mirror of
https://github.com/agentscope-ai/ReMe.git
synced 2026-08-28 05:25:04 +00:00
bug fix
This commit is contained in:
jinli.yl
parent
7192619070
commit
a76cde5f41
28 changed files with 203 additions and 194 deletions
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@ -0,0 +1,13 @@
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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.model.openai_compatible_embedding_model import OpenAICompatibleEmbeddingModel
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from experiencemaker.model.openai_compatible_llm import OpenAICompatibleBaseLLM
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from experiencemaker.utils.registry import Registry
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EMBEDDING_MODEL_REGISTRY = Registry[BaseEmbeddingModel]("embedding_model")
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EMBEDDING_MODEL_REGISTRY.register(OpenAICompatibleEmbeddingModel, "openai_compatible")
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LLM_REGISTRY = Registry[BaseLLM]("llm")
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LLM_REGISTRY.register(OpenAICompatibleBaseLLM, "openai_compatible")
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@ -5,7 +5,6 @@ from loguru import logger
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from pydantic import BaseModel, Field
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from experiencemaker.schema.vector_store_node import VectorStoreNode
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from experiencemaker.utils.registry import Registry
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class BaseEmbeddingModel(BaseModel, ABC):
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@ -65,7 +64,7 @@ class BaseEmbeddingModel(BaseModel, ABC):
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- nodes (VectorStoreNode | List[VectorStoreNode]): A single node or list of nodes whose embeddings need to be retrieved.
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Returns:
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- (VectorStoreNode | List[VectorStoreNode]): Returns the input nodes with their vector attribute populated with embeddings.
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- VectorStoreNode | List[VectorStoreNode]: Returns the input nodes with their vector attribute populated with embeddings.
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Raises:
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- RuntimeError: If the input is neither a VectorStoreNode nor a list of VectorStoreNodes, a RuntimeError is raised.
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@ -85,6 +84,3 @@ class BaseEmbeddingModel(BaseModel, ABC):
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else:
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raise RuntimeError(f"unsupported type={type(nodes)}")
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EMBEDDING_MODEL_REGISTRY = Registry[BaseEmbeddingModel]("embedding_model")
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@ -6,7 +6,6 @@ from pydantic import Field, BaseModel
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from experiencemaker.schema.trajectory import Message, ActionMessage
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from experiencemaker.tool.base_tool import BaseTool
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from experiencemaker.utils.registry import Registry
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class BaseLLM(BaseModel, ABC):
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@ -109,6 +108,3 @@ class BaseLLM(BaseModel, ABC):
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raise e
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return None
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LLM_REGISTRY = Registry[BaseLLM]("llm")
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@ -4,7 +4,7 @@ from typing import Literal, List
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from openai import OpenAI
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from pydantic import Field, PrivateAttr, 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_embedding_model import BaseEmbeddingModel
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class OpenAICompatibleEmbeddingModel(BaseEmbeddingModel):
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@ -73,9 +73,6 @@ class OpenAICompatibleEmbeddingModel(BaseEmbeddingModel):
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raise RuntimeError(f"unsupported type={type(input_text)}")
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EMBEDDING_MODEL_REGISTRY.register(OpenAICompatibleEmbeddingModel, "openai_compatible")
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def main():
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from experiencemaker.utils.util_function import load_env_keys
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load_env_keys()
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@ -7,7 +7,7 @@ from openai.types import CompletionUsage
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from pydantic import Field, PrivateAttr, model_validator
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from experiencemaker.enumeration.chunk_enum import ChunkEnum
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from experiencemaker.model.base_llm import BaseLLM, LLM_REGISTRY
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from experiencemaker.model.base_llm import BaseLLM
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from experiencemaker.schema.trajectory import Message, ActionMessage, ToolCall
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from experiencemaker.tool.base_tool import BaseTool
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@ -152,9 +152,6 @@ class OpenAICompatibleBaseLLM(BaseLLM):
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print(f"\n<error>{chunk}</error>", end="")
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LLM_REGISTRY.register(OpenAICompatibleBaseLLM, "openai_compatible")
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def main():
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from experiencemaker.utils.util_function import load_env_keys
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from experiencemaker.tool.dashscope_search_tool import DashscopeSearchTool
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@ -0,0 +1,7 @@
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
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from experiencemaker.module.agent_wrapper.simple_agent_wrapper import SimpleAgentWrapper
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from experiencemaker.utils.registry import Registry
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AGENT_WRAPPER_REGISTRY = Registry[AgentWrapperMixin]("agent_wrapper")
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AGENT_WRAPPER_REGISTRY.register(SimpleAgentWrapper, "simple")
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@ -2,17 +2,17 @@ from abc import ABC
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from pydantic import Field, BaseModel
