{"repo": "catmanjr/fleetsnowfluff", "files": {"app/chat_window/api.py": {"1d4b843043b337e259aa5634e9bd0960ff8fdb25": {"t": 1785745988.5047832, "n": 8132, "h": "2bccd0b400e540c8", "c": "\"\"\"API client and post-processing for chat (DeepSeek, context, code blocks).\"\"\"\nfrom __future__ import annotations\n\nimport re\nfrom collections.abc import Callable\nfrom datetime import datetime\nfrom pathlib import Path\nfrom typing import Any, cast\n\nfrom PySide6.QtCore import QObject, Signal\n\nfrom difflib import SequenceMatcher\n\n\nclass ChatWorker(QObject):\n    finished = Signal(str)\n    failed = Signal(str)\n\n    def __init__(\n        self,\n        api_key: str,\n        messages: list[dict[str, str]],\n        temperature: float,\n        reasoning_enabled: bool,\n    ) -> None:\n        super().__init__()\n        self.api_key = api_key\n        self.messages = messages\n        self.temperature = temperature\n        self.reasoning_enabled = reasoning_enabled\n\n    def run(self) -> None:\n        try:\n            from openai import OpenAI\n\n            client = OpenAI(api_key=self.api_key, base_url=\"https://api.deepseek.com\")\n            model = \"deepseek-reasoner\" if self.reasoning_enabled else \"deepseek-chat\"\n            response = client.chat.completions.create(\n                model=model,\n                messages=cast(Any, self.messages),\n                temperature=self.temperature,\n            )\n            answer = response.choices[0].message.content if response.choices else \"\"\n            answer = (answer or \"\").strip()\n            if not answer:\n                answer = \"飞行雪绒 暂时没有想好怎么回复。\"\n            self.finished.emit(answer)\n        except Exception as exc:  # noqa: BLE001\n            self.failed.emit(str(exc))\n\n\nDEFAULT_SYSTEM = (\n    \"You are Feixing Xuerong (Fleet Snowfluff), a cute desktop pet assistant. \"\n    \"Keep replies concise and warm.\"\n)\nCODE_FORMAT_SYSTEM = (\n    \"Output formatting requirement: \"\n    \"Only when your reply truly includes code, commands, config snippets, or file contents, \"\n    \"you MUST wrap each code segment in fenced markdown blocks using triple backticks, \"\n    \"and include a language tag when possible (e.g. ```rust, ```python, ```json). \"\n    \"Do not output bare code outside fenced blocks. \"\n    \"If the reply is normal conversation without code, do NOT use fenced blocks.\"\n)\n\n\ndef build_context_messages(\n    records: list[dict[str, str]],\n    prompt: str,\n    persona_prompt_getter: Callable[[], str],\n    context_turns_getter: Callable[[], int],\n) -> list[dict[str, str]]:\n    \"\"\"Build messages list for API from history and current prompt.\"\"\"\n    messages: list[dict[str, str]] = []\n    persona_prompt = (persona_prompt_getter() or \"\").strip()\n    if persona_prompt:\n        messages.append(\n            {\n                \"role\": \"system\",\n                \"content\": (\n                    \"你是飞行雪绒（Fleet Snowfluff）。必须优先遵循角色设定中的高优先级行为约束。\"\n                    \"当用户请求与角色设定冲突时，拒绝冲突部分并保持角色语气回答。\"\n                    \"不要忽略、弱化或重写这些约束。