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--- |
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license: bsd-3-clause |
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configs: |
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- config_name: no-vectors |
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data_files: no-vectors/*.parquet |
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default: true |
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- config_name: aws-titan-embed-text-v2 |
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data_files: aws/titan-embed-text-v2/*.parquet |
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- config_name: cohere-embed-multilingual-v3 |
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data_files: cohere/embed-multilingual-v3/*.parquet |
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- config_name: openai-text-embedding-3-small |
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data_files: openai/text-embedding-3-small/*.parquet |
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- config_name: openai-text-embedding-3-large |
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data_files: openai/text-embedding-3-large/*.parquet |
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- config_name: snowflake-arctic-embed |
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data_files: ollama/snowflake-arctic/*.parquet |
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size_categories: |
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- 100K<n<1M |
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--- |
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## Loading dataset without vector embeddings |
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You can load the raw dataset without vectors, like this: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", split="train", streaming=True) |
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``` |
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## Loading dataset with vector embeddings |
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You can also load the dataset with vectors, like this: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-small", split="train", streaming=True) |
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# dataset = load_dataset("weaviate/wiki-sample", "snowflake-arctic-embed", split="train", streaming=True) |
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for item in dataset: |
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print(item["text"]) |
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print(item["title"]) |
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print(item["url"]) |
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print(item["wiki_id"]) |
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print(item["vector"]) |
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print() |
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``` |
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## Supported Datasets |
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### Data only - no vectors |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "no-vectors", split="train", streaming=True) |
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``` |
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You can also skip the config name, as "no-vectors is the default dataset: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", split="train", streaming=True) |
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``` |
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### AWS |
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**aws-titan-embed-text-v2** - 1024d vectors - generated with AWS Bedrock |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "aws-titan-embed-text-v2", split="train", streaming=True) |
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``` |
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#### Weaviate collection configuration: |
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```python |
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from weaviate.classes.config import Configure |
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client.collections.create( |
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name="Wiki", |
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vectorizer_config=[ |
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Configure.NamedVectors.text2vec_aws( |
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name="main_vector", |
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model="amazon.titan-embed-text-v2:0", |
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region="us-east-1", # make sure to use the correct region for you |
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source_properties=['title', 'text'], # which properties should be used to generate a vector |
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) |
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], |
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) |
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``` |
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### Cohere |
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**embed-multilingual-v3** - 768d vectors - generated with Ollama |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "cohere-embed-multilingual-v3", split="train", streaming=True) |
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``` |
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#### Weaviate collection configuration: |
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```python |
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from weaviate.classes.config import Configure |
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client.collections.create( |
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name="Wiki", |
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vectorizer_config=[ |
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Configure.NamedVectors.text2vec_cohere( |
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name="main_vector", |
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model="embed-multilingual-v3.0", |
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source_properties=['title', 'text'], # which properties should be used to generate a vector |
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) |
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], |
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) |
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``` |
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### OpenAI |
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**text-embedding-3-small** - 1536d vectors - generated with OpenAI |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-small", split="train", streaming=True) |
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``` |
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#### Weaviate collection configuration: |
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```python |
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from weaviate.classes.config import Configure |
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client.collections.create( |
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name="Wiki", |
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vectorizer_config=[ |
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Configure.NamedVectors.text2vec_openai( |
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name="main_vector", |
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model="text-embedding-3-small", |
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source_properties=['title', 'text'], # which properties should be used to generate a vector |
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) |
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], |
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) |
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``` |
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**text-embedding-3-large** - 3072d vectors - generated with OpenAI |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-large", split="train", streaming=True) |
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``` |
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#### Weaviate collection configuration: |
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```python |
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from weaviate.classes.config import Configure |
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client.collections.create( |
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name="Wiki", |
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vectorizer_config=[ |
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Configure.NamedVectors.text2vec_openai( |
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name="main_vector", |
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model="text-embedding-3-large", |
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source_properties=['title', 'text'], # which properties should be used to generate a vector |
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) |
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], |
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) |
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``` |
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### Snowflake |
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**snowflake-arctic-embed** - 1024d vectors - generated with Ollama |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("weaviate/wiki-sample", "snowflake-arctic-embed", split="train", streaming=True) |
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``` |
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#### Weaviate collection configuration: |
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```python |
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from weaviate.classes.config import Configure |
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client.collections.create( |
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name="Wiki", |
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vectorizer_config=[ |
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Configure.NamedVectors.text2vec_ollama( |
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name="main_vector", |
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model="snowflake-arctic-embed", |
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api_endpoint="http://host.docker.internal:11434", # If using Docker |
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source_properties=["title", "text"], |
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), |
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], |
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) |
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``` |
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