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epinzur/astrapy: AstraPy is a Pythonic interface for DataStax Astra DB and the Data API

A pythonic client for DataStax Astra DB.

This README targets AstraPy version 2.0+. Click here for v1 and here for the v0 API (which you should not really be using by now).

Install with pip install astrapy.

Get the API Endpoint and the Token to your Astra DB instance at astra.datastax.com.

Try the following code after replacing the connection parameters:

from astrapy import DataAPIClient
from astrapy.constants import VectorMetric
from astrapy.ids import UUID
from astrapy.info import CollectionDefinition


ASTRA_DB_APPLICATION_TOKEN = "AstraCS:..."
ASTRA_DB_API_ENDPOINT = "https://01234567-....apps.astra.datastax.com"

# Connect and create the Database object
my_client = DataAPIClient()
my_database = my_client.get_database(
    ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)

# Create a vector collection
my_collection = my_database.create_collection(
    "dreams_collection",
    definition=(
        CollectionDefinition.builder()
        .set_vector_dimension(3)
        .set_vector_metric(VectorMetric.COSINE)
        .build()
    )
)

# Populate the collection with some documents
my_collection.insert_many(
    [
        {
            "_id": UUID("018e65c9-e33d-749b-9386-e848739582f0"),
            "summary": "Riding the waves",
            "tags": ["sport"],
            "$vector": [0, 0.2, 1],
        },
        {
            "summary": "Friendly aliens in town",
            "tags": ["scifi"],
            "$vector": [-0.3, 0, 0.8],
        },
        {
            "summary": "Meeting Beethoven at the dentist",
            "$vector": [0.2, 0.6, 0],
        },
    ],
)

my_collection.update_one(
    {"tags": "sport"},
    {"$set": {"summary": "Surfers' paradise"}},
)

# Run a vector search
cursor = my_collection.find(
    {},
    sort={"$vector": [0, 0.2, 0.4]},
    limit=2,
    include_similarity=True,
)

for result in cursor:
    print(f"{result['summary']}: {result['$similarity']}")

# This would print:
#   Surfers' paradise: 0.98238194
#   Friendly aliens in town: 0.91873914

# Resource cleanup
my_collection.drop()

Next steps:

More info and usage patterns are given in the docstrings of classes and methods
Data API reference
AstraPy reference
Package on PyPI

The example above uses a collection, where schemaless "documents" can be stored and retrieved. Here is an equivalent code that uses Tables, i.e. uniform, structured data where each row has the same columns, which are of a specific type:

from astrapy import DataAPIClient
from astrapy.constants import VectorMetric
from astrapy.data_types import DataAPIVector
from astrapy.info import (
    CreateTableDefinition,
    ColumnType,
    TableVectorIndexDefinition,
    TableVectorIndexOptions,
)


ASTRA_DB_APPLICATION_TOKEN = "AstraCS:..."
ASTRA_DB_API_ENDPOINT = "https://01234567-....apps.astra.datastax.com"

# Connect and create the Database object
my_client = DataAPIClient()
my_database = my_client.get_database(
    ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)

# Create a table and a vector index on it
table_definition = (
    CreateTableDefinition.builder()
    .add_column("dream_id", ColumnType.INT)
    .add_column("summary", ColumnType.TEXT)
    .add_set_column("tags", ColumnType.TEXT)
    .add_vector_column("dream_vector", dimension=3)
    .add_partition_by(["dream_id"])
    .build()
)
index_options=TableVectorIndexOptions(
    metric=VectorMetric.COSINE,
)
my_table = my_database.create_table("dreams_table", definition=table_definition, if_not_exists=True)
my_table.create_vector_index("dreams_table_vec_idx", column="dream_vector", options=index_options, if_not_exists=True)

