mirror of https://github.com/microsoft/autogen.git
dev6 (#4129)
This commit is contained in:
Eric Zhu
GitHub
parent
4786f189bc
commit
e27c740961
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@ -38,6 +38,7 @@ jobs:
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{ ref: "v0.4.0.dev3", dest-dir: "0.4.0.dev3" },
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{ ref: "v0.4.0.dev4", dest-dir: "0.4.0.dev4" },
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{ ref: "v0.4.0.dev5", dest-dir: "0.4.0.dev5" },
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{ ref: "v0.4.0.dev6", dest-dir: "0.4.0.dev6" },
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]
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steps:
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- name: Checkout
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@ -101,7 +101,7 @@ We look forward to your contributions!
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First install the packages:
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```bash
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pip install 'autogen-agentchat==0.4.0.dev5' 'autogen-ext[openai]==0.4.0.dev5'
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pip install 'autogen-agentchat==0.4.0.dev6' 'autogen-ext[openai]==0.4.0.dev6'
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```
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The following code uses OpenAI's GPT-4o model and you need to provide your
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@ -36,7 +36,12 @@
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{
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"name": "0.4.0.dev5",
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"version": "0.4.0.dev5",
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"url": "/autogen/0.4.0.dev5/",
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"url": "/autogen/0.4.0.dev5/"
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},
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{
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"name": "0.4.0.dev6",
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"version": "0.4.0.dev6",
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"url": "/autogen/0.4.0.dev6/",
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"preferred": true
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}
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]
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@ -4,7 +4,7 @@ build-backend = "hatchling.build"
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[project]
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name = "autogen-agentchat"
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version = "0.4.0.dev5"
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version = "0.4.0.dev6"
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license = {file = "LICENSE-CODE"}
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description = "AutoGen agents and teams library"
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readme = "README.md"
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@ -15,7 +15,7 @@ classifiers = [
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"Operating System :: OS Independent",
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]
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dependencies = [
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"autogen-core==0.4.0.dev5",
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"autogen-core==0.4.0.dev6",
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]
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[tool.uv]
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@ -61,7 +61,7 @@ AgentChat </div>
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High-level API that includes preset agents and teams for building multi-agent systems.
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```sh
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pip install 'autogen-agentchat==0.4.0.dev5'
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pip install 'autogen-agentchat==0.4.0.dev6'
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```
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💡 *Start here if you are looking for an API similar to AutoGen 0.2*
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@ -82,7 +82,7 @@ Get Started
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Provides building blocks for creating asynchronous, event driven multi-agent systems.
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```sh
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pip install 'autogen-core==0.4.0.dev5'
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pip install 'autogen-core==0.4.0.dev6'
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```
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+++
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@ -29,10 +29,10 @@ myst:
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Library that is at a similar level of abstraction as AutoGen 0.2, including default agents and group chat.
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```sh
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pip install 'autogen-agentchat==0.4.0.dev5'
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pip install 'autogen-agentchat==0.4.0.dev6'
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```
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[{fas}`circle-info;pst-color-primary` User Guide](/user-guide/agentchat-user-guide/index.md) | [{fas}`file-code;pst-color-primary` API Reference](/reference/python/autogen_agentchat/autogen_agentchat.rst) | [{fab}`python;pst-color-primary` PyPI](https://pypi.org/project/autogen-agentchat/0.4.0.dev5/) | [{fab}`github;pst-color-primary` Source](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-agentchat)
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[{fas}`circle-info;pst-color-primary` User Guide](/user-guide/agentchat-user-guide/index.md) | [{fas}`file-code;pst-color-primary` API Reference](/reference/python/autogen_agentchat/autogen_agentchat.rst) | [{fab}`python;pst-color-primary` PyPI](https://pypi.org/project/autogen-agentchat/0.4.0.dev6/) | [{fab}`github;pst-color-primary` Source](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-agentchat)
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:::
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(pkg-info-autogen-core)=
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@ -44,10 +44,10 @@ pip install 'autogen-agentchat==0.4.0.dev5'
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Implements the core functionality of the AutoGen framework, providing basic building blocks for creating multi-agent systems.
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```sh
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pip install 'autogen-core==0.4.0.dev5'
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pip install 'autogen-core==0.4.0.dev6'
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```
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[{fas}`circle-info;pst-color-primary` User Guide](/user-guide/core-user-guide/index.md) | [{fas}`file-code;pst-color-primary` API Reference](/reference/python/autogen_core/autogen_core.rst) | [{fab}`python;pst-color-primary` PyPI](https://pypi.org/project/autogen-core/0.4.0.dev5/) | [{fab}`github;pst-color-primary` Source](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-core)
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[{fas}`circle-info;pst-color-primary` User Guide](/user-guide/core-user-guide/index.md) | [{fas}`file-code;pst-color-primary` API Reference](/reference/python/autogen_core/autogen_core.rst) | [{fab}`python;pst-color-primary` PyPI](https://pypi.org/project/autogen-core/0.4.0.dev6/) | [{fab}`github;pst-color-primary` Source](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-core)
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:::
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(pkg-info-autogen-ext)=
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@ -59,7 +59,7 @@ pip install 'autogen-core==0.4.0.dev5'
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Implementations of core components that interface with external services, or use extra dependencies. For example, Docker based code execution.
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```sh
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pip install 'autogen-ext==0.4.0.dev5'
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pip install 'autogen-ext==0.4.0.dev6'
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```
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Extras:
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@ -69,7 +69,7 @@ Extras:
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- `docker` needed for {py:class}`~autogen_ext.code_executors.DockerCommandLineCodeExecutor`
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- `openai` needed for {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`
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[{fas}`circle-info;pst-color-primary` User Guide](/user-guide/extensions-user-guide/index.md) | [{fas}`file-code;pst-color-primary` API Reference](/reference/python/autogen_ext/autogen_ext.rst) | [{fab}`python;pst-color-primary` PyPI](https://pypi.org/project/autogen-ext/0.4.0.dev5/) | [{fab}`github;pst-color-primary` Source](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-ext)
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[{fas}`circle-info;pst-color-primary` User Guide](/user-guide/extensions-user-guide/index.md) | [{fas}`file-code;pst-color-primary` API Reference](/reference/python/autogen_ext/autogen_ext.rst) | [{fab}`python;pst-color-primary` PyPI](https://pypi.org/project/autogen-ext/0.4.0.dev6/) | [{fab}`github;pst-color-primary` Source](https://github.com/microsoft/autogen/tree/main/python/packages/autogen-ext)
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:::
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(pkg-info-autogen-magentic-one)=
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@ -61,7 +61,7 @@ Install the `autogen-agentchat` package using pip:
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```bash
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pip install 'autogen-agentchat==0.4.0.dev5'
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pip install 'autogen-agentchat==0.4.0.dev6'
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```
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## Install OpenAI for Model Client
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@ -70,7 +70,7 @@ To use the OpenAI and Azure OpenAI models, you need to install the following
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extensions:
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||||
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||||
```bash
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pip install 'autogen-ext[openai]==0.4.0.dev5'
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pip install 'autogen-ext[openai]==0.4.0.dev6'
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```
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## Install Docker for Code Execution
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|
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@ -1,160 +1,160 @@
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{
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||||
"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Quickstart"
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||||
]
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```{include} warning.md\n",
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"\n",
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"```\n",
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"\n",
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":::{note}\n",
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||||
"For installation instructions, please refer to the [installation guide](./installation).\n",
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||||
":::\n",
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||||
"\n",
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||||
"In AutoGen AgentChat, you can build applications quickly using preset agents.\n",
|
||||
"To illustrate this, we will begin with creating a team of a single agent\n",
|
||||
"that can use tools and respond to messages.\n",
|
||||
"\n",
|
||||
"The following code uses the OpenAI model. If you haven't already, you need to\n",
|
||||
"install the following package and extension:"
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||||
]
|
||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": null,
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"metadata": {
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||||
"vscode": {
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||||
"languageId": "shellscript"
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||||
}
|
||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"pip install 'autogen-agentchat==0.4.0.dev5' 'autogen-ext[openai]==0.4.0.dev5'"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"To use Azure OpenAI models and AAD authentication,\n",
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||||
"you can follow the instructions [here](./tutorial/models.ipynb#azure-openai)."
