Guardrails: Updated the Topic Relevance API Endpoint to LLM Prompt Configs

app/api/guardrails/topic_relevance_configs/[topic_relevance_config_id]/route.ts → app/api/guardrails/llm_prompt_configs/[llm_prompt_config_id]/route.ts

@@ -3,13 +3,13 @@ import { NextResponse, NextRequest } from "next/server";
 
 export async function GET(
   request: NextRequest,
-  { params }: { params: Promise<{ topic_relevance_config_id: string }> },
+  { params }: { params: Promise<{ llm_prompt_config_id: string }> },
 ) {
   try {
-    const { topic_relevance_config_id } = await params;
+    const { llm_prompt_config_id } = await params;
     const { status, data } = await guardrailsUserClient(
       request,
-      `/api/v1/guardrails/topic_relevance_configs/${topic_relevance_config_id}`,
+      `/api/v1/guardrails/llm_prompt_configs/${llm_prompt_config_id}`,
     );
     return NextResponse.json(data, { status });
   } catch (e: unknown) {
@@ -20,18 +20,18 @@ export async function GET(
   }
 }
 
-export async function PUT(
+export async function PATCH(
   request: NextRequest,
-  { params }: { params: Promise<{ topic_relevance_config_id: string }> },
+  { params }: { params: Promise<{ llm_prompt_config_id: string }> },
 ) {
   try {
-    const { topic_relevance_config_id } = await params;
+    const { llm_prompt_config_id } = await params;
     const body = await request.json();
     const { status, data } = await guardrailsUserClient(
       request,
-      `/api/v1/guardrails/topic_relevance_configs/${topic_relevance_config_id}`,
+      `/api/v1/guardrails/llm_prompt_configs/${llm_prompt_config_id}`,
       {
-        method: "PUT",
+        method: "PATCH",
         body: JSON.stringify(body),
       },
     );
@@ -46,13 +46,13 @@ export async function PUT(
 
 export async function DELETE(
   request: NextRequest,
-  { params }: { params: Promise<{ topic_relevance_config_id: string }> },
+  { params }: { params: Promise<{ llm_prompt_config_id: string }> },
 ) {
   try {
-    const { topic_relevance_config_id } = await params;
+    const { llm_prompt_config_id } = await params;
     const { status, data } = await guardrailsUserClient(
       request,
-      `/api/v1/guardrails/topic_relevance_configs/${topic_relevance_config_id}`,
+      `/api/v1/guardrails/llm_prompt_configs/${llm_prompt_config_id}`,
       {
         method: "DELETE",
       },

app/api/guardrails/topic_relevance_configs/route.ts → app/api/guardrails/llm_prompt_configs/route.ts

@@ -3,10 +3,10 @@ import { NextResponse, NextRequest } from "next/server";
 
 export async function GET(request: NextRequest) {
   try {
-    const { status, data } = await guardrailsUserClient(
-      request,
-      "/api/v1/guardrails/topic_relevance_configs/",
-    );
+    const url = new URL(request.url);
+    const qs = url.searchParams.toString();
+    const path = `/api/v1/guardrails/llm_prompt_configs/${qs ? `?${qs}` : ""}`;
+    const { status, data } = await guardrailsUserClient(request, path);
     return NextResponse.json(data, { status });
   } catch (e: unknown) {
     return NextResponse.json(
@@ -21,7 +21,7 @@ export async function POST(request: NextRequest) {
     const body = await request.json();
     const { status, data } = await guardrailsUserClient(
       request,
-      "/api/v1/guardrails/topic_relevance_configs/",
+      "/api/v1/guardrails/llm_prompt_configs/",
       {
         method: "POST",
         body: JSON.stringify(body),

app/components/analytics/AnalyticsChartCard.tsx

@@ -15,8 +15,8 @@ import BreakdownPanel from "./BreakdownPanel";
 import LineTrendChart from "./LineTrendChart";
 