# Populate the table with some rows
my_table.insert_many(
    [
        {
            "dream_id": 103,
            "summary": "Riding the waves",
            "tags": ["sport"],
            "dream_vector": DataAPIVector([0, 0.2, 1]),
        },
        {
            "dream_id": 119,
            "summary": "Friendly aliens in town",
            "tags": ["scifi"],
            "dream_vector": DataAPIVector([-0.3, 0, 0.8]),
        },
        {
            "dream_id": 37,
            "summary": "Meeting Beethoven at the dentist",
            "dream_vector": DataAPIVector([0.2, 0.6, 0]),
        },
    ],
)

my_table.update_one(
    {"dream_id": 103},
    {"$set": {"summary": "Surfers' paradise"}},
)

# Run a vector search
cursor = my_table.find(
    {},
    sort={"dream_vector": DataAPIVector([0, 0.2, 0.4])},
    limit=2,
    include_similarity=True,
)

for result in cursor:
    print(f"{result['summary']}: {result['$similarity']}")

# This would print:
#   Surfers' paradise: 0.98238194
#   Friendly aliens in town: 0.91873914

# Resource cleanup
my_table.drop()

For more on Tables, consult the Data API documentation about Tables.

Usage with HCD and other non-Astra installations

The main difference when targeting e.g. a Hyper-Converged Database (HCD) installation is how the client is initialized. Here is a short example showing just how to get to a Database (what comes next is unchaged compared to using Astra DB).

from astrapy import DataAPIClient
from astrapy.constants import Environment
from astrapy.authentication import UsernamePasswordTokenProvider


# Build a token
tp = UsernamePasswordTokenProvider("username", "password")

# Initialize the client and get a "Database" object
client = DataAPIClient(environment=Environment.HCD)
database = client.get_database("http://localhost:8181", token=tp)

For more on this case, please consult the dedicated reference.

AstraPy's abstractions for working at the data and admin layers are structured as depicted by this diagram:

Here's a small admin-oriented example:

from astrapy import DataAPIClient


# this must have "Database Administrator" permissions:
ASTRA_DB_APPLICATION_TOKEN = "AstraCS:..."

my_client = DataAPIClient(ASTRA_DB_APPLICATION_TOKEN)

my_astra_admin = my_client.get_admin()

database_list = list(my_astra_admin.list_databases())

db_info = database_list[0].info
print(db_info.name, db_info.id, db_info.region)

my_database_admin = my_astra_admin.get_database_admin(db_info.id)

my_database_admin.list_keyspaces()
my_database_admin.create_keyspace("my_dreamspace")

The package comes with its own set of exceptions, arranged in this hierarchy:

For more information, and code examples, check out the docstrings and consult the API reference linked above.

You can configure many aspects of the interaction with the API by providing customized "API Options" objects when either spawning a client, copying objects, or spawning "children classes" (such as a Table from a Database).

For the details, please check the docstring for astrapy.api_options.APIOptions and the other classes in that module. Here is a small example script to show a practical starting point:

from astrapy import DataAPIClient
from astrapy.api_options import (
    APIOptions,
    SerdesOptions,
)

# Disable custom datatypes in all reads:
no_cdt_options = APIOptions(
    serdes_options=SerdesOptions(
        custom_datatypes_in_reading=False,
    )
)
my_client = DataAPIClient(api_options=no_cdt_options)

# These spawned objects inherit that setting:
my_database = my_client.get_database(
    "https://...",
    token="my-token-1",
)
my_table = my_database.get_table("my_table")

Working with dates in Collections and Tables

Date and datetime objects, i.e. instances of the standard library datetime.datetime and datetime.date classes, can be used anywhere when sending documents and queries to the API.

By default, what you get back is an instance of astrapy.data_types.DataAPITimestamp (which has a much wider range of expressable timestamps than Python's stdlib). If you want to revert to using the standard library datetime.datetime, you can do so by turn on the APIOptions.SerdesOptions.custom_datatypes_in_reading API Options setting for the collection/table object (note that this setting affects the returned format for several other table data types).

If you choose to have timestamps returned as standard-library datetime.datetime objects, both for collections and tables, you may supply a specific timezone for these (the default is UTC). You do so by providing an appropriate datetime.timezone value to the APIOptions.SerdesOptions.datetime_tz API Options setting for the collection/table object. You can also specify None for a timezone, in which case the resulting values will be timezone-unaware (or "naive") datetimes.

