Merge branch 'infiniflow:main' into feature/confluence-space-key

api/apps/sdk/files.py

@@ -39,7 +39,7 @@ async def upload(tenant_id):
     Upload a file to the system.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -155,7 +155,7 @@ async def create(tenant_id):
     Create a new file or folder.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -233,7 +233,7 @@ async def list_files(tenant_id):
     List files under a specific folder.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -325,7 +325,7 @@ async def get_root_folder(tenant_id):
     Get user's root folder.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     responses:
@@ -361,7 +361,7 @@ async def get_parent_folder():
     Get parent folder info of a file.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -406,7 +406,7 @@ async def get_all_parent_folders(tenant_id):
     Get all parent folders of a file.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -454,7 +454,7 @@ async def rm(tenant_id):
     Delete one or multiple files/folders.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -528,7 +528,7 @@ async def rename(tenant_id):
     Rename a file.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:
@@ -589,7 +589,7 @@ async def get(tenant_id, file_id):
     Download a file.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     produces:
@@ -637,7 +637,7 @@ async def move(tenant_id):
     Move one or multiple files to another folder.
     ---
     tags:
-      - File Management
+      - File
     security:
       - ApiKeyAuth: []
     parameters:

deepdoc/parser/mineru_parser.py

@@ -63,6 +63,7 @@ class MinerUParser(RAGFlowPdfParser):
         self.logger = logging.getLogger(self.__class__.__name__)
 
     def _extract_zip_no_root(self, zip_path, extract_to, root_dir):
+        self.logger.info(f"[MinerU] Extract zip: zip_path={zip_path}, extract_to={extract_to}, root_hint={root_dir}")
         with zipfile.ZipFile(zip_path, "r") as zip_ref:
             if not root_dir:
                 files = zip_ref.namelist()
@@ -72,7 +73,7 @@ class MinerUParser(RAGFlowPdfParser):
                     root_dir = None
 
             if not root_dir or not root_dir.endswith("/"):
-                self.logger.info(f"[MinerU] No root directory found, extracting all...fff{root_dir}")
+                self.logger.info(f"[MinerU] No root directory found, extracting all (root_hint={root_dir})")
                 zip_ref.extractall(extract_to)
                 return
 
@@ -108,7 +109,7 @@ class MinerUParser(RAGFlowPdfParser):
         valid_backends = ["pipeline", "vlm-http-client", "vlm-transformers", "vlm-vllm-engine"]
         if backend not in valid_backends:
             reason = "[MinerU] Invalid backend '{backend}'. Valid backends are: {valid_backends}"
-            logging.warning(reason)
+            self.logger.warning(reason)
             return False, reason
 
         subprocess_kwargs = {
@@ -128,40 +129,40 @@ class MinerUParser(RAGFlowPdfParser):
         if backend == "vlm-http-client" and server_url:
             try:
                 server_accessible = self._is_http_endpoint_valid(server_url + "/openapi.json")
-                logging.info(f"[MinerU] vlm-http-client server check: {server_accessible}")
+                self.logger.info(f"[MinerU] vlm-http-client server check: {server_accessible}")
                 if server_accessible:
                     self.using_api = False  # We are using http client, not API
                     return True, reason
                 else:
                     reason = f"[MinerU] vlm-http-client server not accessible: {server_url}"
-                    logging.warning(f"[MinerU] vlm-http-client server not accessible: {server_url}")
+                    self.logger.warning(f"[MinerU] vlm-http-client server not accessible: {server_url}")
                     return False, reason
             except Exception as e:
-                logging.warning(f"[MinerU] vlm-http-client server check failed: {e}")
+                self.logger.warning(f"[MinerU] vlm-http-client server check failed: {e}")
                 try:
                     response = requests.get(server_url, timeout=5)
-                    logging.info(f"[MinerU] vlm-http-client server connection check: success with status {response.status_code}")
+                    self.logger.info(f"[MinerU] vlm-http-client server connection check: success with status {response.status_code}")
                     self.using_api = False
                     return True, reason
                 except Exception as e:
                     reason = f"[MinerU] vlm-http-client server connection check failed: {server_url}: {e}"
-                    logging.warning(f"[MinerU] vlm-http-client server connection check failed: {server_url}: {e}")
+                    self.logger.warning(f"[MinerU] vlm-http-client server connection check failed: {server_url}: {e}")
                     return False, reason
 
