Go - Tinfoil Documentation

Go SDK

Go SDK for Tinfoil’s secure AI inference API GitHub: 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, TLS certificate pinning, and attestation validation.

Installation

New to Go? Start here - Project Setup

If you don’t have a Go module set up yet, initialize a new one:

# 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

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

DeepSeek R1

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: "deepseek-r1-0528",
    })

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

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

Qwen3-VL 30B

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: "qwen3-vl-30b",
        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)
}

Kimi K2.5

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: Agentic coding with multi-step reasoning
    chatCompletion, err := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
        Messages: []openai.ChatCompletionMessageParamUnion{
            openai.UserMessage("Review this codebase and suggest architectural improvements for better modularity and testability."),
        },
        Model: "kimi-k2-5",
    })

    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, use the SecureClient from the verifier package:

import (
	"fmt"
	"github.com/tinfoilsh/tinfoil-go"
	"github.com/tinfoilsh/tinfoil-go/verifier/client"
)

// Create a secure client for manual verification and HTTP access
secureClient := client.NewSecureClient(
	"inference.tinfoil.sh",
	"tinfoilsh/confidential-model-router",
)

// Verify the enclave attestation
groundTruth, err := secureClient.Verify()
if err != nil {
	return fmt.Errorf("verification failed: %w", err)
}

// Get the verified HTTP client for custom requests
httpClient, err := secureClient.HTTPClient()
if err != nil {
	return fmt.Errorf("failed to get HTTP client: %w", err)
}

// Access enclave and repository information
fmt.Printf("Enclave: %s\n", secureClient.Enclave())
fmt.Printf("Repository: %s\n", secureClient.Repo())

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.

Guides

Tool Calling

Learn how to use function calling capabilities with AI models.

Structured Outputs

Use JSON schema validation for reliable data extraction.

Image Processing

Process images with multimodal AI models.

Document Processing

Upload and process documents securely.

Getting Started

Model catalog

Tinfoil SDKs

Tinfoil Containers

Guides

Verification & Attestation

Tutorials

Admin API

Resources

Go

Go SDK

Overview

Installation

Migration from OpenAI

Usage

Model Examples

Chat Models

Audio Models

Transcription

Audio Q&A

Embedding Models

Advanced Functionality

API Documentation

Guides

Tool Calling

Structured Outputs

Image Processing

Document Processing

Getting Started

Model catalog

Tinfoil SDKs

Tinfoil Containers

Guides

Verification & Attestation

Tutorials

Admin API

Resources

Go SDK

​Overview

​Installation

​Migration from OpenAI

​Usage

​Model Examples

​Chat Models

​Audio Models

​Transcription

​Audio Q&A

​Embedding Models

​Advanced Functionality

​API Documentation

​Guides

Tool Calling

Structured Outputs

Image Processing

Document Processing

Overview

Installation

Migration from OpenAI

Usage

Model Examples

Chat Models

Audio Models

Transcription

Audio Q&A

Embedding Models

Advanced Functionality

API Documentation

Guides