接口修改

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董宏凯 committed 2026-05-25 14:14:31 +08:00
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import { apiFetch } from "@/core/api/fetch-client";
import { getBackendBaseURL } from "@/core/config";
import type { Agent } from "@/core/agents";
import type { IntentSummary } from "./intent";
/**
* Agent-recommendation API. Sits between Step 1 (intent clarification) and
* Step 2 (multi-agent roundtable): given the IntentSummary and a candidate
* agent pool, the built-in `roundtable-recommender` picks 2-6 agents most
* relevant to the task and explains why.
*
* One-shot, stateless: no /init, no thread_id, each call spins up a fresh
* backend-managed thread. Use {@link filterRecommendCandidates} to clean the
* `/api/agents` list before passing it as `candidates`.
*/
/** Single agent pick returned by the recommender. */
export interface RecommendPick {
agent_id: string;
reason: string;
}
/** Final status frame the backend appends after the upstream stream finishes. */
export interface StreamRecommendResult {
/**
* `done` → picks parsed and validated, ready to populate the dialog as
* default-checked selections.
* `asking` → recommender failed to emit a usable [RECOMMEND_READY] block,
* caller should apply its fallback list (typically the 6 roundtable-* seats).
* `error` → upstream / network failure.
*/
status: "done" | "asking" | "error";
/** Full final AI text (includes the natural-language rationale). */
content: string;
/** Populated only when status === "done"; null otherwise. */
picks: RecommendPick[] | null;
}
/** Phase mirror of streamIntent so the UI can reuse one loading-label mapper. */
export type RecommendStreamPhase = "connecting" | "connected" | "streaming";
export interface StreamRecommendRequest {
/** Step 1 output. `objective` is required by the backend. */
intent: IntentSummary;
/** Pre-filtered candidate pool (use {@link filterRecommendCandidates}). */
candidates: RecommendCandidate[];
model?: string;
signal?: AbortSignal;
/** Fires for every AI text chunk from upstream LangGraph messages-tuple frames. */
onTextDelta?: (text: string, messageId: string) => void;
/** Fires whenever the inferred SSE phase changes. */
onPhaseChange?: (phase: RecommendStreamPhase) => void;
}
/** Shape the backend expects in `candidates[]`. */
export interface RecommendCandidate {
agent_id: string;
name: string;
description: string;
}
/**
* Agent ids that must never enter the candidate pool. Centralized here so
* every caller (recommend dialog, future tools) applies the same rule.
*
* - `roundtable-intent` — Step 1 clarification specialist, no role in research.
* - `roundtable-recommender`— the recommender itself, can't recommend itself.
* - `roundtable-coordinator-*` — disposable Step 2 coordinator shells (DB
* accumulates one per session); meaningless as roundtable participants.
*/
const EXCLUDED_AGENT_ID_PREFIX = "roundtable-coordinator-";
const EXCLUDED_AGENT_IDS = new Set<string>(["roundtable-intent", "roundtable-recommender"]);
/**
* Drop excluded ids from a raw `GET /api/agents` list and project each entry
* to the slim shape the backend expects. Returns a fresh array; original
* objects are not mutated.
*/
export function filterRecommendCandidates(agents: Agent[]): RecommendCandidate[] {
const out: RecommendCandidate[] = [];
for (const a of agents) {
if (!a || !a.id) continue;
if (EXCLUDED_AGENT_IDS.has(a.id)) continue;
if (a.id.startsWith(EXCLUDED_AGENT_ID_PREFIX)) continue;
out.push({
agent_id: a.id,
name: a.name ?? a.id,
description: a.description ?? "",
});
}
return out;
}
function isFinalRecommendFrame(value: unknown): value is {
status: "done" | "asking" | "error";
content?: string;
picks?: RecommendPick[] | null;
} {
if (!value || typeof value !== "object") return false;
if (!("status" in value) || !("content" in value)) return false;
const status = (value as { status: unknown }).status;
return status === "done" || status === "asking" || status === "error";
}
/**
* Extract an AI text delta from one LangGraph `messages-tuple` frame. Same
* shape as the helper in intent.ts; duplicated rather than imported to keep
* each helper independently readable.
