🧠 Semantica

Open-Source Semantic Layer & Knowledge Engineering Framework

⭐ Give us a Star • 🍴 Fork us • 💬 Join our Discord

Transform Choas into Intelligence. Build AI systems that are explainable, traceable, and trustworthy — not black boxes.

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🚀 Why Semantica?

Semantica bridges the semantic gap between text similarity and true meaning. It's the semantic intelligence layer that makes your AI agents auditable, explainable, and compliant.

Perfect for high-stakes domains where mistakes have real consequences.

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⚡ Get Started in 30 Seconds

pip install semantica

from semantica.semantic_extract import NERExtractor
from semantica.kg import GraphBuilder

# Extract entities and build knowledge graph
ner = NERExtractor(method="ml", model="en_core_web_sm")
entities = ner.extract("Apple Inc. was founded by Steve Jobs in 1976.")
kg = GraphBuilder().build({"entities": entities, "relationships": []})

print(f"Built KG with {len(kg.get('entities', []))} entities")

📖 Full Quick Start • 🍳 Cookbook Examples • 💬 Join Discord • ⭐ Star Us

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Core Value Proposition

Trustworthy	Explainable	Auditable
Conflict detection & validation	Transparent reasoning paths	Complete provenance tracking
Rule-based governance	Entity relationships & ontologies	Source-level provenance
Production-grade QA	Multi-hop graph reasoning	Audit-ready compliance

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Key Features & Benefits

Not Just Another Agentic Framework

Semantica complements LangChain, LlamaIndex, AutoGen, CrewAI, Google ADK, Agno, and other frameworks to enhance your agents with:

Feature	Benefit
Auditable	Complete provenance tracking with full audit trails
Explainable	Transparent reasoning paths with entity relationships
Provenance-Aware	Source-level provenance from documents to responses
Validated	Built-in conflict detection, deduplication, QA
Governed	Rule-based validation and semantic consistency

Perfect For High-Stakes Use Cases

🏥 Healthcare	💰 Finance	⚖️ Legal
Clinical decisions	Fraud detection	Evidence-backed research
Drug interactions	Regulatory compliance	Contract analysis
Patient safety	Risk assessment	Case law reasoning

🔒 Cybersecurity	🏛️ Government	🏭 Infrastructure	🚗 Autonomous
Threat attribution	Policy decisions	Power grids	Decision logs
Incident response	Classified info	Transportation	Safety validation

Powers Your AI Stack

• GraphRAG Systems — Retrieval with graph reasoning and hybrid search
• AI Agents — Trustworthy, accountable multi-agent systems with semantic memory
• Reasoning Models — Explainable AI decisions with reasoning paths
• Enterprise AI — Governed, auditable platforms for compliance

Integrations

• Docling Support — Document parsing with table extraction (PDF, DOCX, PPTX, XLSX)
• AWS Neptune — Amazon Neptune graph database support with IAM authentication
• Custom Ontology Import — Import existing ontologies (OWL, RDF, Turtle, JSON-LD)

Built for environments where every answer must be explainable and governed.

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🚨 The Problem: The Semantic Gap

Most AI systems fail in high-stakes domains because they operate on text similarity, not meaning.

Understanding the Semantic Gap

The semantic gap is the fundamental disconnect between what AI systems can process (text patterns, vector similarities) and what high-stakes applications require (semantic understanding, meaning, context, and relationships).

Traditional AI approaches:

• Rely on statistical patterns and text similarity
• Cannot understand relationships between entities
• Cannot reason about domain-specific rules
• Cannot explain why decisions were made
• Cannot trace back to original sources with confidence

High-stakes AI requires:

• Semantic understanding of entities and their relationships
• Domain knowledge encoded as formal rules (ontologies)
• Explainable reasoning paths
• Source-level provenance
• Conflict detection and resolution

Semantica bridges this gap by providing a semantic intelligence layer that transforms unstructured data into validated, explainable, and auditable knowledge.

What Organizations Have vs What They Need

Current State	Required for High-Stakes AI
PDFs, DOCX, emails, logs	Formal domain rules (ontologies)
APIs, databases, streams	Structured and validated entities
Conflicting facts and duplicates	Explicit semantic relationships
Siloed systems with no lineage	Explainable reasoning paths
	Source-level provenance
	Audit-ready compliance

The Cost of Missing Semantics

• Decisions cannot be explained — No transparency in AI reasoning
• Errors cannot be traced — No way to debug or improve
• Conflicts go undetected — Contradictory information causes failures
• Compliance becomes impossible — No audit trails for regulations

Trustworthy AI requires semantic accountability.