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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||||
"outputs": [
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||||
{
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||||
"name": "stdout",
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||||
"output_type": "stream",
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||||
"text": [
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||||
"---------- user ----------\n",
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||||
"What is the weather in New York?\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"[FunctionCall(id='call_AhTZ2q3TNL8x0qs00e3wIZ7y', arguments='{\"city\":\"New York\"}', name='get_weather')]\n",
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||||
"[Prompt tokens: 79, Completion tokens: 15]\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_AhTZ2q3TNL8x0qs00e3wIZ7y')]\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"The weather in New York is currently 73 degrees and sunny.\n",
|
||||
"[Prompt tokens: 90, Completion tokens: 14]\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"TERMINATE\n",
|
||||
"[Prompt tokens: 137, Completion tokens: 4]\n",
|
||||
"---------- Summary ----------\n",
|
||||
"Number of messages: 5\n",
|
||||
"Finish reason: Text 'TERMINATE' mentioned\n",
|
||||
"Total prompt tokens: 306\n",
|
||||
"Total completion tokens: 33\n",
|
||||
"Duration: 1.43 seconds\n"
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||||
]
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||||
}
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||||
],
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||||
"source": [
|
||||
"from autogen_agentchat.agents import AssistantAgent\n",
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||||
"from autogen_agentchat.task import Console, TextMentionTermination\n",
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||||
"from autogen_agentchat.teams import RoundRobinGroupChat\n",
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
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||||
"\n",
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||||
"\n",
|
||||
"# Define a tool\n",
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||||
"async def get_weather(city: str) -> str:\n",
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||||
" return f\"The weather in {city} is 73 degrees and Sunny.\"\n",
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||||
"\n",
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||||
"\n",
|
||||
"async def main() -> None:\n",
|
||||
" # Define an agent\n",
|
||||
" weather_agent = AssistantAgent(\n",
|
||||
" name=\"weather_agent\",\n",
|
||||
" model_client=OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o-2024-08-06\",\n",
|
||||
" # api_key=\"YOUR_API_KEY\",\n",
|
||||
" ),\n",
|
||||
" tools=[get_weather],\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # Define termination condition\n",
|
||||
" termination = TextMentionTermination(\"TERMINATE\")\n",
|
||||
"\n",
|
||||
" # Define a team\n",
|
||||
" agent_team = RoundRobinGroupChat([weather_agent], termination_condition=termination)\n",
|
||||
"\n",
|
||||
" # Run the team and stream messages to the console\n",
|
||||
" stream = agent_team.run_stream(task=\"What is the weather in New York?\")\n",
|
||||
" await Console(stream)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# NOTE: if running this inside a Python script you'll need to use asyncio.run(main()).\n",
|
||||
"await main()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The code snippet above introduces two high level concepts in AgentChat: *Agent* and *Team*. An Agent helps us define what actions are taken when a message is received. Specifically, we use the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset - an agent that can be given access to a model (e.g., LLM) and tools (functions) that it can then use to address tasks. A Team helps us define the rules for how agents interact with each other. In the {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team, agents respond in a sequential round-robin fashion.\n",
|
||||
"In this case, we have a single agent, so the same agent is used for each round."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## What's Next?\n",
|
||||
"\n",
|
||||
