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7 changed files with 749 additions and 24 deletions
+243 -5
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@@ -46,8 +46,12 @@ type OpenAIResponsesInputContent = Schema.Schema.Type<typeof OpenAIResponsesInpu
const OpenAIResponsesOutputText = Schema.Struct({
type: Schema.tag("output_text"),
text: Schema.String,
annotations: Schema.Array(Schema.Unknown),
})
const OpenAIResponsesMessagePhase = Schema.Literals(["commentary", "final_answer"])
type OpenAIResponsesMessagePhase = Schema.Schema.Type<typeof OpenAIResponsesMessagePhase>
const OpenAIResponsesReasoningSummaryText = Schema.Struct({
type: Schema.tag("summary_text"),
text: Schema.String,
@@ -78,7 +82,19 @@ const OpenAIResponsesFunctionCallOutput = Schema.Union([
const OpenAIResponsesInputItem = Schema.Union([
Schema.Struct({ role: Schema.tag("system"), content: Schema.String }),
Schema.Struct({ role: Schema.tag("user"), content: Schema.Array(OpenAIResponsesInputContent) }),
Schema.Struct({ role: Schema.tag("assistant"), content: Schema.Array(OpenAIResponsesOutputText) }),
Schema.Struct({
role: Schema.tag("assistant"),
content: Schema.String,
phase: optionalNull(OpenAIResponsesMessagePhase),
}),
Schema.Struct({
type: Schema.tag("message"),
id: Schema.String,
status: Schema.Literals(["in_progress", "completed", "incomplete"]),
role: Schema.tag("assistant"),
content: Schema.Array(OpenAIResponsesOutputText),
phase: optionalNull(OpenAIResponsesMessagePhase),
}),
OpenAIResponsesReasoningItem,
OpenAIResponsesItemReference,
Schema.Struct({
@@ -194,7 +210,9 @@ const OpenAIResponsesStreamItem = Schema.Struct({
server_label: Schema.optional(Schema.String),
output: Schema.optional(Schema.Unknown),
error: Schema.optional(Schema.Unknown),
content: Schema.optional(Schema.Array(Schema.Unknown)),
encrypted_content: optionalNull(Schema.String),
phase: optionalNull(OpenAIResponsesMessagePhase),
})
type OpenAIResponsesStreamItem = Schema.Schema.Type<typeof OpenAIResponsesStreamItem>
@@ -212,7 +230,9 @@ const OpenAIResponsesErrorPayload = Schema.Struct({
const OpenAIResponsesEvent = Schema.Struct({
type: Schema.String,
delta: Schema.optional(Schema.String),
text: Schema.optional(Schema.String),
item_id: Schema.optional(Schema.String),
content_index: Schema.optional(Schema.Number),
summary_index: Schema.optional(Schema.Number),
item: Schema.optional(OpenAIResponsesStreamItem),
response: Schema.optional(
@@ -237,10 +257,18 @@ interface ParserState {
readonly tools: ToolStream.State<string>
readonly hasFunctionCall: boolean
readonly lifecycle: Lifecycle.State
readonly messageItems: Readonly<Record<string, MessageStreamItem>>
readonly messageContentIDs: ReadonlySet<string>
readonly nextMessageContentID: number
readonly reasoningItems: Readonly<Record<string, ReasoningStreamItem>>
readonly store: boolean | undefined
}
interface MessageStreamItem {
readonly providerMetadata?: ProviderMetadata
readonly content: Readonly<Record<number, { readonly id: string; readonly text: string }>>
}
type ReasoningSummaryStatus = "active" | "can-conclude" | "concluded"
interface ReasoningStreamItem {
@@ -298,6 +326,26 @@ const lowerReasoning = (part: ReasoningPart): OpenAIResponsesReasoningInput | un
}
}
const messagePhase = (part: TextPart): OpenAIResponsesMessagePhase | null | undefined => {
const phase = part.providerMetadata?.openai?.phase
return phase === "commentary" || phase === "final_answer" || phase === null ? phase : undefined
}
const messageItemID = (part: TextPart) => {
const itemID = part.providerMetadata?.openai?.itemId
return typeof itemID === "string" && itemID.length > 0 ? itemID : undefined
}
const messageStatus = (part: TextPart) => {
const status = part.providerMetadata?.openai?.status
