docs(index): rewrite landing page as a crisp developer welcome

Replace the long feature-dump landing page with a lean "Welcome to Semantica" page: a two-line problem/positioning statement (deterministic semantic layer, no LLM required for graph construction, reasoning, or provenance), five capability bullets, the multi-provider quickstart snippet, and a 4-step onboarding path. Drops the redundant module table, industry-use-case grid, and duplicate link lists in favor of linking out to Core Concepts, guides, and the API reference. Keeps a collapsed module-list accordion so the page still satisfies docs_check.py's full-module-coverage check.
2026-09-04 04:01:07 +00:00 · 2026-09-03 22:21:30 +05:30
3 changed files with 149 additions and 459 deletions
@@ -1,5 +1,5 @@
 ---
-title: "Semantica"
+title: "Welcome to Semantica"
 description: "The Context and Semantic Layer for AI in High-Stakes Domains: Context Graphs · Decision Intelligence · Full Provenance"
 ---

@@ -7,93 +7,25 @@ description: "The Context and Semantic Layer for AI in High-Stakes Domains: Cont
 pip install semantica
 ```

-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.
+Most AI agents run on embeddings, not meaning. A similarity score has no structure, no relationships, and no way to explain why a result came back.

-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.
+Semantica is the semantic and context layer underneath your LLM, vector store, and agent framework: deterministic infrastructure, not a model. Graph construction, reasoning, and provenance all run without an LLM in the loop. It turns fragmented enterprise data into a structured, queryable context graph and knowledge graph, governed by ontologies, taxonomies, and controlled vocabularies (OWL, SHACL, SKOS), so your data's meaning is explicit rather than approximated by an embedding.

+Provenance and audit trails aren't a bolt-on. They fall out naturally once your data has that structure, so the same graph that powers retrieval and reasoning also gives you a straight answer when a regulator asks why.

-## What Most AI Stacks Are Missing
+## What you get

-**No memory structure.** Agents store embeddings, not meaning.
- No way to ask *why* a fact was recalled
- No link from a recalled fact back to its source document
- Context is a black box that resets on every run
-
-**No decision trail.** Agents act continuously but record nothing.
- No history to hand to a regulator or auditor
- No way to replay or reproduce a past decision
- Debugging means re-running, not reviewing
-
-**No provenance.** Outputs can't be traced to source facts.
- A hard compliance blocker in healthcare, finance, and legal
- No lineage from inference back to the original document
- No way to demonstrate what the agent actually relied on
-
-**No reasoning transparency.** Black-box answers with no explanation.
- No way to validate the reasoning path
- No way to contest a specific conclusion
- No basis for improving or correcting future behavior
-
-**No conflict detection.** Contradictory facts silently coexist in vector stores.
- No detection when two sources disagree
- Outputs become inconsistent and unpredictable over time
- Silent failures compound as the knowledge base grows
-
-
-## What Semantica Adds to Your Stack
-
-Semantica gives every agent the infrastructure it needs to be accountable, and it drops into an existing setup in minutes.
-
-**Context Graphs.** A structured, queryable graph of everything your agent knows, decides, and reasons about.
- Persistent across agent runs, with no context loss between sessions
- Queryable with SPARQL and full graph algorithms
- Temporal model with `valid_from` / `valid_until` on nodes and edges
- Point-in-time snapshots of the full knowledge state
-
-**Decision Intelligence.** Every decision is a first-class object in your system.
- `record_decision()` captures full lifecycle and causal chain
- Hybrid precedent search over past decisions for consistency
- `analyze_decision_impact()` shows downstream consequences
- Causal chain visualization from trigger to outcome
-
-**Full Provenance.** Every fact links to its source document and ingestion event.
- W3C PROV-O compliant lineage across all modules
- Full traceability from raw input to final inference
- `recorded_at` stamping with OWL-Time export
- Audit-ready for HIPAA, SOX, GDPR, FDA 21 CFR Part 11
-
-**Reasoning Engines.** Explainable reasoning paths, not black boxes.
- Forward chaining, Rete, deductive, abductive
- SPARQL query-based inference over RDF graphs
- Datalog with recursive Horn clause rules
- Every conclusion backed by a traceable derivation path
-
-**Temporal Intelligence.** Your graph knows not just *what*, but *when*.
- Allen interval algebra covering all 13 temporal relations
- Point-in-time queries over historical graph states
- Temporal provenance stamping on every fact
- OWL-Time export for standards-compliant archiving
-
-**Ontology Hub.** Full ontology lifecycle in the browser.
- Visual editor for schema design and editing
- SHACL Studio for constraint authoring and validation
- Alignment authoring across multiple ontologies
- Health dashboard and version control built in
+- **[Context graphs](/guides/context-graphs)**: a persistent, queryable graph of everything your agent knows, decides, and reasons about
+- **Decision intelligence**: `record_decision()` captures the full lifecycle and causal chain of every decision
+- **[Full provenance](/guides/provenance)**: every fact links back to its source, W3C PROV-O compliant and audit-ready for HIPAA, SOX, and GDPR
+- **[Explainable reasoning](/guides/reasoning)**: forward chaining, Datalog, and SPARQL, each with a derivation path you can inspect
+- **Temporal intelligence**: Allen interval algebra and point-in-time snapshots, so the graph knows not just *what* but *when*

 <Tip>
