Supabase pgvector Chunk Size 512 vs 1024 RAG Retrieval Results
Chunk size is one of those RAG decisions that feels arbitrary until you run the numbers. The difference between 512 and 1024 tokens isn’t just storage overhead — it reshapes retrieval precision in ways that compound hard across production systems.
Key Takeaways
- 512-token chunks consistently outperform 1024 in precision-focused retrieval, with experiments showing 8–15% higher top-k accuracy on factual Q&A benchmarks.
- Supabase pgvector’s
ivfflatandhnswindexes behave differently at scale depending on vector density — and chunk size directly controls that density.- RAG systems handling multi-topic documents see degraded retrieval quality at 1024-token chunks due to semantic dilution inside a single embedding.
- The right chunk size depends on document type and query pattern. No universal winner exists, but the data leans toward 512 for most developer-facing use cases in 2026.
Background: Why Chunk Size Became a Real Engineering Problem
Two years ago, most teams building RAG pipelines on Supabase pgvector just picked a number. 512, 1024, whatever the tutorial used. It didn’t matter much when document sets were small and retrieval latency wasn’t a bottleneck.
That changed.
According to Supabase’s own 2025 usage reports, vector store adoption on their platform grew over 3x between Q1 2024 and Q1 2026, driven largely by developer teams shipping production RAG apps — customer support bots, internal knowledge bases, code assistants. The moment you’re indexing 500,000+ chunks, chunk size stops being a footnote and becomes a core architectural variable.
The pgvector extension for PostgreSQL supports both ivfflat and hnsw index types, as documented in Supabase’s official pgvector guide. Each index type responds differently to vector count and dimensionality. Smaller chunks mean more vectors. More vectors means index build time climbs — but query recall can improve because each vector carries a tighter semantic signal.
Kalvium Labs published a production RAG analysis in late 2025 covering teams that migrated from ad-hoc vector stores to Supabase pgvector. One consistent finding: teams that started with 1024-token chunks frequently revisited that decision after seeing degraded retrieval on factual, short-answer queries. The chunking decision made at setup became technical debt within months.
The 512 vs 1024 debate isn’t new. But the experiment data that’s emerged through 2025–2026 is cleaner and more actionable than anything that existed before.
The Experiment: What the Data Actually Shows
Semantic Precision at 512 vs 1024 Tokens
The core mechanical difference is straightforward. A 512-token chunk covers roughly 350–400 words of English prose. A 1024-token chunk covers 700–800 words. When you embed those into a 1536-dimensional vector — using OpenAI’s text-embedding-3-small, the default many Supabase teams run — the larger chunk’s embedding has to average more semantic content across that fixed-dimension space.
According to the machinelearningplus analysis of RAG chunk optimization, this averaging effect directly reduces retrieval precision for narrow factual queries. Their experiments showed that at 512 tokens, top-3 retrieval hit rate for specific factual questions was approximately 78%, versus 67% for 1024-token chunks — an 11-point gap that held consistent across multiple document corpora.
The mechanism isn’t mysterious. A chunk describing a product’s pricing model and its API rate limits occupies one vector. A query about pricing pulls that chunk — but half the embedded content is irrelevant noise. Cosine similarity gets diluted. The right answer is technically present, but buried.
Where 1024 Tokens Actually Wins
Longer chunks aren’t universally worse. For summarization tasks and queries requiring contextual breadth — “explain the general architecture of X” — 1024-token chunks outperformed 512 in coherence scores by roughly 12–18%, per the Kalvium Labs production analysis.
The retrieval logic differs too. With 512-token chunks, a well-formed answer often requires assembling 3–5 retrieved chunks in the prompt context. At 1024, 2 chunks may be sufficient. That matters for latency in synchronous, user-facing apps where every round trip counts.
This approach can fail, though, when documents cover multiple distinct topics within a single passage. The 1024-token chunk that seems context-rich becomes a liability when the LLM has to parse through irrelevant material to find the relevant signal.
Index Performance on Supabase pgvector
This part gets overlooked more than it should.
Supabase’s pgvector documentation explicitly notes that hnsw indexes offer better recall at query time than ivfflat, but with higher memory overhead. At 512-token chunking, a 100,000-document corpus produces roughly 2x the vector count versus 1024. That’s a real infrastructure cost, not a theoretical one.
Benchmarks from teams using Supabase’s hosted Postgres — referenced in Kalvium Labs’ production writeup — show hnsw index build times approximately 1.8x longer for 512-chunk corpora versus 1024. But query latency differences at p95 are under 5ms for corpora under 500k rows. Effectively negligible for most applications. The build cost is a one-time penalty. The retrieval benefit is ongoing.
Comparison: 512 vs 1024 Token Chunks in Supabase pgvector
| Criteria | 512 Tokens | 1024 Tokens |
|---|---|---|
| Factual Q&A retrieval accuracy | ~78% top-3 hit rate | ~67% top-3 hit rate |
| Summarization / broad context queries | Lower coherence | Higher coherence (+12–18%) |
| Vector count (100k docs) | ~2x higher | Baseline |
| hnsw index build time | ~1.8x longer | Baseline |
| p95 query latency (<500k rows) | Effectively equal | Effectively equal |
| Chunks needed per LLM context | 3–5 chunks | 1–2 chunks |
| Semantic dilution risk | Low | High for multi-topic docs |
| Best for | Factual Q&A, code docs, support bots | Long-form summarization, narrative corpora |
The trade-off is real but navigable. For most developer-facing RAG applications on Supabase — support chatbots, documentation assistants, internal knowledge search — the data points clearly toward 512.
Practical Implications: Choosing the Right Setting for Your Stack
For teams shipping factual Q&A systems — documentation bots, support tools, code search — start at 512 tokens. The retrieval precision advantage is consistent, and the infrastructure cost is manageable below 1M vectors on Supabase’s hosted plans.
For teams handling narrative or long-form content — legal documents, research papers, book summaries — test 1024 first. Broad semantic queries land better when context isn’t fragmented across too many small pieces. A hybrid approach, using 512 for structured content and 1024 for prose, is architecturally feasible with Supabase’s multi-table setup. It adds query routing complexity, but for corpora with genuinely mixed content types, the accuracy gains justify the overhead.
Overlap and stride matter too — and most teams ignore them.
The machinelearningplus chunking guide found that adding a 10–15% token overlap between chunks — roughly 50 tokens on a 512-token chunk — recovered approximately 4–6% of the retrieval accuracy lost at boundaries. This is underused in most Supabase pgvector implementations. pgvector’s storage cost for overlapping chunks is linear, not exponential. The extra rows are worth it.
One signal worth tracking: Supabase’s vector toolkit roadmap, last updated April 2026, includes planned native support for late chunking and contextual chunking strategies. If that ships in H2 2026, the static 512-vs-1024 decision may get replaced by dynamic chunk sizing at ingest. Watch their GitHub releases. This is the kind of infrastructure shift that makes early adopters look smart and late movers scramble.
Conclusion: Where This Leaves You
The 2026 picture is clearer than it’s been:
- 512 tokens wins on precision for factual, targeted queries — the dominant use case in production RAG today.
- 1024 tokens holds an edge for contextual, broad-answer queries where assembling fragments creates coherence problems.
- Infrastructure cost differences are real but small below 500k vectors. Above that threshold, measure before committing to either.
- Overlap strategies recover meaningful accuracy at chunk boundaries regardless of which size you pick.
The next 6–12 months will push this conversation toward adaptive chunking — models that decide chunk boundaries based on semantic structure rather than fixed token counts. Cohere’s late-chunking research and LlamaIndex’s document hierarchy experiments are both moving in this direction. Static chunk sizing may look as dated as hand-tuned SQL indexes within two years.
For now, the practical answer is simple: if you’re building on Supabase pgvector and haven’t run a chunk size experiment against your actual query distribution, that’s the one benchmark worth running before anything else.
What query patterns does your RAG system handle most — narrow factual lookups or broad contextual summaries? That answer should drive the decision more than any default setting ever will.
References
- Optimizing RAG Chunk Size: Your Definitive Guide to Better Retrieval Accuracy - machinelearningplus
- pgvector: Embeddings and vector similarity | Supabase Docs
- RAG in Production: pgvector vs Pinecone, Embeddings & Chunking | Kalvium Labs
