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https://github.com/semantica-agi/semantica.git
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docs: rewrite README around a sharper narrative, split module reference out (#761)
Trims the README from a full module/API dump into a scannable pitch (hero, why-Semantica, quick start, architecture, decision intelligence, one flagship audit-trail recipe) and moves the exhaustive per-module reference, extra recipes, and full integrations matrix into a new PLATFORM_REFERENCE.md.
This commit is contained in:
Mohd Kaif
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# Platform Reference
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The full module-by-module API reference, additional recipes, the complete integrations matrix, and the capability table. If you're new to Semantica, start with the [README](README.md#quick-start); this doc is the deep end.
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**Jump to:** [Module Reference](#module-reference) · [More Recipes](#more-recipes) · [Features at a Glance](#features-at-a-glance) · [Integrations](#integrations) · [MCP Server](#mcp-server) · [REST API](#rest-api) · [Plugin Bundles](#plugin-bundles)
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---
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## Module Reference
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Every module is independently importable and composable. Below are working examples for each.
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### `semantica.ingest`: Multi-Source Ingestion
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Ingest from files, web, databases, APIs, streams, email, Git repos, Parquet, Databricks, Snowflake, or MCP servers, all through a unified interface.
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```python
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from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, DBIngestor
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# Ingest an entire directory of contracts (PDF, DOCX, HTML, TXT)
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docs = FileIngestor().ingest_directory("./contracts/", recursive=True)
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# Ingest live web content with robots.txt compliance
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pages = WebIngestor().ingest_url("https://example.com/reports/annual-2024.html")
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# Ingest structured data from Parquet with Snappy compression
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records = ParquetIngestor().ingest("./data/transactions.parquet")
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# Ingest from a SQL database - specify which tables to pull
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rows = DBIngestor().ingest_database(
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connection_string="postgresql://user:pass@localhost/mydb",
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include_tables=["customer_events"],
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max_rows_per_table=50_000,
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)
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```
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**Supported sources:** Local files (PDF, DOCX, PPTX, HTML, TXT, CSV, JSON, YAML, Excel, XML) · Web pages · RSS/Atom feeds · REST APIs · Databases (PostgreSQL, MySQL, SQLite, Oracle, SQL Server) · Parquet datasets · Snowflake · Git repositories · Email (IMAP/POP3) · Message streams (Kafka, RabbitMQ, Kinesis, Pulsar) · MCP resources
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---
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### `semantica.semantic_extract`: NER, Relations, Events, Triplets
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Extract structured knowledge from raw text in one pass.
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```python
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from semantica.semantic_extract import (
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NamedEntityRecognizer,
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RelationExtractor,
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EventDetector,
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TripletExtractor,
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)
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text = """
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Anthropic CEO Dario Amodei announced a $7.3B Series E funding round in partnership
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with Google and Spark Capital, valuing the company at $61.5B as of Q4 2024.
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"""
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# Named entity recognition with confidence thresholding
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ner = NamedEntityRecognizer(confidence_threshold=0.7)
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entities = ner.extract_entities(text)
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# → [Entity(name="Dario Amodei", type="PERSON"), Entity(name="Anthropic", type="ORG"),
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# Entity(name="Google", type="ORG"), Entity(name="$7.3B", type="MONEY"), ...]
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# Relationship extraction - bidirectional support
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rel_extractor = RelationExtractor(confidence_threshold=0.6, bidirectional=True)
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relations = rel_extractor.extract_relations(text, entities=entities)
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# → [Relation(subject="Dario Amodei", predicate="ceo_of", object="Anthropic"),
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# Relation(subject="Anthropic", predicate="raised", object="$7.3B Series E"), ...]
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# Event detection with temporal processing
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events = EventDetector(extract_participants=True, extract_time=True).detect_events(text)
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# → [Event(type="FUNDING", participants=["Anthropic","Google","Spark Capital"],
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# amount="$7.3B", date="Q4 2024")]
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# RDF triplets with optional provenance metadata
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triplets = TripletExtractor(include_temporal=True, include_provenance=True).extract_triplets(text)
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# → [("Anthropic", "valuation", "$61.5B"), ("Dario Amodei", "is_ceo_of", "Anthropic"), ...]
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```
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---
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### `semantica.kg`: Knowledge Graph Construction & Analysis
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Build a production knowledge graph from documents and run graph algorithms over it.
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```python
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from semantica.ingest import FileIngestor
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from semantica.kg import (
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GraphBuilder,
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GraphAnalyzer,
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CentralityCalculator,
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CommunityDetector,
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PathFinder,
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LinkPredictor,
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BiTemporalFact,
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)
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from datetime import datetime
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# Build KG - merge duplicate entities, track temporal edges
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sources = FileIngestor().ingest_directory("./contracts/", recursive=True)
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kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(sources)
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# Graph analytics
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analyzer = GraphAnalyzer()
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analysis = analyzer.analyze_graph(kg) # full graph metrics
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centrality = CentralityCalculator()
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degree = centrality.calculate_degree_centrality(kg) # most-connected entities
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betweenness = centrality.calculate_betweenness_centrality(kg)
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communities = CommunityDetector().detect_communities(kg, method="louvain") # natural clusters
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path = PathFinder().find_shortest_path(kg, "alice_chen", "contract_001")
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predictions = LinkPredictor().predict_links(kg, top_k=10) # relationship predictions
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# Bi-temporal facts - track valid time vs. recorded time independently
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fact = BiTemporalFact(
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valid_from=datetime(2024, 3, 1),
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valid_until=datetime(2025, 1, 1),
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recorded_at=datetime(2024, 3, 5),
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)
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```
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---
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### `semantica.reasoning`: Forward Chaining, Rete, Datalog, SPARQL
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Run explainable rule-based inference, not a black box.
