feat: switch AI provider from Anthropic to DeepSeek

- lib/claude/client.ts: replace Anthropic SDK with openai package pointed at DeepSeek baseURL - 4 AI routes: port tool definitions, tool_choice, and output parsing to OpenAI function-calling format - Drop thinking:{type:'adaptive'} (no DeepSeek equivalent); model string → deepseek-chat - settings/route.ts: add DEEPSEEK_API_KEY to ALLOWED_KEYS - migration: seed DEEPSEEK_API_KEY placeholder row in app_settings - tests: update 3 AI route tests to mock createDeepSeekClient + OpenAI response shape Voyage AI embedding path untouched (DeepSeek has no embeddings endpoint). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CPf5Rc8QPx6V8KLEEgfKEQ
2026-07-12 21:51:15 +08:00
parent 8fe036bc1a
commit b2891a433d
12 changed files with 262 additions and 183 deletions
@@ -2,7 +2,7 @@ export const dynamic = 'force-dynamic'

 import { NextResponse } from 'next/server'
 import { createClient } from '@/lib/supabase/server'
-import { createAnthropicClient } from '@/lib/claude/client'
+import { createDeepSeekClient } from '@/lib/claude/client'
 import { getApiKey } from '@/lib/settings'

 type ZoneAggregate = {
@@ -92,41 +92,43 @@ export async function POST() {
    return NextResponse.json({ error: 'Rate limit: wait 60 seconds between AI requests' }, { status: 429 })
  }

-  const anthropicKey = await getApiKey(supabase, 'ANTHROPIC_API_KEY')
-  const anthropic = createAnthropicClient(anthropicKey)
+  const deepseekKey = await getApiKey(supabase, 'DEEPSEEK_API_KEY')
+  const client = createDeepSeekClient(deepseekKey)

-  let message: Awaited<ReturnType<typeof anthropic.messages.create>>
+  let res: Awaited<ReturnType<typeof client.chat.completions.create>>
  try {
-    message = await anthropic.messages.create({
-      model: 'claude-opus-4-8',
-      thinking: { type: 'adaptive' },
+    res = await client.chat.completions.create({
+      model: 'deepseek-chat',
      max_tokens: 2048,
      tools: [{
-        name: 'flag_rising_risk',
-        description: 'Flag warehouse zones showing rising safety risk from 90-day incident aggregates',
-        input_schema: {
-          type: 'object' as const,
-          properties: {
-            flags: {
-              type: 'array',
-              items: {
-                type: 'object',
-                properties: {
-                  zone: { type: 'string' },
-                  site: { type: 'string' },
-                  risk_level: { type: 'string', enum: ['low', 'medium', 'high'] },
-                  rationale: { type: 'string', description: 'One or two sentences citing the numbers' },
-                  recommended_action: { type: 'string', description: 'One concrete preventive action' },
+        type: 'function',
+        function: {
+          name: 'flag_rising_risk',
+          description: 'Flag warehouse zones showing rising safety risk from 90-day incident aggregates',
+          parameters: {
+            type: 'object',
+            properties: {
+              flags: {
+                type: 'array',
+                items: {
+                  type: 'object',
+                  properties: {
+                    zone: { type: 'string' },
+                    site: { type: 'string' },
+                    risk_level: { type: 'string', enum: ['low', 'medium', 'high'] },
+                    rationale: { type: 'string', description: 'One or two sentences citing the numbers' },
+                    recommended_action: { type: 'string', description: 'One concrete preventive action' },
+                  },
+                  required: ['zone', 'site', 'risk_level', 'rationale', 'recommended_action'],
                },
-                required: ['zone', 'site', 'risk_level', 'rationale', 'recommended_action'],
              },
+              summary: { type: 'string', description: 'Two-sentence overall risk picture' },
            },
-            summary: { type: 'string', description: 'Two-sentence overall risk picture' },
+            required: ['flags', 'summary'],
          },
-          required: ['flags', 'summary'],
        },
      }],
-      tool_choice: { type: 'tool', name: 'flag_rising_risk' },
+      tool_choice: { type: 'function', function: { name: 'flag_rising_risk' } },
      messages: [{
        role: 'user',
        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.
@@ -142,11 +144,13 @@ ${JSON.stringify(aggregates, null, 2)}
    return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
  }

