docker-compose.prod.yml

@@ -56,6 +56,10 @@ services:
       OCR_FALLBACK_THRESHOLD: "0.6"
       GOOGLE_VISION_KEY_PATH: /run/secrets/google-wif-config.json
       VISION_MONTHLY_LIMIT: "1000"
+      # Vertex AI / Gemini configuration (maintenance schedule extraction)
+      VERTEX_AI_PROJECT: ${VERTEX_AI_PROJECT:-}
+      VERTEX_AI_LOCATION: us-central1
+      GEMINI_MODEL: gemini-2.5-flash
 
   # PostgreSQL - Remove dev ports, production log level
   mvp-postgres:

docker-compose.staging.yml

@@ -76,6 +76,10 @@ services:
       OCR_FALLBACK_THRESHOLD: "0.6"
       GOOGLE_VISION_KEY_PATH: /run/secrets/google-wif-config.json
       VISION_MONTHLY_LIMIT: "1000"
+      # Vertex AI / Gemini configuration (maintenance schedule extraction)
+      VERTEX_AI_PROJECT: ${VERTEX_AI_PROJECT:-}
+      VERTEX_AI_LOCATION: us-central1
+      GEMINI_MODEL: gemini-2.5-flash
     volumes:
       - ./secrets/app/auth0-ocr-client-id.txt:/run/secrets/auth0-ocr-client-id:ro
       - ./secrets/app/auth0-ocr-client-secret.txt:/run/secrets/auth0-ocr-client-secret:ro

docker-compose.yml

@@ -203,6 +203,10 @@ services:
       OCR_FALLBACK_THRESHOLD: "0.6"
       GOOGLE_VISION_KEY_PATH: /run/secrets/google-wif-config.json
       VISION_MONTHLY_LIMIT: "1000"
+      # Vertex AI / Gemini configuration (maintenance schedule extraction)
+      VERTEX_AI_PROJECT: ${VERTEX_AI_PROJECT:-}
+      VERTEX_AI_LOCATION: us-central1
+      GEMINI_MODEL: gemini-2.5-flash
     volumes:
       - /tmp/vin-debug:/tmp/vin-debug
       - ./secrets/app/auth0-ocr-client-id.txt:/run/secrets/auth0-ocr-client-id:ro

ocr/app/config.py

@@ -29,6 +29,13 @@ class Settings:
             os.getenv("VISION_MONTHLY_LIMIT", "1000")
         )
 
+        # Vertex AI / Gemini configuration
+        self.vertex_ai_project: str = os.getenv("VERTEX_AI_PROJECT", "")
+        self.vertex_ai_location: str = os.getenv(
+            "VERTEX_AI_LOCATION", "us-central1"
+        )
+        self.gemini_model: str = os.getenv("GEMINI_MODEL", "gemini-2.5-flash")
+
         # Redis configuration for job queue
         self.redis_host: str = os.getenv("REDIS_HOST", "mvp-redis")
         self.redis_port: int = int(os.getenv("REDIS_PORT", "6379"))