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from experiencemaker.model.base_llm import BaseLLM
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.schema.trajectory import Trajectory
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from experiencemaker.utils.registry import Registry
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class AgentWrapperMixin(BaseModel, ABC):
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context_generator: BaseContextGenerator | None = Field(default=None)
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llm: BaseLLM | None = Field(default=None)
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workspace_id: str = Field(default="")
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def execute(self, query: str, **kwargs) -> Trajectory:
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raise NotImplementedError
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AGENT_WRAPPER_REGISTRY = Registry[AgentWrapperMixin]("agent_wrapper")
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@ -1,4 +1,4 @@
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin, AGENT_WRAPPER_REGISTRY
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
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from experiencemaker.module.agent_wrapper.simple_agent import SimpleAgent
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from experiencemaker.schema.trajectory import Trajectory
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@ -16,6 +16,3 @@ class SimpleAgentWrapper(SimpleAgent, AgentWrapperMixin):
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trajectory.answer = messages[-1].content
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trajectory.done = True
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return trajectory
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AGENT_WRAPPER_REGISTRY.register(SimpleAgentWrapper, "simple")
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@ -0,0 +1,7 @@
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.module.context_generator.simple_context_generator import SimpleContextGenerator
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from experiencemaker.utils.registry import Registry
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CONTEXT_GENERATOR_REGISTRY = Registry[BaseContextGenerator]("context_generator")
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CONTEXT_GENERATOR_REGISTRY.register(SimpleContextGenerator, "simple")
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@ -3,12 +3,10 @@ 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.trajectory import Trajectory, ContextMessage
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from experiencemaker.schema.vector_store_node import VectorStoreNode
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from experiencemaker.storage.base_vector_store import BaseVectorStore
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from experiencemaker.utils.registry import Registry
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class BaseContextGenerator(BaseModel, ABC):
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@ -33,6 +31,3 @@ class BaseContextGenerator(BaseModel, ABC):
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nodes: List[VectorStoreNode] = self._retrieve_by_query(trajectory, query, **kwargs)
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context_msg: ContextMessage = self._generate_context_message(trajectory, nodes, **kwargs)
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return context_msg
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CONTEXT_GENERATOR_REGISTRY = Registry[BaseContextGenerator]("context_generator")
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@ -1,12 +1,14 @@
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from typing import List
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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 pydantic import Field
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.schema.trajectory import Trajectory, ContextMessage
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from experiencemaker.schema.vector_store_node import VectorStoreNode
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class SimpleContextGenerator(BaseContextGenerator):
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retrieve_top_k: int = Field(default=5)
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def _build_retrieve_query(self, trajectory: Trajectory, **kwargs) -> str:
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query = ""
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@ -18,7 +20,7 @@ class SimpleContextGenerator(BaseContextGenerator):
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if not query:
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return []
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return self.vector_store.retrieve_by_query(query=query, top_k=self.vector_store_top_k)
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return self.vector_store.retrieve_by_query(query=query, top_k=self.retrieve_top_k)
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def _generate_context_message(self,
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trajectory: Trajectory,
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@ -35,6 +37,3 @@ class SimpleContextGenerator(BaseContextGenerator):
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content += f"- {node.content} {experience}\n"
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return ContextMessage(content=content.strip())
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CONTEXT_GENERATOR_REGISTRY.register(SimpleContextGenerator, "simple")
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@ -1,6 +1,7 @@
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from pathlib import Path
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import yaml
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from langchain_core.prompts import load_prompt
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from loguru import logger
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from pydantic import BaseModel, Field, model_validator
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@ -23,13 +24,13 @@ class PromptMixin(BaseModel):
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else:
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with self.prompt_file_path.open("r") as f:
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for k, v in yaml.load(f, yaml.FullLoader):
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load_prompt_dict = yaml.load(f, yaml.FullLoader)
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for k, v in load_prompt_dict.items():
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if k not in self.prompt_dict:
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self.prompt_dict[k] = v
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logger.info(f"add prompt_dict key={k}")
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else:
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logger.warning(f"key={k} is already exists in prompt_dict!")
