Change README instructions to run all samples from root dir (#213)

* AI: make all samples run from root dir

* Add poe to dev group

Author: dandavison

README.md

@@ -84,10 +84,6 @@ Some examples require extra dependencies. See each sample's directory for specif
 
 ## Test
 
-Running the tests requires `poe` to be installed.
+To run the tests:
 
-    uv tool install poethepoet
-
-Once you have `poe` installed you can run:
-
-    poe test
+    uv run poe test

activity_worker/README.md

@@ -6,8 +6,8 @@ First run the Go workflow worker by running this in the `go_workflow` directory
 
     go run .
 
-Then in another terminal, run the sample from this directory:
+Then in another terminal, run the sample from the root directory:
 
-    uv run activity_worker.py
+    uv run activity_worker/activity_worker.py
 
 The Python code will invoke the Go workflow which will execute the Python activity and return.

bedrock/basic/README.md

@@ -2,9 +2,9 @@
 
 A basic Bedrock workflow. Starts a workflow with a prompt, generates a response and ends the workflow.
 
-To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
+To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from the root directory:
 
-1. Run the worker: `uv run run_worker.py`
+1. Run the worker: `uv run bedrock/basic/run_worker.py`
 2. In another terminal run the client with a prompt:
 
-    e.g. `uv run send_message.py 'What animals are marsupials?'`
+    e.g. `uv run bedrock/basic/send_message.py 'What animals are marsupials?'`

bedrock/entity/README.md

@@ -2,18 +2,18 @@
 
 Multi-Turn Chat using an Entity Workflow. The workflow runs forever unless explicitly ended. The workflow continues as new after a configurable number of chat turns to keep the prompt size small and the Temporal event history small. Each continued-as-new workflow receives a summary of the conversation history so far for context.
 
-To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
+To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from the root directory:
 
-1. Run the worker: `uv run run_worker.py`
+1. Run the worker: `uv run bedrock/entity/run_worker.py`
 2. In another terminal run the client with a prompt.
 
-    Example: `uv run send_message.py 'What animals are marsupials?'`
+    Example: `uv run bedrock/entity/send_message.py 'What animals are marsupials?'`
 
 3. View the worker's output for the response.
 4. Give followup prompts by signaling the workflow.
 
-    Example: `uv run send_message.py 'Do they lay eggs?'`
+    Example: `uv run bedrock/entity/send_message.py 'Do they lay eggs?'`
 5. Get the conversation history summary by querying the workflow.
 
-    Example: `uv run get_history.py`
-6. To end the chat session, run `uv run end_chat.py`
+    Example: `uv run bedrock/entity/get_history.py`
+6. To end the chat session, run `uv run bedrock/entity/end_chat.py`

bedrock/signals_and_queries/README.md

@@ -2,18 +2,18 @@
 
 Adding signals & queries to the [basic Bedrock sample](../1_basic). Starts a workflow with a prompt, allows follow-up prompts to be given using Temporal signals, and allows the conversation history to be queried using Temporal queries.
 
-To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from this directory:
+To run, first see `samples-python` [README.md](../../README.md), and `bedrock` [README.md](../README.md) for prerequisites specific to this sample. Once set up, run the following from the root directory:
 
-1. Run the worker: `uv run run_worker.py`
+1. Run the worker: `uv run bedrock/signals_and_queries/run_worker.py`
 2. In another terminal run the client with a prompt.
 
-    Example: `uv run send_message.py 'What animals are marsupials?'`
+    Example: `uv run bedrock/signals_and_queries/send_message.py 'What animals are marsupials?'`
 
 3. View the worker's output for the response.
 4. Give followup prompts by signaling the workflow.
 
-    Example: `uv run send_message.py 'Do they lay eggs?'`
+    Example: `uv run bedrock/signals_and_queries/send_message.py 'Do they lay eggs?'`
 5. Get the conversation history by querying the workflow.
 
