feat: 可配置方案引擎通用原型（Mock 检索）

需求未定稿时可先跑通追问/判定/出方案；槽位与判定树外置 YAML，后续可接 Ragflow。

Co-authored-by: haijd <haijd@163.com>

.gitignore

@@ -0,0 +1,6 @@
+__pycache__/
+.venv/
+venv/
+.env
+*.pyc
+.DS_Store

README.md

@@ -1,37 +1,51 @@
-# 鲜境物联 · AI 智能客服｜技术路线建议
+# 鲜境方案引擎 · 通用原型（需求未定稿版）
 
-综合建议书、验收标准、**委托开发合同**与**附件二进度表**后的实施路线结论。
+业务规则还在变时：**先搭可配置原型，再按真实需求改 YAML，而不是先写死后台。**
 
-## 结论
+## 策略
 
-**推荐：开源自托管二开 + 核心业务定制。不建议纯自研，不建议纯公有云 SaaS。**
+1. 先跑通：多轮追问（槽位）→ 判定树出组合 → 检索补依据 → 出方案草稿  
+2. 槽位 / 判定树 / 示例知识 **全部外置配置**，需求变了主要改配置  
+3. 检索默认 **Mock**（不依赖本机 Ragflow）；有 Ragflow 后改环境变量即可接真库  
+4. 不做大而全后台；控制台能聊天验证闭环即可  
 
-| 路线 | 结论 |
+## 本仓库原型包含
+
+| 路径 | 作用 |
 |------|------|
-| 纯自研 | 不推荐首期（撞上 **7 周 / ¥5 万** 硬顶） |
-| 纯 SaaS（扣子 / 阿里云客服等） | 不推荐（数据自主可控冲突） |
-| **Dify 自托管二开 + 方案判定定制** | **推荐** |
+| `engine/` | FastAPI 方案引擎 + 简易聊天页 |
+| `knowledge/` | `slots.yaml` / `decision_tree.yaml` / 示例检索片段 |
+| `docs/Ragflow实施方案.md` | 完整架构说明 |
 
-**二开首选：Dify**；检索吃力时加 **Ragflow**；人工席位 **Chatwoot**（或企微/飞书）。
+## 快速启动
 
-合同已锁：实施 **¥50,000**、订阅 **¥30,000/年**、周期 **7 周**、线上场景 **只做一个高频渠道**。详见完整建议书。
+```bash
+cd /workspace
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r engine/requirements.txt
+uvicorn engine.app.main:app --host 0.0.0.0 --port 3847
+```
 
-## 文档
+打开：[方案引擎原型](http://127.0.0.1:3847/)
 
-| 文件 | 说明 |
-|------|------|
-| [docs/技术方案建议书.md](./docs/技术方案建议书.md) | 自研/二开决策、Dify vs Ragflow、许可 |
-| [docs/Ragflow实施方案.md](./docs/Ragflow实施方案.md) | **选定 Ragflow 后怎么搭**（架构、知识库、方案引擎、分期） |
-| [uploads/](./uploads/) | 原始合同、进度表、建议书、验收标准 |
+健康检查：`GET /api/health`  
+重载业务配置：`POST /api/admin/reload-config`（改完 YAML 不用重启进程时用）
 
-## 若已定 Ragflow
+## 接真 Ragflow（稍后）
 
-按 [Ragflow实施方案](./docs/Ragflow实施方案.md)：自托管 Ragflow 做检索 → 自研方案引擎做追问/判定/转人工 → 首期只接一个渠道。
+```bash
+export RETRIEVAL_BACKEND=ragflow
+export RAGFLOW_BASE_URL=http://127.0.0.1:9380
+export RAGFLOW_API_KEY=...
+export RAGFLOW_DATASET_IDS=...
+```
 
-## 建议立刻确认
+未配置时保持 `RETRIEVAL_BACKEND=mock`。
 
-1. 首期唯一渠道：网站还是 Facebook？  
-2. 方案判定树的业务拍板人  
-3. 模型：智谱 API 是否可上生产 / 是否必须私有化  
-4. 场景二（录音辅助）是否与场景一同期验收  
-5. 部署：甲方机房还是乙方 VPC
+## 需求清晰后怎么补
+
+1. 改 `knowledge/slots.yaml`（追问项）  
+2. 改 `knowledge/decision_tree.yaml`（推荐规则）  
+3. 灌真实资料进 Ragflow，切 `ragflow` 后端  
+4. 再加渠道 Webhook / 转人工 / Eval，而不是推倒重来  

