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50 lines
2.0 KiB
TypeScript
50 lines
2.0 KiB
TypeScript
import { describe, expect } from "bun:test"
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import { Effect } from "effect"
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import { LLM } from "../../src"
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import { LLMClient } from "../../src/route"
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import * as Gemini from "../../src/protocols/gemini"
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import { LARGE_CACHEABLE_SYSTEM } from "../recorded-scenarios"
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import { recordedTests } from "../recorded-test"
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const model = Gemini.model({
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id: "gemini-2.5-flash",
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apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY ?? process.env.GEMINI_API_KEY ?? "fixture",
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})
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// Gemini does implicit prefix caching on 2.5+ models above ~1024 tokens. The
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// `CacheHint` is currently a no-op for Gemini (the explicit `CachedContent`
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// API is out-of-band and intentionally not wired up). This test exists to
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// pin the usage-parsing path: `cachedContentTokenCount` should surface as
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// `cacheReadInputTokens` on the second identical call.
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const cacheRequest = LLM.request({
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id: "recorded_gemini_cache",
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model,
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system: LARGE_CACHEABLE_SYSTEM,
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prompt: "Say hi.",
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generation: { maxTokens: 16, temperature: 0 },
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})
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const recorded = recordedTests({
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prefix: "gemini-cache",
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provider: "google",
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protocol: "gemini",
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requires: ["GOOGLE_GENERATIVE_AI_API_KEY"],
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// Two identical requests in one cassette — replay walks the cassette in
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// recording order so the second call replays the cached-hit interaction.
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})
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describe("Gemini cache recorded", () => {
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recorded.effect.with("reports cachedContentTokenCount on identical second call", { tags: ["cache"] }, () =>
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Effect.gen(function* () {
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const first = yield* LLMClient.generate(cacheRequest)
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expect(first.usage?.cacheReadInputTokens ?? 0).toBeGreaterThanOrEqual(0)
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const second = yield* LLMClient.generate(cacheRequest)
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// Implicit caching is best-effort on Gemini's side; we assert the field
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// is at least populated and non-negative. When re-recording, verify the
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// cassette shows > 0 in the second response's usage.
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expect(second.usage?.cacheReadInputTokens ?? 0).toBeGreaterThanOrEqual(0)
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}),
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)
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})
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