Reformat deployment-options pages (#346)

Co-authored-by: Max Shkutnyk <max@lightsonsoftware.com>

fern/pages/v2/deployment-options/cohere-on-aws/amazon-bedrock.mdx

@@ -47,24 +47,29 @@ co = cohere.BedrockClient(
 )
 
 # Input parameters for embed. In this example we are embedding hacker news post titles.
-texts = ["Interesting (Non software) books?",
-         "Non-tech books that have helped you grow professionally?",
-         "I sold my company last month for $5m. What do I do with the money?",
-         "How are you getting through (and back from) burning out?",
-         "I made $24k over the last month. Now what?",
-         "What kind of personal financial investment do you do?",
-         "Should I quit the field of software development?"]
+texts = [
+    "Interesting (Non software) books?",
+    "Non-tech books that have helped you grow professionally?",
+    "I sold my company last month for $5m. What do I do with the money?",
+    "How are you getting through (and back from) burning out?",
+    "I made $24k over the last month. Now what?",
+    "What kind of personal financial investment do you do?",
+    "Should I quit the field of software development?",
+]
 input_type = "clustering"
-truncate = "NONE" # optional
-model_id = "cohere.embed-english-v3" # or "cohere.embed-multilingual-v3"
+truncate = "NONE"  # optional
+model_id = (
+    "cohere.embed-english-v3"  # or "cohere.embed-multilingual-v3"
+)
 
 
 # Invoke the model and print the response
 result = co.embed(
-	model=model_id,
-	input_type=input_type,
-	texts=texts,
-	truncate=truncate) # aws_client.invoke_model(**params)
+    model=model_id,
+    input_type=input_type,
+    texts=texts,
+    truncate=truncate,
+)  # aws_client.invoke_model(**params)
 
 print(result)
 ```
@@ -83,9 +88,10 @@ co = cohere.BedrockClient(
     aws_session_token="...",
 )
 
-result = co.chat(message="Write a LinkedIn post about starting a career in tech:",
-                 model='cohere.command-r-plus-v1:0' # or 'cohere.command-r-v1:0'
-                 )
+result = co.chat(
+    message="Write a LinkedIn post about starting a career in tech:",
+    model="cohere.command-r-plus-v1:0",  # or 'cohere.command-r-v1:0'
+)
 
 print(result)
 ```
@@ -98,7 +104,7 @@ You can use this code to invoke our latest Rerank models on Bedrock
 import cohere
 
 co = cohere.BedrockClientV2(
-    aws_region="us-west-2", # pick a region where the model is available
+    aws_region="us-west-2",  # pick a region where the model is available
     aws_access_key="...",
     aws_secret_key="...",
     aws_session_token="...",

fern/pages/v2/deployment-options/cohere-on-aws/amazon-sagemaker-setup-guide.mdx

@@ -58,24 +58,27 @@ co = cohere.SageMakerClient(
 )
 
 # Input parameters for embed. In this example we are embedding hacker news post titles.
-texts = ["Interesting (Non software) books?",
-         "Non-tech books that have helped you grow professionally?",
-         "I sold my company last month for $5m. What do I do with the money?",
-         "How are you getting through (and back from) burning out?",
-         "I made $24k over the last month. Now what?",
-         "What kind of personal financial investment do you do?",
-         "Should I quit the field of software development?"]
+texts = [
+    "Interesting (Non software) books?",
+    "Non-tech books that have helped you grow professionally?",
+    "I sold my company last month for $5m. What do I do with the money?",
+    "How are you getting through (and back from) burning out?",
+    "I made $24k over the last month. Now what?",
+    "What kind of personal financial investment do you do?",
+    "Should I quit the field of software development?",
+]
 input_type = "clustering"
-truncate = "NONE" # optional
-model_id = "<YOUR ENDPOINT NAME>" # On SageMaker, you create a model name that you'll pass here. 
+truncate = "NONE"  # optional
+model_id = "<YOUR ENDPOINT NAME>"  # On SageMaker, you create a model name that you'll pass here.
 
