Feat: include embedding config

src/memos/api/config.py

@@ -887,6 +887,9 @@ def create_user_config(user_name: str, user_id: str) -> tuple[MOSConfig, General
                                 "bm25": bool(os.getenv("BM25_CALL", "false") == "true"),
                                 "cot": bool(os.getenv("VEC_COT_CALL", "false") == "true"),
                             },
+                            "include_embedding": bool(
+                                os.getenv("INCLUDE_EMBEDDING", "false") == "true"
+                            ),
                         },
                     },
                     "act_mem": {}
@@ -960,6 +963,9 @@ def get_default_cube_config() -> GeneralMemCubeConfig | None:
                                 "cot": bool(os.getenv("VEC_COT_CALL", "false") == "true"),
                             },
                             "mode": os.getenv("ASYNC_MODE", "sync"),
+                            "include_embedding": bool(
+                                os.getenv("INCLUDE_EMBEDDING", "false") == "true"
+                            ),
                         },
                     },
                     "act_mem": {}

src/memos/api/handlers/component_init.py

@@ -210,6 +210,7 @@ def init_server() -> dict[str, Any]:
         config=default_cube_config.text_mem.config,
         internet_retriever=internet_retriever,
         tokenizer=tokenizer,
+        include_embedding=bool(os.getenv("INCLUDE_EMBEDDING", "false") == "true"),
     )
 
     logger.debug("Text memory initialized")

src/memos/configs/memory.py

@@ -196,6 +196,10 @@ class TreeTextMemoryConfig(BaseTextMemoryConfig):
         default="sync",
         description=("whether use asynchronous mode in memory add"),
     )
+    include_embedding: bool | None = Field(
+        default=False,
+        description="Whether to include embedding in the memory retrieval",
+    )
 
 
 class SimpleTreeTextMemoryConfig(TreeTextMemoryConfig):

src/memos/graph_dbs/polardb.py

@@ -3602,6 +3602,11 @@ def _build_node_from_agtype(self, node_agtype, embedding=None):
                 return None
 
             if embedding is not None:
+                if isinstance(embedding, str):
+                    try:
+                        embedding = json.loads(embedding)
+                    except (json.JSONDecodeError, TypeError):
+                        logger.warning("Failed to parse embedding for node")
                 props["embedding"] = embedding
 
             # Return standard format directly

src/memos/memories/textual/simple_tree.py

@@ -37,6 +37,7 @@ def __init__(
         internet_retriever: None = None,
         is_reorganize: bool = False,
         tokenizer: FastTokenizer | None = None,
+        include_embedding: bool = False,
     ):
         """Initialize memory with the given configuration."""
         self.config: TreeTextMemoryConfig = config
@@ -65,3 +66,4 @@ def __init__(
             )
         else:
             logger.info("No internet retriever configured")
+        self.include_embedding = include_embedding

src/memos/memories/textual/tree.py

@@ -92,6 +92,7 @@ def __init__(self, config: TreeTextMemoryConfig):
         else:
             logger.info("No internet retriever configured")
         self.tokenizer = None
+        self.include_embedding = config.include_embedding or False
 
     def add(
         self,
@@ -192,6 +193,7 @@ def search(
             search_strategy=self.search_strategy,
             manual_close_internet=manual_close_internet,
             tokenizer=self.tokenizer,
+            include_embedding=self.include_embedding,
         )
         return searcher.search(
             query,

src/memos/memories/textual/tree_text_memory/retrieve/advanced_searcher.py

@@ -35,6 +35,7 @@ def __init__(
         manual_close_internet: bool = True,
         process_llm: Any | None = None,
         tokenizer: FastTokenizer | None = None,
+        include_embedding: bool = False,
     ):
         super().__init__(
             dispatcher_llm=dispatcher_llm,
@@ -46,6 +47,7 @@ def __init__(
             search_strategy=search_strategy,
             manual_close_internet=manual_close_internet,
             tokenizer=tokenizer,
+            include_embedding=include_embedding,
         )
 
