feat: rescope to AI-first DB harness with multi-DB chat agent

Removes enterprise/DBA features and replaces the marginal AI bar with a
central chat agent that has progressive-discovery tools, cross-session
memory, saved-query reuse, and inline result actions. Adds ClickHouse
support alongside PostgreSQL/Greenplum.

Cleanup
- Drop ~10k LOC of advanced features: Docker, Snapshots, Validation,
  Index Advisor, Role/User Management, Data Generator, ERD, Lookup.
- Trim deps: drop @xyflow/react, dagre, @types/dagre; cut tokio features
  to rt-multi-thread/sync/time/net/macros.
- Remove unused TuskError variants and dead helpers (topological_sort,
  invalidate_schema_cache).

Multi-DB (PostgreSQL + ClickHouse)
- New src-tauri/src/db/ module: ChClient (HTTP-based, reuses reqwest),
  sql_guard (cross-flavor read-only whitelist with 8 tests).
- ConnectionConfig gains db_flavor and secure fields with serde defaults
  for backwards-compatible connections.json.
- All connection/query/schema/data commands dispatch by flavor; CH
  covers connect, execute_query, list_databases/schemas/tables/views/
  columns/completion_schema, paginated table fetch.
- Frontend: dbCapabilities matrix, ConnectionDialog engine selector
  with port auto-swap and HTTPS toggle, SqlEditor switches to
  StandardSQL dialect for CH, TableDataView surfaces CH connections as
  read-only.

AI-first chat agent
- New src/components/chat/ panel with composer, message rendering,
  collapsible tool-call/result blocks, top-level ErrorBoundary.
- Backend agent loop in commands/chat.rs with strict-JSON tool
  protocol. Nine tools: list_databases, list_tables, get_columns,
  switch_database, run_query, remember, save_query, find_queries, final.
  Forgiving parser accepts both flat and nested-input shapes.
- Compressed history: only the last 4 run_query results carry sample
  rows (≤10, cells truncated to 200 chars) into LLM context; older
  results marked omitted.
- System prompt uses lite OVERVIEW (DB list + active-DB tables only)
  instead of full DDL — schema details are loaded on demand via
  get_columns. CH OVERVIEW shows cross-DB tables since CH allows
  db.table queries.

Cross-session memory (F1)
- Per-connection markdown file at app_data_dir/memory/<connection_id>.md,
  16KB cap with oldest-block eviction. Agent appends via remember()
  tool; the file is injected into LEARNED NOTES section of every system
  prompt.
- New Memory sidebar tab with editable textarea, badge for note count,
  empty-state with template. Edits picked up on the next agent turn.

Saved-query reuse (F2)
- Tools save_query and find_queries scoped to current connection.
  save_query attaches a UUID + timestamp; find_queries returns top 10
  matches with SQL preview ≤500 chars.
- Storage shared with the sidebar Saved panel.

Inline result actions (F3)
- run_query result block in chat gets Open-full (90vw × 80vh modal with
  full ResultsTable, no row cap) and Export (reuses ExportDialog for
  CSV/JSON via existing exportCsv/exportJson commands).

Verification
- cargo check clean, zero warnings.
- cargo test --lib: 50 pass (20 chat parser + 4 memory + 8 sql_guard +
  6 clean_sql + 12 escape_ident).
- npx tsc --noEmit clean.
- npx vitest run: 20 pass.

