feat: use internal ollama embeddings

harness/tests/test_config_resources.py

@@ -1,16 +1,16 @@
 import pytest
 
+from tht.adapters.evidence import FilesystemEvidenceSource, HttpManifestEvidenceSource
+from tht.adapters.factory import build_evidence_sources
 from tht.config import (
     ConfigError,
     PgvectorDirectConfig,
     PostgresDwhConfig,
     ThothRestDwhConfig,
     ThothVectorHttpConfig,
-    workspace_id_for_config,
     load_config,
+    workspace_id_for_config,
 )
-from tht.adapters.evidence import FilesystemEvidenceSource, HttpManifestEvidenceSource
-from tht.adapters.factory import build_evidence_sources
 
 
 def test_direct_vector_passwords_load_from_file_references(monkeypatch, tmp_path):
@@ -213,6 +213,95 @@ embeddings: {base_url: http://ollama:11434, dim: 768}
     assert cfg.vectors.writer.api_key == "writer"
 
 
+def test_accepts_only_internal_ollama_embedding_contract(tmp_path):
+    workspace = tmp_path / "workspace.yaml"
+    workspace.write_text(
+        """
+dwh:
+  type: postgres_direct
+  connection: {database: analytics, schema: mart, user: reader, password: secret}
+resources:
+  embeddings:
+    provider: ollama_internal
+    base_url: http://embedding:11434
+    model: qwen3-embedding:0.6b
+    dimensions: 1024
+"""
+    )
+
+    cfg = load_config(workspace)
+
+    assert cfg.embeddings.provider == "ollama_internal"
+    assert cfg.embeddings.base_url == "http://embedding:11434"
+    assert cfg.embeddings.model == "qwen3-embedding:0.6b"
+    assert cfg.embeddings.dim == 1024
+
+
+@pytest.mark.parametrize(
+    ("snippet", "pattern"),
+    [
+        (
+            """
+resources:
+  embeddings:
+    provider: openai_compatible
+    base_url: http://embedding:11434
+    model: qwen3-embedding:0.6b
+    dimensions: 1024
+""",
+            "ollama_internal|provider",
+        ),
+        (
+            """
+resources:
+  embeddings:
+    provider: ollama_internal
+    base_url: http://embedding:11434
+    model: qwen3-embedding:0.6b
+    dimensions: 1024
+    api_key: secret
+""",
+            "api_key|extra",
+        ),
+        (
+            """
+resources:
+  embeddings:
+    provider: ollama_internal
+    base_url: https://embedding:11434
+    model: qwen3-embedding:0.6b
+    dimensions: 1024
+""",
+            "base_url|internal|private|host",
+        ),
+        (
+            """
+resources:
+  embeddings:
+    provider: ollama_internal
+    base_url: http://example.com:11434
+    model: qwen3-embedding:0.6b
+    dimensions: 1024
+""",
+            "base_url|internal|private|host",
+        ),
+    ],
+)
+def test_rejects_external_embedding_configuration(tmp_path, snippet, pattern):
+    workspace = tmp_path / "workspace.yaml"
+    workspace.write_text(
+        """
+dwh:
+  type: postgres_direct
+  connection: {database: analytics, schema: mart, user: reader, password: secret}
+"""
+        + snippet
+    )
+
+    with pytest.raises(ConfigError, match=pattern):
+        load_config(workspace)
+
+
 def test_builds_typed_evidence_sources_and_keeps_legacy_compatible(tmp_path):
     common = """
 dwh:

harness/tests/test_internal_embeddings.py

@@ -0,0 +1,139 @@
+import math
+
+import pytest
+
+from tht.config import EmbeddingsConfig
+from tht.vectorstore.embeddings import EmbeddingsError
+
+
+class _Response:
+    def __init__(self, payload, status_code=200):
+        self._payload = payload
+        self.status_code = status_code
+
+    def raise_for_status(self):
+        if self.status_code >= 400:
+            raise RuntimeError(f"http {self.status_code}")
+
+    def json(self):
+        return self._payload
+
+
+class _Session:
+    def __init__(self, responses):
+        self._responses = list(responses)
+        self.calls = []
+
+    def post(self, url, json, timeout):
+        self.calls.append({"url": url, "json": json, "timeout": timeout})
+        if not self._responses:
+            raise AssertionError("unexpected extra request")
+        return self._responses.pop(0)
+
+
+def _vector(value: float, *, dim: int = 1024):
+    return [value] * dim
+
+
+def test_internal_embeddings_posts_model_and_batch_input_without_prefixes():
+    from tht.vectorstore.embeddings import OllamaInternalEmbeddings
+
+    session = _Session([_Response({"embeddings": [_vector(1.0), _vector(2.0)]})])
+    embedder = OllamaInternalEmbeddings(
+        EmbeddingsConfig(
+            provider="ollama_internal",
+            base_url="http://embedding:11434",
+            model="qwen3-embedding:0.6b",
+            dim=1024,
+            batch_size=2,
+            timeout=9,
+            connect_timeout=4,
+        ),
+        session=session,
+    )
+
+    vectors = embedder.embed(["alpha", "beta"])
+
+    assert vectors == [_vector(1.0), _vector(2.0)]
+    assert session.calls == [{
+        "url": "http://embedding:11434/api/embed",
+        "json": {"model": "qwen3-embedding:0.6b", "input": ["alpha", "beta"]},
+        "timeout": (4, 9),
+    }]
+
+
+def test_internal_embeddings_batching_returns_1024d_vectors():
+    from tht.vectorstore.embeddings import OllamaInternalEmbeddings
+
+    session = _Session([
+        _Response({"embeddings": [_vector(1.0), _vector(2.0)]}),
+        _Response({"embeddings": [_vector(3.0)]}),
+    ])
+    embedder = OllamaInternalEmbeddings(
+        EmbeddingsConfig(
+            provider="ollama_internal",
+            base_url="http://embedding:11434",
+            model="qwen3-embedding:0.6b",
+            dim=1024,
+            batch_size=2,
+        ),
+        session=session,
+    )
+
+    vectors = embedder.embed(["one", "two", "three"])
+
+    assert [len(vector) for vector in vectors] == [1024, 1024, 1024]
+    assert [vector[0] for vector in vectors] == [1.0, 2.0, 3.0]
+
+
+def test_internal_embeddings_reject_count_mismatch():
+    from tht.vectorstore.embeddings import OllamaInternalEmbeddings
+
+    embedder = OllamaInternalEmbeddings(
+        EmbeddingsConfig(
+            provider="ollama_internal",
+            base_url="http://embedding:11434",
+            model="qwen3-embedding:0.6b",
+            dim=1024,
+        ),
+        session=_Session([_Response({"embeddings": [_vector(1.0)]})]),
+    )
+
+    with pytest.raises(EmbeddingsError, match="count|numero"):
+        embedder.embed(["alpha", "beta"])
+
+
+def test_internal_embeddings_reject_dimension_mismatch():
+    from tht.vectorstore.embeddings import OllamaInternalEmbeddings
+
+    embedder = OllamaInternalEmbeddings(
+        EmbeddingsConfig(
+            provider="ollama_internal",
+            base_url="http://embedding:11434",
+            model="qwen3-embedding:0.6b",
+            dim=1024,
+        ),
+        session=_Session([_Response({"embeddings": [[1.0] * 8]})]),
+    )
+
+    with pytest.raises(EmbeddingsError, match="dimensione|dimension"):
+        embedder.embed(["alpha"])
+
+
+def test_internal_embeddings_reject_non_finite_values():
+    from tht.vectorstore.embeddings import OllamaInternalEmbeddings
+
+    bad = _vector(0.0)
+    bad[10] = math.nan
+    embedder = OllamaInternalEmbeddings(
+        EmbeddingsConfig(
+            provider="ollama_internal",
+            base_url="http://embedding:11434",
+            model="qwen3-embedding:0.6b",
+            dim=1024,
+        ),
+        session=_Session([_Response({"embeddings": [bad]})]),
+    )
+
+    with pytest.raises(EmbeddingsError, match="finite|finit"):
+        embedder.embed(["alpha"])