Merge pull request #114 from samchon/feat/empty-parameters

Test empty parameters' function calling cases

examples/function-calling/schemas/llama.sale.schema.json

@@ -327,16 +327,16 @@
                 },
                 "minItems": 1
               },
-              "required": {
-                "title": "Whether the unit is required or not",
-                "description": "Whether the unit is required or not.\n\nWhen the unit is required, the customer must select the unit. If do not\nselect, customer can't buy it.\n\nFor example, if there's a sale \"Macbook Set\" and one of the unit is the\n\"Main Body\", is it possible to buy the \"Macbook Set\" without the\n\"Main Body\" unit? This property is for that case.",
-                "type": "boolean"
-              },
               "name": {
                 "title": "Representative name of the unit",
                 "description": "Representative name of the unit.",
                 "type": "string"
               },
+              "required": {
+                "title": "Whether the unit is required or not",
+                "description": "Whether the unit is required or not.\n\nWhen the unit is required, the customer must select the unit. If do not\nselect, customer can't buy it.\n\nFor example, if there's a sale \"Macbook Set\" and one of the unit is the\n\"Main Body\", is it possible to buy the \"Macbook Set\" without the\n\"Main Body\" unit? This property is for that case.",
+                "type": "boolean"
+              },
               "primary": {
                 "title": "Whether the unit is primary or not",
                 "description": "Whether the unit is primary or not.\n\nJust a labeling value.",
@@ -346,8 +346,8 @@
             "required": [
               "options",
               "stocks",
-              "required",
               "name",
+              "required",
               "primary"
             ]
           },
@@ -364,7 +364,6 @@
       },
       "required": [
         "section_code",
-        "status",
         "opened_at",
         "closed_at",
         "content",

test/features/llm/chatgpt/test_chatgpt_function_calling_empty.ts

@@ -0,0 +1,39 @@
+import { TestValidator } from "@nestia/e2e";
+import OpenAI from "openai";
+
+import { TestGlobal } from "../../../TestGlobal";
+
+export const test_chatgpt_function_calling_empty = async (): Promise<void> => {
+  if (TestGlobal.env.CHATGPT_API_KEY === undefined) return;
+
+  const client: OpenAI = new OpenAI({ apiKey: TestGlobal.env.CHATGPT_API_KEY });
+  const completion: OpenAI.ChatCompletion =
+    await client.chat.completions.create({
+      model: "gpt-4o",
+      messages: [
+        {
+          role: "system",
+          content:
+            "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
+        },
+        {
+          role: "user",
+          content: "Call a print function please.",
+        },
+      ],
+      tools: [
+        {
+          type: "function",
+          function: {
+            name: "print",
+            description: "Print to the screen.",
+          },
+        },
+      ],
+      tool_choice: "required",
+      parallel_tool_calls: false,
+    });
+  const call = completion.choices[0].message.tool_calls?.[0];
+  TestValidator.equals("name")(call?.function.name)("print");
+  TestValidator.equals("arguments")(call?.function.arguments)("{}");
+};

test/features/llm/claude/test_claude_function_calling_empty.ts

@@ -0,0 +1,45 @@
+import Anthropic from "@anthropic-ai/sdk";
+import { TestValidator } from "@nestia/e2e";
+
+import { TestGlobal } from "../../../TestGlobal";
+
+export const test_claude_function_calling_empty = async (): Promise<void> => {
+  if (TestGlobal.env.CLAUDE_API_KEY === undefined) return;
+
+  const client: Anthropic = new Anthropic({
+    apiKey: TestGlobal.env.CLAUDE_API_KEY,
+  });
+  const completion: Anthropic.Message = await client.messages.create({
+    model: "claude-3-5-sonnet-latest",
+    max_tokens: 8_192,
+    messages: [
+      {
+        role: "assistant",
+        content:
+          "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
+      },
+      {
+        role: "user",
+        content: "Call a print function please.",
+      },
+    ],
+    tools: [
+      {
+        name: "print",
+        description: "Print to the screen.",
+        input_schema: {
+          type: "object",
+          properties: {},
+          required: [],
+        },
+      },
+    ],
+    tool_choice: {
+      type: "any",
+      disable_parallel_tool_use: true,
+    },
+  });
+  const call = completion.content.filter((c) => c.type === "tool_use")?.[0];
+  TestValidator.equals("name")(call?.name)("print");
+  TestValidator.equals("arguments")(call?.input)({});
+};