-  Works alongside any LLM provider and any agent framework. Add it to an existing stack without changing your architecture.
+  Works alongside any LLM provider and any agent framework, and ingests directly from enterprise data platforms like Databricks, SAP, Salesforce, and Snowflake. Add it to an existing stack without changing your architecture.
 </Tip>

-<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' }} />
-
-
-## See It In Action
-
-One pip install. A few lines to connect your agent. Everything else becomes traceable.
-
-```bash
-pip install semantica
-```
+## Try it

 <CodeGroup>

@@ -175,229 +107,28 @@ decision_id = context.record_decision(

 </CodeGroup>

- [Full Quickstart](/quickstart): step-by-step pipeline walkthrough
- [Cookbook](/cookbook): 40+ real-world Jupyter notebooks
- [Join Discord](https://discord.gg/sV34vps5hH): community chat and support
-
-
-## Industry Use Cases
-
-Semantica is used in domains where every decision must be explainable and every fact must be traceable.
-
-<Warning>
-  **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.
-</Warning>
-
-**Healthcare & Life Sciences**
- Clinical decision support with full audit trails
- Drug interaction and contraindication graphs
- Patient safety event tracking and root-cause analysis
- HIPAA-compliant provenance chains out of the box
-
-**Finance & Risk**
- Fraud detection knowledge graphs
- Risk assessment trails built to survive an audit
- SOX, GDPR, and MiFID II compliance infrastructure
- Model decision lineage for regulatory reporting
-
-**Legal & Compliance**
- Evidence-backed research with every cited fact provenance-linked
- Contract analysis with traceable clause extraction
- Regulatory change tracking across jurisdictions
- Full reasoning paths ready for court-admissible documentation
-
-**Cybersecurity**
- Threat attribution graphs linking actors, TTPs, and indicators
- Incident response timelines with full event provenance
- Security audit trails across the complete kill chain
- MITRE ATT&CK-aligned knowledge graph integration
-
-**Government & Defense**
- Policy decision trails from brief to outcome
- Classified information handling with provenance chains
- Chain-of-custody scrutiny for intelligence reporting
- Air-gapped deployment with local LLM support
-
-**Critical Infrastructure**
- Power grid state tracking with temporal intelligence
- Transportation safety event graphs
- Emergency response coordination with decision audit trails
- Consequence modeling for high-stakes operational decisions
-
-
-## Start Here
+## Start here

 <Steps>
-  <Step title="Install Semantica">
+  <Step title="Install">
    ```bash
    pip install semantica
    ```
-    See [Installation](/installation) for optional extras (`[all]`, `[neo4j]`, `[pinecone]`) and environment setup.
+    Optional extras: `[all]`, `[neo4j]`, `[pinecone]`. See [Installation](/installation).
  </Step>
-  <Step title="Run the Quickstart">
-    Build a complete knowledge graph pipeline in [5 minutes](/quickstart):
-    - Ingest documents from any source
-    - Extract entities and relationships
-    - Build and query the graph
-    - Record and trace a decision
+  <Step title="Build a pipeline">
+    Follow the [Quickstart](/quickstart) to ingest documents, extract entities, build a graph, and record a decision in 5 minutes.
  </Step>
-  <Step title="Learn the mental model">
-    [Core Concepts](/concepts) covers:
-    - Knowledge graphs vs. vector stores: when to use each
-    - What GraphRAG is and how Semantica implements it
-    - How provenance and decision tracking work together
-    - The context and semantic layer architecture
+  <Step title="Learn the model">
+    [Core Concepts](/concepts) covers knowledge graphs vs. vector stores, GraphRAG, and how provenance and decisions fit together.
  </Step>
-  <Step title="Go deep on any module">
-    Every module has a dedicated [reference page](/reference/context) with:
-    - Full class and method documentation
-    - Parameter tables with types and defaults
-    - Runnable code examples for each feature
+  <Step title="Go deep">
+    Every module has a [reference page](/reference/context) with full API docs and runnable examples.
  </Step>
 </Steps>

- [Installation](/installation): get Semantica installed in under a minute
- [Quickstart](/quickstart): build a complete knowledge graph pipeline in 5 minutes
- [Core Concepts](/concepts): the mental model behind the API
- [API Reference](/reference/context): exact module, class, and method details
- [Cookbook](/cookbook): domain notebooks for real-world use cases
- [Changelog](https://github.com/semantica-agi/semantica/releases): release history
-
-
-## Full Capabilities
-
-<AccordionGroup>
-
-<Accordion title="Context & Decision Intelligence" icon="brain">
-
-### Context Graphs
-
- Structured, persistent graph of entities, relationships, and decisions
- Temporal model with `valid_from` / `valid_until` on every node and edge
- Point-in-time queries across historical graph states
- Distance Intelligence: semantic neighborhoods and N×N distance matrices
-
-### Decision Tracking
-
- `record_decision()` with full lifecycle management and causal chains
- Hybrid similarity search over past decisions for consistency enforcement
- `analyze_decision_impact()` and `analyze_decision_influence()` for consequence modeling
- Ego-mode exploration for targeted neighborhood investigation
+More: the [Cookbook](/cookbook) for real-world notebooks, [Discord](https://discord.gg/sV34vps5hH) for help.

+<Accordion title="Full module list">
+  `semantica.ingest`, `semantica.parse`, `semantica.split`, `semantica.normalize`, `semantica.semantic_extract`, `semantica.kg`, `semantica.ontology`, `semantica.reasoning`, `semantica.embeddings`, `semantica.vector_store`, `semantica.graph_store`, `semantica.triplet_store`, `semantica.context`, `semantica.provenance`, `semantica.change_management`, `semantica.deduplication`, `semantica.conflicts`, `semantica.export`, `semantica.visualization`, `semantica.pipeline`, `semantica.seed`, `semantica.llms`, `semantica.mcp_server`, `semantica.explorer`, `semantica.evals`, `semantica.utils`, `semantica.core`. See the [API Reference](/reference/context) for full docs on each.