ocr/app/engines/gemini_engine.py

@@ -0,0 +1,228 @@
+"""Gemini 2.5 Flash engine for maintenance schedule extraction from PDFs.
+
+Standalone module (does NOT extend OcrEngine) because Gemini performs
+semantic document understanding, not traditional OCR word-box extraction.
+Uses Vertex AI SDK with structured JSON output enforcement.
+"""
+
+import json
+import logging
+import os
+from dataclasses import dataclass
+from typing import Any
+
+from app.config import settings
+
+logger = logging.getLogger(__name__)
+
+# 20 MB hard limit for inline base64 PDF delivery
+_MAX_PDF_BYTES = 20 * 1024 * 1024
+
+_EXTRACTION_PROMPT = """\
+Extract all routine scheduled maintenance items from this vehicle owners manual.
+
+For each maintenance item, extract:
+- serviceName: The maintenance task name (e.g., "Engine Oil Change", "Tire Rotation", \
+"Cabin Air Filter Replacement")
+- intervalMiles: The mileage interval as a number, or null if not specified \
+(e.g., 5000, 30000)
+- intervalMonths: The time interval in months as a number, or null if not specified \
+(e.g., 6, 12, 24)
+- details: Any additional details such as fluid specifications, part numbers, \
+or special instructions (e.g., "Use 0W-20 full synthetic oil")
+
+Only include routine scheduled maintenance items with clear intervals. \
+Do not include one-time procedures, troubleshooting steps, or warranty information.
+
+Return the results as a JSON object with a single "maintenanceSchedule" array.\
+"""
+
+_RESPONSE_SCHEMA: dict[str, Any] = {
+    "type": "object",
+    "properties": {
+        "maintenanceSchedule": {
+            "type": "array",
+            "items": {
+                "type": "object",
+                "properties": {
+                    "serviceName": {"type": "string"},
+                    "intervalMiles": {"type": "number", "nullable": True},
+                    "intervalMonths": {"type": "number", "nullable": True},
+                    "details": {"type": "string", "nullable": True},
+                },
+                "required": ["serviceName"],
+            },
+        },
+    },
+    "required": ["maintenanceSchedule"],
+}
+
+
+class GeminiEngineError(Exception):
+    """Base exception for Gemini engine errors."""
+
+
+class GeminiUnavailableError(GeminiEngineError):
+    """Raised when the Gemini engine cannot be initialized."""
+
+
+class GeminiProcessingError(GeminiEngineError):
+    """Raised when Gemini fails to process a document."""
+
+
+@dataclass
+class MaintenanceItem:
+    """A single extracted maintenance schedule item."""
+
+    service_name: str
+    interval_miles: int | None = None
+    interval_months: int | None = None
+    details: str | None = None
+
+
+@dataclass
+class MaintenanceExtractionResult:
+    """Result from Gemini maintenance schedule extraction."""
+
+    items: list[MaintenanceItem]
+    model: str
+
+
+class GeminiEngine:
+    """Gemini 2.5 Flash wrapper for maintenance schedule extraction.
+
+    Standalone class (not an OcrEngine subclass) because Gemini performs
+    semantic document understanding rather than traditional OCR.
+
+    Uses lazy initialization: the Vertex AI client is not created until
+    the first ``extract_maintenance()`` call.
+    """
+
+    def __init__(self) -> None:
+        self._model: Any | None = None
+
+    def _get_model(self) -> Any:
+        """Create the GenerativeModel on first use.
+
+        Authentication uses the same WIF credential path as Google Vision.
+        """
+        if self._model is not None:
+            return self._model
+
+        key_path = settings.google_vision_key_path
+        if not os.path.isfile(key_path):
+            raise GeminiUnavailableError(
+                f"Google credential config not found at {key_path}. "
+                "Set GOOGLE_VISION_KEY_PATH or mount the secret."
+            )
+
+        try:
+            from google.cloud import aiplatform  # type: ignore[import-untyped]
