diff --git a/src/admin/routes.py b/src/admin/routes.py
index cc652ee..9eba29b 100644
--- a/src/admin/routes.py
+++ b/src/admin/routes.py
@@ -1,12 +1,30 @@
 """Admin API routes — system health, Qdrant management, chunk preview, questions, cost."""
 
-import os
-from fastapi import APIRouter, UploadFile, File
+from fastapi import APIRouter, HTTPException, UploadFile, File
+from pydantic import BaseModel, Field
+
 from src.admin import service
 
 router = APIRouter(prefix="/admin")
 
 
+class ActiveEmbeddingModelBody(BaseModel):
+    id: str = Field(..., description="Registry Embedding Model id")
+
+
+class RoleEmbeddingModelBody(BaseModel):
+    id: str = Field(..., description="Registry Embedding Model id for Boundary or Corpus role")
+
+
+class SemanticThresholdBody(BaseModel):
+    semantic_threshold: float = Field(
+        ...,
+        gt=0.0,
+        le=1.0,
+        description="Cosine similarity cutoff for Semantic Boundary Detection",
+    )
+
+
 # ── Health ──────────────────────────────────────────────────
 
 @router.get("/health")
@@ -15,6 +33,50 @@ async def health_check():
     return service.get_health()
 
 
+# ── Embedding Models ────────────────────────────────────────
+
+@router.get("/embedding-models")
+async def list_embedding_models():
+    """List Embedding Model Registry and Boundary/Corpus defaults."""
+    return service.list_embedding_models()
+
+
+@router.put("/embedding-models/active")
+async def set_active_embedding_model(body: ActiveEmbeddingModelBody):
+    """Legacy: set Default Corpus Embedding Model."""
+    result = service.set_corpus_embedding_model(body.id)
+    if "error" in result:
+        raise HTTPException(status_code=400, detail=result["error"])
+    return {**result, "active_id": result.get("corpus_id")}
+
+
+@router.put("/embedding-models/corpus")
+async def set_corpus_embedding_model(body: RoleEmbeddingModelBody):
+    """Set Default Corpus Embedding Model (storage + query)."""
+    result = service.set_corpus_embedding_model(body.id)
+    if "error" in result:
+        raise HTTPException(status_code=400, detail=result["error"])
+    return result
+
+
+@router.put("/embedding-models/boundary")
+async def set_boundary_embedding_model(body: RoleEmbeddingModelBody):
+    """Set Default Boundary Embedding Model (semantic cuts)."""
+    result = service.set_boundary_embedding_model(body.id)
+    if "error" in result:
+        raise HTTPException(status_code=400, detail=result["error"])
+    return result
+
+
+@router.put("/embedding-models/{model_id}/semantic-threshold")
+async def update_semantic_threshold(model_id: str, body: SemanticThresholdBody):
+    """Set per-Embedding-Model semantic_threshold (Admin override in SQLite)."""
+    result = service.update_semantic_threshold(model_id, body.semantic_threshold)
+    if "error" in result:
+        raise HTTPException(status_code=400, detail=result["error"])
+    return result
+
+
 # ── Qdrant Collections ─────────────────────────────────────
 
 @router.get("/qdrant/collections")
diff --git a/src/admin/service.py b/src/admin/service.py
index 74aa699..9bdc63e 100644
--- a/src/admin/service.py
+++ b/src/admin/service.py
@@ -24,7 +24,12 @@ QUESTIONS_DIR = PROJECT_ROOT / "files"
 # ── Health ──────────────────────────────────────────────────
 
 def get_health() -> dict[str, Any]:
-    """Check server, Qdrant, and SQLite status."""
+    """Check server, Qdrant, SQLite, OpenAI, and Ollama status."""
+    from src.chunking.embedding import (
+        get_boundary_embedding_model,
+        get_corpus_embedding_model,
+    )
+
     result: dict[str, Any] = {"status": "ok"}
 
     # Check Qdrant
@@ -51,57 +56,228 @@ def get_health() -> dict[str, Any]:
 
     # Check OpenAI
     try:
-        client = get_openai_client()
-        # Just check the client exists; don't make a real API call
+        get_openai_client()
         result["openai_configured"] = bool(settings.openai_api_key)
     except Exception:
         result["openai_configured"] = False
 
