code format

.flake8

@@ -1,12 +1,6 @@
 [flake8]
 exclude =
-  scripts/*
-  src/agentscope/rpc/*
 max-line-length = 120
 inline-quotes = "
 avoid-escape = no
-ignore =
-    F401
-    F403
-    W503
-    E731
+ignore =

.pre-commit-config.yaml

@@ -25,12 +25,12 @@ repos:
     hooks:
       - id: mypy
         exclude:
-            (?x)(
-                pb2\.py$
-                | grpc\.py$
-                | ^docs
-                | \.html$
-            )
+          (?x)(
+          pb2\.py$
+          | grpc\.py$
+          | ^docs
+          | \.html$
+          )
         args: [ --disallow-untyped-defs,
                 --disallow-incomplete-defs,
                 --ignore-missing-imports,
@@ -42,7 +42,7 @@ repos:
                 --disable-error-code=truthy-function,
                 --follow-imports=skip,
                 --explicit-package-bases,
-                ]
+        ]
   # - repo: https://github.com/numpy/numpydoc
   #   rev: v1.6.0
   #   hooks:
@@ -50,25 +50,25 @@ repos:
   - repo: https://github.com/psf/black
     rev: 23.3.0
     hooks:
-    - id: black
-      args: [--line-length=79]
+      - id: black
+        args: [ --line-length=79 ]
   - repo: https://github.com/PyCQA/flake8
     rev: 6.1.0
     hooks:
       - id: flake8
-        args: ["--extend-ignore=E203"]
+        args: [ "--extend-ignore=E203" ]
   - repo: https://github.com/pylint-dev/pylint
     rev: v3.0.2
     hooks:
       - id: pylint
         exclude:
-            (?x)(
-                ^docs
-                | pb2\.py$
-                | grpc\.py$
-                | \.demo$
-                | \.md$
-                | \.html$
+          (?x)(
+          ^docs
+          | pb2\.py$
+          | grpc\.py$
+          | \.demo$
+          | \.md$
+          | \.html$
           )
         args: [
           --disable=W0511,
@@ -101,4 +101,4 @@ repos:
     rev: "4.0"
     hooks:
       - id: pyroma
-        args: [--min=10, .]
+        args: [ --min=10, . ]

README_ZH.md

@@ -2,10 +2,8 @@
 
 # ModelScope
 
-
 ## 概念解释
 
-
 - service: 在顶层的交互对象，用于定义operation的使用范围
 
 - operation: 读写记忆等，对于记忆的操作方法，是worker的有序组合（workflow）

memory_scope/memory/operation/base_operation.py

@@ -14,7 +14,7 @@ class BaseOperation(metaclass=ABCMeta):
         description (str): A description of the operation.
         kwargs (dict): Additional keyword arguments for operation configuration.
     """
-    
+
     operation_type: OPERATION_TYPE = "frontend"
 
     def __init__(self, name: str, description: str = ""):

memory_scope/memory/operation/base_workflow.py

@@ -50,7 +50,7 @@ class BaseWorkflow(object):
         pattern = r'(\[[^\]]*\]|[^,]+)'
         # Find all matches in the workflow string based on the pattern.
         workflow_split = re.findall(pattern, self.workflow)
-        
+
         for workflow_part in workflow_split:
             # e.g., [d,e,f|g,h]
             workflow_part = workflow_part.strip()
@@ -59,7 +59,7 @@ class BaseWorkflow(object):
 
             # Split the part by '|' to identify potential parallel task groups.
             line_split = [x.strip() for x in workflow_part.split("|") if x]
-            
+
             # Skip if no valid tasks are identified after splitting.
             if len(line_split) <= 0:
                 continue
@@ -75,7 +75,7 @@ class BaseWorkflow(object):
                 # add workers
                 for sub_item in sub_split:
                     self.worker_dict[sub_item] = is_multi_thread
-            
+
             # Append the parsed and structured tasks to the workflow execution plan.
             self.workflow_worker_list.append(line_split_split)
 
@@ -162,7 +162,7 @@ class BaseWorkflow(object):
         """
         with Timer(f"workflow.{self.name}", time_log_type="wrap"):
             self.context[WORKFLOW_NAME] = self.name
-            
+
             # Iterate over each part of the workflow
             for workflow_part in self.workflow_worker_list:
                 # Sequential execution for single-item parts
@@ -175,7 +175,7 @@ class BaseWorkflow(object):
                     # Submit tasks to the thread pool
                     for sub_workflow in workflow_part:
                         t_list.append(G_CONTEXT.thread_pool.submit(self._run_sub_workflow, sub_workflow))
-                    
+
                     # Check results; if any task returns False, stop the workflow
                     flag = True
                     for future in as_completed(t_list):
@@ -183,4 +183,3 @@ class BaseWorkflow(object):
                             flag = False
                     if not flag:
                         break
-            

memory_scope/memory/worker/frontend/extract_time_worker.yaml

@@ -4,7 +4,7 @@ extract_time_system:
 
