[Sweep GHA Fix] Fix failing GitHub Actions on 4ebfc6e (master)

docs/docs_skeleton/ignore_build.sh

@@ -2,9 +2,9 @@
 
 echo "VERCEL_GIT_COMMIT_REF: $VERCEL_GIT_COMMIT_REF"
 
-if [[ $VERCEL_GIT_COMMIT_REF = __docs__* || "$VERCEL_GIT_COMMIT_REF" == "master" ]] ; then
+if [[ $VERCEL_GIT_COMMIT_REF = __docs__* || "$VERCEL_GIT_COMMIT_REF" == "main" || "$VERCEL_GIT_COMMIT_REF" == "master" ]]; then
   # Proceed with the build
-	echo "✅ - Build can proceed"
+	echo "✅ - Build can proceed for __docs__* and main"
   exit 1;
 
 else

docs/extras/guides/deployments/index.mdx

@@ -1,6 +1,6 @@
 # Deployment
 
-In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it's crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories:
+In today's fast-paced technological landscape, deploying Large Language Models (LLMs) presents a significant opportunity for developers to enhance their applications with advanced language capabilities. As a result, it's crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories:
 
 - **Case 1: Utilizing External LLM Providers (OpenAI, Anthropic, etc.)**
     In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc.
@@ -10,7 +10,9 @@ In today's fast-paced technological landscape, the use of Large Language Models
 
 Regardless of the framework that forms the backbone of your product, deploying LLM applications comes with its own set of challenges. It's vital to understand the trade-offs and key considerations when evaluating serving frameworks.
 
-## Outline
+## Challenges and Considerations
+
+LLM applications' deployment involves several potential challenges and considerations when evaluating serving frameworks. Understanding these challenges and considerations is crucial to ensure the successful deployment of LLM applications. This guide aims to provide a comprehensive overview of the potential challenges and considerations when evaluating serving frameworks for LLM applications, as well as the requirements for deploying LLMs in a production setting. It underscores the importance of understanding the challenges and considerations involved in evaluating and selecting the right serving frameworks for LLM applications. The guide also focuses on:
 
 This guide aims to provide a comprehensive overview of the requirements for deploying LLMs in a production setting, focusing on:
 

docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx

@@ -1,5 +1,7 @@
 Install openai,google-search-results packages which are required as the langchain packages call them internally
+\# Install required packages for the OpenAI Functions agent
 
+# Install required packages for the OpenAI Functions agent
 >pip install openai google-search-results
 
 ```python