feat: 品牌替换 + 启动优化 + AGENTS.md 模板定制
- 品牌替换：OpenCode/opencode → AirCoding/aircoding（16+ 文件） - Logo ASCII art：修复 left/right 行数不匹配导致的启动崩溃 - 启动诊断：添加 OPENCODE_PRINT_TIMING 计时探针 - dev 模式默认 --pure 跳过外部插件加载 - AGENTS.md 模板：追加 AirCoding 多 Agent 专项段落 - architect prompt + plugin：强化 AGENTS.md 产出验证
This commit is contained in:
airlongdian
891
packages/llm/test/provider/anthropic-messages.test.ts
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891
packages/llm/test/provider/anthropic-messages.test.ts
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import { describe, expect } from "bun:test"
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import { Effect } from "effect"
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import { HttpClientRequest } from "effect/unstable/http"
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import { CacheHint, LLM, LLMError, Message, ToolCallPart, Usage } from "../../src"
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import { Auth, LLMClient } from "../../src/route"
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import * as AnthropicMessages from "../../src/protocols/anthropic-messages"
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import { continuationRequest, nativeAnthropicMessagesContinuation } from "../continuation-scenarios"
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import { it } from "../lib/effect"
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import { dynamicResponse, fixedResponse } from "../lib/http"
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import { sseEvents } from "../lib/sse"
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const model = AnthropicMessages.route
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.with({ endpoint: { baseURL: "https://api.anthropic.test/v1/" }, auth: Auth.header("x-api-key", "test") })
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.model({ id: "claude-sonnet-4-5" })
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const opus48 = AnthropicMessages.route
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.with({ endpoint: { baseURL: "https://api.anthropic.test/v1/" }, auth: Auth.header("x-api-key", "test") })
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.model({ id: "claude-opus-4-8" })
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const request = LLM.request({
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id: "req_1",
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model,
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system: { type: "text", text: "You are concise.", cache: new CacheHint({ type: "ephemeral" }) },
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prompt: "Say hello.",
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// This fixture predates the `cache: "auto"` default; pin the policy off so
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// existing wire-shape assertions only see the manual hint on the system part.
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cache: "none",
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generation: { maxTokens: 20, temperature: 0 },
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})
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type AnthropicToolResult = Extract<
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AnthropicMessages.AnthropicMessagesBody["messages"][number]["content"][number],
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{ readonly type: "tool_result" }
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>
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const expectToolResult = (body: AnthropicMessages.AnthropicMessagesBody): AnthropicToolResult => {
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const result = body.messages
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.flatMap((message) => (message.role === "user" ? message.content : []))
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.find((block): block is AnthropicToolResult => block.type === "tool_result")
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expect(result).toBeDefined()
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return result!
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}
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describe("Anthropic Messages route", () => {
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it.effect("prepares Anthropic Messages target", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare(request)
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expect(prepared.body).toEqual({
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model: "claude-sonnet-4-5",
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system: [{ type: "text", text: "You are concise.", cache_control: { type: "ephemeral" } }],
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messages: [{ role: "user", content: [{ type: "text", text: "Say hello." }] }],
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stream: true,
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max_tokens: 20,
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temperature: 0,
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})
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}),
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)
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it.effect("lowers chronological system updates natively for Claude Opus 4.8 with cache hints", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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model: opus48,
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messages: [
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Message.user("Before."),
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Message.system([{ type: "text", text: "Operator update.", cache: new CacheHint({ type: "ephemeral" }) }]),
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Message.assistant("After."),
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],
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cache: "none",
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}),
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)
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expect(prepared.body.messages).toEqual([
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{ role: "user", content: [{ type: "text", text: "Before." }] },
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{
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role: "system",
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content: [{ type: "text", text: "Operator update.", cache_control: { type: "ephemeral" } }],
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},
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{ role: "assistant", content: [{ type: "text", text: "After." }] },
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])
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}),
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)
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it.effect("lowers chronological system updates to wrapped user text for unsupported Anthropic models", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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model,
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messages: [
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Message.user("Before."),
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Message.system("Treat </system-update> literally."),
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Message.assistant("After."),
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],
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cache: "none",
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}),
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)
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expect(prepared.body.messages).toEqual([
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{
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role: "user",
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content: [
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{ type: "text", text: "Before." },
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{ type: "text", text: "<system-update>\nTreat </system-update> literally.\n</system-update>" },
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],
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},
