mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-09-05 08:06:15 +00:00
add http sevice
This commit is contained in:
jinli.yl
parent
79a247e114
commit
a17dc024fa
10 changed files with 226 additions and 132 deletions
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@ -8,6 +8,7 @@ from pydantic import Field, BaseModel
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from experiencemaker.model.base_llm import BaseLLM
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from experiencemaker.module.prompt.prompt_mixin import PromptMixin
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from experiencemaker.schema.trajectory import Message, ActionMessage, ToolCall, StateMessage
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from experiencemaker.tool import CodeTool, DashscopeSearchTool, TerminateTool
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from experiencemaker.tool.base_tool import BaseTool
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@ -23,7 +24,7 @@ class SimpleAgentContext(BaseModel):
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class SimpleAgent(PromptMixin):
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llm: BaseLLM | None = Field(default=None)
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max_steps: int = Field(default=10)
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tools: List[BaseTool] = Field(default_factory=list)
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tools: List[BaseTool] = [CodeTool(), DashscopeSearchTool(), TerminateTool()]
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prompt_file_path: Path = Path(__file__).parent / "simple_agent_prompt.yaml"
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def think(self, context: SimpleAgentContext):
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@ -14,7 +14,6 @@ from experiencemaker.utils.registry import Registry
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class BaseContextGenerator(BaseModel, ABC):
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vector_store: BaseVectorStore | None = Field(default=None)
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llm: BaseLLM | None = Field(default=None)
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embedding_model: BaseEmbeddingModel | None = Field(default=None)
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workspace_id: str = Field(default="")
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def _build_retrieve_query(self, trajectory: Trajectory, **kwargs) -> str:
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@ -2,7 +2,7 @@ from abc import ABC
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from pydantic import BaseModel, Field
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from experiencemaker.module.agent_wrapper.base_agent_wrapper import BaseAgentWrapper
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.module.environment.base_environment import BaseEnvironment
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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@ -10,7 +10,7 @@ from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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class BaseEvaluator(BaseModel, ABC):
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data_path: str = Field(default="")
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agent_wrapper: BaseAgentWrapper | None = Field(default=None)
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agent_wrapper: AgentWrapperMixin | None = Field(default=None)
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context_generator: BaseContextGenerator | None = Field(default=None)
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summarizer: BaseSummarizer | None = Field(default=None)
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env: BaseEnvironment | None = Field(default=None)
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@ -2,7 +2,7 @@ from typing import List
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from pydantic import BaseModel, Field
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from experiencemaker.module.agent_wrapper.base_agent_wrapper import BaseAgentWrapper
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.module.environment.base_environment import BaseEnvironment
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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@ -10,7 +10,7 @@ from experiencemaker.schema.trajectory import Trajectory
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class BaseRunner(BaseModel):
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agent_wrapper: BaseAgentWrapper | None = Field(default=None)
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agent_wrapper: AgentWrapperMixin | None = Field(default=None)
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context_generator: BaseContextGenerator | None = Field(default=None)
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summarizer: BaseSummarizer | None = Field(default=None)
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env: BaseEnvironment | None = Field(default=None)
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@ -3,7 +3,6 @@ from typing import List
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from pydantic import Field, BaseModel
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from experiencemaker.model.base_embedding_model import BaseEmbeddingModel
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from experiencemaker.model.base_llm import BaseLLM
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from experiencemaker.schema.experience import Experience
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from experiencemaker.schema.trajectory import Trajectory
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@ -15,7 +14,6 @@ from experiencemaker.utils.registry import Registry
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class BaseSummarizer(BaseModel, ABC):
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vector_store: BaseVectorStore | None = Field(default=None)
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llm: BaseLLM | None = Field(default=None)
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embedding_model: BaseEmbeddingModel | None = Field(default=None)
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workspace_id: str = Field(default="")
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def _extract_experiences(self, trajectories: List[Trajectory], **kwargs) -> List[Experience]:
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@ -1,13 +1,12 @@
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from abc import ABC
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from typing import List
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from pydantic import Field
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from pydantic import BaseModel, Field
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from experiencemaker.schema.module_loader import ModuleLoader
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from experiencemaker.schema.trajectory import Trajectory
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class BaseRequest(ModuleLoader, ABC):
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class BaseRequest(BaseModel, ABC):
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metadata: dict = Field(default_factory=dict)
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@ -21,4 +20,4 @@ class ContextGeneratorRequest(BaseRequest):
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class SummarizerRequest(BaseRequest):
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trajectories: List[Trajectory] = Field(default_factory=dict)
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return_samples: bool = Field(default=False)
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return_experience: bool = Field(default=True)
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@ -3,7 +3,8 @@ from typing import List
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from pydantic import BaseModel, Field
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from experiencemaker.schema.trajectory import Trajectory, ContextMessage, Sample
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from experiencemaker.schema.experience import Experience
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from experiencemaker.schema.trajectory import Trajectory, ContextMessage
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class BaseResponse(BaseModel, ABC):
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@ -20,4 +21,4 @@ class ContextGeneratorResponse(BaseResponse):
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class SummarizerResponse(BaseResponse):
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extract_samples: List[Sample] = Field(default_factory=list)
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experiences: List[Experience] = Field(default_factory=list)
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@ -1,118 +0,0 @@
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from typing import List
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import uvicorn
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from fastapi import FastAPI
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from loguru import logger
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from experiencemaker.model.base_embedding_model import BaseEmbeddingModel, EMBEDDING_MODEL_REGISTRY
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from experiencemaker.model.base_llm import BaseLLM, LLM_REGISTRY
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from experiencemaker.module.agent_wrapper.base_agent_wrapper import BaseAgentWrapper
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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from experiencemaker.schema.request import AgentWrapperRequest, ContextGeneratorRequest, SummarizerRequest
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from experiencemaker.schema.response import AgentWrapperResponse, ContextGeneratorResponse, SummarizerResponse
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from experiencemaker.schema.trajectory import ContextMessage, Trajectory, Sample
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from experiencemaker.storage.base_vector_store import BaseVectorStore, VECTOR_STORE_REGISTRY
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app = FastAPI()
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from pydantic import BaseModel, Field, model_validator
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class ExperienceMakerHttpService(BaseModel):
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host: str = Field(default="0.0.0.0")
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port: int = Field(default=8001)
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timeout_keep_alive: int = Field(default=600000)
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limit_concurrency: int = Field(default=32)
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llm_config: dict = Field(default_factory=dict)
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embedding_model_config: dict = Field(default_factory=dict)
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vector_store_config: dict = Field(default_factory=dict)
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agent_wrapper_config: dict = Field(default_factory=dict)
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context_generator_config: dict = Field(default_factory=dict)
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summarizer_config: dict = Field(default_factory=dict)
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llm: BaseLLM | None = Field(default=None)
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embedding_model: BaseEmbeddingModel | None = Field(default=None)
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vector_store: BaseVectorStore | None = Field(default=None)
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agent_wrapper: BaseAgentWrapper | None = Field(default=None)
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context_generator: BaseContextGenerator | None = Field(default=None)
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summarizer: BaseSummarizer | None = Field(default=None)
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@staticmethod
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def init_llm(llm_config: dict):
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backend = llm_config.pop("backend", None)
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assert backend is not None, "llm must have a backend like `openai_compatible`."
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assert backend in LLM_REGISTRY, f"llm backend={backend} not supported. supported backend={LLM_REGISTRY.registered_modules}"
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llm = LLM_REGISTRY[backend](**llm_config)
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logger.info(f"llm is inited with backend={backend} params={llm_config}")
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return llm
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@staticmethod
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def init_embedding_model(embedding_model_config: dict):
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backend = embedding_model_config.pop("backend", None)
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assert backend is not None, "embedding_model must have a backend like `openai_compatible`."
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assert backend in EMBEDDING_MODEL_REGISTRY, f"embedding_model backend={backend} not supported. supported backend={EMBEDDING_MODEL_REGISTRY.registered_modules}"
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embedding_model = EMBEDDING_MODEL_REGISTRY[backend](**embedding_model_config)
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logger.info(f"embedding_model is inited with backend={backend} params={embedding_model_config}")
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return embedding_model
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@staticmethod
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def init_vector_store(vector_store_config: dict):
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backend = vector_store_config.pop("backend", None)
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assert backend is not None, "vector_store must have a backend like `elasticsearch`."
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assert backend in VECTOR_STORE_REGISTRY, f"vector_store backend={backend} not supported. supported backend={VECTOR_STORE_REGISTRY.registered_modules}"
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vector_store = VECTOR_STORE_REGISTRY[backend](**vector_store_config)
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logger.info(f"vector_store is inited with backend={backend} params={vector_store_config}")
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return vector_store
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@model_validator(mode="after")
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def init_modules(self):
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if self.llm_config:
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self.llm = self.init_llm(self.llm_config)
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if self.embedding_model_config:
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self.embedding_model = self.init_embedding_model(self.embedding_model_config)
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if self.vector_store_config:
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self.vector_store = self.init_vector_store(self.vector_store_config)
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@app.post('/agent_wrapper', response_model=AgentWrapperResponse)
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def call_agent_wrapper(request: AgentWrapperRequest):
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module: BaseAgentWrapper = request.load_from_path()
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trajectory: Trajectory = module.execute(request.query, **request.metadata)
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return AgentWrapperResponse(trajectory=trajectory)
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@app.post('/context_generator', response_model=ContextGeneratorResponse)
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def call_context_generator(request: ContextGeneratorRequest):
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module: BaseContextGenerator = request.load_from_path()
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context_msg: ContextMessage = module.execute(request.trajectory, **request.metadata)
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return ContextGeneratorResponse(context_msg=context_msg)
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@app.post('/summarizer', response_model=SummarizerResponse)
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def call_summarizer(request: SummarizerRequest):
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module: BaseSummarizer = request.load_from_path()
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samples: List[Sample] = module.execute(request.trajectories, request.return_samples, **request.metadata)
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return SummarizerResponse(extract_samples=samples)
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if __name__ == '__main__':
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uvicorn.run(app, host="0.0.0.0", port=8000, timeout_keep_alive=600000, limit_concurrency=32)
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# from experiencemaker.config import summarizer_config
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# print(summarizer_config.simple)
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from experiencemaker.config.config_handler import ConfigHandler
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summarizer_config = ConfigHandler(module_name="summarizer")
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context_generator_config = ConfigHandler(module_name="context_generator")
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print(context_generator_config.config_dict)
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# launch with:
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# python -m experiencemaker.service.model_service
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164
experiencemaker/service/experience_maker_service.py
Normal file
164
experiencemaker/service/experience_maker_service.py
Normal file
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@ -0,0 +1,164 @@
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from typing import List
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from loguru import logger
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from pydantic import BaseModel, Field, model_validator
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from experiencemaker.model.base_embedding_model import BaseEmbeddingModel, EMBEDDING_MODEL_REGISTRY
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from experiencemaker.model.base_llm import BaseLLM, LLM_REGISTRY
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AGENT_WRAPPER_REGISTRY, AgentWrapperMixin
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator, \
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CONTEXT_GENERATOR_REGISTRY
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer, SUMMARIZER_REGISTRY
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from experiencemaker.schema.experience import Experience
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from experiencemaker.schema.request import AgentWrapperRequest, ContextGeneratorRequest, SummarizerRequest
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from experiencemaker.schema.response import AgentWrapperResponse, ContextGeneratorResponse, SummarizerResponse
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from experiencemaker.schema.trajectory import Trajectory, ContextMessage
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from experiencemaker.storage.base_vector_store import BaseVectorStore, VECTOR_STORE_REGISTRY
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class ExperienceMakerService(BaseModel):
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host: str = Field(default="0.0.0.0")
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port: int = Field(default=8001)
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timeout_keep_alive: int = Field(default=600000)
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limit_concurrency: int = Field(default=32)
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llm_config: dict = Field(default_factory=dict)
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embedding_model_config: dict = Field(default_factory=dict)
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vector_store_config: dict = Field(default_factory=dict)
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agent_wrapper_config: dict = Field(default_factory=dict)
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context_generator_config: dict = Field(default_factory=dict)
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summarizer_config: dict = Field(default_factory=dict)
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llm: BaseLLM | None = Field(default=None)
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embedding_model: BaseEmbeddingModel | None = Field(default=None)
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vector_store: BaseVectorStore | None = Field(default=None)
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agent_wrapper: AgentWrapperMixin | None = Field(default=None)
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context_generator: BaseContextGenerator | None = Field(default=None)
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summarizer: BaseSummarizer | None = Field(default=None)
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@staticmethod
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def init_llm(llm_config: dict) -> BaseLLM:
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backend = llm_config.pop("backend", None)
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assert backend is not None, "llm must have a backend like `openai_compatible`."
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assert backend in LLM_REGISTRY, f"llm backend={backend} not supported. " \
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f"supported={LLM_REGISTRY.registered_modules}"
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llm = LLM_REGISTRY[backend](**llm_config)
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logger.info(f"llm is inited with backend={backend} params={llm_config}")
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return llm
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def get_llm(self, config: dict, llm: BaseLLM = None) -> BaseLLM:
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if "llm" in config:
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llm_config = config.pop("llm")
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llm = self.init_llm(llm_config)
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elif llm is None:
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raise RuntimeError("llm must be provided.")
