Refactor settings store to rename proModel to proThinking and update related logic; enhance CSS for energy efficiency and reduced motion preferences; improve i18n translations for better clarity and consistency; modify proBlock utility functions for clearer instruction handling; streamline Vite configuration by removing unnecessary Univer.js dependencies.
This commit is contained in:
“ydy0615”
+148
-178
@@ -2,6 +2,7 @@ import asyncio
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import importlib
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import sys
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from pathlib import Path
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import pytest
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@@ -15,66 +16,27 @@ except ModuleNotFoundError:
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pytest.skip("llm module dependencies are not available", allow_module_level=True)
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def test_extract_message_with_object_message_content_and_thinking():
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class Msg:
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def __init__(self, content, thinking):
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self.content = content
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self.thinking = thinking
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class Resp:
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def __init__(self, message):
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self.message = message
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resp = Resp(Msg("hello world", "thinking about it"))
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def test_extract_message_with_content_and_thinking():
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resp = {"choices": [{"message": {"content": "hello world", "thinking": "reasoning"}}]}
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content, thinking = llm._extract_message(resp)
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assert content == "hello world"
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assert thinking == "thinking about it"
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assert thinking == "reasoning"
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def test_extract_message_with_object_message_empty_content():
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class Msg:
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def __init__(self, content, thinking):
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self.content = content
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self.thinking = thinking
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class Resp:
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def __init__(self, message):
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self.message = message
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resp = Resp(Msg("", None))
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def test_extract_message_empty_content():
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resp = {"choices": [{"message": {"content": "", "thinking": None}}]}
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_extract_message_with_dict_message():
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resp = {"message": {"content": "ok", "thinking": "calc"}}
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content, thinking = llm._extract_message(resp)
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assert content == "ok"
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assert thinking == "calc"
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def test_extract_message_dict_no_message_key():
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resp = {"not_message": {"content": "irrelevant"}}
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def test_extract_message_dict_no_choices():
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resp = {"not_choices": []}
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_extract_message_dict_message_content_none_and_thinking_none():
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resp = {"message": {"content": None, "thinking": None}}
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content, thinking = llm._extract_message(resp)
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assert content == ""
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assert thinking == ""
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def test_extract_message_dict_message_thinking_none():
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resp = {"message": {"content": "val", "thinking": None}}
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content, thinking = llm._extract_message(resp)
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assert content == "val"
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assert thinking == ""
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def test_extract_message_empty_dict():
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resp = {}
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content, thinking = llm._extract_message(resp)
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@@ -82,174 +44,182 @@ def test_extract_message_empty_dict():
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assert thinking == ""
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def test_call_ollama_no_system_message(monkeypatch):
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def test_extract_delta_text_from_chunk():
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chunk = {"choices": [{"delta": {"content": "text"}}]}
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assert llm._extract_delta_text(chunk) == "text"
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def test_extract_delta_thinking_from_chunk():
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chunk = {"choices": [{"delta": {"thinking": "thought"}}]}
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assert llm._extract_delta_thinking(chunk) == "thought"
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def test_call_ollama_no_system(monkeypatch):
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captured = {}
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async def fake_generate(**kwargs):
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captured["kwargs"] = kwargs
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return {"response": "ok"}
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async def fake_post(url, json=None):
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captured["json"] = json
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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class FakeResp:
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def raise_for_status(self): pass
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def json(self): return {"choices": [{"message": {"content": "ok"}}]}
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return FakeResp()
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async def fake_client(*args, **kwargs):
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class Ctx:
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async def __aenter__(self2): return self2
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async def __aexit__(*a): pass
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post = fake_post
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return Ctx()
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monkeypatch.setattr(llm.httpx, "AsyncClient", fake_client)
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monkeypatch.setattr(llm.asyncio, "wait_for", lambda coro, **kw: coro)
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result = asyncio.run(
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llm.call_ollama("user prompt body", system_prompt=None, tag="no-system", temperature=0.1)
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llm.call_ollama("user prompt", system_prompt=None, tag="no-system")
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)
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assert result["content"] == "ok"
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assert captured["kwargs"]["prompt"] == "user prompt body"
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assert captured["kwargs"]["raw"] is True
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# Should only have user message, no system
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assert len(captured["json"]["messages"]) == 1
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def test_call_ollama_whitespace_system_message(monkeypatch):