         try:
             result = subprocess.run([str(self.mineru_path), "--version"], **subprocess_kwargs)
             version_info = result.stdout.strip()
             if version_info:
-                logging.info(f"[MinerU] Detected version: {version_info}")
+                self.logger.info(f"[MinerU] Detected version: {version_info}")
             else:
-                logging.info("[MinerU] Detected MinerU, but version info is empty.")
+                self.logger.info("[MinerU] Detected MinerU, but version info is empty.")
             return True, reason
         except subprocess.CalledProcessError as e:
-            logging.warning(f"[MinerU] Execution failed (exit code {e.returncode}).")
+            self.logger.warning(f"[MinerU] Execution failed (exit code {e.returncode}).")
         except FileNotFoundError:
-            logging.warning("[MinerU] MinerU not found. Please install it via: pip install -U 'mineru[core]'")
+            self.logger.warning("[MinerU] MinerU not found. Please install it via: pip install -U 'mineru[core]'")
         except Exception as e:
-            logging.error(f"[MinerU] Unexpected error during installation check: {e}")
+            self.logger.error(f"[MinerU] Unexpected error during installation check: {e}")
 
         # If executable check fails, try API check
         try:
@@ -171,14 +172,14 @@ class MinerUParser(RAGFlowPdfParser):
                 if not openapi_exists:
                     reason = "[MinerU] Failed to detect vaild MinerU API server"
                     return openapi_exists, reason
-                logging.info(f"[MinerU] Detected {self.mineru_api}/openapi.json: {openapi_exists}")
+                self.logger.info(f"[MinerU] Detected {self.mineru_api}/openapi.json: {openapi_exists}")
                 self.using_api = openapi_exists
                 return openapi_exists, reason
             else:
-                logging.info("[MinerU] api not exists.")
+                self.logger.info("[MinerU] api not exists.")
         except Exception as e:
             reason = f"[MinerU] Unexpected error during api check: {e}"
-            logging.error(f"[MinerU] Unexpected error during api check: {e}")
+            self.logger.error(f"[MinerU] Unexpected error during api check: {e}")
         return False, reason
 
     def _run_mineru(
@@ -314,7 +315,7 @@ class MinerUParser(RAGFlowPdfParser):
         except Exception as e:
             self.page_images = None
             self.total_page = 0
-            logging.exception(e)
+            self.logger.exception(e)
 
     def _line_tag(self, bx):
         pn = [bx["page_idx"] + 1]
@@ -480,15 +481,49 @@ class MinerUParser(RAGFlowPdfParser):
 
         json_file = None
         subdir = None
+        attempted = []
+
+        # mirror MinerU's sanitize_filename to align ZIP naming
+        def _sanitize_filename(name: str) -> str:
+            sanitized = re.sub(r"[/\\\.]{2,}|[/\\]", "", name)
+            sanitized = re.sub(r"[^\w.-]", "_", sanitized, flags=re.UNICODE)
+            if sanitized.startswith("."):
+                sanitized = "_" + sanitized[1:]
+            return sanitized or "unnamed"
+
+        safe_stem = _sanitize_filename(file_stem)
+        allowed_names = {f"{file_stem}_content_list.json", f"{safe_stem}_content_list.json"}
+        self.logger.info(f"[MinerU] Expected output files: {', '.join(sorted(allowed_names))}")
+        self.logger.info(f"[MinerU] Searching output candidates: {', '.join(str(c) for c in candidates)}")
+
         for sub in candidates:
             jf = sub / f"{file_stem}_content_list.json"
+            self.logger.info(f"[MinerU] Trying original path: {jf}")
+            attempted.append(jf)
             if jf.exists():
                 subdir = sub
                 json_file = jf
                 break
 
