update for Dapr 1.16 conversation SDK Python quickstart

conversation/components/ollama.yaml

@@ -0,0 +1,14 @@
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: ollama
+spec:
+  type: conversation.openai
+  version: v1
+  metadata:
+  - name: key
+    value: 'ollama'
+  - name: model
+    value: gpt-oss:20b
+  - name: endpoint
+    value: 'http://localhost:11434/v1' # ollama endpoint https://ollama.com/blog/openai-compatibility

conversation/components/openai.yaml

@@ -0,0 +1,12 @@
+apiVersion: dapr.io/v1alpha1
+kind: Component
+metadata:
+  name: openai
+spec:
+  type: conversation.openai
+  version: v1
+  metadata:
+  - name: key
+    value: "YOUR_OPENAI_API_KEY"
+  - name: model
+    value: gpt-4o-mini-2024-07-18

conversation/python/http/README.md

@@ -24,7 +24,34 @@ name: Install Python dependencies
 
 ```bash
 cd ./conversation
+```
+
+<details open="true">
+<summary>Option 1: Using venv (Python's built-in virtual environment)</summary>
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
 pip3 install -r requirements.txt
+```
+
+</details>
+
+<details>
+<summary>Option 2: 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>
+
+```bash
+# Return to the parent directory
 cd ..
 ```
 
@@ -82,12 +109,39 @@ Open a terminal and run:
 
 ```bash
 cd ./conversation
+```
+
+<details open="true">
+<summary>Option 1: Using venv (Python's built-in virtual environment)</summary>
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
 pip3 install -r requirements.txt
 ```
 
+</details>
+
+<details>
+<summary>Option 2: 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>
+
 2. Run the application:
 
 ```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
 ```
 

conversation/python/sdk/README.md

@@ -1,97 +1,171 @@
 # Dapr Conversation API (Python SDK)
 
-In this quickstart, you'll send an input to a mock Large Language Model (LLM) using Dapr's Conversation API. This API is responsible for providing one consistent API entry point to talk to underlying LLM providers.
+This quickstart demonstrates how to interact with Large Language Models (LLMs) using Dapr's Conversation API. The Conversation API provides a unified interface for communicating with various LLM providers through a consistent entry point.
 
