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Use agent with smart phone app

How To Install

1. Deploy the Workflow Orchestration Engine

OmAgent utilizes Conductor as its workflow orchestration engine. Conductor is an open-source, distributed, and scalable workflow engine that supports a variety of programming languages and frameworks. By default, it uses Redis for persistence and Elasticsearch (7.x) as the indexing backend.
It is recommended to deploy Conductor using Docker:

docker-compose -f docker/conductor/docker-compose.yml up -d
  • Once deployed, you can access the Conductor UI at http://localhost:5001. (Note: Mac system will occupy port 5000 by default, so we use 5001 here. You can specify other ports when deploying Conductor.)
  • The Conductor API can be accessed via http://localhost:8080.
  • More details about the deployment can be found here.

2. Install OmAgent

  • Python Version: Ensure Python 3.10 or higher is installed.

  • Install omagent_core:

    pip install -e omagent-core

  • Install dependencies for the sample project:

    pip install -r requirements.txt

  • Install Optional Components:

    • Install Milvus VectorDB for enhanced support of long-term memory. OmAgent uses Milvus Lite as the default vector database for storing vector data related to long-term memory. To utilize the full Milvus service, you may deploy the Milvus vector database via Docker.
    • Pull git lfs files. We provide sample image files for our examples in the examples/step4_outfit_with_ltm/wardrobe_images directory. To use them, ensure Git LFS is installed. You can install it with the following command: 
      git lfs install
      Then, pull the files by executing: 
      git lfs pull

3. Connect Devices

If you wish to use smart devices to access your agents, we provide a smartphone app and corresponding backend, allowing you to focus on agent functionality without worrying about complex device connection issues.

  • Deploy the app backend
    The APP backend comprises the backend program, along with two middleware components: the MySQL database and MinIO object storage. For installation and deployment instructions, please refer to this link.
  • Download, install, and debug the smartphone app
    At present, we offer an Android APP available for download and testing. For detailed instructions on acquiring and using it, please refer to here. The iOS version is currently under development and will be available soon.

Quick Start

1、Configuration

The container.yaml file is a configuration file that manages dependencies and settings for different components of the system. To set up your configuration:

  1. Generate the container.yaml file:

    cd examples/step2_outfit_with_switch
python compile_container.py

    This will create a container.yaml file with default settings under examples/step2_outfit_with_switch.

  2. Configure your LLM settings in configs/llms/gpt.yml and configs/llms/text_res.yml:

    • Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file
    export custom_openai_key="your_openai_api_key"
export custom_openai_endpoint="your_openai_endpoint"

  3. Update settings in the generated container.yaml:

    • Configure Redis connection settings, including host, port, credentials, and both redis_stream_client and redis_stm_client sections.
    • Update the Conductor server URL under conductor_config section
    • Adjust any other component settings as needed

  4. Websearch gives multiple providers, you can choose one of them by modifying the configs/tools/all_tools.yml file.

    1. [Recommend] Use Tavily as the websearch tool, all_tools.yml file should be like this:
    llm: ${sub|text_res}
tools:
    - ...other tools...
    - name: TavilyWebSearch
      tavily_api_key: ${env|tavily_api_key, null}

    You can get the tavily_api_key from here. It start with tvly-xxx. By setting the tavily_api_key, you can get better search results. 2. Use bing search or duckduckgo search, all_tools.yml file should be like this:

    llm: ${sub|text_res}
tools:
    - ...other tools...
    - name: WebSearch
      bing_api_key: ${env|bing_api_key, null}

    For better results, it is recommended to configure Bing Search setting the bing_api_key.

For more information about the container.yaml configuration, please refer to the container module

2、Running the Example

  1. Run the outfit with switch example:

    For terminal/CLI usage: Input and output are in the terminal window

    cd examples/step2_outfit_with_switch
python run_cli.py

    For app/GUI usage: Input and output are in the app

    cd examples/step2_outfit_with_switch
python run_app.py

    For app backend deployment, please refer to here
    For the connection and usage of the OmAgent app, please check app usage documentation