selfrelease
0c12948fa4
- mock_sync.py: 新增6个减肥场景模板(成交/异议/咨询/对比/复购) + 分配给李娜和王强 - analyze.py: 扩展关键词(减肥/减脂/B420/纤益/代餐等) + 行业识别支持年轻女性 + LLM prompt增加减肥产品背景 - run_demo.sh: 新增4条减肥益生菌预置成交记录(小鹿/阿狸/小鹿复购)
459 lines
18 KiB
Python
459 lines
18 KiB
Python
#!/usr/bin/env python3
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"""AI 分析服务 — 客户识别 + 沟通摘要（规则引擎 / LLM 模式）。
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用法:
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python analyze.py --mode rule
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python analyze.py --mode llm
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python analyze.py --mode llm --force
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环境变量:
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DASHSCOPE_API_KEY — 千问 API Key
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"""
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from __future__ import annotations
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import argparse
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import datetime as dt
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import json
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import os
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import pathlib
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import sys
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import urllib.request
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import urllib.error
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import psycopg2
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TZ = dt.timezone(dt.timedelta(hours=8))
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DEFAULT_SOCKET = str(pathlib.Path(__file__).resolve().parent.parent / "db" / "socket")
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DEFAULT_DSN = f"host={DEFAULT_SOCKET} port=5434 dbname=wxchat_sales"
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DASHSCOPE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions"
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DASHSCOPE_MODEL = "qwen-plus"
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# ---------------------------------------------------------------------------
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# 关键词定义
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# ---------------------------------------------------------------------------
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SYMPTOM_WORDS = [
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"湿疹", "过敏", "鼻炎", "腹泻", "便秘", "免疫力", "体质", "红疹",
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"胀气", "拉肚子", "打喷嚏", "流鼻涕", "感冒", "食物过敏", "牛奶蛋白过敏",
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"减肥", "减脂", "瘦身", "产后", "体脂", "肚子", "大腿", "反弹", "代餐",
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]
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PRODUCT_WORDS = [
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"益生菌", "菌株", "进口", "配方", "成分", "丹麦", "鼠李糖", "无敏",
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"LGG", "临床", "调理", "肠道", "免疫",
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"B420", "纤益", "脂肪代谢", "减脂", "肠道代谢", "膳食纤维",
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]
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PRICE_WORDS = [
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"价格", "多少钱", "费用", "报价", "优惠", "套餐", "盒", "周期",
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"298", "798", "1499", "328", "656", "贵", "便宜", "划算",
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]
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DEAL_WORDS = [
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"下单", "付款", "发货", "来几盒", "定了", "来3盒",
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"来6盒", "已付", "转过去", "签收", "已经转", "麻烦尽快发",
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"来2盒", "付了", "已付款",
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]
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OBJECTION_WORDS = [
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"贵", "太贵", "考虑", "商量", "老公", "副作用", "安全吗", "有没有效",
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"没用过", "合生元", "对比", "担心", "再看看", "网上", "几十块",
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"哺乳期", "反弹", "代餐", "节食", "拉肚子",
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]
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# 阶段关键词（按优先级排序）
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STAGE_KEYWORDS = [
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("成交", DEAL_WORDS),
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("报价", ["多少钱", "价格", "套餐", "298", "798", "1499", "328", "656", "报价"]),
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("异议处理", OBJECTION_WORDS),
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("需求发现", SYMPTOM_WORDS + ["什么情况", "多大了", "几个月", "症状"]),
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("建立联系", ["你好", "在吗", "请问"]),
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]
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def contains_any(text: str, words: list[str]) -> bool:
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return any(w in text for w in words)
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def count_hits(text: str, words: list[str]) -> int:
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return sum(1 for w in words if w in text)
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# ---------------------------------------------------------------------------
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# 客户识别
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# ---------------------------------------------------------------------------
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def identify_customer(messages: list[dict], salesperson_name: str | None = None) -> dict | None:
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"""规则引擎判断联系人是否为客户。返回 None 表示不是客户。"""
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if len(messages) <= 5:
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return None
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# 检查销售是否参与了对话（非客户联系人不会有销售回复）
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if salesperson_name:
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has_sales_reply = any(
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m["sender_display_name"] == salesperson_name for m in messages
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)
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if not has_sales_reply:
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return None
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all_text = " ".join(m["normalized_content"] or "" for m in messages)
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symptom_hits = count_hits(all_text, SYMPTOM_WORDS)
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product_hits = count_hits(all_text, PRODUCT_WORDS)
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price_hits = count_hits(all_text, PRICE_WORDS)
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deal_hits = count_hits(all_text, DEAL_WORDS)
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total_keyword_hits = symptom_hits + product_hits + price_hits + deal_hits
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if total_keyword_hits < 2:
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return None
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# 意向等级
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if deal_hits >= 1 and price_hits >= 1:
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intent_level = "high"
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elif (symptom_hits >= 1 and product_hits >= 1) or price_hits >= 1:
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intent_level = "medium"
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else:
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intent_level = "low"
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# 提取关键需求
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key_needs = []
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for symptom in SYMPTOM_WORDS:
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if symptom in all_text and symptom not in key_needs:
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key_needs.append(symptom)
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if len(key_needs) >= 3:
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break
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# 行业：减肥相关标记为年轻女性，否则宝妈
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diet_words = ["减肥", "减脂", "瘦身", "B420", "纤益", "代餐", "体脂", "产后减肥"]
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industry = "年轻女性" if any(w in all_text for w in diet_words) else "宝妈"
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return {
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"is_customer": True,
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"customer_name": None, # 后续用 display_name 填充
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"industry": industry,
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"intent_level": intent_level,
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"key_needs": key_needs,
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"reason": f"消息数{len(messages)}条，命中关键词{total_keyword_hits}个（症状{symptom_hits}+产品{product_hits}+价格{price_hits}+成交{deal_hits}）",
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}
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# ---------------------------------------------------------------------------
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# 沟通摘要
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# ---------------------------------------------------------------------------
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def generate_summary(messages: list[dict], mode: str = "rule") -> dict:
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"""生成沟通摘要。mode='rule' 用规则引擎，mode='llm' 用千问 LLM。"""
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if mode == "llm":
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llm_result = generate_summary_llm(messages)
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if llm_result:
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return llm_result
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print("[WARN] LLM 调用失败，回退到规则模式", file=sys.stderr)
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return generate_summary_rule(messages)
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def generate_summary_llm(messages: list[dict]) -> dict | None:
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"""调用千问 LLM 生成沟通摘要。"""
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api_key = os.environ.get("DASHSCOPE_API_KEY")
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if not api_key:
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print("[WARN] 未设置 DASHSCOPE_API_KEY，回退到规则模式", file=sys.stderr)
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return None
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# 构建对话文本
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dialog_lines = []
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for m in messages:
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sender = m["sender_display_name"] or "未知"
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content = m["normalized_content"] or f"[{m['message_type']}]"
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dialog_lines.append(f"{sender}: {content}")
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dialog_text = "\n".join(dialog_lines)
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# 截断过长的对话
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if len(dialog_text) > 8000:
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dialog_text = dialog_text[:4000] + "\n...（中间部分省略）...\n" + dialog_text[-4000:]
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prompt = f"""你是一个微信销售对话分析助手。以下是销售与客户的微信对话记录，请分析并输出 JSON 格式的沟通摘要。
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## 产品背景
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1. 益童宝儿童益生菌粉：丹麦进口菌株，主打小儿抗过敏（湿疹、鼻炎、食物过敏），298元/盒，3盒套餐798元，6盒套餐1499元。目标客户是宝妈。
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2. 纤益减肥益生菌：含专利B420菌株，主打健康减脂不反弹，不节食不腹泻，通过调节肠道菌群促进脂肪代谢。328元/盒（30天用量），2盒套餐656元。目标客户是18-35岁有体重管理需求的年轻女性。
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## 对话记录
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{dialog_text}
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## 输出要求
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请输出严格的 JSON，字段如下：
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- summary: 沟通核心要点总结（2-4句话，概括客户需求、销售策略、关键转折和结果）
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- stage: 销售阶段，取值之一：建立联系/需求发现/报价/异议处理/成交
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- key_points: 如果已成交，列出关键成交点（促成成交的关键因素，如信任建立、案例背书、价格拆解等）；如果未成交，列出需要突破的问题点（阻碍成交的核心原因，如价格顾虑、安全担忧、决策链阻碍等）。返回数组，每条一句话。
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- objections: 客户提出的异议或顾虑列表（如价格贵、安全顾虑、效果质疑等，空数组表示无）
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- next_action: 下一步行动建议（具体可执行）
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只输出 JSON，不要其他文字。"""
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body = json.dumps({
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"model": DASHSCOPE_MODEL,
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"messages": [
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{"role": "system", "content": "你是微信销售对话分析助手，擅长从对话中提取核心信息。"},
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{"role": "user", "content": prompt},
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],
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"response_format": {"type": "json_object"},
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"temperature": 0.3,
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}).encode("utf-8")
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req = urllib.request.Request(
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DASHSCOPE_URL,
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data=body,
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headers={
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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},
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method="POST",
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)
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try:
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print(f" [LLM] 调用千问 ({len(messages)} 条消息)...", file=sys.stderr, flush=True)
