Adding VoyageAI EmbeddingEncoder description to embedding.mdx

Author: fzowl

open-source/core-functionality/embedding.mdx

@@ -179,3 +179,47 @@ print(query_embedding, query)
 print(embedding_encoder.is_unit_vector(), embedding_encoder.num_of_dimensions())
 
 ```
+
+
+## `VertexAIEmbeddingEncoder`
+
+The `VoyageAIEmbeddingEncoder` class connects to the VoyageAI to obtain embeddings for pieces of text.
+
+`embed_documents` will receive a list of Elements, and return an updated list which includes the `embeddings` attribute for each Element.
+
+`embed_query` will receive a query as a string, and return a list of floats which is the embedding vector for the given query string.
+
+`num_of_dimensions` is a metadata property that denotes the number of dimensions in any embedding vector obtained via this class.
+
+`is_unit_vector` is a metadata property that denotes if embedding vectors obtained via this class are unit vectors.
+
+The following code block shows an example of how to use `VoyageAIEmbeddingEncoder`. You will see the updated elements list (with the `embeddings` attribute included for each element), the embedding vector for the query string, and some metadata properties about the embedding model.
+
+To use Voyage AI you will need to pass Voyage AI API Key (obtained from [https://dash.voyageai.com/](https://dash.voyageai.com/)) as the `api_key` parameter.
+
+The `model_name` parameter is mandatory, please check the available models at [https://docs.voyageai.com/docs/embeddings](https://docs.voyageai.com/docs/embeddings)
+
+```python
+import os
+
+from unstructured.documents.elements import Text
+from unstructured.embed.voyageai import VoyageAIEmbeddingConfig, VoyageAIEmbeddingEncoder
+
+embedding_encoder = VoyageAIEmbeddingEncoder(
+    config=VoyageAIEmbeddingConfig(
+        api_key=os.environ["VOYAGE_API_KEY"],
+        model_name="voyage-law-2"
+    )
+)
+elements = embedding_encoder.embed_documents(
+    elements=[Text("This is sentence 1"), Text("This is sentence 2")],
+)
+
+query = "This is the query"
+query_embedding = embedding_encoder.embed_query(query=query)
+
+[print(e, e.embeddings) for e in elements]
+print(query, query_embedding)
+print(embedding_encoder.is_unit_vector, embedding_encoder.num_of_dimensions)
+
+```