Merge branch 'main' of github.com:infiniflow/ragflow into feature/1203

agent/canvas.py

@@ -478,7 +478,7 @@ class Canvas(Graph):
                 })
             await _run_batch(idx, to)
             to = len(self.path)
-            # post processing of components invocation
+            # post-processing of components invocation
             for i in range(idx, to):
                 cpn = self.get_component(self.path[i])
                 cpn_obj = self.get_component_obj(self.path[i])

agent/templates/advanced_ingestion_pipeline.json

@@ -193,7 +193,7 @@
                         "presence_penalty": 0.4,
                         "prompts": [
                             {
-                                "content": "Text Content:\n{Splitter:KindDingosJam@chunks}\n",
+                                "content": "Text Content:\n{Splitter:NineTiesSin@chunks}\n",
                                 "role": "user"
                             }
                         ],
@@ -226,7 +226,7 @@
                         "presence_penalty": 0.4,
                         "prompts": [
                             {
-                                "content": "Text Content:\n\n{Splitter:KindDingosJam@chunks}\n",
+                                "content": "Text Content:\n\n{Splitter:TastyPointsLay@chunks}\n",
                                 "role": "user"
                             }
                         ],
@@ -259,7 +259,7 @@
                         "presence_penalty": 0.4,
                         "prompts": [
                             {
-                                "content": "Content: \n\n{Splitter:KindDingosJam@chunks}",
+                                "content": "Content: \n\n{Splitter:CuteBusesBet@chunks}",
                                 "role": "user"
                             }
                         ],
@@ -485,7 +485,7 @@
                             "outputs": {},
                             "presencePenaltyEnabled": false,
                             "presence_penalty": 0.4,
-                            "prompts": "Text Content:\n{Splitter:KindDingosJam@chunks}\n",
+                            "prompts": "Text Content:\n{Splitter:NineTiesSin@chunks}\n",
                             "sys_prompt": "Role\nYou are a text analyzer.\n\nTask\nExtract the most important keywords/phrases of a given piece of text content.\n\nRequirements\n- Summarize the text content, and give the top 5 important keywords/phrases.\n- The keywords MUST be in the same language as the given piece of text content.\n- The keywords are delimited by ENGLISH COMMA.\n- Output keywords ONLY.",
                             "temperature": 0.1,
                             "temperatureEnabled": false,
@@ -522,7 +522,7 @@
                             "outputs": {},
                             "presencePenaltyEnabled": false,
                             "presence_penalty": 0.4,
-                            "prompts": "Text Content:\n\n{Splitter:KindDingosJam@chunks}\n",
+                            "prompts": "Text Content:\n\n{Splitter:TastyPointsLay@chunks}\n",
                             "sys_prompt": "Role\nYou are a text analyzer.\n\nTask\nPropose 3 questions about a given piece of text content.\n\nRequirements\n- Understand and summarize the text content, and propose the top 3 important questions.\n- The questions SHOULD NOT have overlapping meanings.\n- The questions SHOULD cover the main content of the text as much as possible.\n- The questions MUST be in the same language as the given piece of text content.\n- One question per line.\n- Output questions ONLY.",
                             "temperature": 0.1,
                             "temperatureEnabled": false,
@@ -559,7 +559,7 @@
                             "outputs": {},
                             "presencePenaltyEnabled": false,
                             "presence_penalty": 0.4,
-                            "prompts": "Content: \n\n{Splitter:KindDingosJam@chunks}",
+                            "prompts": "Content: \n\n{Splitter:BlueResultsWink@chunks}",
                             "sys_prompt": "Extract important structured information from the given content. Output ONLY a valid JSON string with no additional text. If no important structured information is found, output an empty JSON object: {}.\n\nImportant structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities.",
                             "temperature": 0.1,
                             "temperatureEnabled": false,

agent/tools/yahoofinance.py

@@ -75,7 +75,7 @@ class YahooFinance(ToolBase, ABC):
     @timeout(int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 60)))
     def _invoke(self, **kwargs):
         if self.check_if_canceled("YahooFinance processing"):
-            return
+            return None
 
         if not kwargs.get("stock_code"):
             self.set_output("report", "")
@@ -84,33 +84,33 @@ class YahooFinance(ToolBase, ABC):
         last_e = ""
         for _ in range(self._param.max_retries+1):
             if self.check_if_canceled("YahooFinance processing"):
-                return
+                return None
 
-            yohoo_res = []
+            yahoo_res = []
             try:
                 msft = yf.Ticker(kwargs["stock_code"])
                 if self.check_if_canceled("YahooFinance processing"):
-                    return
+                    return None
 
                 if self._param.info:
-                    yohoo_res.append("# Information:\n" + pd.Series(msft.info).to_markdown() + "\n")
+                    yahoo_res.append("# Information:\n" + pd.Series(msft.info).to_markdown() + "\n")
                 if self._param.history:
-                    yohoo_res.append("# History:\n" + msft.history().to_markdown() + "\n")
+                    yahoo_res.append("# History:\n" + msft.history().to_markdown() + "\n")
                 if self._param.financials:
-                    yohoo_res.append("# Calendar:\n" + pd.DataFrame(msft.calendar).to_markdown() + "\n")
+                    yahoo_res.append("# Calendar:\n" + pd.DataFrame(msft.calendar).to_markdown() + "\n")
                 if self._param.balance_sheet:
-                    yohoo_res.append("# Balance sheet:\n" + msft.balance_sheet.to_markdown() + "\n")
-                    yohoo_res.append("# Quarterly balance sheet:\n" + msft.quarterly_balance_sheet.to_markdown() + "\n")
+                    yahoo_res.append("# Balance sheet:\n" + msft.balance_sheet.to_markdown() + "\n")
+                    yahoo_res.append("# Quarterly balance sheet:\n" + msft.quarterly_balance_sheet.to_markdown() + "\n")
                 if self._param.cash_flow_statement:
-                    yohoo_res.append("# Cash flow statement:\n" + msft.cashflow.to_markdown() + "\n")
-                    yohoo_res.append("# Quarterly cash flow statement:\n" + msft.quarterly_cashflow.to_markdown() + "\n")
+                    yahoo_res.append("# Cash flow statement:\n" + msft.cashflow.to_markdown() + "\n")
+                    yahoo_res.append("# Quarterly cash flow statement:\n" + msft.quarterly_cashflow.to_markdown() + "\n")
                 if self._param.news:
-                    yohoo_res.append("# News:\n" + pd.DataFrame(msft.news).to_markdown() + "\n")
-                self.set_output("report", "\n\n".join(yohoo_res))
+                    yahoo_res.append("# News:\n" + pd.DataFrame(msft.news).to_markdown() + "\n")
+                self.set_output("report", "\n\n".join(yahoo_res))
                 return self.output("report")
             except Exception as e:
                 if self.check_if_canceled("YahooFinance processing"):
-                    return
+                    return None
 
                 last_e = e
                 logging.exception(f"YahooFinance error: {e}")

api/apps/__init__.py

@@ -180,7 +180,7 @@ def login_user(user, remember=False, duration=None, force=False, fresh=True):
     user's `is_active` property is ``False``, they will not be logged in
     unless `force` is ``True``.
 
