Metadata-Version: 2.4
Name: semantica
Version: 0.6.0
Summary: Accountability and context layer for AI agents. Context graphs, decision intelligence, full provenance tracking, and explainable reasoning engines — every AI decision traceable, every output auditable.
Author-email: Semantica <kaif@getsemantica.ai>
Maintainer-email: Semantica <kaif@getsemantica.ai>
License: MIT
Project-URL: Homepage, https://getsemantica.ai
Project-URL: Documentation, https://docs.getsemantica.ai
Project-URL: Repository, https://github.com/semantica-agi/semantica
Project-URL: Changelog, https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md
Project-URL: Bug Tracker, https://github.com/semantica-agi/semantica/issues
Project-URL: Discord, https://discord.gg/sV34vps5hH
Keywords: knowledge-graph,context-graph,ai-agents,llm,decision-intelligence,provenance,explainability,reasoning-engine,entity-extraction,relation-extraction,graph-rag,knowledge-intelligence,semantic-layer,nlp,embeddings,ontology,rdf,triplet-extraction,agentic-ai,knowledge-base,entity-resolution,w3c-prov,audit-trail
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Database :: Database Engines/Servers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Dynamic: license-file

<div align="center">

<img src="Semantica Logo.png" alt="Semantica" width="420"/>

### Graph-Native Infrastructure for Context and Accountable AI Systems

#### *The Open Source Palantir for AI Agents*

> Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.

**Decision Intelligence &nbsp;·&nbsp; Context Management &nbsp;·&nbsp; Deterministic Reasoning &nbsp;·&nbsp; Ontology Management &nbsp;·&nbsp; Knowledge Modeling &nbsp;·&nbsp; End-to-End Traceability**

**Open Source &nbsp;·&nbsp; Self-Hostable &nbsp;·&nbsp; Auditable &nbsp;·&nbsp; Governed &nbsp;·&nbsp; Zero Vendor Lock-In**

**Polyglot Graph Storage &nbsp;·&nbsp; RDF & LPG Support &nbsp;·&nbsp; W3C Standards &nbsp;·&nbsp; Interoperable**

#### Built for High-Stakes, Regulated Domains

[![GitHub Stars](https://img.shields.io/github/stars/semantica-agi/semantica?style=flat-square&color=FFD700&logo=github&logoColor=white&label=Stars)](https://github.com/semantica-agi/semantica) [![GitHub Forks](https://img.shields.io/github/forks/semantica-agi/semantica?style=flat-square&color=6E40C9&logo=github&logoColor=white&label=Forks)](https://github.com/semantica-agi/semantica/network/members) [![Contributors](https://img.shields.io/github/contributors/semantica-agi/semantica?style=flat-square&color=2EA043&logo=github&logoColor=white)](https://github.com/semantica-agi/semantica/graphs/contributors) [![PyPI](https://img.shields.io/pypi/v/semantica.svg?style=flat-square&color=0066CC&logo=pypi&logoColor=white)](https://pypi.org/project/semantica/) [![Total Downloads](https://static.pepy.tech/badge/semantica?style=flat-square)](https://pepy.tech/project/semantica) [![Python 3.8+](https://img.shields.io/badge/python-3.8+-3776AB?style=flat-square&logo=python&logoColor=white)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square)](https://opensource.org/licenses/MIT) [![CI](https://img.shields.io/github/actions/workflow/status/semantica-agi/semantica/ci.yml?style=flat-square&label=CI)](https://github.com/semantica-agi/semantica/actions) [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/semantica-agi/semantica)

[![Website](https://img.shields.io/badge/Website-getsemantica.ai-000000?style=flat-square&logo=googlechrome&logoColor=white)](https://getsemantica.ai/) [![Docs](https://img.shields.io/badge/Docs-docs.getsemantica.ai-0099FF?style=flat-square&logo=readthedocs&logoColor=white)](https://docs.getsemantica.ai/) [![Discord](https://img.shields.io/badge/Discord-Join%20Community-5865F2?style=flat-square&logo=discord&logoColor=white)](https://discord.gg/sV34vps5hH) [![Twitter/X](https://img.shields.io/badge/Follow-%40BuildSemantica-000000?style=flat-square&logo=x&logoColor=white)](https://x.com/BuildSemantica) [![YouTube](https://img.shields.io/badge/YouTube-Watch%20Demos-FF0000?style=flat-square&logo=youtube&logoColor=white)](https://www.youtube.com/watch?v=QfnNZg4-dZA) [![Changelog](https://img.shields.io/badge/Changelog-View-6E40C9?style=flat-square&logo=keepachangelog&logoColor=white)](CHANGELOG.md)

</div>

---

<div align="center">

<a href="https://www.youtube.com/watch?v=QfnNZg4-dZA" target="_blank">
<img
  src="docs/assets/img/semantica-knowledge-explorer-demo.gif"
  alt="Semantica Knowledge Explorer: live graph, decisions, entity resolution, ontology hub"
  width="900"
/>
</a>

*Knowledge Explorer · Context Graphs · Reasoning Engine · Decision Intelligence · Ontology Hub*

**[▶ Watch the full platform walkthrough](https://www.youtube.com/watch?v=QfnNZg4-dZA)**

</div>

---

Most AI agents act without a trail. They store embeddings, not meaning: context that can't be explained, decisions that can't be audited. In lending, that gap is a compliance exposure, not an inconvenience: an underwriting agent's approval has to survive a regulator's "why" months later.

Semantica sits underneath your LLM, vector store, and agent framework as a deterministic infrastructure layer: no LLM required for graph construction, reasoning, or provenance.

**Who it's for:**

- **AI/ML platform teams** shipping agents that make consequential decisions and need structured, queryable context built from fragmented raw data, not just a vector index
- **Compliance, risk, and audit teams** who need a straight answer to "why did the AI do that?" in a format a regulator will actually accept
- **Regulated enterprises** (finance, healthcare, legal, government, defense) that can't ship a black box, and can't send their data to someone else's SaaS to get one
- **Platform and infra engineers** who want the KG, reasoning, and provenance stack self-hosted and swappable, not locked to one vendor's backend
- **Data and knowledge engineers** building a KG from messy, multi-source data: entities and relationships get extracted, conflicting or contradictory facts are flagged instead of silently overwritten, and duplicates are merged before they turn into noise

**[Quick Start](#quick-start)** &nbsp;·&nbsp; **[Architecture](#architecture)** &nbsp;·&nbsp; **[What You Get](#what-semantica-gives-you)** &nbsp;·&nbsp; **[Why Semantica](#why-semantica)** &nbsp;·&nbsp; **[Decision Intelligence](#decision-intelligence)** &nbsp;·&nbsp; **[Context Graphs](#context-graphs)** &nbsp;·&nbsp; **[Recipe: Audit Trail](#recipe-audit-trail-for-a-regulated-decision)** &nbsp;·&nbsp; **[Module Reference](#module-reference)** &nbsp;·&nbsp; **[Integrations](#integrations)** &nbsp;·&nbsp; **[CLI](#cli)** &nbsp;·&nbsp; **[Performance](#performance)** &nbsp;·&nbsp; **[Install](#installation)**

