# π§ Semantica
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**Open Source Framework for Semantic Layer & Knowledge Engineering**
> **Transform chaotic data into intelligent knowledge.**
*The missing fabric between raw data and AI engineering. A comprehensive open-source framework for building semantic layers and knowledge engineering systems that transform unstructured data into AI-ready knowledge β powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.*
**100% Open Source** β’ **MIT Licensed** β’ **Latest Version: 0.1.1** β’ **Production Ready** β’ **Community Driven**
[**Discord**](https://discord.gg/pMHguUzG)
## What is Semantica?
Semantica bridges the gap between raw data chaos and AI-ready knowledge. It's a **semantic intelligence platform** that transforms unstructured data into structured, queryable knowledge graphs powering GraphRAG, AI agents, and multi-agent systems.
### What Makes Semantica Different?
Unlike traditional approaches that process isolated documents and extract text into vectors, Semantica understands **semantic relationships across all content**, provides **automated ontology generation**, and builds a **unified semantic layer** with **production-grade QA**.
| **Traditional Approaches** | **Semantica's Approach** |
|:---------------------------|:-------------------------|
| Process data as isolated documents | Understands semantic relationships across all content |
| Extract text and store vectors | Builds knowledge graphs with meaningful connections |
| Generic entity recognition | General-purpose ontology generation and validation |
| Manual schema definition | Automatic semantic modeling from content patterns |
| Disconnected data silos | Unified semantic layer across all data sources |
| Basic quality checks | Production-grade QA with conflict detection & resolution |
---
## π― The Problem We Solve
### The Semantic Gap
Organizations today face a **fundamental mismatch** between how data exists and how AI systems need it.
#### The Semantic Gap: Problem vs. Solution
Organizations have **unstructured data** (PDFs, emails, logs), **messy data** (inconsistent formats, duplicates, conflicts), and **disconnected silos** (no shared context, missing relationships). AI systems need **clear rules** (formal ontologies), **structured entities** (validated, consistent), and **relationships** (semantic connections, context-aware reasoning).
| **What Organizations Have** | **What AI Systems Require** |
|:------------------------------|:------------------------------|
| **Unstructured Data** | **Clear Rules** |
| PDFs, emails, logs | Formal ontologies |
| Mixed schemas | Graphs & Networks |
| Conflicting facts | |
| **Messy, Noisy Data** | **Structured Entities** |
| Inconsistent formats | Validated entities |
| Duplicate records | Domain Knowledge |
| Missing relationships | |
| **Disconnected, Siloed Data** | **Relationships** |
| Data in separate systems | Semantic connections |
| No shared context | Context-Aware Reasoning |
| Isolated knowledge | |
### **SEMANTICA FRAMEWORK**
Semantica operates through three integrated layers that transform raw data into AI-ready knowledge:
**Input Layer** β Universal ingestion from multiple data formats (PDFs, DOCX, HTML, JSON, CSV, databases, live feeds, APIs, streams, archives, multi-modal content) into a unified pipeline.
**Semantic Layer** β Core intelligence engine performing entity extraction, relationship mapping, ontology generation, context engineering, and quality assurance. Includes **advanced entity deduplication** (Jaro-Winkler, disjoint property handling) to ensure a clean single source of truth.
**Output Layer** β Production-ready knowledge graphs, vector embeddings, and validated ontologies that power GraphRAG systems, AI agents, and multi-agent systems.
**Powers: GraphRAG, AI Agents, Multi-Agent Systems**
### What Happens Without Semantics?
**They Break** β Systems crash due to inconsistent formats and missing structure.
**They Hallucinate** β AI models generate false information without semantic context to validate outputs.
**They Fail Silently** β Systems return wrong answers without warnings, leading to bad decisions.
**Why?** Systems have data β not semantics. They can't connect concepts, understand relationships, validate against domain rules, or detect conflicts.
---
## π‘ The Semantica Solution
**Semantica** is an **open-source framework** that closes the semantic gap between real-world messy data and the structured semantic layers required by advanced AI systems β GraphRAG, agents, multi-agent systems, reasoning models, and more.
### How Semantica Solves These Problems
**Efficient Embeddings** β Uses **FastEmbed** by default for high-performance, lightweight local embedding generation (faster than sentence-transformers).