   en: |
     Instructions: From the sentences and the time when they occurred, infer and extract the time periods indicated in the content of the sentences. Answer with the most complete time periods possible. The format of the answers must strictly adhere to the specifications in the examples provided. If the sentence does not involve time, respond with "none."
-    
+
 
 extract_time_few_shot:
   cn: |
@@ -104,7 +104,7 @@ extract_time_few_shot:
     Time: January 23, 2015, 4th week of 2015, Thursday, 7:38:0.
     Answer:
     None
-    
+
 
 extract_time_user_query:
   cn: |

memory_scope/memory/worker/frontend/fuse_rerank_worker.py

@@ -57,7 +57,7 @@ class FuseRerankWorker(MemoryBaseWorker):
         # Parse input parameters from the worker's context
         extract_time_dict: Dict[str, str] = self.get_context(EXTRACT_TIME_DICT)
         memory_node_list: List[MemoryNode] = self.memory_handler.get_memories(RANKED_MEMORY_NODES)
-        
+
         # Check if memory nodes are available; warn and return if not
         if not memory_node_list:
             self.logger.warning("Ranked memory nodes list is empty.")
@@ -69,16 +69,16 @@ class FuseRerankWorker(MemoryBaseWorker):
             # Skip nodes below the fuse score threshold
             if node.score_rank < self.fuse_score_threshold:
                 continue
-            
+
             # Calculate type-based adjustment factor
             if node.memory_type not in self.fuse_ratio_dict:
                 self.logger.warning(f"{node.memory_type} 'factor is not configured!")
             type_ratio: float = self.fuse_ratio_dict.get(node.memory_type, 0.1)
-            
+
             # Determine time relevance adjustment factor
             match_event_flag, match_msg_flag = self.match_node_time(extract_time_dict=extract_time_dict, node=node)
             fuse_time_ratio: float = self.fuse_time_ratio if match_event_flag or match_msg_flag else 1.0
-            
+
             # Apply reranking score adjustments
             node.score_rerank = node.score_rank * type_ratio * fuse_time_ratio
             reranked_memory_nodes.append(node)
@@ -89,7 +89,6 @@ class FuseRerankWorker(MemoryBaseWorker):
                                        key=lambda x: x.score_rerank,
                                        reverse=True)[: self.fuse_rerank_top_k]
         for i, node in enumerate(reranked_memory_nodes):
-
             # Log reranking details including flags for event and message matches
             self.logger.info(f"Rerank Stage: Content={node.content}, Score={node.score_rerank}, "
                              f"Event Flag={node.meta_data["match_event_flag"]}, "

memory_scope/memory/worker/frontend/semantic_rank_worker.py

@@ -51,7 +51,7 @@ class SemanticRankWorker(MemoryBaseWorker):
 
         # sort by score
         memory_node_list = sorted(memory_node_list, key=lambda n: n.score_rank, reverse=True)
-        
+
         # log ranked nodes
         for node in memory_node_list:
             self.logger.info(f"Rank stage: Content={node.content}, Score={node.score_rank}")

memory_scope/memory/worker/summary/update_insight_worker.py

@@ -199,4 +199,3 @@ class UpdateInsightWorker(MemoryBaseWorker):
         for node in not_reflected_nodes:
             node.obs_updated = 1
             node.action_status = ActionStatusEnum.MODIFIED
-

memory_scope/memory/worker/write/get_observation_with_time_worker.py

@@ -1,4 +1,5 @@
 from typing import List
+
 from memory_scope.constants.common_constants import NEW_OBS_WITH_TIME_NODES
 from memory_scope.constants.language_constants import COLON_WORD
 from memory_scope.memory.worker.write.get_observation_worker import GetObservationWorker
@@ -54,10 +55,10 @@ class GetObservationWithTimeWorker(GetObservationWorker):
         # Construct the system prompt with the count of observations
         system_prompt = self.prompt_handler.get_observation_with_time_system.format(num_obs=len(user_query_list),
                                                                                     user_name=self.target_name)
-        
+
         # Retrieve the few-shot examples for the prompt
         few_shot = self.prompt_handler.get_observation_with_time_few_shot.format(user_name=self.target_name)
-        
+
         # Format the user query section with the concatenated list of timestamped queries
         user_query = self.prompt_handler.get_observation_with_time_user_query.format(
             user_query="\n".join(user_query_list),
@@ -65,9 +66,9 @@ class GetObservationWithTimeWorker(GetObservationWorker):
 
         # Assemble the final message for observation retrieval
         obtain_obs_message = prompt_to_msg(system_prompt=system_prompt, few_shot=few_shot, user_query=user_query)
-        
+
         # Log the constructed message for debugging purposes
         self.logger.info(f"obtain_obs_message={obtain_obs_message}")
-        
+
         # Return the newly created message
         return obtain_obs_message

memory_scope/memory/worker/write/get_observation_worker.py

@@ -70,20 +70,20 @@ class GetObservationWorker(MemoryBaseWorker):
         # Format the system prompt with the number of observations and target name
         system_prompt = self.prompt_handler.get_observation_system.format(num_obs=len(user_query_list),
                                                                           user_name=self.target_name)
-        
+
         # Incorporate few-shot examples into the prompt with the target name
         few_shot = self.prompt_handler.get_observation_few_shot.format(user_name=self.target_name)
-        
+
         # Assemble the user query part of the prompt with the list of formatted user queries
         user_query = self.prompt_handler.get_observation_user_query.format(user_query="\n".join(user_query_list),
                                                                            user_name=self.target_name)
 
         # Combine system prompt, few-shot, and user query into a single message for obtaining observations
         obtain_obs_message = prompt_to_msg(system_prompt=system_prompt, few_shot=few_shot, user_query=user_query)
-        
+
         # Log the constructed observation message
         self.logger.info(f"obtain_obs_message={obtain_obs_message}")
-        
+
         # Return the processed message(s) for further steps in the observation workflow
         return obtain_obs_message
 

memory_scope/memory/worker/write/info_filter_worker.yaml

@@ -14,7 +14,7 @@ info_filter_system:
     Please ensure to output in the following format, and the final result must be enclosed in <>:
     Thought: The basis and process of thinking, within 30 words.
     Result: <Score: 0 or 1 or 2 or 3>
-    
+
 
 info_filter_few_shot:
   cn: |

memory_scope/models/llama_index_embedding_model.py

@@ -24,7 +24,7 @@ class LlamaIndexEmbeddingModel(BaseModel):
             model_class (type): The class of the model to register.
         """
         MODEL_REGISTRY.register(model_name, model_class)
-        
+
     MODEL_REGISTRY.register("dashscope_embedding", DashScopeEmbedding)
 
     def before_call(self, **kwargs):

memory_scope/models/llama_index_generation_model.py

@@ -63,6 +63,7 @@ class LlamaIndexGenerationModel(BaseModel):
                     model_response.message.content += response.delta
                     model_response.delta = response.delta
                     yield model_response
+
             return gen()
         else:
             if isinstance(call_result, CompletionResponse):
@@ -113,6 +114,6 @@ class LlamaIndexGenerationModel(BaseModel):
             response = await self.model.acomplete(**self.data)
         else:
             response = await self.model.achat(**self.data)
-        
+
         results.raw = response
         return results

memory_scope/utils/global_context.py

@@ -5,8 +5,8 @@ from memory_scope.chat.base_memory_chat import BaseMemoryChat
 from memory_scope.enumeration.language_enum import LanguageEnum
 from memory_scope.memory.service.base_memory_service import BaseMemoryService
 from memory_scope.models.base_model import BaseModel
-from memory_scope.storage.base_monitor import BaseMonitor
 from memory_scope.storage.base_memory_store import BaseMemoryStore
+from memory_scope.storage.base_monitor import BaseMonitor
 
 
 class GlobalContext(object):

memory_scope/utils/response_text_parser.py

@@ -1,8 +1,8 @@
 import re
 
-from memory_scope.utils.logger import Logger
 from memory_scope.constants.language_constants import NONE_WORD
 from memory_scope.utils.global_context import G_CONTEXT
+from memory_scope.utils.logger import Logger
 
 
 class ResponseTextParser(object):

memory_scope/utils/tool_functions.py

@@ -102,7 +102,8 @@ def prompt_to_msg(system_prompt: str, few_shot: str, user_query: str) -> List[Me
     return [
         Message(role=MessageRoleEnum.SYSTEM.value, content=system_prompt.strip()),  # System message
         Message(role=MessageRoleEnum.USER.value,
-                content="\n".join([x.strip() for x in [few_shot, system_prompt, user_query]]))  # User message combining few shot, system prompt, and user query
+                content="\n".join([x.strip() for x in [few_shot, system_prompt, user_query]]))
+        # User message combining few shot, system prompt, and user query
     ]
 
 

tests/llama_index_es_memory_store.py

@@ -1,12 +1,10 @@
-import ray
-
 from typing import Dict, List, Any, Optional, cast
 
+import ray
 from llama_index.core import VectorStoreIndex
 from llama_index.core.schema import TextNode, NodeWithScore
 from llama_index.vector_stores.elasticsearch import ElasticsearchStore, AsyncDenseVectorStrategy
 
-from memory_scope.enumeration.action_status_enum import ActionStatusEnum
 from memory_scope.models.base_model import BaseModel
 from memory_scope.scheme.memory_node import MemoryNode
 from memory_scope.storage.base_memory_store import BaseMemoryStore

tests/models/test_models_lli_rank.py

@@ -1,5 +1,6 @@
-import unittest
 import asyncio
+import unittest
+
 from memory_scope.models.llama_index_rank_model import LlamaIndexRankModel
 
 

tests/storages/test_storages_lli_synces.py

@@ -1,8 +1,9 @@
 import unittest
 
+from memory_scope.storage.llama_index_es_memory_store_sync import LlamaIndexEsMemoryStoreSync
+
 from memory_scope.models.llama_index_embedding_model import LlamaIndexEmbeddingModel
 from memory_scope.scheme.memory_node import MemoryNode
-from memory_scope.storage.llama_index_es_memory_store_sync import LlamaIndexEsMemoryStoreSync
 
 
 class TestLlamaIndexElasticSearchStore(unittest.TestCase):