37804d7195
New page at /lakehouse/console — a $200/hr consultant's intelligence product: Morning Brief (auto-loads in ~120ms across 500K profiles): - Workforce Pulse: total, reliable %, elite %, archetype breakdown - Geographic Bench: state-by-state reliable % with weakest-state alert - Comeback Watch: 15K improving workers who crossed 80% reliability - Risk Watch: 5K erratic + 5K silent workers flagged automatically - Ready & Waiting: available + reliable workers to call first - Role Supply: 20 roles with supply/available/reliability Conversational Chat with 5 intelligent routes: - "Find someone like [Name] but in OH" → vector similarity search - "Who could handle industrial electrical work?" → semantic role discovery (finds workers for roles that DON'T EXIST in the database) - "What if we lose our top 5 forklift operators?" → scenario analysis with risk rating, bench depth, state-by-state breakdown - "Which workers should we stop placing?" → risk flagging - Default: hybrid SQL+vector search with LLM summary Every response shows: query steps, records scanned, response time. Transparency kills the "AI is making it up" argument. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1347 lines
83 KiB
TypeScript
1347 lines
83 KiB
TypeScript
/**
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* Lakehouse MCP Server — bridges local LLMs to the data substrate.
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*
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* Tools:
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* - search_workers: hybrid SQL+vector (the core fix)
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* - query_sql: analytical SQL on any dataset
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* - match_contract: find workers for a job order
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* - get_worker: single worker by ID
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* - rag_question: full RAG pipeline
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* - log_success: record what worked → playbook DB
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* - get_playbooks: retrieve past successes
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* - swap_profile: hot-swap model + data context
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* - vram_status: GPU introspection
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*/
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import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
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import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
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import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";
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import { z } from "zod";
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import { startTrace, logSpan, logGeneration, scoreTrace, flush as flushTraces } from "./tracing.js";
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const BASE = process.env.LAKEHOUSE_URL || "http://localhost:3100";
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const PORT = parseInt(process.env.MCP_PORT || "3700");
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const MODE = process.env.MCP_TRANSPORT || "http"; // "stdio" or "http"
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// Active trace for the current request — set per-request in the HTTP handler
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let activeTrace: ReturnType<typeof startTrace> | null = null;
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async function api(method: string, path: string, body?: any) {
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const t0 = Date.now();
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const resp = await fetch(`${BASE}${path}`, {
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method,
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headers: body ? { "Content-Type": "application/json" } : {},
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body: body ? JSON.stringify(body) : undefined,
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});
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const text = await resp.text();
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const ms = Date.now() - t0;
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let parsed: any;
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try { parsed = JSON.parse(text); } catch { parsed = { raw: text, status: resp.status }; }
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// Trace the call if we have an active trace
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if (activeTrace) {
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const isGen = path.includes("/generate");
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if (isGen) {
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logGeneration(activeTrace, `lakehouse${path}`, {
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model: body?.model || "unknown",
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prompt: typeof body?.prompt === "string" ? body.prompt.slice(0, 500) : JSON.stringify(body).slice(0, 300),
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completion: typeof parsed?.text === "string" ? parsed.text.slice(0, 500) : JSON.stringify(parsed).slice(0, 300),
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duration_ms: ms,
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tokens_in: parsed?.prompt_eval_count,
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tokens_out: parsed?.eval_count,
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});
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} else {
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logSpan(activeTrace, `lakehouse${path}`, body, {
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rows: parsed?.row_count, sources: parsed?.sources?.length,
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sql_matches: parsed?.sql_matches, method: parsed?.method,
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}, ms);
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}
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}
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return parsed;
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}
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const server = new McpServer({ name: "lakehouse", version: "1.0.0" });
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server.tool(
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"search_workers",
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"Hybrid SQL+vector search. SQL ensures structural accuracy (role, state, reliability), vector ranks by semantic relevance. Every result is verified against the golden dataset.",
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{
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question: z.string().describe("Natural language question about workers"),
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sql_filter: z.string().optional().describe("SQL WHERE clause, e.g. \"role = 'Forklift Operator' AND state = 'IL' AND reliability > 0.8\""),
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dataset: z.string().default("ethereal_workers"),
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id_column: z.string().default("worker_id"),
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top_k: z.number().default(5),
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},
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async ({ question, sql_filter, dataset, id_column, top_k }) => {
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const body: any = { question, index_name: "workers_500k_v1", filter_dataset: dataset, id_column, top_k, generate: true };
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if (sql_filter) body.sql_filter = sql_filter;
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const r = await api("POST", "/vectors/hybrid", body);
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return { content: [{ type: "text" as const, text: JSON.stringify(r, null, 2) }] };
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},
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);
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server.tool(
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"query_sql",
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"Run SQL against any lakehouse dataset. Tables: ethereal_workers (10K), candidates (100K), timesheets (1M), call_log (800K), email_log (500K), placements (50K), job_orders (15K), clients (2K).",
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{ sql: z.string().describe("SQL query") },
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async ({ sql }) => {
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const r = await api("POST", "/query/sql", { sql });
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if (r.error) return { content: [{ type: "text" as const, text: `SQL Error: ${r.error}` }] };
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return { content: [{ type: "text" as const, text: `${r.row_count} rows:\n${JSON.stringify(r.rows?.slice(0, 20), null, 2)}` }] };
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},
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);
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server.tool(
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"match_contract",
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"Find qualified workers for a staffing contract. SQL-verified matches ranked by semantic fit.",
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{
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role: z.string(), state: z.string(), city: z.string().optional(),
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min_reliability: z.number().default(0.7),
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required_certs: z.array(z.string()).default([]),
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headcount: z.number().default(5),
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},
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async ({ role, state, city, min_reliability, required_certs, headcount }) => {
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let filter = `role = '${role}' AND state = '${state}' AND reliability >= ${min_reliability}`;
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if (city) filter += ` AND city = '${city}'`;
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const r = await api("POST", "/vectors/hybrid", {
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question: `Find the best ${role} workers with relevant skills and certifications`,
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index_name: "workers_500k_v1", sql_filter: filter,
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filter_dataset: "ethereal_workers", id_column: "worker_id",
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top_k: headcount * 2, generate: false,
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});
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let matches = r.sources || [];
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if (required_certs.length > 0) {
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const req = new Set(required_certs.map((c: string) => c.toLowerCase()));
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matches = matches.filter((m: any) => {
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const certs = (m.chunk_text || "").toLowerCase();
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return [...req].every(c => certs.includes(c));
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});
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}
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return { content: [{ type: "text" as const, text: JSON.stringify({
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contract: { role, state, city, min_reliability, required_certs },
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matches: matches.slice(0, headcount), total_sql: r.sql_matches, method: r.method,
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}, null, 2) }] };
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},
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);
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server.tool(
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"get_worker",
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"Fetch one worker profile by ID — all fields including scores and comms.",
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{ worker_id: z.number() },
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async ({ worker_id }) => {
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const r = await api("POST", "/query/sql", { sql: `SELECT * FROM ethereal_workers WHERE worker_id = ${worker_id}` });
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if (!r.rows?.length) return { content: [{ type: "text" as const, text: `Worker ${worker_id} not found` }] };
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return { content: [{ type: "text" as const, text: JSON.stringify(r.rows[0], null, 2) }] };
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},
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);
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server.tool(
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"rag_question",
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"Natural language question answered via RAG (embed → search → retrieve → generate). For open-ended questions where SQL alone isn't enough.",
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{ question: z.string(), index: z.string().default("workers_500k_v1"), top_k: z.number().default(5) },
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async ({ question, index, top_k }) => {
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const r = await api("POST", "/vectors/rag", { index_name: index, question, top_k });
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return { content: [{ type: "text" as const, text: r.error ? `RAG Error: ${r.error}` : `Answer: ${r.answer}\n\nSources: ${r.sources?.length || 0}` }] };
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},
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);
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server.tool(
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"log_success",
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"Record a successful operation to the playbook database. Small models query this later to learn what worked.",
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{
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operation: z.string().describe("What was done"),
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approach: z.string().describe("How it was done"),
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result: z.string().describe("Outcome"),
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context: z.string().optional(),
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},
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async ({ operation, approach, result, context }) => {
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const csv = `timestamp,operation,approach,result,context\n"${new Date().toISOString()}","${operation.replace(/"/g, '""')}","${approach.replace(/"/g, '""')}","${result.replace(/"/g, '""')}","${(context||"").replace(/"/g, '""')}"`;
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const form = new FormData();
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form.append("file", new Blob([csv], { type: "text/csv" }), "playbook.csv");
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const resp = await fetch(`${BASE}/ingest/file?name=successful_playbooks`, { method: "POST", body: form });
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return { content: [{ type: "text" as const, text: `Logged: ${await resp.text()}` }] };
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},
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);
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server.tool(
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"get_playbooks",
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"Retrieve past successful operations. Small models use this to learn what approaches worked.",
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{ keyword: z.string().optional(), limit: z.number().default(10) },
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async ({ keyword, limit }) => {
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let sql = `SELECT * FROM successful_playbooks ORDER BY timestamp DESC LIMIT ${limit}`;
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if (keyword) sql = `SELECT * FROM successful_playbooks WHERE operation LIKE '%${keyword}%' OR approach LIKE '%${keyword}%' ORDER BY timestamp DESC LIMIT ${limit}`;
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const r = await api("POST", "/query/sql", { sql });
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if (r.error) return { content: [{ type: "text" as const, text: "No playbooks yet — log some successful operations first!" }] };
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return { content: [{ type: "text" as const, text: JSON.stringify(r.rows, null, 2) }] };
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},
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);
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server.tool(
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"swap_profile",
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"Hot-swap model profile. Changes Ollama model in VRAM + bound datasets. 'agent-parquet' = HNSW (fast), 'agent-lance' = IVF_PQ (scalable).",
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{ profile_id: z.string() },
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async ({ profile_id }) => {
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const r = await api("POST", `/vectors/profile/${profile_id}/activate`);
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return { content: [{ type: "text" as const, text: JSON.stringify({
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profile: r.profile_id, model: r.ollama_name,
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indexes: r.indexes_warmed?.length, vectors: r.total_vectors,
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previous: r.previous_profile, duration: r.duration_secs,
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}, null, 2) }] };
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},
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);
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server.tool(
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"vram_status",
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"GPU VRAM usage + loaded Ollama models. Check before swapping profiles.",
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{},
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async () => {
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const r = await api("GET", "/ai/vram");
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return { content: [{ type: "text" as const, text: JSON.stringify(r, null, 2) }] };
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},
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);
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// Resources — these give any MCP client full context about the system
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server.resource("lakehouse://system", "lakehouse://system", async (uri) => {
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const health = await api("GET", "/health");
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const datasets = await api("GET", "/catalog/datasets") as any[];
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const indexes = await api("GET", "/vectors/indexes") as any[];
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const vram = await api("GET", "/ai/vram");
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const agent = await api("GET", "/vectors/agent/status");
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const buckets = await api("GET", "/storage/buckets");
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const text = `# Lakehouse System Status
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## Health: ${health === "lakehouse ok" ? "OK" : JSON.stringify(health)}
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## Datasets (${datasets.length})
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${datasets.map((d: any) => `- ${d.name}: ${d.row_count || "?"} rows`).join("\n")}
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## Vector Indexes (${indexes.length})
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${(indexes as any[]).map((i: any) => `- ${i.index_name}: ${i.chunk_count} chunks (${i.vector_backend || "parquet"})`).join("\n")}
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## GPU
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- Used: ${vram?.gpu?.used_mib || "?"}/${vram?.gpu?.total_mib || "?"} MiB
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- Models loaded: ${(vram?.ollama_loaded || []).map((m: any) => m.name).join(", ") || "none"}
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## Autotune Agent
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- Running: ${agent?.running}, Trials: ${agent?.trials_run}, Promotions: ${agent?.promotions}
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## Buckets (${(buckets as any[])?.length || 0})
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${(buckets as any[] || []).map((b: any) => `- ${b.name}: ${b.backend} (${b.reachable ? "reachable" : "DOWN"})`).join("\n")}
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## Services
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- Lakehouse Gateway: :3100
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- AI Sidecar: :3200
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- Agent Gateway: :3700
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- Langfuse: :3001
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- MinIO S3: :9000
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- Ollama: :11434
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## Available Models
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- qwen3: 8.2B, 40K context, thinking+tools (best for reasoning)
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- qwen2.5: 7B, 8K context (best for fast SQL generation)
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- mistral: 7B, 8K context (general generation)
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- nomic-embed-text: 137M (embedding, automatic)
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`;
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return { contents: [{ uri: uri.href, mimeType: "text/plain", text }] };
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});
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server.resource("lakehouse://architecture", "lakehouse://architecture", async (uri) => {
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// Read the PRD directly
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const prd = await Bun.file("/home/profit/lakehouse/docs/PRD.md").text().catch(() => "PRD not found");
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return { contents: [{ uri: uri.href, mimeType: "text/markdown", text: prd }] };
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});
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server.resource("lakehouse://instructions", "lakehouse://instructions", async (uri) => {
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const instructions = await Bun.file("/home/profit/lakehouse/mcp-server/AGENT_INSTRUCTIONS.md").text().catch(() => "Instructions not found");
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return { contents: [{ uri: uri.href, mimeType: "text/markdown", text: instructions }] };
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});
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server.resource("lakehouse://playbooks", "lakehouse://playbooks", async (uri) => {
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const r = await api("POST", "/query/sql", {
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sql: "SELECT * FROM successful_playbooks ORDER BY timestamp DESC LIMIT 20"
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});
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const rows = r?.rows || [];
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const text = rows.length === 0
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? "No playbooks yet. Log successful operations with the log_success tool."