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{ role: "assistant", content: [{ type: "text", text: "After." }] },
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])
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}),
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)
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it.effect("rejects non-text chronological system update content before send", () =>
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Effect.gen(function* () {
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const error = yield* LLMClient.prepare(
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LLM.request({
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model: opus48,
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messages: [
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Message.user("Before."),
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Message.make({ role: "system", content: { type: "media", mediaType: "image/png", data: "AAECAw==" } }),
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],
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}),
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).pipe(Effect.flip)
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expect(error.message).toContain("Anthropic Messages system messages only support text content for now")
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}),
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)
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it.effect("falls back for unsupported native chronological system update placement", () =>
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Effect.gen(function* () {
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expect(
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(yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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model: opus48,
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messages: [Message.assistant("Plain."), Message.system("After plain assistant.")],
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cache: "none",
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}),
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)).body.messages,
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).toEqual([
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{ role: "assistant", content: [{ type: "text", text: "Plain." }] },
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{
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role: "user",
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content: [{ type: "text", text: "<system-update>\nAfter plain assistant.\n</system-update>" }],
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},
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])
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expect(
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(yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({ model: opus48, messages: [Message.system("First.")], cache: "none" }),
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)).body.messages,
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).toEqual([{ role: "user", content: [{ type: "text", text: "<system-update>\nFirst.\n</system-update>" }] }])
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expect(
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(yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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model: opus48,
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messages: [Message.user("Before."), Message.system("One."), Message.system("Two.")],
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cache: "none",
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}),
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)).body.messages,
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).toEqual([
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{
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role: "user",
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content: [
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{ type: "text", text: "Before." },
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{ type: "text", text: "<system-update>\nOne.\n</system-update>" },
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{ type: "text", text: "<system-update>\nTwo.\n</system-update>" },
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],
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},
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])
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}),
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)
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it.effect("rejects a system update between a local tool call and its result", () =>
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Effect.gen(function* () {
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const error = yield* LLMClient.prepare(
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LLM.request({
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model: opus48,
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messages: [
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Message.user("Use the tool."),
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Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })]),
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Message.system("Too early."),
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Message.tool({ id: "call_1", name: "lookup", result: "Done." }),
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],
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cache: "none",
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}),
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).pipe(Effect.flip)
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expect(error.message).toContain("system updates cannot split a local tool call from its tool result")
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}),
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)
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it.effect("prepares tool call and tool result messages", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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id: "req_tool_result",
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model,
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messages: [
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Message.user("What is the weather?"),
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Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: { query: "weather" } })]),
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Message.tool({ id: "call_1", name: "lookup", result: { forecast: "sunny" } }),
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],
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cache: "none",
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}),
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)
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expect(prepared.body).toEqual({
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model: "claude-sonnet-4-5",
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messages: [
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{ role: "user", content: [{ type: "text", text: "What is the weather?" }] },
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{
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role: "assistant",
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content: [{ type: "tool_use", id: "call_1", name: "lookup", input: { query: "weather" } }],
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},
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{ role: "user", content: [{ type: "tool_result", tool_use_id: "call_1", content: '{"forecast":"sunny"}' }] },
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],
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stream: true,
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max_tokens: 4096,
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})
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}),
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)
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// Regression: screenshot/read tool results must stay structured so base64
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// image data is not JSON-stringified into `tool_result.content`.
