docs: rewrite create_robots_whoami.md

Author: maciejmajek

docs/create_robots_whoami.md

@@ -6,66 +6,68 @@ To configure RAI for your robot, provide contents for your robot's so called `wh
 
 Your robot's `whoami` package serves as a configuration package for the `rai_whoami` node.
 
-> [!TIP]
-> The Human-Machine Interface (HMI), both text and voice versions, relies heavily on the whoami package. It uses the robot's identity, constitution, and documentation to provide context-aware responses and ensure the robot behaves according to its defined characteristics.
-
 ## Configuration example - Franka Emika Panda arm
 
 1. Setup the repository using 1st and 2nd step from [Setup](../README.md#setup)
 
-2. Create a whoami package for Panda
-
-   ```shell
-   ./scripts/create_rai_ws.sh --name panda --destination-directory src/examples
-   ```
+2. Fill in the `src/examples/panda_whoami/description` folder with data:
 
-3. Fill in the `src/examples/panda_whoami/description` folder with data:
+   2.1. Save [this image](https://robodk.com/robot/img/Franka-Emika-Panda-robot.png) into `panda/images`
 
-   3.1. Save [this image](https://robodk.com/robot/img/Franka-Emika-Panda-robot.png) into `src/examples/panda_whoami/description/images`
+   2.2. Save [this document](https://github.com/user-attachments/files/16417196/Franka.Emika.Panda.robot.-.RoboDK.pdf) in `panda/documentation`
 
-   3.2. Save [this document](https://github.com/user-attachments/files/16417196/Franka.Emika.Panda.robot.-.RoboDK.pdf) in `src/examples/panda_whoami/description/documentation`
+   2.3. Save [this urdf](https://github.com/frankaemika/franka_ros/blob/develop/franka_description/robots/panda/panda.urdf.xacro) in `panda/urdf`
 
-   3.3. Save [this urdf](https://github.com/frankaemika/franka_ros/blob/develop/franka_description/robots/panda/panda.urdf.xacro) in `src/examples/panda_whoami/description/urdf`
+3. Build the embodiment info using `build_whoami.py`:
 
-4. Run the `parse_whoami_package`. This will process the documentation, building it into a vector database, which is used by RAI agent to reason about its identity.
+```shell
+python src/rai_whoami/rai_whoami/build_whoami.py panda/ --build-vector-db
+```
 
 > [!IMPORTANT]
-> Parsing bigger documents with Cloud vendors might lead to costs. Consider using the
-> local `ollama` provider for this task. Embedding model can be configured in
+> Building the vector database with cloud vendors might lead to costs. Consider using the
+> local `ollama` provider for this task. The embedding model can be configured in
 > [config.toml](../config.toml) (`ollama` works locally, see [docs/vendors.md](./vendors.md#ollama)).
 
-```shell
-./scripts/parse_whoami_package.sh src/examples/panda_whoami
-```
-
-5. Optional: Examine the generated files
+4. Examine the generated files
 
-After running the `parse_whoami_package` command, you can inspect the generated files in the `src/examples/panda_whoami/description/generated` directory. These files contain important information about your robot:
+After running the build command, inspect the generated files in the `panda/generated` directory. The folder should contain a info.json file containing:
 
-- `robot_identity.txt`: Contains a detailed description of the robot's identity, capabilities, and characteristics.
-- `robot_description.urdf.txt`: Provides a summary of the robot's URDF (Unified Robot Description Format), describing its physical structure.
-- `robot_constitution.txt`: Outlines the ethical guidelines and operational rules for the robot.
-- `faiss_index`: A directory containing the vector store of the robot's documentation, used for efficient information retrieval.
+- `rules`: List of rules
+- `capabilities`: List of capabilities
+- `behaviors`: List of behaviors
+- `description`: Description of the robot
+- `images`: Base64 encoded images
 
 ## Testing
 
-You can test your new `panda_whoami` package by calling `rai_whoami` services:
+You can test the generated package by using the RAI Whoami services:
 
-1. Building the `rai_whoami` package and running the `rai_whoami_node` for your `Panda` robot:
+1. Using the RAI Whoami services:
 
 ```shell
-colcon build --symlink-install
-ros2 run rai_whoami rai_whoami_node --ros-args -p robot_description_package:="panda_whoami"
+# Get robot's identity
+ros2 service call /rai_whoami_embodiment_info_service rai_interfaces/srv/EmbodimentInfo
+
+# Query the vector database
+ros2 service call /rai_whoami_documentation_service rai_interfaces/srv/VectorStoreRetrieval "query: 'maximum load'"
 ```
 
-2. Calling the rai_whoami services
+If your service calls succeed and you can access the embodiment info and vector database, your robot's whoami package has been properly initialized.
 
-```shell
-ros2 service call /rai_whoami_identity_service std_srvs/srv/Trigger # ask for identity
-ros2 service call /rai_whoami_selfimages_service std_srvs/srv/Trigger # ask for images folder
-ros2 service call /rai_whoami_constitution_service std_srvs/srv/Trigger # ask for robot constitution
-ros2 service call /rai_whoami_urdf_service std_srvs/srv/Trigger # ask for urdf description
-ros2 service call /rai_whoami_documentation_service rai_interfaces/srv/VectorStoreRetrieval "query:  'maximum load'" # ask for Panda's maximum load
-```
+2. Alternatively, you can use the RAI Whoami tools directly in your Python code:
+
+```python
+from rai_whoami import EmbodimentInfo
+from rai_whoami.tools import QueryDatabaseTool
 
-If your service calls succeed, your `panda_whoami` package has been properly initialized.
+# Load embodiment info
+info = EmbodimentInfo.from_directory("panda/generated")
+
+# Create a system prompt for your LLM
+system_prompt = info.to_langchain()
+
+# Use the vector database tool
+query_tool = QueryDatabaseTool(root_dir="panda/generated")
+query_tool._run(query="maximum load")
+```