gjm
f7fa8f14d9
- checklist.py：系统提示加入结构化输出契约（只输出 {summary, issues[]}，
引用 handle 必须真实存在、不得编造）
- review.py：review_frame 返回 {summary, issues, raw}；仅在「不再请求工具」的回合
解析结论，中间工具回合的 content 永不解析；强制收口那次 chat 不传 tools
- report.py：删除 extract_issues 与 _EXTRACT_SYSTEM（不再需要第二次 LLM 调用）；
新增 format_finding（用 .get，顺带修复缺 frame 键 KeyError）；save_report 的
issues 省略时从 findings 拍平
- __main__.py：主流程改为 确定性检查 → review_frame → build_finding（合并+校验），
issues 由 flatten_issues 派生
顺带修复既有 gap：summarize 硬下标 KeyError、extract_issues 单行围栏解析失败。
118 lines
4.6 KiB
Python
118 lines
4.6 KiB
Python
"""阶段 3：逐图框审查（LLM + 受控只读取证子循环）。
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流程：
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1. 用清单生成系统提示，喂入结构化证据 + 截图。
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2. LLM 若请求只读取证工具 → 执行后把观测回灌，再问（有界循环）。
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3. LLM 不再请求工具时，其输出（受约束的 JSON）即为该图框的审查结论。
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结论是**结构化对象** `{"summary": str, "issues": [dict], "raw": str}`，
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不再依赖第二次 LLM 调用来抽取结构（见 `findings.parse_conclusion`）。
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"""
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import json
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from ..cad_tools import READ_ONLY_TOOLS, is_write_tool
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from ..findings import parse_conclusion
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from ..llm_client import LlmClient, downsample_image_b64
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from ..mcp_client import McpError, McpToolError, result_images, result_texts
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# 单次审查回灌给 LLM 的图片数上限（控制视觉 token 成本）
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_MAX_OBS_IMAGES = 3
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def evidence_payload(evidence):
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"""把证据整理成给 LLM 的 JSON（剔除 base64 图片，避免撑爆上下文）。"""
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frame = evidence.get("frame") or {}
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return {
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"frame_index": evidence.get("index"),
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"frame": {
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k: frame.get(k)
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for k in ("sheet_size", "kind", "layer", "bbox", "confidence")
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},
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"entities": evidence.get("entities"),
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"texts": evidence.get("texts"),
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"dimensions": evidence.get("dimensions"),
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"blocks": evidence.get("blocks"),
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"polylines": evidence.get("polylines"),
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}
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def _run_readonly_tool(ctx, name, args, image_budget):
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"""执行一个只读取证工具，返回回灌给 LLM 的 content 列表。"""
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if name not in READ_ONLY_TOOLS or is_write_tool(name):
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return [{"type": "text", "text": f"工具 {name} 不在只读取证白名单内，已拒绝。"}], image_budget
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try:
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resp = ctx.tools.call(name, args)
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except (McpError, McpToolError) as exc:
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return [{"type": "text", "text": f"工具 {name} 执行失败: {exc}"}], image_budget
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content = []
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for text in result_texts(resp):
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content.append({"type": "text", "text": text})
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for b64, mime in result_images(resp):
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if image_budget[0] <= 0:
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content.append({"type": "text", "text": "（后续截图已省略以控制成本）"})
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break
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image_budget[0] -= 1
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nb, nm = downsample_image_b64(b64, mime)
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content.append(LlmClient.image_content(nb, nm))
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if not content:
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content.append({"type": "text", "text": "（无返回内容）"})
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return content, image_budget
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def review_frame(ctx, evidence, system_prompt, log=print):
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"""审查单个图框，返回结构化结论 `{"summary", "issues", "raw"}`。"""
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payload = evidence_payload(evidence)
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user_content = [
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{
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"type": "text",
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"text": "以下是该图框的审查输入数据(JSON)：\n"
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+ json.dumps(payload, ensure_ascii=False, indent=2),
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}
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]
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image_b64 = evidence.get("image_b64")
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if image_b64:
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nb, nm = downsample_image_b64(image_b64, evidence.get("image_mime") or "image/png")
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user_content.append(LlmClient.image_content(nb, nm))
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_content},
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]
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tools_schema = ctx.tools.openai_tools(names=READ_ONLY_TOOLS)
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image_budget = [_MAX_OBS_IMAGES]
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for _ in range(max(0, ctx.max_iters)):
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resp = ctx.llm.chat(messages, tools=tools_schema)
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msg = resp.choices[0].message
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messages.append(msg)
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# 仅在「不再请求工具」的回合解析结论；中间工具回合的 content 永不解析
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if not msg.tool_calls:
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return parse_conclusion(msg.content or "", log=log)
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for tool_call in msg.tool_calls:
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name = tool_call.function.name
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try:
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args = json.loads(tool_call.function.arguments or "{}")
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except json.JSONDecodeError:
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args = {}
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log(f" [取证] {name} {args if args else ''}")
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content, image_budget = _run_readonly_tool(ctx, name, args, image_budget)
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messages.append(
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{"role": "tool", "tool_call_id": tool_call.id, "content": content}
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)
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# 达到回合上限：强制收口（不传 tools，保证不再请求工具，输出必为终稿 JSON）
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messages.append(
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{
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"role": "user",
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"content": "已达到取证回合上限，请基于现有信息直接给出该图框的审查结论（JSON）。",
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}
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)
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resp = ctx.llm.chat(messages)
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return parse_conclusion(resp.choices[0].message.content or "", log=log)
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