opencrates 3.0.1

Enterprise-grade AI-powered Rust development companion with comprehensive automation, monitoring, and deployment capabilities
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
99
100
101
102
103
104
105
106
107
108
109

use anyhow::Result;
use async_trait::async_trait;
use std::collections::HashMap;
use tracing::{info, debug};

use crate::core::CrateContext;
use crate::providers::{OpenAIProvider, GenerationRequest, LegacyLLMProvider};
use super::Stage;

// TODO: document this
// TODO: document this
// TODO: document this
pub struct OptimizationStage {
    openai_provider: OpenAIProvider,
}

impl OptimizationStage {
    // TODO: document this
    // TODO: document this
    // TODO: document this
    // TODO: document this
    pub fn new(openai_provider: OpenAIProvider) -> Self {
        Self { openai_provider }
    }

    async fn optimize_code(&self, file_path: &str, content: &str) -> Result<String> {
        let prompt = format!(
            "Optimize this Rust code for performance, memory usage, and idiomatic patterns:\n\n{}",
            content
        );

        let request = GenerationRequest {
            prompt,
            context: format!("File: {}", file_path),
            max_tokens: Some(2048),
            temperature: Some(0.3),
            model: None,
        };

        let response = self.openai_provider.generate(request).await?;
        Ok(response.content)
    }

    async fn optimize_dependencies(&self, dependencies: &[String]) -> Result<Vec<String>> {
        let prompt = format!(
            "Optimize these Rust dependencies for minimal compilation time and binary size: {:?}. 
            Suggest alternatives or feature flags to reduce bloat.",
            dependencies
        );

        let request = GenerationRequest {
            prompt,
            context: "Dependency optimization".to_string(),
            max_tokens: Some(1024),
            temperature: Some(0.4),
            model: None,
        };

        let response = self.openai_provider.generate(request).await?;
        
        // Parse optimized dependencies (simplified)
        Ok(dependencies.to_vec())
    }

    async fn add_performance_annotations(&self, context: &CrateContext) -> Result<HashMap<String, String>> {
        let mut optimized_files = HashMap::new();
        
        for (path, content) in &context.file_structure {
            if path.ends_with(".rs") {
                let optimized_content = self.optimize_code(path, content).await?;
                optimized_files.insert(path.clone(), optimized_content);
            } else {
                optimized_files.insert(path.clone(), content.clone());
            }
        }
        
        Ok(optimized_files)
    }
}

#[async_trait]
impl Stage for OptimizationStage {
    type Input = CrateContext;
    type Output = CrateContext;

    async fn execute(&self, input: &Self::Input) -> Result<Self::Output> {
        info!("Starting optimization for crate: {}", input.spec.name);
        
        let optimized_files = self.add_performance_annotations(input).await?;
        let optimized_dependencies = self.optimize_dependencies(&input.dependencies).await?;
        
        let mut optimized_context = input.clone();
        optimized_context.file_structure = optimized_files;
        optimized_context.dependencies = optimized_dependencies;
        
        // Add optimization metadata
        optimized_context.metadata.insert(
            "optimized_at".to_string(),
            chrono::Utc::now().to_rfc3339()
        );
        
        debug!("Optimization complete for crate: {}", input.spec.name);
        Ok(optimized_context)
    }

    fn name(&self) -> &str {
        "optimization"
    }
}