Feat: add full text mem (#544)

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## Checklist:

- [ ] I have performed a self-review of my own code | 我已自行检查了自己的代码
- [ ] I have commented my code in hard-to-understand areas |
我已在难以理解的地方对代码进行了注释
- [ ] I have added tests that prove my fix is effective or that my
feature works | 我已添加测试以证明我的修复有效或功能正常
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[MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) (if applicable) |
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Author: fridayL

src/memos/api/handlers/component_init.py

@@ -40,6 +40,7 @@
 from memos.memories.textual.simple_preference import SimplePreferenceTextMemory
 from memos.memories.textual.simple_tree import SimpleTreeTextMemory
 from memos.memories.textual.tree_text_memory.organize.manager import MemoryManager
+from memos.memories.textual.tree_text_memory.retrieve.retrieve_utils import FastTokenizer
 
 
 if TYPE_CHECKING:
@@ -142,7 +143,7 @@ def init_server() -> dict[str, Any]:
     )
 
     logger.debug("Memory manager initialized")
-
+    tokenizer = FastTokenizer()
     # Initialize text memory
     text_mem = SimpleTreeTextMemory(
         llm=llm,
@@ -153,6 +154,7 @@ def init_server() -> dict[str, Any]:
         memory_manager=memory_manager,
         config=default_cube_config.text_mem.config,
         internet_retriever=internet_retriever,
+        tokenizer=tokenizer,
     )
 
     logger.debug("Text memory initialized")
@@ -270,7 +272,6 @@ def init_server() -> dict[str, Any]:
 
         online_bot = get_online_bot_function() if dingding_enabled else None
         logger.info("DingDing bot is enabled")
-
     # Return all components as a dictionary for easy access and extension
     return {
         "graph_db": graph_db,

src/memos/api/handlers/search_handler.py

@@ -191,7 +191,7 @@ def _fast_search(
         """
         target_session_id = search_req.session_id or "default_session"
         search_filter = {"session_id": search_req.session_id} if search_req.session_id else None
-
+        plugin = bool(search_req.source is not None and search_req.source == "plugin")
         search_results = self.naive_mem_cube.text_mem.search(
             query=search_req.query,
             user_name=user_context.mem_cube_id,
@@ -205,6 +205,7 @@ def _fast_search(
                 "session_id": target_session_id,
                 "chat_history": search_req.chat_history,
             },
+            plugin=plugin,
         )
 
         formatted_memories = [format_memory_item(data) for data in search_results]

src/memos/api/product_models.py

@@ -185,6 +185,7 @@ class APISearchRequest(BaseRequest):
     )
     include_preference: bool = Field(True, description="Whether to handle preference memory")
     pref_top_k: int = Field(6, description="Number of preference results to return")
+    source: str | None = Field(None, description="Source of the search")
 
 
 class APIADDRequest(BaseRequest):

src/memos/graph_dbs/polardb.py

@@ -1450,6 +1450,115 @@ def get_context_chain(self, id: str, type: str = "FOLLOWS") -> list[str]:
         """Get the ordered context chain starting from a node."""
         raise NotImplementedError
 
