mcp-server-qdrant

import numpy as np import pytest from fastembed import TextEmbedding from mcp_server_qdrant.embeddings.fastembed import FastEmbedProvider @pytest.mark.asyncio class TestFastEmbedProviderIntegration: """Integration tests for FastEmbedProvider.""" async def test_initialization(self): """Test that the provider can be initialized with a valid model.""" provider = FastEmbedProvider("sentence-transformers/all-MiniLM-L6-v2") assert provider.model_name == "sentence-transformers/all-MiniLM-L6-v2" assert isinstance(provider.embedding_model, TextEmbedding) async def test_embed_documents(self): """Test that documents can be embedded.""" provider = FastEmbedProvider("sentence-transformers/all-MiniLM-L6-v2") documents = ["This is a test document.", "This is another test document."] embeddings = await provider.embed_documents(documents) # Check that we got the right number of embeddings assert len(embeddings) == len(documents) # Check that embeddings have the expected shape # The exact dimension depends on the model, but should be consistent assert len(embeddings[0]) > 0 assert all(len(embedding) == len(embeddings[0]) for embedding in embeddings) # Check that embeddings are different for different documents # Convert to numpy arrays for easier comparison embedding1 = np.array(embeddings[0]) embedding2 = np.array(embeddings[1]) assert not np.array_equal(embedding1, embedding2) async def test_embed_query(self): """Test that queries can be embedded.""" provider = FastEmbedProvider("sentence-transformers/all-MiniLM-L6-v2") query = "This is a test query." embedding = await provider.embed_query(query) # Check that embedding has the expected shape assert len(embedding) > 0 # Embed the same query again to check consistency embedding2 = await provider.embed_query(query) assert len(embedding) == len(embedding2) # The embeddings should be identical for the same input np.testing.assert_array_almost_equal(np.array(embedding), np.array(embedding2)) def test_get_vector_name(self): """Test that the vector name is generated correctly.""" provider = FastEmbedProvider("sentence-transformers/all-MiniLM-L6-v2") vector_name = provider.get_vector_name() # Check that the vector name follows the expected format assert vector_name.startswith("fast-") assert "minilm" in vector_name.lower()