weaviate/wiki-sample

Loading dataset without vector embeddings

You can load the raw dataset without vectors, like this:

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", split="train", streaming=True)

Loading dataset with vector embeddings

You can also load the dataset with vectors, like this:

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "weaviate-snowflake-arctic-v2", split="train", streaming=True)

for item in dataset:
    print(item["text"])
    print(item["title"])
    print(item["url"])
    print(item["wiki_id"])
    print(item["vector"])
    print()

Supported Datasets

Data only - no vectors

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "no-vectors", split="train", streaming=True)

You can also skip the config name, as "no-vectors is the default dataset:

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", split="train", streaming=True)

Weaviate Embedding Service

snowflake-arctic-embed-l-v2.0 - 1024d vectors - generated with Weaviate Embedding Service

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "weaviate-snowflake-arctic-v2", split="train", streaming=True)

Weaviate collection configuration:

from weaviate.classes.config import Configure

client.collections.create(
    name="Wiki",
    
    vectorizer_config=[
        Configure.NamedVectors.text2vec_weaviate(
            name="main_vector",
            model="Snowflake/snowflake-arctic-embed-l-v2.0",
            source_properties=['title', 'text'], # which properties should be used to generate a vector
        )
    ],
)

AWS

aws-titan-embed-text-v2 - 1024d vectors - generated with AWS Bedrock

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "aws-titan-embed-text-v2", split="train", streaming=True)

Weaviate collection configuration:

from weaviate.classes.config import Configure

client.collections.create(
    name="Wiki",
    
    vectorizer_config=[
        Configure.NamedVectors.text2vec_aws(
            name="main_vector",
            model="amazon.titan-embed-text-v2:0",
            region="us-east-1", # make sure to use the correct region for you

            source_properties=['title', 'text'], # which properties should be used to generate a vector
        )
    ],
)

Cohere

embed-multilingual-v3 - 768d vectors - generated with Ollama

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "cohere-embed-multilingual-v3", split="train", streaming=True)

Weaviate collection configuration:

from weaviate.classes.config import Configure

client.collections.create(
    name="Wiki",
    
    vectorizer_config=[
        Configure.NamedVectors.text2vec_cohere(
            name="main_vector",
            model="embed-multilingual-v3.0",

            source_properties=['title', 'text'], # which properties should be used to generate a vector
        )
    ],
)

OpenAI

text-embedding-3-small - 1536d vectors - generated with OpenAI

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-small", split="train", streaming=True)

Weaviate collection configuration:

from weaviate.classes.config import Configure

client.collections.create(
    name="Wiki",
    
    vectorizer_config=[
        Configure.NamedVectors.text2vec_openai(
            name="main_vector",
            model="text-embedding-3-small",

            source_properties=['title', 'text'], # which properties should be used to generate a vector
        )
    ],
)

text-embedding-3-large - 3072d vectors - generated with OpenAI

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-large", split="train", streaming=True)

Weaviate collection configuration:

from weaviate.classes.config import Configure

client.collections.create(
    name="Wiki",
    
    vectorizer_config=[
        Configure.NamedVectors.text2vec_openai(
            name="main_vector",
            model="text-embedding-3-large",

            source_properties=['title', 'text'], # which properties should be used to generate a vector
        )
    ],
)

Snowflake

snowflake-arctic-embed - 1024d vectors - generated with Ollama

from datasets import load_dataset
dataset = load_dataset("weaviate/wiki-sample", "snowflake-arctic-embed", split="train", streaming=True)

Weaviate collection configuration:

from weaviate.classes.config import Configure

client.collections.create(
    name="Wiki",
    
    vectorizer_config=[
        Configure.NamedVectors.text2vec_ollama(
            name="main_vector",
            model="snowflake-arctic-embed",
            api_endpoint="http://host.docker.internal:11434", # If using Docker

            source_properties=["title", "text"],
        ),  
    ],
)