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```python
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from semantica.reasoning import ReteEngine, Rule, Fact, RuleType
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rete = ReteEngine()
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rete.build_network([
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Rule(
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rule_id="aml_flag",
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name="Flag high-risk transactions",
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conditions=[
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{"field": "amount", "operator": ">", "value": 10_000},
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{"field": "country", "operator": "in", "value": ["IR", "KP", "SY"]},
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],
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conclusion="flag_for_compliance_review",
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rule_type=RuleType.IMPLICATION,
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),
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Rule(
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rule_id="velocity_check",
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name="Flag rapid sequential transfers",
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conditions=[
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{"field": "transfers_in_1h", "operator": ">", "value": 5},
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{"field": "total_amount", "operator": ">", "value": 50_000},
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],
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conclusion="flag_velocity_breach",
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rule_type=RuleType.IMPLICATION,
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),
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])
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rete.add_fact(Fact("tx_001", "transaction", [{"amount": 15_000, "country": "IR"}]))
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flagged = rete.match_patterns()
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# → [{"rule": "aml_flag", "matched_facts": ["tx_001"], "conclusion": "flag_for_compliance_review"}]
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```
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```python
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# Recursive Datalog - natural language for graph queries
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from semantica.reasoning import DatalogReasoner
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engine = DatalogReasoner()
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engine.add_fact("parent(tom, bob)")
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engine.add_fact("parent(bob, ann)")
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engine.add_fact("parent(ann, pat)")
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engine.add_rule("ancestor(X, Y) :- parent(X, Y).")
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engine.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")
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ancestors = engine.query("ancestor(tom, ?X)")
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# → [{"X": "bob"}, {"X": "ann"}, {"X": "pat"}]
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```
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```python
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# Explainable reasoning - trace the path, not just the answer
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from semantica.reasoning import ExplanationGenerator, Reasoner
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reasoner = Reasoner()
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result = reasoner.infer(kg, rules=[...])
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explainer = ExplanationGenerator()
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explanation = explainer.generate(result)
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# → Explanation(conclusion="...", steps=[ReasoningStep(...)], justification=Justification(...))
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```
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---
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### `semantica.vector_store`: Hybrid & Filtered Semantic Search
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Drop-in vector store with multiple backends, hybrid search, and decision-aware retrieval.
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```python
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from semantica.vector_store import VectorStore, HybridSearch
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# Works with FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, or in-memory
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vs = VectorStore(backend="qdrant", dimension=1536)
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# Store a decision with scenario description and outcome
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vs.store_decision(
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scenario="Personal loan A-7291, $85k income, 31% DTI, 3yr employment",
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outcome="approved",
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confidence=0.94,
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category="loan_underwriting",
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)
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# Semantic similarity search
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results = vs.search(
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query="personal loan approval with low DTI",
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limit=10,
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)
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# Hybrid search - dense + sparse retrieval in one pass with RRF fusion
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hs = HybridSearch(vector_store=vs)
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hits = hs.search("high-risk transactions 2024")
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# Explain why a decision was retrieved
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explanation = vs.explain_decision(results[0]["id"])
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```
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---
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### `semantica.split`: GraphRAG-Native Document Chunking
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KG-aware splitting that preserves entity boundaries, relation triplets, and ontology concepts, essential for GraphRAG pipelines.
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```python
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from semantica.split import TextSplitter, EntityAwareChunker, RelationAwareChunker
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text = open("contracts/master_agreement.txt").read()
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# Standard recursive chunking
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chunks = TextSplitter(method="recursive", chunk_size=1000, chunk_overlap=200).split(text)
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# Entity-aware chunking - never splits a named entity across chunks (GraphRAG)
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chunks = TextSplitter(method="entity_aware", ner_method="llm", chunk_size=1000).split(text)
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# Relation-aware chunking - preserves (subject, predicate, object) triplets intact
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chunks = RelationAwareChunker(chunk_size=1000, preserve_triplets=True).chunk(text)
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# Graph-based chunking - uses centrality to find natural community boundaries
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chunks = TextSplitter(method="graph_based", chunk_size=1000).split(text)
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# Hierarchical chunking - multi-level (section → paragraph → sentence)
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chunks = TextSplitter(method="hierarchical", levels=["section", "paragraph"]).split(text)
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```
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**Supported methods:** `recursive` · `token` · `sentence` · `paragraph` · `semantic_transformer` · `entity_aware` · `relation_aware` · `graph_based` · `ontology_aware` · `hierarchical` · `community_detection` · `centrality_based` · `llm`
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---
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### `semantica.provenance`: W3C PROV-O Lineage
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Every fact is linked to its source. No black boxes, no mystery outputs.
