bugfix

internal/infra/llm/embedder_test.go

@@ -0,0 +1,129 @@
+package llm
+
+import (
+	"context"
+	"encoding/json"
+	"errors"
+	"net/http"
+	"net/http/httptest"
+	"testing"
+
+	"github.com/google/uuid"
+	"github.com/stretchr/testify/require"
+)
+
+func TestOpenAI_Embed_ReturnsVectors(t *testing.T) {
+	var gotInputs []string
+	srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		var body struct {
+			Input      []string `json:"input"`
+			Model      string   `json:"model"`
+			Dimensions int      `json:"dimensions"`
+		}
+		_ = json.NewDecoder(r.Body).Decode(&body)
+		gotInputs = body.Input
+		require.Equal(t, PlatformEmbeddingDims, body.Dimensions)
+		// Echo two 3-dim vectors (dimension count here is irrelevant to the test).
+		resp := map[string]any{
+			"object": "list",
+			"data": []map[string]any{
+				{"object": "embedding", "index": 0, "embedding": []float64{0.1, 0.2, 0.3}},
+				{"object": "embedding", "index": 1, "embedding": []float64{0.4, 0.5, 0.6}},
+			},
+			"model": body.Model,
+		}
+		w.Header().Set("Content-Type", "application/json")
+		_ = json.NewEncoder(w).Encode(resp)
+	}))
+	defer srv.Close()
+
+	c, _ := NewOpenAI("k", srv.URL)
+	vecs, err := c.Embed(context.Background(), "text-embedding-3-small", []string{"alpha", "beta"})
+	require.NoError(t, err)
+	require.Len(t, vecs, 2)
+	require.Equal(t, []float32{0.1, 0.2, 0.3}, vecs[0])
+	require.Equal(t, []float32{0.4, 0.5, 0.6}, vecs[1])
+	require.Equal(t, []string{"alpha", "beta"}, gotInputs)
+	require.Equal(t, PlatformEmbeddingDims, c.EmbedDims("text-embedding-3-small"))
+}
+
+func TestOpenAI_Embed_RespectsResponseIndex(t *testing.T) {
+	// Server returns vectors out of order; Embed must place them by Index.
+	srv := httptest.NewServer(http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
+		resp := map[string]any{
+			"object": "list",
+			"data": []map[string]any{
+				{"object": "embedding", "index": 1, "embedding": []float64{9, 9}},
+				{"object": "embedding", "index": 0, "embedding": []float64{1, 1}},
+			},
+			"model": "m",
+		}
+		w.Header().Set("Content-Type", "application/json")
+		_ = json.NewEncoder(w).Encode(resp)
+	}))
+	defer srv.Close()
+
+	c, _ := NewOpenAI("k", srv.URL)
+	vecs, err := c.Embed(context.Background(), "m", []string{"a", "b"})
+	require.NoError(t, err)
+	require.Equal(t, []float32{1, 1}, vecs[0])
+	require.Equal(t, []float32{9, 9}, vecs[1])
+}
+
+func TestOpenAI_Embed_EmptyInputs(t *testing.T) {
+	c, _ := NewOpenAI("k", "")
+	vecs, err := c.Embed(context.Background(), "m", nil)
+	require.NoError(t, err)
+	require.Nil(t, vecs)
+}
+
+func TestAnthropic_Embed_Unsupported(t *testing.T) {
+	c, _ := NewAnthropic("k", "")
+	_, err := c.Embed(context.Background(), "m", []string{"x"})
+	require.ErrorIs(t, err, ErrEmbeddingsUnsupported)
+	require.Equal(t, 0, c.EmbedDims("m"))
+}
+
+func TestFakeEmbedder_Deterministic(t *testing.T) {
+	e := NewFakeEmbedder()
+	v1, err := e.Embed(context.Background(), "m", []string{"hello"})
+	require.NoError(t, err)
+	v2, err := e.Embed(context.Background(), "m", []string{"hello"})
+	require.NoError(t, err)
+	require.Equal(t, v1[0], v2[0], "same input must yield identical vector")
+	require.Len(t, v1[0], PlatformEmbeddingDims)
+
+	other, err := e.Embed(context.Background(), "m", []string{"world"})
+	require.NoError(t, err)
+	require.NotEqual(t, v1[0], other[0], "different inputs must differ")
+}
+
+func TestFakeEmbedder_Error(t *testing.T) {
+	e := &FakeEmbedder{Dims: 4, Err: errors.New("boom")}
+	_, err := e.Embed(context.Background(), "m", []string{"x"})
+	require.Error(t, err)
+}
+
+func TestRegistry_GetEmbedder_TypeAsserts(t *testing.T) {
+	// OpenAI client implements Embedder.
+	id := uuid.New()
+	reg := NewRegistry(&fakeLoader{endpoints: map[uuid.UUID]fakeEndpoint{
+		id: {ID: id, Provider: "openai", BaseURL: "http://localhost", APIKey: "k"},
+	}})
+	emb, err := reg.GetEmbedder(context.Background(), id)
+	require.NoError(t, err)
+	require.NotNil(t, emb)
+	require.Equal(t, PlatformEmbeddingDims, emb.EmbedDims("m"))
+}
+
+func TestRegistry_GetEmbedder_AnthropicSupportedButUnsupportedEmbed(t *testing.T) {
+	// Anthropic client implements Embedder (Embed returns ErrEmbeddingsUnsupported).
+	id := uuid.New()
+	reg := NewRegistry(&fakeLoader{endpoints: map[uuid.UUID]fakeEndpoint{
+		id: {ID: id, Provider: "anthropic", BaseURL: "http://localhost", APIKey: "k"},
+	}})
+	emb, err := reg.GetEmbedder(context.Background(), id)
+	require.NoError(t, err)
+	_, eerr := emb.Embed(context.Background(), "m", []string{"x"})
+	require.ErrorIs(t, eerr, ErrEmbeddingsUnsupported)
+}