Fix documentation (#1075)

* Fix indentation in documentation

* newline

* version

README.md

@@ -14,8 +14,10 @@
     <br>
 </p>
 
-:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web)
+:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).
+
 :fire: [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Auto-Generation) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
+
 :fire: FLAML supports AutoML and Hyperparameter Tuning features in [Microsoft Fabric](https://learn.microsoft.com/en-us/fabric/get-started/microsoft-fabric-overview) private preview. Sign up for these features at: https://aka.ms/fabric/data-science/sign-up.
 
 

notebook/autogen_agent_auto_feedback_from_code_execution.ipynb

@@ -44,7 +44,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[autogen]"
+    "# %pip install flaml[autogen]==2.0.0rc1"
    ]
   },
   {

notebook/autogen_agent_human_feedback.ipynb

@@ -44,7 +44,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[autogen]"
+    "# %pip install flaml[autogen]==2.0.0rc1"
    ]
   },
   {

notebook/autogen_agent_web_info.ipynb

@@ -44,7 +44,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[autogen]"
+    "# %pip install flaml[autogen]==2.0.0rc1"
    ]
   },
   {

website/docs/Getting-Started.md

@@ -23,30 +23,30 @@ There are several ways of using flaml:
 #### (New) [Auto Generation](/docs/Use-Cases/Auto-Generation)
 
 Maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4, including:
-    - A drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalites like tuning, caching, templating, filtering. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
-    ```python
-    from flaml import oai
+- A drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalites like tuning, caching, templating, filtering. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
+```python
+from flaml import oai
 
-    # perform tuning
-    config, analysis = oai.Completion.tune(
-        data=tune_data,
-        metric="success",
-        mode="max",
-        eval_func=eval_func,
-        inference_budget=0.05,
-        optimization_budget=3,
-        num_samples=-1,
-    )
+# perform tuning
+config, analysis = oai.Completion.tune(
+    data=tune_data,
+    metric="success",
+    mode="max",
+    eval_func=eval_func,
+    inference_budget=0.05,
+    optimization_budget=3,
+    num_samples=-1,
+)
 
-    # perform inference for a test instance
-    response = oai.Completion.create(context=test_instance, **config)
-    ```
-    - LLM-driven intelligent agents which can perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,
-    ```python
-    assistant = AssistantAgent("assistant")
-    user = UserProxyAgent("user", human_input_mode="TERMINATE")
-    assistant.receive("Draw a rocket and save to a file named 'rocket.svg'")
-    ```
+# perform inference for a test instance
+response = oai.Completion.create(context=test_instance, **config)
+```
+- LLM-driven intelligent agents which can perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,
+```python
+assistant = AssistantAgent("assistant")
+user = UserProxyAgent("user", human_input_mode="TERMINATE")
+assistant.receive("Draw a rocket and save to a file named 'rocket.svg'")
+```
 
 #### [Task-oriented AutoML](/docs/Use-Cases/task-oriented-automl)