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```python
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from semantica.provenance import ProvenanceManager
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prov = ProvenanceManager(storage_path="./provenance.db")
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# Track where every entity came from
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prov.track_entity(
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entity_id="acme_corp",
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source="contracts/acme_master_agreement_2024.pdf",
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metadata={"page": 1, "confidence": 0.97, "extractor": "NamedEntityRecognizer"},
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)
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prov.track_relationship(
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relationship_id="alice_works_for_acme",
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source_entity_id="alice_chen",
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target_entity_id="acme_corp",
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source="hr_records/employees_q1_2024.csv",
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)
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# Answer "where did this come from?"
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lineage = prov.get_lineage("acme_corp")
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trail = prov.trace_lineage("alice_chen") # full ancestor chain
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entry = prov.get_provenance("acme_corp")
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```
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---
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### `semantica.ontology`: OWL Generation, SHACL Validation
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Generate ontologies from data, validate shapes, and manage your vocabulary.
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```python
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from semantica.ontology import OntologyGenerator, OntologyValidator
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data = {
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"entities": [
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{"id": "acme_corp", "type": "Organization", "industry": "SaaS", "founded": 2012},
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{"id": "alice_chen", "type": "Person", "role": "CTO", "since": 2019},
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],
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"relationships": [
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{"source": "alice_chen", "target": "acme_corp", "type": "works_for"},
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],
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}
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gen = OntologyGenerator(base_uri="https://semantica.dev/ontology/")
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ontology = gen.generate_ontology(data)
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classes = gen.infer_classes(data)
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props = gen.infer_properties(data, classes)
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optimized = gen.optimize_ontology(ontology)
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# Validate against SHACL shapes
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validator = OntologyValidator()
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report = validator.validate(ontology)
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# → ValidationResult(conforms=True, errors=[], warnings=[])
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```
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---
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### `semantica.conflicts`: Conflict Detection & Resolution
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Detect and resolve conflicting facts from multiple sources before they corrupt your knowledge base.
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```python
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from semantica.conflicts import ConflictDetector, ConflictResolver, SourceTracker
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entities_from_source_a = [
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{"id": "alice_chen", "role": "CTO", "salary": 250_000, "start_date": "2019-03-01"},
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]
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entities_from_source_b = [
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{"id": "alice_chen", "role": "VP Eng", "salary": 275_000, "start_date": "2019-03-01"},
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]
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# Detect all conflict types: value, type, relationship, temporal, logical
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detector = ConflictDetector()
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conflicts = detector.detect_conflicts(entities_from_source_a + entities_from_source_b)
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# → [Conflict(entity="alice_chen", field="role", values=["CTO","VP Eng"], severity="HIGH"),
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# Conflict(entity="alice_chen", field="salary", values=[250000,275000], severity="MEDIUM")]
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# Resolve using multiple strategies
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resolver = ConflictResolver()
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resolved = resolver.resolve(conflicts, strategy="credibility_weighted") # weighted by source trust
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resolved = resolver.resolve(conflicts, strategy="temporal") # prefer most recent
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resolved = resolver.resolve(conflicts, strategy="voting") # majority wins
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# Track source credibility over time
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tracker = SourceTracker()
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tracker.track("source_a", credibility=0.85)
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tracker.track("source_b", credibility=0.72)
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```
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---
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### `semantica.deduplication`: Entity Resolution at Scale
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Block, cluster, and merge duplicates with semantic similarity. **6.98× faster** than baseline.
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```python
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from semantica.deduplication import DuplicateDetector, EntityMerger
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entities = [
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{"id": "e1", "name": "Acme Corporation", "domain": "acme.com"},
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{"id": "e2", "name": "Acme Corp.", "domain": "acme.com"},
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{"id": "e3", "name": "ACME Corp", "domain": "acme.co"},
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{"id": "e4", "name": "Globex Industries", "domain": "globex.com"},
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]
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detector = DuplicateDetector(similarity_threshold=0.75, use_clustering=True)
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candidates = detector.detect_duplicates(entities)
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groups = detector.detect_duplicate_groups(entities)
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# → DuplicateGroup(entities=["e1","e2","e3"], confidence=0.91, strategy="semantic+blocking")
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merger = EntityMerger(preserve_provenance=True)
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ops = merger.merge_duplicates(entities, strategy="keep_most_complete")
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history = merger.get_merge_history()
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```
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---
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### `semantica.normalize`: Data Normalization & Cleaning
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Standardize text, entities, dates, numbers, and encodings before building your knowledge graph.
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```python
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from semantica.normalize import (
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TextNormalizer,
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EntityNormalizer,
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DateNormalizer,
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NumberNormalizer,
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DataCleaner,
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)
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# Unicode, whitespace, casing, HTML tags, smart quotes
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text = TextNormalizer().normalize(" Acme Corp.’s Q4 report… ")
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# → "Acme Corp.'s Q4 report..."
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# Alias resolution + entity disambiguation with confidence scores
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names = EntityNormalizer().normalize_entity("ACME Corp.")
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# → NormalizedEntity(canonical="Acme Corporation", type="Organization", confidence=0.91)
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# Natural language date parsing with timezone conversion
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dt = DateNormalizer().normalize_date("3 weeks ago")
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# → datetime(2026, 5, 22, tzinfo=UTC)
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# Unit conversion and currency normalization
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price = NumberNormalizer().normalize("$1.25M USD")
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# → NormalizedNumber(value=1_250_000, currency="USD")
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# Deduplicate and impute missing values across a dataset
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clean = DataCleaner().clean(records, dedup_threshold=0.9, fill_missing="mean")
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```
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---
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### `semantica.pipeline`: Pipeline DSL
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|
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Compose ingestion, extraction, and graph-building into a declarative, parallel pipeline.
|
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|
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```python
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from semantica.pipeline import PipelineBuilder, ExecutionEngine
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|
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pipeline = (
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PipelineBuilder()
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.add_step("ingest", step_type="ingest", source="./contracts/", recursive=True)
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.add_step("extract", step_type="ner_extract")
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.add_step("relations", step_type="relation_extract")
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.add_step("build_kg", step_type="kg_build", merge_entities=True)
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.add_step("deduplicate", step_type="deduplicate", threshold=0.75)
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.add_step("export", step_type="export", format="turtle", output="kg.ttl")
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.connect_steps("ingest", "extract")
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.connect_steps("extract", "relations")
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.connect_steps("relations", "build_kg")
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.connect_steps("build_kg", "deduplicate")
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.connect_steps("deduplicate", "export")
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.set_parallelism(4)
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.build(name="contracts_pipeline")
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)
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engine = ExecutionEngine()
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result = engine.execute(pipeline)
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status = engine.get_status(pipeline)
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progress = engine.get_progress(pipeline)
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```
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|
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---
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||||
|
||||
### Temporal Intelligence: Bi-Temporal Graphs & Time Travel
|
||||
|
||||
Track when facts were true *in the world* vs. when they were *recorded*, and query either axis.
|
||||
|
||||
```python
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.kg import (
|
||||
BiTemporalFact,
|
||||
TemporalGraphQuery,
|
||||
TemporalVersionManager,
|
||||
TemporalNormalizer,
|
||||
)
|
||||
from datetime import datetime
|
||||
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
graph.add_node("alice_chen", "Person", role="VP Engineering")
|
||||
graph.add_node("acme_corp", "Organization", valuation=1_200_000_000)
|
||||
|
||||
# Point-in-time snapshots - replay history without reprocessing
|
||||
snapshot_2023 = graph.state_at("2023-06-01")
|
||||
snapshot_2024 = graph.state_at("2024-01-01")
|
||||
|
||||
# Bi-temporal facts - valid_time is when true in the world;
|
||||
# recorded_at is when you learned about it
|
||||
fact = BiTemporalFact(
|
||||
valid_from=datetime(2024, 3, 1),
|
||||
valid_until=datetime(2025, 1, 1),
|
||||
recorded_at=datetime(2024, 3, 5),
|
||||
)
|
||||
|
||||
# Allen interval algebra - 13 temporal relations (before, during, overlaps, etc.)
|
||||
tq = TemporalGraphQuery(graph)
|
||||
facts_in_window = tq.query_time_range("2024-01-01", "2024-12-31")
|
||||
|
||||
# Normalize natural language temporal expressions
|
||||
norm = TemporalNormalizer()
|
||||
dt = norm.normalize("last quarter") # → datetime range for Q1 2026
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### `semantica.export`: RDF, OWL, Parquet, Cypher, JSON-LD
|
||||
|
||||
Export to any format required by regulators, graph databases, or downstream systems.
|
||||
|
||||
```python
|
||||
from semantica.export import (
|
||||
RDFExporter,
|
||||
JSONExporter,
|
||||
ParquetExporter,
|
||||
LPGExporter,
|
||||
ReportGenerator,
|
||||
)
|
||||
|
||||
kg = {"entities": [...], "relationships": [...]}
|
||||
|
||||
rdf = RDFExporter()
|
||||
turtle_str = rdf.export_to_rdf(kg, format="turtle") # returns string
|
||||
jsonld_str = rdf.export_to_rdf(kg, format="json-ld")
|
||||
|
||||
rdf.export(kg, "kg_audit.ttl", format="turtle")
|
||||
rdf.export(kg, "kg_audit.jsonld", format="json-ld")
|
||||
rdf.export(kg, "kg_audit.nt", format="n-triples")
|
||||
|
||||
# Columnar analytics - Snappy-compressed Parquet
|
||||
ParquetExporter().export(kg, "kg_snapshot.parquet", compression="snappy")
|
||||
|
||||
# JSON knowledge graph
|
||||
JSONExporter().export_knowledge_graph(kg, "kg.json")
|
||||
|
||||
# Neo4j / Memgraph Cypher statements for graph database import
|
||||
LPGExporter().export(kg, "kg_import.cypher", method="cypher")
|
||||
|
||||
# Human-readable HTML / Markdown report
|
||||
ReportGenerator().generate(kg, "audit_report.html", format="html")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### `semantica.visualization`: Interactive Graph Workbench
|
||||
|
||||
Render force-directed graphs, community maps, ontology hierarchies, and temporal dashboards.