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def test_call_ollama_with_system(monkeypatch):
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captured = {}
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async def fake_generate(**kwargs):
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captured["kwargs"] = kwargs
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return {"response": "ok"}
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async def fake_post(url, json=None):
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captured["json"] = json
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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class FakeResp:
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def raise_for_status(self): pass
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def json(self): return {"choices": [{"message": {"content": "ok"}}]}
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return FakeResp()
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async def fake_client(*args, **kwargs):
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class Ctx:
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async def __aenter__(self2): return self2
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async def __aexit__(*a): pass
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post = fake_post
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return Ctx()
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monkeypatch.setattr(llm.httpx, "AsyncClient", fake_client)
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monkeypatch.setattr(llm.asyncio, "wait_for", lambda coro, **kw: coro)
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result = asyncio.run(
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llm.call_ollama("user prompt", system_prompt=" ", tag="whitespace-system", temperature=0.1)
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llm.call_ollama("user prompt", system_prompt="sys prompt", tag="with-system")
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)
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assert result["content"] == "ok"
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assert captured["kwargs"]["prompt"] == "user prompt"
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assert captured["kwargs"]["raw"] is True
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# Should have both system and user messages
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msgs = captured["json"]["messages"]
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assert len(msgs) == 2
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assert msgs[0]["role"] == "system"
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def test_call_ollama_thinking_in_kwargs(monkeypatch):
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def test_call_ollama_with_custom_model(monkeypatch):
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captured = {}
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async def fake_generate(**kwargs):
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captured.update(kwargs)
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return {"response": "ok", "message": {"thinking": "boom"}} # keep thinking for backward test though it might not be perfect
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async def fake_post(url, json=None):
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captured["json"] = json
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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class FakeResp:
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def raise_for_status(self): pass
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def json(self): return {"choices": [{"message": {"content": "ok"}}]}
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res = asyncio.run(
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llm.call_ollama("prompt", thinking="boom", tag="think-flag", temperature=0.7)
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return FakeResp()
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async def fake_client(*args, **kwargs):
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class Ctx:
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async def __aenter__(self2): return self2
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async def __aexit__(*a): pass
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post = fake_post
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return Ctx()
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monkeypatch.setattr(llm.httpx, "AsyncClient", fake_client)
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monkeypatch.setattr(llm.asyncio, "wait_for", lambda coro, **kw: coro)
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result = asyncio.run(
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llm.call_ollama("prompt", model="custom-model")
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)
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assert res["content"] == "ok" and res["think"] == "boom"
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assert captured.get("think") == "boom"
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assert captured["json"]["model"] == "custom-model"
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def test_call_ollama_cancelled_reraises(monkeypatch):
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async def fake_generate(**kwargs):
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raise asyncio.CancelledError
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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with pytest.raises(asyncio.CancelledError):
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asyncio.run(
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llm.call_ollama("prompt", system_prompt=None, tag="cancel", temperature=0.7)
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)
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def test_call_ollama_chat_raises_rethrows(monkeypatch):
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async def fake_generate(**kwargs):
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raise ValueError("boom")
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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with pytest.raises(ValueError):
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asyncio.run(
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llm.call_ollama("prompt", system_prompt=None, tag="exception", temperature=0.7)
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)
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def test_call_ollama_returns_content_and_think_from_response(monkeypatch):
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async def fake_generate(**kwargs):
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return {"response": "final", "message": {"thinking": "process"}}
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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res = asyncio.run(
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llm.call_ollama("prompt", system_prompt=None, tag="return", temperature=0.7)
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)
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assert res["content"] == "final" and res["think"] == "process"
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def test_stream_ollama_uses_requested_model_and_yields_chunks(monkeypatch):
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captured = {}
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class FakeStream:
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def __init__(self, chunks):
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self._chunks = iter(chunks)
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def __aiter__(self):
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return self
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async def __anext__(self):
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try:
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return next(self._chunks)
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except StopIteration:
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raise StopAsyncIteration
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async def fake_generate(**kwargs):
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captured["kwargs"] = kwargs
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return FakeStream([
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{"response": "深度"},
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{"response": "回答"},
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def test_stream_ollama_events_error_handling(monkeypatch):
