Fix: waitForResponse component. (#11172)

### What problem does this PR solve?

#10056

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)

agent/canvas.py

@@ -456,6 +456,7 @@ class Canvas(Graph):
                 for c in path:
                     o = self.get_component_obj(c)
                     if o.component_name.lower() == "userfillup":
+                        o.invoke()
                         another_inputs.update(o.get_input_elements())
                         if o.get_param("enable_tips"):
                             tips = o.output("tips")

agent/component/fillup.py

@@ -13,10 +13,14 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from agent.component.message import MessageParam, Message
+import json
+import re
+from functools import partial
+
+from agent.component.base import ComponentParamBase, ComponentBase
 
 
-class UserFillUpParam(MessageParam):
+class UserFillUpParam(ComponentParamBase):
 
     def __init__(self):
         super().__init__()
@@ -27,13 +31,32 @@ class UserFillUpParam(MessageParam):
         return True
 
 
-class UserFillUp(Message):
+class UserFillUp(ComponentBase):
     component_name = "UserFillUp"
 
     def _invoke(self, **kwargs):
         if self._param.enable_tips:
-            tips, kwargs = self.get_kwargs(self._param.tips)
+            content = self._param.tips
-            self.set_output("tips", tips)
+            for k, v in self.get_input_elements_from_text(self._param.tips).items():
+                v = v["value"]
+                ans = ""
+                if isinstance(v, partial):
+                    for t in v():
+                        ans += t
+                elif isinstance(v, list):
+                    ans = ",".join([str(vv) for vv in v])
+                elif not isinstance(v, str):
+                    try:
+                        ans = json.dumps(v, ensure_ascii=False)
+                    except Exception:
+                        pass
+                else:
+                    ans = v
+                if not ans:
+                    ans = ""
+                content = re.sub(r"\{%s\}"%k, ans, content)
+
+            self.set_output("tips", content)
         for k, v in kwargs.get("inputs", {}).items():
             self.set_output(k, v)
 

api/apps/kb_app.py

@@ -127,6 +127,7 @@ def update():
             logging.error("Link KB errors: ", errors)
         kb = kb.to_dict()
         kb.update(req)
+        kb["connectors"] = connectors
 
         return get_json_result(data=kb)
     except Exception as e:

api/db/services/connector_service.py

@@ -236,6 +236,7 @@ class Connector2KbService(CommonService):
             conn_id = conn["id"]
             connector_ids.append(conn_id)
             if conn_id in old_conn_ids:
+                cls.update_by_id(conn_id, {"auto_parse": conn.get("auto_parse", "1")})
                 continue
             cls.save(**{
                 "id": get_uuid(),

conf/llm_factories.json

@@ -4549,7 +4549,7 @@
             ]
         },
         {
-            "name": "Meituan",
+            "name": "LongCat",
             "logo": "",
             "tags": "LLM",
             "status": "1",

rag/llm/__init__.py

@@ -37,7 +37,7 @@ class SupportedLiteLLMProvider(StrEnum):
     TogetherAI = "TogetherAI"
     Anthropic = "Anthropic"
     Ollama = "Ollama"
-    Meituan = "Meituan"
+    LongCat = "LongCat"
     CometAPI = "CometAPI"
     SILICONFLOW = "SILICONFLOW"
     OpenRouter = "OpenRouter"
@@ -56,7 +56,7 @@ FACTORY_DEFAULT_BASE_URL = {
     SupportedLiteLLMProvider.Dashscope: "https://dashscope.aliyuncs.com/compatible-mode/v1",
     SupportedLiteLLMProvider.Moonshot: "https://api.moonshot.cn/v1",
     SupportedLiteLLMProvider.Ollama: "",
-    SupportedLiteLLMProvider.Meituan: "https://api.longcat.chat/openai",
+    SupportedLiteLLMProvider.LongCat: "https://api.longcat.chat/openai",
     SupportedLiteLLMProvider.CometAPI: "https://api.cometapi.com/v1",
     SupportedLiteLLMProvider.SILICONFLOW: "https://api.siliconflow.cn/v1",
     SupportedLiteLLMProvider.OpenRouter: "https://openrouter.ai/api/v1",
@@ -87,7 +87,7 @@ LITELLM_PROVIDER_PREFIX = {
     SupportedLiteLLMProvider.TogetherAI: "together_ai/",
     SupportedLiteLLMProvider.Anthropic: "",  # don't need a prefix
     SupportedLiteLLMProvider.Ollama: "ollama_chat/",
-    SupportedLiteLLMProvider.Meituan: "openai/",
+    SupportedLiteLLMProvider.LongCat: "openai/",
     SupportedLiteLLMProvider.CometAPI: "openai/",
     SupportedLiteLLMProvider.SILICONFLOW: "openai/",
     SupportedLiteLLMProvider.OpenRouter: "openai/",

