diff --git a/README.md b/README.md
index 4f315690ad12..60a7a35c52c7 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ pip install "pyautogen[blendsearch]"
 
 Find more options in [Installation](https://microsoft.github.io/autogen/docs/Installation).
 <!-- Each of the [`notebook examples`](https://github.com/microsoft/autogen/tree/main/notebook) may require a specific option to be installed. -->
-For [code execution](https://microsoft.github.io/autogen/FAQ#code-execution), we strongly recommend installing the python docker package, and using docker.
+For [code execution](https://microsoft.github.io/autogen/docs/FAQ/#code-execution), we strongly recommend installing the python docker package, and using docker.
 
 For LLM inference configurations, check the [FAQ](https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints).
 
diff --git a/autogen/agentchat/agent.py b/autogen/agentchat/agent.py
index 8a71147297eb..b83709dc30bd 100644
--- a/autogen/agentchat/agent.py
+++ b/autogen/agentchat/agent.py
@@ -28,7 +28,7 @@ def send(self, message: Union[Dict, str], recipient: "Agent", request_reply: Opt
         """(Abstract method) Send a message to another agent."""
 
     async def a_send(self, message: Union[Dict, str], recipient: "Agent", request_reply: Optional[bool] = None):
-        """(Aabstract async method) Send a message to another agent."""
+        """(Abstract async method) Send a message to another agent."""
 
     def receive(self, message: Union[Dict, str], sender: "Agent", request_reply: Optional[bool] = None):
         """(Abstract method) Receive a message from another agent."""
diff --git a/autogen/code_utils.py b/autogen/code_utils.py
index 4e76b70738ca..ff97f567de53 100644
--- a/autogen/code_utils.py
+++ b/autogen/code_utils.py
@@ -259,7 +259,7 @@ def execute_code(
 
     timeout = timeout or DEFAULT_TIMEOUT
     original_filename = filename
-    if WIN32 and lang in ["sh", "shell"]:
+    if WIN32 and lang in ["sh", "shell"] and (not use_docker):
         lang = "ps1"
     if filename is None:
         code_hash = md5(code.encode()).hexdigest()
diff --git a/autogen/version.py b/autogen/version.py
index 0a8da882586d..f1380eede217 100644
--- a/autogen/version.py
+++ b/autogen/version.py
@@ -1 +1 @@
-__version__ = "0.1.6"
+__version__ = "0.1.7"
diff --git a/notebook/agentchat_RetrieveChat.ipynb b/notebook/agentchat_RetrieveChat.ipynb
index 60c2f8861d93..fca0ade9d86f 100644
--- a/notebook/agentchat_RetrieveChat.ipynb
+++ b/notebook/agentchat_RetrieveChat.ipynb
@@ -19,7 +19,7 @@
     "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
-    "RetrieveChat is a convesational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and  `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
+    "RetrieveChat is a conversational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and  `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
     "## Table of Contents\n",
     "We'll demonstrates six examples of using RetrieveChat for code generation and question answering:\n",
diff --git a/website/docs/Examples/AutoGen-AgentChat.md b/website/docs/Examples/AutoGen-AgentChat.md
index 55988818ef0a..4ecd42b5663c 100644
--- a/website/docs/Examples/AutoGen-AgentChat.md
+++ b/website/docs/Examples/AutoGen-AgentChat.md
@@ -1,6 +1,6 @@
 # AutoGen - Automated Multi Agent Chat
 
-AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance via multi-agent conversation.
+AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participance via multi-agent conversation.
 Please find documentation about this feature [here](/docs/Use-Cases/agent_chat).
 
 Links to notebook examples: