docs(bedrock.md): adding docs for calling bedrock models on proxy via config.yaml

docs/my-website/docs/providers/bedrock.md

@@ -4,7 +4,6 @@ import TabItem from '@theme/TabItem';
 # AWS Bedrock
 Anthropic, Amazon Titan, A121 LLMs are Supported on Bedrock
 
-## Pre-Requisites
 LiteLLM requires `boto3` to be installed on your system for Bedrock requests
 ```shell
 pip install boto3>=1.28.57
@@ -51,11 +50,25 @@ export AWS_REGION_NAME=""
 
 ### 2. Start the proxy 
 
+<Tabs>
+<TabItem value="cli" label="CLI">
+
 ```bash
 $ litellm --model anthropic.claude-3-sonnet-20240229-v1:0
 
 # Server running on http://0.0.0.0:4000
 ```
+</TabItem>
+<TabItem value="config" label="config.yaml">
+
+```yaml
+model_list:
+  - model_name: bedrock-claude-v1
+    litellm_params:
+      model: bedrock/anthropic.claude-instant-v1
+```
+</TabItem>
+</Tabs>
 
 ### 3. Test it
 
@@ -67,7 +80,7 @@ $ litellm --model anthropic.claude-3-sonnet-20240229-v1:0
 curl --location 'http://0.0.0.0:4000/chat/completions' \
 --header 'Content-Type: application/json' \
 --data ' {
-      "model": "gpt-3.5-turbo",
+      "model": "bedrock-claude-v1",
       "messages": [
         {
           "role": "user",
@@ -88,7 +101,7 @@ client = openai.OpenAI(
 )
 
 # request sent to model set on litellm proxy, `litellm --model`
-response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
+response = client.chat.completions.create(model="bedrock-claude-v1", messages = [
     {
         "role": "user",
         "content": "this is a test request, write a short poem"
@@ -112,7 +125,7 @@ from langchain.schema import HumanMessage, SystemMessage
 
 chat = ChatOpenAI(
     openai_api_base="http://0.0.0.0:4000", # set openai_api_base to the LiteLLM Proxy
-    model = "gpt-3.5-turbo",
+    model = "bedrock-claude-v1",
     temperature=0.1
 )