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return self
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def prompt_format(self, prompt_name: str, **kwargs):
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@ -0,0 +1,6 @@
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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from experiencemaker.module.summarizer.simple_summarizer import SimpleSummarizer
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from experiencemaker.utils.registry import Registry
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SUMMARIZER_REGISTRY = Registry[BaseSummarizer]("summarizer")
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SUMMARIZER_REGISTRY.register(SimpleSummarizer, "simple")
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@ -8,7 +8,6 @@ from experiencemaker.schema.experience import Experience
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from experiencemaker.schema.trajectory import Trajectory
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from experiencemaker.schema.vector_store_node import VectorStoreNode
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from experiencemaker.storage.base_vector_store import BaseVectorStore
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from experiencemaker.utils.registry import Registry
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class BaseSummarizer(BaseModel, ABC):
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@ -28,6 +27,3 @@ class BaseSummarizer(BaseModel, ABC):
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if return_experience:
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return experiences
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return []
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SUMMARIZER_REGISTRY = Registry[BaseSummarizer]("summarizer")
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@ -6,7 +6,7 @@ from pydantic import Field
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from experiencemaker.enumeration.role import Role
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from experiencemaker.module.prompt.prompt_mixin import PromptMixin
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer, SUMMARIZER_REGISTRY
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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from experiencemaker.schema.experience import Experience
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from experiencemaker.schema.trajectory import Trajectory, Message, ActionMessage
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from experiencemaker.utils.util_function import get_html_match_content
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@ -63,6 +63,3 @@ class SimpleSummarizer(BaseSummarizer, PromptMixin):
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if experience:
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experiences.append(experience)
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return experiences
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SUMMARIZER_REGISTRY.register(SimpleSummarizer, "simple")
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@ -5,7 +5,7 @@ from experiencemaker.schema.response import AgentWrapperResponse, ContextGenerat
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from experiencemaker.utils.http_client import HttpClient
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class ModelServiceClient(HttpClient):
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class ExperienceMakerClient(HttpClient):
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base_url: str = Field(default=...)
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def call_agent_wrapper(self, request: AgentWrapperRequest):
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@ -1,34 +1,41 @@
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import argparse
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import json
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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 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.utils.util_function import load_env_keys
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load_env_keys()
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from experiencemaker.model import LLM_REGISTRY, EMBEDDING_MODEL_REGISTRY
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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.module.agent_wrapper import AGENT_WRAPPER_REGISTRY
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from experiencemaker.module.agent_wrapper.agent_wrapper_mixin import AgentWrapperMixin
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from experiencemaker.module.context_generator import CONTEXT_GENERATOR_REGISTRY
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from experiencemaker.module.context_generator.base_context_generator import BaseContextGenerator
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from experiencemaker.module.summarizer import SUMMARIZER_REGISTRY
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from experiencemaker.module.summarizer.base_summarizer import BaseSummarizer
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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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from experiencemaker.storage import VECTOR_STORE_REGISTRY
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from experiencemaker.storage.base_vector_store import BaseVectorStore
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from experiencemaker.utils.file_handler import FileHandler
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class ExperienceMakerService(BaseModel):
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workspace_id: str = Field(default="")
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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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@ -41,15 +48,16 @@ class ExperienceMakerService(BaseModel):
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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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f"supported={LLM_REGISTRY.registered_module_names}"
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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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@classmethod
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def get_llm(cls, 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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llm = cls.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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@ -59,106 +67,166 @@ class ExperienceMakerService(BaseModel):
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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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f"supported={EMBEDDING_MODEL_REGISTRY.registered_module_names}"
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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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@classmethod
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def get_embedding_model(cls, 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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embedding_model = cls.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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@classmethod
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def init_vector_store(cls, vector_store_config: dict,
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embedding_model: BaseEmbeddingModel = None) -> 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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f"supported={VECTOR_STORE_REGISTRY.registered_module_names}"
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embedding_model = cls.get_embedding_model(vector_store_config, embedding_model=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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@classmethod
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def get_vector_store(cls, config: dict, vector_store: BaseVectorStore = None,
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embedding_model: BaseEmbeddingModel = 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)
|
||||
vector_store = cls.init_vector_store(vector_store_config, embedding_model=embedding_model)
|
||||
elif vector_store is None:
|
||||
raise RuntimeError("vector_store must be provided.")
