Add semantic-index service, deployment assets, and tests

tests/semantic_index/test_embeddings.py

@@ -0,0 +1,46 @@
+import unittest
+
+from semantic_index.embeddings import OpenAIEmbedder
+from semantic_index.models import IndexDocument
+
+
+class FakeOpenAIClient:
+    def __init__(self):
+        self.calls = []
+
+    def create_embeddings(self, model, inputs, dimensions=None):
+        self.calls.append({"model": model, "inputs": list(inputs), "dimensions": dimensions})
+        return [[float(i)] * 3 for i, _ in enumerate(inputs, start=1)]
+
+
+class OpenAIEmbedderTest(unittest.TestCase):
+    def test_batches_embedding_requests(self):
+        client = FakeOpenAIClient()
+        embedder = OpenAIEmbedder(client=client, batch_size=2, dimensions=1536)
+        docs = [
+            IndexDocument(id="a", text="alpha", payload={}),
+            IndexDocument(id="b", text="bravo", payload={}),
+            IndexDocument(id="c", text="charlie", payload={}),
+        ]
+
+        vectors = embedder.embed_documents(docs)
+
+        self.assertEqual([[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [1.0, 1.0, 1.0]], vectors)
+        self.assertEqual(2, len(client.calls))
+        self.assertEqual(["alpha", "bravo"], client.calls[0]["inputs"])
+        self.assertEqual("text-embedding-3-small", client.calls[0]["model"])
+        self.assertEqual(1536, client.calls[0]["dimensions"])
+
+    def test_rejects_empty_or_oversized_chunks_before_api_call(self):
+        client = FakeOpenAIClient()
+        embedder = OpenAIEmbedder(client=client, max_chars=5)
+
+        with self.assertRaises(ValueError):
+            embedder.embed_texts(["ok", "      "])
+        with self.assertRaises(ValueError):
+            embedder.embed_texts(["toolong"])
+        self.assertEqual([], client.calls)
+
+
+if __name__ == "__main__":
+    unittest.main()