-    Example: `uv run get_history.py`
+    Example: `uv run bedrock/signals_and_queries/get_history.py`
 6. The workflow will timeout after inactivity.

cloud_export_to_parquet/README.md

@@ -8,16 +8,16 @@ Please make sure your python is 3.9 above. For this sample, run:
 
 Before you start, please modify workflow input in `create_schedule.py` with your s3 bucket and namespace. Also make sure you've the right AWS permission set up in your environment to allow this workflow read and write to your s3 bucket. 
 
-To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the worker:
+To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the worker:
 
 ```bash
-uv run run_worker.py
+uv run cloud_export_to_parquet/run_worker.py
 ```
 
 This will start the worker. Then, in another terminal, run the following to execute the schedule:
 
 ```bash
-uv run create_schedule.py
+uv run cloud_export_to_parquet/create_schedule.py
 ```
 
 The workflow should convert exported file in your input s3 bucket to parquet in your specified location.

context_propagation/README.md

@@ -3,14 +3,14 @@
 This sample shows how to use an interceptor to propagate contextual information through workflows and activities. For
 this example, [contextvars](https://docs.python.org/3/library/contextvars.html) holds the contextual information.
 
-To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
+To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the
 worker:
 
-    uv run worker.py
+    uv run context_propagation/worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    uv run starter.py
+    uv run context_propagation/starter.py
 
 The starter terminal should complete with the hello result and the worker terminal should show the logs with the
 propagated user ID contextual information flowing through the workflows/activities.

custom_converter/README.md

@@ -2,14 +2,14 @@
 
 This sample shows how to make a custom payload converter for a type not natively supported by Temporal.
 
-To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
+To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the
 worker:
 
-    uv run worker.py
+    uv run custom_converter/worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    uv run starter.py
+    uv run custom_converter/starter.py
 
 The workflow should complete with the hello result. If the custom converter was not set for the custom input and output
 classes, we would get an error on the client side and on the worker side.

custom_decorator/README.md

@@ -3,14 +3,14 @@
 This sample shows a custom decorator can help with Temporal code reuse. Specifically, this makes a `@auto_heartbeater`
 decorator that automatically configures an activity to heartbeat twice as frequently as the heartbeat timeout is set to.
 
-To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
+To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the
 worker:
 
-    uv run worker.py
+    uv run custom_decorator/worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute the workflow:
 
-    uv run starter.py
+    uv run custom_decorator/starter.py
 
 The workflow will be started, and then after 5 seconds will be sent a signal to cancel its forever-running activity.
 The activity has a heartbeat timeout set to 2s, so since it has the `@auto_heartbeater` decorator set, it will heartbeat

dsl/README.md

@@ -8,22 +8,22 @@ For this sample, the optional `dsl` dependency group must be included. To includ
 
     uv sync --group dsl
 
-To run, first see [README.md](../README.md) for prerequisites. Then, run the following from this directory to start the
+To run, first see [README.md](../README.md) for prerequisites. Then, run the following from the root directory to start the
 worker:
 
-    uv run worker.py
+    uv run dsl/worker.py
 
 This will start the worker. Then, in another terminal, run the following to execute a workflow of steps defined in
-[workflow1.yaml](workflow1.yaml):
+[workflow1.yaml](dsl/workflow1.yaml):
 
-    uv run starter.py workflow1.yaml
+    uv run dsl/starter.py dsl/workflow1.yaml
 
 This will run the workflow and show the final variables that the workflow returns. Looking in the worker terminal, each
 step executed will be visible.
 
-Similarly we can do the same for the more advanced [workflow2.yaml](workflow2.yaml) file:
+Similarly we can do the same for the more advanced [workflow2.yaml](dsl/workflow2.yaml) file:
 
-    uv run starter.py workflow2.yaml
+    uv run dsl/starter.py dsl/workflow2.yaml
 
 This sample gives a guide of how one can write a workflow to interpret arbitrary steps from a user-provided DSL. Many
 DSL models are more advanced and are more specific to conform to business logic needs.