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| Author | SHA1 | Date | |
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 KaifAhmad1
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KaifAhmad1
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Mohd Kaif
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837654fc4f |
+30
-29
@@ -2,7 +2,7 @@
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"$schema": "https://mintlify.com/docs.json",
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"theme": "mint",
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"name": "Semantica",
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"description": "The Accountability and Context Layer for AI — Context Graphs · Decision Intelligence · Full Provenance",
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"description": "The Context and Semantic Layer for AI in High-Stakes Domains — Context Graphs · Decision Intelligence · Full Provenance",
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"colors": {
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"primary": "#10B981",
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"light": "#10B981",
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@@ -43,7 +43,7 @@
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"raiseIssue": true
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},
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"metadata": {
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"og:title": "Semantica — Accountability & Context Layer for AI",
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"og:title": "Semantica — Context & Semantic Layer for AI in High-Stakes Domains",
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"og:description": "Build explainable, auditable knowledge graphs with full provenance. Open source. MIT licensed.",
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"og:image": "/assets/img/semantica-logo.png",
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"twitter:card": "summary_large_image",
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@@ -121,6 +121,23 @@
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"pages": [
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"vector_stores/pgvector"
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]
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},
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{
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"group": "FAQ",
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"pages": [
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"faq"
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]
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},
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{
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"group": "Community",
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"pages": [
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"community",
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"community-projects",
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"contributing-guide",
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"governance",
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"citation",
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"project-license"
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]
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}
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]
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},
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@@ -167,7 +184,17 @@
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"guides/policy-engine",
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"guides/visualization",
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"guides/distance-intelligence",
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"guides/graph-analytics",
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"guides/graph-analytics"
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]
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}
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]
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},
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{
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"tab": "API Reference",
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"groups": [
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{
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"group": "Context & Intelligence",
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"pages": [
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"reference/context",
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"reference/kg",
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"reference/temporal",
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@@ -236,32 +263,6 @@
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]
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}
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]
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},
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{
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"tab": "FAQ",
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"groups": [
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{
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"group": "FAQ",
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"pages": [
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"faq"
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]
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},
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{
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"group": "Community",
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"pages": [
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"community",
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"community-projects",
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"contributing-guide",
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"governance",
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"citation",
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"project-license"
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]
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}
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]
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},
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{
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"tab": "Changelog",
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"href": "https://github.com/semantica-agi/semantica/releases"
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}
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]
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},
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+41
-51
@@ -1,97 +1,87 @@
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---
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title: "Semantica"
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description: "The Accountability and Context Layer for AI: Context Graphs · Decision Intelligence · Full Provenance"
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description: "The Context and Semantic Layer for AI in High-Stakes Domains: Context Graphs · Decision Intelligence · Full Provenance"
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---
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```bash
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pip install semantica
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```
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Your AI agent just made a decision. Now someone needs to explain it.
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Most AI agents store embeddings, not meaning. They can't say why a fact was recalled, where it came from, or what led to a decision. In healthcare, finance, legal, and government, that lack of a traceable record blocks production deployment.
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*What did it know at the time? Which facts shaped the outcome? Where did those facts come from? Has it made the same call before: and did that go well?*
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If your stack can't answer those questions with a traceable record, you have a gap. Not a capability gap: an **accountability gap**. It's the reason AI hasn't landed at scale in healthcare, finance, legal, and government. And it's why teams building for those markets keep rebuilding the same guardrails from scratch.
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**Semantica closes that gap.** It's the context and accountability layer that sits beneath your existing agent framework: not a replacement for LangChain or LlamaIndex, but the infrastructure that makes their outputs trustworthy.
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Semantica is the context and semantic layer for AI in high-stakes domains, sitting beneath your existing agent framework. It doesn't replace LangChain or LlamaIndex; it makes their outputs traceable.
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## The Problem Every Production AI Team Hits
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## What Most AI Stacks Are Missing
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Powerful agents aren't automatically trustworthy ones. Five structural blind spots make modern AI systems impossible to deploy in regulated environments:
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**No memory structure** — agents store embeddings, not meaning
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**No memory structure.** Agents store embeddings, not meaning.
