fix: never auto-select embed-only models for chat (#659)

* fix: never auto-select embed-only models for chat

Ollama lists nomic-embed-text alongside chat models. Auto-picking
models[0] / recommending the only available id selected the embedder
and every generation failed with HTTP 400 "does not support chat".

- Filter embed-only ids out of GET /v1/models (chat picker)
- Exclude them from /v1/recommended-model; return empty when none left
- Frontend setModels prefers chat models and clears a bad embed selection
- Regression tests for mixed, embed-only, and classifier cases

* fix: harden chat model capability filtering

---------

Co-authored-by: Elliot Slusky <elliot@slusky.com>

frontend/src/App.tsx

@@ -31,7 +31,6 @@ export default function App() {
   const prevModelRef = useRef<string>('');
   const setModels = useAppStore((s) => s.setModels);
   const setModelsLoading = useAppStore((s) => s.setModelsLoading);
-  const setSelectedModel = useAppStore((s) => s.setSelectedModel);
   const selectedModel = useAppStore((s) => s.selectedModel);
   const setServerInfo = useAppStore((s) => s.setServerInfo);
   const setSavings = useAppStore((s) => s.setSavings);
@@ -70,7 +69,6 @@ export default function App() {
     fetchModels()
       .then((m) => {
         setModels(m);
-        if (!selectedModel && m.length > 0) setSelectedModel(m[0].id);
       })
       .catch(() => setModels([]))
       .finally(() => setModelsLoading(false));

frontend/src/components/SetupScreen.tsx

@@ -7,6 +7,7 @@ import {
   type SetupStatus,
 } from '../lib/api';
 import { useAppStore } from '../lib/store';
+import { isEmbedOnlyModel } from '../lib/model-capabilities';
 
 const STEPS = [
   { key: 'ollama_ready', label: 'Inference Engine', icon: Cpu, detail: 'Starting Ollama...' },
@@ -91,12 +92,14 @@ export function SetupScreen({ onReady }: { onReady: () => void }) {
           fetchRecommendedModel().catch(() => ({ model: '', reason: '' })),
         ]);
         const store = useAppStore.getState();
+        const hadSelection = !!store.selectedModel;
         store.setModels(models);
         store.setModelsLoading(false);
-        const recommended = rec.model && models.some((m) => m.id === rec.model)
+        const chatModels = models.filter((m) => !isEmbedOnlyModel(m.id));
+        const recommended = rec.model && chatModels.some((m) => m.id === rec.model)
           ? rec.model
-          : models[0]?.id || '';
-        if (recommended && !store.selectedModel) {
+          : chatModels[0]?.id || '';
+        if (recommended && !hadSelection) {
           store.setSelectedModel(recommended);
         }
       } catch {

frontend/src/lib/model-capabilities.test.ts

@@ -0,0 +1,19 @@
+import { describe, expect, it } from 'vitest';
+
+import { isEmbedOnlyModel } from './model-capabilities';
+
+describe('isEmbedOnlyModel', () => {
+  it.each([
+    'nomic-embed-text',
+    'mxbai-embed-large',
+    'text-embedding-3-small',
+    'all-minilm:latest',
+    'hf.co/BAAI/bge-m3:latest',
+  ])('classifies %s as embedding-only', (modelId) => {
+    expect(isEmbedOnlyModel(modelId)).toBe(true);
+  });
+
+  it.each(['qwen3.5:4b', 'codegemma:7b'])('keeps %s available for chat', (modelId) => {
+    expect(isEmbedOnlyModel(modelId)).toBe(false);
+  });
+});

frontend/src/lib/model-capabilities.ts

@@ -0,0 +1,22 @@
+const EMBEDDING_MODEL_PREFIXES = [
+  'all-minilm',
+  'bge-',
+  'bge_',
+  'e5-',
+  'e5_',
+  'gte-',
+  'gte_',
+  'jina-embeddings',
+  'nomic-bert',
+  'sentence-transformers',
+];
+
+export function isEmbedOnlyModel(modelId: string): boolean {
+  const name = (modelId || '').trim().toLowerCase();
+  const leaf = name.slice(name.lastIndexOf('/') + 1).split(':')[0];
+  return (
+    leaf.includes('embed') ||
+    leaf.includes('minilm') ||
+    EMBEDDING_MODEL_PREFIXES.some((prefix) => leaf.startsWith(prefix))
+  );
+}