*/
function extractAiTextDelta(parsed: unknown): { text: string; id: string } | null {
if (!Array.isArray(parsed) || parsed.length < 1) return null;
const chunk = parsed[0];
if (!chunk || typeof chunk !== "object") return null;
const obj = chunk as Record<string, unknown>;
const type = String(obj.type ?? "").toLowerCase();
if (type !== "ai" && type !== "aimessagechunk" && type !== "aimessage") return null;
const content = obj.content;
let text = "";
if (typeof content === "string") {
text = content;
} else if (Array.isArray(content)) {
for (const item of content) {
if (typeof item === "string") text += item;
else if (item && typeof item === "object") {
const block = item as Record<string, unknown>;
const blockText = block.text;
if (typeof blockText === "string") text += blockText;
}
}
}
if (!text) return null;
const id = typeof obj.id === "string" ? obj.id : "";
return { text, id };
}
/**
* Open an SSE connection to `/api/recommend/stream` and resolve once the
* upstream stream finishes. Each call is one recommendation round — the
* recommender is stateless, so calling twice in a row produces two
* independent runs.
*/
export async function streamRecommend(req: StreamRecommendRequest): Promise<StreamRecommendResult> {
const res = await apiFetch(`${getBackendBaseURL()}/api/recommend/stream`, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
intent: req.intent,
candidates: req.candidates,
model: req.model,
}),
signal: req.signal,
});
if (!res.ok || !res.body) {
const text = await res.text().catch(() => "");
throw new Error(`recommend/stream failed (${res.status}): ${text}`);
}
const reader = res.body.getReader();
const decoder = new TextDecoder();
let buffer = "";
let lastEvent = "";
let currentPhase: RecommendStreamPhase = "connecting";
req.onPhaseChange?.("connecting");
const setPhase = (next: RecommendStreamPhase) => {
if (next === currentPhase) return;
currentPhase = next;
req.onPhaseChange?.(next);
};
const result: StreamRecommendResult = { status: "asking", content: "", picks: null };
while (true) {
const { done, value } = await reader.read();
if (done) break;
buffer += decoder.decode(value, { stream: true });
const lines = buffer.split("\n");
buffer = lines.pop() ?? "";
for (const rawLine of lines) {
const line = rawLine.trimEnd();
if (!line) continue;
if (line.startsWith("event:")) {
lastEvent = line.slice(6).trim();
if (lastEvent === "metadata" && currentPhase === "connecting") {
setPhase("connected");
}
continue;
}
if (!line.startsWith("data:")) continue;
const payload = line.slice(5).trim();
if (!payload) continue;
let parsed: unknown;
try {
parsed = JSON.parse(payload);
} catch {
continue;
}
if (isFinalRecommendFrame(parsed)) {
result.status = parsed.status;
if (typeof parsed.content === "string") result.content = parsed.content;
if (Array.isArray(parsed.picks)) {
result.picks = parsed.picks as RecommendPick[];
}
continue;
}
if (lastEvent === "messages") {
const delta = extractAiTextDelta(parsed);
if (delta) {
setPhase("streaming");
req.onTextDelta?.(delta.text, delta.id);
}
}
}
}
return result;
}
@@ -0,0 +1,558 @@
"use client";
import { useCallback, useEffect, useMemo, useRef, useState } from "react";
import { CheckIcon, Loader2Icon, RefreshCwIcon, SparklesIcon } from "lucide-react";
import {
Dialog,
DialogContent,
DialogDescription,
DialogFooter,
DialogHeader,
DialogTitle,
} from "@/components/ui/dialog";
import { Button } from "@/components/ui/button";
import { listAgents, type Agent } from "@/core/agents";
import { cn } from "@/lib/utils";
import type { IntentSummary } from "../api/intent";
import {
filterRecommendCandidates,
streamRecommend,
type RecommendCandidate,
type RecommendPick,
type RecommendStreamPhase,
} from "../api/recommend";
/**
* Picks-selection dialog that sits between Step 1 (intent) and Step 2
* (roundtable). Opens with a streaming recommendation from
* `roundtable-recommender`, defaults its picks to checked, and lets the user
* trim or extend the selection from the rest of the agent pool before
* confirming.
