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”
+263
-30
@@ -1,5 +1,6 @@
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import asyncio
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import importlib
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import json
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import sys
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from pathlib import Path
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@@ -16,47 +17,279 @@ except ModuleNotFoundError:
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pytest.skip("llm module dependencies are not available", allow_module_level=True)
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def test_call_ollama_messages_roles_with_system(monkeypatch):
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def test_extract_message_openai_format():
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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 == "reasoning"
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def test_extract_message_openai_reasoning_content():
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resp = {"choices": [{"message": {"content": "answer", "reasoning_content": "deep thought"}}]}
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content, thinking = llm._extract_message(resp)
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assert content == "answer"
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assert thinking == "deep thought"
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def test_extract_message_empty_choices():
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resp = {"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_no_choices_key():
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resp = {}
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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_none_content():
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resp = {"choices": [{"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_delta_text():
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chunk = {"choices": [{"delta": {"content": "hello"}}]}
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assert llm._extract_delta_text(chunk) == "hello"
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def test_extract_delta_text_empty():
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chunk = {"choices": [{"delta": {}}]}
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assert llm._extract_delta_text(chunk) == ""
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def test_extract_delta_thinking():
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chunk = {"choices": [{"delta": {"thinking": "reasoning step"}}]}
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assert llm._extract_delta_thinking(chunk) == "reasoning step"
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def test_extract_delta_reasoning_content():
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chunk = {"choices": [{"delta": {"reasoning_content": "deep thought"}}]}
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assert llm._extract_delta_thinking(chunk) == "deep thought"
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def test_resolve_model_name_explicit():
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assert llm._resolve_model_name("custom-model") == "custom-model"
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def test_resolve_model_name_default():
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assert llm._resolve_model_name() == llm.LLM_MODEL
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def test_resolve_model_name_pro():
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assert llm._resolve_model_name(use_pro_model=True) == llm.PRO_LLM_MODEL
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def test_resolve_system_prompt():
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assert llm._resolve_system_prompt(" system prompt ") == "system prompt"
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assert llm._resolve_system_prompt("") == ""
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assert llm._resolve_system_prompt(None) == ""
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def test_build_chat_payload_with_system():
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payload = llm._build_chat_payload(
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"user prompt", system_prompt="sys prompt", temperature=0.5, model="test-model"
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)
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assert payload["model"] == "test-model"
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assert len(payload["messages"]) == 2
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assert payload["messages"][0]["role"] == "system"
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assert payload["messages"][1]["role"] == "user"
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assert payload["stream"] is False
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def test_build_chat_payload_no_system():
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payload = llm._build_chat_payload("user prompt", system_prompt=None)
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assert len(payload["messages"]) == 1
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assert payload["stream"] is False
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def test_build_chat_payload_with_thinking():
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payload = llm._build_chat_payload("prompt", thinking="low")
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assert "options" in payload
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assert payload["options"]["think"] == "low"
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def test_build_chat_stream_payload():
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payload = llm._build_chat_stream_payload("prompt", system_prompt="sys")
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assert payload["stream"] is True
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assert len(payload["messages"]) == 2
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def test_build_chat_stream_payload_with_thinking():
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payload = llm._build_chat_stream_payload("prompt", thinking="high")
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assert "options" in payload
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assert payload["options"]["think"] == "high"
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def test_call_ollama_non_streaming(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["url"] = url
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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": "done"}}]}
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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(
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"user prompt body",
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system_prompt="system prompt body",
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tag="test",
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temperature=0.1,
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)
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llm.call_ollama("test prompt", system_prompt="sys", tag="t1")
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)
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assert result["content"] == "ok"
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assert captured["kwargs"]["prompt"] == "system prompt body\n\nuser prompt body"
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assert captured["kwargs"]["raw"] is True
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assert result["content"] == "done"
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assert captured["url"] == "/chat/completions"
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assert captured["json"]["stream"] is False
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def test_call_ollama_messages_roles_without_system(monkeypatch):
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def test_stream_ollama_text_deltas(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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def make_lines():
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lines_iter = iter([
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'data: {"choices": [{"delta": {"content": "hel"}}]}',
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'data: {"choices": [{"delta": {"content": "lo"}}]}',
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"data: [DONE]",
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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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result = asyncio.run(
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llm.call_ollama(
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"user prompt only",
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system_prompt="",
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tag="test-no-system",
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temperature=0.1,
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)
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)
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class Response:
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def __init__(self2): self2._lines = LineIterator()
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assert result["content"] == "ok"
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assert captured["kwargs"]["prompt"] == "user prompt only"
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assert captured["kwargs"]["raw"] is True
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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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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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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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captured["called"] = True
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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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results = []
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async def collect():
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async for delta in llm.stream_ollama("prompt", tag="t1"):
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results.append(delta)
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asyncio.run(collect())
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assert captured.get("called") is True
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assert results == ["hel", "lo"]
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def test_stream_ollama_events_thinking_and_content(monkeypatch):
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captured = {}
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def make_lines():
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lines_iter = iter([
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'data: {"choices": [{"delta": {"thinking": "reasoning"}}]}',
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'data: {"choices": [{"delta": {"content": "answer"}}]}',
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"data: [DONE]",
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])
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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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class Response:
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def __init__(self2): self2._lines = LineIterator()
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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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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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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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captured["called"] = True
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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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results = []
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async def collect():
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async for event_type, payload in llm.stream_ollama_events("prompt", tag="t1"):
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results.append((event_type, payload))
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asyncio.run(collect())
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assert captured.get("called") is True
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# First event should be thinking, then content
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assert results[0] == ("thinking", "")
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assert results[1][0] == "content"
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def test_call_vlm_ocr(monkeypatch):
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captured = {}
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async def fake_post(url, json=None):
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captured["url"] = url
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captured["json"] = json
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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 text"}}]}
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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(llm.call_vlm_ocr(b"fake image bytes"))
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assert result == "ocr text"
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# Verify the payload uses OpenAI vision format (image_url)
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assert captured["url"] == "/chat/completions"
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messages = captured["json"]["messages"]
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assert len(messages) == 1
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content_parts = messages[0]["content"]
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# Should have text part and image_url part
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assert any(p.get("type") == "text" for p in content_parts)
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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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assert image_part[0]["image_url"]["url"].startswith("data:image/png;base64,")
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