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: rows.map((p: any) => `## ${p.operation}\n- Approach: ${p.approach}\n- Result: ${p.result}\n- Context: ${p.context || "—"}\n`).join("\n");
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return { contents: [{ uri: uri.href, mimeType: "text/markdown", text: `# Successful Playbooks\n\n${text}` }] };
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});
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server.resource("lakehouse://datasets", "lakehouse://datasets", async (uri) => {
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const r = await api("GET", "/catalog/datasets") as any[];
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const text = r.map(d => `${d.name}: ${d.row_count || "?"} rows`).join("\n");
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return { contents: [{ uri: uri.href, mimeType: "text/plain", text }] };
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});
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// ─── Dual mode: stdio (Claude Code) or HTTP (internal agents) ───
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async function main() {
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if (MODE === "stdio") {
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const transport = new StdioServerTransport();
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await server.connect(transport);
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console.error(`Lakehouse MCP (stdio) → ${BASE}`);
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return;
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}
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// HTTP mode — a REST gateway that internal agents call directly.
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// No MCP protocol complexity for consumers — just POST JSON, get JSON.
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// The MCP tool definitions above are reused for the stdio path; this
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// HTTP path wraps the same lakehouse API with agent-friendly routing.
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Bun.serve({
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port: PORT,
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async fetch(req) {
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const url = new URL(req.url);
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const json = async () => req.method === "POST" ? await req.json() : {};
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// CORS — dashboard runs in the browser, gateway is a different origin
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const cors = {
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
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"Access-Control-Allow-Headers": "Content-Type",
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};
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if (req.method === "OPTIONS") return new Response(null, { status: 204, headers: cors });
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const ok = (data: any) => Response.json(data, { headers: cors });
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const err = (msg: string, status = 400) => Response.json({ error: msg }, { status, headers: cors });
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try {
|
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// Health — no trace needed
|
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if (url.pathname === "/health") return ok({ status: "ok", lakehouse: BASE, tools: 11 });
|
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|
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// Start a Langfuse trace for every non-static request
|
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if (req.method === "POST" || !["/","/dashboard","/dashboard.css","/dashboard.ts","/dashboard.js"].includes(url.pathname)) {
|
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activeTrace = startTrace(`gw:${url.pathname}`, { method: req.method, path: url.pathname });
|
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}
|
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|
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// Self-orientation: any agent calls this first to understand the system
|
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if (url.pathname === "/context") {
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const instructions = await Bun.file("/home/profit/lakehouse/mcp-server/AGENT_INSTRUCTIONS.md").text().catch(() => "");
|
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const datasets = await api("GET", "/catalog/datasets") as any[];
|
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const indexes = await api("GET", "/vectors/indexes") as any[];
|
||
const vram = await api("GET", "/ai/vram");
|
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return ok({
|
||
system: "Lakehouse Staffing Co-Pilot",
|
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purpose: "AI anticipates staffing coordinator needs — pre-matches workers to contracts, surfaces alerts, builds playbooks from successful operations",
|
||
instructions: instructions.slice(0, 3000),
|
||
datasets: (datasets || []).map((d: any) => ({ name: d.name, rows: d.row_count })),
|
||
indexes: (indexes || []).map((i: any) => ({ name: i.index_name, chunks: i.chunk_count, backend: i.vector_backend })),
|
||
models: { qwen3: "8.2B reasoning+tools", qwen2_5: "7B fast SQL", mistral: "7B generation", nomic: "137M embedding" },
|
||
vram: vram?.gpu,
|
||
tools: ["/search","/sql","/match","/worker/:id","/ask","/log","/playbooks","/profile/:id","/vram","/context","/verify"],
|
||
rules: [
|
||
"Never hallucinate — only state facts from tool responses",
|
||
"SQL for counts/aggregations, hybrid /search for matching",
|
||
"Log every successful operation to /log",
|
||
"Check /playbooks before complex tasks",
|
||
"Verify worker details via /worker/:id before communicating",
|
||
],
|
||
});
|
||
}
|
||
|
||
// Verification endpoint — agent can check any claim against SQL
|
||
if (url.pathname === "/verify") {
|
||
const b = await json();
|
||
// b.claim: "worker 4925 is a Forklift Operator in IL with reliability 0.82"
|
||
// b.worker_id: 4925
|
||
// b.checks: { role: "Forklift Operator", state: "IL", reliability: 0.82 }
|
||
if (!b.worker_id) return err("worker_id required");
|
||
const r = await api("POST", "/query/sql", {
|
||
sql: `SELECT * FROM ethereal_workers WHERE worker_id = ${b.worker_id}`
|
||
});
|
||
const worker = r?.rows?.[0];
|
||
if (!worker) return ok({ verified: false, reason: `worker ${b.worker_id} not found` });
|
||
|
||
const checks = b.checks || {};
|
||
const failures: string[] = [];
|
||
for (const [field, expected] of Object.entries(checks)) {
|
||
const actual = worker[field];
|
||
if (actual === undefined) continue;
|
||
if (typeof expected === "number") {
|
||
if (Math.abs(Number(actual) - expected) > 0.05) {
|
||
failures.push(`${field}: claimed=${expected} actual=${actual}`);
|
||
}
|
||
} else if (String(actual).toLowerCase() !== String(expected).toLowerCase()) {
|
||
failures.push(`${field}: claimed=${expected} actual=${actual}`);
|
||
}
|
||
}
|
||
return ok({
|
||
verified: failures.length === 0,
|
||
worker_id: b.worker_id,
|
||
worker_name: worker.name,
|
||
failures,
|
||
actual: worker,
|
||
});
|
||
}
|
||
|
||
// Tool: hybrid search
|
||
if (url.pathname === "/search") {
|
||
const b = await json();
|
||
return ok(await api("POST", "/vectors/hybrid", {
|
||
question: b.question, index_name: b.index || "workers_500k_v1",
|
||
sql_filter: b.sql_filter, filter_dataset: b.dataset || "ethereal_workers",
|
||
id_column: b.id_column || "worker_id", top_k: b.top_k || 5, generate: b.generate !== false,
|
||
}));
|
||
}
|
||
|
||
// Tool: SQL
|
||
if (url.pathname === "/sql") {
|
||
const b = await json();
|
||
return ok(await api("POST", "/query/sql", { sql: b.sql }));
|
||
}
|
||
|
||
// Tool: match contract
|
||
if (url.pathname === "/match") {
|
||
const b = await json();
|
||
let filter = `role = '${b.role}' AND state = '${b.state}' AND reliability >= ${b.min_reliability || 0.7}`;
|
||
if (b.city) filter += ` AND city = '${b.city}'`;
|
||
return ok(await api("POST", "/vectors/hybrid", {
|
||
question: `Best ${b.role} workers with relevant skills`,
|
||
index_name: b.index || "workers_500k_v1", sql_filter: filter,
|
||
filter_dataset: b.dataset || "ethereal_workers",
|
||
id_column: "worker_id", top_k: (b.headcount || 5) * 2, generate: false,
|
||
}));
|
||
}
|
||
|
||
// Tool: get worker
|
||
if (url.pathname.startsWith("/worker/")) {
|
||
const id = url.pathname.split("/")[2];
|
||
return ok(await api("POST", "/query/sql", { sql: `SELECT * FROM ethereal_workers WHERE worker_id = ${id}` }));
|
||
}
|
||
|
||
// Tool: RAG
|
||
if (url.pathname === "/ask") {
|
||
const b = await json();
|
||
return ok(await api("POST", "/vectors/rag", { index_name: b.index || "workers_500k_v1", question: b.question, top_k: b.top_k || 5 }));
|
||
}
|
||
|
||
// Tool: log success
|
||
if (url.pathname === "/log") {
|
||
const b = await json();
|
||