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it.effect("lowers image tool-result content as structured image blocks", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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id: "req_tool_result_image",
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model,
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messages: [
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Message.user("Show me the screenshot."),
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Message.assistant([ToolCallPart.make({ id: "call_1", name: "read", input: { filePath: "shot.png" } })]),
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Message.tool({
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id: "call_1",
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name: "read",
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resultType: "content",
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result: [
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{ type: "text", text: "Image read successfully" },
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{ type: "file", uri: "data:image/png;base64,AAECAw==", mime: "image/png" },
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],
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}),
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],
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cache: "none",
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}),
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)
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expect(expectToolResult(prepared.body).content).toEqual([
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{ type: "text", text: "Image read successfully" },
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{ type: "image", source: { type: "base64", media_type: "image/png", data: "AAECAw==" } },
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])
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}),
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)
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it.effect("lowers single-image tool-result content as a structured image block", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({
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id: "req_tool_result_image_only",
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model,
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messages: [
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Message.assistant([ToolCallPart.make({ id: "call_1", name: "screenshot", input: {} })]),
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Message.tool({
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id: "call_1",
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name: "screenshot",
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resultType: "content",
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result: [{ type: "file", uri: "data:image/jpeg;base64,/9j/AA==", mime: "image/jpeg" }],
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}),
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],
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cache: "none",
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}),
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)
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expect(expectToolResult(prepared.body).content).toEqual([
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{ type: "image", source: { type: "base64", media_type: "image/jpeg", data: "/9j/AA==" } },
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])
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}),
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)
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it.effect("rejects non-image media in tool-result content with a clear error", () =>
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Effect.gen(function* () {
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const error = yield* LLMClient.prepare(
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LLM.request({
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id: "req_tool_result_unsupported_media",
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model,
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messages: [
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Message.assistant([ToolCallPart.make({ id: "call_1", name: "fetch", input: {} })]),
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Message.tool({
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id: "call_1",
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name: "fetch",
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resultType: "content",
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result: [{ type: "file", uri: "data:audio/mpeg;base64,AAECAw==", mime: "audio/mpeg" }],
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}),
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],
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cache: "none",
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}),
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).pipe(Effect.flip)
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expect(error.message).toContain("Anthropic Messages")
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expect(error.message).toContain("audio/mpeg")
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}),
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)
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it.effect("prepares the composed native continuation request", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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continuationRequest({
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id: "req_native_continuation_anthropic",
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model,
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features: nativeAnthropicMessagesContinuation,
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}),
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)
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expect(prepared.body).toMatchObject({
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system: [{ type: "text", text: "You are concise. Continue from the provided history." }],
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messages: [
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{
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role: "user",
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content: [
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{ type: "text", text: "What is shown here?" },
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{ type: "image", source: { type: "base64", media_type: "image/png", data: "AAECAw==" } },
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],
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},
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{
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role: "assistant",
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content: [
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{ type: "thinking", thinking: "I inspected the previous turn.", signature: "sig_continuation_1" },
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{ type: "text", text: "It shows a small test image." },
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],
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},
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{ role: "user", content: [{ type: "text", text: "Check the weather in Paris before continuing." }] },
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{
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role: "assistant",
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content: [{ type: "tool_use", id: "call_weather_1", name: "get_weather", input: { city: "Paris" } }],
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},
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{
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role: "user",
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content: [{ type: "tool_result", tool_use_id: "call_weather_1", content: '{"temperature":22}' }],
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},
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{ role: "assistant", content: [{ type: "text", text: "Paris is 22 degrees." }] },
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{ role: "user", content: [{ type: "text", text: "Continue from this conversation in one short sentence." }] },
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],
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})
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expect(prepared.body.tools).toEqual([expect.objectContaining({ name: "get_weather" })])
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}),
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)
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it.effect("lowers preserved Anthropic reasoning signature metadata", () =>
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Effect.gen(function* () {
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const prepared = yield* LLMClient.prepare(
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LLM.request({
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model,
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messages: [
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Message.assistant([
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{ type: "reasoning", text: "thinking", providerMetadata: { anthropic: { signature: "sig_1" } } },
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]),
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],
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}),
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)
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expect(prepared.body).toMatchObject({
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messages: [{ role: "assistant", content: [{ type: "thinking", thinking: "thinking", signature: "sig_1" }] }],
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})
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}),
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)
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it.effect("parses text, reasoning, and usage stream fixtures", () =>
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Effect.gen(function* () {
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const body = sseEvents(
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{ type: "message_start", message: { usage: { input_tokens: 5, cache_read_input_tokens: 1 } } },
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{ type: "content_block_start", index: 0, content_block: { type: "text", text: "" } },
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{ type: "content_block_delta", index: 0, delta: { type: "text_delta", text: "Hello" } },
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{ type: "content_block_delta", index: 0, delta: { type: "text_delta", text: "!" } },
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{ type: "content_block_stop", index: 0 },
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{ type: "content_block_start", index: 1, content_block: { type: "thinking", thinking: "" } },
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{ type: "content_block_delta", index: 1, delta: { type: "thinking_delta", thinking: "thinking" } },
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{ type: "content_block_delta", index: 1, delta: { type: "signature_delta", signature: "sig_1" } },
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||||
{ type: "content_block_stop", index: 1 },
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{
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type: "message_delta",
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delta: { stop_reason: "end_turn", stop_sequence: "\n\nHuman:" },
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usage: { output_tokens: 2 },
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||||
},
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{ type: "message_stop" },
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||||
)
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const response = yield* LLMClient.generate(request).pipe(Effect.provide(fixedResponse(body)))
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expect(response.text).toBe("Hello!")
