feat: add some more unit tests for structured llm outputs

test/_preload.ts

@@ -1 +0,0 @@
-import 'dotenv/config'

test/_utils.ts

@@ -20,7 +20,7 @@ const redis = new Redis(process.env.REDIS_URL_TEST!)
 const keyvRedis = new KeyvRedis(redis)
 const keyv = new Keyv({ store: keyvRedis, namespace: 'agentic-test' })
 
-// TODO: this is pretty hacky
+// TODO: this is a lil hacky
 const keyvHas = (keyv.has as any).bind(keyv)
 keyv.has = async (key, ...rest) => {
   if (refreshTestCache) {

test/openai.test.ts

@@ -1,11 +1,43 @@
 import test from 'ava'
-// import 'dotenv/config'
 import { expectTypeOf } from 'expect-type'
 import { z } from 'zod'
 
 import { OpenAIChatModelBuilder } from '../src/openai'
 import { createOpenAITestClient } from './_utils'
 
+test('OpenAIChatModel ⇒ string output', async (t) => {
+  t.timeout(2 * 60 * 1000)
+  const client = createOpenAITestClient()
+
+  const builder = new OpenAIChatModelBuilder(client, {
+    modelParams: {
+      temperature: 0,
+      max_tokens: 30
+    },
+    messages: [
+      {
+        role: 'system',
+        content:
+          'You are a human, not a large language model. You are not an AI. Respond like a human would. Your name is Leeroy Jenkins.'
+      },
+      {
+        role: 'user',
+        content: 'what is your name?'
+      }
+    ]
+  })
+
+  const result = await builder.call()
+  t.truthy(typeof result === 'string')
+
+  expectTypeOf(result).toMatchTypeOf<string>()
+
+  const result2 = await builder.output(z.string()).call()
+  t.truthy(typeof result2 === 'string')
+
+  expectTypeOf(result2).toMatchTypeOf<string>()
+})
+
 test('OpenAIChatModel ⇒ json output', async (t) => {
   t.timeout(2 * 60 * 1000)
   const client = createOpenAITestClient()
@@ -30,3 +62,26 @@ test('OpenAIChatModel ⇒ json output', async (t) => {
 
   expectTypeOf(result).toMatchTypeOf<{ foo: string; bar: number }>()
 })
+
+test.only('OpenAIChatModel ⇒ boolean output', async (t) => {
+  t.timeout(2 * 60 * 1000)
+  const client = createOpenAITestClient()
+
+  const builder = new OpenAIChatModelBuilder(client, {
+    modelParams: {
+      temperature: 0,
+      max_tokens: 30
+    },
+    messages: [
+      {
+        role: 'user',
+        content: 'are you alive?'
+      }
+    ]
+  }).output(z.boolean())
+
+  const result = await builder.call()
+  t.truthy(typeof result === 'boolean')
+
+  expectTypeOf(result).toMatchTypeOf<boolean>()
+})