diff --git a/fern/docs/text-gen-solution/using-unstructured-io-for-embedding-documents.mdx b/fern/docs/text-gen-solution/using-unstructured-io-for-embedding-documents.mdx
deleted file mode 100644
index 12f306d..0000000
--- a/fern/docs/text-gen-solution/using-unstructured-io-for-embedding-documents.mdx
+++ /dev/null
@@ -1,50 +0,0 @@
----
-title: Using Unstructured.io for embedding documents
-subtitle: Fast and easy document parsing and embedding using Unstuctured.io and OctoAI.
-slug: text-gen-solution/using-unstructured-io-for-embedding-documents
----
-
-## Introduction
-
-Unstructured is both an open-source library and an API service. The library provides components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more.
-
-It also provides components to very easily embed these documents. In Unstructured's jargon this component is called an `EmbeddingEncoder`. The `OctoAIEmbedingEncoder` is available, so documents parsed with Unstructured can easily be embedded with the OctoAI embeddings endpoint.
-
-## Using the OctoAIEmbeddingEncocer
-
-The `OctoAIEmbeddingEncoder` class connects to the OctoAI Text&Embedding API 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 `OctoAIEmbeddingEncoder`.
-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.
-You will need to set an environment variable named `OCTOAI_API_KEY` to be able to run this example.
-To obtain an API key, visit: [How to create an OctoAI API token](https://octo.ai/docs/getting-started/how-to-create-octoai-access-token).
-
-```Python
-import os
-
-from unstructured.documents.elements import Text
-from unstructured.embed.octoai import OctoAiEmbeddingConfig, OctoAIEmbeddingEncoder
-
-embedding_encoder = OctoAIEmbeddingEncoder(
-    config=OctoAiEmbeddingConfig(api_key=os.environ["OCTOAI_API_KEY"])
-)
-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.embeddings, e) for e in elements]
-print(query_embedding, query)
-print(embedding_encoder.is_unit_vector(), embedding_encoder.num_of_dimensions())
-```