Flow agents

Introduced 2.13

A flow agent is configured with a set of tools that it runs in order. For example, the following agent runs the VectorDBTool and then the MLModelTool. The agent coordinates the tools so that one tool’s output can become another tool’s input. In this example, the VectorDBTool queries the k-NN index, and the agent passes its output ${parameters.VectorDBTool.output} to the MLModelTool as context along with the ${parameters.question} (see the prompt parameter):

POST /_plugins/_ml/agents/_register
{
  "name": "Test_Agent_For_RAG",
  "type": "flow",
  "description": "this is a test agent",
  "tools": [
    {
      "type": "VectorDBTool",
      "parameters": {
        "model_id": "YOUR_TEXT_EMBEDDING_MODEL_ID",
        "index": "my_test_data",
        "embedding_field": "embedding",
        "source_field": ["text"],
        "input": "${parameters.question}"
      }
    },
    {
      "type": "MLModelTool",
      "description": "A general tool to answer any question",
      "parameters": {
        "model_id": "YOUR_LLM_MODEL_ID",
        "prompt": "\n\nHuman:You are a professional data analyst. You will always answer a question based on the given context first. If the answer is not directly shown in the context, you will analyze the data and find the answer. If you don't know the answer, just say you don't know. \n\n Context:\n${parameters.VectorDBTool.output}\n\nHuman:${parameters.question}\n\nAssistant:"
      }
    }
  ]
}

For more information about the Register Agent API request fields, see Request body fields.

For a step-by-step tutorial, see Agents and tools tutorial.

Next steps

To learn more about registering agents, see Register Agent API.
For a list of supported tools, see Tools.
For a step-by-step tutorial, see Agents and tools tutorial.
For supported APIs, see Agent APIs.
To use agents and tools in configuration automation, see Automating configurations.