Load your data

# Insert documents with embeddings into the collection.
documents = [
    {
        "text": "Chat bot integrated sneakers that talk to you",
        "$vector": [0.1, 0.15, 0.3, 0.12, 0.05],
    },
    {
        "text": "An AI quilt to help you sleep forever",
        "$vector": [0.45, 0.09, 0.01, 0.2, 0.11],
    },
    {
        "text": "A deep learning display that controls your mood",
        "$vector": [0.1, 0.05, 0.08, 0.3, 0.6],
    },
]
insertion_result = collection.insert_many(documents)
print(f"* Inserted {len(insertion_result.inserted_ids)} items.\n")

# Insert documents into the collection.
# (UUIDs here are version 7.)
documents = [
    {
        "$vectorize": "Chat bot integrated sneakers that talk to you",
    },
    {
        "$vectorize": "An AI quilt to help you sleep forever",
    },
    {
        "$vectorize": "A deep learning display that controls your mood",
    },
]
insertion_result = collection.insert_many(documents)
print(f"* Inserted {len(insertion_result.inserted_ids)} items.\n")

  // Insert documents with embeddings into the collection.
  const documents = [
    {
      idea: 'Chat bot integrated sneakers that talk to you',
      $vector: [0.1, 0.15, 0.3, 0.12, 0.05],
    },
    {
      idea: 'An AI quilt to help you sleep forever',
      $vector: [0.45, 0.09, 0.01, 0.2, 0.11],
    },
    {
      idea: 'A deep learning display that controls your mood',
      $vector: [0.1, 0.05, 0.08, 0.3, 0.6],
    },
  ];

  const inserted = await collection.insertMany(documents);
  console.log(`* Inserted ${inserted.insertedCount} items.`);

  // Insert documents into the collection (using UUIDv7s)
  const documents = [
    {
      $vectorize: 'Chat bot integrated sneakers that talk to you',
    },
    {
      $vectorize: 'An AI quilt to help you sleep forever',
    },
    {
      $vectorize: 'A deep learning display that controls your mood',
    },
  ];

  try {
    const inserted = await collection.insertMany(documents);
    console.log(`* Inserted ${inserted.insertedCount} items.`);
  } catch (e) {
    console.log('* Documents found on DB already. Let\'s move on!');
  }

    // Insert documents with embeddings into the collection
    collection.insertMany(
            new Document("1")
                    .append("text", "Chat bot integrated sneakers that talk to you")
                    .vector(new float[]{0.1f, 0.15f, 0.3f, 0.12f, 0.05f}),
            new Document("2")
                    .append("text", "An AI quilt to help you sleep forever")
                    .vector(new float[]{0.45f, 0.09f, 0.01f, 0.2f, 0.11f}),
            new Document("3")
                    .append("text", "A deep learning display that controls your mood")
                    .vector(new float[]{0.1f, 0.05f, 0.08f, 0.3f, 0.6f}));
    System.out.println("Inserted documents into the collection");

// Insert documents into the collection
InsertManyResult insertResult = collection.insertMany(
  new Document()
   .vectorize("Chat bot integrated sneakers that talk to you"),
  new Document()
   .vectorize("An AI quilt to help you sleep forever"),
  new Document()
   .vectorize("A deep learning display that controls your mood")
);
System.out.println("Insert " + insertResult.getInsertedIds().size() + " items.");

# Insert documents with embeddings into the collection
curl -sS -L -X POST "$ASTRA_DB_API_ENDPOINT/api/json/v1/default_keyspace/vector_test" \
--header "Token: $ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "text": "Chat bot integrated sneakers that talk to you",
        "$vector": [0.1, 0.15, 0.3, 0.12, 0.05]
      },
      {
        "text": "An AI quilt to help you sleep forever",
        "$vector": [0.45, 0.09, 0.01, 0.2, 0.11]
      },
      {
        "text": "A deep learning display that controls your mood",
        "$vector": [0.1, 0.05, 0.08, 0.3, 0.6]
      }
    ],
    "options": {
      "ordered": false
    }
  }
}' | jq

# Insert documents into the collection and generate embeddings.
curl -sS -L -X POST "$ASTRA_DB_API_ENDPOINT/api/json/v1/default_keyspace/pass:q[**COLLECTION_NAME**]" \
--header "Token: $ASTRA_DB_APPLICATION_TOKEN" \
--header "Content-Type: application/json" \
--data '{
  "insertMany": {
    "documents": [
      {
        "$vectorize": "Chat bot integrated sneakers that talk to you"
      },
      {
        "$vectorize": "An AI quilt to help you sleep forever"
      },
      {
        "$vectorize": "A deep learning display that controls your mood"
      }
    ],
    "options": {
      "ordered": false
    }
  }
}' | jq