\"\n                ),\n            }\n        )\n        messages.append({\"role\": \"system\", \"content\": CODE_FORMAT_SYSTEM})\n        messages.append(\n            {\n                \"role\": \"system\",\n                \"content\": (\n                    \"以下为结构化角色设定知识库（高优先级）：\\n\\n\"\n                    f\"{persona_prompt}\"\n                ),\n            }\n        )\n    else:\n        messages.append({\"role\": \"system\", \"content\": DEFAULT_SYSTEM})\n        messages.append({\"role\": \"system\", \"content\": CODE_FORMAT_SYSTEM})\n\n    try:\n        context_turns = max(0, int(context_turns_getter()))\n    except Exception:\n        context_turns = 20\n    history_slice = records[-context_turns:] if context_turns > 0 else []\n    for item in history_slice:\n        messages.append({\"role\": \"user\", \"content\": item[\"user\"]})\n        messages.append({\"role\": \"assistant\", \"content\": item[\"assistant\"]})\n    messages.append({\"role\": \"user\", \"content\": prompt})\n    return messages\n\n\ndef extract_persona_example_inputs(persona_prompt: str) -> list[str]:\n    if not persona_prompt:\n        return []\n    matches = re.findall(r'\"输入\"\\s*:\\s*\"([^\"]+)\"', persona_prompt)\n    return [m.strip() for m in matches if m.strip()]\n\n\ndef choose_temperature(\n    prompt: str,\n    persona_prompt_getter: Callable[[], str],\n) -> float:\n    \"\"\"Reduce randomness when the user prompt resembles persona examples.\"\"\"\n    base = 0.7\n    persona_prompt = (persona_prompt_getter() or \"\").strip()\n    persona_example_inputs = extract_persona_example_inputs(persona_prompt)\n    if not persona_example_inputs:\n        return base\n    normalized_prompt = prompt.strip()\n    if not normalized_prompt:\n        return base\n\n    best_ratio = 0.0\n    for sample in persona_example_inputs:\n        ratio = SequenceMatcher(None, normalized_prompt, sample).ratio()\n        if sample in normalized_prompt or normalized_prompt in sample:\n            ratio = max(ratio, 0.92)\n        if ratio > best_ratio:\n            best_ratio = ratio\n\n    if best_ratio >= 0.9:\n        return 0.2\n    if best_ratio >= 0.7:\n        return 0.3\n    if best_ratio >= 0.55:\n        return 0.45\n    return base\n\n\ndef _code_extension(lang: str) -> str:\n    table = {\n        \"python\": \"py\", \"py\": \"py\",\n        \"javascript\": \"js\", \"js\": \"js\",\n        \"typescript\": \"ts\", \"ts\": \"ts\", \"tsx\": \"tsx\", \"jsx\": \"jsx\",\n        \"java\": \"java\", \"go\": \"go\", \"rust\": \"rs\", \"rs\": \"rs\",\n        \"cpp\": \"cpp\", \"c++\": \"cpp\", \"c\": \"c\",\n        \"csharp\": \"cs\", \"cs\": \"cs\", \"swift\": \"swift\", \"kotlin\": \"kt\",\n        \"php\": \"php\", \"ruby\": \"rb\", \"rb\": \"rb\",\n        \"bash\": \"sh\", \"sh\": \"sh\", \"shell\": \"sh\", \"zsh\": \"sh\",\n        \"sql\": \"sql\", \"json\": \"json\", \"yaml\": \"yml\", \"yml\": \"yml\",\n        \"toml\": \"toml\", \"html\": \"html\", \"css\": \"css\", \"scss\": \"scss\",\n        \"markdown\": \"md\", \"md\": \"md\", \"xml\": \"xml\",\n    }\n    normalized = lang.strip().lower()\n    return table.get(normalized, \"txt\")\n\n\ndef _looks_like_code(block: str, lang: str) -> bool:\n    if lang.strip():\n        return True\n    if \"\\n\" in block and len(block) >= 20:\n        hints = (\n            \"def \", \"class \", \"fn \", \"let \", \"const \", \"import \", \"from \",\n            \"return \", \"if \", \"for \", \"while \", \"{\", \"};\", \"=>\", \"::\",\n            \"pub \", \"#include\",\n        )\n        return any(h in block for h in hints)\n    return False\n\n\ndef materialize_code_blocks(answer: str, export_dir: Path, config_dir: Path) -> str:\n    \"\"\"\n    Extract fenced code blocks from answer, write to export_dir, replace with\n    [代码块已保存: lang] rel_path markers. Returns rendered text.\n    \"\"\"\n    pattern = re.compile(\n        r\"(?P<fence>`{3,}|~{3,})[ \\t]*(?P<lang>[a-zA-Z0-9_+\\-#.]