cleanup

autogen/agentchat/assistant_agent.py

@@ -43,7 +43,7 @@ def __init__(
             system_message (str): system message for the ChatCompletion inference.
                 Please override this attribute if you want to reprogram the agent.
             llm_config (dict): llm inference configuration.
-                Please refer to [autogen.Completion.create](/docs/reference/autogen/oai/completion#create)
+                Please refer to [Completion.create](/docs/reference/oai/completion#create)
                 for available options.
             is_termination_msg (function): a function that takes a message in the form of a dictionary
                 and returns a boolean value indicating if this received message is a termination message.

autogen/agentchat/contrib/math_user_proxy_agent.py

@@ -156,7 +156,7 @@ def __init__(
                     when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
             default_auto_reply (str or dict or None): the default auto reply message when no code execution or llm based reply is generated.
             max_invalid_q_per_step (int): (ADDED) the maximum number of invalid queries per step.
-            **kwargs (dict): other kwargs in [UserProxyAgent](user_proxy_agent#__init__).
+            **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
         """
         super().__init__(
             name=name,

autogen/agentchat/contrib/retrieve_user_proxy_agent.py

@@ -122,7 +122,7 @@ def __init__(
                     can be found at `https://www.sbert.net/docs/pretrained_models.html`. The default model is a
                     fast model. If you want to use a high performance model, `all-mpnet-base-v2` is recommended.
                 - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
-            **kwargs (dict): other kwargs in [UserProxyAgent](user_proxy_agent#__init__).
+            **kwargs (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
         """
         super().__init__(
             name=name,

autogen/agentchat/conversable_agent.py

@@ -89,7 +89,7 @@ def __init__(
                 - timeout (Optional, int): The maximum execution time in seconds.
                 - last_n_messages (Experimental, Optional, int): The number of messages to look back for code execution. Default to 1.
             llm_config (dict or False): llm inference configuration.
-                Please refer to [autogen.Completion.create](/docs/reference/autogen/oai/completion#create)
+                Please refer to [autogen.Completion.create](/docs/reference/oai/completion#create)
                 for available options.
                 To disable llm-based auto reply, set to False.
             default_auto_reply (str or dict or None): default auto reply when no code execution or llm-based reply is generated.

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 inference tuning](Use-Cases/enhanced_inference)
+* [Documentation about inference tuning](/docs/Use-Cases/enhanced_inference)
 
 *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/be44TCXGpe) 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 using a generic interface offered in [`autogen`](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference#logic-error), yet the result is quite encouraging.
+Our solution is quite simple to implement using a generic interface offered in [`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.

website/docs/Getting-Started.md

@@ -59,7 +59,7 @@ response = autogen.Completion.create(context=test_instance, **config)
 * Understand the use cases for [multi-agent conversation](/docs/Use-Cases/agent_chat).
 * Understand the use cases for [enhanced LLM inference](/docs/Use-Cases/enhanced_inference).
 * Find code examples from [Examples](/docs/Examples/AutoGen-AgentChat).
-* Read [SDK](/docs/reference/autogen/agentchat/conversable_agent/).
+* Read [SDK](/docs/reference/agentchat/conversable_agent/).
 * Learn about [research](/docs/Research) around AutoGen.
 * Chat on [Discord](https://discord.gg/be44TCXGpe).
 

website/docs/Use-Cases/enhanced_inference.md

@@ -30,7 +30,7 @@ There are also complex interactions among subsets of the hyperparameters. For ex
 the temperature and top_p are not recommended to be altered from their default values together because they both control the randomness of the generated text, and changing both at the same time can result in conflicting effects; n and best_of are rarely tuned together because if the application can process multiple outputs, filtering on the server side causes unnecessary information loss; both n and max_tokens will affect the total number of tokens generated, which in turn will affect the cost of the request.
 These interactions and trade-offs make it difficult to manually determine the optimal hyperparameter settings for a given text generation task.
 
-*Do the choices matter? Check this [blogpost](https://microsoft.github.io/autogen/blog/2023/04/21/LLM-tuning-math) to find example tuning results about gpt-3.5-turbo and gpt-4.*
+*Do the choices matter? Check this [blogpost](/blog/2023/04/21/LLM-tuning-math) to find example tuning results about gpt-3.5-turbo and gpt-4.*
 
 
 With AutoGen, the tuning can be performed with the following information:
@@ -111,7 +111,7 @@ The tuend config can be used to perform inference.
 `autogen.Completion.create` is compatible with both `openai.Completion.create` and `openai.ChatCompletion.create`, and both OpenAI API and Azure OpenAI API. So models such as "text-davinci-003", "gpt-3.5-turbo" and "gpt-4" can share a common API.
 When chat models are used and `prompt` is given as the input to `autogen.Completion.create`, the prompt will be automatically converted into `messages` to fit the chat completion API requirement. One advantage is that one can experiment with both chat and non-chat models for the same prompt in a unified API.
 
-For local LLMs, one can spin up an endpoint using a package like [FastChat](https://github.com/lm-sys/FastChat), and then use the same API to send a request. See [here](https://microsoft.github.io/autogen/blog/2023/07/14/Local-LLMs) for examples on how to make inference with local LLMs.
+For local LLMs, one can spin up an endpoint using a package like [FastChat](https://github.com/lm-sys/FastChat), and then use the same API to send a request. See [here](/blog/2023/07/14/Local-LLMs) for examples on how to make inference with local LLMs.
 
 When only working with the chat-based models, `autogen.ChatCompletion` can be used. It also does automatic conversion from prompt to messages, if prompt is provided instead of messages.
 
@@ -183,7 +183,7 @@ response = autogen.Completion.create(
 
 The example above will try to use text-ada-001, gpt-3.5-turbo, and text-davinci-003 iteratively, until a valid json string is returned or the last config is used. One can also repeat the same model in the list for multiple times to try one model multiple times for increasing the robustness of the final response.
 
-*Advanced use case: Check this [blogpost](https://microsoft.github.io/autogen/blog/2023/05/18/GPT-adaptive-humaneval) to find how to improve GPT-4's coding performance from 68% to 90% while reducing the inference cost.*
+*Advanced use case: Check this [blogpost](/blog/2023/05/18/GPT-adaptive-humaneval) to find how to improve GPT-4's coding performance from 68% to 90% while reducing the inference cost.*
 
 ## Templating