**Universal Data Ingestion** β Handles multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams) with unified pipeline, no custom parsers needed.
**Automated Semantic Extraction** β NER, relationship extraction, and triplet generation with LLM enhancement. Includes **auto-chunking** for long documents and **robust error handling** with automatic retry logic.
**Knowledge Graph Construction** β Production-ready graphs with entity resolution, temporal support, and graph analytics. Queryable knowledge ready for AI applications.
**GraphRAG Engine** β Hybrid vector + graph retrieval achieves 91% accuracy (30% improvement) via semantic search + graph traversal for multi-hop reasoning. Features LLM-generated responses grounded in knowledge graph context with reasoning traces. [See Comparison Benchmark](cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb)
**AI Agent Context Engineering** β Persistent memory with RAG + knowledge graphs enables context maintenance, action validation, and structured knowledge access.
**Automated Ontology Generation** β 6-stage LLM pipeline generates validated OWL ontologies with HermiT/Pellet validation, eliminating manual engineering.
**Production-Grade QA** β Conflict detection, deduplication, quality scoring, and provenance tracking ensure trusted, production-ready knowledge graphs.
**Pipeline Orchestration** β Flexible pipeline builder with parallel execution enables scalable processing via orchestrator-worker pattern.
### Core Features at a Glance
| **Feature Category** | **Capabilities** | **Key Benefits** |
|:---------------------|:-----------------|:------------------|
| **Data Ingestion** | Multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams, archives) | Universal ingestion, no custom parsers needed |
| **Semantic Extraction** | NER, relations, triplets, LLM enhancement, **auto-chunking** | Automated discovery with robust error handling |
| **Knowledge Graphs** | Entity resolution, temporal support, graph analytics, query interface | Production-ready, queryable knowledge structures |
| **Ontology Generation** | 6-stage LLM pipeline, OWL generation, HermiT/Pellet validation | Automated ontology creation from documents |
| **GraphRAG** | Hybrid vector + graph retrieval, multi-hop reasoning, LLM-generated responses | 91% accuracy, 30% improvement over vector-only, reasoning traces |
| **LLM Providers** | Unified interface to 100+ LLMs (Groq, OpenAI, HuggingFace, LiteLLM) | Clean imports, multiple providers, structured output |
| **Agent Memory** | Persistent memory (Save/Load), Hybrid Retrieval (Vector+Graph), FastEmbed support | Context-aware agents with semantic understanding |
| **Pipeline Orchestration** | Parallel execution, custom steps, orchestrator-worker pattern | Scalable, flexible data processing |
| **Quality Assurance** | Conflict detection, deduplication, quality scoring, provenance | Trusted knowledge graphs ready for production |
---
## π₯ Who Is This For?
Semantica is designed for **developers, data engineers, and organizations** building the next generation of AI applications that require semantic understanding and knowledge graphs.
### Who Uses Semantica
**AI/ML Engineers & Data Scientists** β Build GraphRAG systems, AI agents, and multi-agent systems.
**Data Engineers** β Build scalable pipelines with semantic enrichment.
**Knowledge Engineers & Ontologists** β Create knowledge graphs and ontologies with automated pipelines.
**Enterprise Data Teams** β Unify semantic layers, improve data quality, resolve conflicts.
**Software & DevOps Engineers** β Build semantic APIs and infrastructure with production-ready SDK.
**Analysts & Researchers** β Transform data into queryable knowledge graphs for insights.
**Security & Compliance Teams** β Threat intelligence, regulatory reporting, audit trails.
**Product Teams & Startups** β Rapid prototyping of AI products and semantic features.
---
## π¦ Installation
> **β
Available on PyPI!** Semantica is now published on PyPI. Install it with a single command: `pip install semantica`
**Prerequisites:** Python 3.8+ (3.9+ recommended) β’ pip (latest version)
### Install from PyPI (Recommended)
```bash
# Install latest version from PyPI
pip install semantica
# Or install with optional dependencies
pip install semantica[all]
# GitHub Workaround (if PyPI version has issues)
pip install git+https://github.com/Hawksight-AI/semantica.git@main
# Verify installation
python -c "from semantica.parse import DoclingParser; DoclingParser(); print('β Semantica ready')"
```
**Current Version:** [](https://pypi.org/project/semantica/) β’ [View on PyPI](https://pypi.org/project/semantica/)
!!! info "Windows PyTorch Note"
If you encounter PyTorch DLL errors on Windows, ensure you have the [Microsoft Visual C++ Redistributable](https://aka.ms/vs/17/release/vc_redist.x64.exe) installed. This is a common environment-specific issue with PyTorch on Windows and not a bug in Semantica.
