Implementation Guide
Date: 2025-11-04
Project: liblevenshtein-rust
Based on: ../../design/grammar-correction/theoretical-analysis/complete-analysis.md
This guide provides concrete implementation strategies to achieve the theoretical properties analyzed in ../../design/grammar-correction/theoretical-analysis/complete-analysis.md.
Requirement: Same input → same candidates in same order
Implementation:
pub struct LexicalCorrector {
dictionary: Dictionary,
max_distance: usize,
deterministic: bool,
}
impl LexicalCorrector {
pub fn correct(&self, token: &str, k: usize) -> Vec<(String, usize)> {
// 1. Build Levenshtein automaton (deterministic)
let automaton = LevenshteinAutomaton::new(token, self.max_distance);
// 2. Query dictionary (deterministic)
let mut candidates: Vec<(String, usize)> = self.dictionary
.iter()
.filter_map(|word| {
if automaton.accepts(word) {
let distance = levenshtein_distance(token, word);
Some((word.to_string(), distance))
} else {
None
}
})
.collect();
// 3. Sort by (distance, lexicographic) for determinism
if self.deterministic {
candidates.sort_by(|a, b| {
a.1.cmp(&b.1) // Primary: distance
.then_with(|| a.0.cmp(&b.0)) // Secondary: lexicographic
});
} else {
// Non-deterministic: sort by distance only (faster)
candidates.sort_by_key(|x| x.1);
}
// 4. Return top-k
candidates.truncate(k);
candidates
}
}
Key Points:
Test:
#[test]
fn test_lexical_determinism() {
let corrector = LexicalCorrector::new(dictionary, 2, true);
let result1 = corrector.correct("teh", 5);
let result2 = corrector.correct("teh", 5);
assert_eq!(result1, result2, "Lexical correction must be deterministic");
}
Requirement: BFS with deterministic queue and tie-breaking
Implementation:
pub struct GrammarCorrector {
parser: TreeSitterParser,
max_distance: usize,
beam_width: usize,
deterministic: bool,
}
impl GrammarCorrector {
pub fn correct(&self, tokens: &[Token]) -> Vec<ParseTree> {
// Use VecDeque for deterministic FIFO queue
let mut queue: VecDeque<SearchState> = VecDeque::new();
let mut results: Vec<ParseTree> = Vec::new();
// Initial state
queue.push_back(SearchState {
parse_state: self.parser.initial_state(),
edits: vec![],
cost: 0,
state_id: 0, // For deterministic ordering
});
let mut next_state_id = 1;
while let Some(state) = queue.pop_front() {
// Prune if cost exceeds max
if state.cost > self.max_distance {
continue;
}
// Check if complete parse
if state.parse_state.is_complete() {
results.push(state.to_parse_tree());
continue;
}
// Generate next states
let mut next_states: Vec<SearchState> = self.parser
.lookahead_iterator(&state.parse_state)
.flat_map(|symbol| {
self.apply_edits(&state, symbol, &mut next_state_id)
})
.collect();
// Deterministic ordering
if self.deterministic {
// Sort by (cost, state_id) for deterministic exploration
next_states.sort_by_key(|s| (s.cost, s.state_id));
}
// Beam search pruning
next_states.truncate(self.beam_width);
// Add to queue
queue.extend(next_states);
}
// Sort results for deterministic output
if self.deterministic {
results.sort_by_key(|tree| tree.cost);
