format fix

experiencemaker/module/agent_wrapper/agent_wrapper_mixin.py

@@ -14,5 +14,3 @@ class AgentWrapperMixin(BaseModel, ABC):
 
     def execute(self, query: str, **kwargs) -> Trajectory:
         raise NotImplementedError
-
-

experiencemaker/module/context_generator/base_context_generator.py

@@ -21,9 +21,9 @@ class BaseContextGenerator(BaseModel, ABC):
         raise NotImplementedError
 
     def _generate_context_message(self,
-                                 trajectory: Trajectory,
-                                 nodes: List[VectorStoreNode],
-                                 **kwargs) -> ContextMessage:
+                                  trajectory: Trajectory,
+                                  nodes: List[VectorStoreNode],
+                                  **kwargs) -> ContextMessage:
         raise NotImplementedError
 
     def execute(self, trajectory: Trajectory, **kwargs) -> ContextMessage:

experiencemaker/module/context_generator/step_context_generator.py

@@ -1,6 +1,7 @@
 import json
 import re
-from typing import List, Dict, Any, Optional
+from typing import List
+
 from loguru import logger
 from pydantic import Field, model_validator
 
@@ -390,4 +391,4 @@ class StepContextGenerator(BaseContextGenerator):
         except json.JSONDecodeError:
             logger.warning(f"Failed to parse JSON response for key '{key}'")
 
-        return ""
+        return ""

experiencemaker/module/prompt/prompt_mixin.py

@@ -1,7 +1,6 @@
 from pathlib import Path
 
 import yaml
-from langchain_core.prompts import load_prompt
 from loguru import logger
 from pydantic import BaseModel, Field, model_validator
 

experiencemaker/module/runner/base_runner.py

@@ -24,4 +24,4 @@ class BaseRunner(BaseModel):
         raise NotImplementedError
 
     def summary(self, **kwargs):
-        raise NotImplementedError
+        raise NotImplementedError

experiencemaker/module/summarizer/simple_summarizer_prompt.yaml

@@ -16,7 +16,7 @@ summary_prompt: |
   Finally, summarize generalized experience from handling such problems to accumulate experience for future similar tasks.  
   The experience should be broadly applicable, such as how to use tools effectively or approaches to solving certain types of problems.  
   Also, specify the conditions or scenarios in which these experience are applicable.
-    
+  
   # Output Format
   <condition> Output the scenarios or conditions in which applying this experience would be particularly effective... </condition>
   <experience> Output generalized experience, concise content is required... </experience>

experiencemaker/module/summarizer/step_summarizer.py

@@ -1,8 +1,9 @@
+import json
 import re
 import uuid
-import json
-from typing import List, Dict, Any, Optional, Tuple
 from datetime import datetime
+from typing import List, Dict, Any, Optional, Tuple
+
 from loguru import logger
 from pydantic import Field, model_validator
 
@@ -577,4 +578,4 @@ class StepSummarizer(BaseSummarizer):
             all_experiences.extend(sample.steps)
 
         if all_experiences:
-            self.store_experiences(all_experiences, **kwargs)
+            self.store_experiences(all_experiences, **kwargs)

experiencemaker/service/experience_maker_service.py

@@ -159,7 +159,8 @@ class ExperienceMakerService(BaseModel):
                 data["embedding_model"] = cls.init_embedding_model(data["embedding_model"])
 
             if "vector_store" in data:
-                data["vector_store"] = cls.init_vector_store(data["vector_store"], embedding_model=data["embedding_model"])
+                data["vector_store"] = cls.init_vector_store(data["vector_store"],
+                                                             embedding_model=data["embedding_model"])
 
             if "context_generator" in data:
                 data["context_generator"] = cls.init_context_generator(data["context_generator"], data)

experiencemaker/storage/base_vector_store.py

@@ -24,5 +24,3 @@ class BaseVectorStore(BaseModel, ABC):
 
     def retrieve_by_query(self, query: str, top_k: int = 3, **kwargs) -> List[VectorStoreNode]:
         raise NotImplementedError
-
-

experiencemaker/tool/dashscope_search_tool.py

@@ -140,6 +140,7 @@ Extract the original content related to the user's question directly from the co
         else:
             return result
 
+
 def main():
     from experiencemaker.utils.util_function import load_env_keys
     load_env_keys()

experiencemaker/tool/mcp_tool.py

@@ -1,4 +1,3 @@
-
 import asyncio
 from typing import List
 

experiencemaker/utils/http_client.py

@@ -24,7 +24,6 @@ class HttpClient(BaseModel):
 
     _client: Any = PrivateAttr()
 
-
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self._client = requests.Session() if self.keep_alive else requests

experiencemaker/utils/registry.py

@@ -40,4 +40,4 @@ class Registry(Generic[T]):
         return self.module_dict[module_name]
 
     def __contains__(self, module_name: str):
-        return module_name in self.module_dict
+        return module_name in self.module_dict

experiencemaker/utils/util_function.py

@@ -1,8 +1,10 @@
 import json
 import os
 import re
+
 from loguru import logger
 
+
 def get_html_match_content(content: str, key: str):
     pattern = rf"<{key}>(.*?)</{key}>"
     match = re.search(pattern, content, re.DOTALL)
@@ -18,4 +20,4 @@ def load_env_keys():
             for k, v in config.items():
                 os.environ[k] = v
     else:
-        logger.warning(".env file not found~")
+        logger.warning(".env file not found~")