|
||||
|
||||
```python
|
||||
from semantica.visualization import (
|
||||
KGVisualizer,
|
||||
OntologyVisualizer,
|
||||
EmbeddingVisualizer,
|
||||
TemporalVisualizer,
|
||||
)
|
||||
import numpy as np
|
||||
|
||||
kg = {"entities": [...], "relationships": [...]}
|
||||
|
||||
# Interactive force-directed graph (opens in browser)
|
||||
viz = KGVisualizer(layout="force", color_scheme="default")
|
||||
viz.visualize_network(kg, output="interactive", file_path="kg.html")
|
||||
viz.visualize_communities(kg, communities, output="interactive")
|
||||
viz.visualize_centrality(kg, centrality, centrality_type="degree")
|
||||
viz.visualize_entity_types(kg, output="html", file_path="entity_types.html")
|
||||
|
||||
# Ontology class hierarchy
|
||||
OntologyVisualizer().visualize_hierarchy(ontology, output="interactive")
|
||||
|
||||
# 2D embedding projection (UMAP / t-SNE / PCA)
|
||||
EmbeddingVisualizer().visualize_2d_projection(
|
||||
embeddings=np.array([...]),
|
||||
labels=["entity_a", "entity_b"],
|
||||
method="umap",
|
||||
)
|
||||
|
||||
# Timeline scrubber - watch the graph evolve
|
||||
TemporalVisualizer().visualize_timeline(kg, output="interactive")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Multi-Agent Shared Context with Agno
|
||||
|
||||
One shared intelligence layer. All agents read and write to the same context graph.
|
||||
|
||||
```python
|
||||
# pip install semantica[agno]
|
||||
from agno.agent import Agent
|
||||
from agno.team import Team
|
||||
from agno.models.anthropic import Claude
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.vector_store import VectorStore
|
||||
from integrations.agno import AgnoSharedContext, AgnoDecisionKit, AgnoKGToolkit
|
||||
|
||||
shared = AgnoSharedContext(
|
||||
vector_store=VectorStore(backend="faiss"),
|
||||
knowledge_graph=ContextGraph(advanced_analytics=True),
|
||||
decision_tracking=True,
|
||||
)
|
||||
|
||||
researcher = Agent(
|
||||
name="Researcher",
|
||||
model=Claude(id="claude-sonnet-4-6"),
|
||||
memory=shared.bind_agent("researcher"),
|
||||
tools=[AgnoKGToolkit(context=shared)],
|
||||
)
|
||||
analyst = Agent(
|
||||
name="Analyst",
|
||||
model=Claude(id="claude-sonnet-4-6"),
|
||||
memory=shared.bind_agent("analyst"),
|
||||
tools=[AgnoDecisionKit(context=shared)],
|
||||
)
|
||||
|
||||
team = Team(agents=[researcher, analyst], mode="coordinate")
|
||||
# Researcher's findings are instantly available to the Analyst - no copy, no sync
|
||||
```
|
||||
|
||||
→ [runnable notebooks in the cookbook](https://github.com/semantica-agi/semantica/tree/main/cookbook), each self-contained and runnable in under 5 minutes
|
||||
|
||||
---
|
||||
|
||||
## More Recipes
|
||||
|
||||
The README covers the flagship audit-trail recipe. Here are three more common patterns.
|
||||
|
||||
### End-to-End GraphRAG Pipeline
|
||||
|
||||
```python
|
||||
from semantica.ingest import FileIngestor
|
||||
from semantica.split import TextSplitter
|
||||
from semantica.semantic_extract import NamedEntityRecognizer, RelationExtractor
|
||||
from semantica.kg import GraphBuilder
|
||||
from semantica.vector_store import VectorStore, HybridSearch
|
||||
from semantica.context import AgentContext
|
||||
|
||||
# 1. Ingest
|
||||
docs = FileIngestor().ingest_directory("./docs/", recursive=True)
|
||||
|
||||
# 2. Entity-aware chunking - never splits an entity across a chunk boundary
|
||||
splitter = TextSplitter(method="entity_aware", chunk_size=1000)
|
||||
chunks = [splitter.split(doc["text"]) for doc in docs]
|
||||
|
||||
# 3. Extract entities and relations
|
||||
ner = NamedEntityRecognizer(confidence_threshold=0.7)
|
||||
rel_ext = RelationExtractor(confidence_threshold=0.6)
|
||||
entities = [ner.extract_entities(chunk) for chunk_group in chunks for chunk in chunk_group]
|
||||
|
||||
# 4. Build KG
|
||||
kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(docs)
|
||||
|
||||
# 5. Hybrid retrieval
|
||||
vs = VectorStore(backend="faiss")
|
||||
ctx = AgentContext(vector_store=vs, knowledge_graph=kg)
|
||||
ctx.store("Alice approved the Acme renewal in Q1 2024", conversation_id="c1")
|
||||
|
||||
results = HybridSearch(vector_store=vs).search("who approved the renewal?")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### AML Rules Engine
|
||||
|
||||
```python
|
||||
from semantica.reasoning import ReteEngine, Rule, Fact, RuleType
|
||||
|
||||
rete = ReteEngine()
|
||||
rete.build_network([
|
||||
Rule(
|
||||
rule_id="sanctions_check",
|
||||
name="Flag sanctioned-country transactions",
|
||||
conditions=[
|
||||
{"field": "amount", "operator": ">", "value": 10_000},
|
||||
{"field": "country", "operator": "in", "value": ["IR", "KP", "SY", "CU"]},
|
||||
],
|
||||
conclusion="flag_for_compliance_review",
|
||||
rule_type=RuleType.IMPLICATION,
|
||||
