feat: prepare GoodBuddy 0.8.0

src/main/knowledge/openai-embedding-client.test.ts

@@ -0,0 +1,94 @@
+import { describe, expect, it, vi } from 'vitest'
+import { OpenAIEmbeddingClient } from './openai-embedding-client'
+
+describe('OpenAIEmbeddingClient', () => {
+  it('sends bounded OpenAI-compatible requests with an optional bearer key', async () => {
+    const transport = vi.fn<typeof fetch>(async (_input, init) => {
+      const body = JSON.parse(String(init?.body)) as {
+        input: string[]
+      }
+      return new Response(
+        JSON.stringify({
+          data: body.input.map((_, index) => ({
+            index,
+            embedding: [index + 1, 2, 3]
+          }))
+        })
+      )
+    })
+    const client = new OpenAIEmbeddingClient({
+      endpoint: 'https://vectors.example/custom/embeddings',
+      model: 'vendor/embed-large',
+      apiKey: 'vector-secret',
+      batchSize: 2,
+      fetch: transport
+    })
+
+    await expect(client.embed(['alpha', 'beta', 'gamma'])).resolves.toEqual([
+      [1, 2, 3],
+      [2, 2, 3],
+      [1, 2, 3]
+    ])
+    expect(transport).toHaveBeenCalledTimes(2)
+    expect(transport.mock.calls[0]?.[0]).toBe(
+      'https://vectors.example/custom/embeddings'
+    )
+    expect(transport.mock.calls[0]?.[1]?.headers).toMatchObject({
+      authorization: 'Bearer vector-secret'
+    })
+    expect(JSON.parse(String(transport.mock.calls[0]?.[1]?.body))).toEqual({
+      model: 'vendor/embed-large',
+      input: ['alpha', 'beta']
+    })
+  })
+
+  it('accepts unauthenticated endpoints and restores response index order', async () => {
+    const transport = vi.fn<typeof fetch>(async () =>
+      new Response(
+        JSON.stringify({
+          data: [
+            { index: 1, embedding: [4, 5] },
+            { index: 0, embedding: [2, 3] }
+          ]
+        })
+      )
+    )
+    const client = new OpenAIEmbeddingClient({
+      endpoint: 'http://127.0.0.1:11434/v1/embeddings',
+      model: 'nomic-embed-text',
+      fetch: transport
+    })
+
+    await expect(client.embed(['first', 'second'])).resolves.toEqual([
+      [2, 3],
+      [4, 5]
+    ])
+    expect(transport.mock.calls[0]?.[1]?.headers).not.toHaveProperty(
+      'authorization'
+    )
+  })
+
+  it('rejects unsafe endpoints and malformed vectors', async () => {
+    expect(
+      () =>
+        new OpenAIEmbeddingClient({
+          endpoint: 'https://user:secret@vectors.example/embeddings',
+          model: 'model'
+        })
+    ).toThrow('must not contain credentials')
+
+    const malformed = new OpenAIEmbeddingClient({
+      endpoint: 'https://vectors.example/v1/embeddings',
+      model: 'model',
+      fetch: async () =>
+        new Response(
+          JSON.stringify({
+            data: [{ index: 0, embedding: [1, Number.NaN] }]
+          })
+        )
+    })
+    await expect(malformed.embed(['safe synthetic input'])).rejects.toThrow(
+      'finite numbers'
+    )
+  })
+})