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with urllib.request.urlopen(req, timeout=60) as resp:
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result = json.loads(resp.read().decode("utf-8"))
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content = result["choices"][0]["message"]["content"]
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parsed = json.loads(content)
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return {
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"summary": parsed.get("summary", ""),
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"stage": parsed.get("stage", "建立联系"),
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"key_points": parsed.get("key_points", []),
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"objections": parsed.get("objections", []),
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"next_action": parsed.get("next_action", "继续跟进"),
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}
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except Exception as exc:
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print(f"[WARN] LLM 调用失败: {exc}", file=sys.stderr)
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return None
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def generate_summary_rule(messages: list[dict]) -> dict:
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"""规则引擎生成沟通摘要。"""
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all_text = " ".join(m["normalized_content"] or "" for m in messages)
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# 阶段判断（按优先级）
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stage = "建立联系"
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for stage_name, keywords in STAGE_KEYWORDS:
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if contains_any(all_text, keywords):
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stage = stage_name
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break
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# 异议提取
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objections = []
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for word in OBJECTION_WORDS:
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if word in all_text and word not in objections:
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objections.append(word)
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if not objections and stage == "异议处理":
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objections = ["其他异议"]
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# 摘要文本：取前 5 条和后 3 条消息拼接
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summary_parts = []
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for m in messages[:5]:
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sender = m["sender_display_name"] or "未知"
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content = m["normalized_content"] or f"[{m['message_type']}]"
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summary_parts.append(f"{sender}: {content}")
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if len(messages) > 8:
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summary_parts.append("...")
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for m in messages[-3:]:
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sender = m["sender_display_name"] or "未知"
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content = m["normalized_content"] or f"[{m['message_type']}]"
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summary_parts.append(f"{sender}: {content}")
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summary = " | ".join(summary_parts)
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# 下一步建议
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next_action = "继续跟进"
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if stage == "成交":
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next_action = "安排发货并跟进使用效果，适时推荐复购"
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elif stage == "异议处理":
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next_action = "发送案例和科普文章，3天后跟进"
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elif stage == "报价":
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next_action = "等待客户反馈，适时推荐套餐"
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elif stage == "需求发现":
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next_action = "深入了解客户需求，介绍产品优势"
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elif stage == "建立联系":
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next_action = "保持互动，寻找需求切入点"
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# 关键点（成交点或突破点）
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key_points = []
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if stage == "成交":
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if "临床" in all_text or "验证" in all_text:
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key_points.append("临床验证数据增强信任")
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if "反馈" in all_text or "案例" in all_text:
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key_points.append("真实案例背书打消顾虑")
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if "798" in all_text or "套餐" in all_text:
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key_points.append("套餐价格拆解降低价格敏感度")
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if "售后" in all_text or "指导" in all_text:
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key_points.append("售后承诺降低试错风险")
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if not key_points:
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key_points.append("客户需求明确，销售及时跟进促成成交")
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else:
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if "贵" in all_text or "太贵" in all_text:
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key_points.append("价格顾虑：需进一步拆解日均成本或强调产品差异")
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if "安全" in all_text or "副作用" in all_text:
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key_points.append("安全顾虑：需提供更多无敏配方和临床数据")
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if "考虑" in all_text or "商量" in all_text or "老公" in all_text:
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key_points.append("决策链阻碍：需提供资料支持客户与家人沟通")
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if "合生元" in all_text or "对比" in all_text:
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key_points.append("竞品对比：需强化菌株差异和临床优势")
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if "效果" in all_text and ("不好" in all_text or "没用" in all_text):
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key_points.append("效果质疑：需提供更多真实反馈和售后保障")
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if not key_points:
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key_points.append("客户意向尚不明确，需持续跟进挖掘需求")
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return {
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"summary": summary[:500],
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"stage": stage,
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"key_points": key_points,
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"objections": objections,
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"next_action": next_action,
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}