+    try:
+        corpus = get_corpus_embedding_model()
+        boundary = get_boundary_embedding_model()
+        result["corpus_embedding_model"] = {
+            "id": corpus.id,
+            "provider": corpus.provider.value,
+            "model_name": corpus.model_name,
+            "dimension": corpus.dimension,
+            "display_name": corpus.display_name,
+        }
+        result["boundary_embedding_model"] = {
+            "id": boundary.id,
+            "provider": boundary.provider.value,
+            "model_name": boundary.model_name,
+            "dimension": boundary.dimension,
+            "display_name": boundary.display_name,
+        }
+        # Legacy alias for older Dashboard code
+        result["active_embedding_model"] = result["corpus_embedding_model"]
+    except Exception as exc:
+        result["corpus_embedding_model"] = None
+        result["boundary_embedding_model"] = None
+        result["active_embedding_model"] = None
+        result["active_embedding_error"] = str(exc)
+
+    result["ollama_base_url"] = settings.ollama_base_url
+    try:
+        import urllib.request
+
+        url = settings.ollama_base_url.rstrip("/") + "/api/tags"
+        with urllib.request.urlopen(url, timeout=2) as resp:
+            result["ollama_reachable"] = resp.status == 200
+    except Exception as exc:
+        result["ollama_reachable"] = False
+        result["ollama_error"] = str(exc)
+
     return result
 
 
+# ── Embedding Models ────────────────────────────────────────
+
+def list_embedding_models() -> dict[str, Any]:
+    """List registry entries and Boundary/Corpus defaults."""
+    from src.chunking.embedding import (
+        get_boundary_embedding_model,
+        get_corpus_embedding_model,
+    )
+    from src.chunking.embedding_models import get_semantic_threshold, list_models
+
+    corpus = get_corpus_embedding_model()
+    boundary = get_boundary_embedding_model()
+    models = []
+    for m in list_models():
+        models.append({
+            "id": m.id,
+            "provider": m.provider.value,
+            "model_name": m.model_name,
+            "dimension": m.dimension,
+            "display_name": m.display_name,
+            "task_prefixes": m.task_prefixes,
+            "default_semantic_threshold": m.default_semantic_threshold,
+            "semantic_threshold": get_semantic_threshold(m.id),
+            "is_corpus_default": m.id == corpus.id,
+            "is_boundary_default": m.id == boundary.id,
+            "is_active": m.id == corpus.id,  # legacy
+        })
+    return {
+        "corpus_id": corpus.id,
+        "boundary_id": boundary.id,
+        "active_id": corpus.id,  # legacy
+        "models": models,
+        "ollama_base_url": settings.ollama_base_url,
+    }
+
+
+def set_active_embedding_model(model_id: str) -> dict[str, Any]:
+    """Legacy: set Corpus default."""
+    return set_corpus_embedding_model(model_id)
+
+
+def set_corpus_embedding_model(model_id: str) -> dict[str, Any]:
+    """Switch the Default Corpus Embedding Model."""
+    from src.chunking.embedding import set_corpus_embedding_model as set_corpus
+
+    try:
+        model = set_corpus(model_id)
+    except KeyError as exc:
+        return {"error": str(exc)}
+
+    return {
+        "corpus_id": model.id,
+        "model": {
+            "id": model.id,
+            "provider": model.provider.value,
+            "model_name": model.model_name,
+            "dimension": model.dimension,
+            "display_name": model.display_name,
+        },
+    }
+
+
+def set_boundary_embedding_model(model_id: str) -> dict[str, Any]:
+    """Switch the Default Boundary Embedding Model."""
+    from src.chunking.embedding import set_boundary_embedding_model as set_boundary
+
+    try:
+        model = set_boundary(model_id)
+    except KeyError as exc:
+        return {"error": str(exc)}
+
+    return {
+        "boundary_id": model.id,
+        "model": {
+            "id": model.id,
+            "provider": model.provider.value,
+            "model_name": model.model_name,
+            "dimension": model.dimension,
+            "display_name": model.display_name,
+        },
+    }
+
+
+def update_semantic_threshold(model_id: str, threshold: float) -> dict[str, Any]:
+    """Persist Admin override of semantic_threshold for a registry Embedding Model."""
+    from src.chunking.embedding_models import (
+        get_model,
+        get_semantic_threshold,
+        set_semantic_threshold,
+    )
+
+    try:
+        model = get_model(model_id)
+        value = set_semantic_threshold(model_id, threshold)
+    except KeyError as exc:
+        return {"error": str(exc)}
+    except ValueError as exc:
+        return {"error": str(exc)}
+
+    return {
+        "id": model.id,
+        "semantic_threshold": value,
+        "default_semantic_threshold": model.default_semantic_threshold,
+        "effective": get_semantic_threshold(model.id),
+    }
+
+
 # ── Qdrant Collections ─────────────────────────────────────
 
 def list_qdrant_collections() -> dict[str, Any]:
-    """List all Qdrant collections with their point counts."""
+    """List all Qdrant collections with point counts and Embedding Model labels."""
+    from src.chunking.embedding import get_active_embedding_model
+
     client = get_qdrant_client()
     collections_data = client.get_collections().collections
+    active = get_active_embedding_model()
 
     result = []
     for col in collections_data:
+        meta = qdrant_store.parse_collection_meta(col.name)
         try:
             info = client.get_collection(collection_name=col.name)
-            result.append({
-                "name": col.name,
-                "points_count": info.points_count or 0,
-            })
+            points = info.points_count or 0
         except Exception as exc:
-            result.append({
-                "name": col.name,
-                "points_count": None,
-                "error": str(exc),
-            })
+            points = None
+            err = str(exc)
+        else:
+            err = None
 
