embedding.go

// File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.

package openai

import (
"context"
"net/http"

"github.com/openai/openai-go/internal/apijson"
"github.com/openai/openai-go/internal/requestconfig"
"github.com/openai/openai-go/option"
"github.com/openai/openai-go/packages/param"
"github.com/openai/openai-go/packages/resp"
"github.com/openai/openai-go/shared/constant"
)

// EmbeddingService contains methods and other services that help with interacting
// with the openai API.
//
// Note, unlike clients, this service does not read variables from the environment
// automatically. You should not instantiate this service directly, and instead use
// the [NewEmbeddingService] method instead.
typeEmbeddingServicestruct {
Options []option.RequestOption
}

// NewEmbeddingService generates a new service that applies the given options to
// each request. These options are applied after the parent client's options (if
// there is one), and before any request-specific options.
funcNewEmbeddingService(opts...option.RequestOption) (rEmbeddingService) {
r=EmbeddingService{}
r.Options=opts
return
}

// Creates an embedding vector representing the input text.
func (r*EmbeddingService) New(ctx context.Context, bodyEmbeddingNewParams, opts...option.RequestOption) (res*CreateEmbeddingResponse, errerror) {
opts=append(r.Options[:], opts...)
path:="embeddings"
err=requestconfig.ExecuteNewRequest(ctx, http.MethodPost, path, body, &res, opts...)
return
}

typeCreateEmbeddingResponsestruct {
// The list of embeddings generated by the model.
Data []Embedding`json:"data,required"`
// The name of the model used to generate the embedding.
Modelstring`json:"model,required"`
// The object type, which is always "list".
Object constant.List`json:"object,required"`
// The usage information for the request.
UsageCreateEmbeddingResponseUsage`json:"usage,required"`
// Metadata for the response, check the presence of optional fields with the
// [resp.Field.IsPresent] method.
JSONstruct {
Data resp.Field
Model resp.Field
Object resp.Field
Usage resp.Field
ExtraFieldsmap[string]resp.Field
rawstring
 } `json:"-"`
}

// Returns the unmodified JSON received from the API
func (rCreateEmbeddingResponse) RawJSON() string { returnr.JSON.raw }
func (r*CreateEmbeddingResponse) UnmarshalJSON(data []byte) error {
returnapijson.UnmarshalRoot(data, r)
}

// The usage information for the request.
typeCreateEmbeddingResponseUsagestruct {
// The number of tokens used by the prompt.
PromptTokensint64`json:"prompt_tokens,required"`
// The total number of tokens used by the request.
TotalTokensint64`json:"total_tokens,required"`
// Metadata for the response, check the presence of optional fields with the
// [resp.Field.IsPresent] method.
JSONstruct {
PromptTokens resp.Field
TotalTokens resp.Field
ExtraFieldsmap[string]resp.Field
rawstring
 } `json:"-"`
}

// Returns the unmodified JSON received from the API
func (rCreateEmbeddingResponseUsage) RawJSON() string { returnr.JSON.raw }
func (r*CreateEmbeddingResponseUsage) UnmarshalJSON(data []byte) error {
returnapijson.UnmarshalRoot(data, r)
}

// Represents an embedding vector returned by embedding endpoint.
typeEmbeddingstruct {
// The embedding vector, which is a list of floats. The length of vector depends on
// the model as listed in the
// [embedding guide](https://platform.openai.com/docs/guides/embeddings).
Embedding []float64`json:"embedding,required"`
// The index of the embedding in the list of embeddings.
Indexint64`json:"index,required"`
// The object type, which is always "embedding".
Object constant.Embedding`json:"object,required"`
// Metadata for the response, check the presence of optional fields with the
// [resp.Field.IsPresent] method.
JSONstruct {
Embedding resp.Field
Index resp.Field
Object resp.Field
ExtraFieldsmap[string]resp.Field
rawstring
 } `json:"-"`
}

// Returns the unmodified JSON received from the API
func (rEmbedding) RawJSON() string { returnr.JSON.raw }
func (r*Embedding) UnmarshalJSON(data []byte) error {
returnapijson.UnmarshalRoot(data, r)
}

typeEmbeddingModel=string

const (
EmbeddingModelTextEmbeddingAda002EmbeddingModel="text-embedding-ada-002"
EmbeddingModelTextEmbedding3SmallEmbeddingModel="text-embedding-3-small"
EmbeddingModelTextEmbedding3LargeEmbeddingModel="text-embedding-3-large"
)

typeEmbeddingNewParamsstruct {
// Input text to embed, encoded as a string or array of tokens. To embed multiple
// inputs in a single request, pass an array of strings or array of token arrays.
// The input must not exceed the max input tokens for the model (8192 tokens for
// `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048
// dimensions or less.
// [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken)
// for counting tokens. Some models may also impose a limit on total number of
// tokens summed across inputs.
InputEmbeddingNewParamsInputUnion`json:"input,omitzero,required"`
// ID of the model to use. You can use the
// [List models](https://platform.openai.com/docs/api-reference/models/list) API to
// see all of your available models, or see our
// [Model overview](https://platform.openai.com/docs/models) for descriptions of
// them.
ModelEmbeddingModel`json:"model,omitzero,required"`
// The number of dimensions the resulting output embeddings should have. Only
// supported in `text-embedding-3` and later models.
Dimensions param.Opt[int64] `json:"dimensions,omitzero"`
// A unique identifier representing your end-user, which can help OpenAI to monitor
// and detect abuse.
// [Learn more](https://platform.openai.com/docs/guides/safety-best-practices#end-user-ids).
User param.Opt[string] `json:"user,omitzero"`
// The format to return the embeddings in. Can be either `float` or
// [`base64`](https://pypi.org/project/pybase64/).
//
// Any of "float", "base64".
EncodingFormatEmbeddingNewParamsEncodingFormat`json:"encoding_format,omitzero"`
paramObj
}

// IsPresent returns true if the field's value is not omitted and not the JSON
// "null". To check if this field is omitted, use [param.IsOmitted].
func (fEmbeddingNewParams) IsPresent() bool { return!param.IsOmitted(f) &&!f.IsNull() }

func (rEmbeddingNewParams) MarshalJSON() (data []byte, errerror) {
typeshadowEmbeddingNewParams
returnparam.MarshalObject(r, (*shadow)(&r))
}

// Only one field can be non-zero.
//
// Use [param.IsOmitted] to confirm if a field is set.
typeEmbeddingNewParamsInputUnionstruct {
OfString param.Opt[string] `json:",omitzero,inline"`
OfArrayOfStrings []string`json:",omitzero,inline"`
OfArrayOfTokens []int64`json:",omitzero,inline"`
OfArrayOfTokenArrays [][]int64`json:",omitzero,inline"`
paramUnion
}

// IsPresent returns true if the field's value is not omitted and not the JSON
// "null". To check if this field is omitted, use [param.IsOmitted].
func (uEmbeddingNewParamsInputUnion) IsPresent() bool { return!param.IsOmitted(u) &&!u.IsNull() }
func (uEmbeddingNewParamsInputUnion) MarshalJSON() ([]byte, error) {
returnparam.MarshalUnion[EmbeddingNewParamsInputUnion](u.OfString, u.OfArrayOfStrings, u.OfArrayOfTokens, u.OfArrayOfTokenArrays)
}

func (u*EmbeddingNewParamsInputUnion) asAny() any {
if!param.IsOmitted(u.OfString) {
return&u.OfString.Value
 } elseif!param.IsOmitted(u.OfArrayOfStrings) {
return&u.OfArrayOfStrings
 } elseif!param.IsOmitted(u.OfArrayOfTokens) {
return&u.OfArrayOfTokens
 } elseif!param.IsOmitted(u.OfArrayOfTokenArrays) {
return&u.OfArrayOfTokenArrays
 }
returnnil
}

// The format to return the embeddings in. Can be either `float` or
// [`base64`](https://pypi.org/project/pybase64/).
typeEmbeddingNewParamsEncodingFormatstring

const (
EmbeddingNewParamsEncodingFormatFloatEmbeddingNewParamsEncodingFormat="float"
EmbeddingNewParamsEncodingFormatBase64EmbeddingNewParamsEncodingFormat="base64"
)