Fix: meta data filter with AND logic operations. (#9687)

### What problem does this PR solve?

Close #9648

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

api/apps/sdk/session.py

@@ -84,18 +84,10 @@ def create_agent_session(tenant_id, agent_id):
     session_id=get_uuid()
     canvas = Canvas(cvs.dsl, tenant_id, agent_id)
     canvas.reset()
-    conv = {
-        "id": session_id,
-        "dialog_id": cvs.id,
-        "user_id": user_id,
-        "message": [],
-        "source": "agent",
-        "dsl": cvs.dsl
-    }
-    API4ConversationService.save(**conv)
     
     cvs.dsl = json.loads(str(canvas))
     conv = {"id": session_id, "dialog_id": cvs.id, "user_id": user_id, "message": [{"role": "assistant", "content": canvas.get_prologue()}], "source": "agent", "dsl": cvs.dsl}
+    API4ConversationService.save(**conv)
     conv["agent_id"] = conv.pop("dialog_id")
     return get_result(data=conv)
 
@@ -570,6 +562,9 @@ def list_agent_session(tenant_id, agent_id):
                             "chunks" in conv["reference"][chunk_num]):
                         chunks = conv["reference"][chunk_num]["chunks"]
                         for chunk in chunks:
+                            # Ensure chunk is a dictionary before calling get method
+                            if not isinstance(chunk, dict):
+                                continue
                             new_chunk = {
                                 "id": chunk.get("chunk_id", chunk.get("id")),
                                 "content": chunk.get("content_with_weight", chunk.get("content")),

api/db/services/canvas_service.py

@@ -182,7 +182,8 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs):
             "user_id": user_id,
             "message": [],
             "source": "agent",
-            "dsl": cvs.dsl
+            "dsl": cvs.dsl,
+            "reference": []
         }
         API4ConversationService.save(**conv)
         conv = API4Conversation(**conv)

api/db/services/dialog_service.py

@@ -256,10 +256,10 @@ def repair_bad_citation_formats(answer: str, kbinfos: dict, idx: set):
 
 
 def meta_filter(metas: dict, filters: list[dict]):
-    doc_ids = []
+    doc_ids = set([])
 
     def filter_out(v2docs, operator, value):
-        nonlocal doc_ids
+        ids = []
         for input, docids in v2docs.items():
             try:
                 input = float(input)
@@ -284,16 +284,24 @@ def meta_filter(metas: dict, filters: list[dict]):
                 ]:
                 try:
                     if all(conds):
-                        doc_ids.extend(docids)
+                        ids.extend(docids)
+                        break
                 except Exception:
                     pass
+        return ids
 
     for k, v2docs in metas.items():
         for f in filters:
             if k != f["key"]:
                 continue
-            filter_out(v2docs, f["op"], f["value"])
-    return doc_ids
+            ids = filter_out(v2docs, f["op"], f["value"])
+            if not doc_ids:
+                doc_ids = set(ids)
+            else:
+                doc_ids = doc_ids & set(ids)
+            if not doc_ids:
+                return []
+    return list(doc_ids)
 
 
 def chat(dialog, messages, stream=True, **kwargs):

api/utils/api_utils.py

@@ -17,6 +17,7 @@ import asyncio
 import functools
 import json
 import logging
+import os
 import queue
 import random
 import threading
@@ -667,7 +668,10 @@ def timeout(seconds: float | int = None, attempts: int = 2, *, exception: Option
 
             for a in range(attempts):
                 try:
+                    if os.environ.get("ENABLE_TIMEOUT_ASSERTION"):
                         result = result_queue.get(timeout=seconds)
+                    else:
+                        result = result_queue.get()
                     if isinstance(result, Exception):
                         raise result
                     return result
@@ -682,8 +686,11 @@ def timeout(seconds: float | int = None, attempts: int = 2, *, exception: Option
 
             for a in range(attempts):
                 try:
+                    if os.environ.get("ENABLE_TIMEOUT_ASSERTION"):
                         with trio.fail_after(seconds):
                             return await func(*args, **kwargs)
+                    else:
+                        return await func(*args, **kwargs)
                 except trio.TooSlowError:
                     if a < attempts - 1:
                         continue

docs/references/http_api_reference.md

@@ -3501,7 +3501,7 @@ Failure:
 
 ### Generate related questions
 
-**POST** `/v1/sessions/related_questions`
+**POST** `/api/v1/sessions/related_questions`
 
 Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results.
 