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return llm
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@staticmethod
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def init_embedding_model(embedding_model_config: dict) -> BaseEmbeddingModel:
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backend = embedding_model_config.pop("backend", None)
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assert backend is not None, "embedding_model must have a backend like `openai_compatible`."
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assert backend in EMBEDDING_MODEL_REGISTRY, f"embedding_model backend={backend} not supported. " \
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f"supported={EMBEDDING_MODEL_REGISTRY.registered_modules}"
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embedding_model = EMBEDDING_MODEL_REGISTRY[backend](**embedding_model_config)
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logger.info(f"embedding_model is inited with backend={backend} params={embedding_model_config}")
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return embedding_model
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def get_embedding_model(self, config: dict, embedding_model: BaseEmbeddingModel = None) -> BaseEmbeddingModel:
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if "embedding_model" in config:
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embedding_model_config = config.pop("embedding_model")
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embedding_model = self.init_embedding_model(embedding_model_config)
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elif embedding_model is None:
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raise RuntimeError("embedding_model must be provided.")
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return embedding_model
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def init_vector_store(self, vector_store_config: dict) -> BaseVectorStore:
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backend = vector_store_config.pop("backend", None)
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assert backend is not None, "vector_store must have a backend like `elasticsearch`."
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assert backend in VECTOR_STORE_REGISTRY, f"vector_store backend={backend} not supported. " \
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f"supported={VECTOR_STORE_REGISTRY.registered_modules}"
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embedding_model = self.get_embedding_model(vector_store_config, embedding_model=self.embedding_model)
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vector_store = VECTOR_STORE_REGISTRY[backend](**vector_store_config, embedding_model=embedding_model)
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logger.info(f"vector_store is inited with backend={backend} params={vector_store_config}")
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return vector_store
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def get_vector_store(self, config: dict, vector_store: BaseVectorStore = None) -> BaseVectorStore:
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if "vector_store" in config:
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vector_store_config = config.pop("vector_store")
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vector_store = self.init_vector_store(vector_store_config)
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elif vector_store is None:
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raise RuntimeError("vector_store must be provided.")
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return vector_store
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def init_context_generator(self, context_generator_config: dict) -> BaseContextGenerator:
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backend = context_generator_config.pop("backend", None)
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assert backend is not None, "context_generator must have a backend like `simple`."
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assert backend in CONTEXT_GENERATOR_REGISTRY, f"context_generator backend={backend} not supported. " \
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f"supported={CONTEXT_GENERATOR_REGISTRY.registered_modules}"
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llm = self.get_llm(context_generator_config, llm=self.llm)
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vector_store = self.get_vector_store(context_generator_config, vector_store=self.vector_store)
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context_generator: BaseContextGenerator = CONTEXT_GENERATOR_REGISTRY[backend](
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**context_generator_config, llm=llm, vector_store=vector_store)
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logger.info(f"context_generator is inited with backend={backend} params={context_generator_config}")
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return context_generator
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def init_summarizer(self, summarizer_config: dict) -> BaseSummarizer:
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backend = summarizer_config.pop("backend", None)
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assert backend is not None, "summarizer must have a backend like `simple`."
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assert backend in SUMMARIZER_REGISTRY, f"summarizer backend={backend} not supported. " \
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f"supported={SUMMARIZER_REGISTRY.registered_modules}"
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llm = self.get_llm(summarizer_config, llm=self.llm)
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vector_store = self.get_vector_store(summarizer_config, vector_store=self.vector_store)
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summarizer: BaseSummarizer = SUMMARIZER_REGISTRY[backend](**summarizer_config,
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llm=llm, vector_store=vector_store)
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logger.info(f"summarizer is inited with backend={backend} params={summarizer_config}")
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return summarizer
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def init_agent_wrapper(self, agent_wrapper_config: dict) -> AgentWrapperMixin:
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backend = agent_wrapper_config.pop("backend", None)
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assert backend is not None, "agent_wrapper must have a backend like `simple`."
