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README.md

@@ -70,13 +70,52 @@ 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
 
-**cohere-text-embedding-3-small** - 768d vectors - generated with Ollama
+**embed-multilingual-v3** - 768d vectors - generated with Ollama
 
 ```python
 from datasets import load_dataset
-dataset = load_dataset("weaviate/wiki-sample", "cohere-text-embedding-3-small", split="train", streaming=True)
+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
@@ -88,6 +127,25 @@ 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
@@ -95,6 +153,25 @@ 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
@@ -103,3 +180,23 @@ dataset = load_dataset("weaviate/wiki-sample", "openai-text-embedding-3-large",
 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"],
+        ),  
+    ],
+)
+```