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- No way to ask *why* a fact was recalled
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- No link from a recalled fact back to its source document
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- Context is a black box that resets on every run
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**No decision trail** — agents act continuously but record nothing
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**No decision trail.** Agents act continuously but record nothing.
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- No history to hand to a regulator or auditor
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- No way to replay or reproduce a past decision
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- Debugging means re-running, not reviewing
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**No provenance** — outputs can't be traced to source facts
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- In healthcare, finance, and legal: this is a hard compliance blocker
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**No provenance.** Outputs can't be traced to source facts.
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- A hard compliance blocker in healthcare, finance, and legal
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- No lineage from inference back to the original document
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- Impossible to demonstrate what the agent actually relied on
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- No way to demonstrate what the agent actually relied on
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**No reasoning transparency** — black-box answers with no explanation
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- Impossible to validate the reasoning path
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- Impossible to contest a specific conclusion
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**No reasoning transparency.** Black-box answers with no explanation.
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- No way to validate the reasoning path
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- No way to contest a specific conclusion
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- No basis for improving or correcting future behavior
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**No conflict detection** — contradictory facts silently coexist in vector stores
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**No conflict detection.** Contradictory facts silently coexist in vector stores.
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- No detection when two sources disagree
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- Outputs become inconsistent and unpredictable over time
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- Silent failures compound as the knowledge base grows
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<Note>
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These aren't edge cases. They're why enterprise AI pilots stall: and why your compliance team keeps saying *not yet*.
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</Note>
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## What Semantica Adds to Your Stack
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Semantica gives every agent the infrastructure it needs to be accountable. Drop it into your existing setup in minutes:
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Semantica gives every agent the infrastructure it needs to be accountable, and it drops into an existing setup in minutes.
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**Context Graphs** — a structured, queryable graph of everything your agent knows, decides, and reasons about
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- Persistent across agent runs: no context loss between sessions
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**Context Graphs.** A structured, queryable graph of everything your agent knows, decides, and reasons about.
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- Persistent across agent runs, with no context loss between sessions
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- Queryable with SPARQL and full graph algorithms
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- Temporal model with `valid_from` / `valid_until` on nodes and edges
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- Point-in-time snapshots of the full knowledge state
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**Decision Intelligence** — every decision is a first-class object in your system
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**Decision Intelligence.** Every decision is a first-class object in your system.
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- `record_decision()` captures full lifecycle and causal chain
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- Hybrid precedent search over past decisions for consistency
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- `analyze_decision_impact()` shows downstream consequences
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- Causal chain visualization from trigger to outcome
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**Full Provenance** — every fact links to its source document and ingestion event
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**Full Provenance.** Every fact links to its source document and ingestion event.
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- W3C PROV-O compliant lineage across all modules
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- Full traceability from raw input to final inference
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- `recorded_at` stamping with OWL-Time export
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- Audit-ready for HIPAA, SOX, GDPR, FDA 21 CFR Part 11
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**Reasoning Engines** — explainable reasoning paths, not black boxes
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**Reasoning Engines.** Explainable reasoning paths, not black boxes.
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- Forward chaining, Rete, deductive, abductive
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- SPARQL query-based inference over RDF graphs
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- Datalog with recursive Horn clause rules
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- Every conclusion backed by a traceable derivation path
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**Temporal Intelligence** — your graph knows not just *what*, but *when*
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- Allen interval algebra: all 13 temporal relations
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**Temporal Intelligence.** Your graph knows not just *what*, but *when*.
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- Allen interval algebra covering all 13 temporal relations
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- Point-in-time queries over historical graph states
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- Temporal provenance stamping on every fact
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- OWL-Time export for standards-compliant archiving
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**Ontology Hub** — full ontology lifecycle in the browser
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**Ontology Hub.** Full ontology lifecycle in the browser.
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- Visual editor for schema design and editing
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- SHACL Studio for constraint authoring and validation
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- Alignment authoring across multiple ontologies
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- Health dashboard and version control built in
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<Tip>
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Works alongside any LLM provider and any agent framework: add it to an existing stack without changing your architecture.
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Works alongside any LLM provider and any agent framework. Add it to an existing stack without changing your architecture.
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</Tip>
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<img src="/assets/img/diagrams/architecture-overview.svg" alt="Semantica four-layer architecture: Ingestion → Processing → Intelligence → Application" style={{ width: '100%', borderRadius: '12px', margin: '24px 0' }} />
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@@ -185,17 +175,17 @@ decision_id = context.record_decision(
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</CodeGroup>
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- [Full Quickstart](/quickstart) — Step-by-step pipeline walkthrough
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- [Cookbook](/cookbook) — 40+ real-world Jupyter notebooks
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- [Join Discord](https://discord.gg/sV34vps5hH) — Community chat and support
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- [Full Quickstart](/quickstart): step-by-step pipeline walkthrough
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- [Cookbook](/cookbook): 40+ real-world Jupyter notebooks
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- [Join Discord](https://discord.gg/sV34vps5hH): community chat and support
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## Built for Where Mistakes Have Consequences
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## Industry Use Cases
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Semantica was designed for domains where every decision must be explainable and every fact must be traceable.
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Semantica is used in domains where every decision must be explainable and every fact must be traceable.
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<Warning>
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**This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model — its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. See [Core Concepts](/concepts) for the full scope note.
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**This is system-level explainability, not foundation-model explainability.** Semantica does not expose, reconstruct, or explain what happens *inside* the LLM/foundation model. Its internal reasoning or chain-of-thought stays opaque, as it does for any external system. What Semantica explains is *outside* the model: the context and data fed in, the decision produced, its provenance, the relevant relationships, the policies applied, and the full execution trail. See [Core Concepts](/concepts) for the full scope note.
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</Warning>
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**Healthcare & Life Sciences**
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@@ -256,7 +246,7 @@ Semantica was designed for domains where every decision must be explainable and
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- Knowledge graphs vs. vector stores: when to use each
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- What GraphRAG is and how Semantica implements it
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- How provenance and decision tracking work together
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- The accountability layer architecture
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- The context and semantic layer architecture
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</Step>
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<Step title="Go deep on any module">
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Every module has a dedicated [reference page](/reference/context) with:
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@@ -266,12 +256,12 @@ Semantica was designed for domains where every decision must be explainable and
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</Step>
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</Steps>
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- [Installation](/installation) — Get Semantica installed in under a minute
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- [Quickstart](/quickstart) — Build a complete knowledge graph pipeline in 5 minutes
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- [Core Concepts](/concepts) — The mental model behind the API
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- [API Reference](/reference/context) — Exact module, class, and method details
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- [Cookbook](/cookbook) — Domain notebooks for real-world use cases
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- [Changelog](https://github.com/semantica-agi/semantica/releases) — Release history
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- [Installation](/installation): get Semantica installed in under a minute
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- [Quickstart](/quickstart): build a complete knowledge graph pipeline in 5 minutes
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- [Core Concepts](/concepts): the mental model behind the API
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- [API Reference](/reference/context): exact module, class, and method details
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- [Cookbook](/cookbook): domain notebooks for real-world use cases
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- [Changelog](https://github.com/semantica-agi/semantica/releases): release history
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## Full Capabilities
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## Why Semantica?
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**Open Source, MIT** — No vendor lock-in. No paywalled features.
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**Open Source, MIT.** No vendor lock-in, no paywalled features.
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- Full source available on GitHub
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- Every line auditable by your security team
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- Fork, extend, and self-host with no restrictions
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- No telemetry, no usage reporting
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**Production Ready** — Built for teams that can't afford surprises.
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**Production Ready.** Built for teams that can't afford surprises.
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- 1,000+ passing tests with full regression coverage
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- `PipelineValidator` catches configuration errors at startup
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- `FailureHandler` with exponential backoff and dead-letter queues
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- Ongoing security hardening: fixes shipped in every release ([CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md))
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- Ongoing security hardening, with fixes shipped in every release ([CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md))
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**Modular by Design** — Import only what you need.
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**Modular by Design.** Import only what you need.
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- Use `NERExtractor` without a graph store
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- Use `ContextGraph` without vector storage
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- Every component independently swappable and testable
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- No framework lock-in: works with any agent stack
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- No framework lock-in, and works with any agent stack
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Reference in New Issue
Block a user