-    This will return ``True`` if the log in attempt succeeds, and ``False`` if
+    This will return ``True`` if the login attempt succeeds, and ``False`` if
     it fails (i.e. because the user is inactive).
 
     :param user: The user object to log in.

api/apps/sdk/doc.py

@@ -552,7 +552,7 @@ def list_docs(dataset_id, tenant_id):
     create_time_from     = int(q.get("create_time_from", 0))
     create_time_to       = int(q.get("create_time_to", 0))
 
-    # map run status (accept text or numeric) - align with API parameter
+    # map run status (text or numeric) - align with API parameter
     run_status_text_to_numeric = {"UNSTART": "0", "RUNNING": "1", "CANCEL": "2", "DONE": "3", "FAIL": "4"}
     run_status_converted = [run_status_text_to_numeric.get(v, v) for v in run_status]
 
@@ -890,7 +890,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
         type: string
         required: false
         default: ""
-        description: Chunk Id.
+        description: Chunk id.
       - in: header
         name: Authorization
         type: string

common/data_source/confluence_connector.py

@@ -126,7 +126,7 @@ class OnyxConfluence:
     def _renew_credentials(self) -> tuple[dict[str, Any], bool]:
         """credential_json - the current json credentials
         Returns a tuple
-        1. The up to date credentials
+        1. The up-to-date credentials
         2. True if the credentials were updated
 
         This method is intended to be used within a distributed lock.
@@ -179,8 +179,8 @@ class OnyxConfluence:
             credential_json["confluence_refresh_token"],
         )
 
-        # store the new credentials to redis and to the db thru the provider
-        # redis: we use a 5 min TTL because we are given a 10 minute grace period
+        # store the new credentials to redis and to the db through the provider
+        # redis: we use a 5 min TTL because we are given a 10 minutes grace period
         # when keys are rotated. it's easier to expire the cached credentials
         # reasonably frequently rather than trying to handle strong synchronization
         # between the db and redis everywhere the credentials might be updated
@@ -690,7 +690,7 @@ class OnyxConfluence:
     ) -> Iterator[dict[str, Any]]:
         """
         This function will paginate through the top level query first, then
-        paginate through all of the expansions.
+        paginate through all the expansions.
         """
 
         def _traverse_and_update(data: dict | list) -> None:
@@ -863,7 +863,7 @@ def get_user_email_from_username__server(
             # For now, we'll just return None and log a warning. This means
             # we will keep retrying to get the email every group sync.
             email = None
-            # We may want to just return a string that indicates failure so we dont
+            # We may want to just return a string that indicates failure so we don't
             # keep retrying
             # email = f"FAILED TO GET CONFLUENCE EMAIL FOR {user_name}"
         _USER_EMAIL_CACHE[user_name] = email
@@ -912,7 +912,7 @@ def extract_text_from_confluence_html(
     confluence_object: dict[str, Any],
     fetched_titles: set[str],
 ) -> str:
-    """Parse a Confluence html page and replace the 'user Id' by the real
+    """Parse a Confluence html page and replace the 'user id' by the real
         User Display Name
 
     Args:

common/data_source/google_drive/doc_conversion.py

@@ -76,7 +76,7 @@ ALL_ACCEPTED_FILE_EXTENSIONS = ACCEPTED_PLAIN_TEXT_FILE_EXTENSIONS + ACCEPTED_DO
 
 MAX_RETRIEVER_EMAILS = 20
 CHUNK_SIZE_BUFFER = 64  # extra bytes past the limit to read
-# This is not a standard valid unicode char, it is used by the docs advanced API to
+# This is not a standard valid Unicode char, it is used by the docs advanced API to
 # represent smart chips (elements like dates and doc links).
 SMART_CHIP_CHAR = "\ue907"
 WEB_VIEW_LINK_KEY = "webViewLink"

common/data_source/google_drive/file_retrieval.py

@@ -141,7 +141,7 @@ def crawl_folders_for_files(
             # Only mark a folder as done if it was fully traversed without errors
             # This usually indicates that the owner of the folder was impersonated.
             # In cases where this never happens, most likely the folder owner is
-            # not part of the google workspace in question (or for oauth, the authenticated
+            # not part of the Google Workspace in question (or for oauth, the authenticated
             # user doesn't own the folder)
             if found_files:
                 update_traversed_ids_func(parent_id)
@@ -232,7 +232,7 @@ def get_files_in_shared_drive(
         **kwargs,
     ):
         # If we found any files, mark this drive as traversed. When a user has access to a drive,
-        # they have access to all the files in the drive. Also not a huge deal if we re-traverse
+        # they have access to all the files in the drive. Also, not a huge deal if we re-traverse
         # empty drives.
         # NOTE: ^^ the above is not actually true due to folder restrictions:
         # https://support.google.com/a/users/answer/12380484?hl=en

common/data_source/google_drive/model.py

@@ -22,7 +22,7 @@ class GDriveMimeType(str, Enum):
     MARKDOWN = "text/markdown"
 
 
-# These correspond to The major stages of retrieval for google drive.
+# These correspond to The major stages of retrieval for Google Drive.
 # The stages for the oauth flow are:
 # get_all_files_for_oauth(),
 # get_all_drive_ids(),
@@ -117,7 +117,7 @@ class GoogleDriveCheckpoint(ConnectorCheckpoint):
 
 class RetrievedDriveFile(BaseModel):
     """
-    Describes a file that has been retrieved from google drive.
+    Describes a file that has been retrieved from Google Drive.
     user_email is the email of the user that the file was retrieved
     by impersonating. If an error worthy of being reported is encountered,
     error should be set and later propagated as a ConnectorFailure.

common/data_source/google_util/resource.py

@@ -29,8 +29,8 @@ class GmailService(Resource):
 
 class RefreshableDriveObject:
     """
-    Running Google drive service retrieval functions
-    involves accessing methods of the service object (ie. files().list())
+    Running Google Drive service retrieval functions
+    involves accessing methods of the service object (i.e. files().list())
     which can raise a RefreshError if the access token is expired.
     This class is a wrapper that propagates the ability to refresh the access token
     and retry the final retrieval function until execute() is called.

common/data_source/html_utils.py

@@ -120,7 +120,7 @@ def format_document_soup(
             # table is standard HTML element
             if e.name == "table":
                 in_table = True
-            # tr is for rows
+            # TR is for rows
             elif e.name == "tr" and in_table:
                 text += "\n"
             # td for data cell, th for header

common/data_source/interfaces.py

@@ -395,8 +395,7 @@ class AttachmentProcessingResult(BaseModel):
 
 
 class IndexingHeartbeatInterface(ABC):
-    """Defines a callback interface to be passed to
-    to run_indexing_entrypoint."""
+    """Defines a callback interface to be passed to run_indexing_entrypoint."""
 
     @abstractmethod
     def should_stop(self) -> bool:

common/data_source/jira/connector.py

@@ -80,7 +80,7 @@ _TZ_OFFSET_PATTERN = re.compile(r"([+-])(\d{2})(:?)(\d{2})$")
 
 
 class JiraConnector(CheckpointedConnectorWithPermSync, SlimConnectorWithPermSync):
-    """Retrieve Jira issues and emit them as markdown documents."""
+    """Retrieve Jira issues and emit them as Markdown documents."""
 
     def __init__(
         self,

common/data_source/models.py

@@ -54,8 +54,8 @@ class ExternalAccess:
         A helper function that returns an *empty* set of external user-emails and group-ids, and sets `is_public` to `False`.
         This effectively makes the document in question "private" or inaccessible to anyone else.
 
-        This is especially helpful to use when you are performing permission-syncing, and some document's permissions aren't able
-        to be determined (for whatever reason). Setting its `ExternalAccess` to "private" is a feasible fallback.
+        This is especially helpful to use when you are performing permission-syncing, and some document's permissions can't
+        be determined (for whatever reason). Setting its `ExternalAccess` to "private" is a feasible fallback.
         """
 
         return cls(

common/http_client.py

@@ -24,7 +24,9 @@ logger = logging.getLogger(__name__)
 # Default knobs; keep conservative to avoid unexpected behavioural changes.
 DEFAULT_TIMEOUT = float(os.environ.get("HTTP_CLIENT_TIMEOUT", "15"))
 # Align with requests default: follow redirects with a max of 30 unless overridden.
-DEFAULT_FOLLOW_REDIRECTS = bool(int(os.environ.get("HTTP_CLIENT_FOLLOW_REDIRECTS", "1")))
+DEFAULT_FOLLOW_REDIRECTS = bool(
+    int(os.environ.get("HTTP_CLIENT_FOLLOW_REDIRECTS", "1"))
+)
 DEFAULT_MAX_REDIRECTS = int(os.environ.get("HTTP_CLIENT_MAX_REDIRECTS", "30"))
 DEFAULT_MAX_RETRIES = int(os.environ.get("HTTP_CLIENT_MAX_RETRIES", "2"))
 DEFAULT_BACKOFF_FACTOR = float(os.environ.get("HTTP_CLIENT_BACKOFF_FACTOR", "0.5"))
@@ -32,7 +34,9 @@ DEFAULT_PROXY = os.environ.get("HTTP_CLIENT_PROXY")
 DEFAULT_USER_AGENT = os.environ.get("HTTP_CLIENT_USER_AGENT", "ragflow-http-client")
 
 
-def _clean_headers(headers: Optional[Dict[str, str]], auth_token: Optional[str] = None) -> Optional[Dict[str, str]]:
+def _clean_headers(
+    headers: Optional[Dict[str, str]], auth_token: Optional[str] = None
+) -> Optional[Dict[str, str]]:
     merged_headers: Dict[str, str] = {}
     if DEFAULT_USER_AGENT:
         merged_headers["User-Agent"] = DEFAULT_USER_AGENT
@@ -59,39 +63,51 @@ async def async_request(
     auth_token: Optional[str] = None,
     retries: Optional[int] = None,
     backoff_factor: Optional[float] = None,
-    proxies: Any = None,
+    proxy: Any = None,
     **kwargs: Any,
 ) -> httpx.Response:
     """Lightweight async HTTP wrapper using httpx.AsyncClient with safe defaults."""
     timeout = timeout if timeout is not None else DEFAULT_TIMEOUT
-    follow_redirects = DEFAULT_FOLLOW_REDIRECTS if follow_redirects is None else follow_redirects
+    follow_redirects = (
+        DEFAULT_FOLLOW_REDIRECTS if follow_redirects is None else follow_redirects
+    )
     max_redirects = DEFAULT_MAX_REDIRECTS if max_redirects is None else max_redirects
     retries = DEFAULT_MAX_RETRIES if retries is None else max(retries, 0)
-    backoff_factor = DEFAULT_BACKOFF_FACTOR if backoff_factor is None else backoff_factor
+    backoff_factor = (
+        DEFAULT_BACKOFF_FACTOR if backoff_factor is None else backoff_factor
+    )
     headers = _clean_headers(headers, auth_token=auth_token)
-    proxies = DEFAULT_PROXY if proxies is None else proxies
+    proxy = DEFAULT_PROXY if proxy is None else proxy
 
     async with httpx.AsyncClient(
         timeout=timeout,
         follow_redirects=follow_redirects,
         max_redirects=max_redirects,
-        proxies=proxies,
+        proxy=proxy,
     ) as client:
         last_exc: Exception | None = None
         for attempt in range(retries + 1):
             try:
                 start = time.monotonic()
-                response = await client.request(method=method, url=url, headers=headers, **kwargs)
+                response = await client.request(
+                    method=method, url=url, headers=headers, **kwargs
+                )
                 duration = time.monotonic() - start
-                logger.debug(f"async_request {method} {url} -> {response.status_code} in {duration:.3f}s")
+                logger.debug(
+                    f"async_request {method} {url} -> {response.status_code} in {duration:.3f}s"
+                )
                 return response
             except httpx.RequestError as exc:
                 last_exc = exc
                 if attempt >= retries:
-                    logger.warning(f"async_request exhausted retries for {method} {url}: {exc}")
+                    logger.warning(
+                        f"async_request exhausted retries for {method} {url}: {exc}"
+                    )
                     raise
                 delay = _get_delay(backoff_factor, attempt)
-                logger.warning(f"async_request attempt {attempt + 1}/{retries + 1} failed for {method} {url}: {exc}; retrying in {delay:.2f}s")
+                logger.warning(
+                    f"async_request attempt {attempt + 1}/{retries + 1} failed for {method} {url}: {exc}; retrying in {delay:.2f}s"
+                )
                 await asyncio.sleep(delay)
         raise last_exc  # pragma: no cover
 
