Merge remote-tracking branch 'origin/main'

# Conflicts:
#	benchmark/longmemeval/eval_longmemeval_reme.py

README.md

@@ -300,15 +300,14 @@ async def main():
     result = await reme.summarize_memory(
         messages=messages,
         user_name="alice",  # Personal memory
-        task_name="code_writing",  # Task memory
+        # task_name="code_writing",  # Task memory
     )
     print(f"Summarize result: {result}")
 
     # 2. Retrieve relevant memory
     memories = await reme.retrieve_memory(
         query="Python programming",
-        user_name="alice",
-        task_name="code_writing",
+        # user_name="alice",
     )
     print(f"Retrieve result: {memories}")
 
@@ -316,7 +315,6 @@ async def main():
     memory_node = await reme.add_memory(
         memory_content="User prefers concise code style",
         user_name="alice",
-        when_to_use="When writing code for the user",
     )
     print(f"Added memory: {memory_node}")
     memory_id = memory_node.memory_id
@@ -330,7 +328,6 @@ async def main():
         memory_id=memory_id,
         user_name="alice",
         memory_content="User prefers concise, well-commented code style",
-        when_to_use="When writing or reviewing code for the user",
     )
     print(f"Updated memory: {updated_memory}")
 

README_ZH.md

@@ -291,7 +291,7 @@ async def main():
     result = await reme.summarize_memory(
         messages=messages,
         user_name="alice",  # 个人记忆
-        task_name="code_writing",  # 任务记忆
+        # task_name="code_writing",  # 任务记忆
     )
     print(f"总结结果: {result}")
 
@@ -299,7 +299,7 @@ async def main():
     memories = await reme.retrieve_memory(
         query="Python 编程",
         user_name="alice",
-        task_name="code_writing",
+        # task_name="code_writing",
     )
     print(f"检索结果: {memories}")
 
@@ -307,7 +307,6 @@ async def main():
     memory_node = await reme.add_memory(
         memory_content="用户喜欢简洁的代码风格",
         user_name="alice",
-        when_to_use="当为用户编写代码时",
     )
     print(f"添加的记忆: {memory_node}")
     memory_id = memory_node.memory_id
@@ -321,7 +320,6 @@ async def main():
         memory_id=memory_id,
         user_name="alice",
         memory_content="用户喜欢简洁且带注释的代码风格",
-        when_to_use="当为用户编写或审查代码时",
     )
     print(f"更新后的记忆: {updated_memory}")
 

benchmark/halumem/eval_reme.py

@@ -191,7 +191,7 @@ async def answer_question_with_memories(
     question: str,
     memories: str,
     user_id: str = None,
-    model_name: str = "qwen3-30b-a3b-instruct-2507",
+    eval_model_name: str = "qwen3-30b-a3b-instruct-2507",
 ):
     """
     Answer a question using retrieved memories with PROMPT_MEMZERO_JSON template.
@@ -201,7 +201,7 @@ async def answer_question_with_memories(
         question: The question to answer
         memories: The retrieved memories (formatted as context)
         user_id: Optional user ID for context formatting
-        model_name: Model name to use for LLM request
+        eval_model_name: Model name to use for LLM request
 
     Returns:
         dict with 'reasoning' and 'answer' fields
@@ -223,7 +223,7 @@ async def answer_question_with_memories(
         question=question,
     )
 
-    result = await reme.get_llm(model_name).simple_request_for_json(
+    result = await reme.get_llm(eval_model_name).simple_request_for_json(
         prompt=prompt,
         model_name=None,
     )
@@ -236,6 +236,7 @@ async def evaluation_for_memory_accuracy(
     dialogue: str,
     golden_memories: list[dict],
     candidate_memory: dict,
+    eval_model_name: str = "qwen-flash",
 ):
     """
     Memory Accuracy Evaluation - Check if an extracted memory is accurate.
@@ -245,7 +246,7 @@ async def evaluation_for_memory_accuracy(
         dialogue: The formatted dialogue string
         golden_memories: List of golden memory points from the session
         candidate_memory: The extracted memory to evaluate
-        model_name: Model name to use for LLM request
+        eval_model_name: Model name to use for LLM request
 
     Returns:
         dict with 'accuracy_score' (0/1/2), 'is_included_in_golden_memories' (true/false), and 'reason'
@@ -265,7 +266,7 @@ async def evaluation_for_memory_accuracy(
         candidate_memory=candidate_content,
     )
 
-    result = await reme.get_llm("qwen-flash").simple_request_for_json(
+    result = await reme.get_llm(eval_model_name).simple_request_for_json(
         prompt=prompt,
         model_name=None,
     )
@@ -454,7 +455,7 @@ class MemoryProcessor:
             question=query,
             memories=memories,
             user_id=user_id,
-            model_name=self.eval_model_name,
+            eval_model_name=self.eval_model_name,
         )
 
         # Add original memories to the result
@@ -1420,8 +1421,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--data_path",
         type=str,
-        # required=True,
-        default="/Users/zhouwk/PycharmProjects/MemAgent/dataset/halumem/HaluMem-Medium.jsonl",
+        required=True,
         help="Path to HaluMem JSONL file",
     )
     parser.add_argument(

reme/__init__.py

@@ -8,7 +8,7 @@ from .reme import ReMe
 from .reme_cli import ReMeCli
 from .reme_fb import ReMeFb
 
-__version__ = "0.3.0.1"
+__version__ = "0.3.0.2"
 
 __all__ = [
     "config",

reme/config/cli.yaml

@@ -21,6 +21,7 @@ embedding_models:
     model_name: text-embedding-v4
     dimensions: 1024
     enable_cache: true
+    use_dimensions: false
 
 file_stores:
   default:

reme/core/embedding/base_embedding_model.py

@@ -30,7 +30,7 @@ class BaseEmbeddingModel(ABC):
         base_url: str | None = None,
         model_name: str = "",
         dimensions: int | None = 1024,
-        use_dimensions: bool = True,
+        use_dimensions: bool = False,
         max_batch_size: int = 10,
         max_retries: int = 3,
         raise_exception: bool = True,