+            from vertexai.generative_models import (  # type: ignore[import-untyped]
+                GenerationConfig,
+                GenerativeModel,
+            )
+
+            # Point ADC at the WIF credential config
+            os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = key_path
+            os.environ["GOOGLE_EXTERNAL_ACCOUNT_ALLOW_EXECUTABLES"] = "1"
+
+            aiplatform.init(
+                project=settings.vertex_ai_project,
+                location=settings.vertex_ai_location,
+            )
+
+            model_name = settings.gemini_model
+            self._model = GenerativeModel(model_name)
+            self._generation_config = GenerationConfig(
+                response_mime_type="application/json",
+                response_schema=_RESPONSE_SCHEMA,
+            )
+
+            logger.info(
+                "Gemini engine initialized (model=%s, project=%s, location=%s)",
+                model_name,
+                settings.vertex_ai_project,
+                settings.vertex_ai_location,
+            )
+            return self._model
+
+        except ImportError as exc:
+            raise GeminiUnavailableError(
+                "google-cloud-aiplatform is not installed. "
+                "Install with: pip install google-cloud-aiplatform"
+            ) from exc
+        except Exception as exc:
+            raise GeminiUnavailableError(
+                f"Failed to initialize Gemini engine: {exc}"
+            ) from exc
+
+    def extract_maintenance(
+        self, pdf_bytes: bytes
+    ) -> MaintenanceExtractionResult:
+        """Extract maintenance schedules from a PDF owners manual.
+
+        Args:
+            pdf_bytes: Raw PDF file bytes (<= 20 MB).
+
+        Returns:
+            Structured maintenance extraction result.
+
+        Raises:
+            GeminiProcessingError: If the PDF is too large or extraction fails.
+            GeminiUnavailableError: If the engine cannot be initialized.
+        """
+        if len(pdf_bytes) > _MAX_PDF_BYTES:
+            size_mb = len(pdf_bytes) / (1024 * 1024)
+            raise GeminiProcessingError(
+                f"PDF size ({size_mb:.1f} MB) exceeds the 20 MB limit for "
+                "inline processing. Upload to GCS and use a gs:// URI instead."
+            )
+
+        model = self._get_model()
+
+        try:
+            from vertexai.generative_models import Part  # type: ignore[import-untyped]
+
+            pdf_part = Part.from_data(
+                data=pdf_bytes,
+                mime_type="application/pdf",
+            )
+
+            response = model.generate_content(
+                [pdf_part, _EXTRACTION_PROMPT],
+                generation_config=self._generation_config,
+            )
+
+            raw = json.loads(response.text)
+            items = [
+                MaintenanceItem(
+                    service_name=item["serviceName"],
+                    interval_miles=item.get("intervalMiles"),
+                    interval_months=item.get("intervalMonths"),
+                    details=item.get("details"),
+                )
+                for item in raw.get("maintenanceSchedule", [])
+            ]
+
+            logger.info(
+                "Gemini extracted %d maintenance items from PDF (%d bytes)",
+                len(items),
+                len(pdf_bytes),
+            )
+
+            return MaintenanceExtractionResult(
+                items=items,
+                model=settings.gemini_model,
+            )
+
+        except (GeminiEngineError,):
+            raise
+        except json.JSONDecodeError as exc:
+            raise GeminiProcessingError(
+                f"Gemini returned invalid JSON: {exc}"
+            ) from exc
+        except Exception as exc:
+            raise GeminiProcessingError(
+                f"Gemini maintenance extraction failed: {exc}"
+            ) from exc