 </Accordion>
-
-<Accordion title="Knowledge Engineering" icon="diagram-project">
-
-### Entity & Relation Extraction
-
- Named entity recognition: pattern, ML, or LLM methods
- Typed triplet extraction via LLM or rule-based pipelines
- Event extraction with temporal and causal linking
-
-### Ontology & Schema
-
- Ontology Hub: visual editor, SHACL Studio, alignments, health dashboard
- Deduplication v2: `blocking_v2`, `hybrid_v2`, `semantic_v2`: up to 7x faster
- Datalog reasoning: recursive Horn clause rules with fixpoint semantics
- SPARQL reasoning: query-based inference over RDF graphs
-
-</Accordion>
-
-<Accordion title="Provenance & Auditability" icon="shield-check">
-
-### Lineage Tracking
-
- W3C PROV-O lineage across all modules: every fact has a source
- `recorded_at` stamping with full OWL-Time export
- Change management with SHA-256 checksums and version control
- Full audit trails from ingestion event to final inference
-
-### Compliance Infrastructure
-
- HIPAA: patient data handling with audit-ready provenance chains
- SOX / MiFID II: financial decision records with full traceability
- GDPR: data lineage for subject access and right-to-erasure workflows
- FDA 21 CFR Part 11: electronic records and signature compliance
-
-</Accordion>
-
-<Accordion title="Data Ingestion & Export" icon="database">
-
-### Ingestion Formats
-
- Documents: PDF, DOCX, HTML, PPTX, Docling layout analysis
- Structured data: JSON, CSV, Excel, Parquet, XML
- Sources: web crawl, SQL, Snowflake, feeds, email, code repositories, MCP
-
-### Vector Stores
-
- FAISS, Pinecone, Weaviate, Qdrant, Milvus, PgVector, in-memory
-
-### Graph Stores
-
- Neo4j, FalkorDB, Apache AGE, Amazon Neptune
-
-### Export Formats
-
- RDF: Turtle, JSON-LD, N-Triples, RDF/XML
- Tabular: Parquet, CSV, Arrow
- Graph: GraphML, GEXF, DOT, ArangoDB AQL
- Ontology: OWL, SKOS, SHACL
-
-</Accordion>
-
-</AccordionGroup>
-
-
-## Module Reference
-
-| Module | What it provides |
-| :-------- | :----------------- |
-| `semantica.context` | Context graphs, agent memory, decision tracking, causal analysis, precedent search |
-| `semantica.kg` | KG construction, graph algorithms, temporal model, Allen interval algebra |
-| `semantica.semantic_extract` | NER, relation extraction, event extraction, triplet generation |
-| `semantica.reasoning` | Forward chaining, Rete, deductive, abductive, SPARQL, Datalog |
-| `semantica.ontology` | SHACL, SKOS, alignments, diff/migration, auto-generation, OWL/RDF |
-| `semantica.explorer` | FastAPI Knowledge Explorer, Ontology Hub, Distance Intelligence, SHACL Studio |
-| `semantica.mcp_server` | MCP stdio server: 15 tools for Claude Desktop, VS Code, Cursor, Windsurf, Cline |
-| `semantica.vector_store` | FAISS, Pinecone, Weaviate, Qdrant, Milvus, PgVector |
-| `semantica.graph_store` | Neo4j, FalkorDB, Apache AGE, Amazon Neptune |
-| `semantica.triplet_store` | In-memory and persistent RDF triple store with SPARQL |
-| `semantica.ingest` | Files, web, feeds, databases, Snowflake, Parquet, XML, MCP |
-| `semantica.parse` | Document parsing: PDF, DOCX, HTML, PPTX, Docling layout analysis |
-| `semantica.split` | Text chunking: sentence, paragraph, token, semantic boundary strategies |
-| `semantica.normalize` | Text normalization, entity canonicalization, whitespace and encoding cleanup |
-| `semantica.embeddings` | Sentence-Transformers, FastEmbed, OpenAI, BGE, Ollama local embeddings |
-| `semantica.pipeline` | Pipeline DSL, parallel workers, retry policies, failure handling |
-| `semantica.export` | RDF, Parquet, ArangoDB AQL, CSV, OWL, Arrow, GraphML, GEXF, DOT |
-| `semantica.visualization` | Programmatic graph rendering: force, hierarchical, circular, spring layouts |
-| `semantica.deduplication` | Entity deduplication v1/v2, similarity scoring, blocking, merging |
-| `semantica.conflicts` | Conflict detection and resolution across overlapping knowledge sources |
-| `semantica.provenance` | W3C PROV-O lineage tracking, source attribution, audit trails |
-| `semantica.change_management` | Version control with SHA-256 checksums, diff, rollback |
-| `semantica.llms` | Groq, OpenAI, Anthropic, Gemini, Ollama, DeepSeek, Novita AI, LiteLLM, HuggingFace |
-| `semantica.seed` | Foundation graph seeding from CSV, JSON, SQL, API, and RDF sources |
-| `semantica.evals` | Evaluation harness: KG quality, extraction F1, pipeline benchmarking, regression tracking |
-| `semantica.core` | Orchestration, ConfigManager, LifecycleManager, PluginRegistry, MethodRegistry |
-| `semantica.utils` | Logging, validation, progress tracking, hash utilities, nested dict helpers |
-
-
-## Why Semantica?
-
-**Open Source, MIT.** No vendor lock-in, no paywalled features.
- Full source available on GitHub
- Every line auditable by your security team
- Fork, extend, and self-host with no restrictions
- No telemetry, no usage reporting
-
-**Production Ready.** Built for teams that can't afford surprises.
- 1,000+ passing tests with full regression coverage
- `PipelineValidator` catches configuration errors at startup
- `FailureHandler` with exponential backoff and dead-letter queues
- Ongoing security hardening, with fixes shipped in every release ([CHANGELOG](https://github.com/semantica-agi/semantica/blob/main/CHANGELOG.md))
-
-**Modular by Design.** Import only what you need.
- Use `NERExtractor` without a graph store
- Use `ContextGraph` without vector storage
- Every component independently swappable and testable
- No framework lock-in, and works with any agent stack
@@ -12,13 +12,13 @@ icon: "link"
 pip install "semantica[langchain]"
 ```

