fix score_similar -> score_recall

memoryscope/memory/worker/frontend/retrieve_memory_worker.py

@@ -133,10 +133,10 @@ class RetrieveMemoryWorker(MemoryBaseWorker):
         if not memory_node_list:
             return
 
-        memory_node_list = sorted(memory_node_list, key=lambda x: x.score_similar, reverse=True)
+        memory_node_list = sorted(memory_node_list, key=lambda x: x.score_recall, reverse=True)
         for node in memory_node_list:
             node.action_status = ActionStatusEnum.NONE.value
-            self.logger.info(f"recall_stage: content={node.content} score={node.score_similar} type={node.memory_type} "
+            self.logger.info(f"recall_stage: content={node.content} score={node.score_rerank} type={node.memory_type} "
                              f"store_status={node.store_status} action_status={node.action_status}")
 
             self.memory_handler.set_memories(RETRIEVE_MEMORY_NODES, memory_node_list)

memoryscope/memory/worker/summary/get_reflection_subject_worker.py

@@ -101,7 +101,7 @@ class GetReflectionSubjectWorker(MemoryBaseWorker):
             return
 
         # Parse LLM response for new insight keys and update memory
-        new_insight_keys = ResponseTextParser(response.message.content).parse_v2(self.__class__.__name__)
+        new_insight_keys = ResponseTextParser(response.message.content, self.__class__.__name__).parse_v2()
         if new_insight_keys:
             for insight_key in new_insight_keys:
                 self.memory_handler.add_memories(INSIGHT_NODES, self.new_insight_node(insight_key))

memoryscope/memory/worker/summary/long_contra_repeat_worker.py

@@ -49,7 +49,7 @@ class LongContraRepeatWorker(MemoryBaseWorker):
                                                              top_k=self.long_contra_repeat_top_k,
                                                              filter_dict=filter_dict)
         # Filter retrieved nodes based on the similarity threshold
-        return node, [n for n in retrieve_nodes if n.score_similar >= self.long_contra_repeat_threshold]
+        return node, [n for n in retrieve_nodes if n.score_recall >= self.long_contra_repeat_threshold]
 
     def _run(self):
         """
@@ -111,7 +111,7 @@ class LongContraRepeatWorker(MemoryBaseWorker):
             return
 
         # Parses the model's response text to identify updates for memory nodes
-        idx_obs_info_list = ResponseTextParser(response.message.content).parse_v1(self.__class__.__name__)
+        idx_obs_info_list = ResponseTextParser(response.message.content, self.__class__.__name__).parse_v1()
         if len(idx_obs_info_list) <= 0:
             self.logger.warning("idx_obs_info_list is empty!")
             return

memoryscope/memory/worker/summary/update_insight_worker.py

@@ -136,7 +136,8 @@ class UpdateInsightWorker(MemoryBaseWorker):
         if not response.status or not response.message.content:
             return insight_node
 
-        insight_value_list = ResponseTextParser(response.message.content).parse_v1(f"update_{insight_node.key}")
+        insight_value_list = ResponseTextParser(response.message.content,
+                                                f"update_{insight_node.key}").parse_v1()
         if not insight_value_list:
             self.logger.warning(f"update_{insight_node.key} insight_value_list is empty!")
             return insight_node

memoryscope/memory/worker/write/contra_repeat_worker.py

@@ -80,7 +80,7 @@ class ContraRepeatWorker(MemoryBaseWorker):
         response_text = response.message.content
 
         # parse text
-        idx_merge_obs_list = ResponseTextParser(response_text).parse_v1(self.__class__.__name__)
+        idx_merge_obs_list = ResponseTextParser(response_text, self.__class__.__name__).parse_v1()
         if len(idx_merge_obs_list) <= 0:
             self.logger.warning("idx_merge_obs_list is empty!")
             return

memoryscope/memory/worker/write/get_observation_worker.py

@@ -139,7 +139,7 @@ class GetObservationWorker(MemoryBaseWorker):
         response_text = response.message.content
 
