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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteTo store and query embeddings with pgvector, enable the extension in the database, create a column whose vector dimension matches your embedding model, insert vectors, and sort query results by the distance metric you need. PostgreSQL performs exact nearest-neighbor search by default; add an HNSW or IVFFlat index only when approximate search’s speed-versus-recall trade-off suits your workload.
Enable pgvector and create a vector column
Install pgvector for your PostgreSQL environment, then enable it in each database where you intend to use it. The project README describes pgvector 0.8.6 and PostgreSQL 13+; installation and supported versions can change, so check the pgvector project README for the version deployed on your server.
CREATE EXTENSION vector;
CREATE TABLE items (
id bigserial PRIMARY KEY,
embedding vector(3)
);The number in vector(3) is illustrative. Replace it with the output dimension of the embedding model that populates the column. Each stored vector and each query vector must have the dimension expected by that column.
Insert embeddings and run a nearest-neighbor query
Represent a vector as a bracketed list of numeric values. The following sample uses small illustrative vectors and asks PostgreSQL for the five rows nearest to a query vector under L2 distance:
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INSERT INTO items (embedding)
VALUES ('[1,2,3]'), ('[4,5,6]');
SELECT *
FROM items
ORDER BY embedding <-> '[3,1,2]'
LIMIT 5;The distance operator in ORDER BY determines what “nearest” means. pgvector documents these operators:
| Operator | Metric or value | How to interpret ascending order |
|---|---|---|
<-> |
L2 (Euclidean) distance | Smaller distance is nearer. |
<#> |
Negative inner product | Use the operator as documented in ascending order for index scans; it returns the negative inner product, not the positive inner product. |
<=> |
Cosine distance | Smaller distance is nearer. |
<+> |
L1 (Manhattan) distance | Smaller distance is nearer. |
For cosine distance, change the ordering expression to embedding <=> '[3,1,2]'; for inner product, use embedding <#> '[3,1,2]'; for L1 distance, use embedding <+> '[3,1,2]'. A returned distance is not automatically a similarity score: name and interpret it according to the metric and ordering convention you chose.
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Choose between exact search, HNSW, and IVFFlat
Without an approximate index, pgvector performs exact nearest-neighbor search, which gives perfect recall: it returns the true nearest results for the chosen metric. Whether exact search is fast enough depends on the data and query workload. If you add an approximate index, retrieval can become faster, but results can differ from exact search.
| Approach | Trade-off described by the project | When to consider it |
|---|---|---|
| Exact search | Perfect recall; no approximate index required. | Use as a correctness baseline, or keep it when its query performance meets your needs. |
| HNSW | Generally a better speed-recall trade-off than IVFFlat, at higher index-build time and memory cost. It has no training step, so it can be created before the table contains data. | Consider it when query speed and recall justify the additional build and memory costs. |
| IVFFlat | Typically faster to build and lower in memory use than HNSW, but lower query performance in the project’s qualitative comparison. It needs data for useful index training. | Consider it when lower build and memory costs matter and you can tune lists and probes on representative data. |
These are qualitative project-level comparisons, not performance guarantees for a particular PostgreSQL deployment. Compare approximate results with exact results on representative data, and measure query latency, recall, build time, and resource use before choosing an index.
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Create an index for the metric you query
Approximate indexes need an operator class compatible with the distance operator used in the query. For example, an HNSW index using L2 distance can be created as follows:
CREATE INDEX ON items USING hnsw (embedding vector_l2_ops);Match the operator class to the metric: the project documents vector_l2_ops for L2 distance, vector_cosine_ops for cosine distance, vector_ip_ops for inner product, and vector_l1_ops for L1 distance. For example, a cosine index uses vector_cosine_ops with the <=> operator. The project README covers index options and query details in its indexing documentation.
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IVFFlat needs populated data to train its lists, so create that index after loading data. The README gives approximate starting heuristics: use about rows / 1000 lists for up to one million rows, and about sqrt(rows) lists above one million rows. Probes can start around sqrt(lists); increasing probes generally improves recall at a speed cost. These are tuning starting points, not universal settings. Benchmark with your data and queries.
Handle filters and tenant isolation
A filtered nearest-neighbor query may have different behavior with an approximate index than an unfiltered one. Approximate index filtering happens after the index scan. If a WHERE condition is selective, too few scanned candidates may pass the filter to fill the requested result limit.
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The project README illustrates the effect: if a filter matches 10% of rows and HNSW uses the default hnsw.ef_search of 40, an average of four matching rows would be expected from that scan. This is an illustration of filtering behavior, not a general performance benchmark.
Choose a strategy based on how many values the filter has and how selective it is:
- Try exact search with a filter-column index when the filter selects a small fraction of rows; exact search can perform well for that case.
- Use iterative index scans when an approximate scan needs to continue further to find enough rows after filtering.
- Consider a partial index when there are only a few commonly queried filter values.
- Consider partitioning when there are many distinct filter values.
- For tenant isolation, use list partitioning or separate tables. With tenants sharing an approximate index, vectors belonging to one tenant can affect another tenant’s recall and speed.
Validate the chosen approach with the actual filters, tenant distribution, and requested result limits. A query that works well without a filter may not return enough filtered candidates from the same approximate scan.
Quick Recap
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