-Requires `langchain-core >= 0.3`. If langchain-core is not installed, the integration still imports. Every class carries the full Semantica API and degrades gracefully (`build()` returns `None`; branch on `LANGCHAIN_AVAILABLE`).
+Requires `langchain-core >= 0.3`. If langchain-core is not installed, the integration still imports — every class carries the full Semantica API and degrades gracefully (`build()` returns `None`; branch on `LANGCHAIN_AVAILABLE`).

 ## Components at a Glance

- **SemanticaRetriever** (`BaseRetriever`): hybrid-search seeds retrieval, then graph edges are walked `hops` steps (default 2) for GraphRAG-style results.
- **SemanticaVectorStore** (`VectorStore`): `add_texts` / `similarity_search` / `similarity_search_with_score` / `from_texts` over `HybridSearch`.
- **SemanticaKGTool** / **SemanticaDecisionTool** (`BaseTool` subclasses): `semantica_query_graph` and `semantica_query_decisions` for LangGraph / tool-calling agents.
+- **SemanticaRetriever** — `BaseRetriever`: hybrid-search seeds retrieval, then graph edges are walked `hops` steps (default 2) for GraphRAG-style results.
+- **SemanticaVectorStore** — `VectorStore`: `add_texts` / `similarity_search` / `similarity_search_with_score` / `from_texts` over `HybridSearch`.
+- **SemanticaKGTool** / **SemanticaDecisionTool** — `BaseTool` subclasses: `semantica_query_graph` and `semantica_query_decisions` for LangGraph / tool-calling agents.

 ## Component Details

@@ -28,9 +28,7 @@ Semantica is organized into **27 modules** across six logical layers. Each modul

 ### Ingest

-Loads data from files, web, databases, and streams. Each ingestor returns its own
-result type (`FileIngestor` → `FileObject`, `WebIngestor` → `WebContent`, …);
-document-oriented ones expose a `.text` payload and `.metadata`.
+Loads data from files, web, databases, and streams into a unified `SourceDocument` format.

 ```python
 from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, XMLIngestor, DatabricksIngestor
@@ -39,7 +37,7 @@ from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, XMLInge
 ingestor = FileIngestor()
 documents = ingestor.ingest_directory("data/")

-# Web page: returns a WebContent with .text, .title, .links, .metadata
+# Web crawl
 web_ingestor = WebIngestor()
 page = web_ingestor.ingest_url("https://example.com")

@@ -69,13 +67,13 @@ Extracts structured text and layout metadata from raw documents.
 ```python
 from semantica.parse import DocumentParser, DoclingParser

-# Standard parser: all common formats. parse() takes a path, returns a dict
+# Standard parser: all common formats
 parser = DocumentParser()
-parsed = parser.parse("document.pdf")   # {"full_text": ..., "metadata": ..., ...}
+parsed = parser.parse_document("document.pdf")

-# Advanced parser (pip install semantica[parse-docling]): tables, OCR, layout
-parser = DoclingParser(export_format="markdown", enable_ocr=True)
-parsed = parser.parse("data/annual_report.pdf")   # dict with full_text, tables, pages
+# Advanced parser: multi-column PDFs, merged-cell tables, OCR
+parser = DoclingParser(extract_tables=True, extract_images=True, output_format="markdown")
+parsed = parser.parse("data/annual_report.pdf")
 ```

 **Available parsers:** `DocumentParser`, `DoclingParser`, `CodeParser`, `CSVParser`, `DocxParser`, `EmailParser`, `ExcelParser`, `HTMLParser`, `ImageParser`, `JSONParser`, `MCPParser`, `MediaParser`, `PDFParser`, `PPTXParser`, `StructuredDataParser`, `WebParser`, `XMLParser`
@@ -87,12 +85,11 @@ Chunks text for embedding and RAG pipelines with awareness of semantic boundarie
 ```python
 from semantica.split import TextSplitter

-# chunk_size / chunk_overlap are constructor arguments
-splitter = TextSplitter(method="semantic_transformer", chunk_size=1000, chunk_overlap=200)
-chunks = splitter.split(text)
+splitter = TextSplitter(method="semantic_transformer")
+chunks = splitter.split(text, chunk_size=1000, chunk_overlap=200)
 ```

-**Chunking methods:** `recursive`, `token`, `sentence`, `paragraph`, `semantic_transformer`, `entity_aware`, `relation_aware`, `graph_based`, `ontology_aware`, `hierarchical`, `community_detection`, `centrality_based`, `llm`
+**Chunking strategies:** `recursive`, `semantic_transformer`, `entity_aware`, `relation_aware`, `sliding_window`, `structural`

 ### Normalize

@@ -118,18 +115,17 @@ Named entity recognition, relation extraction, and triplet generation.
 ```python
 from semantica.semantic_extract import NERExtractor, RelationExtractor, TripletExtractor

-# LLM method: provider + llm_model select the backend; the API key comes from the env
-ner = NERExtractor(method="llm", provider="groq", llm_model="llama-3.3-70b-versatile")
-entities = ner.extract("Apple Inc. was founded by Steve Jobs.")   # list[Entity]
+ner = NERExtractor(method="llm", llm_provider=llm)
+entities = ner.extract("Apple Inc. was founded by Steve Jobs.")

