You've already forked DataMate
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* fix(chart): update Helm chart helpers and values for improved configuration * feat(SynthesisTaskTab): enhance task table with tooltip support and improved column widths * feat(CreateTask, SynthFileTask): improve task creation and detail view with enhanced payload handling and UI updates * feat(SynthFileTask): enhance file display with progress tracking and delete action * feat(SynthFileTask): enhance file display with progress tracking and delete action * feat(SynthDataDetail): add delete action for chunks with confirmation prompt * feat(SynthDataDetail): update edit and delete buttons to icon-only format * feat(SynthDataDetail): add confirmation modals for chunk and synthesis data deletion * feat(DocumentSplitter): add enhanced document splitting functionality with CJK support and metadata detection * feat(DataSynthesis): refactor data synthesis models and update task handling logic * feat(DataSynthesis): streamline synthesis task handling and enhance chunk processing logic * feat(DataSynthesis): refactor data synthesis models and update task handling logic * fix(generation_service): ensure processed chunks are incremented regardless of question generation success * feat(CreateTask): enhance task creation with new synthesis templates and improved configuration options * feat(CreateTask): enhance task creation with new synthesis templates and improved configuration options * feat(CreateTask): enhance task creation with new synthesis templates and improved configuration options * feat(CreateTask): enhance task creation with new synthesis templates and improved configuration options * feat(model_chat): enhance JSON parsing by removing additional thought tags and improving fallback logic
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
from openai import OpenAI
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def call_openai_style_model(base_url, api_key, model_name, prompt, **kwargs):
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client = OpenAI(
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base_url=base_url,
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api_key=api_key
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)
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response = client.chat.completions.create(
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model=model_name,
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messages=[{"role": "user", "content": prompt}],
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**kwargs
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)
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return response.choices[0].message.content
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def extract_json_substring(raw: str) -> str:
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"""从 LLM 的原始回答中提取最可能的 JSON 字符串片段。
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处理思路：
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- 原始回答可能是：说明文字 + JSON + 说明文字，甚至带有 Markdown 代码块。
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- 优先在文本中查找第一个 '{' 或 '[' 作为 JSON 起始；
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- 再从后向前找最后一个 '}' 或 ']' 作为结束；
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- 如果找不到合适的边界，就退回原始字符串。
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- 部分模型可能会在回复中加入 `<think>...</think>` 内部思考内容，应在解析前先去除。
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- 也有模型会在 JSON 前后增加如 <reasoning>...</reasoning>、<analysis>...</analysis> 等标签，本方法会一并去除。
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该方法不会保证截取的一定是合法 JSON，但能显著提高 json.loads 的成功率。
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"""
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if not raw:
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return raw
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try:
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import re
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# 1. 先把所有完整的思考标签块整体去掉：<think>...</think> 等
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thought_tags = [
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"think",
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"thinking",
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"analysis",
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"reasoning",
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"reflection",
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"inner_thoughts",
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]
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for tag in thought_tags:
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pattern = rf"<{tag}>[\s\S]*?</{tag}>"
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raw = re.sub(pattern, "", raw, flags=re.IGNORECASE)
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# 2. 再做一次“截取最后一个 </think>（或其它思考标签结束）之后的内容”的兜底
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# 这样就算标签不成对或嵌套异常，也能保留尾部真正的回答
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last_pos = -1
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for tag in thought_tags:
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# 匹配类似 </think> 或 </THINK>
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m = list(re.finditer(rf"</{tag}>", raw, flags=re.IGNORECASE))
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if m:
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last_pos = max(last_pos, m[-1].end())
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if last_pos != -1 and last_pos < len(raw):
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raw = raw[last_pos:]
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except Exception:
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# 正则异常时不影响后续逻辑，继续使用当前文本
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pass
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start = None
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end = None
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# 查找第一个 JSON 起始符号
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for i, ch in enumerate(raw):
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if ch in "[{":
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start = i
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break
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# 查找最后一个 JSON 结束符号
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for i in range(len(raw) - 1, -1, -1):
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if raw[i] in "]}":
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end = i + 1 # 切片是左闭右开
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break
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if start is not None and end is not None and start < end:
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return raw[start:end].strip()
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# 兜底：去掉常见 Markdown 包裹（```json ... ```）
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stripped = raw.strip()
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if stripped.startswith("```"):
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# 去掉首尾 ``` 标记
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stripped = stripped.strip("`")
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return stripped
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