fix: switch AI endpoints from tool-calling to JSON output mode
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>
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@@ -111,56 +111,46 @@ export async function POST() {
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res = await client.chat.completions.create({
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model: 'deepseek-v4-pro',
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max_tokens: 2048,
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tools: [{
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type: 'function',
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function: {
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name: 'flag_rising_risk',
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description: 'Flag warehouse zones showing rising safety risk from 90-day incident aggregates',
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parameters: {
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type: 'object',
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properties: {
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flags: {
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type: 'array',
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items: {
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type: 'object',
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properties: {
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zone: { type: 'string' },
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site: { type: 'string' },
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risk_level: { type: 'string', enum: ['low', 'medium', 'high'] },
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rationale: { type: 'string', description: 'One or two sentences citing the numbers' },
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recommended_action: { type: 'string', description: 'One concrete preventive action' },
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},
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required: ['zone', 'site', 'risk_level', 'rationale', 'recommended_action'],
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},
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},
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summary: { type: 'string', description: 'Two-sentence overall risk picture' },
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},
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required: ['flags', 'summary'],
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},
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messages: [
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{
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role: 'system',
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content: `You are an HSE risk analyst for a Malaysian 3PL warehouse operator. You must respond with valid JSON only — no markdown, no explanation outside the JSON object.
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Output exactly this JSON structure:
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{
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"flags": [
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{
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"zone": "string",
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"site": "string",
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"risk_level": "low" | "medium" | "high",
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"rationale": "One or two sentences citing the numbers",
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"recommended_action": "One concrete preventive action"
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}
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],
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"summary": "Two-sentence overall risk picture"
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}
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Flag only zones with rising or elevated risk (at most 5 flags; do not flag healthy zones). Base every rationale strictly on the numbers given. "first_half" is incidents in days 90-46, "second_half" is days 45-0 — a rising second_half means worsening trend. Near-miss and hazard reports are leading indicators; injuries are lagging.`,
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},
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}],
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tool_choice: { type: 'function', function: { name: 'flag_rising_risk' } },
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messages: [{
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role: 'user',
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content: `You are an HSE risk analyst for a Malaysian 3PL warehouse operator. Below are 90-day incident aggregates per zone. "first_half" is incidents in days 90-46, "second_half" is days 45-0 — a rising second_half means worsening trend. Near-miss and hazard reports are leading indicators; injuries are lagging.
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Flag zones with rising or elevated risk (at most 5 flags; do not flag healthy zones). Base every rationale strictly on the numbers given.
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<zone_data>
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${JSON.stringify(aggregates, null, 2)}
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</zone_data>`,
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}],
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{
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role: 'user',
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content: `Below are 90-day incident aggregates per zone. Respond with JSON only.\n\n<zone_data>\n${JSON.stringify(aggregates, null, 2)}\n</zone_data>`,
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},
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],
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})
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} catch (err) {
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console.error('DeepSeek risk-flags error:', err)
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return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
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}
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const call = res.choices[0]?.message?.tool_calls?.[0]
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if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI suggestion failed' }, { status: 500 })
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const raw = res.choices[0]?.message?.content
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if (!raw) return NextResponse.json({ error: 'AI returned empty response' }, { status: 500 })
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// Strip markdown code fences if present
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const json = raw.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/i, '').trim()
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let input: { flags?: unknown; summary?: unknown }
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try { input = JSON.parse(call.function.arguments) }
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try { input = JSON.parse(json) }
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catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }
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if (!Array.isArray(input.flags) || typeof input.summary !== 'string')
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