Initial scaffold: local invoice OCR pipeline with Ollama, classifier branches, structured extraction, review, and Odoo XML export

- Add AGENTS.md and project-specific Zed skills (odoo-ocr-pipeline, odoo-xml-import, local-vlm-client).
- Implement Pydantic schemas for documents, invoices, review results, and VLM responses.
- Add unified BaseVLMClient with Ollama implementation and llama.cpp stub.
- Build pipeline stages: loader, classifier, digital_pdf/scanned_print/handwritten/mixed_unknown branches, extractor, reviewer, xml_builder.
- Add CLI entry point  with sidecar JSON and confidence-gated XML output.
- Include prompts for classifier, OCR, extraction, and review models.
- Add tests with FakeVLMClient; pytest, ruff, and mypy all pass.

tests/conftest.py

@@ -0,0 +1,54 @@
+"""Shared pytest fixtures."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import pytest
+from PIL import Image
+
+from odoo_ocr.clients.base import BaseVLMClient
+from odoo_ocr.config import Settings
+from odoo_ocr.schemas import VLMResponse
+
+
+@pytest.fixture
+def settings() -> Settings:
+    """Default test settings with cache disabled."""
+    return Settings(
+        ollama_base_url="http://test-ollama:11435",
+        cache=Settings().model_dump()["cache"] | {"enabled": False},
+    )
+
+
+class FakeVLMClient(BaseVLMClient):
+    """Test double that returns canned responses based on prompt content."""
+
+    def __init__(self, responses: dict[str, str] | None = None) -> None:
+        super().__init__(Settings())
+        self.responses = responses or {}
+        self.calls: list[dict[str, Any]] = []
+
+    async def complete(
+        self,
+        system_prompt: str,
+        user_prompt: str,
+        images: list[Image.Image],
+        response_format: type[Any] | None = None,
+        temperature: float = 0.1,
+        max_tokens: int = 4096,
+        model: str | None = None,
+    ) -> VLMResponse:
+        self.calls.append(
+            {
+                "system_prompt": system_prompt,
+                "user_prompt": user_prompt,
+                "image_count": len(images),
+                "model": model,
+            }
+        )
+        for key, value in self.responses.items():
+            if key in system_prompt or key in user_prompt:
+                return VLMResponse(content=json.dumps(value), model="fake")
+        return VLMResponse(content=json.dumps({}), model="fake")