PostgreSQL vs. Dedicated Vector DBs in 2025: The RAG Architecture Debate

 

The Architecture Trap: "Just Add Another Database"

It is 2025. You are designing a Retrieval-Augmented Generation (RAG) pipeline for a high-traffic SaaS application. Your application data—users, permissions, document metadata—already lives in PostgreSQL. When the requirement for "Semantic Search" lands on your desk, the immediate impulse is often to provision a dedicated vector database like Pinecone, Weaviate, or Qdrant.

This is often a mistake.

While dedicated vector databases offer impressive niche features, introducing them creates a Distributed State Problem. You now have two sources of truth. When a user updates a document in PostgreSQL, you must synchronously update the vector store. If the PostgreSQL transaction commits but the API call to the vector DB fails (or lags), your user sees inconsistent search results. You are essentially fighting the CAP theorem unnecessarily.

For 95% of use cases—specifically those under 100 million vectors—the architectural overhead of a dedicated vector DB outweighs the marginal performance gains. The superior architecture for most teams is the "Single Pane of Glass" approach using PostgreSQL with pgvector.

The Root Cause: The Distributed Transaction & Metadata Gap

The friction arises from two specific technical constraints:

1. ACID Compliance across Boundaries: To keep a primary DB and a vector DB in sync, you need a two-phase commit or an elaborate event-driven architecture (CDC pipelines via Kafka/Debezium). This introduces latency and operational fragility.
2. The Metadata Filtering Problem: RAG is rarely just "find similar vectors." It is "find similar vectors owned by Tenant A, created after 2024, with status 'published'."
   • Dedicated DB Approach: You must replicate all metadata (tenant_id, timestamps, status) into the vector store. This is data duplication.
   • Post-Filtering Latency: If you don't replicate metadata, you perform the vector search first, get top-k results, and then filter them in your application code. If the top-k are all from the wrong tenant, you return zero results despite valid matches existing. This is the "Pre-filtering vs. Post-filtering" dilemma.

PostgreSQL solves this by treating the vector embedding as just another column type, allowing the query planner to execute vector similarity and metadata filtering in a single, atomic, optimized query plan.

The Fix: Implementing Hybrid Search in PostgreSQL 17

We will implement a production-grade RAG solution using PostgreSQL 17 with pgvector (v0.7.0+), Node.js (v22), and Drizzle ORM. This solution solves the synchronization problem by keeping data and embeddings in the same row.

Prerequisites

You must enable the extension in your database migration:

CREATE EXTENSION IF NOT EXISTS vector;

1. The Schema (Drizzle ORM)

We define a table that handles both the raw content for keyword search (using tsvector) and the embedding for semantic search (using vector).

// db/schema.ts
import { 
  pgTable, 
  serial, 
  text, 
  timestamp, 
  vector, 
  index,
  integer
} from 'drizzle-orm/pg-core';
import { sql } from 'drizzle-orm';

export const documents = pgTable(
  'documents',
  {
    id: serial('id').primaryKey(),
    tenantId: integer('tenant_id').notNull(),
    content: text('content').notNull(),
    // OpenAI's text-embedding-3-small outputs 1536 dimensions
    embedding: vector('embedding', { dimensions: 1536 }),
    createdAt: timestamp('created_at').defaultNow(),
  },
  (table) => [
    // HNSW Index for fast approximate nearest neighbor search
    // 'm' and 'ef_construction' are tuned for a balance of recall vs build speed
    index('embedding_hnsw_idx').using(
      'hnsw',
      table.embedding.op('vector_cosine_ops'), 
      sql`with (m = 16, ef_construction = 64)`
    ),
    // GIN Index for full-text keyword search
    index('content_search_idx').using(
      'gin',
      sql`to_tsvector('english', ${table.content})`
    )
  ]
);

2. The Hybrid Search Implementation

This function performs Hybrid Search: it combines keyword matching (BM25 via ts_rank) with semantic similarity (Cosine Distance). This ensures that exact keyword matches (like part numbers or specific acronyms) are boosted even if the semantic embedding is slightly off.

