Integrate BACKBEAT SDK and resolve KACHING license validation
Major integrations and fixes: - Added BACKBEAT SDK integration for P2P operation timing - Implemented beat-aware status tracking for distributed operations - Added Docker secrets support for secure license management - Resolved KACHING license validation via HTTPS/TLS - Updated docker-compose configuration for clean stack deployment - Disabled rollback policies to prevent deployment failures - Added license credential storage (CHORUS-DEV-MULTI-001) Technical improvements: - BACKBEAT P2P operation tracking with phase management - Enhanced configuration system with file-based secrets - Improved error handling for license validation - Clean separation of KACHING and CHORUS deployment stacks 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
anthonyrawlins
297
vendor/github.com/sashabaranov/go-openai/embeddings.go
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vendor/github.com/sashabaranov/go-openai/embeddings.go
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package openai
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import (
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"context"
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"encoding/base64"
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"encoding/binary"
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"encoding/json"
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"errors"
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"math"
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"net/http"
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)
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var ErrVectorLengthMismatch = errors.New("vector length mismatch")
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// EmbeddingModel enumerates the models which can be used
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// to generate Embedding vectors.
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type EmbeddingModel string
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const (
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// Deprecated: The following block is shut down. Use text-embedding-ada-002 instead.
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AdaSimilarity EmbeddingModel = "text-similarity-ada-001"
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BabbageSimilarity EmbeddingModel = "text-similarity-babbage-001"
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CurieSimilarity EmbeddingModel = "text-similarity-curie-001"
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DavinciSimilarity EmbeddingModel = "text-similarity-davinci-001"
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AdaSearchDocument EmbeddingModel = "text-search-ada-doc-001"
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AdaSearchQuery EmbeddingModel = "text-search-ada-query-001"
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BabbageSearchDocument EmbeddingModel = "text-search-babbage-doc-001"
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BabbageSearchQuery EmbeddingModel = "text-search-babbage-query-001"
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CurieSearchDocument EmbeddingModel = "text-search-curie-doc-001"
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CurieSearchQuery EmbeddingModel = "text-search-curie-query-001"
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DavinciSearchDocument EmbeddingModel = "text-search-davinci-doc-001"
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DavinciSearchQuery EmbeddingModel = "text-search-davinci-query-001"
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AdaCodeSearchCode EmbeddingModel = "code-search-ada-code-001"
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AdaCodeSearchText EmbeddingModel = "code-search-ada-text-001"
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BabbageCodeSearchCode EmbeddingModel = "code-search-babbage-code-001"
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BabbageCodeSearchText EmbeddingModel = "code-search-babbage-text-001"
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AdaEmbeddingV2 EmbeddingModel = "text-embedding-ada-002"
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SmallEmbedding3 EmbeddingModel = "text-embedding-3-small"
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LargeEmbedding3 EmbeddingModel = "text-embedding-3-large"
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)
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// Embedding is a special format of data representation that can be easily utilized by machine
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// learning models and algorithms. The embedding is an information dense representation of the
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// semantic meaning of a piece of text. Each embedding is a vector of floating point numbers,
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// such that the distance between two embeddings in the vector space is correlated with semantic similarity
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// between two inputs in the original format. For example, if two texts are similar,
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// then their vector representations should also be similar.
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type Embedding struct {
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Object string `json:"object"`
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Embedding []float32 `json:"embedding"`
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Index int `json:"index"`
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}
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// DotProduct calculates the dot product of the embedding vector with another
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// embedding vector. Both vectors must have the same length; otherwise, an
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// ErrVectorLengthMismatch is returned. The method returns the calculated dot
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// product as a float32 value.
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func (e *Embedding) DotProduct(other *Embedding) (float32, error) {
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if len(e.Embedding) != len(other.Embedding) {
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return 0, ErrVectorLengthMismatch
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}
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var dotProduct float32
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for i := range e.Embedding {
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dotProduct += e.Embedding[i] * other.Embedding[i]
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}
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return dotProduct, nil
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}
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// EmbeddingResponse is the response from a Create embeddings request.
