vertexai

package

v0.0.2 Latest Latest Go to latest Published: Nov 29, 2024 License: MIT Imports: 14 Imported by: 0

Details

Valid go.mod file
Redistributable license
Tagged version
Stable version
Learn more about best practices

Repository

github.com/modfin/bellman

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Documentation ¶

Index ¶

Constants
Variables
type EmbedType
type Google
- func New(config GoogleConfig) (*Google, error)
- func (g *Google) Embed(text string, model string) ([]float64, error)
- func (g *Google) Generator(options ...bellman.GeneratorOption) bellman.Generator
type GoogleConfig
type GoogleEmbedRequest
type GoogleEmbedResponse

Constants ¶

View Source

const EmbedDimensions = 768

Variables ¶

View Source

var EmbedMode_multilang_002 = bellman.EmbedModel{
	Name:             "text-multilingual-embedding-002",
	Description:      "see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api",
	InputMaxTokens:   2048,
	OutputDimensions: 768,
}

View Source

var EmbedModel_text_004 = bellman.EmbedModel{
	Name:             "text-embedding-004",
	Description:      "see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api",
	InputMaxTokens:   2048,
	OutputDimensions: 768,
}

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var EmbedModel_text_005 = bellman.EmbedModel{
	Name:             "text-embedding-005",
	Description:      "see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api",
	InputMaxTokens:   2048,
	OutputDimensions: 768,
}

View Source

var EmbedModel_text_gecko_001 = bellman.EmbedModel{
	Name:             "textembedding-gecko@001",
	Description:      "see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api",
	InputMaxTokens:   2048,
	OutputDimensions: 768,
}

View Source

var EmbedModel_text_gecko_003 = bellman.EmbedModel{
	Name:             "textembedding-gecko@003",
	Description:      "see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api",
	InputMaxTokens:   2048,
	OutputDimensions: 768,
}

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var EmbedModel_text_gecko_multilang_001 = bellman.EmbedModel{
	Name:             "textembedding-gecko-multilingual@001",
	Description:      "see https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api",
	InputMaxTokens:   2048,
	OutputDimensions: 768,
}

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var GenModel_gemini_1_5_flash = bellman.GenModel{
	Name:                    "gemini-1.5-flash",
	Description:             "Fast and versatile performance across a diverse variety of tasks",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

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var GenModel_gemini_1_5_flash_001 = bellman.GenModel{
	Name:                    "gemini-1.5-flash-002",
	Description:             "Fast and versatile performance across a diverse variety of tasks",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

View Source

var GenModel_gemini_1_5_flash_002 = bellman.GenModel{
	Name:                    "gemini-1.5-flash-001",
	Description:             "Fast and versatile performance across a diverse variety of tasks",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

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var GenModel_gemini_1_5_flash_8b = bellman.GenModel{
	Name:                    "gemini-1.5-flash-8b",
	Description:             "High volume and lower intelligence tasks",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

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var GenModel_gemini_1_5_flash_8b_001 = bellman.GenModel{
	Name:                    "gemini-1.5-flash-8b-001",
	Description:             "High volume and lower intelligence tasks",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

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var GenModel_gemini_1_5_pro = bellman.GenModel{
	Name:                    "gemini-1.5-pro",
	Description:             "Complex reasoning tasks requiring more intelligence",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

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var GenModel_gemini_1_5_pro_001 = bellman.GenModel{
	Name:                    "gemini-1.5-pro-001",
	Description:             "Complex reasoning tasks requiring more intelligence",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

View Source

var GenModel_gemini_1_5_pro_002 = bellman.GenModel{
	Name:                    "gemini-1.5-pro-002",
	Description:             "Complex reasoning tasks requiring more intelligence",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

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var GenModel_gemini_Experiment_114 = bellman.GenModel{
	Name:                    "gemini-exp-1114",
	Description:             "",
	InputContentTypes:       nil,
	InputMaxToken:           0,
	OutputMaxToken:          0,
	SupportTools:            false,
	SupportStructuredOutput: false,
}

Functions ¶

This section is empty.

Types ¶

type EmbedType ¶

type EmbedType string

const EmbedTypeClassification EmbedType = "CLASSIFICATION"

const EmbedTypeClustring EmbedType = "CLUSTERING"

const EmbedTypeCode EmbedType = "CODE_RETRIEVAL_QUERY"

const EmbedTypeDocument EmbedType = "RETRIEVAL_DOCUMENT"

const EmbedTypeQA EmbedType = "QUESTION_ANSWERING"

const EmbedTypeQuery EmbedType = "RETRIEVAL_QUERY"

const EmbedTypeSimilarity EmbedType = "SEMANTIC_SIMILARITY"

const EmbedTypeVerification EmbedType = "FACT_VERIFICATION"

type Google ¶

type Google struct {
	// contains filtered or unexported fields
}

func New ¶

func New(config GoogleConfig) (*Google, error)

func (*Google) Embed ¶

func (g *Google) Embed(text string, model string) ([]float64, error)

func (*Google) Generator ¶ added in v0.0.2

func (g *Google) Generator(options ...bellman.GeneratorOption) bellman.Generator

type GoogleConfig ¶

type GoogleConfig struct {
	Project    string `cli:"ai-google-project"`
	Region     string `cli:"ai-google-region"`
	Credential string `cli:"ai-google-credential"`
}

type GoogleEmbedRequest ¶

type GoogleEmbedRequest struct {
	Instances []struct {
		TaskType string `json:"task_type"`
		//Title    string `json:"title"`
		Content string `json:"content"`
	} `json:"instances"`
}

type GoogleEmbedResponse ¶

type GoogleEmbedResponse struct {
	Predictions []struct {
		Embeddings struct {
			Statistics struct {
				Truncated  bool `json:"truncated"`
				TokenCount int  `json:"token_count"`
			} `json:"statistics"`
			Values []float64 `json:"values"`
		} `json:"embeddings"`
	} `json:"predictions"`
}

Source Files ¶

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