}
results
}
fn apply_edits(
&self,
state: &SearchState,
symbol: Symbol,
next_state_id: &mut usize,
) -> Vec<SearchState> {
let mut new_states = Vec::new();
// Match (cost 0)
if state.next_token() == Some(symbol) {
new_states.push(SearchState {
parse_state: state.parse_state.advance(symbol),
edits: state.edits.clone(),
cost: state.cost,
state_id: *next_state_id,
});
*next_state_id += 1;
}
// Insert (cost 1)
new_states.push(SearchState {
parse_state: state.parse_state.advance(symbol),
edits: {
let mut e = state.edits.clone();
e.push(Edit::Insert(symbol));
e
},
cost: state.cost + 1,
state_id: *next_state_id,
});
*next_state_id += 1;
// Delete (cost 1) - skip current token
if let Some(token) = state.next_token() {
new_states.push(SearchState {
parse_state: state.parse_state.clone(),
edits: {
let mut e = state.edits.clone();
e.push(Edit::Delete(token));
e
},
cost: state.cost + 1,
state_id: *next_state_id,
});
*next_state_id += 1;
}
// Substitute (cost 1)
if let Some(token) = state.next_token() {
new_states.push(SearchState {
parse_state: state.parse_state.advance(symbol),
edits: {
let mut e = state.edits.clone();
e.push(Edit::Substitute(token, symbol));
e
},
cost: state.cost + 1,
state_id: *next_state_id,
});
*next_state_id += 1;
}
new_states
}
}
Key Points:
state_id ensures deterministic ordering when costs are equalTest:
#[test]
fn test_grammar_determinism() {
let corrector = GrammarCorrector::new(parser, 2, 20, true);
let tokens = tokenize("1 + 2 3");
let result1 = corrector.correct(&tokens);
let result2 = corrector.correct(&tokens);
assert_eq!(result1, result2, "Grammar correction must be deterministic");
}
Requirement: Deterministic type inference
Implementation:
pub struct SemanticValidator {
fresh_counter: Cell<usize>, // Deterministic fresh variable generation
}
impl SemanticValidator {
pub fn validate(&self, parse_tree: &ParseTree) -> Result<Type, TypeError> {
// Reset fresh counter for determinism
self.fresh_counter.set(0);
let ast = parse_tree_to_ast(parse_tree);
self.type_infer(&ast, &Context::empty())
}
fn type_infer(&self, ast: &AST, ctx: &Context) -> Result<Type, TypeError> {
// Algorithm W implementation with deterministic fresh variables
match ast {
AST::Var(name) => {
ctx.lookup(name).ok_or_else(|| TypeError::Unbound(name.clone()))
}
AST::Lambda(param, body) => {
let param_type = self.fresh_type_var();
let ctx_extended = ctx.extend(param.clone(), param_type.clone());
let body_type = self.type_infer(body, &ctx_extended)?;
Ok(Type::Arrow(Box::new(param_type), Box::new(body_type)))
}
AST::App(func, arg) => {
let func_type = self.type_infer(func, ctx)?;
let arg_type = self.type_infer(arg, ctx)?;
let result_type = self.fresh_type_var();
let expected = Type::Arrow(Box::new(arg_type.clone()), Box::new(result_type.clone()));
self.unify(&func_type, &expected)?;
Ok(result_type)
}
// ... more cases
}
}
fn fresh_type_var(&self) -> Type {
let id = self.fresh_counter.get();
self.fresh_counter.set(id + 1);
Type::Var(format!("t{}", id)) // Deterministic: t0, t1, t2, ...
}
}
Key Points:
Test:
#[test]
fn test_semantic_determinism() {
let validator = SemanticValidator::new();
let tree = parse("(λx. x) 5");
let result1 = validator.validate(&tree);
let result2 = validator.validate(&tree);
assert_eq!(result1, result2, "Type inference must be deterministic");
}
Requirement: Deterministic SMT solver
Implementation:
pub struct SemanticRepairer {
solver: Z3Solver,
deterministic: bool,
}
impl SemanticRepairer {
pub fn new(deterministic: bool) -> Self {
let mut config = Z3Config::new();
if deterministic {
config.set("smt.random_seed", "42"); // Fixed seed for reproducibility
config.set("sat.random_seed", "42");
}
Self {
solver: Z3Solver::new(config),
deterministic,
}
}
pub fn repair(&self, ast: &AST, type_error: &TypeError) -> Vec<AST> {
// 1. Localize error (deterministic)
let error_location = self.localize_error(ast, type_error);
// 2. Generate repair constraints
let constraints = self.generate_constraints(ast, error_location);
// 3. Solve MaxSMT
let models = self.solver.max_smt(&constraints);
// 4. Extract repairs
let mut repairs: Vec<AST> = models
.iter()
.map(|model| self.apply_model(ast, model))
.collect();
// 5. Verify repairs
repairs.retain(|repair| self.is_well_typed(repair));
// 6. Sort for determinism
if self.deterministic {
repairs.sort_by_key(|repair| self.repair_cost(ast, repair));
}
repairs
}
}
Key Points:
Test:
#[test]
fn test_repair_determinism() {
let repairer = SemanticRepairer::new(true);
let ast = parse("x + \"hello\""); // Type error
let result1 = repairer.repair(&ast, &type_error);
let result2 = repairer.repair(&ast, &type_error);
assert_eq!(result1, result2, "Semantic repair must be deterministic");
}
Strategy: Generate random inputs, verify properties hold
Implementation (using proptest):
use proptest::prelude::*;
proptest! {
#[test]
fn prop_lexical_correctness(s in "[a-z]{1,10}", d in 1..=3usize) {
let corrector = LexicalCorrector::new(dictionary(), d, true);
let results = corrector.correct(&s, 10);
// Property: All results are within distance d
for (candidate, distance) in results {
prop_assert!(distance <= d);
prop_assert!(levenshtein_distance(&s, &candidate) <= d);
}
}
#[test]
fn prop_grammar_correctness(tokens in prop::collection::vec(any::<Token>(), 1..20)) {
let corrector = GrammarCorrector::new(parser(), 2, 20, true);
let results = corrector.correct(&tokens);
// Property: All results are syntactically valid
for tree in results {
prop_assert!(tree.is_syntactically_valid());
prop_assert!(!tree.has_error_nodes());
}
}
#[test]
fn prop_semantic_correctness(ast in arb_ast()) {
let validator = SemanticValidator::new();
// Property: If validation succeeds, program is well-typed
if let Ok(ty) = validator.validate(&ast) {
prop_assert!(is_well_typed(&ast, &ty));
}
}
#[test]
fn prop_repair_correctness(ast in arb_ast_with_type_error()) {
let repairer = SemanticRepairer::new(true);
let validator = SemanticValidator::new();
if let Some(error) = find_type_error(&ast) {
let repairs = repairer.repair(&ast, &error);
// Property: All repairs are well-typed
for repair in repairs {
prop_assert!(validator.validate(&repair).is_ok());
}
}
}
}
Strategy: Assert invariants at key points
Implementation:
impl GrammarCorrector {
fn correct(&self, tokens: &[Token]) -> Vec<ParseTree> {
let results = self.bfs_search(tokens);
// Invariant: All results are valid parse trees
debug_assert!(results.iter().all(|tree| tree.is_valid()));
// Invariant: All results are within max distance
debug_assert!(results.iter().all(|tree| tree.cost <= self.max_distance));
results
}
}
Strategy: Verify repairs with independent validator
Implementation:
impl SemanticRepairer {
pub fn repair(&self, ast: &AST, type_error: &TypeError) -> Vec<AST> {
