Merge branch 'main' into feature/OND211-2329-Check-existing-REST-endponts-and-extend-with-new-requested-endpoints

README.md

@@ -194,7 +194,7 @@ releases! 🌟
   
    # git checkout v0.22.1
    # Optional: use a stable tag (see releases: https://github.com/infiniflow/ragflow/releases)
-   # This steps ensures the **entrypoint.sh** file in the code matches the Docker image version.
+   # This step ensures the **entrypoint.sh** file in the code matches the Docker image version.
    
    # Use CPU for DeepDoc tasks:
    $ docker compose -f docker-compose.yml up -d

agent/tools/code_exec.py

@@ -69,7 +69,7 @@ class CodeExecParam(ToolParamBase):
         self.meta: ToolMeta = {
             "name": "execute_code",
             "description": """
-This tool has a sandbox that can execute code written in 'Python'/'Javascript'. It recieves a piece of code and return a Json string.
+This tool has a sandbox that can execute code written in 'Python'/'Javascript'. It receives a piece of code and return a Json string.
 Here's a code example for Python(`main` function MUST be included):
 def main() -> dict:
     \"\"\"

api/apps/__init__.py

@@ -82,6 +82,11 @@ app.url_map.strict_slashes = False
 app.json_encoder = CustomJSONEncoder
 app.errorhandler(Exception)(server_error_response)
 
+# Configure Quart timeouts for slow LLM responses (e.g., local Ollama on CPU)
+# Default Quart timeouts are 60 seconds which is too short for many LLM backends
+app.config["RESPONSE_TIMEOUT"] = int(os.environ.get("QUART_RESPONSE_TIMEOUT", 600))
+app.config["BODY_TIMEOUT"] = int(os.environ.get("QUART_BODY_TIMEOUT", 600))
+
 ## convince for dev and debug
 # app.config["LOGIN_DISABLED"] = True
 app.config["SESSION_PERMANENT"] = False

api/apps/document_app.py

@@ -706,6 +706,7 @@ async def set_meta():
     except Exception as e:
         return server_error_response(e)
 
+
 @manager.route("/upload_info", methods=["POST"])  # noqa: F821
 async def upload_info():
     files = await request.files

api/db/services/dialog_service.py

@@ -676,7 +676,11 @@ Please write the SQL, only SQL, without any other explanations or text.
         if kb_ids:
             kb_filter = "(" + " OR ".join([f"kb_id = '{kb_id}'" for kb_id in kb_ids]) + ")"
             if "where" not in sql.lower():
-                sql += f" WHERE {kb_filter}"
+                o = sql.lower().split("order by")
+                if len(o) > 1:
+                    sql = o[0] + f" WHERE {kb_filter}  order by " + o[1]
+                else:
+                    sql += f" WHERE {kb_filter}"
             else:
                 sql += f" AND {kb_filter}"
 
@@ -684,10 +688,9 @@ Please write the SQL, only SQL, without any other explanations or text.
         tried_times += 1
         return settings.retriever.sql_retrieval(sql, format="json"), sql
 
-    tbl, sql = get_table()
-    if tbl is None:
-        return None
-    if tbl.get("error") and tried_times <= 2:
+    try:
+        tbl, sql = get_table()
+    except Exception as e:
         user_prompt = """
         Table name: {};
         Table of database fields are as follows:
@@ -701,16 +704,14 @@ Please write the SQL, only SQL, without any other explanations or text.
         The SQL error you provided last time is as follows:
         {}
 
-        Error issued by database as follows:
-        {}
-
         Please correct the error and write SQL again, only SQL, without any other explanations or text.
-        """.format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, sql, tbl["error"])
-        tbl, sql = get_table()
-        logging.debug("TRY it again: {}".format(sql))
+        """.format(index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, e)
+        try:
+            tbl, sql = get_table()
+        except Exception:
+            return
 
-    logging.debug("GET table: {}".format(tbl))
-    if tbl.get("error") or len(tbl["rows"]) == 0:
+    if len(tbl["rows"]) == 0:
         return None
 
     docid_idx = set([ii for ii, c in enumerate(tbl["columns"]) if c["name"] == "doc_id"])

api/utils/validation_utils.py

@@ -14,6 +14,7 @@
 #  limitations under the License.
 #
 from collections import Counter
+import string
 from typing import Annotated, Any, Literal
 from uuid import UUID
 
@@ -25,6 +26,7 @@ from pydantic import (
     StringConstraints,
     ValidationError,
     field_validator,
+    model_validator,
 )
 from pydantic_core import PydanticCustomError
 from werkzeug.exceptions import BadRequest, UnsupportedMediaType
@@ -362,10 +364,9 @@ class CreateDatasetReq(Base):
     embedding_model: Annotated[str | None, Field(default=None, max_length=255, serialization_alias="embd_id")]
     permission: Annotated[Literal["me", "team"], Field(default="me", min_length=1, max_length=16)]
     shared_tenant_id: Annotated[str | None, Field(default=None, max_length=32, description="Specific tenant ID to share with when permission is 'team'")]
-    chunk_method: Annotated[
-        Literal["naive", "book", "email", "laws", "manual", "one", "paper", "picture", "presentation", "qa", "table", "tag"],
-        Field(default="naive", min_length=1, max_length=32, serialization_alias="parser_id"),
-    ]
+    chunk_method: Annotated[str | None, Field(default=None, serialization_alias="parser_id")]
+    parse_type: Annotated[int | None, Field(default=None, ge=0, le=64)]
+    pipeline_id: Annotated[str | None, Field(default=None, min_length=32, max_length=32, serialization_alias="pipeline_id")]
     parser_config: Annotated[ParserConfig | None, Field(default=None)]
     
