Minor grammar and wording issues (microsoft#854)

* wording

* grammar and wording

* readability suggested by shruti222patel

* period

notebook/agentchat_RetrieveChat.ipynb

@@ -19,10 +19,10 @@
     "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 participation 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 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",
+    "RetrieveChat is a conversational system for retrieval-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",
+    "We'll demonstrate six examples of using RetrieveChat for code generation and question answering:\n",
     "\n",
     "[Example 1: Generate code based off docstrings w/o human feedback](#example-1)\n",
     "\n",

notebook/agentchat_groupchat_RAG.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Group Chat with Retrieval Augmented Generation\n",
     "\n",
-    "AutoGen supports conversable agents powered by LLMs, tools or humans, performing tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
+    "AutoGen supports conversable agents powered by LLMs, tools, or humans, performing tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "## Requirements\n",
@@ -224,7 +224,7 @@
     "    )\n",
     "    manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
     "\n",
-    "    # Start chatting with boss as this is the user proxy agent.\n",
+    "    # Start chatting with the boss as this is the user proxy agent.\n",
     "    boss.initiate_chat(\n",
     "        manager,\n",
     "        message=PROBLEM,\n",
@@ -294,7 +294,7 @@
     "    )\n",
     "    manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
     "\n",
-    "    # Start chatting with boss as this is the user proxy agent.\n",
+    "    # Start chatting with the boss as this is the user proxy agent.\n",
     "    boss.initiate_chat(\n",
     "        manager,\n",
     "        message=PROBLEM,\n",

notebook/agentchat_teaching.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Teaching\n",
     "\n",
-    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework makes it easy to build many advanced applications of LLMs.\n",
+    "AutoGen offers conversable agents powered by LLMs, tools, or humans, which can be used to perform tasks collectively via automated chat. This framework makes it easy to build many advanced applications of LLMs.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "This notebook demonstrates how AutoGen enables a user to teach AI new skills via natural agent interactions, without requiring knowledge of programming language. It is modified based on https://github.com/microsoft/FLAML/blob/evaluation/notebook/research_paper/teaching.ipynb and https://github.com/microsoft/FLAML/blob/evaluation/notebook/research_paper/teaching_recipe_reuse.ipynb.\n",

notebook/oai_chatgpt_gpt4.ipynb

@@ -127,7 +127,7 @@
     "        'base_url': '<your first Azure OpenAI API base here>',\n",
     "        'api_type': 'azure',\n",
     "        'api_version': '2023-06-01-preview',\n",
-    "    },  # only if the at least one Azure OpenAI API key is found\n",
+    "    },  # only if at least one Azure OpenAI API key is found\n",
     "    {\n",
     "        'api_key': '<your second Azure OpenAI API key here>',\n",
     "        'base_url': '<your second Azure OpenAI API base here>',\n",
@@ -277,7 +277,7 @@
    "source": [
     "## Define Success Metric\n",
     "\n",
-    "Before we start tuning, we need to define the success metric we want to optimize. For each math task, we use voting to select a response with the most common answers out of all the generated responses. If it has an equivalent answer to the canonical solution, we consider the task as successfully solved. Then we can optimize the mean success rate of a collection of tasks."
+    "Before we start tuning, we must define the success metric we want to optimize. For each math task, we use voting to select a response with the most common answers out of all the generated responses. We consider the task successfully solved if it has an equivalent answer to the canonical solution. Then we can optimize the mean success rate of a collection of tasks."
    ]
   },
   {
@@ -346,9 +346,9 @@
     "\n",
     "The tuning will take a while to finish, depending on the optimization budget. The tuning will be performed under the specified optimization budgets.\n",
     "\n",
-    "* `inference_budget` is the target average inference budget per instance in the benchmark. For example, 0.004 means the target inference budget is 0.004 dollars, which translates to 2000 tokens (input + output combined) if the gpt-3.5-turbo model is used.\n",
-    "* `optimization_budget` is the total budget allowed to perform the tuning. For example, 1 means 1 dollars are allowed in total, which translates to 500K tokens for the gpt-3.5-turbo model.\n",
-    "* `num_sumples` is the number of different hyperparameter configurations which is allowed to try. The tuning will stop after either num_samples trials or after optimization_budget dollars spent, whichever happens first. -1 means no hard restriction in the number of trials and the actual number is decided by `optimization_budget`.\n",
+    "* `inference_budget` is the benchmark's target average inference budget per instance. For example, 0.004 means the target inference budget is 0.004 dollars, which translates to 2000 tokens (input + output combined) if the gpt-3.5-turbo model is used.\n",
+    "* `optimization_budget` is the total budget allowed for tuning. For example, 1 means 1 dollar is allowed in total, which translates to 500K tokens for the gpt-3.5-turbo model.\n",
+    "* `num_sumples` is the number of different hyperparameter configurations allowed to be tried. The tuning will stop after either num_samples trials are completed or optimization_budget dollars are spent, whichever happens first. -1 means no hard restriction in the number of trials and the actual number is decided by `optimization_budget`.\n",
     "\n",
     "Users can specify tuning data, optimization metric, optimization mode, evaluation function, search spaces etc.. The default search space is:\n",
     "\n",
@@ -371,7 +371,7 @@
     "```\n",
     "\n",
     "The default search space can be overridden by users' input.\n",
-    "For example, the following code specifies a fixed prompt template. For hyperparameters which don't appear in users' input, the default search space will be used."
+    "For example, the following code specifies a fixed prompt template. The default search space will be used for hyperparameters that don't appear in users' input."
    ]
   },
   {

