Tianyi Cui
b5e8e4e9c1
Model responses, replayed session data, tool presenter output, question metadata, configuration, and diagnostics all cross into ANSI-aware pi-tui renderers. Passing their C0 or C1 controls through unchanged lets an otherwise ordinary transcript emit OSC, CSI, cursor, or title operations in the user terminal. Introduce one displayText boundary that preserves line-feed layout but renders every other C0/C1 control as visible \\xNN text before application styling is applied. Route transcript blocks, streaming output, tool cards, diffs, plans, dialogs, headers, cwd/title data, notices, errors, and pre-mount startup failures through that boundary while leaving pi-tui and the theme responsible for legitimate terminal control sequences. Pin the contract at three levels: a settled headless-terminal golden spans the main untrusted display sources, unit coverage checks the pre-fullscreen failure path, and the real Loader/PTY conversation streams hostile OSC, cursor, and C1 probes and proves only their inert textual forms reach the terminal stream.
62 lines
2.5 KiB
TypeScript
62 lines
2.5 KiB
TypeScript
import type { Context } from 'cordis'
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import type { GenerateOptions, StreamChunk } from '@deepseek-ai/dsh-llm'
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import { CallId, LlmAdapter } from '@deepseek-ai/dsh-llm'
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const CONTROL_PROBE = '\u001b]2;MODEL_CONTROLLED\u0007\u001b[999CMODEL_CURSOR\u009b31mMODEL_C1'
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const INITIAL_TEXT = `I need one decision before I continue. ${CONTROL_PROBE}`
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const FINAL_TEXT = 'Decision received. Scripted TUI run complete.'
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function textChunks(text: string): StreamChunk[] {
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return [
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{ type: 'block-start', index: 0, blockType: 'text' },
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...Array.from(text, (char): StreamChunk => ({ type: 'text-delta', index: 0, text: char })),
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{ type: 'block-end', index: 0, block: { type: 'text', text } },
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{ type: 'usage', usage: { inputTokens: 20, outputTokens: text.length } },
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{ type: 'finish', reason: { kind: 'stop' } },
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]
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}
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/** Keyless two-step adapter for the real-PTY TUI conversation test. */
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class ScriptedTuiAdapter extends LlmAdapter {
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async * stream(options: GenerateOptions): AsyncIterable<StreamChunk> {
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const hasToolResult = options.messages.at(-1)?.content.some(block => block.type === 'tool-result') ?? false
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if (hasToolResult) {
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for (const chunk of textChunks(FINAL_TEXT)) yield chunk
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return
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}
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const args = JSON.stringify({
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questions: [{
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id: 'mode',
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header: 'Execution mode',
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question: 'How should the scripted run proceed?',
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options: [
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{ label: 'Safe', description: 'Use the guarded path.' },
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{ label: 'Fast', description: 'Use the shorter path.' },
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],
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}],
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})
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const callId = CallId('call-ask-mode')
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yield { type: 'block-start', index: 0, blockType: 'text' }
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for (const char of INITIAL_TEXT) yield { type: 'text-delta', index: 0, text: char }
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yield { type: 'block-end', index: 0, block: { type: 'text', text: INITIAL_TEXT } }
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yield { type: 'block-start', index: 1, blockType: 'tool-call' }
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yield { type: 'tool-call-delta', index: 1, id: callId, name: 'ask_user_question', argumentsDelta: args }
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yield {
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type: 'block-end',
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index: 1,
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block: { type: 'tool-call', id: callId, name: 'ask_user_question', arguments: args },
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}
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yield { type: 'usage', usage: { inputTokens: 20, outputTokens: 10 } }
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yield { type: 'finish', reason: { kind: 'tool-calls' } }
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}
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}
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export const name = 'tui-scripted-llm'
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export const inject = ['llm']
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/** Register the network-free adapter used by the PTY fixture. */
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export function apply(ctx: Context): void {
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ctx.llm.registerAdapter(['tui-scripted'], new ScriptedTuiAdapter())
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}
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