AstraPy is a Pythonic SDK for DataStax Astra
Part III - Inserting Documents
- 3.1 - Inserting a document
- 3.2 - Inserting multiple documents
- 3.3 - Inserting multiple documents
- 3.4 - Creating a subdocument
- 3.5 - Create a document without an ID
- 5.1 - Find documents using vector search
- 5.2 - Find documents using vector search and projection
- 5.3 - Find one and update with vector search
- 5.4 Find one and replace with vector search
pip install astrapyCreate a .env file with the appropriate values:
ASTRA_DB_APPLICATION_TOKEN="<AstraCS:...>"
ASTRA_DB_API_ENDPOINT="<https://...>"If you have Astra CLI installed, you can create the
.envfile withastra db create-dotenv DATABASE_NAME.
Load the variables in and then create the client. This collections client can make non-vector and vector calls, depending on the call configuration.
import os
from dotenv import load_dotenv
from astrapy.db import AstraDB, AstraDBCollection
from astrapy.ops import AstraDBOps
load_dotenv()
# Grab the Astra token and api endpoint from the environment
token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
# Initialize our vector db
astra_db = AstraDB(token=token, api_endpoint=api_endpoint)Create a vector collection with dimension of 5 If you were using OpenAI here you would use 1376 as the value
# Create a collection and then delete it
astra_db.create_collection(collection_name="collection_test_delete", dimension=5)
astra_db.delete_collection(collection_name="collection_test_delete")
# Double check the collections in your vector store
astra_db.get_collections()At this point you have a collection named "collection_test" to do the following operations
In the next section, you will be creating the object for your collection
# The return of create_collection() will return the collection
collection = astra_db.create_collection(
collection_name="collection_test", dimension=5
)
# Or you can connect to an existing connection directly
collection = AstraDBCollection(
collection_name="collection_test", astra_db=astra_db
)
# You don't even need the astra_db object
collection = AstraDBCollection(
collection_name="collection_test", token=token, api_endpoint=api_endpoint
)Here is an example of inserting a vector object into your vector store (collection), followed by running a find command to retrieve the document. The first find command fails because that object does not exist. The second find command should succeed.
collection.insert_one(
{
"_id": "5",
"name": "Coded Cleats Copy",
"description": "ChatGPT integrated sneakers that talk to you",
"$vector": [0.25, 0.25, 0.25, 0.25, 0.25],
}
)
collection.find_one({"name": "potato"}) # Not found
collection.find_one({"name": "Coded Cleats Copy"})Here is an example of inserting a number of documents into your collection. Note that the json object is 'documents' here, not 'document' as it is in insert_one.
In the first insert, the default behavior is in place. If you are inserting documents that already exist, you will get an error and the process will end.
These two examples are using non-vector objects.
documents = [
{
"_id": "id_1",
"first_name": "Dang",
"last_name": "Son",
},
{
"_id": "id_2",
"first_name": "Yep",
"last_name": "Boss",
},
]
response = collection.insert_many(documents=documents)In the following insert_many example, options are set so that it skips errors and only inserts successful entries.
documents2 = [
{
"_id": "id_2",
"first_name": "Yep",
"last_name": "Boss",
},
{
"_id": "id_3",
"first_name": "Miv",
"last_name": "Fuff",
},
]
response = collection.insert_many(
documents=documents2,
partial_failures_allowed=True,
)The following code inserts vector objects into the collection in your vector store.
json_query = [
{
"_id": str(uuid.uuid4()),
"name": "Coded Cleats",
"description": "ChatGPT integrated sneakers that talk to you",
"$vector": [0.1, 0.15, 0.3, 0.12, 0.05],
},
{
"_id": str(uuid.uuid4()),
"name": "Logic Layers",
"description": "An AI quilt to help you sleep forever",
"$vector": [0.45, 0.09, 0.01, 0.2, 0.11],
},
{
"_id": vv_uuid,
"name": "Vision Vector Frame",
"description": "Vision Vector Frame - A deep learning display that controls your mood",
"$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
},
]
res = collection.insert_many(documents=json_query)The following code uses update to create or update a sub-document under one of your existing documents.
document = collection.update_one(
filter={"_id": "id_1"},
update={"$set": {"name": "Eric"}},
)
document = collection.find_one(filter={"_id": "id_1"})response = collection.insert_one(
document={
"first_name": "New",
"last_name": "Guy",
}
)
document = collection.find_one(filter={"first_name": "New"})collection.update_one(
filter={"_id": cliff_uuid},
update={"$set": {"name": "Bob"}},
)
document = collection.find_one(filter={"_id": "id_1"})collection.find_one_and_replace(
filter={"_id": "id_1"},
replacement={
"_id": "id_1",
"addresses": {
"work": {
"city": "New York",
"state": "NY",
}
},
},
)
document = collection.find_one(filter={"_id": "id_1"})
document_2 = collection.find_one(
filter={"_id": cliff_uuid}, projection={"addresses.work.city": 1}
)The below examples show our high-level interfaces for finding documents. Note that corresponding low-level functionality exists, i.e., the high-level interface vector_find is a wrapper around the find function, as available in the JSON API.
documents = collection.vector_find(
[0.15, 0.1, 0.1, 0.35, 0.55],
limit=100,
)documents = collection.vector_find(
[0.15, 0.1, 0.1, 0.35, 0.55],
limit=100,
fields=["$vector"],
)update = {"$set": {"status": "active"}}
document = collection.find_one(filter={"status": "active"})
collection.vector_find_one_and_update(
[0.15, 0.1, 0.1, 0.35, 0.55],
update=update,
)
document = collection.find_one(filter={"status": "active"})replacement = {
"_id": "1",
"name": "Vision Vector Frame",
"description": "Vision Vector Frame - A deep learning display that controls your mood",
"$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
"status": "inactive",
}
collection.vector_find_one_and_replace(
[0.15, 0.1, 0.1, 0.35, 0.55],
replacement=replacement,
)
document = collection.find_one(filter={"name": "Vision Vector Frame"})response = collection.delete_subdocument(id="id_1", subdoc="addresses")
document = collection.find(filter={"_id": "id_1"})response = collection.delete(id="id_1")Check out the notebook which has examples for finding and inserting information into the database, including vector commands.
Take a look at the Astra DB DML tests for specific endpoint examples. Further examples are available in the other corresponding test files.
You can use the Ops client to work with the Astra DevOps API. Check the devops tests
Install poetry
pip install poetryInstall the project dependencies
poetry installAstraPy tries to be consistent in code style and linting. Moreover, type annotations are used everywhere.
To ensure the code complies, you should get no errors (Success: no issues found...) when running the following in the root dir:
poetry run black --check astrapy && poetry run ruff astrapy && poetry run mypy astrapyLikewise, for the tests:
poetry run black --check tests && poetry run ruff tests && poetry run mypy testsEnsure you provide all required environment variables (you can do so by editing tests/.env after tests/.env.template):
export ASTRA_DB_APPLICATION_TOKEN="..."
export ASTRA_DB_API_ENDPOINT="..."
export ASTRA_DB_KEYSPACE="..." # Optional
export ASTRA_DB_ID="..." # For the Ops testing only
export ASTRA_DB_OPS_APPLICATION_TOKEN="..." # Ops-only, falls back to the other tokenthen you can run:
poetry run pytestTo remove the noise from the logs (on by default), run pytest -o log_cli=0.
To skip all collection deletions (done by default):
TEST_SKIP_COLLECTION_DELETE=1 poetry run pytest [...]To enable the AstraDBOps testing (off by default):
TEST_ASTRADBOPS=1 poetry run pytest [...]