datalevin 0.10.18

Python bindings for Datalevin over the JVM interop bridge

Datalevin Python Bindings

Python bindings for Datalevin over the JVM interop bridge.

Install

python -m pip install datalevin

Requirements:

• Python 3.10+
• Java 21+

Published wheels bundle the shared Datalevin runtime jar, so normal usage does not require building Datalevin from source.

Quick Start

from datalevin import connect

with connect(
    "/tmp/dtlv-py",
    schema={
        ":name": {
            ":db/valueType": ":db.type/string",
            ":db/unique": ":db.unique/identity",
        }
    },
) as conn:
    conn.transact(
        [
            {":db/id": -1, ":name": "Ada"},
            {":db/id": -2, ":name": "Bob"},
        ]
    )

    names = conn.query("[:find [?name ...] :where [?e :name ?name]]")
    ada = conn.pull([":name"], 1)

    print(names)
    print(ada)

Structured query forms and inputs can also be passed as normal Python lists and dictionaries when that is more convenient than EDN strings.

KV Example

from datalevin import open_kv

with open_kv("/tmp/dtlv-py-kv") as kv:
    kv.open_dbi("items")
    kv.transact(
        [
            (":put", 1, "alpha"),
            (":put", 2, "beta"),
        ],
        dbi_name="items",
        k_type=":long",
        v_type=":string",
    )

    print(
        kv.get_value(
            "items",
            2,
            k_type=":long",
            v_type=":string",
            ignore_key=True,
        )
    )
    print(kv.get_range("items", [":all"], k_type=":long", v_type=":string"))

Remote Client Example

Use new_client() for server administration against a running Datalevin server:

from datalevin import new_client

CLIENT_OPTS = {
    ":pool-size": 1,
    ":time-out": 5000,
    ":ha-write-retry-timeout-ms": 5000,
    ":ha-write-retry-delay-ms": 100,
}

client = new_client("dtlv://datalevin:datalevin@localhost", opts=CLIENT_OPTS)
created = False
opened = False

try:
    client.create_database("demo", "datalog")
    created = True
    info = client.open_database(
        "demo",
        "datalog",
        schema={
            ":name": {
                ":db/valueType": ":db.type/string",
                ":db/unique": ":db.unique/identity",
            }
        },
        info=True,
    )
    opened = True

    print(info)
    print(client.list_databases())
finally:
    if opened:
        client.close_database("demo")
    if created:
        client.drop_database("demo")
    client.disconnect()

Embedding Search Options

Python bindings pass Datalevin option maps through unchanged, so newer store features such as :embedding-opts, :embedding-domains, and remote :openai-compatible embedding providers are available directly from connect():

from datalevin import connect

with connect(
    "/tmp/dtlv-py-embed",
    schema={
        ":doc/id": {
            ":db/valueType": ":db.type/string",
            ":db/unique": ":db.unique/identity",
        },
        ":doc/text": {
            ":db/valueType": ":db.type/string",
            ":db/embedding": True,
            ":db.embedding/domains": ["docs"],
            ":db.embedding/autoDomain": True,
        },
    },
    opts={
        ":embedding-opts": {
            ":provider": ":openai-compatible",
            ":model": "text-embedding-3-small",
            ":base-url": "https://api.openai.com/v1",
            ":api-key-env": "OPENAI_API_KEY",
            ":request-dimensions": 1536,
            ":metric-type": ":cosine",
        }
    },
) as conn:
    pass

Notes

• Datalevin values come back as ordinary Python values where possible.
• Remote client options such as :ha-write-retry-timeout-ms and :ha-write-retry-delay-ms can be passed to new_client().
• interop() is available for advanced raw-handle access when you need it.

Development

From this repo, the wrapper can run against:

1. DATALEVIN_JAR=/path/to/datalevin-runtime-<version>.jar
2. a vendored jar under src/datalevin/jars/
3. a repo-local build in target/

Typical local flow:

clojure -T:build vendor-jar
cd bindings/python
python -m venv .venv
. .venv/bin/activate
pip install -e '.[dev]'
pytest

vendor-jar builds a platform-specific runtime jar for the current build host by default. To keep the cross-platform native payloads, pass clojure -T:build vendor-jar :native-platform all.

Wheel builds do this automatically with the all-platform runtime jar and produce a universal py3-none-any wheel. The supported release path is wheel-only:

python -m pip wheel --no-build-isolation bindings/python -w dist/

FreeBSD users should use the platform's own package instead of the PyPI wheel.

.github/workflows/release.python.yml builds the universal wheel on demand, smoke-tests it on Linux amd64, Linux arm64, macOS arm64, and Windows amd64, then uploads the wheel as an artifact. It does not publish to PyPI or TestPyPI.

For a local manual packaging helper, see script/deploy-python.md.

The hosted package workflow currently smoke-tests Linux amd64, Linux arm64, macOS arm64, and Windows amd64.

For ad hoc development against a different build, set DATALEVIN_JAR to point at another embeddable Datalevin runtime jar, preferably target/datalevin-runtime-<version>.jar.