### Install from Source (Development)
```bash
# 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]"
```
## π Resources
> **New to Semantica?** Check out the [**Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook) for hands-on examples!
- [**Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook) - Interactive notebooks
- [Introduction](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction) - Getting started tutorials
- [Advanced](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced) - Advanced techniques
- [Use Cases](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases) - Real-world applications
## β¨ Core Capabilities
| **Data Ingestion** | **Semantic Extract** | **Knowledge Graphs** | **Ontology** |
|:--------------------:|:----------------------:|:----------------------:|:--------------:|
| [Multiple Formats](#universal-data-ingestion) | [Entity & Relations](#semantic-intelligence-engine) | [Graph Analytics](#knowledge-graph-construction) | [Auto Generation](#ontology-generation--management) |
| **Context** | **GraphRAG** | **LLM Providers** | **Pipeline** |
| [Agent Memory, Context Graph, Context Retriever](#context-engineering--memory-systems) | [Hybrid RAG](#knowledge-graph-powered-rag-graphrag) | [100+ LLMs](#llm-providers-module) | [Parallel Workers](#pipeline-orchestration--parallel-processing) |
| **QA** | **Reasoning** | | |
| [Conflict Resolution](#production-ready-quality-assurance) | [Rule-based Inference](#reasoning--inference-engine) | | |
---
### Universal Data Ingestion
> **Multiple file formats** β’ PDF, DOCX, HTML, JSON, CSV, databases, feeds, archives
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/02_Data_Ingestion.ipynb)
### Document Parsing & Processing
> **Multi-format parsing** β’ **Text normalization** β’ **Intelligent chunking**
```python
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")
# Enhanced parsing with Docling (recommended 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/03_Document_Parsing.ipynb) β’ [**Data Normalization**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/04_Data_Normalization.ipynb) β’ [**Chunking & Splitting**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/11_Chunking_and_Splitting.ipynb)
### Semantic Intelligence Engine
> **Entity & Relation Extraction** β’ NER, Relationships, Events, Triplets with LLM Enhancement
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/05_Entity_Extraction.ipynb) β’ [**Relation Extraction**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/06_Relation_Extraction.ipynb) β’ [**Advanced Extraction**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/01_Advanced_Extraction.ipynb)
### Knowledge Graph Construction
> **Production-Ready KGs** β’ Entity Resolution β’ Temporal Support β’ Graph Analytics
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/07_Building_Knowledge_Graphs.ipynb) β’ [**Graph Analytics**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/10_Graph_Analytics.ipynb)
### Embeddings & Vector Store
> **FastEmbed by default** β’ **Multiple backends** β’ **Semantic search**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/12_Embedding_Generation.ipynb) β’ [**Vector Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/13_Vector_Store.ipynb)
### Graph Store & Triplet Store
> **Neo4j, FalkorDB support** β’ **SPARQL queries** β’ **RDF triplets**
```python
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"}}])
# 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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/09_Graph_Store.ipynb) β’ [**Triplet Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/20_Triplet_Store.ipynb)
### Ontology Generation & Management
> **6-Stage LLM Pipeline** β’ Automatic OWL Generation β’ HermiT/Pellet Validation
```python
from semantica.ontology import OntologyGenerator
generator = OntologyGenerator(llm_provider="openai", model="gpt-4")
ontology = generator.generate_from_documents(sources=["domain_docs/"])
print(f"Classes: {len(ontology.classes)}")
```
[**Cookbook: Ontology**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/14_Ontology.ipynb)
### Context Engineering & Memory Systems
> **Persistent Memory** β’ **Context Graph** β’ **Context Retriever** β’ **Hybrid Retrieval (Vector + Graph)** β’ **Production Graph Store (Neo4j)** β’ **Entity Linking** β’ **Multi-Hop Reasoning**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/19_Context_Module.ipynb) - Basic memory and storage.
- [**Advanced Context Engineering**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/11_Advanced_Context_Engineering.ipynb) - Hybrid retrieval, graph builders, and custom memory policies.