ocr/requirements.txt

@@ -21,6 +21,9 @@ google-cloud-vision>=3.7.0
 # PDF Processing
 PyMuPDF>=1.23.0
 
+# Vertex AI / Gemini (maintenance schedule extraction)
+google-cloud-aiplatform>=1.40.0
+
 # Redis for job queue
 redis>=5.0.0
 

ocr/tests/test_gemini_engine.py

@@ -0,0 +1,353 @@
+"""Tests for Gemini engine maintenance schedule extraction.
+
+Covers: GeminiEngine initialization, PDF size validation,
+successful extraction, empty results, and error handling.
+All Vertex AI SDK calls are mocked.
+"""
+
+import json
+from unittest.mock import MagicMock, patch, PropertyMock
+
+import pytest
+
+from app.engines.gemini_engine import (
+    GeminiEngine,
+    GeminiEngineError,
+    GeminiProcessingError,
+    GeminiUnavailableError,
+    MaintenanceExtractionResult,
+    MaintenanceItem,
+    _MAX_PDF_BYTES,
+)
+
+
+# --- Helpers ---
+
+
+def _make_pdf_bytes(size: int = 1024) -> bytes:
+    """Create fake PDF bytes of a given size."""
+    # Minimal PDF header so it looks plausible, padded to size
+    header = b"%PDF-1.4 fake"
+    return header + b"\x00" * max(0, size - len(header))
+
+
+def _make_gemini_response(schedule: list[dict]) -> MagicMock:
+    """Create a mock Gemini generate_content response."""
+    response = MagicMock()
+    response.text = json.dumps({"maintenanceSchedule": schedule})
+    return response
+
+
+# --- Exception hierarchy ---
+
+
+class TestExceptionHierarchy:
+    """Verify the Gemini exception class relationships."""
+
+    def test_processing_error_is_engine_error(self):
+        assert issubclass(GeminiProcessingError, GeminiEngineError)
+
+    def test_unavailable_error_is_engine_error(self):
+        assert issubclass(GeminiUnavailableError, GeminiEngineError)
+
+    def test_engine_error_is_exception(self):
+        assert issubclass(GeminiEngineError, Exception)
+
+
+# --- Data types ---
+
+
+class TestMaintenanceItem:
+    """Verify MaintenanceItem dataclass construction."""
+
+    def test_required_fields_only(self):
+        item = MaintenanceItem(service_name="Oil Change")
+        assert item.service_name == "Oil Change"
+        assert item.interval_miles is None
+        assert item.interval_months is None
+        assert item.details is None
+
+    def test_all_fields(self):
+        item = MaintenanceItem(
+            service_name="Tire Rotation",
+            interval_miles=5000,
+            interval_months=6,
+            details="Rotate front to rear on same side.",
+        )
+        assert item.service_name == "Tire Rotation"
+        assert item.interval_miles == 5000
+        assert item.interval_months == 6
+        assert item.details == "Rotate front to rear on same side."
+
+
+class TestMaintenanceExtractionResult:
+    """Verify MaintenanceExtractionResult dataclass."""
+
+    def test_construction(self):
+        result = MaintenanceExtractionResult(
+            items=[MaintenanceItem(service_name="Oil Change")],
+            model="gemini-2.5-flash",
+        )
+        assert len(result.items) == 1
+        assert result.model == "gemini-2.5-flash"
+
+    def test_empty_items(self):
+        result = MaintenanceExtractionResult(items=[], model="gemini-2.5-flash")
+        assert result.items == []
+
+
+# --- PDF size validation ---
+
+
+class TestPdfSizeValidation:
+    """Verify the 20 MB PDF size limit."""
+
+    def test_oversized_pdf_rejected(self):
+        """PDFs exceeding 20 MB must be rejected with a clear error."""
+        engine = GeminiEngine()
+        oversized = _make_pdf_bytes(_MAX_PDF_BYTES + 1)
+
+        with pytest.raises(GeminiProcessingError, match="exceeds the 20 MB limit"):
+            engine.extract_maintenance(oversized)
+
+    def test_exactly_at_limit_accepted(self):
+        """PDFs exactly at 20 MB should pass size validation.
+
+        The engine will still fail at model init (mocked away in other tests),
+        but the size check itself should pass.
+        """
+        engine = GeminiEngine()
+        exact = _make_pdf_bytes(_MAX_PDF_BYTES)
+
+        # Should fail at _get_model, not at size check
+        with pytest.raises(GeminiUnavailableError):
+            engine.extract_maintenance(exact)
+
+
+# --- Successful extraction ---
+
+
+class TestExtractMaintenance:
+    """Verify successful maintenance schedule extraction."""
+