),
|
||||
])
|
||||
rete.add_fact(Fact("tx_99", "transaction", [{"amount": 25_000, "country": "IR"}]))
|
||||
matches = rete.match_patterns()
|
||||
# → [{"rule": "sanctions_check", "matched_facts": ["tx_99"],
|
||||
# "conclusion": "flag_for_compliance_review"}]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Ontology-to-Knowledge-Graph in One Pass
|
||||
|
||||
```python
|
||||
from semantica.ingest import FileIngestor
|
||||
from semantica.semantic_extract import NamedEntityRecognizer, RelationExtractor
|
||||
from semantica.kg import GraphBuilder
|
||||
from semantica.ontology import OntologyGenerator, OntologyValidator
|
||||
from semantica.export import RDFExporter
|
||||
|
||||
sources = FileIngestor().ingest_directory("./contracts/")
|
||||
ner = NamedEntityRecognizer(confidence_threshold=0.7)
|
||||
entities = ner.extract_entities_batch([s["text"] for s in sources])
|
||||
|
||||
kg = GraphBuilder(merge_entities=True).build(sources)
|
||||
gen = OntologyGenerator(base_uri="https://myco.dev/ontology/")
|
||||
ont = gen.generate_ontology({"entities": entities[0], "relationships": []})
|
||||
|
||||
report = OntologyValidator().validate(ont)
|
||||
if report.conforms:
|
||||
RDFExporter().export({"entities": entities[0]}, "ontology.ttl", format="turtle")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Features at a Glance
|
||||
|
||||
| Capability | Highlights |
|
||||
| --- | --- |
|
||||
| **Context Graphs** | Queryable graph of entities, decisions, relationships; causal links; cross-graph navigation |
|
||||
| **Decision Intelligence** | `record_decision` · `trace_decision_chain` · `find_similar_decisions` · `analyze_decision_impact` · `check_decision_rules` |
|
||||
| **Temporal Intelligence** | Point-in-time snapshots · Allen interval algebra (13 relations) · `TemporalNormalizer` · bi-temporal provenance |
|
||||
| **Distance Intelligence** | N×N semantic distance matrices · ego-mode visualization · distance bands · 10× embedding cache |
|
||||
| **Semantic Extraction** | NER · relation extraction · event detection · triplet generation · coreference · **6.98×** faster dedup |
|
||||
| **Reasoning Engines** | Forward chaining · Rete · deductive · abductive · SPARQL · Datalog with explainable output |
|
||||
| **GraphRAG Chunking** | Entity-aware · relation-aware · graph-based · ontology-aware · community-detection chunking |
|
||||
| **Conflict Detection** | Value / type / relationship / temporal / logical conflicts · 5 resolution strategies |
|
||||
| **Provenance** | W3C PROV-O · every fact traced to source · audit log export JSON/CSV/RDF |
|
||||
| **Ontology Hub** | SHACL Studio · visual editor · cross-ontology alignments · 5-dimension health dashboard |
|
||||
| **Vector Store** | FAISS · Pinecone · Weaviate · Qdrant · Milvus · PgVector · hybrid + filtered search |
|
||||
| **Graph Databases (LPG)** | Neo4j · FalkorDB · Apache AGE · AWS Neptune |
|
||||
| **Triple Stores (RDF)** | Blazegraph · Apache Jena · Eclipse RDF4J · unified `TripletStore` interface · SPARQL query & bulk load |
|
||||
| **LLM Providers** | **All already supported today:** OpenAI (GPT-4o, o1, o3) · Anthropic (Claude 4) · Google Gemini · Mistral · Meta Llama · Groq · Cohere · Azure OpenAI · AWS Bedrock · Ollama · DeepSeek · Perplexity · Together AI · Fireworks AI · Replicate · HuggingFace · via `semantica.llms` and LiteLLM |
|
||||
|
||||
---
|
||||
|
||||
## Integrations
|
||||
|
||||
Native plugin bundles across major editors, a full-featured MCP server, a comprehensive REST API, and first-class Agno support. All LLM providers already supported: OpenAI · Anthropic · Gemini · Mistral · Llama · Groq · Cohere · Azure · Bedrock · Ollama · DeepSeek · HuggingFace and more via LiteLLM
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th colspan="3" align="left">Native Plugin Bundle</th>
|
||||
<th colspan="5" align="left">MCP Server + Plugin</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://claude.com/product/claude-code"><img src="https://github.com/anthropics.png?size=120" alt="Claude Code" width="48" height="48" /></a><br/>
|
||||
<strong>Claude Code</strong><br/>
|
||||
<sub>Skills · agents · hooks</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://cursor.com"><img src="https://www.freelogovectors.net/wp-content/uploads/2025/06/cursor-logo-freelogovectors.net_.png" alt="Cursor" width="48" height="48" /></a><br/>
|
||||
<strong>Cursor</strong><br/>
|
||||
<sub>Skills · agents</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/openai/codex"><img src="https://github.com/openai.png?size=120" alt="Codex CLI" width="48" height="48" /></a><br/>
|
||||
<strong>Codex CLI</strong><br/>
|
||||
<sub>Skills · agents</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://windsurf.com"><img src="https://exafunction.github.io/public/brand/windsurf-black-symbol.svg" alt="Windsurf" width="48" height="48" /></a><br/>