// lib/search.ts
import { db } from './db';
import { documents } from './db/schema';
import { sql, desc, and, eq } from 'drizzle-orm';

interface SearchParams {
  tenantId: number;
  queryText: string;
  queryEmbedding: number[]; // Result from OpenAI/Cohere
  limit?: number;
}

export async function searchDocuments({ 
  tenantId, 
  queryText, 
  queryEmbedding, 
  limit = 5 
}: SearchParams) {
  
  // Weights for Reciprocal Rank Fusion or Linear Combination
  const SEMANTIC_WEIGHT = 0.7;
  const KEYWORD_WEIGHT = 0.3;

  // We use raw SQL interpolation for the complex ranking logic 
  // because ORM wrappers often obscure the specificity of vector math.
  const results = await db.select({
    id: documents.id,
    content: documents.content,
    // Calculate Cosine Similarity (1 - Cosine Distance)
    similarity: sql<number>`1 - (${documents.embedding} <=> ${JSON.stringify(queryEmbedding)})`,
    // Calculate Keyword Rank
    rank: sql<number>`ts_rank_cd(to_tsvector('english', ${documents.content}), websearch_to_tsquery('english', ${queryText}))`
  })
  .from(documents)
  .where(
    and(
      eq(documents.tenantId, tenantId),
      // Optimization: Only consider documents with minimal relevance to save CPU
      sql`1 - (${documents.embedding} <=> ${JSON.stringify(queryEmbedding)}) > 0.5`
    )
  )
  .orderBy(
    desc(sql`
      (
        (${SEMANTIC_WEIGHT} * (1 - (${documents.embedding} <=> ${JSON.stringify(queryEmbedding)}))) + 
        (${KEYWORD_WEIGHT} * ts_rank_cd(to_tsvector('english', ${documents.content}), websearch_to_tsquery('english', ${queryText})))
      )
    `)
  )
  .limit(limit);

  return results;
}

3. Atomic Updates (The Architecture Fix)

This is where the architecture shines. When a document is updated, the embedding is regenerated and updated in the same transaction.

// services/documentService.ts
import { db } from '../db';
import { documents } from '../db/schema';
import { eq } from 'drizzle-orm';
import { generateEmbedding } from './ai'; // Your OpenAI wrapper

export async function updateDocument(id: number, newContent: string) {
  // 1. Generate new vector
  const newEmbedding = await generateEmbedding(newContent);

  // 2. Transactional Update
  // If the DB write fails, the embedding generation is wasted (low cost).
  // If the DB write succeeds, data and vector are guaranteed consistent.
  await db.transaction(async (tx) => {
    await tx.update(documents)
      .set({
        content: newContent,
        embedding: newEmbedding,
        // Drizzle handles the date update via schema default, 
        // but explicit updates are better for audit trails.
        createdAt: new Date()
      })
      .where(eq(documents.id, id));
  });
}

The Explanation: HNSW and Atomic Consistency

Why this works at scale (HNSW)

Early versions of vector search in SQL (like pgvector before 0.5.0) used IVFFlat (Inverted File Flat) indexing. IVFFlat is fast but suffers from recall degradation if the distribution of data changes significantly without re-indexing.

The code above uses HNSW (Hierarchical Navigable Small World) indexing.

1. Graph-based: It builds a multi-layered graph of vectors.
2. Performance: It offers logarithmic time complexity O(log N) for searches, comparable to Pinecone or Milvus.
3. WAL Integration: Because pgvector indexes are first-class citizens in Postgres, they are written to the Write-Ahead Log (WAL). This means your vectors are covered by Point-in-Time Recovery (PITR), replication, and standard backups.

The "Hybrid Score" Logic

In the query, we combine two scores:

1. 1 - (embedding <=> query): The semantic closeness.
2. ts_rank_cd(...): The density of keyword usage.

By sorting on a weighted sum (0.7 * semantic) + (0.3 * keyword), we solve a classic AI failure mode: the "confident hallucination." If a user searches for a specific error code "ERR-505", a purely semantic search might return documents about "network errors" generally. Hybrid search forces the exact match "ERR-505" to bubble up because the ts_rank will be massive for that document, overriding a mediocre semantic score.

Conclusion

The decision to use a dedicated vector database in 2025 should be driven by scale (billions of vectors) or specific exotic features (like on-device quantization or proprietary re-ranking engines provided by the vendor).

For standard RAG applications, PostgreSQL is the correct choice. By colocating your embeddings with your operational data, you eliminate the distributed state problem, simplify your testing strategy, and gain the ability to perform complex, joined metadata filtering with zero network overhead.

Stop over-engineering your stack. Use the database you already have.