---

## What Semantica Gives You

- **Context Graphs:** A structured, queryable graph of everything your agent knows, decides, and reasons about
- **Decision Intelligence:** Every decision is a first-class object: traceable, searchable by precedent, and causally linked
- **AI Governance & Ontology:** SHACL constraints, conflict detection, compliance rules, OWL generation, and SKOS vocabulary management with a visual editor
- **Full Auditability:** W3C PROV-O provenance on every fact, with audit trails exportable to JSON, CSV, or RDF
- **Deterministic Reasoning:** Forward chaining, Rete network, Datalog, and SPARQL with fully explainable paths, not black boxes
- **Knowledge Pipeline:** Multi-source ingestion, entity-aware chunking, NER/relation/event extraction, and knowledge graph construction, with semantic deduplication and provenance-preserving merges throughout
- **Graph Analytics:** Centrality, community detection, link prediction, and shortest-path queries over the graph you just built
- **Polyglot Graph Storage:** Native RDF (Blazegraph, Apache Jena, Eclipse RDF4J via SPARQL) and Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune via Cypher), plus vector stores, all swappable without touching your code
- **Visualization:** Explore any graph, ontology, or timeline in an interactive browser workbench
- **Drop-in Integrations:** Native Agno support, a full-featured MCP server, a comprehensive CLI, a REST API, and plugins across major editors

---

## Why Semantica

| | Vector DB + RAG | Plain LLM Memory | **Semantica** |
| --- | --- | --- | --- |
| **Recall method** | Embedding similarity | Token window | Graph traversal + semantic search |
| **Decision history** | Not stored | Not stored | First-class queryable objects |
| **Provenance** | None | None | W3C PROV-O, source-linked |
| **Reasoning** | None | Black box | Forward chain, Rete, Datalog, SPARQL |
| **Conflict detection** | Silent overwrite | Silent overwrite | Detected, flagged, resolved |
| **Time travel** | No | No | Point-in-time graph snapshots |
| **Compliance export** | None | None | PROV-O, SHACL, OWL, RDF |
| **Policy enforcement** | None | None | Built-in rule engine + SHACL |
| **Entity resolution** | No | No | Blocking + semantic deduplication |
| **Multi-agent context** | Separate per agent | Separate per agent | Single shared intelligence layer |

Semantica complements your existing stack rather than replacing it. Keep your LLM, vector store, and agent framework exactly as they are; Semantica adds the decision records, causal reasoning, provenance, ontology governance, conflict detection, and audit trails on top. The reasoning engines, KG construction, and provenance layer are fully deterministic; no LLM is required to use them.

---

## Quick Start

```bash
pip install semantica
```

```python
from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Every agent decision becomes a queryable, auditable knowledge node
decision_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, and existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)

# Ask "why did this happen?" and get a real, structured answer
chain     = graph.trace_decision_chain(decision_id)       # full causal ancestry
similar   = graph.find_similar_decisions("cloud vendor", max_results=5)  # precedents
impact    = graph.analyze_decision_impact(decision_id)    # downstream influence map
compliant = graph.check_decision_rules({"category": "vendor_selection"})  # policy gate
```

**Verify your install in 5 seconds:**

```bash
semantica doctor
# Python 3.11.9         pass
# semantica 0.6.0       pass
# faiss vector store    pass
# Config file           pass    ~/.semantica/config.yaml
```

<div align="center">

If Semantica solves a real problem for you, a star helps others find it.

**[⭐ Star on GitHub](https://github.com/semantica-agi/semantica)** &nbsp;·&nbsp; **[Join Discord](https://discord.gg/sV34vps5hH)**

</div>

---

## Architecture

Semantica is a real end-to-end pipeline, not a single library with a marketing name. Every stage below is a shipping module, independently importable:

```
Sources → Ingest → Parse → Normalize → Split → Extract → Conflict Detection → Deduplication
   → Knowledge Graph → [ Ontology · Reasoning · Provenance · Decisions ] → Enriched KG
   → Vector Store + Polyglot Graph Store (RDF & LPG) → Export / Visualize / REST · MCP · CLI
```

- **Ingest:** files, web, databases, enterprise data platforms (Databricks, Snowflake), cloud (Google Drive, Elasticsearch), streams (Kafka, Kinesis), Git, email, MCP
- **Parse → Normalize → Split:** document parsing, text/entity/date normalization, GraphRAG-native entity-aware chunking
- **Extract → Conflict Detection → Deduplication:** NER, relations, events, triplets; conflicting facts flagged and resolved before they merge
- **Knowledge Graph:** `GraphBuilder` constructs the graph; bi-temporal facts and full graph analytics (centrality, communities, link prediction) run on top of it
- **Ontology · Reasoning · Provenance · Decisions:** the intelligence layer sitting on the KG, with SHACL/OWL governance, Rete/Datalog/SPARQL inference, W3C PROV-O lineage, and first-class decision records
- **Storage:** polyglot by design, with RDF triple stores (Blazegraph, Apache Jena, Eclipse RDF4J), Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune), and vector stores, all swappable without touching your code
- **Outputs:** export (RDF, OWL, Parquet, Cypher, JSON-LD), interactive visualization, and access via REST API, MCP server, or CLI

**→ [Full Mermaid diagrams for the pipeline and the decision intelligence lifecycle](ARCHITECTURE.md)**

---

## Decision Intelligence

Decision Intelligence turns every AI choice from an ephemeral inference into a permanent, auditable, queryable record. It answers *"what did your AI decide, why, and what happened next?"*: the question regulators and enterprise risk teams ask with increasing urgency.

