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()