You are a sentiment analysis engine for a business management system.
Classify the sentiment of each piece of text.

Input: array of { id, text, source, language }
Output — strict JSON array:
[
  {
    "id": <string>,
    "sentiment": "positive|neutral|negative",
    "intensity": "strong|moderate|mild",
    "key_phrase": "<the phrase that most drives the sentiment, max 8 words>",
    "language_detected": "<ISO 639-1 code>"
  }
]

Rules:
- Detect language automatically; do not require English input.
- Do not infer sentiment from punctuation alone — read meaning.
- If text is too short to judge (< 3 words), return sentiment: "neutral", intensity: "mild".

// Aggregate sentiment results by tenant for the dashboard
$summary = DB::table('nlp_results')
    ->where('tenant_id', $tenantId)
    ->where('period', $period)
    ->selectRaw('
        sentiment,
        COUNT(*) as count,
        ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (), 1) as pct
    ')
    ->groupBy('sentiment')
    ->get();

// Store individual results
NLPResult::create([
    'tenant_id'   => $tenantId,
    'source_type' => 'parent_feedback',
    'source_id'   => $feedbackId,
    'sentiment'   => $result['sentiment'],
    'intensity'   => $result['intensity'],
    'key_phrase'  => $result['key_phrase'],
    'period'      => now()->format('Y-m'),
]);

Feedback Sentiment — This Term
Positive  ████████████░░░░  74%  (148 responses)
Neutral   ███░░░░░░░░░░░░░  18%  (36 responses)
Negative  ██░░░░░░░░░░░░░░   8%  (16 responses)

Top Negative Themes:
- "Fees too high" (6 mentions)
- "Poor communication from teachers" (4 mentions)
- "Long waiting times at the clinic" (3 mentions)

You are a text classification engine.
Classify each item into exactly one category from the provided list.

Categories: [<list from caller>]

Input: array of { id, text }
Output — strict JSON array:
[
  {
    "id": <string>,
    "category": "<one of the provided categories>",
    "confidence": "high|medium|low",
    "secondary_category": "<second best category or null>"
  }
]

If the text does not fit any category, use the category: "uncategorised".

["Fee query", "Grade query", "Attendance query", "Technical issue",
 "Complaint — teacher", "Complaint — facilities", "Admission enquiry", "Other"]

["Travel", "Accommodation", "Meals", "Office supplies", "IT equipment",
 "Professional services", "Utilities", "Marketing", "Miscellaneous"]

["Wait time", "Staff conduct", "Treatment quality", "Billing",
 "Facility cleanliness", "Medication", "Communication", "Other"]

You are a document intelligence engine.
Extract structured data from the provided invoice or receipt text.

Output — strict JSON:
{
  "vendor_name": "<string or null>",
  "vendor_tin": "<string or null>",
  "invoice_number": "<string or null>",
  "invoice_date": "<YYYY-MM-DD or null>",
  "due_date": "<YYYY-MM-DD or null>",
  "currency": "<ISO 4217 code>",
  "subtotal": <float or null>,
  "tax_amount": <float or null>,
  "total_amount": <float or null>,
  "line_items": [
    { "description": "<string>", "quantity": <float>, "unit_price": <float>, "amount": <float> }
  ],
  "extraction_confidence": "high|medium|low",
  "flags": ["<any field that could not be reliably extracted>"]
}

If a field is not present in the document, return null.
Do not invent or guess values — only extract what is explicitly stated.

// Android — OCR via ML Kit, then send text to AI Service
val recognizer = TextRecognition.getClient(TextRecognizerOptions.DEFAULT_OPTIONS)
recognizer.process(InputImage.fromBitmap(bitmap, 0))
    .addOnSuccessListener { visionText ->
        val extractedText = visionText.text
        viewModel.extractInvoiceData(extractedText)  // calls AI Service
    }

You are a qualitative research analyst.
Read the following responses and identify the top themes expressed.

Responses: [<array of text responses>]

Output — strict JSON:
{
  "total_responses_analysed": <int>,
  "themes": [
    {
      "theme": "<short label, max 5 words>",
      "description": "<one sentence explaining the theme>",
      "frequency": "<approximate number of responses mentioning this>",
      "sentiment": "positive|negative|mixed",
      "representative_quotes": ["<verbatim quote 1>", "<verbatim quote 2>"]
    }
  ],
  "overall_summary": "<2–3 sentence executive summary>",
  "top_recommended_action": "<one sentence — most impactful thing to address>"
}

Identify 3–7 distinct themes. Do not overlap themes.

Language handling:
- Accept input in any language including Luganda, Swahili, and East African English varieties.
- Output must always be in [target_language — default English].
- Do not transliterate names or places.

$languageDetected = $nlpResult['language_detected']; // 'lg' = Luganda, 'sw' = Swahili

// Store for analytics — track which languages clients use
NLPResult::create([
    'language' => $languageDetected,
    // ...
]);

// Show language breakdown on admin dashboard
// "Feedback received: 62% English | 24% Luganda | 14% Swahili"

CREATE TABLE nlp_results (
    id              BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,
    tenant_id       BIGINT UNSIGNED NOT NULL,
    source_type     VARCHAR(64) NOT NULL,  -- 'feedback', 'ticket', 'invoice', 'survey'
    source_id       BIGINT UNSIGNED NOT NULL,
    nlp_task        VARCHAR(32) NOT NULL,  -- 'sentiment', 'classification', 'extraction', 'themes'
    result_json     JSON NOT NULL,
    sentiment       ENUM('positive','neutral','negative') NULL,
    category        VARCHAR(128) NULL,
    confidence      ENUM('high','medium','low') NULL,
    language        CHAR(5) NULL,
    period          CHAR(7) NOT NULL,
    created_at      DATETIME DEFAULT CURRENT_TIMESTAMP,
    INDEX idx_tenant_period  (tenant_id, period),
    INDEX idx_source         (source_type, source_id),
    INDEX idx_sentiment      (tenant_id, sentiment, period)
);