|
||||
<strong>Windsurf</strong><br/>
|
||||
<sub><a href="plugins/.windsurf-plugin/">plugin</a></sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/cline/cline"><img src="https://github.com/cline.png?size=120" alt="Cline" width="48" height="48" /></a><br/>
|
||||
<strong>Cline</strong><br/>
|
||||
<sub><a href="plugins/.cline-plugin/">plugin</a></sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/continuedev/continue"><img src="https://github.com/continuedev.png?size=120" alt="Continue" width="48" height="48" /></a><br/>
|
||||
<strong>Continue</strong><br/>
|
||||
<sub><a href="plugins/.continue-plugin/">plugin</a></sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/microsoft/vscode"><img src="https://github.com/microsoft.png?size=120" alt="VS Code" width="48" height="48" /></a><br/>
|
||||
<strong>VS Code</strong><br/>
|
||||
<sub><a href="plugins/.vscode-plugin/">plugin</a></sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="integrations/openclaw/"><img src="https://github.com/openclaw.png?size=120" alt="OpenClaw" width="48" height="48" /></a><br/>
|
||||
<strong>OpenClaw</strong><br/>
|
||||
<sub>MCP + <a href="integrations/openclaw/">plugin</a></sub>
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th colspan="1" align="left">MCP Server</th>
|
||||
<th colspan="7" align="left">REST API</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://claude.ai/download"><img src="https://github.com/anthropics.png?size=120" alt="Claude Desktop" width="48" height="48" /></a><br/>
|
||||
<strong>Claude Desktop</strong><br/>
|
||||
<sub>MCP server</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/features/copilot"><img src="https://github.com/github.png?size=120" alt="GitHub Copilot" width="48" height="48" /></a><br/>
|
||||
<strong>GitHub Copilot</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/RooCodeInc/Roo-Code"><img src="https://github.com/RooCodeInc.png?size=120" alt="Roo Code" width="48" height="48" /></a><br/>
|
||||
<strong>Roo Code</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/block/goose"><img src="https://github.com/block.png?size=120" alt="Goose" width="48" height="48" /></a><br/>
|
||||
<strong>Goose</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/Kilo-Org/kilocode"><img src="https://github.com/Kilo-Org.png?size=120" alt="Kilo Code" width="48" height="48" /></a><br/>
|
||||
<strong>Kilo Code</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/Aider-AI/aider"><img src="https://github.com/Aider-AI.png?size=120" alt="Aider" width="48" height="48" /></a><br/>
|
||||
<strong>Aider</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/aws/amazon-q-developer-cli"><img src="https://github.com/aws.png?size=120" alt="Amazon Q" width="48" height="48" /></a><br/>
|
||||
<strong>Amazon Q</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://zed.dev"><img src="https://github.com/zed-industries.png?size=120" alt="Zed" width="48" height="48" /></a><br/>
|
||||
<strong>Zed</strong><br/>
|
||||
<sub>REST API</sub>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
### Agentic Frameworks
|
||||
|
||||
<table>
|
||||
<tr>
|
||||
<th colspan="8" align="left">Native Integration</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/agno-agi/agno"><img src="https://github.com/agno-agi.png?size=120" alt="Agno" width="48" height="48" /></a><br/>
|
||||
<strong>Agno</strong><br/>
|
||||
<sub>First-class · <code>pip install semantica[agno]</code></sub>
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th colspan="8" align="left">Already Supported via REST API & MCP</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/langchain-ai/langchain"><img src="https://github.com/langchain-ai.png?size=120" alt="LangChain" width="48" height="48" /></a><br/>
|
||||
<strong>LangChain</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/langchain-ai/langgraph"><img src="https://github.com/langchain-ai.png?size=120" alt="LangGraph" width="48" height="48" /></a><br/>
|
||||
<strong>LangGraph</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/crewAIInc/crewAI"><img src="https://github.com/crewAIInc.png?size=120" alt="CrewAI" width="48" height="48" /></a><br/>
|
||||
<strong>CrewAI</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/run-llama/llama_index"><img src="https://github.com/run-llama.png?size=120" alt="LlamaIndex" width="48" height="48" /></a><br/>
|
||||
<strong>LlamaIndex</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/microsoft/autogen"><img src="https://github.com/microsoft.png?size=120" alt="AutoGen" width="48" height="48" /></a><br/>
|
||||
<strong>AutoGen</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/openai/openai-agents-python"><img src="https://github.com/openai.png?size=120" alt="OpenAI Agents SDK" width="48" height="48" /></a><br/>