@@ -107,39 +123,51 @@ def sync_request(
     auth_token: Optional[str] = None,
     retries: Optional[int] = None,
     backoff_factor: Optional[float] = None,
-    proxies: Any = None,
+    proxy: Any = None,
     **kwargs: Any,
 ) -> httpx.Response:
     """Synchronous counterpart to async_request, for CLI/tests or sync contexts."""
     timeout = timeout if timeout is not None else DEFAULT_TIMEOUT
-    follow_redirects = DEFAULT_FOLLOW_REDIRECTS if follow_redirects is None else follow_redirects
+    follow_redirects = (
+        DEFAULT_FOLLOW_REDIRECTS if follow_redirects is None else follow_redirects
+    )
     max_redirects = DEFAULT_MAX_REDIRECTS if max_redirects is None else max_redirects
     retries = DEFAULT_MAX_RETRIES if retries is None else max(retries, 0)
-    backoff_factor = DEFAULT_BACKOFF_FACTOR if backoff_factor is None else backoff_factor
+    backoff_factor = (
+        DEFAULT_BACKOFF_FACTOR if backoff_factor is None else backoff_factor
+    )
     headers = _clean_headers(headers, auth_token=auth_token)
-    proxies = DEFAULT_PROXY if proxies is None else proxies
+    proxy = DEFAULT_PROXY if proxy is None else proxy
 
     with httpx.Client(
         timeout=timeout,
         follow_redirects=follow_redirects,
         max_redirects=max_redirects,
-        proxies=proxies,
+        proxy=proxy,
     ) as client:
         last_exc: Exception | None = None
         for attempt in range(retries + 1):
             try:
                 start = time.monotonic()
-                response = client.request(method=method, url=url, headers=headers, **kwargs)
+                response = client.request(
+                    method=method, url=url, headers=headers, **kwargs
+                )
                 duration = time.monotonic() - start
-                logger.debug(f"sync_request {method} {url} -> {response.status_code} in {duration:.3f}s")
+                logger.debug(
+                    f"sync_request {method} {url} -> {response.status_code} in {duration:.3f}s"
+                )
                 return response
             except httpx.RequestError as exc:
                 last_exc = exc
                 if attempt >= retries:
-                    logger.warning(f"sync_request exhausted retries for {method} {url}: {exc}")
+                    logger.warning(
+                        f"sync_request exhausted retries for {method} {url}: {exc}"
+                    )
                     raise
                 delay = _get_delay(backoff_factor, attempt)
-                logger.warning(f"sync_request attempt {attempt + 1}/{retries + 1} failed for {method} {url}: {exc}; retrying in {delay:.2f}s")
+                logger.warning(
+                    f"sync_request attempt {attempt + 1}/{retries + 1} failed for {method} {url}: {exc}; retrying in {delay:.2f}s"
+                )
                 time.sleep(delay)
         raise last_exc  # pragma: no cover
 

common/settings.py

@@ -210,7 +210,10 @@ def init_settings():
     IMAGE2TEXT_CFG = _resolve_per_model_config(image2text_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
 
     CHAT_MDL = CHAT_CFG.get("model", "") or ""
-    EMBEDDING_MDL = os.getenv("TEI_MODEL", "BAAI/bge-small-en-v1.5") if "tei-" in os.getenv("COMPOSE_PROFILES", "") else ""
+    EMBEDDING_MDL = EMBEDDING_CFG.get("model", "") or ""
+    compose_profiles = os.getenv("COMPOSE_PROFILES", "")
+    if "tei-" in compose_profiles:
+        EMBEDDING_MDL = os.getenv("TEI_MODEL", EMBEDDING_MDL or "BAAI/bge-small-en-v1.5")
     RERANK_MDL = RERANK_CFG.get("model", "") or ""
     ASR_MDL = ASR_CFG.get("model", "") or ""
     IMAGE2TEXT_MDL = IMAGE2TEXT_CFG.get("model", "") or ""

common/string_utils.py

@@ -61,7 +61,7 @@ def clean_markdown_block(text):
         str: Cleaned text with Markdown code block syntax removed, and stripped of surrounding whitespace
 
     """
-    # Remove opening ```markdown tag with optional whitespace and newlines
+    # Remove opening ```Markdown tag with optional whitespace and newlines
     # Matches: optional whitespace + ```markdown + optional whitespace + optional newline
     text = re.sub(r'^\s*```markdown\s*\n?', '', text)
 

conf/infinity_mapping.json

@@ -2,6 +2,7 @@
 	"id": {"type": "varchar", "default": ""},
 	"doc_id": {"type": "varchar", "default": ""},
 	"kb_id": {"type": "varchar", "default": ""},
+	"mom_id": {"type": "varchar", "default": ""},
 	"create_time": {"type": "varchar", "default": ""},
 	"create_timestamp_flt": {"type": "float", "default": 0.0},
 	"img_id": {"type": "varchar", "default": ""},

deepdoc/README.md

@@ -51,7 +51,7 @@ We use vision information to resolve problems as human being.
     ```bash
         python deepdoc/vision/t_ocr.py --inputs=path_to_images_or_pdfs --output_dir=path_to_store_result
      ```
-    The inputs could be directory to images or PDF, or a image or PDF. 
+    The inputs could be directory to images or PDF, or an image or PDF. 
     You can look into the folder 'path_to_store_result' where has images which demonstrate the positions of results,
     txt files which contain the OCR text.
     <div align="center" style="margin-top:20px;margin-bottom:20px;">
@@ -78,7 +78,7 @@ We use vision information to resolve problems as human being.
      ```bash
         python deepdoc/vision/t_recognizer.py --inputs=path_to_images_or_pdfs --threshold=0.2 --mode=layout --output_dir=path_to_store_result
      ```
-    The inputs could be directory to images or PDF, or a image or PDF. 
+    The inputs could be directory to images or PDF, or an image or PDF. 
     You can look into the folder 'path_to_store_result' where has images which demonstrate the detection results as following:
     <div align="center" style="margin-top:20px;margin-bottom:20px;">
     <img src="https://github.com/infiniflow/ragflow/assets/12318111/07e0f625-9b28-43d0-9fbb-5bf586cd286f" width="1000"/>

deepdoc/parser/excel_parser.py

@@ -41,7 +41,7 @@ class RAGFlowExcelParser:
 
             try:
                 file_like_object.seek(0)
-                df = pd.read_csv(file_like_object)
+                df = pd.read_csv(file_like_object, on_bad_lines='skip')
                 return RAGFlowExcelParser._dataframe_to_workbook(df)
 
