feat(react): add simple react operator and update related functionalities

- Add SimpleReactOp to react module
- Update reme_ai/__init__.py to include react module
- Modify contra_repeat_op.py to use memory_id instead of id
- Adjust datetime_handler.py to handle string datetime conversion
- Update default.yaml to include react flow content
- Modify get_observation_op.py and get_observation_with_time_op.py to use workspace_id from context
- Update test/http_client_test.py to test new react functionality
- Adjust messages.jsonl to reflect new analysis approach for Xiaomi Corporation

cookbook/simple_demo/simple_demo.py

@@ -4,13 +4,12 @@ import requests
 from dotenv import load_dotenv
 
 load_dotenv()
-base_url = "http://0.0.0.0:8001/"
-workspace_id = "test_workspace1"
+base_url = "http://0.0.0.0:8002/"
+workspace_id = "test_workspace4"
 
 
 def run_agent(query: str, dump_messages: bool = False):
-
-    response = requests.post(url=base_url + "agent", json={"query": query})
+    response = requests.post(url=base_url + "react", json={"query": query})
     if response.status_code != 200:
         print(response.text)
         return []
@@ -28,10 +27,10 @@ def run_agent(query: str, dump_messages: bool = False):
     return messages
 
 
-def run_summary(messages: list, dump_experience: bool = True):
-    response = requests.post(url=base_url + "summarizer", json={
+def run_summary(messages: list, enable_dump_memory: bool = True):
+    response = requests.post(url=base_url + "summary_task_memory_simple", json={
         "workspace_id": workspace_id,
-        "traj_list": [
+        "trajectories": [
             {"messages": messages, "score": 1.0}
         ]
     })
@@ -41,14 +40,14 @@ def run_summary(messages: list, dump_experience: bool = True):
         return
 
     response = response.json()
-    experience_list = response["experience_list"]
-    if dump_experience:
-        with open("experience.jsonl", "w") as f:
-            f.write(json.dumps(experience_list, indent=2, ensure_ascii=False))
+    memory_list = response["metadata"]["memory_list"]
+    if enable_dump_memory:
+        with open("memory.jsonl", "w") as f:
+            f.write(json.dumps(memory_list, indent=2, ensure_ascii=False))
 
 
-def run_retriever(query: str):
-    response = requests.post(url=base_url + "retriever", json={
+def run_retrieve(query: str):
+    response = requests.post(url=base_url + "retrieve_task_memory_simple", json={
         "workspace_id": workspace_id,
         "query": query,
     })
@@ -58,20 +57,20 @@ def run_retriever(query: str):
         return ""
 
     response = response.json()
-    experience_merged: str = response["experience_merged"]
-    print(f"experience_merged={experience_merged}")
-    return experience_merged
+    answer: str = response["answer"]
+    print(f"answer={answer}")
+    return answer
 
 
-def run_agent_with_experience(query_first: str, query_second: str, dump_experience: bool = True):
+def run_agent_with_memory(query_first: str, query_second: str, enable_dump_memory: bool = True):
     messages = run_agent(query=query_second)
-    run_summary(messages, dump_experience)
-    experience_merged = run_retriever(query_first)
-    messages = run_agent(query=f"{experience_merged}\n\nUser Question:\n{query_first}")
+    run_summary(messages, enable_dump_memory)
+    retrieved_memory = run_retrieve(query_first)
+    messages = run_agent(query=f"{retrieved_memory}\n\nUser Question:\n{query_first}")
     return messages
 
 
-def dump_experience():
+def dump_memory():
     response = requests.post(url=base_url + "vector_store", json={
         "workspace_id": workspace_id,
         "action": "dump",
@@ -85,7 +84,7 @@ def dump_experience():
     print(response.json())
 
 
-def load_experience():
+def load_memory():
     response = requests.post(url=base_url + "vector_store", json={
         "workspace_id": "test_workspace2",
         "action": "load",
@@ -104,6 +103,6 @@ if __name__ == "__main__":
     query2 = "Analyze the company Tesla."
 
     run_agent(query=query1, dump_messages=True)
-    run_agent_with_experience(query_first=query1, query_second=query2)
-    dump_experience()
-    load_experience()
+    run_agent_with_memory(query_first=query1, query_second=query2)
+    dump_memory()
+    load_memory()

reme_ai/__init__.py

@@ -1,3 +1,4 @@
+from reme_ai import react
 from reme_ai import retrieve
 from reme_ai import summary
 from reme_ai import vector_store

reme_ai/config/default.yaml

@@ -40,7 +40,7 @@ flow:
     input_schema:
       query:
         type: "str"
-        description: "user query"
+        description: "current query"
         required: true
 
   summary_task_memory_simple:
@@ -51,44 +51,10 @@ flow:
         type: "list"
         description: "A list of conversation trajectory information, including message content and score. This field does not need to be filled in, the system will complete it automatically."
         required: false
-  
-  record_task_memory:
-    flow_content: update_memory_freq_op >> update_memory_utility_op >> update_vector_store_op
-    description: "Update the freq & utility attributes of retrieved task memories"
-    input_schema:
-      workspace_id:
-        type: "str"
-        description: "workspace id"
-        required: true
-      memory_dicts:
-        type: "list"
-        description: "A list of retrieved task memory corresponding to the current task."
-        required: true
-      update_utility:
-        type: "bool"
-        description: "Whether to update the utility attribute of the retrieved task memory."
-        required: true
-  
-  delete_task_memory:
-    flow_content: delete_memory_op >> update_vector_store_op
-    description: "Delete task memories when utility/freq < utility_threshold and freq >= freq_threshold"
-    input_schema:
-      workspace_id:
-        type: "str"
-        description: "workspace id"
-        required: true
-      freq_threshold:
-        type: "int"
-        description: "The retrieved frequency threshold for deleting task memory."
-        required: true
-      utility_threshold:
-        type: "float"
-        description: "The utility/freq threshold for deleting task memory."
-        required: true
 
   vector_store:
     flow_content: vector_store_action_op
-    description: "directly operate the vector store."
+    description: "Directly operates on the vector store with various management actions"
     input_schema:
       action:
         type: "str"
@@ -113,22 +79,11 @@ flow:
         type: "list"
         description: "A list of conversation messages information. This field does not need to be filled in, the system will complete it automatically."
         required: false
-  
-op:
-  # retriever ops
-  rerank_memory_op:
-    backend: rerank_memory_op
-    llm: default
-    params:
-      enable_llm_rerank: true
-      enable_score_filter: false
-      top_k: 5
 
-  rewrite_memory_op:
-    backend: rewrite_memory_op
-    llm: default
-    params:
-      enable_llm_rewrite: true
+#  reconsolidate_personal_memory:
+#    flow_content: load_not_reflected_memory_op >> get_reflection_subject_op >> update_insight_op >> long_contra_repeat_op >> update_vector_store_op
+#    description: "Consolidate personal memories by generating topic insights, updating values, resolving conflicts, and updating vector store"
+
 
 llm:
   default:
@@ -137,7 +92,6 @@ llm:
     model_name: qwen3-30b-a3b-instruct-2507
     params:
       temperature: 0.6
-      
 
 embedding_model:
   default:

reme_ai/react/__init__.py

@@ -0,0 +1 @@
+from .simple_react_op import SimpleReactOp

reme_ai/react/simple_react_op.py

@@ -0,0 +1,21 @@
+from flowllm import C
+from flowllm.context.flow_context import FlowContext
+from flowllm.op.agent.react_op import ReactOp
+
+
+@C.register_op()
+class SimpleReactOp(ReactOp):
+    ...
+
+
+if __name__ == "__main__":
+    from reme_ai.config.config_parser import ConfigParser
+
+    C.set_default_service_config(parser=ConfigParser).init_by_service_config()
+    context = FlowContext(query="茅台和五粮现在股价多少？")
+
+    op = SimpleReactOp()
+    op(context=context)
+    # from reme_ai.schema import Message
+    # result = op.llm.chat(messages=[Message(**{"role": "user", "content": "你叫什么名字？"})])
+    # print("!!!", result)