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type EmbeddingResponse struct {
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Object string `json:"object"`
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Data []Embedding `json:"data"`
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Model EmbeddingModel `json:"model"`
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Usage Usage `json:"usage"`
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httpHeader
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}
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type base64String string
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func (b base64String) Decode() ([]float32, error) {
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decodedData, err := base64.StdEncoding.DecodeString(string(b))
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if err != nil {
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return nil, err
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}
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const sizeOfFloat32 = 4
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floats := make([]float32, len(decodedData)/sizeOfFloat32)
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for i := 0; i < len(floats); i++ {
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floats[i] = math.Float32frombits(binary.LittleEndian.Uint32(decodedData[i*4 : (i+1)*4]))
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}
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return floats, nil
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}
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// Base64Embedding is a container for base64 encoded embeddings.
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type Base64Embedding struct {
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Object string `json:"object"`
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Embedding base64String `json:"embedding"`
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Index int `json:"index"`
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}
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// EmbeddingResponseBase64 is the response from a Create embeddings request with base64 encoding format.
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type EmbeddingResponseBase64 struct {
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Object string `json:"object"`
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Data []Base64Embedding `json:"data"`
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Model EmbeddingModel `json:"model"`
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Usage Usage `json:"usage"`
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httpHeader
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}
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// ToEmbeddingResponse converts an embeddingResponseBase64 to an EmbeddingResponse.
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func (r *EmbeddingResponseBase64) ToEmbeddingResponse() (EmbeddingResponse, error) {
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data := make([]Embedding, len(r.Data))
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for i, base64Embedding := range r.Data {
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embedding, err := base64Embedding.Embedding.Decode()
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if err != nil {
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return EmbeddingResponse{}, err
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}
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data[i] = Embedding{
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Object: base64Embedding.Object,
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Embedding: embedding,
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Index: base64Embedding.Index,
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}
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}
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return EmbeddingResponse{
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Object: r.Object,
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Model: r.Model,
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Data: data,
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Usage: r.Usage,
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}, nil
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}
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type EmbeddingRequestConverter interface {
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// Needs to be of type EmbeddingRequestStrings or EmbeddingRequestTokens
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Convert() EmbeddingRequest
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}
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// EmbeddingEncodingFormat is the format of the embeddings data.
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// Currently, only "float" and "base64" are supported, however, "base64" is not officially documented.
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// If not specified OpenAI will use "float".
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type EmbeddingEncodingFormat string
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const (
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EmbeddingEncodingFormatFloat EmbeddingEncodingFormat = "float"
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EmbeddingEncodingFormatBase64 EmbeddingEncodingFormat = "base64"
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)
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type EmbeddingRequest struct {
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Input any `json:"input"`
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Model EmbeddingModel `json:"model"`
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User string `json:"user,omitempty"`
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EncodingFormat EmbeddingEncodingFormat `json:"encoding_format,omitempty"`
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// Dimensions The number of dimensions the resulting output embeddings should have.
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// Only supported in text-embedding-3 and later models.
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Dimensions int `json:"dimensions,omitempty"`
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// The ExtraBody field allows for the inclusion of arbitrary key-value pairs
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// in the request body that may not be explicitly defined in this struct.
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ExtraBody map[string]any `json:"extra_body,omitempty"`
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}
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func (r EmbeddingRequest) Convert() EmbeddingRequest {
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return r
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}
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// EmbeddingRequestStrings is the input to a create embeddings request with a slice of strings.
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type EmbeddingRequestStrings struct {
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// Input is a slice of strings for which you want to generate an Embedding vector.
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// Each input must not exceed 8192 tokens in length.
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// OpenAPI suggests replacing newlines (\n) in your input with a single space, as they
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// have observed inferior results when newlines are present.
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// E.g.
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// "The food was delicious and the waiter..."
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Input []string `json:"input"`
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// ID of the model to use. You can use the List models API to see all of your available models,
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// or see our Model overview for descriptions of them.
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Model EmbeddingModel `json:"model"`
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// A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
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User string `json:"user"`
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// EmbeddingEncodingFormat is the format of the embeddings data.
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// Currently, only "float" and "base64" are supported, however, "base64" is not officially documented.
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// If not specified OpenAI will use "float".
