feat(embeddings): add embedding dimensions request field (#317)

openai-client/src/commonTest/kotlin/com/aallam/openai/client/TestEmbeddings.kt

@@ -23,6 +23,20 @@ class TestEmbeddings : TestOpenAI() {
         assertEquals(0, embedding.index)
     }
 
+    @Test
+    fun embeddingDimensions() = test {
+        val response = openAI.embeddings(request = embeddingRequest {
+            model = ModelId("text-embedding-3-small")
+            input = listOf("The food was delicious and the waiter...")
+            dimensions = 1024
+        })
+        assertTrue { response.embeddings.isNotEmpty() }
+        val embedding = response.embeddings.first()
+        assertTrue { embedding.embedding.isNotEmpty() }
+        assertEquals(1024, embedding.embedding.size)
+        assertEquals(0, embedding.index)
+    }
+
     @Test
     fun similarityEqual() = test {
         val embedding1 = Embedding(embedding = listOf(1.0, 2.0, 3.0, 4.0), index = 0)

openai-core/src/commonMain/kotlin/com.aallam.openai.api/embedding/EmbeddingRequest.kt

@@ -26,6 +26,13 @@ public class EmbeddingRequest(
      */
     @SerialName("input") public val input: List<String>,
 
+    /**
+     * The number of dimensions the resulting output embeddings should have.
+     *
+     * Only supported in text-embedding-3 and later models.
+     */
+    @SerialName("dimensions") public val dimensions: Int? = null,
+
     /**
      * A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
      */
@@ -59,6 +66,13 @@ public class EmbeddingRequestBuilder {
      */
     public var input: List<String>? = null
 
+    /**
+     * The number of dimensions the resulting output embeddings should have.
+     *
+     * Only supported in text-embedding-3 and later models.
+     */
+    public var dimensions: Int? = null
+
     /**
      * A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
      */
@@ -70,6 +84,7 @@ public class EmbeddingRequestBuilder {
     public fun build(): EmbeddingRequest = EmbeddingRequest(
         model = requireNotNull(model) { "model is required" },
         input = requireNotNull(input) { "input is required" },
+        dimensions = dimensions,
         user = user
     )
 }