@@ -3516,7 +3516,7 @@ The chat model autonomously determines the number of questions to generate based
 #### Request
 
 - Method: POST
-- URL: `/v1/sessions/related_questions`
+- URL: `/api/v1/sessions/related_questions`
 - Headers:
   - `'content-Type: application/json'`
   - `'Authorization: Bearer <YOUR_LOGIN_TOKEN>'`
@@ -3528,7 +3528,7 @@ The chat model autonomously determines the number of questions to generate based
 
 ```bash
 curl --request POST \
-     --url http://{address}/v1/sessions/related_questions \
+     --url http://{address}/api/v1/sessions/related_questions \
      --header 'Content-Type: application/json' \
      --header 'Authorization: Bearer <YOUR_LOGIN_TOKEN>' \
      --data '

graphrag/entity_resolution.py

@@ -15,6 +15,7 @@
 #
 import logging
 import itertools
+import os
 import re
 from dataclasses import dataclass
 from typing import Any, Callable
@@ -106,7 +107,8 @@ class EntityResolution(Extractor):
             nonlocal remain_candidates_to_resolve, callback
             async with semaphore:
                 try:
-                    with trio.move_on_after(280) as cancel_scope:
+                    enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION")
+                    with trio.move_on_after(280 if enable_timeout_assertion else 1000000000) as cancel_scope:
                         await self._resolve_candidate(candidate_batch, result_set, result_lock)
                         remain_candidates_to_resolve = remain_candidates_to_resolve - len(candidate_batch[1])
                         callback(msg=f"Resolved {len(candidate_batch[1])} pairs, {remain_candidates_to_resolve} are remained to resolve. ")
@@ -169,7 +171,8 @@ class EntityResolution(Extractor):
         logging.info(f"Created resolution prompt {len(text)} bytes for {len(candidate_resolution_i[1])} entity pairs of type {candidate_resolution_i[0]}")
         async with chat_limiter:
             try:
-                with trio.move_on_after(280) as cancel_scope:
+                enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION")
+                with trio.move_on_after(280 if enable_timeout_assertion else 1000000000) as cancel_scope:
                     response = await trio.to_thread.run_sync(self._chat, text, [{"role": "user", "content": "Output:"}], {})
                 if cancel_scope.cancelled_caught:
                     logging.warning("_resolve_candidate._chat timeout, skipping...")

graphrag/general/community_reports_extractor.py

@@ -7,6 +7,7 @@ Reference:
 
 import logging
 import json
+import os
 import re
 from typing import Callable
 from dataclasses import dataclass
@@ -51,6 +52,7 @@ class CommunityReportsExtractor(Extractor):
         self._max_report_length = max_report_length or 1500
 
     async def __call__(self, graph: nx.Graph, callback: Callable | None = None):
+        enable_timeout_assertion = os.environ.get("ENABLE_TIMEOUT_ASSERTION")
         for node_degree in graph.degree:
             graph.nodes[str(node_degree[0])]["rank"] = int(node_degree[1])
 
@@ -92,7 +94,7 @@ class CommunityReportsExtractor(Extractor):
             text = perform_variable_replacements(self._extraction_prompt, variables=prompt_variables)
             async with chat_limiter:
                 try:
-                    with trio.move_on_after(180) as cancel_scope:
+                    with trio.move_on_after(180 if enable_timeout_assertion else 1000000000) as cancel_scope:
                         response = await trio.to_thread.run_sync( self._chat, text, [{"role": "user", "content": "Output:"}], {})
                     if cancel_scope.cancelled_caught:
                         logging.warning("extract_community_report._chat timeout, skipping...")

graphrag/general/index.py

@@ -15,6 +15,8 @@
 #
 import json
 import logging
+import os
+
 import networkx as nx
 import trio
 
@@ -49,6 +51,7 @@ async def run_graphrag(
     embedding_model,
     callback,
 ):
+    enable_timeout_assertion=os.environ.get("ENABLE_TIMEOUT_ASSERTION")
     start = trio.current_time()
     tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
     chunks = []
@@ -57,7 +60,7 @@ async def run_graphrag(
     ):
         chunks.append(d["content_with_weight"])
 
-    with trio.fail_after(max(120, len(chunks)*60*10)):
+    with trio.fail_after(max(120, len(chunks)*60*10) if enable_timeout_assertion else 10000000000):
         subgraph = await generate_subgraph(
             LightKGExt
             if "method" not in row["kb_parser_config"].get("graphrag", {}) or row["kb_parser_config"]["graphrag"]["method"] != "general"

