Add action-agent runtime and generated config pipeline

embodichain/gen_sim/action_agent_pipeline/__init__.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# 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 __future__ import annotations
+
+"""Action-agent graph compilation and atomic-action runtime."""
+
+__all__: list[str] = []

embodichain/gen_sim/action_agent_pipeline/agents/__init__.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# 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 __future__ import annotations
+
+__all__ = [
+    "agent_base",
+    "compile_agent",
+    "llm",
+    "task_agent",
+]

embodichain/gen_sim/action_agent_pipeline/agents/agent_base.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# 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 __future__ import annotations
+
+from abc import ABCMeta
+import os
+
+from embodichain.utils.utility import load_txt
+
+
+def _resolve_prompt_path(file_name: str, config_dir: str | None = None) -> str:
+    # If absolute path, use directly
+    if os.path.isabs(file_name):
+        if os.path.exists(file_name):
+            return file_name
+        raise FileNotFoundError(f"Prompt file not found: {file_name}")
+
+    # Try config directory first (for task-specific prompts)
+    if config_dir:
+        config_path = os.path.join(config_dir, file_name)
+        if os.path.exists(config_path):
+            return config_path
+
+    # Try action_agent_pipeline/prompts directory for reusable prompts.
+    agents_prompts_dir = os.path.join(
+        os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "prompts"
+    )
+    agents_path = os.path.join(agents_prompts_dir, file_name)
+    if os.path.exists(agents_path):
+        return agents_path
+
+    # If still not found, raise error with search paths
+    searched_paths = []
+    if config_dir:
+        searched_paths.append(f"  - {config_dir}/{file_name}")
+    searched_paths.append(f"  - {agents_prompts_dir}/{file_name}")
+
+    raise FileNotFoundError(
+        f"Prompt file not found: {file_name}\n"
+        f"Searched in:\n" + "\n".join(searched_paths)
+    )
+
+
+class AgentBase(metaclass=ABCMeta):
+    def __init__(self, **kwargs) -> None:
+
+        assert (
+            "prompt_kwargs" in kwargs.keys()
+        ), "Key prompt_kwargs must exist in config."
+
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+        # Get config directory if provided
+        config_dir = kwargs.get("config_dir", None)
+        if config_dir:
+            config_dir = os.path.dirname(os.path.abspath(config_dir))
+
+        # Preload and store prompt contents inside self.prompt_kwargs
+        for key, val in self.prompt_kwargs.items():
+            if val["type"] == "text":
+                file_path = _resolve_prompt_path(val["name"], config_dir)
+                val["content"] = load_txt(file_path)
+            else:
+                raise ValueError(
+                    f"Now only support `text` type but {val['type']} is given."
+                )
+
+    def generate(self, *args, **kwargs):
+        pass
+
+    def act(self, *args, **kwargs):
+        pass
+
+    def get_composed_observations(self, **kwargs):
+        ret = {}
+        for key, val in self.prompt_kwargs.items():
+            ret[key] = val["content"]
+        ret.update(kwargs)
+        return ret

