397 lines
17 KiB
Python
397 lines
17 KiB
Python
"""数据处理任务的大模型生成适配器。"""
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from __future__ import annotations
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import hashlib
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import json
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import re
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from collections.abc import Callable, Iterable, Mapping
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from typing import Any
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from urllib.parse import urlsplit, urlunsplit
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import httpx
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from app.modules.data_process.algorithms import normalize_text, stable_split_assignments
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from app.modules.data_process.constants import (
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MAX_QA_PAIRS_PER_ITEM,
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MODEL_GENERATION_BATCH_SIZE,
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)
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class ModelGenerationError(ValueError):
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"""模型配置、响应或调用失败。"""
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OUTPUT_TYPE_STANDARD = "standard"
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OUTPUT_TYPE_REASONING = "reasoning"
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SUPPORTED_OUTPUT_TYPES = {OUTPUT_TYPE_STANDARD, OUTPUT_TYPE_REASONING}
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REASONING_DETAIL_NORMAL = "normal"
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REASONING_DETAIL_DETAILED = "detailed"
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SUPPORTED_REASONING_DETAILS = {
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REASONING_DETAIL_NORMAL,
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REASONING_DETAIL_DETAILED,
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}
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def chat_completions_url(value: str) -> str:
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"""把域名、基础 URL 或完整地址统一为 chat completions 地址。"""
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raw = (value or "").strip()
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if not raw:
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raise ModelGenerationError("generation model api_url is required")
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if "://" not in raw:
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raw = f"https://{raw}"
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parsed = urlsplit(raw)
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if parsed.scheme not in {"http", "https"} or not parsed.hostname:
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raise ModelGenerationError("generation model api_url must be an HTTP(S) host or URL")
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if parsed.username or parsed.password:
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raise ModelGenerationError("generation model api_url must not contain credentials")
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path = parsed.path.rstrip("/")
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if path.endswith("/chat/completions"):
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target_path = path
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elif path.endswith("/v1"):
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target_path = f"{path}/chat/completions"
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elif not path:
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target_path = "/v1/chat/completions"
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else:
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target_path = f"{path}/v1/chat/completions"
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return urlunsplit((parsed.scheme, parsed.netloc, target_path, "", ""))
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def _message_content(payload: Mapping[str, Any]) -> str:
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try:
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content = payload["choices"][0]["message"]["content"]
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except (KeyError, IndexError, TypeError) as exc:
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raise ModelGenerationError(
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"model response does not contain choices[0].message.content"
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) from exc
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts = [
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str(item.get("text") or "")
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for item in content
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if isinstance(item, Mapping) and item.get("type") in {None, "text", "output_text"}
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]
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if parts:
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return "".join(parts)
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raise ModelGenerationError("model response content must be text")
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def _json_payload(content: str) -> Any:
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# 只移除模型在 JSON 之前自行输出的思考过程，不能破坏 JSON 字段中的训练内容。
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cleaned = re.sub(
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r"^\s*<think>[\s\S]*?</think>\s*",
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"",
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content,
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count=1,
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flags=re.IGNORECASE,
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).strip()
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fenced = re.fullmatch(r"```(?:json)?\s*([\s\S]*?)\s*```", cleaned, flags=re.IGNORECASE)
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if fenced:
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cleaned = fenced.group(1).strip()
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try:
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return json.loads(cleaned)
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except json.JSONDecodeError as exc:
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raise ModelGenerationError(
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f"model response is not valid JSON at line {exc.lineno}, column {exc.colno}"
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) from exc
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def _result_items(payload: Any) -> list[Mapping[str, Any]]:
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if isinstance(payload, list):
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values = payload
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elif isinstance(payload, Mapping):
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nested = next(
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(
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payload[key]
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for key in ("items", "results", "data", "records")
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if isinstance(payload.get(key), list)
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),
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None,
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)
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values = nested if isinstance(nested, list) else [payload]
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else:
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raise ModelGenerationError("model JSON must be an object or array")
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items = [item for item in values if isinstance(item, Mapping)]
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if not items:
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raise ModelGenerationError("model JSON does not contain result objects")
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return items
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def _prompt_messages(
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prompt: str,
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content: str,
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count: int,
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*,
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start_index: int,
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total_count: int,
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output_type: str,
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reasoning_detail: str,
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) -> list[dict[str, str]]:
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end_index = start_index + count - 1
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if output_type == OUTPUT_TYPE_REASONING:
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schema = '{"items":[{"instruction":"...","input":"...","reasoning":"...","answer":"..."}]}'
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detail_rule = (
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"推理详细程度为“详细”：完整展开问题条件、来源依据、中间计算或推导，"
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"并在得出答案前核对结论；每一步都必须能从来源内容中验证。"
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if reasoning_detail == REASONING_DETAIL_DETAILED
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else
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"推理详细程度为“普通”：只保留得出答案所需的关键依据和必要步骤，"