graphrag/light/graph_prompt.py

@@ -130,7 +130,36 @@ Output:
 
 PROMPTS[
     "entiti_continue_extraction"
-] = """MANY entities were missed in the last extraction.  Add them below using the same format:
+] = """
+MANY entities and relationships were missed in the last extraction. Please find only the missing entities and relationships from previous text.
+
+---Remember Steps---
+
+1. Identify all entities. For each identified entity, extract the following information:
+- entity_name: Name of the entity, use same language as input text. If English, capitalized the name
+- entity_type: One of the following types: [{entity_types}]
+- entity_description: Provide a comprehensive description of the entity's attributes and activities *based solely on the information present in the input text*. **Do not infer or hallucinate information not explicitly stated.** If the text provides insufficient information to create a comprehensive description, state "Description not available in text."
+Format each entity as ("entity"{tuple_delimiter}<entity_name>{tuple_delimiter}<entity_type>{tuple_delimiter}<entity_description>)
+
+2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other.
+For each pair of related entities, extract the following information:
+- source_entity: name of the source entity, as identified in step 1
+- target_entity: name of the target entity, as identified in step 1
+- relationship_description: explanation as to why you think the source entity and the target entity are related to each other
+- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity
+- relationship_keywords: one or more high-level key words that summarize the overarching nature of the relationship, focusing on concepts or themes rather than specific details
+Format each relationship as ("relationship"{tuple_delimiter}<source_entity>{tuple_delimiter}<target_entity>{tuple_delimiter}<relationship_description>{tuple_delimiter}<relationship_keywords>{tuple_delimiter}<relationship_strength>)
+
+3. Identify high-level key words that summarize the main concepts, themes, or topics of the entire text. These should capture the overarching ideas present in the document.
+Format the content-level key words as ("content_keywords"{tuple_delimiter}<high_level_keywords>)
+
+4. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.
+
+5. When finished, output {completion_delimiter}
+
+---Output---
+
+Add new entities and relations below using the same format, and do not include entities and relations that have been previously extracted. :
 """
 
 PROMPTS[

graphrag/utils.py

@@ -307,6 +307,7 @@ def chunk_id(chunk):
 
 async def graph_node_to_chunk(kb_id, embd_mdl, ent_name, meta, chunks):
     global chat_limiter
+    enable_timeout_assertion=os.environ.get("ENABLE_TIMEOUT_ASSERTION")
     chunk = {
         "id": get_uuid(),
         "important_kwd": [ent_name],
@@ -324,7 +325,7 @@ async def graph_node_to_chunk(kb_id, embd_mdl, ent_name, meta, chunks):
     ebd = get_embed_cache(embd_mdl.llm_name, ent_name)
     if ebd is None:
         async with chat_limiter:
-            with trio.fail_after(3):
+            with trio.fail_after(3 if enable_timeout_assertion else 30000000):
                 ebd, _ = await trio.to_thread.run_sync(lambda: embd_mdl.encode([ent_name]))
         ebd = ebd[0]
         set_embed_cache(embd_mdl.llm_name, ent_name, ebd)
@@ -362,6 +363,7 @@ def get_relation(tenant_id, kb_id, from_ent_name, to_ent_name, size=1):
 
 
 async def graph_edge_to_chunk(kb_id, embd_mdl, from_ent_name, to_ent_name, meta, chunks):
+    enable_timeout_assertion=os.environ.get("ENABLE_TIMEOUT_ASSERTION")
     chunk = {
         "id": get_uuid(),
         "from_entity_kwd": from_ent_name,
@@ -380,7 +382,7 @@ async def graph_edge_to_chunk(kb_id, embd_mdl, from_ent_name, to_ent_name, meta,
     ebd = get_embed_cache(embd_mdl.llm_name, txt)
     if ebd is None:
         async with chat_limiter:
-            with trio.fail_after(3):
+            with trio.fail_after(3 if enable_timeout_assertion else 300000000):
                 ebd, _ = await trio.to_thread.run_sync(lambda: embd_mdl.encode([txt+f": {meta['description']}"]))
         ebd = ebd[0]
         set_embed_cache(embd_mdl.llm_name, txt, ebd)
@@ -514,9 +516,10 @@ async def set_graph(tenant_id: str, kb_id: str, embd_mdl, graph: nx.Graph, chang
         callback(msg=f"set_graph converted graph change to {len(chunks)} chunks in {now - start:.2f}s.")
     start = now
 
+    enable_timeout_assertion=os.environ.get("ENABLE_TIMEOUT_ASSERTION")