         self.stage_retrieve_top = 3

src/memos/memories/textual/tree_text_memory/retrieve/recall.py

@@ -22,13 +22,15 @@ def __init__(
         graph_store: Neo4jGraphDB,
         embedder: OllamaEmbedder,
         bm25_retriever: EnhancedBM25 | None = None,
+        include_embedding: bool = False,
     ):
         self.graph_store = graph_store
         self.embedder = embedder
         self.bm25_retriever = bm25_retriever
         self.max_workers = 10
         self.filter_weight = 0.6
         self.use_bm25 = bool(self.bm25_retriever)
+        self.include_embedding = include_embedding
 
     def retrieve(
         self,
@@ -72,7 +74,7 @@ def retrieve(
             # For working memory, retrieve all entries (no session-oriented filtering)
             working_memories = self.graph_store.get_all_memory_items(
                 scope="WorkingMemory",
-                include_embedding=False,
+                include_embedding=self.include_embedding,
                 user_name=user_name,
                 filter=search_filter,
             )
@@ -244,7 +246,9 @@ def process_node(node):
                 return []
 
             # Load nodes and post-filter
-            node_dicts = self.graph_store.get_nodes(list(candidate_ids), include_embedding=False)
+            node_dicts = self.graph_store.get_nodes(
+                list(candidate_ids), include_embedding=self.include_embedding
+            )
 
             final_nodes = []
             for node in node_dicts:
@@ -291,7 +295,7 @@ def process_node(node):
 
             # Load nodes and post-filter
             node_dicts = self.graph_store.get_nodes(
-                list(candidate_ids), include_embedding=False, user_name=user_name
+                list(candidate_ids), include_embedding=self.include_embedding, user_name=user_name
             )
 
             final_nodes = []
@@ -385,7 +389,10 @@ def search_path_b():
         unique_ids = {r["id"] for r in all_hits if r.get("id")}
         node_dicts = (
             self.graph_store.get_nodes(
-                list(unique_ids), include_embedding=False, cube_name=cube_name, user_name=user_name
+                list(unique_ids),
+                include_embedding=self.include_embedding,
+                cube_name=cube_name,
+                user_name=user_name,
             )
             or []
         )
@@ -416,7 +423,9 @@ def _bm25_recall(
                 key_filters.append({"field": key, "op": "=", "value": value})
             corpus_name += "".join(list(search_filter.values()))
         candidate_ids = self.graph_store.get_by_metadata(key_filters, user_name=user_name)
-        node_dicts = self.graph_store.get_nodes(list(candidate_ids), include_embedding=False)
+        node_dicts = self.graph_store.get_nodes(
+            list(candidate_ids), include_embedding=self.include_embedding
+        )
 
         bm25_query = " ".join(list({query, *parsed_goal.keys}))
         bm25_results = self.bm25_retriever.search(
@@ -471,7 +480,10 @@ def _fulltext_recall(
         unique_ids = {r["id"] for r in all_hits if r.get("id")}
         node_dicts = (
             self.graph_store.get_nodes(
-                list(unique_ids), include_embedding=False, cube_name=cube_name, user_name=user_name
+                list(unique_ids),
+                include_embedding=self.include_embedding,
+                cube_name=cube_name,
+                user_name=user_name,
             )
             or []
         )

src/memos/memories/textual/tree_text_memory/retrieve/searcher.py

@@ -47,13 +47,16 @@ def __init__(
         search_strategy: dict | None = None,
         manual_close_internet: bool = True,
         tokenizer: FastTokenizer | None = None,
+        include_embedding: bool = False,
     ):
         self.graph_store = graph_store
         self.embedder = embedder
         self.llm = dispatcher_llm
 
         self.task_goal_parser = TaskGoalParser(dispatcher_llm)
-        self.graph_retriever = GraphMemoryRetriever(graph_store, embedder, bm25_retriever)
+        self.graph_retriever = GraphMemoryRetriever(
+            graph_store, embedder, bm25_retriever, include_embedding=include_embedding
+        )
         self.reranker = reranker
         self.reasoner = MemoryReasoner(dispatcher_llm)