let repairs = self.smt_repair(ast, type_error);
// Double-check: Verify all repairs are well-typed
let validator = SemanticValidator::new();
let verified_repairs: Vec<AST> = repairs
.into_iter()
.filter(|repair| validator.validate(repair).is_ok())
.collect();
if verified_repairs.is_empty() {
warn!("SMT repair failed verification");
}
verified_repairs
}
}
Strategy: Expand beam when quality drops
Implementation:
pub struct AdaptiveBeamSearch {
initial_width: usize,
max_width: usize,
expansion_threshold: f64,
}
impl AdaptiveBeamSearch {
pub fn search(&self, initial_state: State) -> Vec<Solution> {
let mut beam_width = self.initial_width;
let mut prev_best_score = f64::INFINITY;
loop {
let results = self.beam_search(initial_state.clone(), beam_width);
let best_score = results.first().map(|s| s.score).unwrap_or(f64::INFINITY);
// Check if expanding beam improves quality
let improvement = (prev_best_score - best_score) / prev_best_score;
if improvement < self.expansion_threshold || beam_width >= self.max_width {
return results;
}
// Expand beam
beam_width = (beam_width * 2).min(self.max_width);
prev_best_score = best_score;
}
}
}
Strategy: Use admissible heuristic to guide search
Implementation:
impl GrammarCorrector {
fn heuristic(&self, state: &SearchState, target: &[Token]) -> usize {
// Admissible heuristic: underestimate remaining cost
let remaining_tokens = target.len() - state.position;
// Lower bound: at least need to process remaining tokens
// (could all match perfectly, so cost 0 per token)
0.max(remaining_tokens.saturating_sub(state.remaining_budget()))
}
fn a_star_search(&self, tokens: &[Token]) -> Vec<ParseTree> {
// Priority queue by f(n) = g(n) + h(n)
let mut queue: BinaryHeap<Reverse<(usize, SearchState)>> = BinaryHeap::new();
queue.push(Reverse((0, initial_state())));
while let Some(Reverse((f_score, state))) = queue.pop() {
if state.is_complete() {
return vec![state.to_parse_tree()];
}
for next_state in self.expand(&state) {
let g_score = next_state.cost; // Cost so far
let h_score = self.heuristic(&next_state, tokens); // Estimated remaining
let f_score = g_score + h_score;
queue.push(Reverse((f_score, next_state)));
}
}
vec![]
}
}
Strategy: Keep all non-dominated solutions
Implementation:
pub struct ParetoOptimizer {
objectives: Vec<Box<dyn Fn(&Solution) -> f64>>,
}
impl ParetoOptimizer {
pub fn compute_frontier(&self, candidates: Vec<Solution>) -> Vec<Solution> {
let mut frontier: Vec<Solution> = Vec::new();
'candidate: for candidate in candidates {
// Check if candidate is dominated by any solution in frontier
for existing in &frontier {
if self.dominates(existing, &candidate) {
continue 'candidate; // Skip dominated candidate
}
}
// Remove solutions dominated by candidate
frontier.retain(|existing| !self.dominates(&candidate, existing));
// Add candidate to frontier
frontier.push(candidate);
}
frontier
}
fn dominates(&self, a: &Solution, b: &Solution) -> bool {
let mut strictly_better = false;
for objective in &self.objectives {
let score_a = objective(a);
let score_b = objective(b);
if score_a > score_b {
return false; // a worse in some objective
} else if score_a < score_b {
strictly_better = true; // a better in this objective
}
}
strictly_better // a at least as good in all, better in at least one
}
}
// Usage
let optimizer = ParetoOptimizer {
objectives: vec![
Box::new(|s| s.lexical_cost as f64),
Box::new(|s| s.grammar_cost as f64),
Box::new(|s| s.semantic_cost as f64),
],
};
let candidates = pipeline.generate_candidates(input);
let pareto_frontier = optimizer.compute_frontier(candidates);
// Present to user
for (i, solution) in pareto_frontier.iter().enumerate() {
println!("{}. {} (lex: {}, gram: {}, sem: {})",
i + 1,
solution.text,
solution.lexical_cost,
solution.grammar_cost,
solution.semantic_cost
);
}
Strategy: Reuse previous parse when input changes
Implementation:
pub struct IncrementalParser {
parser: TreeSitterParser,
prev_tree: Option<Tree>,
prev_input: String,
}
impl IncrementalParser {
pub fn parse(&mut self, input: &str) -> Tree {
let tree = if let Some(prev) = &self.prev_tree {
// Incremental parse (O(log n) typically)
self.parser.parse_incremental(input, prev)
} else {
// Full parse (O(n))
self.parser.parse(input, None)
};
self.prev_tree = Some(tree.clone());
self.prev_input = input.to_string();
tree
}
}
Strategy: Cache expensive computations
Implementation:
use lru::LruCache;
pub struct CachedLexicalCorrector {
corrector: LexicalCorrector,
cache: Mutex<LruCache<String, Vec<(String, usize)>>>,
}
impl CachedLexicalCorrector {
pub fn correct(&self, token: &str, k: usize) -> Vec<(String, usize)> {
let cache_key = format!("{}:{}", token, k);
// Check cache
{
let mut cache = self.cache.lock().unwrap();
if let Some(result) = cache.get(&cache_key) {
return result.clone();
}
}
// Compute
let result = self.corrector.correct(token, k);
// Store in cache
{
let mut cache = self.cache.lock().unwrap();
cache.put(cache_key, result.clone());
}
result
}
}
Strategy: Process independent candidates in parallel
Implementation:
use rayon::prelude::*;
impl SemanticValidator {
pub fn validate_batch(&self, parse_trees: Vec<ParseTree>) -> Vec<Result<Type, TypeError>> {
// Parallel validation (candidates are independent)
parse_trees
.par_iter()
.map(|tree| self.validate(tree))
.collect()
}
}
impl Pipeline {
pub fn correct(&self, input: &str) -> Vec<Correction> {
// Layer 1: Lexical (sequential)
let token_candidates = self.lexical.correct_all_tokens(input);
// Layer 2: Grammar (parallel for each token combination)
let parse_trees: Vec<ParseTree> = token_candidates
.par_iter()
.flat_map(|tokens| self.grammar.correct(tokens))
.collect();
// Layer 3: Semantic (parallel)
let validated: Vec<(ParseTree, Type)> = parse_trees
.into_par_iter()
.filter_map(|tree| {
self.semantic.validate(&tree).ok().map(|ty| (tree, ty))
})
.collect();
// ... continue pipeline
validated.into_iter().map(|(tree, ty)| Correction { tree, ty }).collect()
}
}
Strategy: Bound expensive operations
Implementation:
use std::time::{Duration, Instant};
impl SemanticRepairer {
pub fn repair_with_timeout(
&self,
ast: &AST,
type_error: &TypeError,
timeout: Duration,
) -> Vec<AST> {
let start = Instant::now();
let mut repairs = Vec::new();
// Set solver timeout
self.solver.set_timeout(timeout.as_millis() as u32);
// Attempt repair
match self.smt_repair(ast, type_error) {
Ok(result) if start.elapsed() < timeout => {
repairs = result;
}
Ok(_) => {
warn!("SMT repair timed out");
}
Err(e) => {
error!("SMT repair failed: {}", e);
}
}
repairs
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_lexical_empty() {
let corrector = LexicalCorrector::new(dictionary(), 2, true);
let result = corrector.correct("", 5);
assert!(result.is_empty());
}
#[test]
fn test_grammar_balanced_parens() {
let corrector = GrammarCorrector::new(parser(), 1, 20, true);
let tokens = tokenize("(x + y"); // Missing ')'
let result = corrector.correct(&tokens);
assert!(result.iter().any(|tree| tree.is_balanced()));
}
#[test]
fn test_semantic_simple_types() {
let validator = SemanticValidator::new();
let ast = parse("λx. x"); // Identity function
let result = validator.validate(&ast);
assert!(result.is_ok());
}
}
#[test]
fn test_full_pipeline() {
let pipeline = Pipeline::new(config());
let input = "prnt(x + y"; // Typo + missing ')'
let results = pipeline.correct(input);
// Check at least one valid correction exists
assert!(!results.is_empty());
