feat(copilot): enhance prompt system and add Markdown rendering for ghost text
- Rewrite prompt builder with comprehensive rules for seamless text completion - Implement Markdown parsing for ghost text with proper mark handling - Update LLM parameters (temperature 0.7, repeat_penalty, think mode) - Add CSS styles for formatted ghost text elements - Add planning documentation for Copilot prompt system analysis
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“ydy0615”
@@ -1,52 +1,38 @@
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import os
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import json
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import ollama
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from typing import AsyncGenerator
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from dotenv import load_dotenv
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load_dotenv()
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OLLAMA_MODEL = os.getenv('OLLAMA_MODEL', 'gpt-oss:20b')
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OLLAMA_HOST = os.getenv('OLLAMA_HOST', 'http://192.168.0.120:11434')
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if OLLAMA_HOST.endswith('/v1/'):
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OLLAMA_HOST = OLLAMA_HOST[:-4]
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elif OLLAMA_HOST.endswith('/v1'):
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OLLAMA_HOST = OLLAMA_HOST[:-3]
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os.environ['OLLAMA_HOST'] = OLLAMA_HOST
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print(f"[LLM] Ollama host: {OLLAMA_HOST}")
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print(f"[LLM] Model: {OLLAMA_MODEL}")
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client = ollama.AsyncClient(host=OLLAMA_HOST)
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async def stream_openai(prompt: str) -> AsyncGenerator[str, None]:
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print(f"[LLM] Calling Ollama API with prompt length: {len(prompt)}")
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async def call_ollama(prompt: str) -> dict:
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"""
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调用 Ollama API 并返回 content 和 thinking。
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"""
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response = await client.chat(
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model=OLLAMA_MODEL,
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messages=[{'role': 'user', 'content': prompt}],
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stream=False,
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options={
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'temperature': 0.7,
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'repeat_penalty': 1.1,
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},
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think='high'
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)
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try:
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print(f"[LLM] Awaiting client.chat...")
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stream = await client.chat(
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model=OLLAMA_MODEL,
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messages=[{'role': 'user', 'content': prompt}],
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stream=True,
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options={
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'temperature': 0.7,
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'repeat_penalty': 1.1,
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},
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think='high'
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)
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print(f"[LLM] Got stream object, starting iteration...")
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chunk_count = 0
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async for chunk in stream:
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if chunk['message'] and chunk['message']['content']:
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content = chunk['message']['content']
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chunk_count += 1
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print(f"[LLM] Chunk {chunk_count}: {content}")
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yield json.dumps({"content": content})
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print(f"[LLM] Stream complete, total chunks: {chunk_count}")
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except Exception as e:
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error_msg = f"Error: {str(e)}"
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print(f"[LLM] Error: {error_msg}")
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import traceback
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traceback.print_exc()
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yield json.dumps({"error": str(e)})
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content = ""
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thinking = ""
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if hasattr(response, 'message') and response.message:
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content = response.message.content or ""
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thinking = getattr(response.message, 'thinking', '') or ""
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elif isinstance(response, dict):
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msg = response.get('message', {})
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content = msg.get('content', '') or ""
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thinking = msg.get('thinking', '') or ""
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return {"content": content, "thinking": thinking}
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106
backend/main.py
106
backend/main.py
@@ -1,10 +1,11 @@
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from fastapi import FastAPI, HTTPException
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import StreamingResponse, JSONResponse
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from pydantic import BaseModel
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import os
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import json
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import re
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from prompt import build_prompt
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from llm import call_ollama
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app = FastAPI()
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@@ -21,121 +22,26 @@ class CompletionRequest(BaseModel):
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suffix: str
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languageId: str = 'markdown'
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def extract_completion_from_thinking(thinking: str) -> str:
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"""
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从模型的 thinking 输出中提取实际的续写内容。
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移除推理过程，保留实际的续写。
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"""
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if not thinking:
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return ""
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# 尝试找到实际的续写内容
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# 模型通常会在 thinking 中描述上下文，然后输出实际续写
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# 常见的模式是：推理过程以描述开始，然后直接输出续写
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# 查找 "Continuation:" 或类似标记之后的内容
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continuation_match = re.search(r'Continuation[:\s]*([\s\S]*)', thinking, re.IGNORECASE)
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if continuation_match:
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result = continuation_match.group(1).strip()
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# 移除可能的后续推理说明
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result = re.sub(r'\s*It seems like.*$', '', result, flags=re.IGNORECASE)
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return result.strip()
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# 如果没有明确标记，尝试移除描述性内容
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# 查找 "We need to continue" 或类似开头
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continue_match = re.search(r'(?:We need to|Then we should|So we|I will|The|Thus)[,\s]+([A-Z][^.!?]*(?:[.!?]|$))', thinking)
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if continue_match:
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# 取找到的句子及其后续内容
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start_idx = continue_match.start(1)
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result = thinking[start_idx:].strip()
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# 移除 "Probably " 开头及其后续内容
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result = re.sub(r'^Probably\s+', '', result)
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# 如果有 "It seems like" 或类似短语，截断
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result = re.split(r'\s*It seems like\s', result, flags=re.IGNORECASE)[0]
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return result.strip()
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# 最后的策略：直接返回 thinking，移除末尾的推理说明
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result = thinking.strip()
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# 移除 "Probably" 及其后续内容
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result = re.split(r'\s+Probably\s', result, flags=re.IGNORECASE, maxsplit=1)[0]
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# 移除 "The instruction:" 及其后续内容
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result = re.split(r'\s+The instruction:', result, flags=re.IGNORECASE, maxsplit=1)[0]
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return result.strip()
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@app.post("/v1/completions")
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async def create_completion(request: CompletionRequest):
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from prompt import build_prompt
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import ollama
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print(f"[Backend] POST /v1/completions called")
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print(f"[Backend] Received request - prefix length: {len(request.prefix)}, suffix length: {len(request.suffix)}")
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OLLAMA_MODEL = os.getenv('OLLAMA_MODEL', 'gpt-oss:20b')
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OLLAMA_HOST = os.getenv('OLLAMA_HOST', 'http://192.168.0.120:11434')
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print(f"[LLM] Using host: {OLLAMA_HOST}, model: {OLLAMA_MODEL}")
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try:
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prompt = build_prompt(request.prefix, request.suffix)
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print(f"[Backend] Built prompt (first 100 chars): {prompt[:100]}...")
