Files
go-openai/embeddings_test.go
e. alvarez 84f77a0acd Add DotProduct Method and README Example for Embedding Similarity Search (#492)
* Add DotProduct Method and README Example for Embedding Similarity Search

- Implement a DotProduct() method for the Embedding struct to calculate the dot product between two embeddings.
- Add a custom error type for vector length mismatch.
- Update README.md with a complete example demonstrating how to perform an embedding similarity search for user queries.
- Add unit tests to validate the new DotProduct() method and error handling.

* Update README to focus on Embedding Semantic Similarity
2023-10-02 18:39:10 +04:00

274 lines
7.6 KiB
Go

package openai_test
import (
"bytes"
"context"
"encoding/json"
"errors"
"fmt"
"math"
"net/http"
"reflect"
"testing"
. "github.com/sashabaranov/go-openai"
"github.com/sashabaranov/go-openai/internal/test/checks"
)
func TestEmbedding(t *testing.T) {
embeddedModels := []EmbeddingModel{
AdaSimilarity,
BabbageSimilarity,
CurieSimilarity,
DavinciSimilarity,
AdaSearchDocument,
AdaSearchQuery,
BabbageSearchDocument,
BabbageSearchQuery,
CurieSearchDocument,
CurieSearchQuery,
DavinciSearchDocument,
DavinciSearchQuery,
AdaCodeSearchCode,
AdaCodeSearchText,
BabbageCodeSearchCode,
BabbageCodeSearchText,
}
for _, model := range embeddedModels {
// test embedding request with strings (simple embedding request)
embeddingReq := EmbeddingRequest{
Input: []string{
"The food was delicious and the waiter",
"Other examples of embedding request",
},
Model: model,
}
// marshal embeddingReq to JSON and confirm that the model field equals
// the AdaSearchQuery type
marshaled, err := json.Marshal(embeddingReq)
checks.NoError(t, err, "Could not marshal embedding request")
if !bytes.Contains(marshaled, []byte(`"model":"`+model.String()+`"`)) {
t.Fatalf("Expected embedding request to contain model field")
}
// test embedding request with strings
embeddingReqStrings := EmbeddingRequestStrings{
Input: []string{
"The food was delicious and the waiter",
"Other examples of embedding request",
},
Model: model,
}
marshaled, err = json.Marshal(embeddingReqStrings)
checks.NoError(t, err, "Could not marshal embedding request")
if !bytes.Contains(marshaled, []byte(`"model":"`+model.String()+`"`)) {
t.Fatalf("Expected embedding request to contain model field")
}
// test embedding request with tokens
embeddingReqTokens := EmbeddingRequestTokens{
Input: [][]int{
{464, 2057, 373, 12625, 290, 262, 46612},
{6395, 6096, 286, 11525, 12083, 2581},
},
Model: model,
}
marshaled, err = json.Marshal(embeddingReqTokens)
checks.NoError(t, err, "Could not marshal embedding request")
if !bytes.Contains(marshaled, []byte(`"model":"`+model.String()+`"`)) {
t.Fatalf("Expected embedding request to contain model field")
}
}
}
func TestEmbeddingModel(t *testing.T) {
var em EmbeddingModel
err := em.UnmarshalText([]byte("text-similarity-ada-001"))
checks.NoError(t, err, "Could not marshal embedding model")
if em != AdaSimilarity {
t.Errorf("Model is not equal to AdaSimilarity")
}
err = em.UnmarshalText([]byte("some-non-existent-model"))
checks.NoError(t, err, "Could not marshal embedding model")
if em != Unknown {
t.Errorf("Model is not equal to Unknown")
}
}
func TestEmbeddingEndpoint(t *testing.T) {
client, server, teardown := setupOpenAITestServer()
defer teardown()
sampleEmbeddings := []Embedding{
{Embedding: []float32{1.23, 4.56, 7.89}},
{Embedding: []float32{-0.006968617, -0.0052718227, 0.011901081}},
}
sampleBase64Embeddings := []Base64Embedding{
{Embedding: "pHCdP4XrkUDhevxA"},
{Embedding: "/1jku0G/rLvA/EI8"},
}
server.RegisterHandler(
"/v1/embeddings",
func(w http.ResponseWriter, r *http.Request) {
var req struct {
EncodingFormat EmbeddingEncodingFormat `json:"encoding_format"`
User string `json:"user"`
}
_ = json.NewDecoder(r.Body).Decode(&req)
var resBytes []byte
switch {
case req.User == "invalid":
w.WriteHeader(http.StatusBadRequest)
return