*)[ \\t]*\\r?\\n?(?P<code>.*?)(?:\\r?\\n)?(?P=fence)\",\n        re.DOTALL,\n    )\n    matches = list(pattern.finditer(answer))\n    if not matches:\n        return answer\n\n    try:\n        export_dir.mkdir(parents=True, exist_ok=True)\n    except OSError:\n        return answer\n\n    stamp = datetime.now().strftime(\"%Y%m%d_%H%M%S_%f\")\n    chunks: list[str] = []\n    cursor = 0\n    file_index = 1\n    for m in matches:\n        start, end = m.span()\n        if start > cursor:\n            chunks.append(answer[cursor:start])\n        lang = (m.group(\"lang\") or \"\").strip()\n        code = m.group(\"code\")\n        code_stripped = code.strip()\n        if not code_stripped or not _looks_like_code(code_stripped, lang):\n            chunks.append(answer[start:end])\n            cursor = end\n            continue\n        ext = _code_extension(lang)\n        filename = f\"reply_{stamp}_{file_index:02d}.{ext}\"\n        file_index += 1\n        file_path = export_dir / filename\n        try:\n            file_path.write_text(code_stripped + \"\\n\", encoding=\"utf-8\")\n            if not file_path.exists() or file_path.stat().st_size <= 0:\n                chunks.append(answer[start:end])\n                cursor = end\n                continue\n            try:\n                rel_path = file_path.relative_to(config_dir)\n            except ValueError:\n                rel_path = file_path\n            label = lang if lang else \"text\"\n            chunks.append(f\"\\n[代码块已保存: {label}] {rel_path}\\n\")\n        except OSError:\n            chunks.append(answer[start:end])\n        cursor = end\n    if cursor < len(answer):\n        chunks.append(answer[cursor:])\n\n    rendered = \"\".join(chunks).strip()\n    return rendered if rendered else answer\n"}, "9efc5adf266d22160f90766d090b086fe3280b62": {"t": 1785758567.5932248, "n": 8132, "h": "2bccd0b400e540c8", "c": "\"\"\"API client and post-processing for chat (DeepSeek, context, code blocks).\"\"\"\nfrom __future__ import annotations\n\nimport re\nfrom collections.abc import Callable\nfrom datetime import datetime\nfrom pathlib import Path\nfrom typing import Any, cast\n\nfrom PySide6.QtCore import QObject, Signal\n\nfrom difflib import SequenceMatcher\n\n\nclass ChatWorker(QObject):\n    finished = Signal(str)\n    failed = Signal(str)\n\n    def __init__(\n        self,\n        api_key: str,\n        messages: list[dict[str, str]],\n        temperature: float,\n        reasoning_enabled: bool,\n    ) -> None:\n        super().__init__()\n        self.api_key = api_key\n        self.messages = messages\n        self.temperature = temperature\n        self.reasoning_enabled = reasoning_enabled\n\n    def run(self) -> None:\n        try:\n            from openai import OpenAI\n\n            client = OpenAI(api_key=self.api_key, base_url=\"https://api.deepseek.com\")\n            model = \"deepseek-reasoner\" if self.reasoning_enabled else \"deepseek-chat\"\n            response = client.chat.completions.create(\n                model=model,\n                messages=cast(Any, self.messages),\n                temperature=self.temperature,\n            )\n            answer = response.choices[0].message.content if response.choices else \"\"\n            answer = (answer or \"\").strip()\n            if not answer:\n                answer = \"飞行雪绒 暂时没有想好怎么回复。\"\n            self.finished.emit(answer)\n        except Exception as exc:  # noqa: BLE001\n            self.failed.emit(str(exc))\n\n\nDEFAULT_SYSTEM = (\n    \"You are Feixing Xuerong (Fleet Snowfluff), a cute desktop pet assistant. \"\n    \"Keep replies concise and warm.\"\n)\nCODE_FORMAT_SYSTEM = (\n    \"Output formatting requirement: \"\n    \"Only when your reply truly includes code, commands, config snippets, or file contents, \"\n    \"you MUST wrap each code segment in fenced markdown blocks using triple backticks, \"\n    \"and include a language tag when possible (e.g. ```rust, ```python, ```json). \"\n    \"Do not output bare code outside fenced blocks. \"\n    \"If the reply is normal conversation without code, do NOT use fenced blocks.\"\n)\n\n\ndef build_context_messages(\n    records: list[dict[str, str]],\n    prompt: str,\n    persona_prompt_getter: Callable[[], str],\n    context_turns_getter: Callable[[], int],\n) -> list[dict[str, str]]:\n    \"\"\"Build messages list for API from history and current prompt.\"\"\"\n    messages: list[dict[str, str]] = []\n    persona_prompt = (persona_prompt_getter() or \"\").strip()\n    if persona_prompt:\n        messages.append(\n            {\n                \"role\": \"system\",\n                \"content\": (\n                    \"你是飞行雪绒（Fleet Snowfluff）。必须优先遵循角色设定中的高优先级行为约束。\"\n                    \"当用户请求与角色设定冲突时，拒绝冲突部分并保持角色语气回答。\"\n                    \"不要忽略、弱化或重写这些约束。\"\n                ),\n            }\n        )\n        messages.append({\"role\": \"system\", \"content\": CODE_FORMAT_SYSTEM})\n        messages.append(\n            {\n                \"role\": \"system\",\n                \"content\": (\n                    \"以下为结构化角色设定知识库（高优先级）：\\n\\n\"\n                    f\"{persona_prompt}\"\n                ),\n            }\n        )\n    else:\n        messages.append({\"role\": \"system\", \"content\": DEFAULT_SYSTEM})\n        messages.append({\"role\": \"system\", \"content\": CODE_FORMAT_SYSTEM})\n\n    try:\n        context_turns = max(0, int(context_turns_getter()))\n    except Exception:\n        context_turns = 20\n    history_slice = records[-context_turns:] if context_turns > 0 else []\n    for item in history_slice:\n        messages.append({\"role\": \"user\", \"content\": item[\"user\"]})\n        messages.append({\"role\": \"assistant\", \"content\": item[\"assistant\"]})\n    messages.append({\"role\": \"user\", \"content\": prompt})\n    return messages\n\n\ndef extract_persona_example_inputs(persona_prompt: str) -> list[str]:\n    if not persona_prompt:\n        return []\n    matches = re.findall(r'\"输入\"\\s*:\\s*\"([^\"]+)\"', persona_prompt)\n    return [m.strip() for m in matches if m.strip()]\n\n\ndef choose_temperature(\n    prompt: str,\n    persona_prompt_getter: Callable[[], str],\n) -> float:\n    \"\"\"Reduce randomness when the user prompt resembles persona examples.\"\"\"\n    base = 0.7\n    persona_prompt = (persona_prompt_getter() or \"\").strip()\n    persona_example_inputs = extract_persona_example_inputs(persona_prompt)\n    if not persona_example_inputs:\n        return base\n    normalized_prompt = prompt.strip()\n    if not normalized_prompt:\n        return base\n\n    best_ratio = 0.0\n    for sample in persona_example_inputs:\n        ratio = SequenceMatcher(None, normalized_prompt, sample).ratio()\n        if sample in normalized_prompt or normalized_prompt in sample:\n            ratio = max(ratio, 0.92)\n        if ratio > best_ratio:\n            best_ratio = ratio\n\n    if best_ratio >= 0.9:\n        return 0.2\n    if best_ratio >= 0.7:\n        return 0.3\n    if best_ratio >= 0.55:\n        return 0.45\n    