Fix for blog links due to docs site change.

ag2ai · Dec 20, 2024 · 4ce03c6 · 4ce03c6
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diff --git a/README.md b/README.md
@@ -47,7 +47,7 @@ We adopt the Apache 2.0 license from v0.3. This enhances our commitment to open-
 
 :tada: Apr 17, 2024: Andrew Ng cited AutoGen in [The Batch newsletter](https://www.deeplearning.ai/the-batch/issue-245/) and [What's next for AI agentic workflows](https://youtu.be/sal78ACtGTc?si=JduUzN_1kDnMq0vF) at Sequoia Capital's AI Ascent (Mar 26).
 
-:tada: Mar 3, 2024: What's new in AutoGen? 📰[Blog](https://ag2ai.github.io/ag2/blog/2024/03/03/AutoGen-Update); 📺[Youtube](https://www.youtube.com/watch?v=j_mtwQiaLGU).
+:tada: Mar 3, 2024: What's new in AutoGen? 📰[Blog](https://docs.ag2.ai/blog/2024-03-03-AutoGen-Update); 📺[Youtube](https://www.youtube.com/watch?v=j_mtwQiaLGU).
 
 <!-- :tada: Mar 1, 2024: the first AutoGen multi-agent experiment on the challenging [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) benchmark achieved the No. 1 accuracy in all the three levels. -->
 
@@ -57,7 +57,7 @@ We adopt the Apache 2.0 license from v0.3. This enhances our commitment to open-
 
 <!-- :fire: Nov 24: pyautogen [v0.2](https://github.com/ag2ai/ag2/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://ag2ai.github.io/ag2/docs/Installation#python). -->
 
-<!-- :fire: Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://ag2ai.github.io/ag2/blog/2023/11/13/OAI-assistants) for details and examples. -->
+<!-- :fire: Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://docs.ag2.ai/blog/2023-11-13-OAI-assistants) for details and examples. -->
 
 :tada: Nov 8, 2023: AutoGen is selected into [Open100: Top 100 Open Source achievements](https://www.benchcouncil.org/evaluation/opencs/annual.html) 35 days after spinoff from [FLAML](https://github.com/microsoft/FLAML).
 
@@ -234,7 +234,7 @@ You can find detailed documentation about AG2 [here](https://ag2ai.github.io/ag2
 
 In addition, you can find:
 
-- [Research](https://ag2ai.github.io/ag2/docs/Research), [blogposts](https://ag2ai.github.io/ag2/blog) around AG2, and [Transparency FAQs](https://github.com/ag2ai/ag2/blob/main/TRANSPARENCY_FAQS.md)
+- [Research](https://ag2ai.github.io/ag2/docs/Research), [blogposts](https://docs.ag2.ai/blog) around AG2, and [Transparency FAQs](https://github.com/ag2ai/ag2/blob/main/TRANSPARENCY_FAQS.md)
 
 - [Discord](https://discord.gg/pAbnFJrkgZ)
 

diff --git a/autogen/agentchat/contrib/agent_eval/README.md b/autogen/agentchat/contrib/agent_eval/README.md
@@ -1,7 +1,7 @@
-Agents for running the [AgentEval](https://ag2ai.github.io/ag2/blog/2023/11/20/AgentEval/) pipeline.
+Agents for running the [AgentEval](https://docs.ag2.ai/blog/2023-11-20-AgentEval/) pipeline.
 
 AgentEval is a process for evaluating a LLM-based system's performance on a given task.
 
 When given a task to evaluate and a few example runs, the critic and subcritic agents create evaluation criteria for evaluating a system's solution. Once the criteria has been created, the quantifier agent can evaluate subsequent task solutions based on the generated criteria.
 
