Secretary

Secretary is a Rust library that transforms natural language into structured data using large language models (LLMs). With its powerful derive macro system, you can extract structured information from unstructured text with minimal boilerplate code.

Features

• 🚀 Unified Task Trait: Single trait combining data extraction, schema definition, and system prompt generation with #[derive(Task)]
• 🔍 Schema-Based Extraction: Define your data structure using Rust structs with field-level instructions
• 🔄 Context-Aware Conversations: Maintain conversation state for multi-turn interactions
• 📋 Declarative Field Instructions: Use #[task(instruction = "...")] attributes to guide extraction
• ⚡ Async Support: Built-in async/await support for concurrent processing
• 🔌 Extensible LLM Support: Currently supports OpenAI API with more providers planned
• 🛡️ Type Safety: Leverage Rust's type system for reliable data extraction
• 🧹 Simplified API: Consolidated traits reduce boilerplate and complexity

Quick Start

cargo add secretary

Basic Example

use secretary::Task;
use secretary::llm_providers::openai::OpenAILLM;
use secretary::traits::GenerateData;
use serde::{Serialize, Deserialize};

// Define your data structure with extraction instructions
#[derive(Task, Serialize, Deserialize, Debug, Default)]
struct PersonInfo {
    // Required fields for Task trait
    #[serde(skip)]
    pub context: secretary::MessageList,
    #[serde(skip)]
    pub additional_instructions: Vec<String>,
    
    // Data fields with specific extraction instructions
    #[task(instruction = "Extract the person's full name")]
    pub name: String,
    
    #[task(instruction = "Extract age as a number")]
    pub age: u32,
    
    #[task(instruction = "Extract email address if mentioned")]
    pub email: Option<String>,
    
    #[task(instruction = "List all hobbies or interests mentioned")]
    pub interests: Vec<String>,
}

fn main() -> anyhow::Result<()> {
    // Create a task instance with additional instructions
    let task = PersonInfo::new(vec![
        "Be precise with personal information".to_string(),
        "Use 'Unknown' for missing data".to_string(),
    ]);
    
    // Initialize LLM client
    let llm = OpenAILLM::new(
        "https://api.openai.com/v1",
        "your-api-key",
        "gpt-4"
    )?;
    
    // Process natural language input
    let input = "Hi, I'm Jane Smith, 29 years old. My email is jane@example.com. I love hiking, coding, and playing piano.";
    // Process natural language input and get structured data directly
    let person: PersonInfo = llm.generate_data(&task, input)?;
    println!("{:#?}", person);
    
    Ok(())
}

How It Works

1. Define Your Schema: Create a Rust struct with #[derive(Task)] and field-level instructions
2. Add Required Fields: Include context and additional_instructions fields (marked with #[serde(skip)])
3. Annotate Fields: Use #[task(instruction = "...")] to guide the LLM on how to extract each field
4. Automatic Implementation: The derive macro implements all necessary traits (data model, system prompt generation, context management)
5. Create Task Instance: Initialize with YourStruct::new(additional_instructions)
6. Process Text: Send natural language input to an LLM through the Secretary API
7. Get Structured Data: Receive JSON that can be parsed back into your struct

Field Instructions

The #[task(instruction = "...")] attribute tells the LLM how to extract each field:

#[derive(Task, Serialize, Deserialize, Debug, Default)]
struct ProductInfo {
    #[serde(skip)]
    pub context: secretary::MessageList,
    #[serde(skip)]
    pub additional_instructions: Vec<String>,
    
    #[task(instruction = "Extract the product name or title")]
    pub name: String,
    
    #[task(instruction = "Extract price as a number without currency symbols")]
    pub price: f64,
    
    #[task(instruction = "Categorize the product type (electronics, clothing, etc.)")]
    pub category: String,
    
    #[task(instruction = "Extract brand name if mentioned, otherwise null")]
    pub brand: Option<String>,
    
    #[task(instruction = "Determine if product is available (true/false)")]
    pub in_stock: bool,
}

Advanced Features

Async Processing

Secretary provides full async support for concurrent processing:

use secretary::traits::AsyncGenerateData;
use tokio;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let llm = OpenAILLM::new("https://api.openai.com/v1", "your-api-key", "gpt-4")?;
    let task = PersonInfo::new(vec!["Extract accurately".to_string()]);
    
