// LangChain Python:                  Rust equivalent:
// ChatOpenAI(model="gpt-4o")        → LlmClient struct with reqwest
// PromptTemplate(...)                → String formatting functions
// LLMChain(prompt, llm)             → Function composition
// ConversationBufferMemory()         → Vec<Message> + context window management
// VectorStore(embeddings)            → Custom Vec<Embedding> + cosine search
// RetrievalQA                        → retrieve() + build_prompt() + complete()

use serde::{Deserialize, Serialize};

#[derive(Clone, Serialize, Deserialize)]
struct Message { role: String, content: String }

/// Rust equivalent of LangChain's PromptTemplate
struct PromptTemplate {
    template: String,
}

impl PromptTemplate {
    fn new(template: &str) -> Self {
        Self { template: template.to_string() }
    }

    /// Fill template variables — Rust version of .format_messages()
    fn format(&self, vars: &[(&str, &str)]) -> String {
        let mut result = self.template.clone();
        for (key, value) in vars {
            result = result.replace(&format!("{{{{{}}}}}", key), value);
        }
        result
    }
}

fn main() {
    let template = PromptTemplate::new(
        "You are a {{role}}. Answer the following: {{question}}"
    );
    let prompt = template.format(&[
        ("role", "Rust expert"),
        ("question", "How do I use async/await?"),
    ]);
    println!("{}", prompt);
}

/// Rust equivalent of LangChain's RetrievalQAChain

struct Document { id: String, content: String, embedding: Vec<f32> }

struct RagChain {
    documents: Vec<Document>,
    top_k: usize,
}

impl RagChain {
    fn new(top_k: usize) -> Self { Self { documents: Vec::new(), top_k } }

    fn add_document(&mut self, id: &str, content: &str, embed: Vec<f32>) {
        self.documents.push(Document {
            id: id.to_string(), content: content.to_string(), embedding: embed,
        });
    }

    fn retrieve(&self, query_embed: &[f32]) -> Vec<&Document> {
        let cosine = |a: &[f32], b: &[f32]| -> f32 {
            let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
            let na = a.iter().map(|x| x * x).sum::<f32>().sqrt();
            let nb = b.iter().map(|x| x * x).sum::<f32>().sqrt();
            if na < 1e-8 || nb < 1e-8 { 0.0 } else { dot / (na * nb) }
        };

        let mut scored: Vec<(usize, f32)> = self.documents.iter()
            .enumerate()
            .map(|(i, d)| (i, cosine(query_embed, &d.embedding)))
            .collect();
        scored.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
        scored.into_iter().take(self.top_k).map(|(i, _)| &self.documents[i]).collect()
    }

    fn build_prompt(&self, question: &str, docs: &[&Document]) -> Vec<Message> {
        let context = docs.iter()
            .map(|d| format!("[{}]: {}", d.id, d.content))
            .collect::<Vec<_>>()
            .join("\n\n");

        vec![
            Message {
                role: "system".to_string(),
                content: "Answer based on context only. If not in context, say you don't know.".to_string(),
            },
            Message {
                role: "user".to_string(),
                content: format!("Context:\n{}\n\nQuestion: {}", context, question),
            },
        ]
    }

    fn run(&self, question: &str, query_embed: &[f32]) -> Vec<Message> {
        let docs = self.retrieve(query_embed);
        self.build_prompt(question, &docs)
    }
}

fn mock_embed(text: &str) -> Vec<f32> {
    let mut v = vec![0.0f32; 4];
    for (i, c) in text.chars().enumerate() { v[i % 4] += c as f32 / 1000.0; }
    let n = v.iter().map(|x| x * x).sum::<f32>().sqrt().max(1e-8);
    v.iter().map(|x| x / n).collect()
}

fn main() {
    let mut rag = RagChain::new(2);
    rag.add_document("doc1", "Rust ownership ensures memory safety without GC.", mock_embed("ownership memory safety"));
    rag.add_document("doc2", "Tokio enables async I/O in Rust applications.", mock_embed("tokio async io"));
    rag.add_document("doc3", "Candle is a Rust ML framework from Hugging Face.", mock_embed("candle ml framework"));

    let query = "How does Rust handle memory?";
    let query_emb = mock_embed("memory management rust");
    let messages = rag.run(query, &query_emb);

    println!("Generated {} messages for LLM", messages.len());
    println!("Context preview: {}...", &messages[1].content[..100]);
}

/// Use feature flags to migrate gradually
#[derive(Debug, Clone, Copy)]
enum LlmBackend {
    Python, // Old Python service
    Rust,   // New Rust implementation
}

struct FeatureRouter {
    /// Percentage of traffic to route to Rust (0-100)
    rust_percentage: u8,
    counter: std::sync::atomic::AtomicU64,
}

impl FeatureRouter {
    fn new(rust_percentage: u8) -> Self {
        Self {
            rust_percentage,
            counter: std::sync::atomic::AtomicU64::new(0),
        }
    }

    fn select_backend(&self) -> LlmBackend {
        let n = self.counter.fetch_add(1, std::sync::atomic::Ordering::Relaxed);
        if n % 100 < self.rust_percentage as u64 {
            LlmBackend::Rust
        } else {
            LlmBackend::Python
        }
    }
}

fn main() {
    // Migration phases:
    let phases = [
        ("Phase 1: Shadow mode", 0),   // 0% Rust traffic, log discrepancies
        ("Phase 2: Canary", 5),        // 5% Rust
        ("Phase 3: Ramp up", 50),      // 50% Rust
        ("Phase 4: Full migration", 100), // 100% Rust
    ];

    for (phase_name, percentage) in phases {
        let router = FeatureRouter::new(percentage);
        let rust_count = (0..100).filter(|_| {
            matches!(router.select_backend(), LlmBackend::Rust)
        }).count();
        println!("{}: {}% Rust ({}/100 requests)", phase_name, percentage, rust_count);
    }
}