phase 44 (part 1): migrate TS callers to /v1/chat + add regression guard

Migrates the four TypeScript /generate callers to the gateway's
/v1/chat surface so every LLM call lands on /v1/usage and Langfuse:

  tests/multi-agent/agent.ts::generate()      provider="ollama"
  tests/agent_test/agent_harness.ts::callAgent provider="ollama"
  bot/propose.ts::generateProposal             provider="ollama_cloud"
  mcp-server/observer.ts (error analysis)      provider="ollama"

Each migration follows the same pattern as the prior generateCloud()
migration (already on /v1/chat from 2026-04-24): replace
`fetch(SIDECAR/generate)` with `fetch(GATEWAY/v1/chat)`, swap the
prompt-style body for OpenAI-compat messages array, extract
content from `choices[0].message.content` instead of `text`.

Same upstream models in every case — gateway is the new home for
the call, transport otherwise unchanged.

Adds scripts/check_phase44_callers.sh — fail-loud regression guard
that exits non-zero if any non-adapter file fetches /generate or
api/generate. Adapter files (crates/gateway, crates/aibridge,
sidecar/) are exempt. Pre-tightening regex flagged prose mentions
in comments; the shipped regex requires `fetch(...)` or
`client.post(...)` shape so comments don't trip it.

Verification:
  bun build mcp-server/observer.ts                       compiles
  bun build tests/multi-agent/agent.ts                   compiles
  bun build tests/agent_test/agent_harness.ts            compiles
  bun build bot/propose.ts                               compiles
  ./scripts/check_phase44_callers.sh                     ✅ clean
  systemctl restart lakehouse-observer                   active

Phase 44 part 2 (deferred):
  - crates/aibridge/src/client.rs:118 still posts to sidecar /generate
    directly. AiClient is the foundational Rust LLM caller used by
    8+ vectord modules; migrating it is a workspace-wide refactor
    that needs its own commit. Plan: keep AiClient as the local-
    transport layer for the gateway's `provider=ollama` arm, but
    introduce a thin `/v1/chat` wrapper for external callers (vectord
    autotune, agent, rag, refresh, supervisor, playbook_memory).
  - tests/real-world/hard_task_escalation.ts: comment mentions
    /api/generate but doesn't actually call it. Comment is left
    intentionally as historical context; regex no longer flags it.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

mcp-server/observer.ts

@@ -550,13 +550,19 @@ async function analyzeErrors() {
     `[${f.endpoint}] ${f.input_summary}: ${f.error}`
   ).join("\n");
 
-  // Ask local model to diagnose
+  // Ask local model to diagnose. Phase 44 migration (2026-04-27):
+  // /v1/chat instead of legacy /ai/generate so /v1/usage tracks the
+  // call + Langfuse traces it. Same upstream model (qwen2.5 local).
   try {
-    const resp = await fetch(`${LAKEHOUSE}/ai/generate`, {
+    const resp = await fetch(`${LAKEHOUSE}/v1/chat`, {
       method: "POST",
       headers: { "Content-Type": "application/json" },
       body: JSON.stringify({
-        prompt: `You are a system reliability observer. Analyze these recent failures and suggest fixes:
+        model: "qwen2.5",
+        provider: "ollama",
+        messages: [{
+          role: "user",
+          content: `You are a system reliability observer. Analyze these recent failures and suggest fixes:
 
 ${errorSummary}
 
@@ -566,14 +572,15 @@ For each error:
 3. Should this be added to the playbook as a "don't do this"?
 
 Be specific and actionable. Under 200 words.`,
-        model: "qwen2.5",
+        }],
         max_tokens: 400,
         temperature: 0.2,
       }),
     });
-    const analysis = await resp.json();
-    if (analysis.text) {
-      console.error(`[observer] Error analysis:\n${analysis.text}`);
+    const analysis = await resp.json() as any;
+    const analysisText = analysis?.choices?.[0]?.message?.content ?? "";
+    if (analysisText) {
+      console.error(`[observer] Error analysis:\n${analysisText}`);
       // Log the analysis as a playbook entry
       await fetch(`${GATEWAY}/log`, {
         method: "POST",
@@ -581,7 +588,7 @@ Be specific and actionable. Under 200 words.`,
         body: JSON.stringify({
           operation: `error_analysis: ${failures.length} failures`,
           approach: "LLM-analyzed error patterns",
-          result: analysis.text.slice(0, 500),
+          result: analysisText.slice(0, 500),
           context: errorSummary.slice(0, 500),
         }),
       });