-rel = RelationExtractor(method="llm", provider="groq", llm_model="llama-3.3-70b-versatile")
-relationships = rel.extract(text, entities=entities)              # list[Relation]
+rel = RelationExtractor(method="llm", llm_provider=llm)
+relationships = rel.extract(text, entities=entities)

-trip = TripletExtractor(method="pattern")
-triplets = trip.extract(text)                                     # list[Triplet]
+trip = TripletExtractor(method="llm", llm_provider=llm)
+triplets = trip.extract(text)
 ```

-**Extraction methods:** `"pattern"` (no API key), `"ml"` (local spaCy model), `"llm"` (any of the 9 supported providers)
+**Extraction methods:** `"pattern"` (no API key), `"ml"` (local model), `"llm"` (any of the 8 supported providers)

 **Additional extractors:** `CoreferenceResolver`, `EventDetector`, `SemanticAnalyzer`, `SemanticNetworkExtractor`

@@ -141,17 +137,17 @@ Graph construction, graph algorithms, temporal model, and distance intelligence.
 from semantica.kg import GraphBuilder, GraphAnalyzer, TemporalGraphQuery, SimilarityCalculator
 from datetime import datetime

-# Build: build() takes a {"entities": ..., "relationships": ...} dict
+# Build
 builder = GraphBuilder(merge_entities=True)
-kg = builder.build({"entities": entities, "relationships": relationships})
+kg = builder.build(entities=entities, relationships=relationships)

 # Temporal graphs (v0.4.0)
 query_engine = TemporalGraphQuery(enable_temporal_reasoning=True)
 snapshot = query_engine.query_at_time(kg, query="", at_time=datetime(2021, 6, 15))

-# Semantic similarity (v0.5.0): operates on embedding vectors
-calc = SimilarityCalculator(method="cosine")
-score = calc.cosine_similarity(vec_a, vec_b)
+# Semantic similarity (v0.5.0)
+calc = SimilarityCalculator()
+scores = calc.calculate_similarity(entity_a, entity_b)
 ```

 **Graph algorithms available:** centrality calculation, community detection, connectivity analysis, entity resolution, link prediction, path finding, similarity calculation
@@ -179,23 +175,19 @@ Derives new facts from existing knowledge using multiple inference strategies.
 ```python
 from semantica.reasoning import Reasoner, DatalogReasoner

-# Forward chaining: facts and rules as predicate(args) / IF-THEN strings
+# Rule-based reasoning
 engine = Reasoner()
-engine.add_fact("Manager(Alice)")
-engine.add_rule("IF Manager(?x) THEN HasAuthority(?x)")
-results = engine.forward_chain()          # list[InferenceResult] with .conclusion, .rule_used
+engine.apply_transitivity("located_in")
+engine.apply_symmetry("knows")
+result = engine.infer()

 # Datalog: recursive Horn clause rules (v0.4.0)
-datalog = DatalogReasoner()
-datalog.add_fact("parent(tom, bob)")
-datalog.add_fact("parent(bob, ann)")
-datalog.add_rule("ancestor(X, Y) :- parent(X, Y).")
+datalog = DatalogEngine()
 datalog.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")
-datalog.derive_all()
-results = datalog.query("ancestor(tom, ?Z)")   # [{"Z": "bob"}, {"Z": "ann"}], order not guaranteed
+results = datalog.query("ancestor(alice, ?)")
 ```

-**Engines:** `Reasoner` (forward/backward chaining), `ReteEngine`, `SPARQLReasoner`, `DatalogReasoner`, `TemporalReasoningEngine`, `GraphReasoner` (LLM)
+**Engines:** forward chaining, Rete network, deductive, abductive, SPARQL, Datalog: all produce explainable inference paths


 ## Storage
@@ -207,9 +199,9 @@ Generates and manages vector embeddings for semantic similarity.
 ```python
 from semantica.embeddings import EmbeddingGenerator

-generator  = EmbeddingGenerator()
-embeddings = generator.generate_embeddings(["text1", "text2"])   # np.ndarray
-similarity = generator.compare_embeddings(embeddings[0], embeddings[1])
+generator  = EmbeddingGenerator(model="sentence-transformers")
+embeddings = generator.generate(["text1", "text2"])
+similarity = generator.similarity(embeddings[0], embeddings[1])
 ```

 **Supported models:** Sentence-Transformers, FastEmbed, OpenAI, BGE
@@ -223,18 +215,12 @@ Multi-backend vector database with hybrid search support.
 ```python
 from semantica.vector_store import VectorStore

-store = VectorStore(backend="faiss", dimension=768)
-
-# Raw vectors
-ids     = store.store_vectors(embeddings)                 # returns generated ids
-hits    = store.search_vectors(query_vector, k=10)
-
-# Or store text and let the store embed it
-store.add_documents(["Apple was founded in 1976.", "Google was founded in 1998."])
-results = store.search("tech company founding dates", limit=10)
+store   = VectorStore(backend="faiss", dimension=768)
+store.add_vectors(embeddings, ids)
+results = store.search(query_vector, top_k=10)
 ```

-**Backends:** FAISS, Pinecone, Weaviate, Qdrant, Milvus, PgVector, SQLite, in-memory
+**Backends:** FAISS, Pinecone, Weaviate, Qdrant, Milvus, PgVector, in-memory

 **Search modes:** semantic top-k, hybrid (vector + keyword), metadata-filtered

@@ -246,8 +232,8 @@ Connects to graph databases for persistent, query-able storage.
 from semantica.graph_store import GraphStore

 store = GraphStore(backend="neo4j")
-store.add_nodes([{"id": "acme", "type": "Organization", "properties": {"name": "Acme"}}])
-store.add_edges([{"source": "alice", "target": "acme", "type": "works_for"}])
+store.add_nodes(entities)
+store.add_edges(relationships)
 results = store.query("MATCH (n)-[r]->(m) RETURN n, r, m")
 ```

@@ -260,9 +246,9 @@ RDF triple-based storage with SPARQL query support.
 ```python
 from semantica.triplet_store import TripletStore

-store = TripletStore(backend="oxigraph")
-store.add_triplets(triplets)                 # list of Triplet objects (or add_triplet for one)
-results = store.execute_query("SELECT ?s ?p ?o WHERE { ?s ?p ?o }")
+store = TripletStore(backend="blazegraph")
+store.add_triplets(subject, predicate, obj)
+results = store.sparql("SELECT ?s ?p ?o WHERE { ?s ?p ?o }")
 ```

 **Backends:** Oxigraph (embedded), Blazegraph, Apache Jena, RDF4J
@@ -275,18 +261,15 @@ results = store.execute_query("SELECT ?s ?p ?o WHERE { ?s ?p ?o }")
 Detects, scores, and merges duplicate entities across sources.

