packages/ai/test/extraction.test.ts
Rama dev · réplica de lectura del repositorio original.
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { Extraction } from "../src/experimental.js"
import { CloudflareWorkersAI } from "../src/providers.js"
import { it } from "./lib/effect.js"
import { dynamicResponse, json } from "./lib/http.js"
const model = CloudflareWorkersAI.configure({
accountId: "test-account",
apiKey: "cloudflare-key",
}).experimental.extraction("@cf/cloudflare/clairic")
describe("Extraction", () => {
it.effect("sends Clairic fields and decodes typed answers", () =>
Extraction.generate({
model,
input: "Invoice INV-7 issued March 2, net 30, total $1,080.00. Contacts: Dana, Priya.",
fields: {
invoice: { type: "text", nullThreshold: 0.8 },
total: { type: "number", decimals: 2, unit: "USD" },
due: { type: "date", instructions: "Issue date plus net days" },
contacts: { type: "list", items: "text" },
payDays: { type: "estimate", min: 0, max: 120, step: 1 },
sales: { type: "forecast", min: 0, max: 500, horizon: 2, interval: "month" },
},
referenceDate: "2026-01-01",
providerOptions: { options: { include_quantiles: true } },
}).pipe(
Effect.tap((response) =>
Effect.sync(() => {
expect(response.answers).toEqual({
invoice: {
value: "INV-7",
guess: "INV-7",
nullProbability: 0.01,
confidence: 0.99,
span: [8, 13],
},
total: { value: null, guess: 1080, nullProbability: 0.6, confidence: 0.9 },
due: { value: "2026-04-01", nullProbability: 0, confidence: 1 },
contacts: {
value: ["Dana", "Priya"],
spans: [
[67, 71],
[73, 78],
],
confidence: 0.98,
},
payDays: {
value: 30,
mean: 29.5,
median: 30,
p10: 21,
p90: 38,
belowProbability: 0.001,
aboveProbability: 0.002,
quantiles: [20, 30, 40],
},
sales: {
value: [140, 148],
steps: [
{ value: 140, mean: 140, median: 141, p10: 130, p90: 151 },
{ value: 148, mean: 148, median: 148, p10: 131, p90: 165 },
],
},
})
expect(response.usage?.inputTokens).toBe(655)
expect(response.usage?.providerMetadata?.["cloudflare-workers-ai"]).toMatchObject({ passes: 3 })
expect(response.providerMetadata).toEqual({ "cloudflare-workers-ai": { model: "clairic", truncated: false } })
}),
),
Effect.provide(
dynamicResponse((input) => {
expect(input.request.url).toBe(
"https://api.cloudflare.com/client/v4/accounts/test-account/ai/run/@cf/cloudflare/clairic",
)
expect(input.request.headers.authorization).toBe("Bearer cloudflare-key")
expect(JSON.parse(input.text)).toEqual({
options: { include_quantiles: true },
model: "@cf/cloudflare/clairic",
state: "Invoice INV-7 issued March 2, net 30, total $1,080.00. Contacts: Dana, Priya.",
fields: {
invoice: { type: "text", null_threshold: 0.8 },
total: { type: "number", decimals: 2, unit: "USD" },
due: { type: "date", instructions: "Issue date plus net days" },
contacts: { type: "list", items: "text" },
payDays: { type: "estimate", min: 0, max: 120, step: 1 },
sales: { type: "forecast", min: 0, max: 500, horizon: 2, interval: "month" },
},
reference_date: "2026-01-01",
})
return Effect.succeed(
json(input, {
result: {
model: "clairic",
answers: {
invoice: {
type: "text",
p_null: 0.01,
span: [8, 13],
guess: "INV-7",
confidence: 0.99,
value: "INV-7",
},
total: { type: "number", p_null: 0.6, guess: 1080, confidence: 0.9, value: null },
due: { type: "date", p_null: 0, confidence: 1, value: "2026-04-01" },
contacts: {
type: "list",
value: ["Dana", "Priya"],
spans: [
[67, 71],
[73, 78],
],
confidence: 0.98,
},
payDays: {
type: "estimate",
value: 30,
mean: 29.5,
median: 30,
p10: 21,
p90: 38,
p_below: 0.001,
p_above: 0.002,
quantiles: [20, 30, 40],
},
sales: {
type: "forecast",
value: [140, 148],
median: [141, 148],
p10: [130, 131],
p90: [151, 165],
steps: [
{ value: 140, mean: 140, median: 141, p10: 130, p90: 151 },
{ value: 148, mean: 148, median: 148, p10: 131, p90: 165 },
],
},
},
truncated: false,
usage: {
state_tokens: 61,
image_tokens: 0,
schema_tokens: 472,
input_tokens: 655,
output_tokens: 0,
passes: 3,
},
},
success: true,
errors: [],
messages: [],
}),
)
}),
),
),
)
it.effect("passes unknown field types through and drops extra answers", () =>
Extraction.generate({
model,
input: "Ship by May 20 in EUR.",
fields: { ship: { type: "date" }, money: { type: "currency", codes: ["EUR"] } },
}).pipe(
Effect.tap((response) =>
Effect.sync(() =>
expect(response.answers).toEqual({
ship: { value: "2026-05-20", confidence: 0.97 },
money: { type: "currency", value: "EUR", p_null: 0 },
}),
),
),
Effect.provide(
dynamicResponse((input) => {
expect(JSON.parse(input.text).fields.money).toEqual({ type: "currency", codes: ["EUR"] })
return Effect.succeed(
json(input, {
result: {
model: "clairic",
answers: {
ship: { type: "date", value: "2026-05-20", confidence: 0.97, calendar: "gregorian" },
money: { type: "currency", value: "EUR", p_null: 0 },
extra: { type: "hologram", value: { x: 1 } },
},
future_flag: true,
},
}),
)
}),
),
),
)
it.effect("fails when a requested field has no answer", () =>
Extraction.generate({ model, input: "Nothing here.", fields: { total: { type: "number" } } }).pipe(
Effect.flip,
Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidProviderOutput"))),
Effect.provide(
dynamicResponse((input) =>
Effect.succeed(json(input, { result: { model: "clairic", answers: {}, usage: { input_tokens: 1 } } })),
),
),
),
)
it.effect("fails when an answer value does not match its field", () =>
Extraction.generate({ model, input: "Total: lots.", fields: { total: { type: "number" } } }).pipe(
Effect.flip,
Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidProviderOutput"))),
Effect.provide(
dynamicResponse((input) =>
Effect.succeed(
json(input, { result: { model: "clairic", answers: { total: { type: "number", value: "lots" } } } }),
),
),
),
),
)
it.effect("rejects an empty field map before the network", () =>
Extraction.generate({ model, input: "x", fields: {} }).pipe(
Effect.flip,
Effect.tap((error) => Effect.sync(() => expect(error.reason._tag).toBe("InvalidRequest"))),
Effect.provide(dynamicResponse(() => Effect.die("an invalid extraction request reached the network"))),
),
)
})