docs/原型搭建步骤.md

@@ -0,0 +1,18 @@
+# 需求未定稿时：先搭通用原型的步骤
+
+## 要不要先搭原型？
+
+**要。** 做成「配置驱动的通用骨架」，不要等需求 100% 精确再动手。
+
+- 先验证：追问 → 判定 → 检索引用 → 出方案草稿  
+- 业务变了：主要改 `knowledge/*.yaml`  
+- 后台/渠道：闭环跑通后再补  
+
+## 具体步骤
+
+1. **拉起本原型**（不必先装 Ragflow）  
+2. **和业务改槽位/判定树 YAML**（边聊边改，`POST /api/admin/reload-config`）  
+3. **灌真实小样本到 Ragflow**，切 `RETRIEVAL_BACKEND=ragflow`  
+4. **再补**转人工、单渠道、Eval、薄后台  
+
+不要颠倒：先做大后台或双渠道。

engine/__init__.py

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+# Package

engine/app/__init__.py

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+# Package marker

engine/app/config_loader.py

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+from __future__ import annotations
+
+from typing import Any
+
+import yaml
+
+from .settings import settings
+
+
+def _load_yaml(name: str) -> dict[str, Any]:
+    path = settings.knowledge_dir / name
+    with path.open(encoding="utf-8") as f:
+        data = yaml.safe_load(f) or {}
+    if not isinstance(data, dict):
+        raise ValueError(f"{name} must be a mapping")
+    return data
+
+
+class BusinessConfig:
+    def __init__(self) -> None:
+        self.reload()
+
+    def reload(self) -> None:
+        self.slots_doc = _load_yaml("slots.yaml")
+        self.tree_doc = _load_yaml("decision_tree.yaml")
+        self.chunks_doc = _load_yaml("sample_chunks.yaml")
+
+    @property
+    def slots(self) -> list[dict[str, Any]]:
+        return list(self.slots_doc.get("slots") or [])
+
+    @property
+    def handoff_phrases(self) -> list[str]:
+        return list(self.slots_doc.get("handoff_phrases") or [])
+
+    @property
+    def closing_prompt(self) -> str:
+        return str(self.slots_doc.get("closing_prompt") or "正在生成方案…")
+
+    @property
+    def rules(self) -> list[dict[str, Any]]:
+        return sorted(self.tree_doc.get("rules") or [], key=lambda r: r.get("order", 100))
+
+    @property
+    def handoff_confidence_below(self) -> float:
+        return float(self.tree_doc.get("handoff_confidence_below", 0.55))
+
+    @property
+    def chunks(self) -> list[dict[str, Any]]:
+        return list(self.chunks_doc.get("chunks") or [])
+
+
+business_config = BusinessConfig()

engine/app/decision.py

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+from __future__ import annotations
+
+from typing import Any
+
+
+def _match_condition(cond: dict[str, Any], slots: dict[str, str]) -> bool:
+    ctype = cond.get("type")
+    if ctype == "always":
+        return True
+    slot = str(cond.get("slot") or "")
+    value = str(cond.get("value") or "")
+    actual = (slots.get(slot) or "").strip()
+    if ctype == "slot_equals":
+        return actual == value
+    if ctype == "slot_contains":
+        return value.lower() in actual.lower()
+    return False
+
+
+def evaluate_tree(rules: list[dict[str, Any]], slots: dict[str, str]) -> dict[str, Any]:
+    for rule in rules:
+        conditions = rule.get("when_all") or []
+        if all(_match_condition(c, slots) for c in conditions):
+            then = dict(rule.get("then") or {})
+            return {
+                "rule_id": rule.get("id"),
+                "description": rule.get("description"),
+                **then,
+            }
+    return {
+        "rule_id": "empty",
+        "product_bundle": ["待专家确认"],
+        "tech_line": "待判定",
+        "summary": "未匹配到规则",
+        "confidence": 0.0,
+        "handoff": True,
+    }

engine/app/main.py

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+from __future__ import annotations
+
+from pathlib import Path
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import FileResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel, Field
+
+from .config_loader import business_config
+from .scheme_engine import create_session, handle_message
+from .settings import settings
+
+STATIC_DIR = Path(__file__).resolve().parent / "static"
+
+app = FastAPI(title=settings.app_name, version="0.1.0")
+app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
+
+
+class ChatRequest(BaseModel):
+    message: str = Field(..., min_length=1)
+    session_id: str | None = None
+
+
+class SessionResponse(BaseModel):
+    session_id: str
+
+
+@app.get("/")
+def index() -> FileResponse:
+    return FileResponse(STATIC_DIR / "index.html")
+
+
+@app.get("/api/health")
+def health() -> dict:
+    return {
+        "ok": True,
+        "app": settings.app_name,
+        "retrieval_backend": settings.retrieval_backend,
+        "slots": len(business_config.slots),
+        "rules": len(business_config.rules),
+    }
+
+
+@app.get("/api/config/slots")
+def get_slots() -> dict:
+    return business_config.slots_doc
+
+
+@app.get("/api/config/decision-tree")
+def get_tree() -> dict:
+    return business_config.tree_doc
+
+
+@app.post("/api/admin/reload-config")
+def reload_config() -> dict:
+    business_config.reload()
+    return {"reloaded": True, "slots": len(business_config.slots), "rules": len(business_config.rules)}
+
+
+@app.post("/api/sessions", response_model=SessionResponse)
+def api_create_session() -> SessionResponse:
+    session = create_session()
+    return SessionResponse(session_id=session.id)
+
+
+@app.post("/api/chat")
+def api_chat(body: ChatRequest) -> dict:
+    session_id = body.session_id
+    if not session_id:
+        session_id = create_session().id
+    try:
+        return handle_message(session_id, body.message)
+    except KeyError:
+        raise HTTPException(status_code=404, detail="session_not_found") from None

engine/app/retrieval.py

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+from __future__ import annotations
+
+from typing import Any
+
+import httpx
+
+from .config_loader import business_config
+from .settings import settings
+
+
+class RetrievalResult:
+    def __init__(self, chunks: list[dict[str, Any]]):
+        self.chunks = chunks
+
+
+def retrieve_for_scheme(slots: dict[str, str], bundle: list[str]) -> RetrievalResult:
+    backend = (settings.retrieval_backend or "mock").lower()
+    if backend == "ragflow":
+        return _retrieve_ragflow(slots, bundle)
+    return _retrieve_mock(slots, bundle)
+
+
+def _retrieve_mock(slots: dict[str, str], bundle: list[str]) -> RetrievalResult:
+    chunks = business_config.chunks
+    picked: list[dict[str, Any]] = []
+    bundle_set = set(bundle)
+    for c in chunks:
+        if c.get("product") in bundle_set:
+            picked.append(c)
+    # tag overlap with slot values
+    blob = " ".join(slots.values())
+    for c in chunks:
+        if c in picked:
+            continue
+        tags = c.get("tags") or []
+        if any(str(t) in blob for t in tags):
+            picked.append(c)
+        if len(picked) >= 4:
+            break
+    if not picked:
+        picked = chunks[:2]
+    return RetrievalResult(picked[:4])
+
+
+def _retrieve_ragflow(slots: dict[str, str], bundle: list[str]) -> RetrievalResult:
+    """Minimal Ragflow retrieval hook. Falls back to mock if not configured/unavailable."""
+    if not settings.ragflow_api_key:
+        return _retrieve_mock(slots, bundle)
+
+    query = " ".join(
+        [
+            slots.get("tech_category", ""),
+            slots.get("scenario", ""),
+            slots.get("key_metric", ""),
+            " ".join(bundle),
+        ]
+    ).strip() or "产品方案"
+    dataset_ids = [x for x in settings.ragflow_dataset_ids.split(",") if x.strip()]
+    url = settings.ragflow_base_url.rstrip("/") + "/api/v1/retrieval"
+    headers = {"Authorization": f"Bearer {settings.ragflow_api_key}"}
+    payload: dict[str, Any] = {"question": query, "top_k": 4}
+    if dataset_ids:
+        payload["dataset_ids"] = dataset_ids
+
+    try:
+        with httpx.Client(timeout=20.0) as client:
+            resp = client.post(url, headers=headers, json=payload)
+            resp.raise_for_status()
+            data = resp.json()
+    except Exception:
+        return _retrieve_mock(slots, bundle)
+
+    # Ragflow response shapes vary by version; normalize best-effort
+    records = []
+    if isinstance(data, dict):
+        records = data.get("data") or data.get("chunks") or data.get("records") or []
+        if isinstance(records, dict):
+            records = records.get("chunks") or records.get("records") or []
+    normalized = []
+    for i, r in enumerate(records[:4]):
+        if not isinstance(r, dict):
+            continue
+        normalized.append(
+            {
+                "id": r.get("id") or f"rf-{i}",
+                "title": r.get("document_keyword") or r.get("title") or "Ragflow片段",
+                "product": r.get("document_id") or "",
+                "content": r.get("content") or r.get("text") or "",
+                "tags": [],
+            }
+        )
+    if not normalized:
+        return _retrieve_mock(slots, bundle)
+    return RetrievalResult(normalized)

engine/app/scheme_engine.py

@@ -0,0 +1,113 @@
+from __future__ import annotations
+
+from typing import Any
+
+from .config_loader import business_config
+from .decision import evaluate_tree
+from .retrieval import retrieve_for_scheme
+from .session_store import (
+    Session,
+    looks_like_handoff,
+    naive_fill_slots,
+    next_slot_prompt,
+    store,
+)
+
+
+def create_session() -> Session:
+    return store.create()
+
+
+def handle_message(session_id: str, message: str) -> dict[str, Any]:
+    session = store.get(session_id)
+    if not session:
+        raise KeyError("session_not_found")
+
+    text = (message or "").strip()
+    session.messages.append({"role": "user", "content": text})
+
+    if looks_like_handoff(text, business_config.handoff_phrases):
+        session.phase = "handoff"
+        reply = "已为您标记转人工。请留下称呼与联系方式，销售同事会跟进。（原型：仅记录意图，未接 Chatwoot）"
+        return _finish(session, reply, need_handoff=True)
+
+    # fill slots while collecting
+    if session.phase in ("collecting", "scheme_ready"):
+        session.slots = naive_fill_slots(text, business_config.slots, session.slots)
+
+    nxt = next_slot_prompt(business_config.slots, session.slots)
+    if nxt and session.phase == "collecting":
+        # first turn with empty slots: greet + first question
+        if len(session.messages) == 1 and not any(session.slots.values()):
+            # put first user utterance into first slot if they already answered something
+            session.slots = naive_fill_slots(text, business_config.slots, session.slots)
+            nxt = next_slot_prompt(business_config.slots, session.slots)
+        if nxt:
+            reply = nxt["prompt"]
+            return _finish(session, reply, asking_slot=nxt["id"])
+
+    # all required (+ optional asked or filled) → scheme
+    decision = evaluate_tree(business_config.rules, session.slots)
+    confidence = float(decision.get("confidence") or 0)
+    need_handoff = bool(decision.get("handoff")) or confidence < business_config.handoff_confidence_below
+    bundle = list(decision.get("product_bundle") or [])
+    retrieval = retrieve_for_scheme(session.slots, bundle)
+
+    scheme = {
+        "tech_line": decision.get("tech_line"),
+        "product_bundle": bundle,
+        "summary": decision.get("summary"),
+        "confidence": confidence,
+        "rule_id": decision.get("rule_id"),
+        "citations": [
+            {"id": c.get("id"), "title": c.get("title"), "content": c.get("content")}
+            for c in retrieval.chunks
+        ],
+        "slots": dict(session.slots),
+    }
+    session.last_scheme = scheme
+    session.phase = "handoff" if need_handoff else "scheme_ready"
+
+    lines = [
+        business_config.closing_prompt,
+        "",
+        f"技术路线：{scheme['tech_line']}",
+        f"推荐组合：{', '.join(bundle)}",
+        f"说明：{scheme['summary']}",
+        f"置信度：{confidence:.0%}",
+    ]
+    if scheme["citations"]:
+        lines.append("")
+        lines.append("知识依据（原型检索）：")
+        for c in scheme["citations"][:3]:
+            lines.append(f"- {c['title']}：{c['content']}")
+    if need_handoff:
+        lines.append("")
+        lines.append("系统建议转人工确认后再对外发送方案。回复「转人工」可进入人工流程。")
+    else:
+        lines.append("")
+        lines.append("可继续补充约束，或回复「转人工」交给销售。")
+
+    reply = "\n".join(lines)
+    return _finish(session, reply, scheme=scheme, need_handoff=need_handoff)
+
+
+def _finish(
+    session: Session,
+    reply: str,
+    *,
+    asking_slot: str | None = None,
+    scheme: dict[str, Any] | None = None,
+    need_handoff: bool = False,
+) -> dict[str, Any]:
+    session.messages.append({"role": "assistant", "content": reply})
+    store.touch(session)
+    return {
+        "session_id": session.id,
+        "reply": reply,
+        "phase": session.phase,
+        "slots": session.slots,
+        "asking_slot": asking_slot,
+        "scheme": scheme or session.last_scheme,
+        "need_handoff": need_handoff,
+    }

engine/app/session_store.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import re
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Any
+
+
+def _now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+@dataclass
+class Session:
+    id: str
+    slots: dict[str, str] = field(default_factory=dict)
+    messages: list[dict[str, Any]] = field(default_factory=list)
+    phase: str = "collecting"  # collecting | scheme_ready | handoff
+    last_scheme: dict[str, Any] | None = None
+    created_at: str = field(default_factory=_now)
+    updated_at: str = field(default_factory=_now)
+
+
+class SessionStore:
+    def __init__(self) -> None:
+        self._sessions: dict[str, Session] = {}
+
+    def create(self) -> Session:
+        session = Session(id=str(uuid.uuid4()))
+        self._sessions[session.id] = session
+        return session
+
+    def get(self, session_id: str) -> Session | None:
+        return self._sessions.get(session_id)
+
+    def touch(self, session: Session) -> None:
+        session.updated_at = _now()
+
+
+store = SessionStore()
+
+
+def looks_like_handoff(text: str, phrases: list[str]) -> bool:
+    t = text.strip().lower()
+    return any(p.lower() in t for p in phrases)
+
+
+def naive_fill_slots(message: str, slots_meta: list[dict[str, Any]], current: dict[str, str]) -> dict[str, str]:
+    """每轮通常只推进一个槽：先做明确关键词，再把整句写入当前优先空槽。"""
+    updated = dict(current)
+    text = message.strip()
+    if not text:
+        return updated
+
+    touched = False
+
+    def set_slot(slot_id: str, value: str) -> None:
+        nonlocal touched
+        if not updated.get(slot_id):
+            updated[slot_id] = value
+            touched = True
+
+    if re.search(r"A类|传感", text, re.I):
+        set_slot("tech_category", "A类传感")
+    elif re.search(r"B类|控制", text, re.I):
+        set_slot("tech_category", "B类控制")
+    elif "不确定" in text:
+        set_slot("tech_category", "不确定")
+
+    if "产线" in text:
+        set_slot("scenario", "产线")
+    elif "户外" in text:
+        set_slot("scenario", "户外监测")
+    elif "实验室" in text:
+        set_slot("scenario", "实验室")
+
+    # 本轮若已通过关键词写入至少一个槽，就不再把整句灌进其它空槽
+    if touched:
+        return updated
+
+    pending = sorted(
+        [s for s in slots_meta if s.get("required") and not updated.get(s["id"])],
+        key=lambda s: s.get("priority", 100),
+    )
+    if pending:
+        updated[pending[0]["id"]] = text
+        return updated
+
+    optional = sorted(
+        [s for s in slots_meta if not s.get("required") and not updated.get(s["id"])],
+        key=lambda s: s.get("priority", 100),
+    )
+    if optional:
+        updated[optional[0]["id"]] = text
+    return updated
+
+
+def next_slot_prompt(slots_meta: list[dict[str, Any]], filled: dict[str, str]) -> dict[str, str] | None:
+    pending = sorted(
+        [s for s in slots_meta if s.get("required") and not (filled.get(s["id"]) or "").strip()],
+        key=lambda s: s.get("priority", 100),
+    )
+    if not pending:
+        # optional slots: ask once if empty
+        optional = sorted(
+            [s for s in slots_meta if not s.get("required") and not (filled.get(s["id"]) or "").strip()],
+            key=lambda s: s.get("priority", 100),
+        )
+        if optional:
+            s = optional[0]
+            return {"id": s["id"], "label": s["label"], "prompt": s["prompt"]}
+        return None
+    s = pending[0]
+    return {"id": s["id"], "label": s["label"], "prompt": s["prompt"]}

engine/app/settings.py

@@ -0,0 +1,22 @@
+from pathlib import Path
+
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+ROOT = Path(__file__).resolve().parents[2]
+KNOWLEDGE_DIR = ROOT / "knowledge"
+
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file=".env", extra="ignore")
+
+    app_name: str = "鲜境方案引擎原型"
+    knowledge_dir: Path = KNOWLEDGE_DIR
+    retrieval_backend: str = "mock"  # mock | ragflow
+    ragflow_base_url: str = "http://127.0.0.1:9380"
+    ragflow_api_key: str = ""
+    ragflow_dataset_ids: str = ""
+    llm_mode: str = "template"  # template（无 Key 可跑）| openai_compatible（预留）
+
+
+settings = Settings()

