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* update intro * update intro * tutorial * update notebook * update notebooks * update * merge * add conversation patterns * rename; delete unused files. * Reorganize new guides * Improve intro, fix typos * add what is next * outline for code executor * initiate chats png * Improve language * Improve language of human in the loop tutorial * update * update * Update group chat * code executor * update convsersation patterns * update code executor section to use legacy code executor * update conversation pattern * redirect * update figures * update whats next * Break down chapter 2 into two chapters * udpate * fix website build * Minor corrections of typos and grammar. * remove broken links, update sidebar * code executor update * Suggest changes to the code executor section * update what is next * reorder * update getting started * title * update navbar * Delete website/docs/tutorial/what-is-next.ipynb * update conversable patterns * Improve language * Fix typo * minor fixes --------- Co-authored-by: Jack Gerrits <jack@jackgerrits.com> Co-authored-by: gagb <gagb@users.noreply.github.com> Co-authored-by: Joshua Kim <joshua@spectdata.com> Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
41 lines
1.8 KiB
Markdown
41 lines
1.8 KiB
Markdown
# What is Next?
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Now that you have learned the basics of AutoGen, you can start to build your own
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agents. Here are some ideas to get you started without going to the advanced
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topics:
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1. **Chat with LLMs**: In [Human in the Loop](./human-in-the-loop) we covered
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the basic human-in-the-loop usage. You can try to hook up different LLMs
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using proxy servers like [Ollama](https://github.com/ollama/ollama), and
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chat with them using the human-in-the-loop component of your human proxy
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agent.
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2. **Prompt Engineering**: In [Code Executors](./code-executors) we
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covered the simple two agent scenario using GPT-4 and Python code executor.
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To make this scenario work for different LLMs and programming languages, you
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probably need to tune the system message of the code writer agent. Same with
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other scenarios that we have covered in this tutorial, you can also try to
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tune system messages for different LLMs.
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3. **Complex Tasks**: In [ConversationPatterns](./conversation-patterns)
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we covered the basic conversation patterns. You can try to find other tasks
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that can be decomposed into these patterns, and leverage the code executors
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to make the agents more powerful.
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## Dig Deeper
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- Read the [topic docs](/docs/topics) to learn more
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- Read the examples and guides in the [notebooks section](/docs/notebooks)
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## Get Help
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If you have any questions, you can ask in our [GitHub
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Discussions](https://github.com/microsoft/autogen/discussions), or join
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our [Discord Server](https://discord.gg/pAbnFJrkgZ).
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[](https://discord.gg/pAbnFJrkgZ)
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## Get Involved
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- Contribute your work to our [gallery](../Gallery)
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- Follow our [contribution guide](../Contribute) to make a pull request to AutoGen
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- You can also share your work with the community on the Discord server.
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