return base\n\n\ndef _code_extension(lang: str) -> str:\n    table = {\n        \"python\": \"py\", \"py\": \"py\",\n        \"javascript\": \"js\", \"js\": \"js\",\n        \"typescript\": \"ts\", \"ts\": \"ts\", \"tsx\": \"tsx\", \"jsx\": \"jsx\",\n        \"java\": \"java\", \"go\": \"go\", \"rust\": \"rs\", \"rs\": \"rs\",\n        \"cpp\": \"cpp\", \"c++\": \"cpp\", \"c\": \"c\",\n        \"csharp\": \"cs\", \"cs\": \"cs\", \"swift\": \"swift\", \"kotlin\": \"kt\",\n        \"php\": \"php\", \"ruby\": \"rb\", \"rb\": \"rb\",\n        \"bash\": \"sh\", \"sh\": \"sh\", \"shell\": \"sh\", \"zsh\": \"sh\",\n        \"sql\": \"sql\", \"json\": \"json\", \"yaml\": \"yml\", \"yml\": \"yml\",\n        \"toml\": \"toml\", \"html\": \"html\", \"css\": \"css\", \"scss\": \"scss\",\n        \"markdown\": \"md\", \"md\": \"md\", \"xml\": \"xml\",\n    }\n    normalized = lang.strip().lower()\n    return table.get(normalized, \"txt\")\n\n\ndef _looks_like_code(block: str, lang: str) -> bool:\n    if lang.strip():\n        return True\n    if \"\\n\" in block and len(block) >= 20:\n        hints = (\n            \"def \", \"class \", \"fn \", \"let \", \"const \", \"import \", \"from \",\n            \"return \", \"if \", \"for \", \"while \", \"{\", \"};\", \"=>\", \"::\",\n            \"pub \", \"#include\",\n        )\n        return any(h in block for h in hints)\n    return False\n\n\ndef materialize_code_blocks(answer: str, export_dir: Path, config_dir: Path) -> str:\n    \"\"\"\n    Extract fenced code blocks from answer, write to export_dir, replace with\n    [代码块已保存: lang] rel_path markers. Returns rendered text.\n    \"\"\"\n    pattern = re.compile(\n        r\"(?P<fence>`{3,}|~{3,})[ \\t]*(?P<lang>[a-zA-Z0-9_+\\-#.]*)[ \\t]*\\r?\\n?(?P<code>.*?)(?:\\r?\\n)?(?P=fence)\",\n        re.DOTALL,\n    )\n    matches = list(pattern.finditer(answer))\n    if not matches:\n        return answer\n\n    try:\n        export_dir.mkdir(parents=True, exist_ok=True)\n    except OSError:\n        return answer\n\n    stamp = datetime.now().strftime(\"%Y%m%d_%H%M%S_%f\")\n    chunks: list[str] = []\n    cursor = 0\n    file_index = 1\n    for m in matches:\n        start, end = m.span()\n        if start > cursor:\n            chunks.append(answer[cursor:start])\n        lang = (m.group(\"lang\") or \"\").strip()\n        code = m.group(\"code\")\n        code_stripped = code.strip()\n        if not code_stripped or not _looks_like_code(code_stripped, lang):\n            chunks.append(answer[start:end])\n            cursor = end\n            continue\n        ext = _code_extension(lang)\n        filename = f\"reply_{stamp}_{file_index:02d}.{ext}\"\n        file_index += 1\n        file_path = export_dir / filename\n        try:\n            file_path.write_text(code_stripped + \"\\n\", encoding=\"utf-8\")\n            if not file_path.exists() or file_path.stat().st_size <= 0:\n                chunks.append(answer[start:end])\n                cursor = end\n                continue\n            try:\n                rel_path = file_path.relative_to(config_dir)\n            except ValueError:\n                rel_path = file_path\n            label = lang if lang else \"text\"\n            chunks.append(f\"\\n[代码块已保存: {label}] {rel_path}\\n\")\n        except OSError:\n            chunks.append(answer[start:end])\n        cursor = end\n    if cursor < len(answer):\n        chunks.append(answer[cursor:])\n\n    rendered = \"\".join(chunks).strip()\n    return rendered if rendered else answer\n"}}}}