docs: add Temporal & Distance Intelligence reference pages with accurate API (#650)

- Add docs/reference/temporal.md: full Temporal Intelligence reference covering
  bi-temporal model (TemporalBound.OPEN sentinel, BiTemporalFact.from_relationship()
  factory), TemporalGraphQuery (query_at_time, reconstruct_at_time, query_time_range,
  find_temporal_paths, analyze_evolution, validate_temporal_consistency),
  TemporalPatternDetector, TemporalReasoningEngine with all 13 Allen interval
  relations over TemporalInterval objects, TemporalNormalizer (returns
  Optional[Tuple[datetime, datetime]]), TemporalQueryRewriter.rewrite() returning
  TemporalQueryResult, and TemporalVersionManager with SQLite storage and correct
  method names (list_versions, compare_versions, get_version, apply_revision,
  validate_snapshot, verify_checksum)

- Add docs/reference/distance.md: Distance Intelligence reference with corrected
  SimilarityCalculator API (pairwise_similarity, batch_similarity, find_most_similar)
  and semantic neighborhood / proximity-blended retrieval patterns

- Update docs/reference/kg.md: expand Exported Classes table to include all
  TemporalPatternDetector, TemporalInterval, IntervalRelation, TemporalQueryResult,
  AlgorithmTrackerWithProvenance, AlgorithmRegistry, ProvenanceTracker, SeedManager,
  KGConfig; fix all temporal code examples to use correct constructors and method names

- Update docs/reference/context.md: add Distance Intelligence section

- Update docs/index.md: add v0.3.0 release accordion with feature highlights

- Update docs/docs.json: wire temporal and distance pages into Modules navigation

Author: KaifAhmad1

docs/docs.json

@@ -120,6 +120,8 @@
             "pages": [
               "reference/context",
               "reference/kg",
+              "reference/temporal",
+              "reference/distance",
               "reference/semantic_extract",
               "reference/reasoning",
               "reference/ontology"

docs/index.md

@@ -15,10 +15,12 @@ If your stack can't answer those questions with a traceable record, you have a g
 
 **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.
 
-<div style={{display:"flex",flexWrap:"wrap",gap:"3rem",margin:"2rem 0",padding:"1.5rem 2rem",borderRadius:"10px",border:"1px solid rgba(16,185,129,0.2)",background:"rgba(16,185,129,0.03)"}}>
+<div style={{display:"flex",flexWrap:"wrap",gap:"2rem",margin:"2rem 0",padding:"1.5rem 2rem",borderRadius:"10px",border:"1px solid rgba(16,185,129,0.2)",background:"rgba(16,185,129,0.03)"}}>
   <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>1,000+</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>passing tests</div></div>
-  <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>25+</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>modules</div></div>
-  <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>12</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>LLM providers</div></div>
+  <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>29</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>modules</div></div>
+  <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>13</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>LLM providers</div></div>
+  <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>6,000×</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>search speedup</div></div>
+  <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>109</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>REST endpoints</div></div>
   <div><div style={{fontSize:"1.75rem",fontWeight:700,color:"#10B981",lineHeight:1.1}}>MIT</div><div style={{fontSize:"0.8rem",color:"rgba(255,255,255,0.5)",marginTop:"4px"}}>open source</div></div>
 </div>
 
@@ -300,11 +302,37 @@ pip install semantica==0.5.0
 
 <Accordion title="v0.4.0: Temporal Intelligence & Knowledge Explorer" icon="clock">
 
