chore: merge dev into v2 (#34788)
Co-authored-by: Brendan Allan <14191578+Brendonovich@users.noreply.github.com> Co-authored-by: Kit Langton <kit.langton@gmail.com> Co-authored-by: opencode-agent[bot] <opencode-agent[bot]@users.noreply.github.com> Co-authored-by: Affan Ali <93028901+affanali2k3@users.noreply.github.com> Co-authored-by: affanali2k3 <affanalikhanxx@gmail.com> Co-authored-by: Frank <frank@anoma.ly> Co-authored-by: opencode-agent[bot] <219766164+opencode-agent[bot]@users.noreply.github.com> Co-authored-by: 𝓛𝓲𝓽𝓽𝓵𝓮 𝓕𝓻𝓪𝓷𝓴 <little-frank@opencord.local> Co-authored-by: Aiden Cline <63023139+rekram1-node@users.noreply.github.com> Co-authored-by: Jay V <air@live.ca> Co-authored-by: Dax Raad <d@ironbay.co> Co-authored-by: Aarav Sareen <96787824+arvsrn@users.noreply.github.com> Co-authored-by: OpeOginni <107570612+OpeOginni@users.noreply.github.com> Co-authored-by: Luke Parker <10430890+Hona@users.noreply.github.com> Co-authored-by: Ben Guthrie <benjee.012@gmail.com> Co-authored-by: Dax <mail@thdxr.com> Co-authored-by: Filip <34747899+neriousy@users.noreply.github.com> Co-authored-by: Max Anderson <max.a.anderson95@gmail.com> Co-authored-by: Brendan Allan <git@brendonovich.dev> Co-authored-by: Jack <jack@anoma.ly> Co-authored-by: Shoubhit Dash <shoubhit2005@gmail.com> Co-authored-by: Dustin Deus <deusdustin@gmail.com> Co-authored-by: starptech <starptech@starptechs-MBP.fritz.box> Co-authored-by: Aiden Cline <aidenpcline@gmail.com> Co-authored-by: usrnk1 <7547651+usrnk1@users.noreply.github.com> Co-authored-by: Jay <53023+jayair@users.noreply.github.com> Co-authored-by: runvip <164729189+runvip@users.noreply.github.com> Co-authored-by: opencode <opencode@sst.dev> Co-authored-by: Julian Coy <julian@ex-machina.co> Co-authored-by: Vladimir Glafirov <vglafirov@gitlab.com>
This commit is contained in:
James Long
@@ -1,6 +1,6 @@
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import { describe, expect } from "bun:test"
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import { Effect, Schema, Stream } from "effect"
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import { LLM } from "../src"
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import { LLM, LLMResponse } from "../src"
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import { Route, Endpoint, LLMClient, Protocol, type FramingDef } from "../src/route"
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import { Model } from "../src/schema"
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import { testEffect } from "./lib/effect"
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@@ -112,9 +112,16 @@ describe("llm route", () => {
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const llm = yield* LLMClient.Service
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const events = Array.from(yield* llm.stream(request).pipe(Stream.runCollect))
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const response = yield* llm.generate(request)
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const reduced = LLMResponse.fromEvents(events)
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expect(events.map((event) => event.type)).toEqual(["text-delta", "finish"])
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expect(response.events.map((event) => event.type)).toEqual(["text-delta", "finish"])
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expect(reduced).toBeDefined()
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if (!reduced) throw new Error("stream reducer did not produce a completed response")
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expect(response.events).toEqual(events)
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expect(response.message).toEqual(reduced.message)
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expect(response.usage).toEqual(reduced.usage)
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expect(response.finishReason).toEqual(reduced.finishReason)
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expect(response.message.content).toEqual([{ type: "text", text: 'echo:{"body":"hello"}' }])
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}),
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)
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+2
-2
@@ -21,7 +21,7 @@
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"headers": {
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"content-type": "text/event-stream"
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},
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"body": ""
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"body": "data: {\"candidates\":[{\"content\":{\"role\":\"model\",\"parts\":[{\"text\":\"Hi.\"}]},\"finishReason\":\"STOP\"}],\"usageMetadata\":{\"promptTokenCount\":1200,\"candidatesTokenCount\":2,\"totalTokenCount\":1202}}\n\n"
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}
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},
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{
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@@ -39,7 +39,7 @@
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"headers": {
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"content-type": "text/event-stream"
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},
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"body": ""
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"body": "data: {\"candidates\":[{\"content\":{\"role\":\"model\",\"parts\":[{\"text\":\"Hi.\"}]},\"finishReason\":\"STOP\"}],\"usageMetadata\":{\"cachedContentTokenCount\":1100,\"promptTokenCount\":1200,\"candidatesTokenCount\":2,\"totalTokenCount\":1202}}\n\n"
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}
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}
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]
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@@ -90,15 +90,47 @@ describe("llm constructors", () => {
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provider: "fake",
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route: chatRoute,
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})
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const updated = Model.update(base, { route: responsesRoute })
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const updated = Model.update(base, {