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def make_lines():
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lines_iter = iter([
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'data: {"error": "model not found"}',
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])
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monkeypatch.setattr(llm.client, "generate", fake_generate)
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class LineIterator:
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async def __anext__(self):
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try:
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return next(lines_iter)
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except StopIteration:
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raise StopAsyncIteration()
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chunks = []
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class Response:
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def __init__(self2): self2._lines = LineIterator()
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async def collect_stream():
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async for chunk in llm.stream_ollama(
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"prompt",
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system_prompt="system",
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tag="stream",
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temperature=0.8,
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model="pro-model",
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use_pro_model=True,
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):
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chunks.append(chunk)
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async def raise_for_status(self2): pass
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async def aiter_lines(self2): return self2._lines
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asyncio.run(collect_stream())
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class StreamCtx:
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async def __aenter__(self2): return Response()
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async def __aexit__(*a): pass
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assert "".join(chunks) == "深度回答"
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assert captured["kwargs"]["model"] == "pro-model"
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assert captured["kwargs"]["stream"] is True
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class Client:
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stream = lambda self2, *args, **kw: StreamCtx()
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return Client()
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async def fake_client(*args, **kwargs):
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return make_lines()
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monkeypatch.setattr(llm.httpx, "AsyncClient", fake_client)
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monkeypatch.setattr(llm.asyncio, "wait_for", lambda coro, **kw: coro)
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async def collect():
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try:
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async for _ in llm.stream_ollama_events("prompt", tag="err"):
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pass
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except RuntimeError as e:
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return str(e)
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result = asyncio.run(collect())
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assert "model not found" in str(result)
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def test_call_vlm_ocr_passes_image_and_prompt(monkeypatch):
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image_bytes = b"image-bytes"
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called = {}
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monkeypatch.setattr(llm, "get_vlm_ocr_prompt", lambda: "OCR PROMPT")
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def test_call_vlm_ocr_payload_format(monkeypatch):
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captured = {}
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async def fake_chat(**kwargs):
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called["kwargs"] = kwargs
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return {"message": {"content": "ocr result", "thinking": ""}}
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async def fake_post(url, json=None):
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captured["json"] = json
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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class FakeResp:
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def raise_for_status(self): pass
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def json(self): return {"choices": [{"message": {"content": "ocr result"}}]}
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result = asyncio.run(llm.call_vlm_ocr(image_bytes, language="auto"))
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return FakeResp()
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messages = called["kwargs"].get("messages", [])
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assert messages[0]["role"] == "user"
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assert messages[0]["content"] == "OCR PROMPT"
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assert messages[0]["images"] == [image_bytes]
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async def fake_client(*args, **kwargs):
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class Ctx:
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async def __aenter__(self2): return self2
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async def __aexit__(*a): pass
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post = fake_post
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return Ctx()
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monkeypatch.setattr(llm.httpx, "AsyncClient", fake_client)
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monkeypatch.setattr(llm.asyncio, "wait_for", lambda coro, **kw: coro)
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result = asyncio.run(llm.call_vlm_ocr(b"image"))
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assert result == "ocr result"
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def test_call_vlm_ocr_chat_raises_rethrows(monkeypatch):
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image_bytes = b"image-bytes"
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monkeypatch.setattr(llm, "get_vlm_ocr_prompt", lambda: "OCR PROMPT")
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async def fake_chat(**kwargs):
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raise RuntimeError("ocr fail")
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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with pytest.raises(RuntimeError):
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asyncio.run(llm.call_vlm_ocr(image_bytes))
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def test_call_vlm_ocr_returns_content_from_response(monkeypatch):
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image_bytes = b"img"
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monkeypatch.setattr(llm, "get_vlm_ocr_prompt", lambda: "OCR PROMPT")
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async def fake_chat(**kwargs):
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return {"message": {"content": "ocr text", "thinking": ""}}
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monkeypatch.setattr(llm.client, "chat", fake_chat)
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content = asyncio.run(llm.call_vlm_ocr(image_bytes))
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assert content == "ocr text"
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# Verify vision format: image_url content part with base64
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msgs = captured["json"]["messages"]
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assert len(msgs) == 1
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content_parts = msgs[0]["content"]
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image_part = [p for p in content_parts if p.get("type") == "image_url"]
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assert len(image_part) == 1
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Block a user