Feat:update embedding

src/memos/api/config.py

@@ -395,6 +395,32 @@ def get_reranker_config() -> dict[str, Any]:
                 },
             }
 
+    @staticmethod
+    def get_feedback_reranker_config() -> dict[str, Any]:
+        """Get embedder configuration."""
+        embedder_backend = os.getenv("MOS_FEEDBACK_RERANKER_BACKEND", "http_bge")
+
+        if embedder_backend in ["http_bge", "http_bge_strategy"]:
+            return {
+                "backend": embedder_backend,
+                "config": {
+                    "url": os.getenv("MOS_RERANKER_URL"),
+                    "model": os.getenv("MOS_FEEDBACK_RERANKER_MODEL", "bge-reranker-v2-m3"),
+                    "timeout": 10,
+                    "headers_extra": json.loads(os.getenv("MOS_RERANKER_HEADERS_EXTRA", "{}")),
+                    "rerank_source": os.getenv("MOS_RERANK_SOURCE"),
+                    "reranker_strategy": os.getenv("MOS_RERANKER_STRATEGY", "single_turn"),
+                },
+            }
+        else:
+            return {
+                "backend": "cosine_local",
+                "config": {
+                    "level_weights": {"topic": 1.0, "concept": 1.0, "fact": 1.0},
+                    "level_field": "background",
+                },
+            }
+
     @staticmethod
     def get_embedder_config() -> dict[str, Any]:
         """Get embedder configuration."""

src/memos/api/handlers/component_init.py

@@ -13,6 +13,7 @@
 from memos.api.handlers.config_builders import (
     build_chat_llm_config,
     build_embedder_config,
+    build_feedback_reranker_config,
     build_graph_db_config,
     build_internet_retriever_config,
     build_llm_config,
@@ -159,6 +160,7 @@ def init_server() -> dict[str, Any]:
     embedder_config = build_embedder_config()
     mem_reader_config = build_mem_reader_config()
     reranker_config = build_reranker_config()
+    feedback_reranker_config = build_feedback_reranker_config()
     internet_retriever_config = build_internet_retriever_config()
     vector_db_config = build_vec_db_config()
     pref_extractor_config = build_pref_extractor_config()
@@ -179,6 +181,7 @@ def init_server() -> dict[str, Any]:
     embedder = EmbedderFactory.from_config(embedder_config)
     mem_reader = MemReaderFactory.from_config(mem_reader_config)
     reranker = RerankerFactory.from_config(reranker_config)
+    feedback_reranker = RerankerFactory.from_config(feedback_reranker_config)
     internet_retriever = InternetRetrieverFactory.from_config(
         internet_retriever_config, embedder=embedder
     )
@@ -305,7 +308,7 @@ def init_server() -> dict[str, Any]:
         memory_manager=memory_manager,
         mem_reader=mem_reader,
         searcher=searcher,
-        reranker=reranker,
+        reranker=feedback_reranker,
     )
 
     # Initialize Scheduler

src/memos/api/handlers/config_builders.py

@@ -140,6 +140,16 @@ def build_reranker_config() -> dict[str, Any]:
     return RerankerConfigFactory.model_validate(APIConfig.get_reranker_config())
 
 
+def build_feedback_reranker_config() -> dict[str, Any]:
+    """
+    Build reranker configuration.
+
+    Returns:
+        Validated reranker configuration dictionary
+    """
+    return RerankerConfigFactory.model_validate(APIConfig.get_feedback_reranker_config())
+
+
 def build_internet_retriever_config() -> dict[str, Any]:
     """
     Build internet retriever configuration.

src/memos/graph_dbs/polardb.py

@@ -1160,13 +1160,15 @@ def get_nodes(
                         properties = properties_json if properties_json else {}
 
                     # Parse embedding from JSONB if it exists
-                    if embedding_json is not None:
+                    if embedding_json is not None and kwargs.get("include_embedding"):
                         try:
                             # remove embedding
-                            """
-                            embedding = json.loads(embedding_json) if isinstance(embedding_json, str) else embedding_json
-                            # properties["embedding"] = embedding
-                            """
+                            embedding = (
+                                json.loads(embedding_json)
+                                if isinstance(embedding_json, str)
+                                else embedding_json
+                            )
+                            properties["embedding"] = embedding
                         except (json.JSONDecodeError, TypeError):
                             logger.warning(f"Failed to parse embedding for node {node_id}")
                     nodes.append(

src/memos/memories/textual/tree.py

@@ -144,6 +144,7 @@ def get_searcher(
             manual_close_internet=manual_close_internet,
             process_llm=process_llm,
             tokenizer=self.tokenizer,
+            include_embedding=self.include_embedding,
         )
         return searcher