In Semantica, a decision is not a log line. It is a first-class graph node with a full lifecycle. In regulated domains, every AI decision must be traceable to a source and defensible to an auditor: `record_decision()` creates a permanent, structured record exportable as W3C PROV-O, the format most compliance frameworks accept for regulator submission.

```
record_decision()             → stored as a graph node with full structured context
add_causal_relationship()     → linked to upstream causes and downstream effects
find_similar_decisions()      → semantic precedent search across all past decisions
trace_decision_chain()        → full causal ancestry back to root causes
analyze_decision_impact()     → downstream influence map - everything this decision affected
check_decision_rules()        → policy compliance gate against configurable rule sets
export / audit trail          → W3C PROV-O, CSV, or JSON for regulator submission
```

```python
from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Record decisions with full structured context
app_id = graph.record_decision(
    category="credit_application",
    scenario="Personal loan, $85k income, 31% DTI, 3yr employment",
    reasoning="Income meets threshold; employment stable; no adverse credit events",
    outcome="proceed_to_underwriting",
    confidence=0.88,
    metadata={"applicant_id": "A-7291"},
)
uw_id = graph.record_decision(
    category="loan_underwriting",
    scenario="Underwriting review for A-7291",
    reasoning="DTI within policy; clean 36-month credit history",
    outcome="approved",
    confidence=0.94,
)
rate_id = graph.record_decision(
    category="interest_rate",
    scenario="Rate assignment for approved loan A-7291",
    outcome="rate_set_8.9pct",
    reasoning="Prime + 2.4% based on risk tier B2",
    confidence=0.99,
)

# Build the auditable causal chain - relationship_type must be one of
# CAUSED, INFLUENCED, or PRECEDENT_FOR
graph.add_causal_relationship(app_id, uw_id,   relationship_type="CAUSED")
graph.add_causal_relationship(uw_id,  rate_id, relationship_type="INFLUENCED")

# Query the intelligence
chain     = graph.trace_decision_chain(rate_id)
similar   = graph.find_similar_decisions("personal loan approval, 31% DTI", max_results=5)
impact    = graph.analyze_decision_impact(uw_id)
compliant = graph.check_decision_rules({"category": "loan_underwriting", "confidence": 0.94})
insights  = graph.get_decision_insights()
```

---

## Context Graphs

A Context Graph is the structured memory layer that traditional RAG is missing. Instead of flat embeddings that answer *"what is similar?"*, a Context Graph answers *"what is connected, why, and how?"* Every entity, relationship, decision, and fact is a first-class node, queryable by graph traversal. Entities link to source documents, decisions link to evidence and consequences, facts carry full provenance, and conflicts are detected, not silently overwritten.

```python
from semantica.context import ContextGraph, AgentContext
from semantica.vector_store import VectorStore

graph = ContextGraph(advanced_analytics=True)

# Add nodes with typed properties
graph.add_node("acme_corp",    "Organization", name="Acme Corp", industry="SaaS")
graph.add_node("alice_chen",   "Person",       name="Alice Chen", role="CTO")
graph.add_node("contract_001", "Contract",     value=2_400_000, currency="USD")

# Add typed, weighted edges (extra kwargs become edge metadata)
graph.add_edge("alice_chen", "acme_corp",    edge_type="works_for",  since="2019-03-01")
graph.add_edge("acme_corp",  "contract_001", edge_type="party_to",   signed="2024-01-15")

# BFS traversal - hop through the graph from any node
neighbors = graph.get_neighbors("acme_corp", hops=2)

# Point-in-time snapshot - the graph as it existed on any past date
snapshot  = graph.state_at("2024-01-01")

# AgentContext - high-level API for agent memory workflows
vs  = VectorStore(backend="faiss")
ctx = AgentContext(vector_store=vs, knowledge_graph=graph)
ctx.store("Alice approved the Acme renewal in Q1 2024", conversation_id="conv_001")
retrieved = ctx.retrieve("who approved the Acme contract?")
```

**Why graph over embeddings:** traversal finds connections embeddings miss (a person 3 hops from a contract); every node carries provenance so you can always ask *"where did this come from?"*; conflicts are flagged before they corrupt your knowledge base; point-in-time snapshots let you replay history without reprocessing.

---

## Recipe: Audit Trail for a Regulated Decision

The flagship pattern: record a causally-linked decision chain, attach provenance to every entity, and export a regulator-ready audit trail.

```python
from semantica.context import ContextGraph
from semantica.provenance import ProvenanceManager
from semantica.export import RDFExporter

graph = ContextGraph(advanced_analytics=True)
prov  = ProvenanceManager(storage_path="./audit.db")

# Record the decision chain
d1 = graph.record_decision(
    category="drug_interaction_check", scenario="Patient P-4821: warfarin + amiodarone co-prescribed",
    reasoning="Amiodarone potentiates warfarin's anticoagulant effect", outcome="flag_for_review", confidence=0.91,
)
d2 = graph.record_decision(
    category="dosage_adjustment", scenario="INR monitoring plan for P-4821",
    reasoning="Reduce warfarin dose per interaction severity; recheck INR in 5 days", outcome="dose_reduced_30pct", confidence=0.87,
)
# relationship_type must be one of CAUSED, INFLUENCED, or PRECEDENT_FOR
graph.add_causal_relationship(d1, d2, relationship_type="CAUSED")

# Track provenance for every entity
prov.track_entity("patient_P4821", source="ehr/medication_orders_2024.json",
                  metadata={"extractor": "NamedEntityRecognizer"})

# Export W3C PROV-O for regulator submission - RDFExporter expects
# {"entities": [...], "relationships": [...]}, so map ContextGraph.to_dict()'s
# {"nodes": [...], "edges": [...]} shape onto it first
graph_dict = graph.to_dict()
kg = {
    "entities": [{"id": n["id"], "type": n["type"], "text": n["content"]} for n in graph_dict["nodes"]],
    "relationships": [
        {"source_id": e["source"], "target_id": e["target"], "type": e["type"]}
        for e in graph_dict["edges"]
    ],
}
RDFExporter().export(kg, "audit_trail.ttl", format="turtle")
```

More recipes (GraphRAG pipelines, an AML rules engine, ontology-to-KG in one pass) are in **[More Recipes](#more-recipes)** below.

---

## Explore the Platform

Every module below is independently importable, with working code samples verified against the current source tree; use one or all of them.

| Module | What it does |
| --- | --- |
| [`semantica.ingest`](#semanticaingest-multi-source-ingestion) | Files, web, databases, APIs, streams, email, Git, Parquet, Snowflake, MCP |
| [`semantica.semantic_extract`](#semanticasemantic_extract-ner-relations-events-triplets) | NER, relation extraction, event detection, triplet generation |
| [`semantica.kg`](#semanticakg-knowledge-graph-construction--analysis) | Graph construction, centrality, communities, link prediction |
| [`semantica.reasoning`](#semanticareasoning-forward-chaining-rete-datalog-sparql) | Forward chaining, Rete, Datalog, SPARQL, fully explainable |
| [`semantica.vector_store`](#semanticavector_store-hybrid--filtered-semantic-search) | FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, hybrid search |
| [`semantica.split`](#semanticasplit-graphrag-native-document-chunking) | Entity-aware, relation-aware, ontology-aware chunking for GraphRAG |
| [`semantica.provenance`](#semanticaprovenance-w3c-prov-o-lineage) | W3C PROV-O lineage on every fact |
| [`semantica.ontology`](#semanticaontology-owl-generation-shacl-validation) | OWL generation, SHACL validation, SKOS vocabularies |
| [`semantica.conflicts`](#semanticaconflicts-conflict-detection--resolution) | Detect and resolve conflicting facts across sources |
| [`semantica.deduplication`](#semanticadeduplication-entity-resolution-at-scale) | Entity resolution at scale |
| [`semantica.normalize`](#semanticanormalize-data-normalization--cleaning) | Text, entity, date, and number normalization; dataset cleaning |
| [`semantica.pipeline`](#semanticapipeline-pipeline-dsl) | Declarative, parallel pipeline DSL for ingest → extract → build → export |
| [`semantica.export`](#semanticaexport-rdf-owl-parquet-cypher-json-ld) | RDF, OWL, Parquet, Cypher, JSON-LD |
| [`semantica.visualization`](#semanticavisualization-interactive-graph-workbench) | Force-directed graphs, ontology hierarchies, temporal dashboards |
| [Temporal Intelligence](#temporal-intelligence-bi-temporal-graphs--time-travel) | Bi-temporal facts, Allen interval algebra, time travel |
| [Multi-Agent (Agno)](#multi-agent-shared-context-with-agno) | One shared context graph across every agent on a team |