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assert backend in AGENT_WRAPPER_REGISTRY, f"agent_wrapper backend={backend} not supported. " \
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f"supported={AGENT_WRAPPER_REGISTRY.registered_modules}"
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llm = self.get_llm(agent_wrapper_config, llm=self.llm)
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agent_wrapper: AgentWrapperMixin = AGENT_WRAPPER_REGISTRY[backend](
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**agent_wrapper_config, llm=llm, context_generator=self.context_generator)
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logger.info(f"agent_wrapper is inited with backend={backend} params={agent_wrapper_config}")
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return agent_wrapper
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@model_validator(mode="after")
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def init_modules(self):
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if self.llm_config:
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||||
self.llm = self.init_llm(self.llm_config)
|
||||
|
||||
if self.embedding_model_config:
|
||||
self.embedding_model = self.init_embedding_model(self.embedding_model_config)
|
||||
|
||||
if self.vector_store_config:
|
||||
self.vector_store = self.init_vector_store(self.vector_store_config)
|
||||
|
||||
if self.context_generator_config:
|
||||
self.context_generator = self.init_context_generator(self.context_generator_config)
|
||||
|
||||
if self.summarizer_config:
|
||||
self.summarizer = self.init_summarizer(self.summarizer_config)
|
||||
|
||||
if self.agent_wrapper_config:
|
||||
self.agent_wrapper = self.init_agent_wrapper(self.agent_wrapper_config)
|
||||
|
||||
def call_agent_wrapper(self, request: AgentWrapperRequest) -> AgentWrapperResponse:
|
||||
assert self.agent_wrapper is not None, "agent_wrapper must be provided."
|
||||
trajectory: Trajectory = self.agent_wrapper.execute(request.query, **request.metadata)
|
||||
return AgentWrapperResponse(trajectory=trajectory)
|
||||
|
||||
def call_context_generator(self, request: ContextGeneratorRequest) -> ContextGeneratorResponse:
|
||||
assert self.context_generator is not None, "context_generator must be provided."
|
||||
context_msg: ContextMessage = self.context_generator.execute(request.trajectory, **request.metadata)
|
||||
return ContextGeneratorResponse(context_msg=context_msg)
|
||||
|
||||
def call_summarizer(self, request: SummarizerRequest) -> SummarizerResponse:
|
||||
assert self.summarizer is not None, "summarizer must be provided."
|
||||
experiences: List[Experience] = self.summarizer.execute(request.trajectories, request.return_experience,
|
||||
**request.metadata)
|
||||
return SummarizerResponse(experiences=experiences)
|
||||
50
experiencemaker/service/http_service.py
Normal file
50
experiencemaker/service/http_service.py
Normal file
|
|
@ -0,0 +1,50 @@
|
|||
import argparse
|
||||
import json
|
||||
|
||||
import uvicorn
|
||||
from fastapi import FastAPI
|
||||
|
||||
from experiencemaker.schema.request import AgentWrapperRequest, ContextGeneratorRequest, SummarizerRequest
|
||||
from experiencemaker.schema.response import AgentWrapperResponse, ContextGeneratorResponse, SummarizerResponse
|
||||
from experiencemaker.service.experience_maker_service import ExperienceMakerService
|
||||
from experiencemaker.utils.file_handler import FileHandler
|
||||
|
||||
app = FastAPI()
|
||||
service: ExperienceMakerService | None = None
|
||||
|
||||
@app.post('/agent_wrapper', response_model=AgentWrapperResponse)
|
||||
def call_agent_wrapper(request: AgentWrapperRequest):
|
||||
return service.call_agent_wrapper(request)
|
||||
|
||||
|
||||
@app.post('/context_generator', response_model=ContextGeneratorResponse)
|
||||
def call_context_generator(request: ContextGeneratorRequest):
|
||||
return service.call_context_generator(request)
|
||||
|
||||
|
||||
@app.post('/summarizer', response_model=SummarizerResponse)
|
||||
def call_summarizer(request: SummarizerRequest):
|
||||
return service.call_summarizer(request)
|
||||
|
||||
|
||||
# launch with: python -m experiencemaker.service.http_service
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--config', type=str, help='config dict')
|
||||
parser.add_argument('--config_path', type=str, help='config load path')
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.config_path:
|
||||
config = FileHandler(file_path=args.config_path).load()
|
||||
elif args.config:
|
||||
config = json.loads(args.config)
|
||||
else:
|
||||
raise RuntimeError("both config and config_path are not specified")
|
||||
|
||||
service = ExperienceMakerService(**config)
|
||||
uvicorn.run(app,
|
||||
host=service.host,
|
||||
port=service.port,
|
||||
timeout_keep_alive=service.timeout_keep_alive,
|
||||
limit_concurrency=service.limit_concurrency)
|
||||
Loading…
Add table
Reference in a new issue