             except Exception as e_csv:
@@ -164,7 +164,7 @@ class RAGFlowExcelParser:
         except Exception as e:
             logging.warning(f"Parse spreadsheet error: {e}, trying to interpret as CSV file")
             file_like_object.seek(0)
-            df = pd.read_csv(file_like_object)
+            df = pd.read_csv(file_like_object, on_bad_lines='skip')
         df = df.replace(r"^\s*$", "", regex=True)
         return df.to_markdown(index=False)
 

deepdoc/vision/table_structure_recognizer.py

@@ -155,7 +155,7 @@ class TableStructureRecognizer(Recognizer):
         while i < len(boxes):
             if TableStructureRecognizer.is_caption(boxes[i]):
                 if is_english:
-                    cap + " "
+                    cap += " "
                 cap += boxes[i]["text"]
                 boxes.pop(i)
                 i -= 1

docker/docker-compose.yml

@@ -26,8 +26,8 @@ services:
     #   - --no-json-response # Disable JSON response mode in Streamable HTTP transport (instead of SSE over HTTP)
 
     # Example configuration to start Admin server:
-    # command:
-    #   - --enable-adminserver
+    command:
+      - --enable-adminserver
     ports:
       - ${SVR_WEB_HTTP_PORT}:80
       - ${SVR_WEB_HTTPS_PORT}:443
@@ -75,8 +75,8 @@ services:
     #   - --no-json-response # Disable JSON response mode in Streamable HTTP transport (instead of SSE over HTTP)
 
     # Example configuration to start Admin server:
-    # command:
-    #   - --enable-adminserver
+    command:
+      - --enable-adminserver
     ports:
       - ${SVR_WEB_HTTP_PORT}:80
       - ${SVR_WEB_HTTPS_PORT}:443

docs/references/http_api_reference.md

@@ -4013,7 +4013,7 @@ Failure:
 
 **DELETE** `/api/v1/agents/{agent_id}/sessions`
 
-Deletes sessions of a agent by ID.
+Deletes sessions of an agent by ID.
 
 #### Request
 
@@ -4072,7 +4072,7 @@ Failure:
 
 Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results.
 
-This operation requires a `Bearer Login Token`, which typically expires with in 24 hours. You can find the it in the Request Headers in your browser easily as shown below:
+This operation requires a `Bearer Login Token`, which typically expires with in 24 hours. You can find it in the Request Headers in your browser easily as shown below:
 
 ![Image](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/login_token.jpg)
 

