dapr-agents/quickstarts/02_llm_call_open_ai

OpenAI LLM calls with Dapr Agents

This quickstart demonstrates how to use Dapr Agents' LLM capabilities to interact with language models and generate both free-form text and structured data. You'll learn how to make basic calls to LLMs and how to extract structured information in a type-safe manner.

Prerequisites

• Python 3.10 (recommended)
• pip package manager
• OpenAI API key

Environment Setup

# Create a virtual environment
python3.10 -m venv .venv

# Activate the virtual environment 
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Configuration

Create a .env file in the project root:

OPENAI_API_KEY=your_api_key_here

Replace your_api_key_here with your actual OpenAI API key.

Examples

Text

1. Run the basic text completion example:

python text_completion.py

The script demonstrates basic usage of Dapr Agents' OpenAIChatClient for text generation:

from dapr_agents import OpenAIChatClient
from dapr_agents.types import UserMessage
from dotenv import load_dotenv

# Load environment variables from .env
load_dotenv()

# Basic chat completion
llm = OpenAIChatClient()
response = llm.generate("Name a famous dog!")

if len(response.get_content()) > 0:
    print("Response: ", response.get_content())

# Chat completion using a prompty file for context
llm = OpenAIChatClient.from_prompty('basic.prompty')
response = llm.generate(input_data={"question":"What is your name?"})

if len(response.get_content()) > 0:
    print("Response with prompty: ", response.get_content())

# Chat completion with user input
llm = OpenAIChatClient()
response = llm.generate(messages=[UserMessage("hello")])


if len(response.get_content()) > 0 and "hello" in response.get_content().lower():
    print("Response with user input: ", response.get_content())

2. Expected output: The LLM will respond with the name of a famous dog (e.g., "Lassie", "Hachiko", etc.).

Run the structured text completion example:

python structured_completion.py

This example shows how to use Pydantic models to get structured data from LLMs:

import json

from dapr_agents import OpenAIChatClient
from dapr_agents.types import UserMessage
from pydantic import BaseModel
from dotenv import load_dotenv

# Load environment variables from .env
load_dotenv()

# Define our data model
class Dog(BaseModel):
    name: str
    breed: str
    reason: str

# Initialize the chat client
llm = OpenAIChatClient()

# Get structured response
response = llm.generate(
    messages=[UserMessage("One famous dog in history.")],
    response_format=Dog
)

print(json.dumps(response.model_dump(), indent=2))

Expected output: A structured Dog object with name, breed, and reason fields (e.g., Dog(name='Hachiko', breed='Akita', reason='Known for his remarkable loyalty...'))

Audio

You can use the OpenAIAudioClient in dapr-agents for basic tasks with the OpenAI Audio API. We will explore:

• Generating speech from text and saving it as an MP3 file.
• Transcribing audio to text.
• Translating audio content to English.

1. Run the text to speech example:

python text_to_speech.py

2. Run the speech to text transcription example:

python audio_transcription.py

2. Run the speech to text translation example:

Embeddings

You can use the OpenAIEmbedder in dapr-agents for generating text embeddings.

1. Embeddings a single text:

python embeddings.py

Troubleshooting

1. Authentication Errors: If you encounter authentication failures, check your OpenAI API key in the .env file
2. Structured Output Errors: If the model fails to produce valid structured data, try refining your model or prompt
3. Module Not Found: Ensure you've activated your virtual environment and installed the requirements

Next Steps

After completing these examples, move on to the Agent Tool Call quickstart to learn how to build agents that can use tools to interact with external systems.