setup tests

tests/test_ebook_search_http.py

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+"""Tests for EPUB search HTTP model adapters."""
+
+from __future__ import annotations
+
+import httpx
+import pytest
+
+from python.ebook_search.answer import answer_query
+from python.ebook_search.config import EbookSearchConfig, RerankConfig
+from python.ebook_search.embeddings import embed_texts
+from python.ebook_search.search import SearchResult
+
+
+def test_answer_query_uses_httpx_chat_completions(monkeypatch) -> None:
+    captured: dict[str, object] = {}
+
+    def fake_post(url: str, **kwargs: object) -> httpx.Response:
+        captured["url"] = url
+        captured["kwargs"] = kwargs
+        return httpx.Response(
+            200,
+            json={"choices": [{"message": {"content": "grounded answer"}}]},
+            request=httpx.Request("POST", url),
+        )
+
+    monkeypatch.setattr(httpx, "post", fake_post)
+    config = EbookSearchConfig(
+        rerank=RerankConfig(enabled=False),
+        vllm_base_url="https://ollama.com/v1",
+        vllm_api_key="secret",
+        chat_model="deepseek-v4-flash",
+    )
+
+    answer = answer_query("question", [SearchResult(chunk_id=1, text="source", source_title="Book")], config)
+
+    assert answer == "grounded answer"
+    assert captured["url"] == "https://ollama.com/v1/chat/completions"
+    kwargs = captured["kwargs"]
+    assert isinstance(kwargs, dict)
+    assert kwargs["headers"] == {"Authorization": "Bearer secret"}
+    payload = kwargs["json"]
+    assert isinstance(payload, dict)
+    assert payload["model"] == "deepseek-v4-flash"
+
+
+def test_embed_texts_uses_httpx_embeddings(monkeypatch) -> None:
+    captured: dict[str, object] = {}
+    vector = [0.0] * 1024
+
+    def fake_post(url: str, **kwargs: object) -> httpx.Response:
+        captured["url"] = url
+        captured["kwargs"] = kwargs
+        return httpx.Response(
+            200,
+            json={"data": [{"embedding": vector}]},
+            request=httpx.Request("POST", url),
+        )
+
+    monkeypatch.setattr(httpx, "post", fake_post)
+    config = EbookSearchConfig(
+        rerank=RerankConfig(enabled=False),
+        embedding_base_url="http://bob:8000/v1",
+        embedding_model="qwen3-embedding-0.6b",
+    )
+
+    embeddings = embed_texts(["hello"], config)
+
+    assert embeddings == [vector]
+    assert captured["url"] == "http://bob:8000/v1/embeddings"
+    kwargs = captured["kwargs"]
+    assert isinstance(kwargs, dict)
+    assert kwargs["headers"] == {}
+    assert kwargs["json"] == {"model": "qwen3-embedding-0.6b", "input": ["hello"]}
+
+
+def test_embed_texts_rejects_bad_response_shape(monkeypatch) -> None:
+    def fake_post(url: str, **_kwargs: object) -> httpx.Response:
+        return httpx.Response(200, json={"data": [{}]}, request=httpx.Request("POST", url))
+
+    monkeypatch.setattr(httpx, "post", fake_post)
+    config = EbookSearchConfig(rerank=RerankConfig(enabled=False))
+
+    with pytest.raises(RuntimeError, match="Embedding request failed"):
+        embed_texts(["hello"], config)