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"避免冗长复述、套话和无依据扩展。"
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)
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output_rule = (
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"你正在生成用于训练推理模型的思维链数据，而不是普通问答数据。"
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"instruction、reasoning 和 answer 均不得为空；reasoning 必须是基于来源内容、"
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f"可核对的推理过程，answer 只写最终答案。{detail_rule}"
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"这是思维链输出模式，即使其他提示语要求省略分析，也不得省略 reasoning。"
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"不要自行添加 <think> 标签，系统会在保存时统一组装。"
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)
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else:
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schema = '{"items":[{"instruction":"...","input":"...","output":"..."}]}'
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output_rule = (
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"你正在生成标准监督微调问答数据。instruction 和 output 不得为空；"
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"output 只写最终答案，禁止输出分析、推理过程或 <think> 标签。"
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)
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schema_instruction = (
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f"必须只返回 JSON 对象，格式为 {schema}；items 必须包含 {count} 条。"
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f"这是总计 {total_count} 条中的第 {start_index}-{end_index} 条，"
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"各条必须使用不同的提问角度和表述，避免重复。"
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f"{output_rule}不要输出 Markdown 代码围栏或 JSON 之外的说明。"
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)
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base_prompt = normalize_text(prompt) or "请根据来源内容生成可用于监督微调的问答数据。"
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if "{{ content }}" in base_prompt:
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user_prompt = base_prompt.replace("{{ content }}", content)
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return [
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{"role": "system", "content": schema_instruction},
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{"role": "user", "content": user_prompt},
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]
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return [
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{"role": "system", "content": f"{base_prompt}\n{schema_instruction}"},
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{"role": "user", "content": f"来源内容：\n{content}"},
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]
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def generate_model_records(
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preview_items: Iterable[Mapping[str, Any]],
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*,
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model: Mapping[str, Any],
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config: Mapping[str, Any],
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task_id: str,
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split: Mapping[str, int],
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qa_pairs_per_item: int,
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client: httpx.Client | None = None,
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on_progress: Callable[[int, int], None] | None = None,
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) -> list[dict[str, Any]]:
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"""调用 OpenAI 兼容接口，将预览切片生成标准训练记录。
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每个切片按安全批次调用模型；失败批次会产生一条可人工修复的
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invalid 结果，已经成功的批次不会丢失。
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"""
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if not 1 <= qa_pairs_per_item <= MAX_QA_PAIRS_PER_ITEM:
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raise ModelGenerationError(f"qa_pairs_per_item must be in [1, {MAX_QA_PAIRS_PER_ITEM}]")
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output_type = str(config.get("output_type") or OUTPUT_TYPE_STANDARD).strip().lower()
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if output_type not in SUPPORTED_OUTPUT_TYPES:
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raise ModelGenerationError(f"output_type must be one of {sorted(SUPPORTED_OUTPUT_TYPES)}")
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reasoning_detail = str(
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config.get("reasoning_detail") or REASONING_DETAIL_NORMAL
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).strip().lower()
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if reasoning_detail not in SUPPORTED_REASONING_DETAILS:
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raise ModelGenerationError(
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f"reasoning_detail must be one of {sorted(SUPPORTED_REASONING_DETAILS)}"
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)
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endpoint = chat_completions_url(str(model.get("api_url") or ""))
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model_name = str(model.get("online_model_name") or model.get("name") or "").strip()
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if not model_name:
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raise ModelGenerationError("generation model name is required")
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temperature = float(config.get("temperature", 0.7))
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max_tokens = int(config.get("max_tokens", 1024))
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timeout = max(1.0, min(120.0, float(config.get("request_timeout_seconds", 60))))
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retries = max(0, min(5, int(config.get("generation_retries", 2))))
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headers = {"Content-Type": "application/json"}
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api_key = str(model.get("api_key") or "").strip()
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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owns_client = client is None
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http_client = client or httpx.Client(timeout=timeout)
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results: list[dict[str, Any]] = []
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try:
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preview_list = list(preview_items)
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total_items = len(preview_list)
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for item_index, item in enumerate(preview_list):
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preview_id = str(item.get("id") or f"preview-{item_index + 1}")
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content = normalize_text(
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str(item.get("edited_content") or item.get("original_content") or "")
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)
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for batch_offset in range(0, qa_pairs_per_item, MODEL_GENERATION_BATCH_SIZE):
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batch_count = min(
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MODEL_GENERATION_BATCH_SIZE,
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qa_pairs_per_item - batch_offset,
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)
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batch_start = batch_offset + 1
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batch_end = batch_offset + batch_count
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request_payload: dict[str, Any] = {
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"model": model_name,
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"messages": _prompt_messages(
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str(config.get("generation_prompt") or ""),
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content,
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batch_count,
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start_index=batch_start,
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total_count=qa_pairs_per_item,
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output_type=output_type,
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reasoning_detail=reasoning_detail,
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),
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"temperature": temperature,
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"max_tokens": max_tokens,
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}
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if bool(config.get("json_mode", False)):
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request_payload["response_format"] = {"type": "json_object"}
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last_error: Exception | None = None
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generated_items: list[Mapping[str, Any]] | None = None
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for _ in range(retries + 1):
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try:
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response = http_client.post(
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endpoint,
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headers=headers,
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json=request_payload,
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)
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response.raise_for_status()
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body = response.json()