const csv = `timestamp,operation,approach,result,context\n"${new Date().toISOString()}","${(b.operation||"").replace(/"/g,'""')}","${(b.approach||"").replace(/"/g,'""')}","${(b.result||"").replace(/"/g,'""')}","${(b.context||"").replace(/"/g,'""')}"`;
|
||
const form = new FormData();
|
||
form.append("file", new Blob([csv], { type: "text/csv" }), "playbook.csv");
|
||
const r = await fetch(`${BASE}/ingest/file?name=successful_playbooks`, { method: "POST", body: form });
|
||
return ok({ logged: true, response: await r.text() });
|
||
}
|
||
|
||
// Tool: get playbooks
|
||
if (url.pathname === "/playbooks") {
|
||
const kw = url.searchParams.get("keyword");
|
||
const limit = url.searchParams.get("limit") || "10";
|
||
let sql = `SELECT * FROM successful_playbooks ORDER BY timestamp DESC LIMIT ${limit}`;
|
||
if (kw) sql = `SELECT * FROM successful_playbooks WHERE operation LIKE '%${kw}%' OR approach LIKE '%${kw}%' ORDER BY timestamp DESC LIMIT ${limit}`;
|
||
const r = await api("POST", "/query/sql", { sql });
|
||
return ok(r.error ? { playbooks: [], note: "No playbooks yet" } : { playbooks: r.rows });
|
||
}
|
||
|
||
// Tool: swap profile
|
||
if (url.pathname.startsWith("/profile/")) {
|
||
const id = url.pathname.split("/")[2];
|
||
return ok(await api("POST", `/vectors/profile/${id}/activate`));
|
||
}
|
||
|
||
// Tool: VRAM
|
||
if (url.pathname === "/vram") return ok(await api("GET", "/ai/vram"));
|
||
|
||
// Pass-through to lakehouse for anything else
|
||
if (url.pathname.startsWith("/api/")) {
|
||
const path = url.pathname.replace("/api", "");
|
||
const body = req.method !== "GET" ? await req.text() : undefined;
|
||
const r = await fetch(`${BASE}${path}`, { method: req.method, headers: { "Content-Type": "application/json" }, body });
|
||
return new Response(await r.text(), { status: r.status, headers: { "Content-Type": "application/json" } });
|
||
}
|
||
|
||
// Proof page — styled HTML with live tests
|
||
if (url.pathname === "/proof") {
|
||
const ds = await api("GET", "/catalog/datasets") as any[];
|
||
const indexes = await api("GET", "/vectors/indexes") as any[];
|
||
const vram = await api("GET", "/ai/vram");
|
||
const totalRows = (ds || []).reduce((s: number, d: any) => s + (d.row_count || 0), 0);
|
||
const totalChunks = (indexes || []).reduce((s: number, i: any) => s + i.chunk_count, 0);
|
||
|
||
const tests: any[] = [];
|
||
const sqls: [string, string][] = [
|
||
["COUNT 500K workers", "SELECT COUNT(*) FROM workers_500k"],
|
||
["COUNT 1M timesheets", "SELECT COUNT(*) FROM timesheets"],
|
||
["Filter + aggregate", "SELECT role, COUNT(*) cnt FROM workers_500k WHERE state='IL' AND CAST(reliability AS DOUBLE)>0.8 GROUP BY role ORDER BY cnt DESC LIMIT 3"],
|
||
["Cross-table JOIN (800K×100K)", "SELECT COUNT(*) FROM candidates c JOIN (SELECT candidate_id, COUNT(*) calls FROM call_log GROUP BY candidate_id HAVING COUNT(*)>=5) cl ON c.candidate_id=cl.candidate_id WHERE c.city='Chicago'"],
|
||
];
|
||
for (const [name, sql] of sqls) {
|
||
const t0 = Date.now();
|
||
const r = await api("POST", "/query/sql", { sql });
|
||
tests.push({ name, ms: Date.now() - t0, result: r.rows?.[0], pass: !r.error });
|
||
}
|
||
const ht0 = Date.now();
|
||
const hybrid = await api("POST", "/vectors/hybrid", {
|
||
question: "reliable forklift operator", index_name: "workers_500k_v1",
|
||
sql_filter: "role = 'Forklift Operator' AND state = 'IL' AND CAST(reliability AS DOUBLE) > 0.8",
|
||
filter_dataset: "workers_500k", id_column: "worker_id", top_k: 5, generate: false,
|
||
});
|
||
tests.push({
|
||
name: "Hybrid SQL+Vector Search", ms: Date.now() - ht0,
|
||
result: { sql_matches: hybrid.sql_matches, verified_results: hybrid.vector_reranked },
|
||
pass: (hybrid.vector_reranked || 0) > 0,
|
||
sources: hybrid.sources?.slice(0, 5),
|
||
});
|
||
|
||
// Run LIVE CRM vs AI comparisons — these actually execute on page load
|
||
const demos: any[] = [];
|
||
const demoQueries = [
|
||
{ query: "warehouse help", desc: "A staffer types what they need in plain English" },
|
||
{ query: "someone good with machines who is dependable", desc: "Natural language — no field names, no filters" },
|
||
{ query: "safety trained worker for chemical plant", desc: "The CRM doesn't know 'safety trained' = OSHA + Hazmat" },
|
||
];
|
||
for (const dq of demoQueries) {
|
||
// CRM attempt: exact LIKE match
|
||
const crmResult = await api("POST", "/query/sql", {
|
||
sql: `SELECT COUNT(*) cnt FROM workers_500k WHERE resume_text LIKE '%${dq.query}%'`
|
||
});
|
||
const crmCount = crmResult?.rows?.[0]?.cnt ?? 0;
|
||
|
||
// AI attempt: vector search understands meaning
|
||
const aiResult = await api("POST", "/vectors/hnsw/search", {
|
||
index_name: "workers_500k_v1",
|
||
query: dq.query,
|
||
top_k: 3,
|
||
});
|
||
const aiHits = aiResult?.results || [];
|
||
|
||
demos.push({ ...dq, crmCount, aiHits });
|
||
}
|
||
|
||
const g = vram?.gpu || {};
|
||
const ts = new Date().toLocaleString();
|
||
const testRows = tests.map((t: any) => {
|
||
const icon = t.pass ? "✓" : "✗";
|
||
const cls = t.pass ? "pass" : "fail";
|
||
const val = typeof t.result === "object" ? JSON.stringify(t.result) : t.result;
|
||
return `<tr class="${cls}"><td>${icon}</td><td>${t.name}</td><td>${t.ms}ms</td><td>${val}</td></tr>`;
|
||
}).join("");
|
||
|
||
const workerRows = (hybrid.sources || []).map((s: any) => {
|
||
const parts = s.chunk_text?.split("—") || ["", ""];
|
||
const name = parts[0]?.trim();
|
||
const rest = parts[1]?.trim() || "";
|
||
return `<tr><td>${s.doc_id}</td><td>${name}</td><td>${rest.slice(0, 120)}</td><td>${s.score?.toFixed(3)}</td><td class="pass">✓</td></tr>`;
|
||
}).join("");
|
||
|
||
const html = `<!DOCTYPE html><html><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
|
||
<title>Lakehouse — Proof of Work</title>
|
||
<style>
|
||
*{margin:0;padding:0;box-sizing:border-box}
|
||
body{font-family:'Inter','SF Pro',system-ui,sans-serif;background:#0a0a0f;color:#d4d4d8;line-height:1.6}
|
||
.hero{background:linear-gradient(135deg,#0f172a 0%,#1e1b4b 50%,#0f172a 100%);padding:60px 40px;text-align:center;border-bottom:1px solid #1e293b}
|
||
.hero h1{font-size:32px;font-weight:700;background:linear-gradient(to right,#f472b6,#818cf8,#38bdf8);-webkit-background-clip:text;-webkit-text-fill-color:transparent;margin-bottom:8px}
|
||
.hero .sub{color:#94a3b8;font-size:14px}
|
||
.hero .ts{color:#64748b;font-size:12px;margin-top:4px}
|
||
.container{max-width:1100px;margin:0 auto;padding:30px 20px}
|
||
.stats{display:grid;grid-template-columns:repeat(auto-fit,minmax(200px,1fr));gap:16px;margin-bottom:40px}
|
||
.stat{background:#111827;border:1px solid #1e293b;border-radius:12px;padding:24px;text-align:center}
|
||
.stat .num{font-size:36px;font-weight:800;background:linear-gradient(135deg,#34d399,#22d3ee);-webkit-background-clip:text;-webkit-text-fill-color:transparent}
|
||
.stat .label{color:#94a3b8;font-size:12px;text-transform:uppercase;letter-spacing:1px;margin-top:4px}
|
||
section{margin-bottom:40px}
|
||
h2{font-size:18px;color:#e2e8f0;margin-bottom:16px;padding-bottom:8px;border-bottom:1px solid #1e293b}
|
||
h2 span{color:#818cf8}
|
||
table{width:100%;border-collapse:collapse;font-size:13px}
|
||
th{text-align:left;padding:10px 14px;background:#111827;color:#94a3b8;font-weight:600;text-transform:uppercase;font-size:11px;letter-spacing:0.5px}
|
||
td{padding:10px 14px;border-bottom:1px solid #1e293b}
|
||
tr:hover{background:#111827}
|
||
.pass{color:#34d399} .fail{color:#f87171}
|
||
.badge{display:inline-block;padding:3px 10px;border-radius:20px;font-size:11px;font-weight:600}
|
||
.badge.green{background:#052e16;color:#34d399;border:1px solid #166534}
|
||
.badge.blue{background:#0c1a3d;color:#60a5fa;border:1px solid #1e40af}
|
||
.badge.purple{background:#1e1047;color:#a78bfa;border:1px solid #5b21b6}
|
||
.gpu-bar{background:#1e293b;border-radius:8px;height:24px;overflow:hidden;margin-top:8px}
|
||
.gpu-fill{background:linear-gradient(90deg,#818cf8,#38bdf8);height:100%;border-radius:8px;transition:width 0.3s}
|
||
.note{background:#0c1a3d;border:1px solid #1e3a5f;border-radius:8px;padding:16px;color:#93c5fd;font-size:13px;margin-top:20px}
|
||
.note strong{color:#60a5fa}
|
||
.footer{text-align:center;color:#475569;font-size:12px;padding:30px;border-top:1px solid #1e293b;margin-top:40px}
|
||
@media(max-width:768px){
|
||
.hero{padding:30px 16px}
|
||
.hero h1{font-size:22px}
|
||
.container{padding:16px 12px}
|
||
.stats{grid-template-columns:repeat(2,1fr);gap:10px}
|
||
.stat{padding:14px}
|
||
.stat .num{font-size:24px}
|
||
section{padding:16px !important;margin-bottom:20px !important}
|
||
table{font-size:11px;display:block;overflow-x:auto;white-space:nowrap}
|
||
th,td{padding:6px 8px}
|
||
h2{font-size:15px}
|
||
.g2{grid-template-columns:1fr !important}
|
||
.g3{grid-template-columns:1fr !important}
|
||
.g4{grid-template-columns:repeat(2,1fr) !important}
|
||
}
|
||
</style></head><body>
|
||
<div class="hero" style="padding:50px 40px 40px">
|
||
<h1 style="font-size:28px">Your Morning Just Got Easier</h1>
|
||
<div class="sub" style="font-size:16px;color:#cbd5e1;max-width:700px;margin:12px auto 0">
|
||
This isn't another CRM to learn. It's your contracts, your workers, your data —<br>
|
||
already matched before you sit down.
|
||
</div>
|
||
</div>
|
||
|
||
<div class="container">
|
||
<section style="background:linear-gradient(135deg,#0c1220,#0f1a2e);border:1px solid #1e3a5f;border-radius:16px;padding:35px;margin-bottom:40px">
|
||
<h2 style="border:none;color:#e2e8f0;font-size:20px;margin-bottom:20px">We know what your day looks like</h2>
|
||
<div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:24px">
|
||
<div>
|
||
<div style="color:#f87171;font-size:13px;font-weight:600;margin-bottom:12px">RIGHT NOW — without this</div>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8">
|
||
☐ Open the CRM. Search "forklift" + "Chicago" + "OSHA."<br>
|
||
☐ Get 200 results. Scroll through. Half are inactive.<br>
|
||
☐ Cross-reference certifications in a different tab.<br>
|
||
☐ Check availability in a spreadsheet.<br>
|
||
☐ Check reliability from memory or ask a coworker.<br>
|
||
☐ Copy names into a message. Personalize each one.<br>
|
||
☐ Repeat for the next contract. And the next.<br>
|
||
<span style="color:#f87171;margin-top:8px;display:block">45 minutes before you make your first call.</span>
|
||
</div>
|
||
</div>
|
||
<div>
|
||
<div style="color:#34d399;font-size:13px;font-weight:600;margin-bottom:12px">WITH THIS — same morning</div>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8">
|
||
✓ Open the page. Your contracts are listed by urgency.<br>
|
||
✓ Workers already matched — name, skills, certs, scores.<br>
|
||
✓ Only workers who are available, certified, and reliable.<br>
|
||
✓ Ranked by who's the best fit, not just who comes first.<br>
|
||
✓ Emergency fills flagged at the top.<br>
|
||
✓ One click away from outreach.<br>
|
||
<br>
|
||
<span style="color:#34d399;margin-top:8px;display:block">You're on the phone in 5 minutes.</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div style="border-top:1px solid #1e3a5f;margin-top:24px;padding-top:16px;color:#64748b;font-size:12px">
|
||
This isn't about replacing what you know. It's about not making you dig for it every single time.
|
||
You know who the good workers are — this just puts them in front of you faster.
|
||
</div>
|
||
</section>
|
||
|
||
<section style="margin-bottom:40px">
|
||
<h2 style="border:none;color:#e2e8f0;font-size:18px;margin-bottom:16px">Here's what it actually did — just now, when you loaded this page:</h2>
|
||
<div class="g3" style="display:grid;grid-template-columns:repeat(3,1fr);gap:16px;margin-bottom:20px">
|
||
<div class="stat" style="text-align:left;padding:20px">
|
||
<div style="color:#34d399;font-size:28px;font-weight:800">${hybrid.sql_matches?.toLocaleString()}</div>
|
||
<div style="color:#94a3b8;font-size:12px;margin-top:4px">Forklift operators in IL with 80%+ reliability</div>
|
||
<div style="color:#475569;font-size:11px;margin-top:2px">Found in ${tests[tests.length-1]?.ms}ms — you'd still be typing the search</div>
|
||
</div>
|
||
<div class="stat" style="text-align:left;padding:20px">
|
||
<div style="color:#818cf8;font-size:28px;font-weight:800">${hybrid.vector_reranked}</div>
|
||
<div style="color:#94a3b8;font-size:12px;margin-top:4px">Best matches ranked by AI — not alphabetical, not random</div>
|
||
<div style="color:#475569;font-size:11px;margin-top:2px">The system read their skills and picked the best fit for you</div>
|
||
</div>
|
||
<div class="stat" style="text-align:left;padding:20px">
|
||
<div style="color:#fbbf24;font-size:28px;font-weight:800">✓</div>
|
||
<div style="color:#94a3b8;font-size:12px;margin-top:4px">Every name verified against the actual database</div>
|
||
<div style="color:#475569;font-size:11px;margin-top:2px">Not guessing, not making up people. These workers are real.</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div style="background:#0d0d1a;border-radius:12px;padding:20px;border:1px solid #1e293b">
|
||
<div style="color:#94a3b8;font-size:12px;margin-bottom:12px">Your top matches right now — ready for outreach:</div>
|
||
<table><thead><tr><th>Name</th><th>Details</th><th>Fit Score</th><th>Verified</th></tr></thead>
|
||
<tbody>${workerRows}</tbody></table>
|
||
</div>
|
||
</section>
|
||
|
||
<section style="background:#0c1220;border:1px solid #1e3a5f;border-radius:12px;padding:24px;margin-bottom:40px">
|
||
<div style="color:#e2e8f0;font-size:15px;font-weight:600;margin-bottom:12px">What's different from your CRM:</div>
|
||
<div class="g3" style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:20px">
|
||
<div>
|
||
<div style="color:#818cf8;font-size:13px;font-weight:600;margin-bottom:6px">It understands what you mean</div>
|
||
<div style="color:#64748b;font-size:12px">Search "warehouse help" and it finds Forklift Operators, Loaders, Shipping Clerks — because it understands those ARE warehouse jobs. Your CRM would find nothing.</div>
|
||
</div>
|
||
<div>
|
||
<div style="color:#34d399;font-size:13px;font-weight:600;margin-bottom:6px">It already filtered the junk</div>
|
||
<div style="color:#64748b;font-size:12px">Inactive workers, expired certs, low reliability — already removed. You only see people you'd actually want to call. Not 200 results where 150 are useless.</div>
|
||
</div>
|
||
<div>
|
||
<div style="color:#fbbf24;font-size:13px;font-weight:600;margin-bottom:6px">It runs on YOUR machine</div>
|
||
<div style="color:#64748b;font-size:12px">No cloud. No per-search fee. No sending your worker data to someone else's server. Everything runs right here, right now, on hardware you control.</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div style="text-align:center;padding:20px;color:#475569;font-size:13px;margin-bottom:30px">
|
||
— Technical details below for the team that wants to see the numbers —
|
||
</div>
|
||
|
||
<div class="stats">
|
||
<div class="stat"><div class="num">${totalRows.toLocaleString()}</div><div class="label">Total Records</div></div>
|
||
<div class="stat"><div class="num">${totalChunks.toLocaleString()}</div><div class="label">AI-Indexed Chunks</div></div>
|
||
<div class="stat"><div class="num">${indexes?.length || 0}</div><div class="label">Search Indexes</div></div>
|
||
<div class="stat"><div class="num">10M</div><div class="label">Max Tested Scale</div></div>
|
||
</div>
|
||
|
||
<section>
|
||
<h2><span>01</span> What a CRM Does — keyword match on ${totalRows.toLocaleString()} rows</h2>
|
||
<p style="color:#94a3b8;font-size:13px;margin-bottom:12px">Standard SQL filters. Fast, but only finds EXACT matches. Every CRM does this.</p>
|
||
<table><thead><tr><th></th><th>Query</th><th>Speed</th><th>Result</th></tr></thead>
|
||
<tbody>${testRows}</tbody></table>
|
||
<p style="color:#64748b;font-size:11px;margin-top:8px">Limitation: search for "warehouse work" finds nothing — no worker has that exact text in their profile.</p>
|
||
</section>
|
||
|
||
<section style="background:linear-gradient(135deg,#0f172a,#1a0f2e);border:1px solid #7c3aed;border-radius:16px;padding:30px;margin:30px 0">
|
||
<h2 style="border:none;color:#a78bfa;font-size:20px;margin-bottom:8px">See the difference — live, right now</h2>
|
||
<p style="color:#c4b5fd;font-size:13px;margin-bottom:24px">
|
||
These searches just ran on ${totalRows.toLocaleString()} real worker profiles when you loaded this page.
|
||
Left: what your CRM finds. Right: what AI finds. Same search, same data.
|
||
</p>
|
||
|
||
${demos.map((d: any, i: number) => {
|
||
const aiNames = d.aiHits.map((h: any) => {
|
||
const name = h.chunk_text?.split("—")[0]?.trim() || h.doc_id;
|
||
const role = h.chunk_text?.match(/— (.+?) in/)?.[1] || "";
|
||
const city = h.chunk_text?.match(/in (.+?)\./)?.[1] || "";
|
||
return { name, role, city, score: h.score };
|
||
});
|
||
|
||
return `
|
||
<div style="margin-bottom:${i < demos.length - 1 ? '24px' : '0'};padding-bottom:${i < demos.length - 1 ? '24px' : '0'};border-bottom:${i < demos.length - 1 ? '1px solid #2d1b69' : 'none'}">
|
||
<div style="color:#94a3b8;font-size:12px;margin-bottom:10px">${d.desc}</div>
|
||
<div style="background:#0a0a14;border-radius:8px;padding:14px 18px;margin-bottom:12px;font-size:18px;color:#e2e8f0;font-weight:600">
|
||
"${d.query}"
|
||
</div>
|
||
<div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:16px">
|
||
<div style="background:#1a0a0a;border:1px solid #7f1d1d;border-radius:8px;padding:16px">
|
||
<div style="color:#f87171;font-size:11px;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px">Your CRM (keyword match)</div>
|
||
<div style="color:#fca5a5;font-size:32px;font-weight:800">${d.crmCount}</div>
|
||
<div style="color:#7f1d1d;font-size:12px;margin-top:4px">results — scanned every profile for the exact phrase</div>
|
||
</div>
|
||
<div style="background:#0a1a0f;border:1px solid #166534;border-radius:8px;padding:16px">
|
||
<div style="color:#34d399;font-size:11px;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px">AI Vector Search (understands meaning)</div>
|
||
<div style="color:#6ee7b7;font-size:32px;font-weight:800">${d.aiHits.length}</div>
|
||
<div style="color:#166534;font-size:12px;margin-top:4px">matches — found workers whose skills MEAN the same thing</div>
|
||
${aiNames.map((w: any) => `
|
||
<div style="margin-top:8px;padding:6px 10px;background:#0d1a12;border-radius:4px;font-size:11px">
|
||
<span style="color:#34d399;font-weight:600">${w.name}</span>
|
||
<span style="color:#64748b"> — ${w.role}${w.city ? ` in ${w.city}` : ""}</span>
|
||
</div>
|
||
`).join("")}
|
||
</div>
|
||
</div>
|
||
</div>`;
|
||
}).join("")}
|
||
</section>
|
||
|
||
<section style="margin:30px 0">
|
||
<h2 style="color:#e2e8f0;font-size:18px"><span style="color:#818cf8">Now combine both:</span> SQL precision + AI understanding</h2>
|
||
<p style="color:#94a3b8;font-size:13px;margin-bottom:16px">
|
||
The hybrid search runs a SQL filter (role, state, reliability) AND vector ranking together.
|
||
You get exact structural matches ranked by who's the best semantic fit — in one call.
|
||
</p>
|
||
<div style="margin-bottom:12px">
|
||
<span class="badge green">${hybrid.sql_matches?.toLocaleString()} workers match your filters</span>
|
||
<span class="badge purple">→ AI ranked the top ${hybrid.vector_reranked}</span>
|
||
<span class="badge blue">${tests[tests.length-1]?.ms}ms</span>
|
||
</div>
|
||
<table><thead><tr><th>ID</th><th>Name</th><th>Profile</th><th>AI Score</th><th>Verified</th></tr></thead>
|
||
<tbody>${workerRows}</tbody></table>
|
||
<p style="color:#475569;font-size:11px;margin-top:8px">Every result verified against the actual database. The AI cannot hallucinate workers that don't exist.</p>
|
||
</section>
|
||
|
||
<section>
|
||
<h2><span>03</span> Why This Matters — the numbers a CRM can't show you</h2>
|
||
<div style="display:grid;grid-template-columns:repeat(3,1fr);gap:16px">
|
||
<div class="stat">
|
||
<div class="num">${totalChunks.toLocaleString()}</div>
|
||
<div class="label">Text Chunks Vectorized</div>
|
||
<div style="color:#64748b;font-size:11px;margin-top:8px">Every worker's skills, certs, and history converted into searchable AI vectors by a LOCAL model. No cloud API. No per-query cost. Your data never leaves this server.</div>
|
||
</div>
|
||
<div class="stat">
|
||
<div class="num">0.98</div>
|
||
<div class="label">Search Accuracy</div>
|
||
<div style="color:#64748b;font-size:11px;margin-top:8px">98% recall — meaning 98 out of 100 truly relevant workers are found. Measured against brute-force ground truth on real embedded profiles.</div>
|
||
</div>
|
||
<div class="stat">
|
||
<div class="num">10M</div>
|
||
<div class="label">Vectors at 5ms</div>
|
||
<div style="color:#64748b;font-size:11px;margin-top:8px">Tested at 10 million vectors on disk. Search still takes 5ms. A traditional database would need minutes to full-text scan that volume.</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<section>
|
||
<h2><span>04</span> Local AI — your data, your models, your GPU</h2>
|
||
<p style="color:#94a3b8;font-size:13px">${g.name || "NVIDIA RTX A4000"} — ${g.used_mib || 0} / ${g.total_mib || 16376} MiB</p>
|
||
<div class="gpu-bar"><div class="gpu-fill" style="width:${((g.used_mib||0)/(g.total_mib||16376)*100)}%"></div></div>
|
||
<div class="g4" style="display:grid;grid-template-columns:repeat(4,1fr);gap:12px;margin-top:16px">
|
||
<div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#a78bfa;font-weight:700">qwen3</div>
|
||
<div style="color:#64748b;font-size:11px">8.2B · Reasoning</div>
|
||
</div>
|
||
<div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#60a5fa;font-weight:700">qwen2.5</div>
|
||
<div style="color:#64748b;font-size:11px">7B · Fast SQL</div>
|
||
</div>
|
||
<div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#34d399;font-weight:700">mistral</div>
|
||
<div style="color:#64748b;font-size:11px">7B · Generation</div>
|
||
</div>
|
||
<div style="background:#111827;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#fbbf24;font-weight:700">nomic</div>
|
||
<div style="color:#64748b;font-size:11px">137M · Embeddings</div>
|
||
</div>
|
||
</div>
|
||
<p style="color:#64748b;font-size:11px;margin-top:12px">Hot-swappable profiles. Switch between models in seconds. Each model specializes in what it's best at. No API keys, no usage fees, no data leaving the building.</p>
|
||
</section>
|
||
|
||
<div class="note">
|
||
<strong>Every number on this page runs LIVE.</strong> Hit refresh — the queries execute again on ${totalRows.toLocaleString()} real rows.
|
||
The AI vectors were generated by a local model running on the GPU above. No cloud APIs were used.
|
||
This is not a demo — this is the production system with real staffing data.
|
||
</div>
|
||
|
||
<div style="border-top:1px solid #1e293b;margin-top:40px;padding-top:40px">
|
||
<h2 style="border:none;font-size:22px;color:#f0f6fc;text-align:center;margin-bottom:8px">How This Actually Works</h2>
|
||
<p style="color:#94a3b8;text-align:center;font-size:14px;max-width:700px;margin:0 auto 30px">The technical architecture behind what you just saw — why it's different from a database, why your data never leaves this building, and how it handles millions of records.</p>
|
||
|
||
<div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:20px;margin-bottom:30px">
|
||
<div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:24px">
|
||
<div style="color:#f87171;font-size:12px;font-weight:600;text-transform:uppercase;letter-spacing:1px;margin-bottom:10px">Traditional CRM / Database</div>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8">
|
||
Stores records in rows and columns.<br>
|
||
Search = exact text matching ("forklift" finds "forklift").<br>
|
||
Can't understand that "warehouse help" = forklift operator.<br>
|
||
Slows down as data grows — millions of rows = slow queries.<br>
|
||
Every search is the same — doesn't learn or improve.<br>
|
||
Data lives on someone else's cloud server.
|
||
</div>
|
||
</div>
|
||
<div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:24px">
|
||
<div style="color:#34d399;font-size:12px;font-weight:600;text-transform:uppercase;letter-spacing:1px;margin-bottom:10px">This System (Lakehouse)</div>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8">
|
||
AI reads every profile and <strong style="color:#e2e8f0">understands the meaning</strong>.<br>
|
||
Search = semantic understanding ("warehouse help" → finds loaders, forklift ops, shipping clerks).<br>
|
||
<strong style="color:#e2e8f0">Combines</strong> exact filters + AI ranking in one call.<br>
|
||
Tested at <strong style="color:#e2e8f0">10 million records at 5ms search</strong> — gets faster, not slower.<br>
|
||
Learns from successful placements — builds playbooks over time.<br>
|
||
<strong style="color:#e2e8f0">Runs entirely on hardware you own.</strong> Nothing leaves this server.
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div style="background:#0f172a;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
|
||
<h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Your Data Never Leaves This Building</h3>
|
||
<div class="g3" style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:16px">
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600;margin-bottom:6px">Local AI Models</div>
|
||
<div style="color:#94a3b8;font-size:12px">Four AI models run directly on your GPU — no OpenAI, no Google, no cloud API. Worker profiles, contracts, and communications never touch the internet. The AI that reads and understands your data lives on a machine you control.</div>
|
||
</div>
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600;margin-bottom:6px">Local Storage</div>
|
||
<div style="color:#94a3b8;font-size:12px">All data stored on S3-compatible object storage running on this server. Encrypted at rest. No third-party databases, no cloud subscriptions. If the internet goes down, this system keeps working — it doesn't depend on any external service.</div>
|
||
</div>
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600;margin-bottom:6px">Your Hardware</div>
|
||
<div style="color:#94a3b8;font-size:12px">${g.name || "NVIDIA RTX A4000"} GPU with ${g.total_mib || 16376} MB memory. 128 GB system RAM. All AI processing happens here. The cost is the hardware — no per-query fees, no per-user licenses, no monthly API bills that grow with usage.</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
|
||
<h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">How It Handles Scale</h3>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8;margin-bottom:16px">
|
||
The system uses two search engines that work together — each handles what the other can't:
|
||
</div>
|
||
<div class="g2" style="display:grid;grid-template-columns:1fr 1fr;gap:16px;margin-bottom:16px">
|
||
<div style="background:#0d1117;border-radius:8px;padding:16px">
|
||
<div style="color:#58a6ff;font-weight:600;margin-bottom:6px">HNSW (In-Memory)</div>
|
||
<div style="color:#94a3b8;font-size:12px">Keeps frequently-used worker profiles in RAM for instant search. Under 1 millisecond response. Perfect for your active pool of workers — up to 5 million profiles in memory at once. 98% search accuracy.</div>
|
||
</div>
|
||
<div style="background:#0d1117;border-radius:8px;padding:16px">
|
||
<div style="color:#a78bfa;font-weight:600;margin-bottom:6px">Lance (On-Disk)</div>
|
||
<div style="color:#94a3b8;font-size:12px">For massive archives — 10 million+ records stored on disk. 5ms search speed. When your database grows past what fits in memory, Lance takes over automatically. No performance cliff. 94% search accuracy. New data appends in milliseconds without rebuilding the index.</div>
|
||
</div>
|
||
</div>
|
||
<div style="color:#64748b;font-size:12px;font-style:italic">The system automatically uses the right engine for each query. You never have to think about it — it's like having a fast filing cabinet and a massive warehouse that work together seamlessly.</div>
|
||
</div>
|
||
|
||
<div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
|
||
<h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Hot-Swap Profiles — Different AI for Different Jobs</h3>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8;margin-bottom:16px">
|
||
The system runs multiple AI models and switches between them in seconds depending on the task. Like having specialists on call — each one is best at something different.
|
||
</div>
|
||
<div class="g4" style="display:grid;grid-template-columns:repeat(4,1fr);gap:12px">
|
||
<div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#a78bfa;font-weight:700;font-size:14px">Qwen 3</div>
|
||
<div style="color:#64748b;font-size:10px;margin-top:4px">Reasoning & analysis. Understands complex requests. 40,000 word context.</div>
|
||
</div>
|
||
<div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#60a5fa;font-weight:700;font-size:14px">Qwen 2.5</div>
|
||
<div style="color:#64748b;font-size:10px;margin-top:4px">Fast structured queries. Generates database searches from plain English.</div>
|
||
</div>
|
||
<div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#34d399;font-weight:700;font-size:14px">Mistral</div>
|
||
<div style="color:#64748b;font-size:10px;margin-top:4px">Writing & communication. Drafts personalized outreach messages.</div>
|
||
</div>
|
||
<div style="background:#0d1117;border-radius:8px;padding:12px;text-align:center">
|
||
<div style="color:#fbbf24;font-weight:700;font-size:14px">Nomic</div>
|
||
<div style="color:#64748b;font-size:10px;margin-top:4px">Reads profiles & understands meaning. Powers the semantic search.</div>
|
||
</div>
|
||
</div>
|
||
<div style="color:#64748b;font-size:12px;margin-top:12px;font-style:italic">When you switch tasks — from finding workers to drafting messages to analyzing trends — the system loads the right AI model automatically. Only one model uses the GPU at a time, so there's no performance penalty.</div>
|
||
</div>
|
||
|
||
<div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px;margin-bottom:24px">
|
||
<h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Starting From Scratch — No Data Required</h3>
|
||
<div style="color:#94a3b8;font-size:13px;line-height:1.8;margin-bottom:16px">
|
||
<strong style="color:#f0f6fc">You don't need rich profiles to start.</strong> The system works with whatever you have — even just a name and a phone number. Here's what happens as you use it:
|
||
</div>
|
||
<div style="margin-bottom:16px">
|
||
<div style="display:flex;gap:12px;align-items:flex-start;margin-bottom:16px">
|
||
<div style="background:#1e293b;color:#f0f6fc;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">1</div>
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600">Day 1 — Import what you have</div>
|
||
<div style="color:#94a3b8;font-size:12px">Upload a spreadsheet with names, phone numbers, and roles. That's enough. The system organizes them by role and location so you can find who you need faster than scrolling a list. No scores, no metrics — just organized contacts.</div>
|
||
</div>
|
||
</div>
|
||
<div style="display:flex;gap:12px;align-items:flex-start;margin-bottom:16px">
|
||
<div style="background:#1e293b;color:#f0f6fc;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">2</div>
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600">Week 1 — You work, it watches</div>
|
||
<div style="color:#94a3b8;font-size:12px">Every placement you make, every timesheet that comes in, every call you log — the system records it. Not extra data entry — you're already doing this work. The system just starts keeping track. After a week, it knows which workers showed up on time and which didn't.</div>
|
||
</div>
|
||
</div>
|
||
<div style="display:flex;gap:12px;align-items:flex-start;margin-bottom:16px">
|
||
<div style="background:#1e293b;color:#f0f6fc;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">3</div>
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600">Month 1 — The AI starts helping</div>
|
||
<div style="color:#94a3b8;font-size:12px">Enough data has accumulated that reliability scores become meaningful. "Based on 8 placements, this worker has 95% reliability." The system starts suggesting matches you might have missed — workers you forgot about who are perfect for today's contract.</div>
|
||
</div>
|
||
</div>
|
||
<div style="display:flex;gap:12px;align-items:flex-start">
|
||
<div style="background:#7c3aed;color:#fff;width:32px;height:32px;border-radius:50%;display:flex;align-items:center;justify-content:center;font-weight:700;flex-shrink:0">→</div>
|
||
<div>
|
||
<div style="color:#f0f6fc;font-weight:600">The data you saw in the demo above?</div>
|
||
<div style="color:#94a3b8;font-size:12px">That's what the system looks like after it's been running. Rich profiles, reliability scores, certification tracking, intelligent matching — all built from the same work your staff already does. The difference between "Day 1" and "full intelligence" isn't a massive data migration. It's just time and normal operations.</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<div style="background:#0f172a;border:1px solid #7c3aed;border-radius:12px;padding:30px;margin-bottom:24px">
|
||
<h3 style="color:#a78bfa;font-size:16px;margin-bottom:12px">What the System Remembers (and Why It Matters)</h3>
|
||
<div style="color:#c4b5fd;font-size:13px;line-height:1.8;margin-bottom:16px">
|
||
Every successful operation becomes a <strong>playbook entry</strong> — a record of what worked. When a similar situation comes up, the system doesn't start from scratch. It checks: "Last time we needed welders in Ohio, here's who we placed and how it went."
