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westey
2025-10-01 17:43:17 +01:00
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python/samples/SAMPLE_GUIDELINES.md
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@@ -22,14 +22,6 @@ Try not to include too many concepts in a single sample. The goal is to demonstr
The getting started samples are the simplest samples that require minimal setup. These samples should be named in the following format: `step<number>_<name>.py`. One exception to this rule is when the sample is a notebook, in which case the sample should be named in the following format: `<number>_<name>.ipynb`.
#### Concept Samples
Concept samples under [./concepts](./concepts) should be grouped by feature or capability. These samples should be relatively short and demonstrate a specific concept. These samples are more advanced than the getting started samples.
#### Demos
Demos under [./demos](./demos) are full console applications that demonstrate a specific set of features or capabilities of Agent Framework, potentially with external dependencies. Each of the demos should have a README.md file that explains the purpose of the demo and how to run it.
### **Incremental Complexity**
Try to do a best effort to make sure that the samples are incremental in complexity. For example, in the getting started samples, each step should build on the previous step, and the concept samples should build on the getting started samples, same with the demos.
python/samples/getting_started/observability/README.md
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@@ -106,7 +106,7 @@ This folder contains different samples demonstrating how to use telemetry in var
| Sample | Description |
|--------|-------------|
| [setup_observability_with_parameters.py](./setup_observability_with_parameters.py) | A simple example showing how to setup telemetry by passing in parameters to the `setup_observability()` function. |
| [setup_observability_with_env_vars.py](./setup_observability_with_env_vars.py) | A simple example showing how to setup telemetry with the `setup_observability()` function using environment variables. |
| [setup_observability_with_env_var.py](./setup_observability_with_env_var.py) | A simple example showing how to setup telemetry with the `setup_observability()` function using environment variables. |
| [agent_observability.py](./agent_observability.py) | A simple example showing how to setup telemetry for an agentic application. |
| [azure_ai_agent_observability.py](./azure_ai_agent_observability.py) | A simple example showing how to setup telemetry for an agentic application with an Azure AI project. |
| [azure_ai_chat_client_with_observability.py](./azure_ai_chat_client_with_observability.py) | A simple example showing how to setup telemetry for a chat client with an Azure AI project. |
@@ -244,4 +244,4 @@ Once your sample finishes running, navigate to <http://localhost:18888> in a web
You won't have to deploy an Application Insights resource or install Docker to run Aspire Dashboard if you choose to inspect telemetry data in a console. However, it is difficult to navigate through all the spans and logs produced, so **this method is only recommended when you are just getting started**.
Use the guides from OpenTelemetry to setup exporters for [the console](https://opentelemetry.io/docs/languages/python/getting-started/), or use [manual_setup_console_output](./manual_setup_console_output.py) as a reference, just know that there are a lot of options you can setup and this is not a comprehensive example.
Use the guides from OpenTelemetry to setup exporters for [the console](https://opentelemetry.io/docs/languages/python/getting-started/), or use [advanced_manual_setup_console_output](./advanced_manual_setup_console_output.py) as a reference, just know that there are a lot of options you can setup and this is not a comprehensive example.
python/samples/getting_started/workflows/README.md
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@@ -36,7 +36,8 @@ Once comfortable with these, explore the rest of the samples below.
|---|---|---|
| Azure Chat Agents (Streaming) | [agents/azure_chat_agents_streaming.py](./agents/azure_chat_agents_streaming.py) | Add Azure agents as edges and handle streaming events |
| Custom Agent Executors | [agents/custom_agent_executors.py](./agents/custom_agent_executors.py) | Create executors to handle agent run methods |
| Azure AI Chat Agents (Streaming) | [agents/azure_ai_chat_agents_streaming.py](./agents/azure_ai_chat_agents_streaming.py) | Add Azure AI agents as edges and handle streaming events |
| Azure AI Chat Agents (Streaming) | [agents/azure_ai_agents_streaming.py](./agents/azure_ai_agents_streaming.py) | Add Azure AI agents as edges and handle streaming events |
| Azure Chat Agents (Streaming) | [agents/azure_chat_agents_streaming.py](./agents/azure_chat_agents_streaming.py) | Add Azure Chat agents as edges and handle streaming events |
| Workflow as Agent (Reflection Pattern) | [agents/workflow_as_agent_reflection_pattern.py](./agents/workflow_as_agent_reflection_pattern.py) | Wrap a workflow so it can behave like an agent (reflection pattern) |
| Workflow as Agent + HITL | [agents/workflow_as_agent_human_in_the_loop.py](./agents/workflow_as_agent_human_in_the_loop.py) | Extend workflow-as-agent with human-in-the-loop capability |