**↓ Expand [Module Reference](#module-reference) below** for every module's working example, or jump to [More Recipes](#more-recipes), the full [Integrations](#integrations) matrix, [MCP tool list](#mcp-server), and [REST endpoints](#rest-api).

---

## Module Reference

Expand any module below for its runnable example.

<details>
<summary><b><code>semantica.ingest</code></b>: Multi-Source Ingestion</summary>
<a id="semanticaingest-multi-source-ingestion"></a>

Ingest from files, web, databases, APIs, streams, email, Git repos, Parquet, Databricks, Snowflake, or MCP servers, all through a unified interface.

```python
from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, DBIngestor

# Ingest an entire directory of contracts (PDF, DOCX, HTML, TXT)
docs = FileIngestor().ingest_directory("./contracts/", recursive=True)

# Ingest live web content with robots.txt compliance
pages = WebIngestor().ingest_url("https://example.com/reports/annual-2024.html")

# Ingest structured data from Parquet with Snappy compression
records = ParquetIngestor().ingest("./data/transactions.parquet")

# Ingest from a SQL database - specify which tables to pull
rows = DBIngestor().ingest_database(
    connection_string="postgresql://user:pass@localhost/mydb",
    include_tables=["customer_events"],
    max_rows_per_table=50_000,
)
```

**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 · Apache Arrow/Feather/IPC (`ArrowIngestor`)

Elasticsearch and Google Drive ingestion also ship (`ElasticIngestor`, `GDriveIngestor`) but aren't re-exported from the top-level `semantica.ingest` namespace yet — import them directly: `from semantica.ingest.elastic_ingestor import ElasticIngestor`.

</details>

<details>
<summary><b><code>semantica.semantic_extract</code></b>: NER, Relations, Events, Triplets</summary>
<a id="semanticasemantic_extract-ner-relations-events-triplets"></a>

Extract structured knowledge from raw text in one pass.

```python
from semantica.semantic_extract import (
    NamedEntityRecognizer,
    RelationExtractor,
    EventDetector,
    TripletExtractor,
)

text = """
Anthropic CEO Dario Amodei announced a $7.3B Series E funding round in partnership
with Google and Spark Capital, valuing the company at $61.5B as of Q4 2024.
"""

# Named entity recognition with confidence thresholding
ner = NamedEntityRecognizer(confidence_threshold=0.7)
entities = ner.extract_entities(text)
# → [Entity(name="Dario Amodei", type="PERSON"), Entity(name="Anthropic", type="ORG"),
#    Entity(name="Google", type="ORG"), Entity(name="$7.3B", type="MONEY"), ...]

# Relationship extraction - bidirectional support
rel_extractor = RelationExtractor(confidence_threshold=0.6, bidirectional=True)
relations = rel_extractor.extract_relations(text, entities=entities)
# → [Relation(subject="Dario Amodei", predicate="ceo_of", object="Anthropic"),
#    Relation(subject="Anthropic", predicate="raised", object="$7.3B Series E"), ...]

# Event detection with temporal processing
events = EventDetector(extract_participants=True, extract_time=True).detect_events(text)
# → [Event(type="FUNDING", participants=["Anthropic","Google","Spark Capital"],
#          amount="$7.3B", date="Q4 2024")]

# RDF triplets with optional provenance metadata
triplets = TripletExtractor(include_temporal=True, include_provenance=True).extract_triplets(text)
# → [("Anthropic", "valuation", "$61.5B"), ("Dario Amodei", "is_ceo_of", "Anthropic"), ...]
```

Batch processing across many documents uses `ner.process_batch([...])`, not a per-call `extract_entities_batch` on the facade class.

</details>

<details>
<summary><b><code>semantica.kg</code></b>: Knowledge Graph Construction & Analysis</summary>
<a id="semanticakg-knowledge-graph-construction--analysis"></a>

Build a production knowledge graph from documents and run graph algorithms over it.

```python
from semantica.ingest import FileIngestor
from semantica.kg import (
    GraphBuilder,
    GraphAnalyzer,
    CentralityCalculator,
    CommunityDetector,
    PathFinder,
    LinkPredictor,
    BiTemporalFact,
)
from datetime import datetime

# Build KG - merge duplicate entities, track temporal edges
sources = FileIngestor().ingest_directory("./contracts/", recursive=True)
kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(sources)

# Graph analytics
analyzer    = GraphAnalyzer()
analysis    = analyzer.analyze_graph(kg)             # full graph metrics

centrality  = CentralityCalculator()
degree      = centrality.calculate_degree_centrality(kg)    # most-connected entities
betweenness = centrality.calculate_betweenness_centrality(kg)

communities = CommunityDetector().detect_communities(kg, method="louvain")  # natural clusters
path        = PathFinder().find_shortest_path(kg, "alice_chen", "contract_001")
predictions = LinkPredictor().predict_links(kg, top_k=10)   # relationship predictions

# Bi-temporal facts - track valid time vs. recorded time independently
fact = BiTemporalFact(
    valid_from=datetime(2024, 3, 1),
    valid_until=datetime(2025, 1, 1),
    recorded_at=datetime(2024, 3, 5),
)
```

</details>

<details>
<summary><b><code>semantica.reasoning</code></b>: Forward Chaining, Rete, Datalog, SPARQL</summary>
<a id="semanticareasoning-forward-chaining-rete-datalog-sparql"></a>

Run explainable rule-based inference, not a black box.