Naive datetimes (i.e. those without a timezone information attached) are inherently ambiguous when it comes to translating them into a unambiguous timestamp. For this reason, if you want to work with naive datetimes, and in particular you want AstraPy to accept them for writes, you need to explicitly turn on the APIOptions.SerdesOptions.accept_naive_datetimes API Options setting for the collection/table object, otherwise AstraPy will raise an error.

Remember that what effectively gets written to DB is always a (numeric) timestamp: for naive quantities, this timestamp value depends on the implied timezone used in the conversion, potentially leading to unexpected results e.g. if multiple applications are running with different locale settings.

The following diagram summarizes the behaviour of the write and read paths for datetime objects, depending on the SerdesOptions settings:

Here an example code snippet showing how to switch to having reads return regular datetime objects and have them set to one's desired timezone offset:

from datetime import timezone,timedelta

from astrapy import DataAPIClient
from astrapy.api_options import APIOptions, SerdesOptions

my_timezone = timezone(timedelta(hours=4, minutes=30))

my_client = DataAPIClient()
my_database = my_client.get_database(
    ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
    spawn_api_options=APIOptions(
        serdes_options=SerdesOptions(
            custom_datatypes_in_reading=False,
            datetime_tzinfo=my_timezone,
        ),
    ),
)

my_collection = my_database.get_collection("my_collection")
# This document will have datetimes set to the desired timezone
document = my_collection.find_one({"code": 123})

Working with ObjectIds and UUIDs in Collections

Astrapy repackages the ObjectId from bson and the UUID class and utilities from the uuid package and its uuidv6 extension. You can also use them directly.

Even when setting a default ID type for a collection, you still retain the freedom to use any ID type for any document:

from astrapy import DataAPIClient
from astrapy.constants import DefaultIdType
from astrapy.ids import ObjectId, uuid8, UUID

import bson

ASTRA_DB_APPLICATION_TOKEN = "AstraCS:..."
ASTRA_DB_API_ENDPOINT = "https://01234567-....apps.astra.datastax.com"

my_client = DataAPIClient()
my_database = my_client.get_database(
    ASTRA_DB_API_ENDPOINT,
    token=ASTRA_DB_APPLICATION_TOKEN,
)

my_collection = my_database.create_collection(
    "ecommerce",
    definition=CollectionDefinition.builder().set_default_id(
        DefaultIdType.UUIDV6
    ).build(),
)

my_collection.insert_one({"_id": ObjectId("65fd9b52d7fabba03349d013")})
my_collection.find({
    "_id": UUID("018e65c9-e33d-749b-9386-e848739582f0"),
})

my_collection.update_one(
    {"tag": "in_stock"},
    {"$set": {"inventory_id": bson.objectid.ObjectId()}},
    upsert=True,
)

my_collection.insert_one({"_id": uuid8()})

First install poetry with pip install poetry and then the project dependencies with poetry install --with dev.

Linter, style and typecheck should all pass for a PR:

With make format-fix the style and imports are autofixed (by ruff)

Features must be thoroughly covered in tests (have a look at tests/* to infer naming convention and module structure).

Tests are grouped in:

"base", covering general-purpose astrapy functionality. Divided in unit/integration;
"vectorize", extensively running a base workload on all provider/integration choices;
"admin", doing a whole sweep of admin operations. Very slow on Astra DB.

Astrapy's CI only runs "base". The others are to be checked manually when it's needed.

Tests can be run on three types of Data API targets (with slight differences in what is applicable):

DockerCompose: DSE+Data API, started by the test initialization with docker-compose. Note that in this case you will have to manually destroy the created containers.
nonAstra: a ready-to-use (user-supplied) local Data API (e.g. using tests/dse_compose)
Astra: an Astra DB target account (or two, as some tests are specific to dev environment)

Depending on the test, different environment variables are needed: refer to the templates in tests/env_templates. The "basic" credentials (one of the three options) are always required, even for unit testing.

