/* Datavize AI - assistant sidebar. A real query engine grounded in the live dataset (instant, reliable, clickable answers), with an optional "Live AI" toggle that calls window.__ai.complete. Exports: AssistantPanel. */ function matchEntities(q) { const D = window.DATA; const ql = " " + q.toLowerCase().replace(/[^a-z0-9 ]/g, " ") + " "; const stop = new Set(["the", "and", "fund", "trust", "network", "project", "institute", "foundation", "collective", "research", "studio", "alliance", "academy", "commons", "clinics", "centre", "center", "group", "lab", "media", "ground", "scholars", "sound", "data", "open", "new", "hall", "town", "well"]); const all = [D.funder, ...D.orgs, ...D.people, ...D.projects, ...D.events]; const scored = []; all.forEach((e) => { const name = e.name.toLowerCase(); let score = 0; if (ql.includes(" " + name + " ") || ql.includes(name)) score += 100; name.split(/[^a-z0-9]+/).forEach((w) => { if (w.length >= 4 && !stop.has(w) && ql.includes(w)) score += 22; }); if (e.codename && ql.includes(e.codename.toLowerCase())) score += 70; if (e.title && ql.includes(e.title.toLowerCase())) score += 10; if (score > 0) scored.push({ e, score }); }); scored.sort((a, b) => b.score - a.score); return scored.map((s) => s.e); } /* the local, grounded engine - returns { text, refs, verdict } */ function localAnswer(q) { const D = window.DATA; const ql = q.toLowerCase(); const ents = matchEntities(q); const orgHit = ents.find((e) => D.kindOf(e.id) === "org"); const has = (...words) => words.some((w) => ql.includes(w)); // 1) MILESTONES - did X hit their milestones? if (has("milestone", "on track", "on-track", "hit what", "supposed to", "delivered", "behind")) { if (orgHit) { const mh = D.milestoneHealth(orgHit.id); const verdict = mh.verdict === "behind" ? "NO" : (mh.verdict === "met" || mh.verdict === "on-track") ? "YES" : "PARTLY"; const detail = mh.list.map((m) => `• ${m[0]} - ${m[1].toUpperCase()}`).join("\n"); const lead = verdict === "YES" ? `Yes - ${orgHit.name} is hitting its milestones.` : verdict === "NO" ? `No - ${orgHit.name} is behind on its milestones.` : `Partly - ${orgHit.name} has met some milestones but at least one is behind.`; return { verdict, refs: [orgHit.id], text: `${lead}\n${mh.met}/${mh.total} met${mh.behind ? `, ${mh.behind} behind` : ""}.\n\n${detail}` }; } // portfolio-wide const behind = D.orgs.filter((o) => D.milestoneHealth(o.id).verdict === "behind"); return { refs: behind.map((o) => o.id), text: behind.length ? `${behind.length} org${behind.length > 1 ? "s are" : " is"} behind on milestones:\n` + behind.map((o) => `• ${o.name} - ${D.milestoneHealth(o.id).behind} behind`).join("\n") : "No organisations are currently behind on their milestones." }; } // 2) FUNDING GAPS - who needs more funding? if (has("need", "more funding", "underfunded", "gap", "unfunded", "fund more", "who should we fund", "shortfall")) { const list = D.needsFunding(); if (orgHit && D.orgGap(orgHit) >= 0 && !has("who", "which", "list", "all", "everyone")) { const g = D.orgGap(orgHit); return { verdict: g > 0 ? "YES" : "NO", refs: [orgHit.id], text: g > 0 ? `Yes - ${orgHit.name} has an open funding gap of ${D.fmtMoney(g)} (asked ${D.fmtMoney(orgHit.funding.ask)}, awarded ${D.fmtMoney(orgHit.funding.awarded)}). Risk level: ${orgHit.risk.level}.` : `No - ${orgHit.name} is fully funded against its ask (${D.fmtMoney(orgHit.funding.awarded)}).` }; } const top = list.slice(0, 6); return { refs: top.map((o) => o.id), text: `${list.length} organisations have an open funding gap, totalling ${D.fmtMoney(D.totalGap())}. Largest needs:\n` + top.map((o) => `• ${o.name} - ${D.fmtMoney(D.orgGap(o))}${o.priority === "need" ? " (high need)" : ""}`).join("\n") }; } // 3) RISK - who's at risk / going cold? if (has("risk", "at risk", "cold", "cooling", "worry", "concern", "watch", "flag")) { const r = D.risks(); return { refs: r.map((x) => x.org.id), text: `${r.length} high-risk relationships flagged:\n` + r.map((x) => `• ${x.org.name} - ${x.note}`).join("\n") }; } // 4) INTRO / PATH - how do I reach X / connection between A and B if (has("reach", "intro", "introduce", "connect", "path", "between", "get to", "how do i", "link")) { if (ents.length >= 2) { const path = D.shortestPath(ents[0].id, ents[1].id); if (path) return { refs: path, text: `Shortest path (${path.length - 1} hop${path.length - 1 === 1 ? "" : "s"}):\n` + path.map((id) => D.byId[id].name).join(" → ") }; return { refs: [ents[0].id, ents[1].id], text: `No connection found between ${ents[0].name} and ${ents[1].name}.