-See our [blog post](https://ag2ai.github.io/ag2/blog/2024/06/21/AgentEval) for usage examples and general explanations.
+See our [blog post](https://docs.ag2.ai/blog/2024-06-21-AgentEval) for usage examples and general explanations.
diff --git a/notebook/JSON_mode_example.ipynb b/notebook/JSON_mode_example.ipynb
@@ -19,7 +19,7 @@
     "\n",
     "\n",
     "Please find documentation about this feature in OpenAI  [here](https://platform.openai.com/docs/guides/text-generation/json-mode).\n",
-    "More information about Agent Descriptions is located [here](https://ag2ai.github.io/ag2/blog/2023/12/29/AgentDescriptions/)\n",
+    "More information about Agent Descriptions is located [here](https://docs.ag2.ai/blog/2023-12-29-AgentDescriptions/)\n",
     "\n",
     "Benefits\n",
     "- This contribution provides a method to implement precise speaker transitions based on content of the input message. The example can prevent Prompt hacks that use coersive language.\n",

diff --git a/notebook/agentchat_MathChat.ipynb b/notebook/agentchat_MathChat.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation. Please find documentation about this feature [here](https://ag2ai.github.io/ag2/docs/Use-Cases/agent_chat).\n",
     "\n",
-    "MathChat is an experimental conversational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://ag2ai.github.io/ag2/blog/2023/06/28/MathChat).\n",
+    "MathChat is an experimental conversational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://docs.ag2.ai/blog/2023-06-28-MathChat).\n",
     "\n",
     "````{=mdx}\n",
     ":::info Requirements\n",

diff --git a/notebook/agentchat_realtime_swarm.ipynb b/notebook/agentchat_realtime_swarm.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "AG2 supports **RealtimeAgent**, a powerful agent type that connects seamlessly to OpenAI's [Realtime API](https://openai.com/index/introducing-the-realtime-api). With RealtimeAgent, you can add voice interaction and listening capabilities to your swarms, enabling dynamic and natural communication.\n",
     "\n",
-    "AG2 provides an intuitive programming interface to build and orchestrate swarms of agents. With RealtimeAgent, you can enhance swarm functionality, integrating real-time interactions alongside task automation. Check the [Documentation](https://ag2ai.github.io/ag2/docs/topics/swarm) and [Blog](https://ag2ai.github.io/ag2/blog/2024/11/17/Swarm) for further insights.\n",
+    "AG2 provides an intuitive programming interface to build and orchestrate swarms of agents. With RealtimeAgent, you can enhance swarm functionality, integrating real-time interactions alongside task automation. Check the [Documentation](https://ag2ai.github.io/ag2/docs/topics/swarm) and [Blog](https://docs.ag2.ai/blog/2024-11-17-Swarm) for further insights.\n",
     "\n",
     "In this notebook, we implement OpenAI's [airline customer service example](https://github.com/openai/swarm/tree/main/examples/airline) in AG2 using the RealtimeAgent for enhanced interaction."
    ]

diff --git a/notebook/agentchat_swarm.ipynb b/notebook/agentchat_swarm.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "AG2 offers conversable agents, powered by LLMs, tools or a human, that can perform tasks collectively via an automated chat. Recently, OpenAI released a [Swarm](https://github.com/openai/swarm) framework that focuses on making agent coordination and execution lightweight. \n",
     "\n",
-    "In AG2 we offer a simple programming interface to build and orchestrate a swarm of agents. Please check the [Documentation](https://ag2ai.github.io/ag2/docs/topics/swarm) and [Blog](https://ag2ai.github.io/ag2/blog/2024/11/17/Swarm) for more details.\n",
+    "In AG2 we offer a simple programming interface to build and orchestrate a swarm of agents. Please check the [Documentation](https://ag2ai.github.io/ag2/docs/topics/swarm) and [Blog](https://docs.ag2.ai/blog/2024-11-17-Swarm) for more details.\n",
     "\n",
     "After learning the fundamentals of AG2's swarm in this notebook, check out [this notebook](https://ag2ai.github.io/ag2/docs/notebooks/agentchat_swarm) where we take on some more advanced techniques that provide greater control and predicability for your swarms.\n",
     "\n",

