adding support for generic AI provider (OpenAI compatible)

packages/backend/.env

@@ -3,4 +3,14 @@
 # In production / Docker, supply these as environment variables.
 
 DATABASE_URL=postgres://user:password@localhost:5432/recommender
+
+# AI provider selection: OPENAI (default) or GENERIC
+AI_PROVIDER=OPENAI
+
+# OpenAI provider settings (used when AI_PROVIDER=OPENAI)
 OPENAI_API_KEY=your-openai-api-key-here
+
+# Generic provider settings (used when AI_PROVIDER=GENERIC)
+PROVIDER_URL=https://your-provider.example.com/v1
+BEARER_TOKEN=your-bearer-token
+MODEL_NAME=your-model-name

packages/backend/src/agent.ts

@@ -2,9 +2,22 @@ import OpenAI from 'openai';
 import * as dotenv from 'dotenv';
 dotenv.config({ path: ['.env.local', '.env'] });
 
-export const openai = new OpenAI({
-  apiKey: process.env.OPENAI_API_KEY,
-});
+const AI_PROVIDER = process.env.AI_PROVIDER ?? 'OPENAI';
+const isGeneric = AI_PROVIDER === 'GENERIC';
+
+export const openai = isGeneric
+  ? new OpenAI({
+      apiKey: process.env.BEARER_TOKEN,
+      baseURL: process.env.PROVIDER_URL,
+    })
+  : new OpenAI({
+      apiKey: process.env.OPENAI_API_KEY,
+    });
+
+export const defaultModel = isGeneric ? (process.env.MODEL_NAME ?? 'default') : 'gpt-5.4';
+export const miniModel = isGeneric ? (process.env.MODEL_NAME ?? 'default') : 'gpt-5.4-mini';
+export const serviceOptions = isGeneric ? {} : { service_tier: 'flex' as const };
+export const supportsWebSearch = !isGeneric;
 
 export async function askAgent(prompt: string) {
   try {

packages/backend/src/agents/curator.ts

@@ -1,4 +1,4 @@
-import { openai } from '../agent.js';
+import { openai, defaultModel, serviceOptions, supportsWebSearch } from '../agent.js';
 import type { InterpreterOutput, RankingOutput, CuratorOutput, MediaType } from '../types/agents.js';
 import { z } from 'zod';
 import { zodTextFormat } from 'openai/helpers/zod';
@@ -32,11 +32,12 @@ export async function runCurator(
     .map((s) => `- "${s.title}" (${s.category})`)
     .join('\n');
 
+  const canSearch = useWebSearch && supportsWebSearch;
   const response = await openai.responses.parse({
-    model: 'gpt-5.4',
+    model: defaultModel,
     temperature: 0.5,
-    service_tier: 'flex',
-    ...(useWebSearch ? { tools: [{ type: 'web_search' as const }] } : {}),
+    ...serviceOptions,
+    ...(canSearch ? { tools: [{ type: 'web_search' as const }] } : {}),
     text: { format: zodTextFormat(CuratorSchema, "shows") },
     instructions: `You are a ${mediaLabel} recommendation curator. For each ${mediaLabel}, write a concise 1-2 sentence explanation of why it was assigned to its category based on the user's preferences.${useWebSearch ? '\n\nUse web search to verify details and enrich explanations with accurate information.' : ''}
 

packages/backend/src/agents/interpreter.ts

@@ -1,4 +1,4 @@
-import { openai } from '../agent.js';
+import { openai, defaultModel, serviceOptions } from '../agent.js';
 import type { InterpreterOutput, MediaType } from '../types/agents.js';
 import { z } from 'zod';
 import { zodTextFormat } from 'openai/helpers/zod';
@@ -28,9 +28,9 @@ export async function runInterpreter(input: InterpreterInput): Promise<Interpret
     : '';
 
   const response = await openai.responses.parse({
-    model: 'gpt-5.4-mini',
+    model: defaultModel,
     temperature: 0.2,
-    service_tier: 'flex',
+    ...serviceOptions,
     text: { format: zodTextFormat(InterpreterSchema, "preferences") },
     instructions: `You are a ${mediaLabel} preference interpreter. Transform raw user input into structured, normalized preferences.
 

packages/backend/src/agents/ranking.ts

@@ -1,4 +1,4 @@
-import { openai } from '../agent.js';
+import { openai, defaultModel, serviceOptions } from '../agent.js';
 import type { InterpreterOutput, RetrievalOutput, RankingOutput, MediaType } from '../types/agents.js';
 import { z } from 'zod';
 import { zodTextFormat } from 'openai/helpers/zod';
@@ -42,9 +42,9 @@ export async function runRanking(
     const chunkTitles = chunk.map((c) => `- ${c.title}: ${c.reason}`).join('\n');
 
     const response = await openai.responses.parse({
-      model: 'gpt-5.4',
+      model: defaultModel,
       temperature: 0.2,
-      service_tier: 'flex',
+      ...serviceOptions,
       text: { format: zodTextFormat(RankingSchema, "ranking") },
       instructions: `You are a ${mediaLabel} ranking critic. Assign each ${mediaLabel} to exactly one of four confidence buckets based on how well it matches the user's preferences.
 

packages/backend/src/agents/retrieval.ts

@@ -1,4 +1,4 @@
-import { openai } from '../agent.js';
+import { openai, defaultModel, serviceOptions, supportsWebSearch } from '../agent.js';
 import type { InterpreterOutput, RetrievalOutput, MediaType } from '../types/agents.js';
 import { z } from 'zod';
 import { zodTextFormat } from 'openai/helpers/zod';
@@ -19,11 +19,12 @@ export async function runRetrieval(
   const mediaLabel = mediaType === 'movie' ? 'movie' : 'TV show';
   const mediaLabelPlural = mediaType === 'movie' ? 'movies' : 'TV shows';
 
+  const canSearch = useWebSearch && supportsWebSearch;
   const response = await openai.responses.parse({
-    model: 'gpt-5.4',
+    model: defaultModel,
     temperature: 0.9,
-    service_tier: 'flex',
-    ...(useWebSearch ? { tools: [{ type: 'web_search' as const }] } : {}),
+    ...serviceOptions,
+    ...(canSearch ? { tools: [{ type: 'web_search' as const }] } : {}),
     text: { format: zodTextFormat(RetrievalSchema, "candidates") },
     instructions: `You are a ${mediaLabel} candidate generator. Your goal is to brainstorm a LARGE, DIVERSE pool of ${brainstormCount} ${mediaLabel} candidates that match the user's structured preferences.${useWebSearch ? '\n\nUse web search to find recent and accurate titles, including newer releases.' : ''}
 

packages/backend/src/agents/titleGenerator.ts

@@ -1,13 +1,13 @@
-import { openai } from '../agent.js';
+import { openai, miniModel, serviceOptions } from '../agent.js';
 import type { InterpreterOutput, MediaType } from '../types/agents.js';
 
 export async function generateTitle(interpreter: InterpreterOutput, mediaType: MediaType = 'tv_show'): Promise<string> {
   const mediaLabel = mediaType === 'movie' ? 'movie' : 'TV show';
 
   const response = await openai.responses.create({
-    model: 'gpt-5.4-mini',
+    model: miniModel,
     temperature: 0.7,
-    service_tier: 'flex',
+    ...serviceOptions,
     instructions: `Generate a concise 5-8 word title for a ${mediaLabel} recommendation session.
 Capture the essence of the user's taste — genre, tone, key themes.
 Respond with ONLY the title. No quotes, no trailing punctuation.