feat: use internal ollama embeddings

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"])