|
||||
<strong>OpenAI Agents</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/google/adk-python"><img src="https://github.com/google.png?size=120" alt="Google ADK" width="48" height="48" /></a><br/>
|
||||
<strong>Google ADK</strong><br/>
|
||||
<sub>REST API · MCP</sub>
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th colspan="8" align="left">Native SDK Integration (Coming Soon)</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/langchain-ai/langchain"><img src="https://github.com/langchain-ai.png?size=120" alt="LangChain" width="48" height="48" /></a><br/>
|
||||
<strong>LangChain</strong><br/>
|
||||
<sub>Dedicated toolkit</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/crewAIInc/crewAI"><img src="https://github.com/crewAIInc.png?size=120" alt="CrewAI" width="48" height="48" /></a><br/>
|
||||
<strong>CrewAI</strong><br/>
|
||||
<sub>Dedicated toolkit</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/run-llama/llama_index"><img src="https://github.com/run-llama.png?size=120" alt="LlamaIndex" width="48" height="48" /></a><br/>
|
||||
<strong>LlamaIndex</strong><br/>
|
||||
<sub>Dedicated toolkit</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/microsoft/autogen"><img src="https://github.com/microsoft.png?size=120" alt="AutoGen" width="48" height="48" /></a><br/>
|
||||
<strong>AutoGen</strong><br/>
|
||||
<sub>Dedicated toolkit</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/openai/openai-agents-python"><img src="https://github.com/openai.png?size=120" alt="OpenAI Agents SDK" width="48" height="48" /></a><br/>
|
||||
<strong>OpenAI Agents</strong><br/>
|
||||
<sub>Dedicated toolkit</sub>
|
||||
</td>
|
||||
<td align="center" width="12.5%">
|
||||
<a href="https://github.com/google/adk-python"><img src="https://github.com/google.png?size=120" alt="Google ADK" width="48" height="48" /></a><br/>
|
||||
<strong>Google ADK</strong><br/>
|
||||
<sub>Dedicated toolkit</sub>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
---
|
||||
|
||||
## MCP Server
|
||||
|
||||
Connect any MCP-compatible client (Claude Desktop, Windsurf, Cline, VS Code) in 30 seconds:
|
||||
|
||||
```bash
|
||||
python -m semantica.mcp_server
|
||||
# or via the installed entry point
|
||||
semantica-mcp
|
||||
```
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"semantica": { "command": "python", "args": ["-m", "semantica.mcp_server"] }
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Tools exposed over MCP:**
|
||||
|
||||
| Tool | What it does |
|
||||
| --- | --- |
|
||||
| `extract_entities` | NER on any text |
|
||||
| `extract_relations` | Relation extraction |
|
||||
| `record_decision` | Persist a decision node |
|
||||
| `query_decisions` | Search decision history |
|
||||
| `find_precedents` | Semantic precedent lookup |
|
||||
| `get_causal_chain` | Full causal ancestry |
|
||||
| `add_entity` | Add a KG node |
|
||||
| `add_relationship` | Add a KG edge |
|
||||
| `run_reasoning` | Execute rule set |
|
||||
| `get_graph_analytics` | Centrality, communities |
|
||||
| `export_graph` | Export to RDF/JSON/Parquet |
|
||||
| `get_graph_summary` | Graph statistics |
|
||||
|
||||
---
|
||||
|
||||
## REST API
|
||||
|
||||
```bash
|
||||
# Start the backend
|
||||
python -m semantica.server # port 8000
|
||||
|
||||
# Extract entities via REST
|
||||
curl -X POST http://localhost:8000/api/extract/entities \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"text": "Apple CEO Tim Cook announced record earnings."}'
|
||||
|
||||
# Record a decision
|
||||
curl -X POST http://localhost:8000/api/decisions \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"category": "vendor_selection",
|
||||
"scenario": "Choose ML cloud provider",
|
||||
"reasoning": "Best GPU availability and pricing",
|
||||
"outcome": "selected_aws",
|
||||
"confidence": 0.91
|
||||
}'
|
||||
|
||||
# Query the knowledge graph
|
||||
curl http://localhost:8000/api/graph/neighbors/acme_corp?hops=2
|
||||
```
|
||||
|
||||
**REST endpoints span:** `extract` · `kg` · `decisions` · `reasoning` · `provenance` · `ontology` · `embeddings` · `search` · `export` · `pipeline` · `temporal` · `deduplication`
|
||||
|
||||
---
|
||||
|
||||
## Plugin Bundles
|
||||
|
||||
**Domain skills:** `extract` · `ingest` · `query` · `ontology` · `validate` · `deduplicate` · `embed` · `reason` · `decision` · `causal` · `temporal` · `provenance` · `policy` · `explain` · `export` · `change` · `visualize`
|
||||
|
||||
**Specialized agents:** `kg-assistant` · `decision-advisor` · `explainability`
|
||||
|
||||
Bundles for Claude Code, Cursor, Codex, Windsurf, Cline, Continue, VS Code, and OpenClaw in [`plugins/`](plugins/).
|
||||
|
||||
---
|
||||
|
||||
[← Back to README](README.md)
|
||||
Reference in New Issue
Block a user