- [**Fraud Detection**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases/finance/02_Fraud_Detection.ipynb) - Demonstrates Context Graph and Context Retriever for fraud detection with GraphRAG.
**Related Components:**
[**Vector Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/13_Vector_Store.ipynb) β’ [**Embedding Generation**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/12_Embedding_Generation.ipynb) β’ [**Advanced Vector Store**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/Advanced_Vector_Store_and_Search.ipynb)
### Knowledge Graph-Powered RAG (GraphRAG)
> **30% Accuracy Improvement** β’ Vector + Graph Hybrid Search β’ 91% Accuracy β’ **Multi-Hop Reasoning** β’ **LLM-Generated Responses**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb) β’ [**Real-Time Anomaly Detection**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb)
### LLM Providers Module
> **Unified LLM Interface** β’ **100+ LLM Support via LiteLLM** β’ **Clean Imports** β’ **Multiple Providers**
```python
from semantica.llms import Groq, OpenAI, HuggingFaceLLM, LiteLLM
import os
# Groq - Fast inference
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**: Fast 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** β’ **Forward/Backward Chaining** β’ **Rete Algorithm** β’ **Explanation Generation**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/08_Reasoning_and_Inference.ipynb) β’ [**Rete Engine**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/09_Rete_Engine.ipynb)
### Pipeline Orchestration & Parallel Processing
> **Orchestrator-Worker Pattern** β’ Parallel Execution β’ Scalable Processing
```python
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** β’ Conflict Detection β’ Deduplication
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/17_Conflict_Detection_and_Resolution.ipynb) β’ [**Deduplication**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/18_Deduplication.ipynb)
### Visualization & Export
> **Interactive graphs** β’ **Multi-format export** β’ **Graph analytics**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/16_Visualization.ipynb) β’ [**Export**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/15_Export.ipynb)
### Seed Data Integration
> **Foundation data** β’ **Entity resolution** β’ **Domain knowledge**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced/07_Seed_Data_Integration.ipynb)
## π Quick Start
> **For comprehensive examples, see the [**Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook) with interactive notebooks!**
```python
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**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb)
## π― Use Cases
**Enterprise Knowledge Engineering** β Unify data sources into knowledge graphs, breaking down silos.
**AI Agents & Autonomous Systems** β Build agents with persistent memory and semantic understanding.
**Multi-Format Document Processing** β Process multiple formats through a unified pipeline.
**Data Pipeline Processing** β Build scalable pipelines with parallel execution.
**Intelligence & Security** β Analyze networks, threat intelligence, forensic analysis.
**Finance & Trading** β Fraud detection, market intelligence, risk assessment.
**Biomedical** β Drug discovery, medical literature analysis.
## π³ Semantica Cookbook
> **Interactive Jupyter Notebooks** designed to take you from beginner to expert.
[**View Full Cookbook**](https://github.com/Hawksight-AI/semantica/tree/main/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](cookbook/use_cases/advanced_rag/01_GraphRAG_Complete.ipynb) |
| **RAG vs. GraphRAG** | Side-by-side comparison. Demonstrates the **Reasoning Gap** and how GraphRAG solves it with **Inference Engines**. | [Open Notebook](cookbook/use_cases/advanced_rag/02_RAG_vs_GraphRAG_Comparison.ipynb) |
| **First Knowledge Graph** | Go from raw text to a queryable knowledge graph in 20 minutes. | [Open Notebook](cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb) |
| **Real-Time Anomalies** | Detect anomalies in streaming data using temporal knowledge graphs and pattern detection. | [Open Notebook](cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb) |
### Core Tutorials
- [**Welcome to Semantica**](cookbook/introduction/01_Welcome_to_Semantica.ipynb) - Framework Overview
- [**Data Ingestion**](cookbook/introduction/02_Data_Ingestion.ipynb) - Universal Ingestion
- [**Entity Extraction**](cookbook/introduction/05_Entity_Extraction.ipynb) - NER & Relationships
