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/test_ollama_client.py

@@ -0,0 +1,74 @@
+"""Tests for the Ollama VLM client."""
+
+import base64
+import json
+from io import BytesIO
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+from PIL import Image
+
+from odoo_ocr.clients.base import BaseVLMClient
+from odoo_ocr.clients.ollama import OllamaClient
+from odoo_ocr.config import Settings
+from odoo_ocr.schemas import ExtractedInvoice
+
+
+@pytest.fixture
+def settings() -> Settings:
+    return Settings(
+        ollama_base_url="http://test-ollama:11434",
+        cache=Settings().model_dump()["cache"] | {"enabled": False},
+    )
+
+
+@pytest.mark.asyncio
+async def test_complete_parses_response(settings: Settings) -> None:
+    client = OllamaClient(settings)
+    fake_response = {
+        "model": "glm-ocr",
+        "message": {"role": "assistant", "content": '{"text": "Invoice 123"}'},
+        "done": True,
+        "prompt_eval_count": 100,
+        "eval_count": 20,
+    }
+
+    resp = MagicMock()
+    resp.json.return_value = fake_response
+    resp.raise_for_status = lambda: None
+    client.http = AsyncMock()
+    client.http.post.return_value = resp
+
+    img = Image.new("RGB", (50, 50), color="red")
+    response = await client.complete(
+        system_prompt="ocr",
+        user_prompt="read",
+        images=[img],
+    )
+
+    assert response.content == '{"text": "Invoice 123"}'
+    assert response.model == "glm-ocr"
+    assert response.prompt_tokens == 100
+    assert response.completion_tokens == 20
+
+
+def test_parse_json_extracted_invoice() -> None:
+    data = {
+        "vendor_name": "Acme",
+        "invoice_number": "1",
+        "invoice_date": "2024-01-01",
+        "line_items": [],
+        "subtotal": "100",
+        "tax_total": "20",
+        "total": "120",
+    }
+    result = BaseVLMClient.parse_json(json.dumps(data), ExtractedInvoice)
+    assert isinstance(result, ExtractedInvoice)
+    assert result.vendor_name == "Acme"
+
+
+def test_encode_image_roundtrip() -> None:
+    img = Image.new("RGB", (10, 10), color="blue")
+    encoded = OllamaClient._encode_image(img)  # type: ignore[attr-defined]
+    decoded = Image.open(BytesIO(base64.b64decode(encoded)))
+    assert decoded.size == (10, 10)