update for Dapr 1.16 conversation SDK Python quickstart

conversation/python/http/README.md

@@ -16,108 +16,173 @@ This section shows how to run the application using the [multi-app run template
 
 This example uses the default LLM Component provided by Dapr which simply echoes the input provided, for testing purposes. Here are other [supported Conversation components](https://docs.dapr.io/reference/components-reference/supported-conversation/).
 
-1. Install dependencies:
-
-<!-- STEP
-name: Install Python dependencies
--->
-
-```bash
-cd ./conversation
-pip3 install -r requirements.txt
-cd ..
-```
-
-<!-- END_STEP -->
+1.  Install dependencies:
+
+    <details open="true">
+    <summary>Option 2: Using uv (faster modern alternative to pip)</summary>
+
+    ```
+    cd conversation
+
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+
+    # If you don't have uv installed yet, install it first:
+    # pip install uv
+    uv pip install -r requirements.txt
+    ```
+
+    </details>
+
+    <details>
+    <summary>Option 1: Using classic pip</summary>
+
+    <!-- STEP
+    name: Install Python dependencies
+    -->
+
+    ```bash
+    cd conversation
+
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+
+    pip install -r requirements.txt 
+    ```
+
+    <!-- END_STEP -->
+
+    </details>
+
+    ```bash
+    # Return to the parent directory
+    cd ..
+    ```
 
 2. Open a new terminal window and run the multi app run template:
 
-<!-- STEP
-name: Run multi app run template
-expected_stdout_lines:
-  - '== APP - conversation == Conversation input sent: What is dapr?'
-  - '== APP - conversation == Output response: What is dapr?'
-  - '== APP - conversation == Tool calling input sent: What is the weather like in San Francisco in celsius?'
-  - '== APP - conversation == Output message: What is the weather like in San Francisco in celsius?'
-  - '== APP - conversation == No tool calls in response'
-expected_stderr_lines:
-output_match_mode: substring
-match_order: none
-background: true
-sleep: 15
-timeout_seconds: 30
--->
-
-```bash
-dapr run -f .
-```
-
-The terminal console output should look similar to this, where:
-
-- The app first sends an input `What is dapr?` to the `echo` Component mock LLM.
-- The mock LLM echoes `What is dapr?`.
-- The app then sends a weather request to the component with tools available to the LLM.
-- The LLM will either respond back with a tool call for the user, or an ask for more information.
-
-```text
-== APP - conversation == Input sent: What is dapr?
-== APP - conversation == Output response: What is dapr?
-```
-
-- The app then sends an input `What is the weather like in San Francisco in celsius?` to the `echo` Component mock LLM.
-- The mock LLM echoes `What is the weather like in San Francisco in celsius?` and calls the `get_weather` tool.
-- Since we are using the `echo` Component mock LLM, the tool call is not executed and the LLM returns `No tool calls in response`.
-
-```text
-== APP == Tool calling input sent: What is the weather like in San Francisco in celsius?
-== APP == Output message: What is the weather like in San Francisco in celsius?
-== APP == No tool calls in response
-```
-```
-
-<!-- END_STEP -->
-
-2. Stop and clean up application processes.
-
-<!-- STEP
-name: Stop multi-app run 
-sleep: 5
--->
-
-```bash
-dapr stop -f .
-```
-
-<!-- END_STEP -->
+    <!-- STEP
+    name: Run multi app run template
+    expected_stdout_lines:
+      - '== APP - conversation == Conversation input sent: What is dapr?'
+      - '== APP - conversation == Output response: What is dapr?'
+      - '== APP - conversation == Tool calling input sent: What is the weather like in San Francisco in celsius?'
+      - '== APP - conversation == Output message: What is the weather like in San Francisco in celsius?'
+      - '== APP - conversation == Tool calls detected:'
+      - '== APP - conversation == Tool call: {'id': '0', 'function': {'name': 'get_weather', 'arguments': 'location,unit'}}'
+      - '== APP - conversation == Function name: get_weather'
+      - '== APP - conversation == Function arguments: location,unit'   
+    expected_stderr_lines:
+    output_match_mode: substring
+    match_order: none
+    background: true
+    sleep: 15
+    timeout_seconds: 30
+    -->
+
+    ```bash
+    source conversation/.venv/bin/activate
+    dapr run -f .
+    ```
+
+    The terminal console output should look similar to this, where:
+
+    - The app first sends an input `What is dapr?` to the `echo` Component mock LLM.
+    - The mock LLM echoes `What is dapr?`.
+    - The app then sends a weather request to the component with tools available to the LLM.
+    - The LLM will either respond back with a tool call for the user, or an ask for more information.
+
+    ```text
+    == APP - conversation == Input sent: What is dapr?
+    == APP - conversation == Output response: What is dapr?
+    ```
+
+    - The app then sends an input `What is the weather like in San Francisco in celsius?` to the `echo` Component mock LLM.
+    - The mock LLM echoes `What is the weather like in San Francisco in celsius?` and calls the `get_weather` tool.
+    - Since we are using the `echo` Component mock LLM, the tool call is not executed and the LLM returns `No tool calls in response`.
+
+    ```text
+    == APP == Tool calling input sent: What is the weather like in San Francisco in celsius?
+    == APP == Output message: What is the weather like in San Francisco in celsius?
+    == APP - conversation == Tool calls detected:
+    == APP - conversation == Tool call: {'id': '0', 'function': {'name': 'get_weather', 'arguments': 'location,unit'}}
+    == APP - conversation == Function name: get_weather
+    == APP - conversation == Function arguments: location,unit
+    ```
+
+    <!-- END_STEP -->
+
+3. Stop and clean up application processes.
+
+    <!-- STEP
+    name: Stop multi-app run 
+    sleep: 5
+    -->
+
+    ```bash
+    dapr stop -f .
+    ```
+
+    <!-- END_STEP -->
 