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route: responsesRoute,
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defaults: { generation: { maxTokens: 20 } },
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compatibility: { toolSchema: "gemini" },
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})
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const updatedInput = Model.input(updated)
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expect(updated).toBeInstanceOf(Model)
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expect(String(updated.id)).toBe("fake-model")
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expect(updated.route).toBe(responsesRoute)
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expect(String(Model.input(updated).provider)).toBe("fake")
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expect(updated.defaults?.generation).toEqual({ maxTokens: 20 })
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expect(updated.compatibility).toEqual({ toolSchema: "gemini" })
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expect(updatedInput.defaults).toBe(updated.defaults)
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expect(updatedInput.compatibility).toBe(updated.compatibility)
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expect(String(updatedInput.provider)).toBe("fake")
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expect(Model.update(updated, {})).toBe(updated)
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})
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test("carries model defaults and compatibility through route model selection", () => {
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const model = chatRoute.model({
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id: "kimi-k2",
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defaults: {
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limits: { context: 128_000, output: 8_192 },
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generation: { maxTokens: 1_024, stop: ["END"] },
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providerOptions: { openai: { parallelToolCalls: false } },
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http: { body: { extra_body: true } },
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},
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compatibility: { toolSchema: "moonshot" },
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})
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const request = LLM.request({ model, prompt: "Say hello." })
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expect(request.model.defaults?.limits).toEqual({ context: 128_000, output: 8_192 })
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expect(request.model.defaults?.generation).toEqual({ maxTokens: 1_024, stop: ["END"] })
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expect(request.model.defaults?.providerOptions).toEqual({ openai: { parallelToolCalls: false } })
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expect(request.model.defaults?.http).toEqual({ body: { extra_body: true } })
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expect(request.model.compatibility).toEqual({ toolSchema: "moonshot" })
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expect(request.generation).toBeUndefined()
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expect(request.providerOptions).toBeUndefined()
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expect(request.http).toBeUndefined()
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})
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test("builds tool choices from names and tools", () => {
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const tool = ToolDefinition.make({ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } })
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@@ -0,0 +1,178 @@
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import { describe, expect, test } from "bun:test"
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import { Effect, Schema } from "effect"
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import { HttpClientRequest } from "effect/unstable/http"
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import { LLM, mergeProviderOptions } from "../src"
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import { AnthropicMessages, OpenAIChat } from "../src/protocols"
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import { Auth, LLMClient } from "../src/route"
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import { it } from "./lib/effect"
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import { dynamicResponse } from "./lib/http"
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import { deltaChunk } from "./lib/openai-chunks"
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import { sseEvents } from "./lib/sse"
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const TargetJson = Schema.fromJsonString(Schema.Unknown)
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const decodeJson = Schema.decodeUnknownSync(TargetJson)
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describe("request option precedence", () => {
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test("deep-merges provider option records and replaces arrays, primitives, and null", () => {
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const merged = mergeProviderOptions(
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{
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openai: {
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include: ["route"],
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metadata: { route: true, shared: "route" },
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nullable: "route",
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primitive: "route",
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},
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},
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{
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openai: {
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include: ["model"],
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metadata: { model: true, shared: "model" },