// Check all corrections are valid
for correction in results {
assert!(correction.is_syntactically_valid());
assert!(correction.is_well_typed());
}
}
#[test]
fn test_error_cascade() {
let pipeline = Pipeline::new(config());
let input = "for i in rang(10):"; // Typo causes cascade
let results = pipeline.correct(input);
// Should fix both typo and missing block
let best = results.first().unwrap();
assert!(best.text.contains("range")); // Typo fixed
assert!(best.text.contains("pass") || best.text.contains("break")); // Block added
}
#[test]
fn test_performance_budget() {
use std::time::Instant;
let pipeline = Pipeline::new(config());
let input = generate_input_with_errors(100); // 100 tokens
let start = Instant::now();
let results = pipeline.correct(&input);
let duration = start.elapsed();
assert!(!results.is_empty());
assert!(duration < Duration::from_millis(500), "Exceeds 500ms budget");
}
#[bench]
fn bench_lexical_correction(b: &mut Bencher) {
let corrector = LexicalCorrector::new(dictionary(), 2, false);
b.iter(|| {
corrector.correct("prnt", 5)
});
}
#[derive(Debug, Clone)]
pub struct PipelineConfig {
// Determinism
pub deterministic_mode: bool,
// Lexical
pub lexical_max_distance: usize,
pub lexical_top_k: usize,
// Grammar
pub grammar_max_distance: usize,
pub grammar_beam_width: usize,
pub grammar_use_astar: bool,
// Semantic
pub type_inference_timeout: Duration,
pub smt_repair_timeout: Duration,
pub smt_random_seed: Option<u32>,
// Session Types
pub session_type_depth: usize,
// Feedback
pub enable_feedback: bool,
pub feedback_model_path: Option<PathBuf>,
// Performance
pub enable_caching: bool,
pub enable_parallelization: bool,
pub cache_size: usize,
}
impl Default for PipelineConfig {
fn default() -> Self {
Self {
deterministic_mode: false,
lexical_max_distance: 2,
lexical_top_k: 5,
grammar_max_distance: 2,
grammar_beam_width: 20,
grammar_use_astar: true,
type_inference_timeout: Duration::from_secs(1),
smt_repair_timeout: Duration::from_secs(2),
smt_random_seed: None, // Non-deterministic by default
session_type_depth: 10,
enable_feedback: true,
feedback_model_path: None,
enable_caching: true,
enable_parallelization: true,
cache_size: 1000,
}
}
}
impl PipelineConfig {
pub fn for_testing() -> Self {
Self {
deterministic_mode: true,
smt_random_seed: Some(42),
enable_feedback: false,
..Default::default()
}
}
pub fn for_production() -> Self {
Self {
deterministic_mode: false,
grammar_beam_width: 50, // Higher quality
smt_repair_timeout: Duration::from_secs(5), // More time
..Default::default()
}
}
pub fn for_interactive() -> Self {
Self {
grammar_beam_width: 10, // Faster
smt_repair_timeout: Duration::from_millis(500), // Lower latency
..Default::default()
}
}
}
impl PipelineConfig {
pub fn from_env() -> Self {
use std::env;
let mut config = Self::default();
if let Ok(val) = env::var("DETERMINISTIC") {
config.deterministic_mode = val.parse().unwrap_or(false);
}
if let Ok(val) = env::var("BEAM_WIDTH") {
config.grammar_beam_width = val.parse().unwrap_or(20);
}
// ... more env vars
config
}
}
pub struct MultiLayerPipeline {
config: PipelineConfig,
lexical: LexicalCorrector,
grammar: GrammarCorrector,
semantic: SemanticValidator,
repair: SemanticRepairer,
process: ProcessVerifier,
}
impl MultiLayerPipeline {
pub fn new(config: PipelineConfig) -> Self {
Self {
lexical: LexicalCorrector::new(
load_dictionary(),
config.lexical_max_distance,
config.deterministic_mode,
),
grammar: GrammarCorrector::new(
load_parser(),
config.grammar_max_distance,
config.grammar_beam_width,
config.deterministic_mode,
),
semantic: SemanticValidator::new(),
repair: SemanticRepairer::new(config.deterministic_mode),
process: ProcessVerifier::new(config.session_type_depth),
config,
}
}