-- **Temporal Intelligence** — 6-PR system: temporal data model, point-in-time queries, Allen interval algebra (all 13 relations), OWL-Time export
-- **Knowledge Explorer API** — Full FastAPI backend: 99 tests, 12 export formats, WebSocket progress, thread-safe sessions, audit trail
-- **Ontology Foundations** — SHACL generation/validation, SKOS vocabulary, ontology alignment API, diff & migration tooling
+Released **April 8, 2026**
+
+- **Temporal Intelligence** — Bi-temporal data model (`BiTemporalFact`, `TemporalBound`), point-in-time query engine, Allen interval algebra (all 13 relations), `TemporalNormalizer` (zero LLM calls), `TemporalQueryRewriter`, OWL-Time export
+- **Knowledge Explorer API** — Full FastAPI backend: 99 integration tests, 12 export formats, WebSocket progress, thread-safe `RLock` sessions, audit trail, rollback protection
+- **Ontology Foundations** — SHACL generation/validation (basic/standard/strict tiers), SKOS vocabulary, ontology alignment API, diff & migration with change impact analysis
 - **Datalog Reasoning** — Pure-Python bottom-up semi-naive fixpoint, recursive Horn clause rules, guaranteed termination
 - **Agno Integration** — 5 components: graph-backed memory, multi-hop GraphRAG, decision toolkit, KG toolkit, shared team context; 110 tests
+- **Novita AI provider** — OpenAI-compatible provider for 200+ open-weight models
+
+```bash
+pip install semantica==0.4.0
+```
+
+</Accordion>
+
+<Accordion title="v0.3.0: Context Graph Features & Decision Intelligence" icon="brain">
+
+Released **March 10, 2026**
+
+- **Context Graph completeness** — `valid_from`/`valid_until` on every node, `find_active_nodes()` temporal filtering, `get_neighbors(min_weight)` confidence-filtered traversal
+- **Cross-graph navigation** — `link_graph()`, `navigate_to()`, `resolve_links()`, `cross_graph_path()` for multi-graph agent architectures
+- **Decision tracking system** — Full lifecycle: `record_decision()` → `add_causal_relationship()` → `find_similar_decisions()` → `trace_decision_chain()` → `analyze_decision_impact()` → `check_decision_rules()` → `get_decision_insights()`
+- **Advanced KG algorithms** — Node2Vec embeddings, betweenness / closeness / eigenvector centrality, Louvain community detection
+- **Policy management** — Versioned policy storage with policy exception tracking and approver audit trail
+- **Vector store** — Hybrid search (dense + sparse), PgVector HNSW/IVFFlat indexing with JSONB metadata filtering
+- **Apache AGE backend** — Full graph store support for PostgreSQL-native AGE graphs
+- **ResourceScheduler fix** — `RLock` deadlock fix for concurrent agent pipelines
+
+```bash
+pip install semantica==0.3.0
+```
 
 </Accordion>
 

docs/reference/context.md

@@ -443,7 +443,81 @@ print("Nodes: {}, Edges: {}".format(stats["node_count"], stats["edge_count"]))
 | `cross_graph_path(source_node_id, target_graph, target_node_id, max_hops)` | `Dict` | Shortest path across linked graphs |
 | `clear()` | `None` | Reset graph state and all indexes |
 
-### Cross-Graph Navigation
+### Distance Intelligence (v0.5.0)
+
+`ContextGraph` exposes a full Distance Intelligence API for exploring semantic neighborhoods and blending proximity into retrieval.
+
+<Info>
+  Full Distance Intelligence reference — distance matrices, API endpoints, embedding cache, Explorer UI — is covered in the dedicated [Distance Intelligence](distance) page. This section documents the context-layer API.
+</Info>
+
+### Neighbors with Distance Metadata
+
+Pass `include_distance_metadata=True` to `get_neighbors()` to receive distance band, confidence decay, and path information alongside every neighbor:
+
+```python
+graph = ContextGraph(advanced_analytics=True)
+
+# ... populate graph ...
+
+neighbors = graph.get_neighbors(
+    "python",
+    hops=3,
+    include_distance_metadata=True,
+    min_weight=0.3,   # exclude low-confidence edges
+)
+
+for n in neighbors:
+    print(
+        f"{n['node_id']:15s}  "
+        f"band={n['distance_band']:10s}  "
+        f"decay={n['confidence_decay']:.3f}  "
+        f"hops={n['hop_count']}"
+    )
+```
+
+| Added field | Type | Description |
+| :---------- | :---- | :----------- |
+| `distance_band` | `str` | `"direct"` (1 hop) / `"near"` (2) / `"mid-range"` (3–4) / `"distant"` (5+) |
+| `confidence_decay` | `float` | `edge_weight ^ hop_count` — decays with each hop |
+| `path_to_anchor` | `List[str]` | Shortest path from anchor node to this neighbor |
+| `hop_count` | `int` | BFS depth from anchor |
+
+### Proximity-Blended Retrieval
+
+Set `proximity_weight` on `AgentContext` to blend graph proximity into every `retrieve()` and `find_precedents()` call:
+
+```python
+context = AgentContext(
+    vector_store=VectorStore(backend="faiss", dimension=768),
+    knowledge_graph=ContextGraph(advanced_analytics=True),
+    proximity_weight=0.3,   # 0.7×semantic + 0.3×proximity
+)
+
+# combined_score is returned alongside semantic_score and proximity_score
+results = context.retrieve("web API frameworks", max_results=10)
+for r in results:
+    print(
+        f"[{r['combined_score']:.3f}]  "
+        f"semantic={r['semantic_score']:.3f}  "
+        f"proximity={r['proximity_score']:.3f}  "
+        f"{r['content'][:60]}"
+    )
+
+# Override weight per-call
+precedents = context.find_precedents(
+    "infrastructure scaling decisions",
+    proximity_weight=0.5,
+    limit=5,
+)
+```
+
+<Tip>
+  `proximity_weight=0.0` disables proximity blending entirely (pure semantic). `proximity_weight=1.0` returns results ranked purely by graph proximity to the query anchor. Values between `0.2`–`0.4` work well for most production use cases.
+</Tip>
+
+
+## Cross-Graph Navigation
 
 Link multiple independent `ContextGraph` instances so agents can traverse across problem spaces: