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convert EmbeddingModel to string type (sashabaranov#629)
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This gives the user the ability to pass in models for embeddings that are not
already defined in the library. Also more closely matches how the completions
API works.
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@jaffee
jaffee committed Jan 15, 2024
1 parent 682b7ad commit e01a2d7
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Showing 2 changed files with 24 additions and 118 deletions.
120 changes: 21 additions & 99 deletions embeddings.go
View file
Original file line number Diff line number Diff line change
Expand Up @@ -13,108 +13,30 @@ var ErrVectorLengthMismatch = errors.New("vector length mismatch")

// EmbeddingModel enumerates the models which can be used
// to generate Embedding vectors.
type EmbeddingModel int

// String implements the fmt.Stringer interface.
func (e EmbeddingModel) String() string {
return enumToString[e]
}

// MarshalText implements the encoding.TextMarshaler interface.
func (e EmbeddingModel) MarshalText() ([]byte, error) {
return []byte(e.String()), nil
}

// UnmarshalText implements the encoding.TextUnmarshaler interface.
// On unrecognized value, it sets |e| to Unknown.
func (e *EmbeddingModel) UnmarshalText(b []byte) error {
if val, ok := stringToEnum[(string(b))]; ok {
*e = val
return nil
}

*e = Unknown

return nil
}
type EmbeddingModel string

const (
Unknown EmbeddingModel = iota
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
AdaSimilarity
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
BabbageSimilarity
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
CurieSimilarity
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
DavinciSimilarity
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
AdaSearchDocument
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
AdaSearchQuery
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
BabbageSearchDocument
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
BabbageSearchQuery
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
CurieSearchDocument
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
CurieSearchQuery
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
DavinciSearchDocument
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
DavinciSearchQuery
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
AdaCodeSearchCode
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
AdaCodeSearchText
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
BabbageCodeSearchCode
// Deprecated: Will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
BabbageCodeSearchText
AdaEmbeddingV2
// Deprecated: The following block will be shut down on January 04, 2024. Use text-embedding-ada-002 instead.
AdaSimilarity EmbeddingModel = "text-similarity-ada-001"
BabbageSimilarity EmbeddingModel = "text-similarity-babbage-001"
CurieSimilarity EmbeddingModel = "text-similarity-curie-001"
DavinciSimilarity EmbeddingModel = "text-similarity-davinci-001"
AdaSearchDocument EmbeddingModel = "text-search-ada-doc-001"
AdaSearchQuery EmbeddingModel = "text-search-ada-query-001"
BabbageSearchDocument EmbeddingModel = "text-search-babbage-doc-001"
BabbageSearchQuery EmbeddingModel = "text-search-babbage-query-001"
CurieSearchDocument EmbeddingModel = "text-search-curie-doc-001"
CurieSearchQuery EmbeddingModel = "text-search-curie-query-001"
DavinciSearchDocument EmbeddingModel = "text-search-davinci-doc-001"
DavinciSearchQuery EmbeddingModel = "text-search-davinci-query-001"
AdaCodeSearchCode EmbeddingModel = "code-search-ada-code-001"
AdaCodeSearchText EmbeddingModel = "code-search-ada-text-001"
BabbageCodeSearchCode EmbeddingModel = "code-search-babbage-code-001"
BabbageCodeSearchText EmbeddingModel = "code-search-babbage-text-001"

AdaEmbeddingV2 EmbeddingModel = "text-embedding-ada-002"
)

var enumToString = map[EmbeddingModel]string{
AdaSimilarity: "text-similarity-ada-001",
BabbageSimilarity: "text-similarity-babbage-001",
CurieSimilarity: "text-similarity-curie-001",
DavinciSimilarity: "text-similarity-davinci-001",
AdaSearchDocument: "text-search-ada-doc-001",
AdaSearchQuery: "text-search-ada-query-001",
BabbageSearchDocument: "text-search-babbage-doc-001",
BabbageSearchQuery: "text-search-babbage-query-001",
CurieSearchDocument: "text-search-curie-doc-001",
CurieSearchQuery: "text-search-curie-query-001",
DavinciSearchDocument: "text-search-davinci-doc-001",
DavinciSearchQuery: "text-search-davinci-query-001",
AdaCodeSearchCode: "code-search-ada-code-001",
AdaCodeSearchText: "code-search-ada-text-001",
BabbageCodeSearchCode: "code-search-babbage-code-001",
BabbageCodeSearchText: "code-search-babbage-text-001",
AdaEmbeddingV2: "text-embedding-ada-002",
}

