feat(llm): add embedding adapter with cache and similarity utils (S1.3)

Add OpenAI-compatible embedding support (works with both OpenAI and
OpenRouter), file-based embedding cache with content-digest invalidation,
and pure-Python cosine similarity utilities for downstream redundancy
detection.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

tests/unit/llm/test_embeddings.py

@@ -0,0 +1,235 @@
+"""Tests for embedding adapter, cache, similarity, and factory."""
+
+from pathlib import Path
+from unittest import mock
+
+import pytest
+
+from markitect.llm.similarity import (
+    cosine_similarity,
+    similarity_matrix,
+    find_similar_pairs,
+)
+from markitect.llm.embedding_cache import EmbeddingCache
+from markitect.llm.embedding_openai import OpenAICompatibleEmbeddingAdapter
+from markitect.llm.embedding_factory import create_embedding_adapter
+from markitect.llm.exceptions import LLMConfigurationError, LLMRateLimitError
+
+
+# ── Similarity math ─────────────────────────────────────────────────
+
+
+class TestCosineSimilarity:
+    def test_identical_vectors(self):
+        v = [1.0, 2.0, 3.0]
+        assert cosine_similarity(v, v) == pytest.approx(1.0)
+
+    def test_orthogonal_vectors(self):
+        a = [1.0, 0.0, 0.0]
+        b = [0.0, 1.0, 0.0]
+        assert cosine_similarity(a, b) == pytest.approx(0.0)
+
+    def test_opposite_vectors(self):
+        a = [1.0, 0.0]
+        b = [-1.0, 0.0]
+        assert cosine_similarity(a, b) == pytest.approx(-1.0)
+
+    def test_zero_vector(self):
+        assert cosine_similarity([0.0, 0.0], [1.0, 2.0]) == 0.0
+
+
+class TestSimilarityMatrix:
+    def test_diagonal_is_one(self):
+        vecs = [[1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
+        mat = similarity_matrix(vecs)
+        for i in range(len(vecs)):
+            assert mat[i][i] == pytest.approx(1.0)
+
+    def test_symmetric(self):
+        vecs = [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]
+        mat = similarity_matrix(vecs)
+        n = len(vecs)
+        for i in range(n):
+            for j in range(n):
+                assert mat[i][j] == pytest.approx(mat[j][i])
+
+
+class TestFindSimilarPairs:
+    def test_threshold_filters(self):
+        emb = {
+            "a": [1.0, 0.0],
+            "b": [0.0, 1.0],
+            "c": [1.0, 0.01],  # very similar to "a"
+        }
+        pairs = find_similar_pairs(emb, threshold=0.90)
+        slugs_in_pairs = {(s1, s2) for s1, s2, _ in pairs}
+        assert ("a", "c") in slugs_in_pairs
+        # a-b are orthogonal, should not appear
+        assert ("a", "b") not in slugs_in_pairs
+
+    def test_sorted_descending(self):
+        emb = {
+            "x": [1.0, 0.0, 0.0],
+            "y": [0.9, 0.1, 0.0],
+            "z": [0.95, 0.05, 0.0],
+        }
+        pairs = find_similar_pairs(emb, threshold=0.0)
+        sims = [s for _, _, s in pairs]
+        assert sims == sorted(sims, reverse=True)
+
+    def test_empty_embeddings(self):
+        assert find_similar_pairs({}) == []
+
+    def test_single_embedding(self):
+        assert find_similar_pairs({"only": [1.0, 0.0]}) == []
+
+
+# ── Embedding cache ─────────────────────────────────────────────────
+
+
+class TestEmbeddingCache:
+    def test_put_get_roundtrip(self, tmp_path: Path):
+        cache = EmbeddingCache(tmp_path)
+        cache.put("division-of-labour", "abc123", [0.1, 0.2, 0.3])
+        assert cache.get("division-of-labour", "abc123") == [0.1, 0.2, 0.3]
+
+    def test_wrong_digest_returns_none(self, tmp_path: Path):
+        cache = EmbeddingCache(tmp_path)
+        cache.put("slug", "digest-v1", [1.0])
+        assert cache.get("slug", "digest-v2") is None
+
+    def test_missing_slug_returns_none(self, tmp_path: Path):
+        cache = EmbeddingCache(tmp_path)
+        assert cache.get("nonexistent", "any") is None
+
+    def test_save_load_persists(self, tmp_path: Path):
+        cache = EmbeddingCache(tmp_path)
+        cache.put("slug-a", "d1", [0.5, 0.6])
+        cache.save()
+
+        cache2 = EmbeddingCache(tmp_path)
+        assert cache2.get("slug-a", "d1") == [0.5, 0.6]
+
+    def test_stats_tracks_hits_and_misses(self, tmp_path: Path):
+        cache = EmbeddingCache(tmp_path)
+        cache.put("s", "d", [1.0])
+        cache.get("s", "d")       # hit
+        cache.get("s", "wrong")   # miss
+        cache.get("missing", "x") # miss
+        s = cache.stats()
+        assert s["entries"] == 1