*
* Selection bounds (2 ≤ N ≤ 8) are enforced on the confirm button — the
* backend also clamps but UI feedback is faster here.
*/
export interface RecommendAgentsDialogProps {
open: boolean;
intent: IntentSummary | null;
/** Falls back to the recommender's own default if omitted. */
model?: string;
onCancel: () => void;
/** Called with the user's final selection of agent ids (in display order). */
onConfirm: (agentIds: string[]) => void;
}
const MIN_SELECTED = 2;
const MAX_SELECTED = 8;
/**
* Default selection when the recommender fails or returns < 2 valid picks.
* Intersected with the live candidate pool at runtime so missing built-ins
* don't break the dialog.
*/
const FALLBACK_AGENT_IDS = [
"roundtable-intelligence",
"roundtable-environment",
"roundtable-solution-design",
"roundtable-risk-review",
"roundtable-execution-plan",
"roundtable-summary",
];
type DialogPhase =
| "idle"
| "loading_agents"
| "recommending"
| "ready"
| "fallback"
| "error";
interface InternalState {
phase: DialogPhase;
/** Streaming + final rationale text from the recommender. */
rationale: string;
/** Streaming SSE phase from the recommender helper. */
streamPhase: RecommendStreamPhase | null;
/** Pre-filtered candidate pool fetched from `/api/agents`. */
candidates: RecommendCandidate[];
/** Reason-by-agent_id, only for picks returned by the recommender. */
reasons: Record<string, string>;
/** Agent ids the recommender returned (in order). */
recommendedIds: string[];
/** User-visible warning ("推荐失败,已使用默认席位") for the fallback case. */
warning: string | null;
/** Latest error message; only shown when `phase === "error"`. */
errorMessage: string | null;
}
const INITIAL_STATE: InternalState = {
phase: "idle",
rationale: "",
streamPhase: null,
candidates: [],
reasons: {},
recommendedIds: [],
warning: null,
errorMessage: null,
};
/**
* Resolve which candidate ids should be checked by default given a set of
* recommended ids. The result preserves recommendation order and silently
* drops ids that aren't in the candidate pool.
*/
function intersectInOrder(preferred: string[], pool: RecommendCandidate[]): string[] {
const poolIds = new Set(pool.map((c) => c.agent_id));
const seen = new Set<string>();
const out: string[] = [];
for (const id of preferred) {
if (!poolIds.has(id) || seen.has(id)) continue;
out.push(id);
seen.add(id);
}
return out;
}
export function RecommendAgentsDialog({
open,
intent,
model,
onCancel,
onConfirm,
}: RecommendAgentsDialogProps) {
const [state, setState] = useState<InternalState>(INITIAL_STATE);
const [selectedIds, setSelectedIds] = useState<Set<string>>(new Set());
// Latest stream abort controller, so closing or re-running cancels the
// in-flight request instead of leaking it.
const abortRef = useRef<AbortController | null>(null);
// Generation token guards against stale completion handlers writing into
// state after a re-run (e.g. user clicks "重新推荐" mid-stream).
const generationRef = useRef(0);
const cancelInflight = useCallback(() => {
if (abortRef.current) {
abortRef.current.abort();
abortRef.current = null;
}
}, []);
/**
* The full pipeline: fetch agents → run recommendation → seed selection.
* Wrapped so the user can re-trigger it via the "重新推荐" button.
*/
const runRecommendation = useCallback(async () => {
if (!intent || !intent.objective) {
setState({
...INITIAL_STATE,
phase: "error",
errorMessage: "缺少任务目标,无法启动推荐",
});
return;
}
cancelInflight();
const myGen = ++generationRef.current;
const controller = new AbortController();
abortRef.current = controller;
setState({ ...INITIAL_STATE, phase: "loading_agents" });
setSelectedIds(new Set());
let agents: Agent[];
try {
agents = await listAgents();
} catch (err) {
if (myGen !== generationRef.current) return;
setState({
...INITIAL_STATE,
phase: "error",
errorMessage: `加载智能体列表失败:${err instanceof Error ? err.message : String(err)}`,
});
return;
}
if (myGen !== generationRef.current) return;
const candidates = filterRecommendCandidates(agents);
if (candidates.length === 0) {
setState({
...INITIAL_STATE,
phase: "error",
errorMessage: "候选智能体池为空,请先在系统里创建至少 2 个智能体",
});
return;
}
setState((prev) => ({
...prev,
phase: "recommending",
candidates,
rationale: "",
streamPhase: "connecting",
}));
try {
const result = await streamRecommend({
intent,
candidates,
model,
signal: controller.signal,
// Append every AI text delta to the rationale display. The
// recommender's [RECOMMEND_READY] + json block ends up in here too;
// that's fine — it's terse and rendering it shows users the model
// actually emitted the structured handoff.
onTextDelta: (text) => {
if (myGen !== generationRef.current) return;
setState((prev) => ({ ...prev, rationale: prev.rationale + text }));
},
onPhaseChange: (phase) => {
if (myGen !== generationRef.current) return;
setState((prev) => ({ ...prev, streamPhase: phase }));
},
});
if (myGen !== generationRef.current) return;
const reasons: Record<string, string> = {};
const recommendedIds: string[] = [];
if (result.picks) {
for (const p of result.picks) {
reasons[p.agent_id] = p.reason;
recommendedIds.push(p.agent_id);
}
}
if (result.status === "done" && recommendedIds.length >= MIN_SELECTED) {
const seed = intersectInOrder(recommendedIds, candidates);
setSelectedIds(new Set(seed));
setState((prev) => ({
...prev,
phase: "ready",
rationale: result.content || prev.rationale,
recommendedIds,
reasons,
streamPhase: null,
warning: null,
}));
return;
}
// Fallback: status === "asking" or picks too few. Use the 6 built-in
// roundtable seats, intersected against the live pool.
const fallback = intersectInOrder(FALLBACK_AGENT_IDS, candidates);
const seed = fallback.length >= MIN_SELECTED
? fallback
: candidates.slice(0, MIN_SELECTED).map((c) => c.agent_id);
setSelectedIds(new Set(seed));
setState((prev) => ({
...prev,
phase: "fallback",
rationale: result.content || prev.rationale,
recommendedIds: seed,
reasons,
streamPhase: null,
warning: "智能推荐未生成有效结果,已为你勾选默认的圆桌席位,可手动调整。",
}));
} catch (err) {
if (myGen !== generationRef.current) return;
// AbortError fires when the user closes the dialog mid-stream — no
// state update needed because the dialog is going away.
if (err instanceof DOMException && err.name === "AbortError") return;
setState((prev) => ({
...prev,
phase: "error",
streamPhase: null,
errorMessage: `推荐过程出错:${err instanceof Error ? err.message : String(err)}`,
}));
}
}, [intent, model, cancelInflight]);
// Trigger the pipeline whenever the dialog opens. Closing it aborts any
// in-flight stream so the next open starts clean.
useEffect(() => {
if (!open) {
cancelInflight();
return;
}
void runRecommendation();
return () => {
cancelInflight();
};
}, [open, runRecommendation, cancelInflight]);
const handleToggle = useCallback((agentId: string) => {
setSelectedIds((prev) => {
const next = new Set(prev);
if (next.has(agentId)) next.delete(agentId);
else next.add(agentId);
return next;
});
}, []);
// Split candidates into "recommended" and "other" — order within each
// group matches the recommender's pick order / the API's list order so the
// dialog is deterministic between renders.
const { recommendedCards, otherCards } = useMemo(() => {
const recSet = new Set(state.recommendedIds);
const byId = new Map(state.candidates.map((c) => [c.agent_id, c]));
const recommended: RecommendCandidate[] = [];
for (const id of state.recommendedIds) {
const c = byId.get(id);
if (c) recommended.push(c);
}
const others = state.candidates.filter((c) => !recSet.has(c.agent_id));
return { recommendedCards: recommended, otherCards: others };
}, [state.candidates, state.recommendedIds]);
const selectedCount = selectedIds.size;
const canConfirm =
(state.phase === "ready" || state.phase === "fallback") &&
selectedCount >= MIN_SELECTED &&
selectedCount <= MAX_SELECTED;
const selectionHint = (() => {
if (selectedCount < MIN_SELECTED) {
return `至少再选 ${MIN_SELECTED - selectedCount} 个`;
}
if (selectedCount > MAX_SELECTED) {
return `已超出上限 ${selectedCount - MAX_SELECTED} 个`;
}
return `已选 ${selectedCount} / ${MAX_SELECTED}`;
})();
const handleConfirm = useCallback(() => {
if (!canConfirm) return;
// Order: recommended-and-selected first (in recommender order), then
// user-added picks (in pool order). Keeps the leader's "可调度智能体"
// list visually stable.
const ordered: string[] = [];
for (const c of recommendedCards) {
if (selectedIds.has(c.agent_id)) ordered.push(c.agent_id);
}
for (const c of otherCards) {
if (selectedIds.has(c.agent_id)) ordered.push(c.agent_id);
}
onConfirm(ordered);
}, [canConfirm, recommendedCards, otherCards, selectedIds, onConfirm]);
const handleOpenChange = useCallback(
(next: boolean) => {
if (!next) onCancel();
},
[onCancel],
);
return (
<Dialog open={open} onOpenChange={handleOpenChange}>
<DialogContent className="sm:max-w-3xl max-h-[85vh] flex flex-col">
<DialogHeader>
<DialogTitle className="flex items-center gap-2">
<SparklesIcon className="size-5 text-purple-500" />
选择参会智能体
</DialogTitle>
<DialogDescription>
根据你的任务目标,系统为你推荐了最合适的智能体。你可以取消勾选,也可以从下方添加其他智能体。
</DialogDescription>
</DialogHeader>
{/* Rationale + status block */}
<div className="border rounded-md bg-muted/30 p-3 text-sm">
<RationaleBlock state={state} />
</div>
{/* Selection area: scrollable, takes remaining height */}
<div className="flex-1 min-h-0 overflow-y-auto pr-1 -mr-1 space-y-4">
{state.phase === "error" ? (
<div className="text-sm text-destructive p-4 border border-destructive/30 rounded-md bg-destructive/5">
{state.errorMessage ?? "未知错误"}
</div>
) : (state.phase === "ready" || state.phase === "fallback") ? (
<>
{state.warning && (
<div className="text-xs text-amber-700 dark:text-amber-300 p-2 border border-amber-300/50 dark:border-amber-700/50 rounded-md bg-amber-50 dark:bg-amber-950/30">
{state.warning}
</div>
)}
{recommendedCards.length > 0 && (
<Section title={`推荐参会 (${recommendedCards.length})`}>
{recommendedCards.map((c) => (
<AgentCard
key={c.agent_id}
candidate={c}
reason={state.reasons[c.agent_id]}
recommended
checked={selectedIds.has(c.agent_id)}
onToggle={() => handleToggle(c.agent_id)}
/>
))}
</Section>
)}
{otherCards.length > 0 && (
<Section title={`其他候选 (${otherCards.length})`}>
{otherCards.map((c) => (
<AgentCard
key={c.agent_id}
candidate={c}
reason={undefined}
recommended={false}
checked={selectedIds.has(c.agent_id)}
onToggle={() => handleToggle(c.agent_id)}
/>
))}
</Section>
)}
</>
) : null}
</div>
<DialogFooter className="flex-row sm:justify-between items-center gap-2 flex-wrap">
<div className="flex items-center gap-3 text-xs">
<span
className={cn(
"tabular-nums",
selectedCount < MIN_SELECTED || selectedCount > MAX_SELECTED
? "text-destructive"
: "text-muted-foreground",
)}
>
{selectionHint}
</span>
{(state.phase === "ready" || state.phase === "fallback" || state.phase === "error") && (
<Button
variant="ghost"
size="sm"
onClick={() => void runRecommendation()}
className="h-7 px-2 text-xs"
>
<RefreshCwIcon className="size-3 mr-1" />
重新推荐
</Button>
)}
</div>
<div className="flex gap-2">
<Button variant="outline" onClick={onCancel}>
取消
</Button>
<Button onClick={handleConfirm} disabled={!canConfirm}>
进入研讨
</Button>
</div>
</DialogFooter>
</DialogContent>
</Dialog>
);
}
function RationaleBlock({ state }: { state: InternalState }) {
if (state.phase === "loading_agents") {
return (
<div className="flex items-center gap-2 text-muted-foreground">
<Loader2Icon className="size-4 animate-spin" />
正在加载候选智能体...