 const SERIES_COLORS = [
-  "#2563eb",
-  "#e11d48",
+  "#1f4496",
+  "#ffce00",
   "#16a34a",
   "#f59e0b",
   "#7c3aed",

app/components/analytics/AnalyticsTotalsRow.tsx

@@ -2,7 +2,16 @@
 
 import { ReactNode } from "react";
 import { InfoTooltip, Loader } from "@/app/components/ui";
-import { AnalyticsTotalsRowProps } from "@/app/lib/types/analytics";
+import {
+  AnalyticsCardAccent,
+  AnalyticsTotalsRowProps,
+} from "@/app/lib/types/analytics";
+
+const ACCENT_CARD: Record<AnalyticsCardAccent, string> = {
+  cost: "bg-status-success-bg/40 border-status-success-border/40",
+  usage: "bg-accent-primary/5 border-accent-primary/20",
+  activity: "bg-status-warning-bg/40 border-status-warning-border/40",
+};
 
 function formatTokens(n: number): string {
   if (!Number.isFinite(n)) return "0";
@@ -52,29 +61,26 @@ export default function AnalyticsTotalsRow({
         <h3 className="text-sm font-semibold text-text-primary tracking-tight">
           All-time totals
         </h3>
-        <p className="text-[11px] text-text-secondary">
-          Real users on the left, quality checks on the right.
-        </p>
       </div>
 
       <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-2.5">
         <StatCard
           accent="cost"
-          label="Production cost"
+          label="AI cost"
           value={formatCurrency(totals.cost.value)}
           hint="Real users using your AI"
           tooltip="Money spent on AI requests made by your real users (excludes any testing or quality checks you ran)."
         />
         <StatCard
           accent="usage"
-          label="Production tokens"
+          label="AI usage tokens"
           value={`${formatTokens(totals.cost.totalTokens)} tokens`}
           hint={`${formatCount(totals.cost.inputTokens)} in · ${formatCount(totals.cost.outputTokens)} out`}
           tooltip="The amount of text your AI processed for real users. Tokens are how AI providers measure work and bill you — roughly one word ≈ 1.3 tokens."
         />
         <StatCard
           accent="activity"
-          label="Production requests"
+          label="AI requests"
           value={formatCount(totals.requests.value)}
           hint="Real-user AI requests"
           tooltip="How many times your real users asked the AI to do something."
@@ -83,36 +89,28 @@ export default function AnalyticsTotalsRow({
           accent="cost"
           label="Eval cost"
           value={formatCurrency(totals.eval_cost.value)}
-          hint="Quality checks you ran"
-          tooltip="Money spent on AI requests used for testing and quality checks (separate from real-user activity)."
+          hint="Evaluations you ran"
+          tooltip="Money spent on AI requests used for running evaluations (separate from user activity)."
         />
         <StatCard
           accent="usage"
           label="Eval tokens"
           value={`${formatTokens(totals.eval_cost.totalTokens)} tokens`}
           hint={`${formatCount(totals.eval_cost.inputTokens)} in · ${formatCount(totals.eval_cost.outputTokens)} out`}
-          tooltip="The amount of text processed during your quality checks. Tokens are how AI providers measure work and bill you."
+          tooltip="The amount of text processed during your evaluations. Tokens are how AI providers measure work and bill you."
         />
         <StatCard
           accent="activity"
           label="Eval runs"
           value={formatCount(totals.eval_runs.value)}
-          hint="Quality-check batches"
-          tooltip="How many times you ran a quality check on your AI. Each run can test the AI on many cases at once."
+          hint="Eval-run activity"
+          tooltip="How many times you ran an evaluation on your AI. Each run can test it on many cases at once."
         />
       </div>
     </div>
   );
 }
 