embodichain/gen_sim/action_agent_pipeline/agents/compile_agent.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# 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 __future__ import annotations
+
+import hashlib
+import json
+from pathlib import Path
+from typing import Any
+
+from embodichain.gen_sim.action_agent_pipeline.agents.agent_base import AgentBase
+from embodichain.gen_sim.action_agent_pipeline.utils.llm_json import (
+    extract_json_object,
+    normalize_json_content,
+)
+from embodichain.data import database_agent_prompt_dir
+
+__all__ = ["CompileAgent"]
+
+COMPILED_GRAPH_SCHEMA_VERSION = "nominal_graph_v1"
+
+
+class CompileAgent(AgentBase):
+    """Compile and execute nominal atomic-action graph specs."""
+
+    query_prefix = "# "
+    query_suffix = "."
+    prompt_kwargs: dict[str, dict[str, Any]]
+
+    def __init__(self, **kwargs) -> None:
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+        self.prompt_kwargs = kwargs.get("prompt_kwargs", {})
+
+    def generate(self, **kwargs):
+        log_dir = kwargs.get(
+            "log_dir", Path(database_agent_prompt_dir) / self.task_name
+        )
+        file_path = Path(log_dir) / "agent_compiled_graph.json"
+        task_graph = extract_json_object(kwargs["task_graph"])
+        task_graph_hash = _stable_json_hash(task_graph)
+
+        if not kwargs.get("regenerate", False) and file_path.exists():
+            existing_bundle = extract_json_object(file_path.read_text(encoding="utf-8"))
+            metadata = existing_bundle.get("metadata", {})
+            if (
+                metadata.get("schema_version") == COMPILED_GRAPH_SCHEMA_VERSION
+                and metadata.get("task_graph_hash") == task_graph_hash
+            ):
+                print(f"Compiled graph artifact already exists at {file_path}.")
+                return file_path, kwargs, None
+
+        content = normalize_json_content(
+            {
+                "task_graph": task_graph,
+                "metadata": {
+                    "schema_version": COMPILED_GRAPH_SCHEMA_VERSION,
+                    "task_graph_hash": task_graph_hash,
+                },
+            }
+        )
+
+        file_path.parent.mkdir(parents=True, exist_ok=True)
+        file_path.write_text(content, encoding="utf-8")
+        print(f"Compiled graph artifact saved to {file_path}")
+        return file_path, kwargs, content
+
+    def act(self, graph_file_path, **kwargs):
+        graph_file_path = Path(graph_file_path)
+        if graph_file_path.suffix != ".json":
+            raise ValueError("CompileAgent executes compiled graph JSON artifacts.")
+
+        from embodichain.gen_sim.action_agent_pipeline.runtime.graph_compiler import (
+            compile_agent_graph_from_file,
+        )
+
+        runtime_kwargs = _runtime_kwargs(kwargs, getattr(self, "prompt_kwargs", {}))
+        graph = compile_agent_graph_from_file(graph_file_path)
+        result = graph.run(**runtime_kwargs)
+        print("Compiled agent graph executed successfully.")
+        return result
+
+    def get_composed_observations(self, **kwargs):
+        return dict(kwargs)
+
+
+def _stable_json_hash(content: dict[str, Any]) -> str:
+    payload = json.dumps(
+        content, ensure_ascii=False, sort_keys=True, separators=(",", ":")
+    )
+    return hashlib.sha256(payload.encode("utf-8")).hexdigest()
+
+
+def _runtime_kwargs(
+    kwargs: dict[str, Any],
+    prompt_kwargs: dict[str, dict[str, Any]],
+) -> dict[str, Any]:
+    prompt_only_keys = set(prompt_kwargs)
+    prompt_only_keys.update(
+        {
+            "task_graph",
+            "observations",
+            "regenerate",
+        }
+    )
+    return {key: value for key, value in kwargs.items() if key not in prompt_only_keys}

embodichain/gen_sim/action_agent_pipeline/agents/llm.py

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+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# 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 __future__ import annotations
+
+from embodichain.gen_sim.action_agent_pipeline.utils.mllm import create_chat_openai
+
+__all__ = ["create_llm", "task_llm"]
+
+
+# ------------------------------------------------------------------------------
+# LLM factory
+# ------------------------------------------------------------------------------
+
+
+def create_llm(*, temperature=0.0, model=None, usage_stage=None):
+    return create_chat_openai(
+        temperature=temperature,
+        model=model,
+        usage_stage=usage_stage,
+    )
+
+
+# ------------------------------------------------------------------------------
+# LLM instances
+# ------------------------------------------------------------------------------
+
+
+# Initialize LLM instances, but handle errors gracefully for documentation builds
+def _create_llm_safe(*, temperature=0.0, model=None, usage_stage=None):
+    try:
+        return create_llm(
+            temperature=temperature,
+            model=model,
+            usage_stage=usage_stage,
+        )
+    except Exception:
+        return None
+
+
+task_llm = _create_llm_safe(
+    temperature=0.0,
+    usage_stage="action_agent.task_graph",
+)
+
+if __name__ == "__main__":
+
+    def call_llm(prompt, temperature=0.0, model=None):
+        llm = create_llm(
+            temperature=temperature,
+            model=model,
+            usage_stage="action_agent.debug",
+        )
+        response = llm.invoke(prompt)
+        return response.content
+
+    response = call_llm(prompt="Which model you are?", temperature=0.0)
+    print(response)