var stringToEnum = map[string]EmbeddingModel{
"text-similarity-ada-001": AdaSimilarity,
"text-similarity-babbage-001": BabbageSimilarity,
"text-similarity-curie-001": CurieSimilarity,
"text-similarity-davinci-001": DavinciSimilarity,
"text-search-ada-doc-001": AdaSearchDocument,
"text-search-ada-query-001": AdaSearchQuery,
"text-search-babbage-doc-001": BabbageSearchDocument,
"text-search-babbage-query-001": BabbageSearchQuery,
"text-search-curie-doc-001": CurieSearchDocument,
"text-search-curie-query-001": CurieSearchQuery,
"text-search-davinci-doc-001": DavinciSearchDocument,
"text-search-davinci-query-001": DavinciSearchQuery,
"code-search-ada-code-001": AdaCodeSearchCode,
"code-search-ada-text-001": AdaCodeSearchText,
"code-search-babbage-code-001": BabbageCodeSearchCode,
"code-search-babbage-text-001": BabbageCodeSearchText,
"text-embedding-ada-002": AdaEmbeddingV2,
}

// Embedding is a special format of data representation that can be easily utilized by machine
// learning models and algorithms. The embedding is an information dense representation of the
// semantic meaning of a piece of text. Each embedding is a vector of floating point numbers,
Expand Down Expand Up @@ -306,7 +228,7 @@ func (c *Client) CreateEmbeddings(
conv EmbeddingRequestConverter,
) (res EmbeddingResponse, err error) {
baseReq := conv.Convert()
req, err := c.newRequest(ctx, http.MethodPost, c.fullURL("/embeddings", baseReq.Model.String()), withBody(baseReq))
req, err := c.newRequest(ctx, http.MethodPost, c.fullURL("/embeddings", baseReq.Model), withBody(baseReq))
if err != nil {
return
}
Expand Down
22 changes: 3 additions & 19 deletions embeddings_test.go
View file
Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ func TestEmbedding(t *testing.T) {
// 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()+`"`)) {
if !bytes.Contains(marshaled, []byte(`"model":"`+model+`"`)) {
t.Fatalf("Expected embedding request to contain model field")
}

Expand All @@ -61,7 +61,7 @@ func TestEmbedding(t *testing.T) {
}
marshaled, err = json.Marshal(embeddingReqStrings)
checks.NoError(t, err, "Could not marshal embedding request")
if !bytes.Contains(marshaled, []byte(`"model":"`+model.String()+`"`)) {
if !bytes.Contains(marshaled, []byte(`"model":"`+model+`"`)) {
t.Fatalf("Expected embedding request to contain model field")
}

Expand All @@ -75,28 +75,12 @@ func TestEmbedding(t *testing.T) {
}
marshaled, err = json.Marshal(embeddingReqTokens)
checks.NoError(t, err, "Could not marshal embedding request")
if !bytes.Contains(marshaled, []byte(`"model":"`+model.String()+`"`)) {
if !bytes.Contains(marshaled, []byte(`"model":"`+model+`"`)) {
t.Fatalf("Expected embedding request to contain model field")
}
}
}

func TestEmbeddingModel(t *testing.T) {
var em openai.EmbeddingModel
err := em.UnmarshalText([]byte("text-similarity-ada-001"))
checks.NoError(t, err, "Could not marshal embedding model")

if em != openai.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 != openai.Unknown {
t.Errorf("Model is not equal to Unknown")
}
}

func TestEmbeddingEndpoint(t *testing.T) {
client, server, teardown := setupOpenAITestServer()
defer teardown()
Expand Down

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