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feat(runtime): Execute codes in a sandbox environment (#2119)
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Fangyin Cheng
GitHub
322
dbgpt/app/operators/code.py
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322
dbgpt/app/operators/code.py
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"""Code operators for DB-GPT.
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The code will be executed in a sandbox environment, which is isolated from the host
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system. You can limit the memory and file system access of the code execution.
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"""
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import json
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import logging
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import os
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from dbgpt.core import ModelRequest
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from dbgpt.core.awel import MapOperator
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from dbgpt.core.awel.flow import (
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TAGS_ORDER_HIGH,
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IOField,
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OperatorCategory,
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OptionValue,
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Parameter,
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ViewMetadata,
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ui,
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)
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from dbgpt.util.code.server import get_code_server
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from dbgpt.util.i18n_utils import _
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logger = logging.getLogger(__name__)
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_FN_PYTHON_MAP = """
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import os
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import json
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import lyric_task
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from lyric_py_task.imports import msgpack
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def fn_map(args: dict[str, any]) -> dict[str, any]:
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text = args.get("text")
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return {
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"text": text,
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"key0": "customized key",
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"key1": "hello, world",
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"key2": [1, 2, 3],
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"key3": {"a": 1, "b": 2},
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}
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"""
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_FN_JAVASCRIPT_MAP = """
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function fn_map(args) {
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var text = args.text;
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return {
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text: text,
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key0: "customized key",
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key1: "hello, world",
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key2: [1, 2, 3],
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key3: {a: 1, b: 2},
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};
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}
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"""
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class CodeMapOperator(MapOperator[dict, dict]):
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metadata = ViewMetadata(
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label=_("Code Map Operator"),
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name="default_code_map_operator",
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description=_(
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"Handle input dictionary with code and return output dictionary after execution."
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),
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category=OperatorCategory.CODE,
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parameters=[
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Parameter.build_from(
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_("Code Editor"),
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"code",
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type=str,
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optional=True,
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default=_FN_PYTHON_MAP,
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placeholder=_("Please input your code"),
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description=_("The code to be executed."),
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ui=ui.UICodeEditor(
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language="python",
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),
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),
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Parameter.build_from(
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_("Language"),
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"lang",
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type=str,
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optional=True,
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default="python",
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placeholder=_("Please select the language"),
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description=_("The language of the code."),
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options=[
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OptionValue(label="Python", name="python", value="python"),
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OptionValue(
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label="JavaScript", name="javascript", value="javascript"
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),
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],
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ui=ui.UISelect(),
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),
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Parameter.build_from(
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_("Call Name"),
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"call_name",
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type=str,
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optional=True,
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default="fn_map",
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placeholder=_("Please input the call name"),
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description=_("The call name of the function."),
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),
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],
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inputs=[
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IOField.build_from(
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_("Input Data"),
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"input",
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type=dict,
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description=_("The input dictionary."),
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)
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],
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outputs=[
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IOField.build_from(
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_("Output Data"),
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"output",
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type=dict,
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description=_("The output dictionary."),
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)
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],
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tags={"order": TAGS_ORDER_HIGH},
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)
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def __init__(
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self,
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code: str = _FN_PYTHON_MAP,
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lang: str = "python",
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call_name: str = "fn_map",
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**kwargs,
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):
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super().__init__(**kwargs)
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self.code = code
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self.lang = lang
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self.call_name = call_name
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async def map(self, input_value: dict) -> dict:
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exec_input_data_bytes = json.dumps(input_value).encode("utf-8")
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code_server = await get_code_server()
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result = await code_server.exec1(
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self.code, exec_input_data_bytes, call_name=self.call_name, lang=self.lang
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)
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logger.info(f"Code execution result: {result}")
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return result.output
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_REQ_BUILD_PY_FUNC = """
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import os
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def fn_map(args: dict[str, any]) -> dict[str, any]:
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llm_model = args.get("model", os.getenv("DBGPT_RUNTIME_LLM_MODEL"))
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messages: str | list[str] = args.get("messages", [])
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if isinstance(messages, str):
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human_message = messages
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else:
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human_message = messages[0]
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temperature = float(args.get("temperature") or 0.5)
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max_new_tokens = int(args.get("max_new_tokens") or 2048)
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conv_uid = args.get("conv_uid", "")
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print("Conv uid is: ", conv_uid)
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "human", "content": human_message}
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]
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return {
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"model": llm_model,
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"messages": messages,
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"temperature": temperature,
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"max_new_tokens": max_new_tokens
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}
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"""
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_REQ_BUILD_JS_FUNC = """
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function fn_map(args) {
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var llm_model = args.model || "chatgpt_proxyllm";
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var messages = args.messages || [];
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var human_message = messages[0];
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var temperature = parseFloat(args.temperature) || 0.5;
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var max_new_tokens = parseInt(args.max_new_tokens) || 2048;
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var conv_uid = args.conv_uid || "";
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console.log("Conv uid is: ", conv_uid);
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "human", "content": human_message}
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];
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return {
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model: llm_model,
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messages: messages,
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temperature: temperature,
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max_new_tokens: max_new_tokens
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};
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}
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"""
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class CodeDictToModelRequestOperator(MapOperator[dict, ModelRequest]):
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metadata = ViewMetadata(
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label=_("Code Dict to Model Request Operator"),
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name="default_code_dict_to_model_request_operator",
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description=_(
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"Handle input dictionary with code and return output ModelRequest after execution."
