mirror of
https://github.com/Silentely/eSIM-Tools.git
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♻️ refactor: 重构 AI Issue 智能回复工作流，移除索引检索并增强上下文分析
- 移除独立的 AI 索引构建工作流（ai-build-index.yml）及相关脚本，简化架构 - 删除 ai/ 目录下所有索引文件（index.jsonl、embeddings.jsonl、index_manifest.json） - 将 retrieve_ai_context.py 脚本替换为内联的仓库扫描逻辑，直接在工作流中实现上下文检索 - 新增富化上下文（enriched context）收集功能，自动提取 Issue 中的错误堆栈、代码块、相关文件内容和 Git 历史 - 增强 triage 阶段输出结构，新增 error_pattern_summary、related_files 和 actionable_steps 字段 - 优化触发条件，支持 issue_comment 事件并通过 `/ai-analyze` 命令触发重新分析 - 改进 LLM 调用验证逻辑，增加字段完整性检查和自动重试机制 - 重构输出展示模板，提供更结构化的 AI 分析结果和用户操作指引
This commit is contained in:
Abner
280
.github/scripts/retrieve_ai_context.py
vendored
280
.github/scripts/retrieve_ai_context.py
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#!/usr/bin/env python3
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import argparse
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import json
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import math
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import pathlib
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import re
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import urllib.request
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from typing import Dict, List, Tuple
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TEXT_EXTS = {
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".md", ".mdx", ".rst", ".txt", ".js", ".jsx", ".ts", ".tsx", ".py", ".go", ".java",
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".rs", ".php", ".rb", ".yml", ".yaml", ".json", ".toml", ".sh", ".vue"
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}
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def tokenize(text: str) -> List[str]:
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return re.findall(r"[A-Za-z0-9_./:#-]{3,}", text.lower())
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def load_json(path: str) -> Dict:
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return json.loads(pathlib.Path(path).read_text(encoding="utf-8"))
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def score_keywords(query_tokens: set, keywords: List[str], weight: int = 1) -> float:
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if not query_tokens:
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return 0.0
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kw = set([k.lower() for k in (keywords or [])])
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inter = len(query_tokens & kw)
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union = max(len(query_tokens | kw), 1)
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return inter * 4 + (inter / union) * 100 + weight * 3
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def dot(a: List[float], b: List[float]) -> float:
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return sum(x * y for x, y in zip(a, b))
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def norm(a: List[float]) -> float:
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return math.sqrt(sum(x * x for x in a))
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def cosine_similarity(a: List[float], b: List[float]) -> float:
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na = norm(a)
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nb = norm(b)
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if na == 0.0 or nb == 0.0:
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return 0.0
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return dot(a, b) / (na * nb)
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def embed_text(base_url: str, api_key: str, model: str, text: str) -> List[float]:
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url = base_url.rstrip("/") + "/embeddings"
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payload = json.dumps({
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"model": model,
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"input": [text]
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}).encode("utf-8")
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req = urllib.request.Request(
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url,
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data=payload,
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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},
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method="POST"
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)
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with urllib.request.urlopen(req, timeout=180) as resp:
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data = json.loads(resp.read().decode("utf-8"))
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items = data.get("data")
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if not isinstance(items, list) or not items:
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raise RuntimeError("Invalid embedding response")
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vec = items[0].get("embedding")
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if not isinstance(vec, list):
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raise RuntimeError("Missing embedding vector")
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return vec
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def build_query_text(rewrite: Dict, issue: Dict) -> str:
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parts = []
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parts.extend(rewrite.get("search_queries", []))
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parts.extend(rewrite.get("keywords", []))
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parts.extend(rewrite.get("components", []))
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parts.extend(rewrite.get("likely_paths", []))
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parts.append(issue.get("title", ""))
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parts.append(issue.get("body", ""))
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return "\n".join([p for p in parts if isinstance(p, str) and p.strip()])
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def emit_rows(rows: List[Dict], max_chars: int):
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parts = []
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cur = 0
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seen = set()
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for row in rows:
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key = (row["path"], row["start_line"], row["end_line"])
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if key in seen:
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continue
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seen.add(key)
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block = f"--- FILE: {row['path']} (lines {row['start_line']}-{row['end_line']}) ---\n{row['text']}\n"
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if cur + len(block) > max_chars:
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break
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parts.append(block)
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cur += len(block)
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print("\n".join(parts))
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def retrieve_from_index(index_path: str, rewrite: Dict, issue: Dict, max_chars: int):
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query_text = build_query_text(rewrite, issue)
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q = set(tokenize(query_text))
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rows = []
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with open(index_path, "r", encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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row = json.loads(line)
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row["score"] = score_keywords(q, row.get("keywords"), row.get("weight", 1))
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rows.append(row)
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rows.sort(key=lambda x: (-x["score"], x["path"], x["start_line"]))
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emit_rows(rows[:20], max_chars)
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def retrieve_from_index_with_embeddings(
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index_path: str,
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embedding_index_path: str,
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rewrite: Dict,
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issue: Dict,
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max_chars: int,
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embedding_api_key: str,
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embedding_base_url: str,
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embedding_model: str
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):
