diff --git a/docs/my-website/docs/providers/bedrock_vector_store.md b/docs/my-website/docs/providers/bedrock_vector_store.md
new file mode 100644
index 0000000000..779c4fd041
--- /dev/null
+++ b/docs/my-website/docs/providers/bedrock_vector_store.md
@@ -0,0 +1,144 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Image from '@theme/IdealImage';
+
+# Bedrock Knowledge Bases
+
+AWS Bedrock Knowledge Bases allows you to connect your LLM's to your organization's data, letting your models retrieve and reference information specific to your business.
+
+| Property | Details |
+|----------|---------|
+| Description | Bedrock Knowledge Bases connects your data to LLM's, enabling them to retrieve and reference your organization's information in their responses. |
+| Provider Route on LiteLLM | `bedrock` in the litellm vector_store_registry |
+| Provider Doc | [AWS Bedrock Knowledge Bases ↗](https://aws.amazon.com/bedrock/knowledge-bases/) |
+
+## Quick Start
+
+### LiteLLM Python SDK
+
+```python showLineNumbers title="Example using LiteLLM Python SDK"
+import os
+import litellm
+
+from litellm.vector_stores.vector_store_registry import VectorStoreRegistry, LiteLLM_ManagedVectorStore
+
+# Init vector store registry with your Bedrock Knowledge Base
+litellm.vector_store_registry = VectorStoreRegistry(
+    vector_stores=[
+        LiteLLM_ManagedVectorStore(
+            vector_store_id="YOUR_KNOWLEDGE_BASE_ID",  # KB ID from AWS Bedrock
+            custom_llm_provider="bedrock"
+        )
+    ]
+)
+
+# Make a completion request using your Knowledge Base
+response = await litellm.acompletion(
+    model="anthropic/claude-3-5-sonnet", 
+    messages=[{"role": "user", "content": "What does our company policy say about remote work?"}],
+    tools=[
+        {
+            "type": "file_search",
+            "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
+        }
+    ],
+)
+
+print(response.choices[0].message.content)
+```
+
+### LiteLLM Proxy
+
+#### 1. Configure your vector_store_registry
+
+<Tabs>
+<TabItem value="config-yaml" label="config.yaml">
+
+```yaml
+model_list:
+  - model_name: claude-3-5-sonnet
+    litellm_params:
+      model: anthropic/claude-3-5-sonnet
+      api_key: os.environ/ANTHROPIC_API_KEY
+
+vector_store_registry:
+  - vector_store_name: "bedrock-company-docs"
+    litellm_params:
+      vector_store_id: "YOUR_KNOWLEDGE_BASE_ID"
+      custom_llm_provider: "bedrock"
+      vector_store_description: "Bedrock Knowledge Base for company documents"
+      vector_store_metadata:
+        source: "Company internal documentation"
+```
+
+</TabItem>
+
+<TabItem value="litellm-ui" label="LiteLLM UI">
+
+On the LiteLLM UI, Navigate to Experimental > Vector Stores > Create Vector Store. On this page you can create a vector store with a name, vector store id and credentials.
+
+<Image 
+  img={require('../../img/kb_2.png')}
+  style={{width: '50%'}}
+/>
+
+</TabItem>
+</Tabs>
+
+#### 2. Make a request with vector_store_ids parameter
+
+<Tabs>
+<TabItem value="curl" label="Curl">
+
+```bash
+curl http://localhost:4000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer $LITELLM_API_KEY" \
+  -d '{
+    "model": "claude-3-5-sonnet",
+    "messages": [{"role": "user", "content": "What does our company policy say about remote work?"}],
+    "tools": [
+        {
+            "type": "file_search",
+            "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
+        }
+    ]
+  }'
+```
+
+</TabItem>
+
+<TabItem value="openai-sdk" label="OpenAI Python SDK">
+
+```python
+from openai import OpenAI
+
+# Initialize client with your LiteLLM proxy URL
+client = OpenAI(
+    base_url="http://localhost:4000",
+    api_key="your-litellm-api-key"
+)
+
+# Make a completion request with vector_store_ids parameter
+response = client.chat.completions.create(
+    model="claude-3-5-sonnet",
+    messages=[{"role": "user", "content": "What does our company policy say about remote work?"}],
+    tools=[
+        {
+            "type": "file_search",
+            "vector_store_ids": ["YOUR_KNOWLEDGE_BASE_ID"]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+</TabItem>
+</Tabs>
+
+
+Futher Reading Vector Stores:
+- [Always on Vector Stores](https://docs.litellm.ai/docs/completion/knowledgebase#always-on-for-a-model)
+- [Listing available vector stores on litellm proxy](https://docs.litellm.ai/docs/completion/knowledgebase#listing-available-vector-stores)
+- [How LiteLLM Vector Stores Work](https://docs.litellm.ai/docs/completion/knowledgebase#how-it-works)
\ No newline at end of file
diff --git a/docs/my-website/docs/proxy/guardrails/bedrock.md b/docs/my-website/docs/proxy/guardrails/bedrock.md
index 0da2238bcf..81c561fcfc 100644
--- a/docs/my-website/docs/proxy/guardrails/bedrock.md
+++ b/docs/my-website/docs/proxy/guardrails/bedrock.md
@@ -2,7 +2,7 @@ import Image from '@theme/IdealImage';
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-# Bedrock
+# Bedrock Guardrails
 
 LiteLLM supports Bedrock guardrails via the [Bedrock ApplyGuardrail API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ApplyGuardrail.html). 
 
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index e278b36ed7..1e97e76b15 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -208,7 +208,14 @@ const sidebars = {
         },
         "providers/anthropic",
         "providers/aws_sagemaker",
-        "providers/bedrock",
+        {
+          type: "category",
+          label: "Bedrock",
+          items: [
+            "providers/bedrock",
+            "providers/bedrock_vector_store",
+          ]
+        },
         "providers/litellm_proxy",
         "providers/mistral",
         "providers/codestral",