Kit Langton
109 lines
3.8 KiB
TypeScript
109 lines
3.8 KiB
TypeScript
import { describe, expect, test } from "bun:test"
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import { LLM, ProviderPatch } from "../src"
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import { Model, Patch, context, plan } from "../src/patch"
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const request = LLM.request({
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id: "req_1",
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model: LLM.model({
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id: "devstral-small",
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provider: "mistral",
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protocol: "openai-chat",
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}),
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prompt: "hi",
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})
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describe("llm patch", () => {
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test("constructors prefix ids and registry groups by phase", () => {
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const prompt = Patch.prompt("mistral.test", {
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reason: "test prompt",
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when: Model.provider("mistral"),
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apply: (request) => request,
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})
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const target = Patch.target("fake.test", {
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reason: "test target",
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apply: (draft: { value: number }) => draft,
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})
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const registry = Patch.registry([prompt, target])
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expect(prompt.id).toBe("prompt.mistral.test")
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expect(target.id).toBe("target.fake.test")
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expect(registry.prompt).toEqual([prompt])
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expect(registry.target.map((item) => item.id)).toEqual([target.id])
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})
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test("predicates compose", () => {
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const ctx = context({ request })
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expect(Model.provider("mistral").and(Model.protocol("openai-chat"))(ctx)).toBe(true)
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expect(Model.provider("anthropic").or(Model.idIncludes("devstral"))(ctx)).toBe(true)
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expect(Model.provider("mistral").not()(ctx)).toBe(false)
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})
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test("plan filters, sorts, applies, and traces deterministically", () => {
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const patches = [
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Patch.prompt("b", {
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reason: "second alphabetically",
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order: 1,
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apply: (request) => ({ ...request, metadata: { ...request.metadata, b: true } }),
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}),
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Patch.prompt("a", {
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reason: "first alphabetically",
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order: 1,
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apply: (request) => ({ ...request, metadata: { ...request.metadata, a: true } }),
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}),
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Patch.prompt("skip", {
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reason: "not selected",
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when: Model.provider("anthropic"),
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apply: (request) => ({ ...request, metadata: { ...request.metadata, skip: true } }),
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}),
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]
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const patchPlan = plan({ phase: "prompt", context: context({ request }), patches })
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const output = patchPlan.apply(request)
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expect(patchPlan.trace.map((item) => item.id)).toEqual(["prompt.a", "prompt.b"])
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expect(output.metadata).toEqual({ a: true, b: true })
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})
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test("provider patch examples remove empty Anthropic content", () => {
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const input = LLM.request({
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id: "anthropic_empty",
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model: LLM.model({ id: "claude-sonnet", provider: "anthropic", protocol: "anthropic-messages" }),
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system: "",
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messages: [
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LLM.user([{ type: "text", text: "" }, { type: "text", text: "hello" }]),
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LLM.assistant({ type: "reasoning", text: "" }),
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],
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})
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const output = plan({
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phase: "prompt",
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context: context({ request: input }),
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patches: [ProviderPatch.removeEmptyAnthropicContent],
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}).apply(input)
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expect(output.system).toEqual([])
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expect(output.messages).toHaveLength(1)
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expect(output.messages[0]?.content).toEqual([{ type: "text", text: "hello" }])
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})
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test("provider patch examples scrub model-specific tool call ids", () => {
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const input = LLM.request({
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id: "mistral_tool_ids",
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model: LLM.model({ id: "devstral-small", provider: "mistral", protocol: "openai-chat" }),
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messages: [
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LLM.assistant([LLM.toolCall({ id: "call.bad/value-long", name: "lookup", input: {} })]),
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LLM.toolMessage({ id: "call.bad/value-long", name: "lookup", result: "ok", resultType: "text" }),
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],
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})
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const output = plan({
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phase: "prompt",
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context: context({ request: input }),
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patches: [ProviderPatch.scrubMistralToolIds],
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}).apply(input)
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expect(output.messages[0]?.content[0]).toMatchObject({ type: "tool-call", id: "callbadva" })
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expect(output.messages[1]?.content[0]).toMatchObject({ type: "tool-result", id: "callbadva" })
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})
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})
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