+    @timed
+    def search_by_fulltext(
+        self,
+        query_words: list[str],
+        top_k: int = 10,
+        scope: str | None = None,
+        status: str | None = None,
+        threshold: float | None = None,
+        search_filter: dict | None = None,
+        user_name: str | None = None,
+        tsvector_field: str = "properties_tsvector_zh",
+        tsquery_config: str = "jiebaqry",
+        **kwargs,
+    ) -> list[dict]:
+        """
+        Full-text search functionality using PostgreSQL's full-text search capabilities.
+
+        Args:
+            query_text: query text
+            top_k: maximum number of results to return
+            scope: memory type filter (memory_type)
+            status: status filter, defaults to "activated"
+            threshold: similarity threshold filter
+            search_filter: additional property filter conditions
+            user_name: username filter
+            tsvector_field: full-text index field name, defaults to properties_tsvector_zh_1
+            tsquery_config: full-text search configuration, defaults to jiebaqry (Chinese word segmentation)
+            **kwargs: other parameters (e.g. cube_name)
+
+        Returns:
+            list[dict]: result list containing id and score
+        """
+        # Build WHERE clause dynamically, same as search_by_embedding
+        where_clauses = []
+
+        if scope:
+            where_clauses.append(
+                f"ag_catalog.agtype_access_operator(properties, '\"memory_type\"'::agtype) = '\"{scope}\"'::agtype"
+            )
+        if status:
+            where_clauses.append(
+                f"ag_catalog.agtype_access_operator(properties, '\"status\"'::agtype) = '\"{status}\"'::agtype"
+            )
+        else:
+            where_clauses.append(
+                "ag_catalog.agtype_access_operator(properties, '\"status\"'::agtype) = '\"activated\"'::agtype"
+            )
+
+        # Add user_name filter
+        user_name = user_name if user_name else self.config.user_name
+        where_clauses.append(
+            f"ag_catalog.agtype_access_operator(properties, '\"user_name\"'::agtype) = '\"{user_name}\"'::agtype"
+        )
+
+        # Add search_filter conditions
+        if search_filter:
+            for key, value in search_filter.items():
+                if isinstance(value, str):
+                    where_clauses.append(
+                        f"ag_catalog.agtype_access_operator(properties, '\"{key}\"'::agtype) = '\"{value}\"'::agtype"
+                    )
+                else:
+                    where_clauses.append(
+                        f"ag_catalog.agtype_access_operator(properties, '\"{key}\"'::agtype) = {value}::agtype"
+                    )
+
+        # Add fulltext search condition
+        # Convert query_text to OR query format: "word1 | word2 | word3"
+        tsquery_string = " | ".join(query_words)
+
+        where_clauses.append(f"{tsvector_field} @@ to_tsquery('{tsquery_config}', %s)")
+
+        where_clause = f"WHERE {' AND '.join(where_clauses)}" if where_clauses else ""
+
+        # Build fulltext search query
+        query = f"""
+            SELECT
+                ag_catalog.agtype_access_operator(properties, '"id"'::agtype) AS old_id,
+                agtype_object_field_text(properties, 'memory') as memory_text,
+                ts_rank({tsvector_field}, to_tsquery('{tsquery_config}', %s)) as rank
+            FROM "{self.db_name}_graph"."Memory"
+            {where_clause}
+            ORDER BY rank DESC
+            LIMIT {top_k};
+        """
+
+        params = [tsquery_string, tsquery_string]
+
+        conn = self._get_connection()
+        try:
+            with conn.cursor() as cursor:
+                cursor.execute(query, params)
+                results = cursor.fetchall()
+                output = []
+                for row in results:
+                    oldid = row[0]  # old_id
+                    rank = row[2]  # rank score
+
+                    id_val = str(oldid)
+                    score_val = float(rank)
+
+                    # Apply threshold filter if specified
+                    if threshold is None or score_val >= threshold:
+                        output.append({"id": id_val, "score": score_val})
+
+                return output[:top_k]
+        finally:
+            self._return_connection(conn)
+
     @timed
     def search_by_embedding(
         self,

src/memos/memories/textual/item.py

@@ -199,6 +199,7 @@ class PreferenceTextualMemoryMetadata(TextualMemoryMetadata):
     preference: str | None = Field(default=None, description="Preference.")
     created_at: str | None = Field(default=None, description="Timestamp of the dialog.")
     mem_cube_id: str | None = Field(default=None, description="ID of the MemCube.")
+    score: float | None = Field(default=None, description="Score of the retrieval result.")
 
 
 class TextualMemoryItem(BaseModel):

src/memos/memories/textual/prefer_text_memory/extractor.py

@@ -90,7 +90,8 @@ def extract_implicit_preference(self, qa_pair: MessageList | str) -> dict[str, A
             response = self.llm_provider.generate([{"role": "user", "content": prompt}])
             response = response.strip().replace("```json", "").replace("```", "").strip()
             result = json.loads(response)
-            result["preference"] = result.pop("implicit_preference")
+            for d in result:
+                d["preference"] = d.pop("implicit_preference")
             return result
         except Exception as e:
             logger.error(f"Error extracting implicit preferences: {e}, return None")
@@ -136,20 +137,24 @@ def _process_single_chunk_implicit(
         if not implicit_pref:
             return None
 
-        vector_info = {
-            "embedding": self.embedder.embed([implicit_pref["context_summary"]])[0],
-        }
+        memories = []
+        for pref in implicit_pref:
+            vector_info = {
+                "embedding": self.embedder.embed([pref["context_summary"]])[0],
+            }
 
-        extract_info = {**basic_info, **implicit_pref, **vector_info, **info}
+            extract_info = {**basic_info, **pref, **vector_info, **info}
 
-        metadata = PreferenceTextualMemoryMetadata(
-            type=msg_type, preference_type="implicit_preference", **extract_info
-        )
-        memory = TextualMemoryItem(
-            id=extract_info["dialog_id"], memory=implicit_pref["context_summary"], metadata=metadata
-        )
+            metadata = PreferenceTextualMemoryMetadata(
+                type=msg_type, preference_type="implicit_preference", **extract_info
+            )
+            memory = TextualMemoryItem(
+                id=str(uuid.uuid4()), memory=pref["context_summary"], metadata=metadata
+            )
 
-        return memory
+            memories.append(memory)
+
+        return memories
 
     def extract(
         self,

src/memos/memories/textual/prefer_text_memory/retrievers.py

@@ -1,3 +1,5 @@
+import os
+
 from abc import ABC, abstractmethod
 from typing import Any
 