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print(f"[LLM] Full prompt:\n{prompt}\n")
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result = await call_ollama(prompt)
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# 使用非流式 API 获取完整响应
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print(f"[LLM] Calling Ollama API (non-streaming)...")
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client = ollama.AsyncClient(host=OLLAMA_HOST)
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response = await client.chat(
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model=OLLAMA_MODEL,
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messages=[{'role': 'user', 'content': prompt}],
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stream=False,
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options={
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'temperature': 0.2,
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}
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)
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content = result["content"]
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print(f"[LLM] Response type: {type(response)}")
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# 提取 content 和 thinking
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content = ""
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thinking = ""
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if hasattr(response, 'message') and response.message:
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content = response.message.content or ""
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thinking = getattr(response.message, 'thinking', '') or ""
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elif isinstance(response, dict):
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msg = response.get('message', {})
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content = msg.get('content', '') or ""
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thinking = msg.get('thinking', '') or ""
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print(f"[LLM] Original content: {repr(content[:100] if content else '')}...")
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print(f"[LLM] Thinking length: {len(thinking)}")
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print(f"[LLM] Thinking (first 200): {thinking[:200]}...")
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# 如果 content 为空，尝试从 thinking 中提取
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if not content and thinking:
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print(f"[LLM] Content is empty, extracting from thinking...")
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content = extract_completion_from_thinking(thinking)
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print(f"[LLM] Extracted completion: {repr(content[:100])}...")
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print(f"[LLM] Final content length: {len(content)}")
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# 返回完整内容
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async def generate():
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if content:
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print(f"[LLM] Yielding full content: {repr(content)}")
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yield f"data: {json.dumps({'content': content})}\n\n"
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yield f"data: {json.dumps({'done': True})}\n\n"
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return StreamingResponse(generate(), media_type="text/event-stream")
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except Exception as e:
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error_msg = f"{{\"error\": \"{str(e)}\"}}"
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print(f"[Backend] Error: {e}")
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import traceback
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traceback.print_exc()
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return JSONResponse(content={"error": str(e)}, status_code=500)
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if __name__ == "__main__":
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import uvicorn
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print("[Backend] Starting server on http://0.0.0.0:8000")
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uvicorn.run(app, host="0.0.0.0", port=8000)
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77
plans/refactor-backend.md
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77
plans/refactor-backend.md
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# 重构计划：统一 backend/llm.py 和 backend/main.py
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## 目标
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消除 `llm.py` 和 `main.py` 之间的代码冗余，建立清晰的职责分离。
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## 当前问题
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```mermaid
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graph LR
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A[llm.py] -->|流式调用| B[Ollama API]
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C[main.py] -->|非流式调用| B
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A -.->|未被使用| D[❌ 冗余]
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```
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## 重构后架构
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```mermaid
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graph LR
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A[main.py] -->|导入调用| B[llm.py]
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B -->|非流式调用| C[Ollama API]
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A --> D[FastAPI 路由处理]
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```
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## 具体步骤
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### 步骤 1：重构 llm.py
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将 `stream_openai` 函数改为非流式调用，参考 main.py 的实现：
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```python
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# 新的 llm.py 结构
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async def call_ollama(prompt: str) -> dict:
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# 非流式调用
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# 返回 {"content": str, "thinking": str}
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```
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关键改动：
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- 移除 `AsyncGenerator` 类型，改为返回 `dict`
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- 设置 `stream=False`
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- 使用 `temperature=0.2`（与 main.py 一致）
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- 返回 content 和 thinking 字段
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### 步骤 2：重构 main.py
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导入并使用 llm.py：
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```python
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# main.py 改动
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from llm import call_ollama
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@app.post("/v1/completions")
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async def create_completion(request: CompletionRequest):
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prompt = build_prompt(request.prefix, request.suffix)
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result = await call_ollama(prompt)
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# 使用 result["content"] 和 result["thinking"]
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```
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删除的代码：
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- 直接导入 `ollama` 的代码
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- 重复创建 `AsyncClient` 的代码
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- 重复的 API 调用逻辑
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- 重复的环境变量读取
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### 步骤 3：清理冗余
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- 移除 llm.py 中不再需要的 `AsyncGenerator` 导入
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- 移除 main.py 中重复的环境变量定义
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- 确保调试日志保留但不过度
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## 文件职责划分
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| 文件 | 职责 |
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|------|------|
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| `llm.py` | Ollama API 调用封装、模型配置 |
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| `main.py` | FastAPI 路由、请求解析、响应格式化 |
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| `prompt.py` | Prompt 构建逻辑 |
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