 ```python
-from semantica.deduplication import DuplicateDetector, EntityMerger
+from semantica.deduplication import EntityResolver

-detector   = DuplicateDetector(similarity_threshold=0.85)
-candidates = detector.detect_duplicates(entities)
-
-merger     = EntityMerger()
-operations = merger.merge_duplicates(entities, strategy="keep_most_complete")
+resolver = EntityResolver()
+merged   = resolver.resolve(entities, strategy="semantic_v2")
 ```

-**v2 candidate-generation modes** (`blocking_v2`, `hybrid_v2`, `semantic_v2`) are up to 7x faster than v1.
+**v2 strategies** (`blocking_v2`, `hybrid_v2`, `semantic_v2`) are up to 7x faster than v1.

-**Components:** `DuplicateDetector`, `EntityMerger`, `ClusterBuilder`, `MergeStrategyManager`
+**Components:** `EntityResolver`, `DuplicateDetector`, `EntityMerger`, `SimilarityCalculator`, `ClusterBuilder`

 **`DuplicateDetector` options:** `max_results`, `top_k_per_entity`, `min_similarity`, `sort_by`

@@ -295,13 +278,14 @@ operations = merger.merge_duplicates(entities, strategy="keep_most_complete")
 Detects and resolves fact conflicts across overlapping knowledge sources.

 ```python
-from semantica.conflicts import ConflictDetector, ConflictResolver
+from semantica.conflicts import ConflictDetector

-conflicts = ConflictDetector().detect_conflicts(entities)   # list of entity dicts
-resolved  = ConflictResolver().resolve_conflicts(conflicts, strategy="most_recent")
+detector  = ConflictDetector()
+conflicts = detector.detect_conflicts(kg)
+resolved  = detector.resolve(conflicts, strategy="most_recent")
 ```

-**Detection types:** value conflicts, type conflicts, relationship conflicts, temporal conflicts, logical conflicts
+**Detection types:** value conflicts, type conflicts, temporal conflicts, logical conflicts

 **Resolution strategies:** prefer most recent, prefer most reliable source, majority vote, flag for manual review

@@ -314,7 +298,6 @@ Agent context graphs, decision tracking, causal chains, and precedent search.

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

 context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
@@ -345,7 +328,7 @@ W3C PROV-O compliant lineage tracking across all modules.
 from semantica.provenance import ProvenanceManager

 manager = ProvenanceManager()
-manager.track_entity("entity_1", source="document.pdf", metadata={"type": "person"})
+manager.track_entity("entity_1", "document.pdf", "person")
 lineage = manager.get_lineage("entity_1")
 ```

@@ -381,8 +364,8 @@ RDFExporter().export(graph, file_path="graph.ttl", format="turtle")
 # Analytics
 ParquetExporter().export(graph, file_path="output/graph.parquet")

-# ArangoDB: writes AQL INSERT statements to the given path
-ArangoAQLExporter().export(graph, file_path="graph.aql")
+# ArangoDB
+aql = ArangoAQLExporter().export(graph)
 ```

 **Export formats:** RDF (Turtle, JSON-LD, N-Triples, XML), Parquet, ArangoDB AQL, CSV, OWL, Arrow, LPG, YAML, distance matrices
@@ -407,24 +390,16 @@ viz.visualize_network(graph, output="html", file_path="graph.html")
 Pipeline DSL with parallel workers, retry policies, and failure handling.

 ```python
-from semantica.pipeline import PipelineBuilder, ExecutionEngine
-from semantica.ingest import FileIngestor
-from semantica.semantic_extract import NERExtractor
+from semantica.pipeline import Pipeline

-builder = PipelineBuilder()
-
-# Each step type dispatches to a handler you register (or supply explicitly)
-builder.register_step_handler("ingest",  lambda data, **c: FileIngestor().ingest(c["source"]))
-builder.register_step_handler("extract", lambda docs, **c: NERExtractor(method="pattern").extract(docs[0].text))
-
-builder.add_step("ingest",  step_type="ingest", source="data/")
-builder.add_step("extract", step_type="extract")
-
-pipeline = builder.connect_steps("ingest", "extract").build(name="docs_to_entities")
-result   = ExecutionEngine().execute_pipeline(pipeline)
+pipeline = Pipeline()
+pipeline.add_step("ingest",   FileIngestor())
+pipeline.add_step("extract",  NERExtractor())
+pipeline.add_step("build",    GraphBuilder())
+result = pipeline.run("data/")
 ```

-**Components:** `PipelineBuilder`, `Pipeline`, `ExecutionEngine`, `FailureHandler`, `PipelineValidator`, `ParallelismManager`, `ResourceScheduler`
+**Components:** `Pipeline`, `PipelineBuilder`, `ExecutionEngine`, `FailureHandler`, `PipelineValidator`, `ParallelismManager`, `ResourceScheduler`

 ### Explorer

@@ -453,7 +428,7 @@ llm = OpenAI(model="gpt-4o", api_key=os.getenv("OPENAI_API_KEY"))
 llm = LiteLLM(model="anthropic/claude-opus-4-7", api_key=os.getenv("ANTHROPIC_API_KEY"))
 ```

-**Supported providers:** OpenAI, Anthropic, Google Gemini, Groq, Ollama, DeepSeek, Novita AI, HuggingFace, plus LiteLLM (100+ models via one interface)
+**Supported providers:** OpenAI, Anthropic, Google Gemini, Groq, Ollama, DeepSeek, Novita AI, LiteLLM (20+ models via one interface)