+    @patch("app.engines.gemini_engine.settings")
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_valid_pdf_returns_structured_schedules(
+        self, mock_isfile, mock_settings
+    ):
+        """Normal: Valid PDF returns structured maintenance schedules."""
+        mock_settings.google_vision_key_path = "/fake/creds.json"
+        mock_settings.vertex_ai_project = "test-project"
+        mock_settings.vertex_ai_location = "us-central1"
+        mock_settings.gemini_model = "gemini-2.5-flash"
+
+        schedule = [
+            {
+                "serviceName": "Engine Oil Change",
+                "intervalMiles": 5000,
+                "intervalMonths": 6,
+                "details": "Use 0W-20 full synthetic oil.",
+            },
+            {
+                "serviceName": "Tire Rotation",
+                "intervalMiles": 5000,
+                "intervalMonths": 6,
+                "details": None,
+            },
+        ]
+
+        mock_model = MagicMock()
+        mock_model.generate_content.return_value = _make_gemini_response(schedule)
+
+        with (
+            patch(
+                "app.engines.gemini_engine.importlib_vertex_ai"
+            ) if False else patch.dict("sys.modules", {
+                "google.cloud": MagicMock(),
+                "google.cloud.aiplatform": MagicMock(),
+                "vertexai": MagicMock(),
+                "vertexai.generative_models": MagicMock(),
+            }),
+        ):
+            engine = GeminiEngine()
+            engine._model = mock_model
+            engine._generation_config = MagicMock()
+
+            result = engine.extract_maintenance(_make_pdf_bytes())
+
+        assert isinstance(result, MaintenanceExtractionResult)
+        assert len(result.items) == 2
+        assert result.model == "gemini-2.5-flash"
+
+        oil = result.items[0]
+        assert oil.service_name == "Engine Oil Change"
+        assert oil.interval_miles == 5000
+        assert oil.interval_months == 6
+        assert oil.details == "Use 0W-20 full synthetic oil."
+
+        tire = result.items[1]
+        assert tire.service_name == "Tire Rotation"
+        assert tire.details is None
+
+    @patch("app.engines.gemini_engine.settings")
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_no_maintenance_content_returns_empty_array(
+        self, mock_isfile, mock_settings
+    ):
+        """Edge: PDF with no maintenance content returns empty array."""
+        mock_settings.google_vision_key_path = "/fake/creds.json"
+        mock_settings.vertex_ai_project = "test-project"
+        mock_settings.vertex_ai_location = "us-central1"
+        mock_settings.gemini_model = "gemini-2.5-flash"
+
+        mock_model = MagicMock()
+        mock_model.generate_content.return_value = _make_gemini_response([])
+
+        engine = GeminiEngine()
+        engine._model = mock_model
+        engine._generation_config = MagicMock()
+
+        result = engine.extract_maintenance(_make_pdf_bytes())
+
+        assert isinstance(result, MaintenanceExtractionResult)
+        assert result.items == []
+
+    @patch("app.engines.gemini_engine.settings")
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_nullable_fields_handled(self, mock_isfile, mock_settings):
+        """Items with only serviceName (nullable fields omitted) parse correctly."""
+        mock_settings.google_vision_key_path = "/fake/creds.json"
+        mock_settings.vertex_ai_project = "test-project"
+        mock_settings.vertex_ai_location = "us-central1"
+        mock_settings.gemini_model = "gemini-2.5-flash"
+
+        schedule = [{"serviceName": "Brake Fluid Replacement"}]
+
+        mock_model = MagicMock()
+        mock_model.generate_content.return_value = _make_gemini_response(schedule)
+
+        engine = GeminiEngine()
+        engine._model = mock_model
+        engine._generation_config = MagicMock()
+
+        result = engine.extract_maintenance(_make_pdf_bytes())
+
+        assert len(result.items) == 1
+        item = result.items[0]
+        assert item.service_name == "Brake Fluid Replacement"
+        assert item.interval_miles is None
+        assert item.interval_months is None
+        assert item.details is None
+
+
+# --- Error handling ---
+
+
+class TestErrorHandling:
+    """Verify error handling for various failure modes."""
+