reme_ai/summary/personal/contra_repeat_op.py

@@ -130,7 +130,7 @@ class ContraRepeatOp(BaseLLMOp):
                 judgment_lower = judgment.lower()
                 if judgment_lower in ['矛盾', 'contradiction', '被包含', 'contained']:
                     indices_to_remove.add(idx)
-                    deleted_memory_ids.append(memories[idx].id)
+                    deleted_memory_ids.append(memories[idx].memory_id)
                     logger.info(f"Marking memory {idx + 1} for removal: {judgment} - {memories[idx].content[:100]}...")
 
             except ValueError:

reme_ai/summary/personal/get_observation_op.py

@@ -96,7 +96,8 @@ class GetObservationOp(BaseLLMOp):
 
                 # Create observation memory
                 observation = PersonalMemory(
-                    workspace_id=self.context.get("workspace_id", ""),
+                    workspace_id=self.context.workspace_id,
+                    when_to_use=obs["keywords"],
                     content=obs["content"],
                     target=user_name,
                     author=self.llm.model_name,

reme_ai/summary/personal/get_observation_with_time_op.py

@@ -105,7 +105,8 @@ class GetObservationWithTimeOp(BaseLLMOp):
 
                 # Create observation memory
                 observation = PersonalMemory(
-                    workspace_id=self.context.get("workspace_id", ""),
+                    workspace_id=self.context.workspace_id,
+                    when_to_use=obs["keywords"],
                     content=obs["content"],
                     target=user_name,
                     author=getattr(self.llm, "model_name", "system"),

reme_ai/utils/datetime_handler.py

@@ -29,7 +29,11 @@ class DatetimeHandler(object):
         """
         if isinstance(dt, str | int | float):
             if isinstance(dt, str):
-                dt = float(dt)
+                try:
+                    dt = float(dt)
+                except:
+                    dt = datetime.datetime.strptime(dt, "%Y-%m-%d %H:%M:%S")
+                    dt = dt.timestamp()
             self._dt: datetime.datetime = datetime.datetime.fromtimestamp(dt)
         elif isinstance(dt, datetime.datetime):
             self._dt: datetime.datetime = dt

reme_ai/vector_store/recall_vector_store_op.py

@@ -19,7 +19,6 @@ class RecallVectorStoreOp(BaseLLMOp):
 
         workspace_id: str = self.context.workspace_id
         nodes: List[VectorNode] = self.vector_store.search(query=query, workspace_id=workspace_id, top_k=top_k)
-
         memory_list: List[BaseMemory] = []
         memory_content_list: List[str] = []
         for node in nodes:

test/http_client_test.py

@@ -78,7 +78,13 @@ async def run2(session):
         result = await response.json()
         print(json.dumps(result, ensure_ascii=False))
 
-    messages = [{"role": "user", "content": "我喜欢吃西瓜🍉"}]
+    messages = [
+        {"role": "user", "content": "我喜欢吃西瓜🍉"},
+        {"role": "user", "content": "昨天吃了苹果，很好吃"},
+        {"role": "user", "content": "我不太喜欢吃西瓜"},
+        {"role": "user", "content": "上周我去了日本，得了肠胃炎"},
+        {"role": "user", "content": "这周只能在家里，喝粥"},
+    ]
 
     async with session.post(
             f"{base_url}/summary_personal_memory",
@@ -96,7 +102,7 @@ async def run2(session):
     async with session.post(
             f"{base_url}/retrieve_personal_memory",
             json={
-                "query": "茅台怎么样？",
+                "query": "你知道我喜欢吃什么？",
                 "workspace_id": workspace_id,
             },
             headers={"Content-Type": "application/json"}