     es_bulk_size = 4
     for b in range(0, len(chunks), es_bulk_size):
-        with trio.fail_after(3):
+        with trio.fail_after(3 if enable_timeout_assertion else 30000000):
             doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b:b + es_bulk_size], search.index_name(tenant_id), kb_id))
         if b % 100 == es_bulk_size and callback:
             callback(msg=f"Insert chunks: {b}/{len(chunks)}")

rag/llm/__init__.py

@@ -36,6 +36,7 @@ class SupportedLiteLLMProvider(StrEnum):
     Nvidia = "NVIDIA"
     TogetherAI = "TogetherAI"
     Anthropic = "Anthropic"
+    Ollama = "Ollama"
 
 
 FACTORY_DEFAULT_BASE_URL = {
@@ -59,6 +60,7 @@ LITELLM_PROVIDER_PREFIX = {
     SupportedLiteLLMProvider.Nvidia: "nvidia_nim/",
     SupportedLiteLLMProvider.TogetherAI: "together_ai/",
     SupportedLiteLLMProvider.Anthropic: "",  # don't need a prefix
+    SupportedLiteLLMProvider.Ollama: "ollama_chat/",
 }
 
 ChatModel = globals().get("ChatModel", {})

rag/llm/chat_model.py

@@ -29,7 +29,6 @@ import json_repair
 import litellm
 import openai
 import requests
-from ollama import Client
 from openai import OpenAI
 from openai.lib.azure import AzureOpenAI
 from strenum import StrEnum
@@ -683,73 +682,6 @@ class ZhipuChat(Base):
         return super().chat_streamly_with_tools(system, history, gen_conf)
 
 
-class OllamaChat(Base):
-    _FACTORY_NAME = "Ollama"
-
-    def __init__(self, key, model_name, base_url=None, **kwargs):
-        super().__init__(key, model_name, base_url=base_url, **kwargs)
-
-        self.client = Client(host=base_url) if not key or key == "x" else Client(host=base_url, headers={"Authorization": f"Bearer {key}"})
-        self.model_name = model_name
-        self.keep_alive = kwargs.get("ollama_keep_alive", int(os.environ.get("OLLAMA_KEEP_ALIVE", -1)))
-
-    def _clean_conf(self, gen_conf):
-        options = {}
-        if "max_tokens" in gen_conf:
-            options["num_predict"] = gen_conf["max_tokens"]
-        for k in ["temperature", "top_p", "presence_penalty", "frequency_penalty"]:
-            if k not in gen_conf:
-                continue
-            options[k] = gen_conf[k]
-        return options
-
-    def _chat(self, history, gen_conf={}, **kwargs):
-        # Calculate context size
-        ctx_size = self._calculate_dynamic_ctx(history)
-
-        gen_conf["num_ctx"] = ctx_size
-        response = self.client.chat(model=self.model_name, messages=history, options=gen_conf, keep_alive=self.keep_alive)
-        ans = response["message"]["content"].strip()
-        token_count = response.get("eval_count", 0) + response.get("prompt_eval_count", 0)
-        return ans, token_count
-
-    def chat_streamly(self, system, history, gen_conf={}, **kwargs):
-        if system:
-            history.insert(0, {"role": "system", "content": system})
-        if "max_tokens" in gen_conf:
-            del gen_conf["max_tokens"]
-        try:
-            # Calculate context size
-            ctx_size = self._calculate_dynamic_ctx(history)
-            options = {"num_ctx": ctx_size}
-            if "temperature" in gen_conf:
-                options["temperature"] = gen_conf["temperature"]
-            if "max_tokens" in gen_conf:
-                options["num_predict"] = gen_conf["max_tokens"]
-            if "top_p" in gen_conf:
-                options["top_p"] = gen_conf["top_p"]
-            if "presence_penalty" in gen_conf:
-                options["presence_penalty"] = gen_conf["presence_penalty"]
-            if "frequency_penalty" in gen_conf:
-                options["frequency_penalty"] = gen_conf["frequency_penalty"]
-
-            ans = ""
-            try:
-                response = self.client.chat(model=self.model_name, messages=history, stream=True, options=options, keep_alive=self.keep_alive)
-                for resp in response:
-                    if resp["done"]:
-                        token_count = resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
-                        yield token_count
-                    ans = resp["message"]["content"]
-                    yield ans
-            except Exception as e:
-                yield ans + "\n**ERROR**: " + str(e)
-            yield 0
-        except Exception as e:
-            yield "**ERROR**: " + str(e)
-            yield 0
-
-
 class LocalAIChat(Base):
     _FACTORY_NAME = "LocalAI"
 
@@ -1422,7 +1354,7 @@ class Ai302Chat(Base):
 
 
 class LiteLLMBase(ABC):
-    _FACTORY_NAME = ["Tongyi-Qianwen", "Bedrock", "Moonshot", "xAI", "DeepInfra", "Groq", "Cohere", "Gemini", "DeepSeek", "NVIDIA", "TogetherAI", "Anthropic"]
