Leonid PershinandCursor
4081890b4c
Pull nomic-embed-text for Assistent memory, write ollama-roles.json, CPU Modelfile, and raise MAX_LOADED_MODELS/NUM_PARALLEL to 2. Co-authored-by: Cursor <cursoragent@cursor.com>
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
"""Ollama manifest use: chat|memory parsing."""
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from pathlib import Path
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from gpu_rent.llm_runtime import (
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MEMORY_EMBED_MODEL,
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ensure_memory_model_entries,
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ollama_roles_payload,
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parse_ollama_models,
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preferred_ollama_model,
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write_ollama_models_preset,
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)
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def test_parse_use_chat_and_memory(tmp_path: Path):
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path = tmp_path / "ollama-models.yaml"
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path.write_text(
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"""
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models:
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- name: chat-model:7b
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use: chat
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default: true
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- name: nomic-embed-text
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use: memory
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""",
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encoding="utf-8",
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)
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entries = parse_ollama_models(path)
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assert len(entries) == 2
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assert entries[0].use == "chat" and entries[0].default
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assert entries[1].use == "memory" and not entries[1].default
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assert preferred_ollama_model(path) == "chat-model:7b"
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roles = ollama_roles_payload(entries)
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assert roles["chat"] == ["chat-model:7b"]
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assert roles["memory"] == ["nomic-embed-text"]
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def test_ensure_memory_appended():
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from gpu_rent.llm_runtime import OllamaModelEntry
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entries = [OllamaModelEntry(name="vl:7b", default=True, use="chat")]
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out = ensure_memory_model_entries(entries)
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assert any(e.use == "memory" and e.name == MEMORY_EMBED_MODEL for e in out)
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def test_write_preset_includes_memory(tmp_path: Path):
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path = tmp_path / "m.yaml"
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write_ollama_models_preset(path, "recommended")
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text = path.read_text(encoding="utf-8")
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assert "use: chat" in text
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assert "use: memory" in text
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assert MEMORY_EMBED_MODEL in text
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entries = parse_ollama_models(path)
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assert preferred_ollama_model(path) is not None
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assert preferred_ollama_model(path) != MEMORY_EMBED_MODEL
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