Dev v2.0.15 (#1678)

## Description

Please include a summary of the change, the problem it solves, the
implementation approach, and relevant context. List any dependencies
required for this change.

Related Issue (Required):  Fixes #issue_number

## Type of change

Please delete options that are not relevant.

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] Refactor (does not change functionality, e.g. code style
improvements, linting)
- [ ] Documentation update

## How Has This Been Tested?

Please describe the tests that you ran to verify your changes. Provide
instructions so we can reproduce. Please also list any relevant details
for your test configuration

- [ ] Unit Test
- [ ] Test Script Or Test Steps (please provide)
- [ ] Pipeline Automated API Test (please provide)

## 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
- [ ] I have created related documentation issue/PR in
[MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) (if applicable) |
我已在 [MemOS-Docs](https://github.com/MemTensor/MemOS-Docs) 中创建了相关的文档
issue/PR（如果适用）
- [ ] I have linked the issue to this PR (if applicable) | 我已将 issue
链接到此 PR（如果适用）
- [ ] I have mentioned the person who will review this PR | 我已提及将审查此 PR
的人

## Reviewer Checklist
- [ ] closes #xxxx (Replace xxxx with the GitHub issue number)
- [ ] Made sure Checks passed
- [ ] Tests have been provided

Author: CaralHsi

evaluation/README.md

@@ -64,6 +64,12 @@ First prepare the dataset `longmemeval_s` from https://huggingface.co/datasets/x
 ./scripts/run_lme_eval.sh
 ```
 
+#### Question date and `reference_time`
+
+LongMemEval gives each question a **question date**; evaluation should use that as the reference “now”, not the time when you run the script. The LongMemEval search script passes `question_date` as **`reference_time`** where the backend supports it.
+
+**MemOS Cloud** currently does not support supplying question date on search the same way, so LongMemEval scores there may differ from a spec-faithful run. **Prefer evaluating LongMemEval against the open-source MemOS server** when you need comparable numbers.
+
 ### PrefEval Evaluation
 Downloading benchmark_dataset/filtered_inter_turns.json from https://github.com/amazon-science/PrefEval/blob/main/benchmark_dataset/filtered_inter_turns.json and save it as `./data/prefeval/filtered_inter_turns.json`.
 To evaluate the **Prefeval** dataset — run the following [script](./scripts/run_prefeval_eval.sh):

evaluation/scripts/longmemeval/lme_search.py

@@ -41,9 +41,11 @@ def mem0_search(client, query, user_id, top_k):
     return context, duration_ms
 
 
-def memos_search(client, query, user_id, top_k):
+def memos_search(client, query, user_id, top_k, reference_time=None):
     start = time()
-    results = client.search(query=query, user_id=user_id, top_k=top_k)
+    results = client.search(
+        query=query, user_id=user_id, top_k=top_k, reference_time=reference_time
+    )
     context = (
         "\n".join([i["memory"] for i in results["text_mem"][0]["memories"]])
         + f"\n{results.get('pref_string', '')}"
@@ -122,12 +124,16 @@ def process_user(lme_df, conv_idx, frame, version, top_k=20):
         from utils.client import MemosApiClient
 
         client = MemosApiClient()
-        context, duration_ms = memos_search(client, question, user_id, top_k)
+        context, duration_ms = memos_search(
+            client, question, user_id, top_k, reference_time=question_date
+        )
     elif frame == "memos-api-online":
         from utils.client import MemosApiOnlineClient
 
         client = MemosApiOnlineClient()
-        context, duration_ms = memos_search(client, question, user_id, top_k)
+        context, duration_ms = memos_search(
+            client, question, user_id, top_k, reference_time=question_date
+        )
     elif frame == "memu":
         from utils.client import MemuClient
 

src/memos/api/handlers/search_handler.py

@@ -20,6 +20,7 @@
 from memos.multi_mem_cube.composite_cube import CompositeCubeView
 from memos.multi_mem_cube.single_cube import SingleCubeView
 from memos.multi_mem_cube.views import MemCubeView
+from memos.plugins.hooks import hookable
 
 
 logger = get_logger(__name__)
@@ -44,6 +45,7 @@ def __init__(self, dependencies: HandlerDependencies):
             "naive_mem_cube", "mem_scheduler", "searcher", "deepsearch_agent"
         )
 
+    @hookable("search")
     def handle_search_memories(self, search_req: APISearchRequest) -> SearchResponse:
         """
         Main handler for search memories endpoint.

src/memos/api/product_models.py

@@ -438,6 +438,14 @@ class APISearchRequest(BaseRequest):
     )
 
     # ==== Context ====
+    reference_time: str | None = Field(
+        None,
+        description=(
+            "Optional reference time for time-sensitive search parsing. "
+            "If omitted, search uses the current server time."
+        ),
+    )
+
     chat_history: MessageList | None = Field(
         None,
         description=(
@@ -608,6 +616,17 @@ class APIADDRequest(BaseRequest):
         description=("Whether this request represents user feedback. Default: False."),
     )
 
+    # ==== Upload skill flag ====
+    is_upload_skill: bool = Field(
+        False,
+        description=(
+            "Whether this request is an upload skill request. "
+            "When True, the messages field should contain file items "
+            "with zip file download URLs for pre-built skill packages. "
+            "Default: False."
+        ),
+    )
+
     # ==== Backward compatibility fields (will delete later) ====
     mem_cube_id: str | None = Field(
         None,

src/memos/mem_reader/multi_modal_struct.py

@@ -17,7 +17,7 @@
 from memos.mem_reader.utils import parse_json_result
 from memos.memories.textual.item import TextualMemoryItem, TreeNodeTextualMemoryMetadata
 from memos.plugins.hook_defs import H
-from memos.plugins.hooks import trigger_single_hook
+from memos.plugins.hooks import trigger_hook, trigger_single_hook
 from memos.templates.mem_reader_prompts import MEMORY_MERGE_PROMPT_EN, MEMORY_MERGE_PROMPT_ZH
 from memos.templates.tool_mem_prompts import TOOL_TRAJECTORY_PROMPT_EN, TOOL_TRAJECTORY_PROMPT_ZH
 from memos.types import MessagesType
@@ -785,6 +785,14 @@ def _process_one_item(
                 )
                 rawfile_node.metadata.summary_ids = [mem_node.id for mem_node in fine_items]
                 fine_items.append(rawfile_node)
+            enriched_items = trigger_hook(
+                H.MEMORY_ITEMS_AFTER_FINE_EXTRACT,
+                items=fine_items,
+                user_context=kwargs.get("user_context"),
+                mem_reader=self,
+                extract_mode="fine",
+            )
+            fine_items = enriched_items if enriched_items is not None else fine_items
             return fine_items
 
         fine_memory_items: list[TextualMemoryItem] = []
@@ -993,6 +1001,7 @@ def _process_multi_modal_data(
             return fast_memory_items
 
         # Stage: llm_extract — fine mode 4-way parallel LLM + per-source serial
+        is_upload_skill = kwargs.pop("is_upload_skill", False)
         non_file_url_fast_items = [
             item for item in fast_memory_items if not self._is_file_url_only_item(item)
         ]
@@ -1009,14 +1018,17 @@ def _process_multi_modal_data(
                 )
                 future_skill = executor.submit(
                     process_skill_memory_fine,
-                    fast_memory_items=non_file_url_fast_items,
+                    fast_memory_items=fast_memory_items
+                    if is_upload_skill
+                    else non_file_url_fast_items,
                     info=info,
                     searcher=self.searcher,
                     graph_db=self.graph_db,
                     llm=self.general_llm,
                     embedder=self.embedder,
                     oss_config=self.oss_config,
                     skills_dir_config=self.skills_dir_config,
+                    is_upload_skill=is_upload_skill,
                     **kwargs,
                 )
                 future_pref = executor.submit(
@@ -1039,6 +1051,10 @@ def _process_multi_modal_data(
             fine_memory_items.extend(fine_memory_items_pref_parser)
 
             # Part B: per-source serial processing
+            if is_upload_skill:
+                # (skip for upload skill to avoid zip being parsed)
+                return fine_memory_items
+
             with timed_stage("add", "per_source") as ts_ps:
                 for fast_item in fast_memory_items:
                     sources = fast_item.metadata.sources
@@ -1072,6 +1088,8 @@ def _process_transfer_multi_modal_data(
             logger.warning("[MultiModalStruct] No raw nodes found.")
             return []
 
+        is_upload_skill = kwargs.pop("is_upload_skill", False)
+
         # Extract info from raw_nodes (same as simple_struct.py)
         info = {
             "user_id": raw_nodes[0].metadata.user_id,
@@ -1093,14 +1111,15 @@ def _process_transfer_multi_modal_data(
             )
             future_skill = executor.submit(
                 process_skill_memory_fine,
-                non_file_url_nodes,
+                raw_nodes if is_upload_skill else non_file_url_nodes,
                 info,