frontend/src/lib/store.models.test.ts

@@ -0,0 +1,64 @@
+import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest';
+
+import type { ModelInfo } from '../types';
+
+class MemoryStorage {
+  private store = new Map<string, string>();
+
+  getItem(key: string): string | null {
+    return this.store.get(key) ?? null;
+  }
+
+  setItem(key: string, value: string): void {
+    this.store.set(key, String(value));
+  }
+}
+
+const model = (id: string): ModelInfo => ({
+  id,
+  object: 'model',
+  created: 0,
+  owned_by: 'openjarvis',
+});
+
+beforeEach(() => {
+  vi.resetModules();
+  (globalThis as unknown as { localStorage: MemoryStorage }).localStorage =
+    new MemoryStorage();
+});
+
+afterEach(() => {
+  (globalThis as unknown as { localStorage?: MemoryStorage }).localStorage =
+    undefined;
+});
+
+describe('setModels', () => {
+  it('does not select an embedding-only model', async () => {
+    const { useAppStore } = await import('./store');
+
+    useAppStore.getState().setModels([model('nomic-embed-text')]);
+
+    expect(useAppStore.getState().selectedModel).toBe('');
+  });
+
+  it('clears a missing selection when no chat fallback exists', async () => {
+    const { useAppStore } = await import('./store');
+    useAppStore.getState().setSelectedModel('deleted-chat-model');
+
+    useAppStore.getState().setModels([model('nomic-embed-text')]);
+
+    expect(useAppStore.getState().selectedModel).toBe('');
+  });
+
+  it('replaces an embedding selection with an available chat model', async () => {
+    const { useAppStore } = await import('./store');
+    useAppStore.getState().setSelectedModel('all-minilm:latest');
+
+    useAppStore.getState().setModels([
+      model('all-minilm:latest'),
+      model('qwen3.5:4b'),
+    ]);
+
+    expect(useAppStore.getState().selectedModel).toBe('qwen3.5:4b');
+  });
+});

frontend/src/lib/store.ts

@@ -15,6 +15,7 @@ import type {
   TokenUsage,
 } from '../types';
 import type { ManagedAgent } from './api';
+import { isEmbedOnlyModel } from './model-capabilities';
 
 export interface CachedConnector {
   connector_id: string;
@@ -444,11 +445,36 @@ export const useAppStore = create<AppState>((set, get) => {
     // ── Models & server ────────────────────────────────────────────
 
     setModels: (models: ModelInfo[]) =>
-      set((state) =>
-        !state.selectedModel && models.length > 0
-          ? { models, selectedModel: models[0].id }
-          : { models },
-      ),
+      set((state) => {
+        // Ollama returns embed-only models (e.g. nomic-embed-text) in the
+        // same list as chat models. Auto-picking models[0] selected the
+        // embedder and every chat failed with HTTP 400 "does not support
+        // chat". Prefer a real chat model for selection / fallback.
+        const chatModels = models.filter((m) => !isEmbedOnlyModel(m.id));
+        const preferred =
+          (state.settings.defaultModel &&
+            chatModels.some((m) => m.id === state.settings.defaultModel) &&
+            state.settings.defaultModel) ||
+          chatModels[0]?.id ||
+          models.find((m) => !isEmbedOnlyModel(m.id))?.id ||
+          '';
+
+        const currentIsBad =
+          !!state.selectedModel && isEmbedOnlyModel(state.selectedModel);
+        const currentMissing =
+          !!state.selectedModel &&
+          !models.some((m) => m.id === state.selectedModel);
+
+        if (!state.selectedModel || currentIsBad || currentMissing) {
+          // Prefer a real chat model. If none exist, clear a bad/missing
+          // selection rather than keeping an embed-only id that 400s on chat.
+          return {
+            models,
+            selectedModel: preferred,
+          };
+        }
+        return { models };
+      }),
     setModelsLoading: (loading: boolean) => set({ modelsLoading: loading }),
     setSelectedModel: (model: string) => set({ selectedModel: model }),
     setServerInfo: (info: ServerInfo | null) => set({ serverInfo: info }),

src/openjarvis/server/agent_manager_routes.py

@@ -20,6 +20,7 @@ from openjarvis.agents.tool_resolver import (
 from openjarvis.agents.tool_resolver import (
     ensure_registries_populated as _ensure_registries_populated,
 )
+from openjarvis.server.model_capabilities import is_embed_only_model
 
 try:
     from fastapi import APIRouter, HTTPException, Request
@@ -332,16 +333,29 @@ _CLOUD_PREFIXES = ("gpt-", "claude-", "gemini-", "o1-", "o3-", "o4-")
 def _pick_recommended_model(
     model_ids: list[str],
 ) -> dict[str, str]:
-    """Pick the second-largest local model from a list."""
-    local = [m for m in model_ids if not any(m.startswith(p) for p in _CLOUD_PREFIXES)]
+    """Pick the second-largest local *chat* model from a list.
+
+    Embedding-only models (nomic-embed-text, etc.) are excluded — they return
+    HTTP 400 "does not support chat" when used as the generation model.
+    """
+    local = [
+        m
+        for m in model_ids
+        if not any(m.startswith(p) for p in _CLOUD_PREFIXES)
+        and not is_embed_only_model(m)
+    ]
     if not local:
+        # Fall back to any non-cloud model, still skipping embedders.
+        local = [m for m in model_ids if not is_embed_only_model(m)]
+    if not local:
+        # Never recommend an embed-only model — chat would 400.
         return {
-            "model": model_ids[0] if model_ids else "",
-            "reason": "Only model available",
+            "model": "",
+            "reason": "No local chat model available",
         }
     sized = sorted(local, key=_parse_param_count, reverse=True)
     if len(sized) == 1:
-        return {"model": sized[0], "reason": "Only local model available"}
+        return {"model": sized[0], "reason": "Only local chat model available"}
     pick = sized[1]  # second-largest
     params = _parse_param_count(pick)
     return {