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

rete = ReteEngine()
rete.build_network([
    Rule(
        rule_id="aml_flag",
        name="Flag high-risk transactions",
        conditions=[
            {"field": "amount",  "operator": ">",  "value": 10_000},
            {"field": "country", "operator": "in", "value": ["IR", "KP", "SY"]},
        ],
        conclusion="flag_for_compliance_review",
        rule_type=RuleType.IMPLICATION,
    ),
    Rule(
        rule_id="velocity_check",
        name="Flag rapid sequential transfers",
        conditions=[
            {"field": "transfers_in_1h", "operator": ">", "value": 5},
            {"field": "total_amount",    "operator": ">", "value": 50_000},
        ],
        conclusion="flag_velocity_breach",
        rule_type=RuleType.IMPLICATION,
    ),
])

rete.add_fact(Fact("tx_001", "transaction", [{"amount": 15_000, "country": "IR"}]))
flagged = rete.match_patterns()
# → [{"rule": "aml_flag", "matched_facts": ["tx_001"], "conclusion": "flag_for_compliance_review"}]
```

> **Current limitation:** `ReteEngine`'s alpha-node condition matcher is intentionally simple in this release — validate `match_patterns()` output against your actual rule set before wiring it into a production compliance gate; more selective condition evaluation is on the roadmap.

```python
# Recursive Datalog - natural language for graph queries
from semantica.reasoning import DatalogReasoner

engine = DatalogReasoner()
engine.add_fact("parent(tom, bob)")
engine.add_fact("parent(bob, ann)")
engine.add_fact("parent(ann, pat)")
engine.add_rule("ancestor(X, Y) :- parent(X, Y).")
engine.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")
ancestors = engine.query("ancestor(tom, ?X)")
# → [{"X": "bob"}, {"X": "ann"}, {"X": "pat"}]
```

```python
# Explainable reasoning - trace the path, not just the answer
from semantica.reasoning import ExplanationGenerator, Reasoner

reasoner = Reasoner()
reasoner.add_fact("parent(tom, bob)")
reasoner.add_rule("ancestor(X, Y) :- parent(X, Y)")
result = reasoner.forward_chain()

explainer = ExplanationGenerator()
explanation = explainer.generate_explanation(result)
# → Explanation(conclusion="...", steps=[ReasoningStep(...)], justification=Justification(...))
```

</details>

<details>
<summary><b><code>semantica.vector_store</code></b>: Hybrid & Filtered Semantic Search</summary>
<a id="semanticavector_store-hybrid--filtered-semantic-search"></a>

Drop-in vector store with multiple backends, hybrid search, and decision-aware retrieval.

```python
from semantica.vector_store import VectorStore, HybridSearch

# In-memory backend shown here: HybridSearch and explain_decision() work out of the box.
# Swap backend="qdrant" / "weaviate" / "milvus" / "pinecone" / "pgvector" / "faiss" once you
# scale past a single process — search() and store_decision() work identically on all of them.
vs = VectorStore(backend="inmemory", dimension=1536)

# Store a decision with scenario description and outcome
vs.store_decision(
    scenario="Personal loan A-7291, $85k income, 31% DTI, 3yr employment",
    outcome="approved",
    confidence=0.94,
    category="loan_underwriting",
)

# Semantic similarity search
results = vs.search(
    query="personal loan approval with low DTI",
    limit=10,
)

# Hybrid search - dense + sparse retrieval in one pass with RRF fusion
hs   = HybridSearch(vector_store=vs)
hits = hs.search("high-risk transactions 2024")

# Explain why a decision was retrieved
explanation = vs.explain_decision(results[0]["id"])
```

**Backends:** `faiss` · `qdrant` · `weaviate` · `milvus` · `pinecone` · `pgvector` · `sqlite` · `inmemory`

</details>

<details>
<summary><b><code>semantica.split</code></b>: GraphRAG-Native Document Chunking</summary>
<a id="semanticasplit-graphrag-native-document-chunking"></a>

KG-aware splitting that preserves entity boundaries, relation triplets, and ontology concepts, essential for GraphRAG pipelines.

```python
from semantica.split import TextSplitter, EntityAwareChunker, RelationAwareChunker

text = open("contracts/master_agreement.txt").read()

# Standard recursive chunking
chunks = TextSplitter(method="recursive", chunk_size=1000, chunk_overlap=200).split(text)

# Entity-aware chunking - never splits a named entity across chunks (GraphRAG)
chunks = TextSplitter(method="entity_aware", ner_method="llm", chunk_size=1000).split(text)

# Relation-aware chunking - preserves (subject, predicate, object) triplets intact
chunks = RelationAwareChunker(chunk_size=1000, preserve_triplets=True).chunk(text)

# Graph-based chunking - uses centrality to find natural community boundaries
chunks = TextSplitter(method="graph_based", chunk_size=1000).split(text)

# Hierarchical chunking - multi-level (section → paragraph → sentence)
chunks = TextSplitter(method="hierarchical", levels=["section", "paragraph"]).split(text)
```

**Supported methods:** `recursive` · `token` · `sentence` · `paragraph` · `semantic_transformer` · `entity_aware` · `relation_aware` · `graph_based` · `ontology_aware` · `hierarchical` · `community_detection` · `centrality_based` · `llm`

</details>

<details>
<summary><b><code>semantica.provenance</code></b>: W3C PROV-O Lineage</summary>
<a id="semanticaprovenance-w3c-prov-o-lineage"></a>

Every fact is linked to its source. No black boxes, no mystery outputs.

```python
from semantica.provenance import ProvenanceManager

prov = ProvenanceManager(storage_path="./provenance.db")

# Track where every entity came from
prov.track_entity(
    entity_id="acme_corp",
    source="contracts/acme_master_agreement_2024.pdf",
    metadata={"page": 1, "confidence": 0.97, "extractor": "NamedEntityRecognizer"},
)

# Track a relationship's provenance - entity linkage travels in metadata
prov.track_relationship(
    relationship_id="alice_works_for_acme",
    source="hr_records/employees_q1_2024.csv",
    metadata={"source_entity_id": "alice_chen", "target_entity_id": "acme_corp"},
)

# Answer "where did this come from?"
lineage = prov.get_lineage("acme_corp")
trail   = prov.trace_lineage("alice_chen")   # full ancestor chain
entry   = prov.get_provenance("acme_corp")
```

</details>

<details>
<summary><b><code>semantica.ontology</code></b>: OWL Generation, SHACL Validation</summary>
<a id="semanticaontology-owl-generation-shacl-validation"></a>

Generate ontologies from data, validate shapes, and manage your vocabulary.

```python
from semantica.ontology import OntologyGenerator, OntologyValidator

data = {
    "entities": [
        {"id": "acme_corp",  "type": "Organization", "industry": "SaaS", "founded": 2012},
        {"id": "alice_chen", "type": "Person",        "role": "CTO",     "since": 2019},
    ],
    "relationships": [
        {"source": "alice_chen", "target": "acme_corp", "type": "works_for"},
    ],
}

gen       = OntologyGenerator(base_uri="https://semantica.dev/ontology/")
ontology  = gen.generate_ontology(data)
classes   = gen.infer_classes(data)
props     = gen.infer_properties(data, classes)
optimized = gen.optimize_ontology(ontology)

# Validate against SHACL shapes
validator = OntologyValidator()
report    = validator.validate(ontology)
# → ValidationResult(valid=True, consistent=True, satisfiable=True, errors=[], warnings=[])
```

</details>

<details>
<summary><b><code>semantica.conflicts</code></b>: Conflict Detection & Resolution</summary>
<a id="semanticaconflicts-conflict-detection--resolution"></a>

Detect and resolve conflicting facts from multiple sources before they corrupt your knowledge base.

