From cd287a4947cf80d45b50a30dfaab2dc2542b81ba Mon Sep 17 00:00:00 2001
From: "sweep-ai[bot]" <128439645+sweep-ai[bot]@users.noreply.github.com>
Date: Thu, 25 Jan 2024 13:17:50 +0000
Subject: [PATCH 1/3] feat: Updated docs/docs_skeleton/ignore_build.sh

---
 docs/docs_skeleton/ignore_build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/docs_skeleton/ignore_build.sh b/docs/docs_skeleton/ignore_build.sh
index 8669cde18833d..ccb537dc6e5cd 100755
--- a/docs/docs_skeleton/ignore_build.sh
+++ b/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

From 02b0074eff6c05a32411dda134e6d4fc5b65ce19 Mon Sep 17 00:00:00 2001
From: "sweep-ai[bot]" <128439645+sweep-ai[bot]@users.noreply.github.com>
Date: Thu, 25 Jan 2024 13:18:26 +0000
Subject: [PATCH 2/3] feat: Updated docs/extras/guides/deployments/index

---
 docs/extras/guides/deployments/index.mdx | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/extras/guides/deployments/index.mdx b/docs/extras/guides/deployments/index.mdx
index 09841cff14761..a6a4565a00e79 100644
--- a/docs/extras/guides/deployments/index.mdx
+++ b/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:
 

From 1870bfe628807d27bc3edbcea4ac8407fa37a3cb Mon Sep 17 00:00:00 2001
From: "sweep-ai[bot]" <128439645+sweep-ai[bot]@users.noreply.github.com>
Date: Thu, 25 Jan 2024 13:19:38 +0000
Subject: [PATCH 3/3] feat: Updated docs/snippets/modules/agents/agent_t

---
 .../modules/agents/agent_types/openai_functions_agent.mdx       | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx b/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx
index cb5e085e4ce36..c44a0a11534b1 100644
--- a/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx
+++ b/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