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Native data AI application framework based on AWEL+AGENT (eosphoros-a…
…i#1152) Co-authored-by: Fangyin Cheng <[email protected]> Co-authored-by: lcx01800250 <[email protected]> Co-authored-by: licunxing <[email protected]> Co-authored-by: Aralhi <[email protected]> Co-authored-by: xuyuan23 <[email protected]> Co-authored-by: aries_ckt <[email protected]> Co-authored-by: hzh97 <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| import json | ||
| from abc import ABC | ||
| from typing import ( | ||
| Any, | ||
| Dict, | ||
| Generic, | ||
| List, | ||
| Optional, | ||
| Tuple, | ||
| Type, | ||
| TypeVar, | ||
| Union, | ||
| get_args, | ||
| get_origin, | ||
| ) | ||
|
|
||
| from pydantic import BaseModel | ||
|
|
||
| from dbgpt.agent.resource.resource_api import AgentResource, ResourceType | ||
| from dbgpt.agent.resource.resource_loader import ResourceLoader | ||
| from dbgpt.util.json_utils import find_json_objects | ||
|
|
||
| T = TypeVar("T", None, BaseModel, List[BaseModel]) | ||
|
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||
|
|
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| class ActionOutput(BaseModel): | ||
| content: str | ||
| is_exe_success: bool = True | ||
| view: str = None | ||
| resource_type: Optional[str] = None | ||
| resource_value: Optional[Any] = None | ||
|
|
||
| @staticmethod | ||
| def from_dict(param: Optional[Dict]): | ||
| if not param: | ||
| return None | ||
| return ActionOutput.parse_obj(param) | ||
|
|
||
|
|
||
| class Action(ABC, Generic[T]): | ||
| def __init__(self): | ||
| self.resource_loader: ResourceLoader = None | ||
|
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||
| def init_resource_loader(self, resource_loader: ResourceLoader): | ||
| self.resource_loader = resource_loader | ||
|
|
||
| @property | ||
| def resource_need(self) -> Optional[ResourceType]: | ||
| return None | ||
|
|
||
| @property | ||
| def render_protocal(self): | ||
| raise NotImplementedError("The run method should be implemented in a subclass.") | ||
|
|
||
| def render_prompt(self): | ||
| if self.render_protocal is None: | ||
| return None | ||
| else: | ||
| return self.render_protocal.render_prompt() | ||
|
|
||
| def _create_example( | ||
| self, | ||
| model_type: Union[Type[BaseModel], Type[List[BaseModel]]], | ||
| ) -> Union[Dict[str, Any], List[Dict[str, Any]]]: | ||
| if model_type is None: | ||
| return None | ||
| origin = get_origin(model_type) | ||
| args = get_args(model_type) | ||
| if origin is None: | ||
| example = {} | ||
| for field_name, field in model_type.__fields__.items(): | ||
| field_info = field.field_info | ||
| if field_info.description: | ||
| example[field_name] = field_info.description | ||
| elif field_info.default: | ||
| example[field_name] = field_info.default | ||
| else: | ||
| example[field_name] = "" | ||
| return example | ||
| elif origin is list or origin is List: | ||
| element_type = args[0] | ||
| if issubclass(element_type, BaseModel): | ||
| return [self._create_example(element_type)] | ||
| else: | ||
| raise TypeError("List elements must be BaseModel subclasses") | ||
| else: | ||
| raise ValueError( | ||
| f"Model type {model_type} is not an instance of BaseModel." | ||
| ) | ||
|
|
||
| @property | ||
| def out_model_type(self) -> T: | ||
| return None | ||
|
|
||
| @property | ||
| def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: | ||
| if self.out_model_type is None: | ||
| return None | ||
|
|
||
| return f"""Please response in the following json format: | ||
| {json.dumps(self._create_example(self.out_model_type), indent=2, ensure_ascii=False)} | ||
| Make sure the response is correct json and can be parsed by Python json.loads. | ||
| """ | ||
|
|
||
| def _ai_mesage_2_json(self, ai_message: str) -> json: | ||
| json_objects = find_json_objects(ai_message) | ||
| json_count = len(json_objects) | ||
| if json_count != 1: | ||
| raise ValueError("Unable to obtain valid output.") | ||
| return json_objects[0] | ||
|
|
||
| def _input_convert(self, ai_message: str, cls: Type[T]) -> Union[T, List[T]]: | ||
| json_result = self._ai_mesage_2_json(ai_message) | ||
| if get_origin(cls) == list: | ||
| inner_type = get_args(cls)[0] | ||
| return [inner_type.parse_obj(item) for item in json_result] | ||
| else: | ||
| return cls.parse_obj(json_result) | ||
|
|
||
| async def a_run( | ||
| self, | ||
| ai_message: str, | ||
| resource: Optional[AgentResource] = None, | ||
| rely_action_out: Optional[ActionOutput] = None, | ||
| need_vis_render: bool = True, | ||
| **kwargs, | ||
| ) -> ActionOutput: | ||
| raise NotImplementedError("The run method should be implemented in a subclass.") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,40 @@ | ||
| import json | ||
| import logging | ||
| from typing import Any, Dict, List, Optional, Union | ||
|
|
||
| from pydantic import BaseModel, Field | ||
|
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||
| from dbgpt.agent.actions.action import Action, ActionOutput, T | ||
| from dbgpt.agent.common.schema import Status | ||
| from dbgpt.agent.plugin.generator import PluginPromptGenerator | ||
| from dbgpt.agent.resource.resource_api import AgentResource, ResourceType | ||
| from dbgpt.agent.resource.resource_plugin_api import ResourcePluginClient | ||
| from dbgpt.vis.tags.vis_plugin import Vis, VisPlugin | ||
|
|
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| logger = logging.getLogger(__name__) | ||
|
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||
|
|
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| class BlankAction(Action): | ||
| def __init__(self, **kwargs): | ||
| super().__init__(**kwargs) | ||
|
|
||
| @property | ||
| def resource_need(self) -> Optional[ResourceType]: | ||
| return None | ||
|
|
||
| @property | ||
| def render_protocal(self) -> Optional[Vis]: | ||
| return None | ||
|
|
||
| @property | ||
| def ai_out_schema(self) -> Union[Dict[str, Any], List[Dict[str, Any]]]: | ||
| return None | ||
|
|
||
| async def a_run( | ||
| self, | ||
| ai_message: str, | ||
| resource: Optional[AgentResource] = None, | ||
| rely_action_out: Optional[ActionOutput] = None, | ||
| need_vis_render: bool = True, | ||
| ) -> ActionOutput: | ||
| return ActionOutput(is_exe_success=True, content=ai_message, view=ai_message) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,83 @@ | ||
| import json | ||
| import logging | ||
| from typing import Optional | ||
|
|
||
| from pydantic import BaseModel, Field | ||
|
|
||
| from dbgpt.agent.actions.action import ActionOutput, T | ||
| from dbgpt.agent.resource.resource_api import AgentResource, ResourceType | ||
| from dbgpt.agent.resource.resource_db_api import ResourceDbClient | ||
| from dbgpt.vis.tags.vis_chart import Vis, VisChart | ||
|
|
||
| from .action import Action | ||
|
|
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| logger = logging.getLogger(__name__) | ||
|
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|
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| class SqlInput(BaseModel): | ||
| display_type: str = Field( | ||
| ..., | ||
| description="The chart rendering method selected for SQL. If you don’t know what to output, just output 'response_table' uniformly.", | ||
| ) | ||
| sql: str = Field( | ||
| ..., description="Executable sql generated for the current target/problem" | ||
| ) | ||
| thought: str = Field(..., description="Summary of thoughts to the user") | ||
|
|
||
|
|
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| class ChartAction(Action[SqlInput]): | ||
| def __init__(self): | ||
| self._render_protocal = VisChart() | ||
|
|
||
| @property | ||
| def resource_need(self) -> Optional[ResourceType]: | ||
| return ResourceType.DB | ||
|
|
||
| @property | ||
| def render_protocal(self) -> Optional[Vis]: | ||
| return self._render_protocal | ||
|
|
||
| @property | ||
| def out_model_type(self): | ||
| return SqlInput | ||
|
|
||
| async def a_run( | ||
| self, | ||
| ai_message: str, | ||
| resource: Optional[AgentResource] = None, | ||
| rely_action_out: Optional[ActionOutput] = None, | ||
| need_vis_render: bool = True, | ||
| ) -> ActionOutput: | ||
| try: | ||
| param: SqlInput = self._input_convert(ai_message, SqlInput) | ||
| except Exception as e: | ||
| logger.exception(f"str(e)! \n {ai_message}") | ||
| return ActionOutput( | ||
| is_exe_success=False, | ||
| content="The requested correctly structured answer could not be found.", | ||
| ) | ||
| try: | ||
| resource_db_client: ResourceDbClient = ( | ||
| self.resource_loader.get_resesource_api(self.resource_need) | ||
| ) | ||
| if not resource_db_client: | ||
| raise ValueError( | ||
| "There is no implementation class bound to database resource execution！" | ||
| ) | ||
| data_df = await resource_db_client.a_query_to_df(resource.value, param.sql) | ||
| view = await self.render_protocal.disply( | ||
| chart=json.loads(param.json()), data_df=data_df | ||
| ) | ||
| return ActionOutput( | ||
| is_exe_success=True, | ||
| content=param.json(), | ||
| view=view, | ||
| resource_type=self.resource_need.value, | ||
| resource_value=resource.value, | ||
| ) | ||
| except Exception as e: | ||
| logger.exception("Check your answers, the sql run failed！") | ||
| return ActionOutput( | ||
| is_exe_success=False, | ||
| content=f"Check your answers, the sql run failed!Reason:{str(e)}", | ||
| ) |
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