website/blog/2023-10-18-RetrieveChat/index.mdx

@@ -369,7 +369,7 @@ groupchat = autogen.GroupChat(
 )
 manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
 
-# Start chatting with boss as this is the user proxy agent.
+# Start chatting with the boss as this is the user proxy agent.
 boss.initiate_chat(
     manager,
     message="How to use spark for parallel training in FLAML? Give me sample code.",

website/docs/FAQ.md

@@ -52,7 +52,7 @@ When you call `initiate_chat` the conversation restarts by default. You can use
 
 ## How do we decide what LLM is used for each agent? How many agents can be used? How do we decide how many agents in the group?
 
-Each agent can be customized. You can use LLMs, tools or human behind each agent. If you use an LLM for an agent, use the one best suited for its role. There is no limit of the number of agents, but start from a small number like 2, 3. The more capable is the LLM and the fewer roles you need, the fewer agents you need.
+Each agent can be customized. You can use LLMs, tools, or humans behind each agent. If you use an LLM for an agent, use the one best suited for its role. There is no limit of the number of agents, but start from a small number like 2, 3. The more capable is the LLM and the fewer roles you need, the fewer agents you need.
 
 The default user proxy agent doesn't use LLM. If you'd like to use an LLM in UserProxyAgent, the use case could be to simulate user's behavior.
 

website/docs/Getting-Started.md

@@ -22,7 +22,7 @@ Install from pip: `pip install pyautogen`. Find more options in [Installation](/
 For [code execution](/docs/FAQ#code-execution), we strongly recommend installing the python docker package, and using docker.
 
 #### Multi-Agent Conversation Framework
-Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human.
+Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools, and humans.
 By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For [example](https://github.com/microsoft/autogen/blob/main/test/twoagent.py),
 ```python
 from autogen import AssistantAgent, UserProxyAgent, config_list_from_json

website/docs/Use-Cases/agent_chat.md

@@ -1,6 +1,6 @@
 # Multi-agent Conversation Framework
 
-AutoGen offers a unified multi-agent conversation framework as a high-level abstraction of using foundation models. It features capable, customizable and conversable agents which integrate LLM, tool and human via automated agent chat.
+AutoGen offers a unified multi-agent conversation framework as a high-level abstraction of using foundation models. It features capable, customizable and conversable agents which integrate LLMs, tools, and humans via automated agent chat.
 By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.
 
 This framework simplifies the orchestration, automation and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcome their weaknesses. It enables building next-gen LLM applications based on multi-agent conversations with minimal effort.