sqlalchemy-paradedb 0.3.0

Typed SQLAlchemy helpers for ParadeDB

Simple, Elastic-quality search for Postgres

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sqlalchemy-paradedb

ParadeDB integration for SQLAlchemy: typed helpers for BM25 indexes, search predicates, scoring, snippets, facets, and migration ergonomics.

Requirements & Compatibility

Component	Supported
Python	3.10+
SQLAlchemy	2.0.32+
ParadeDB	0.22.0+
PostgreSQL	15+ (with ParadeDB extension)

Installation

uv add sqlalchemy-paradedb

For local development:

uv sync --extra test --extra dev

Quick Start

Prerequisites

Install pg_search in your Postgres database and connect SQLAlchemy to that database.

Create a BM25 Index

from sqlalchemy import Index
from paradedb.sqlalchemy import indexing

products_bm25_idx = Index(
    "products_bm25_idx",
    indexing.BM25Field(Product.id),
    indexing.BM25Field(
        Product.description,
        tokenizer=indexing.tokenize.unicode(lowercase=True),
    ),
    indexing.BM25Field(
        Product.category,
        tokenizer=indexing.tokenize.literal(),
    ),
    postgresql_using="bm25",
    postgresql_with={"key_field": "id"},
)

For JSON columns named metadata, use metadata_ as the ORM attribute name.

Query with ParadeDB Predicates

from sqlalchemy import select
from sqlalchemy.orm import Session
from paradedb.sqlalchemy import pdb, search

stmt = (
    select(Product.id, Product.description)
    .where(search.match_any(Product.description, "running", "shoes"))
    .order_by(pdb.score(Product.id).desc())
    .limit(10)
)

with Session(engine) as session:
    rows = session.execute(stmt).all()

Rows + Facets in a Single Query

from sqlalchemy import select
from sqlalchemy.orm import Session
from paradedb.sqlalchemy import facets, search

base = (
    select(Product.id, Product.description)
    .where(search.match_all(Product.description, "running"))
    .order_by(Product.id)
    .limit(10)
)

stmt = facets.with_rows(
    base,
    agg=facets.multi(
        facets.value_count(field="id"),
        facets.terms(field="category", size=10),
    ),
    key_field=Product.id,
)

with Session(engine) as session:
    rows = session.execute(stmt).all()
    facet_payload = facets.extract(rows)

Search Patterns

Fuzzy Matching

from paradedb.sqlalchemy import search

search.term(Product.description, "shose", distance=1)
search.match_any(Product.description, "wirless", distance=1, prefix=True)
search.term(Product.description, "rnnuing", distance=1, transpose_cost_one=True)

Use fuzzy options on term, match_any, or match_all; there is no separate search.fuzzy(...) helper.

Phrase Prefix and More-Like-This

from paradedb.sqlalchemy import search

search.phrase_prefix(Product.description, ["running", "sh"])
search.more_like_this(Product.id, document_id=1, fields=["description"])

Proximity Composition

from sqlalchemy import select
from paradedb.sqlalchemy import search

prox = search.prox_array("running").within(1, search.prox_regex("sho.*"), ordered=True)
stmt = select(Product.id).where(search.proximity(Product.description, prox))

Indexing and Tokenizers

Tokenizer config can be expressed as a structured mapping:

from sqlalchemy import Index
from paradedb.sqlalchemy import indexing

products_bm25_idx = Index(
    "products_bm25_idx",
    indexing.BM25Field(Product.id),
    indexing.BM25Field(
        Product.description,
        tokenizer=indexing.tokenize.from_config(
            {
                "tokenizer": "simple",
                "filters": ["lowercase", "stemmer"],
                "stemmer": "english",
                "alias": "description_simple",
            }
        ),
    ),
    indexing.BM25Field(
        Product.description,
        tokenizer=indexing.tokenize.from_config(
            {
                "tokenizer": "ngram",
                "args": [3, 8],
                "named_args": {"prefix_only": True},
                "alias": "description_ngram",
            }
        ),
    ),
    postgresql_using="bm25",
    postgresql_with={"key_field": "id"},
)

Validate that a field is indexed with the expected tokenizer:

from paradedb.sqlalchemy import indexing

indexing.assert_indexed(engine, Product.category, tokenizer="literal")

Inspect BM25 metadata for a mapped table:

from paradedb.sqlalchemy import indexing

meta = indexing.describe(engine, Product.__table__)

Alembic Operations

Import once in migration environment startup so Alembic registers ParadeDB operations:

import paradedb.sqlalchemy.alembic  # noqa: F401

Use custom operations in migrations:

op.create_bm25_index(
    "products_bm25_idx",
    "products",
    ["id", "description"],
    key_field="id",
    table_schema="public",
)
op.reindex_bm25("products_bm25_idx", concurrently=True, schema="public")
op.drop_bm25_index("products_bm25_idx", if_exists=True, schema="public")

op.reindex_bm25(..., concurrently=True) must run outside a transaction (autocommit block).

Diagnostics Helpers

paradedb.sqlalchemy.diagnostics exposes wrapper functions for ParadeDB diagnostics:

from paradedb.sqlalchemy import diagnostics

indexes = diagnostics.paradedb_indexes(engine)
segments = diagnostics.paradedb_index_segments(engine, "products_bm25_idx")
check = diagnostics.paradedb_verify_index(engine, "products_bm25_idx", sample_rate=0.1)
all_checks = diagnostics.paradedb_verify_all_indexes(engine, schema_pattern="public")

Common Errors

with_rows requires ORDER BY

from sqlalchemy import select
from paradedb.sqlalchemy import facets

# Missing order_by(...)
base = select(Product.id).limit(10)
facets.with_rows(base, agg=facets.value_count(field="id"), key_field=Product.id)

with_rows requires LIMIT

from sqlalchemy import select
from paradedb.sqlalchemy import facets

# Missing limit(...)
base = select(Product.id).order_by(Product.id)
facets.with_rows(base, agg=facets.value_count(field="id"), key_field=Product.id)

with_rows requires a ParadeDB predicate

from sqlalchemy import select
from paradedb.sqlalchemy import facets

# ensure_predicate=False disables automatic search.all(...) injection
facets.with_rows(
    select(Product.id).order_by(Product.id).limit(10),
    agg=facets.value_count(field="id"),
    key_field=Product.id,
    ensure_predicate=False,
)

tokenizer config requires 'tokenizer'

from paradedb.sqlalchemy import indexing

indexing.tokenize.from_config({"filters": ["lowercase"]})

Examples

• Quick Start
• Faceted Search
• Autocomplete
• More Like This
• Hybrid Search (RRF)
• RAG

Documentation

• ParadeDB Official Docs: https://docs.paradedb.com
• ParadeDB Website: https://paradedb.com

Contributing

See CONTRIBUTING.md for development setup, running tests, linting, and the PR workflow.

Support

If you're missing a feature or have found a bug, please open a GitHub Issue.

To get community support, you can:

• Post a question in the ParadeDB Slack Community
• Ask for help on our GitHub Discussions

If you need commercial support, please contact the ParadeDB team.

License

sqlalchemy-paradedb is licensed under the MIT License.