- 4 skill pipeline (doc_parser, conflict_detection, ir_generation, resolution_application) - CI workflow on push/PR (.gitea/workflows/ci.yml) - Auto-issue on CI failure (.gitea/workflows/auto-issue.yml) - Pytest smoke tests (tests/test_sample.py) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""Generate JSON intermediate representation from ``_parsed.json`` or ``_updated.json``.
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Sends the JSON document directly to the LLM for analysis. If the document exceeds
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``MAX_ANALYSIS_TOKENS``, sections are batched greedily without splitting any
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individual section. Conflict corrections from ``resolved_conflicts`` are included
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so the output respects user arbitration decisions.
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Usage::
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python scripts/ir_generator.py output/<basename>_updated.json [output_dir] [--dry-run]
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Output: ``<basename>_ir.json``
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"""
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import argparse
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import json
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import logging
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import os
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import sys
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import time
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from LLM import LLMClient
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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RATE_LIMIT_DELAY = 0.5
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MAX_ANALYSIS_TOKENS = 6000 # max content size per LLM call
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# ---------------------------------------------------------------------------
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# Prompt
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# ---------------------------------------------------------------------------
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PROMPT = """你是一个需求文档分析助手。请分析以下需求文档的JSON内容，输出结构化JSON。
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## 已知修正（来自冲突检测）
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以下内容已确认修正，生成JSON时请**使用修正后的值**，不要同时输出两个版本。
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{conflict_context}
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## 待分析内容（JSON格式）
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{content}
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## JSON字段说明
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- sections: 文档章节列表，每个章节含 source（章节标题）和 blocks（内容块数组）
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- blocks: 类型含 para（段落，字段 text）和 table（表格，字段 rows，每行含 columns 数组）
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- image_sources: 图片所在章节映射，key 为图片 rid
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- image_analysis: 图片分析结果，每个含 rid、type（流程图/架构图/状态图等）、description
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- resolved_conflicts: 已知修正列表，每个含 section、conflict_type、correction、source
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## 功能点定义
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只有满足以下**全部条件**的才视为功能点：
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1. 描述了一个**系统或软件要实现的具体行为**（有触发条件、执行动作、状态变化或逻辑规则）
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2. 该行为直接由**系统或框架**执行（不是人的操作流程、管理流程）
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3. 对用户或系统有**可观察的效果**
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**以下内容不是功能点，不要输出：**
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- 术语/缩略词定义（
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- 文档背景、范围说明（如 "本文档涵盖xxx"）
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- 变更日志、版本记录、编制人信息
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- 文档结构描述（如 "产品简介用户场景说明"）
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- 纯文本的概述、没有具体行为的介绍
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## 决策树/流程图分解规则（重要）
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图片分析（image_analysis）中的流程图和决策树描述包含丰富的功能逻辑，**必须完全分解**：
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1. **每个叶子路径 = 一个独立 function**：从根节点到每个最终结果的完整路径，都拆成一个 function
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2. **每个判断分支 = 一个独立 function**：菱形判断节点的每个分支方向和对应的结果，单独作为一个 function
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3. **不同约束条件 = 不同 function**：例如"通过接入SDK限制"和"通过系统限制"是不同约束机制，必须分别列出
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4. **不要合并不同路径**：即使最终结果相同，只要到达路径不同，就是不同的 function
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## 输出格式
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只输出功能点，每个功能点格式如下：
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{
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"function": "功能名称",
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"source": {
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"section": "章节名",
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"location": "原文位置（如：正文第1段、表格1第2行、图片rId13）"
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},
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"trigger": {
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"type": "AND或者OR",
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"conditions": [
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"触发条件1",
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"触发条件2"
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]
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},
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"actions": {
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"场景/角色": [
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"动作1",
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"动作2"
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]
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}
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}
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## 输出原则
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1. **只输出功能点**，没有功能点就输出空数组 []
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2. 每个功能点**必须**包含 source.section 和 source.location
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3. location 必须是具体的原文位置标签（如 "正文第1段"、"表格1"、"图片rId13"）
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4. **一个 function 只对应一种行为逻辑（一条完整路径）**。决策树中的每个分支路径（从根到叶子）必须拆成独立 function，conditions 中明确写出该路径上的所有判断条件和分支方向。
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5. **穷举所有分支**：流程图/决策树中的每一条分支路径都要输出对应的 function，不能遗漏任何子逻辑。
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6. 没有 trigger 或 actions 的字段直接**省略**，不要写 null 或空列表/空对象
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7. 所有功能点全部列出，**宁多勿漏**
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8. **已知修正**中确认的信息，使用修正后的值
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9. 输出一个JSON数组，不要用 ```json 代码块包裹，直接输出纯JSON
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"""
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _parse_llm_response(raw: str) -> list | dict | str | None:
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"""Parse JSON from LLM response, handling markdown code fences."""
