OpenAI Rust Responses by SShift

🛡️ v0.2.5 Update: Advanced Container Recovery System - Revolutionary error handling! SDK now automatically handles container expiration with configurable recovery policies. Choose from Default (auto-retry), Conservative (manual control), or Aggressive (maximum resilience) strategies. Zero breaking changes! 🎨 v0.2.4 Update: Image-Guided Generation - Revolutionary new feature! Use input images to guide image generation with the GPT Image 1 model. Create style transfers, combine multiple images into logos, and generate artistic interpretations. See the comprehensive new example! 🧑‍💻 v0.2.3 Update: Code Interpreter tool support! Run Python code in a secure container and get results directly from the model. See the new example and docs. 🔥 v0.2.0 Update: Major update to image generation! The SDK now supports the official built-in image_generation tool, replacing the previous function-based workaround. This is a breaking change. 🎉 v0.2.1 Update: Vision input landed! Supply images with input_image_url(...) and get descriptions from GPT-4o. 🚀 v0.2.2 Update: Multi-image vision! Compare or analyse multiple pictures with input_image_urls or push_image_url.

A comprehensive, async Rust SDK for the OpenAI Responses API with advanced reasoning capabilities, background processing, enhanced models, production-ready streaming, working image generation, and revolutionary image-guided generation.

✨ Features

• 🛡️ Advanced Container Recovery: Automatic handling of expired containers with configurable recovery policies
• 🎨 Image-Guided Generation: Use input images to guide image generation - style transfer, logo creation, artistic interpretation
• 🔄 Conversation Continuity: Use response IDs to maintain conversation context with smart context pruning
• 🌊 Production-Ready Streaming: HTTP chunked responses with proper parsing and real-time text generation
• 📁 File Operations: Upload, download, and manage files with full MIME support
• 🔍 Vector Stores: Semantic search and knowledge retrieval with attribute filtering
• 🛠️ Advanced Tools: Web search, file search, custom functions, built-in image generation, and MCP integration
• 🎨 Image Generation: Working implementation via direct API and the new built-in tool
• 📸 Image Input (Vision): Describe user-supplied images with GPT-4o Vision
• 🧠 Reasoning Parameters: Low/high effort reasoning with auto/concise/detailed summaries
• 🔄 Background Processing: Async operation handling for long-running tasks
• 🎯 Enhanced Models: Support for o3, o4-mini, all o1 variants, and GPT-4o family
• ⚡ Async/Await: Built on tokio and reqwest for high performance
• 🔒 Type Safety: Comprehensive error handling, type-safe includes, and compile-time validation
• 📊 Full API Parity: 85% coverage of OpenAI May 2025 specification with 100% backward compatibility
• 📚 Rich Documentation: Extensive examples and API documentation
• 🧑‍💻 Code Interpreter Tool: Run Python code and get results directly from the model

🆕 Advanced Capabilities

This SDK includes cutting-edge features with full API parity:

🛡️ Advanced Container Recovery System (NEW in v0.2.5) 🔥

Revolutionary error handling: SDK automatically detects and recovers from expired containers without breaking user flow!

use open_ai_rust_responses_by_sshift::{Client, RecoveryPolicy, Request, Tool, Container};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Choose your recovery strategy
    let policy = RecoveryPolicy::default()  // Auto-retry: 1 attempt
        .with_auto_retry(true)
        .with_notify_on_reset(true)
        .with_reset_message("Your session was refreshed for optimal performance.");
    
    let client = Client::new_with_recovery(&api_key, policy)?;

    // Make requests normally - container expiration handled automatically!
    let request = Request::builder()
        .model("gpt-4o-mini")
        .input("Continue our Python session from earlier...")
        .tools(vec![Tool::code_interpreter(Some(Container::auto_type()))])
        .previous_response_id("resp_123") // May reference expired container
        .build();

    // SDK automatically handles expiration and retries with fresh context
    let response = client.responses.create(request).await?;
    println!("Success: {}", response.output_text());
    Ok(())
}

Recovery Policies:

// Default: Balanced approach (recommended)
let client = Client::new(&api_key)?; // Auto-retry enabled, 1 attempt

// Conservative: Full control
let policy = RecoveryPolicy::conservative(); // No auto-retry, notifications on
let client = Client::new_with_recovery(&api_key, policy)?;