```python
from semantica.conflicts import ConflictDetector, ConflictResolver, SourceTracker

entities_from_source_a = [
    {"id": "alice_chen", "role": "CTO",   "salary": 250_000, "start_date": "2019-03-01"},
]
entities_from_source_b = [
    {"id": "alice_chen", "role": "VP Eng", "salary": 275_000, "start_date": "2019-03-01"},
]

# Detect all conflict types: value, type, relationship, temporal, logical
detector   = ConflictDetector()
conflicts  = detector.detect_conflicts(entities_from_source_a + entities_from_source_b)
# → [Conflict(entity="alice_chen", field="role",   values=["CTO","VP Eng"], severity="HIGH"),
#    Conflict(entity="alice_chen", field="salary",  values=[250000,275000],   severity="MEDIUM")]

# Resolve using multiple strategies
resolver = ConflictResolver()
resolved = resolver.resolve_conflicts(conflicts, strategy="credibility_weighted")  # weighted by source trust
resolved = resolver.resolve_conflicts(conflicts, strategy="most_recent")          # prefer most recent
resolved = resolver.resolve_conflicts(conflicts, strategy="voting")               # majority wins

# Track source credibility over time
tracker = SourceTracker()
tracker.register_source("source_a", source_type="document", credibility_score=0.85)
tracker.register_source("source_b", source_type="document", credibility_score=0.72)
```

</details>

<details>
<summary><b><code>semantica.deduplication</code></b>: Entity Resolution at Scale</summary>
<a id="semanticadeduplication-entity-resolution-at-scale"></a>

Block, cluster, and merge duplicates with semantic similarity.

```python
from semantica.deduplication import DuplicateDetector, EntityMerger

entities = [
    {"id": "e1", "name": "Acme Corporation",  "domain": "acme.com"},
    {"id": "e2", "name": "Acme Corp.",         "domain": "acme.com"},
    {"id": "e3", "name": "ACME Corp",          "domain": "acme.co"},
    {"id": "e4", "name": "Globex Industries",  "domain": "globex.com"},
]

detector   = DuplicateDetector(similarity_threshold=0.75, use_clustering=True)
candidates = detector.detect_duplicates(entities)
groups     = detector.detect_duplicate_groups(entities)
# → DuplicateGroup(entities=["e1","e2","e3"], confidence=0.91, strategy="semantic+blocking")

merger  = EntityMerger(preserve_provenance=True)
ops     = merger.merge_duplicates(entities, strategy="keep_most_complete")
history = merger.get_merge_history()
```

</details>

<details>
<summary><b><code>semantica.normalize</code></b>: Data Normalization & Cleaning</summary>
<a id="semanticanormalize-data-normalization--cleaning"></a>

Standardize text, entities, dates, numbers, and encodings before building your knowledge graph.

```python
from semantica.normalize import (
    TextNormalizer,
    EntityNormalizer,
    DateNormalizer,
    NumberNormalizer,
    DataCleaner,
)

# Unicode, whitespace, casing, HTML tags, smart quotes
text  = TextNormalizer().normalize("  Acme Corp.'s Q4 report...  ")
# → "Acme Corp.'s Q4 report..."

# Alias resolution + entity disambiguation with confidence scores
canonical = EntityNormalizer().normalize_entity("ACME Corp.")
# → NormalizedEntity(canonical="Acme Corporation", type="Organization", confidence=0.91)

# Natural language date parsing with timezone conversion
dt    = DateNormalizer().normalize_date("3 weeks ago")
# → datetime(2026, 7, 1, tzinfo=UTC)

# Unit conversion and currency normalization
price = NumberNormalizer().normalize_number("$1.25M USD")
# → NormalizedNumber(value=1_250_000, currency="USD")

# Deduplicate, validate, and impute missing values across a dataset
clean = DataCleaner().clean_data(records, remove_duplicates=True, handle_missing=True)
```

</details>

<details>
<summary><b><code>semantica.pipeline</code></b>: Pipeline DSL</summary>
<a id="semanticapipeline-pipeline-dsl"></a>

Compose ingestion, extraction, and graph-building into a declarative, parallel pipeline.

```python
from semantica.pipeline import PipelineBuilder, ExecutionEngine

builder = PipelineBuilder()

# add_step() returns the created PipelineStep, not the builder, so these don't chain
builder.add_step("ingest",      step_type="ingest",           source="./contracts/", recursive=True)
builder.add_step("extract",     step_type="ner_extract")
builder.add_step("relations",   step_type="relation_extract")
builder.add_step("build_kg",    step_type="kg_build",         merge_entities=True)
builder.add_step("deduplicate", step_type="deduplicate",      threshold=0.75)
builder.add_step("export",      step_type="export",           format="turtle", output="kg.ttl")

# connect_steps() and set_parallelism() return the builder, so these do chain
pipeline = (
    builder
    .connect_steps("ingest",      "extract")
    .connect_steps("extract",     "relations")
    .connect_steps("relations",   "build_kg")
    .connect_steps("build_kg",    "deduplicate")
    .connect_steps("deduplicate", "export")
    .set_parallelism(4)
    .build(name="contracts_pipeline")
)

engine   = ExecutionEngine()
result   = engine.execute_pipeline(pipeline)
status   = engine.get_pipeline_status(pipeline.name)
progress = engine.get_progress(pipeline.name)
```

</details>

<details>
<summary><b>Temporal Intelligence</b>: Bi-Temporal Graphs & Time Travel</summary>
<a id="temporal-intelligence-bi-temporal-graphs--time-travel"></a>

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,
    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)

# A temporally-bounded edge - valid_from/valid_until define when it held true
graph.add_edge(
    "alice_chen", "acme_corp", edge_type="works_for",
    valid_from="2024-03-01T00:00:00", valid_until="2025-01-01T00:00:00",
)

# 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),
)

# Query facts valid within a time window - query_time_range() expects
# {"relationships": [...]} with source_id/target_id keys, which differs from
# ContextGraph.to_dict()'s {"nodes", "edges"} shape, so map it first
graph_dict = graph.to_dict()
kg_relationships = {
    "relationships": [
        {**e, "source_id": e["source"], "target_id": e["target"]}
        for e in graph_dict["edges"]
    ]
}

tq = TemporalGraphQuery()
facts_in_window = tq.query_time_range(
    kg_relationships, query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
)

# Normalize natural language temporal expressions - returns a (start, end) range
norm = TemporalNormalizer()
start, end = norm.normalize("last quarter")
```