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if raw is None:
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return None
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stripped = raw.strip()
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if stripped.startswith("```"):
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nl = stripped.find("\n")
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stripped = stripped[nl + 1:] if nl != -1 else stripped[3:]
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if stripped.endswith("```"):
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stripped = stripped[:-3]
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try:
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return json.loads(stripped)
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except json.JSONDecodeError:
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logger.warning(" Failed to parse JSON, returning raw text")
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return raw
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def _build_conflict_context(
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section_name: str | None,
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resolved_conflicts: list[dict],
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) -> str:
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"""Build conflict correction context for a section, or all if section_name is None."""
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if section_name is None:
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relevant = resolved_conflicts
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else:
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relevant = [c for c in resolved_conflicts if c.get("section", "") == section_name]
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if not relevant:
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return "没有"
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lines: list[str] = []
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for c in relevant:
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correction = c.get("correction", "")
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conflict_type = c.get("conflict_type", "")
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source = c.get("source", "")
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lines.append(f"- 冲突类型：{conflict_type}，依据：{source}")
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lines.append(f" 修正后的值：{correction}")
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return "\n".join(lines)
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# ---------------------------------------------------------------------------
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# LLM analysis
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# ---------------------------------------------------------------------------
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def _analyze_content(
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content: str,
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conflict_context: str,
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llm: LLMClient,
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*,
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dry_run: bool = False,
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) -> list[dict]:
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"""Send content to the LLM and return IR entries."""
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prompt = PROMPT.replace("{conflict_context}", conflict_context).replace("{content}", content)
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if dry_run:
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est = llm.estimate_tokens(prompt)
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logger.info(" [DRY RUN] prompt ~%d tokens", est)
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return []
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try:
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raw = llm.chat(
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model=LLMClient.TEXT_MODEL,
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messages=[{"role": "user", "content": prompt}],
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response_format={"type": "json_object"},
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)
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logger.info(" Response: %d chars", len(raw))
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except RuntimeError as e:
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logger.error(" Analysis failed: %s", e)
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return []
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parsed = _parse_llm_response(raw)
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if isinstance(parsed, list):
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return parsed
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elif isinstance(parsed, dict):
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return [parsed]
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else:
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logger.warning(" Unparseable response, raw length: %d", len(raw))
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return []
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def generate_ir(
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parsed_path: str,
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output_dir: str = "output",
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*,
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dry_run: bool = False,
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) -> dict:
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"""Read parsed/updated JSON and generate JSON IR.
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Produces ``<basename>_ir.json`` in *output_dir*.
