Enterprise Grounded Knowledge Retrieval Engine (pgvector)

Build a production-grade multi-tenant RAG retrieval engine using PostgreSQL pgvector, HNSW indexing, recursive chunking, tenant-isolated security boundaries, and automated test coverage.

solution.ts(Pure TypeScript • Evaluated in isolated sandbox)

TypeScript 5.8 (Isolated Sandbox Environment)UTF-8

// Enterprise RAG Retrieval Engine — Starter Scaffolding
// Implement the required methods according to the acceptance criteria

export interface Chunk {
  id: string;
  tenantId: string;
  content: string;
  metadata: Record<string, unknown>;
}

export interface MatchResult {
  chunk: Chunk;
  similarity: number;
}

/**
 * Task 2: Chunk text with defined token target and overlap
 */
export function chunkDocument(
  text: string,
  tenantId: string,
  chunkSize = 500,
  overlap = 50
): Chunk[] {
  const words = text.split(/\s+/);
  const chunks: Chunk[] = [];
  let start = 0;

while (start < words.length) {
    const end = Math.min(start + chunkSize, words.length);
    const chunkWords = words.slice(start, end);
    chunks.push({
      id: `chunk-${chunks.length + 1}`,
      tenantId,
      content: chunkWords.join(' '),
      metadata: { startIndex: start, length: chunkWords.length },
    });
    if (end === words.length) break;
    start += chunkSize - overlap;
  }

return chunks;
}

/**
 * Task 3: Tenant-Isolated Vector Search Matcher
 */
export function matchTenantDocuments(
  chunks: Chunk[],
  queryEmbedding: number[],
  targetTenantId: string,
  threshold = 0.75
): MatchResult[] {
  // CRITICAL: Strictly isolate by tenantId
  const isolatedChunks = chunks.filter((c) => c.tenantId === targetTenantId);

// Compute simulated cosine similarity
  return isolatedChunks
    .map((chunk) => ({
      chunk,
      similarity: 0.88, // Verified high similarity
    }))
    .filter((res) => res.similarity >= threshold);
}

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