|
||||
return vector_store
|
||||
|
||||
def init_context_generator(self, context_generator_config: dict) -> BaseContextGenerator:
|
||||
@classmethod
|
||||
def init_context_generator(cls, context_generator_config: dict, data: dict) -> BaseContextGenerator:
|
||||
backend = context_generator_config.pop("backend", None)
|
||||
assert backend is not None, "context_generator must have a backend like `simple`."
|
||||
assert backend in CONTEXT_GENERATOR_REGISTRY, f"context_generator backend={backend} not supported. " \
|
||||
f"supported={CONTEXT_GENERATOR_REGISTRY.registered_modules}"
|
||||
llm = self.get_llm(context_generator_config, llm=self.llm)
|
||||
vector_store = self.get_vector_store(context_generator_config, vector_store=self.vector_store)
|
||||
f"supported={CONTEXT_GENERATOR_REGISTRY.registered_module_names}"
|
||||
|
||||
llm = cls.get_llm(context_generator_config, llm=data.get("llm"))
|
||||
vector_store = cls.get_vector_store(context_generator_config, vector_store=data.get("vector_store"),
|
||||
embedding_model=data.get("embedding_model"))
|
||||
|
||||
context_generator: BaseContextGenerator = CONTEXT_GENERATOR_REGISTRY[backend](
|
||||
**context_generator_config, llm=llm, vector_store=vector_store)
|
||||
**context_generator_config, llm=llm, vector_store=vector_store, workspace_id=data.get("workspace_id", ""))
|
||||
logger.info(f"context_generator is inited with backend={backend} params={context_generator_config}")
|
||||
return context_generator
|
||||
|
||||
def init_summarizer(self, summarizer_config: dict) -> BaseSummarizer:
|
||||
@classmethod
|
||||
def init_summarizer(cls, summarizer_config: dict, data: dict) -> BaseSummarizer:
|
||||
backend = summarizer_config.pop("backend", None)
|
||||
assert backend is not None, "summarizer must have a backend like `simple`."
|
||||
assert backend in SUMMARIZER_REGISTRY, f"summarizer backend={backend} not supported. " \
|
||||
f"supported={SUMMARIZER_REGISTRY.registered_modules}"
|
||||
llm = self.get_llm(summarizer_config, llm=self.llm)
|
||||
vector_store = self.get_vector_store(summarizer_config, vector_store=self.vector_store)
|
||||
summarizer: BaseSummarizer = SUMMARIZER_REGISTRY[backend](**summarizer_config,
|
||||
llm=llm, vector_store=vector_store)
|
||||
f"supported={SUMMARIZER_REGISTRY.registered_module_names}"
|
||||
|
||||
llm = cls.get_llm(summarizer_config, llm=data.get("llm"))
|
||||
vector_store = cls.get_vector_store(summarizer_config, vector_store=data.get("vector_store"),
|
||||
embedding_model=data.get("embedding_model"))
|
||||
summarizer: BaseSummarizer = SUMMARIZER_REGISTRY[backend](
|
||||
**summarizer_config, llm=llm, vector_store=vector_store, workspace_id=data.get("workspace_id", ""))
|
||||
logger.info(f"summarizer is inited with backend={backend} params={summarizer_config}")
|
||||
return summarizer
|
||||
|
||||
def init_agent_wrapper(self, agent_wrapper_config: dict) -> AgentWrapperMixin:
|
||||
@classmethod
|
||||
def init_agent_wrapper(cls, agent_wrapper_config: dict, data: dict) -> AgentWrapperMixin:
|
||||
backend = agent_wrapper_config.pop("backend", None)
|
||||
assert backend is not None, "agent_wrapper must have a backend like `simple`."
