mirror of https://github.com/dapr/dapr-agents.git
124 lines
4.9 KiB
Markdown
124 lines
4.9 KiB
Markdown
# Installation
|
|
|
|
## Install Dapr Agents
|
|
|
|
!!! info
|
|
make sure you have Python already installed. `Python >=3.10`
|
|
|
|
### As a Python package using Pip
|
|
|
|
```bash
|
|
pip install dapr-agents
|
|
```
|
|
|
|
## Install Dapr CLI
|
|
|
|
Install the Dapr CLI to manage Dapr-related tasks like running applications with sidecars, viewing logs, and launching the Dapr dashboard. It works seamlessly with both self-hosted and Kubernetes environments. For a complete step-by-step guide, visit the official [Dapr CLI installation page](https://docs.dapr.io/getting-started/install-dapr-cli/).
|
|
|
|
Verify the CLI is installed by restarting your terminal/command prompt and running the following:
|
|
|
|
```bash
|
|
dapr -h
|
|
```
|
|
|
|
## Initialize Dapr in Local Mode
|
|
|
|
!!! info
|
|
Make sure you have [Docker](https://docs.docker.com/get-started/get-docker/) already installed. I use [Docker Desktop](https://www.docker.com/products/docker-desktop/).
|
|
|
|
Initialize Dapr locally to set up a self-hosted environment for development. This process fetches and installs the Dapr sidecar binaries, runs essential services as Docker containers, and prepares a default components folder for your application. For detailed steps, see the official [guide on initializing Dapr locally](https://docs.dapr.io/getting-started/install-dapr-selfhost/).
|
|
|
|
|
|
|
|
To initialize the Dapr control plane containers and create a default configuration file, run:
|
|
|
|
```bash
|
|
dapr init
|
|
```
|
|
|
|
Verify you have container instances with `daprio/dapr`, `openzipkin/zipkin`, and `redis` images running:
|
|
|
|
```bash
|
|
docker ps
|
|
```
|
|
|
|
## Enable Redis Insights
|
|
|
|
Dapr uses [Redis](https://docs.dapr.io/reference/components-reference/supported-state-stores/setup-redis/) by default for state management and pub/sub messaging, which are fundamental to Dapr Agents's agentic workflows. These capabilities enable the following:
|
|
|
|
* Viewing Pub/Sub Messages: Monitor and inspect messages exchanged between agents in event-driven workflows.
|
|
* Inspecting State Information: Access and analyze shared state data among agents.
|
|
* Debugging and Monitoring Events: Track workflow events in real time to ensure smooth operations and identify issues.
|
|
|
|
To make these insights more accessible, you can leverage Redis Insight.
|
|
|
|
```bash
|
|
docker run --rm -d --name redisinsight -p 5540:5540 redis/redisinsight:latest
|
|
```
|
|
|
|
Once running, access the Redis Insight interface at `http://localhost:5540/`
|
|
|
|
### Connection Configuration
|
|
|
|
* Port: 6379
|
|
* Host (Linux): 172.17.0.1
|
|
* Host (Windows/Mac): host.docker.internal (example `host.docker.internal:6379`)
|
|
|
|
Redis Insight makes it easy to visualize and manage the data powering your agentic workflows, ensuring efficient debugging, monitoring, and optimization.
|
|
|
|
|
|
|
|
## Using custom endpoints
|
|
|
|
### Azure hosted OpenAI endpoint
|
|
|
|
In order to use Azure OpenAI for the model you'll need the following `.env` file:
|
|
|
|
```env
|
|
AZURE_OPENAI_API_KEY=your_custom_key
|
|
AZURE_OPENAI_ENDPOINT=your_custom_endpoint
|
|
AZURE_OPENAI_DEPLOYMENT=your_custom_model
|
|
AZURE_OPENAI_API_VERSION="azure_openai_api_version"
|
|
```
|
|
|
|
**NB!** the `AZURE_OPENAI_DEPLOYMENT` refers to the _model_, e.g., `gpt-4o`. `AZURE_OPENAI_API_VERSION` has been tested to work against `2024-08-01-preview`.
|
|
|
|
Then instantiate the agent(s) as well as the orchestrator as follows:
|
|
|
|
```python
|
|
from dapr_agents import AssistantAgent, OpenAIChatClient
|
|
from dotenv import load_dotenv
|
|
import asyncio
|
|
import logging
|
|
import os
|
|
|
|
async def main():
|
|
llm = OpenAIChatClient(
|
|
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
|
|
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
|
|
azure_deployment=os.getenv("AZURE_OPENAI_DEPLOYMENT"),
|
|
api_version=os.getenv("AZURE_OPENAI_API_VERSION")
|
|
)
|
|
|
|
try:
|
|
elf_service = AssistantAgent(
|
|
name="Legolas", role="Elf",
|
|
goal="Act as a scout, marksman, and protector, using keen senses and deadly accuracy to ensure the success of the journey.",
|
|
instructions=[
|
|
"Speak like Legolas, with grace, wisdom, and keen observation.",
|
|
"Be swift, silent, and precise, moving effortlessly across any terrain.",
|
|
"Use superior vision and heightened senses to scout ahead and detect threats.",
|
|
"Excel in ranged combat, delivering pinpoint arrow strikes from great distances.",
|
|
"Respond concisely, accurately, and relevantly, ensuring clarity and strict alignment with the task."],
|
|
llm=llm, # Note the explicit reference to the above OpenAIChatClient
|
|
message_bus_name="messagepubsub",
|
|
state_store_name="workflowstatestore",
|
|
state_key="workflow_state",
|
|
agents_registry_store_name="agentstatestore",
|
|
agents_registry_key="agents_registry",
|
|
)
|
|
...
|
|
```
|
|
|
|
The above is taken from [multi-agent quick starter](https://github.com/dapr/dapr-agents/blob/main/quickstarts/05-multi-agent-workflow-dapr-workflows/services/elf/app.py#L1-L23).
|