+            # MinerU API sanitizes non-ASCII filenames inside the ZIP root and file names.
+            alt = sub / f"{safe_stem}_content_list.json"
+            self.logger.info(f"[MinerU] Trying sanitized filename: {alt}")
+            attempted.append(alt)
+            if alt.exists():
+                subdir = sub
+                json_file = alt
+                break
+
+            nested_alt = sub / safe_stem / f"{safe_stem}_content_list.json"
+            self.logger.info(f"[MinerU] Trying sanitized nested path: {nested_alt}")
+            attempted.append(nested_alt)
+            if nested_alt.exists():
+                subdir = nested_alt.parent
+                json_file = nested_alt
+                break
+
         if not json_file:
-            raise FileNotFoundError(f"[MinerU] Missing output file, tried: {', '.join(str(c / (file_stem + '_content_list.json')) for c in candidates)}")
+            raise FileNotFoundError(f"[MinerU] Missing output file, tried: {', '.join(str(p) for p in attempted)}")
 
         with open(json_file, "r", encoding="utf-8") as f:
             data = json.load(f)

docker/.env

@@ -170,7 +170,7 @@ TZ=Asia/Shanghai
 # Uncomment the following line if your operating system is MacOS:
 # MACOS=1
 
-# The maximum file size limit (in bytes) for each upload to your knowledge base or File Management.
+# The maximum file size limit (in bytes) for each upload to your dataset or RAGFlow's File system.
 # To change the 1GB file size limit, uncomment the line below and update as needed.
 # MAX_CONTENT_LENGTH=1073741824
 # After updating, ensure `client_max_body_size` in nginx/nginx.conf is updated accordingly.

docs/guides/agent/agent_component_reference/begin.mdx

@@ -76,5 +76,5 @@ No. Files uploaded to an agent as input are not stored in a dataset and hence wi
 There is no _specific_ file size limit for a file uploaded to an agent. However, note that model providers typically have a default or explicit maximum token setting, which can range from 8196 to 128k: The plain text part of the uploaded file will be passed in as the key value, but if the file's token count exceeds this limit, the string will be truncated and incomplete.
 
 :::tip NOTE
-The variables `MAX_CONTENT_LENGTH` in `/docker/.env` and `client_max_body_size` in `/docker/nginx/nginx.conf` set the file size limit for each upload to a dataset or **File Management**. These settings DO NOT apply in this scenario.
+The variables `MAX_CONTENT_LENGTH` in `/docker/.env` and `client_max_body_size` in `/docker/nginx/nginx.conf` set the file size limit for each upload to a dataset or RAGFlow's File system. These settings DO NOT apply in this scenario.
 :::

docs/guides/chat/start_chat.md

@@ -9,7 +9,7 @@ Initiate an AI-powered chat with a configured chat assistant.
 
 ---
 
-Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. Chats in RAGFlow are based on a particular dataset or multiple datasets. Once you have created your dataset, finished file parsing, and [run a retrieval test](../dataset/run_retrieval_test.md), you can go ahead and start an AI conversation.
+Chats in RAGFlow are based on a particular dataset or multiple datasets. Once you have created your dataset, finished file parsing, and [run a retrieval test](../dataset/run_retrieval_test.md), you can go ahead and start an AI conversation.
 
 ## Start an AI chat
 

docs/guides/dataset/configure_knowledge_base.md

@@ -5,7 +5,7 @@ slug: /configure_knowledge_base
 
 # Configure dataset
 
-Most of RAGFlow's chat assistants and Agents are based on datasets. Each of RAGFlow's datasets serves as a knowledge source, *parsing* files uploaded from your local machine and file references generated in **File Management** into the real 'knowledge' for future AI chats. This guide demonstrates some basic usages of the dataset feature, covering the following topics:
+Most of RAGFlow's chat assistants and Agents are based on datasets. Each of RAGFlow's datasets serves as a knowledge source, *parsing* files uploaded from your local machine and file references generated in RAGFlow's File system into the real 'knowledge' for future AI chats. This guide demonstrates some basic usages of the dataset feature, covering the following topics:
 