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# ---------------------------------------------------------------------------
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# 主流程
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# ---------------------------------------------------------------------------
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def main():
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ap = argparse.ArgumentParser(description="AI 分析服务")
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ap.add_argument("--dsn", default=DEFAULT_DSN)
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ap.add_argument("--mode", choices=["rule", "llm"], default="rule")
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ap.add_argument("--force", action="store_true", help="强制重新分析")
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args = ap.parse_args()
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if args.mode == "llm" and not os.environ.get("DASHSCOPE_API_KEY"):
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print("[WARN] 未设置 DASHSCOPE_API_KEY 环境变量，将使用规则模式", file=sys.stderr)
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pg = psycopg2.connect(args.dsn)
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pg.autocommit = False
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try:
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with pg.cursor() as cur:
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# 获取需要分析的联系人
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if args.force:
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cur.execute("""
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SELECT c.id, c.salesperson_id, c.display_name, c.is_group,
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c.wx_username
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FROM contact c
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WHERE c.is_group = FALSE
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ORDER BY c.id
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""")
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else:
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cur.execute("""
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SELECT c.id, c.salesperson_id, c.display_name, c.is_group,
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c.wx_username
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FROM contact c
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LEFT JOIN customer cu ON cu.contact_id = c.id AND cu.salesperson_id = c.salesperson_id
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WHERE c.is_group = FALSE
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AND (cu.id IS NULL OR cu.last_analysis IS NULL)
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ORDER BY c.id
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""")
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contacts = cur.fetchall()
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customers_created = 0
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summaries_generated = 0
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for contact_id, sp_id, display_name, is_group, wx_username in contacts:
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# 获取该联系人的所有消息
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with pg.cursor() as cur:
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cur.execute("""
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SELECT m.normalized_content, m.sender_display_name, m.message_type,
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m.created_at, conv.id
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FROM message m
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JOIN conversation conv ON conv.id = m.conversation_id
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WHERE conv.contact_id = %s
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ORDER BY m.created_at
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""", (contact_id,))
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rows = cur.fetchall()
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if not rows:
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continue
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messages = [
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{
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"normalized_content": r[0],
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"sender_display_name": r[1],
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"message_type": r[2],
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"created_at": r[3],
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}
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for r in rows
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]
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|
||
# 获取销售名用于过滤
|
||
with pg.cursor() as cur2:
|
||
cur2.execute("SELECT name FROM salesperson WHERE id=%s", (sp_id,))
|
||
sp_name = cur2.fetchone()[0]
|
||
|
||
# 客户识别
|
||
result = identify_customer(messages, salesperson_name=sp_name)
|
||
|
||
if result is None:
|
||
# 不是客户，跳过
|
||
continue
|
||
|
||
conv_id = rows[0][4]
|
||
|
||
# 沟通摘要
|
||
summary_result = generate_summary(messages, mode=args.mode)
|
||
|
||
# 写入 customer 表
|
||
with pg.cursor() as cur:
|
||
cur.execute("""
|
||
INSERT INTO customer (salesperson_id, contact_id, customer_name, industry,
|
||
intent_level, key_needs, reason, summary, stage, key_points, objections,
|
||
next_action, last_analysis)
|
||
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, now())
|
||
ON CONFLICT (salesperson_id, contact_id) DO UPDATE SET
|
||
customer_name=excluded.customer_name, industry=excluded.industry,
|
||
intent_level=excluded.intent_level, key_needs=excluded.key_needs,
|
||
reason=excluded.reason, summary=excluded.summary, stage=excluded.stage,
|
||
key_points=excluded.key_points,
|
||
objections=excluded.objections, next_action=excluded.next_action,
|
||
last_analysis=now()
|
||
""", (
|
||
sp_id, contact_id, display_name, result["industry"],
|
||
result["intent_level"], result["key_needs"], result["reason"],
|
||
summary_result["summary"], summary_result["stage"],
|
||
summary_result["key_points"],
|
||
summary_result["objections"], summary_result["next_action"],
|
||
))
|
||
|
||
customers_created += 1
|
||
summaries_generated += 1
|
||
|
||
pg.commit()
|
||
|
||
# 统计
|
||
with pg.cursor() as cur:
|
||
cur.execute("SELECT count(*) FROM customer")
|
||
total_customers = cur.fetchone()[0]
|
||
cur.execute("SELECT intent_level, count(*) FROM customer GROUP BY intent_level ORDER BY intent_level")
|
||
intent_dist = {r[0]: r[1] for r in cur.fetchall()}
|
||
cur.execute("SELECT stage, count(*) FROM customer GROUP BY stage ORDER BY count(*) DESC")
|
||
stage_dist = {r[0]: r[1] for r in cur.fetchall()}
|
||
|
||
print(json.dumps({
|
||
"mode": args.mode,
|
||
"contacts_analyzed": len(contacts),
|
||
"customers_identified": customers_created,
|
||
"summaries_generated": summaries_generated,
|
||
"total_customers": total_customers,
|
||
"intent_distribution": intent_dist,
|
||
"stage_distribution": stage_dist,
|
||
}, ensure_ascii=False, indent=2))
|
||
return 0
|
||
|
||
except Exception as exc:
|
||
pg.rollback()
|
||
print(f"[ERROR] {exc}", file=sys.stderr)
|
||
return 1
|
||
finally:
|
||
pg.close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
sys.exit(main())
|