How to Use ParadeDB in PostgreSQL

What are common ParadeDB use cases in PostgreSQL?

ParadeDB extends PostgreSQL with lightning-fast vector similarity search, letting you blend AI-powered semantic queries with standard SQL filters.

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Description

Example H2

Example H3

Why choose ParadeDB for Postgres-based AI search?

ParadeDB adds approximate-nearest-neighbor (ANN) indexing, hybrid BM25+vector ranking, and GPU support directly inside PostgreSQL. You keep ACID compliance, schema, and SQL while unlocking ChatGPT-style relevance for product catalogs or support docs.

How do I install ParadeDB?

Run CREATE EXTENSION paradedb; after adding the ParadeDB binaries to $PGHOME/lib or using the prebuilt Docker image. No external service is required; everything stays inside your existing cluster.

How do I store embeddings for my Products table?

Add a vector column sized to your embedding length (e.g., 384). Populate it with your favorite model offline or use paradedb.embed() for on-the-fly generation.

ALTER TABLE Products ADD COLUMN embedding vector(384); UPDATE Products SET embedding = paradedb.embed(name || ' ' || coalesce(description,''));

How do I index vectors for fast search?

Create a ParadeDB ivfflat index. Choose a reasonable number of lists (√rows is a rule of thumb).

CREATE INDEX products_embedding_idx ON Products USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);

How do I run a vector similarity query?

Pass the query embedding to <-> (L2 distance) or <#> (cosine). Limit for speed.

SELECT id, name, price FROM Products ORDER BY embedding <#> $1 -- $1 = 384-dim array LIMIT 10;

How do I mix vector and relational filters?

Filter first, then order by distance to avoid scanning unnecessary rows.

SELECT id, name FROM Products WHERE stock > 0 AND price < 50 ORDER BY embedding <#> $1 LIMIT 5;

What are practical ParadeDB use cases?

Product recommendations: find similar SKUs by embedding names and descriptions.

Semantic customer support: embed FAQs and fetch the closest answers.

Hybrid ranked search: combine BM25 text relevance with vectors for better recall.

AI chat memory: store conversation chunks as vectors for retrieval-augmented generation.

What are best practices for ParadeDB?

1. Deduplicate embeddings with a materialized view to avoid recomputation.

2. Use ANALYZE after bulk loads so the planner respects your indexes.

3. Benchmark list counts and probes per workload; there is no one-size-fits-all.

4. Keep embeddings in smaller tables or partitions when possible for cache locality.

Why How to Use ParadeDB in PostgreSQL is important

How to Use ParadeDB in PostgreSQL Example Usage


-- Recommend accessories similar to product 42 that cost under $30
WITH target AS (
  SELECT embedding FROM Products WHERE id = 42
)
SELECT p.id, p.name, p.price
FROM   Products p, target t
WHERE  p.price < 30 AND p.id <> 42
ORDER  BY p.embedding <#> t.embedding
LIMIT  3;

How to Use ParadeDB in PostgreSQL Syntax


-- 1. Enable extension
CREATE EXTENSION IF NOT EXISTS paradedb;

-- 2. Add embedding column
ALTER TABLE Products ADD COLUMN embedding vector(384);

-- 3. Populate embeddings (offline Python example)
-- UPDATE Products SET embedding = %s WHERE id = %s

-- 4. Create ANN index
CREATE INDEX products_embedding_idx
ON Products USING ivfflat (embedding vector_l2_ops)
WITH (lists = 100);

-- 5. Query: show top 5 similar in-stock products under $50
SELECT id, name, price
FROM   Products
WHERE  stock > 0 AND price < 50
ORDER  BY embedding <#> $1  -- 384-length float[]
LIMIT  5;

Common Mistakes

Skipping <code>ANALYZE</code> after bulk embedding loads. Without fresh statistics, the planner may ignore your IVFFlat index and perform slow sequential scans. Run <code>ANALYZE Products;</code> or schedule autovacuum.
Building an IVFFlat index before populating embeddings. The index captures empty pages and must be rebuilt. Always load data first, then create the index or <code>REINDEX</code> afterward.