engine/app/static/app.js

@@ -0,0 +1,73 @@
+let sessionId = null;
+
+const logEl = document.getElementById("log");
+const slotsEl = document.getElementById("slots");
+const schemeEl = document.getElementById("scheme");
+const metaEl = document.getElementById("meta");
+const form = document.getElementById("form");
+const input = document.getElementById("input");
+const resetBtn = document.getElementById("reset");
+
+function addBubble(role, text) {
+  const div = document.createElement("div");
+  div.className = `bubble ${role}`;
+  div.textContent = text;
+  logEl.appendChild(div);
+  logEl.scrollTop = logEl.scrollHeight;
+}
+
+async function ensureSession() {
+  if (sessionId) return sessionId;
+  const res = await fetch("/api/sessions", { method: "POST" });
+  const data = await res.json();
+  sessionId = data.session_id;
+  metaEl.textContent = `session: ${sessionId.slice(0, 8)}…`;
+  return sessionId;
+}
+
+async function sendMessage(message) {
+  await ensureSession();
+  addBubble("user", message);
+  const res = await fetch("/api/chat", {
+    method: "POST",
+    headers: { "Content-Type": "application/json" },
+    body: JSON.stringify({ session_id: sessionId, message }),
+  });
+  if (!res.ok) {
+    addBubble("sys", `错误：${res.status}`);
+    return;
+  }
+  const data = await res.json();
+  addBubble("bot", data.reply);
+  slotsEl.textContent = JSON.stringify(data.slots || {}, null, 2);
+  schemeEl.textContent = data.scheme
+    ? JSON.stringify(data.scheme, null, 2)
+    : "尚未生成";
+  metaEl.textContent = `session: ${sessionId.slice(0, 8)}… · phase: ${data.phase}`;
+}
+
+form.addEventListener("submit", async (e) => {
+  e.preventDefault();
+  const text = input.value.trim();
+  if (!text) return;
+  input.value = "";
+  form.querySelector("button").disabled = true;
+  try {
+    await sendMessage(text);
+  } finally {
+    form.querySelector("button").disabled = false;
+    input.focus();
+  }
+});
+
+resetBtn.addEventListener("click", () => {
+  sessionId = null;
+  logEl.innerHTML = "";
+  slotsEl.textContent = "{}";
+  schemeEl.textContent = "尚未生成";
+  metaEl.textContent = "";
+  addBubble("bot", "新会话已开始。可以直接说场景，例如：「产线检测，偏向 B 类」。");
+  input.focus();
+});
+
+addBubble("bot", "你好，我是鲜境方案引擎原型。先收集几个关键信息，再给出产品组合草稿。\n试试：「我们在户外做监测，指标还不确定」。");

engine/app/static/index.html

@@ -0,0 +1,43 @@
+<!DOCTYPE html>
+<html lang="zh-CN">
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>鲜境方案引擎原型</title>
+  <link rel="preconnect" href="https://fonts.googleapis.com" />
+  <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin />
+  <link href="https://fonts.googleapis.com/css2?family=DM+Sans:wght@400;600;700&family=Noto+Sans+SC:wght@400;500;700&display=swap" rel="stylesheet" />
+  <link rel="stylesheet" href="/static/styles.css" />
+</head>
+<body>
+  <div class="page">
+    <header class="hero">
+      <p class="brand">鲜境物联 · 方案引擎原型</p>
+      <h1>先跑通「追问 → 判定 → 出方案」</h1>
+      <p class="sub">槽位与判定树在 <code>knowledge/*.yaml</code>，需求变了改配置即可。当前检索为 Mock，可后再接 Ragflow。</p>
+    </header>
+
+    <main class="layout">
+      <section class="chat panel">
+        <div id="log" class="log" aria-live="polite"></div>
+        <form id="form" class="composer">
+          <input id="input" type="text" placeholder="描述需求，或直接回答追问…" autocomplete="off" required />
+          <button type="submit">发送</button>
+        </form>
+        <div class="toolbar">
+          <button type="button" id="reset" class="ghost">新会话</button>
+          <span id="meta" class="meta"></span>
+        </div>
+      </section>
+
+      <aside class="side panel">
+        <h2>当前槽位</h2>
+        <pre id="slots">{}</pre>
+        <h2>最近方案</h2>
+        <pre id="scheme">尚未生成</pre>
+      </aside>
+    </main>
+  </div>
+  <script src="/static/app.js"></script>
+</body>
+</html>