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expect(response.reasoning).toBe("thinking")
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expect(response.usage).toMatchObject({
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inputTokens: 6,
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outputTokens: 2,
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nonCachedInputTokens: 5,
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cacheReadInputTokens: 1,
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totalTokens: 8,
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})
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expect(response.events.find((event) => event.type === "reasoning-end")).toMatchObject({
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providerMetadata: { anthropic: { signature: "sig_1" } },
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})
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expect(response.events.at(-1)).toMatchObject({
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type: "finish",
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reason: "stop",
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providerMetadata: { anthropic: { stopSequence: "\n\nHuman:" } },
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||||
})
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||||
}),
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||||
)
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it.effect("assembles streamed tool call input", () =>
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||||
Effect.gen(function* () {
|
||||
const body = sseEvents(
|
||||
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
|
||||
{ type: "content_block_start", index: 0, content_block: { type: "tool_use", id: "call_1", name: "lookup" } },
|
||||
{ type: "content_block_delta", index: 0, delta: { type: "input_json_delta", partial_json: '{"query"' } },
|
||||
{ type: "content_block_delta", index: 0, delta: { type: "input_json_delta", partial_json: ':"weather"}' } },
|
||||
{ type: "content_block_stop", index: 0 },
|
||||
{ type: "message_delta", delta: { stop_reason: "tool_use" }, usage: { output_tokens: 1 } },
|
||||
)
|
||||
const response = yield* LLMClient.generate(
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||||
LLM.updateRequest(request, {
|
||||
tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
|
||||
}),
|
||||
).pipe(Effect.provide(fixedResponse(body)))
|
||||
const usage = new Usage({
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||||
inputTokens: 5,
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||||
outputTokens: 1,
|
||||
nonCachedInputTokens: 5,
|
||||
cacheReadInputTokens: undefined,
|
||||
cacheWriteInputTokens: undefined,
|
||||
totalTokens: 6,
|
||||
providerMetadata: { anthropic: { input_tokens: 5, output_tokens: 1 } },
|
||||
})
|
||||
|
||||
expect(response.toolCalls).toEqual([
|
||||
{
|
||||
type: "tool-call",
|
||||
id: "call_1",
|
||||
name: "lookup",
|
||||
input: { query: "weather" },
|
||||
providerExecuted: undefined,
|
||||
providerMetadata: undefined,
|
||||
},
|
||||
])
|
||||
expect(response.events).toEqual([
|
||||
{ type: "step-start", index: 0 },
|
||||
{ type: "tool-input-start", id: "call_1", name: "lookup" },
|
||||
{ type: "tool-input-delta", id: "call_1", name: "lookup", text: '{"query"' },
|
||||
{ type: "tool-input-delta", id: "call_1", name: "lookup", text: ':"weather"}' },
|
||||
{ type: "tool-input-end", id: "call_1", name: "lookup", providerMetadata: undefined },
|
||||
{
|
||||
type: "tool-call",
|
||||
id: "call_1",
|
||||
name: "lookup",
|
||||
input: { query: "weather" },
|
||||
providerExecuted: undefined,
|
||||
providerMetadata: undefined,
|
||||
},
|
||||
{ type: "step-finish", index: 0, reason: "tool-calls", usage, providerMetadata: undefined },
|
||||
{
|
||||
type: "finish",
|
||||
reason: "tool-calls",
|
||||
providerMetadata: undefined,
|
||||
usage,
|
||||
},
|
||||
])
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("emits provider-error events for mid-stream provider errors", () =>
|
||||
Effect.gen(function* () {
|
||||
const response = yield* LLMClient.generate(request).pipe(
|
||||
Effect.provide(
|
||||
fixedResponse(sseEvents({ type: "error", error: { type: "overloaded_error", message: "Overloaded" } })),
|
||||
),
|
||||
)
|
||||
|
||||
// Prefix the error type so consumers can distinguish overloads, rate
|
||||
// limits, and quota errors without parsing the message string.