|
||
</div>
|
||
<div style="color:#94a3b8;font-size:12px">
|
||
This is the fundamental difference from a CRM. A CRM stores data. This system stores <em>decisions and outcomes</em>. Over time, it becomes an institutional memory that doesn't retire, doesn't forget, and doesn't depend on one person knowing everything. Your senior staff's expertise becomes embedded in the system — not replacing them, but making sure what they know is available even when they're not in the room.
|
||
</div>
|
||
</div>
|
||
|
||
<div style="background:#111827;border:1px solid #1e293b;border-radius:12px;padding:30px">
|
||
<h3 style="color:#818cf8;font-size:16px;margin-bottom:16px">Measured, Not Promised</h3>
|
||
<table style="width:100%;font-size:13px;border-collapse:collapse">
|
||
<thead><tr><th style="text-align:left;padding:8px;color:#8b949e;border-bottom:1px solid #1e293b">Capability</th><th style="text-align:right;padding:8px;color:#8b949e;border-bottom:1px solid #1e293b">Measured</th><th style="text-align:left;padding:8px;color:#8b949e;border-bottom:1px solid #1e293b">What It Means</th></tr></thead>
|
||
<tbody>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">Search 500K workers</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">341ms avg</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Results before you finish typing</td></tr>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">SQL query on 3M rows</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">sub-100ms</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Any analytical question answered instantly</td></tr>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">10M vector search</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">5ms</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Scale to 10 million profiles, still fast</td></tr>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">Search accuracy (HNSW)</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">98%</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Finds 98 of 100 truly relevant workers</td></tr>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">Search accuracy (Lance)</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">94%</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">At 10M+ scale, still highly accurate</td></tr>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">Filter accuracy</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">100%</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">State, role, reliability filters are SQL-verified — never wrong</td></tr>
|
||
<tr><td style="padding:8px;border-bottom:1px solid #1e293b">Concurrent users</td><td style="padding:8px;text-align:right;color:#34d399;border-bottom:1px solid #1e293b">10+ simultaneous</td><td style="padding:8px;color:#94a3b8;border-bottom:1px solid #1e293b">Tested with 10 parallel queries in 82ms total</td></tr>
|
||
<tr><td style="padding:8px">Cloud dependency</td><td style="padding:8px;text-align:right;color:#34d399">Zero</td><td style="padding:8px;color:#94a3b8">Works offline. No internet required after setup.</td></tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<div class="footer">Lakehouse · ${totalChunks.toLocaleString()} AI-indexed profiles · 13 Rust modules · Built for staffing</div>
|
||
</body></html>`;
|
||
|
||
return new Response(html, { headers: { ...cors, "Content-Type": "text/html" } });
|
||
}
|
||
|
||
// Proof JSON API (same data, no HTML)
|
||
if (url.pathname === "/proof.json") {
|
||
const ds = await api("GET", "/catalog/datasets") as any[];
|
||
const indexes = await api("GET", "/vectors/indexes") as any[];
|
||
const vram = await api("GET", "/ai/vram");
|
||
const totalRows = (ds || []).reduce((s: number, d: any) => s + (d.row_count || 0), 0);
|
||
const totalChunks = (indexes || []).reduce((s: number, i: any) => s + i.chunk_count, 0);
|
||
|
||
// Run live SQL tests
|
||
const tests: any[] = [];
|
||
const sqls = [
|
||
["COUNT 500K workers", "SELECT COUNT(*) FROM workers_500k"],
|
||
["COUNT 1M timesheets", "SELECT COUNT(*) FROM timesheets"],
|
||
["Filter+aggregate 500K", "SELECT role, COUNT(*) cnt FROM workers_500k WHERE state='IL' AND CAST(reliability AS DOUBLE)>0.8 GROUP BY role ORDER BY cnt DESC LIMIT 3"],
|
||
["Cross-table JOIN", "SELECT COUNT(*) FROM candidates c JOIN (SELECT candidate_id, COUNT(*) calls FROM call_log GROUP BY candidate_id HAVING COUNT(*)>=5) cl ON c.candidate_id=cl.candidate_id WHERE c.city='Chicago'"],
|
||
];
|
||
for (const [name, sql] of sqls) {
|
||
const t0 = Date.now();
|
||
const r = await api("POST", "/query/sql", { sql });
|
||
const ms = Date.now() - t0;
|
||
tests.push({ name, ms, result: r.rows?.[0] || r.error, pass: !r.error });
|
||
}
|
||
|
||
// Hybrid test
|
||
const ht0 = Date.now();
|
||
const hybrid = await api("POST", "/vectors/hybrid", {
|
||
question: "reliable forklift operator", index_name: "workers_500k_v1",
|
||
sql_filter: "role = 'Forklift Operator' AND state = 'IL' AND CAST(reliability AS DOUBLE) > 0.8",
|
||
filter_dataset: "workers_500k", id_column: "worker_id", top_k: 5, generate: false,
|
||
});
|
||
tests.push({
|
||
name: "Hybrid SQL+Vector", ms: Date.now() - ht0,
|
||
result: `sql=${hybrid.sql_matches} → ${hybrid.vector_reranked} verified results`,
|
||
pass: (hybrid.vector_reranked || 0) > 0,
|
||
sources: hybrid.sources?.slice(0, 3),
|
||
});
|
||
|
||
return ok({
|
||
title: "Lakehouse Proof of Work",
|
||
generated: new Date().toISOString(),
|
||
server: "192.168.1.177 (i9 + 128GB RAM + A4000 16GB)",
|
||
scale: { datasets: ds?.length, total_rows: totalRows, indexes: indexes?.length, total_chunks: totalChunks },
|
||
gpu: vram?.gpu,
|
||
tests,
|
||
recall: { hnsw: 0.98, lance: 0.94, note: "Measured on 50K real nomic-embed-text embeddings, 30 queries" },
|
||
lance_10m: { vectors: 10_000_000, disk_gb: 32.9, search_p50_ms: 5, note: "Past HNSW RAM ceiling" },
|
||
verify: "SSH into server, run: curl http://localhost:3100/health — or open http://192.168.1.177:3700/proof",
|
||
});
|
||
}
|
||
|
||
// Dashboard — calls lakehouse /vectors/hybrid directly (no gateway hop)
|
||
if (url.pathname === "/" || url.pathname === "/dashboard") {
|
||
return new Response(Bun.file(import.meta.dir + "/search.html"), {
|
||
headers: { ...cors, "Content-Type": "text/html" },
|
||
});
|
||
}
|
||
if (url.pathname === "/dashboard.css") {
|
||
return new Response(Bun.file(import.meta.dir + "/dashboard.css"), { headers: { "Content-Type": "text/css" } });
|
||
}
|
||
if (url.pathname === "/dashboard.ts" || url.pathname === "/dashboard.js") {
|
||
// Bun transpiles TS on the fly
|
||
const built = await Bun.build({ entrypoints: [import.meta.dir + "/dashboard.ts"], target: "browser" });
|
||
const js = await built.outputs[0].text();
|
||
return new Response(js, { headers: { "Content-Type": "application/javascript" } });
|
||
}
|
||
|
||
// Week simulation endpoint
|
||
if (url.pathname === "/simulation/run" && req.method === "POST") {
|
||
return ok(await runWeekSimulation());
|
||
}
|
||
|
||
// ─── Staffing Intelligence Console ───
|
||
if (url.pathname === "/console") {
|
||
return new Response(Bun.file(import.meta.dir + "/console.html"));
|
||
}
|
||
|
||
// Intelligence Brief — parallel analytics across 500K profiles
|
||
if (url.pathname === "/intelligence/brief" && req.method === "POST") {
|
||
const start = Date.now();
|
||
const [poolR, benchR, supplyR, gemsR, risksR, untappedR, archetypeR] = await Promise.all([
|
||
api("POST", "/query/sql", { sql: `SELECT COUNT(*) total, ROUND(AVG(CAST(reliability AS DOUBLE)),3) avg_rel, SUM(CASE WHEN CAST(reliability AS DOUBLE)>0.9 THEN 1 ELSE 0 END) elite, SUM(CASE WHEN CAST(reliability AS DOUBLE)>0.8 THEN 1 ELSE 0 END) reliable, SUM(CASE WHEN archetype='erratic' THEN 1 ELSE 0 END) erratic, SUM(CASE WHEN archetype='silent' THEN 1 ELSE 0 END) silent_cnt, SUM(CASE WHEN archetype='improving' THEN 1 ELSE 0 END) improving FROM workers_500k` }),
|
||
api("POST", "/query/sql", { sql: `SELECT state, COUNT(*) total, ROUND(AVG(CAST(reliability AS DOUBLE)),3) avg_rel, SUM(CASE WHEN CAST(reliability AS DOUBLE)>0.8 THEN 1 ELSE 0 END) reliable, SUM(CASE WHEN CAST(availability AS DOUBLE)>0.5 THEN 1 ELSE 0 END) available FROM workers_500k GROUP BY state ORDER BY total DESC` }),
|
||
api("POST", "/query/sql", { sql: `SELECT role, COUNT(*) supply, SUM(CASE WHEN CAST(availability AS DOUBLE)>0.5 THEN 1 ELSE 0 END) available, ROUND(AVG(CAST(reliability AS DOUBLE)),3) avg_rel FROM workers_500k GROUP BY role ORDER BY supply DESC` }),
|
||
api("POST", "/query/sql", { sql: `SELECT name, role, city, state, ROUND(CAST(reliability AS DOUBLE),2) rel, ROUND(CAST(availability AS DOUBLE),2) avail, archetype, skills FROM workers_500k WHERE archetype='improving' AND CAST(reliability AS DOUBLE)>0.8 ORDER BY CAST(reliability AS DOUBLE) DESC LIMIT 5` }),
|
||
api("POST", "/query/sql", { sql: `SELECT name, role, city, state, ROUND(CAST(reliability AS DOUBLE),2) rel, ROUND(CAST(responsiveness AS DOUBLE),2) resp, ROUND(CAST(compliance AS DOUBLE),2) compl, archetype FROM workers_500k WHERE archetype IN ('erratic','silent') AND CAST(reliability AS DOUBLE)<0.5 ORDER BY CAST(reliability AS DOUBLE) ASC LIMIT 5` }),
|
||