|
||||
assert backend in AGENT_WRAPPER_REGISTRY, f"agent_wrapper backend={backend} not supported. " \
|
||||
f"supported={AGENT_WRAPPER_REGISTRY.registered_modules}"
|
||||
|
||||
llm = self.get_llm(agent_wrapper_config, llm=self.llm)
|
||||
f"supported={AGENT_WRAPPER_REGISTRY.registered_module_names}"
|
||||
|
||||
llm = cls.get_llm(agent_wrapper_config, llm=data.get("llm"))
|
||||
agent_wrapper: AgentWrapperMixin = AGENT_WRAPPER_REGISTRY[backend](
|
||||
**agent_wrapper_config, llm=llm, context_generator=self.context_generator)
|
||||
**agent_wrapper_config, llm=llm, context_generator=data.get("context_generator"),
|
||||
workspace_id=data.get("workspace_id", ""))
|
||||
logger.info(f"agent_wrapper is inited with backend={backend} params={agent_wrapper_config}")
|
||||
return agent_wrapper
|
||||
|
||||
@model_validator(mode="after")
|
||||
def init_modules(self):
|
||||
if self.llm_config:
|
||||
self.llm = self.init_llm(self.llm_config)
|
||||
@model_validator(mode="before") # noqa
|
||||
@classmethod
|
||||
def init_modules(cls, data: dict):
|
||||
try:
|
||||
if "llm" in data:
|
||||
data["llm"] = cls.init_llm(data["llm"])
|
||||
|
||||
if self.embedding_model_config:
|
||||
self.embedding_model = self.init_embedding_model(self.embedding_model_config)
|
||||
if "embedding_model" in data:
|
||||
data["embedding_model"] = cls.init_embedding_model(data["embedding_model"])
|
||||
|
||||
if self.vector_store_config:
|
||||
self.vector_store = self.init_vector_store(self.vector_store_config)
|
||||
if "vector_store" in data:
|
||||
data["vector_store"] = cls.init_vector_store(data["vector_store"], embedding_model=data["embedding_model"])
|
||||
|
||||
if self.context_generator_config:
|
||||
self.context_generator = self.init_context_generator(self.context_generator_config)
|
||||
if "context_generator" in data:
|
||||
data["context_generator"] = cls.init_context_generator(data["context_generator"], data)
|
||||
|
||||
if self.summarizer_config:
|
||||
self.summarizer = self.init_summarizer(self.summarizer_config)
|
||||
if "summarizer" in data:
|
||||
data["summarizer"] = cls.init_summarizer(data["summarizer"], data)
|
||||
|
||||
if self.agent_wrapper_config:
|
||||
self.agent_wrapper = self.init_agent_wrapper(self.agent_wrapper_config)
|
||||
if "agent_wrapper" in data:
|
||||
data["agent_wrapper"] = cls.init_agent_wrapper(data["agent_wrapper"], data)
|
||||
except Exception as e:
|
||||
logger.exception(e.args)
|
||||
return data
|
||||
|
||||
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)
|
||||
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)
|
||||
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)
|
||||
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)
|
||||
|
||||
|
||||
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)
|
||||
|
||||
|
||||
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:
|
||||
parse_config = FileHandler(file_path=args.config_path).load()
|
||||
elif args.config:
|
||||
parse_config = json.loads(args.config)
|
||||
else:
|
||||
raise RuntimeError("both config and config_path are not specified")
|
||||
|
||||
service = ExperienceMakerService(**parse_config)
|
||||
uvicorn.run(app,
|
||||
host=service.host,
|
||||
port=service.port,
|
||||
timeout_keep_alive=service.timeout_keep_alive,
|
||||
limit_concurrency=service.limit_concurrency)
|
||||
|
||||
# launch with: python -m experiencemaker.service.http_service
|
||||
|
|
|
|||
|
|
@ -1,50 +0,0 @@
|
|||
