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Stabilize pro editing without heavy office runtime

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The workspace now carries the pro editing flow, streaming completion path, and lighter Office preview state as one checkpoint so the remote has the current runnable project shape.

Constraint: Preserve the current workspace as a single reviewable project commit while excluding local agent state and verification artifacts. Removed stale Univer runtime dependencies from the lockfile so installs match package.json.

Rejected: Commit runtime screenshots, .omx state, and coverage files | they are local artifacts rather than source state.

Confidence: medium

Scope-risk: broad

Directive: Keep package.json and package-lock.json synchronized when changing frontend dependencies.

Tested: npm run build; C:\Users\ydy\.conda\envs\llmwebsite\python.exe -m pytest backend/tests/test_main_endpoints.py backend/tests/test_main_cancel.py backend/tests/test_llm.py backend/tests/test_llm_extended.py -v -o addopts= (44 passed).

Not-tested: Full pytest with repository coverage addopts currently reports 0% coverage because pytest-cov watches backend.* module names while tests import top-level backend modules.

Co-authored-by: OmX <omx@oh-my-codex.dev>
This commit is contained in:
ydy0615
2026-05-24 23:30:32 +08:00
parent 6dc9933853
commit 59334e4057
41 changed files with 4438 additions and 4875 deletions
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backend/llm.py
+171 -19
View File
@@ -3,6 +3,7 @@ import time
import logging
import asyncio
from datetime import datetime
from typing import AsyncIterator
import ollama
from dotenv import load_dotenv
@@ -11,6 +12,7 @@ from prompts import get_vlm_ocr_prompt
load_dotenv()
OLLAMA_MODEL = os.getenv('OLLAMA_MODEL', 'gpt-oss:20b')
PRO_OLLAMA_MODEL = os.getenv('PRO_OLLAMA_MODEL', OLLAMA_MODEL)
OLLAMA_HOST = os.getenv('OLLAMA_HOST', 'http://localhost:11434')
VLM_MODEL = os.getenv('VLM_MODEL', 'qwen3-vl:30b')
@@ -29,14 +31,73 @@ def _extract_message(response) -> tuple[str, str]:
if hasattr(response, 'message') and response.message:
content = response.message.content or ""
thinking = getattr(response.message, 'thinking', '') or ""
elif isinstance(response, dict):
elif isinstance(response, dict) and 'message' in response:
msg = response.get('message', {})
content = msg.get('content', '') or ""
thinking = msg.get('thinking', '') or ""
# fallback for generate
if not content:
if hasattr(response, 'response'):
content = getattr(response, 'response', '') or ""
elif isinstance(response, dict) and 'response' in response:
content = response.get('response', '') or ""
return content, thinking
def _build_prompt(prompt: str, system_prompt: str | None = None) -> str:
if system_prompt and system_prompt.strip():
return f"{system_prompt}\n\n{prompt}"
return prompt
def _resolve_model_name(model: str | None = None, *, use_pro_model: bool = False) -> str:
candidate = (model or '').strip()
if candidate:
return candidate
return PRO_OLLAMA_MODEL if use_pro_model else OLLAMA_MODEL
def _build_generate_kwargs(
prompt: str,
*,
system_prompt: str | None = None,
temperature: float = 0.7,
thinking: str | None = None,
model: str | None = None,
use_pro_model: bool = False,
stream: bool = False,
) -> dict:
kwargs = {
"model": _resolve_model_name(model, use_pro_model=use_pro_model),
"prompt": _build_prompt(prompt, system_prompt),
"stream": stream,
"raw": True,
"options": {
'temperature': temperature,
'repeat_penalty': 1.1,
},
}
if thinking:
kwargs["think"] = thinking
return kwargs
def _extract_stream_text(chunk) -> str:
content, _ = _extract_message(chunk)
if content:
return content
if isinstance(chunk, dict):
return chunk.get('response', '') or ''
if hasattr(chunk, 'response'):
