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https://github.com/open-jarvis/OpenJarvis.git
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kelliott-cloudandElliot Slusky
2ed885eb11
* 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>
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
"""Tests for /v1/recommended-model endpoint."""
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from __future__ import annotations
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import pytest
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try:
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import fastapi # noqa: F401
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HAS_FASTAPI = True
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except ImportError:
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HAS_FASTAPI = False
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_recommended_model_picks_second_largest():
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"""Should pick the second-largest local model."""
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from openjarvis.server.agent_manager_routes import _pick_recommended_model
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models = ["qwen3.5:0.8b", "qwen3.5:4b", "qwen3.5:9b", "qwen3.5:35b"]
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result = _pick_recommended_model(models)
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assert result["model"] == "qwen3.5:9b"
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_recommended_model_single_model():
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"""With only one model, pick it."""
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from openjarvis.server.agent_manager_routes import _pick_recommended_model
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result = _pick_recommended_model(["qwen3.5:4b"])
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assert result["model"] == "qwen3.5:4b"
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_recommended_model_filters_cloud():
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"""Cloud models should be excluded from recommendation."""
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from openjarvis.server.agent_manager_routes import _pick_recommended_model
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models = ["qwen3.5:4b", "gpt-4o", "claude-3.5-sonnet", "qwen3.5:9b"]
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result = _pick_recommended_model(models)
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assert result["model"] in ("qwen3.5:4b", "qwen3.5:9b")
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_parse_param_count():
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"""Parse parameter counts from model names."""
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from openjarvis.server.agent_manager_routes import _parse_param_count
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assert _parse_param_count("qwen3.5:9b") == 9.0
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assert _parse_param_count("qwen3.5:0.8b") == 0.8
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assert _parse_param_count("qwen3.5:35b") == 35.0
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assert _parse_param_count("gpt-4o") == 0.0
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_recommended_model_skips_embed_only():
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"""Embed-only models must never be recommended for chat."""
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from openjarvis.server.agent_manager_routes import _pick_recommended_model
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models = [
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"nomic-embed-text",
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"qwen3.5:4b",
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"mxbai-embed-large",
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"qwen3.5:9b",
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]
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result = _pick_recommended_model(models)
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assert result["model"] == "qwen3.5:4b"
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assert "embed" not in result["model"]
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_recommended_model_embed_only_returns_empty():
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"""If only embedders are installed, recommend nothing (not nomic-embed)."""
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from openjarvis.server.agent_manager_routes import _pick_recommended_model
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result = _pick_recommended_model(["nomic-embed-text", "mxbai-embed-large"])
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assert result["model"] == ""
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assert "No local chat model" in result["reason"]
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@pytest.mark.skipif(not HAS_FASTAPI, reason="fastapi not installed")
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def test_is_embed_only_model():
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from openjarvis.server.model_capabilities import is_embed_only_model
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assert is_embed_only_model("nomic-embed-text")
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assert is_embed_only_model("mxbai-embed-large")
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assert is_embed_only_model("text-embedding-3-small")
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assert is_embed_only_model("all-minilm:latest")
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assert is_embed_only_model("hf.co/BAAI/bge-m3:latest")
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assert not is_embed_only_model("qwen3.5:4b")
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assert not is_embed_only_model("codegemma:7b")
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