src/openjarvis/server/model_capabilities.py

@@ -0,0 +1,34 @@
+"""Model capability helpers shared by server model-selection routes."""
+
+_EMBEDDING_MODEL_PREFIXES = (
+    "all-minilm",
+    "bge-",
+    "bge_",
+    "e5-",
+    "e5_",
+    "gte-",
+    "gte_",
+    "jina-embeddings",
+    "nomic-bert",
+    "sentence-transformers",
+)
+
+
+def is_embed_only_model(model_name: str) -> bool:
+    """Return whether a model identifier denotes a non-chat embedder.
+
+    Ollama does not expose capabilities through its model-list response, so
+    model selection needs a conservative name-based guard.  Most embedding
+    models contain ``embed``; the explicit prefixes cover common families
+    such as MiniLM, BGE, E5, and GTE whose names do not.
+    """
+    name = (model_name or "").strip().lower()
+    leaf = name.rsplit("/", 1)[-1].split(":", 1)[0]
+    return (
+        "embed" in leaf
+        or "minilm" in leaf
+        or leaf.startswith(_EMBEDDING_MODEL_PREFIXES)
+    )
+
+
+__all__ = ["is_embed_only_model"]

src/openjarvis/server/routes.py

@@ -12,6 +12,7 @@ from fastapi.responses import StreamingResponse
 
 from openjarvis.core.paths import get_config_dir
 from openjarvis.core.types import Message, Role
+from openjarvis.server.model_capabilities import is_embed_only_model
 from openjarvis.server.models import (
     ChatCompletionChunk,
     ChatCompletionRequest,
@@ -892,6 +893,11 @@ async def list_models(request: Request) -> ModelListResponse:
     if not model_ids:
         model_ids = await list_local_models()
 
+    # Keep embed-only models out of the chat model picker. They still work for
+    # memory/retrieval via the embedder path; putting them in /v1/models made
+    # the UI auto-select nomic-embed-text and fail every generation with 400.
+    model_ids = [m for m in model_ids if not is_embed_only_model(m)]
+
     return ModelListResponse(
         data=[
             ModelObject(

tests/server/test_model_management.py

@@ -265,6 +265,28 @@ class TestModelsEndpointExtended:
         assert "qwen3.5:9b" in ids
         assert "qwen3:0.6b" in ids
 
+    def test_models_list_filters_embedding_only_models(self):
+        engine = _make_engine(
+            models=["nomic-embed-text", "all-minilm:latest", "qwen3.5:4b"],
+        )
+        client = TestClient(create_app(engine, "qwen3.5:4b"))
+
+        resp = client.get("/v1/models")
+
+        assert resp.status_code == 200
+        assert [m["id"] for m in resp.json()["data"]] == ["qwen3.5:4b"]
+
+    def test_models_list_returns_empty_when_only_embedders_are_installed(self):
+        engine = _make_engine(
+            models=["nomic-embed-text", "hf.co/BAAI/bge-m3:latest"],
+        )
+        client = TestClient(create_app(engine, "nomic-embed-text"))
+
+        resp = client.get("/v1/models")
+
+        assert resp.status_code == 200
+        assert resp.json()["data"] == []
+
     def test_models_empty_engine(self):
         """When engine.list_models() returns empty, endpoint still succeeds."""
         engine = _make_engine(models=[])

tests/server/test_recommended_model.py

@@ -50,3 +50,42 @@ def test_parse_param_count():
     assert _parse_param_count("qwen3.5:0.8b") == 0.8
     assert _parse_param_count("qwen3.5:35b") == 35.0
     assert _parse_param_count("gpt-4o") == 0.0
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+def test_recommended_model_skips_embed_only():
+    """Embed-only models must never be recommended for chat."""
+    from openjarvis.server.agent_manager_routes import _pick_recommended_model
+
+    models = [
+        "nomic-embed-text",
+        "qwen3.5:4b",
+        "mxbai-embed-large",
+        "qwen3.5:9b",
+    ]
+    result = _pick_recommended_model(models)
+    assert result["model"] == "qwen3.5:4b"
+    assert "embed" not in result["model"]
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+def test_recommended_model_embed_only_returns_empty():
+    """If only embedders are installed, recommend nothing (not nomic-embed)."""
+    from openjarvis.server.agent_manager_routes import _pick_recommended_model
+
+    result = _pick_recommended_model(["nomic-embed-text", "mxbai-embed-large"])
+    assert result["model"] == ""
+    assert "No local chat model" in result["reason"]
+
+
+@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
+def test_is_embed_only_model():
+    from openjarvis.server.model_capabilities import is_embed_only_model
+
+    assert is_embed_only_model("nomic-embed-text")
+    assert is_embed_only_model("mxbai-embed-large")
+    assert is_embed_only_model("text-embedding-3-small")
+    assert is_embed_only_model("all-minilm:latest")
+    assert is_embed_only_model("hf.co/BAAI/bge-m3:latest")
+    assert not is_embed_only_model("qwen3.5:4b")
+    assert not is_embed_only_model("codegemma:7b")