|
||||
expect(response.events).toEqual([{ type: "provider-error", message: "overloaded_error: Overloaded" }])
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("classifies prompt-too-long provider errors", () =>
|
||||
Effect.gen(function* () {
|
||||
const response = yield* LLMClient.generate(request).pipe(
|
||||
Effect.provide(
|
||||
fixedResponse(
|
||||
sseEvents({
|
||||
type: "error",
|
||||
error: { type: "invalid_request_error", message: "prompt is too long: 210000 tokens" },
|
||||
}),
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
expect(response.events).toEqual([
|
||||
{
|
||||
type: "provider-error",
|
||||
message: "invalid_request_error: prompt is too long: 210000 tokens",
|
||||
classification: "context-overflow",
|
||||
},
|
||||
])
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("falls back to error type when no message is present", () =>
|
||||
Effect.gen(function* () {
|
||||
const response = yield* LLMClient.generate(request).pipe(
|
||||
Effect.provide(fixedResponse(sseEvents({ type: "error", error: { type: "overloaded_error", message: "" } }))),
|
||||
)
|
||||
|
||||
expect(response.events).toEqual([{ type: "provider-error", message: "overloaded_error" }])
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("falls back to a stable default when error payload is absent", () =>
|
||||
Effect.gen(function* () {
|
||||
const response = yield* LLMClient.generate(request).pipe(
|
||||
Effect.provide(fixedResponse(sseEvents({ type: "error" }))),
|
||||
)
|
||||
|
||||
expect(response.events).toEqual([{ type: "provider-error", message: "Anthropic Messages stream error" }])
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("fails HTTP provider errors before stream parsing", () =>
|
||||
Effect.gen(function* () {
|
||||
const error = yield* LLMClient.generate(request).pipe(
|
||||
Effect.provide(
|
||||
fixedResponse('{"type":"error","error":{"type":"invalid_request_error","message":"Bad request"}}', {
|
||||
status: 400,
|
||||
headers: { "content-type": "application/json" },
|
||||
}),
|
||||
),
|
||||
Effect.flip,
|
||||
)
|
||||
|
||||
expect(error).toBeInstanceOf(LLMError)
|
||||
expect(error.reason).toMatchObject({ _tag: "InvalidRequest" })
|
||||
expect(error.message).toContain("HTTP 400")
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("decodes server_tool_use + web_search_tool_result as provider-executed events", () =>
|
||||
Effect.gen(function* () {
|
||||
const body = sseEvents(
|
||||
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
|
||||
{
|
||||
type: "content_block_start",
|
||||
index: 0,
|
||||
content_block: { type: "server_tool_use", id: "srvtoolu_abc", name: "web_search" },
|
||||
},
|
||||
{
|
||||
type: "content_block_delta",
|
||||
index: 0,
|
||||
delta: { type: "input_json_delta", partial_json: '{"query":"effect 4"}' },
|
||||
},
|
||||
{ type: "content_block_stop", index: 0 },
|
||||
{
|
||||
type: "content_block_start",
|
||||
index: 1,
|
||||
content_block: {
|
||||
type: "web_search_tool_result",
|
||||
tool_use_id: "srvtoolu_abc",
|
||||
content: [{ type: "web_search_result", url: "https://example.com", title: "Example" }],
|
||||
},
|
||||
},
|
||||
{ type: "content_block_stop", index: 1 },
|
||||
{ type: "content_block_start", index: 2, content_block: { type: "text", text: "" } },
|
||||
{ type: "content_block_delta", index: 2, delta: { type: "text_delta", text: "Found it." } },
|
||||
{ type: "content_block_stop", index: 2 },
|
||||
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 8 } },
|
||||
)
|
||||
const response = yield* LLMClient.generate(
|
||||
LLM.updateRequest(request, {
|
||||
tools: [{ name: "web_search", description: "Web search", inputSchema: { type: "object" } }],
|
||||
}),
|
||||
).pipe(Effect.provide(fixedResponse(body)))
|
||||
|
||||
const toolCall = response.events.find((event) => event.type === "tool-call")
|
||||
expect(toolCall).toEqual({
|
||||
type: "tool-call",
|
||||
id: "srvtoolu_abc",
|
||||
name: "web_search",
|
||||
input: { query: "effect 4" },
|
||||
providerExecuted: true,
|
||||
})
|
||||
const toolResult = response.events.find((event) => event.type === "tool-result")
|
||||
expect(toolResult).toEqual({
|
||||
type: "tool-result",
|
||||
id: "srvtoolu_abc",
|
||||
name: "web_search",
|
||||
result: { type: "json", value: [{ type: "web_search_result", url: "https://example.com", title: "Example" }] },
|
||||
providerExecuted: true,
|
||||
providerMetadata: { anthropic: { blockType: "web_search_tool_result" } },
|
||||
})
|
||||
expect(response.text).toBe("Found it.")
