README.md

---
license: bsd-3-clause
configs:
- config_name: no-vectors
  data_files: no-vectors/*.parquet
  default: true
- config_name: aws-titan-embed-text-v2
  data_files: aws/titan-embed-text-v2/*.parquet
- config_name: cohere-embed-multilingual-v3
  data_files: cohere/embed-multilingual-v3/*.parquet
- config_name: openai-text-embedding-3-small
  data_files: openai/text-embedding-3-small/*.parquet
- config_name: openai-text-embedding-3-large
  data_files: openai/text-embedding-3-large/*.parquet
- config_name: snowflake-arctic-embed
  data_files: ollama/snowflake-arctic/*.parquet
- config_name: weaviate-snowflake-arctic-v2
  data_files: weaviate/snowflake-arctic-v2/*.parquet
size_categories:
- 100K<n<1M
---

##  Loading dataset without vector embeddings

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

```python
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:

```python
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

```python
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:

```python
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

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

#### Weaviate collection configuration:

```python
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

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

#### Weaviate collection configuration:

```python
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

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

#### Weaviate collection configuration:

```python
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

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

#### Weaviate collection configuration:

```python
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

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

#### Weaviate collection configuration:

```python
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

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

#### Weaviate collection configuration:

```python
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"],
        ),  
    ],
)
```