Base:

# choose one:
poetry run pytest tests/base
poetry run pytest tests/base/unit
poetry run pytest tests/base/integration

Admin:

# depending on the environment, different 'admin tests' will run:
poetry run pytest tests/admin

Extended vectorize:

# very many env. variables required for this one:
poetry run pytest tests/vectorize

# restrict to some combination(s) with e.g.:
EMBEDDING_MODEL_TAGS="openai/text-embedding-3-large/HEADER/0,voyageAI/voyage-finance-2/SHARED_SECRET/f" \
    poetry run pytest tests/vectorize/integration/test_vectorize_providers.py \
    -k test_vectorize_usage_auth_type_header_sync

All the usual pytest ways of restricting the test selection hold (e.g. poetry run pytest tests/idiomatic/unit or [...] -k <test_name_selector>). Also e.g.:

# suppress log noise
poetry run pytest [...] -o log_cli=0

# increase log level
poetry run pytest [...] -o log_cli=1 --log-cli-level=10

Appendix A: quick reference for key imports

Note: check tests/base/unit/test_imports.py for more.

Client, data and admin abstractions

from astrapy import (
    AstraDBAdmin,
    AstraDBDatabaseAdmin,
    AsyncCollection,
    AsyncDatabase,
    AsyncTable,
    Collection,
    Database,
    DataAPIClient,
    DataAPIDatabaseAdmin,
    Table,
)

Constants for data-related use:

from astrapy.constants import (
    DefaultIdType,
    Environment,
    ReturnDocument,
    SortMode,
    VectorMetric,
)

ObjectIds and UUIDs:

from astrapy.ids import (
    UUID,
    ObjectId,
    uuid1,
    uuid3,
    uuid4,
    uuid5,
    uuid6,
    uuid7,
    uuid8,
)

API Options:

from astrapy.api_options import (
    APIOptions,
    DataAPIURLOptions,
    DevOpsAPIURLOptions,
    SerdesOptions,
    TimeoutOptions,
)

Data types:

from astrapy.data_types import (
    DataAPITimestamp,
    DataAPIVector,
    DataAPIDate,
    DataAPIDuration,
    DataAPIMap,
    DataAPISet,
    DataAPITime,
)

Info/metadata classes:

from astrapy.info import (
    AlterTableAddColumns,
    AlterTableAddVectorize,
    AlterTableDropColumns,
    AlterTableDropVectorize,
    CollectionDefaultIDOptions,
    CollectionDefinition,
    CollectionVectorOptions,
    ColumnType,
    CreateTableDefinition,
    EmbeddingProvider,
    EmbeddingProviderAuthentication,
    EmbeddingProviderModel,
    EmbeddingProviderParameter,
    EmbeddingProviderToken,
    TableBaseIndexDefinition,
    TableIndexDefinition,
    TableIndexOptions,
    TableKeyValuedColumnType,
    TableKeyValuedColumnTypeDescriptor,
    TablePrimaryKeyDescriptor,
    TableScalarColumnTypeDescriptor,
    TableUnsupportedColumnTypeDescriptor,
    TableValuedColumnType,
    TableValuedColumnTypeDescriptor,
    TableVectorColumnTypeDescriptor,
    TableVectorIndexDefinition,
    TableVectorIndexOptions,
    VectorServiceOptions,
)

Authentication:

from astrapy.authentication import (
    StaticTokenProvider,
    UsernamePasswordTokenProvider,
    EmbeddingAPIKeyHeaderProvider,
    AWSEmbeddingHeadersProvider,
)

Appendix B: compatibility with pre-1.0.0 library

If your code still uses the pre-1.0.0 astrapy (i.e. from astrapy.db import AstraDB, AstraDBCollection and so on) you are strongly advised to migrate to the current API. All of the astrapy pre-1.0 API (later dubbed "core") works throughout astrapy v1, albeit with a deprecation warning on astrapy v. 1.5.

Version 2 drops "core" support entirely. In order to use astrapy version 2.0+, you need to migrate your application. Check the links at the beginning of this README for the updated documentation and API reference.

Check out previous versions of this README for more on "core": 1.5.2 and pre-1.0.

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