init the project
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evyzacq
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"""
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Stage 1: Semantic Index Generation.
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Reads the full parsed PRD JSON, calls the LLM once to produce a semantic index
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identifying all function units, concepts, and their document sources.
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Output: output/semantic_index.json
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"""
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import json
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import re
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import sys
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import time
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from pathlib import Path
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import config
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def format_document_for_prompt(doc: dict) -> str:
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"""Render the full parsed document as a readable string for the LLM prompt."""
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lines = []
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# ---- Sections ----
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lines.append("=== SECTIONS ===")
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for i, section in enumerate(doc.get("sections", [])):
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source = section.get("source", f"(无标题-章节{i})")
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lines.append(f"\n--- Section: {source} ---")
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for block in section.get("blocks", []):
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if block["type"] == "para":
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lines.append(f"[段落 {block['index']}] {block['text']}")
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elif block["type"] == "table":
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lines.append(f"[表格 {block.get('table', '?')}]")
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headers = block.get("headers", [])
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lines.append(f" 表头: {' | '.join(headers)}")
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for row in block.get("rows", []):
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cols = row.get("columns", [])
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cell_texts = []
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for c in cols:
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cell_texts.append(f"[行{c.get('row','?')}]{c.get('name','')}: {c.get('text','')}")
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lines.append(f" {'; '.join(cell_texts)}")
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images = section.get("images", [])
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if images:
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lines.append(f" 图片引用: {', '.join(images)}")
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# ---- Image Analysis ----
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lines.append("\n\n=== IMAGE_ANALYSIS (流程图逻辑树) ===")
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for img in doc.get("image_analysis", []):
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rid = img.get("rid", "?")
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img_type = img.get("type", "?")
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lines.append(f"\n--- Image: {rid} (type={img_type}) ---")
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lines.append(f" 描述: {img.get('description', '')[:300]}")
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lt = img.get("logic_tree")
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if lt:
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lines.append(f" 逻辑树根节点: {lt.get('root', '?')}")
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lines.append(" 节点详情:")
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for node in lt.get("nodes", []):
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nid = node.get("id", "?")
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ntype = node.get("type", "?")
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desc = node.get("description", "") or node.get("condition", "")
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lines.append(f" [{ntype}] {nid}: {desc}")
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branches = node.get("branches", [])
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if branches:
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for br in branches:
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lines.append(f" → {br['value']} → {br['target']}")
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# ---- Resolved Conflicts ----
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conflicts = doc.get("resolved_conflicts", [])
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if conflicts:
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lines.append("\n\n=== RESOLVED_CONFLICTS (图文冲突仲裁) ===")
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for c in conflicts:
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lines.append(
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f" [{c.get('conflict_type','?')}] {c.get('section','?')}: "
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f"以{c.get('source','?')}为准 — {c.get('correction','')}"
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)
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return "\n".join(lines)
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def build_prompt(doc: dict) -> str:
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"""Load the prompt template and inject the formatted document."""
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template_path = Path(config.PROMPTS_DIR) / "step1_semantic_index.txt"
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template = template_path.read_text(encoding="utf-8")
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formatted_doc = format_document_for_prompt(doc)
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prompt = template.replace("{document_json}", formatted_doc)
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return prompt
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def extract_json_from_response(text: str) -> str:
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"""Robustly extract JSON from LLM response, handling markdown fences."""
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# Try ```json ... ``` first
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m = re.search(r"```(?:json)?\s*([\s\S]*?)```", text)
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if m:
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return m.group(1).strip()
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# Try to find the outermost { ... }
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start = text.find("{")
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if start == -1:
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raise ValueError("No JSON object found in LLM response")
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depth = 0
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for i in range(start, len(text)):
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if text[i] == "{":
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depth += 1
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elif text[i] == "}":
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depth -= 1
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if depth == 0:
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return text[start : i + 1]
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raise ValueError("Unclosed JSON object in LLM response")
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def call_llm(prompt: str, max_retries: int = 2) -> dict:
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"""Send the prompt to the LLM and return the parsed semantic index JSON."""
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client = config.llm_client()
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for attempt in range(max_retries + 1):
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print(f" LLM 调用 (尝试 {attempt + 1}/{max_retries + 1})...", flush=True)
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try:
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resp = client.chat.completions.create(
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model=config.MODEL_NAME,
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messages=[
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{"role": "system", "content": "你是一个精确的 JSON 输出引擎。只输出合法的 JSON，不输出任何其他文字。"},
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{"role": "user", "content": prompt},
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],
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temperature=config.TEMPERATURE,
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max_tokens=config.MAX_TOKENS,
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)
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content = resp.choices[0].message.content
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if content is None:
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raise RuntimeError("LLM returned empty response")
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json_str = extract_json_from_response(content)
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result = json.loads(json_str)
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# Quick structural validation
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_validate_schema(result)
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return result
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except (json.JSONDecodeError, ValueError) as e:
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print(f" JSON 解析失败: {e}")
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if attempt < max_retries:
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time.sleep(2)
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else:
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raise RuntimeError(f"无法从 LLM 响应中解析 JSON: {e}") from e
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def _validate_schema(data: dict) -> None:
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"""Validate the top-level schema of the semantic index."""
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if "feature_name" not in data:
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raise ValueError("semantic_index 缺少 'feature_name' 字段")
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if "function_units" not in data:
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raise ValueError("semantic_index 缺少 'function_units' 字段")
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units = data["function_units"]
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if not isinstance(units, list) or len(units) == 0:
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raise ValueError("function_units 必须是非空数组")
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for i, fu in enumerate(units):
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if not fu.get("unit_id"):
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raise ValueError(f"function_unit[{i}] 缺少 unit_id")
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if not fu.get("name"):
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raise ValueError(f"function_unit[{i}] 缺少 name")
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def main():
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print("=" * 60)
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print("阶段一：宏观语义索引 (Semantic Index)")
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print("=" * 60)
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# 1. Load input
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print(f"\n[1/4] 加载输入文档: {config.INPUT_JSON}")
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doc = config.load_input_document()
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print(f" 已加载 {len(doc.get('sections', []))} 个 section, "
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f"{len(doc.get('image_analysis', []))} 张图片分析")
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# 2. Build prompt
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print(f"\n[2/4] 构造 Prompt...")
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prompt = build_prompt(doc)
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print(f" Prompt 长度: {len(prompt)} 字符 (~{len(prompt)//3} tokens)")
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# 3. Call LLM
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print(f"\n[3/4] 调用 LLM ({config.MODEL_NAME})...")
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semantic_index = call_llm(prompt)
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# 4. Save result
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print(f"\n[4/4] 保存语义索引: {config.SEMANTIC_INDEX_JSON}")
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config.save_json(semantic_index, config.SEMANTIC_INDEX_JSON)
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n_concepts = len(semantic_index.get("concepts", []))
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n_units = len(semantic_index.get("function_units", []))
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print(f"\n完成! 提取了 {n_concepts} 个概念, {n_units} 个功能单元.")
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print(f"输出: {config.SEMANTIC_INDEX_JSON}")
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if __name__ == "__main__":
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main()
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