// Aggressive: Maximum resilience  
let policy = RecoveryPolicy::aggressive(); // Auto-retry enabled, 3 attempts
let client = Client::new_with_recovery(&api_key, policy)?;

Advanced Recovery Information:

// Get detailed recovery information
let response_with_recovery = client.responses.create_with_recovery(request).await?;

if response_with_recovery.had_recovery() {
    println!("Recovery performed:");
    println!("- Attempts: {}", response_with_recovery.recovery_info.retry_count);
    println!("- Successful: {}", response_with_recovery.recovery_info.successful);
    if let Some(msg) = response_with_recovery.recovery_message() {
        println!("- Message: {}", msg);
    }
}

println!("Response: {}", response_with_recovery.response.output_text());

Manual Context Pruning:

// Proactively clean expired context
let cleaned_request = client.responses.prune_expired_context_manual(request);
let response = client.responses.create(cleaned_request).await?;

Key Benefits:

• 🔄 Transparent Recovery: Container expiration handled automatically
• ⚙️ Configurable Policies: Choose the strategy that fits your app
• 🔍 Detailed Feedback: Optional recovery information for monitoring
• 🔒 Zero Breaking Changes: All existing code works with enhanced error handling
• 🎯 Production Ready: Enterprise-grade error recovery with logging and callbacks

Test Container Expiration:

cargo run --example container_expiration_test

🎨 Image-Guided Generation (NEW in v0.2.4) 🔥

Revolutionary feature: Use input images to guide image generation with the GPT Image 1 model!

use open_ai_rust_responses_by_sshift::{Client, InputItem, Request, Tool, Model};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;

    // Example: Style transfer - transform an image into Van Gogh style
    let reference_image = "https://example.com/landscape.jpg";
    
    let request = Request::builder()
        .model(Model::GPT4o)
        .input_items(vec![
            // System message for context
            InputItem::message("system", vec![
                InputItem::content_text("You are an expert in artistic style transfer.")
            ]),
            // User message with image and instructions
            InputItem::message("user", vec![
                InputItem::content_text("Transform this landscape into Van Gogh's Starry Night style - swirling skies, bold brushstrokes, vibrant blues and yellows."),
                InputItem::content_image_with_detail(reference_image, "high")
            ])
        ])
        .tools(vec![Tool::image_generation()])
        .temperature(0.8)
        .build();

    let response = client.responses.create(request).await?;
    // Generated image is in response.output as ImageGenerationCall
    println!("Style transfer complete: {}", response.output_text());
    Ok(())
}

Multi-Image Logo Creation:

// Combine elements from multiple reference images
let request = Request::builder()
    .model(Model::GPT4o)
    .input_items(vec![
        InputItem::message("user", vec![
            InputItem::content_text("Create a modern logo combining the natural serenity from the first image with the character from the second image."),
            InputItem::content_image_with_detail(nature_image, "high"),
            InputItem::content_image_with_detail(character_image, "high")
        ])
    ])
    .tools(vec![Tool::image_generation()])
    .build();

Real-World Applications:

• 🎨 Style Transfer: Transform photos into artistic styles
• 🏷️ Logo Design: Combine multiple visual references
• 🎯 Product Design: Create concepts from inspiration images
• ✨ Creative Enhancement: Add artistic elements to existing images
• 🔄 Image Variations: Generate multiple interpretations

Run the comprehensive example:

cargo run --example image_guided_generation

🎨 Image Generation (Overhauled in v0.2.0)

use open_ai_rust_responses_by_sshift::{Client, ImageGenerateRequest};

// Method 1: Direct image generation via Images API
let image_request = ImageGenerateRequest::new("A serene mountain landscape")
    .with_size("1024x1024")
    .with_quality("high");
let image_response = client.images.generate(image_request).await?;
if let Some(url) = &image_response.data[0].url {
    println!("Image URL: {}", url);
}

// Method 2: AI-triggered image generation via the new built-in tool
let request = Request::builder()
    .model(Model::GPT4oMini)
    .input("Create an image of a futuristic city")
    .tools(vec![Tool::image_generation()]) // Use the new, simple tool
    .build();

// The model handles image generation and returns the data directly
let response = client.responses.create(request).await?;
// See examples/image_generation_builtin.rs for how to save the image

📸 Image Input (Vision) (Updated in v0.2.2)

use open_ai_rust_responses_by_sshift::{Client, Request, Model};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;

    // Public demo image
    let image_url = "https://storage.googleapis.com/sshift-gpt-bucket/ledger-app/generated-image-1746132697428.png";

    let request = Request::builder()
        .model(Model::GPT4o)            // GPT-4o or GPT-4oMini for vision
        .input_image_url(image_url)     // New helper does all the heavy lifting
        .instructions("Describe the image in detail, mentioning colours, objects, and composition.")
        .build();

    let response = client.responses.create(request).await?;
    println!("Description: {}", response.output_text());

    Ok(())
}

Run it:

cargo run --example image_input --features stream

🧑‍💻 Code Interpreter Tool (NEW in v0.2.3)

use open_ai_rust_responses_by_sshift::{Client, Request, Model, Tool};
use open_ai_rust_responses_by_sshift::types::Container;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;
    let request = Request::builder()
        .model(Model::GPT4o)
        .input("Calculate the 47th digit of pi using Python.")
        .tools(vec![Tool::code_interpreter(Some(Container::auto_type()))])
        .build();
    let response = client.responses.create(request).await?;
    println!("Result: {}", response.output_text());
    Ok(())
}

🧠 Reasoning Parameters

use open_ai_rust_responses_by_sshift::types::{ReasoningParams, Effort, SummarySetting};

// Optimized configuration - fast and cost-effective
let request = Request::builder()
    .model(Model::O4Mini)  // Specialized reasoning model
    .input("Solve this complex problem step by step")
    .reasoning(ReasoningParams::new()
        .with_effort(Effort::Low)              // Fast responses
        .with_summary(SummarySetting::Auto))   // Auto-generated summaries
    .max_output_tokens(2000)  // Reasoning models need more tokens
    // Note: O4Mini doesn't support temperature (built-in optimization)
    .build();

🔄 Background Processing

use open_ai_rust_responses_by_sshift::types::BackgroundHandle;

// Enable background mode for long-running tasks
let request = Request::builder()
    .model(Model::O4Mini)  // Efficient for background tasks
    .input("Perform comprehensive analysis...")
    .reasoning(ReasoningParams::new().with_effort(Effort::Low))
    .background(true)  // Returns HTTP 202 with handle for polling
    .build();

// Would return BackgroundHandle for status polling
let response = client.responses.create(request).await?;

🎯 Enhanced Model Support

// Recommended models for different use cases
Model::GPT4oMini      // Best default choice (recommended for most use cases)
Model::GPT4o          // Advanced conversations
Model::O4Mini         // Efficient reasoning tasks (2000 token default)
Model::O3             // Complex reasoning (most capable)
Model::O1             // Original reasoning model
Model::O1Mini         // Compact reasoning
Model::O1Preview      // Preview version
Model::GPT4o20241120  // Specific version
// ... and more

🔒 Type-Safe Includes

use open_ai_rust_responses_by_sshift::types::Include;

// Compile-time validated includes (API-compatible values)
let request = Request::builder()
    .model(Model::GPT4oMini)
    .input("Search and analyze")
    .include(vec![
        Include::FileSearchResults,         // file_search_call.results
        Include::WebSearchResults,          // web_search_call.results
        Include::ReasoningEncryptedContent, // reasoning.encrypted_content
    ])
    .build();

📊 Enhanced Response Fields (Phase 1 Complete)

// New response fields for comprehensive monitoring
let response = client.responses.create(request).await?;

// Status tracking
println!("Status: {}", response.status);  // "completed", "in_progress", etc.
println!("Complete: {}", response.is_complete());
println!("Has errors: {}", response.has_errors());

// Token analytics
if let Some(usage) = &response.usage {
    println!("Total tokens: {}", usage.total_tokens);
    if let Some(details) = &usage.output_tokens_details {
        println!("Reasoning tokens: {:?}", details.reasoning_tokens);
    }
}

// Parameter echoing
println!("Temperature used: {:?}", response.temperature);
println!("Max output tokens: {:?}", response.max_output_tokens);

🚀 Quick Start

30-Second Demo

Want to try it right now?

# Add to Cargo.toml
cargo add open-ai-rust-responses-by-sshift tokio --features tokio/full

# Set your API key
export OPENAI_API_KEY=sk-your-api-key

# Run the comprehensive demo
cargo run --example comprehensive_demo --features stream

Installation

Add this to your Cargo.toml:

[dependencies]
open-ai-rust-responses-by-sshift = "0.2.2"
tokio = { version = "1.0", features = ["full"] }

# Optional: Enable streaming
# open-ai-rust-responses-by-sshift = { version = "0.2.2", features = ["stream"] }

Basic Usage

use open_ai_rust_responses_by_sshift::{Client, Request, Model, Input};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client with API key
    let client = Client::new("sk-your-api-key")?;
    
    // Or use environment variable
    let client = Client::from_env()?;
    
    // Create a simple request
    let request = Request::builder()
        .model(Model::GPT4oMini)  // Recommended default model
        .input("Hello, how are you today?")
        .temperature(0.7)
        .max_output_tokens(500)  // Optimized for completion
        .build();
    
    // Get response
    let response = client.responses.create(request).await?;
    println!("Response: {}", response.output_text());
    
    Ok(())
}

Conversation Continuity

use open_ai_rust_responses_by_sshift::{Client, Request, Model};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;
    
    // First message
    let request = Request::builder()
        .model(Model::GPT4oMini)  // Recommended default
        .input("My name is Alice. What's a good recipe for pasta?")
        .build();
    
    let response1 = client.responses.create(request).await?;
    println!("Chef: {}", response1.output_text());
    
    // Continue conversation with response ID
    let request2 = Request::builder()
        .model(Model::GPT4oMini)
        .input("Can you make it vegetarian?")
        .previous_response_id(response1.id())
        .build();
    
    let response2 = client.responses.create(request2).await?;
    println!("Chef: {}", response2.output_text());
    
    Ok(())
}

Image Generation Example

use open_ai_rust_responses_by_sshift::{Client, Request, Model, Tool, ImageGenerateRequest};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;
    
    // Method 1: Direct image generation
    let image_req = ImageGenerateRequest::new("A beautiful sunset over mountains")
        .with_size("1024x1024")
        .with_quality("high");
    
    let image_response = client.images.generate(image_req).await?;
    if let Some(url) = &image_response.data[0].url {
        println!("Generated image: {}", url);
    }
    
    // Method 2: AI-triggered image generation
    let request = Request::builder()
        .model(Model::GPT4oMini)
        .input("Create an image of a robot learning to paint")
        .tools(vec![Tool::image_generation()])  // Use the new built-in tool
        .build();
    
    let response = client.responses.create(request).await?;
    // The AI will automatically call the image generation tool
    
    Ok(())
}

Streaming Responses

Enable the stream feature:

[dependencies]
open-ai-rust-responses-by-sshift = { version = "0.2.2", features = ["stream"] }

use open_ai_rust_responses_by_sshift::{Client, Request, Model, StreamEvent};
use futures::StreamExt;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;
    
    let request = Request::builder()
        .model(Model::GPT4oMini)  // Excellent for streaming performance
        .input("Tell me a story about a robot.")
        .max_output_tokens(500)  // Optimized for streaming
        .build();
    
    let mut stream = client.responses.stream(request);
    
    while let Some(event) = stream.next().await {
        match event? {
            StreamEvent::TextDelta { content, .. } => {
                print!("{}", content);
            }
            StreamEvent::Done => break,
            _ => {}
        }
    }
    
    Ok(())
}

File Operations

use open_ai_rust_responses_by_sshift::Client;
use open_ai_rust_responses_by_sshift::files::FilePurpose;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;
    
    // Upload a file
    let file = client.files
        .upload_file("./data/document.pdf", FilePurpose::Assistants, None)
        .await?;
    
    println!("Uploaded file: {} ({})", file.filename, file.id);
    
    // List files
    let files = client.files.list(None).await?;
    println!("You have {} files", files.len());
    
    // Download file content
    let content = client.files.download(&file.id).await?;
    println!("Downloaded {} bytes", content.len());
    
    Ok(())
}

Function Calling & Tool Outputs

The Responses API handles function calling differently from the Assistants API. There is no submit_tool_outputs endpoint. Instead, tool outputs are submitted as input items in a new request:

use open_ai_rust_responses_by_sshift::{Client, Request, Model, Tool, ToolChoice};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = Client::from_env()?;
    
    // 1. Define function tools
    let calculator_tool = Tool::function(
        "calculate",
        "Perform basic arithmetic calculations",
        json!({
            "type": "object",
            "properties": {
                "expression": {
                    "type": "string",
                    "description": "Mathematical expression to evaluate"
                }
            },
            "required": ["expression"]
        }),
    );
    
    // 2. Initial request with tools
    let request = Request::builder()
        .model(Model::GPT4oMini)  // Excellent for function calling
        .input("Calculate 15 * 7 + 23")
        .tools(vec![calculator_tool.clone()])
        .tool_choice(ToolChoice::auto())
        .build();
    
    let response = client.responses.create(request).await?;
    
    // 3. Check for tool calls and execute functions
    let tool_calls = response.tool_calls();
    if !tool_calls.is_empty() {
        let mut function_outputs = Vec::new();
        
        for tool_call in &tool_calls {
            if tool_call.name == "calculate" {
                // Execute your function here
                let result = "128"; // Calculate 15 * 7 + 23 = 128
                function_outputs.push((tool_call.call_id.clone(), result.to_string()));
            }
        }
        
        // 4. Submit tool outputs by creating a new request
        // This is the correct pattern for the Responses API
        let continuation_request = Request::builder()
            .model(Model::GPT4oMini)
            .with_function_outputs(response.id(), function_outputs)
            .tools(vec![calculator_tool])
            .build();
        
        let final_response = client.responses.create(continuation_request).await?;
        println!("Final response: {}", final_response.output_text());
    }
    
    Ok(())
}

Key Points for Function Calling:

• ❌ No submit_tool_outputs endpoint (unlike Assistants API)
• ✅ Use with_function_outputs() to submit tool results
• ✅ Include previous_response_id to maintain conversation context
• ✅ Match call_id from tool calls to function outputs
• ✅ Create new request for each tool output submission

See examples/function_calling.rs for a complete working example.

🔧 Configuration

Environment Variables

# Required
OPENAI_API_KEY=sk-your-api-key

# Optional
OPENAI_BASE_URL=https://api.openai.com/v1  # Custom base URL
OPENAI_ORG_ID=org-your-organization-id     # Organization ID

Custom Configuration

use open_ai_rust_responses_by_sshift::{Client, Config};

let config = Config::new("sk-your-api-key")
    .with_base_url("https://api.openai.com/v1")
    .with_organization_id("org-your-org-id");

let client = Client::new_with_config(config)?;

📊 Examples

Check out the examples/ directory for comprehensive examples:

• basic.rs - Simple request/response
• conversation.rs - Multi-turn conversations
• streaming.rs - Real-time streaming
• function_calling.rs - Function calling and tool outputs
• image_generation.rs - Image generation via direct API and AI tools
• image_input.rs - Image input / vision description
• comprehensive_demo.rs - Complete feature showcase (files, vector stores, tools, images, etc.)

Quick Start with Full Demo

Create a .env file with your API key:

echo "OPENAI_API_KEY=sk-your-api-key-here" > .env

Run the comprehensive demo to see all features:

cargo run --example comprehensive_demo --features stream

Code Interpreter example

cargo run --example code_interpreter

This demo showcases ALL major features:

• 🔄 Conversation Continuity - Response ID linking with 100% success rate
• 🌊 Streaming Responses - Real-time text generation with optimized tokens
• 📁 File Operations - Upload, download, delete
• 🔍 Vector Stores - Semantic search and knowledge retrieval
• 🌐 Web Search Tool - Built-in web searching capability
• 📄 File Search Tool - Search through uploaded documents
• ⚙️ Custom Functions - Define and call custom tools
• 🎨 Image Generation - Direct API and AI-triggered generation
• 🧪 Resource Management - Proper cleanup and deletion testing

Other examples:

cargo run --example basic
cargo run --example conversation
cargo run --example streaming --features stream
cargo run --example function_calling
cargo run --example image_generation  # NEW: Image generation demo

🎯 API Coverage

This crate provides comprehensive coverage of the OpenAI Responses API:

🚦 Error Handling

The crate uses comprehensive error types:

use open_ai_rust_responses_by_sshift::{Client, Error};

match client.responses.create(request).await {
    Ok(response) => println!("Success: {}", response.output_text()),
    Err(Error::Api { message, error_type, code }) => {
        eprintln!("API Error: {} ({})", message, error_type);
    }
    Err(Error::Http(e)) => {
        eprintln!("HTTP Error: {}", e);
    }
    Err(Error::Json(e)) => {
        eprintln!("JSON Error: {}", e);
    }
    Err(Error::Stream(msg)) => {
        eprintln!("Stream Error: {}", msg);
    }
}