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"""
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with open(parsed_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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basename = os.path.splitext(os.path.basename(parsed_path))[0]
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for suffix in ("_parsed", "_updated"):
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if basename.endswith(suffix):
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basename = basename[:-len(suffix)]
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break
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os.makedirs(output_dir, exist_ok=True)
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llm = LLMClient()
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ir_output: list[dict] = []
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sections = data.get("sections", [])
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image_sources = data.get("image_sources", {})
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image_analysis = data.get("image_analysis", [])
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resolved_conflicts = data.get("resolved_conflicts", [])
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# Build full document JSON to measure size
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full_doc = {
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"sections": sections,
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"image_sources": image_sources,
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"image_analysis": image_analysis,
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}
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full_json = json.dumps(full_doc, ensure_ascii=False)
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total_chars = len(full_json)
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logger.info("Total document JSON chars: %d", total_chars)
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if total_chars < MAX_ANALYSIS_TOKENS:
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logger.info("Document fits in one request (< %d chars)", MAX_ANALYSIS_TOKENS)
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conflict_ctx = _build_conflict_context(None, resolved_conflicts)
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entries = _analyze_content(full_json, conflict_ctx, llm, dry_run=dry_run)
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ir_output.extend(entries)
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else:
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logger.info("Document is large (>= %d chars), batching sections", MAX_ANALYSIS_TOKENS)
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# Filter to non-empty sections, measure effective size per section
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# (section JSON + image_sources + image_analysis for images in that section)
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sec_sizes = []
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for sec in sections:
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if not sec.get("blocks"):
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continue
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sec_json = json.dumps(sec, ensure_ascii=False)
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sec_chars = len(sec_json)
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# Add image overhead for this section
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sec_name = sec.get("source", "")
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sec_rids = [rid for rid, src in image_sources.items()
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if src.get("section", "") == sec_name]
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if sec_rids:
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overhead_doc = {
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"image_sources": {rid: image_sources[rid] for rid in sec_rids},
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"image_analysis": [img for img in image_analysis
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if img.get("rid", "") in sec_rids],
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}
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sec_chars += len(json.dumps(overhead_doc, ensure_ascii=False))
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sec_sizes.append((sec, sec_chars))
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# Greedy batch: never split a section, keep adding until next exceeds limit
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i = 0
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while i < len(sec_sizes):
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batch = []
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batch_size = 0
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while i < len(sec_sizes) and batch_size + sec_sizes[i][1] <= MAX_ANALYSIS_TOKENS:
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batch.append(sec_sizes[i][0])
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batch_size += sec_sizes[i][1]
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i += 1
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if not batch:
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i += 1
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continue
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# Collect sections and their images for this batch
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batch_names = [s.get("source", "") for s in batch]
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batch_image_sources = {
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rid: src for rid, src in image_sources.items()
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if src.get("section", "") in batch_names
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}
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batch_images = [
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img for img in image_analysis
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if image_sources.get(img.get("rid", ""), {}).get("section", "") in batch_names
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]
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batch_doc = {
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"sections": batch,
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"image_sources": batch_image_sources,
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"image_analysis": batch_images,
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}
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batch_json = json.dumps(batch_doc, ensure_ascii=False)
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# Merge conflict contexts
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ctx_parts = []
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for sn in batch_names:
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ctx = _build_conflict_context(sn, resolved_conflicts)
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if ctx != "没有":
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ctx_parts.append(ctx)
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conflict_ctx = "\n".join(ctx_parts) if ctx_parts else "没有"
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label = " + ".join(batch_names)
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logger.info("Batch [%s]: %d sections, %d chars", label, len(batch), len(batch_json))
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entries = _analyze_content(batch_json, conflict_ctx, llm, dry_run=dry_run)
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ir_output.extend(entries)
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time.sleep(RATE_LIMIT_DELAY)
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# ---- save ----------------------------------------------------------------
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ir_path = os.path.join(output_dir, f"{basename}_ir.json")
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os.makedirs(os.path.dirname(ir_path) or ".", exist_ok=True)
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with open(ir_path, "w", encoding="utf-8") as f:
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json.dump(ir_output, f, ensure_ascii=False, indent=2)
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logger.info("Saved: %s (%d entries)", ir_path, len(ir_output))
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# ---- summary -------------------------------------------------------------
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usg = llm.usage
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logger.info("Tokens: %d prompt + %d completion = %d total",
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usg["prompt_tokens"], usg["completion_tokens"], usg["total_tokens"])
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logger.info("Output: %s", ir_path)
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return {"ir": ir_output, "path": ir_path}
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Generate JSON intermediate representation from parsed/updated JSON.",
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)
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parser.add_argument("input", metavar="parsed.json",
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help="Path to _parsed.json or _updated.json")
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parser.add_argument("output_dir", nargs="?", default="output", metavar="output_dir",
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help="Directory for output files (default: output/)")
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parser.add_argument("--dry-run", action="store_true",
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help="Print token estimates without calling the API.")
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args = parser.parse_args()
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generate_ir(args.input, args.output_dir, dry_run=args.dry_run)
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Reference in New Issue
Block a user