pub fn correct(&self, input: &str) -> Vec<Correction> {
// Layer 1: Lexical
let lexical_start = Instant::now();
let token_candidates = self.lexical.correct_all_tokens(input, self.config.lexical_top_k);
debug!("Lexical: {:?}", lexical_start.elapsed());
// Layer 2: Grammar
let grammar_start = Instant::now();
let parse_trees: Vec<ParseTree> = token_candidates
.into_iter()
.flat_map(|tokens| self.grammar.correct(&tokens))
.collect();
debug!("Grammar: {:?}", grammar_start.elapsed());
// Layer 3: Semantic Validation
let semantic_start = Instant::now();
let mut validated: Vec<(ParseTree, Type)> = parse_trees
.into_iter()
.filter_map(|tree| {
self.semantic.validate(&tree).ok().map(|ty| (tree, ty))
})
.collect();
debug!("Semantic Validation: {:?}", semantic_start.elapsed());
// Layer 4: Semantic Repair (if no valid programs)
if validated.is_empty() {
let repair_start = Instant::now();
let repaired = self.repair.repair_batch(&parse_trees, self.config.smt_repair_timeout);
validated.extend(repaired.into_iter().filter_map(|tree| {
self.semantic.validate(&tree).ok().map(|ty| (tree, ty))
}));
debug!("Semantic Repair: {:?}", repair_start.elapsed());
}
// Layer 5: Process Verification (if applicable)
if self.config.session_type_depth > 0 {
let process_start = Instant::now();
validated.retain(|(tree, _)| {
self.process.verify(tree).is_ok()
});
debug!("Process Verification: {:?}", process_start.elapsed());
}
// Convert to corrections
validated
.into_iter()
.map(|(tree, ty)| Correction {
text: tree.to_string(),
tree,
ty,
cost: self.total_cost(input, &tree),
})
.collect()
}
pub fn correct_with_pareto(&self, input: &str) -> Vec<Correction> {
let candidates = self.correct(input);
// Compute Pareto frontier
let optimizer = ParetoOptimizer {
objectives: vec![
Box::new(|c: &Correction| c.lexical_cost() as f64),
Box::new(|c: &Correction| c.grammar_cost() as f64),
Box::new(|c: &Correction| c.semantic_cost() as f64),
],
};
optimizer.compute_frontier(candidates)
}
}
fn main() {
// Example 1: Testing mode (deterministic)
let config = PipelineConfig::for_testing();
let pipeline = MultiLayerPipeline::new(config);
let input = "prnt(x + y";
let corrections = pipeline.correct(input);
for correction in corrections {
println!("{}: cost {}", correction.text, correction.cost);
}
// Example 2: Production mode (quality)
let config = PipelineConfig::for_production();
let pipeline = MultiLayerPipeline::new(config);
let input = "for i in rang(10):";
let corrections = pipeline.correct_with_pareto(input);
for (i, correction) in corrections.iter().enumerate() {
println!("{}. {} (lex: {}, gram: {}, sem: {})",
i + 1,
correction.text,
correction.lexical_cost(),
correction.grammar_cost(),
correction.semantic_cost()
);
}
// Example 3: Interactive mode (low latency)
let config = PipelineConfig::for_interactive();
let pipeline = MultiLayerPipeline::new(config);
loop {
let input = read_user_input();
let start = Instant::now();
let corrections = pipeline.correct(&input);
let latency = start.elapsed();
println!("Found {} corrections in {:?}", corrections.len(), latency);
// ... show to user
}
}
This implementation guide provides concrete strategies to achieve the theoretical properties analyzed in ../../design/grammar-correction/theoretical-analysis/complete-analysis.md:
For complete theoretical analysis, see ../../design/grammar-correction/theoretical-analysis/complete-analysis.md.
For quick reference, see ../../design/grammar-correction/theoretical-analysis/quick-reference.md.
For visual representations, see ../../design/grammar-correction/theoretical-analysis/visual-guide.md.
Document Version: 1.0 Last Updated: 2025-11-04 Status: Implementation Guide
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