Enforce your style guide: fix Deepgram and LangChain page style

partner-services/deepgram.mdx

@@ -40,9 +40,9 @@ Consult your Deepgram representative on how to achieve parity with the Deepgram
 cerebrium init deepgram
 ```
 
-2. Create a self-hosted API key from the <b>Deepgram</b> dashboard. Navigate to the **Secrets** tab in the <b>Cerebrium</b> dashboard and add the API key with the name `DEEPGRAM_API_KEY`. This secret automatically becomes available as an environment variable in the deployment.
+2. Create a self-hosted API key from the **Deepgram** dashboard. Navigate to the **Secrets** tab in the **Cerebrium** dashboard and add the API key with the name `DEEPGRAM_API_KEY`. This secret automatically becomes available as an environment variable in the deployment.
 
-3. Download model files from Deepgram's self-hosted section in the <b>Deepgram</b> dashboard using the guide available [here](https://developers.deepgram.com/docs/deploy-deepgram-services#pull-deepgram-container-images). Select the 'license proxy' deployment type. Upload downloaded model files using the links provided by your Account Representative (with `.dg` extension) to persistent-storage in the `/deepgram-models` folder. This folder automatically attaches to the engine container. Use this command to upload the files:
+3. Download model files from Deepgram's self-hosted section in the **Deepgram** dashboard using the guide available [here](https://developers.deepgram.com/docs/deploy-deepgram-services#pull-deepgram-container-images). Select the 'license proxy' deployment type. Upload downloaded model files using the links provided by your Account Representative (with `.dg` extension) to persistent storage in the `/deepgram-models` folder. This folder automatically attaches to the engine container. Use this command to upload the files:
 
 ```bash
 cerebrium cp <model>.dg deepgram-models/<model>.dg
@@ -53,12 +53,12 @@ cerebrium cp nova-3-general.en.streaming.123456.dg deepgram-models/nova-3-genera
 
 <Note>
   The `deepgram-models` directory remains at the root level of persistent
-  storage and is shared across all Deepgram apps in the project. Configuration
-  files (`api.toml` and `engine.toml`), however, must be placed under the app
-  name directory (see steps 4 and 5 below).
+  storage and is shared across all Deepgram apps in the project. The
+  configuration files (`api.toml` and `engine.toml`), however, belong under the
+  app name directory (see steps 4 and 5 below).
 </Note>
 
-4. Create a file named <b>engine.toml</b> with the following content and upload to your persistent storage under the app name directory (e.g., `{appName}/engine.toml`). These are
+4. Create a file named `engine.toml` with the following content and upload to your persistent storage under the app name directory (e.g., `{appName}/engine.toml`). These are
    the default settings. Adjust as needed.
 
    For example, if your app is named `deepgram`:
@@ -156,7 +156,7 @@ step = 0.2
   it, remove it when you next update the file.
 </Note>
 
-5. Create a file named <b>api.toml</b> with the following content and upload to your persistent storage under the app name directory (e.g., `{appName}/api.toml`). These are
+5. Create a file named `api.toml` with the following content and upload to your persistent storage under the app name directory (e.g., `{appName}/api.toml`). These are
    the default settings. Adjust as needed.
 
    For example, if your app is named `deepgram`:
@@ -331,7 +331,7 @@ curl -X POST --data-binary @bueller.wav "https://api.cerebrium.ai/v4/p-xxxxxxxx/
 You can find the parameters accepted by the Deepgram service in the [speech-to-text API reference](https://developers.deepgram.com/reference/speech-to-text-api/listen-streaming).
 
 <Note>
-  If 'disable_auth' in cerebrium.toml is set to false, include the inference
+  If `disable_auth` in `cerebrium.toml` is set to false, include the inference
   token in the Authorization header to authenticate with the Cerebrium service.
   Cerebrium pulls the Deepgram API key automatically from secrets.
 </Note>

v4/examples/langchain-langsmith.mdx

@@ -3,9 +3,9 @@ title: "Build a LangChain agent with LangSmith monitoring"
 description: Build an executive assistant agent with LangChain tool calling, monitor it in LangSmith and deploy it on Cerebrium to manage Cal.com bookings.
 ---
 
-This tutorial builds Cal-vin, an executive assistant that manages calendar appointments (via Cal.com) with employees, customers, partners, and friends. It uses the LangChain SDK for agent creation and the LangSmith platform for monitoring scheduling activities and identifying failure points, deployed on Cerebrium for seamless scaling.
+This tutorial builds Cal-vin, an executive assistant that manages calendar appointments (via Cal.com) with employees, customers, partners, and friends. It uses the LangChain SDK for agent creation and the LangSmith platform for monitoring scheduling activities and identifying failure points. The app deploys on Cerebrium for seamless scaling.
 
-You can find the final version of the code [here](https://github.com/CerebriumAI/examples/tree/master/4-integrations/2-tool-calling-langsmith)
+You can find the final version of the code [here](https://github.com/CerebriumAI/examples/tree/master/4-integrations/2-tool-calling-langsmith).
 
 ### Concepts
 
@@ -45,7 +45,7 @@ agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 agent_executor.invoke({"input": "what's 3 plus 5 raised to the 2.743. also what's 17.24 - 918.1241", })
 ```
 
-### Setup Cal.com
+### Cal.com Setup
 
 [Cal.com](https://cal.com) provides the calendar management foundation. Create an account [here](https://app.cal.com/signup) if needed. Cal serves as the source of truth. Updates to time zones or working hours automatically reflect in the assistant's responses.
 
@@ -284,9 +284,9 @@ The agent executor consists of:
 - The GPT-3.5 model serves as the LLM. Swap to Anthropic or any other provider by replacing this one line. LangChain makes this seamless.
 - Finally, these components combine with the tools to create an agent executor.
 
-### Setup Chatbot
+### Chatbot Setup
 
-The above code only handles a single question. A multi-turn conversation is needed to find a mutually suitable time. LangChain’s RunnableWithMessageHistory() adds tool calling capabilities and message memory.
+The above code only handles a single question. Finding a mutually suitable time requires a multi-turn conversation. LangChain’s RunnableWithMessageHistory() adds tool calling capabilities and message memory.
 
 It stores previous replies in the chat_history variable (from the prompt template) and ties them to a session identifier, so the API remembers information per user/session:
 
@@ -436,7 +436,7 @@ You can find the final version of the code [here](https://github.com/CerebriumAI
 Consider implementing:
 
 1. Response streaming for seamless user experience
-2. Email integration for context-aware scheduling when Claire is tagged
+2. Email integration for context-aware scheduling when Cal-vin is tagged
 3. Voice capabilities for phone-based scheduling
 
 ### Conclusion