+    _FACTORY_NAME = ["Tongyi-Qianwen", "Bedrock", "Moonshot", "xAI", "DeepInfra", "Groq", "Cohere", "Gemini", "DeepSeek", "NVIDIA", "TogetherAI", "Anthropic", "Ollama"]
 
     def __init__(self, key, model_name, base_url=None, **kwargs):
         self.timeout = int(os.environ.get("LM_TIMEOUT_SECONDS", 600))

rag/svr/task_executor.py

@@ -21,7 +21,7 @@ import sys
 import threading
 import time
 
-from api.utils.api_utils import timeout, is_strong_enough
+from api.utils.api_utils import timeout
 from api.utils.log_utils import init_root_logger, get_project_base_directory
 from graphrag.general.index import run_graphrag
 from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
@@ -478,8 +478,6 @@ async def embedding(docs, mdl, parser_config=None, callback=None):
 
 @timeout(3600)
 async def run_raptor(row, chat_mdl, embd_mdl, vector_size, callback=None):
-    # Pressure test for GraphRAG task
-    await is_strong_enough(chat_mdl, embd_mdl)
     chunks = []
     vctr_nm = "q_%d_vec"%vector_size
     for d in settings.retrievaler.chunk_list(row["doc_id"], row["tenant_id"], [str(row["kb_id"])],
@@ -553,7 +551,6 @@ async def do_handle_task(task):
     try:
         # bind embedding model
         embedding_model = LLMBundle(task_tenant_id, LLMType.EMBEDDING, llm_name=task_embedding_id, lang=task_language)
-        await is_strong_enough(None, embedding_model)
         vts, _ = embedding_model.encode(["ok"])
         vector_size = len(vts[0])
     except Exception as e:
@@ -568,7 +565,6 @@ async def do_handle_task(task):
     if task.get("task_type", "") == "raptor":
         # bind LLM for raptor
         chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
-        await is_strong_enough(chat_model, None)
         # run RAPTOR
         async with kg_limiter:
             chunks, token_count = await run_raptor(task, chat_model, embedding_model, vector_size, progress_callback)
@@ -580,7 +576,6 @@ async def do_handle_task(task):
         graphrag_conf = task["kb_parser_config"].get("graphrag", {})
         start_ts = timer()
         chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
-        await is_strong_enough(chat_model, None)
         with_resolution = graphrag_conf.get("resolution", False)
         with_community = graphrag_conf.get("community", False)
         async with kg_limiter:

web/src/app.tsx

@@ -2,7 +2,6 @@ import { Toaster as Sonner } from '@/components/ui/sonner';
 import { Toaster } from '@/components/ui/toaster';
 import i18n from '@/locales/config';
 import { QueryClient, QueryClientProvider } from '@tanstack/react-query';
-import { ReactQueryDevtools } from '@tanstack/react-query-devtools';
 import { App, ConfigProvider, ConfigProviderProps, theme } from 'antd';
 import pt_BR from 'antd/lib/locale/pt_BR';
 import deDE from 'antd/locale/de_DE';
@@ -85,7 +84,7 @@ function Root({ children }: React.PropsWithChildren) {
         <Sonner position={'top-right'} expand richColors closeButton></Sonner>
         <Toaster />
       </ConfigProvider>
-      <ReactQueryDevtools buttonPosition={'top-left'} initialIsOpen={false} />
+      {/* <ReactQueryDevtools buttonPosition={'top-left'} initialIsOpen={false} /> */}
     </>
   );
 }

web/src/components/layout-recognize-form-field.tsx

@@ -68,7 +68,7 @@ export function LayoutRecognizeFormField() {
             <div className="flex items-center">
               <FormLabel
                 tooltip={t('layoutRecognizeTip')}
-                className="text-sm text-muted-foreground whitespace-nowrap w-1/4"
+                className="text-sm text-muted-foreground whitespace-wrap w-1/4"
               >
                 {t('layoutRecognize')}
               </FormLabel>

web/src/components/originui/select-with-search.tsx

@@ -28,6 +28,7 @@ import {
   PopoverTrigger,
 } from '@/components/ui/popover';
 import { cn } from '@/lib/utils';
+import { t } from 'i18next';
 import { RAGFlowSelectOptionType } from '../ui/select';
 import { Separator } from '../ui/separator';
 
@@ -114,7 +115,9 @@ export const SelectWithSearch = forwardRef<
                 <span className="leading-none truncate">{selectLabel}</span>
               </span>
             ) : (
-              <span className="text-muted-foreground">Select value</span>
+              <span className="text-muted-foreground">
+                {t('common.selectPlaceholder')}
+              </span>
             )}
             <div className="flex items-center justify-between">
               {value && allowClear && (

