Polygres Python SDK

Build Python applications with Polygres graph, vector, text, and hybrid retrieval.

The SDK connects to one project's Runtime API using a Polygres API key. It does not open PostgreSQL connections or expose database passwords.

• Documentation
• Polygres
• Discord

Install

The SDK requires Python 3.10 or newer.

pip install polygres-sdk

The SDK is a Python library and does not install the polygres terminal command. Install polygres-cli separately for project setup, imports, migrations, and retrieval configuration.

Quick start

Create a Project API Key in Settings and copy the Runtime API URL from the project's Connect page. Store both values in your application's secret configuration.

import os

from polygres import Polygres

client = Polygres(
    api_key=os.environ["POLYGRES_API_KEY"],
    runtime_url=os.environ["POLYGRES_RUNTIME_URL"],
)
project = client.project()

readiness = project.readiness()
print(readiness.graph, readiness.vector, readiness.hybrid)

Use the Runtime API URL with the SDK. Do not use a direct or pooled PostgreSQL connection string.

Choose a retrieval method

The corresponding graph, vector, or text configuration must be ready before the application sends retrieval requests. New vector setup uses project.context.create_collection() with a native pgcontext.vector column. Existing project.vector retrieval methods remain available for applications using previously registered vector configurations.

Vector retrieval

Generate the query embedding with the same model and dimensions used by the saved vector configuration.

query_embedding = [0.1] * 768

page = project.vector.search(
    query_embedding,
    config="documents_embedding",
    filters={"status": "published"},
    min_similarity=0.75,
    limit=10,
)

for result in page.results:
    print(result.id, result.score, result.properties)

Find rows similar to an existing row without generating another embedding:

page = project.vector.similar_to(
    row_id="doc_123",
    config="documents_embedding",
    limit=10,
)

Text retrieval

Full-text search:

page = project.text.tsvector(
    "refund policy",
    config="documents_body_tsv",
    filters={"status": "published"},
    limit=10,
)

Fuzzy text search:

page = project.text.fuzzy(
    "acme corpration",
    config="customer_name_fuzzy",
    limit=10,
)

Graph retrieval

Graph methods start from real rows in graph-registered tables. Use an ID from trusted application data or a previous retrieval result.

start = {
    "schema": "public",
    "table": "documents",
    "id": "doc_123",
}

page = project.graph.expand(
    start,
    max_depth=2,
    direction="any",
    limit=20,
)

for result in page.results:
    print(result.node.id, result.depth, result.readable_path)

Other graph methods include:

neighbors = project.graph.neighborhood(start, radius=2, limit=20)
related = project.graph.related(start, limit=20)

target = {"schema": "public", "table": "documents", "id": "doc_456"}
paths = project.graph.path(start, target, max_depth=3)
connections = project.graph.connection([start, target], max_depth=3)

If a graph method returns Node not found, confirm that the row exists, its table is registered, and the graph was rebuilt after the latest relevant changes.

Hybrid retrieval

Graph-first retrieval starts from a known row and adds vector relevance:

page = project.hybrid.graph_first(
    start,
    embedding=query_embedding,
    config="documents_embedding",
    max_depth=2,
    limit=10,
)

Vector-first retrieval finds semantic candidates before expanding graph context:

page = project.hybrid.vector_first(
    query_embedding,
    config="documents_embedding",
    vector_limit=20,
    max_depth=1,
    limit=10,
)

Joint retrieval lets vector and graph rankings contribute independently:

page = project.hybrid.joint(
    query_embedding,
    start,
    config="documents_embedding",
    vector_weight=0.7,
    graph_weight=0.3,
    max_depth=2,
    limit=10,
)

Pagination

Retrieval methods return a Page with results, has_more, and next_cursor.

page = project.vector.search(
    query_embedding,
    config="documents_embedding",
    limit=25,
)

for result in page.results:
    print(result.id)

if page.has_more:
    next_page = project.vector.search(
        query_embedding,
        config="documents_embedding",
        limit=25,
        cursor=page.next_cursor,
    )

Use auto_paging_iter() when you want the SDK to follow every page:

for result in page.auto_paging_iter():
    print(result.id, result.score)

Error handling

SDK exceptions include the HTTP status, stable error code, safe details, and request ID when available.

from polygres import PolygresAPIError

try:
    page = project.graph.expand(start, max_depth=2)
except PolygresAPIError as exc:
    print(exc.status_code)
    print(exc.code)
    print(exc.request_id)
    print(exc.details)

Keep the request ID when reporting a problem. Never log or send the Project API Key.

Connection information

connection_info() returns project hosts and passwordless connection strings. It never returns the database password.

connection = project.connection_info()
print(connection.direct_host)
print(connection.pooled_host)
print(connection.direct_url_without_password)

Use a PostgreSQL driver such as psycopg or SQLAlchemy when your application needs a database connection. The Polygres SDK is an HTTP retrieval client and does not bundle a PostgreSQL driver.

Version and support

Package version: 0.2.0.

When contacting support, include the installed SDK version and the request ID.

See the SDK 0.2.0 release notes for release changes.

Optional Agent Skill

The polygres-sdk Agent Skill helps compatible coding agents write and review Polygres application code.

npx skills add Evokoa/polygres-skills --skill polygres-sdk

See the Agent Skills repository for Codex and Claude Code installation options.