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query_text = build_query_text(rewrite, issue)
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q_tokens = set(tokenize(query_text))
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q_vec = embed_text(embedding_base_url, embedding_api_key, embedding_model, query_text)
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base_rows: Dict[str, Dict] = {}
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with open(index_path, "r", encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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row = json.loads(line)
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base_rows[row["id"]] = row
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scored = []
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with open(embedding_index_path, "r", encoding="utf-8") as f:
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for line in f:
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if not line.strip():
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continue
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item = json.loads(line)
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rid = item.get("id")
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if rid not in base_rows:
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continue
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base = base_rows[rid]
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emb = item.get("embedding")
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if not isinstance(emb, list):
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continue
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sim = cosine_similarity(q_vec, emb)
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kw_score = score_keywords(q_tokens, base.get("keywords"), base.get("weight", 1))
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fused = sim * 100 + kw_score
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row = dict(base)
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row["score"] = fused
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scored.append(row)
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scored.sort(key=lambda x: (-x["score"], x["path"], x["start_line"]))
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emit_rows(scored[:20], max_chars)
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def retrieve_from_repo(repo_root: str, rewrite: Dict, issue: Dict, max_chars: int):
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root = pathlib.Path(repo_root)
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query_terms = rewrite.get("keywords", []) + rewrite.get("components", []) + rewrite.get("likely_paths", [])
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query_terms = [x for x in query_terms if isinstance(x, str) and x.strip()]
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rows = []
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for path in root.rglob("*"):
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if not path.is_file():
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continue
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rel = path.relative_to(root).as_posix()
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if any(part in rel for part in [".git/", "node_modules/", "dist/", "build/", "coverage/", ".next/", ".nuxt/"]):
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continue
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if path.suffix.lower() not in TEXT_EXTS and not path.name.startswith("README") and not path.name.startswith("CHANGELOG"):
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continue
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try:
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text = path.read_text(encoding="utf-8", errors="ignore")
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except Exception:
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continue
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lower = text.lower()
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score = 0
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for t in query_terms:
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tl = t.lower()
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if tl in lower:
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score += 2
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if tl in rel.lower():
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score += 4
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if score <= 0:
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continue
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lines = text.splitlines()
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for i, line in enumerate(lines):
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ls = line.lower()
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hit = 0
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for t in query_terms:
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if t.lower() in ls:
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hit += 1
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if hit <= 0:
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continue
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start = max(0, i - 18)
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end = min(len(lines), i + 19)
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block = "\n".join(f"{j+1:>5}: {lines[j]}" for j in range(start, end))
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rows.append({
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"path": rel,
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"start_line": start + 1,
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"end_line": end,
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"text": block,
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"score": score + hit * 5
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})
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rows.sort(key=lambda x: (-x["score"], x["path"], x["start_line"]))
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emit_rows(rows[:20], max_chars)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--index", default="")
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ap.add_argument("--embedding-index", default="")
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ap.add_argument("--repo-root", default="")
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ap.add_argument("--rewrite", required=True)
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ap.add_argument("--issue", required=True)
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ap.add_argument("--max-chars", type=int, default=50000)
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ap.add_argument("--embedding-api-key", default="")
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ap.add_argument("--embedding-base-url", default="")
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ap.add_argument("--embedding-model", default="")
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args = ap.parse_args()
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rewrite = load_json(args.rewrite)
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issue = load_json(args.issue)
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if args.index and args.embedding_index and args.embedding_api_key and args.embedding_base_url and args.embedding_model:
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retrieve_from_index_with_embeddings(
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args.index,
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args.embedding_index,
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rewrite,
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issue,
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args.max_chars,
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args.embedding_api_key,
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args.embedding_base_url,
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args.embedding_model
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)
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return
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if args.index:
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retrieve_from_index(args.index, rewrite, issue, args.max_chars)
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return
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if args.repo_root:
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retrieve_from_repo(args.repo_root, rewrite, issue, args.max_chars)
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return
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print("")
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if __name__ == "__main__":
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main()
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