web/src/components/parse-configuration/raptor-form-fields.tsx

@@ -3,7 +3,7 @@ import { DocumentParserType } from '@/constants/knowledge';
 import { useTranslate } from '@/hooks/common-hooks';
 import random from 'lodash/random';
 import { Plus } from 'lucide-react';
-import { useCallback, useEffect } from 'react';
+import { useCallback } from 'react';
 import { useFormContext, useWatch } from 'react-hook-form';
 import { SliderInputFormField } from '../slider-input-form-field';
 import { Button } from '../ui/button';
@@ -57,15 +57,19 @@ const RaptorFormFields = () => {
   const form = useFormContext();
   const { t } = useTranslate('knowledgeConfiguration');
   const useRaptor = useWatch({ name: UseRaptorField });
-  useEffect(() => {
-    if (useRaptor) {
+
+  const changeRaptor = useCallback(
+    (isUseRaptor: boolean) => {
+      if (isUseRaptor) {
         form.setValue(MaxTokenField, 256);
         form.setValue(ThresholdField, 0.1);
         form.setValue(MaxCluster, 64);
         form.setValue(RandomSeedField, 0);
         form.setValue(Prompt, t('promptText'));
       }
-  }, [form, useRaptor, t]);
+    },
+    [form],
+  );
 
   const handleGenerate = useCallback(() => {
     form.setValue(RandomSeedField, random(10000));
@@ -97,7 +101,10 @@ const RaptorFormFields = () => {
                   <FormControl>
                     <Switch
                       checked={field.value}
-                      onCheckedChange={field.onChange}
+                      onCheckedChange={(e) => {
+                        changeRaptor(e);
+                        field.onChange(e);
+                      }}
                     ></Switch>
                   </FormControl>
                 </div>
@@ -127,7 +134,13 @@ const RaptorFormFields = () => {
                     </FormLabel>
                     <div className="w-3/4">
                       <FormControl>
-                        <Textarea {...field} rows={8} />
+                        <Textarea
+                          {...field}
+                          rows={8}
+                          onChange={(e) => {
+                            field.onChange(e?.target?.value);
+                          }}
+                        />
                       </FormControl>
                     </div>
                   </div>

web/src/components/ui/multi-select.tsx

@@ -209,10 +209,16 @@ export const MultiSelect = React.forwardRef<
     const [isAnimating, setIsAnimating] = React.useState(false);
 
     React.useEffect(() => {
-      if (selectedValues === undefined) {
+      if (!selectedValues && props.value) {
+        setSelectedValues(props.value as string[]);
+      }
+    }, [props.value, selectedValues]);
+
+    React.useEffect(() => {
+      if (!selectedValues && !props.value && defaultValue) {
         setSelectedValues(defaultValue);
       }
-    }, [defaultValue, selectedValues]);
+    }, [defaultValue, props.value, selectedValues]);
 
     const flatOptions = React.useMemo(() => {
       return options.flatMap((option) =>
@@ -293,15 +299,18 @@ export const MultiSelect = React.forwardRef<
                         variant="secondary"
                         className={cn(
                           isAnimating ? 'animate-bounce' : '',
+                          'px-1',
                           multiSelectVariants({ variant }),
                         )}
                         style={{ animationDuration: `${animation}s` }}
                       >
-                        <div className="flex items-center gap-1">
+                        <div className="flex justify-between items-center gap-1">
                           {IconComponent && (
                             <IconComponent className="h-4 w-4" />
                           )}
-                          <div>{option?.label}</div>
+                          <div className="max-w-28 text-ellipsis overflow-hidden">
+                            {option?.label}
+                          </div>
                           <XCircle
                             className="h-4 w-4 cursor-pointer"
                             onClick={(event) => {

web/src/locales/en.ts

@@ -1,6 +1,7 @@
 export default {
   translation: {
     common: {
+      selectPlaceholder: 'select value',
       delete: 'Delete',
       deleteModalTitle: 'Are you sure to delete this item?',
       ok: 'Yes',
@@ -94,6 +95,16 @@ export default {
       noMoreData: `That's all. Nothing more.`,
     },
     knowledgeDetails: {
+      created: 'Created',
+      learnMore: 'Learn More',