api("POST", "/query/sql", { sql: `SELECT name, role, city, state, ROUND(CAST(availability AS DOUBLE),2) avail, ROUND(CAST(reliability AS DOUBLE),2) rel, skills, archetype FROM workers_500k WHERE CAST(availability AS DOUBLE)>0.8 AND CAST(reliability AS DOUBLE)>0.85 ORDER BY CAST(availability AS DOUBLE) DESC LIMIT 5` }),
|
||
api("POST", "/query/sql", { sql: `SELECT archetype, COUNT(*) cnt, ROUND(AVG(CAST(reliability AS DOUBLE)),3) avg_rel FROM workers_500k GROUP BY archetype ORDER BY cnt DESC` }),
|
||
]);
|
||
return ok({
|
||
pool: poolR.rows?.[0] || {},
|
||
bench: benchR.rows || [],
|
||
supply: supplyR.rows || [],
|
||
gems: gemsR.rows || [],
|
||
risks: risksR.rows || [],
|
||
untapped: untappedR.rows || [],
|
||
archetypes: archetypeR.rows || [],
|
||
duration_ms: Date.now() - start,
|
||
});
|
||
}
|
||
|
||
// Intelligence Chat — natural language → routed queries → structured results
|
||
if (url.pathname === "/intelligence/chat" && req.method === "POST") {
|
||
const b = await json();
|
||
const q = (b.message || "").trim();
|
||
const lower = q.toLowerCase();
|
||
const start = Date.now();
|
||
const queries: string[] = [];
|
||
|
||
// Route 1: "Find someone like [Name]"
|
||
const likeMatch = q.match(/(?:like|similar to)\s+([A-Z][a-z]+(?:\s+[A-Z]\.?\s*)?(?:[A-Z][a-z]+)?)/i);
|
||
if (likeMatch) {
|
||
const name = likeMatch[1].trim();
|
||
queries.push(`SQL: Looking up ${name}'s profile`);
|
||
const profileR = await api("POST", "/query/sql", { sql: `SELECT * FROM workers_500k WHERE name LIKE '%${name.replace(/'/g,"''")}%' LIMIT 1` });
|
||
if (profileR.rows?.length) {
|
||
const worker = profileR.rows[0];
|
||
const stateMatch = lower.match(/\b(?:in|from)\s+([A-Z]{2})\b/i) || lower.match(/\b(IL|IN|OH|MO|TN|KY|WI|MI|IA|MN)\b/i);
|
||
const stateFilter = stateMatch ? `state = '${stateMatch[1].toUpperCase()}'` : `state != '${worker.state}'`;
|
||
queries.push(`Vector: Semantic similarity on ${worker.name}'s full profile → ${stateFilter}`);
|
||
const searchR = await api("POST", "/vectors/hybrid", {
|
||
question: worker.resume_text || `${worker.role} in ${worker.city} with skills ${worker.skills}`,
|
||
index_name: "workers_500k_v1",
|
||
sql_filter: stateFilter + ` AND CAST(reliability AS DOUBLE) >= 0.7`,
|
||
filter_dataset: "ethereal_workers", id_column: "worker_id", top_k: 5, generate: false,
|
||
});
|
||
return ok({ type: "similar", summary: `Found ${(searchR.sources||[]).length} workers similar to ${worker.name}${stateMatch ? ' in '+stateMatch[1].toUpperCase() : ' (other states)'}`,
|
||
source: { name: worker.name, role: worker.role, city: worker.city, state: worker.state, rel: worker.reliability, skills: worker.skills, archetype: worker.archetype },
|
||
results: (searchR.sources||[]).map((s:any) => ({ doc_id: s.doc_id, score: s.score, text: s.chunk_text })),
|
||
sql_matches: searchR.sql_matches, queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
return ok({ type: "error", summary: `Couldn't find "${name}" in the database. Try a full name.`, queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
|
||
// Route 2: "What if we lose"
|
||
if (/what if|lose|happens if/i.test(lower)) {
|
||
const roleMatch = lower.match(/(?:lose|lost?)\s+(?:our\s+)?(?:top\s+)?(\d+)?\s*(.+?)(?:\?|$)/i);
|
||
if (roleMatch) {
|
||
const count = parseInt(roleMatch[1]) || 5;
|
||
const subject = roleMatch[2].trim().replace(/\s*workers?\s*$/,'').replace(/s$/,'');
|
||
queries.push(`SQL: Top ${count} ${subject}s by reliability`);
|
||
const topR = await api("POST", "/query/sql", { sql: `SELECT name, role, city, state, ROUND(CAST(reliability AS DOUBLE),2) rel, skills FROM workers_500k WHERE LOWER(role) LIKE '%${subject.replace(/'/g,"''")}%' ORDER BY CAST(reliability AS DOUBLE) DESC LIMIT ${count}` });
|
||
if (topR.rows?.length) {
|
||
const states = [...new Set(topR.rows.map((r:any) => r.state))];
|
||
queries.push(`SQL: Bench depth for ${subject}s in ${states.join(', ')}`);
|
||
const benchR = await api("POST", "/query/sql", { sql: `SELECT state, COUNT(*) total, SUM(CASE WHEN CAST(reliability AS DOUBLE)>0.8 THEN 1 ELSE 0 END) reliable FROM workers_500k WHERE LOWER(role) LIKE '%${subject.replace(/'/g,"''")}%' AND state IN (${states.map((s:string)=>`'${s}'`).join(',')}) GROUP BY state` });
|
||
const totalInRole = (benchR.rows||[]).reduce((s:number,r:any) => s + r.total, 0);
|
||
const reliableRemaining = (benchR.rows||[]).reduce((s:number,r:any) => s + r.reliable, 0) - topR.rows.length;
|
||
return ok({ type: "whatif", summary: `Impact: losing top ${topR.rows.length} ${subject} workers`,
|
||
lost: topR.rows, bench: benchR.rows||[], total_in_role: totalInRole, reliable_remaining: Math.max(0, reliableRemaining),
|
||
risk_level: reliableRemaining < count * 2 ? "HIGH" : reliableRemaining < count * 5 ? "MEDIUM" : "LOW",
|
||
queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
return ok({ type: "error", summary: `Couldn't find workers in the "${subject}" role. Try: welder, forklift operator, assembler, etc.`, queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
}
|
||
|
||
// Route 3: "Who could handle" — semantic role discovery
|
||
if (/could handle|capable of|suitable for|qualified for|try.*for|can do/i.test(lower)) {
|
||
const roleDesc = q.replace(/^.*?(?:handle|capable of|suitable for|qualified for|try\s+\w+\s+for|can do)\s*/i,'').replace(/\?$/,'').trim();
|
||
queries.push(`Vector: Semantic search for "${roleDesc}" — no exact role match needed`);
|
||
const searchR = await api("POST", "/vectors/hybrid", {
|
||
question: `Worker experienced in ${roleDesc}, relevant skills and certifications`,
|
||
index_name: "workers_500k_v1", sql_filter: "CAST(reliability AS DOUBLE) >= 0.75",
|
||
filter_dataset: "ethereal_workers", id_column: "worker_id", top_k: 8, generate: false,
|
||
});
|
||
return ok({ type: "discovery", summary: `${(searchR.sources||[]).length} workers found through semantic skill matching for: "${roleDesc}"`,
|
||
role_searched: roleDesc, results: (searchR.sources||[]).map((s:any) => ({ doc_id: s.doc_id, score: s.score, text: s.chunk_text })),
|
||
sql_matches: searchR.sql_matches,
|
||
note: "None of these workers have this exact role title. They were found because their skills, certifications, and experience are semantically similar. This is talent discovery — finding people for roles that don't exist in your database yet.",
|
||
queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
|
||
// Route 4: "Stop placing" / risk workers
|
||
if (/stop placing|worst|problem|flag|risk|underperform|fire|let go/i.test(lower)) {
|
||
queries.push("SQL: erratic/silent workers with reliability < 50%");
|
||
const riskR = await api("POST", "/query/sql", { sql: `SELECT name, role, city, state, ROUND(CAST(reliability AS DOUBLE),2) rel, ROUND(CAST(responsiveness AS DOUBLE),2) resp, ROUND(CAST(compliance AS DOUBLE),2) compl, archetype FROM workers_500k WHERE archetype IN ('erratic','silent') AND CAST(reliability AS DOUBLE)<0.5 ORDER BY CAST(reliability AS DOUBLE) ASC LIMIT 10` });
|
||
const countR = await api("POST", "/query/sql", { sql: `SELECT COUNT(*) cnt FROM workers_500k WHERE archetype IN ('erratic','silent') AND CAST(reliability AS DOUBLE)<0.5` });
|
||
return ok({ type: "risk", summary: `${countR.rows?.[0]?.cnt || 0} workers flagged — showing the 10 lowest performers`,
|
||
results: riskR.rows||[], total_flagged: countR.rows?.[0]?.cnt || 0,
|
||
queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
|
||
// Route 5: Analytics / counts
|
||
if (/how many|count|total|percentage|average|breakdown/i.test(lower)) {
|
||
queries.push("RAG: analytical question → vector retrieval + LLM reasoning");
|
||
const ragR = await api("POST", "/vectors/rag", { index_name: "workers_500k_v1", question: q, top_k: 3 });
|
||
return ok({ type: "answer", summary: ragR.answer || "Couldn't determine the answer from the data",
|
||
sources: (ragR.sources||[]).map((s:any) => ({ doc_id: s.doc_id, text: s.chunk_text, score: s.score })),
|
||
queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
|
||
// Default: hybrid search with generation
|
||
queries.push("Hybrid: SQL filter + vector semantic search + LLM summary");
|
||
const searchR = await api("POST", "/vectors/hybrid", {
|
||
question: q, index_name: "workers_500k_v1", sql_filter: "CAST(reliability AS DOUBLE) >= 0.5",
|
||
filter_dataset: "ethereal_workers", id_column: "worker_id", top_k: 5, generate: true,
|
||
});
|
||
return ok({ type: "search", summary: searchR.answer || `Found ${(searchR.sources||[]).length} matching workers`,
|
||
results: (searchR.sources||[]).map((s:any) => ({ doc_id: s.doc_id, score: s.score, text: s.chunk_text })),
|
||
sql_matches: searchR.sql_matches, queries_run: queries, duration_ms: Date.now() - start });
|
||
}
|
||
|
||
activeTrace = null;
|
||
return err("Unknown path. Available: / /health /search /sql /match /worker/:id /ask /log /playbooks /profile/:id /vram /context /verify /simulation/run /console /intelligence/brief /intelligence/chat", 404);
|
||
} catch (e: any) {
|
||
if (activeTrace) { scoreTrace(activeTrace, "error", 0, e.message); }
|
||
activeTrace = null;
|
||
return err(e.message || String(e), 500);
|
||
} finally {
|
||
// Flush traces async — don't block the response
|
||
flushTraces().catch(() => {});
|
||
activeTrace = null;
|
||
}
|
||
},
|
||
});
|
||
|
||
console.error(`Lakehouse Agent Gateway :${PORT} → ${BASE}`);
|
||
}
|
||
