diff --git a/backend/ollama_client.py b/backend/ollama_client.py
index fd00e8e..37e3cbf 100644
--- a/backend/ollama_client.py
+++ b/backend/ollama_client.py
@@ -1,4 +1,4 @@
-
+import asyncio
 import httpx
 import json
 import re
@@ -10,6 +10,87 @@ from .app_settings import get_ollama_api_url
 _MODEL_DETAILS_CACHE: Dict[Tuple[str, str], Tuple[float, Dict[str, Any]]] = {}
 _MODEL_DETAILS_TTL_S = 15.0
 
+
+def _string_tokens(value: Any) -> List[str]:
+    if isinstance(value, str):
+        trimmed = value.strip()
+        return [trimmed] if trimmed else []
+    if isinstance(value, dict):
+        out: List[str] = []
+        for key, item in value.items():
+            out.extend(_string_tokens(key))
+            out.extend(_string_tokens(item))
+        return out
+    if isinstance(value, (list, tuple, set)):
+        out: List[str] = []
+        for item in value:
+            out.extend(_string_tokens(item))
+        return out
+    return []
+
+
+def _normalize_capabilities(model_data: Dict[str, Any]) -> List[str]:
+    out = []
+    for item in model_data.get("capabilities") or []:
+        text = str(item).strip().lower()
+        if text and text not in out:
+            out.append(text)
+    return out
+
+
+def _is_embedding_model(name: str, model_data: Dict[str, Any], tag_item: Dict[str, Any]) -> bool:
+    capabilities = set(_normalize_capabilities(model_data))
+    if "embedding" in capabilities or "embeddings" in capabilities:
+        return True
+
+    lowered_tokens = " ".join(
+        token.lower()
+        for token in _string_tokens(name) + _string_tokens(tag_item.get("details")) + _string_tokens(model_data)
+    )
+    return any(
+        marker in lowered_tokens
+        for marker in (
+            " embed ",
+            " embedding ",
+            "embed-",
+            "-embed",
+            "nomic-embed",
+            "mxbai-embed",
+            "snowflake-arctic-embed",
+            "bge-m3",
+            "bge ",
+        )
+    ) or lowered_tokens.startswith("bge")
+
+
+def _is_rerank_model(name: str, model_data: Dict[str, Any], tag_item: Dict[str, Any]) -> bool:
+    lowered_tokens = " ".join(
+        token.lower()
+        for token in _string_tokens(name) + _string_tokens(tag_item.get("details")) + _string_tokens(model_data)
+    )
+    return (
+        _is_embedding_model(name, model_data, tag_item)
+        or "rerank" in lowered_tokens
+        or "cross-encoder" in lowered_tokens
+    )
+
+
+def _build_model_catalog_entry(tag_item: Dict[str, Any], model_data: Dict[str, Any]) -> Dict[str, Any]:
+    name = str((tag_item or {}).get("name") or "").strip()
+    capabilities = _normalize_capabilities(model_data)
+    is_embedding = _is_embedding_model(name, model_data, tag_item)
+    is_rerank = _is_rerank_model(name, model_data, tag_item)
+    has_vision = supports_vision(model_data)
+    return {
+        "name": name,
+        "capabilities": capabilities,
+        "supports_vision": has_vision,
+        "is_embedding": is_embedding,
+        "can_chat": not is_embedding,
+        "can_rerank": is_rerank,
+    }
+
+
 async def list_models() -> Dict[str, Any]:
     ollama_url = get_ollama_api_url()
     async with httpx.AsyncClient(timeout=30.0) as client:
@@ -20,6 +101,40 @@ async def list_models() -> Dict[str, Any]:
         models = [m.get('name') for m in data.get('models', [])]
         return {"models": models}
 
+
+async def list_model_catalog(*, refresh: bool = False) -> Dict[str, Any]:
+    ollama_url = get_ollama_api_url()
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        r = await client.get(f"{ollama_url}/api/tags")
+        r.raise_for_status()
+        payload = r.json()
+
+    raw_models = payload.get("models", []) or []
+    names = [str((item or {}).get("name") or "").strip() for item in raw_models]
+    details = await asyncio.gather(
+        *(show_model(name, refresh=refresh) for name in names if name),
+        return_exceptions=True,
+    )
+
+    detail_by_name: Dict[str, Dict[str, Any]] = {}
+    for name, detail in zip([name for name in names if name], details):
+        if isinstance(detail, dict):
+            detail_by_name[name] = detail
+
+    models = [
+        _build_model_catalog_entry(item or {}, detail_by_name.get(str((item or {}).get("name") or "").strip(), {}))
+        for item in raw_models
+        if str((item or {}).get("name") or "").strip()
+    ]
+
+    return {
+        "models": models,
+        "chat_models": [model["name"] for model in models if model["can_chat"]],
+        "embedding_models": [model["name"] for model in models if model["is_embedding"]],
+        "vision_models": [model["name"] for model in models if model["supports_vision"]],
+        "reranking_models": [model["name"] for model in models if model["can_rerank"]],
+    }
+
 async def show_model(model: str, *, refresh: bool = False) -> Dict[str, Any]:
     ollama_url = get_ollama_api_url()
     cache_key = (ollama_url.rstrip('/'), str(model or '').strip())