 
 # Invoke the model and print the response
 result = co.embed(
-            model=model_id,
-            input_type=input_type,
-            texts=texts,
-            truncate=truncate) 
+    model=model_id,
+    input_type=input_type,
+    texts=texts,
+    truncate=truncate,
+)
 
 print(result)
 ```

fern/pages/v2/deployment-options/cohere-on-microsoft-azure.mdx

@@ -72,14 +72,19 @@ data = {
 body = str.encode(json.dumps(data))
 
 # Replace the url with your API endpoint
-url = "https://your-endpoint.inference.ai.azure.com/v1/chat/completions"
+url = (
+    "https://your-endpoint.inference.ai.azure.com/v1/chat/completions"
+)
 
 # Replace this with the key for the endpoint
 api_key = "your-auth-key"
 if not api_key:
     raise Exception("API Key is missing")
 
-headers = {"Content-Type": "application/json", "Authorization": (api_key)}
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": (api_key),
+}
 
 req = urllib.request.Request(url, body, headers)
 
@@ -114,9 +119,7 @@ import urllib.request
 import json
 
 # Configure payload data sending to API endpoint
-data = {
-    "input": ["hi"]
-}
+data = {"input": ["hi"]}
 
 body = str.encode(json.dumps(data))
 
@@ -128,7 +131,10 @@ api_key = "your-auth-key"
 if not api_key:
     raise Exception("API Key is missing")
 
-headers = {"Content-Type": "application/json", "Authorization": (api_key)}
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": (api_key),
+}
 
 req = urllib.request.Request(url, body, headers)
 
@@ -152,7 +158,7 @@ import cohere
 
 co = cohere.Client(
     base_url="https://<endpoint>.<region>.inference.ai.azure.com/v1",
-    api_key="<key>"
+    api_key="<key>",
 )
 
 documents = [
@@ -217,19 +223,19 @@ import cohere
 # For Command models
 co_chat = cohere.Client(
     api_key="AZURE_INFERENCE_CREDENTIAL",
-    base_url="AZURE_MODEL_ENDPOINT", # Example - https://Cohere-command-r-plus-08-2024-xyz.eastus.models.ai.azure.com/
+    base_url="AZURE_MODEL_ENDPOINT",  # Example - https://Cohere-command-r-plus-08-2024-xyz.eastus.models.ai.azure.com/
 )
 
 # For Embed models
 co_embed = cohere.Client(
     api_key="AZURE_INFERENCE_CREDENTIAL",
-    base_url="AZURE_MODEL_ENDPOINT", # Example - https://cohere-embed-v3-multilingual-xyz.eastus.models.ai.azure.com/
+    base_url="AZURE_MODEL_ENDPOINT",  # Example - https://cohere-embed-v3-multilingual-xyz.eastus.models.ai.azure.com/
 )
 
 # For Rerank models
 co_rerank = cohere.Client(
     api_key="AZURE_INFERENCE_CREDENTIAL",
-    base_url="AZURE_MODEL_ENDPOINT", # Example - https://cohere-rerank-v3-multilingual-xyz.eastus.models.ai.azure.com/
+    base_url="AZURE_MODEL_ENDPOINT",  # Example - https://cohere-rerank-v3-multilingual-xyz.eastus.models.ai.azure.com/
 )
 ```
 
@@ -289,7 +295,10 @@ faqs_short = [
 query = "Are there fitness-related perks?"
 
 results = co_rerank.rerank(
-    query=query, documents=faqs_short, top_n=2, model="rerank-english-v3.0"
+    query=query,
+    documents=faqs_short,
+    top_n=2,
+    model="rerank-english-v3.0",
 )
 ```
 

fern/pages/v2/deployment-options/cohere-works-everywhere.mdx

@@ -196,8 +196,7 @@ const cohere = new CohereClientV2({
 ```python PYTHON
 import cohere
 
-co = cohere.ClientV2(api_key="",
-                     base_url="<YOUR_DEPLOYMENT_URL>")
+co = cohere.ClientV2(api_key="", base_url="<YOUR_DEPLOYMENT_URL>")
 
 response = co.chat(
     chat_history=[
@@ -328,7 +327,7 @@ co = cohere.BedrockClient(
 )
 
 response = co.chat(
-  	model="cohere.command-r-plus-v1:0",
+    model="cohere.command-r-plus-v1:0",
     chat_history=[
         {"role": "USER", "message": "Who discovered gravity?"},
         {
@@ -521,8 +520,8 @@ const cohere = new CohereClient({
 import cohere
 
 co = cohere.Client(
-  api_key="<azure token>",
-  base_url="https://Cohere-command-r-plus-phulf-serverless.eastus2.inference.ai.azure.com/v1",
+    api_key="<azure token>",
+    base_url="https://Cohere-command-r-plus-phulf-serverless.eastus2.inference.ai.azure.com/v1",
 )
 
 response = co.chat(