feat: add full text memory by jieba

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.info is not None and search_req.info.get("origin_model"))
         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")
+    info: dict | None = Field(None, description="Info for 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/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(

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

@@ -143,6 +143,26 @@ def retrieve_from_cube(
 
         return list(combined.values())
 
+    def retrieve_from_mixed(
+        self,
+        top_k: int,
+        memory_scope: str | None = None,
+        query_embedding: list[list[float]] | None = None,
+        search_filter: dict | None = None,
+        user_name: str | None = None,
+        use_fast_graph: bool = False,
+    ) -> list[TextualMemoryItem]:
+        """Retrieve from mixed and memory"""
+        vector_results = self._vector_recall(
+            query_embedding or [],
+            memory_scope,
+            top_k,
+            search_filter=search_filter,
+            user_name=user_name,
+        )  # Merge and deduplicate by ID
+        combined = {item.id: item for item in vector_results}
+        return list(combined.values())
+
     def _graph_recall(
         self, parsed_goal: ParsedTaskGoal, memory_scope: str, user_name: str | None = None, **kwargs
     ) -> list[TextualMemoryItem]:
@@ -270,7 +290,7 @@ def _vector_recall(
         query_embedding: list[list[float]],
         memory_scope: str,
         top_k: int = 20,
-        max_num: int = 5,
+        max_num: int = 20,
         status: str = "activated",
         cube_name: str | None = None,
         search_filter: dict | None = None,

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

@@ -3,6 +3,8 @@
 
 from pathlib import Path
 
+import numpy as np
+
 from memos.dependency import require_python_package
 from memos.log import get_logger
 
@@ -376,3 +378,28 @@ def detect_lang(text):
         return "en"
     except Exception:
         return "en"
+
+
+def find_best_unrelated_subgroup(sentences: list, similarity_matrix: list, bar: float = 0.8):
+    assert len(sentences) == len(similarity_matrix)
+
+    num_sentence = len(sentences)
+    selected_sentences = []
+    selected_indices = []
+    for i in range(num_sentence):
+        can_add = True
+        for j in selected_indices:
+            if similarity_matrix[i][j] > bar:
+                can_add = False
+                break
+        if can_add:
+            selected_sentences.append(i)
+            selected_indices.append(i)
+    return selected_sentences, selected_indices
+
+
+def cosine_similarity_matrix(embeddings: list[list[float]]) -> list[list[float]]:
+    norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
+    x_normalized = embeddings / norms
+    similarity_matrix = np.dot(x_normalized, x_normalized.T)
+    return similarity_matrix