version update post release v1.2.2 (#1005)

Author: sonichi

notebook/autogen_chatgpt.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai,blendsearch] option:\n",
     "```bash\n",
-    "pip install flaml[openai,blendsearch]==1.2.1\n",
+    "pip install flaml[openai,blendsearch]==1.2.2\n",
     "```"
    ]
   },
@@ -40,7 +40,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[openai,blendsearch]==1.2.1 datasets"
+    "# %pip install flaml[openai,blendsearch]==1.2.2 datasets"
    ]
   },
   {

notebook/autogen_openai.ipynb

@@ -23,7 +23,7 @@
     "\n",
     "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen,blendsearch] option:\n",
     "```bash\n",
-    "pip install flaml[autogen,blendsearch]==1.2.1\n",
+    "pip install flaml[autogen,blendsearch]==1.2.2\n",
     "```"
    ]
   },
@@ -40,7 +40,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[autogen,blendsearch]==1.2.1 datasets"
+    "# %pip install flaml[autogen,blendsearch]==1.2.2 datasets"
    ]
   },
   {

notebook/research/autogen_code.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen] option:\n",
     "```bash\n",
-    "pip install flaml[autogen]==1.2.1\n",
+    "pip install flaml[autogen]==1.2.2\n",
     "```"
    ]
   },
@@ -38,7 +38,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[autogen]==1.2.1 datasets"
+    "# %pip install flaml[autogen]==1.2.2 datasets"
    ]
   },
   {

notebook/research/math_level5counting.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
     "```bash\n",
-    "pip install flaml[openai]==1.2.1\n",
+    "pip install flaml[openai]==1.2.2\n",
     "```"
    ]
   },
@@ -38,7 +38,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[openai]==1.2.1 datasets"
+    "# %pip install flaml[openai]==1.2.2 datasets"
    ]
   },
   {

website/docs/Examples/AutoGen-OpenAI.md

@@ -5,9 +5,9 @@ In this example, we will tune several hyperparameters for the OpenAI's completio
 
 ### Prerequisites
 
-Install the [autogen,blendsearch] option. The OpenAI integration is in preview.
+Install the [autogen,blendsearch] option.
 ```bash
-pip install "flaml[autogen,blendsearch]==1.2.1 datasets"
+pip install "flaml[autogen,blendsearch]==1.2.2 datasets"
 ```
 
 Setup your OpenAI key:
@@ -64,7 +64,9 @@ Before starting tuning, you need to define the metric for the optimization. For
 from functools import partial
 from flaml.autogen.code_utils import eval_function_completions, generate_assertions
 
-eval_with_generated_assertions = partial(eval_function_completions, assertions=generate_assertions)
+eval_with_generated_assertions = partial(
+    eval_function_completions, assertions=generate_assertions,
+)
 ```
 
 This function will first generate assertion statements for each problem. Then, it uses the assertions to select the generated responses.

website/docs/Use-Cases/Auto-Generation.md

@@ -1,6 +1,6 @@
 # Auto Generation
 
-`flaml.autogen` is a subpackage for automating generation tasks. It uses [`flaml.tune`](../reference/tune/tune) to find good hyperparameter configurations under budget constraints.
+`flaml.autogen` is a package for automating generation tasks (in preview). It uses [`flaml.tune`](../reference/tune/tune) to find good hyperparameter configurations under budget constraints.
 Such optimization has several benefits:
 * Maximize the utility out of using expensive foundation models.
 * Reduce the inference cost by using cheaper models or configurations which achieve equal or better performance.