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🆚 Semantica vs Traditional RAG

Feature	Traditional RAG	Semantica
Reasoning	❌ Black-box answers	✅ Explainable reasoning paths
Provenance	❌ No provenance	✅ Source-level provenance
Search	⚠️ Vector similarity only	✅ Semantic + graph reasoning
Quality	❌ No conflict handling	✅ Explicit contradiction detection
Safety	⚠️ Unsafe for high-stakes	✅ Designed for governed environments
Compliance	❌ No audit trails	✅ Audit-ready provenance

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🧩 Semantica Architecture

1️⃣ Input Layer — Governed Ingestion

• 📄 Multiple Formats — PDFs, DOCX, HTML, JSON, CSV, Excel, PPTX
• 🔧 Docling Support — Docling parser for table extraction
• 💾 Data Sources — Databases, APIs, streams, archives, web content
• 🎨 Media Support — Image parsing with OCR, audio/video metadata extraction
• 📊 Single Pipeline — Unified ingestion with metadata and source tracking

2️⃣ Semantic Layer — Trust & Reasoning Engine

• 🔍 Entity Extraction — NER, normalization, classification
• 🔗 Relationship Discovery — Triplet generation, semantic links
• 📐 Ontology Induction — Automated domain rule generation
• 🔄 Deduplication — Jaro-Winkler similarity, conflict resolution
• ✅ Quality Assurance — Conflict detection, validation
• 📊 Provenance Tracking — Source, time, confidence metadata
• 🧠 Reasoning Traces — Explainable inference paths

3️⃣ Output Layer — Auditable Knowledge Assets

• 📊 Knowledge Graphs — Queryable, temporal, explainable
• 📐 OWL Ontologies — HermiT/Pellet validated, custom ontology import support
• 🔢 Vector Embeddings — FastEmbed by default
• ☁️ AWS Neptune — Amazon Neptune graph database support
• 🔍 Provenance — Every AI response links back to:
  • 📄 Source documents
  • 🏷️ Extracted entities & relations
  • 📐 Ontology rules applied
  • 🧠 Reasoning steps used

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🏥 Built for High-Stakes Domains

Designed for domains where mistakes have real consequences and every decision must be accountable:

• 🏥 Healthcare & Life Sciences — Clinical decision support, drug interaction analysis, medical literature reasoning, patient safety compliance
• 💰 Finance & Risk — Fraud detection, regulatory compliance (SOX, GDPR, MiFID II), credit risk assessment, algorithmic trading validation
• ⚖️ Legal & Compliance — Evidence-backed legal research, contract analysis, regulatory change management, case law reasoning
• 🔒 Cybersecurity & Intelligence — Threat attribution, incident response, security audit trails, intelligence analysis
• 🏛️ Government & Defense — Governed AI systems, policy decisions, classified information handling, defense intelligence
• 🏭 Critical Infrastructure — Power grid management, transportation safety, water treatment, emergency response
• 🚗 Autonomous Systems — Self-driving vehicles, drone navigation, robotics safety, industrial automation

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👥 Who Uses Semantica?

• 🤖 AI / ML Engineers — Building explainable GraphRAG & agents
• ⚙️ Data Engineers — Creating governed semantic pipelines
• 📊 Knowledge Engineers — Managing ontologies & KGs at scale
• 🏢 Enterprise Teams — Requiring trustworthy AI infrastructure
• 🛡️ Risk & Compliance Teams — Needing audit-ready systems

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📦 Installation

Install from PyPI (Recommended)

pip install semantica
# or
pip install semantica[all]

Install from Source (Development)

# Clone and install in editable mode
git clone https://github.com/Hawksight-AI/semantica.git
cd semantica
pip install -e .

# Or with all optional dependencies
pip install -e ".[all]"

# Development setup
pip install -e ".[dev]"

📚 Resources

New to Semantica? Check out the Cookbook for hands-on examples!

• Cookbook - Interactive notebooks
  • Introduction - Getting started tutorials
  • Advanced - Advanced techniques
  • Use Cases - Real-world applications

✨ Core Capabilities

Data Ingestion	Semantic Extract	Knowledge Graphs	Ontology
Multiple Formats	Entity & Relations	Graph Analytics	Auto Generation
Context	GraphRAG	LLM Providers	Pipeline
Agent Memory, Context Graph, Context Retriever	Hybrid RAG	100+ LLMs	Parallel Workers
QA	Reasoning
Conflict Resolution	Rule-based Inference

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Universal Data Ingestion

Multiple file formats • PDF, DOCX, HTML, JSON, CSV, databases, feeds, archives

from semantica.ingest import FileIngestor, WebIngestor, DBIngestor

file_ingestor = FileIngestor(recursive=True)
web_ingestor = WebIngestor(max_depth=3)
db_ingestor = DBIngestor(connection_string="postgresql://...")

sources = []
sources.extend(file_ingestor.ingest("documents/"))
sources.extend(web_ingestor.ingest("https://example.com"))
sources.extend(db_ingestor.ingest(query="SELECT * FROM articles"))

print(f" Ingested {len(sources)} sources")

Cookbook: Data Ingestion

Document Parsing & Processing

Multi-format parsing • Docling Support • Text normalization • Intelligent chunking

from semantica.parse import DocumentParser, DoclingParser
from semantica.normalize import TextNormalizer
from semantica.split import TextSplitter

# Standard parsing
parser = DocumentParser()
parsed = parser.parse("document.pdf", format="auto")

# Parsing with Docling (for complex layouts/tables)
# Requires: pip install docling
docling_parser = DoclingParser(enable_ocr=True)
result = docling_parser.parse("complex_table.pdf")

print(f"Text (Markdown): {result['full_text'][:100]}...")
print(f"Extracted {len(result['tables'])} tables")
for i, table in enumerate(result['tables']):
    print(f"Table {i+1} headers: {table.get('headers', [])}")

# Normalize text
normalizer = TextNormalizer()
normalized = normalizer.normalize(parsed, clean_html=True, normalize_entities=True)

# Split into chunks
splitter = TextSplitter(method="token", chunk_size=1000, chunk_overlap=200)
chunks = splitter.split(normalized)

Cookbook: Document Parsing • Data Normalization • Chunking & Splitting

Semantic Intelligence Engine

Entity & Relation Extraction • NER, Relationships, Events, Triplets with LLM Enhancement

from semantica.semantic_extract import NERExtractor, RelationExtractor

text = "Apple Inc., founded by Steve Jobs in 1976, acquired Beats Electronics for $3 billion."

# Extract entities
ner_extractor = NERExtractor(method="ml", model="en_core_web_sm")
entities = ner_extractor.extract(text)

# Extract relationships
relation_extractor = RelationExtractor(method="dependency", model="en_core_web_sm")
relationships = relation_extractor.extract(text, entities=entities)

print(f"Entities: {len(entities)}, Relationships: {len(relationships)}")

Cookbook: Entity Extraction • Relation Extraction • Advanced Extraction

Knowledge Graph Construction

Production-Ready KGs • Entity Resolution • Temporal Support • Graph Analytics

from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder

# Extract entities and relationships
ner_extractor = NERExtractor(method="ml", model="en_core_web_sm")
relation_extractor = RelationExtractor(method="dependency", model="en_core_web_sm")

entities = ner_extractor.extract(text)
relationships = relation_extractor.extract(text, entities=entities)

# Build knowledge graph
builder = GraphBuilder()
kg = builder.build({"entities": entities, "relationships": relationships})

print(f"Nodes: {len(kg.get('entities', []))}, Edges: {len(kg.get('relationships', []))}")

Cookbook: Building Knowledge Graphs • Graph Analytics

Embeddings & Vector Store

FastEmbed by default • Multiple backends • Semantic search

from semantica.embeddings import EmbeddingGenerator
from semantica.vector_store import VectorStore

# Generate embeddings
embedding_gen = EmbeddingGenerator(model_name="sentence-transformers/all-MiniLM-L6-v2", dimension=384)
embeddings = embedding_gen.generate_embeddings(chunks, data_type="text")

# Store in vector database
vector_store = VectorStore(backend="faiss", dimension=384)
vector_store.store_vectors(vectors=embeddings, metadata=[{"text": chunk} for chunk in chunks])

# Search
results = vector_store.search(query="supply chain", top_k=5)

Cookbook: Embedding Generation • Vector Store

Graph Store & Triplet Store

Neo4j, FalkorDB, Amazon Neptune • SPARQL queries • RDF triplets

from semantica.graph_store import GraphStore
from semantica.triplet_store import TripletStore

# Graph Store (Neo4j, FalkorDB)
graph_store = GraphStore(backend="neo4j", uri="bolt://localhost:7687", user="neo4j", password="password")
graph_store.add_nodes([{"id": "n1", "labels": ["Person"], "properties": {"name": "Alice"}}])

# Amazon Neptune Graph Store (OpenCypher via HTTP with IAM Auth)
neptune_store = GraphStore(
    backend="neptune",
    endpoint="your-cluster.us-east-1.neptune.amazonaws.com",
    port=8182,
    region="us-east-1",
    iam_auth=True,  # Uses AWS credential chain (boto3, env vars, or IAM role)
)

# Node Operations
neptune_store.add_nodes([
    {"labels": ["Person"], "properties": {"id": "alice", "name": "Alice", "age": 30}},
    {"labels": ["Person"], "properties": {"id": "bob", "name": "Bob", "age": 25}},
])

# Query Operations
result = neptune_store.execute_query("MATCH (p:Person) RETURN p.name, p.age")

# Triplet Store (Blazegraph, Jena, RDF4J)
triplet_store = TripletStore(backend="blazegraph", endpoint="http://localhost:9999/blazegraph")
triplet_store.add_triplet({"subject": "Alice", "predicate": "knows", "object": "Bob"})
results = triplet_store.execute_query("SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10")

Cookbook: Graph Store • Triplet Store

Ontology Generation & Management

6-Stage LLM Pipeline • Automatic OWL Generation • HermiT/Pellet Validation • Custom Ontology Import (OWL, RDF, Turtle, JSON-LD)

from semantica.ontology import OntologyGenerator
from semantica.ingest import ingest_ontology

# Generate ontology automatically
generator = OntologyGenerator(llm_provider="openai", model="gpt-4")
ontology = generator.generate_from_documents(sources=["domain_docs/"])

# Or import your existing ontology
custom_ontology = ingest_ontology("my_ontology.ttl")  # Supports OWL, RDF, Turtle, JSON-LD
print(f"Classes: {len(custom_ontology.classes)}")

Cookbook: Ontology

Context Engineering & Memory Systems

Persistent Memory • Context Graph • Context Retriever • Hybrid Retrieval (Vector + Graph) • Production Graph Store (Neo4j) • Entity Linking • Multi-Hop Reasoning

from semantica.context import AgentContext, ContextGraph, ContextRetriever
from semantica.vector_store import VectorStore
from semantica.graph_store import GraphStore
from semantica.llms import Groq

# Initialize Context with Hybrid Retrieval (Graph + Vector)
context = AgentContext(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=GraphStore(backend="neo4j"), # Optional: Use persistent graph
    hybrid_alpha=0.75  # 75% weight to Knowledge Graph, 25% to Vector
)

# Build Context Graph from entities and relationships
graph_stats = context.build_graph(
    entities=kg.get('entities', []),
    relationships=kg.get('relationships', []),
    link_entities=True
)

# Store memory with automatic entity linking
context.store(
    "User is building a RAG system with Semantica",
    metadata={"priority": "high", "topic": "rag"}
)

# Use Context Retriever for hybrid retrieval
retriever = context.retriever  # Access underlying ContextRetriever
results = retriever.retrieve(
    query="What is the user building?",
    max_results=10,
    use_graph_expansion=True
)

# Retrieve with context expansion
results = context.retrieve("What is the user building?", use_graph_expansion=True)

# Query with reasoning and LLM-generated responses
llm_provider = Groq(model="llama-3.1-8b-instant", api_key=os.getenv("GROQ_API_KEY"))
reasoned_result = context.query_with_reasoning(
    query="What is the user building?",
    llm_provider=llm_provider,
    max_hops=2
)

Core Components:

• ContextGraph: Builds and manages context graphs from entities and relationships for enhanced retrieval
• ContextRetriever: Performs hybrid retrieval combining vector search, graph traversal, and memory for optimal context relevance
• AgentContext: High-level interface integrating Context Graph and Context Retriever for GraphRAG applications

Core Notebooks:

• Context Module Introduction - Basic memory and storage.
• Advanced Context Engineering - Hybrid retrieval, graph builders, and custom memory policies.
• Fraud Detection - Demonstrates Context Graph and Context Retriever for fraud detection with GraphRAG.

Related Components: Vector Store • Embedding Generation • Advanced Vector Store

Knowledge Graph-Powered RAG (GraphRAG)

Vector + Graph Hybrid Search • Multi-Hop Reasoning • LLM-Generated Responses • Semantic Re-ranking

from semantica.context import AgentContext
from semantica.llms import Groq, OpenAI, LiteLLM
from semantica.vector_store import VectorStore
import os

# Initialize GraphRAG with hybrid retrieval
context = AgentContext(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=kg
)

# Configure LLM provider (supports Groq, OpenAI, HuggingFace, LiteLLM)
llm_provider = Groq(
    model="llama-3.1-8b-instant",
    api_key=os.getenv("GROQ_API_KEY")
)

# Query with multi-hop reasoning and LLM-generated responses
result = context.query_with_reasoning(
    query="What IPs are associated with security alerts?",
    llm_provider=llm_provider,
    max_results=10,
    max_hops=2
)

print(f"Response: {result['response']}")
print(f"Reasoning Path: {result['reasoning_path']}")
print(f"Confidence: {result['confidence']:.3f}")

Key Features:

• Multi-Hop Reasoning: Traverses knowledge graph up to N hops to find related entities
• LLM-Generated Responses: Natural language answers grounded in graph context
• Reasoning Trace: Shows entity relationship paths used in reasoning
• Multiple LLM Providers: Supports Groq, OpenAI, HuggingFace, and LiteLLM (100+ LLMs)

Cookbook: GraphRAG • Real-Time Anomaly Detection

LLM Providers Module

Unified LLM Interface • 100+ LLM Support via LiteLLM • Clean Imports • Multiple Providers

from semantica.llms import Groq, OpenAI, HuggingFaceLLM, LiteLLM
import os

# Groq
groq = Groq(
    model="llama-3.1-8b-instant",
    api_key=os.getenv("GROQ_API_KEY")
)
response = groq.generate("What is AI?")

# OpenAI
openai = OpenAI(
    model="gpt-4",
    api_key=os.getenv("OPENAI_API_KEY")
)
response = openai.generate("What is AI?")

# HuggingFace - Local models
hf = HuggingFaceLLM(model_name="gpt2")
response = hf.generate("What is AI?")

# LiteLLM - Unified interface to 100+ LLMs
litellm = LiteLLM(
    model="openai/gpt-4o",  # or "anthropic/claude-sonnet-4-20250514", "groq/llama-3.1-8b-instant", etc.
    api_key=os.getenv("OPENAI_API_KEY")
)
response = litellm.generate("What is AI?")

# Structured output
structured = groq.generate_structured("Extract entities from: Apple Inc. was founded by Steve Jobs.")

Supported Providers:

• Groq: Inference with Llama models
• OpenAI: GPT-3.5, GPT-4, and other OpenAI models
• HuggingFace: Local LLM inference with Transformers
• LiteLLM: Unified interface to 100+ LLM providers (OpenAI, Anthropic, Azure, Bedrock, Vertex AI, and more)

Reasoning & Inference Engine

Rule-based Inference • Forward/Backward Chaining • Rete Algorithm • Explanation Generation

from semantica.reasoning import Reasoner

# Initialize Reasoner
reasoner = Reasoner()

# Define rules and facts
rules = ["IF Parent(?a, ?b) AND Parent(?b, ?c) THEN Grandparent(?a, ?c)"]
facts = ["Parent(Alice, Bob)", "Parent(Bob, Charlie)"]

# Infer new facts (Forward Chaining)
inferred = reasoner.infer_facts(facts, rules)
print(f"Inferred: {inferred}") # ['Grandparent(Alice, Charlie)']

# Explain reasoning
from semantica.reasoning import ExplanationGenerator
explainer = ExplanationGenerator()
# ... generate explanation for inferred facts

Cookbook: Reasoning • Rete Engine

Pipeline Orchestration & Parallel Processing

Orchestrator-Worker Pattern • Parallel Execution • Scalable Processing

from semantica.pipeline import PipelineBuilder, ExecutionEngine

pipeline = PipelineBuilder() \
    .add_step("ingest", "custom", func=ingest_data) \
    .add_step("extract", "custom", func=extract_entities) \
    .add_step("build", "custom", func=build_graph) \
    .build()

result = ExecutionEngine().execute_pipeline(pipeline, parallel=True)