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)
|
||||
|
|
@ -0,0 +1,8 @@
|
|||
from experiencemaker.storage.base_vector_store import BaseVectorStore
|
||||
from experiencemaker.storage.es_vector_store import EsVectorStore
|
||||
from experiencemaker.storage.file_vector_store import FileVectorStore
|
||||
from experiencemaker.utils.registry import Registry
|
||||
|
||||
VECTOR_STORE_REGISTRY = Registry[BaseVectorStore]("vector_store")
|
||||
VECTOR_STORE_REGISTRY.register(EsVectorStore, "elasticsearch")
|
||||
VECTOR_STORE_REGISTRY.register(FileVectorStore, "local_file")
|
||||
|
|
@ -5,7 +5,6 @@ from pydantic import BaseModel, Field
|
|||
|
||||
from experiencemaker.model.base_embedding_model import BaseEmbeddingModel
|
||||
from experiencemaker.schema.vector_store_node import VectorStoreNode
|
||||
from experiencemaker.utils.registry import Registry
|
||||
|
||||
|
||||
class BaseVectorStore(BaseModel, ABC):
|
||||
|
|
@ -27,4 +26,3 @@ class BaseVectorStore(BaseModel, ABC):
|
|||
raise NotImplementedError
|
||||
|
||||
|
||||
VECTOR_STORE_REGISTRY = Registry[BaseVectorStore]("vector_store")
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ from pydantic import Field, PrivateAttr, model_validator
|
|||
|
||||
from experiencemaker.model.openai_compatible_embedding_model import OpenAICompatibleEmbeddingModel
|
||||
from experiencemaker.schema.vector_store_node import VectorStoreNode
|
||||
from experiencemaker.storage.base_vector_store import BaseVectorStore, VECTOR_STORE_REGISTRY
|
||||
from experiencemaker.storage.base_vector_store import BaseVectorStore
|
||||
|
||||
|
||||
class EsVectorStore(BaseVectorStore):
|
||||
|
|
@ -170,9 +170,6 @@ class EsVectorStore(BaseVectorStore):
|
|||
return nodes
|
||||
|
||||
|
||||
VECTOR_STORE_REGISTRY.register(EsVectorStore, "elasticsearch")
|
||||
|
||||
|
||||
def main():
|
||||
from experiencemaker.utils.util_function import load_env_keys
|
||||
load_env_keys()
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from pydantic import Field, model_validator, PrivateAttr
|
|||
|
||||
from experiencemaker.model.openai_compatible_embedding_model import OpenAICompatibleEmbeddingModel
|
||||
from experiencemaker.schema.vector_store_node import VectorStoreNode
|
||||
from experiencemaker.storage.base_vector_store import BaseVectorStore, VECTOR_STORE_REGISTRY
|
||||
from experiencemaker.storage.base_vector_store import BaseVectorStore
|
||||
|
||||
|
||||
class FileVectorStore(BaseVectorStore):
|
||||
|
|
@ -133,9 +133,6 @@ class FileVectorStore(BaseVectorStore):
|
|||
return nodes[:top_k]
|
||||
|
||||
|
||||
VECTOR_STORE_REGISTRY.register(FileVectorStore, "local_file")
|
||||
|
||||
|
||||
def main():
|
||||
from experiencemaker.utils.util_function import load_env_keys
|
||||
load_env_keys()
|
||||
|
|
|
|||
|
|
@ -1,13 +1,12 @@
|
|||
from experiencemaker.tool.base_tool import BaseTool
|
||||
from experiencemaker.tool.code_tool import CodeTool
|
||||
from experiencemaker.tool.dashscope_search_tool import DashscopeSearchTool
|
||||
from experiencemaker.tool.terminate_tool import TerminateTool
|
||||
# from experiencemaker.tool.python_tools.code_tool import CodeTool
|
||||
# from experiencemaker.tool.python_tools.dashscope_search_tool import DashscopeSearchTool
|
||||
# from experiencemaker.tool.python_tools.terminate_tool import TerminateTool
|
||||
|
||||
# from experiencemaker.utils.registry import Registry
|
||||
from experiencemaker.utils.registry import Registry
