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 — powering Knowledge Graph-Powered RAG (GraphRAG), AI Agents, Multi-Agent Systems, and AI applications with structured semantic knowledge.
100% Open Source • MIT Licensed • Production Ready • Community Driven
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.
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 |
Organizations today face a fundamental mismatch between how data exists and how AI systems need it.
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 operates through three integrated layers that transform raw data into AI-ready knowledge:
Input Layer — 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 — 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 — 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
They Break — Systems crash due to inconsistent formats and missing structure.
They Hallucinate — AI models generate false information without semantic context to validate outputs.
They Fail Silently — Systems return wrong answers without warnings, leading to bad decisions.
Why? Systems have data — not semantics. They can't connect concepts, understand relationships, validate against domain rules, or detect conflicts.
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 — GraphRAG, agents, multi-agent systems, reasoning models, and more.
Efficient Embeddings — Uses FastEmbed by default for high-performance, lightweight local embedding generation (faster than sentence-transformers).
Universal Data Ingestion — Handles multiple formats (PDF, DOCX, HTML, JSON, CSV, databases, APIs, streams) with unified pipeline, no custom parsers needed.
Automated Semantic Extraction — NER, relationship extraction, and triplet generation with LLM enhancement discovers entities and relationships automatically.
Knowledge Graph Construction — Production-ready graphs with entity resolution, temporal support, and graph analytics. Queryable knowledge ready for AI applications.
GraphRAG Engine — 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
AI Agent Context Engineering — Persistent memory with RAG + knowledge graphs enables context maintenance, action validation, and structured knowledge access.
Automated Ontology Generation — 6-stage LLM pipeline generates validated OWL ontologies with HermiT/Pellet validation, eliminating manual engineering.
Production-Grade QA — Conflict detection, deduplication, quality scoring, and provenance tracking ensure trusted, production-ready knowledge graphs.
Pipeline Orchestration — Flexible pipeline builder with parallel execution enables scalable processing via orchestrator-worker pattern.
| 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, relationship extraction, triplet generation, LLM enhancement | Automated discovery of entities and relationships |
| 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 |
Semantica is designed for developers, data engineers, and organizations building the next generation of AI applications that require semantic understanding and knowledge graphs.
AI/ML Engineers & Data Scientists — Build GraphRAG systems, AI agents, and multi-agent systems.
Data Engineers — Build scalable pipelines with semantic enrichment.
Knowledge Engineers & Ontologists — Create knowledge graphs and ontologies with automated pipelines.
Enterprise Data Teams — Unify semantic layers, improve data quality, resolve conflicts.
Software & DevOps Engineers — Build semantic APIs and infrastructure with production-ready SDK.
Analysts & Researchers — Transform data into queryable knowledge graphs for insights.
Security & Compliance Teams — Threat intelligence, regulatory reporting, audit trails.
Product Teams & Startups — Rapid prototyping of AI products and semantic features.
✅ 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) • pip (latest version)
# Install latest version from PyPI
pip install semantica
# Or install with optional dependencies
pip install semantica[all]
# Verify installation
python -c "import semantica; print(semantica.__version__)"Current Version:
# 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]"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
| 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 |
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")Multi-format parsing • Text normalization • Intelligent chunking
from semantica.parse import DocumentParser
from semantica.normalize import TextNormalizer
from semantica.split import TextSplitter
# Parse documents
parser = DocumentParser()
parsed = parser.parse("document.pdf", format="auto")
# 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
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
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
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
Neo4j, FalkorDB support • 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"}}])
# 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
6-Stage LLM Pipeline • Automatic OWL Generation • HermiT/Pellet Validation
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)}")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
30% Accuracy Improvement • Vector + Graph Hybrid Search • 91% Accuracy • Multi-Hop Reasoning • LLM-Generated Responses
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
Unified LLM Interface • 100+ LLM Support via LiteLLM • Clean Imports • Multiple Providers
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)
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 factsCookbook: Reasoning • Rete Engine
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)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
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
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)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
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.
Interactive Jupyter Notebooks designed to take you from beginner to expert.
| 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 |
- Welcome to Semantica - Framework Overview
- Data Ingestion - Universal Ingestion
- Entity Extraction - NER & Relationships
- Building Knowledge Graphs - Graph Construction
Domain-Specific Cookbooks showcasing real-world applications with real data sources, advanced chunking strategies, temporal KGs, GraphRAG, and comprehensive Semantica module integration:
- Drug Discovery Pipeline - PubMed RSS, entity-aware chunking, GraphRAG, vector similarity search
- Genomic Variant Analysis - bioRxiv RSS, temporal KGs, deduplication, pathway analysis
- 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
- DeFi Protocol Intelligence - CoinDesk RSS, ontology-aware chunking, conflict detection, ontology generation
- Transaction Network Analysis - Blockchain APIs, deduplication, network analytics
- Real-Time Anomaly Detection - CVE RSS, Kafka streams, temporal KGs, sentence chunking
- Threat Intelligence Hybrid RAG - Security RSS, entity-aware chunking, enhanced GraphRAG, deduplication
- Criminal Network Analysis - OSINT RSS, deduplication, network centrality, graph analytics
- Intelligence Analysis Orchestrator Worker - Pipeline orchestrator, multi-source integration, conflict detection
- Energy Market Analysis - Energy RSS, EIA API, temporal KGs, TemporalPatternDetector, trend prediction
- 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.
Incremental Updates — Real-time stream processing with Kafka, RabbitMQ, Kinesis for live updates.
Multi-Language Support — Process multiple languages with automatic detection.
Custom Ontology Import — Import and extend Schema.org and custom ontologies.
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.
- 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
- Multi-modal processing
| Channel | Purpose |
|---|---|
| Discord | Real-time help, showcases |
| GitHub Discussions | Q&A, feature requests |
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!
# 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- Code - New features, bug fixes
- Documentation - Improvements, tutorials
- Bug Reports - Create issue
- Feature Requests - Request feature
Semantica is licensed under the MIT License - see the LICENSE file for details.