         # Parses the generated text to extract observation indices, times, contents, and keywords
-        idx_obs_list = ResponseTextParser(response_text).parse_v1(self.__class__.__name__)
+        idx_obs_list = ResponseTextParser(response_text, self.__class__.__name__).parse_v1()
         if len(idx_obs_list) <= 0:
             self.logger.warning("idx_obs_list is empty!")
             return

memoryscope/memory/worker/write/info_filter_worker.py

@@ -76,7 +76,7 @@ class InfoFilterWorker(MemoryBaseWorker):
         response_text = response.message.content
 
         # parse text
-        info_score_list = ResponseTextParser(response_text).parse_v1(self.__class__.__name__)
+        info_score_list = ResponseTextParser(response_text, self.__class__.__name__).parse_v1()
         if len(info_score_list) != len(info_messages):
             self.logger.warning(f"score_size != messages_size, {len(info_score_list)} vs {len(info_messages)}")
 

memoryscope/storage/llama_index_es_memory_store.py

@@ -149,8 +149,5 @@ class LlamaIndexEsMemoryStore(BaseMemoryStore):
         Returns:
             MemoryNode: The converted MemoryNode with text and metadata from the NodeWithScore.
         """
-        embedding = text_node.embedding
-        print("textnode embedding", embedding)
-        if not embedding:
-            embedding = []
-        return MemoryNode(content=text_node.text, vector=embedding, **text_node.metadata)
+        text_node.metadata["vector"] = text_node.embedding
+        return MemoryNode(content=text_node.text, **text_node.metadata)

memoryscope/utils/response_text_parser.py

@@ -14,23 +14,27 @@ class ResponseTextParser(object):
 
     pattern_v1 = re.compile(r"<(.*?)>")  # Regular expression pattern to match content within angle brackets
 
-    def __init__(self, response_text: str):
+    def __init__(self, response_text: str, logger_prefix: str = ""):
         """
         Initializes the `ResponseTextParser` instance with the provided response text and sets up a logger.
 
         Args:
             response_text (str): The raw response text that needs to be parsed and processed.
         """
-        self.response_text: str = response_text.strip()  # Strips leading and trailing whitespace from the response text
-        self.logger: Logger = Logger.get_logger()  # Initializes a logger instance for logging parsing activities
 
-    def parse_v1(self, prefix: str = "") -> List[str]:
+        # Strips leading and trailing whitespace from the response text
+        self.response_text: str = response_text.strip()
+
+        # The prefix of log. Defaults to "".
+        self.logger_prefix: str = logger_prefix
+
+        # Initializes a logger instance for logging parsing activities
+        self.logger: Logger = Logger.get_logger()
+
+    def parse_v1(self) -> List[List[str]]:
         """
         Extract specific patterns from the text which match content within angle brackets.
 
-        Args:
-            prefix (str): The prefix of log. Defaults to "".
-        
         Returns:
             Contents match the specific patterns.
         """
@@ -42,10 +46,10 @@ class ResponseTextParser(object):
             matches = [match.group(1) for match in self.pattern_v1.finditer(line)]
             if matches:
                 result.append(matches)
-        self.logger.info(f"{prefix} response_text={self.response_text} result={result}", stacklevel=2)
+        self.logger.info(f"{self.logger_prefix} response_text={self.response_text} result={result}", stacklevel=2)
         return result
 
-    def parse_v2(self, prefix: str = "") -> List[str]:
+    def parse_v2(self) -> List[str]:
         """
         Extract lines which contain NONE_WORD in Chinese or English.
 
@@ -61,5 +65,5 @@ class ResponseTextParser(object):
             if not line or line.lower() == NONE_WORD.get(G_CONTEXT.language):
                 continue
             result.append(line)
-        self.logger.info(f"{prefix} response_text={self.response_text} result={result}", stacklevel=2)
+        self.logger.info(f"{self.logger_prefix} response_text={self.response_text} result={result}", stacklevel=2)
         return result