Do not use mocks in embeddings test

src/haiku/rag/embeddings/__init__.py

@@ -17,20 +17,14 @@ def get_embedder() -> EmbedderBase:
         except ImportError:
             raise ImportError(
                 "VoyageAI embedder requires the 'voyageai' package. "
-                "Please install haiku.rag with the 'voyageai' extra:"
+                "Please install haiku.rag with the 'voyageai' extra: "
                 "uv pip install haiku.rag[voyageai]"
             )
         return VoyageAIEmbedder(Config.EMBEDDINGS_MODEL, Config.EMBEDDINGS_VECTOR_DIM)
 
     if Config.EMBEDDINGS_PROVIDER == "openai":
-        try:
+        from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
-            from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
+
-        except ImportError:
-            raise ImportError(
-                "OpenAI embedder requires the 'openai' package. "
-                "Please install haiku.rag with the 'openai' extra:"
-                "uv pip install haiku.rag[openai]"
-            )
         return OpenAIEmbedder(Config.EMBEDDINGS_MODEL, Config.EMBEDDINGS_VECTOR_DIM)
 
     raise ValueError(f"Unsupported embedding provider: {Config.EMBEDDINGS_PROVIDER}")

src/haiku/rag/embeddings/openai.py

@@ -1,16 +1,13 @@
-try:
+from openai import AsyncOpenAI
-    from openai import AsyncOpenAI
 
-    from haiku.rag.embeddings.base import EmbedderBase
+from haiku.rag.embeddings.base import EmbedderBase
 
-    class Embedder(EmbedderBase):
-        async def embed(self, text: str) -> list[float]:
-            client = AsyncOpenAI()
-            response = await client.embeddings.create(
-                model=self._model,
-                input=text,
-            )
-            return response.data[0].embedding
 
-except ImportError:
+class Embedder(EmbedderBase):
-    pass
+    async def embed(self, text: str) -> list[float]:
+        client = AsyncOpenAI()
+        response = await client.embeddings.create(
+            model=self._model,
+            input=text,
+        )
+        return response.data[0].embedding

tests/test_embedder.py

@@ -1,19 +1,26 @@
 import numpy as np
 import pytest
 
-from haiku.rag.embeddings import get_embedder
+from haiku.rag.config import Config
+from haiku.rag.embeddings.ollama import Embedder as OllamaEmbedder
+from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
+
+OPENAI_AVAILABLE = bool(Config.OPENAI_API_KEY)
+VOYAGEAI_AVAILABLE = bool(Config.VOYAGE_API_KEY)
+
+
+# Calculate cosine similarity
+def similarities(embeddings, test_embedding):
+    return [
+        np.dot(embedding, test_embedding)
+        / (np.linalg.norm(embedding) * np.linalg.norm(test_embedding))
+        for embedding in embeddings
+    ]
 
 
 @pytest.mark.asyncio
-async def test_embedder():
+async def test_ollama_embedder():
-    embedder = get_embedder()
+    embedder = OllamaEmbedder("mxbai-embed-large", 1024)
-    embedding = await embedder.embed("hello world")
-    assert len(embedding) == embedder._vector_dim
-
-
-@pytest.mark.asyncio
-async def test_similarity():
-    embedder = get_embedder()
     phrases = [
         "I enjoy eating great food.",
         "Python is my favorite programming language.",
@@ -21,14 +28,6 @@ async def test_similarity():
     ]
     embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
 
-    # Calculate cosine similarity
-    def similarities(embeddings, test_embedding):
-        return [
-            np.dot(embedding, test_embedding)
-            / (np.linalg.norm(embedding) * np.linalg.norm(test_embedding))
-            for embedding in embeddings
-        ]
-
     test_phrase = "I am going for a camping trip."
     test_embedding = await embedder.embed(test_phrase)
 
@@ -49,80 +48,66 @@ async def test_similarity():
 
 
 @pytest.mark.asyncio
-async def test_openai_embedder(monkeypatch):
+@pytest.mark.skipif(not OPENAI_AVAILABLE, reason="OpenAI API key not available")
-    monkeypatch.setenv("EMBEDDINGS_PROVIDER", "openai")
+async def test_openai_embedder():
-    monkeypatch.setenv("EMBEDDINGS_MODEL", "text-embedding-3-small")
+    embedder = OpenAIEmbedder("text-embedding-3-small", 1536)
+    phrases = [
+        "I enjoy eating great food.",
+        "Python is my favorite programming language.",
+        "I love to travel and see new places.",
+    ]
+    embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
 
-    try:
+    test_phrase = "I am going for a camping trip."
-        from haiku.rag.embeddings.openai import Embedder as OpenAIEmbedder
+    test_embedding = await embedder.embed(test_phrase)
 
-        embedder = OpenAIEmbedder("text-embedding-3-small", 1536)
+    sims = similarities(embeddings, test_embedding)
+    assert max(sims) == sims[2]
 
-        # Mock the OpenAI client
+    test_phrase = "When is dinner ready?"
-        class MockEmbeddingData:
+    test_embedding = await embedder.embed(test_phrase)
-            def __init__(self, embedding):
-                self.embedding = embedding
 