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nullable: null,
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primitive: "model",
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},
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},
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{ openai: { metadata: { request: true }, primitive: false } },
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)
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expect(merged).toEqual({
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openai: {
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include: ["model"],
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metadata: { route: true, model: true, request: true, shared: "model" },
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nullable: null,
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primitive: false,
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},
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})
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})
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it.effect("prepares bodies with route defaults, model defaults, and call options in order", () =>
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Effect.gen(function* () {
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const route = OpenAIChat.route.with({
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endpoint: { baseURL: "https://api.openai.test/v1/" },
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auth: Auth.bearer("test"),
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generation: { maxTokens: 10, temperature: 1, stop: ["route"] },
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providerOptions: { openai: { store: false, reasoningEffort: "low" } },
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})
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const model = route.model({
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id: "gpt-4o-mini",
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defaults: {
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generation: { maxTokens: 20, temperature: 0.5, frequencyPenalty: 0.25, stop: ["model"] },
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providerOptions: { openai: { reasoningEffort: "medium" } },
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},
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})
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const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
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LLM.request({
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model,
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prompt: "Say hello.",
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generation: { maxTokens: 30, topP: 0.9, stop: ["request"] },
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providerOptions: { openai: { store: true } },
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}),
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)
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expect(prepared.body).toMatchObject({
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model: "gpt-4o-mini",
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stream: true,
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max_tokens: 30,
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temperature: 0.5,
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top_p: 0.9,
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frequency_penalty: 0.25,
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store: true,
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reasoning_effort: "medium",
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})
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expect(prepared.body.stop).toEqual(["request"])
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}),
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)
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it.effect("applies model HTTP defaults before request HTTP overlays", () =>
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LLMClient.generate(
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LLM.request({
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model: OpenAIChat.route
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.with({
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endpoint: { baseURL: "https://api.openai.test/v1/" },
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auth: Auth.bearer("fresh-key"),
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http: {
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body: { metadata: { route: true, shared: "route" }, value: "route" },
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headers: { "x-route": "route", "x-shared": "route" },
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query: { route: "1", shared: "route" },
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},
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})
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.model({
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id: "gpt-4o-mini",
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defaults: {
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http: {
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body: { metadata: { model: true, shared: "model" }, value: "model" },
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headers: { "x-model": "model", "x-shared": "model" },
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query: { model: "1", shared: "model" },
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},
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},
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}),
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prompt: "Say hello.",
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http: {
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body: { metadata: { request: true }, value: null },
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headers: { "x-request": "request" },
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query: { request: "1" },
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},
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}),