Production-Ready Quality Assurance

Enterprise-Grade QA • Conflict Detection • Deduplication

from semantica.deduplication import DuplicateDetector
from semantica.conflicts import ConflictDetector

entities = kg.get("entities", [])
conflicts = ConflictDetector().detect_conflicts(entities)
duplicates = DuplicateDetector(similarity_threshold=0.85).detect_duplicates(entities)

print(f"Conflicts: {len(conflicts)} | Duplicates: {len(duplicates)}")

Cookbook: Conflict Detection & Resolution • Deduplication

Visualization & Export

Interactive graphs • Multi-format export • Graph analytics

from semantica.visualization import KGVisualizer
from semantica.export import GraphExporter

# Visualize knowledge graph
viz = KGVisualizer(layout="force")
fig = viz.visualize_network(kg, output="interactive")
fig.show()

# Export to multiple formats
exporter = GraphExporter()
exporter.export(kg, format="json", output_path="graph.json")
exporter.export(kg, format="graphml", output_path="graph.graphml")

Cookbook: Visualization • Export

Seed Data Integration

Foundation data • Entity resolution • Domain knowledge

from semantica.seed import SeedDataManager

seed_manager = SeedDataManager()
seed_manager.seed_data.entities = [
    {"id": "s1", "text": "Supplier A", "type": "Supplier", "source": "foundation", "verified": True}
]

# Use seed data for entity resolution
resolved = seed_manager.resolve_entities(extracted_entities)

Cookbook: Seed Data

🚀 Quick Start

For comprehensive examples, see the Cookbook with interactive notebooks!

from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder
from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

# Extract entities and relationships
ner_extractor = NERExtractor(method="ml", model="en_core_web_sm")
relation_extractor = RelationExtractor(method="dependency", model="en_core_web_sm")

text = "Apple Inc. was founded by Steve Jobs in 1976."
entities = ner_extractor.extract(text)
relationships = relation_extractor.extract(text, entities=entities)

# Build knowledge graph
builder = GraphBuilder()
kg = builder.build({"entities": entities, "relationships": relationships})

# Query using GraphRAG
vector_store = VectorStore(backend="faiss", dimension=384)
context_graph = ContextGraph()
context_graph.build_from_entities_and_relationships(
    entities=kg.get('entities', []),
    relationships=kg.get('relationships', [])
)
context = AgentContext(vector_store=vector_store, knowledge_graph=context_graph)

results = context.retrieve("Who founded Apple?", max_results=5)
print(f"Found {len(results)} results")

Cookbook: Your First Knowledge Graph

🎯 Use Cases

Enterprise Knowledge Engineering — Unify data sources into knowledge graphs, breaking down silos.

AI Agents & Autonomous Systems — Build agents with persistent memory and semantic understanding.

Multi-Format Document Processing — Process multiple formats through a unified pipeline.

Data Pipeline Processing — Build scalable pipelines with parallel execution.

Intelligence & Security — Analyze networks, threat intelligence, forensic analysis.

Finance & Trading — Fraud detection, market intelligence, risk assessment.

Biomedical — Drug discovery, medical literature analysis.

🍳 Semantica Cookbook

Interactive Jupyter Notebooks designed to take you from beginner to expert.

View Full Cookbook

Featured Recipes

Recipe	Description	Link
GraphRAG Complete	Build a production-ready Graph Retrieval Augmented Generation system. Features Graph Validation, Hybrid Retrieval, and Logical Inference.	Open Notebook
RAG vs. GraphRAG	Side-by-side comparison. Demonstrates the Reasoning Gap and how GraphRAG solves it with Inference Engines.	Open Notebook
First Knowledge Graph	Go from raw text to a queryable knowledge graph in 20 minutes.	Open Notebook
Real-Time Anomalies	Detect anomalies in streaming data using temporal knowledge graphs and pattern detection.	Open Notebook

Core Tutorials

• Welcome to Semantica - Framework Overview
• Data Ingestion - Universal Ingestion
• Entity Extraction - NER & Relationships
• Building Knowledge Graphs - Graph Construction

Industry Use Cases (14 Cookbooks)