@@ -34,9 +36,12 @@ def _naive_reranker(
         self, query: str, prefs_mem: list[TextualMemoryItem], top_k: int, **kwargs: Any
     ) -> list[TextualMemoryItem]:
         if self.reranker:
-            prefs_mem = self.reranker.rerank(query, prefs_mem, top_k)
-            return [item for item, _ in prefs_mem]
-        return prefs_mem
+            prefs_mem_reranked = []
+            prefs_mem_tuple = self.reranker.rerank(query, prefs_mem, top_k)
+            for item, score in prefs_mem_tuple:
+                item.metadata.score = score
+                prefs_mem_reranked.append(item)
+        return prefs_mem_reranked
 
     def _original_text_reranker(
         self,
@@ -52,11 +57,22 @@ def _original_text_reranker(
             prefs_mem_for_reranker = deepcopy(prefs_mem)
             for pref_mem, pref in zip(prefs_mem_for_reranker, prefs, strict=False):
                 pref_mem.memory = pref_mem.memory + "\n" + pref.original_text
-            prefs_mem_for_reranker = self.reranker.rerank(query, prefs_mem_for_reranker, top_k)
-            prefs_mem_for_reranker = [item for item, _ in prefs_mem_for_reranker]
+            reranked_results = self.reranker.rerank(query, prefs_mem_for_reranker, top_k)
+            prefs_mem_for_reranker = [item for item, _ in reranked_results]
             prefs_ids = [item.id for item in prefs_mem_for_reranker]
             prefs_dict = {item.id: item for item in prefs_mem}
-            return [prefs_dict[item_id] for item_id in prefs_ids if item_id in prefs_dict]
+
+            # Create mapping from id to score from reranked results
+            reranked_scores = {item.id: score for item, score in reranked_results}
+
+            # Assign scores to the original items
+            result_items = []
+            for item_id in prefs_ids:
+                if item_id in prefs_dict:
+                    original_item = prefs_dict[item_id]
+                    original_item.metadata.score = reranked_scores.get(item_id)
+                    result_items.append(original_item)
+            return result_items
         return prefs_mem
 
     def retrieve(
@@ -119,9 +135,6 @@ def retrieve(
             if pref.payload.get("preference", None)
         ]
 
-        # store explicit id and score, use it after reranker
-        explicit_id_scores = {item.id: item.score for item in explicit_prefs}
-
         reranker_map = {
             "naive": self._naive_reranker,
             "original_text": self._original_text_reranker,
@@ -136,7 +149,14 @@ def retrieve(
 
         # filter explicit mem by score bigger than threshold
         explicit_prefs_mem = [
-            item for item in explicit_prefs_mem if explicit_id_scores.get(item.id, 0) >= 0.0
+            item
+            for item in explicit_prefs_mem
+            if item.metadata.score >= float(os.getenv("PREFERENCE_SEARCH_THRESHOLD", 0.0))
+        ]
+        implicit_prefs_mem = [
+            item
+            for item in implicit_prefs_mem
+            if item.metadata.score >= float(os.getenv("PREFERENCE_SEARCH_THRESHOLD", 0.0))
         ]
 
         return explicit_prefs_mem + implicit_prefs_mem

src/memos/memories/textual/simple_tree.py

@@ -9,6 +9,7 @@
 from memos.memories.textual.tree import TreeTextMemory
 from memos.memories.textual.tree_text_memory.organize.manager import MemoryManager
 from memos.memories.textual.tree_text_memory.retrieve.bm25_util import EnhancedBM25
+from memos.memories.textual.tree_text_memory.retrieve.retrieve_utils import FastTokenizer
 from memos.reranker.base import BaseReranker
 
 
@@ -35,6 +36,7 @@ def __init__(
         config: TreeTextMemoryConfig,
         internet_retriever: None = None,
         is_reorganize: bool = False,
+        tokenizer: FastTokenizer | None = None,
     ):
         """Initialize memory with the given configuration."""
         self.config: TreeTextMemoryConfig = config
@@ -51,6 +53,7 @@ def __init__(
             if self.search_strategy and self.search_strategy.get("bm25", False)
             else None
         )
+        self.tokenizer = tokenizer
         self.reranker = reranker
         self.memory_manager: MemoryManager = memory_manager
         # Create internet retriever if configured

src/memos/memories/textual/tree.py

@@ -89,6 +89,7 @@ def __init__(self, config: TreeTextMemoryConfig):
             )
         else:
             logger.info("No internet retriever configured")
+        self.tokenizer = None
 
     def add(
         self,
@@ -165,6 +166,7 @@ def search(
         moscube: bool = False,
         search_filter: dict | None = None,
         user_name: str | None = None,
+        **kwargs,
     ) -> list[TextualMemoryItem]:
         """Search for memories based on a query.
         User query -> TaskGoalParser -> MemoryPathResolver ->
@@ -199,6 +201,7 @@ def search(
                 moscube=moscube,
                 search_strategy=self.search_strategy,
                 manual_close_internet=manual_close_internet,
+                tokenizer=self.tokenizer,
             )
         else:
             searcher = Searcher(
@@ -211,9 +214,17 @@ def search(
                 moscube=moscube,
                 search_strategy=self.search_strategy,
                 manual_close_internet=manual_close_internet,
+                tokenizer=self.tokenizer,
             )
         return searcher.search(
-            query, top_k, info, mode, memory_type, search_filter, user_name=user_name
+            query,
+            top_k,
+            info,
+            mode,
+            memory_type,
+            search_filter,
+            user_name=user_name,
+            plugin=kwargs.get("plugin", False),
         )
 
     def get_relevant_subgraph(