+    def test_missing_credential_file_raises_unavailable(self):
+        """Auth failure: Missing credential file raises GeminiUnavailableError."""
+        engine = GeminiEngine()
+
+        with (
+            patch("app.engines.gemini_engine.os.path.isfile", return_value=False),
+            pytest.raises(GeminiUnavailableError, match="credential config not found"),
+        ):
+            engine.extract_maintenance(_make_pdf_bytes())
+
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_missing_sdk_raises_unavailable(self, mock_isfile):
+        """Auth failure: Missing SDK raises GeminiUnavailableError."""
+        engine = GeminiEngine()
+
+        with (
+            patch("app.engines.gemini_engine.settings") as mock_settings,
+            patch.dict("sys.modules", {
+                "google.cloud.aiplatform": None,
+            }),
+        ):
+            mock_settings.google_vision_key_path = "/fake/creds.json"
+
+            with pytest.raises(GeminiUnavailableError):
+                engine.extract_maintenance(_make_pdf_bytes())
+
+    @patch("app.engines.gemini_engine.settings")
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_generate_content_exception_raises_processing_error(
+        self, mock_isfile, mock_settings
+    ):
+        """Runtime error from Gemini API is wrapped as GeminiProcessingError."""
+        mock_settings.google_vision_key_path = "/fake/creds.json"
+        mock_settings.vertex_ai_project = "test-project"
+        mock_settings.vertex_ai_location = "us-central1"
+        mock_settings.gemini_model = "gemini-2.5-flash"
+
+        mock_model = MagicMock()
+        mock_model.generate_content.side_effect = RuntimeError("API quota exceeded")
+
+        engine = GeminiEngine()
+        engine._model = mock_model
+        engine._generation_config = MagicMock()
+
+        with pytest.raises(GeminiProcessingError, match="maintenance extraction failed"):
+            engine.extract_maintenance(_make_pdf_bytes())
+
+    @patch("app.engines.gemini_engine.settings")
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_invalid_json_response_raises_processing_error(
+        self, mock_isfile, mock_settings
+    ):
+        """Gemini returning invalid JSON is caught and wrapped."""
+        mock_settings.google_vision_key_path = "/fake/creds.json"
+        mock_settings.vertex_ai_project = "test-project"
+        mock_settings.vertex_ai_location = "us-central1"
+        mock_settings.gemini_model = "gemini-2.5-flash"
+
+        mock_response = MagicMock()
+        mock_response.text = "not valid json {{"
+
+        mock_model = MagicMock()
+        mock_model.generate_content.return_value = mock_response
+
+        engine = GeminiEngine()
+        engine._model = mock_model
+        engine._generation_config = MagicMock()
+
+        with pytest.raises(GeminiProcessingError, match="invalid JSON"):
+            engine.extract_maintenance(_make_pdf_bytes())
+
+
+# --- Lazy initialization ---
+
+
+class TestLazyInitialization:
+    """Verify the model is not created until first use."""
+
+    def test_model_is_none_after_construction(self):
+        """GeminiEngine should not initialize the model in __init__."""
+        engine = GeminiEngine()
+        assert engine._model is None
+
+    @patch("app.engines.gemini_engine.settings")
+    @patch("app.engines.gemini_engine.os.path.isfile", return_value=True)
+    def test_model_reused_on_second_call(self, mock_isfile, mock_settings):
+        """Once initialized, the same model instance is reused."""
+        mock_settings.google_vision_key_path = "/fake/creds.json"
+        mock_settings.vertex_ai_project = "test-project"
+        mock_settings.vertex_ai_location = "us-central1"
+        mock_settings.gemini_model = "gemini-2.5-flash"
+
+        schedule = [{"serviceName": "Oil Change", "intervalMiles": 5000}]
+        mock_model = MagicMock()
+        mock_model.generate_content.return_value = _make_gemini_response(schedule)
+
+        engine = GeminiEngine()
+        engine._model = mock_model
+        engine._generation_config = MagicMock()
+
+        engine.extract_maintenance(_make_pdf_bytes())
+        engine.extract_maintenance(_make_pdf_bytes())
+
+        # Model's generate_content should have been called twice
+        assert mock_model.generate_content.call_count == 2