return getattr(chunk, 'response', '') or ''
return ''
async def call_ollama(
prompt: str,
*,
@@ -44,16 +105,19 @@ async def call_ollama(
tag: str = "default",
temperature: float = 0.7,
thinking: str | None = None,
model: str | None = None,
use_pro_model: bool = False,
) -> dict:
"""
调用 Ollama API 并返回 content 和 thinking。
"""
start = time.perf_counter()
start_dt = datetime.now()
model_name = _resolve_model_name(model, use_pro_model=use_pro_model)
logger.info(
"[LLM][%s] request model=%s host=%s prompt_chars=%d system_chars=%d temp=%.2f thinking=%s",
tag,
OLLAMA_MODEL,
model_name,
OLLAMA_HOST,
len(prompt),
len(system_prompt or ""),
@@ -62,24 +126,17 @@ async def call_ollama(
)
try:
messages = []
if system_prompt and system_prompt.strip():
messages.append({"role": "system", "content": system_prompt})
messages.append({"role": "user", "content": prompt})
kwargs = _build_generate_kwargs(
prompt,
system_prompt=system_prompt,
temperature=temperature,
thinking=thinking,
model=model,
use_pro_model=use_pro_model,
stream=False,
)
kwargs = {
"model": OLLAMA_MODEL,
"messages": messages,
"stream": False,
"options": {
'temperature': temperature,
'repeat_penalty': 1.1,
},
}
if thinking:
kwargs["think"] = thinking
response = await asyncio.wait_for(client.chat(**kwargs), timeout=COMPLETION_TIMEOUT)
response = await asyncio.wait_for(client.generate(**kwargs), timeout=COMPLETION_TIMEOUT)
except asyncio.CancelledError:
elapsed_ms = (time.perf_counter() - start) * 1000
end_dt = datetime.now()
@@ -126,6 +183,101 @@ async def call_ollama(
return {"content": content, "think": thinking}
async def stream_ollama(
prompt: str,
*,
system_prompt: str | None = None,
tag: str = "default-stream",
temperature: float = 0.7,
thinking: str | None = None,
model: str | None = None,
use_pro_model: bool = False,
) -> AsyncIterator[str]:
start = time.perf_counter()
start_dt = datetime.now()
model_name = _resolve_model_name(model, use_pro_model=use_pro_model)
yielded_chars = 0
logger.info(
"[LLM][%s] stream request model=%s host=%s prompt_chars=%d system_chars=%d temp=%.2f thinking=%s",
tag,
model_name,
OLLAMA_HOST,
len(prompt),
len(system_prompt or ""),
temperature,
thinking,
)
try:
kwargs = _build_generate_kwargs(
prompt,
system_prompt=system_prompt,
temperature=temperature,
thinking=thinking,
model=model,
use_pro_model=use_pro_model,
stream=True,
)
stream = await client.generate(**kwargs)
iterator = stream.__aiter__()
deadline = time.perf_counter() + COMPLETION_TIMEOUT
while True:
remaining = deadline - time.perf_counter()
if remaining <= 0:
raise TimeoutError("LLM stream timed out")
try:
chunk = await asyncio.wait_for(iterator.__anext__(), timeout=remaining)
except StopAsyncIteration:
break
text = _extract_stream_text(chunk)
if not text:
continue
yielded_chars += len(text)
yield text
except asyncio.CancelledError:
elapsed_ms = (time.perf_counter() - start) * 1000
end_dt = datetime.now()
logger.info(
"[LLM][%s] stream_time [%s --> %s]",
tag,
start_dt.strftime("%H:%M:%S"),
end_dt.strftime("%H:%M:%S"),
)
logger.warning("[LLM][%s] stream cancelled after %.1fms", tag, elapsed_ms)
raise
except Exception:
elapsed_ms = (time.perf_counter() - start) * 1000
end_dt = datetime.now()
logger.info(
"[LLM][%s] stream_time [%s --> %s]",
tag,
start_dt.strftime("%H:%M:%S"),
end_dt.strftime("%H:%M:%S"),
)
logger.exception("[LLM][%s] stream failed after %.1fms", tag, elapsed_ms)
raise
elapsed_ms = (time.perf_counter() - start) * 1000
end_dt = datetime.now()
logger.info(
"[LLM][%s] stream_time [%s --> %s]",
tag,
start_dt.strftime("%H:%M:%S"),
end_dt.strftime("%H:%M:%S"),
)
logger.info(
"[LLM][%s] stream finished in %.1fms yielded_chars=%d",
tag,
elapsed_ms,
yielded_chars,
)
async def call_vlm_ocr(image_bytes: bytes, language: str = 'auto') -> str:
start = time.perf_counter()
start_dt = datetime.now()