docs/references/python_api_reference.md

@@ -1740,7 +1740,7 @@ for session in sessions:
 Agent.delete_sessions(ids: list[str] = None)
 ```
 
-Deletes sessions of a agent by ID.
+Deletes sessions of an agent by ID.
 
 #### Parameters
 

plugin/README.md

@@ -92,6 +92,6 @@ def get_metadata(cls) -> LLMToolMetadata:
 
 The `get_metadata` method is a `classmethod`. It will provide the description of this tool to LLM.
 
-The fields starts with `display` can use a special notation: `$t:xxx`, which will use the i18n mechanism in the RAGFlow frontend, getting text from the `llmTools` category. The frontend will display what you put here if you don't use this notation.
+The fields start with `display` can use a special notation: `$t:xxx`, which will use the i18n mechanism in the RAGFlow frontend, getting text from the `llmTools` category. The frontend will display what you put here if you don't use this notation.
 
 Now our tool is ready. You can select it in the `Generate` component and try it out.

plugin/embedded_plugins/llm_tools/bad_calculator.py

@@ -5,7 +5,7 @@ from plugin.llm_tool_plugin import LLMToolMetadata, LLMToolPlugin
 class BadCalculatorPlugin(LLMToolPlugin):
     """
     A sample LLM tool plugin, will add two numbers with 100.
-    It only present for demo purpose. Do not use it in production.
+    It only presents for demo purpose. Do not use it in production.
     """
     _version_ = "1.0.0"
 

rag/app/book.py

@@ -70,7 +70,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
     """
         Supported file formats are docx, pdf, txt.
         Since a book is long and not all the parts are useful, if it's a PDF,
-        please setup the page ranges for every book in order eliminate negative effects and save elapsed computing time.
+        please set up the page ranges for every book in order eliminate negative effects and save elapsed computing time.
     """
     parser_config = kwargs.get(
         "parser_config", {

rag/app/laws.py

@@ -201,12 +201,23 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
 
     elif re.search(r"\.doc$", filename, re.IGNORECASE):
         callback(0.1, "Start to parse.")
-        binary = BytesIO(binary)
-        doc_parsed = parser.from_buffer(binary)
-        sections = doc_parsed['content'].split('\n')
-        sections = [s for s in sections if s]
-        callback(0.8, "Finish parsing.")
+        try:
+            from tika import parser as tika_parser
+        except Exception as e:
+            callback(0.8, f"tika not available: {e}. Unsupported .doc parsing.")
+            logging.warning(f"tika not available: {e}. Unsupported .doc parsing for {filename}.")
+            return []
 
+        binary = BytesIO(binary)
+        doc_parsed = tika_parser.from_buffer(binary)
+        if doc_parsed.get('content', None) is not None:
+            sections = doc_parsed['content'].split('\n')
+            sections = [s for s in sections if s]
+            callback(0.8, "Finish parsing.")
+        else:
+            callback(0.8, f"tika.parser got empty content from {filename}.")
+            logging.warning(f"tika.parser got empty content from {filename}.")
+            return []
     else:
         raise NotImplementedError(
             "file type not supported yet(doc, docx, pdf, txt supported)")

rag/app/picture.py

@@ -14,6 +14,7 @@
 #  limitations under the License.
 #
 
+import asyncio
 import io
 import re
 
@@ -50,7 +51,7 @@ def chunk(filename, binary, tenant_id, lang, callback=None, **kwargs):
                 }
             )
             cv_mdl = LLMBundle(tenant_id, llm_type=LLMType.IMAGE2TEXT, lang=lang)
-            ans = cv_mdl.chat(system="", history=[], gen_conf={}, video_bytes=binary, filename=filename)
+            ans = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=binary, filename=filename))
             callback(0.8, "CV LLM respond: %s ..." % ans[:32])
             ans += "\n" + ans
             tokenize(doc, ans, eng)

rag/app/qa.py

@@ -313,7 +313,7 @@ def mdQuestionLevel(s):
 def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", callback=None, **kwargs):
     """
         Excel and csv(txt) format files are supported.
-        If the file is in excel format, there should be 2 column question and answer without header.
+        If the file is in Excel format, there should be 2 column question and answer without header.
         And question column is ahead of answer column.
         And it's O.K if it has multiple sheets as long as the columns are rightly composed.
 

rag/app/tag.py

@@ -37,7 +37,7 @@ def beAdoc(d, q, a, eng, row_num=-1):
 def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
     """
         Excel and csv(txt) format files are supported.
-        If the file is in excel format, there should be 2 column content and tags without header.
+        If the file is in Excel format, there should be 2 column content and tags without header.
         And content column is ahead of tags column.
         And it's O.K if it has multiple sheets as long as the columns are rightly composed.
 

rag/flow/parser/parser.py

@@ -12,6 +12,7 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
+import asyncio
 import io
 import json
 import os
@@ -634,7 +635,7 @@ class Parser(ProcessBase):
         self.set_output("output_format", conf["output_format"])
 
         cv_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.IMAGE2TEXT, llm_name=conf["llm_id"])
-        txt = cv_mdl.chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name)
+        txt = asyncio.run(cv_mdl.async_chat(system="", history=[], gen_conf={}, video_bytes=blob, filename=name))
 
         self.set_output("text", txt)
 

rag/llm/chat_model.py

@@ -28,7 +28,7 @@ import json_repair
 import litellm
 import openai
 from openai import AsyncOpenAI, OpenAI
-from openai.lib.azure import AzureOpenAI
+from openai.lib.azure import AzureOpenAI, AsyncAzureOpenAI
 from strenum import StrEnum
 
 from common.token_utils import num_tokens_from_string, total_token_count_from_response
@@ -535,6 +535,7 @@ class AzureChat(Base):
         api_version = json.loads(key).get("api_version", "2024-02-01")
         super().__init__(key, model_name, base_url, **kwargs)
         self.client = AzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
+        self.async_client = AsyncAzureOpenAI(api_key=key, base_url=base_url, api_version=api_version)
         self.model_name = model_name
 
     @property

rag/llm/cv_model.py

@@ -14,6 +14,7 @@
 #  limitations under the License.
 #
 
+import asyncio
 import base64
 import json
 import logging
@@ -27,9 +28,8 @@ from pathlib import Path
 from urllib.parse import urljoin
 
 import requests
-from openai import OpenAI
-from openai.lib.azure import AzureOpenAI
-from zhipuai import ZhipuAI
+from openai import OpenAI, AsyncOpenAI
+from openai.lib.azure import AzureOpenAI, AsyncAzureOpenAI
 
 from common.token_utils import num_tokens_from_string, total_token_count_from_response
 from rag.nlp import is_english
@@ -76,9 +76,9 @@ class Base(ABC):
             pmpt.append({"type": "image_url", "image_url": {"url": img if isinstance(img, str) and img.startswith("data:") else f"data:image/png;base64,{img}"}})
         return pmpt
 
-    def chat(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
         try:
-            response = self.client.chat.completions.create(
+            response = await self.async_client.chat.completions.create(
                 model=self.model_name,
                 messages=self._form_history(system, history, images),
                 extra_body=self.extra_body,
@@ -87,17 +87,17 @@ class Base(ABC):
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         ans = ""
         tk_count = 0
         try:
-            response = self.client.chat.completions.create(
+            response = await self.async_client.chat.completions.create(
                 model=self.model_name,
                 messages=self._form_history(system, history, images),
                 stream=True,
                 extra_body=self.extra_body,
             )
-            for resp in response:
+            async for resp in response:
                 if not resp.choices[0].delta.content:
                     continue
                 delta = resp.choices[0].delta.content
@@ -191,6 +191,7 @@ class GptV4(Base):
             base_url = "https://api.openai.com/v1"
         self.api_key = key
         self.client = OpenAI(api_key=key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         super().__init__(**kwargs)
@@ -221,6 +222,7 @@ class AzureGptV4(GptV4):
         api_key = json.loads(key).get("api_key", "")
         api_version = json.loads(key).get("api_version", "2024-02-01")
         self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
+        self.async_client = AsyncAzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -243,7 +245,7 @@ class QWenCV(GptV4):
             base_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
         super().__init__(key, model_name, lang=lang, base_url=base_url, **kwargs)
 
-    def chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
         if video_bytes:
             try:
                 summary, summary_num_tokens = self._process_video(video_bytes, filename)
@@ -313,7 +315,8 @@ class Zhipu4V(GptV4):
     _FACTORY_NAME = "ZHIPU-AI"
 
     def __init__(self, key, model_name="glm-4v", lang="Chinese", **kwargs):
-        self.client = ZhipuAI(api_key=key)
+        self.client = OpenAI(api_key=key, base_url="https://open.bigmodel.cn/api/paas/v4/")
+        self.async_client = AsyncOpenAI(api_key=key, base_url="https://open.bigmodel.cn/api/paas/v4/")
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -342,20 +345,20 @@ class Zhipu4V(GptV4):
         )
         return response.json()
 
-    def chat(self, system, history, gen_conf, images=None, stream=False, **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
         if system and history and history[0].get("role") != "system":
             history.insert(0, {"role": "system", "content": system})
 
         gen_conf = self._clean_conf(gen_conf)
 
         logging.info(json.dumps(history, ensure_ascii=False, indent=2))
-        response = self.client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=False, **gen_conf)
+        response = await self.async_client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=False, **gen_conf)
         content = response.choices[0].message.content.strip()
 
         cleaned = re.sub(r"<\|(begin_of_box|end_of_box)\|>", "", content).strip()
         return cleaned, total_token_count_from_response(response)
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         from rag.llm.chat_model import LENGTH_NOTIFICATION_CN, LENGTH_NOTIFICATION_EN
         from rag.nlp import is_chinese
 
@@ -366,8 +369,8 @@ class Zhipu4V(GptV4):
         tk_count = 0
         try:
             logging.info(json.dumps(history, ensure_ascii=False, indent=2))
-            response = self.client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=True, **gen_conf)
-            for resp in response:
+            response = await self.async_client.chat.completions.create(model=self.model_name, messages=self._form_history(system, history, images), stream=True, **gen_conf)
+            async for resp in response:
                 if not resp.choices[0].delta.content:
                     continue
                 delta = resp.choices[0].delta.content
@@ -412,6 +415,7 @@ class StepFunCV(GptV4):
         if not base_url:
             base_url = "https://api.stepfun.com/v1"
         self.client = OpenAI(api_key=key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -425,6 +429,7 @@ class VolcEngineCV(GptV4):
             base_url = "https://ark.cn-beijing.volces.com/api/v3"
         ark_api_key = json.loads(key).get("ark_api_key", "")
         self.client = OpenAI(api_key=ark_api_key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=ark_api_key, base_url=base_url)
         self.model_name = json.loads(key).get("ep_id", "") + json.loads(key).get("endpoint_id", "")
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -438,6 +443,7 @@ class LmStudioCV(GptV4):
             raise ValueError("Local llm url cannot be None")
         base_url = urljoin(base_url, "v1")
         self.client = OpenAI(api_key="lm-studio", base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key="lm-studio", base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -451,6 +457,7 @@ class OpenAI_APICV(GptV4):
             raise ValueError("url cannot be None")
         base_url = urljoin(base_url, "v1")
         self.client = OpenAI(api_key=key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name.split("___")[0]
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -491,6 +498,7 @@ class OpenRouterCV(GptV4):
             base_url = "https://openrouter.ai/api/v1"
         api_key = json.loads(key).get("api_key", "")
         self.client = OpenAI(api_key=api_key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=api_key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -522,6 +530,7 @@ class LocalAICV(GptV4):
             raise ValueError("Local cv model url cannot be None")
         base_url = urljoin(base_url, "v1")
         self.client = OpenAI(api_key="empty", base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key="empty", base_url=base_url)
         self.model_name = model_name.split("___")[0]
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -533,6 +542,7 @@ class XinferenceCV(GptV4):
     def __init__(self, key, model_name="", lang="Chinese", base_url="", **kwargs):
         base_url = urljoin(base_url, "v1")
         self.client = OpenAI(api_key=key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -546,6 +556,7 @@ class GPUStackCV(GptV4):
             raise ValueError("Local llm url cannot be None")
         base_url = urljoin(base_url, "v1")
         self.client = OpenAI(api_key=key, base_url=base_url)
+        self.async_client = AsyncOpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
         self.lang = lang
         Base.__init__(self, **kwargs)
@@ -635,19 +646,19 @@ class OllamaCV(Base):
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
-    def chat(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
         try:
-            response = self.client.chat(model=self.model_name, messages=self._form_history(system, history, images), options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
+            response = await asyncio.to_thread(self.client.chat, model=self.model_name, messages=self._form_history(system, history, images), options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
 
             ans = response["message"]["content"].strip()
             return ans, response["eval_count"] + response.get("prompt_eval_count", 0)
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         ans = ""
         try:
-            response = self.client.chat(model=self.model_name, messages=self._form_history(system, history, images), stream=True, options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
+            response = await asyncio.to_thread(self.client.chat, model=self.model_name, messages=self._form_history(system, history, images), stream=True, options=self._clean_conf(gen_conf), keep_alive=self.keep_alive)
             for resp in response:
                 if resp["done"]:
                     yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
@@ -780,41 +791,41 @@ class GeminiCV(Base):
         )
         return res.text, total_token_count_from_response(res)
 
-    def chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, video_bytes=None, filename="", **kwargs):
         if video_bytes:
             try:
                 size = len(video_bytes) if video_bytes else 0
-                logging.info(f"[GeminiCV] chat called with video: filename={filename} size={size}")
-                summary, summary_num_tokens = self._process_video(video_bytes, filename)
+                logging.info(f"[GeminiCV] async_chat called with video: filename={filename} size={size}")
+                summary, summary_num_tokens = await asyncio.to_thread(self._process_video, video_bytes, filename)
                 return summary, summary_num_tokens
             except Exception as e:
-                logging.info(f"[GeminiCV] chat video error: {e}")
+                logging.info(f"[GeminiCV] async_chat video error: {e}")
                 return "**ERROR**: " + str(e), 0
 
         from google.genai import types
 
         history_len = len(history) if history else 0
         images_len = len(images) if images else 0
-        logging.info(f"[GeminiCV] chat called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
+        logging.info(f"[GeminiCV] async_chat called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
 
         generation_config = types.GenerateContentConfig(
             temperature=gen_conf.get("temperature", 0.3),
             top_p=gen_conf.get("top_p", 0.7),
         )
         try:
-            response = self.client.models.generate_content(
+            response = await self.client.aio.models.generate_content(
                 model=self.model_name,
                 contents=self._form_history(system, history, images),
                 config=generation_config,
             )
             ans = response.text
-            logging.info("[GeminiCV] chat completed")
+            logging.info("[GeminiCV] async_chat completed")
             return ans, total_token_count_from_response(response)
         except Exception as e:
-            logging.warning(f"[GeminiCV] chat error: {e}")
+            logging.warning(f"[GeminiCV] async_chat error: {e}")
             return "**ERROR**: " + str(e), 0
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         ans = ""
         response = None
         try:
@@ -826,15 +837,15 @@ class GeminiCV(Base):
             )
             history_len = len(history) if history else 0
             images_len = len(images) if images else 0
-            logging.info(f"[GeminiCV] chat_streamly called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
+            logging.info(f"[GeminiCV] async_chat_streamly called: history_len={history_len} images_len={images_len} gen_conf={gen_conf}")
 