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EncodingFormat EmbeddingEncodingFormat `json:"encoding_format,omitempty"`
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// Dimensions The number of dimensions the resulting output embeddings should have.
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// Only supported in text-embedding-3 and later models.
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Dimensions int `json:"dimensions,omitempty"`
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// The ExtraBody field allows for the inclusion of arbitrary key-value pairs
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// in the request body that may not be explicitly defined in this struct.
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ExtraBody map[string]any `json:"extra_body,omitempty"`
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}
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func (r EmbeddingRequestStrings) Convert() EmbeddingRequest {
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return EmbeddingRequest{
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Input: r.Input,
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Model: r.Model,
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User: r.User,
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EncodingFormat: r.EncodingFormat,
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Dimensions: r.Dimensions,
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ExtraBody: r.ExtraBody,
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}
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}
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type EmbeddingRequestTokens struct {
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// Input is a slice of slices of ints ([][]int) for which you want to generate an Embedding vector.
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// Each input must not exceed 8192 tokens in length.
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// OpenAPI suggests replacing newlines (\n) in your input with a single space, as they
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// have observed inferior results when newlines are present.
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// E.g.
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// "The food was delicious and the waiter..."
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Input [][]int `json:"input"`
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// ID of the model to use. You can use the List models API to see all of your available models,
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// or see our Model overview for descriptions of them.
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Model EmbeddingModel `json:"model"`
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// A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
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User string `json:"user"`
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// EmbeddingEncodingFormat is the format of the embeddings data.
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// Currently, only "float" and "base64" are supported, however, "base64" is not officially documented.
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// If not specified OpenAI will use "float".
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EncodingFormat EmbeddingEncodingFormat `json:"encoding_format,omitempty"`
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// Dimensions The number of dimensions the resulting output embeddings should have.
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// Only supported in text-embedding-3 and later models.
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Dimensions int `json:"dimensions,omitempty"`
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// The ExtraBody field allows for the inclusion of arbitrary key-value pairs
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// in the request body that may not be explicitly defined in this struct.
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ExtraBody map[string]any `json:"extra_body,omitempty"`
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}
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func (r EmbeddingRequestTokens) Convert() EmbeddingRequest {
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return EmbeddingRequest{
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Input: r.Input,
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Model: r.Model,
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User: r.User,
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EncodingFormat: r.EncodingFormat,
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Dimensions: r.Dimensions,
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ExtraBody: r.ExtraBody,
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}
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}
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// CreateEmbeddings returns an EmbeddingResponse which will contain an Embedding for every item in |body.Input|.
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// https://beta.openai.com/docs/api-reference/embeddings/create
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//
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// Body should be of type EmbeddingRequestStrings for embedding strings or EmbeddingRequestTokens
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// for embedding groups of text already converted to tokens.
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func (c *Client) CreateEmbeddings(
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ctx context.Context,
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conv EmbeddingRequestConverter,
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) (res EmbeddingResponse, err error) {
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baseReq := conv.Convert()
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// The body map is used to dynamically construct the request payload for the embedding API.
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// Instead of relying on a fixed struct, the body map allows for flexible inclusion of fields
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// based on their presence, avoiding unnecessary or empty fields in the request.
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extraBody := baseReq.ExtraBody
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baseReq.ExtraBody = nil
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// Serialize baseReq to JSON
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jsonData, err := json.Marshal(baseReq)
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if err != nil {
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return
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}
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// Deserialize JSON to map[string]any
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var body map[string]any
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_ = json.Unmarshal(jsonData, &body)
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req, err := c.newRequest(
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ctx,
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http.MethodPost,
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c.fullURL("/embeddings", withModel(string(baseReq.Model))),
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withBody(body), // Main request body.
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withExtraBody(extraBody), // Merge ExtraBody fields.
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)
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if err != nil {
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return
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}
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if baseReq.EncodingFormat != EmbeddingEncodingFormatBase64 {
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err = c.sendRequest(req, &res)
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return
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}
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base64Response := &EmbeddingResponseBase64{}
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err = c.sendRequest(req, base64Response)
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if err != nil {
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return
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}
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res, err = base64Response.ToEmbeddingResponse()
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return
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}
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