- [**Building Knowledge Graphs**](cookbook/introduction/07_Building_Knowledge_Graphs.ipynb) - 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**](cookbook/use_cases/biomedical/01_Drug_Discovery_Pipeline.ipynb) - PubMed RSS, entity-aware chunking, GraphRAG, vector similarity search
- [**Genomic Variant Analysis**](cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb) - bioRxiv RSS, temporal KGs, deduplication, pathway analysis
#### Finance
- [**Financial Data Integration MCP**](cookbook/use_cases/finance/01_Financial_Data_Integration_MCP.ipynb) - Alpha Vantage API, MCP servers, seed data, real-time ingestion
- [**Fraud Detection**](cookbook/use_cases/finance/02_Fraud_Detection.ipynb) - Transaction streams, temporal KGs, pattern detection, conflict resolution, **Context Graph**, **Context Retriever**, GraphRAG with Groq LLM
#### Blockchain
- [**DeFi Protocol Intelligence**](cookbook/use_cases/blockchain/01_DeFi_Protocol_Intelligence.ipynb) - CoinDesk RSS, ontology-aware chunking, conflict detection, ontology generation
- [**Transaction Network Analysis**](cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb) - Blockchain APIs, deduplication, network analytics
#### Cybersecurity
- [**Real-Time Anomaly Detection**](cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb) - CVE RSS, Kafka streams, temporal KGs, sentence chunking
- [**Threat Intelligence Hybrid RAG**](cookbook/use_cases/cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb) - Security RSS, entity-aware chunking, enhanced GraphRAG, deduplication
#### Intelligence & Law Enforcement
- [**Criminal Network Analysis**](cookbook/use_cases/intelligence/01_Criminal_Network_Analysis.ipynb) - OSINT RSS, deduplication, network centrality, graph analytics
- [**Intelligence Analysis Orchestrator Worker**](cookbook/use_cases/intelligence/02_Intelligence_Analysis_Orchestrator_Worker.ipynb) - Pipeline orchestrator, multi-source integration, conflict detection
#### Renewable Energy
- [**Energy Market Analysis**](cookbook/use_cases/renewable_energy/01_Energy_Market_Analysis.ipynb) - Energy RSS, EIA API, temporal KGs, TemporalPatternDetector, trend prediction
#### Supply Chain
- [**Supply Chain Data Integration**](cookbook/use_cases/supply_chain/01_Supply_Chain_Data_Integration.ipynb) - Logistics RSS, deduplication, relationship mapping
[**Explore Use Case Examples**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/use_cases) β 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.
## π¬ Advanced Features
**Incremental Updates** β Real-time stream processing with Kafka, RabbitMQ, Kinesis for live updates.
**Multi-Language Support** β Process multiple languages with automatic detection.
**Custom Ontology Import** β Import and extend Schema.org and custom ontologies.
**Advanced Reasoning** β Forward/backward chaining, Rete-based pattern matching, and automated explanation generation.
**Graph Analytics** β Centrality, community detection, path finding, temporal analysis.
**Custom Pipelines** β Build custom pipelines with parallel execution.
**API Integration** β Integrate external APIs for entity enrichment.
[**See Advanced Examples**](https://github.com/Hawksight-AI/semantica/tree/main/cookbook/advanced) β Advanced extraction, graph analytics, reasoning, and more.
## πΊοΈ Roadmap
### Q1 2026
- [x] Core framework (v1.0)
- [x] GraphRAG engine
- [x] 6-stage ontology pipeline
- [x] 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
---
## π€ Community & Support
### Join Our Community
| **Channel** | **Purpose** |
|:-----------:|:-----------|
| [**Discord**](https://discord.gg/pMHguUzG) | Real-time help, showcases |
| [**GitHub Discussions**](https://github.com/Hawksight-AI/semantica/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](https://discord.gg/pMHguUzG) and [GitHub Discussions](https://github.com/Hawksight-AI/semantica/discussions)
- **Bug Reports** - [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues)
**Future Enterprise Offerings:**
- Professional support with SLA
- Enterprise licensing
- Custom development services
- Priority feature requests
- Dedicated support channels
Stay tuned for updates!
## π€ Contributing
### How to Contribute
```bash
# 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](https://github.com/Hawksight-AI/semantica/issues/new)
4. **Feature Requests** - [Request feature](https://github.com/Hawksight-AI/semantica/issues/new)
### Contributors
**Built by the Semantica Community**
[GitHub](https://github.com/Hawksight-AI/semantica) β’ [Discord](https://discord.gg/pMHguUzG)