secretary 0.4.42

Transform natural language into structured data using large language models (LLMs) with powerful derive macros
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98

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

#[derive(Task, Serialize, Deserialize, Debug)]
struct Details {
    #[task(instruction = "Extract the price as a float")]
    pub price: f64,

    #[task(instruction = "Extract the product category or type")]
    pub category: String,

    #[task(instruction = "Extract the brand name if mentioned")]
    pub brand: Option<String>,
}

#[derive(Task, Serialize, Deserialize, Debug)]
pub struct Feature {
    #[task(instruction = "description of the feature")]
    pub description: String,
}

/// Example data structure for extracting product information
#[derive(Task, Serialize, Deserialize, Debug)]
struct ProductExtraction {
    /// Product data fields with specific extraction instructions
    #[task(instruction = "Extract the product name or title")]
    pub name: String,

    #[task(instruction = "Extract key features or description")]
    pub description: Vec<Feature>,

    #[task(instruction = "Determine if the product is in stock (true/false)")]
    pub in_stock: bool,

    pub details: Details,
}

/// Async example demonstrating the Task derive macro
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync + 'static>> {
    println!("Secretary Async Example - Product Information Extraction");
    println!("{}", "=".repeat(60));

    // Create a task instance
    let task = ProductExtraction::new();

    // Additional instructions for the LLM
    let additional_instructions = vec![
        "Be precise with numerical values".to_string(),
        "Use 'Unknown' for missing information".to_string(),
        "Ensure boolean values are accurate".to_string(),
    ];

    // Example product description text
    let product_text = "
        Apple MacBook Pro 16-inch - $2,499
        
        The latest MacBook Pro features the powerful M3 Pro chip, 
        16GB unified memory, and 512GB SSD storage. Perfect for 
        professional video editing and software development.
        
        Category: Laptop Computer
        Status: In Stock
        Brand: Apple
    ";

    println!("Input text:");
    println!("{}", product_text);
    println!();

    // Display the generated system prompt
    println!("Generated System Prompt:");
    println!("{}", task.get_system_prompt());
    println!();

    // Note: This would require actual API credentials to work
    // For demonstration, we'll show how to set up the async call
    println!("Setting up async LLM call (requires API credentials):");

    let llm = OpenAILLM::new(
        &std::env::var("SECRETARY_OPENAI_API_BASE").unwrap(),
        &std::env::var("SECRETARY_OPENAI_API_KEY").unwrap(),
        &std::env::var("SECRETARY_OPENAI_MODEL").unwrap(),
    )?;

    println!("Making async request to LLM...");
    let result: ProductExtraction = llm
        .async_generate_data(&task, product_text, &additional_instructions)
        .await?;
    println!("Generated Data Structure: {:#?}", result);

    println!();
    println!("Example completed successfully!");

    Ok(())
}