How to Choose a Retrieval Product When Precision Matters More Than Speed in 2026
Choose for relevant passages, not index settings alone: compare retrieval quality at a fixed cutoff, tune query controls first, and match the product to your existing stack or file-to-search workflow.
For precision-critical retrieval, choose a product whose controls fit your corpus and workflow, then validate relevance on labelled examples. Index settings can affect approximate-neighbor retrieval, but parsing, chunking, embedding choice and result ranking also shape what users see.
Key takeaways
How to choose for retrieval precision
- Measure relevance: Score Precision@k and Recall@k on human-labelled passages at the same fixed k; measure ANN recall separately against exact nearest neighbors.
- Tune in order: Compare query-time search and fusion controls before rebuilding an index or changing creation-bound settings.
- Match the workflow: Consider pgvector for an existing Postgres stack, Qdrant for a dedicated configurable retrieval engine, Weaviate for configurable vector-and-BM25F hybrid search, or Seahorse Cloud for an integrated file-to-search pipeline.
- Check the whole pipeline: Parsing, chunking, embedding choice and any reranker you add or use where supported can change relevance; HNSW settings do not guarantee answer accuracy.
What precision means in retrieval
Retrieval precision is the share of returned items that are relevant. Recall is the share of all relevant items that the results found. At a fixed cutoff k, Precision@k measures relevant passages in the top k; Recall@k measures how many of the human-labelled relevant passages appear there.
ANN recall is different: it measures how many of the exact nearest neighbors an approximate nearest-neighbor search recovers. HNSW settings such as M and EF Construction shape the graph, while query-time search breadth affects how it is traversed. They can influence ANN recall and performance, but do not guarantee that a passage is relevant or that a generated answer is correct.
Parsing and chunk boundaries determine what text is searchable. The embedding model affects semantic matches, and a reranker can reorder retrieved candidates when it is supported by the product or added to your pipeline. These are separate levers from HNSW tuning.
Dense vectors can also rank an exact identifier below semantically similar text. Hybrid search combines vector results with keyword or sparse results. Token-based matching can help with identifiers, but it is not a guarantee of literal substring matching; check how the chosen system tokenizes and matches your data.
Reciprocal Rank Fusion (RRF) combines ranked lists rather than raw scores, so the lists do not need comparable score scales. Its rank constant and the depth of candidates supplied to fusion can still change results, so evaluate both rather than treating RRF as a no-tuning setting. See Elasticsearch’s RRF documentation and the original RRF paper.
How the options expose index and search controls
Defaults are documented starting points, not quality rankings. Confirm the current documentation and test on your own corpus before choosing settings.
| Product | HNSW index-build settings | Query-time controls | Keyword and vector search | When settings change |
|---|---|---|---|---|
| pgvector (in PostgreSQL) | m 16; ef_construction 64 | hnsw.ef_search 40 by default | Combine with Postgres full-text search; use RRF or a cross-encoder in your query pipeline | Build settings are set on index creation; ef_search can be adjusted for queries or transactions |
| Qdrant (open-source or managed) | m 16; ef_construct 100 | Search breadth is adjustable with hnsw_ef | RRF or DBSF can fuse dense and sparse or BM25 results | Collection HNSW settings can be updated; an index rebuild may apply, depending on the change |
| Weaviate (open-source or managed) | maxConnections 32; efConstruction 128 | ef is mutable and defaults to dynamic (-1) | Hybrid search combines vector and BM25F results with configurable fusion and weight | maxConnections and efConstruction are creation-bound; query-time ef is mutable |
| Seahorse Cloud (integrated platform) | M Value 16; EF Construction 128 | Search Mode (Hybrid/Dense/Sparse), Top K 5, EF Search 200 and RRF K 60 | Dense plus Sparse (BM25 sparse) supports Hybrid mode with RRF | Endpoints, dimension, M and EF Construction are set at Storage creation; search controls can be adjusted per search |
The pgvector documentation describes HNSW as having a better speed–recall trade-off than IVFFlat, with slower builds and greater memory use. That is pgvector-specific guidance, not a benchmark conclusion for every product in this comparison.
Seahorse Cloud: tune searches before rebuilding
Seahorse Cloud combines file upload, embedding and vector-table creation with Semantic Search. When a Storage has both Dense and Sparse endpoints configured, you can compare Hybrid, Dense and Sparse search on that same Storage. The Semantic Search controls include Top K, EF Search and RRF K; documented defaults are 5, 200 and 60 respectively. This gives you query-time controls to evaluate before considering a new Storage.
Storage creation binds the embedding endpoints and dimension to the table and sets HNSW parameters such as M and EF Construction. The Storage creation guide says these settings cannot be edited after creation. If one of them must change, create a new Storage and reprocess the files. Check its processing and search results, repoint consuming applications to it, and retire the old Storage only after the new path is working.
A five-step precision evaluation
- Build a representative test set. Include exact identifiers, paraphrased questions and questions answered in tables. Have people label the passages that should be retrieved.
- Choose a fixed k and score relevance. Run searches with the same top-k cutoff and calculate Precision@k and Recall@k against the labels. For ANN recall, compare approximate results with exact nearest neighbors separately.
- Tune query-time controls first. Compare Dense and Hybrid search, then adjust controls such as EF Search, Top K and RRF K where available. Keep the evaluation cutoff k fixed; for Seahorse Cloud, Dense-versus-Hybrid comparisons and EF Search tuning can use the same Storage when Dense and Sparse are configured.
- Improve the retrieval pipeline, then consider rebuilds. If relevant passages are still missed, inspect parsing, chunking and embedding choice; test a reranker if your product or pipeline supports one. Raise M or EF Construction only when the measured gap points to ANN recall, and record latency, memory and build cost.
- Choose the least costly setup that meets your target. If a creation-bound Seahorse setting needs to change, validate the new Storage and repoint consumers before retiring the old one.
FAQ
Should I raise M above 16 to improve precision?
Not automatically. M is an index-build setting that can affect ANN recall, not a guarantee of more relevant results or correct answers. First measure Precision@k and Recall@k on labelled passages, and check whether approximate search is failing to recover exact neighbors. A change to creation-bound settings may require an index rebuild or, in Seahorse Cloud, a new Storage.
When can hybrid search help?
It can help when semantic matches and keyword signals contribute different useful candidates, such as a paraphrase alongside a product code or clause term. Token-based sparse or BM25 search does not guarantee literal substring matching. Compare Dense and Hybrid results against labelled passages, and tune the fusion constant and candidate depth where those controls are available.
Can I change Seahorse Cloud settings after creating a Storage?
Creation-bound settings—including embedding endpoints, dimension, M and EF Construction—cannot be edited on that Storage. Search-time controls are separate: with Dense and Sparse endpoints configured, Semantic Search lets you adjust Search Mode, Top K, EF Search and RRF K on the same Storage. See the official Semantic Search controls guide.
Test precision on your documents
Create a test Storage with hybrid search and compare retrieval results on a sample corpus.