    // Process multiple inputs concurrently
    let inputs = vec![
        "John Doe, 25, loves gaming",
        "Alice Smith, 30, enjoys reading and cooking",
        "Bob Johnson, 35, passionate about photography",
    ];
    
    let futures: Vec<_> = inputs.into_iter().map(|input| {
        let llm = &llm;
        let task = &task;
        async move {
            llm.async_generate_data(task, input).await
        }
    }).collect();
    
    let results = futures::future::join_all(futures).await;
    
    for result in results {
        match result {
            Ok(json) => println!("Extracted: {}", json),
            Err(e) => eprintln!("Error: {}", e),
        }
    }
    
    Ok(())
}

Context-Aware Conversations

Maintain conversation state for multi-turn interactions:

use secretary::message_list::Role;

fn main() -> anyhow::Result<()> {
    let mut task = PersonInfo::new(vec!["Gather information progressively".to_string()]);
    let llm = OpenAILLM::new("https://api.openai.com/v1", "your-api-key", "gpt-4")?;
    
    // First interaction
    task.push(Role::User, "Hi, I'm John")?;
    let response1: PersonInfo = llm.generate_data_with_context(&task)?;
    task.push(Role::Assistant, &serde_json::to_string(&response1)?)?;
    
    // Continue conversation with context
    task.push(Role::User, "I'm 25 years old and love programming")?;
    let response2: PersonInfo = llm.generate_data_with_context(&task)?;
    
    println!("Final result: {:#?}", response2);
    Ok(())
}

System Prompt Generation

The derive macro automatically generates comprehensive system prompts:

let task = PersonInfo::new(vec!["Be accurate".to_string()]);
let prompt = task.get_system_prompt();
println!("{}", prompt);

// Output includes:
// - JSON structure specification
// - Field-specific instructions
// - Additional instructions
// - Formatting guidelines

Examples

The examples/ directory contains practical demonstrations:

Basic Usage

• sync.rs - Basic person information extraction using synchronous API
• async.rs - Async product information extraction with comprehensive testing

Run examples with:

# Basic synchronous example
cargo run --example sync

# Async example with comprehensive testing
cargo run --example async

# To test with real API, set environment variables:
export SECRETARY_OPENAI_API_BASE="https://api.openai.com/v1"
export SECRETARY_OPENAI_API_KEY="your-api-key"
export SECRETARY_OPENAI_MODEL="gpt-4"
cargo run --example async

Environment Setup

For production use with OpenAI:

export SECRETARY_OPENAI_API_BASE="https://api.openai.com/v1"
export SECRETARY_OPENAI_API_KEY="your-openai-api-key"
export SECRETARY_OPENAI_MODEL="gpt-4"

In your code:

let api_base = std::env::var("SECRETARY_OPENAI_API_BASE")
    .expect("SECRETARY_OPENAI_API_BASE environment variable not set");
let api_key = std::env::var("SECRETARY_OPENAI_API_KEY")
    .expect("SECRETARY_OPENAI_API_KEY environment variable not set");
let model = std::env::var("SECRETARY_OPENAI_MODEL")
    .expect("SECRETARY_OPENAI_MODEL environment variable not set");

let llm = OpenAILLM::new(&api_base, &api_key, &model)?;

API Reference

Core Traits

Derive Macro Attributes

• #[derive(Task)] - Implements the Task trait automatically
• #[task(instruction = "...")] - Provides field-specific extraction instructions
• #[serde(skip)] - Required for context and additional_instructions fields

Troubleshooting

Common Issues

"Failed to execute function" Error

• Check your API key and endpoint configuration
• Verify network connectivity
• Ensure the model name is correct

Serialization Errors

• Ensure all data fields implement Serialize and Deserialize
• Check that field types match the expected JSON structure
• Verify that optional fields are properly handled

Context Management Issues

• Remember to include required fields: context and additional_instructions
• Mark these fields with #[serde(skip)]
• Use push() method to add messages to context

Performance Tips

• Use async methods for concurrent processing
• Batch multiple requests when possible
• Consider caching LLM responses for repeated queries
• Use specific field instructions to improve extraction accuracy

Roadmap

• Support for additional LLM providers (Anthropic, Azure OpenAI, etc.)
• Enhanced error handling and validation
• Performance optimizations and caching
• Integration with more serialization formats
• Advanced prompt engineering features
• Streaming response support

Contributing

Contributions are welcome!

License

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