From 6e74e2d51a23331d3d1055ec8480a25440975844 Mon Sep 17 00:00:00 2001
From: Chi Wang <wang.chi@microsoft.com>
Date: Mon, 23 Oct 2023 08:34:32 -0700
Subject: [PATCH] headsup about dependency change (#378)

* headsup about dependency change

* more change
---
 README.md                                    | 9 ++++-----
 website/docs/Getting-Started.md              | 4 ++--
 website/docs/Installation.md                 | 3 +++
 website/docs/Use-Cases/enhanced_inference.md | 2 +-
 4 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 9d425abd1..430a431d9 100644
--- a/README.md
+++ b/README.md
@@ -13,10 +13,9 @@ This project is a spinoff from [FLAML](https://github.com/microsoft/FLAML).
     <br>
 </p> -->
 
-:fire: autogen has graduated from [FLAML](https://github.com/microsoft/FLAML) into a new project.
-
-<!-- :fire: Heads-up: We're preparing to migrate [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) into a dedicated Github repository. Alongside this move, we'll also launch a dedicated Discord server and a website for comprehensive documentation.
+:fire: Heads-up: pyautogen v0.2 will switch to using openai v1.
 
+<!--
 :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/autogen/) 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).
@@ -33,7 +32,7 @@ AutoGen is a framework that enables the development of LLM applications using mu
 - It supports **diverse conversation patterns** for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
   the number of agents, and agent conversation topology.
 - It provides a collection of working systems with different complexities. These systems span a **wide range of applications** from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
-- AutoGen provides a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` as an **enhanced inference API**. It allows easy performance tuning, utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
+- AutoGen provides **enhanced LLM inference**. It offers easy performance tuning, plus utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
 
 AutoGen is powered by collaborative [research studies](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and the University of Washington.
 
@@ -111,7 +110,7 @@ Please find more [code examples](https://microsoft.github.io/autogen/docs/Exampl
 
 ## Enhanced LLM Inferences
 
-Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` adding powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.
+Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers enhanced LLM inference with powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics, and budgets.
 
 ```python
 # perform tuning
diff --git a/website/docs/Getting-Started.md b/website/docs/Getting-Started.md
index 937e8e513..c0e53aea5 100644
--- a/website/docs/Getting-Started.md
+++ b/website/docs/Getting-Started.md
@@ -12,7 +12,7 @@ AutoGen is a framework that enables development of LLM applications using multip
 * It supports **diverse conversation patterns** for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
 the number of agents, and agent conversation topology.
 * It provides a collection of working systems with different complexities. These systems span a **wide range of applications** from various domains and complexities. They demonstrate how AutoGen can easily support different conversation patterns.
-* AutoGen provides a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` as an **enhanced inference API**. It allows easy performance tuning, utilities like API unification & caching, and advanced usage patterns, such as error handling, multi-config inference, context programming etc.
+* AutoGen provides **enhanced LLM inference**. It offers easy performance tuning, plus utilities like API unification & caching, and advanced usage patterns, such as error handling, multi-config inference, context programming etc.
 
 AutoGen is powered by collaborative [research studies](/docs/Research) from Microsoft, Penn State University, and University of Washington.
 
@@ -44,7 +44,7 @@ The figure below shows an example conversation flow with AutoGen.
 * [Documentation](/docs/Use-Cases/agent_chat).
 
 #### Enhanced LLM Inferences
-Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
+Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers enhanced LLM inference with powerful functionalites like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
 ```python
 # perform tuning
 config, analysis = autogen.Completion.tune(
diff --git a/website/docs/Installation.md b/website/docs/Installation.md
index 9d064d4ab..5b75b9229 100644
--- a/website/docs/Installation.md
+++ b/website/docs/Installation.md
@@ -26,6 +26,9 @@ AutoGen requires **Python version >= 3.8**. It can be installed from pip:
 ```bash
 pip install pyautogen
 ```
+
+`pyautogen<0.2` requires `openai<1`. Starting from pyautogen v0.2, `openai>=1` is required.
+
 <!--
 or conda:
 ```
diff --git a/website/docs/Use-Cases/enhanced_inference.md b/website/docs/Use-Cases/enhanced_inference.md
index 2766fd9a4..8b5dd5c69 100644
--- a/website/docs/Use-Cases/enhanced_inference.md
+++ b/website/docs/Use-Cases/enhanced_inference.md
@@ -1,6 +1,6 @@
 # Enhanced Inference
 
-`autogen.Completion` is a drop-in replacement of `openai.Completion` and `openai.ChatCompletion` as an enhanced inference API.
+`autogen.Completion` is a drop-in replacement of `openai.Completion` and `openai.ChatCompletion` for enhanced LLM inference.
 There are a number of benefits of using `autogen` to perform inference: performance tuning, API unification, caching, error handling, multi-config inference, result filtering, templating and so on.
 
 ## Tune Inference Parameters