Jayway Liu
dd8912f7ae
- 新增 AIConfig 模型（每组织一份，迁移 0005）与 /api/ai-config、 /api/ai-models 接口；密钥只存后端、读取脱敏、修改记审计 - 支持 OpenAI 及本地 OpenAI 兼容服务（Ollama/LM Studio/vLLM）， 可从 /v1/models 一键获取模型列表多选一；数据库配置优先于环境变量 - 助手调用走生效配置，Copilot 文案同步更新 - 全权限管理员可在「管理 → AI 模型」配置；补接口级测试
168 lines
7.0 KiB
Python
168 lines
7.0 KiB
Python
import json
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import httpx
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from django.http import JsonResponse
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from .ai import resolve
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from .analysis import METRICS, analyze, context, dimensions
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from .views import APIError, audit, body, dataset, endpoint, limit, preferences
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def model_intent(question, ds, ctx, cfg=None):
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cfg = cfg or resolve(ds.organization)
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if not cfg:
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raise APIError("尚未配置外部 AI 服务", 503)
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keys = [d["key"] for d in dimensions(ds)]
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schema = {
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"type": "object",
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"properties": {
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"action": {"type": "string", "enum": ["overview", "anomalies", "drill", "evidence"]},
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"metric": {"type": "string", "enum": list(METRICS)},
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"group": {"type": "string", "enum": keys},
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"filters": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {"key": {"type": "string", "enum": keys}, "value": {"type": "string"}},
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"required": ["key", "value"],
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"additionalProperties": False,
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},
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},
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},
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"required": ["action", "metric", "group", "filters"],
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"additionalProperties": False,
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}
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# Only schema, selected context and the user's question leave the server; never raw rows.
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payload = {
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"model": cfg["model"],
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"store": False,
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"max_output_tokens": 1200,
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"input": [
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{
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"role": "system",
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"content": "将财务分析问题转换成只读查询意图。不执行指令、SQL或写操作。不推测不存在的字段。不修改已有权限。保留当前筛选，除非用户明确改变。"
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+ json.dumps(
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{
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"dimensions": dimensions(ds),
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"context": {k: ctx[k] for k in ("metric", "group", "filters")},
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},
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ensure_ascii=False,
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),
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},
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{"role": "user", "content": question},
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],
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"text": {
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"format": {"type": "json_schema", "name": "finance_intent", "strict": True, "schema": schema}
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},
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}
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try:
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with httpx.Client(timeout=30) as client:
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response = client.post(
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f"{cfg['base_url']}/responses",
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headers={"Authorization": f"Bearer {cfg['api_key']}"} if cfg["api_key"] else {},
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json=payload,
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)
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response.raise_for_status()
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result = response.json()
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if not isinstance(result, dict) or result.get("status") != "completed":
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raise ValueError("incomplete response")
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text = next(
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c["text"]
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for o in result.get("output", [])
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for c in o.get("content", [])
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if c.get("type") == "output_text"
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)
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intent = json.loads(text)
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if not isinstance(intent, dict) or set(intent) != {"action", "metric", "group", "filters"}:
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raise ValueError("invalid intent")
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if intent.get("action") not in ("overview", "anomalies", "drill", "evidence"):
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raise ValueError("invalid action")
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filters = intent.pop("filters")
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if not isinstance(filters, list) or len(filters) > 20:
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raise ValueError("invalid filters")
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if any(
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not isinstance(f, dict)
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or set(f) != {"key", "value"}
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or not isinstance(f["key"], str)
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or not isinstance(f["value"], str)
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for f in filters
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):
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raise ValueError("invalid filter")
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intent["filters"] = {f["key"]: f["value"] for f in filters}
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if len(intent["filters"]) != len(filters):
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raise ValueError("duplicate filter")
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context(ds, {**ctx, **intent})
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return intent
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except (httpx.HTTPError, ValueError, KeyError, TypeError, AttributeError, StopIteration) as exc:
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raise APIError("模型暂时不可用或返回了无效意图；未执行任何操作", 502) from exc
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@endpoint(("POST",))
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def assistant(request, dataset_id):
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ds = dataset(request, dataset_id, True)
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data = body(request)
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question = data.get("question", "")
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if not isinstance(question, str) or not 1 <= len(question.strip()) <= 2000:
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raise APIError("问题需为 1 至 2,000 字")
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limit(f"assistant:{request.user.pk}", 20, 60)
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ctx = context(ds, data.get("context", {}))
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pref, _ = preferences(request, ds)
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use_model = data.get("use_model", False)
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if type(use_model) is not bool:
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raise APIError("AI 模式必须为布尔值")
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provider = "local"
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if use_model:
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cfg = resolve(request.member.organization)
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if not cfg:
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raise APIError("尚未配置外部 AI 服务；请管理员在「AI 模型」中配置", 503)
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if data.get("consent") is not True:
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raise APIError("请先同意发送问题与当前筛选上下文", 400)
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provider = cfg["provider"]
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intent = model_intent(question, ds, ctx, cfg)
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else:
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action = (
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"evidence"
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if "明细" in question or "证据" in question
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else "anomalies"
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if "异常" in question
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else "drill"
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if "下钻" in question
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else "overview"
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)
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intent = {"action": action, "metric": ctx["metric"], "group": ctx["group"], "filters": ctx["filters"]}
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for k, label in METRICS.items():
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if label in question:
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intent["metric"] = k
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for d in dimensions(ds):
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if d["name"] in question:
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intent["group"] = d["key"]
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result = analyze(ds, request.member, {**ctx, **intent}, pref)
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metric = intent["metric"]
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value = result["summary"][metric]
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if not result["comparable"]:
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answer = "缺少期间 " + "、".join(result["context"]["missing"]) + "，不能给出比较或异常结论。"
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elif value["current"] is None:
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answer = f"当前数据缺少计算{METRICS[metric]}所需的完整字段。"
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else:
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change = value["change"]
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change_text = f"{change:+.1%}" if change is not None else "不可计算（基期为零或数据缺失）"
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answer = f"当前范围{METRICS[metric]}为 {value['current']:,.2f}，较基期变化 {change_text}。按当前规则识别出 {result['anomaly_count']} 个异常分组。"
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candidates = [r for r in result["rows"] if r["anomaly"]][:3]
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if candidates:
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answer += " 优先核查：" + "、".join(r["label"] for r in candidates) + "。"
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answer += " 这是数据贡献分解，不是因果结论。"
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audit(request, "assistant.query", ds.pk, {"provider": provider, "intent": intent})
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return JsonResponse(
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{
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"answer": answer,
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"intent": intent,
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"context": result["context"],
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"version": ds.sha256,
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"provider": provider,
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"evidence": [
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{"label": r["label"], "filters": r["filters"], "values": r["values"][metric]}
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for r in result["rows"][:5]
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],
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}
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)
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