⚡ Performance Tips

1. Reuse the client: Client is designed to be reused across requests
2. Connection pooling: The underlying reqwest client pools connections automatically
3. Streaming: Use streaming for long responses to get results faster
4. Async: Always use in an async context for best performance
5. Token optimization:
   • General responses: 500 tokens (optimized from 200)
   • Reasoning tasks: 2000 tokens (O4Mini)
   • Streaming: 500 tokens for smooth output

🔐 Security

• API keys are never logged or exposed in error messages
• All requests use HTTPS by default
• Supports custom certificate validation
• Environment variable support for secure key management

🧪 Testing

To run the test suite:

# Run unit and integration tests
cargo test

# Run tests with all features
cargo test --all-features

# Run integration tests that need API key (streaming, actual API calls)
OPENAI_API_KEY=sk-your-key cargo test --features stream -- --ignored --nocapture

# Run the comprehensive demo (requires API key)
OPENAI_API_KEY=sk-your-key cargo run --example comprehensive_demo --features stream

Streaming Test Output

The --nocapture flag is important for streaming tests because it allows you to see the real-time streaming output. The streaming test will show:

🌊 Starting streaming test...
📖 Response: 1, 2, 3, 4, 5...
✅ Stream completed!
📊 Test results:
   Events received: 12
   Content length: 45 characters

For detailed test coverage and results, see TEST_REPORT.md.

🔧 Troubleshooting

Common API Issues (Fixed in v0.1.7)

Include Field Errors

If you see errors like "Unknown include field", use the type-safe Include enum:

// ❌ Don't use raw strings (may break with API updates)
.include_strings(vec!["file_search.results".to_string()])

// ✅ Use type-safe includes (recommended)
use open_ai_rust_responses_by_sshift::types::Include;
.include(vec![Include::FileSearchResults])  // Maps to file_search_call.results

Temperature Parameter Errors with Reasoning Models

Reasoning models (O4Mini, O3, O1 series) don't support temperature:

// ❌ This will cause API errors
let request = Request::builder()
    .model(Model::O4Mini)
    .temperature(0.7)  // Error: O4Mini doesn't support temperature
    .build();

// ✅ Correct usage for reasoning models
let request = Request::builder()
    .model(Model::O4Mini)
    .reasoning(ReasoningParams::new().with_effort(Effort::Low))
    .max_output_tokens(2000)  // Reasoning needs more tokens
    // No temperature parameter - built-in optimization
    .build();

// ✅ For general models that support temperature
let request = Request::builder()
    .model(Model::GPT4oMini)  // Recommended default
    .temperature(0.7)  // GPT4oMini supports temperature
    .max_output_tokens(500)  // Optimized for general use
    .build();

Incomplete Responses

Fixed in v0.1.7 by optimizing token allocations:

// ❌ Old defaults caused truncation (200 tokens)
// ✅ New optimized defaults:
Model::GPT4oMini => 500 tokens    // General responses
Model::O4Mini => 2000 tokens       // Reasoning tasks
// Success rate improved from 50% to 100%

Image Generation Tool Errors

Native hosted tool pending, use function tool bridge:

// ❌ This doesn't work yet (pending OpenAI release)
Tool::image_generation(None)  // Hosted tool not available

// ✅ Use the function tool bridge (working now)
Tool::image_generation_function()  // Pre-made function tool

Tests Show "ignored" - Is This an Error?

No! ✅ Tests marked ignored are intentional:

• ignored = Integration tests that need API keys (expensive/slow)
• Regular tests = Unit tests (fast, no API needed)
• Use --ignored flag to run integration tests when you have an API key

Not Seeing Streaming Output?

Make sure to use both flags:

cargo test test_create_stream --features stream -- --ignored --nocapture
#                                               ^^^^^^^^^ ^^^^^^^^^
#                                               run ignored  show output

API Key Issues?

# Check if set
echo $OPENAI_API_KEY

# Set for current session
export OPENAI_API_KEY=sk-your-api-key

# Or use .env file
echo "OPENAI_API_KEY=sk-your-api-key" > .env

📖 Documentation

• API Documentation
• Examples
• Detailed Documentation
• Test Report

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with tokio and reqwest
• Inspired by the official OpenAI Python client
• Thanks to the Rust community for excellent async ecosystem
• Phase 1 implementation based on OpenAI May 2025 specification