-        class MockResponse:
+    sims = similarities(embeddings, test_embedding)
-            def __init__(self, embedding):
+    assert max(sims) == sims[0]
-                self.data = [MockEmbeddingData(embedding)]
 
-        class MockAsyncOpenAI:
+    test_phrase = "I work as a software developer."
-            class MockEmbeddings:
+    test_embedding = await embedder.embed(test_phrase)
-                async def create(self, model, input):
-                    return MockResponse([0.1] * 1536)
 
-            def __init__(self):
+    sims = similarities(embeddings, test_embedding)
-                self.embeddings = self.MockEmbeddings()
+    assert max(sims) == sims[1]
-
-        # Patch the AsyncOpenAI import
-        import haiku.rag.embeddings.openai
-
-        original_client = haiku.rag.embeddings.openai.AsyncOpenAI
-        haiku.rag.embeddings.openai.AsyncOpenAI = MockAsyncOpenAI
-
-        try:
-            embedding = await embedder.embed("test text")
-            assert len(embedding) == 1536
-            assert all(isinstance(x, float) for x in embedding)
-        finally:
-            haiku.rag.embeddings.openai.AsyncOpenAI = original_client
-
-    except ImportError:
-        pytest.skip("OpenAI package not installed")
 
 
 @pytest.mark.asyncio
-async def test_voyageai_embedder(monkeypatch):
+@pytest.mark.skipif(not VOYAGEAI_AVAILABLE, reason="VoyageAI API key not available")
-    monkeypatch.setenv("EMBEDDINGS_PROVIDER", "voyageai")
+async def test_voyageai_embedder():
-    monkeypatch.setenv("EMBEDDINGS_MODEL", "voyage-3.5")
-
     try:
         from haiku.rag.embeddings.voyageai import Embedder as VoyageAIEmbedder
 
         embedder = VoyageAIEmbedder("voyage-3.5", 1024)
+        phrases = [
+            "I enjoy eating great food.",
+            "Python is my favorite programming language.",
+            "I love to travel and see new places.",
+        ]
+        embeddings = [np.array(await embedder.embed(phrase)) for phrase in phrases]
 
-        # Mock the VoyageAI client
+        test_phrase = "I am going for a camping trip."
-        class MockEmbeddings:
+        test_embedding = await embedder.embed(test_phrase)
-            def __init__(self, embeddings):
-                self.embeddings = embeddings
 
-        class MockClient:
+        sims = similarities(embeddings, test_embedding)
-            def embed(self, texts, model, output_dtype):
+        assert max(sims) == sims[2]
-                return MockEmbeddings([[0.1] * 1024])
 
-        # Patch the Client import
+        test_phrase = "When is dinner ready?"
-        import haiku.rag.embeddings.voyageai
+        test_embedding = await embedder.embed(test_phrase)
 
-        original_client = haiku.rag.embeddings.voyageai.Client
+        sims = similarities(embeddings, test_embedding)
-        haiku.rag.embeddings.voyageai.Client = MockClient
+        assert max(sims) == sims[0]
 
-        try:
+        test_phrase = "I work as a software developer."
-            embedding = await embedder.embed("test text")
+        test_embedding = await embedder.embed(test_phrase)
-            assert len(embedding) == 1024
+
-            assert all(isinstance(x, float) for x in embedding)
+        sims = similarities(embeddings, test_embedding)
-        finally:
+        assert max(sims) == sims[1]
-            haiku.rag.embeddings.voyageai.Client = original_client
 
     except ImportError:
         pytest.skip("VoyageAI package not installed")

uv.lock

@@ -1027,18 +1027,9 @@ dependencies = [
 ]
 
 [package.optional-dependencies]
-anthropic = [
-    { name = "anthropic" },
-]
-cohere = [
-    { name = "cohere" },
-]
 mxbai = [
     { name = "mxbai-rerank" },
 ]
-openai = [
-    { name = "openai" },
-]
 voyageai = [
     { name = "voyageai" },
 ]
@@ -1058,14 +1049,11 @@ dev = [
 
 [package.metadata]
 requires-dist = [
-    { name = "anthropic", marker = "extra == 'anthropic'", specifier = ">=0.56.0" },
-    { name = "cohere", marker = "extra == 'cohere'", specifier = ">=5.16.1" },
     { name = "docling", specifier = ">=2.15.0" },
     { name = "fastmcp", specifier = ">=2.8.1" },
     { name = "httpx", specifier = ">=0.28.1" },
     { name = "mxbai-rerank", marker = "extra == 'mxbai'", specifier = ">=0.1.6" },
     { name = "ollama", specifier = ">=0.5.3" },
-    { name = "openai", marker = "extra == 'openai'", specifier = ">=1.0.0" },
     { name = "pydantic", specifier = ">=2.11.7" },
     { name = "pydantic-ai", specifier = ">=0.7.2" },
     { name = "python-dotenv", specifier = ">=1.1.0" },
@@ -1076,7 +1064,7 @@ requires-dist = [
     { name = "voyageai", marker = "extra == 'voyageai'", specifier = ">=0.3.2" },
     { name = "watchfiles", specifier = ">=1.1.0" },
 ]
-provides-extras = ["voyageai", "openai", "anthropic", "cohere", "mxbai"]
+provides-extras = ["voyageai", "mxbai"]
 
 [package.metadata.requires-dev]
 dev = [