case req.EncodingFormat == EmbeddingEncodingFormatBase64:
resBytes, _ = json.Marshal(EmbeddingResponseBase64{Data: sampleBase64Embeddings})
default:
resBytes, _ = json.Marshal(EmbeddingResponse{Data: sampleEmbeddings})
}
fmt.Fprintln(w, string(resBytes))
},
)
// test create embeddings with strings (simple embedding request)
res, err := client.CreateEmbeddings(context.Background(), EmbeddingRequest{})
checks.NoError(t, err, "CreateEmbeddings error")
if !reflect.DeepEqual(res.Data, sampleEmbeddings) {
t.Errorf("Expected %#v embeddings, got %#v", sampleEmbeddings, res.Data)
}
// test create embeddings with strings (simple embedding request)
res, err = client.CreateEmbeddings(
context.Background(),
EmbeddingRequest{
EncodingFormat: EmbeddingEncodingFormatBase64,
},
)
checks.NoError(t, err, "CreateEmbeddings error")
if !reflect.DeepEqual(res.Data, sampleEmbeddings) {
t.Errorf("Expected %#v embeddings, got %#v", sampleEmbeddings, res.Data)
}
// test create embeddings with strings
res, err = client.CreateEmbeddings(context.Background(), EmbeddingRequestStrings{})
checks.NoError(t, err, "CreateEmbeddings strings error")
if !reflect.DeepEqual(res.Data, sampleEmbeddings) {
t.Errorf("Expected %#v embeddings, got %#v", sampleEmbeddings, res.Data)
}
// test create embeddings with tokens
res, err = client.CreateEmbeddings(context.Background(), EmbeddingRequestTokens{})
checks.NoError(t, err, "CreateEmbeddings tokens error")
if !reflect.DeepEqual(res.Data, sampleEmbeddings) {
t.Errorf("Expected %#v embeddings, got %#v", sampleEmbeddings, res.Data)
}
// test failed sendRequest
_, err = client.CreateEmbeddings(context.Background(), EmbeddingRequest{
User: "invalid",
EncodingFormat: EmbeddingEncodingFormatBase64,
})
checks.HasError(t, err, "CreateEmbeddings error")
}
func TestEmbeddingResponseBase64_ToEmbeddingResponse(t *testing.T) {
type fields struct {
Object string
Data []Base64Embedding
Model EmbeddingModel
Usage Usage
}
tests := []struct {
name string
fields fields
want EmbeddingResponse
wantErr bool
}{
{
name: "test embedding response base64 to embedding response",
fields: fields{
Data: []Base64Embedding{
{Embedding: "pHCdP4XrkUDhevxA"},
{Embedding: "/1jku0G/rLvA/EI8"},
},
},
want: EmbeddingResponse{
Data: []Embedding{
{Embedding: []float32{1.23, 4.56, 7.89}},
{Embedding: []float32{-0.006968617, -0.0052718227, 0.011901081}},
},
},
wantErr: false,
},
{
name: "Invalid embedding",
fields: fields{
Data: []Base64Embedding{
{
Embedding: "----",
},
},
},
want: EmbeddingResponse{},
wantErr: true,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
r := &EmbeddingResponseBase64{
Object: tt.fields.Object,
Data: tt.fields.Data,
Model: tt.fields.Model,
Usage: tt.fields.Usage,
}
got, err := r.ToEmbeddingResponse()
if (err != nil) != tt.wantErr {
t.Errorf("EmbeddingResponseBase64.ToEmbeddingResponse() error = %v, wantErr %v", err, tt.wantErr)
return
}
if !reflect.DeepEqual(got, tt.want) {
t.Errorf("EmbeddingResponseBase64.ToEmbeddingResponse() = %v, want %v", got, tt.want)
}
})
}
}
func TestDotProduct(t *testing.T) {
v1 := &Embedding{Embedding: []float32{1, 2, 3}}
v2 := &Embedding{Embedding: []float32{2, 4, 6}}
expected := float32(28.0)
result, err := v1.DotProduct(v2)
if err != nil {
t.Errorf("Unexpected error: %v", err)
}
if math.Abs(float64(result-expected)) > 1e-12 {
t.Errorf("Unexpected result. Expected: %v, but got %v", expected, result)
}
v1 = &Embedding{Embedding: []float32{1, 0, 0}}
v2 = &Embedding{Embedding: []float32{0, 1, 0}}
expected = float32(0.0)
result, err = v1.DotProduct(v2)
if err != nil {
t.Errorf("Unexpected error: %v", err)
}
if math.Abs(float64(result-expected)) > 1e-12 {
t.Errorf("Unexpected result. Expected: %v, but got %v", expected, result)
}
// Test for VectorLengthMismatchError
v1 = &Embedding{Embedding: []float32{1, 0, 0}}
v2 = &Embedding{Embedding: []float32{0, 1}}
_, err = v1.DotProduct(v2)
if !errors.Is(err, ErrVectorLengthMismatch) {
t.Errorf("Expected Vector Length Mismatch Error, but got: %v", err)
}
}