engine/app/static/styles.css

@@ -0,0 +1,144 @@
+:root {
+  --bg0: #0f1c17;
+  --bg1: #163528;
+  --panel: rgba(248, 250, 246, 0.94);
+  --ink: #14231c;
+  --muted: #5c7268;
+  --accent: #1f7a4c;
+  --accent-2: #c4f06a;
+  --line: rgba(20, 35, 28, 0.12);
+  --bot: #e7f2ea;
+  --user: #1f7a4c;
+}
+
+* { box-sizing: border-box; }
+body {
+  margin: 0;
+  min-height: 100vh;
+  font-family: "Noto Sans SC", "DM Sans", sans-serif;
+  color: var(--ink);
+  background:
+    radial-gradient(1200px 600px at 10% -10%, #2f6b4f 0%, transparent 55%),
+    radial-gradient(900px 500px at 100% 0%, #8fd36a 0%, transparent 45%),
+    linear-gradient(160deg, var(--bg0), var(--bg1));
+}
+
+.page { max-width: 1100px; margin: 0 auto; padding: 32px 20px 48px; }
+.hero { color: #f4faf6; margin-bottom: 22px; }
+.brand {
+  margin: 0 0 8px;
+  font-family: "DM Sans", sans-serif;
+  letter-spacing: 0.04em;
+  font-weight: 700;
+  font-size: 14px;
+  color: var(--accent-2);
+}
+.hero h1 {
+  margin: 0 0 10px;
+  font-size: clamp(1.6rem, 3vw, 2.2rem);
+  line-height: 1.25;
+  font-weight: 700;
+}
+.sub { margin: 0; max-width: 52rem; color: rgba(244, 250, 246, 0.82); }
+.sub code {
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-size: 0.9em;
+  background: rgba(0,0,0,0.25);
+  padding: 0.1em 0.35em;
+  border-radius: 4px;
+}
+
+.layout {
+  display: grid;
+  grid-template-columns: 1.4fr 0.9fr;
+  gap: 16px;
+}
+@media (max-width: 860px) {
+  .layout { grid-template-columns: 1fr; }
+}
+
+.panel {
+  background: var(--panel);
+  border-radius: 18px;
+  border: 1px solid rgba(255,255,255,0.35);
+  box-shadow: 0 18px 50px rgba(0,0,0,0.22);
+}
+
+.chat { display: flex; flex-direction: column; min-height: 560px; overflow: hidden; }
+.log {
+  flex: 1;
+  padding: 18px;
+  overflow-y: auto;
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+}
+.bubble {
+  max-width: 92%;
+  padding: 10px 12px;
+  border-radius: 12px;
+  white-space: pre-wrap;
+  line-height: 1.5;
+  font-size: 14px;
+}
+.bubble.bot { align-self: flex-start; background: var(--bot); }
+.bubble.user { align-self: flex-end; background: var(--user); color: #fff; }
+.bubble.sys { align-self: center; background: transparent; color: var(--muted); font-size: 12px; }
+
+.composer {
+  display: flex;
+  gap: 8px;
+  padding: 12px 14px;
+  border-top: 1px solid var(--line);
+}
+.composer input {
+  flex: 1;
+  border: 1px solid var(--line);
+  border-radius: 10px;
+  padding: 12px 14px;
+  font: inherit;
+}
+.composer button, .ghost {
+  border: 0;
+  border-radius: 10px;
+  padding: 12px 16px;
+  font: inherit;
+  font-weight: 600;
+  cursor: pointer;
+  background: var(--accent);
+  color: #fff;
+}
+.ghost {
+  background: transparent;
+  color: var(--accent);
+  border: 1px solid var(--line);
+  padding: 8px 12px;
+}
+.toolbar {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  gap: 10px;
+  padding: 0 14px 14px;
+}
+.meta { color: var(--muted); font-size: 12px; }
+
+.side { padding: 16px 18px 20px; }
+.side h2 {
+  margin: 12px 0 8px;
+  font-size: 13px;
+  letter-spacing: 0.04em;
+  text-transform: uppercase;
+  color: var(--muted);
+}
+.side h2:first-child { margin-top: 0; }
+.side pre {
+  margin: 0;
+  padding: 12px;
+  background: #eef5f0;
+  border-radius: 10px;
+  font-size: 12px;
+  line-height: 1.45;
+  overflow: auto;
+  max-height: 220px;
+}

engine/requirements.txt

@@ -0,0 +1,7 @@
+fastapi==0.115.6
+uvicorn[standard]==0.32.1
+pydantic==2.10.3
+pydantic-settings==2.6.1
+pyyaml==6.0.2
+httpx==0.28.1
+python-multipart==0.0.20