"Now that you have a basic understanding of how to define an agent and a team, consider following the [tutorial](./tutorial/index) for a walkthrough on other features of AgentChat.\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Quickstart"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```{include} warning.md\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
":::{note}\n",
|
||||
"For installation instructions, please refer to the [installation guide](./installation).\n",
|
||||
":::\n",
|
||||
"\n",
|
||||
"In AutoGen AgentChat, you can build applications quickly using preset agents.\n",
|
||||
"To illustrate this, we will begin with creating a team of a single agent\n",
|
||||
"that can use tools and respond to messages.\n",
|
||||
"\n",
|
||||
"The following code uses the OpenAI model. If you haven't already, you need to\n",
|
||||
"install the following package and extension:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-agentchat==0.4.0.dev6' 'autogen-ext[openai]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To use Azure OpenAI models and AAD authentication,\n",
|
||||
"you can follow the instructions [here](./tutorial/models.ipynb#azure-openai)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"---------- user ----------\n",
|
||||
"What is the weather in New York?\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"[FunctionCall(id='call_AhTZ2q3TNL8x0qs00e3wIZ7y', arguments='{\"city\":\"New York\"}', name='get_weather')]\n",
|
||||
"[Prompt tokens: 79, Completion tokens: 15]\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_AhTZ2q3TNL8x0qs00e3wIZ7y')]\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"The weather in New York is currently 73 degrees and sunny.\n",
|
||||
"[Prompt tokens: 90, Completion tokens: 14]\n",
|
||||
"---------- weather_agent ----------\n",
|
||||
"TERMINATE\n",
|
||||
"[Prompt tokens: 137, Completion tokens: 4]\n",
|
||||
"---------- Summary ----------\n",
|
||||
"Number of messages: 5\n",
|
||||
"Finish reason: Text 'TERMINATE' mentioned\n",
|
||||
"Total prompt tokens: 306\n",
|
||||
"Total completion tokens: 33\n",
|
||||
"Duration: 1.43 seconds\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from autogen_agentchat.agents import AssistantAgent\n",
|
||||
"from autogen_agentchat.task import Console, TextMentionTermination\n",
|
||||
"from autogen_agentchat.teams import RoundRobinGroupChat\n",
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Define a tool\n",
|
||||
"async def get_weather(city: str) -> str:\n",
|
||||
" return f\"The weather in {city} is 73 degrees and Sunny.\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"async def main() -> None:\n",
|
||||
" # Define an agent\n",
|
||||
" weather_agent = AssistantAgent(\n",
|
||||
" name=\"weather_agent\",\n",
|
||||
" model_client=OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o-2024-08-06\",\n",
|
||||
" # api_key=\"YOUR_API_KEY\",\n",
|
||||
" ),\n",
|
||||
" tools=[get_weather],\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # Define termination condition\n",
|
||||
" termination = TextMentionTermination(\"TERMINATE\")\n",
|
||||
"\n",
|
||||
" # Define a team\n",
|
||||
" agent_team = RoundRobinGroupChat([weather_agent], termination_condition=termination)\n",
|
||||
"\n",
|
||||
" # Run the team and stream messages to the console\n",
|
||||
" stream = agent_team.run_stream(task=\"What is the weather in New York?\")\n",
|
||||
" await Console(stream)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# NOTE: if running this inside a Python script you'll need to use asyncio.run(main()).\n",
|
||||
"await main()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The code snippet above introduces two high level concepts in AgentChat: *Agent* and *Team*. An Agent helps us define what actions are taken when a message is received. Specifically, we use the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset - an agent that can be given access to a model (e.g., LLM) and tools (functions) that it can then use to address tasks. A Team helps us define the rules for how agents interact with each other. In the {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team, agents respond in a sequential round-robin fashion.\n",
|
||||
"In this case, we have a single agent, so the same agent is used for each round."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## What's Next?\n",
|
||||
"\n",
|
||||