</div>
);
}
if (state.phase === "recommending") {
const label =
state.streamPhase === "connecting"
? "调度推荐智能体..."
: state.streamPhase === "connected"
? "已连接,等待响应..."
: "正在生成推荐...";
return (
<div className="space-y-2">
<div className="flex items-center gap-2 text-muted-foreground text-xs">
<Loader2Icon className="size-3 animate-spin" />
{label}
</div>
{state.rationale && (
<pre className="whitespace-pre-wrap break-words font-sans text-xs text-foreground">
{state.rationale}
</pre>
)}
</div>
);
}
if (state.phase === "ready" || state.phase === "fallback") {
if (!state.rationale) {
return <div className="text-xs text-muted-foreground">推荐完成。</div>;
}
return (
<pre className="whitespace-pre-wrap break-words font-sans text-xs text-foreground max-h-32 overflow-y-auto">
{state.rationale}
</pre>
);
}
if (state.phase === "error") {
return <div className="text-xs text-destructive">推荐失败,请使用下方的"重新推荐"重试,或取消后再次进入。</div>;
}
return null;
}
function Section({ title, children }: { title: string; children: React.ReactNode }) {
return (
<div className="space-y-2">
<div className="text-xs font-medium text-muted-foreground uppercase tracking-wide">
{title}
</div>
<div className="grid gap-2">{children}</div>
</div>
);
}
interface AgentCardProps {
candidate: RecommendCandidate;
reason: string | undefined;
recommended: boolean;
checked: boolean;
onToggle: () => void;
}
function AgentCard({ candidate, reason, recommended, checked, onToggle }: AgentCardProps) {
return (
<button
type="button"
onClick={onToggle}
className={cn(
"text-left rounded-md border p-3 transition-colors flex gap-3 items-start",
checked
? "border-primary bg-primary/5 hover:bg-primary/10"
: "border-border hover:bg-muted/40",
)}
>
{/* Visual check box — simulated, since the project ships no Checkbox primitive */}
<span
className={cn(
"mt-0.5 size-4 rounded border flex items-center justify-center shrink-0",
checked ? "border-primary bg-primary text-primary-foreground" : "border-input bg-background",
)}
>
{checked && <CheckIcon className="size-3" />}
</span>
<div className="flex-1 min-w-0">
<div className="flex items-center gap-2 flex-wrap">
<div className="font-medium text-sm truncate">{candidate.name || candidate.agent_id}</div>
{recommended && (
<span className="text-[10px] px-1.5 py-0.5 rounded bg-purple-500/10 text-purple-700 dark:text-purple-300 font-medium">
推荐
</span>
)}
</div>
{candidate.description && (
<div className="text-xs text-muted-foreground mt-0.5 line-clamp-2">
{candidate.description}
</div>
)}
{reason && (
<div className="text-xs text-primary/80 mt-1 italic">
<span className="font-medium not-italic">推荐理由:</span> {reason}
</div>
)}
</div>
</button>
);
}