knowledge/decision_tree.yaml

@@ -0,0 +1,71 @@
+# 判定树：规则按 order 依次匹配，命中第一条即输出推荐
+# 条件支持：slot_equals / slot_contains / always
+version: 1
+
+rules:
+  - id: prefer_a_line
+    order: 10
+    description: 明确倾向 A 类或传感场景
+    when_all:
+      - type: slot_contains
+        slot: tech_category
+        value: A
+    then:
+      product_bundle: ["XJ-A100", "XJ-A-GW"]
+      tech_line: A类传感
+      summary: 按 A 类传感路线匹配主机 + 网关组合，适合监测类诉求。
+      confidence: 0.82
+
+  - id: prefer_b_line
+    order: 20
+    description: 明确倾向 B 类或控制/产线
+    when_all:
+      - type: slot_contains
+        slot: tech_category
+        value: B
+    then:
+      product_bundle: ["XJ-B200", "XJ-B-IO"]
+      tech_line: B类控制
+      summary: 按 B 类控制路线匹配控制器 + IO 扩展，适合产线联动。
+      confidence: 0.8
+
+  - id: scenario_production
+    order: 30
+    description: 未指定大类但场景像产线 → 偏 B
+    when_all:
+      - type: slot_contains
+        slot: scenario
+        value: 产线
+    then:
+      product_bundle: ["XJ-B200", "XJ-B-IO"]
+      tech_line: B类控制（场景推断）
+      summary: 根据产线场景初步偏向 B 类控制方案，建议人工确认指标后锁定型号。
+      confidence: 0.68
+
+  - id: scenario_outdoor
+    order: 40
+    description: 户外监测 → 偏 A
+    when_all:
+      - type: slot_contains
+        slot: scenario
+        value: 户外
+    then:
+      product_bundle: ["XJ-A100", "XJ-A-GW"]
+      tech_line: A类传感（场景推断）
+      summary: 根据户外监测场景初步偏向 A 类传感方案，需确认防护与供电约束。
+      confidence: 0.66
+
+  - id: fallback_uncertain
+    order: 1000
+    description: 兜底：信息不足或不确定
+    when_all:
+      - type: always
+    then:
+      product_bundle: ["待专家确认"]
+      tech_line: 待判定
+      summary: 当前信息尚不足以稳定定型，建议补充指标或转人工销售确认。
+      confidence: 0.4
+      handoff: true
+
+# 低于该置信度则建议转人工
+handoff_confidence_below: 0.55

knowledge/sample_chunks.yaml

@@ -0,0 +1,31 @@
+# Mock 检索语料：无 Ragflow 时用于演示「带引用的方案」
+chunks:
+  - id: c1
+    title: XJ-A100 产品要点
+    product: XJ-A100
+    content: XJ-A100 适用于户外与环境监测类场景，支持宽温工作，需配合 XJ-A-GW 网关做数据汇聚。
+    tags: [A, 户外, 传感]
+
+  - id: c2
+    title: XJ-A-GW 网关
+    product: XJ-A-GW
+    content: XJ-A-GW 提供多节点接入与协议转换，常与 XJ-A100 组成监测方案。
+    tags: [A, 网关]
+
+  - id: c3
+    title: XJ-B200 控制主机
+    product: XJ-B200
+    content: XJ-B200 面向产线联动与设备控制，支持本地逻辑与扩展 IO。
+    tags: [B, 产线, 控制]
+
+  - id: c4
+    title: XJ-B-IO 扩展
+    product: XJ-B-IO
+    content: XJ-B-IO 用于扩展数字量/模拟量通道，与 XJ-B200 配套。
+    tags: [B, IO]
+
+  - id: c5
+    title: 方案注意事项
+    product: GENERAL
+    content: 正式报价前需确认现场供电、安装空间与是否已有同类设备，避免重复建设。
+    tags: [通用, 约束]

knowledge/slots.yaml

@@ -0,0 +1,36 @@
+# 追问槽位：需求未定稿时可随时增删字段；引擎按 priority 顺序问未填项
+version: 1
+intent: solution_consult
+
+slots:
+  - id: tech_category
+    label: 技术大类倾向
+    prompt: 目前更倾向哪一类技术路线？也可以说「不确定，希望你们推荐」。（示例：A类传感 / B类控制 / 不确定）
+    required: true
+    priority: 10
+    extract_hint: 用户提到的技术大类或不确定
+
+  - id: scenario
+    label: 应用场景
+    prompt: 客户主要用在什么场景或工况？（例如产线检测、户外监测、实验室标定等）
+    required: true
+    priority: 20
+
+  - id: key_metric
+    label: 关键指标
+    prompt: 有没有必须满足的关键指标或参数范围？（量程、精度、响应时间等，没有可以说「暂无」）
+    required: true
+    priority: 30
+
+  - id: constraints
+    label: 现场约束
+    prompt: 现场有哪些约束？（空间、已有设备、预算区间、交付区域等，没有可说「暂无」）
+    required: false
+    priority: 40
+
+closing_prompt: 信息已较完整，正在为您匹配方案…
+handoff_phrases:
+  - 转人工
+  - 人工客服
+  - 找销售
+  - 真人