+        assert s["hits"] == 1
+        assert s["misses"] == 2
+
+
+# ── Adapter (mocked HTTP) ──────────────────────────────────────────
+
+
+def _make_embedding_response(vectors):
+    """Build a mock API response for the /embeddings endpoint."""
+    return {
+        "data": [
+            {"embedding": vec, "index": i}
+            for i, vec in enumerate(vectors)
+        ],
+        "usage": {"prompt_tokens": 5, "total_tokens": 5},
+    }
+
+
+class TestOpenAICompatibleEmbeddingAdapter:
+    def _adapter(self, **kwargs):
+        defaults = {"api_key": "sk-test", "provider": "openai"}
+        defaults.update(kwargs)
+        return OpenAICompatibleEmbeddingAdapter(**defaults)
+
+    @mock.patch("markitect.llm.embedding_openai.post_json")
+    def test_embed_returns_vectors_in_order(self, mock_post):
+        # Return indices out of order to verify sorting
+        mock_post.return_value = {
+            "data": [
+                {"embedding": [0.2, 0.3], "index": 1},
+                {"embedding": [0.1, 0.2], "index": 0},
+            ],
+            "usage": {},
+        }
+        adapter = self._adapter()
+        result = adapter.embed(["text1", "text2"])
+        assert result == [[0.1, 0.2], [0.2, 0.3]]
+
+    @mock.patch("markitect.llm.embedding_openai.post_json")
+    def test_embed_payload_structure(self, mock_post):
+        mock_post.return_value = _make_embedding_response([[0.1]])
+        adapter = self._adapter(model="text-embedding-3-large")
+        adapter.embed(["hello"])
+
+        call_args = mock_post.call_args
+        url = call_args[0][0]
+        payload = call_args[0][1]
+        assert url == "https://api.openai.com/v1/embeddings"
+        assert payload["model"] == "text-embedding-3-large"
+        assert payload["input"] == ["hello"]
+
+    def test_embed_raises_without_api_key(self):
+        adapter = OpenAICompatibleEmbeddingAdapter(api_key=None, provider="openai")
+        adapter._api_key = None
+        with pytest.raises(LLMConfigurationError):
+            adapter.embed(["test"])
+
+    def test_validate_true_with_key(self):
+        adapter = self._adapter()
+        assert adapter.validate() is True
+
+    def test_validate_false_without_key(self):
+        adapter = OpenAICompatibleEmbeddingAdapter(api_key=None, provider="openai")
+        adapter._api_key = None
+        assert adapter.validate() is False
+
+    @mock.patch("markitect.llm.embedding_openai.post_json")
+    @mock.patch("markitect.llm.embedding_openai.time.sleep")
+    def test_retry_on_429(self, mock_sleep, mock_post):
+        mock_post.side_effect = [
+            LLMRateLimitError("rate limited", status_code=429),
+            _make_embedding_response([[0.1, 0.2]]),
+        ]
+        adapter = self._adapter(max_retries=2)
+        result = adapter.embed(["test"])
+        assert result == [[0.1, 0.2]]
+        assert mock_sleep.call_count == 1
+
+    def test_openai_provider_base_url(self):
+        adapter = self._adapter(provider="openai")
+        assert adapter._api_base == "https://api.openai.com/v1"
+
+    def test_openrouter_provider_base_url(self):
+        adapter = self._adapter(provider="openrouter")
+        assert adapter._api_base == "https://openrouter.ai/api/v1"
+
+    def test_unknown_provider_raises(self):
+        with pytest.raises(LLMConfigurationError):
+            OpenAICompatibleEmbeddingAdapter(api_key="sk-test", provider="unknown")
+
+
+# ── Factory ─────────────────────────────────────────────────────────
+
+
+class TestCreateEmbeddingAdapter:
+    def test_openai_provider(self):
+        adapter = create_embedding_adapter("openai", api_key="sk-test")
+        assert isinstance(adapter, OpenAICompatibleEmbeddingAdapter)
+        assert adapter._provider == "openai"
+
+    def test_openrouter_provider(self):
+        adapter = create_embedding_adapter("openrouter", api_key="sk-test")
+        assert isinstance(adapter, OpenAICompatibleEmbeddingAdapter)
+        assert adapter._provider == "openrouter"
+
+    def test_unknown_provider_raises(self):
+        with pytest.raises(LLMConfigurationError) as exc_info:
+            create_embedding_adapter("unknown")
+        assert "unknown" in str(exc_info.value)
+
+    def test_model_passed_through(self):
+        adapter = create_embedding_adapter(
+            "openai", model="text-embedding-3-large", api_key="sk-test"
+        )
+        assert adapter._model == "text-embedding-3-large"