|
||||
expect(response.events.at(-1)).toMatchObject({ type: "finish", reason: "stop" })
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("decodes web_search_tool_result_error as provider-executed error result", () =>
|
||||
Effect.gen(function* () {
|
||||
const body = sseEvents(
|
||||
{ type: "message_start", message: { usage: { input_tokens: 5 } } },
|
||||
{
|
||||
type: "content_block_start",
|
||||
index: 0,
|
||||
content_block: { type: "server_tool_use", id: "srvtoolu_x", name: "web_search" },
|
||||
},
|
||||
{ type: "content_block_delta", index: 0, delta: { type: "input_json_delta", partial_json: '{"query":"q"}' } },
|
||||
{ type: "content_block_stop", index: 0 },
|
||||
{
|
||||
type: "content_block_start",
|
||||
index: 1,
|
||||
content_block: {
|
||||
type: "web_search_tool_result",
|
||||
tool_use_id: "srvtoolu_x",
|
||||
content: { type: "web_search_tool_result_error", error_code: "max_uses_exceeded" },
|
||||
},
|
||||
},
|
||||
{ type: "content_block_stop", index: 1 },
|
||||
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 1 } },
|
||||
)
|
||||
const response = yield* LLMClient.generate(
|
||||
LLM.updateRequest(request, {
|
||||
tools: [{ name: "web_search", description: "Web search", inputSchema: { type: "object" } }],
|
||||
}),
|
||||
).pipe(Effect.provide(fixedResponse(body)))
|
||||
|
||||
const toolResult = response.events.find((event) => event.type === "tool-result")
|
||||
expect(toolResult).toMatchObject({
|
||||
type: "tool-result",
|
||||
id: "srvtoolu_x",
|
||||
name: "web_search",
|
||||
result: { type: "error" },
|
||||
providerExecuted: true,
|
||||
})
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("round-trips provider-executed assistant content into server tool blocks", () =>
|
||||
Effect.gen(function* () {
|
||||
const prepared = yield* LLMClient.prepare(
|
||||
LLM.request({
|
||||
id: "req_round_trip",
|
||||
model,
|
||||
messages: [
|
||||
Message.user("Search for something."),
|
||||
Message.assistant([
|
||||
{
|
||||
type: "tool-call",
|
||||
id: "srvtoolu_abc",
|
||||
name: "web_search",
|
||||
input: { query: "effect 4" },
|
||||
providerExecuted: true,
|
||||
},
|
||||
{
|
||||
type: "tool-result",
|
||||
id: "srvtoolu_abc",
|
||||
name: "web_search",
|
||||
result: { type: "json", value: [{ url: "https://example.com" }] },
|
||||
providerExecuted: true,
|
||||
},
|
||||
{ type: "text", text: "Found it." },
|
||||
]),
|
||||
Message.user("Thanks."),
|
||||
],
|
||||
}),
|
||||
)
|
||||
|
||||
expect(prepared.body).toMatchObject({
|
||||
messages: [
|
||||
{ role: "user", content: [{ type: "text", text: "Search for something." }] },
|
||||
{
|
||||
role: "assistant",
|
||||
content: [
|
||||
{ type: "server_tool_use", id: "srvtoolu_abc", name: "web_search", input: { query: "effect 4" } },
|
||||
{
|
||||
type: "web_search_tool_result",
|
||||
tool_use_id: "srvtoolu_abc",
|
||||
content: [{ url: "https://example.com" }],
|
||||
},
|
||||
{ type: "text", text: "Found it." },
|
||||
],
|
||||
},
|
||||
{ role: "user", content: [{ type: "text", text: "Thanks." }] },
|
||||
],
|
||||
})
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("rejects round-trip for unknown server tool names", () =>
|
||||
Effect.gen(function* () {
|
||||
const error = yield* LLMClient.prepare(
|
||||
LLM.request({
|
||||
id: "req_unknown_server_tool",
|
||||
model,
|
||||
messages: [
|
||||
Message.assistant([
|
||||
{
|
||||
type: "tool-result",
|
||||
id: "srvtoolu_abc",
|
||||
name: "future_server_tool",
|
||||
result: { type: "json", value: {} },
|
||||
providerExecuted: true,
|
||||
},
|
||||
]),
|
||||
],
|
||||
}),
|
||||
).pipe(Effect.flip)
|
||||
|
||||
expect(error.message).toContain("future_server_tool")
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("continues a conversation with user image content", () =>
|
||||
Effect.gen(function* () {
|
||||
const response = yield* LLMClient.generate(
|
||||
LLM.request({
|
||||
id: "req_media",
|
||||
model,
|
||||
messages: [
|
||||
Message.user([
|
||||
{ type: "text", text: "What is in this image?" },
|
||||
{ type: "media", mediaType: "image/png", data: "AAECAw==" },
|
||||
]),
|
||||
],
|
||||
}),
|
||||
).pipe(
|
||||
Effect.provide(
|
||||
dynamicResponse((input) =>
|
||||
Effect.gen(function* () {
|
||||
const web = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
|
||||
expect(yield* Effect.promise(() => web.json())).toMatchObject({
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: [
|
||||
{ type: "text", text: "What is in this image?" },
|
||||
{ type: "image", source: { type: "base64", media_type: "image/png", data: "AAECAw==" } },
|
||||
],
|
||||
},
|
||||
],
|
||||
})
|
||||
return input.respond(
|
||||
sseEvents(
|
||||
{ type: "content_block_start", index: 0, content_block: { type: "text", text: "" } },
|
||||
{ type: "content_block_delta", index: 0, delta: { type: "text_delta", text: "An image." } },
|
||||
{ type: "content_block_stop", index: 0 },
|
||||
{ type: "message_delta", delta: { stop_reason: "end_turn" }, usage: { output_tokens: 3 } },
|
||||
{ type: "message_stop" },
|
||||
),
|
||||
{ headers: { "content-type": "text/event-stream" } },
|
||||
)
|
||||
}),
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
expect(response.text).toBe("An image.")