                 searcher=self.searcher,
                 llm=self.general_llm,
                 embedder=self.embedder,
                 graph_db=self.graph_db,
                 oss_config=self.oss_config,
                 skills_dir_config=self.skills_dir_config,
+                is_upload_skill=is_upload_skill,
                 **kwargs,
             )
             # Add preference memory extraction
@@ -1125,6 +1144,9 @@ def _process_transfer_multi_modal_data(
         fine_memory_items.extend(fine_memory_items_pref_parser)
 
         # Part B: get fine multimodal items
+        if is_upload_skill:
+            # (skip for upload skill to avoid zip being parsed)
+            return fine_memory_items
         for raw_node in raw_nodes:
             sources = raw_node.metadata.sources
             for source in sources:

src/memos/mem_reader/read_skill_memory/process_skill_memory.py

@@ -252,36 +252,6 @@ def create_task(skill_mem, gen_type, prompt, requirements, context, **kwargs):
     return [item[0] for item in raw_skills_data]
 
 
-def add_id_to_mysql(memory_id: str, mem_cube_id: str):
-    """Add id to mysql, will deprecate this function in the future"""
-    # TODO: tmp function, deprecate soon
-    import requests
-
-    skill_mysql_url = os.getenv("SKILLS_MYSQL_URL", "")
-    skill_mysql_bearer = os.getenv("SKILLS_MYSQL_BEARER", "")
-
-    if not skill_mysql_url or not skill_mysql_bearer:
-        logger.warning("[PROCESS_SKILLS] SKILLS_MYSQL_URL or SKILLS_MYSQL_BEARER is not set")
-        return None
-    headers = {"Authorization": skill_mysql_bearer, "Content-Type": "application/json"}
-    data = {"memCubeId": mem_cube_id, "skillId": memory_id}
-    try:
-        response = requests.post(skill_mysql_url, headers=headers, json=data)
-
-        logger.info(f"[PROCESS_SKILLS] response: \n\n{response.json()}")
-        logger.info(f"[PROCESS_SKILLS] memory_id: \n\n{memory_id}")
-        logger.info(f"[PROCESS_SKILLS] mem_cube_id: \n\n{mem_cube_id}")
-        logger.info(f"[PROCESS_SKILLS] skill_mysql_url: \n\n{skill_mysql_url}")
-        logger.info(f"[PROCESS_SKILLS] skill_mysql_bearer: \n\n{skill_mysql_bearer}")
-        logger.info(f"[PROCESS_SKILLS] headers: \n\n{headers}")
-        logger.info(f"[PROCESS_SKILLS] data: \n\n{data}")
-
-        return response.json()
-    except Exception as e:
-        logger.warning(f"[PROCESS_SKILLS] Error adding id to mysql: {e}")
-        return None
-
-
 @require_python_package(
     import_name="alibabacloud_oss_v2",
     install_command="pip install alibabacloud-oss-v2",
@@ -948,6 +918,7 @@ def create_skill_memory_item(
         scripts=skill_memory.get("scripts"),
         others=skill_memory.get("others"),
         url=skill_memory.get("url", ""),
+        skill_source=skill_memory.get("skill_source"),
         manager_user_id=manager_user_id,
         project_id=project_id,
     )
@@ -1024,6 +995,21 @@ def process_skill_memory_fine(
     complete_skill_memory: bool = True,
     **kwargs,
 ) -> list[TextualMemoryItem]:
+    is_upload_skill = kwargs.pop("is_upload_skill", False)
+    if is_upload_skill:
+        from memos.mem_reader.read_skill_memory.upload_skill_memory import (
+            process_upload_skill_memory,
+        )
+
+        return process_upload_skill_memory(
+            fast_memory_items=fast_memory_items,
+            info=info,
+            embedder=embedder,
+            oss_config=oss_config,
+            skills_dir_config=skills_dir_config,
+            **kwargs,
+        )
+
     skills_repo_backend = _get_skill_file_storage_location()
     oss_client, _missing_keys, flag = _skill_init(
         skills_repo_backend, oss_config, skills_dir_config
@@ -1252,6 +1238,7 @@ def _full_extract():
                     if source:
                         skill_sources.append(source)
 
+            skill_memory["skill_source"] = "auto_create"
             memory_item = create_skill_memory_item(
                 skill_memory, info, embedder, sources=skill_sources, **kwargs
             )
@@ -1260,12 +1247,4 @@ def _full_extract():
             logger.warning(f"[PROCESS_SKILLS] Error creating skill memory item: {e}")
             continue
 
-    # TODO: deprecate this funtion and call
-    for skill_memory, skill_memory_item in zip(skill_memories, skill_memory_items, strict=False):
-        if skill_memory.get("update", False) and skill_memory.get("old_memory_id", ""):
-            continue
-        add_id_to_mysql(
-            memory_id=skill_memory_item.id,
-            mem_cube_id=kwargs.get("user_name", info.get("user_id", "")),
-        )
     return skill_memory_items