test/features/llm/gemini/test_gemini_function_calling_empty.ts

@@ -0,0 +1,58 @@
+import {
+  FunctionCallingMode,
+  GenerateContentResult,
+  GenerativeModel,
+  GoogleGenerativeAI,
+} from "@google/generative-ai";
+import { TestValidator } from "@nestia/e2e";
+
+import { TestGlobal } from "../../../TestGlobal";
+
+export const test_gemini_function_calling_empty = async (): Promise<void> => {
+  if (TestGlobal.env.GEMINI_API_KEY === undefined) return;
+
+  const model: GenerativeModel = new GoogleGenerativeAI(
+    TestGlobal.env.GEMINI_API_KEY,
+  ).getGenerativeModel({
+    model: "gemini-1.5-pro",
+  });
+  const completion: GenerateContentResult = await model.generateContent({
+    contents: [
+      {
+        role: "model",
+        parts: [
+          {
+            text: "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
+          },
+        ],
+      },
+      {
+        role: "user",
+        parts: [
+          {
+            text: "Call a print function please.",
+          },
+        ],
+      },
+    ],
+    tools: [
+      {
+        functionDeclarations: [
+          {
+            name: "print",
+            description: "Print to the screen.",
+          },
+        ],
+      },
+    ],
+    toolConfig: {
+      functionCallingConfig: {
+        mode: FunctionCallingMode.ANY,
+        allowedFunctionNames: ["print"],
+      },
+    },
+  });
+  const call = (completion.response.functionCalls() ?? [])?.[0];
+  TestValidator.equals("name")(call?.name)("print");
+  TestValidator.equals("arguments")(call?.args)({});
+};

test/features/llm/llama/test_llama_function_calling_empty.ts

@@ -0,0 +1,47 @@
+import { TestValidator } from "@nestia/e2e";
+import OpenAI from "openai";
+
+import { TestGlobal } from "../../../TestGlobal";
+
+export const test_llama_function_calling_empty = async (): Promise<void> => {
+  if (TestGlobal.env.LLAMA_API_KEY === undefined) return;
+
+  const client: OpenAI = new OpenAI({
+    apiKey: TestGlobal.env.LLAMA_API_KEY,
+    baseURL: "https://api.llama-api.com",
+  });
+  const completion: OpenAI.ChatCompletion =
+    await client.chat.completions.create({
+      model: "llama3.3-70b",
+      messages: [
+        {
+          role: "system",
+          content:
+            "You are a helpful customer support assistant. Use the supplied tools to assist the user.",
+        },
+        {
+          role: "user",
+          content: "Call a print function please.",
+        },
+      ],
+      tools: [
+        {
+          type: "function",
+          function: {
+            name: "print",
+            description: "Print to the screen.",
+            parameters: {
+              type: "object",
+              properties: {},
+              required: [],
+            },
+          },
+        },
+      ],
+      tool_choice: "required",
+      parallel_tool_calls: false,
+    });
+  const call = completion.choices[0].message.tool_calls?.[0];
+  TestValidator.equals("name")(call?.function.name)("print");
+  TestValidator.equals("arguments")(call?.function.arguments)("{}");
+};

test/utils/LlamaFunctionCaller.ts

@@ -72,7 +72,7 @@ export namespace LlamaFunctionCaller {
     });
     const completion: OpenAI.ChatCompletion =
       await client.chat.completions.create({
-        model: "llama3.2-90b-vision",
+        model: "llama3.3-70b",
         messages: previous
           ? [
               ...props.texts.slice(0, -1),