Merge branch 'main' into filinto/dapr-default-better-devex

Author: filintod

dapr_agents/llm/dapr/chat.py

@@ -23,11 +23,13 @@
 from dapr_agents.prompt.prompty import Prompty
 from dapr_agents.tool import AgentTool
 from dapr_agents.types.exceptions import DaprRuntimeVersionNotSupportedError
+from dapr_agents.types.exceptions import DaprRuntimeVersionNotSupportedError
 from dapr_agents.types.message import (
     BaseMessage,
     LLMChatResponse,
 )
 from dapr_agents.utils import is_version_supported
+from dapr_agents.utils import is_version_supported
 
 
 # Lazy import to avoid import issues during test collection
@@ -80,6 +82,8 @@ class DaprChatClient(DaprInferenceClientBase, ChatClientBase):
 
     component_name: Optional[str] = None
 
+    component_name: Optional[str] = None
+
     # Only function_call–style structured output is supported
     SUPPORTED_STRUCTURED_MODES: ClassVar[set[str]] = {"function_call"}
 

quickstarts/01-hello-world/03_durable_agent.py

@@ -9,6 +9,7 @@
 import asyncio
 import logging
 
+
 from typing import List
 from pydantic import BaseModel, Field
 from dapr_agents import tool, DurableAgent
@@ -85,6 +86,8 @@ async def main():
         print(f"Error starting service: {e}")
     finally:
         travel_planner.graceful_shutdown()
+    finally:
+        travel_planner.graceful_shutdown()
 
 
 if __name__ == "__main__":

quickstarts/01-hello-world/README.md

@@ -24,6 +24,22 @@ uv pip install -r requirements.txt
 
 </details>
 
+<details>
+<summary><strong>Option 2: Using pip</strong></summary>
+<details open>
+<summary><strong>Option 1: Using uv (Recommended)</strong></summary>
+
+```bash
+# Create and activate virtual environment
+uv venv .venv
+source .venv/bin/activate
+
+# Install core dependencies
+uv pip install -r requirements.txt
+```
+
+</details>
+
 <details>
 <summary><strong>Option 2: Using pip</strong></summary>
 
@@ -44,12 +60,18 @@ pip install -r requirements.txt
 
 </details>
 
+</details>
+
 
 ## Configuration
 
 > **Warning**
 > The examples will not work if you do not have a OpenAI API key exported in the environment.
 
+Create a `.env` file in the project root and add your OpenAI API key:
+> **Warning**
+> The examples will not work if you do not have a OpenAI API key exported in the environment.
+
 Create a `.env` file in the project root and add your OpenAI API key:
 
 ```env
@@ -63,6 +85,11 @@ Export the environment variables from the .env file to your shell:
 export $(grep -v '^#' .env | xargs) # or if .env is in the root directory, you can just run `export $(grep -v '^#' ../../.env | xargs)`
 ```
 
+Export the environment variables from the .env file to your shell:
+```bash
+export $(grep -v '^#' .env | xargs) # or if .env is in the root directory, you can just run `export $(grep -v '^#' ../../.env | xargs)`
+```
+
 ## Examples
 
 ### 1. Basic LLM Usage
@@ -167,6 +194,9 @@ A stateful agent that uses Dapr Workflows to ensure durability and persistence o
 We are using the Dapr ChatClient to interact with the OpenAI API. In the components folder, we have a `openai.yaml` file that contains the configuration for the OpenAI API.
 You need to replace the `{YOUR_OPENAI_API_KEY}` with your actual OpenAI API key.
 
+We are using the Dapr ChatClient to interact with the OpenAI API. In the components folder, we have a `openai.yaml` file that contains the configuration for the OpenAI API.
+You need to replace the `{YOUR_OPENAI_API_KEY}` with your actual OpenAI API key.
+
 Make sure Dapr is initialized on your system:
 
 ```bash
@@ -185,15 +215,31 @@ output_match_mode: substring
 -->
 
 
+We are using the `resolve_env_templates.py` script to resolve the environment variables in the components folder and substitute them with the actual values in your .env file, like the OpenAI API key.
+
+<!-- STEP
+name: Run basic LLM example
+expected_stdout_lines:
+  - "I want to find flights to Paris"
+  - "TravelBuddy"
+timeout_seconds: 30
+output_match_mode: substring
+-->
+
+
 We are using the `resolve_env_templates.py` script to resolve the environment variables in the components folder and substitute them with the actual values in your .env file, like the OpenAI API key.
 
 ```bash
 dapr run --app-id stateful-llm --dapr-http-port 3500 --resources-path $(../resolve_env_templates.py ./components) -- python 03_durable_agent.py
+dapr run --app-id stateful-llm --dapr-http-port 3500 --resources-path $(../resolve_env_templates.py ./components) -- python 03_durable_agent.py
 ```
 
 <!-- END_STEP -->
 
 
+<!-- END_STEP -->
+
+
 This example demonstrates a stateful travel planning assistant that:
 1. Remembers user context persistently (across restarts)
 2. Uses a tool to search for flight options
@@ -322,6 +368,7 @@ output_match_mode: substring
 -->
 ```bash
 dapr run --app-id dapr-agent-wf --resources-path $(../resolve_env_templates.py ./components) -- python 04_chain_tasks.py
+dapr run --app-id dapr-agent-wf --resources-path $(../resolve_env_templates.py ./components) -- python 04_chain_tasks.py
 ```
 <!-- END_STEP -->
 
@@ -392,6 +439,8 @@ Run the vector store agent example to see how to create an agent that can search
 
 <!-- STEP
 name: Run agent with vector store example
+expected_stderr_lines:
+  - "Batches"
 expected_stderr_lines:
   - "Batches"
 expected_stdout_lines: