Anchor Engine Code Patterns Guide

A tour of the programming patterns used throughout the codebase.

Overview

The Anchor Engine uses a hybrid approach - mixing functional and imperative patterns where each makes sense:

• ~31,000 lines of TypeScript
• 125 files across 12 modules
• Functional for data transformations
• Imperative for performance-critical loops
• Async/await for I/O operations

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1. Functional Patterns

Pure Functions

Functions that don't mutate state and return predictable outputs.

Example: calculateLightweightScore (search.ts)

function calculateLightweightScore(
  result: SearchResult,
  queryTerms: string[],
  query: string
): number {
  // Immutable operations only
  const contentWords = new Set(content.split(/\s+/).filter(w => w.length > 2));
  
  // No side effects - just calculations
  return Math.min(1.0, baseScore * 0.3 + termScore * 0.5 + ...);
}

Used for:

• Scoring algorithms
• Data transformations
• Configuration merging

Higher-Order Functions

Functions that take or return other functions.

Example: processWithAdaptiveConcurrency (adaptive-concurrency.ts)

export async function processWithAdaptiveConcurrency<T, R>(
  items: T[],
  processor: (item: T, index: number) => Promise<R>,  // ← Function argument
  config?: ConcurrencyConfig
): Promise<R[]> {
  // Abstracts the concurrency logic
  const results = await processor(item, index);
}

Used for:

• Abstracting processing patterns
• Middleware chains
• Batch operations

Array Methods (Map/Filter/Reduce)

Declarative data transformations.

Example: Result processing (search.ts)

const scoredAtoms = rawAtoms.map(atom => ({
  ...atom,
  score: calculateLightweightScore(atom, terms, sanitizedQuery)
}))
.sort((a, b) => (b.score || 0) - (a.score || 0))
.slice(0, maxResultsPerTerm * terms.length);

Used for:

• Data pipelines
• Result formatting
• Scoring and ranking

---

2. Imperative Patterns

For Loops with Index

When you need precise control over iteration.

Example: Range merging deduplication (search.ts)

for (let i = 1; i < compoundAnchors.length; i++) {
  const next = compoundAnchors[i];
  const currentEnd = (current.end_byte || 0);
  const nextStart = (next.start_byte || 0);
  
  // Complex overlap logic requires mutable state
  if (nextStart <= currentEnd + 50) {
    // Merge or skip based on conditions
    continue;
  }
}

Used for:

• Complex deduplication
• Sliding window algorithms
• Performance-critical paths

While Loops

For unknown iteration counts.

Example: BFS traversal (explore.ts)

for (let depth = 0; depth < maxDepth && frontier.length > 0; depth++) {
  const nextFrontier: string[] = [];
  
  for (const chunk of chunks) {
    // Process current frontier
    // Build nextFrontier for next iteration
  }
  
  frontier = nextFrontier;  // ← State mutation
}

Used for:

• Graph traversals
• Queue processing
• Streaming data

Mutable State

When performance matters more than purity.

Example: Batch processing (context-inflator.ts)

const results: R[] = [];

for (let i = 0; i < items.length; i += batchSize) {
  const batch = items.slice(i, i + batchSize);
  const batchResults = await Promise.all(
    batch.map((item, batchIndex) => processor(item, i + batchIndex))
  );
  results.push(...batchResults);  // ← Mutating results array
}

Used for:

• Accumulating results
• Caching
• Performance optimization

---

3. Async/Await Patterns

Sequential Processing

One at a time - memory safe.

for (const item of items) {
  const result = await processor(item);
  results.push(result);
}

Used for: Low-memory environments (mobile)

Parallel Processing with Promise.all

Batch concurrency for speed.

const batchPromises = batch.map((item, batchIndex) =>
  processor(item, i + batchIndex)
);
const batchResults = await Promise.all(batchPromises);

Used for: High-memory systems (desktops)

Error Handling with Try/Catch

Graceful degradation.

try {
  const result = await db.run(query, params);
  return result.rows;
} catch (e) {
  console.error('[Search] Query failed:', e);
  return [];  // ← Graceful fallback
}

Used for: Database queries, file I/O, external APIs

---

4. Object-Oriented Patterns

Static Methods

Utility functions grouped by domain.

Example: ContextInflator (context-inflator.ts)

export class ContextInflator {
  static async inflate(results: SearchResult[], ...): Promise<SearchResult[]> {
    // Implementation
  }
  
  static async inflateFromAtomPositions(...): Promise<SearchResult[]> {
    // Implementation
  }
}

Used for: Namespacing utility functions

Interfaces for Type Safety

export interface ConcurrencyConfig {
  forceSequential?: boolean;
  sequentialThresholdMB?: number;
  // ...
}

Used for: Configuration objects, API contracts

---

5. Design Patterns

Strategy Pattern

Different algorithms based on conditions.

Example: Adaptive concurrency

if (concurrency === 1) {
  // Sequential strategy
  for (let i = 0; i < items.length; i++) { ... }
} else {
  // Parallel strategy
  for (let i = 0; i < items.length; i += batchSize) { ... }
}

Memoization/Caching

let cachedSettings: any = null;
let settingsLastRead = 0;

function loadUserSettings(): any {
  if (cachedSettings && (now - settingsLastRead) < SETTINGS_CACHE_MS) {
    return cachedSettings;  // ← Return cached
  }
  // ... load fresh
}

Guard Clauses

Early returns for edge cases.

if (!result.content) return result.score || 0;
if (terms.length === 0) return [];
if (!databaseReady) throw new Error('Not ready');

---

Pattern Selection Guide

Pattern	Use When	Example
Pure Function	Data transformation, scoring	calculateLightweightScore
Higher-Order	Abstract processing logic	processWithAdaptiveConcurrency
For Loop	Complex iteration logic	Range merging dedup
Array Methods	Simple transformations	Result formatting
Sequential	Memory-constrained	Mobile search
Parallel	Speed needed, memory available	Desktop search
Mutable State	Performance critical	Batch accumulation

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Key Takeaways

1. Functional for data - Transformations, scoring, formatting
2. Imperative for control - Complex loops, performance, memory management
3. Async for I/O - Database, files, network
4. Pure when possible - Easier to test and reason about
5. Mutable when necessary - But isolate the mutation

The codebase prioritizes pragmatism over purity - using whatever pattern solves the problem best while keeping the code readable and maintainable.