-  const toolBlock = message.content.find(b => b.type === 'tool_use')
-  if (!toolBlock || toolBlock.type !== 'tool_use')
-    return NextResponse.json({ error: 'AI suggestion failed' }, { status: 500 })
+  const call = res.choices[0]?.message?.tool_calls?.[0]
+  if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI suggestion failed' }, { status: 500 })
+
+  let input: { flags?: unknown; summary?: unknown }
+  try { input = JSON.parse(call.function.arguments) }
+  catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }

-  const input = toolBlock.input as { flags?: unknown; summary?: unknown }
  if (!Array.isArray(input.flags) || typeof input.summary !== 'string')
    return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 })

@@ -164,7 +168,7 @@ ${JSON.stringify(aggregates, null, 2)}
    p_table_name: 'incidents',
    p_record_id: user.id,
    p_action: 'ai_risk_flags',
-    p_new_value: { flags, summary: input.summary, model: 'claude-opus-4-8' } as never,
+    p_new_value: { flags, summary: input.summary, model: 'deepseek-chat' } as never,
  })

  return NextResponse.json({ flags, summary: input.summary })
@@ -2,7 +2,7 @@ export const dynamic = 'force-dynamic'

 import { NextRequest, NextResponse } from 'next/server'
 import { createClient } from '@/lib/supabase/server'
-import { createAnthropicClient } from '@/lib/claude/client'
+import { createDeepSeekClient } from '@/lib/claude/client'
 import { getApiKey } from '@/lib/settings'

 export async function POST(
@@ -28,8 +28,8 @@ export async function POST(
  if ((recentCount ?? 0) > 0)
    return NextResponse.json({ error: 'Rate limited — please wait 60 seconds' }, { status: 429 })

-  const anthropicKey = await getApiKey(supabase, 'ANTHROPIC_API_KEY')
-  const anthropic = createAnthropicClient(anthropicKey)
+  const deepseekKey = await getApiKey(supabase, 'DEEPSEEK_API_KEY')
+  const client = createDeepSeekClient(deepseekKey)

  const { data: incident } = await supabase
    .from('incidents')
@@ -55,44 +55,46 @@ export async function POST(
  const siteName = (incident.sites as unknown as { name: string } | null)?.name ?? 'Unknown'
  const zoneName = (incident.zones as unknown as { name: string } | null)?.name ?? 'Unknown'

-  let message: Awaited<ReturnType<typeof anthropic.messages.create>>
+  let res: Awaited<ReturnType<typeof client.chat.completions.create>>
  try {
-    message = await anthropic.messages.create({
-      model: 'claude-opus-4-8',
-      thinking: { type: 'adaptive' },
+    res = await client.chat.completions.create({
+      model: 'deepseek-chat',
      max_tokens: 2048,
      tools: [{
-        name: 'draft_rca',
-        description: 'Draft a 5-Why root cause analysis and CAPA suggestions for an HSE incident',
-        input_schema: {
-          type: 'object' as const,
-          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' },
+        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'],
                },
-                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',
              },
-              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'],
          },
-          required: ['five_why_steps', 'root_cause_summary', 'capa_suggestions'],
        },
      }],
-      tool_choice: { type: 'tool', name: 'draft_rca' },
+      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.
@@ -114,15 +116,12 @@ Provide 3–5 Why steps drilling from immediate cause to root cause. Give a one-
    return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
  }

-  const toolBlock = message.content.find(b => b.type === 'tool_use')
-  if (!toolBlock || toolBlock.type !== 'tool_use')
-    return NextResponse.json({ error: 'AI draft failed' }, { status: 500 })
+  const call = res.choices[0]?.message?.tool_calls?.[0]
+  if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI draft failed' }, { status: 500 })