rag/llm/chat_model.py

@@ -1390,7 +1390,7 @@ class LiteLLMBase(ABC):
         "TogetherAI",
         "Anthropic",
         "Ollama",
-        "Meituan",
+        "LongCat",
         "CometAPI",
         "SILICONFLOW",
         "OpenRouter",

rag/raptor.py

@@ -97,7 +97,7 @@ class RecursiveAbstractiveProcessing4TreeOrganizedRetrieval:
     async def __call__(self, chunks, random_state, callback=None, task_id: str = ""):
         if len(chunks) <= 1:
             return []
-        chunks = [(s, a) for s, a in chunks if s and len(a) > 0]
+        chunks = [(s, a) for s, a in chunks if s and a and len(a) > 0]
         layers = [(0, len(chunks))]
         start, end = 0, len(chunks)
 

rag/svr/task_executor.py

@@ -642,47 +642,64 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
     fake_doc_id = GRAPH_RAPTOR_FAKE_DOC_ID
 
     raptor_config = kb_parser_config.get("raptor", {})
-
-    chunks = []
     vctr_nm = "q_%d_vec"%vector_size
-    for doc_id in doc_ids:
-        for d in settings.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
-                                                 fields=["content_with_weight", vctr_nm],
-                                                 sort_by_position=True):
-            chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))
 
-    raptor = Raptor(
-        raptor_config.get("max_cluster", 64),
-        chat_mdl,
-        embd_mdl,
-        raptor_config["prompt"],
-        raptor_config["max_token"],
-        raptor_config["threshold"],
-    )
-    original_length = len(chunks)
-    chunks = await raptor(chunks, kb_parser_config["raptor"]["random_seed"], callback, row["id"])
-    doc = {
-        "doc_id": fake_doc_id,
-        "kb_id": [str(row["kb_id"])],
-        "docnm_kwd": row["name"],
-        "title_tks": rag_tokenizer.tokenize(row["name"]),
-        "raptor_kwd": "raptor"
-    }
-    if row["pagerank"]:
-        doc[PAGERANK_FLD] = int(row["pagerank"])
     res = []
     tk_count = 0
-    for content, vctr in chunks[original_length:]:
+    async def generate(chunks):
-        d = copy.deepcopy(doc)
+        nonlocal tk_count, res
-        d["id"] = xxhash.xxh64((content + str(fake_doc_id)).encode("utf-8")).hexdigest()
+        raptor = Raptor(
-        d["create_time"] = str(datetime.now()).replace("T", " ")[:19]
+            raptor_config.get("max_cluster", 64),
-        d["create_timestamp_flt"] = datetime.now().timestamp()
+            chat_mdl,
-        d[vctr_nm] = vctr.tolist()
+            embd_mdl,
-        d["content_with_weight"] = content
+            raptor_config["prompt"],
-        d["content_ltks"] = rag_tokenizer.tokenize(content)
+            raptor_config["max_token"],
-        d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+            raptor_config["threshold"],
-        res.append(d)
+        )
-        tk_count += num_tokens_from_string(content)
+        original_length = len(chunks)
+        chunks = await raptor(chunks, kb_parser_config["raptor"]["random_seed"], callback, row["id"])
+        doc = {
+            "doc_id": fake_doc_id,
+            "kb_id": [str(row["kb_id"])],
+            "docnm_kwd": row["name"],
+            "title_tks": rag_tokenizer.tokenize(row["name"]),
+            "raptor_kwd": "raptor"
+        }
+        if row["pagerank"]:
+            doc[PAGERANK_FLD] = int(row["pagerank"])
+
+        for content, vctr in chunks[original_length:]:
+            d = copy.deepcopy(doc)
+            d["id"] = xxhash.xxh64((content + str(fake_doc_id)).encode("utf-8")).hexdigest()
+            d["create_time"] = str(datetime.now()).replace("T", " ")[:19]
+            d["create_timestamp_flt"] = datetime.now().timestamp()
+            d[vctr_nm] = vctr.tolist()
+            d["content_with_weight"] = content
+            d["content_ltks"] = rag_tokenizer.tokenize(content)
+            d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+            res.append(d)
+            tk_count += num_tokens_from_string(content)
+
+    if raptor_config.get("scope", "file") == "file":
+        for x, doc_id in enumerate(doc_ids):
+            chunks = []
+            for d in settings.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
+                                                 fields=["content_with_weight", vctr_nm],
+                                                 sort_by_position=True):
+                chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))
+            callback(progress=(x+1.)/len(doc_ids))
+            await generate(chunks)
+
+    else:
+        chunks = []
+        for doc_id in doc_ids:
+            for d in settings.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
+                                                 fields=["content_with_weight", vctr_nm],
+                                                 sort_by_position=True):
+                chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))
+
+        await generate(chunks)
+
     return res, tk_count
 
 
@@ -795,6 +812,7 @@ async def do_handle_task(task):
                         "threshold": 0.1,
                         "max_cluster": 64,
                         "random_seed": 0,
+                        "scope": "file"
                     },
                 }
             )