diff --git a/docs/docs/examples/retrievers/bedrock_retriever.ipynb b/docs/docs/examples/retrievers/bedrock_retriever.ipynb
index f1569351e3fe25982381921ec708859981f7f0db..016e93ef2ac9fc0dc7c2fc47f79f08d39b9d2f65 100644
--- a/docs/docs/examples/retrievers/bedrock_retriever.ipynb
+++ b/docs/docs/examples/retrievers/bedrock_retriever.ipynb
@@ -6,15 +6,15 @@
    "source": [
     "# Bedrock (Knowledge Bases)\n",
     "\n",
-    "> [Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
+    "[Knowledge bases for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/) is an Amazon Web Services (AWS) offering which lets you quickly build RAG applications by using your private data to customize FM response.\n",
     "\n",
-    "> Implementing `RAG` requires organizations to perform several cumbersome steps to convert data into embeddings (vectors), store the embeddings in a specialized vector database, and build custom integrations into the database to search and retrieve text relevant to the user’s query. This can be time-consuming and inefficient.\n",
+    "Implementing `RAG` requires organizations to perform several cumbersome steps to convert data into embeddings (vectors), store the embeddings in a specialized vector database, and build custom integrations into the database to search and retrieve text relevant to the user’s query. This can be time-consuming and inefficient.\n",
     "\n",
-    "> With `Knowledge Bases for Amazon Bedrock`, simply point to the location of your data in `Amazon S3`, and `Knowledge Bases for Amazon Bedrock` takes care of the entire ingestion workflow into your vector database. If you do not have an existing vector database, Amazon Bedrock creates an Amazon OpenSearch Serverless vector store for you.\n",
+    "With `Knowledge Bases for Amazon Bedrock`, simply point to the location of your data in `Amazon S3`, and `Knowledge Bases for Amazon Bedrock` takes care of the entire ingestion workflow into your vector database. If you do not have an existing vector database, Amazon Bedrock creates an Amazon OpenSearch Serverless vector store for you.\n",
     "\n",
-    "> Knowledge base can be configured through [AWS Console](https://aws.amazon.com/console/) or by using [AWS SDKs](https://aws.amazon.com/developer/tools/).\n",
+    "Knowledge base can be configured through [AWS Console](https://aws.amazon.com/console/) or by using [AWS SDKs](https://aws.amazon.com/developer/tools/).\n",
     "\n",
-    "> In this notebook, we introduce AmazonKnowledgeBasesRetriever - Amazon Bedrock integration in Llama Index via the Retrieve API to retrieve relevant results for a user query from knowledge bases."
+    "In this notebook, we introduce AmazonKnowledgeBasesRetriever - Amazon Bedrock integration in Llama Index via the Retrieve API to retrieve relevant results for a user query from knowledge bases."
    ]
   },
   {
@@ -31,7 +31,8 @@
    "outputs": [],
    "source": [
     "%pip install --upgrade --quiet  boto3 botocore\n",
-    "%pip install llama-index"
+    "%pip install llama-index\n",
+    "%pip install llama-index-retrievers-bedrock"
    ]
   },
   {
@@ -76,13 +77,6 @@
     "print(retrieved_results[0].get_content())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Milky Way is a large spiral galaxy, but in the grand scheme of the universe, it's relatively small. The observable universe is estimated to be about 93 billion light-years in diameter. In comparison, the Milky Way galaxy has a diameter of about 100,000 light-years. So, the Milky Way is just a tiny speck within the vastness of the observable universe. Keep in mind, however, that the universe may extend beyond the observable universe, but our ability to observe it is limited by the speed of light and the age of the universe."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/docs/docs/module_guides/querying/retriever/retrievers.md b/docs/docs/module_guides/querying/retriever/retrievers.md
index 3de7794ebe0432c9c19f095c1d8fcd5cdd269f8b..dace94e70011355e27cec93dee718c3e229afed4 100644
--- a/docs/docs/module_guides/querying/retriever/retrievers.md
+++ b/docs/docs/module_guides/querying/retriever/retrievers.md
@@ -60,6 +60,7 @@ hierarchical retrieval and query decomposition.
 - [Vectara](../../../examples/managed/vectaraDemo.ipynb)
 - [VideoDB](../../../examples/retrievers/videodb_retriever.ipynb)
 - [Zilliz](../../../examples/managed/zcpDemo.ipynb)
+- [Amazon Bedrock](../../../examples/retrievers/bedrock_retriever.ipynb)
 
 ### Other Retrievers
 
diff --git a/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml b/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml
index d365e8ee8dc9bd45f2c6f5d5be93cd4f20a53625..b5720f6c54708ee280f6831af18e5df3219f659c 100644
--- a/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml
+++ b/llama-index-integrations/retrievers/llama-index-retrievers-bedrock/pyproject.toml
@@ -8,11 +8,11 @@ check-hidden = true
 skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb"
 
 [tool.llamahub]
-contains_example = true
+contains_example = false
 import_path = "llama_index.retrievers.bedrock"
 
 [tool.llamahub.class_authors]
-BedrockRetriever = "AmazonBedrock"
+AmazonKnowledgeBasesRetriever = "llama-index"
 
 [tool.mypy]
 disallow_untyped_defs = true