-type Accent = "cost" | "usage" | "activity";
-
-const ACCENT_CARD: Record<Accent, string> = {
-  cost: "bg-status-success-bg/40 border-status-success-border/40",
-  usage: "bg-accent-primary/5 border-accent-primary/20",
-  activity: "bg-status-warning-bg/40 border-status-warning-border/40",
-};
-
 function StatCard({
   label,
   value,
@@ -124,7 +122,7 @@ function StatCard({
   value: string;
   hint?: string;
   tooltip?: ReactNode;
-  accent: Accent;
+  accent: AnalyticsCardAccent;
 }) {
   return (
     <div className={`rounded-lg border p-3 ${ACCENT_CARD[accent]}`}>

app/components/guardrails/TopicRelevanceField.tsx

@@ -49,25 +49,25 @@ export default function TopicRelevanceField({
 
     guardrailsFetch<{
       data?:
-        | { topic_relevance_configs?: TopicRelevanceConfig[] }
+        | { llm_prompt_configs?: TopicRelevanceConfig[] }
         | TopicRelevanceConfig[];
-      topic_relevance_configs?: TopicRelevanceConfig[];
-    }>("/api/guardrails/topic_relevance_configs", apiKey)
+      llm_prompt_configs?: TopicRelevanceConfig[];
+    }>(
+      "/api/guardrails/llm_prompt_configs?validator_name=topic_relevance",
+      apiKey,
+    )
       .then((data) => {
         const nested = data?.data;
         const list: TopicRelevanceConfig[] = Array.isArray(
-          (nested as { topic_relevance_configs?: TopicRelevanceConfig[] })
-            ?.topic_relevance_configs,
+          (nested as { llm_prompt_configs?: TopicRelevanceConfig[] })
+            ?.llm_prompt_configs,
         )
-          ? (
-              nested as {
-                topic_relevance_configs: TopicRelevanceConfig[];
-              }
-            ).topic_relevance_configs
+          ? (nested as { llm_prompt_configs: TopicRelevanceConfig[] })
+              .llm_prompt_configs
           : Array.isArray(nested)
             ? (nested as TopicRelevanceConfig[])
-            : Array.isArray(data?.topic_relevance_configs)
-              ? data.topic_relevance_configs!
+            : Array.isArray(data?.llm_prompt_configs)
+              ? data.llm_prompt_configs!
               : [];
         setConfigs(list);
       })
@@ -101,16 +101,17 @@ export default function TopicRelevanceField({
     let cancelled = false;
     setDetailsLoading(true);
     guardrailsFetch<{
-      data?: TopicRelevanceConfigDetails;
+      data?: TopicRelevanceConfigDetails & { llm_prompt?: string | null };
       description?: string | null;
       configuration?: string | null;
-    }>(`/api/guardrails/topic_relevance_configs/${value}`, apiKey)
+      llm_prompt?: string | null;
+    }>(`/api/guardrails/llm_prompt_configs/${value}`, apiKey)
       .then((d) => {
         if (cancelled) return;
         const entity = d?.data ?? d;
         setDetails({
           description: entity?.description ?? null,
-          configuration: entity?.configuration ?? null,
+          configuration: entity?.llm_prompt ?? entity?.configuration ?? null,
         });
       })
       .catch(() => {

app/components/guardrails/TopicRelevanceModal.tsx

@@ -41,13 +41,14 @@ export default function TopicRelevanceModal({
     try {
       setIsSaving(true);
       const body = {
+        validator_name: "topic_relevance",
         name: name.trim(),
         description: description.trim(),
-        configuration: configuration.trim(),
+        llm_prompt: configuration.trim(),
         prompt_schema_version: promptSchemaVersion,
       };
       const data = await guardrailsFetch<{ id: string; name?: string }>(
-        "/api/guardrails/topic_relevance_configs",
+        "/api/guardrails/llm_prompt_configs",
         apiKey,
         { method: "POST", body: JSON.stringify(body) },
       );

app/lib/types/analytics.ts

@@ -100,6 +100,8 @@ export interface AnalyticsTotalsRowProps {
   error: string | null;
 }
 
+export type AnalyticsCardAccent = "cost" | "usage" | "activity";
+
 export interface AnalyticsChartRow {
   month: string;
   monthIso: string;