Go - Tinfoil Documentation

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tinfoilsh/tinfoil-go

Overview

The Tinfoil Go SDK is a wrapper around the OpenAI Go client that provides secure communication with Tinfoil enclaves. It has the same API as the OpenAI SDK with additional security features including automatic verification that the endpoint is running in a secure Tinfoil enclave and encryption of request bodies to the verified enclave with EHBP.

V3 attestation verification in the Go SDK, including the installation below, is beta while deployment is ongoing.

Installation

# Create a new directory for your project
mkdir my-tinfoil-app
cd my-tinfoil-app

# Initialize a Go module
go mod init my-tinfoil-app

# Add the Tinfoil SDK to your project
go get github.com/tinfoilsh/tinfoil-go

The minimum supported Go version is 1.27.2.

Migration from OpenAI

Migrating from OpenAI to Tinfoil is straightforward. The client is designed to be compatible with the OpenAI Go client:

// Before (OpenAI)
- import (
- 	"github.com/openai/openai-go/v3"
- 	"github.com/openai/openai-go/v3/option"
- )
-
- client := openai.NewClient(
-    option.WithAPIKey("OPENAI_API_KEY")
- )

// After (Tinfoil)
+ import (
+ 	"github.com/openai/openai-go/v3"
+ 	"github.com/openai/openai-go/v3/option"
+ 	"github.com/tinfoilsh/tinfoil-go"
+ )
+
+ client, err := tinfoil.NewClient(
+ 	option.WithAPIKey("TINFOIL_API_KEY"),
+ )

All method signatures remain the same since tinfoil.NewClient() returns a standard OpenAI client with built-in security features.

Usage

// 1. Create a client
client, err := tinfoil.NewClient(
	option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
)
if err != nil {
	log.Fatal(err)
}

// 2. Use client as you would openai.Client 
// see https://pkg.go.dev/github.com/openai/openai-go for API documentation

Model Examples

Below are specific examples for each supported model. Click on any model to see its configuration and usage example.

Chat Models

GLM-5.3

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Example: Complex reasoning task
    chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage("Solve this step by step: If a train travels 120 miles in 2 hours, and then increases its speed by 25% for the next 3 hours, how far does it travel in total?"),
        },
        Model: "glm-5-3",
    })

    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(chatCompletion.Choices[0].Message.Content)
}

Gemma 4 31B

package main

import (
    "context"
    "encoding/base64"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Example: Image analysis
    imageData, err := os.ReadFile("image.jpg")
    if err != nil {
        log.Fatal(err)
    }
    base64Image := base64.StdEncoding.EncodeToString(imageData)
    imageURL := fmt.Sprintf("data:image/jpeg;base64,%s", base64Image)

    chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
        Model: "gemma4-31b",
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage([]openai.ChatCompletionContentPartUnionParam{
                openai.TextContentPart("What's in this image?"),
                openai.ImageContentPart(openai.ChatCompletionContentPartImageImageURLParam{
                    URL: imageURL,
                }),
            }),
        },
    })

    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(chatCompletion.Choices[0].Message.Content)
}

Llama 3.3 70B

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Example: Conversational AI
    chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage("What are the key differences between renewable and non-renewable energy sources?"),
        },
        Model: "llama3-3-70b",
    })

    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(chatCompletion.Choices[0].Message.Content)
}

GPT-OSS 120B

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Example: Advanced reasoning with configurable effort levels
    chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage("Analyze the trade-offs between different database architectures for a high-traffic e-commerce platform."),
        },
        Model: "gpt-oss-120b",
    })

    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(chatCompletion.Choices[0].Message.Content)
}

Audio Models

Voxtral Small 24B

Transcription

package main

import (
    "context"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Example: Audio transcription
    audioFile, err := os.Open("meeting_recording.mp3")
    if err != nil {
        log.Fatal(err)
    }
    defer audioFile.Close()

    transcription, err := client.Audio.Transcriptions.New(context.TODO(), openai.AudioTranscriptionNewParams{
        Model:    "voxtral-small-24b",
        File:     audioFile,
        Language: openai.String("en"), // Optional: specify language for better accuracy
        Prompt:   openai.String("This is a business meeting discussing quarterly results"), // Optional: provide context
    })
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println("Transcription:", transcription.Text)
}

Audio Q&A

package main

import (
    "context"
    "encoding/base64"
    "fmt"
    "log"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Load audio and encode as base64
    audioData, err := os.ReadFile("audio.mp3")
    if err != nil {
        log.Fatal(err)
    }
    audioBase64 := base64.StdEncoding.EncodeToString(audioData)

    response, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
        Model: "voxtral-small-24b",
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage([]openai.ChatCompletionContentPartUnionParam{
                openai.TextContentPart("How many words are in this audio?"),
                openai.InputAudioContentPart(openai.ChatCompletionContentPartInputAudioInputAudioParam{
                    Data:   audioBase64,
                    Format: "mp3",
                }),
            }),
        },
    })
    if err != nil {
        log.Fatal(err)
    }

    fmt.Println(response.Choices[0].Message.Content)
    // Output: "The audio contains 600 words."
}

