and retriever interfaces
MCP Server	[semantica/mcp/server.py](https://github.com/semantica-agi/semantica/blob/main/semantica/mcp/server.py)	Model-Control-Protocol API
CLI/REST	Built-in endpoints	Human and programmatic access

Summary

• Context Graph is Semantica's bi-temporal, provenance-rich knowledge core in semantica/context/context_graph.py
• Decision Intelligence records decisions as first-class nodes with causal relationships and similarity search
• Deterministic Reasoning via Rete, Datalog, and SPARQL engines produces explainable inferences without LLM black boxes
• Full Auditability through W3C PROV-O provenance with regulator-ready export formats
• Enterprise Connectors for Databricks, Snowflake, SAP, and generic sources
• Polyglot Storage avoids vendor lock-in across RDF and property graph backends
• Agent Integrations for Agno, CrewAI, LangChain, plus MCP server and REST API

Frequently Asked Questions

What makes Semantica different from a standard vector database?

Semantica's Context Graph treats provenance, decisions, and temporal state as first-class entities rather than metadata. While vector databases optimize for similarity search, Semantica provides bi-temporal querying, causal relationship tracking, deterministic reasoning, and full W3C PROV-O audit trails—capabilities essential for regulated industries.

How does Semantica ensure deterministic, explainable AI reasoning?

The platform routes inference through explicit engines (Rete network, forward-chaining, Datalog, SPARQL) in semantica/reasoning/ rather than opaque LLM calls. Every conclusion carries a complete derivation path showing which rules fired and which facts were involved, satisfying explainability requirements for high-stakes decisions.

Can Semantica integrate with existing enterprise data platforms?

Yes. Native connectors in semantica/ingest/ support Databricks Unity Catalog, Snowflake, and SAP OData out of the box. The modular pipeline architecture allows organizations to plug Semantica into existing ETL workflows without data migration, processing source data through normalization, extraction, and knowledge graph construction before exposing it to downstream agents.

What deployment options does Semantica support for storage backends?

The polyglot storage layer in semantica/vector_store/registry.py abstracts over multiple backends: vector stores (FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, SQLite), RDF triple stores (Oxigraph, Blazegraph, Jena, RDF4J), and labeled property graphs (Neo4j, FalkorDB, Apache AGE, Neptune). This enables on-premise, hybrid, and multi-cloud deployments without vendor lock-in.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}