""" 售后报告生成引擎 - 从 topic_messages 拿到所有 msg_seq - 通过 chatlog batch 接口批量拉回消息原文 - 用配置的总结模型生成 Markdown 售后事件报告 - 写入 knowledge_docs + knowledge_fts（jieba 分词） """ import asyncio import logging import json import aiosqlite from urllib.parse import quote from database import get_active_db_path from services.ai_client import get_openai_client from services.fts import tokenize from services.message_formatter import append_quote_text, extract_contents, extract_quote from services.report_learning import build_report_learning_context log = logging.getLogger(__name__) CHATLOG_BATCH_SIZE = 80 SUMMARY_LLM_TIMEOUT_SECONDS = 300 async def _get_client(): return await get_openai_client() def _message_line(item: dict, fallback_seq: int = 0) -> tuple[int, str] | None: if not item: return None seq = item.get("seq") or item.get("Seq") or item.get("sort_seq") or fallback_seq or 0 time_str = item.get("create_time") or item.get("time") or item.get("CreateTime") or "" sender = ( item.get("sender_name") or item.get("senderName") or item.get("SenderName") or item.get("sender") or item.get("Sender") or "" ) content = _message_text(item) if not content: return None return int(seq), f"[{time_str}] {sender}: {content}" def _message_meta(item: dict, fallback_seq: int = 0) -> dict: return { "seq": int(item.get("seq") or item.get("Seq") or item.get("sort_seq") or fallback_seq or 0), "time": item.get("create_time") or item.get("time") or item.get("CreateTime") or "", "sender": ( item.get("sender_name") or item.get("senderName") or item.get("SenderName") or item.get("sender") or item.get("Sender") or "" ), "type": item.get("type") or item.get("Type") or 1, } def _extract_contents(item: dict) -> dict: return extract_contents(item) def _message_text(item: dict) -> str: content = item.get("content") or item.get("Content") or "" contents = _extract_contents(item) if isinstance(content, str) and content.lstrip().startswith("<") and extract_quote(item): content = "" link_title = contents.get("title") or item.get("link_title") or "" link_desc = contents.get("desc") or item.get("link_desc") or "" link_source = contents.get("sourceName") or contents.get("source_name") or item.get("link_source") or "" link_url = contents.get("url") or item.get("link_url") or "" if link_title: parts = [f"[链接卡片] {link_title}"] if link_desc: parts.append(link_desc) if link_source: parts.append(f"来源：{link_source}") if link_url: parts.append(f"URL：{link_url}") if content and content not in parts: parts.append(content) return append_quote_text("；".join(parts), item) return append_quote_text(content, item) def _extract_image_key(item: dict) -> str: contents = _extract_contents(item) key = ( contents.get("rawmd5") or contents.get("md5") or contents.get("path") or item.get("media_key") or item.get("mediaKey") or item.get("image_path") or "" ) return str(key).replace("\\", "/") def _is_image_message(item: dict) -> bool: try: return int(item.get("type") or item.get("Type") or 0) == 3 except Exception: return False def _media_path(kind: str, key: str) -> str: return f"/{kind}/" + "/".join(quote(part) for part in key.split("/")) def _image_url(key: str) -> str: return f"{_media_path('image', key)}?thumb=1" def _collect_image_evidence(messages: list[dict]) -> tuple[list[dict], list[dict]]: images: list[dict] = [] failures: list[dict] = [] for item in messages: if not _is_image_message(item): continue meta = _message_meta(item) key = _extract_image_key(item) if not key: failures.append({**meta, "url": "", "reason": "图片无法展示，缺少图片文件标识"}) continue url = _image_url(key) images.append({**meta, "key": key, "url": url}) return images, failures def _image_evidence_context(images: list[dict], failures: list[dict]) -> str: lines: list[str] = [] if images: lines.append("系统将作为原始材料插入报告的现场图片：") for img in images: lines.append(f"- [{img['time']}] {img['sender']} seq={img['seq']} url={img['url']}") if failures: lines.append("无法展示的图片清单：") for img in failures: link = f"，查看图片：{img['url']}" if img.get("url") else "" lines.append(f"- [{img['time']}] {img['sender']} seq={img['seq']}：{img['reason']}{link}") return "\n".join(lines) def _image_success_markdown(images: list[dict]) -> str: if not images: return "" blocks = ["### 现场图片"] for img in images: alt = f"现场图片 - {img['time']} {img['sender']}".strip() blocks.extend( [ f"![{alt}]({img['url']})", f"来源：{img['time']} {img['sender']} seq={img['seq']}", "", ] ) return "\n".join(blocks).strip() def _image_failure_markdown(failures: list[dict]) -> str: if not failures: return "" lines = ["## 图片展示提示"] for img in failures: link = f"，查看图片：{img['url']}" if img.get("url") else "" lines.append(f"- [{img['time']}] {img['sender']} seq={img['seq']}：{img['reason']}{link}") return "\n".join(lines) def _insert_after_heading(content: str, heading: str, addition: str) -> str: if not addition: return content lines = content.splitlines() for i, line in enumerate(lines): if line.strip() == heading: return "\n".join(lines[: i + 1] + ["", addition, ""] + lines[i + 1 :]).strip() for i, line in enumerate(lines): if line.startswith("# "): return "\n".join(lines[: i + 1] + ["", heading, "", addition, ""] + lines[i + 1 :]).strip() return f"{heading}\n\n{addition}\n\n{content}".strip() def _merge_image_sections(content: str, successes: list[dict], failures: list[dict]) -> str: result = _insert_after_heading(content, "## 关键聊天依据", _image_success_markdown(successes)) failure_md = _image_failure_markdown(failures) if failure_md: result = f"{result.rstrip()}\n\n{failure_md}" return result.strip() def _line_from_snapshot(raw: str | None, fallback_seq: int) -> str | None: if not raw: return None try: item = json.loads(raw) except Exception: return None line = _message_line(item, fallback_seq) return line[1] if line else None MARKDOWN_TEMPLATE = """\ # {title} 请按聊天记录中的实际内容生成一份【具体售后问题点】报告，不要照抄固定字段，也不要输出占位文案。 必须围绕以下结构组织，按内容决定是否保留章节，不要输出空章节： ## 问题摘要 ## 关键聊天依据 ## 当前处理状态 ## 是否解决 ## AI 建议/解决方法 输出规则： - 只写聊天记录中能直接识别或合理归纳的信息。 - 没有识别到的客户、门店、联系人、合同、订单、物流、日期、价格、原因等信息直接省略。 - 不要写“未从聊天记录中识别”“待补充”“未知”“无”等占位内容。 - “是否解决”只能从聊天记录判断，取值限定为：已解决、未解决、处理中、待确认。 - 如果聊天内容不足以形成明确售后问题点，仍然按当前话题内容整理，但用更保守的“待确认”结论。 - “AI 建议/解决方法”必须放在文档下方，并附注：注：此方法由 AI 生成，仅供参考，请以人工复核和现场实际情况为准。 - 只输出 Markdown 报告，不要输出这些规则本身。 """ async def _mark_summarize_failed(topic_id: int, task_id: int | None, error: str): path = get_active_db_path() message = error or "AI 报告生成失败" try: async with aiosqlite.connect(path) as db: await db.execute( "UPDATE topics SET status = 'error', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,), ) if task_id is not None: await db.execute( """ UPDATE ai_tasks SET status='error', progress=?, error=?, updated_at=CURRENT_TIMESTAMP WHERE id=? """, (json.dumps({"processed": 0, "total": 1}), message, task_id), ) await db.commit() except Exception as exc: log.warning(f"[summarize] 标记失败状态失败 topic={topic_id} task={task_id}: {exc}") async def _run_summarize_impl(topic_id: int, topic: dict, task_id: int | None = None): """ 为指定话题生成/更新 Markdown 售后事件报告。 