Domain-Specific Cookbooks showcasing real-world applications with real data sources, advanced chunking strategies, temporal KGs, GraphRAG, and comprehensive Semantica module integration:

Biomedical

• Drug Discovery Pipeline - PubMed RSS, entity-aware chunking, GraphRAG, vector similarity search
• Genomic Variant Analysis - bioRxiv RSS, temporal KGs, deduplication, pathway analysis

Finance

• Financial Data Integration MCP - Alpha Vantage API, MCP servers, seed data, real-time ingestion
• Fraud Detection - Transaction streams, temporal KGs, pattern detection, conflict resolution, Context Graph, Context Retriever, GraphRAG with Groq LLM

Blockchain

• DeFi Protocol Intelligence - CoinDesk RSS, ontology-aware chunking, conflict detection, ontology generation
• Transaction Network Analysis - Blockchain APIs, deduplication, network analytics

Cybersecurity

• Real-Time Anomaly Detection - CVE RSS, Kafka streams, temporal KGs, sentence chunking
• Threat Intelligence Hybrid RAG - Security RSS, entity-aware chunking, GraphRAG, deduplication

Intelligence & Law Enforcement

• Criminal Network Analysis - OSINT RSS, deduplication, network centrality, graph analytics
• Intelligence Analysis Orchestrator Worker - Pipeline orchestrator, multi-source integration, conflict detection

Renewable Energy

• Energy Market Analysis - Energy RSS, EIA API, temporal KGs, TemporalPatternDetector, trend prediction

Supply Chain

• Supply Chain Data Integration - Logistics RSS, deduplication, relationship mapping

Explore Use Case Examples — See real-world implementations in finance, biomedical, cybersecurity, and more. 14 comprehensive domain-specific cookbooks with real data sources, advanced chunking strategies, temporal KGs, GraphRAG, and full Semantica module integration.

🔬 Advanced Features

Docling Integration — Document parsing with table extraction for PDFs, DOCX, PPTX, and XLSX files. Supports OCR and multiple export formats.

AWS Neptune Support — Amazon Neptune graph database integration with IAM authentication and OpenCypher queries.

Custom Ontology Import — Import existing ontologies (OWL, RDF, Turtle, JSON-LD, N3) and extend Schema.org, FOAF, Dublin Core, or custom ontologies.

Incremental Updates — Real-time stream processing with Kafka, RabbitMQ, Kinesis for live updates.

Multi-Language Support — Process multiple languages with automatic detection.

Advanced Reasoning — Forward/backward chaining, Rete-based pattern matching, and automated explanation generation.

Graph Analytics — Centrality, community detection, path finding, temporal analysis.

Custom Pipelines — Build custom pipelines with parallel execution.

API Integration — Integrate external APIs for entity enrichment.

See Advanced Examples — Advanced extraction, graph analytics, reasoning, and more.

🗺️ Roadmap

Q1 2026

• Core framework (v1.0)
• GraphRAG engine
• 6-stage ontology pipeline
• Advanced reasoning v2 (Rete, Forward/Backward Chaining)
• Quality assurance features and Quality Assurance module
• Enhanced multi-language support
• Evals
• Real-time streaming improvements

Q2 2026

• Multi-modal processing

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Join Our Community

Channel	Purpose
Discord	Real-time help, showcases
GitHub Discussions	Q&A, feature requests

Learning Resources

Enterprise Support

Enterprise support, professional services, and commercial licensing will be available in the future. For now, we offer community support through Discord and GitHub Discussions.

Current Support:

• Community Support - Free support via Discord and GitHub Discussions
• Bug Reports - GitHub Issues

Future Enterprise Offerings:

• Professional support with SLA
• Enterprise licensing
• Custom development services
• Priority feature requests
• Dedicated support channels

Stay tuned for updates!

🤝 Contributing

How to Contribute

# Fork and clone
git clone https://github.com/your-username/semantica.git
cd semantica

# Create branch
git checkout -b feature/your-feature

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

# Make changes and test
pytest tests/
black semantica/
flake8 semantica/

# Commit and push
git commit -m "Add feature"
git push origin feature/your-feature

Contribution Types

1. Code - New features, bug fixes
2. Documentation - Improvements, tutorials
3. Bug Reports - Create issue
4. Feature Requests - Request feature

📜 License

Semantica is licensed under the MIT License - see the LICENSE file for details.

Built by the Semantica Community

GitHub • Discord