tv shows to tv series

packages/backend/src/agents/curator.ts

@@ -4,7 +4,7 @@ import { z } from 'zod';
 import { zodTextFormat } from 'openai/helpers/zod';
 
 const CuratorSchema = z.object({
-  shows: z.array(z.object({
+  series: z.array(z.object({
     title: z.string(),
     explanation: z.string(),
     category: z.enum(["Full Match", "Definitely Like", "Might Like", "Questionable", "Will Not Like"]),
@@ -24,7 +24,7 @@ export async function runCurator(
 ): Promise<CuratorOutput[]> {
   const mediaLabel = mediaType === 'movie' ? 'movie' : 'TV show';
 
-  const allShows = [
+  const allSeries = [
     ...(ranking.full_match ?? []).map((t) => ({ title: t, category: 'Full Match' as const })),
     ...ranking.definitely_like.map((t) => ({ title: t, category: 'Definitely Like' as const })),
     ...ranking.might_like.map((t) => ({ title: t, category: 'Might Like' as const })),
@@ -32,7 +32,7 @@ export async function runCurator(
     ...ranking.will_not_like.map((t) => ({ title: t, category: 'Will Not Like' as const })),
   ];
 
-  if (allShows.length === 0) return [];
+  if (allSeries.length === 0) return [];
 
   const canSearch = useWebSearch && supportsWebSearch;
   const preferenceSummary = `User preferences summary:
@@ -53,9 +53,9 @@ Rules:
 - cons: up to 3 short bullet points about what the user might not like based on their preferences
 - Be honest — explain why "Questionable" or "Will Not Like" ${mediaLabel}s got that rating`;
 
-  const chunks: typeof allShows[] = [];
-  for (let i = 0; i < allShows.length; i += CHUNK_SIZE) {
-    chunks.push(allShows.slice(i, i + CHUNK_SIZE));
+  const chunks: typeof allSeries[] = [];
+  for (let i = 0; i < allSeries.length; i += CHUNK_SIZE) {
+    chunks.push(allSeries.slice(i, i + CHUNK_SIZE));
   }
 
   const results: CuratorOutput[] = [];
@@ -66,14 +66,14 @@ Rules:
       temperature: 0.5,
       ...serviceOptions,
       ...(canSearch ? { tools: [{ type: 'web_search' as const }] } : {}),
-      text: { format: zodTextFormat(CuratorSchema, "shows") },
+      text: { format: zodTextFormat(CuratorSchema, "series") },
       instructions,
       input: `${preferenceSummary}
 
 ${mediaLabel}s to describe:
 ${showList}`,
     }));
-    results.push(...(response.output_parsed?.shows ?? []));
+    results.push(...(response.output_parsed?.series ?? []));
   }
 
   return results;

packages/backend/src/agents/interpreter.ts

@@ -15,8 +15,8 @@ const InterpreterSchema = z.object({
 
 interface InterpreterInput {
   main_prompt: string;
-  liked_shows: string;
-  disliked_shows: string;
+  liked_series: string;
+  disliked_series: string;
   themes: string;
   media_type: MediaType;
   feedback_context?: string;
@@ -43,8 +43,8 @@ Rules:
 - Be specific and concrete, not vague
 - For "requirements": capture explicit hard requirements the user stated that recommendations must satisfy — things like "must be from the 2000s onward", "must have subtitles", "must feature a female lead". Leave empty if no such constraints were stated.`,
     input: `Main prompt: ${input.main_prompt}
-Liked ${mediaLabel}s: ${input.liked_shows || '(none)'}
-Disliked ${mediaLabel}s: ${input.disliked_shows || '(none)'}
+Liked ${mediaLabel}s: ${input.liked_series || '(none)'}
+Disliked ${mediaLabel}s: ${input.disliked_series || '(none)'}
 Themes and requirements: ${input.themes || '(none)'}${feedbackSection}`,
   }));
 

packages/backend/src/agents/retrieval.ts

@@ -19,7 +19,7 @@ export async function runRetrieval(
   previousFullMatches: string[] = [],
 ): Promise<RetrievalOutput> {
   const mediaLabel = mediaType === 'movie' ? 'movie' : 'TV show';
-  const mediaLabelPlural = mediaType === 'movie' ? 'movies' : 'TV shows';
+  const mediaLabelPlural = mediaType === 'movie' ? 'movies' : 'TV series';
 
   const canSearch = useWebSearch && supportsWebSearch;
   const response = await parseWithRetry(() => openai.responses.parse({

packages/backend/src/agents/retrievalContinuous.ts

@@ -38,7 +38,7 @@ function buildSystemPrompt(
   seenTitles: string[]
 ): string {
   const mediaLabel = mediaType === 'movie' ? 'movie' : 'TV show';
-  const mediaLabelPlural = mediaType === 'movie' ? 'movies' : 'TV shows';
+  const mediaLabelPlural = mediaType === 'movie' ? 'movies' : 'TV series';
 
   let prompt = `You are a ${mediaLabel} recommendation specialist. Your task is to recommend titles that match the user's taste profile.
 

