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docs: split apart installation docs into parts (#1420)
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* docs: split apart installation file into parts

* missing newline

* fix link

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Co-authored-by: Chi Wang <[email protected]>
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jackgerrits and sonichi authored Jan 27, 2024
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2 changes: 1 addition & 1 deletion website/docs/FAQ.md
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Expand Up @@ -89,7 +89,7 @@ The `AssistantAgent` doesn't save all the code by default, because there are cas

We strongly recommend using docker to execute code. There are two ways to use docker:

1. Run AutoGen in a docker container. For example, when developing in [GitHub codespace](https://codespaces.new/microsoft/autogen?quickstart=1), AutoGen runs in a docker container. If you are not developing in Github codespace, follow instructions [here](Installation.md#option-1-install-and-run-autogen-in-docker) to install and run AutoGen in docker.
1. Run AutoGen in a docker container. For example, when developing in [GitHub codespace](https://codespaces.new/microsoft/autogen?quickstart=1), AutoGen runs in a docker container. If you are not developing in Github codespace, follow instructions [here](installation/Docker.md#option-1-install-and-run-autogen-in-docker) to install and run AutoGen in docker.
2. Run AutoGen outside of a docker, while performing code execution with a docker container. For this option, make sure docker is up and running. If you want to run the code locally (not recommended) then `use_docker` can be set to `False` in `code_execution_config` for each code-execution agent, or set `AUTOGEN_USE_DOCKER` to `False` as an environment variable.

### Enable Python 3 docker image
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306 changes: 0 additions & 306 deletions website/docs/Installation.md

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23 changes: 23 additions & 0 deletions website/docs/Migration-Guide.md
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# Migration Guide

## Migrating to 0.2

openai v1 is a total rewrite of the library with many breaking changes. For example, the inference requires instantiating a client, instead of using a global class method.
Therefore, some changes are required for users of `pyautogen<0.2`.

- `api_base` -> `base_url`, `request_timeout` -> `timeout` in `llm_config` and `config_list`. `max_retry_period` and `retry_wait_time` are deprecated. `max_retries` can be set for each client.
- MathChat is unsupported until it is tested in future release.
- `autogen.Completion` and `autogen.ChatCompletion` are deprecated. The essential functionalities are moved to `autogen.OpenAIWrapper`:

```python
from autogen import OpenAIWrapper
client = OpenAIWrapper(config_list=config_list)
response = client.create(messages=[{"role": "user", "content": "2+2="}])
print(client.extract_text_or_completion_object(response))
```

- Inference parameter tuning and inference logging features are currently unavailable in `OpenAIWrapper`. Logging will be added in a future release.
Inference parameter tuning can be done via [`flaml.tune`](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
- `seed` in autogen is renamed into `cache_seed` to accommodate the newly added `seed` param in openai chat completion api. `use_cache` is removed as a kwarg in `OpenAIWrapper.create()` for being automatically decided by `cache_seed`: int | None. The difference between autogen's `cache_seed` and openai's `seed` is that:
- autogen uses local disk cache to guarantee the exactly same output is produced for the same input and when cache is hit, no openai api call will be made.
- openai's `seed` is a best-effort deterministic sampling with no guarantee of determinism. When using openai's `seed` with `cache_seed` set to None, even for the same input, an openai api call will be made and there is no guarantee for getting exactly the same output.
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