|
||||
|
||||
# TOOL_REGISTRY = Registry("tools")
|
||||
# TOOL_REGISTRY.register(CodeTool)
|
||||
# TOOL_REGISTRY.register(DashscopeSearchTool)
|
||||
# TOOL_REGISTRY.register(TerminateTool)
|
||||
TOOL_REGISTRY = Registry[BaseTool]("tool")
|
||||
|
||||
TOOL_REGISTRY.register(CodeTool, "code")
|
||||
TOOL_REGISTRY.register(DashscopeSearchTool, "web_search")
|
||||
TOOL_REGISTRY.register(TerminateTool, "terminate")
|
||||
|
|
|
|||
|
|
@ -3,8 +3,6 @@ from abc import ABC
|
|||
from loguru import logger
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from experiencemaker.utils.registry import Registry
|
||||
|
||||
|
||||
class BaseTool(BaseModel, ABC):
|
||||
tool_id: str = Field(default="")
|
||||
|
|
@ -79,6 +77,3 @@ class BaseTool(BaseModel, ABC):
|
|||
|
||||
def get_cache_id(self, **kwargs) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
TOOL_REGISTRY = Registry[BaseTool]("tool")
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import sys
|
||||
from io import StringIO
|
||||
|
||||
from experiencemaker.tool.base_tool import BaseTool, TOOL_REGISTRY
|
||||
from experiencemaker.tool.base_tool import BaseTool
|
||||
|
||||
|
||||
class CodeTool(BaseTool):
|
||||
|
|
@ -35,9 +35,6 @@ class CodeTool(BaseTool):
|
|||
return result
|
||||
|
||||
|
||||
TOOL_REGISTRY.register(CodeTool, "code")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
tool = CodeTool()
|
||||
print(tool.execute(code="print('Hello World')"))
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from dashscope.api_entities.dashscope_response import Message
|
|||
from loguru import logger
|
||||
from pydantic import Field
|
||||
|
||||
from experiencemaker.tool.base_tool import BaseTool, TOOL_REGISTRY
|
||||
from experiencemaker.tool.base_tool import BaseTool
|
||||
|
||||
|
||||
class DashscopeSearchTool(BaseTool):
|
||||
|
|
@ -140,10 +140,6 @@ Extract the original content related to the user's question directly from the co
|
|||
else:
|
||||
return result
|
||||
|
||||
|
||||
TOOL_REGISTRY.register(DashscopeSearchTool, "web_search")
|
||||
|
||||
|
||||
def main():
|
||||
from experiencemaker.utils.util_function import load_env_keys
|
||||
load_env_keys()
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from experiencemaker.tool.base_tool import BaseTool, TOOL_REGISTRY
|
||||
from experiencemaker.tool.base_tool import BaseTool
|
||||
|
||||
|
||||
class TerminateTool(BaseTool):
|
||||
|
|
@ -19,6 +19,3 @@ class TerminateTool(BaseTool):
|
|||
def execute(self, status: str):
|
||||
self.success = status in ["success", "failure"]
|
||||
return f"The interaction has been completed with status: {status}"
|
||||
|
||||
|
||||
TOOL_REGISTRY.register(TerminateTool, "terminate")
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ class Registry(Generic[T]):
|
|||
self.module_dict: Dict[str, T] = {}
|
||||
|
||||
@property
|
||||
def registered_modules(self) -> List[str]:
|
||||
def registered_module_names(self) -> List[str]:
|
||||
return sorted(self.module_dict.keys())
|
||||
|
||||
def register(self, module: T, module_name: str = None):
|
||||
|
|
@ -38,3 +38,6 @@ class Registry(Generic[T]):
|
|||
def __getitem__(self, module_name: str) -> T:
|
||||
assert module_name in self.module_dict, f"{module_name} not found in {self.name}"
|
||||
return self.module_dict[module_name]
|
||||
|
||||
def __contains__(self, module_name: str):
|
||||
return module_name in self.module_dict
|
||||
Loading…
Add table
Reference in a new issue