diff --git a/notebook/agentchat_swarm_w_groupchat_legacy.ipynb b/notebook/agentchat_swarm_w_groupchat_legacy.ipynb
@@ -23,7 +23,7 @@
     "\n",
     "AG2 offers conversable agents, powered by LLMs, tools or a human, that can perform tasks collectively via an automated chat. Recently, OpenAI has released a [Swarm](https://github.com/openai/swarm) framework that focuses on making agent coordination and execution lightweight.\n",
     "\n",
-    "In AG2, the groupchat allows customized speaker selection, which can be used to achieve the same orchestration pattern. This feature is also supported by our research paper [StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows](https://ag2ai.github.io/ag2/blog/2024/02/29/StateFlow).\n",
+    "In AG2, the groupchat allows customized speaker selection, which can be used to achieve the same orchestration pattern. This feature is also supported by our research paper [StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows](https://docs.ag2.ai/blog/2024-02-29-StateFlow).\n",
     "\n",
     "In this notebook, we implement OpenAI's [airline customer service example](https://github.com/openai/swarm/tree/main/examples/airline) in AG2 using group chat."
    ]

diff --git a/notebook/agenteval_cq_math.ipynb b/notebook/agenteval_cq_math.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "![AgentEval](https://media.githubusercontent.com/media/ag2ai/ag2/main/website/blog/2023-11-20-AgentEval/img/agenteval-CQ.png)\n",
     "\n",
-    "For more detailed explanations, please refer to the accompanying [blog post](https://ag2ai.github.io/ag2/blog/2023/11/20/AgentEval)\n",
+    "For more detailed explanations, please refer to the accompanying [blog post](https://docs.ag2.ai/blog/2023-11-20-AgentEval)\n",
     "\n",
     "## Requirements\n",
     "\n",

diff --git a/notebook/autogen_uniformed_api_calling.ipynb b/notebook/autogen_uniformed_api_calling.ipynb
@@ -22,7 +22,7 @@
     "\n",
     "... and more to come!\n",
     "\n",
-    "You can also [plug in your local deployed LLM](https://ag2ai.github.io/ag2/blog/2024/01/26/Custom-Models) into AutoGen if needed."
+    "You can also [plug in your local deployed LLM](https://docs.ag2.ai/blog/2024-01-26-Custom-Models) into AutoGen if needed."
    ]
   },
   {

diff --git a/notebook/oai_chatgpt_gpt4.ipynb b/notebook/oai_chatgpt_gpt4.ipynb
@@ -33,7 +33,7 @@
     "\n",
     "In this notebook, we tune OpenAI ChatGPT (both GPT-3.5 and GPT-4) models for math problem solving. We use [the MATH benchmark](https://crfm.stanford.edu/helm/latest/?group=math_chain_of_thought) for measuring mathematical problem solving on competition math problems with chain-of-thoughts style reasoning.\n",
     "\n",
-    "Related link: [Blogpost](https://ag2ai.github.io/ag2/blog/2023/04/21/LLM-tuning-math) based on this experiment.\n",
+    "Related link: [Blogpost](https://docs.ag2.ai/blog/2023-04-21-LLM-tuning-math) based on this experiment.\n",
     "\n",
     "## Requirements\n",
     "\n",
-Original file line number
+Diff line change
@@ Expand Up / @@ -22,7 +22,7 @@ @@
         "\n",
         "... and more to come!\n",
         "\n",
-        "You can also [plug in your local deployed LLM](https://ag2ai.github.io/ag2/blog/2024/01/26/Custom-Models) into AutoGen if needed."
+        "You can also [plug in your local deployed LLM](https://docs.ag2.ai/blog/2024-01-26-Custom-Models) into AutoGen if needed."
        ]
       },
       {
@@ Expand Down @@