Files
boocode/apps/server/src/services/inference/error-handler.ts
indifferentketchup ac1a71f583 v1.13.1-C: port ask_user_input correlation to parts + wire reasoning_parts end-to-end
Pass 1 — ask_user_input correlation port (messages.ts:478, :549):

- The two correlation queries that backed the elicitation flow used to scan
  messages.tool_calls and messages.tool_results JSON columns directly. They
  now JOIN message_parts on payload->>'id' (for the caller assistant) and
  payload->>'tool_call_id' (for the pending tool row). Semantics preserved:
  ORDER BY m.created_at DESC LIMIT 1 still picks the latest issuance, the
  already-answered 409 guard now reads payload.output, and the UPDATE +
  parts replace inside sql.begin is unchanged from v1.13.0.
- Pre-v1.13.0 history has no parts rows and is unreachable to this lookup
  path (404). Acceptable per dispatch decision — no pending elicitation
  from before v1.13.0 will still be open. JSON-column fallback can land as
  a hotfix if it ever surfaces.

Pass 2 — reasoning_parts wired end-to-end:

- types.ts/StreamResult gains `reasoning: string`. stream-phase.ts accumulates
  reasoning-delta text per stream (replacing the v1.13.1-A counter-only
  diagnostic) and returns it on the result.
- parts.ts/partsFromAssistantMessage gains an optional `reasoning` param.
  When present it emits a kind='reasoning' part at sequence 0, ahead of
  the text and tool_call parts.
- error-handler.ts/finalizeCompletion and tool-phase.ts/executeToolPhase
  both thread result.reasoning into the dual-write call so reasoning-channel
  models (qwen3.6) get persistent reasoning rows.
- payload.ts: loadContext SELECT pulls reasoning_parts from the v1.13.1-B
  view; OpenAiMessage gains an optional `reasoning` field; buildMessagesPayload
  collapses reasoning_parts into a single string per assistant message.
- stream-phase.ts/toModelMessages converts assistant messages with reasoning
  into an AI SDK ModelMessage content array starting with a ReasoningPart,
  matching the @ai-sdk/provider-utils AssistantContent union. Reasoning
  models can now replay prior reasoning context across tool-call boundaries.
- types/api.ts and apps/web/src/api/types.ts Message interface gain
  reasoning_parts (optional, nullable). Frontend doesn't render this yet —
  field reserved for a v1.14 UI surface.

Tests: 2 new in parts.test.ts cover reasoning-at-sequence-0 with and
without text content. 172 tests pass (170 prior + 2 new).

Smoke verified against the live container:
- A reasoning-prompt ("walk through 17 × 23 step by step") produced one
  message with kind='reasoning' (361 chars) at sequence 0 and kind='text'
  (429 chars) at sequence 1. Adapter log confirmed reasoning capture.
- The new correlation SQL was validated against existing tool_call /
  tool_result parts: returns the expected message_id + payload shape with
  pending state correctly identified via payload.output IS NULL.
- ask_user_input end-to-end through the UI is Sam's smoke — the Prompt
  Builder agent does not always trigger ask_user_input for these prompts,
  so synthetic verification via SQL substituted for traffic-driven cover.

Annotation: the v1.13.1-A abort-throw site in stream-phase.ts got a
one-liner comment ("AI SDK v6 fullStream returns normally on abort; check
signal explicitly.") to prevent a future refactor removing it.

v1.13.2 drops the dual-write + the JSON columns + collapses the view.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-22 06:34:10 +00:00

168 lines
6.5 KiB
TypeScript

import type { MessageMetadata, Session } from '../../types/api.js';
import * as modelContext from '../model-context.js';
import { maybeFlagForCompaction } from './payload.js';
import { insertParts, partsFromAssistantMessage } from './parts.js';
import type { InferenceContext, StreamResult, TurnArgs } from './turn.js';
export async function handleAbortOrError(
ctx: InferenceContext,
args: TurnArgs,
accumulated: string,
err: unknown
): Promise<void> {
const { sessionId, chatId, assistantMessageId } = args;
const isAbort = err instanceof Error && err.name === 'AbortError';
const finalStatus = isAbort ? 'cancelled' : 'failed';
const errMsg = err instanceof Error ? err.message : String(err);
// v1.8.2: persist a structured error metadata blob on genuine failures so
// the bubble can render the reason on reload without re-deriving from the
// (one-shot) WS error frame. User-initiated abort skips this — there's no
// "reason" to surface for a stop the user already explicitly chose.
const errorMetadata: MessageMetadata | null = isAbort
? null
: { kind: 'error', error_reason: 'llm_provider_error', error_text: errMsg };
if (errorMetadata) {
await ctx.sql`
UPDATE messages
SET status = ${finalStatus},
content = ${accumulated},
finished_at = clock_timestamp(),
metadata = ${ctx.sql.json(errorMetadata as never)}
WHERE id = ${assistantMessageId}
`;
} else {
await ctx.sql`
UPDATE messages
SET status = ${finalStatus},
content = ${accumulated},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
`;
}
const [failSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: failSessRow!.project_id, name: failSessRow!.name, updated_at: failSessRow!.updated_at });
// v1.8 mobile-tabs: cancellation is a user-initiated stop, treat as idle;
// genuine errors flip the dot red. v1.8.2: error path also carries a
// machine-readable `reason` so the UI can render specifics inline.
if (isAbort) {
// v1.12.1: defensive cancellation write. The status=${finalStatus} UPDATE
// above already sets 'cancelled' for the AbortError case, but a row can
// leak as 'streaming' when the abort fires between the post-tool-phase
// INSERT (executeToolPhase) and the next runAssistantTurn's stream setup,
// bypassing the try/catch around executeStreamPhase. The status guard
// makes this a no-op when the earlier write already landed.
await ctx.sql`
UPDATE messages
SET status = 'cancelled', content = ${accumulated}, finished_at = clock_timestamp()
WHERE id = ${args.assistantMessageId} AND status = 'streaming'
`;
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
});
ctx.log.info({ sessionId, chatId, assistantMessageId }, 'inference cancelled');
} else {
ctx.publishUser({
type: 'chat_status',
chat_id: chatId,
status: 'error',
at: new Date().toISOString(),
reason: 'llm_provider_error',
});
ctx.publish(sessionId, {
type: 'error',
message_id: assistantMessageId,
chat_id: chatId,
error: errMsg,
reason: 'llm_provider_error',
});
ctx.log.error({ err, sessionId, assistantMessageId }, 'inference failed');
}
}
export async function finalizeCompletion(
ctx: InferenceContext,
args: TurnArgs,
result: StreamResult,
startedAt: string | null,
session: Session
): Promise<void> {
const { sessionId, chatId, assistantMessageId } = args;
const { content, finishReason, promptTokens, completionTokens } = result;
// v1.11.3: see executeToolPhase for the rationale.
const mctx = await modelContext.getModelContext(session.model);
const nCtx = mctx?.n_ctx ?? null;
const [updated] = await ctx.sql<
{ tokens_used: number | null; ctx_used: number | null; ctx_max: number | null; finished_at: string | null }[]
>`
UPDATE messages
SET content = ${content},
status = 'complete',
tokens_used = ${completionTokens},
ctx_used = ${promptTokens},
ctx_max = ${nCtx},
finished_at = clock_timestamp()
WHERE id = ${assistantMessageId}
RETURNING tokens_used, ctx_used, ctx_max, finished_at
`;
// v1.13.0: dual-write the text part. finalizeCompletion is the terminal
// path for text-only assistant turns (no tool calls); tool_calls are null
// here by construction (the tool-bearing path goes through executeToolPhase).
// v1.13.1-C: include result.reasoning so reasoning-channel models capture
// a kind='reasoning' part alongside the text.
// TODO(v1.13.1): wrap the UPDATE above and this insertParts in a single
// sql.begin before flipping read authority to message_parts.
await insertParts(
ctx.sql,
partsFromAssistantMessage({
content,
tool_calls: null,
reasoning: result.reasoning,
}).map((p) => ({
...p,
message_id: assistantMessageId,
})),
);
// v1.11: flag for compaction on the terminal turn too. Catches the common
// case of a turn that hit the limit without invoking tools.
await maybeFlagForCompaction(ctx, chatId, updated);
const [completeSessRow] = await ctx.sql<{ project_id: string; name: string; updated_at: string }[]>`
UPDATE sessions SET updated_at = clock_timestamp()
WHERE id = ${sessionId}
RETURNING project_id, name, updated_at
`;
ctx.publishUser({ type: 'session_updated', session_id: sessionId, project_id: completeSessRow!.project_id, name: completeSessRow!.name, updated_at: completeSessRow!.updated_at });
ctx.publishUser({ type: 'chat_status', chat_id: chatId, status: 'idle', at: new Date().toISOString() });
ctx.publish(sessionId, {
type: 'message_complete',
message_id: assistantMessageId,
chat_id: chatId,
tokens_used: updated?.tokens_used ?? null,
ctx_used: updated?.ctx_used ?? null,
ctx_max: updated?.ctx_max ?? null,
started_at: startedAt,
finished_at: updated?.finished_at ?? null,
model: session.model,
});
ctx.log.info(
{
sessionId,
chatId,
assistantMessageId,
finishReason,
chars: content.length,
tokens_used: updated?.tokens_used,
ctx_used: updated?.ctx_used,
},
'inference complete'
);
}