` }; } if (ents.length === 1) { const path = D.shortestPath("funder", ents[0].id); const handler = D.kindOf(ents[0].id) === "org" && ents[0].account ? D.byId[ents[0].account] : null; return { refs: path || [ents[0].id], text: `To reach ${ents[0].name}${handler ? `, go through ${handler.name} (account holder)` : ""}.` + (path ? `\nPath: ` + path.map((id) => D.byId[id].name).join(" → ") : "") }; } } // 5) SUMMARY / WHO IS - brief me on X if (ents.length) { const e = ents[0]; return { refs: [e.id], text: D.brief(e.id) }; } // 6) counts / overview if (has("how many", "overview", "summary", "portfolio", "total", "ecosystem")) { return { refs: ["funder"], text: `The ecosystem has ${D.counts.orgs} organisations (${D.counts.funded} funded), ${D.counts.people} people, ${D.counts.projects} projects and ${D.counts.events} events across 6 domains. Open funding gap: ${D.fmtMoney(D.totalGap())} across ${D.needsFunding().length} orgs.` }; } // fallback return { text: "I can answer questions about funding gaps, milestones, risk, introductions/paths, and summaries. Try “who needs more funding?”, “did Haven Therapy hit their milestones?”, or “how do I reach Priya Nair?”" }; } async function liveAnswer(q) { const D = window.DATA; // compact grounding context const ctx = D.orgs.map((o) => D.brief(o.id)).join("\n"); const people = D.people.map((p) => `${p.name} - ${p.title}${p.org ? " at " + D.byId[p.org].name : p.team ? " (Wellspring team)" : ""}`).join("\n"); const prompt = `You are NeuroAI, an assistant embedded in a grant-funder's relationship tool. Answer ONLY from the data below. Be concise (2-5 sentences). When the question is yes/no, start with "Yes", "No" or "Partly". Use £ figures where relevant.\n\n=== ORGANISATIONS ===\n${ctx}\n\n=== PEOPLE ===\n${people}\n\n=== QUESTION ===\n${q}`; const text = await window.__ai.complete({ messages: [{ role: "user", content: prompt }] }); // attach refs by name-matching the answer + question const refs = matchEntities(q + " " + text).slice(0, 5).map((e) => e.id); return { text: (text || "").trim(), refs }; } const SUGGESTED = [ "Who needs more funding?", "Did Haven Therapy hit their milestones?", "Who's at risk?", "How do I reach Priya Nair?", "Brief me on Beacon Research Lab", ]; function Bubble({ m, onOpenRecord }) { const D = window.DATA; if (m.role === "user") { return
{m.text}
; } const vmap = { YES: ["YES", "var(--good)", "var(--good-bg)"], NO: ["NO", "var(--rose)", "var(--rose-bg)"], PARTLY: ["PARTLY", "var(--warn)", "var(--warn-bg)"] }; const v = m.verdict && vmap[m.verdict]; return (
{v &&
{v[0]}
} {m.text}
{m.refs && m.refs.length > 0 && (
{m.refs.map((id) => { const e = D.byId[id]; if (!e) return null; const k = D.kindOf(id); return ; })}
)}
); } function AssistantPanel({ open, onClose, onOpenRecord }) { const D = window.DATA; const [messages, setMessages] = React.useState([ { role: "ai", text: "I'm NeuroAI - grounded in your live ecosystem data. Ask me about funding gaps, milestones, risk, or how to reach someone.", refs: [] }, ]); const [input, setInput] = React.useState(""); const [live, setLive] = React.useState(false); const [busy, setBusy] = React.useState(false); const scrollRef = React.useRef(null); const hasAI = typeof window !== "undefined" && window.__ai && typeof window.__ai.complete === "function"; React.useEffect(() => { const el = scrollRef.current; if (el) el.scrollTop = el.scrollHeight; }, [messages, busy]); const ask = async (q) => { const query = (q != null ? q : input).trim(); if (!query || busy) return; setInput(""); setMessages((m) => [...m, { role: "user", text: query }]); setBusy(true); try { let ans; if (live && hasAI) { try { ans = await liveAnswer(query); if (!ans.text) ans = localAnswer(query); } catch (e) { ans = localAnswer(query); } } else { await new Promise((r) => setTimeout(r, 340)); // brief "thinking" ans = localAnswer(query); } setMessages((m) => [...m, { role: "ai", ...ans }]); } finally { setBusy(false); } }; if (!open) return null; return (
{/* header */}
NeuroAI
Grounded in your ecosystem
{hasAI && ( )}
{/* transcript */}
{messages.map((m, i) => )} {busy && (
{[0, 1, 2].map((d) => )}
)}
{/* suggestions */} {messages.length <= 1 && (
{SUGGESTED.map((s) => )}
)} {/* input */}
setInput(e.target.value)} onKeyDown={(e) => { if (e.key === "Enter") ask(); }} />
); } window.AssistantPanel = AssistantPanel;