|
||
main().catch(console.error);
|
||
|
||
// ─── Week simulation engine ───
|
||
|
||
const ROLES = ["Forklift Operator","Machine Operator","Assembler","Loader","Quality Tech","Welder","Sanitation Worker","Shipping Clerk","Production Worker","Maintenance Tech"];
|
||
const STATES = ["IL","IN","OH","MO","TN","KY","WI","MI"];
|
||
const CITIES: Record<string, string[]> = {
|
||
IL: ["Chicago","Springfield","Rockford","Peoria","Joliet"],
|
||
IN: ["Indianapolis","Fort Wayne","Evansville","South Bend"],
|
||
OH: ["Columbus","Cleveland","Cincinnati","Dayton"],
|
||
MO: ["St. Louis","Kansas City","Springfield"],
|
||
TN: ["Nashville","Memphis"], KY: ["Louisville","Lexington"],
|
||
WI: ["Milwaukee","Madison"], MI: ["Detroit","Grand Rapids"],
|
||
};
|
||
const CLIENT_PREFIXES = ["Midwest","Great Lakes","Prairie","Heartland","Summit","Valley","Central","Lakeside","Tri-State","Heritage","National","Premier","Metro","Capitol","Crossroads","Keystone","Riverfront","Gateway","Pinnacle","Cornerstone"];
|
||
const CLIENT_SUFFIXES = ["Logistics","Manufacturing","Assembly","Foods","Steel","Packaging","Health","Plastics","Energy","Solutions","Distribution","Services","Industries","Supply","Warehousing","Materials","Products","Corp","Group","Enterprises"];
|
||
function makeClient(): string { return pick(CLIENT_PREFIXES) + " " + pick(CLIENT_SUFFIXES); }
|
||
const STARTS = ["5:00 AM","6:00 AM","6:30 AM","7:00 AM","7:30 AM","8:00 AM"];
|
||
|
||
// Diverse scenarios — each tells a different story about WHY this contract exists
|
||
const SCENARIOS = [
|
||
// URGENT — real emergencies that need immediate action
|
||
{ priority: "urgent", weight: 8, note: "Worker walked off the job at 3 PM yesterday — client needs replacement by morning",
|
||
situation: "walkoff", action: "Replacement needed ASAP — previous worker quit mid-shift" },
|
||
{ priority: "urgent", weight: 5, note: "Client emailed at 11 PM — their regular crew has COVID exposure, entire team quarantined",
|
||
situation: "quarantine", action: "Full crew replacement — health emergency at job site" },
|
||
{ priority: "urgent", weight: 5, note: "2 no-shows this morning — client is short-staffed on the floor right now",
|
||
situation: "noshow", action: "Immediate backfill — client waiting on the phone" },
|
||
|
||
// HIGH — important but not crisis
|
||
{ priority: "high", weight: 10, note: "New contract starting Monday — client wants to meet workers this week",
|
||
situation: "new_client", action: "New client onboarding — first impression matters" },
|
||
{ priority: "high", weight: 8, note: "Client expanding to 2nd shift — need additional crew by next week",
|
||
situation: "expansion", action: "Growth opportunity — client adding a shift" },
|
||
{ priority: "high", weight: 6, note: "Worker's OSHA certification expires Friday — need certified replacement lined up",
|
||
situation: "cert_expiry", action: "Cert compliance — current worker can't continue without renewal" },
|
||
{ priority: "high", weight: 5, note: "Client requested specific workers back from last month's project",
|
||
situation: "client_request", action: "Client relationship — they asked for specific people" },
|
||
|
||
// MEDIUM — standard day-to-day operations
|
||
{ priority: "medium", weight: 15, note: "Ongoing weekly fill — same client, same role, reliable pipeline",
|
||
situation: "recurring", action: "Recurring contract — steady work" },
|
||
{ priority: "medium", weight: 12, note: "Seasonal uptick — warehouse volume increasing ahead of holidays",
|
||
situation: "seasonal", action: "Seasonal planning — volume ramping up" },
|
||
{ priority: "medium", weight: 10, note: "Backfill for worker on approved medical leave — returns in 3 weeks",
|
||
situation: "medical_leave", action: "Temporary coverage — worker returning soon" },
|
||
{ priority: "medium", weight: 8, note: "Client testing new role — wants to try 2 workers for a week before committing",
|
||
situation: "trial", action: "Trial placement — client evaluating the role" },
|
||
{ priority: "medium", weight: 6, note: "Cross-training opportunity — client wants workers who can learn a new skill",
|
||
situation: "cross_train", action: "Development opportunity — workers can learn new skills" },
|
||
|
||
// LOW — planning ahead
|
||
{ priority: "low", weight: 10, note: "Future fill — project starts in 2 weeks, gathering candidates now",
|
||
situation: "future", action: "Pipeline building — no rush, quality over speed" },
|
||
{ priority: "low", weight: 8, note: "Client exploring staffing options — not committed yet, just want to see who's available",
|
||
situation: "exploratory", action: "Exploratory — client shopping, impress them with quality" },
|
||
{ priority: "low", weight: 5, note: "Internal transfer — moving a worker from one site to another, need replacement at original",
|
||
situation: "transfer", action: "Planned transition — smooth handoff between sites" },
|
||
];
|
||
|
||
function pick<T>(arr: T[]): T { return arr[Math.floor(Math.random() * arr.length)]; }
|
||
|
||
async function runWeekSimulation() {
|
||
const days = ["Monday","Tuesday","Wednesday","Thursday","Friday"];
|
||
const staffers = ["Sarah (Lead)","Mike (Senior)","Kim (Junior)"];
|
||
const results: any[] = [];
|
||
let totalFilled = 0, totalNeeded = 0, emergencies = 0, handoffs = 0, playbookEntries = 0;
|
||
|
||
for (let d = 0; d < days.length; d++) {
|
||
const dayLabel = days[d];
|
||
const numContracts = 4 + Math.floor(Math.random() * 5); // 4-8 per day
|
||
const contracts: any[] = [];
|
||
const staffer = staffers[d % staffers.length];
|
||
const handoffTo = staffers[(d + 1) % staffers.length];
|
||
|
||
for (let c = 0; c < numContracts; c++) {
|
||
const state = pick(STATES);
|
||
const city = pick(CITIES[state] || [state]);
|
||
const role = pick(ROLES);
|
||
// Weighted scenario selection
|
||
const totalWeight = SCENARIOS.reduce((s, sc) => s + sc.weight, 0);
|
||
let r = Math.random() * totalWeight;
|
||
let scenario = SCENARIOS[0];
|
||
for (const sc of SCENARIOS) { r -= sc.weight; if (r <= 0) { scenario = sc; break; } }
|
||
const priority = scenario.priority;
|
||
const headcount = priority === "urgent" ? 3 + Math.floor(Math.random() * 4) :
|
||
priority === "high" ? 2 + Math.floor(Math.random() * 3) :
|
||
priority === "medium" ? 2 + Math.floor(Math.random() * 3) :
|
||
1 + Math.floor(Math.random() * 2);
|
||
const minRel = priority === "urgent" ? 0.6 : priority === "high" ? 0.75 : 0.8;
|
||
const cid = `W${d+1}-${String(c+1).padStart(3,"0")}`;
|
||
|
||
if (priority === "urgent") emergencies++;
|
||
totalNeeded += headcount;
|
||
|
||
// Run hybrid search
|
||
let filled = 0;
|
||
let matches: any[] = [];
|
||
try {
|
||
const filt = `role = '${role}' AND state = '${state}' AND reliability >= ${minRel}`;
|
||
const r = await api("POST", "/vectors/hybrid", {
|
||
question: `Find ${role} workers in ${city}, ${state} for ${scenario.situation}`,
|
||
index_name: "workers_500k_v1",
|
||
sql_filter: filt,
|
||
filter_dataset: "ethereal_workers",
|
||
id_column: "worker_id",
|
||
top_k: headcount + 2,
|
||
generate: false,
|
||
});
|
||
matches = (r.sources || []).slice(0, headcount).map((s: any) => ({
|
||
doc_id: s.doc_id,
|
||
name: s.chunk_text?.split("—")[0]?.trim() || s.doc_id,
|
||
score: s.score,
|
||
chunk_text: s.chunk_text || "",
|
||
}));
|
||
filled = matches.length;
|
||
} catch {}
|
||
totalFilled += Math.min(filled, headcount);
|
||
|
||
contracts.push({
|
||
id: cid, client: makeClient(), role, state, city,
|
||
headcount, filled: Math.min(filled, headcount), priority,
|
||
start: pick(STARTS), notes: scenario.note, situation: scenario.situation,
|
||
action: scenario.action, matches,
|
||
staffer, handoff_to: d < 4 ? handoffTo : null,
|
||
});
|
||
}
|
||
|
||
// End of day: log playbook + prepare handoff
|
||
if (d < 4) {
|
||
handoffs++;
|
||
try {
|
||
await api("POST", "/api/ingest/file?name=successful_playbooks", null); // just trigger
|
||
} catch {}
|
||
}
|
||
playbookEntries++;
|
||
|
||
results.push({
|
||
label: dayLabel,
|
||
staffer,
|
||
handoff_to: d < 4 ? handoffTo : null,
|
||
contracts,
|
||
filled: contracts.reduce((s: number, c: any) => s + c.filled, 0),
|
||
needed: contracts.reduce((s: number, c: any) => s + c.headcount, 0),
|
||
});
|
||
}
|
||
|
||
const summary = {
|
||
total_contracts: results.reduce((s, d) => s + d.contracts.length, 0),
|
||
total_needed: totalNeeded,
|
||
total_filled: totalFilled,
|
||
fill_pct: Math.round(totalFilled / Math.max(totalNeeded, 1) * 100),
|
||
emergencies,
|
||
handoffs,
|
||
playbook_entries: playbookEntries,
|
||
};
|
||
|
||
// Log the week to playbooks
|
||
try {
|
||
const form = new FormData();
|
||
const csv = `timestamp,operation,approach,result,context\n"${new Date().toISOString()}","week_simulation: ${summary.total_contracts} contracts over 5 days","hybrid SQL+vector with multi-model routing","${summary.total_filled}/${summary.total_needed} filled (${summary.fill_pct}%)","${summary.emergencies} emergencies, ${summary.handoffs} handoffs"`;
|
||
form.append("file", new Blob([csv], { type: "text/csv" }), "playbook.csv");
|
||
await fetch(`${BASE}/ingest/file?name=successful_playbooks`, { method: "POST", body: form });
|
||
} catch {}
|
||
|
||
return { days: results, summary };
|
||
}
|