+      general: 'General',
+      chunkMethodTab: 'Chunk Method',
+      testResults: 'Test Results',
+      testSetting: 'Test Setting',
+      retrievalTesting: 'Retrieval Testing',
+      retrievalTestingDescription:
+        'Conduct a retrieval test to check if RAGFlow can recover the intended content for the LLM.',
+      Parse: 'Parse',
       dataset: 'Dataset',
       testing: 'Retrieval testing',
       files: 'files',

web/src/locales/zh.ts

@@ -1,6 +1,7 @@
 export default {
   translation: {
     common: {
+      selectPlaceholder: '请选择',
       delete: '删除',
       deleteModalTitle: '确定删除吗?',
       ok: '是',
@@ -86,6 +87,16 @@ export default {
       noMoreData: '没有更多数据了',
     },
     knowledgeDetails: {
+      created: '创建于',
+      learnMore: '了解更多',
+      general: '通用',
+      chunkMethodTab: '切片方法',
+      testResults: '测试结果',
+      testSetting: '测试设置',
+      retrievalTesting: '知识检索测试',
+      retrievalTestingDescription:
+        '进行检索测试，检查 RAGFlow 是否能够为大语言模型（LLM）恢复预期的内容。',
+      Parse: '解析',
       dataset: '数据集',
       testing: '检索测试',
       configuration: '配置',

web/src/pages/dataset/index.tsx

@@ -30,7 +30,9 @@ export default function DatasetWrapper() {
             </BreadcrumbItem>
             <BreadcrumbSeparator />
             <BreadcrumbItem>
-              <BreadcrumbPage>{data.name}</BreadcrumbPage>
+              <BreadcrumbPage className="w-28 whitespace-nowrap text-ellipsis overflow-hidden">
+                {data.name}
+              </BreadcrumbPage>
             </BreadcrumbItem>
           </BreadcrumbList>
         </Breadcrumb>

web/src/pages/dataset/setting/chunk-method-learn-more.tsx

@@ -1,5 +1,6 @@
 import { Button } from '@/components/ui/button';
 import { cn } from '@/lib/utils';
+import { t } from 'i18next';
 import { X } from 'lucide-react';
 import { useState } from 'react';
 import CategoryPanel from './category-panel';
@@ -26,7 +27,7 @@ export default ({
             setVisible(!visible);
           }}
         >
-          Learn More
+          {t('knowledgeDetails.learnMore')}
         </Button>
       </div>
       <div

web/src/pages/dataset/setting/configuration/common-item.tsx

@@ -29,7 +29,7 @@ export function ChunkMethodItem() {
           <div className="flex items-center">
             <FormLabel
               tooltip={t('chunkMethodTip')}
-              className="text-sm text-muted-foreground whitespace-nowrap w-1/4"
+              className="text-sm text-muted-foreground whitespace-wrap w-1/4"
             >
               {t('chunkMethod')}
             </FormLabel>
@@ -69,7 +69,7 @@ export function EmbeddingModelItem() {
           <div className="flex items-center">
             <FormLabel
               tooltip={t('embeddingModelTip')}
-              className="text-sm text-muted-foreground whitespace-nowrap w-1/4"
+              className="text-sm text-muted-foreground whitespace-wrap w-1/4"
             >
               {t('embeddingModel')}
             </FormLabel>

web/src/pages/dataset/setting/index.tsx

@@ -107,7 +107,7 @@ export default function DatasetSettings() {
                 >
                   <div className="flex w-full h-full justify-center	items-center">
                     <span className="h-full group-data-[state=active]:border-b-2 border-foreground	">
-                      General
+                      {t('knowledgeDetails.general')}
                     </span>
                   </div>
                 </TabsTrigger>
@@ -117,7 +117,7 @@ export default function DatasetSettings() {
                 >
                   <div className="flex w-full h-full justify-center	items-center">
                     <span className="h-full group-data-[state=active]:border-b-2 border-foreground	">
-                      Chunk Method
+                      {t('knowledgeDetails.chunkMethodTab')}
                     </span>
                   </div>
                 </TabsTrigger>

web/src/pages/dataset/sidebar/index.tsx

@@ -67,10 +67,14 @@ export function SideBar({ refreshCount }: PropType) {
             {data.name}
           </h3>
           <div className="flex justify-between">
-            <span>{data.doc_num} files</span>
+            <span>
+              {data.doc_num} {t('knowledgeDetails.files')}
+            </span>
             <span>{formatBytes(data.size)}</span>
           </div>
-          <div>Created {formatPureDate(data.create_time)}</div>
+          <div>
+            {t('knowledgeDetails.created')} {formatPureDate(data.create_time)}
+          </div>
         </div>