src/components/chat/ChatPanel.tsx

@@ -0,0 +1,108 @@
+import { useEffect, useRef } from "react";
+import { useChat } from "@/hooks/use-chat";
+import { ChatComposer } from "./ChatComposer";
+import { ChatMessageView } from "./ChatMessageView";
+import { Button } from "@/components/ui/button";
+import { Eraser, Sparkles } from "lucide-react";
+import { useAppStore } from "@/stores/app-store";
+import { useAiSettings } from "@/hooks/use-ai";
+
+interface Props {
+  tabId: string;
+  connectionId: string;
+}
+
+export function ChatPanel({ tabId, connectionId }: Props) {
+  const { messages, pending, send, clear } = useChat(tabId, connectionId);
+  const dbFlavors = useAppStore((s) => s.dbFlavors);
+  const flavor = dbFlavors[connectionId];
+  const { data: aiSettings } = useAiSettings();
+  const aiReady = !!aiSettings?.model;
+
+  const scrollerRef = useRef<HTMLDivElement>(null);
+  useEffect(() => {
+    scrollerRef.current?.scrollTo({
+      top: scrollerRef.current.scrollHeight,
+      behavior: "smooth",
+    });
+  }, [messages.length, pending]);
+
+  return (
+    <div className="flex h-full flex-col">
+      <div className="flex h-9 items-center justify-between border-b border-border/40 px-3">
+        <div className="flex items-center gap-2 text-xs text-muted-foreground">
+          <Sparkles className="h-3.5 w-3.5 text-primary/70" />
+          <span className="font-medium">AI Assistant</span>
+          {flavor && <span className="text-[10px] uppercase tracking-wider text-muted-foreground/60">· {flavor}</span>}
+          {aiSettings?.model && (
+            <span className="text-[10px] text-muted-foreground/60">· {aiSettings.model}</span>
+          )}
+        </div>
+        <Button
+          size="xs"
+          variant="ghost"
+          onClick={clear}
+          disabled={messages.length === 0 || pending}
+          className="h-6 gap-1 text-xs text-muted-foreground hover:text-foreground"
+        >
+          <Eraser className="h-3 w-3" />
+          Clear
+        </Button>
+      </div>
+
+      <div ref={scrollerRef} className="min-h-0 flex-1 overflow-y-auto">
+        {messages.length === 0 && !pending ? (
+          <EmptyState aiReady={aiReady} flavor={flavor} />
+        ) : (
+          <div className="flex flex-col gap-3 px-4 py-3">
+            {messages.map((m) => (
+              <ChatMessageView key={m.id} message={m} />
+            ))}
+            {pending && <PendingIndicator />}
+          </div>
+        )}
+      </div>
+
+      <div className="border-t border-border/40 bg-background/40 p-2">
+        <ChatComposer
+          onSend={send}
+          disabled={pending || !aiReady}
+          placeholder={
+            aiReady
+              ? "Ask in plain language. The agent will browse schema and run read-only queries."
+              : "Configure an AI model in Settings to enable chat."
+          }
+        />
+      </div>
+    </div>
+  );
+}
+
+function PendingIndicator() {
+  return (
+    <div className="flex items-center gap-2 text-xs text-muted-foreground/70">
+      <span className="inline-flex gap-0.5">
+        <span className="h-1.5 w-1.5 animate-pulse rounded-full bg-primary/70" />
+        <span className="h-1.5 w-1.5 animate-pulse rounded-full bg-primary/70 [animation-delay:120ms]" />
+        <span className="h-1.5 w-1.5 animate-pulse rounded-full bg-primary/70 [animation-delay:240ms]" />
+      </span>
+      Thinking...
+    </div>
+  );
+}
+
+function EmptyState({ aiReady, flavor }: { aiReady: boolean; flavor: string | undefined }) {
+  return (
+    <div className="flex h-full items-center justify-center p-6">
+      <div className="max-w-md space-y-3 text-center">
+        <Sparkles className="mx-auto h-8 w-8 text-primary/50" />
+        <h3 className="text-sm font-medium">Ask anything about your data</h3>
+        <p className="text-xs text-muted-foreground">
+          {aiReady
+            ? `Connected to ${flavor ?? "database"}. Try: "How many rows in each table?", "Top 10 customers by total spend", "Show me last week's orders".`
+            : "Open Settings → AI to choose an Ollama model. Tusk will then assist with natural-language queries."}
+        </p>
+      </div>
+    </div>
+  );
+}