-Visit [this](https://docs.dapr.io/developing-applications/building-blocks/conversation/conversation-overview/) link for more information about Dapr and the Conversation API.
-
-This quickstart includes one app:
-
-- `app.py`, responsible for sending an input to the underlying LLM and retrieving an output.
-
-## Run the app with the template file
-
-This section shows how to run the application using the [multi-app run template files](https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-overview/) with `dapr run -f .`.  
-
-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 -->
-
-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 == Input sent: What is dapr?'
-  - '== APP - conversation == Output response: What is dapr?'
-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 sends an input `What is dapr?` to the `echo` Component mock LLM.
-- The mock LLM echoes `What is dapr?`.
-
-```text
-== APP - conversation == Input sent: What is dapr?
-== APP - conversation == Output response: What is dapr?
-```
-
-<!-- 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
-```
-
-2. Run the application:
-
-```bash
-dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
-```
-
-You should see the output:
-
-```bash
-== APP == Input sent: What is dapr?
-== APP == Output response: What is dapr?
-```
+For comprehensive documentation on Dapr's Conversation API, see the [official documentation](https://docs.dapr.io/developing-applications/building-blocks/conversation/conversation-overview/).
+
+## Sample Applications
+
+This quickstart includes three example applications:
+
+- `app.py`: Basic example that sends a prompt to an LLM and retrieves the response
+- `tool_calling.py`: Advanced example that defines a tool and sends a request to an LLM that supports tool calling
+- `tool_calling_from_function.py`: Similar to `tool_calling.py` but uses a helper function to generate the JSON schema for function calling
+
+## LLM Providers
+
+By default, this quickstart uses Dapr's mock LLM Echo Component, which simply echoes back the input for testing purposes.
+
+The repository also includes pre-configured components for the following LLM providers:
+- [OpenAI](../../components/openai.yaml)
+- [Ollama](../../components/ollama.yaml) (via its OpenAI compatibility layer)
+
+To use one of these alternative provider, modify the `provider_component` value in your application code from `echo` to either `openai` or `ollama`.
+
+Of course, you can also play adding components for other LLM providers supported by Dapr.
+
+### OpenAI Configuration
+
+To use the OpenAI provider:
+
+1. Change the `provider_component` parameter in your application code to `openai`
+2. Edit the [openai.yaml](../../components/openai.yaml) component file and replace `YOUR_OPENAI_API_KEY` with your actual OpenAI API key
+
+### Ollama Configuration
+
+To use the Ollama provider:
+
+1. Change the `provider_component` parameter in your application code to `ollama`
+2. Install and run Ollama locally on your machine
+3. Pull a model with tool-calling support from the [Ollama models repository](https://ollama.com/search?c=tools)
+
+The default configuration uses the `gpt-oss:20b` model, but you can modify the component file to use any compatible model that your system can run.
+
+## Running the Application
+
+You can run the sample applications using either the Dapr multi-app template or the Dapr CLI directly.
+
+### Option 1: Using the Multi-App Template
+
+This approach uses [Dapr's multi-app run template files](https://docs.dapr.io/developing-applications/local-development/multi-app-dapr-run/multi-app-overview/) to simplify deployment with `dapr run -f .`.
+
+For more LLM options, see the [supported Conversation components](https://docs.dapr.io/reference/components-reference/supported-conversation/) documentation.
+
+1. **Install dependencies:**
+
+    <!-- STEP
+    name: Install Python dependencies
+    -->
+
+    ```bash
+    cd ./conversation
+    ```
+
+    <details open="true">
+    <summary>Option 1: Using pip</summary>
+
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+    pip3 install -r requirements.txt
+    ```
+
+    </details>
+
+    <details>
+    <summary>Option 2: Using uv (faster alternative to pip)</summary>
+
+    ```bash
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+    # If you do not have uv installed yet, install it first:
+    # pip install uv
+    uv pip install -r requirements.txt
+    ```
+
+    </details>
+
+    ```bash
+    # Return to the parent directory
+    cd ..
+    ```
+    <!-- END_STEP -->
+
+2. **Run the application:**
+
+    <!-- STEP
+    name: Run multi app run template
+    expected_stdout_lines:
+      - '== APP - conversation == Input sent: What is dapr?'
+      - '== APP - conversation == Output response: What is dapr?'
+    expected_stderr_lines:
+    output_match_mode: substring
+    match_order: none
+    background: true
+    sleep: 15
+    timeout_seconds: 30
+    -->
+
+    ```bash
+    dapr run -f .
+    ```
+
+    Expected output:
+
+    ```text
+    == APP - conversation == Input sent: What is dapr?
+    == APP - conversation == Output response: What is dapr?
+    ```
+
+    <!-- END_STEP -->
+
+3. **Stop the application:**
+
+    <!-- STEP
+    name: Stop multi-app run 
+    sleep: 5
+    -->
+
+    ```bash
+    dapr stop -f .
+    ```
+
+    <!-- END_STEP -->
+
+### Option 2: Using the Dapr CLI Directly
+
+As an alternative to the multi-app template, you can run the application directly with the Dapr CLI.
+
+1. **Install dependencies:**
+
+    ```bash
+    cd ./conversation
+    python3 -m venv .venv
+    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
+    pip3 install -r requirements.txt
+    ```
+
+2. **Run the application:**
+
+    ```bash
+    dapr run --app-id conversation --resources-path ../../../components -- python3 app.py
+    ```
+
+    Expected output:
+
+    ```text
+    == APP == Input sent: What is dapr?
+    == APP == Output response: What is dapr?
+    ```
+
+3. **Try the tool calling examples:**
+
+    You can run the other example applications similarly:
+
+    ```bash
+    # For tool calling example
+    dapr run --app-id conversation --resources-path ../../../components -- python3 tool_calling.py
+    
+    # For tool calling with function helper example
+    dapr run --app-id conversation --resources-path ../../../components -- python3 tool_calling_from_function.py
+    ```