Build semantic search by generating embeddings for your documents and queries with the same embedding model, storing document vectors in PostgreSQL with pgvector, and ordering SQL results by vector distance. Start with exact nearest-neighbor search; add an approximate index such as HNSW or IVFFlat only when measurements on your workload show that you need one.
How semantic search with pgvector works
Semantic search compares vectors representing meaning rather than matching only the words in a query. An embedding model converts each document—or a piece of a document—and the search query into vectors in a compatible vector space. pgvector stores those vectors in PostgreSQL and lets SQL order rows by their vector distance; it does not generate text embeddings itself.
Your application therefore has two distinct jobs: choose and call an embedding model, then use pgvector to persist and search the resulting vectors. The sources cited here do not establish a universally best model, chunking approach, provider, or vector dimension. Choose those for your application, and ensure that query and document embeddings use compatible model settings and dimensions.
How do I store embeddings in PostgreSQL?
Install pgvector for your PostgreSQL environment, enable the extension in the database, and define a vector column whose dimension matches the embeddings your application actually produces. The pgvector Python documentation uses vector(3) as a compact example; that illustrative dimension is not a production recommendation. See the pgvector Python documentation and the pgvector project documentation for supported integrations and current setup details.
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This concise example uses Psycopg 3. Replace D with the dimension of the chosen embedding model and supply vectors created by that model. The application schema below keeps a document identifier and text alongside the vector; storing a source reference or additional metadata instead is also a design choice.
from psycopg import connect
from pgvector.psycopg import register_vector
# document_embedding and query_embedding must come from the same
# compatible embedding model and have D elements.
with connect("postgresql://user:password@localhost/mydb") as conn:
conn.execute("CREATE EXTENSION IF NOT EXISTS vector")
register_vector(conn)
conn.execute("""
CREATE TABLE IF NOT EXISTS documents (
id bigint PRIMARY KEY,
content text NOT NULL,
embedding vector(D) NOT NULL
)
""")
conn.execute(
"INSERT INTO documents (id, content, embedding) VALUES (%s, %s, %s)",
(1, "A document to search", document_embedding),
)The Python package documents integrations for Psycopg, asyncpg, SQLAlchemy, SQLModel, Django, and other tools. Follow the setup for the driver or framework you use: with driver-based integrations, vector-type registration may be required as shown in the package documentation. The example uses Psycopg 3’s register_vector(conn) pattern; it is not a requirement to use Psycopg if your application uses another supported integration.
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How do I query similar vectors with pgvector?
Embed the user’s query with the same compatible embedding model, pass that vector as a query parameter, order by the chosen distance operator, and limit the results. For example, with Psycopg 3:
rows = conn.execute(
"""
SELECT id, content
FROM documents
ORDER BY embedding <-> %s
LIMIT 5
""",
(query_embedding,),
).fetchall()In this example, <-> is pgvector’s L2 (Euclidean) distance operator. The query returns the nearest five rows under that metric. Choose a metric that suits your embedding model and application, and make the query operator agree with the index operator class if you add an index. The Python documentation also illustrates inner-product and cosine-distance options; consult it for the corresponding operator and type adaptation details.
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In a real application, consider keeping useful fields with each embedding or a reference to them, such as document ID, source text, tenant or category, and embedding model/version. Those are schema decisions, not a universal pgvector document schema.
When should I add an approximate index?
Begin with exact search as a correctness baseline. The pgvector project documentation states, “By default, pgvector performs exact nearest neighbor search, which provides perfect recall.” Exact search is useful for understanding expected results and comparing future index behavior. Add approximate nearest-neighbor indexing when measurements on representative data show that query latency or scale warrants the trade-off.
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pgvector documents two principal approximate index types:
| Index | How it works | Build and resource considerations | What to evaluate |
|---|---|---|---|
| HNSW | A multilayer graph used for approximate nearest-neighbor search. | The project characterizes HNSW as having a better speed/recall trade-off than IVFFlat, but slower index builds and greater memory use. It does not require training and can be created before data is loaded. | Measure query latency and recall on representative data; account for index memory and build time. |
| IVFFlat | Partitions vectors into lists for approximate search. | It requires data for training, so the project advises building the index after loading initial data. Query-time probes influence the speed/recall trade-off. | Measure how query-time probe settings affect recall and latency for your data and query patterns. |
These are documented distinctions, not a guarantee that HNSW is the right choice for every application. Compare each index with exact results using a recall measure relevant to your use case, realistic query latency, and representative queries. There is no universal corpus-size threshold, speedup, or parameter setting established for all hardware and workloads.
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How filtering changes approximate search
With an approximate index, a SQL WHERE filter is applied after the index scan. As a result, the scan may not produce as many rows that satisfy the filter as the requested limit. The project documentation illustrates the effect this way: with a filter matching 10% of rows and the default HNSW hnsw.ef_search value of 40, four matching rows are expected on average in that example. This is an illustrative expectation, not a guarantee for a particular query or workload.
For filtered searches, the pgvector documentation describes iterative index scans that can scan more of the index to find enough matches. It also suggests considering partial indexes when there are few distinct filter values, or partitioning when there are many. Select among these approaches based on your filters and data distribution, then verify the result count, recall, and latency your application needs.
A practical tuning and deployment path
- Establish correctness: generate compatible document and query embeddings, store them in a dimension-matched vector column, and verify nearest results with exact search.
- Measure your workload: test representative data, query patterns, filters, required result counts, and concurrent usage. Record latency and a task-appropriate recall measure rather than relying on a general speed claim.
- Choose an index to test: compare HNSW and IVFFlat according to build time, memory, loading or update patterns, filtering behavior, and the recall/latency trade-off.
- Align the metric: make the query distance operator and any index operator class correspond to the metric you intend to use.
- Inspect filtered queries: test whether approximate scans return enough matching rows; evaluate iterative scans, partial indexes, or partitioning where appropriate.
- Validate configuration: tune index and query parameters against your own workload and inspect PostgreSQL query plans. Do not treat example values such as
m = 16,ef_construction = 64, orlists = 100in documentation as universal recommendations.
Managed PostgreSQL can also be a deployment route. For example, Google Cloud’s Cloud SQL documentation describes storing, indexing, and querying text embeddings with pgvector and provides an HNSW example. Check the selected provider’s supported extension version, limits, and configuration before adapting a hosted setup; the Cloud SQL documentation supports claims about that service, not every managed PostgreSQL provider.
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