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),
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category=OperatorCategory.CODE,
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parameters=[
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Parameter.build_from(
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_("Code Editor"),
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"code",
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type=str,
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optional=True,
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default=_REQ_BUILD_PY_FUNC,
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placeholder=_("Please input your code"),
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description=_("The code to be executed."),
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ui=ui.UICodeEditor(
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language="python",
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),
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),
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Parameter.build_from(
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_("Language"),
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"lang",
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type=str,
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optional=True,
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default="python",
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placeholder=_("Please select the language"),
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description=_("The language of the code."),
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options=[
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OptionValue(label="Python", name="python", value="python"),
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OptionValue(
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label="JavaScript", name="javascript", value="javascript"
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),
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],
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ui=ui.UISelect(),
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),
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Parameter.build_from(
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_("Call Name"),
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"call_name",
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type=str,
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optional=True,
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default="fn_map",
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placeholder=_("Please input the call name"),
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description=_("The call name of the function."),
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),
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],
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inputs=[
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IOField.build_from(
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_("Input Data"),
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"input",
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type=dict,
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description=_("The input dictionary."),
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)
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],
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outputs=[
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IOField.build_from(
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_("Output Data"),
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"output",
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type=ModelRequest,
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description=_("The output ModelRequest."),
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)
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],
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tags={"order": TAGS_ORDER_HIGH},
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)
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def __init__(
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self,
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code: str = _REQ_BUILD_PY_FUNC,
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lang: str = "python",
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call_name: str = "fn_map",
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**kwargs,
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):
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super().__init__(**kwargs)
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self.code = code
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self.lang = lang
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self.call_name = call_name
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async def map(self, input_value: dict) -> ModelRequest:
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from lyric import PyTaskFsConfig, PyTaskMemoryConfig, PyTaskResourceConfig
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exec_input_data_bytes = json.dumps(input_value).encode("utf-8")
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code_server = await get_code_server()
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model_name = os.getenv("LLM_MODEL")
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fs = PyTaskFsConfig(
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preopens=[
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# Mount the /tmp directory to the /tmp directory in the sandbox
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# Directory permissions are set to 3 (read and write)
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# File permissions are set to 3 (read and write)
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("/tmp", "/tmp", 3, 3),
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# Mount the current directory to the /home directory in the sandbox
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# Directory and file permissions are set to 1 (read)
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(".", "/home", 1, 1),
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]
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)
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memory = PyTaskMemoryConfig(memory_limit=50 * 1024 * 1024) # 50MB in bytes
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resources = PyTaskResourceConfig(
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fs=fs,
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memory=memory,
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env_vars=[
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("DBGPT_RUNTIME_LLM_MODEL", model_name),
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],
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)
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result = await code_server.exec1(
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self.code,
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exec_input_data_bytes,
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call_name=self.call_name,
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lang=self.lang,
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resources=resources,
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)
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logger.info(f"Code execution result: {result}")
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if result.exit_code != 0:
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raise RuntimeError(f"Code execution failed: {result.logs}")
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if not result.output:
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raise RuntimeError(f"Code execution failed: {result.logs}")
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if not isinstance(result.output, dict):
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raise RuntimeError(
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f"Code execution failed, invalid output: {result.output}"
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)
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logger.info(f"Code execution result: {result}")
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return ModelRequest(**result.output)
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