RAI Whoami

Overview

RAI Whoami is a Python package designed to extract and synthesize robot embodiment information from a structured directory of documentation, images, and URDFs.
It generates a comprehensive system prompt (embodiment info) for robots controlled by LLMs, enabling advanced reasoning guided by the robot's embodiment setup.

How It Works

Given a directory containing robot documentation (documents, images, URDFs), RAI Whoami processes these resources to produce a structured representation of the robot, including:

• Rules: Extracted operational or safety rules.
• Behaviors: Descriptions of robot behaviors.
• Capabilities: Functional and physical capabilities.
• Images: Visual representations.
• Vector Database: Embeddings of the robot's documentation. (optional)

This embodiment info is then used to create a system prompt for LLM-based agents, enabling them to reason about and interact with the robot effectively.

Directory Structure

Initialize the documentation directory:

initialize-docs documentation_dir

Populate your robot documentation directory:

documentation_dir/
├── images/          # png, jpg, jpeg files
├── documentation/   # pdf, docx, doc, md files
├── urdf/            # urdf files

Building the Embodiment Info

To generate the system prompt from your documentation directory:

build-whoami documentation_dir [--output_dir output_dir] [--build-vector-db]

Generated files will be saved in the output_dir / generated directory or documentation_dir / generated if not specified.

Using with ROS2 and ReActAgent

Integrate the generated embodiment info into your LLM-powered robot agent:

from rai_whoami import EmbodimentInfo
from rai.agents import ReActAgent, wait_for_shutdown
from rai.communication.ros2 import ROS2HRIConnector

info = EmbodimentInfo.from_directory("output_dir/")
system_prompt = info.to_langchain()  # Convert EmbodimentInfo to a system prompt

# example usage with langchain runnable
from rai.agents.langchain import create_react_runnable

react_agent = create_react_runnable(
    tools=[],
    system_prompt=system_prompt
)

# example usage with RAI Agent
connector = ROS2HRIConnector()
agent = ReActAgent(
    target_connectors={"/to_human": connector},
    system_prompt=system_prompt,
)
agent.subscribe_source("/from_human", connector)

agent.run()
wait_for_shutdown([agent])

Using generated Vector Database

rai whoami provides a langchain tool to query the generated vector database. There are a couple of ways to use it:

1. Through a langchain runnable

from langchain_core.messages import HumanMessage
from rai_whoami.tools import QueryDatabaseTool
from rai.agents.langchain import create_react_runnable

query_tool = QueryDatabaseTool(root_dir="output_dir")

react_agent = create_react_runnable(tools=[query_tool])
print(
    react_agent.invoke(
        {"messages": [HumanMessage(content="Check the db for Robot's name")]}
    )
)

2. Through a RAI Agent

from rai.agents import ReActAgent, wait_for_shutdown
from rai.communication.ros2 import ROS2HRIConnector

from rai_whoami.tools import QueryDatabaseTool

query_tool = QueryDatabaseTool(root_dir="output_dir")

connector = ROS2HRIConnector()
agent = ReActAgent(
    target_connectors={"/to_human": connector}, system_prompt="", tools=[query_tool]
)
agent.subscribe_source("/from_human", connector)

agent.run()
wait_for_shutdown([agent])

Using ROS2 Vector Store Retrieval Agent

The ROS2VectorStoreRetrievalAgent is a ROS2 agent that can be used to retrieve information from the vector database through a ROS 2 service.

from rai_whoami.agents.ros2 import ROS2VectorStoreRetrievalAgent
from rai.agents import wait_for_shutdown

agent = ROS2VectorStoreRetrievalAgent(
    service_name="rai_whoami_documentation_service",
    root_dir="output_dir",
    k=4,
)
agent.run()
wait_for_shutdown([agent])

With the agent running, you can query the vector database through a ROS 2 service:

ros2 service call /rai_whoami_documentation_service rai_interfaces/srv/VectorStoreRetrieval "query:  'maximum load'"

Using ROS2 Embodiment Info Agent

The ROS2EmbodimentInfoAgent is a ROS2 agent that can be used to retrieve information from the embodiment info through a ROS 2 service.

from rai_whoami.agents.ros2 import ROS2EmbodimentInfoAgent
from rai.agents import wait_for_shutdown

agent = ROS2EmbodimentInfoAgent(
    service_name="rai_whoami_embodiment_info_service",
    root_dir="output_dir",
)
agent.run()
wait_for_shutdown([agent])

With the agent running, you can query the embodiment info through a ROS 2 service:

ros2 service call /rai_whoami_embodiment_info_service rai_interfaces/srv/EmbodimentInfo