eric-schneider
commented
8 months ago Stress-tested things with the following syntax, and things look good!
= {astra_db} FAQs
:slug: datastax-astra-faq
Frequently asked questions about {company} {astra_db}.
[IMPORTANT]
======
Data is not directly protected by TDE when you access the data using the following utilities.
[cols="1,3"]
|===
| Utility | Reason utility is not encrypted
| nodetool
| Uses only JMX, so data is not accessed.
| sstableloader
| Operates directly on the SSTables.
| sstablescrub
| Operates directly on the SSTables.
| sstableutil
| Operates directly on the SSTables.
| sstableverify
| Operates directly on the SSTables.
|===
[NOTE]
====
A note.
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[NOTE]
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A note.
[NOTE]
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Another admonition.
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[NOTE]
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A note.
[IMPORTANT]
====
Another admonition.
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[NOTE]
======
A note.
[TIP]
====
Another admonition.
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[NOTE]
======
A note.
[WARNING]
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Another admonition.
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[NOTE]
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A note.
[CAUTION]
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Another admonition.
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[tabs]
======
Python::
+
.quickstart.py
[source,python]
----
# tag::init[]
import os
from astrapy.db import AstraDB
# Initialize the client
db = AstraDB(
token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
)
# end::init[]
# tag::collection[]
# Create a collection
collection = db.create_collection("vector_test", dimension=5, metric="cosine")
# end::collection[]
# tag::data[]
# Insert documents into the collection
documents = [
{
"_id": "1",
"text": "ChatGPT integrated sneakers that talk to you",
"$vector": [0.1, 0.15, 0.3, 0.12, 0.05],
},
{
"_id": "2",
"text": "An AI quilt to help you sleep forever",
"$vector": [0.45, 0.09, 0.01, 0.2, 0.11],
},
{
"_id": "3",
"text": "A deep learning display that controls your mood",
"$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
},
]
res = collection.insert_many(documents)
# end::data[]
# tag::search[]
# Perform a similarity search
query = [0.15, 0.1, 0.1, 0.35, 0.55]
results = collection.vector_find(query, limit=2, fields={"text", "$vector"})
for document in results:
print(document)
# end::search[]
# tag::delete[]
# Delete the collection
db.delete_collection(collection_name="vector_test")
# end::delete[]
----
+
[NOTE]
====
Don't name the file `astrapy.py` to avoid a namespace collision.
====
TypeScript::
+
.quickstart.ts
[source,typescript]
----
// tag::init[]
import { AstraDB } from "@datastax/astra-db-ts";
async function main() {
const { ASTRA_DB_APPLICATION_TOKEN, ASTRA_DB_API_ENDPOINT } = process.env;
// Initialize the client
const db = new AstraDB(ASTRA_DB_APPLICATION_TOKEN, ASTRA_DB_API_ENDPOINT);
// end::init[]
// tag::collection[]
// Create a collection
await db.createCollection(
"vector_test",
{
"vector": {
"dimension": 5,
"metric": "cosine"
}
}
);
const collection = await db.collection("vector_test");
// end::collection[]
// tag::data[]
// Insert documents into the collection
const documents = [
{
"_id": "1",
"text": "ChatGPT integrated sneakers that talk to you",
"$vector": [0.1, 0.15, 0.3, 0.12, 0.05],
},
{
"_id": "2",
"text": "An AI quilt to help you sleep forever",
"$vector": [0.45, 0.09, 0.01, 0.2, 0.11],
},
{
"_id": "3",
"text": "A deep learning display that controls your mood",
"$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
}
];
const results = await collection.insertMany(documents);
// end::data[]
// tag::search[]
// Define the search options
const options = {
sort: {
"$vector": [0.15, 0.1, 0.1, 0.35, 0.55],
},
limit: 5
};
// Perform a similarity search
collection.find({}, options).toArray().then(docs => {
docs.forEach(doc => console.log(doc));
});
// end::search[]
// tag::delete[]
// Delete the collection
db.dropCollection("vector_test").then(response => {
console.log(response);
});
// end::delete[]
// tag::end[]
}
main().catch(console.error);
// end::end[]
----
Java::
+
.src/main/java/com/example/Quickstart.java
[source,java]
----
// tag::init[]
package com.example;
import com.dtsx.astra.sdk.AstraDB;
import com.dtsx.astra.sdk.AstraDBCollection;
import io.stargate.sdk.data.domain.JsonDocument;
import io.stargate.sdk.data.domain.JsonDocumentResult;
import io.stargate.sdk.data.domain.SimilarityMetric;
import io.stargate.sdk.data.domain.CollectionDefinition;
import java.util.List;
import java.util.stream.Stream;
public class Quickstart {
public static void main(String[] args) {
// Loading Arguments
String astraToken = System.getenv("ASTRA_DB_APPLICATION_TOKEN");
String astraApiEndpoint = System.getenv("ASTRA_DB_API_ENDPOINT");
// Initialize the client
AstraDB db = new AstraDB(astraToken, astraApiEndpoint);
System.out.println("Connected to AstraDB");
// end::init[]
// tag::collection[]
// Create a collection
CollectionDefinition colDefinition = CollectionDefinition.builder()
.name("vector_test")
.vector(5, SimilarityMetric.cosine)
.build();
db.createCollection(colDefinition);
AstraDBCollection collection = db.collection("vector_test");
// end::collection[]
// tag::data[]
// Insert documents into the collection
collection.insertMany(List.of(
new JsonDocument()
.id("1")
.put("text", "ChatGPT integrated sneakers that talk to you")
.vector(new float[]{0.1f, 0.15f, 0.3f, 0.12f, 0.05f}),
new JsonDocument()
.id("2")
.put("text", "An AI quilt to help you sleep forever")
.vector(new float[]{0.45f, 0.09f, 0.01f, 0.2f, 0.11f}),
new JsonDocument()
.id("3")
.put("text", "A deep learning display that controls your mood")
.vector(new float[]{0.1f, 0.05f, 0.08f, 0.3f, 0.6f})
));
// end::data[]
// tag::search[]
// Perform a similarity search
Stream<JsonDocumentResult> resultsSet = collection.findVector(
new float[]{0.15f, 0.1f, 0.1f, 0.35f, 0.55f},
10
);
resultsSet.forEach(System.out::println);
// end::search[]
// tag::delete[]
// Delete the collection
db.deleteCollection("vector_test");
// end::delete[]
// tag::end[]
}
}
// end::end[]
----
======Ignore the streaking green coming off the NOTE (artifact of screenshot app).
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