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).pipe(
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Effect.provide(
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dynamicResponse((input) =>
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Effect.gen(function* () {
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const web = yield* HttpClientRequest.toWeb(input.request).pipe(Effect.orDie)
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expect(web.url).toBe("https://api.openai.test/v1/chat/completions?route=1&shared=model&model=1&request=1")
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expect(web.headers.get("authorization")).toBe("Bearer fresh-key")
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expect(web.headers.get("x-route")).toBe("route")
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expect(web.headers.get("x-model")).toBe("model")
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expect(web.headers.get("x-request")).toBe("request")
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expect(web.headers.get("x-shared")).toBe("model")
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expect(decodeJson(input.text)).toMatchObject({
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metadata: { route: true, model: true, request: true, shared: "model" },
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value: null,
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})
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return input.respond(sseEvents(deltaChunk({}, "stop")), {
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headers: { "content-type": "text/event-stream" },
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})
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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 raw body overlays for protocol-owned roots", () =>
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Effect.gen(function* () {
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const model = OpenAIChat.route
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.with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
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.model({ id: "gpt-4o-mini" })
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const error = yield* LLMClient.prepare(
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LLM.request({
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model,
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prompt: "Say hello.",
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http: { body: { model: "gpt-5", messages: [], tools: [] } },
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}),
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).pipe(Effect.flip)
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expect(error.reason).toMatchObject({
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_tag: "InvalidRequest",
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message: "http.body cannot overlay protocol-owned field(s): model, messages, tools",
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})
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}),
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)
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it.effect("uses model output limits after route limits and before call maxTokens", () =>
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Effect.gen(function* () {
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const route = AnthropicMessages.route.with({
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endpoint: { baseURL: "https://api.anthropic.test/v1/" },
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auth: Auth.header("x-api-key", "test"),
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limits: { output: 128 },
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})
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const model = route.model({ id: "claude-sonnet-4-5", defaults: { limits: { output: 64 } } })
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const withoutMaxTokens = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({ model, prompt: "Say hello.", cache: "none" }),
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)
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const withMaxTokens = yield* LLMClient.prepare<AnthropicMessages.AnthropicMessagesBody>(
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LLM.request({ model, prompt: "Say hello.", cache: "none", generation: { maxTokens: 32 } }),
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)
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expect(withoutMaxTokens.body.max_tokens).toBe(64)
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expect(withMaxTokens.body.max_tokens).toBe(32)
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}),
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)
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})
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@@ -395,6 +395,10 @@ describe("Anthropic Messages route", () => {
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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.message.content).toEqual([
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{ type: "text", text: "Hello!" },
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{ type: "reasoning", text: "thinking", 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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@@ -1,7 +1,7 @@
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import { describe, expect } from "bun:test"
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import { Effect, Schema, Stream } from "effect"
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import { HttpClientRequest } from "effect/unstable/http"
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import { LLM, LLMError, Message, Model, ToolCallPart, Usage } from "../../src"
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import { LLM, LLMError, LLMEvent, Message, Model, ToolCallPart, Usage } from "../../src"
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import * as Azure from "../../src/providers/azure"
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import * as OpenAI from "../../src/providers/openai"
