Add new Embeddings endpoint (sashabaranov#12)

api_test.go

@@ -51,4 +51,15 @@ func TestAPI(t *testing.T) {
 	if err != nil {
 		t.Fatalf("Search error: %v", err)
 	}
+
+	embeddingReq := EmbeddingRequest{
+		Input: []string{
+			"The food was delicious and the waiter",
+			"Other examples of embedding request",
+		},
+	}
+	_, err = c.CreateEmbeddings(ctx, embeddingReq, AdaSearchQuery)
+	if err != nil {
+		t.Fatalf("Embedding error: %v", err)
+	}
 }

embeddings.go

@@ -0,0 +1,144 @@
+package gogpt
+
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"net/http"
+)
+
+// 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
+}
+
+const (
+	Unknown EmbeddingModel = iota
+	AdaSimilarity
+	BabbageSimilarity
+	CurieSimilarity
+	DavinciSimilarity
+	AdaSearchDocument
+	AdaSearchQuery
+	BabbageSearchDocument
+	BabbageSearchQuery
+	CurieSearchDocument
+	CurieSearchQuery
+	DavinciSearchDocument
+	DavinciSearchQuery
+	AdaCodeSearchCode
+	AdaCodeSearchText
+	BabbageCodeSearchCode
+	BabbageCodeSearchText
+)
+
+var enumToString = map[EmbeddingModel]string{
+	AdaSimilarity:         "ada-similarity",
+	BabbageSimilarity:     "babbage-similarity",
+	CurieSimilarity:       "curie-similarity",
+	DavinciSimilarity:     "davinci-similarity",
+	AdaSearchDocument:     "ada-search-document",
+	AdaSearchQuery:        "ada-search-query",
+	BabbageSearchDocument: "babbage-search-document",
+	BabbageSearchQuery:    "babbage-search-query",
+	CurieSearchDocument:   "curie-search-document",
+	CurieSearchQuery:      "curie-search-query",
+	DavinciSearchDocument: "davinci-search-document",
+	DavinciSearchQuery:    "davinci-search-query",
+	AdaCodeSearchCode:     "ada-code-search-code",
+	AdaCodeSearchText:     "ada-code-search-text",
+	BabbageCodeSearchCode: "babbage-code-search-code",
+	BabbageCodeSearchText: "babbage-code-search-text",
+}
+
+var stringToEnum = map[string]EmbeddingModel{
+	"ada-similarity":           AdaSimilarity,
+	"babbage-similarity":       BabbageSimilarity,
+	"curie-similarity":         CurieSimilarity,
+	"davinci-similarity":       DavinciSimilarity,
+	"ada-search-document":      AdaSearchDocument,
+	"ada-search-query":         AdaSearchQuery,
+	"babbage-search-document":  BabbageSearchDocument,
+	"babbage-search-query":     BabbageSearchQuery,
+	"curie-search-document":    CurieSearchDocument,
+	"curie-search-query":       CurieSearchQuery,
+	"davinci-search-document":  DavinciSearchDocument,
+	"davinci-search-query":     DavinciSearchQuery,
+	"ada-code-search-code":     AdaCodeSearchCode,
+	"ada-code-search-text":     AdaCodeSearchText,
+	"babbage-code-search-code": BabbageCodeSearchCode,
+	"babbage-code-search-text": BabbageCodeSearchText,
+}
+
+// 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, such that the distance between two embeddings in the vector space is correlated with semantic similarity
+// between two inputs in the original format. For example, if two texts are similar, then their vector representations should
+// also be similar.
+type Embedding struct {
+	Object    string    `json:"object"`
+	Embedding []float64 `json:"embedding"`
+	Index     int       `json:"index"`
+}
+
+// EmbeddingResponse is the response from a Create embeddings request.
+type EmbeddingResponse struct {
+	Object string         `json:"object"`
+	Data   []Embedding    `json:"data"`
+	Model  EmbeddingModel `json:"model"`
+}
+
+// EmbeddingRequest is the input to a Create embeddings request.
+type EmbeddingRequest struct {
+	// Input is a slice of strings for which you want to generate an Embedding vector.
+	// Each input must not exceed 2048 tokens in length.
+	// OpenAPI suggests replacing newlines (\n) in your input with a single space, as they
+	// have observed inferior results when newlines are present.
+	// E.g.
+	//	"The food was delicious and the waiter..."
+	Input []string `json:"input"`
+}
+
+// CreateEmbeddings returns an EmbeddingResponse which will contain an Embedding for every item in |request.Input|.
+// https://beta.openai.com/docs/api-reference/embeddings/create
+func (c *Client) CreateEmbeddings(ctx context.Context, request EmbeddingRequest, model EmbeddingModel) (resp EmbeddingResponse, err error) {
+	var reqBytes []byte
+	reqBytes, err = json.Marshal(request)
+	if err != nil {
+		return
+	}
+
+	urlSuffix := fmt.Sprintf("/engines/%s/embeddings", model)
+	req, err := http.NewRequest(http.MethodPost, c.fullURL(urlSuffix), bytes.NewBuffer(reqBytes))
+	if err != nil {
+		return
+	}
+
+	req = req.WithContext(ctx)
+	err = c.sendRequest(req, &resp)
+
+	return
+}