-    return {"collections": result}
+        entry = {
+            "name": col.name,
+            "points_count": points,
+            "strategy": meta.get("strategy"),
+            "embedding_model_id": meta.get("embedding_model_id"),
+            "is_legacy": meta.get("is_legacy", False),
+            "is_active_corpus": meta.get("embedding_model_id") == active.id,
+        }
+        if err:
+            entry["error"] = err
+        result.append(entry)
+
+    # Active Model Corpus first
+    result.sort(key=lambda c: (not c.get("is_active_corpus", False), c["name"]))
+    return {
+        "collections": result,
+        "active_embedding_model_id": active.id,
+    }
 
 
 def create_qdrant_collection(collection_name: str) -> dict[str, Any]:
-    """Create a new Qdrant collection."""
+    """Create a new Qdrant collection using Active Embedding Model dimension."""
+    from src.chunking.embedding import get_active_embedding_model
+
     client = get_qdrant_client()
+    active = get_active_embedding_model()
 
     existing = [c.name for c in client.get_collections().collections]
     if collection_name in existing:
         return {"created": False, "message": f"Collection '{collection_name}' already exists"}
 
+    meta = qdrant_store.parse_collection_meta(collection_name)
+    dimension = active.dimension
+    if meta.get("embedding_model_id"):
+        try:
+            from src.chunking.embedding_models import get_model
+            dimension = get_model(meta["embedding_model_id"]).dimension
+        except KeyError:
+            pass
+
     client.create_collection(
         collection_name=collection_name,
         vectors_config=VectorParams(
-            size=qdrant_store.VECTOR_DIMENSION,
+            size=dimension,
             distance=Distance.COSINE,
         ),
     )
-    logger.info("Created Qdrant collection: %s", collection_name)
-    return {"created": True, "collection": collection_name}
+    logger.info("Created Qdrant collection: %s (dim=%d)", collection_name, dimension)
+    return {"created": True, "collection": collection_name, "dimension": dimension}
 
 
 def delete_qdrant_collection(collection_name: str) -> dict[str, Any]:
@@ -134,8 +310,11 @@ def wipe_qdrant_collection_points(collection_name: str) -> dict[str, Any]:
 # ── Chunk Preview ───────────────────────────────────────────
 
 def preview_chunks(doc_id: str, strategy: str | None = None) -> dict[str, Any]:
-    """Preview chunks for a document. Uses Qdrant scroll to fetch chunks with payload."""
+    """Preview chunks for a document from the Active Embedding Model's corpus."""
+    from src.chunking.embedding import get_active_embedding_model
+
     client = get_qdrant_client()
+    active = get_active_embedding_model()
 
     from qdrant_client.models import Filter, FieldCondition, MatchValue
 
@@ -155,12 +334,12 @@ def preview_chunks(doc_id: str, strategy: str | None = None) -> dict[str, Any]:
 
     results = {}
     for strat_name in strategies_to_search:
-        col_name = f"{strat_name}_collection"
+        col_name = qdrant_store.collection_name(strat_name, active.id)
 
         try:
             existing = [c.name for c in client.get_collections().collections]
             if col_name not in existing:
-                results[strat_name] = {"chunks": [], "count": 0}
+                results[strat_name] = {"chunks": [], "count": 0, "collection": col_name}
                 continue
 
             scroll_filter = Filter(
@@ -188,12 +367,21 @@ def preview_chunks(doc_id: str, strategy: str | None = None) -> dict[str, Any]:
 
             # Sort by chunk_index
             chunks.sort(key=lambda c: c.get("chunk_index") or 0)
-            results[strat_name] = {"chunks": chunks, "count": len(chunks)}
+            results[strat_name] = {
+                "chunks": chunks,
+                "count": len(chunks),
+                "collection": col_name,
+            }
 
         except Exception as exc:
             results[strat_name] = {"error": str(exc), "chunks": [], "count": 0}
 
-    return {"document_id": doc_id, "filename": doc_name, "strategies": results}
+    return {
+        "document_id": doc_id,
+        "filename": doc_name,
+        "embedding_model_id": active.id,
+        "strategies": results,
+    }
 
 
 # ── Questions Dataset ───────────────────────────────────────