-  const draft = toolBlock.input as {
-    five_why_steps?: unknown
-    root_cause_summary?: unknown
-    capa_suggestions?: unknown
-  }
+  let draft: { five_why_steps?: unknown; root_cause_summary?: unknown; capa_suggestions?: unknown }
+  try { draft = JSON.parse(call.function.arguments) }
+  catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }

  if (
    !Array.isArray(draft.five_why_steps) ||
@@ -138,7 +137,7 @@ Provide 3–5 Why steps drilling from immediate cause to root cause. Give a one-
    p_action: 'ai_rca_draft',
    p_new_value: {
      root_cause_summary: draft.root_cause_summary,
-      model: 'claude-opus-4-8',
+      model: 'deepseek-chat',
    } as never,
  })

@@ -2,7 +2,7 @@ export const dynamic = 'force-dynamic'

 import { NextRequest, NextResponse } from 'next/server'
 import { createClient } from '@/lib/supabase/server'
-import { createAnthropicClient } from '@/lib/claude/client'
+import { createDeepSeekClient } from '@/lib/claude/client'
 import { getApiKey } from '@/lib/settings'

 export async function POST(
@@ -28,8 +28,8 @@ export async function POST(
  if ((recentCount ?? 0) > 0)
    return NextResponse.json({ error: 'Rate limited — please wait 60 seconds' }, { status: 429 })

-  const anthropicKey = await getApiKey(supabase, 'ANTHROPIC_API_KEY')
-  const anthropic = createAnthropicClient(anthropicKey)
+  const deepseekKey = await getApiKey(supabase, 'DEEPSEEK_API_KEY')
+  const client = createDeepSeekClient(deepseekKey)

  const { data: incident } = await supabase
    .from('incidents')
@@ -46,32 +46,34 @@ export async function POST(
    medical_status: string | null
  }

-  let message: Awaited<ReturnType<typeof anthropic.messages.create>>
+  let res: Awaited<ReturnType<typeof client.chat.completions.create>>
  try {
-    message = await anthropic.messages.create({
-      model: 'claude-opus-4-8',
-      thinking: { type: 'adaptive' },
+    res = await client.chat.completions.create({
+      model: 'deepseek-chat',
      max_tokens: 1024,
      tools: [{
-        name: 'suggest_triage',
-        description: 'Suggest severity rating and NADOPOD 2004 DOSH classification for a warehouse incident',
-        input_schema: {
-          type: 'object' as const,
-          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' },
+        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',
+            ],
          },
-          required: [
-            'severity', 'is_fatality', 'is_serious_bodily_injury',
-            'is_dangerous_occurrence', 'is_occupational_disease', 'rationale',
-          ],
        },
      }],
-      tool_choice: { type: 'tool', name: 'suggest_triage' },
+      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.
@@ -89,11 +91,10 @@ Suggest severity (1–5) and tick the appropriate NADOPOD 2004 flags. Give a one
    return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
  }

-  const toolBlock = message.content.find(b => b.type === 'tool_use')
-  if (!toolBlock || toolBlock.type !== 'tool_use')
-    return NextResponse.json({ error: 'AI suggestion failed' }, { status: 500 })
+  const call = res.choices[0]?.message?.tool_calls?.[0]
+  if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI suggestion failed' }, { status: 500 })

-  const input = toolBlock.input as {
+  let input: {
    severity?: unknown
    is_fatality?: unknown
    is_serious_bodily_injury?: unknown
@@ -101,6 +102,8 @@ 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) }
+  catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }

  if (
    typeof input.severity !== 'number' ||
@@ -117,8 +120,8 @@ Suggest severity (1–5) and tick the appropriate NADOPOD 2004 flags. Give a one
    p_table_name: 'incidents',
    p_record_id: id,
    p_action: 'ai_triage_suggest',
-    p_new_value: { suggestion: toolBlock.input, model: 'claude-opus-4-8' } as never,
+    p_new_value: { suggestion: input, model: 'deepseek-chat' } as never,
  })

-  return NextResponse.json(toolBlock.input)
+  return NextResponse.json(input)
 }
@@ -2,7 +2,7 @@ export const dynamic = 'force-dynamic'

 import { NextRequest, NextResponse } from 'next/server'
 import { createClient } from '@/lib/supabase/server'
-import { createAnthropicClient } from '@/lib/claude/client'
+import { createDeepSeekClient } from '@/lib/claude/client'
 import { getApiKey } from '@/lib/settings'

 export async function POST(request: NextRequest) {
@@ -20,8 +20,8 @@ export async function POST(request: NextRequest) {
  if ((recentCount ?? 0) > 0)
    return NextResponse.json({ error: 'Rate limited — please wait 60 seconds' }, { status: 429 })

-  const anthropicKey = await getApiKey(supabase, 'ANTHROPIC_API_KEY')
-  const anthropic = createAnthropicClient(anthropicKey)
+  const deepseekKey = await getApiKey(supabase, 'DEEPSEEK_API_KEY')
+  const client = createDeepSeekClient(deepseekKey)

  let body: { description?: string; incident_type?: string }
  try {
@@ -33,31 +33,33 @@ export async function POST(request: NextRequest) {
    return NextResponse.json({ error: 'description and incident_type required' }, { status: 422 })
  }

-  let message: Awaited<ReturnType<typeof anthropic.messages.create>>
+  let res: Awaited<ReturnType<typeof client.chat.completions.create>>
  try {
-    message = await anthropic.messages.create({
-      model: 'claude-opus-4-8',
-      thinking: { type: 'adaptive' },
+    res = await client.chat.completions.create({
+      model: 'deepseek-chat',
      max_tokens: 1024,
      tools: [{
-        name: 'assess_quality',
-        description: 'Assess HSE incident report description quality',
-        input_schema: {
-          type: 'object' as const,
-          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',
+        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'],
          },
-          required: ['score', 'passes', 'feedback', 'suggestions'],
        },
      }],
-      tool_choice: { type: 'tool', name: 'assess_quality' },
+      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.
@@ -72,17 +74,13 @@ Score 1–10 based on: specificity (location, time, persons involved), completen
    return NextResponse.json({ error: 'AI service unavailable' }, { status: 503 })
  }

-  const toolBlock = message.content.find(b => b.type === 'tool_use')
-  if (!toolBlock || toolBlock.type !== 'tool_use') {
-    return NextResponse.json({ error: 'AI assessment failed' }, { status: 500 })
-  }
+  const call = res.choices[0]?.message?.tool_calls?.[0]
+  if (!call || call.type !== 'function') return NextResponse.json({ error: 'AI assessment failed' }, { status: 500 })
+
+  let input: { score?: unknown; passes?: unknown; feedback?: unknown; suggestions?: unknown }
+  try { input = JSON.parse(call.function.arguments) }
+  catch { return NextResponse.json({ error: 'AI returned unexpected structure' }, { status: 500 }) }

-  const input = toolBlock.input as {
-    score?: unknown
-    passes?: unknown
-    feedback?: unknown
-    suggestions?: unknown
-  }
  if (
    typeof input.score !== 'number' ||
    typeof input.passes !== 'boolean' ||
@@ -96,7 +94,7 @@ Score 1–10 based on: specificity (location, time, persons involved), completen
    p_table_name: 'incidents',
    p_record_id: user.id,
    p_action: 'ai_quality_check',
-    p_new_value: { score: input.score, passes: input.passes, model: 'claude-opus-4-8' } as never,
+    p_new_value: { score: input.score, passes: input.passes, model: 'deepseek-chat' } as never,
  })

  return NextResponse.json(input)
@@ -5,6 +5,7 @@ import { createClient } from '@/lib/supabase/server'

 const ALLOWED_KEYS = [
  'ANTHROPIC_API_KEY',
+  'DEEPSEEK_API_KEY',
  'VOYAGE_API_KEY',
  'META_WHATSAPP_PHONE_NUMBER_ID',
  'META_WHATSAPP_ACCESS_TOKEN',