Corrected framework guide docs (#3929)

* Corrected grammatical errors and typos

* Corrected formating issues
This commit is contained in:
Rohan Thacker
2024-10-24 02:12:40 +05:30
committed by GitHub
parent 8f4d5ee5ec
commit fb494534b8
7 changed files with 16 additions and 16 deletions

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@@ -41,10 +41,10 @@
"source": [
"## Implementing an Agent\n",
"\n",
"To implement an agent, developer must subclass the {py:class}`~autogen_core.base.BaseAgent` class\n",
"To implement an agent, the developer must subclass the {py:class}`~autogen_core.base.BaseAgent` class\n",
"and implement the {py:meth}`~autogen_core.base.BaseAgent.on_message` method.\n",
"This method is invoked when the agent receives a message. For example,\n",
"the following agent handles a simple message type and simply prints message it receives:"
"the following agent handles a simple message type and prints the message it receives:"
]
},
{
@@ -101,7 +101,7 @@
"The factory function is expected to return an instance of the agent class \n",
"on which the {py:meth}`~autogen_core.base.BaseAgent.register` class method is invoked.\n",
"Read [Agent Identity and Lifecycles](../core-concepts/agent-identity-and-lifecycle.md)\n",
"about agent type an identity.\n",
"to learn more about agent type and identity.\n",
"\n",
"```{note}\n",
"Different agent types can be registered with factory functions that return \n",

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@@ -71,7 +71,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combining Application in Docker with a Docker based executor\n",
"### Combining an Application in Docker with a Docker based executor\n",
"\n",
"It is desirable to bundle your application into a Docker image. But then, how do you allow your containerised application to execute code in a different container?\n",
"\n",

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@@ -40,11 +40,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The above code starts the host service in the background and accepting\n",
"worker connections at port 50051.\n",
"The above code starts the host service in the background and accepts\n",
"worker connections on port 50051.\n",
"\n",
"Before running worker runtimes, let's define our agent.\n",
"The agent publishes a new message on every message it receives.\n",
"The agent will publish a new message on every message it receives.\n",
"It also keeps track of how many messages it has published, and \n",
"stops publishing new messages once it has published 5 messages."
]

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@@ -6,10 +6,10 @@ There are two kinds of logging:
- **Trace logging**: This is used for debugging and is human readable messages to indicate what is going on. This is intended for a developer to understand what is happening in the code. The content and format of these logs should not be depended on by other systems.
- Name: {py:attr}`~autogen_core.application.logging.TRACE_LOGGER_NAME`.
- **Structured logging**: This logger emits structured events that can be consumed by other systems. The content and format of these logs should be can be depended on by other systems.
- **Structured logging**: This logger emits structured events that can be consumed by other systems. The content and format of these logs can be depended on by other systems.
- Name: {py:attr}`~autogen_core.application.logging.EVENT_LOGGER_NAME`.
- See the module {py:mod}`autogen_core.application.logging.events` to see the available events.
- {py:attr}`~autogen_core.application.logging.ROOT_LOGGER` can be used to enable or disable all logs at the same time.
- {py:attr}`~autogen_core.application.logging.ROOT_LOGGER` can be used to enable or disable all logs.
## Enabling logging output

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@@ -477,7 +477,7 @@
"metadata": {},
"source": [
"Subscriptions are registered with the agent runtime, either as part of\n",
"agent type's registeration or through a separate API method.\n",
"agent type's registration or through a separate API method.\n",
"Here is how we register {py:class}`~autogen_core.components.TypeSubscription`\n",
"for the receiving agent with the {py:meth}`~autogen_core.components.type_subscription` decorator,\n",
"and for the broadcasting agent without the decorator."
@@ -544,7 +544,7 @@
"However, when there is a single scope of publishing, that is, \n",
"all agents publish and subscribe to all broadcasted messages,\n",
"we can use the convenience classes {py:class}`~autogen_core.components.DefaultTopicId`\n",
"and {py:meth}`~autogen_core.components.default_subscription` to simply our code.\n",
"and {py:meth}`~autogen_core.components.default_subscription` to simplify our code.\n",
"\n",
"{py:class}`~autogen_core.components.DefaultTopicId` is\n",
"for creating a topic that uses `\"default\"` as the default value for the topic type\n",

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@@ -18,11 +18,11 @@
"## Built-in Model Clients\n",
"\n",
"Currently there are two built-in model clients:\n",
"{py:class}~autogen_ext.models.OpenAIChatCompletionClient` and\n",
"{py:class}`~autogen_ext.models.OpenAIChatCompletionClient` and\n",
"{py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`.\n",
"Both clients are asynchronous.\n",
"\n",
"To use the {py:class}~autogen_ext.models.OpenAIChatCompletionClient`, you need to provide the API key\n",
"To use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`, you need to provide the API key\n",
"either through the environment variable `OPENAI_API_KEY` or through the `api_key` argument."
]
},
@@ -45,7 +45,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"You can call the {py:meth}~autogen_ext.models.OpenAIChatCompletionClient.create` method to create a\n",
"You can call the {py:meth}`~autogen_ext.models.OpenAIChatCompletionClient.create` method to create a\n",
"chat completion request, and await for an {py:class}`~autogen_core.components.models.CreateResult` object in return."
]
},
@@ -79,7 +79,7 @@
"source": [
"### Streaming Response\n",
"\n",
"You can use the {py:meth}~autogen_ext.models.OpenAIChatCompletionClient.create_streaming` method to create a\n",
"You can use the {py:meth}`~autogen_ext.models.OpenAIChatCompletionClient.create_streaming` method to create a\n",
"chat completion request with streaming response."
]
},

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@@ -8,7 +8,7 @@
"\n",
"Tools are code that can be executed by an agent to perform actions. A tool\n",
"can be a simple function such as a calculator, or an API call to a third-party service\n",
"such as stock price lookup and weather forecast.\n",
"such as stock price lookup or weather forecast.\n",
"In the context of AI agents, tools are designed to be executed by agents in\n",
"response to model-generated function calls.\n",
"\n",