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import * as OpenAIChat from "../../src/protocols/openai-chat"
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@@ -597,19 +597,22 @@ describe("OpenAI Chat route", () => {
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}),
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deltaChunk({ tool_calls: [{ index: 0, function: { arguments: ':"weather"}' } }] }),
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)
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const response = yield* LLMClient.generate(
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LLM.updateRequest(request, {
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tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
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}),
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).pipe(Effect.provide(fixedResponse(body)))
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const input = LLM.updateRequest(request, {
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tools: [{ name: "lookup", description: "Lookup data", inputSchema: { type: "object" } }],
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})
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const events = Array.from(
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yield* LLMClient.stream(input).pipe(Stream.runCollect, Effect.provide(fixedResponse(body))),
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)
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const error = yield* LLMClient.generate(input).pipe(Effect.provide(fixedResponse(body)), Effect.flip)
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expect(response.events).toEqual([
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expect(events).toEqual([
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{ type: "step-start", index: 0 },
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{ type: "tool-input-start", id: "call_1", name: "lookup", providerMetadata: undefined },
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{ type: "tool-input-delta", id: "call_1", name: "lookup", text: '{"query"' },
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{ type: "tool-input-delta", id: "call_1", name: "lookup", text: ':"weather"}' },
|
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])
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expect(response.toolCalls).toEqual([])
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expect(events.filter(LLMEvent.is.toolCall)).toEqual([])
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expect(error.message).toContain("Provider stream ended without a terminal finish event")
|
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}),
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)
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|
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@@ -778,6 +778,11 @@ describe("OpenAI Responses route", () => {
|
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{ type: "step-finish", index: 0, reason: "stop" },
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{ type: "finish", reason: "stop" },
|
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])
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expect(response.events.filter((event) => event.type === "finish")).toHaveLength(1)
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expect(response.message.content).toEqual([
|
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{ type: "reasoning", text: "thinking" },
|
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{ type: "text", text: "Hello" },
|
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])
|
||||
}),
|
||||
)
|
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|
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|
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@@ -0,0 +1,98 @@
|
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import { describe, expect, test } from "bun:test"
|
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import { LLMEvent, LLMResponse } from "../src"
|
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|
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const reduce = (events: ReadonlyArray<LLMEvent>) => events.reduce(LLMResponse.reduce, LLMResponse.empty())
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const finishEvents = (events: ReadonlyArray<LLMEvent>) => events.filter(LLMEvent.is.finish)
|
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|
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describe("LLMResponse reducer", () => {
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test("assembles interleaved reasoning and text with end metadata", () => {
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const events = [
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LLMEvent.reasoningStart({ id: "r1" }),
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LLMEvent.reasoningDelta({ id: "r1", text: "I should " }),
|
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LLMEvent.textStart({ id: "t1" }),
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LLMEvent.reasoningDelta({ id: "r1", text: "compare..." }),
|
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LLMEvent.reasoningEnd({ id: "r1", providerMetadata: { anthropic: { signature: "sig" } } }),
|
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LLMEvent.textDelta({ id: "t1", text: "Answer" }),
|
||||
LLMEvent.textEnd({ id: "t1" }),
|
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LLMEvent.finish({ reason: "stop", usage: { outputTokens: 5 } }),
|
||||
]
|
||||
const response = LLMResponse.fromEvents(events)
|
||||
|
||||
expect(response?.finishReason).toBe("stop")
|
||||
expect(response?.usage).toMatchObject({ outputTokens: 5 })
|
||||
expect(response?.events).toEqual(events)
|
||||
expect(response?.events.map((event) => event.type)).toEqual([
|
||||
"reasoning-start",
|
||||
"reasoning-delta",
|
||||
"text-start",
|
||||
"reasoning-delta",
|
||||
"reasoning-end",
|
||||
"text-delta",
|
||||
"text-end",
|
||||
"finish",
|
||||
])
|
||||
expect(finishEvents(response?.events ?? [])).toHaveLength(1)
|
||||
expect(response?.message.content).toEqual([
|
||||
{
|
||||
type: "reasoning",
|
||||
text: "I should compare...",
|
||||
providerMetadata: { anthropic: { signature: "sig" } },
|
||||
},
|
||||