由 POST /api/topics/{id}/summarize（手动触发）调用。 task_id: 若提供，则更新 ai_tasks 表的状态和进度。 """ path = get_active_db_path() async def _update_task(status: str, processed: int = 0, total: int = 1, error: str = ""): """辅助函数：更新 ai_tasks 状态和进度""" if task_id is None: return try: async with aiosqlite.connect(path) as _db: _db.row_factory = aiosqlite.Row await _db.execute( """ UPDATE ai_tasks SET status=?, progress=?, error=?, updated_at=CURRENT_TIMESTAMP WHERE id=? """, (status, json.dumps({"processed": processed, "total": total}), error or None, task_id) ) await _db.commit() except Exception as e: log.warning(f"[summarize] 更新 task {task_id} 失败: {e}") path = get_active_db_path() async with aiosqlite.connect(path) as db: db.row_factory = aiosqlite.Row # 将话题状态置为 processing await db.execute("UPDATE topics SET status = 'processing', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,)) await db.commit() await _update_task("running", 0, 1) # 1. 拿到该话题的所有消息 seq 和群 talker async with db.execute( """ SELECT tm.msg_seq, tm.talker, tm.message_json FROM topic_messages tm WHERE tm.topic_id = ? ORDER BY tm.msg_seq """, (topic_id,), ) as cur: msg_rows = await cur.fetchall() if not msg_rows: log.warning(f"[summarize] topic={topic_id} 没有消息，跳过") error = "该话题没有关联消息，无法生成 AI 报告" await db.execute("UPDATE topics SET status = 'error', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,)) await db.commit() await _update_task("error", 0, 1, error) return seqs = [r["msg_seq"] for r in msg_rows] # talker 在 topic_messages 里存的是群 ID（chatlog 叫 talker） group_talker = msg_rows[0]["talker"] # 2. 批量从 chatlog 拉取消息原文（最多 100 条/批） from services.chatlog_client import chatlog_client messages_text: list[str] = [] message_items: dict[int, dict] = {} fetched_lines: dict[int, str] = {} for i in range(0, len(seqs), CHATLOG_BATCH_SIZE): chunk_seqs = seqs[i: i + CHATLOG_BATCH_SIZE] try: result = await chatlog_client.get_messages_batch(group_talker, chunk_seqs) for m in result.get("items", []): meta = _message_meta(m) if meta["seq"]: message_items[meta["seq"]] = m line = _message_line(m) if line: fetched_lines[line[0]] = line[1] except Exception as e: log.error(f"[summarize] batch 拉取失败 topic={topic_id}: {e}") for r in msg_rows: seq = int(r["msg_seq"]) if seq in fetched_lines: messages_text.append(fetched_lines[seq]) continue snap_raw = r["message_json"] if "message_json" in r.keys() else None if seq not in message_items and snap_raw: try: snap_item = json.loads(snap_raw) if isinstance(snap_item, dict): message_items[seq] = snap_item except Exception: pass snap_line = _line_from_snapshot(snap_raw, seq) if snap_line: messages_text.append(snap_line) image_successes, image_failures = _collect_image_evidence( [message_items[seq] for seq in seqs if seq in message_items] ) if not messages_text and not image_successes and not image_failures: log.warning(f"[summarize] topic={topic_id} 从 chatlog 获取到 0 条有效消息") error = "未能从 chatlog 获取到有效消息，无法生成 AI 报告" await db.execute("UPDATE topics SET status = 'error', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,)) await db.commit() await _update_task("error", 0, 1, error) return chat_text = "\n".join(messages_text) if messages_text else "无文字消息，仅有图片或媒体证据。" image_context = _image_evidence_context(image_successes, image_failures) learning_context = await build_report_learning_context( db, group_id=topic.get("group_id"), query=f"{topic.get('title', '')}\n{chat_text[:2000]}", exclude_topic_id=topic_id, purpose="summary", ) # 3. 