docs: update readme

feat: export tools

Author: maciejmajek

src/rai_whoami/README.md

@@ -40,6 +40,8 @@ To generate the system prompt from your documentation directory:
 python src/rai_whoami/rai_whoami/build_whoami.py 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
@@ -51,16 +53,66 @@ from rai_whoami import EmbodimentInfo
 from rai.agents import ReActAgent, wait_for_shutdown
 from rai.communication.ros2 import ROS2HRIConnector
 
-info = EmbodimentInfo.from_directory("path/to/documentation_dir/")
+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={"ros2": connector},
+    target_connectors={"/to_human": connector},
     system_prompt=system_prompt,
 )
 
 agent.run()
 connector.register_callback("/from_human", agent)
 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
+
+```python
+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/generated")
+
+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
+
+```python
+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/generated")
+
+connector = ROS2HRIConnector()
+agent = ReActAgent(
+    target_connectors={"/to_human": connector}, system_prompt="", tools=[query_tool]
+)
+agent.run()
+connector.register_callback("/from_human", agent)
+wait_for_shutdown([agent])
+
+```

src/rai_whoami/rai_whoami/__init__.py

@@ -15,12 +15,14 @@
 from .models import EmbodimentInfo, EmbodimentSource
 from .pipeline import Pipeline, PipelineBuilder
 from .processors import get_default_postprocessors, get_default_preprocessors
+from .tools import QueryDatabaseTool
 
 __all__ = [
     "EmbodimentInfo",
     "EmbodimentSource",
     "Pipeline",
     "PipelineBuilder",
+    "QueryDatabaseTool",
     "get_default_postprocessors",
     "get_default_preprocessors",
 ]

src/rai_whoami/rai_whoami/tools/__init__.py

@@ -0,0 +1,17 @@
+# Copyright (C) 2025 Robotec.AI
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#         http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from rai_whoami.tools.vdb_client import QueryDatabaseTool
+
+__all__ = ["QueryDatabaseTool"]

src/rai_whoami/rai_whoami/tools/vdb_client.py

@@ -60,11 +60,9 @@ def __init__(self, **kwargs: Any):
             from langchain_community.vectorstores import FAISS
 
             if self.embeddings_model is None:
-                embeddings_kwargs = json.load(
-                    open(Path(self.root_dir) / "embeddings_kwargs.json")
-                )
+                vdb_kwargs = json.load(open(Path(self.root_dir) / "vdb_kwargs.json"))
                 self.embeddings_model = initialize_embeddings(
-                    embeddings_kwargs["class"], **embeddings_kwargs
+                    vdb_kwargs["embeddings"]["class"], **vdb_kwargs
                 )
 
             self.vdb_client = FAISS.load_local(

src/rai_whoami/rai_whoami/vector_db/builder.py

@@ -52,6 +52,4 @@ def _build(self, data: EmbodimentSource) -> VectorStore:
         """
 
     def dump_model_kwargs(self):
-        (self.root_dir / "embeddings_kwargs.json").write_text(
-            json.dumps(self.model_kwargs)
-        )
+        (self.root_dir / "vdb_kwargs.json").write_text(json.dumps(self.model_kwargs))