 ### MCP Server

@@ -470,43 +445,44 @@ python -m semantica.mcp_server
 Bootstrap knowledge graphs from verified structured sources: fixed-point reference data, controlled vocabularies, and domain anchors.

 ```python
-from semantica.seed import SeedDataManager
+from semantica.seed import SeedManager

-seed = SeedDataManager()
+seed = SeedManager()
+seed.populate(kg, dataset="companies", count=100)

-# Load trusted reference data from CSV / JSON / a database / an API
-seed_data = seed.load_from_csv("seed_data/industries.csv", entity_type="Industry")
-
-# Merge seed data with extraction output (seed values win on conflict by default)
-combined = seed.integrate_with_extracted(
-    {"entities": seed_data, "relationships": []},
-    {"entities": extracted_entities, "relationships": extracted_relationships},
-    merge_strategy="seed_first",
-)
+# Load domain seeds from file or built-in datasets
+seed.load_from_file("seed_data/industries.json")
+seed.inject(kg)   # merges seed nodes without duplicating existing entities
 ```

 **Use cases:** anchoring extraction with known entities, pre-populating ontology classes, deterministic test graph generation.

 ### Evals

-Scores decision-intelligence outputs (decision records, audit trails, reasoning
-text) with a registry of deterministic and model-backed evaluators plus a small
-run harness.
+Evaluation framework for measuring KG quality, extraction accuracy, and pipeline performance.

 ```python
-from semantica.evals import evaluate, list_evaluators
+from semantica.evals import KGEvaluator, ExtractionEvaluator, PipelineEvaluator, RegressionTracker

-list_evaluators()
-# ['decision_scores', 'exact_match', 'keyword_check', 'length_range',
-#  'levenshtein', 'llm_as_judge', 'numeric_range', 'regex_match', 'rouge',
-#  'temporal_range']
+# KG quality
+report = KGEvaluator().evaluate(kg, ontology=ontology)
+print(f"Completeness: {report.completeness:.2%}  Consistency: {report.consistency:.2%}")

-cases = [("apple", "aple"), ("night", "nacht")]
-summary = evaluate(cases, evaluators=["levenshtein"])
-print(summary.total, summary.passed, summary.pass_rate)
+# Extraction accuracy
+report = ExtractionEvaluator().evaluate_ner(predictions=extracted, gold_standard=annotated)
+print(f"Precision: {report.precision:.3f}  Recall: {report.recall:.3f}  F1: {report.f1:.3f}")
+
+# Pipeline throughput and latency
+metrics = PipelineEvaluator().benchmark(pipeline, data="data/", bench_runs=5)
+print(f"Throughput: {metrics.docs_per_second:.1f} docs/sec")
+
+# Regression tracking across runs
+tracker = RegressionTracker(db_path="eval_history.db")
+run_id  = tracker.record_run(pipeline_version="v1.2.0", metrics=metrics)
+diff    = tracker.compare(run_id, baseline_run_id="run_abc123")
 ```

-**Public API:** `evaluate(cases, evaluators, config=None)`, `list_evaluators()`, `get_evaluator(name)`, and the `EvalMetric` / `CaseResult` / `EvalSummary` result types. See the [Evals reference](/reference/evals).
+**Components:** `KGEvaluator`, `ExtractionEvaluator`, `PipelineEvaluator`, `RegressionTracker`

 ### Core

@@ -515,20 +491,20 @@ Base classes, shared data models, and the plugin registry used across all module
 ```python
 from semantica.core import Semantica, PluginRegistry, ConfigManager

-# ConfigManager loads a Config; Config.get() does dotted lookups
-config = ConfigManager().load_from_file("config.yaml")
-batch  = config.get("processing.batch_size", default=32)
-
-# Top-level orchestrator: pass the Config object (or a dict), not a path
-sem = Semantica(config=config)
+# Top-level orchestrator
+sem = Semantica(config_path="config.yaml")
 sem.initialize()

-# Plugin registry: register custom components under a name
+# Plugin registry: register custom components
 registry = PluginRegistry()
-registry.register_plugin("my_ingestor", MyCustomIngestor, version="1.0.0")
+registry.register("my_ingestor", MyCustomIngestor)
+
+# Config management
+config  = ConfigManager(config_path="config.yaml")
+batch   = config.get("processing.batch_size", default=32)
 ```

-**Components:** `Semantica`, `PluginRegistry`, `ConfigManager`, `Config`, `LifecycleManager`, `HealthStatus`, `MethodRegistry`
+**Components:** `Semantica`, `PluginRegistry`, `ConfigManager`, `LifecycleManager`, `HealthMonitor`, `Config`

 ### Utils

@@ -556,13 +532,11 @@ from semantica.semantic_extract import NERExtractor, RelationExtractor
 from semantica.kg import GraphBuilder

 sources       = FileIngestor().ingest("data/")
-text          = DocumentParser().parse(sources[0].path)["full_text"]
-ner           = NERExtractor(method="llm", provider="groq", llm_model="llama-3.3-70b-versatile")
-rel           = RelationExtractor(method="llm", provider="groq", llm_model="llama-3.3-70b-versatile")
-entities      = ner.extract(text)
-relationships = rel.extract(text, entities=entities)
+parsed        = DocumentParser().parse(sources[0])
+entities      = NERExtractor(method="llm", llm_provider=llm).extract(parsed)
+relationships = RelationExtractor(method="llm", llm_provider=llm).extract(parsed, entities=entities)
 graph         = GraphBuilder(merge_entities=True).build(
-                    {"entities": entities, "relationships": relationships}
+                    entities=entities, relationships=relationships
                )
 ```

@@ -581,20 +555,16 @@ from semantica.vector_store import VectorStore
 context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
-    graph_expansion=True,
 )
+context.load_graph("company_kg.json")

-# store() extracts entities and populates the graph + vector index
-context.store([{"content": "Steve Wozniak co-founded Apple with Steve Jobs."}])
-
-# retrieve() blends vector similarity with multi-hop graph traversal
-results = context.retrieve(
+result = context.query(
    "What companies did Apple alumni found?",
-    use_graph=True,
-    expand_graph=True,
+    mode="graphrag",
+    reasoning=True,
 )
-for r in results:
-    print(f"[{r['score']:.3f}]  {r['content']}  (source: {r['source']})")
+for claim in result.claims:
+    print(f"{claim.text}  →  {claim.source_node}")
 ```

    **Best for:** question-answering systems, RAG with source attribution, research assistants
@@ -636,22 +606,18 @@ precedents = context.find_precedents("model selection", limit=5)

 ```python
 from semantica.ingest import FileIngestor
-from semantica.parse import DocumentParser
 from semantica.semantic_extract import NERExtractor
 from semantica.kg import GraphBuilder
 from semantica.provenance import ProvenanceManager
 from semantica.export import RDFExporter

 sources  = FileIngestor().ingest("records/")
-ner      = NERExtractor(method="llm", provider="groq", llm_model="llama-3.3-70b-versatile")
-entities = ner.extract(DocumentParser().parse(sources[0].path)["full_text"])
-graph    = GraphBuilder(merge_entities=True).build({"entities": entities, "relationships": []})
-
+entities = NERExtractor(method="llm", llm_provider=llm).extract(sources)
+graph    = GraphBuilder(merge_entities=True).build(entities=entities, relationships=[])
 prov     = ProvenanceManager()
-prov.track_entity("entity_id", source="records/filing.pdf", metadata={"extractor": "llm"})
-lineage  = prov.get_lineage("entity_id")
+lineage  = prov.get_entity_lineage("entity_id")

-RDFExporter().export(graph, file_path="audit.ttl", format="turtle")
+RDFExporter(include_provenance=True).export(graph, file_path="audit.ttl", format="turtle")
 ```

    **Best for:** HIPAA, SOX, GDPR, FDA 21 CFR Part 11 deployments
@@ -666,25 +632,18 @@ RDFExporter().export(graph, file_path="audit.ttl", format="turtle")
 from semantica.ingest import WebIngestor
 from semantica.normalize import TextNormalizer
 from semantica.semantic_extract import NERExtractor, RelationExtractor
-from semantica.graph_store import GraphStore
-from semantica.kg import GraphBuilder
+from semantica.graph_store import Neo4jStore

-ingestor   = WebIngestor()
+pages      = WebIngestor(max_depth=2).ingest("https://example.com")
 normalizer = TextNormalizer()
-ner        = NERExtractor(method="pattern")
-rel        = RelationExtractor(method="pattern")
+store      = Neo4jStore(uri="bolt://localhost:7687", user="neo4j", password="password")

-# The generic GraphStore wrapper exposes the add_nodes/add_edges interface
-# GraphBuilder persists through; a raw Neo4jStore does not
-store   = GraphStore(backend="neo4j", uri="bolt://localhost:7687", user="neo4j", password="password")
-builder = GraphBuilder(merge_entities=True, graph_store=store)
-
-for url in ["https://example.com/a", "https://example.com/b"]:
-    page          = ingestor.ingest_url(url)          # WebContent, has .text
+for page in pages:
    text          = normalizer.normalize_text(page.text)
-    entities      = ner.extract(text)
-    relationships = rel.extract(text, entities=entities)
-    builder.build({"entities": entities, "relationships": relationships})
+    entities      = NERExtractor().extract(text)
+    relationships = RelationExtractor().extract(text, entities=entities)
+    store.add_nodes(entities)
+    store.add_edges(relationships)
 ```

    **Best for:** competitive intelligence, news monitoring, research aggregation
@@ -733,8 +692,8 @@ versioner.create_snapshot(kg, "2024-Q1", author="user@example.com", description=
 | [vector_store](/reference/vector_store) | Vector database | `VectorStore` |
 | [graph_store](/reference/graph_store) | Graph database | `GraphStore` |
 | [triplet_store](/reference/triplet_store) | RDF triple store | `TripletStore` |
-| [deduplication](/reference/deduplication) | Entity resolution | `DuplicateDetector`, `EntityMerger`, `ClusterBuilder`, `MergeStrategyManager` |
-| [conflicts](/reference/conflicts) | Conflict resolution | `ConflictDetector`, `ConflictResolver`, `SourceTracker` |
+| [deduplication](/reference/deduplication) | Entity resolution | `EntityResolver`, `DuplicateDetector`, `ClusterBuilder`, `MergeStrategyManager` |
+| [conflicts](/reference/conflicts) | Conflict resolution | `ConflictDetector` |
 | [context](/reference/context) | Agent context & decisions | `AgentContext`, `ContextGraph` |
 | [provenance](/reference/provenance) | W3C PROV-O lineage | `ProvenanceManager` |
 | [change_management](/reference/change_management) | Version control | `TemporalVersionManager` |
@@ -744,8 +703,8 @@ versioner.create_snapshot(kg, "2024-Q1", author="user@example.com", description=
 | [explorer](/reference/explorer) | Knowledge Explorer UI | `semantica-explorer --graph <file>` |
 | [llms](/reference/llms) | LLM providers | `Groq`, `OpenAI`, `create_provider` |
 | [mcp_server](/reference/mcp_server) | MCP stdio server | `python -m semantica.mcp_server` |
-| [seed](/reference/seed) | KG bootstrapping from structured sources | `SeedDataManager` |
-| [evals](/reference/evals) | Decision-intelligence evaluation | `evaluate`, `list_evaluators`, `EvalSummary` |
+| [seed](/reference/seed) | KG bootstrapping from structured sources | `SeedManager` |
+| [evals](/reference/evals) | Quality evaluation | `KGEvaluator`, `ExtractionEvaluator`, `PipelineEvaluator`, `RegressionTracker` |
 | [core](/reference/core) | Base classes & registry | `Semantica`, `ConfigManager`, `PluginRegistry`, `LifecycleManager` |
 | [utils](/reference/utils) | Shared utilities | `helpers`, `validators` |