</details>

<details>
<summary><b><code>semantica.export</code></b>: RDF, OWL, Parquet, Cypher, JSON-LD</summary>
<a id="semanticaexport-rdf-owl-parquet-cypher-json-ld"></a>

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 (writes kg_snapshot_entities.parquet
# and kg_snapshot_relationships.parquet)
ParquetExporter(compression="snappy").export_knowledge_graph(kg, "kg_snapshot")

# JSON knowledge graph
JSONExporter().export_knowledge_graph(kg, "kg.json")

# Neo4j / Memgraph Cypher statements for graph database import
LPGExporter().export(kg, "kg_import.cypher")

# Human-readable HTML report
ReportGenerator().generate_report(
    {"title": "KG Audit Report", "summary": "Weekly ingestion summary", "metrics": {"entities": len(kg["entities"])}},
    file_path="audit_report.html",
    format="html",
)
```

</details>

<details>
<summary><b><code>semantica.visualization</code></b>: Interactive Graph Workbench</summary>
<a id="semanticavisualization-interactive-graph-workbench"></a>

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")
```

</details>

<details>
<summary><b>Multi-Agent Shared Context with Agno</b></summary>
<a id="multi-agent-shared-context-with-agno"></a>

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-4-5"),
    memory=shared.bind_agent("researcher"),
    tools=[AgnoKGToolkit(context=shared)],
)
analyst = Agent(
    name="Analyst",
    model=Claude(id="claude-sonnet-4-5"),
    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

</details>

---

## More Recipes

The flagship audit-trail recipe is [above](#recipe-audit-trail-for-a-regulated-decision). Here are three more common patterns.

<details>
<summary><b>End-to-End GraphRAG Pipeline</b></summary>

```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="inmemory")
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?")
```

</details>

<details>
<summary><b>AML Rules Engine</b></summary>

```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,
    ),
])

# Run the rule across a batch of incoming transactions, not just one
for tx in [
    Fact("tx_101", "transaction", [{"amount": 25_000, "country": "IR"}]),
    Fact("tx_102", "transaction", [{"amount": 4_500,  "country": "DE"}]),
    Fact("tx_103", "transaction", [{"amount": 60_000, "country": "KP"}]),
]:
    rete.add_fact(tx)

flagged = rete.match_patterns()
```

Same condition-matcher caveat as [above](#semanticareasoning-forward-chaining-rete-datalog-sparql) applies — validate against your rule set before production use.

</details>

<details>
<summary><b>Ontology-to-Knowledge-Graph in One Pass</b></summary>

```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.process_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.valid:
    RDFExporter().export({"entities": entities[0]}, "ontology.ttl", format="turtle")
```

</details>

---

## 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 · embedding cache |
| **Semantic Extraction** | NER · relation extraction · event detection · triplet generation · coreference |
| **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 · multiple 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 · 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) · 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 |

---

## Performance

Benchmarks from v0.5.0 on a 118,000-node production graph:

| Operation | Before | After | Improvement |
| --- | --- | --- | --- |
| Node search (118k nodes) | 24 ms | 0.004 ms | **6,000×** faster |
| Embedding cache hit | cold load | revision-based cache | **10×** throughput |
| Semantic deduplication | baseline | optimized candidate gen | **6.98×** faster |
| Candidate generation | baseline | blocking strategy | **63.6%** faster |

*Measured on a 118,000-node production graph (AMD EPYC, 64 GB RAM); the deduplication/candidate-generation figures are historical measurements recorded in [CHANGELOG.md](CHANGELOG.md) rather than an automated `tests/` assertion. Results vary by hardware, dataset topology, and backend selection — run `pytest tests/vector_store/test_performance_benchmarks.py -s` to measure your own data.*

---

## CLI

Every capability is available from the terminal. The CLI ships with the package, no separate install required.

```bash
pip install semantica
semantica        # startup dashboard
semantica doctor # health check
semantica --help # full grouped command reference
```

Start with `semantica`, verify with `doctor`, build a graph, and explore the command groups from one terminal.

**Command groups:** `ingest` · `parse` · `extract` · `kg` · `reason` · `decision` · `temporal` · `provenance` · `ontology` · `embed` · `deduplicate` · `validate` · `export` · `visualize` · `pipeline` · `server` · `explorer` · `mcp` · `doctor` · `shell` · `init` · `watch`

→ [Full CLI reference](https://docs.getsemantica.ai/)

---

## Integrations

Native plugin bundles for Claude Code, Cursor, Codex, Windsurf, Cline, Continue, VS Code, and OpenClaw; a full-featured MCP server for any MCP-compatible client; a comprehensive REST API; and first-class Agno support for multi-agent shared context. Every major LLM provider is already supported via `semantica.llms` and LiteLLM: OpenAI, Anthropic, Gemini, Mistral, Llama, Groq, Cohere, Azure, Bedrock, Ollama, DeepSeek, HuggingFace, and more.

MCP setup takes 30 seconds — see [MCP Server](#mcp-server) below.

<details>
<summary><b>Full integrations matrix</b> (editors, MCP clients, REST clients, agentic frameworks)</summary>

<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 &amp; 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>

</details>

### 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 & relations via REST
curl -X POST http://localhost:8000/api/enrich/extract \
  -H "Content-Type: application/json" \
  -d '{"text": "Apple CEO Tim Cook announced record earnings."}'

# List recorded decisions
curl "http://localhost:8000/api/decisions?category=vendor_selection"

# Query the knowledge graph
curl "http://localhost:8000/api/graph/node/acme_corp/neighbors?depth=2"
```

**REST endpoints span:** `enrich` (extract) · `graph` · `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/).

---

## Knowledge Explorer

A browser-based graph workbench. Pan and zoom live graphs, scrub the timeline, review every decision's causal chain, resolve duplicates, and author your ontology visually. Built on React 19 + Sigma.js.

| Workspace | What you can do |
| --- | --- |
| **Knowledge Graph** | Live Sigma.js canvas with ForceAtlas2 layout, Ego Mode, semantic distance heatmap |
| **Timeline** | Scrub through temporal events and watch the graph evolve |
| **Decisions** | Browse the causal chain behind every recorded decision |
| **Registry** | Live audit log of every graph mutation |
| **Entity Resolution** | Review and merge duplicates |
| **Ontology Hub** | SHACL Studio, visual editor, cross-ontology alignments, SKOS browser |
| **Lineage** | W3C PROV-O provenance visualization for any entity |

Quickest way to start (no Node.js required):