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if not isinstance(body, Mapping):
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raise ModelGenerationError("model response body must be a JSON object")
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candidate_items = _result_items(_json_payload(_message_content(body)))
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if len(candidate_items) < batch_count:
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raise ModelGenerationError(
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"model response contains fewer result objects than requested: "
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f"expected {batch_count}, got {len(candidate_items)}"
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)
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generated_items = candidate_items
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break
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except (
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httpx.HTTPError,
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json.JSONDecodeError,
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ModelGenerationError,
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) as exc:
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last_error = exc
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if generated_items is None:
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error_message = str(last_error or "model generation failed")[:2000]
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failure_instruction = (
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f"模型生成失败，请人工补充（第 {batch_start}-{batch_end} 条）"
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)
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result_id = (
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"result_"
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f"{hashlib.sha256(f'{preview_id}:error:{batch_start}'.encode()).hexdigest()[:16]}"
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)
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results.append(
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{
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"id": result_id,
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"preview_item_id": preview_id,
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"instruction": failure_instruction,
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"input": content,
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"output": "",
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"original_instruction": failure_instruction,
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"original_input": content,
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"original_output": "",
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"status": "invalid",
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"error": error_message,
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"split": "train",
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}
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)
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continue
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for batch_index, value in enumerate(generated_items[:batch_count]):
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variant_index = batch_offset + batch_index
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instruction = normalize_text(
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str(value.get("instruction") or value.get("question") or "")
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)
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input_text = normalize_text(
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str(value.get("input") or value.get("context") or "")
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)
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if output_type == OUTPUT_TYPE_REASONING:
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reasoning = normalize_text(
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re.sub(
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r"</?think>",
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"",
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str(value.get("reasoning") or value.get("analysis") or ""),
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flags=re.IGNORECASE,
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)
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)
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answer = normalize_text(
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re.sub(
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r"</?think>",
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"",
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str(
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value.get("answer")
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or value.get("final_answer")
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or value.get("output")
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or ""
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),
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flags=re.IGNORECASE,
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)
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)
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output = (
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f"<think>\n{reasoning}\n</think>\n{answer}"
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if reasoning and answer
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else answer or (f"<think>\n{reasoning}\n</think>" if reasoning else "")
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)
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valid = bool(instruction and reasoning and answer)
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missing_error = "model result is missing instruction, reasoning or answer"
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else:
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output = normalize_text(
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str(
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value.get("output")
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or value.get("answer")
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or value.get("response")
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or ""
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)
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)
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output = normalize_text(
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re.sub(
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r"<think>[\s\S]*?(?:</think>|$)",
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"",
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output,
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flags=re.IGNORECASE,
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)
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)
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valid = bool(instruction and output)
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missing_error = "model result is missing instruction or output"
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raw_id = f"{preview_id}:{variant_index + 1}:{instruction}:{output}"
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result_id = f"result_{hashlib.sha256(raw_id.encode()).hexdigest()[:16]}"
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results.append(
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{
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"id": result_id,
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"preview_item_id": preview_id,
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"instruction": instruction,
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"input": input_text,
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"output": output,
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"original_instruction": instruction,
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"original_input": input_text,
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"original_output": output,
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"status": "valid" if valid else "invalid",
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"error": (None if valid else missing_error),
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"split": "train",
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}
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)
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if on_progress:
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on_progress(item_index + 1, total_items)
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finally:
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if owns_client:
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http_client.close()
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assignments = stable_split_assignments(
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[str(result["id"]) for result in results],
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split,
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seed=task_id,
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)
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for result, assignment in zip(results, assignments, strict=True):
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result["split"] = assignment
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return results
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__all__ = ["ModelGenerationError", "chat_completions_url", "generate_model_records"]
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