 - Create a dataset
 - Configure a dataset
@@ -82,10 +82,10 @@ Some embedding models are optimized for specific languages, so performance may b
 
 ### Upload file
 
-- RAGFlow's **File Management** allows you to link a file to multiple datasets, in which case each target dataset holds a reference to the file.
+- RAGFlow's File system allows you to link a file to multiple datasets, in which case each target dataset holds a reference to the file.
 - In **Knowledge Base**, you are also given the option of uploading a single file or a folder of files (bulk upload) from your local machine to a dataset, in which case the dataset holds file copies. 
 
-While uploading files directly to a dataset seems more convenient, we *highly* recommend uploading files to **File Management** and then linking them to the target datasets. This way, you can avoid permanently deleting files uploaded to the dataset. 
+While uploading files directly to a dataset seems more convenient, we *highly* recommend uploading files to RAGFlow's File system and then linking them to the target datasets. This way, you can avoid permanently deleting files uploaded to the dataset. 
 
 ### Parse file
 
@@ -142,6 +142,6 @@ As of RAGFlow v0.22.1, the search feature is still in a rudimentary form, suppor
 You are allowed to delete a dataset. Hover your mouse over the three dot of the intended dataset card and the **Delete** option appears. Once you delete a dataset, the associated folder under **root/.knowledge** directory is AUTOMATICALLY REMOVED. The consequence is:
 
 - The files uploaded directly to the dataset are gone;  
-- The file references, which you created from within **File Management**, are gone, but the associated files still exist in **File Management**. 
+- The file references, which you created from within RAGFlow's File system, are gone, but the associated files still exist. 
 
 ![delete dataset](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/delete_datasets.jpg)

graphrag/general/index.py

@@ -57,7 +57,7 @@ async def run_graphrag(
     start = trio.current_time()
     tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
     chunks = []
-    for d in settings.retriever.chunk_list(doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"], sort_by_position=True):
+    for d in settings.retriever.chunk_list(doc_id, tenant_id, [kb_id], max_count=10000, fields=["content_with_weight", "doc_id"], sort_by_position=True):
         chunks.append(d["content_with_weight"])
 
     with trio.fail_after(max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000):
@@ -174,13 +174,19 @@ async def run_graphrag_for_kb(
         chunks = []
         current_chunk = ""
 
-        for d in settings.retriever.chunk_list(
+        # DEBUG: Obtener todos los chunks primero
+        raw_chunks = list(settings.retriever.chunk_list(
             doc_id,
             tenant_id,
             [kb_id],
+            max_count=10000,  # FIX: Aumentar límite para procesar todos los chunks
             fields=fields_for_chunks,
             sort_by_position=True,
-        ):
+        ))
+
+        callback(msg=f"[DEBUG] chunk_list() returned {len(raw_chunks)} raw chunks for doc {doc_id}")
+
+        for d in raw_chunks:
             content = d["content_with_weight"]
             if num_tokens_from_string(current_chunk + content) < 1024:
                 current_chunk += content

rag/nlp/search.py

@@ -537,7 +537,8 @@ class Dealer:
                 doc["id"] = id
             if dict_chunks:
                 res.extend(dict_chunks.values())
-            if len(dict_chunks.values()) < bs:
+            # FIX: Solo terminar si no hay chunks, no si hay menos de bs
+            if len(dict_chunks.values()) == 0:
                 break
         return res
 

web/src/locales/zh.ts

@@ -1905,16 +1905,5 @@ Tokenizer 会根据所选方式将内容存储为对应的数据结构。`,
       searchTitle: '尚未创建搜索应用',
       addNow: '立即添加',
     },
-
-    deleteModal: {
-      delAgent: '删除智能体',
-      delDataset: '删除知识库',
-      delSearch: '删除搜索',
-      delFile: '删除文件',
-      delFiles: '删除文件',
-      delFilesContent: '已选择 {{count}} 个文件',
-      delChat: '删除聊天',
-      delMember: '删除成员',
-    },
   },
 };