 ## Run the app with the Dapr CLI
 
 1. Install dependencies:
 
-Open a terminal and run:
-
-```bash
-cd ./conversation
-pip3 install -r requirements.txt
-```
+    Open a terminal and run:
+
+    ```bash
+    cd ./conversation
+    ```
+
+    <details open="true">
+    <summary>Option 1: Using uv (faster alternative to pip)</summary>
+
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+    # If you don't have uv installed yet, install it first:
+    # pip install uv
+    uv pip install -r requirements.txt
+    ```
+
+    </details>
+
+    <details>
+    <summary>Option 2: Using classic pip</summary>
+
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+    pip3 install -r requirements.txt
+    ```    
+
+    </details>
 
 2. Run the application:
 
-```bash
-dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
-```
-
-The terminal console output should look similar to this, where:
-
-- The app first sends an input `What is dapr?` to the `echo` Component mock LLM.
-- The mock LLM echoes `What is dapr?`.
-- The app then sends an input `What is the weather like in San Francisco in celsius?` to the `echo` Component mock LLM.
-- The mock LLM echoes `What is the weather like in San Francisco in celsius?`
-
-```text
-== APP - conversation == Conversation input sent: What is dapr?
-== APP - conversation == Output response: What is dapr?
-== APP - conversation == Tool calling input sent: What is the weather like in San Francisco in celsius?
-== APP - conversation == Output message: What is the weather like in San Francisco in celsius?
-== APP - conversation == No tool calls in response
-```
+    ```bash
+    # Make sure your virtual environment is activated
+    # If not already activated, run:
+    # source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+
+    dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
+    ```
+
+    The terminal console output should look similar to this, where:
+
+    - The app first sends an input `What is dapr?` to the `echo` Component mock LLM.
+    - The mock LLM echoes `What is dapr?`.
+    - The app then sends an input `What is the weather like in San Francisco in celsius?` to the `echo` Component mock LLM.
+    - The mock LLM echoes `What is the weather like in San Francisco in celsius?`
+
+    ```text
+    == APP - conversation == Conversation input sent: What is dapr?
+    == APP - conversation == Output response: What is dapr?
+    == APP - conversation == Tool calling input sent: What is the weather like in San Francisco in celsius?
+    == APP - conversation == Output message: What is the weather like in San Francisco in celsius?
+    == APP - conversation == Tool calls detected:
+    == APP - conversation == Tool call: {'id': '0', 'function': {'name': 'get_weather', 'arguments': 'location,unit'}}
+    == APP - conversation == Function name: get_weather
+    == APP - conversation == Function arguments: location,unit
+    ```