Iris Getting Started

Getting Started

This guide walks you through installing Iris, configuring your first provider, and making your first LLM request. By the end, you’ll have a working setup ready for building AI applications.

Prerequisites

Before installing Iris, ensure you have:

• Go 1.21+: Iris uses generics and other modern Go features
• API Key: At least one provider API key (OpenAI, Anthropic, etc.)
• Git: For installing from source (optional)

Verify your Go installation:

go version
# go version go1.22.0 darwin/arm64

Installation

SDK Installation

Add Iris to your Go project:

go get github.com/petal-labs/iris

This installs the core SDK with all provider packages.

CLI Installation (Optional)

The CLI provides a command-line interface for quick experiments and prompt testing:

go install github.com/petal-labs/iris/cli/cmd/iris@latest

Verify the installation:

iris version
# iris v0.12.0

Tip

The CLI is optional for SDK usage but invaluable for testing prompts, experimenting with models, and quick debugging sessions.

Your First Request

Let’s make a simple chat request with OpenAI. This example demonstrates the core workflow: create a provider, wrap it in a client, and use the fluent builder API.

OpenAI

package main

import (
    "context"
    "fmt"
    "os"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/openai"
)

func main() {
    // Create provider with API key from environment
    provider := openai.New(os.Getenv("OPENAI_API_KEY"))

    // Wrap in client for retry logic and telemetry
    client := core.NewClient(provider)

    // Build and execute request
    resp, err := client.Chat("gpt-4o").
        System("You are a helpful assistant.").
        User("What is the capital of France?").
        Temperature(0.7).
        GetResponse(context.Background())

    if err != nil {
        panic(err)
    }

    fmt.Println(resp.Output)
}

Anthropic

package main

import (
    "context"
    "fmt"
    "os"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/anthropic"
)

func main() {
    // Anthropic requires both API key and model specification
    provider := anthropic.New(os.Getenv("ANTHROPIC_API_KEY"))
    client := core.NewClient(provider)

    resp, err := client.Chat("claude-3-5-sonnet-20241022").
        System("You are a helpful assistant.").
        User("What is the capital of France?").
        Temperature(0.7).
        GetResponse(context.Background())

    if err != nil {
        panic(err)
    }

    fmt.Println(resp.Output)
}

Ollama (Local)

package main

import (
    "context"
    "fmt"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/ollama"
)

func main() {
    // Ollama runs locally, no API key needed
    provider := ollama.New("http://localhost:11434")
    client := core.NewClient(provider)

    resp, err := client.Chat("llama3.2").
        System("You are a helpful assistant.").
        User("What is the capital of France?").
        Temperature(0.7).
        GetResponse(context.Background())

    if err != nil {
        panic(err)
    }

    fmt.Println(resp.Output)
}

Run your code:

export OPENAI_API_KEY=sk-...
go run main.go
# The capital of France is Paris.

Streaming Responses

For chat applications and real-time interfaces, streaming delivers tokens as they’re generated:

package main

import (
    "context"
    "fmt"
    "os"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/openai"
)

func main() {
    provider := openai.New(os.Getenv("OPENAI_API_KEY"))
    client := core.NewClient(provider)

    // GetStream returns immediately with channels
    stream, err := client.Chat("gpt-4o").
        System("You are a helpful assistant.").
        User("Write a haiku about programming.").
        GetStream(context.Background())

    if err != nil {
        panic(err)
    }

    // Process chunks as they arrive
    for chunk := range stream.Ch {
        fmt.Print(chunk.Content) // Print without newline for streaming effect
    }
    fmt.Println() // Final newline

    // Check for errors after stream completes
    if err := <-stream.Err; err != nil {
        panic(err)
    }

    // Optional: get the final aggregated response
    final := <-stream.Final
    fmt.Printf("\nTotal tokens: %d\n", final.Usage.TotalTokens)
}

Note

The ChatStream type provides three channels:

• Ch: Incremental content chunks
• Err: Error channel (receives at most one error)
• Final: Aggregated response after stream completes

Tool Calling

Tools let models invoke functions with structured arguments. Here’s a simple example:

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "os"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/openai"
)

func main() {
    provider := openai.New(os.Getenv("OPENAI_API_KEY"))
    client := core.NewClient(provider)

    // Define a tool
    weatherTool := core.Tool{
        Name:        "get_weather",
        Description: "Get the current weather for a location",
        Parameters: core.ToolParameters{
            Type: "object",
            Properties: map[string]core.Property{
                "location": {
                    Type:        "string",
                    Description: "City name, e.g., 'San Francisco, CA'",
                },
                "unit": {
                    Type:        "string",
                    Enum:        []string{"celsius", "fahrenheit"},
                    Description: "Temperature unit",
                },
            },
            Required: []string{"location"},
        },
    }

    // Request with tool
    resp, err := client.Chat("gpt-4o").
        System("You are a helpful weather assistant.").
        User("What's the weather in Tokyo?").
        Tools(weatherTool).
        GetResponse(context.Background())

    if err != nil {
        panic(err)
    }

    // Check if model wants to call a tool
    if len(resp.ToolCalls) > 0 {
        tc := resp.ToolCalls[0]
        fmt.Printf("Tool called: %s\n", tc.Name)

        var args map[string]string
        json.Unmarshal(tc.Arguments, &args)
        fmt.Printf("Arguments: %+v\n", args)

        // In a real app, you'd execute the tool and continue the conversation
    }
}

See the Tools Guide for complete tool calling patterns including multi-turn conversations with tool results.

Secure Key Management

Iris provides an encrypted keystore for securely storing API keys locally. This is more secure than environment variables for development machines.

Set Up Keystore Encryption

The keystore uses AES-256-GCM encryption with Argon2id key derivation. Set your master password:

export IRIS_KEYSTORE_KEY=your-secure-master-password

Caution

If IRIS_KEYSTORE_KEY is not set, the CLI will prompt interactively. For automated environments, always set this variable.

Store Provider Keys

# Store keys (prompts for value)
iris keys set openai
iris keys set anthropic
iris keys set gemini

# List stored keys (shows key names, not values)
iris keys list
# openai
# anthropic
# gemini

# Remove a key
iris keys remove gemini

The encrypted keystore is stored at ~/.iris/keys.enc with 0600 permissions (owner read/write only).

Using Stored Keys in Code

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/openai"
)

func main() {
    // Load key from keystore
    keystore, err := core.LoadKeystore()
    if err != nil {
        log.Fatal(err)
    }

    apiKey, err := keystore.Get("openai")
    if err != nil {
        log.Fatal(err)
    }

    // Use the key
    provider := openai.New(apiKey)
    client := core.NewClient(provider)

    resp, _ := client.Chat("gpt-4o").
        User("Hello!").
        GetResponse(context.Background())

    fmt.Println(resp.Output)
}

Configuration File

For consistent settings across sessions, create a configuration file at ~/.iris/config.yaml:

# Default provider and model
default_provider: openai
default_model: gpt-4o

# Provider-specific settings
providers:
  openai:
    api_key_env: OPENAI_API_KEY  # Or use keystore
    default_model: gpt-4o
    organization: org-xxx  # Optional

  anthropic:
    api_key_env: ANTHROPIC_API_KEY
    default_model: claude-3-5-sonnet-20241022

  ollama:
    base_url: http://localhost:11434
    default_model: llama3.2

# Telemetry settings
telemetry:
  enabled: true
  log_level: info

# Retry policy
retry:
  max_attempts: 3
  initial_backoff: 1s
  max_backoff: 30s
  backoff_multiplier: 2.0

Load Configuration in Code

package main

import (
    "log"

    "github.com/petal-labs/iris/core"
)

func main() {
    // Load config from default location (~/.iris/config.yaml)
    config, err := core.LoadConfig()
    if err != nil {
        log.Fatal(err)
    }

    // Create provider from config
    provider, err := config.GetProvider("openai")
    if err != nil {
        log.Fatal(err)
    }

    client := core.NewClient(provider)
    // Use client...
}

CLI Quick Reference

The CLI provides commands for quick testing and experimentation:

Chat Commands

# Simple chat
iris chat "Explain quantum entanglement"

# With specific provider/model
iris chat --provider anthropic --model claude-3-opus "Write a poem"

# Streaming output
iris chat --stream "Tell me a story"

# With system prompt
iris chat --system "You are a pirate" "What's the weather like?"

# With temperature
iris chat --temperature 0.9 "Be creative: invent a new word"

# Multi-turn conversation (interactive mode)
iris chat --interactive

Key Management

# Set a key
iris keys set openai

# List keys
iris keys list

# Remove a key
iris keys remove openai

# Test a key
iris keys test openai

Model Information

# List available models for a provider
iris models list openai

# Get model details
iris models info gpt-4o

Error Handling

Iris provides typed errors for different failure modes:

package main

import (
    "context"
    "errors"
    "fmt"
    "os"

    "github.com/petal-labs/iris/core"
    "github.com/petal-labs/iris/providers/openai"
)

func main() {
    provider := openai.New(os.Getenv("OPENAI_API_KEY"))
    client := core.NewClient(provider)

    resp, err := client.Chat("gpt-4o").
        User("Hello").
        GetResponse(context.Background())

    if err != nil {
        // Check for specific error types
        var rateLimitErr *core.RateLimitError
        var authErr *core.AuthenticationError
        var contextErr *core.ContextLengthError

        switch {
        case errors.As(err, &rateLimitErr):
            fmt.Printf("Rate limited. Retry after: %v\n", rateLimitErr.RetryAfter)
        case errors.As(err, &authErr):
            fmt.Println("Authentication failed. Check your API key.")
        case errors.As(err, &contextErr):
            fmt.Printf("Input too long. Max tokens: %d\n", contextErr.MaxTokens)
        default:
            fmt.Printf("Request failed: %v\n", err)
        }
        return
    }

    fmt.Println(resp.Output)
}

Common Issues

”API key not found”

Ensure your API key is set correctly:

# Check environment variable
echo $OPENAI_API_KEY

# Or check keystore
iris keys list

“Model not found”

Verify the model name is correct for your provider:

# List available models
iris models list openai

“Context length exceeded”

Your input is too long for the model. Options:

1. Use a model with larger context (e.g., gpt-4o-128k)
2. Truncate or summarize your input
3. Use streaming with the MaxTokens() builder method

”Rate limit exceeded”

The provider is throttling requests. Options:

1. Add delays between requests
2. Use Iris’s built-in retry policy
3. Upgrade your API tier with the provider

Connection Issues with Ollama

Ensure Ollama is running:

# Start Ollama
ollama serve

# Verify it's accessible
curl http://localhost:11434/api/tags

Next Steps

Concepts

Understand Iris’s architecture and building blocks. Read Concepts →

Providers

Configure and use different LLM providers. Provider Setup →

Streaming Guide

Deep dive into streaming patterns. Streaming Guide →

Tools Guide

Build tool-augmented agents. Tools Guide →