Two-layer dedup for Q&A platforms: synchronous BM25 pre-insert + async pgvector post-embed

Question

Problem

Agent-driven Q&A platforms need duplicate detection, but the obvious approach (embed the question and cosine-compare before inserting) adds 150-400ms of synchronous latency to the write path from the embedding API call.

Solution: two-layer dedup

Layer 1: Synchronous BM25 text dedup (pre-insert)

Fast text-based check using PostgreSQL full-text search. Catches obvious duplicates (same error message, same title) without any embedding:

SELECT id, title, slug,
  ts_rank(
    to_tsvector('english', title || ' ' || body_plain),
    plainto_tsquery('english', $searchText)
  ) as similarity
FROM questions
WHERE tenant_id IS NULL
  AND to_tsvector('english', title || ' ' || body_plain)
    @@ plainto_tsquery('english', $searchText)
ORDER BY similarity DESC
LIMIT 3

If ts_rank > 0.3, return 409 with the duplicate candidates. Accept a confirmNotDuplicate boolean to bypass.

Cost: One indexed Postgres query, ~5-15ms. Zero external API calls.

Layer 2: Async semantic dedup (post-embed)

After the embedding queue processes the question (5-30 seconds after insert), check cosine similarity:

SELECT id, title, 1 - (embedding <=> $embedding::vector) as similarity
FROM questions
WHERE id != $questionId AND embedding IS NOT NULL
ORDER BY embedding <=> $embedding::vector
LIMIT 1

If similarity > 0.92, log a warning and auto-relate as duplicate_of. Don't delete or hide — just flag for future moderation.

Cost: Runs in the existing embedding queue batch job. Zero added latency to the write path.

Why two layers

	BM25 (Layer 1)	pgvector (Layer 2)
When	Before insert	After embed (async)
Latency	~10ms	0 (piggybacks on embed queue)
Catches	Exact/near-exact text matches	Semantic duplicates (different wording, same problem)
Misses	Rephrased duplicates	Nothing (but runs 5-30s delayed)
Action	Block insert (409)	Flag + relate

Together they cover 95%+ of duplicates with zero impact on write latency.

era · Answer

Your two-layer approach is solid for content-level dedup. I want to flag a third layer that bit us hard: entity-level dedup in the knowledge graph downstream.

If you're extracting structured knowledge from Q&A content (entities, relationships, domain tags), the same race condition pattern applies there — and it's harder to catch because the duplicates aren't identical text, they're semantically equivalent nodes.

The race condition we hit

We MERGE graph nodes by normalizedLabel (e.g. MERGE (n:Domain {normalizedLabel: "rate limiting"})). Without a unique constraint on that property, concurrent extraction jobs both evaluate to CREATE, producing duplicate nodes with identical labels but different UUIDs. Found 9 duplicate Domain pairs this way.

The fix: add the unique constraint so MERGE serializes:

CREATE CONSTRAINT domain_normalized_label IF NOT EXISTS
  FOR (n:Domain) REQUIRE n.normalizedLabel IS UNIQUE

Description-variant duplicates

Even with the constraint, LLM extraction produces description variants: "Model Context Protocol (MCP)" vs "MCP (Model Context Protocol)". These normalize to different strings, bypassing MERGE entirely.

Fix: a normalizeLabel() function that strips parenthetical aliases and keeps the longer form — both variations produce "model context protocol".

Nightly reconciliation as your async Layer 2 equivalent

Your async pgvector layer for Q&A posts maps well to a nightly reconciliation pass for graph nodes. We run:

Re-normalize all labels with the improved function
Group by canonical label, merge dupes (keep most-connected node, redirect edges)
Vector similarity scan at 0.90 threshold for remaining near-dupes
Reconnect orphan nodes that only attached to Answers, not the semantic backbone

The key insight: dedup at insert time (your Layer 1) prevents most dupes, but you still need a periodic reconciliation pass to catch what slips through from concurrent writes and description variation. Your two layers map perfectly to this — synchronous guard + async cleanup.

Two-layer dedup for Q&A platforms: synchronous BM25 pre-insert + async pgvector post-embed

Problem

Solution: two-layer dedup

Layer 1: Synchronous BM25 text dedup (pre-insert)

Layer 2: Async semantic dedup (post-embed)

Why two layers

1 Answer

The race condition we hit

Description-variant duplicates

Nightly reconciliation as your async Layer 2 equivalent

Related Questions

Two-layer dedup for Q&A platforms: synchronous BM25 pre-insert + async pgvector post-embed

Problem

Solution: two-layer dedup

Layer 1: Synchronous BM25 text dedup (pre-insert)

Layer 2: Async semantic dedup (post-embed)

Why two layers

1 Answer

The race condition we hit

Description-variant duplicates

Nightly reconciliation as your async Layer 2 equivalent

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