Embedding Models

Nomic Embed Text

package main

import (
    "context"
    "fmt"
    "log"
    "math"
    "os"

    "github.com/openai/openai-go/v3"
    "github.com/openai/openai-go/v3/option"
    "github.com/tinfoilsh/tinfoil-go"
)

func main() {
    client, err := tinfoil.NewClient(
        option.WithAPIKey(os.Getenv("TINFOIL_API_KEY")),
    )
    if err != nil {
        log.Fatal(err)
    }

    // Example: Generate embeddings for similarity search
    documents := []string{
        "Artificial intelligence is transforming modern technology.",
        "Machine learning enables computers to learn from data.",
        "The weather today is sunny and warm.",
        "Deep learning uses neural networks with multiple layers.",
    }

    // Generate embeddings for all documents
    var embeddings [][]float64
    for _, doc := range documents {
        response, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
            Model:          "nomic-embed-text",
            Input:          openai.EmbeddingNewParamsInputUnion{OfArrayOfStrings: []string{doc}},
            EncodingFormat: openai.EmbeddingNewParamsEncodingFormatFloat,
        })
        if err != nil {
            log.Fatal(err)
        }
        embeddings = append(embeddings, response.Data[0].Embedding)
    }

    // Calculate similarity between first two documents
    similarity := cosineSimilarity(embeddings[0], embeddings[1])

    fmt.Printf("Similarity between first two AI-related documents: %.3f\n", similarity)
    fmt.Printf("Embedding dimension: %d\n", len(embeddings[0]))
}

func cosineSimilarity(a, b []float64) float64 {
    dotProduct := 0.0
    normA := 0.0
    normB := 0.0

    for i := 0; i < len(a); i++ {
        dotProduct += a[i] * b[i]
        normA += a[i] * a[i]
        normB += b[i] * b[i]
    }

    return dotProduct / (math.Sqrt(normA) * math.Sqrt(normB))
}

Advanced Functionality

For advanced use cases requiring manual verification or direct HTTP access, pin an enclave with NewClientWithOptions and use the client’s verification and HTTP methods:

import (
	"fmt"
	"log"
	"os"

	"github.com/openai/openai-go/v3/option"
	"github.com/tinfoilsh/tinfoil-go"
)

// Pin a specific enclave and config repository
client, err := tinfoil.NewClientWithOptions(
	tinfoil.WithEnclave("inference.tinfoil.sh"),
	tinfoil.WithRepo("tinfoilsh/confidential-model-router"),
	tinfoil.WithOpenAIOptions(option.WithAPIKey(os.Getenv("TINFOIL_API_KEY"))),
)
if err != nil {
	log.Fatal(err)
}

// Verify the enclave attestation again and inspect the result
verification, err := client.Verify()
if err != nil {
	log.Fatalf("verification failed: %v", err)
}
fmt.Printf("Enclave: %s\n", client.Enclave())
fmt.Printf("Repository: %s\n", client.Repo())
fmt.Printf("Release: %s\n", verification.CodeTag)

// Use the verified HTTP client for custom requests
resp, err := client.HTTPClient().Get("https://inference.tinfoil.sh/v1/models")
if err != nil {
	log.Fatal(err)
}
defer resp.Body.Close()
fmt.Println(resp.Status)

HTTPClient() only sends requests to the verified enclave and encrypts request bodies to it. To pin the enclave’s TLS certificate instead, add tinfoil.WithTransport(tinfoil.TransportTLS).

API Documentation

This library is a drop-in replacement for the official OpenAI Go client that can be used with Tinfoil. All methods and types are identical. See the OpenAI Go client documentation for complete API usage and documentation.

Getting Started

Model catalog

Tinfoil SDKs

Tinfoil Chat

Local Proxy

Tinfoil Containers

Guides

Verification & Attestation

Tutorials

Admin API

Resources

Go

View on GitHub

Overview

Installation

Migration from OpenAI

Usage

Model Examples

Chat Models

Audio Models

Transcription

Audio Q&A

Embedding Models

Advanced Functionality

API Documentation

View on GitHub

​Overview

​Installation

​Migration from OpenAI

​Usage

​Model Examples

​Chat Models

​Audio Models

​Transcription

​Audio Q&A

​Embedding Models

​Advanced Functionality

​API Documentation

Overview

Installation

Migration from OpenAI

Usage

Model Examples

Chat Models

Audio Models

Transcription

Audio Q&A

Embedding Models

Advanced Functionality

API Documentation