update autogen doc link

flaml/autogen/oai/completion.py

@@ -697,7 +697,7 @@ class Completion(openai_Completion):
                 E.g., `prompt="Complete the following sentence: {prefix}, context={"prefix": "Today I feel"}`.
                 The actual prompt will be:
                 "Complete the following sentence: Today I feel".
-                More examples can be found at [templating](/docs/Use-Cases/Autogen#templating).
+                More examples can be found at [templating](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#templating).
             use_cache (bool, Optional): Whether to use cached responses.
             config_list (List, Optional): List of configurations for the completion to try.
                 The first one that does not raise an error will be used.

website/blog/2023-04-21-LLM-tuning-math/index.mdx

@@ -69,6 +69,6 @@ The need for model selection, parameter tuning and cost saving is not specific t
 ## For Further Reading
 
 * [Research paper about the tuning technique](https://arxiv.org/abs/2303.04673)
-* [Documentation about `flaml.autogen`](/docs/Use-Cases/Autogen)
+* [Documentation about `flaml.autogen`](https://microsoft.github.io/autogen/)
 
 *Do you have any experience to share about LLM applications? Do you like to see more support or research of LLM optimization or automation? Please join our [Discord](https://discord.gg/Cppx2vSPVP) server for discussion.*

website/blog/2023-05-07-1M-milestone/index.mdx

@@ -37,7 +37,7 @@ We invite contributions from anyone interested in this topic and look forward to
 
 ## For Further Reading
 
-* [Documentation about `flaml.autogen`](/docs/Use-Cases/Autogen)
+* [Documentation about `flaml.autogen`](https://microsoft.github.io/autogen/)
 * [Code Example: Tune chatGPT for Math Problem Solving with FLAML](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_chatgpt_gpt4.ipynb)
 
 *Do you have any experience to share about LLM applications? Do you like to see more support or research of LLMOps? Please join our [Discord](https://discord.gg/Cppx2vSPVP) server for discussion.*

website/blog/2023-05-18-GPT-adaptive-humaneval/index.mdx

@@ -144,7 +144,7 @@ An example notebook to run this experiment can be found at: https://github.com/m
 
 ## Discussion
 
-Our solution is quite simple to [implement](/docs/reference/autogen/code_utils#implement) using a generic interface offered in [`flaml.autogen`](/docs/Use-Cases/Autogen#logic-error), yet the result is quite encouraging.
+Our solution is quite simple to implement using a generic interface offered in [`flaml.autogen`](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#logic-error), yet the result is quite encouraging.
 
 While the specific way of generating assertions is application-specific, the main ideas are general in LLM operations:
 * Generate multiple responses to select - especially useful when selecting a good response is relatively easier than generating a good response at one shot.
@@ -164,5 +164,5 @@ There are many directions of extensions in research and development:
 
 ## For Further Reading
 
-* [Documentation](/docs/Use-Cases/Autogen) about `flaml.autogen` and [Research paper](https://arxiv.org/abs/2303.04673).
+* [Documentation](https://microsoft.github.io/autogen/) about `flaml.autogen` and [Research paper](https://arxiv.org/abs/2303.04673).
 * [Blog post](/blog/2023/04/21/LLM-tuning-math) about a related study for math.

website/blog/2023-06-28-MathChat/index.mdx

@@ -89,6 +89,6 @@ Further work can be done to enhance this framework or math problem-solving in ge
 ## For Further Reading
 
 * [Research paper of MathChat](https://arxiv.org/abs/2306.01337)
-* [Documentation about `flaml.autogen`](/docs/Use-Cases/Autogen)
+* [Documentation about `flaml.autogen`](https://microsoft.github.io/autogen/)
 
 *Are you working on applications that involve math problem-solving? Would you appreciate additional research or support on the application of LLM-based agents for math problem-solving? Please join our [Discord](https://discord.gg/Cppx2vSPVP) server for discussion.*

website/blog/2023-07-14-Local-LLMs/index.mdx

@@ -143,5 +143,5 @@ print(response)
 
 ## For Further Reading
 
-* [Documentation](/docs/Use-Cases/Autogen) about `flaml.autogen`
+* [Documentation](https://microsoft.github.io/autogen/) about `flaml.autogen`
 * [Documentation](https://github.com/lm-sys/FastChat) about FastChat.

website/docs/Examples/AutoGen-AgentChat.md

@@ -2,7 +2,7 @@
 <!-- Keep aligned with notebooks in docs/Use-Cases/Autogen#notebook-examples -->
 
 `flaml.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.
-Please find documentation about this feature [here](/docs/Use-Cases/Autogen#agents).
+Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).
 
 Links to notebook examples:
 * [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)

website/docs/Examples/AutoGen-OpenAI.md

@@ -1,7 +1,7 @@
 # AutoGen - Tune GPT Models
 
 `flaml.autogen` offers a cost-effective hyperparameter optimization technique [EcoOptiGen](https://arxiv.org/abs/2303.04673) for tuning Large Language Models. The research study finds that tuning hyperparameters can significantly improve the utility of them.
-Please find documentation about this feature [here](/docs/Use-Cases/Autogen#enhanced-inference).
+Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/#enhanced-inference).
 
 Links to notebook examples:
 * [Optimize for Code Generation](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_openai_completion.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_openai_completion.ipynb)

website/docs/Getting-Started.md

@@ -20,7 +20,7 @@ Install FLAML from pip: `pip install flaml`. Find more options in [Installation]
 
 There are several ways of using flaml:
 
-#### (New) [Autogen](/docs/Use-Cases/Autogen)
+#### (New) [Autogen](https://microsoft.github.io/autogen/)
 
 Autogen enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
 It offers customizable and conversable agents which integrate LLMs, tools and human.
@@ -118,7 +118,7 @@ Then, you can use it just like you use the original `LGMBClassifier`. Your other
 
 ### Where to Go Next?
 
-* Understand the use cases for [Autogen](/docs/Use-Cases/Autogen), [Task-oriented AutoML](/docs/Use-Cases/Task-Oriented-Automl), [Tune user-defined function](/docs/Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](/docs/Use-Cases/Zero-Shot-AutoML).
+* Understand the use cases for [Autogen](https://microsoft.github.io/autogen/), [Task-oriented AutoML](/docs/Use-Cases/Task-Oriented-Automl), [Tune user-defined function](/docs/Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](/docs/Use-Cases/Zero-Shot-AutoML).
 * Find code examples under "Examples": from [AutoGen - AgentChat](/docs/Examples/AutoGen-AgentChat) to [Tune - PyTorch](/docs/Examples/Tune-PyTorch).
 * Learn about [research](/docs/Research) around FLAML and check [blogposts](/blog).
 * Chat on [Discord](https://discord.gg/Cppx2vSPVP).