packages/backend/src/db/schema.ts

@@ -5,8 +5,8 @@ export const recommendations = pgTable('recommendations', {
   id: uuid('id').defaultRandom().primaryKey(),
   title: text('title').notNull(),
   main_prompt: text('main_prompt').notNull(),
-  liked_shows: text('liked_shows').notNull().default(''),
-  disliked_shows: text('disliked_shows').notNull().default(''),
+  liked_series: text('liked_series').notNull().default(''),
+  disliked_series: text('disliked_series').notNull().default(''),
   themes: text('themes').notNull().default(''),
   brainstorm_count: integer('brainstorm_count').notNull().default(100),
   media_type: text('media_type').notNull().default('tv_show'),

packages/backend/src/pipelines/continuous.ts

@@ -39,8 +39,8 @@ function mergeCuratorOutputs(a: CuratorOutput[], b: CuratorOutput[]): CuratorOut
 }
 
 interface ContinuousPipelineInput {
-  likedShows: string;
-  dislikedShows?: string;
+  likedSeries: string;
+  dislikedSeries?: string;
   themes?: string;
   requirements?: string;
   avoid?: string;
@@ -57,8 +57,8 @@ export async function runContinuousPipeline(
 ): Promise<CuratorOutput[]> {
   const startTime = Date.now();
   const {
-    likedShows,
-    dislikedShows = '',
+    likedSeries,
+    dislikedSeries = '',
     themes = '',
     requirements = '',
     avoid = '',
@@ -83,9 +83,9 @@ export async function runContinuousPipeline(
     const t0 = Date.now();
 
     interpreterOutput = await runInterpreter({
-      main_prompt: themes || 'recommend shows based on user preferences',
-      liked_shows: likedShows,
-      disliked_shows: dislikedShows,
+      main_prompt: themes || 'recommend series based on user preferences',
+      liked_series: likedSeries,
+      disliked_series: dislikedSeries,
       themes: themes,
       media_type: mediaType,
     });
@@ -267,8 +267,8 @@ export async function runContinuousPipeline(
       .set({
         title: aiTitle,
         main_prompt: themes || 'Continuous recommendations',
-        liked_shows: likedShows,
-        disliked_shows: dislikedShows,
+        liked_series: likedSeries,
+        disliked_series: dislikedSeries,
         themes: themes,
         brainstorm_count: totalCount,
         media_type: mediaType,

packages/backend/src/pipelines/recommendation.ts

@@ -201,8 +201,8 @@ export async function runPipeline(
     const t0 = Date.now();
     const interpreterOutput = await runInterpreter({
       main_prompt: rec.main_prompt,
-      liked_shows: rec.liked_shows,
-      disliked_shows: rec.disliked_shows,
+      liked_series: rec.liked_series,
+      disliked_series: rec.disliked_series,
       themes: rec.themes,
       media_type: mediaType,
       ...(feedbackContext !== undefined ? { feedback_context: feedbackContext } : {}),

packages/backend/src/routes/recommendations.ts

@@ -29,8 +29,8 @@ export default async function recommendationsRoute(fastify: FastifyInstance) {
   fastify.post('/recommendations', async (request, reply) => {
     const body = request.body as {
       main_prompt: string;
-      liked_shows?: string;
-      disliked_shows?: string;
+      liked_series?: string;
+      disliked_series?: string;
       themes?: string;
       brainstorm_count?: number;
       media_type?: string;
@@ -64,8 +64,8 @@ export default async function recommendationsRoute(fastify: FastifyInstance) {
       .values({
         title: title || 'Untitled',
         main_prompt: body.main_prompt ?? '',
-        liked_shows: body.liked_shows ?? '',
-        disliked_shows: body.disliked_shows ?? '',
+        liked_series: body.liked_series ?? '',
+        disliked_series: body.disliked_series ?? '',
         themes: body.themes ?? '',
         brainstorm_count,
         media_type,
@@ -85,8 +85,8 @@ export default async function recommendationsRoute(fastify: FastifyInstance) {
   // POST /recommendations/continuous — create record and run continuous pipeline with SSE
   fastify.post('/recommendations/continuous', async (request, reply) => {
     const body = request.body as {
-      liked_shows: string;
-      disliked_shows?: string;
+      liked_series: string;
+      disliked_series?: string;
       themes?: string;
       requirements?: string;
       avoid?: string;
@@ -108,8 +108,8 @@ export default async function recommendationsRoute(fastify: FastifyInstance) {
       .values({
         title,
         main_prompt: body.themes ?? 'Continuous recommendations',
-        liked_shows: body.liked_shows,
-        disliked_shows: body.disliked_shows ?? '',
+        liked_series: body.liked_series,
+        disliked_series: body.disliked_series ?? '',
         themes: body.themes ?? '',
         brainstorm_count: totalCount,
         media_type: mediaType,
@@ -155,8 +155,8 @@ export default async function recommendationsRoute(fastify: FastifyInstance) {
 
       // Run the continuous pipeline (it will now update the existing record)
       await runContinuousPipeline(recId, {
-        likedShows: body.liked_shows,
-        dislikedShows: body.disliked_shows ?? '',
+        likedSeries: body.liked_series,
+        dislikedSeries: body.disliked_series ?? '',
         themes: body.themes ?? '',
         requirements: body.requirements ?? '',
         avoid: body.avoid ?? '',
@@ -305,11 +305,11 @@ export default async function recommendationsRoute(fastify: FastifyInstance) {
       const feedbackContext =
         feedbackRows.length > 0
           ? feedbackRows
-              .map(
-                (f) =>
-                  `${mediaLabel}: "${f.item_name}" — Rating: ${f.stars}/3 stars${f.feedback ? ` — Comment: ${f.feedback}` : ''}`,
-              )
-              .join('\n')
+            .map(
+              (f) =>
+                `${mediaLabel}: "${f.item_name}" — Rating: ${f.stars}/3 stars${f.feedback ? ` — Comment: ${f.feedback}` : ''}`,
+            )
+            .join('\n')
           : undefined;
 
       await runPipeline(rec, (event) => {

packages/backend/src/types/agents.ts

@@ -83,8 +83,8 @@ export interface ContinuousSession {
 
 export interface ContinuousStartRequest {
   mediaType: 'tv_show' | 'movie';
-  likedShows: string;
-  dislikedShows?: string;
+  likedSeries: string;
+  dislikedSeries?: string;
   themes?: string;
   requirements?: string;
   avoid?: string;