{ type: "text", text: "Answer" },
|
||||
])
|
||||
})
|
||||
|
||||
test("preserves partial content without completing a failed stream", () => {
|
||||
const state = reduce([LLMEvent.textStart({ id: "t1" }), LLMEvent.textDelta({ id: "t1", text: "partial" })])
|
||||
|
||||
expect(LLMResponse.complete(state)).toBeUndefined()
|
||||
expect(state.message.content).toEqual([{ type: "text", text: "partial" }])
|
||||
})
|
||||
|
||||
test("does not complete ended content without a terminal finish", () => {
|
||||
const state = reduce([
|
||||
LLMEvent.textStart({ id: "t1" }),
|
||||
LLMEvent.textDelta({ id: "t1", text: "partial" }),
|
||||
LLMEvent.textEnd({ id: "t1" }),
|
||||
])
|
||||
|
||||
expect(LLMResponse.complete(state)).toBeUndefined()
|
||||
expect(state.message.content).toEqual([{ type: "text", text: "partial" }])
|
||||
})
|
||||
|
||||
test("uses terminal usage when present and keeps prior usage when finish omits it", () => {
|
||||
const withFinishUsage = LLMResponse.fromEvents([
|
||||
LLMEvent.stepFinish({ index: 0, reason: "stop", usage: { inputTokens: 3 } }),
|
||||
LLMEvent.finish({ reason: "stop", usage: { outputTokens: 2 } }),
|
||||
])
|
||||
const withoutFinishUsage = LLMResponse.fromEvents([
|
||||
LLMEvent.stepFinish({ index: 0, reason: "stop", usage: { inputTokens: 3 } }),
|
||||
LLMEvent.finish({ reason: "stop" }),
|
||||
])
|
||||
|
||||
expect(withFinishUsage?.usage).toMatchObject({ outputTokens: 2 })
|
||||
expect(withoutFinishUsage?.usage).toMatchObject({ inputTokens: 3 })
|
||||
})
|
||||
|
||||
test("assembles tool-call content only after the completed tool call event", () => {
|
||||
const pending = reduce([
|
||||
LLMEvent.toolInputStart({ id: "call_1", name: "lookup" }),
|
||||
LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: '{"query"' }),
|
||||
])
|
||||
|
||||
expect(pending.message.content).toEqual([])
|
||||
expect(pending.toolInputs.call_1?.text).toBe('{"query"')
|
||||
|
||||
const response = LLMResponse.fromEvents([
|
||||
...pending.events,
|
||||
LLMEvent.toolInputDelta({ id: "call_1", name: "lookup", text: ':"weather"}' }),
|
||||
LLMEvent.toolInputEnd({ id: "call_1", name: "lookup" }),
|
||||
LLMEvent.toolCall({ id: "call_1", name: "lookup", input: { query: "weather" } }),
|
||||
LLMEvent.finish({ reason: "tool-calls" }),
|
||||
])
|
||||
|
||||
expect(response?.message.content).toEqual([
|
||||
{ type: "tool-call", id: "call_1", name: "lookup", input: { query: "weather" } },
|
||||
])
|
||||
})
|
||||
})
|
||||
@@ -0,0 +1,117 @@
|
||||
import { describe, expect, test } from "bun:test"
|
||||
import { Effect } from "effect"
|
||||
import { LLM } from "../src"
|
||||
import { OpenAIChat } from "../src/protocols"
|
||||
import { ToolSchemaProjection } from "../src/protocols/utils/tool-schema"
|
||||
import { Auth, LLMClient } from "../src/route"
|
||||
import { it } from "./lib/effect"
|
||||
|
||||
describe("tool schema projections", () => {
|
||||
test("moonshot strips $ref siblings and converts tuple arrays to a schema object", () => {
|
||||
expect(
|
||||
ToolSchemaProjection.moonshot({
|
||||
type: "object",
|
||||
properties: {
|
||||
linked: { $ref: "#/$defs/Linked", description: "drop me" },
|
||||
tuple: { type: "array", items: [{ type: "string" }, { type: "number" }] },
|
||||
prefixTuple: { type: "array", prefixItems: [{ type: "boolean" }, { type: "string" }] },
|
||||
},
|
||||
}),
|
||||
).toEqual({
|
||||
type: "object",
|
||||
properties: {
|
||||
linked: { $ref: "#/$defs/Linked" },
|
||||
tuple: { type: "array", items: { anyOf: [{ type: "string" }, { type: "number" }] } },
|
||||
prefixTuple: { type: "array", items: { anyOf: [{ type: "boolean" }, { type: "string" }] } },
|
||||
},
|
||||
})
|
||||
})
|
||||
|
||||
test("gemini handles numeric enums, dangling required fields, untyped arrays, and scalar object keys", () => {
|
||||
expect(
|
||||
ToolSchemaProjection.gemini({
|
||||
type: "object",
|
||||
required: ["status", "missing"],
|
||||
properties: {
|
||||
status: { type: "integer", enum: [1, 2] },
|
||||
tags: { type: "array" },
|
||||
name: { type: "string", properties: { ignored: { type: "string" } }, required: ["ignored"] },
|
||||
},
|
||||
}),
|
||||
).toEqual({
|
||||
type: "object",
|
||||
required: ["status"],
|
||||
properties: {
|
||||
status: { type: "string", enum: ["1", "2"] },
|
||||
tags: { type: "array", items: { type: "string" } },
|
||||
name: { type: "string" },
|
||||
},
|
||||
})
|
||||
})
|
||||
|
||||
test("openai keeps one flat object top-level schema", () => {
|
||||
expect(
|
||||
ToolSchemaProjection.openAI({
|
||||
anyOf: [
|
||||
{
|
||||
type: "object",
|
||||
properties: {
|
||||
path: { type: "string" },
|
||||
maybe: { anyOf: [{ type: "string" }, { type: "null" }] },
|
||||
},
|
||||
},
|
||||
{ type: "object", properties: { resource: { type: "string" } } },
|
||||
],
|
||||
}),
|
||||
).toEqual({
|
||||
type: "object",
|
||||
properties: {
|
||||
path: { type: "string" },
|
||||
maybe: { type: "string" },
|
||||
resource: { type: "string" },
|
||||
},
|
||||
additionalProperties: false,
|
||||
})
|
||||
})
|
||||
|
||||
it.effect("applies model compatibility before protocol projection", () =>
|
||||
Effect.gen(function* () {
|
||||
const model = OpenAIChat.route
|
||||
.with({ endpoint: { baseURL: "https://api.openai.test/v1/" }, auth: Auth.bearer("test") })
|
||||
.model({ id: "kimi-k2", compatibility: { toolSchema: "moonshot" } })
|
||||
const prepared = yield* LLMClient.prepare<OpenAIChat.OpenAIChatBody>(
|
||||
LLM.request({
|
||||
model,
|
||||
prompt: "Use the tool.",
|
||||
tools: [
|
||||
{
|
||||
name: "lookup",
|
||||
description: "Lookup data.",
|
||||
inputSchema: {
|
||||
type: "object",
|
||||
anyOf: [
|
||||
{
|
||||
type: "object",
|
||||
properties: {
|
||||
tuple: { type: "array", items: [{ type: "string" }, { type: "number" }] },
|
||||
linked: { $ref: "#/$defs/Linked", description: "drop me" },
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
],
|
||||
}),
|
||||
)
|
||||
|
||||
expect(prepared.body.tools?.[0]?.function.parameters).toEqual({
|
||||
type: "object",
|
||||
properties: {
|
||||
tuple: { type: "array", items: { anyOf: [{ type: "string" }, { type: "number" }] } },
|
||||
linked: { $ref: "#/$defs/Linked" },
|
||||
},
|
||||
additionalProperties: false,
|
||||
})
|
||||
}),
|
||||
)
|
||||
})
|
||||
Reference in New Issue
Block a user