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("maps ttlSeconds >= 3600 to cache_control ttl: '1h'", () =>
|
||||
Effect.gen(function* () {
|
||||
const prepared = yield* LLMClient.prepare(
|
||||
LLM.request({
|
||||
model,
|
||||
system: { type: "text", text: "system", cache: new CacheHint({ type: "ephemeral", ttlSeconds: 3600 }) },
|
||||
prompt: "hi",
|
||||
}),
|
||||
)
|
||||
|
||||
expect(prepared.body).toMatchObject({
|
||||
system: [{ type: "text", text: "system", cache_control: { type: "ephemeral", ttl: "1h" } }],
|
||||
})
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("emits cache_control on tool definitions and tool-result blocks", () =>
|
||||
Effect.gen(function* () {
|
||||
const prepared = yield* LLMClient.prepare(
|
||||
LLM.request({
|
||||
model,
|
||||
tools: [
|
||||
{
|
||||
name: "lookup",
|
||||
description: "lookup tool",
|
||||
inputSchema: { type: "object", properties: {} },
|
||||
cache: new CacheHint({ type: "ephemeral" }),
|
||||
},
|
||||
],
|
||||
messages: [
|
||||
Message.user("What's the weather?"),
|
||||
Message.assistant([ToolCallPart.make({ id: "call_1", name: "lookup", input: {} })]),
|
||||
Message.tool({
|
||||
id: "call_1",
|
||||
name: "lookup",
|
||||
result: { temp: 72 },
|
||||
cache: new CacheHint({ type: "ephemeral" }),
|
||||
}),
|
||||
],
|
||||
}),
|
||||
)
|
||||
|
||||
expect(prepared.body).toMatchObject({
|
||||
tools: [{ name: "lookup", cache_control: { type: "ephemeral" } }],
|
||||
messages: [
|
||||
{ role: "user", content: [{ type: "text", text: "What's the weather?" }] },
|
||||
{ role: "assistant", content: [{ type: "tool_use", id: "call_1", name: "lookup" }] },
|
||||
{
|
||||
role: "user",
|
||||
content: [{ type: "tool_result", tool_use_id: "call_1", cache_control: { type: "ephemeral" } }],
|
||||
},
|
||||
],
|
||||
})
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("drops cache_control breakpoints past the 4-per-request cap", () =>
|
||||
Effect.gen(function* () {
|
||||
const hint = new CacheHint({ type: "ephemeral" })
|
||||
const prepared = yield* LLMClient.prepare(
|
||||
LLM.request({
|
||||
model,
|
||||
system: [
|
||||
{ type: "text", text: "a", cache: hint },
|
||||
{ type: "text", text: "b", cache: hint },
|
||||
{ type: "text", text: "c", cache: hint },
|
||||
{ type: "text", text: "d", cache: hint },
|
||||
{ type: "text", text: "e", cache: hint },
|
||||
{ type: "text", text: "f", cache: hint },
|
||||
],
|
||||
prompt: "hi",
|
||||
}),
|
||||
)
|
||||
|
||||
const system = (prepared.body as { system: Array<{ cache_control?: unknown }> }).system
|
||||
const marked = system.filter((part) => part.cache_control !== undefined)
|
||||
expect(marked).toHaveLength(4)
|
||||
expect(system[4]?.cache_control).toBeUndefined()
|
||||
expect(system[5]?.cache_control).toBeUndefined()
|
||||
}),
|
||||
)
|
||||
|
||||
it.effect("spends breakpoint budget on tools before system before messages", () =>
|
||||
Effect.gen(function* () {
|
||||
const hint = new CacheHint({ type: "ephemeral" })
|
||||
const prepared = yield* LLMClient.prepare(
|
||||
LLM.request({
|
||||
model,
|
||||
tools: [
|
||||
{
|
||||
name: "t1",
|
||||
description: "t1",
|
||||
inputSchema: { type: "object", properties: {} },
|
||||
cache: hint,
|
||||
},
|
||||
{
|
||||
name: "t2",
|
||||
description: "t2",
|
||||
inputSchema: { type: "object", properties: {} },
|
||||
cache: hint,
|
||||
},
|
||||
{
|
||||
name: "t3",
|
||||
description: "t3",
|
||||
inputSchema: { type: "object", properties: {} },
|
||||
cache: hint,
|
||||
},
|
||||
{
|
||||
name: "t4",
|
||||
description: "t4",
|
||||
inputSchema: { type: "object", properties: {} },
|
||||
cache: hint,
|
||||
},
|
||||
],
|
||||
system: [{ type: "text", text: "system-tail", cache: hint }],
|
||||
messages: [Message.user([{ type: "text", text: "message-tail", cache: hint }])],
|
||||
}),
|
||||
)
|
||||
|
||||
const body = prepared.body as {
|
||||
tools: Array<{ cache_control?: unknown }>
|
||||
system: Array<{ cache_control?: unknown }>
|
||||
messages: Array<{ content: Array<{ cache_control?: unknown }> }>
|
||||
}
|
||||
expect(body.tools.every((t) => t.cache_control !== undefined)).toBe(true)
|
||||
expect(body.system[0]?.cache_control).toBeUndefined()
|
||||
expect(body.messages[0]?.content[0]?.cache_control).toBeUndefined()
|
||||
}),
|
||||
)
|
||||
})
|
||||
Reference in New Issue
Block a user