fix: logo 右半部分从 CODING 改为 CODE

去掉难以正确渲染的 N 和 G 字母，右半部分简化为 CODE（4 字母），
与左半部分 AIR 组合为 AIR CODE。

packages/llm/test/generate-object.test.ts

@@ -0,0 +1,184 @@
+import { describe, expect, test } from "bun:test"
+import { Effect, Schema } from "effect"
+import { LLM } from "../src"
+import * as OpenAIChat from "../src/protocols/openai-chat"
+import { Auth } from "../src/route"
+import { Tool, toDefinitions } from "../src/tool"
+import { it } from "./lib/effect"
+import { dynamicResponse } from "./lib/http"
+import { finishChunk, toolCallChunk } from "./lib/openai-chunks"
+import { sseEvents } from "./lib/sse"
+
+type OpenAIChatBody = {
+  readonly tool_choice?: unknown
+  readonly tools?: ReadonlyArray<{
+    readonly function: {
+      readonly parameters: unknown
+    }
+  }>
+}
+
+const model = OpenAIChat.route
+  .with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
+  .model({ id: "gpt-4o-mini" })
+
+const Json = Schema.fromJsonString(Schema.Unknown)
+const decodeJson = Schema.decodeUnknownSync(Json)
+const decodeBody = (text: string): OpenAIChatBody => decodeJson(text) as OpenAIChatBody
+
+describe("Tool.make (dynamic JSON Schema)", () => {
+  test("forwards JSON Schema and description through toDefinitions", () => {
+    const jsonSchema = {
+      type: "object" as const,
+      properties: { city: { type: "string" } },
+      required: ["city"],
+    }
+    const lookup = Tool.make({
+      description: "Look up something",
+      jsonSchema,
+      execute: () => Effect.succeed({ ok: true }),
+    })
+    const [definition] = toDefinitions({ lookup })
+    expect(definition?.name).toBe("lookup")
+    expect(definition?.description).toBe("Look up something")
+    expect(definition?.inputSchema).toEqual(jsonSchema)
+  })
+
+  test("execute receives the raw input untouched", async () => {
+    const seen: unknown[] = []
+    const tool = Tool.make({
+      description: "echo",
+      jsonSchema: { type: "object" },
+      execute: (params) =>
+        Effect.sync(() => {
+          seen.push(params)
+          return { ok: true }
+        }),
+    })
+    const result = await Effect.runPromise(tool.execute({ hello: "world" }))
+    expect(seen).toEqual([{ hello: "world" }])
+    expect(result).toEqual({ ok: true })
+  })
+})
+
+describe("LLM.generateObject", () => {
+  it.effect("forces a synthetic tool call and decodes the input", () =>
+    Effect.gen(function* () {
+      const bodies: OpenAIChatBody[] = []
+      const layer = dynamicResponse((input) =>
+        Effect.sync(() => {
+          bodies.push(decodeBody(input.text))
+          return input.respond(
+            sseEvents(
+              toolCallChunk("call_1", "generate_object", '{"city":"Paris","temp":22}'),
+              finishChunk("tool_calls"),
+            ),
+            { headers: { "content-type": "text/event-stream" } },
+          )
+        }),
+      )
+
+      const response = yield* LLM.generateObject({
+        model,
+        prompt: "Return a structured weather report.",
+        schema: Schema.Struct({ city: Schema.String, temp: Schema.Number }),
+      }).pipe(Effect.provide(layer))
+
+      expect(response.object).toEqual({ city: "Paris", temp: 22 })
+      expect(response.response.toolCalls).toHaveLength(1)
+      expect(bodies).toHaveLength(1)
+      expect(bodies[0].tool_choice).toEqual({ type: "function", function: { name: "generate_object" } })
+      const tool = bodies[0].tools?.[0]
+      expect(bodies[0].tools).toHaveLength(1)
+      expect(tool).toMatchObject({
+        type: "function",
+        function: { name: "generate_object" },
+      })
+      const params = tool?.function.parameters as {
+        readonly type?: unknown
+        readonly required?: unknown
+        readonly properties?: Record<string, unknown>
+      }
+      expect(params.type).toBe("object")
+      expect(params.required).toEqual(["city", "temp"])
+      expect(params.properties?.city).toMatchObject({ type: "string" })
+      expect(params.properties?.temp).toBeDefined()
+    }),
+  )
+
+  it.effect("accepts a raw JSON Schema and returns the input untouched", () =>
+    Effect.gen(function* () {
+      const bodies: OpenAIChatBody[] = []
+      const layer = dynamicResponse((input) =>
+        Effect.sync(() => {
+          bodies.push(decodeBody(input.text))
+          return input.respond(
+            sseEvents(toolCallChunk("call_1", "generate_object", '{"name":"Ada","age":30}'), finishChunk("tool_calls")),
+            { headers: { "content-type": "text/event-stream" } },
+          )
+        }),
+      )
+
+      const response = yield* LLM.generateObject({
+        model,
+        prompt: "Extract the user.",
+        jsonSchema: {
+          type: "object",
+          properties: { name: { type: "string" }, age: { type: "number" } },
+          required: ["name", "age"],
+        },
+      }).pipe(Effect.provide(layer))
+
+      expect(response.object).toEqual({ name: "Ada", age: 30 })
+      expect(bodies[0].tools?.[0]?.function.parameters).toEqual({
+        type: "object",
+        properties: { name: { type: "string" }, age: { type: "number" } },
+        required: ["name", "age"],
+      })
+    }),
+  )
+
+  it.effect("fails when the model does not call the synthetic tool", () =>
+    Effect.gen(function* () {
+      const layer = dynamicResponse((input) =>
+        Effect.sync(() =>
+          input.respond(sseEvents({ id: "x", choices: [{ delta: { content: "no thanks" }, finish_reason: "stop" }] }), {
+            headers: { "content-type": "text/event-stream" },
+          }),
+        ),
+      )
+
+      const exit = yield* LLM.generateObject({
+        model,
+        prompt: "Return a structured value.",
+        schema: Schema.Struct({ value: Schema.Number }),
+      }).pipe(Effect.provide(layer), Effect.exit)
+
+      expect(exit._tag).toBe("Failure")
+    }),
+  )
+
+  it.effect("fails with a decode error when the tool input does not match the schema", () =>
+    Effect.gen(function* () {
+      const layer = dynamicResponse((input) =>
+        Effect.sync(() =>
+          input.respond(
+            sseEvents(
+              toolCallChunk("call_1", "generate_object", '{"value":"not-a-number"}'),
+              finishChunk("tool_calls"),
+            ),
+            { headers: { "content-type": "text/event-stream" } },
+          ),
+        ),
+      )
+
+      const exit = yield* LLM.generateObject({
+        model,
+        prompt: "Return a structured value.",
+        schema: Schema.Struct({ value: Schema.Number }),
+      }).pipe(Effect.provide(layer), Effect.exit)
+
+      expect(exit._tag).toBe("Failure")
+    }),
+  )
+})