```bash
pip install "semantica[explorer]"
semantica-explorer --graph my_graph.json
# Dashboard opens at http://127.0.0.1:8000
```

For contributor / dev-server setup: **[explorer/README.md: Local Setup Guide](explorer/README.md)**

---

## What's New in v0.6.0

- **Named-Graph Support for `JenaStore`:** Migrated onto `rdflib.Dataset(default_union=False)`, completing cross-backend named-graph parity across Blazegraph, RDF4J, and Jena; `add_triplets()` gains a `graph=` option
- **SPARQL CONSTRUCT Query Templates:** Parameterized, injection-safe `CONSTRUCT` templates extended from Blazegraph-only to RDF4J and Jena, plus pipeline integration via the `construct_template` step type
- **Databricks Connector:** `DatabricksIngestor` for Unity Catalog + Delta Lake ingestion, with PAT/OAuth M2M auth, table/query ingestion, and catalog/schema/table/lineage introspection. Install with `pip install "semantica[db-databricks]"`
- **SQLite Vector Store Backend:** `SQLiteVecStore`, a disk-backed local vector store on `sqlite-vec`'s `vec0` virtual tables, with Cosine/L2 metrics, metadata filtering, and WAL mode. Install with `pip install semantica[vectorstore-sqlite]`

→ [Full release notes](RELEASE_NOTES.md) · [Changelog](CHANGELOG.md)

---

## Built for High-Stakes Domains

Semantica is designed for environments where AI outputs must be explainable, auditable, and defensible, and where the data itself can't leave your infrastructure. Self-hostable with zero vendor lock-in, it's built as much for organizations handling confidential or classified data as for regulated industries chasing an audit trail:

- **Finance:** Loan underwriting audit trails, fraud detection, AML compliance, regulatory risk knowledge graphs
- **Healthcare:** Clinical decision support, drug interaction graphs, and patient safety audit trails
- **Legal:** Evidence-backed research, contract analysis, case law reasoning, and privilege tracking
- **Government & Defense:** Policy decision records, classified information governance, and regulatory reporting, fully self-hosted with no data leaving your perimeter
- **Law Enforcement:** Case linkage, evidence provenance chains, and investigative knowledge graphs that hold up under legal scrutiny
- **Cybersecurity:** Threat attribution, incident response timelines, and IOC provenance tracking
- **Autonomous Systems:** Decision logs, safety validation, and explainable AI for certification

---

## Installation

```bash
pip install semantica           # core
pip install semantica[all]      # everything
```

```bash
pip install semantica[agno]                 # Agno multi-agent integration
pip install semantica[llm-litellm]          # OpenAI, Anthropic, Gemini, Mistral, Llama, Groq, Cohere, Bedrock, Ollama, DeepSeek, and more
pip install semantica[graph-neo4j]          # Neo4j graph store (LPG)
pip install semantica[graph-falkordb]       # FalkorDB graph store (LPG)
pip install semantica[graph-apache-age]     # Apache AGE graph store (LPG)
pip install semantica[graph-amazon-neptune] # AWS Neptune graph store (LPG)
# RDF triple stores (Blazegraph, Apache Jena, Eclipse RDF4J) need no extra:
# semantica.triplet_store talks SPARQL over HTTP using the core `requests` dependency
pip install semantica[vectorstore-qdrant]   # Qdrant vector store
pip install semantica[vectorstore-pinecone] # Pinecone vector store
pip install semantica[db-snowflake]         # Snowflake
pip install semantica[db-databricks]        # Databricks (SDK + SQL connector)
pip install semantica[ingest-parquet]       # Parquet / PyArrow
pip install semantica[ingest-arrow]        # Apache Arrow, Feather, IPC
pip install semantica[viz]                  # HTML interactive visualization
pip install semantica[watch]                # Directory file watcher
pip install semantica[explorer]             # Knowledge Explorer dashboard
```

For production deployments, use Docker or Kubernetes rather than a local `pip install`. Set `SEMANTICA_SECRET_KEY`, configure a persistent LPG graph store (Neo4j / FalkorDB / Apache AGE / AWS Neptune) and/or RDF triple store (Blazegraph / Apache Jena / Eclipse RDF4J), and point the vector store at a hosted backend (Qdrant / Pinecone). See [ARCHITECTURE.md](ARCHITECTURE.md) for the full deployment topology.

```bash
# From source
git clone https://github.com/semantica-agi/semantica.git
cd semantica && pip install -e ".[dev]" && pytest tests/
```

---

## Enterprise

On-premises deployment · Private cloud · Custom domain implementations · SLA-backed support · Professional services for regulated industries (finance, healthcare, legal, government).

**[getsemantica.ai](https://getsemantica.ai/)** for enterprise solutions and pricing.

---

## Community & Support

| | |
| --- | --- |
| **Discord** | [discord.gg/sV34vps5hH](https://discord.gg/sV34vps5hH): real-time help, showcases, and announcements |
| **GitHub Discussions** | [Q&A and feature requests](https://github.com/semantica-agi/semantica/discussions) |
| **GitHub Issues** | [Bug reports](https://github.com/semantica-agi/semantica/issues) |
| **Documentation** | [docs.getsemantica.ai](https://docs.getsemantica.ai/) |
| **Cookbook** | [Runnable Jupyter notebooks](https://github.com/semantica-agi/semantica/tree/main/cookbook) |
| **Changelog** | [CHANGELOG.md](CHANGELOG.md) · [Release Notes](RELEASE_NOTES.md) |

---

## Star History

<a href="https://www.star-history.com/?repos=semantica-agi%2Fsemantica&type=date&legend=top-left">
 <picture>
   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=semantica-agi/semantica&type=date&theme=dark&legend=top-left" />
   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=semantica-agi/semantica&type=date&legend=top-left" />
   <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=semantica-agi/semantica&type=date&legend=top-left" />
 </picture>
</a>

---

## Contributors

<div align="center">

[![Contributors](https://contrib.rocks/image?repo=semantica-agi/semantica&max=500)](https://github.com/semantica-agi/semantica/graphs/contributors)

</div>

---

## Contributing

All contributions are welcome: bug fixes, features, tests, and documentation.

1. Fork the repo and create a branch
2. `pip install -e ".[dev]"`
3. Write tests alongside your changes (`pytest tests/`)
4. Open a PR and tag `@KaifAhmad1` for review

See [CONTRIBUTING.md](CONTRIBUTING.md) for full guidelines.

---

<div align="center">

MIT License · Built by [Semantica](https://github.com/semantica-agi)

[GitHub](https://github.com/semantica-agi/semantica) &nbsp;·&nbsp;
[Discord](https://discord.gg/sV34vps5hH) &nbsp;·&nbsp;
[Twitter/X](https://x.com/BuildSemantica) &nbsp;·&nbsp;
[Website](https://getsemantica.ai/) &nbsp;·&nbsp;
[Docs](https://docs.getsemantica.ai/) &nbsp;·&nbsp;
[PyPI](https://pypi.org/project/semantica/)

If this project helps you build better AI, a star means a lot.

**[⭐ Star on GitHub →](https://github.com/semantica-agi/semantica)**

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