构建 Prompt template_filled = MARKDOWN_TEMPLATE.format(title=topic["title"]) prompt = ( f"售后问题点话题：{topic['title']}\n\n" f"以下是该售后问题点关联的完整微信群聊天记录（按时间顺序）：\n\n" f"{chat_text}\n\n" f"以下是系统将插入报告的现场图片信息（如有）：\n\n{image_context or '无现场图片。'}\n\n" "请根据上述聊天记录输出一份 Markdown 报告。\n" "报告要求：\n" "1. 保持售后问题点口径，优先提炼问题现象、涉及产品/部件、现场材料、处理过程和处理结果。\n" "2. 只能使用聊天记录中能直接识别或合理归纳的信息，不要编造客户、合同、订单、物流、日期、价格、原因或处理结果。\n" "3. 不要输出空字段、空项目、空章节、空表格；某个章节没有有效内容时整段省略。\n" "4. 「是否解决」必须写在文档中，并使用：已解决 / 未解决 / 处理中 / 待确认。\n" "5. 「AI 建议/解决方法」必须写在文档中，且在段末附上固定注释：注：此方法由 AI 生成，仅供参考，请以人工复核和现场实际情况为准。\n" "6. 如果聊天内容不足以形成明确售后问题点，也不要编造结论；只按聊天中已有事实给出保守的待确认判断。\n" "7. 图片会由系统作为「现场图片」原始材料插入「关键聊天依据」；你不要猜测图片内容，也不要自行输出图片 Markdown 或图片说明。\n" "8. 如果聊天文字中有人描述图片内容，可以引用这些文字；但不要根据图片本身编造故障细节。\n" "9. 聊天记录中的「[引用消息]」属于当前回复的上下文证据，可以用于理解被回复的问题和处理过程。\n" "10. 只输出 Markdown 报告，不要输出模板说明或额外解释。\n\n" f"以下是本企业报告库中人工修订过的历史报告示例（如有）。请只学习它们的栏目结构、措辞风格、问题关注点和结论表达方式；不得复制历史事实、客户名、设备状态或处理结果到当前报告：\n\n{learning_context or '暂无可学习的人工修订报告。'}\n\n" f"{template_filled}" ) # 4. 调用 LLM try: _client, _ai = await _get_client() async with asyncio.timeout(SUMMARY_LLM_TIMEOUT_SECONDS): resp = await _client.chat.completions.create( model=_ai["summary_model"], messages=[ { "role": "system", "content": ( "你是资深售后运营与设备服务工程师，负责根据微信群聊天记录整理具体售后问题点报告。" "你必须忠实依据聊天记录，只输出已识别到的有效信息，缺失信息直接省略，不得编造。" "你要在文档中明确给出是否解决结论，并给出 AI 建议/解决方法和免责声明。只输出 Markdown 报告，不要有任何额外说明。" ), }, {"role": "user", "content": prompt}, ], temperature=0.2, ) content = resp.choices[0].message.content.strip() content = _merge_image_sections(content, image_successes, image_failures) except TimeoutError: error = "AI 报告生成超时，请检查模型/API或稍后重试" log.error(f"[summarize] LLM 调用超时 topic={topic_id}") await db.execute("UPDATE topics SET status = 'error', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,)) await db.commit() await _update_task("error", 0, 1, error) return except Exception as e: log.error(f"[summarize] LLM 调用失败 topic={topic_id}: {e}", exc_info=True) await db.execute("UPDATE topics SET status = 'error', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,)) await db.commit() await _update_task("error", 0, 1, str(e) or "LLM 调用失败") return # 5. 写入 knowledge_docs async with db.execute( "SELECT id FROM knowledge_docs WHERE topic_id = ?", (topic_id,) ) as cur: existing = await cur.fetchone() if existing: doc_id = existing["id"] await db.execute( "UPDATE knowledge_docs SET content = ?, updated_at = CURRENT_TIMESTAMP WHERE id = ?", (content, doc_id), ) else: await db.execute( "INSERT INTO knowledge_docs (topic_id, content) VALUES (?, ?)", (topic_id, content), ) async with db.execute("SELECT last_insert_rowid() AS id") as cur: doc_id = (await cur.fetchone())["id"] # 6. 更新 FTS（先删后插） await db.execute("DELETE FROM knowledge_fts WHERE doc_id = ?", (doc_id,)) await db.execute( "INSERT INTO knowledge_fts (doc_id, title, content) VALUES (?, ?, ?)", (doc_id, tokenize(topic["title"]), tokenize(content)), ) await db.execute("UPDATE topics SET status = 'completed', updated_at = CURRENT_TIMESTAMP WHERE id = ?", (topic_id,)) await db.commit() await _update_task("done", 1, 1) log.info(f"[summarize] topic={topic_id} doc={doc_id} 生成完成（{len(content)} 字符）") async def run_summarize(topic_id: int, topic: dict, task_id: int | None = None): try: await _run_summarize_impl(topic_id, topic, task_id) except Exception as e: error = str(e) or e.__class__.__name__ log.error(f"[summarize] 未捕获异常 topic={topic_id}: {error}", exc_info=True) await _mark_summarize_failed(topic_id, task_id, error)