Update README.md

README.md

@@ -14,6 +14,8 @@ configs:
   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
 ---
@@ -33,8 +35,7 @@ You can also load the dataset with vectors, like this:
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-small", split="train", streaming=True)
-# dataset = load_dataset("weaviate/wiki-sample", "snowflake-arctic-embed", split="train", streaming=True)
+dataset = load_dataset("weaviate/wiki-sample", "weaviate-snowflake-arctic-v2", split="train", streaming=True)
 
 for item in dataset:
     print(item["text"])
@@ -61,6 +62,33 @@ 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