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fix: switch AI endpoints from tool-calling to JSON output mode

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DeepSeek v4-pro reasoning model rejects tool_choice parameter.
All 4 endpoints now use system prompts with JSON schema
instructions and parse content as JSON instead of tool_calls.

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
admin
2026-07-26 10:49:44 +08:00
co-authored by Claude
parent 7bf3b6a409
commit ed2a2f65e4
4 changed files with 104 additions and 165 deletions
Download Patch File Download Diff File Expand all files Collapse all files
app/api/incidents/[id]/ai/rca-draft/route.ts
+25 -53
View File
@@ -61,67 +61,39 @@ export async function POST(
res = await client.chat.completions.create({
model: 'deepseek-v4-pro',
max_tokens: 2048,
tools: [{
type: 'function',
function: {
name: 'draft_rca',
description: 'Draft a 5-Why root cause analysis and CAPA suggestions for an HSE incident',
parameters: {
type: 'object',
properties: {
five_why_steps: {
type: 'array',
items: {
type: 'object',
properties: {
why: { type: 'string', description: 'The why question' },
answer: { type: 'string', description: 'The finding or answer' },
},
required: ['why', 'answer'],
},
description: '3 to 5 why steps',
},
root_cause_summary: {
type: 'string',
description: 'One-sentence root cause statement',
},
capa_suggestions: {
type: 'array',
items: { type: 'string' },
description: 'Up to 3 corrective/preventive action suggestions',
},
},
required: ['five_why_steps', 'root_cause_summary', 'capa_suggestions'],
},
messages: [
{
role: 'system',
content: `You are an experienced HSE investigator for a Malaysian 3PL warehouse. Draft a 5-Why root cause analysis for incidents. You must respond with valid JSON only — no markdown, no explanation outside the JSON object.
Output exactly this JSON structure:
{
"five_why_steps": [
{ "why": "<the why question>", "answer": "<the finding or answer>" }
],
"root_cause_summary": "<one-sentence root cause statement>",
"capa_suggestions": ["<up to 3 corrective/preventive actions>"]
}
Provide 3–5 Why steps drilling from immediate cause to root cause. Frame CAPA suggestions appropriate for a Malaysian warehouse context.`,
},
}],
tool_choice: { type: 'function', function: { name: 'draft_rca' } },
messages: [{
role: 'user',
content: `You are an experienced HSE investigator for a Malaysian 3PL warehouse. Draft a 5-Why root cause analysis for this incident.
Site: ${incident.siteName ?? 'Unknown'}
Zone: ${incident.zoneName ?? 'Unknown'}
Incident type: ${incident.incidentType}
Description: ${incident.description}
Severity: ${incident.severity ?? 'not yet assigned'}/5
Injury involved: ${incident.injuryInvolved ? `yes — ${incident.medicalStatus}` : 'no'}
Fatality: ${incident.isFatality ? 'yes' : 'no'}
Serious bodily injury: ${incident.isSeriousBodilyInjury ? 'yes' : 'no'}
Triage notes: ${incident.triageNotes ?? 'none'}
Provide 3–5 Why steps drilling from immediate cause to root cause. Give a one-sentence root cause statement. Suggest 3 corrective/preventive actions appropriate for a Malaysian warehouse context.`,
}],
{
role: 'user',
content: `Draft a 5-Why root cause analysis for this incident. Respond with JSON only.\n\nSite: ${incident.siteName ?? 'Unknown'}\nZone: ${incident.zoneName ?? 'Unknown'}\nIncident type: ${incident.incidentType}\nDescription: ${incident.description}\nSeverity: ${incident.severity ?? 'not yet assigned'}/5\nInjury involved: ${incident.injuryInvolved ? `yes — ${incident.medicalStatus}` : 'no'}\nFatality: ${incident.isFatality ? 'yes' : 'no'}\nSerious bodily injury: ${incident.isSeriousBodilyInjury ? 'yes' : 'no'}\nTriage notes: ${incident.triageNotes ?? 'none'}`,
},
],
})
} catch {
return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
}
const call = res.choices[0]?.message?.tool_calls?.[0]
if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI draft failed' }, { status: 500 })
const raw = res.choices[0]?.message?.content
if (!raw) return NextResponse.json({ error: 'AI returned empty response' }, { status: 500 })
const json = raw.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/i, '').trim()
let draft: { five_why_steps?: unknown; root_cause_summary?: unknown; capa_suggestions?: unknown }
try { draft = JSON.parse(call.function.arguments) }
try { draft = JSON.parse(json) }
catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }
if (
app/api/incidents/[id]/ai/triage-suggest/route.ts
+24 -37
View File
@@ -52,48 +52,35 @@ export async function POST(
res = await client.chat.completions.create({
model: 'deepseek-v4-pro',
max_tokens: 1024,
tools: [{
type: 'function',
function: {
name: 'suggest_triage',
description: 'Suggest severity rating and NADOPOD 2004 DOSH classification for a warehouse incident',
parameters: {
type: 'object',
properties: {
severity: { type: 'number', description: '1=minor, 2=low, 3=moderate, 4=serious, 5=critical/fatality' },
is_fatality: { type: 'boolean' },
is_serious_bodily_injury: { type: 'boolean', description: 'Fracture, amputation, blindness, serious burn, or similar' },
is_dangerous_occurrence: { type: 'boolean', description: 'Structural collapse, explosion, fire, scaffold collapse, etc.' },
is_occupational_disease: { type: 'boolean', description: 'Disease arising from workplace exposure' },
rationale: { type: 'string', description: 'One-sentence rationale citing NADOPOD 2004 where applicable' },
},
required: [
'severity', 'is_fatality', 'is_serious_bodily_injury',
'is_dangerous_occurrence', 'is_occupational_disease', 'rationale',
],
},
messages: [
{
role: 'system',
content: `You are an HSE triage specialist for a Malaysian 3PL warehouse. Assess incidents under NADOPOD 2004. You must respond with valid JSON only — no markdown, no explanation outside the JSON object.
Output exactly this JSON structure:
{
"severity": <number 1-5, where 1=minor, 2=low, 3=moderate, 4=serious, 5=critical/fatality>,
"is_fatality": <boolean>,
"is_serious_bodily_injury": <boolean, true for fracture, amputation, blindness, serious burn, or similar>,
"is_dangerous_occurrence": <boolean, true for structural collapse, explosion, fire, scaffold collapse, etc.>,
"is_occupational_disease": <boolean, true for disease arising from workplace exposure>,
"rationale": "<one-sentence rationale citing NADOPOD 2004 where applicable>"
}`,
},
}],
tool_choice: { type: 'function', function: { name: 'suggest_triage' } },
messages: [{
role: 'user',
content: `You are an HSE triage specialist for a Malaysian 3PL warehouse. Assess this incident under NADOPOD 2004.
Incident type: ${incident.incidentType}
Description: ${incident.description}
Injury involved: ${incident.injuryInvolved ? 'yes' : 'no'}
Medical status: ${incident.medicalStatus ?? 'N/A'}
Asset/equipment involved: ${incident.assetInvolved ? 'yes' : 'no'}
Suggest severity (1–5) and tick the appropriate NADOPOD 2004 flags. Give a one-sentence rationale.`,
}],
{
role: 'user',
content: `Assess this incident under NADOPOD 2004. Respond with JSON only.\n\nIncident type: ${incident.incidentType}\nDescription: ${incident.description}\nInjury involved: ${incident.injuryInvolved ? 'yes' : 'no'}\nMedical status: ${incident.medicalStatus ?? 'N/A'}\nAsset/equipment involved: ${incident.assetInvolved ? 'yes' : 'no'}`,
},
],
})
} catch {
return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
}
const call = res.choices[0]?.message?.tool_calls?.[0]
if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI suggestion failed' }, { status: 500 })
const raw = res.choices[0]?.message?.content
if (!raw) return NextResponse.json({ error: 'AI returned empty response' }, { status: 500 })
const json = raw.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/i, '').trim()
let input: {
severity?: unknown
@@ -103,7 +90,7 @@ Suggest severity (1–5) and tick the appropriate NADOPOD 2004 flags. Give a one
is_occupational_disease?: unknown
rationale?: unknown
}
try { input = JSON.parse(call.function.arguments) }
try { input = JSON.parse(json) }
catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }
if (
app/api/incidents/ai/quality-check/route.ts
+24 -34
View File
@@ -44,47 +44,38 @@ export async function POST(request: NextRequest) {
res = await client.chat.completions.create({
model: 'deepseek-v4-pro',
max_tokens: 1024,
tools: [{
type: 'function',
function: {
name: 'assess_quality',
description: 'Assess HSE incident report description quality',
parameters: {
type: 'object',
properties: {
score: { type: 'number', description: '1-10 quality score' },
passes: { type: 'boolean', description: 'True when score is 6 or above' },
feedback: { type: 'string', description: 'One-sentence quality summary' },
suggestions: {
type: 'array',
items: { type: 'string' },
description: 'Up to 3 concrete suggestions to improve the description',
},
},
required: ['score', 'passes', 'feedback', 'suggestions'],
},
messages: [
{
role: 'system',
content: `You are an HSE reporting assistant for a Malaysian 3PL warehouse. You must respond with valid JSON only — no markdown, no explanation outside the JSON object.
Output exactly this JSON structure:
{
"score": <number 1-10>,
"passes": <boolean, true when score >= 6>,
"feedback": "<one-sentence quality summary>",
"suggestions": ["<up to 3 concrete suggestions, empty array if score >= 6>"]
}
Scoring criteria: specificity (location, time, persons involved), completeness (what happened + immediate actions), clarity. Score 6 or above passes.`,
},
}],
tool_choice: { type: 'function', function: { name: 'assess_quality' } },
messages: [{
role: 'user',
content: `You are an HSE reporting assistant for a Malaysian 3PL warehouse. Assess this incident report description.
Incident type: ${body.incident_type}
Description: ${body.description}
Score 1–10 based on: specificity (location, time, persons involved), completeness (what happened + immediate actions), and clarity. Score 6 or above passes. If score is below 6, give up to 3 actionable suggestions.`,
}],
{
role: 'user',
content: `Assess this incident report description. Respond with JSON only.\n\nIncident type: ${body.incident_type}\nDescription: ${body.description}`,
},
],
})
} catch {
return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
}
const call = res.choices[0]?.message?.tool_calls?.[0]
if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI assessment failed' }, { status: 500 })
const raw = res.choices[0]?.message?.content
if (!raw) return NextResponse.json({ error: 'AI returned empty response' }, { status: 500 })
const json = raw.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/i, '').trim()
let input: { score?: unknown; passes?: unknown; feedback?: unknown; suggestions?: unknown }
try { input = JSON.parse(call.function.arguments) }
try { input = JSON.parse(json) }
catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }
if (
@@ -96,7 +87,6 @@ Score 1–10 based on: specificity (location, time, persons involved), completen
return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 })
}
// audit write uses session.sub as a pseudo record-id (no incident_id at this stage)
await withUser(session.sub, async tx => {
await writeAuditLog(tx, 'incidents', session.sub, 'ai_quality_check', {
score: input.score, passes: input.passes, model: 'deepseek-v4-pro',