-            response_stream = self.client.models.generate_content_stream(
+            response_stream = await self.client.aio.models.generate_content_stream(
                 model=self.model_name,
                 contents=self._form_history(system, history, images),
                 config=generation_config,
             )
 
-            for chunk in response_stream:
+            async for chunk in response_stream:
                 if chunk.text:
                     ans += chunk.text
                     yield chunk.text
@@ -939,17 +950,17 @@ class NvidiaCV(Base):
         response = self._request(vision_prompt)
         return (response["choices"][0]["message"]["content"].strip(), total_token_count_from_response(response))
 
-    def chat(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
         try:
-            response = self._request(self._form_history(system, history, images), gen_conf)
+            response = await asyncio.to_thread(self._request, self._form_history(system, history, images), gen_conf)
             return (response["choices"][0]["message"]["content"].strip(), total_token_count_from_response(response))
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         total_tokens = 0
         try:
-            response = self._request(self._form_history(system, history, images), gen_conf)
+            response = await asyncio.to_thread(self._request, self._form_history(system, history, images), gen_conf)
             cnt = response["choices"][0]["message"]["content"]
             total_tokens += total_token_count_from_response(response)
             for resp in cnt:
@@ -967,6 +978,7 @@ class AnthropicCV(Base):
         import anthropic
 
         self.client = anthropic.Anthropic(api_key=key)
+        self.async_client = anthropic.AsyncAnthropic(api_key=key)
         self.model_name = model_name
         self.system = ""
         self.max_tokens = 8192
@@ -1012,17 +1024,18 @@ class AnthropicCV(Base):
             gen_conf["max_tokens"] = self.max_tokens
         return gen_conf
 
-    def chat(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
         gen_conf = self._clean_conf(gen_conf)
         ans = ""
         try:
-            response = self.client.messages.create(
+            response = await self.async_client.messages.create(
                 model=self.model_name,
                 messages=self._form_history(system, history, images),
                 system=system,
                 stream=False,
                 **gen_conf,
-            ).to_dict()
+            )
+            response = response.to_dict()
             ans = response["content"][0]["text"]
             if response["stop_reason"] == "max_tokens":
                 ans += "...\nFor the content length reason, it stopped, continue?" if is_english([ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
@@ -1033,11 +1046,11 @@ class AnthropicCV(Base):
         except Exception as e:
             return ans + "\n**ERROR**: " + str(e), 0
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         gen_conf = self._clean_conf(gen_conf)
         total_tokens = 0
         try:
-            response = self.client.messages.create(
+            response = self.async_client.messages.create(
                 model=self.model_name,
                 messages=self._form_history(system, history, images),
                 system=system,
@@ -1045,7 +1058,7 @@ class AnthropicCV(Base):
                 **gen_conf,
             )
             think = False
-            for res in response:
+            async for res in response:
                 if res.type == "content_block_delta":
                     if res.delta.type == "thinking_delta" and res.delta.thinking:
                         if not think:
@@ -1117,18 +1130,18 @@ class GoogleCV(AnthropicCV, GeminiCV):
         else:
             return GeminiCV.describe_with_prompt(self, image, prompt)
 
-    def chat(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat(self, system, history, gen_conf, images=None, **kwargs):
         if "claude" in self.model_name:
-            return AnthropicCV.chat(self, system, history, gen_conf, images)
+            return await AnthropicCV.async_chat(self, system, history, gen_conf, images)
         else:
-            return GeminiCV.chat(self, system, history, gen_conf, images)
+            return await GeminiCV.async_chat(self, system, history, gen_conf, images)
 
-    def chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
+    async def async_chat_streamly(self, system, history, gen_conf, images=None, **kwargs):
         if "claude" in self.model_name:
-            for ans in AnthropicCV.chat_streamly(self, system, history, gen_conf, images):
+            async for ans in AnthropicCV.async_chat_streamly(self, system, history, gen_conf, images):
                 yield ans
         else:
-            for ans in GeminiCV.chat_streamly(self, system, history, gen_conf, images):
+            async for ans in GeminiCV.async_chat_streamly(self, system, history, gen_conf, images):
                 yield ans
 
 

rag/nlp/search.py

@@ -91,7 +91,7 @@ class Dealer:
                       ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd", "position_int",
                        "doc_id", "page_num_int", "top_int", "create_timestamp_flt", "knowledge_graph_kwd",
                        "question_kwd", "question_tks", "doc_type_kwd",
-                       "available_int", "content_with_weight", PAGERANK_FLD, TAG_FLD])
+                       "available_int", "content_with_weight", "mom_id", PAGERANK_FLD, TAG_FLD])
         kwds = set([])
 
         qst = req.get("question", "")
@@ -469,6 +469,7 @@ class Dealer:
                 "vector": chunk.get(vector_column, zero_vector),
                 "positions": position_int,
                 "doc_type_kwd": chunk.get("doc_type_kwd", ""),
+                "mom_id": chunk.get("mom_id", ""),
             }
             if highlight and sres.highlight:
                 if id in sres.highlight:
@@ -650,7 +651,8 @@ class Dealer:
         i = 0
         while i < len(chunks):
             ck = chunks[i]
-            if not ck.get("mom_id"):
+            mom_id = ck.get("mom_id")
+            if not isinstance(mom_id, str) or not mom_id.strip():
                 i += 1
                 continue
             mom_chunks[ck["mom_id"]].append(chunks.pop(i))

rag/prompts/generator.py

@@ -781,7 +781,10 @@ async def run_toc_from_text(chunks, chat_mdl, callback=None):
 
     # Merge structure and content (by index)
     prune = len(toc_with_levels) > 512
-    max_lvl = sorted([t.get("level", "0") for t in toc_with_levels if isinstance(t, dict)])[-1]
+    max_lvl = "0"
+    sorted_list = sorted([t.get("level", "0") for t in toc_with_levels if isinstance(t, dict)])
+    if sorted_list:
+        max_lvl = sorted_list[-1]
     merged = []
     for _ , (toc_item, src_item) in enumerate(zip(toc_with_levels, filtered)):
         if prune and toc_item.get("level", "0") >= max_lvl:

rag/svr/task_executor.py

@@ -727,17 +727,17 @@ async def insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_c
         if not mom:
             continue
         id = xxhash.xxh64(mom.encode("utf-8")).hexdigest()
+        ck["mom_id"] = id
         if id in mother_ids:
             continue
         mother_ids.add(id)
-        ck["mom_id"] = id
         mom_ck = copy.deepcopy(ck)
         mom_ck["id"] = id
         mom_ck["content_with_weight"] = mom
         mom_ck["available_int"] = 0
         flds = list(mom_ck.keys())
         for fld in flds:
-            if fld not in ["id", "content_with_weight", "doc_id", "kb_id", "available_int", "position_int"]:
+            if fld not in ["id", "content_with_weight", "doc_id", "docnm_kwd", "kb_id", "available_int", "position_int"]:
                 del mom_ck[fld]
         mothers.append(mom_ck)
 

web/src/components/json-edit/interface.ts

@@ -244,7 +244,7 @@ export interface JsonEditorOptions {
   timestampFormat?: string;
 
   /**
-   * If true, unicode characters are escaped. false by default.
+   * If true, Unicode characters are escaped. false by default.
    */
   escapeUnicode?: boolean;
 

web/src/services/admin.service.d.ts

@@ -66,7 +66,7 @@ declare module AdminService {
     title: string;
   };
 
-  export type TaskExectorHeartbeatItem = {
+  export type TaskExecutorHeartbeatItem = {
     name: string;
     boot_at: string;
     now: string;
@@ -79,7 +79,7 @@ declare module AdminService {
     pid: number;
   };
 
-  export type TaskExecutorInfo = Record<string, TaskExectorHeartbeatItem[]>;
+  export type TaskExecutorInfo = Record<string, TaskExecutorHeartbeatItem[]>;
 
   export type ListServicesItem = {
     extra: Record<string, unknown>;
@@ -134,7 +134,7 @@ declare module AdminService {
   export type RoleDetail = {
     id: string;
     name: string;
-    descrtiption: string;
+    description: string;
     create_date: string;
     update_date: string;
   };
@@ -162,7 +162,7 @@ declare module AdminService {
     id: number;
     email: string;
     create_date: string;
-    createt_time: number;
+    create_time: number;
     update_date: string;
     update_time: number;
   };