"Now that you have a basic understanding of how to define an agent and a team, consider following the [tutorial](./tutorial/index) for a walkthrough on other features of AgentChat.\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,181 +1,181 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Models\n",
|
||||
"\n",
|
||||
"In many cases, agents need access to model services such as OpenAI, Azure OpenAI, and local models.\n",
|
||||
"AgentChat utilizes model clients provided by the\n",
|
||||
"[`autogen-ext`](../../core-user-guide/framework/model-clients.ipynb) package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## OpenAI\n",
|
||||
"\n",
|
||||
"To access OpenAI models, you need to install the `openai` extension to use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai]==0.4.0.dev5'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You will also need to obtain an [API key](https://platform.openai.com/account/api-keys) from OpenAI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
|
||||
"\n",
|
||||
"opneai_model_client = OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o-2024-08-06\",\n",
|
||||
" # api_key=\"sk-...\", # Optional if you have an OPENAI_API_KEY environment variable set.\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To test the model client, you can use the following code:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CreateResult(finish_reason='stop', content='The capital of France is Paris.', usage=RequestUsage(prompt_tokens=15, completion_tokens=7), cached=False, logprobs=None)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from autogen_core.components.models import UserMessage\n",
|
||||
"\n",
|
||||
"result = await opneai_model_client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\n",
|
||||
"print(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Azure OpenAI\n",
|
||||
"\n",
|
||||
"Install the `azure` and `openai` extensions to use the {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai,azure]==0.4.0.dev5'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To use the client, you need to provide your deployment id, Azure Cognitive Services endpoint, api version, and model capabilities.\n",
|
||||
"For authentication, you can either provide an API key or an Azure Active Directory (AAD) token credential.\n",
|
||||
"\n",
|
||||
"The following code snippet shows how to use AAD authentication.\n",
|
||||
"The identity used must be assigned the [Cognitive Services OpenAI User](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-user) role."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import AzureOpenAIChatCompletionClient\n",
|
||||
"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
|
||||
"\n",
|
||||
"# Create the token provider\n",
|
||||
"token_provider = get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\")\n",
|
||||
"\n",
|
||||
"az_model_client = AzureOpenAIChatCompletionClient(\n",
|
||||
" model=\"{your-azure-deployment}\",\n",
|
||||
" api_version=\"2024-06-01\",\n",
|
||||
" azure_endpoint=\"https://{your-custom-endpoint}.openai.azure.com/\",\n",
|
||||
" azure_ad_token_provider=token_provider, # Optional if you choose key-based authentication.\n",
|
||||
" # api_key=\"sk-...\", # For key-based authentication.\n",
|
||||
" model_capabilities={\n",
|
||||
" \"vision\": True,\n",
|
||||
" \"function_calling\": True,\n",
|
||||
" \"json_output\": True,\n",
|
||||
" },\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"See [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/managed-identity#chat-completions) for how to use the Azure client directly or for more info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Local Models\n",
|
||||
"\n",
|
||||
"We are working on it. Stay tuned!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Models\n",
|
||||
"\n",
|
||||
"In many cases, agents need access to model services such as OpenAI, Azure OpenAI, and local models.\n",
|
||||
"AgentChat utilizes model clients provided by the\n",
|
||||
"[`autogen-ext`](../../core-user-guide/framework/model-clients.ipynb) package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## OpenAI\n",
|
||||
"\n",
|
||||
"To access OpenAI models, you need to install the `openai` extension to use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You will also need to obtain an [API key](https://platform.openai.com/account/api-keys) from OpenAI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
|
||||
"\n",
|
||||
"opneai_model_client = OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o-2024-08-06\",\n",
|
||||
" # api_key=\"sk-...\", # Optional if you have an OPENAI_API_KEY environment variable set.\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To test the model client, you can use the following code:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CreateResult(finish_reason='stop', content='The capital of France is Paris.', usage=RequestUsage(prompt_tokens=15, completion_tokens=7), cached=False, logprobs=None)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from autogen_core.components.models import UserMessage\n",
|
||||
"\n",
|
||||
"result = await opneai_model_client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\n",
|
||||
"print(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Azure OpenAI\n",
|
||||
"\n",
|
||||
"Install the `azure` and `openai` extensions to use the {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai,azure]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To use the client, you need to provide your deployment id, Azure Cognitive Services endpoint, api version, and model capabilities.\n",
|
||||
"For authentication, you can either provide an API key or an Azure Active Directory (AAD) token credential.\n",
|
||||
"\n",
|
||||
"The following code snippet shows how to use AAD authentication.\n",
|
||||
"The identity used must be assigned the [Cognitive Services OpenAI User](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-user) role."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import AzureOpenAIChatCompletionClient\n",
|
||||
"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
|
||||
"\n",
|
||||
"# Create the token provider\n",
|
||||
"token_provider = get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\")\n",
|
||||
"\n",
|
||||
"az_model_client = AzureOpenAIChatCompletionClient(\n",
|
||||
" model=\"{your-azure-deployment}\",\n",
|
||||
" api_version=\"2024-06-01\",\n",
|
||||
" azure_endpoint=\"https://{your-custom-endpoint}.openai.azure.com/\",\n",
|
||||
" azure_ad_token_provider=token_provider, # Optional if you choose key-based authentication.\n",
|
||||
" # api_key=\"sk-...\", # For key-based authentication.\n",
|
||||
" model_capabilities={\n",
|
||||
" \"vision\": True,\n",
|
||||
" \"function_calling\": True,\n",
|
||||
" \"json_output\": True,\n",
|
||||
" },\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"See [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/managed-identity#chat-completions) for how to use the Azure client directly or for more info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Local Models\n",
|
||||
"\n",
|
||||
"We are working on it. Stay tuned!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
|||
|
||||
[project]
|
||||
name = "autogen-core"
|
||||
version = "0.4.0.dev5"
|
||||
version = "0.4.0.dev6"
|
||||
license = {file = "LICENSE-CODE"}
|
||||
description = "Foundational interfaces and agent runtime implementation for AutoGen"
|
||||
readme = "README.md"
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ build-backend = "hatchling.build"
|
|||
|
||||
[project]
|
||||
name = "autogen-ext"
|
||||
version = "0.4.0.dev5"
|
||||
version = "0.4.0.dev6"
|
||||
license = {file = "LICENSE-CODE"}
|
||||
description = "AutoGen extensions library"
|
||||
readme = "README.md"
|
||||
|
|
@ -15,7 +15,7 @@ classifiers = [
|
|||
"Operating System :: OS Independent",
|
||||
]
|
||||
dependencies = [
|
||||
"autogen-core==0.4.0.dev5",
|
||||
"autogen-core==0.4.0.dev6",
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -330,7 +330,7 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "autogen-agentchat"
|
||||
version = "0.4.0.dev5"
|
||||
version = "0.4.0.dev6"
|
||||
source = { editable = "packages/autogen-agentchat" }
|
||||
dependencies = [
|
||||
{ name = "autogen-core" },
|
||||
|
|
@ -341,7 +341,7 @@ requires-dist = [{ name = "autogen-core", editable = "packages/autogen-core" }]
|
|||
|
||||
[[package]]
|
||||
name = "autogen-core"
|
||||
version = "0.4.0.dev5"
|
||||
version = "0.4.0.dev6"
|
||||
source = { editable = "packages/autogen-core" }
|
||||
dependencies = [
|
||||
{ name = "aiohttp" },
|
||||
|
|
@ -454,7 +454,7 @@ dev = [
|
|||
|
||||
[[package]]
|
||||
name = "autogen-ext"
|
||||
version = "0.4.0.dev5"
|
||||
version = "0.4.0.dev6"
|
||||
source = { editable = "packages/autogen-ext" }
|
||||
dependencies = [
|
||||
{ name = "autogen-core" },
|
||||
|
|
|
|||
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Reference in New Issue