       </div>
 

web/src/pages/dataset/testing/index.tsx

@@ -1,4 +1,5 @@
 import { useTestRetrieval } from '@/hooks/use-knowledge-request';
+import { t } from 'i18next';
 import { useState } from 'react';
 import { TopTitle } from '../dataset-title';
 import TestingForm from './testing-form';
@@ -23,8 +24,8 @@ export default function RetrievalTesting() {
     <div className="p-5">
       <section className="flex justify-between items-center">
         <TopTitle
-          title={'Retrieval testing'}
-          description={`Conduct a retrieval test to check if RAGFlow can recover the intended content for the LLM.`}
+          title={t('knowledgeDetails.retrievalTesting')}
+          description={t('knowledgeDetails.retrievalTestingDescription')}
         ></TopTitle>
         {/* <Button>Save as Preset</Button> */}
       </section>
@@ -33,7 +34,7 @@ export default function RetrievalTesting() {
           <div className="p-4 flex-1">
             <div className="flex justify-between pb-2.5">
               <span className="text-text-primary font-semibold text-2xl">
-                Test setting
+                {t('knowledgeDetails.testSetting')}
               </span>
               {/* <Button variant={'outline'} onClick={addCount}>
                 <Plus /> Add New Test

web/src/pages/dataset/testing/testing-result.tsx

@@ -6,6 +6,7 @@ import { RAGFlowPagination } from '@/components/ui/ragflow-pagination';
 import { useTranslate } from '@/hooks/common-hooks';
 import { useTestRetrieval } from '@/hooks/use-knowledge-request';
 import { ITestingChunk } from '@/interfaces/database/knowledge';
+import { t } from 'i18next';
 import camelCase from 'lodash/camelCase';
 import { useMemo } from 'react';
 
@@ -66,7 +67,7 @@ export function TestingResult({
     <div className="p-4 flex-1">
       <div className="flex justify-between pb-2.5">
         <span className="text-text-primary font-semibold text-2xl">
-          Test results
+          {t('knowledgeDetails.testResults')}
         </span>
         <FilterPopover
           filters={filters}

web/src/pages/datasets/dataset-card.tsx

@@ -39,7 +39,7 @@ export function SeeAllCard() {
   const { navigateToDatasetList } = useNavigatePage();
 
   return (
-    <Card className="w-40 flex-none" onClick={navigateToDatasetList}>
+    <Card className="w-40 flex-none h-full" onClick={navigateToDatasetList}>
       <CardContent className="p-2.5 pt-1 w-full h-full flex items-center justify-center gap-1.5 text-text-secondary">
         See All <ChevronRight className="size-4" />
       </CardContent>

web/src/pages/home/datasets.tsx

@@ -31,16 +31,20 @@ export function Datasets() {
           </div>
         ) : (
           <div className="grid gap-6 sm:grid-cols-1 md:grid-cols-2 lg:grid-cols-4 xl:grid-cols-6 2xl:grid-cols-8 max-h-[78vh] overflow-auto">
-            {kbs.slice(0, 6).map((dataset) => (
+            {kbs
+              ?.slice(0, 6)
+              .map((dataset) => (
                 <DatasetCard
                   key={dataset.id}
                   dataset={dataset}
                   showDatasetRenameModal={showDatasetRenameModal}
                 ></DatasetCard>
               ))}
+            <div className="min-h-24">
+              <SeeAllCard></SeeAllCard>
+            </div>
           </div>
         )}
-        <SeeAllCard></SeeAllCard>
       </div>
       {datasetRenameVisible && (
         <RenameDialog

web/src/pages/next-chats/chat/app-settings/chat-settings.tsx

@@ -9,6 +9,7 @@ import {
   setLLMSettingEnabledValues,
 } from '@/utils/form';
 import { zodResolver } from '@hookform/resolvers/zod';
+import { omit } from 'lodash';
 import { X } from 'lucide-react';
 import { useEffect } from 'react';
 import { useForm } from 'react-hook-form';
@@ -69,7 +70,7 @@ export function ChatSettings({ switchSettingVisible }: ChatSettingsProps) {
         ? await transformFile2Base64(icon[0])
         : '';
     setDialog({
-      ...data,
+      ...omit(data, 'operator_permission'),
       ...nextValues,
       icon: avatar,
       dialog_id: id,

web/src/pages/next-chats/hooks/use-rename-chat.ts

@@ -48,7 +48,10 @@ export const useRenameChat = () => {
       const nextChat = {
         ...(isEmpty(chat)
           ? InitialData
-          : { ...omit(chat, 'nickname', 'tenant_avatar'), dialog_id: chat.id }),
+          : {
+              ...omit(chat, 'nickname', 'tenant_avatar', 'operator_permission'),
+              dialog_id: chat.id,
+            }),
         name,
       };
       const ret = await setDialog(nextChat);