return status === "in_progress" || status === "completed" || status === "incomplete" ? status : undefined
}
const messageAnnotations = (part: TextPart) => {
const annotations = part.providerMetadata?.openai?.annotations
return Array.isArray(annotations) ? annotations : []
}
const hostedToolItemID = (part: ToolResultPart) => {
const openai = part.providerMetadata?.openai
return ProviderShared.isRecord(openai) && typeof openai.itemId === "string" && openai.itemId.length > 0
@@ -369,16 +417,47 @@ const lowerMessages = Effect.fn("OpenAIResponses.lowerMessages")(function* (requ
if (message.role === "assistant") {
const content: TextPart[] = []
let phase: OpenAIResponsesMessagePhase | null | undefined
let itemID: string | undefined
let status: "in_progress" | "completed" | "incomplete" | undefined
const reasoningItems: Record<string, OpenAIResponsesReasoningReplay> = {}
const reasoningReferences = new Set<string>()
const hostedToolReferences = new Set<string>()
const flushText = () => {
if (content.length === 0) return
input.push({ role: "assistant", content: content.map((part) => ({ type: "output_text", text: part.text })) })
input.push(
itemID
? {
type: "message",
id: itemID,
status: status ?? "completed",
role: "assistant",
content: content.map((part) => ({
type: "output_text",
text: part.text,
annotations: messageAnnotations(part),
})),
...(phase !== undefined ? { phase } : {}),
}
: {
role: "assistant",
content: ProviderShared.joinText(content),
...(phase !== undefined ? { phase } : {}),
},
)
content.splice(0, content.length)
phase = undefined
itemID = undefined
status = undefined
}
for (const part of message.content) {
if (part.type === "text") {
const nextPhase = messagePhase(part)
const nextItemID = messageItemID(part)
if (content.length > 0 && (phase !== nextPhase || itemID !== nextItemID)) flushText()
phase = nextPhase
itemID = nextItemID
status = messageStatus(part) ?? status
content.push(part)
continue
}
@@ -612,15 +691,127 @@ const NO_EVENTS: StepResult["1"] = []
// the protocol's `terminal` predicate stay in sync.
const TERMINAL_TYPES = new Set(["response.completed", "response.incomplete", "response.failed"])
const onOutputTextDelta = (state: ParserState, event: OpenAIResponsesEvent): StepResult => {
if (!event.delta) return [state, NO_EVENTS]
const messageMetadata = (item: OpenAIResponsesStreamItem, id: string, previous?: ProviderMetadata) => {
const openai = previous?.openai
const phase = item.phase !== undefined ? item.phase : openai?.phase
const status =
item.status === "in_progress" || item.status === "completed" || item.status === "incomplete"
? item.status
: openai?.status
return openaiMetadata({
itemId: id,
...(phase === "commentary" || phase === "final_answer" || phase === null ? { phase } : {}),
...(status === "in_progress" || status === "completed" || status === "incomplete" ? { status } : {}),
})
}
const messageContentMetadata = (
providerMetadata: ProviderMetadata,
item: OpenAIResponsesStreamItem,
index: number,
): ProviderMetadata => {
const content = item.content?.[index]
if (!ProviderShared.isRecord(content) || content.type !== "output_text" || !Array.isArray(content.annotations))
return providerMetadata
return openaiMetadata({ ...providerMetadata.openai, annotations: content.annotations })
}
const ensureMessageContent = (state: ParserState, event: OpenAIResponsesEvent) => {
const itemID = event.item_id ?? "text-0"
const index = event.content_index ?? 0
const item = state.messageItems[itemID] ?? { content: {} }
const existing = item.content[index]
if (existing) return { state, itemID, index, item, content: existing }
const findID = (next: number): readonly [string, number] => {
const id = `openai-text-${next}`
return state.messageContentIDs.has(id) ? findID(next + 1) : [id, next + 1]
}
const [id, nextMessageContentID] =
index === 0 && !state.messageContentIDs.has(itemID)
? ([itemID, state.nextMessageContentID] as const)
: findID(state.nextMessageContentID)
const content = { id, text: "" }
const nextItem = { ...item, content: { ...item.content, [index]: content } }
return {
state: {
...state,
messageItems: { ...state.messageItems, [itemID]: nextItem },
messageContentIDs: new Set([...state.messageContentIDs, id]),
nextMessageContentID,
},
itemID,
index,
item: nextItem,
content,
}
}
const updateMessageContent = (
state: ParserState,
itemID: string,
index: number,
content: { readonly id: string; readonly text: string },
): ParserState => ({
...state,
messageItems: {
...state.messageItems,
[itemID]: {
...state.messageItems[itemID],
content: { ...state.messageItems[itemID]?.content, [index]: content },
},
},
})
const appendOutputText = (state: ParserState, event: OpenAIResponsesEvent, text: string): StepResult => {
const ensured = ensureMessageContent(state, event)
const events: LLMEvent[] = []
const lifecycle = Lifecycle.textStart(
ensured.state.lifecycle,
events,
ensured.content.id,
ensured.item.providerMetadata,
)
return [
{ ...state, lifecycle: Lifecycle.textDelta(state.lifecycle, events, event.item_id ?? "text-0", event.delta) },
{
...updateMessageContent(ensured.state, ensured.itemID, ensured.index, {
...ensured.content,
text: ensured.content.text + text,
}),
lifecycle: Lifecycle.textDelta(lifecycle, events, ensured.content.id, text),
},
events,
]
}
const onOutputTextDelta = (state: ParserState, event: OpenAIResponsesEvent): StepResult => {
if (!event.delta) return [state, NO_EVENTS]
return appendOutputText(state, event, event.delta)
}
const onOutputTextDone = (state: ParserState, event: OpenAIResponsesEvent): StepResult => {
if (event.text === undefined) return [state, NO_EVENTS]
const ensured = ensureMessageContent(state, event)
if (event.text === ensured.content.text) {
if (ensured.state.lifecycle.text.has(ensured.content.id)) return [ensured.state, NO_EVENTS]
const events: LLMEvent[] = []
return [
{
...ensured.state,
lifecycle: Lifecycle.textStart(
ensured.state.lifecycle,
events,
ensured.content.id,
ensured.item.providerMetadata,
),
},
events,
]
}
if (event.text.startsWith(ensured.content.text))
return appendOutputText(ensured.state, event, event.text.slice(ensured.content.text.length))
return [ensured.state, NO_EVENTS]
}
const onReasoningDelta = (state: ParserState, event: OpenAIResponsesEvent): StepResult => {
if (!event.delta) return [state, NO_EVENTS]
const events: LLMEvent[] = []
@@ -655,6 +846,23 @@ const reasoningMetadata = (item: OpenAIResponsesStreamItem & { id: string }) =>
// best-effort, not guaranteed.
const onOutputItemAdded = (state: ParserState, event: OpenAIResponsesEvent): StepResult => {
const item = event.item
if (item?.type === "message" && item.id) {
const existing = state.messageItems[item.id]
return [
{
...state,
messageItems: {
...state.messageItems,
[item.id]: {
...existing,
providerMetadata: messageMetadata(item, item.id, existing?.providerMetadata),
content: existing?.content ?? {},
},
},
},
NO_EVENTS,
]
}
if (item && isReasoningItem(item)) {
const events: LLMEvent[] = []
return [
@@ -812,6 +1020,32 @@ const onOutputItemDone = Effect.fn("OpenAIResponses.onOutputItemDone")(function*
const item = event.item
if (!item) return [state, NO_EVENTS] satisfies StepResult
if (item.type === "message" && item.id) {
const events: LLMEvent[] = []
const itemID = item.id
const messageItem = state.messageItems[itemID]
const { [itemID]: _finished, ...messageItems } = state.messageItems
const providerMetadata = messageMetadata(item, itemID, messageItem?.providerMetadata)
const lifecycle = Object.entries(messageItem?.content ?? {}).reduce(
(lifecycle, entry) =>
Lifecycle.textEnd(
lifecycle,
events,
entry[1].id,
messageContentMetadata(providerMetadata, item, Number(entry[0])),
),
state.lifecycle,
)
return [
{
...state,
lifecycle,
messageItems,
},
events,
] satisfies StepResult
}
if (item.type === "function_call") {
if (!item.id || !item.call_id || !item.name) return [state, NO_EVENTS] satisfies StepResult
const tools = state.tools[item.id]
@@ -939,6 +1173,7 @@ const step = (state: ParserState, event: OpenAIResponsesEvent) => {
if (event.type === "response.reasoning_summary_part.done")
return Effect.succeed(onReasoningSummaryPartDone(state, event))
if (event.type === "response.output_item.added") return Effect.succeed(onOutputItemAdded(state, event))
if (event.type === "response.output_text.done") return Effect.succeed(onOutputTextDone(state, event))
if (event.type === "response.function_call_arguments.delta") return onFunctionCallArgumentsDelta(state, event)
if (event.type === "response.output_item.done") return onOutputItemDone(state, event)
if (event.type === "response.completed" || event.type === "response.incomplete")
@@ -968,6 +1203,9 @@ export const protocol = Protocol.make({
hasFunctionCall: false,
tools: ToolStream.empty<string>(),
lifecycle: Lifecycle.initial(),
messageItems: {},
messageContentIDs: new Set<string>(),
nextMessageContentID: 0,
reasoningItems: {},
store: OpenAIOptions.store(request),
}),
+10 -7
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@@ -14,16 +14,19 @@ export const stepStart = (state: State, events: LLMEvent[]): State => {
return { ...state, stepStarted: true }
}
export const textDelta = (state: State, events: LLMEvent[], id: string, text: string): State => {
export const textStart = (state: State, events: LLMEvent[], id: string, providerMetadata?: ProviderMetadata): State => {
if (state.text.has(id)) return state
const stepped = stepStart(state, events)
if (stepped.text.has(id)) {
events.push(LLMEvent.textDelta({ id, text }))
return stepped
}
events.push(LLMEvent.textStart({ id }), LLMEvent.textDelta({ id, text }))
events.push(LLMEvent.textStart({ id, ...(providerMetadata ? { providerMetadata } : {}) }))
return { ...stepped, text: new Set([...stepped.text, id]) }
}
export const textDelta = (state: State, events: LLMEvent[], id: string, text: string): State => {
const started = textStart(state, events, id)
events.push(LLMEvent.textDelta({ id, text }))
return started
}
export const reasoningStart = (
state: State,
events: LLMEvent[],
@@ -65,7 +68,7 @@ export const reasoningEnd = (
export const textEnd = (state: State, events: LLMEvent[], id: string, providerMetadata?: ProviderMetadata): State => {
if (!state.text.has(id)) return state
const stepped = stepStart(state, events)
events.push(LLMEvent.textEnd({ id, providerMetadata }))
events.push(LLMEvent.textEnd({ id, ...(providerMetadata ? { providerMetadata } : {}) }))
const text = new Set(stepped.text)
text.delete(id)
return { ...stepped, text }
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File diff suppressed because one or more lines are too long
@@ -0,0 +1,71 @@
import { describe, expect } from "bun:test"
import { Effect } from "effect"
import { LLM, Message } from "../../src"
import * as OpenAI from "../../src/providers/openai"
import { LLMClient } from "../../src/route"
import { weatherTool } from "../recorded-scenarios"
import { recordedTests } from "../recorded-test"
const model = OpenAI.configure({
apiKey: process.env.OPENAI_API_KEY ?? "fixture",
}).responses("gpt-5.6-sol")
const recorded = recordedTests({
prefix: "openai-responses-phase",
provider: "openai",
protocol: "openai-responses",
requires: ["OPENAI_API_KEY"],
})
describe("OpenAI Responses phase recorded", () => {
recorded.effect.with("round-trips commentary into a final answer", { tags: ["phase", "tool"] }, () =>
Effect.gen(function* () {
const user = Message.user("What is the weather in Paris?")
const first = yield* LLMClient.generate(
LLM.request({
model,
system:
"Before calling get_weather, briefly tell the user you are checking. Then call get_weather exactly once. Do not provide the final answer until its result is available.",
messages: [user],
tools: [weatherTool],
generation: { maxTokens: 100 },
}),
)
const call = first.toolCalls[0]
if (!call) throw new Error("OpenAI Responses did not return the expected weather tool call")
expect(call).toMatchObject({ name: "get_weather", input: { city: "Paris" } })
expect(
first.message.content.some(
(part) => part.type === "text" && part.providerMetadata?.openai?.phase === "commentary",
),
).toBeTrue()
const second = yield* LLMClient.generate(
LLM.request({
model,
system:
"Before calling get_weather, briefly tell the user you are checking. Then call get_weather exactly once. After its result, answer exactly: Paris is sunny.",
messages: [
user,
first.message,
Message.tool({
id: call.id,
name: call.name,
result: { temperature: 22, condition: "sunny" },
}),
],
tools: [weatherTool],
generation: { maxTokens: 100 },
}),
)
expect(second.text.trim()).toBe("Paris is sunny.")
expect(
second.message.content.some(
(part) => part.type === "text" && part.providerMetadata?.openai?.phase === "final_answer",
),
).toBeTrue()
}),
)
})
@@ -153,7 +153,7 @@ describe("OpenAI Responses route", () => {
{ type: "input_text", text: "<system-update>\nTreat &lt;/system-update&gt; literally.\n</system-update>" },
],
},
{ role: "assistant", content: [{ type: "output_text", text: "After." }] },
{ role: "assistant", content: "After." },
])
}),
)
@@ -529,11 +529,11 @@ describe("OpenAI Responses route", () => {
encrypted_content: "encrypted-continuation-state",
summary: [{ type: "summary_text", text: "I inspected the previous turn." }],
},
{ role: "assistant", content: [{ type: "output_text", text: "It shows a small test image." }] },
{ role: "assistant", content: "It shows a small test image." },
{ role: "user", content: [{ type: "input_text", text: "Check the weather in Paris before continuing." }] },
{ type: "function_call", call_id: "call_weather_1", name: "get_weather", arguments: '{"city":"Paris"}' },
{ type: "function_call_output", call_id: "call_weather_1", output: '{"temperature":22}' },
{ role: "assistant", content: [{ type: "output_text", text: "Paris is 22 degrees." }] },
{ role: "assistant", content: "Paris is 22 degrees." },
{
role: "user",
content: [{ type: "input_text", text: "Continue from this conversation in one short sentence." }],
@@ -754,6 +754,311 @@ describe("OpenAI Responses route", () => {
}),
)
it.effect("preserves streamed assistant message phases", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{
type: "response.output_item.added",
item: { type: "message", id: "msg_commentary", phase: "commentary" },
},
{ type: "response.output_text.delta", item_id: "msg_commentary", delta: "Checking first." },
{ type: "response.output_text.done", item_id: "msg_commentary" },
{
type: "response.output_item.done",
item: { type: "message", id: "msg_commentary", phase: "commentary" },
},
{
type: "response.output_item.added",
item: { type: "message", id: "msg_final", phase: "final_answer" },
},
{ type: "response.output_text.delta", item_id: "msg_final", delta: "Finished." },
{ type: "response.output_text.done", item_id: "msg_final" },
{
type: "response.output_item.done",
item: { type: "message", id: "msg_final", phase: "final_answer" },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
),
),
)
expect(response.events.filter((event) => event.type.startsWith("text-"))).toEqual([
{
type: "text-start",
id: "msg_commentary",
providerMetadata: { openai: { itemId: "msg_commentary", phase: "commentary" } },
},
{ type: "text-delta", id: "msg_commentary", text: "Checking first." },
{
type: "text-end",
id: "msg_commentary",
providerMetadata: { openai: { itemId: "msg_commentary", phase: "commentary" } },
},
{
type: "text-start",
id: "msg_final",
providerMetadata: { openai: { itemId: "msg_final", phase: "final_answer" } },
},
{ type: "text-delta", id: "msg_final", text: "Finished." },
{
type: "text-end",
id: "msg_final",
providerMetadata: { openai: { itemId: "msg_final", phase: "final_answer" } },
},
])
expect(response.message.content).toEqual([
{
type: "text",
text: "Checking first.",
providerMetadata: { openai: { itemId: "msg_commentary", phase: "commentary" } },
},
{
type: "text",
text: "Finished.",
providerMetadata: { openai: { itemId: "msg_final", phase: "final_answer" } },
},
])
}),
)
it.effect("preserves phased message and content boundaries", () =>
Effect.gen(function* () {
const response = yield* LLMClient.generate(request).pipe(
Effect.provide(
fixedResponse(
sseEvents(
{
type: "response.output_item.added",
item: { type: "message", id: "msg_commentary", phase: "commentary" },
},
{
type: "response.output_text.delta",
item_id: "msg_commentary",
content_index: 0,
delta: "First.",
},
{
type: "response.output_item.added",
item: { type: "message", id: "msg_commentary" },
},
{
type: "response.output_text.done",
item_id: "msg_commentary",
content_index: 0,
text: "First.",
},
{
type: "response.output_text.done",
item_id: "msg_commentary",
content_index: 1,
text: "Second.",
},
{
type: "response.output_item.done",
item: { type: "message", id: "msg_commentary" },
},
{
type: "response.output_item.added",
item: { type: "message", id: "msg_commentary_2", phase: "commentary" },
},
{
type: "response.output_text.delta",
item_id: "msg_commentary_2",
content_index: 0,
delta: "Thi",
},
{
type: "response.output_text.done",
item_id: "msg_commentary_2",
content_index: 0,
text: "Third.",
},
{
type: "response.output_item.done",
item: { type: "message", id: "msg_commentary_2", phase: "commentary" },
},
{
type: "response.output_item.added",
item: { type: "message", id: "openai-text-0" },
},
{
type: "response.output_text.done",
item_id: "openai-text-0",
content_index: 0,
text: "Final.",
},
{
type: "response.output_item.done",
item: {
type: "message",
id: "openai-text-0",
phase: "final_answer",
content: [
{
type: "output_text",
text: "Final.",
annotations: [
{
type: "url_citation",
url: "https://example.com",
title: "Example",
start_index: 0,
end_index: 6,
},
],
},
],
},
},
{
type: "response.output_item.added",
item: { type: "message", id: "msg_null", phase: null },
},
{
type: "response.output_text.done",
item_id: "msg_null",
content_index: 0,
text: "Nullable.",
},
{
type: "response.output_item.done",
item: { type: "message", id: "msg_null", phase: null },
},
{
type: "response.output_item.added",
item: { type: "message", id: "msg_unphased" },
},
{
type: "response.output_text.done",
item_id: "msg_unphased",
content_index: 0,
text: "Unphased.",
},
{
type: "response.output_item.done",
item: { type: "message", id: "msg_unphased" },
},
{ type: "response.completed", response: { id: "resp_1" } },
),
),
),
)
expect(response.message.content).toEqual([
{
type: "text",
text: "First.",
providerMetadata: { openai: { itemId: "msg_commentary", phase: "commentary" } },
},
{
type: "text",
text: "Second.",
providerMetadata: { openai: { itemId: "msg_commentary", phase: "commentary" } },
},
{
type: "text",
text: "Third.",
providerMetadata: { openai: { itemId: "msg_commentary_2", phase: "commentary" } },
},
{
type: "text",
text: "Final.",
providerMetadata: {
openai: {
itemId: "openai-text-0",
phase: "final_answer",
annotations: [
{
type: "url_citation",
url: "https://example.com",
title: "Example",
start_index: 0,
end_index: 6,
},
],
},
},
},
{
type: "text",
text: "Nullable.",
providerMetadata: { openai: { itemId: "msg_null", phase: null } },
},
{
type: "text",
text: "Unphased.",
providerMetadata: { openai: { itemId: "msg_unphased" } },
},
])
const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
LLM.request({ model, messages: [response.message] }),
)
expect(prepared.body.input).toEqual([
{
type: "message",
id: "msg_commentary",
status: "completed",
role: "assistant",
phase: "commentary",
content: [
{ type: "output_text", text: "First.", annotations: [] },
{ type: "output_text", text: "Second.", annotations: [] },
],
},
{
type: "message",
id: "msg_commentary_2",
status: "completed",
role: "assistant",
phase: "commentary",
content: [{ type: "output_text", text: "Third.", annotations: [] }],
},
{
type: "message",
id: "openai-text-0",
status: "completed",
role: "assistant",
phase: "final_answer",
content: [
{
type: "output_text",
text: "Final.",
annotations: [
{
type: "url_citation",
url: "https://example.com",
title: "Example",
start_index: 0,
end_index: 6,
},
],
},
],
},
{
type: "message",
id: "msg_null",
status: "completed",
role: "assistant",
phase: null,
content: [{ type: "output_text", text: "Nullable.", annotations: [] }],
},
{
type: "message",
id: "msg_unphased",
status: "completed",
role: "assistant",
content: [{ type: "output_text", text: "Unphased.", annotations: [] }],
},
])
}),
)
it.effect("parses reasoning summary stream fixtures", () =>
Effect.gen(function* () {
const body = sseEvents(
@@ -947,7 +1252,7 @@ describe("OpenAI Responses route", () => {
encrypted_content: "encrypted-state",
summary: [{ type: "summary_text", text: "Checked the previous diff." }],
},
{ role: "assistant", content: [{ type: "output_text", text: "The parser changed." }] },
{ role: "assistant", content: "The parser changed." },
{ role: "user", content: [{ type: "input_text", text: "Summarize it." }] },
],
})
@@ -995,13 +1300,69 @@ describe("OpenAI Responses route", () => {
)
expect(prepared.body.input).toEqual([
{ role: "assistant", content: [{ type: "output_text", text: "Before." }] },
{ role: "assistant", content: "Before." },
{
type: "reasoning",
encrypted_content: "encrypted-state",
summary: [{ type: "summary_text", text: "Checked order." }],
},
{ role: "assistant", content: [{ type: "output_text", text: "After." }] },
{ role: "assistant", content: "After." },
])
}),
)
it.effect("round-trips assistant message phases", () =>
Effect.gen(function* () {
const prepared = yield* LLMClient.prepare<OpenAIResponses.OpenAIResponsesBody>(
LLM.request({
model,
messages: [
Message.assistant([
{
type: "text",
text: "Checking first.",
providerMetadata: { openai: { itemId: "msg_commentary", phase: "commentary" } },
},
{
type: "text",
text: "Still checking.",
providerMetadata: { openai: { itemId: "msg_commentary_2", phase: "commentary" } },
},
{
type: "text",
text: "Finished.",
providerMetadata: { openai: { itemId: "msg_final", phase: "final_answer" } },
},
]),
],
}),
)
expect(prepared.body.input).toEqual([
{
type: "message",
id: "msg_commentary",
status: "completed",
role: "assistant",
phase: "commentary",
content: [{ type: "output_text", text: "Checking first.", annotations: [] }],
},
{
type: "message",
id: "msg_commentary_2",
status: "completed",
role: "assistant",
phase: "commentary",
content: [{ type: "output_text", text: "Still checking.", annotations: [] }],
},
{
type: "message",
id: "msg_final",
status: "completed",
role: "assistant",
phase: "final_answer",
content: [{ type: "output_text", text: "Finished.", annotations: [] }],
},
])
}),
)
@@ -1131,7 +1492,7 @@ describe("OpenAI Responses route", () => {
expect(prepared.body).toMatchObject({
input: [
{ role: "user", content: [{ type: "input_text", text: "What changed?" }] },
{ role: "assistant", content: [{ type: "output_text", text: "The parser changed." }] },
{ role: "assistant", content: "The parser changed." },
{ role: "user", content: [{ type: "input_text", text: "Summarize it." }] },
],
store: false,
@@ -119,7 +119,7 @@ const storedSession = {
const openAIResponses = {
user: (text: string) => ({ role: "user", content: [{ type: "input_text", text }] }),
assistant: (text: string) => ({ role: "assistant", content: [{ type: "output_text", text }] }),
assistant: (text: string) => ({ role: "assistant", content: text }),
openaiReasoning: (text: string, encryptedContent: string) => ({
type: "reasoning",
encrypted_content: encryptedContent,