     @field_validator("shared_tenant_id", mode="after")
@@ -535,6 +536,93 @@ class CreateDatasetReq(Base):
             raise PydanticCustomError("string_too_long", "Parser config exceeds size limit (max 65,535 characters). Current size: {actual}", {"actual": len(json_str)})
         return v
 
+    @field_validator("pipeline_id", mode="after")
+    @classmethod
+    def validate_pipeline_id(cls, v: str | None) -> str | None:
+        """Validate pipeline_id as 32-char lowercase hex string if provided.
+
+        Rules:
+        - None or empty string: treat as None (not set)
+        - Must be exactly length 32
+        - Must contain only hex digits (0-9a-fA-F); normalized to lowercase
+        """
+        if v is None:
+            return None
+        if v == "":
+            return None
+        if len(v) != 32:
+            raise PydanticCustomError("format_invalid", "pipeline_id must be 32 hex characters")
+        if any(ch not in string.hexdigits for ch in v):
+            raise PydanticCustomError("format_invalid", "pipeline_id must be hexadecimal")
+        return v.lower()
+
+    @model_validator(mode="after")
+    def validate_parser_dependency(self) -> "CreateDatasetReq":
+        """
+        Mixed conditional validation:
+        - If parser_id is omitted (field not set):
+            * If both parse_type and pipeline_id are omitted → default chunk_method = "naive"
+            * If both parse_type and pipeline_id are provided → allow ingestion pipeline mode
+        - If parser_id is provided (valid enum) → parse_type and pipeline_id must be None (disallow mixed usage)
+
+        Raises:
+            PydanticCustomError with code 'dependency_error' on violation.
+        """
+        # Omitted chunk_method (not in fields) logic
+        if self.chunk_method is None and "chunk_method" not in self.model_fields_set:
+            # All three absent → default naive
+            if self.parse_type is None and self.pipeline_id is None:
+                object.__setattr__(self, "chunk_method", "naive")
+                return self
+            # parser_id omitted: require BOTH parse_type & pipeline_id present (no partial allowed)
+            if self.parse_type is None or self.pipeline_id is None:
+                missing = []
+                if self.parse_type is None:
+                    missing.append("parse_type")
+                if self.pipeline_id is None:
+                    missing.append("pipeline_id")
+                raise PydanticCustomError(
+                    "dependency_error",
+                    "parser_id omitted → required fields missing: {fields}",
+                    {"fields": ", ".join(missing)},
+                )
+            # Both provided → allow pipeline mode
+            return self
+
+        # parser_id provided (valid): MUST NOT have parse_type or pipeline_id
+        if isinstance(self.chunk_method, str):
+            if self.parse_type is not None or self.pipeline_id is not None:
+                invalid = []
+                if self.parse_type is not None:
+                    invalid.append("parse_type")
+                if self.pipeline_id is not None:
+                    invalid.append("pipeline_id")
+                raise PydanticCustomError(
+                    "dependency_error",
+                    "parser_id provided → disallowed fields present: {fields}",
+                    {"fields": ", ".join(invalid)},
+                )
+        return self
+
+    @field_validator("chunk_method", mode="wrap")
+    @classmethod
+    def validate_chunk_method(cls, v: Any, handler) -> Any:
+        """Wrap validation to unify error messages, including type errors (e.g. list)."""
+        allowed = {"naive", "book", "email", "laws", "manual", "one", "paper", "picture", "presentation", "qa", "table", "tag"}
+        error_msg = "Input should be 'naive', 'book', 'email', 'laws', 'manual', 'one', 'paper', 'picture', 'presentation', 'qa', 'table' or 'tag'"
+        # Omitted field: handler won't be invoked (wrap still gets value); None treated as explicit invalid
+        if v is None:
+            raise PydanticCustomError("literal_error", error_msg)
+        try:
+            # Run inner validation (type checking)
+            result = handler(v)
+        except Exception:
+            raise PydanticCustomError("literal_error", error_msg)
+        # After handler, enforce enumeration
+        if not isinstance(result, str) or result == "" or result not in allowed:
+            raise PydanticCustomError("literal_error", error_msg)
+        return result
+
 
 class UpdateDatasetReq(CreateDatasetReq):
     dataset_id: Annotated[str, Field(...)]

conf/service_conf.yaml

@@ -38,6 +38,7 @@ oceanbase:
     port: 2881
 redis:
   db: 1
+  username: ''
   password: 'infini_rag_flow'
   host: 'localhost:6379'
 task_executor:

deepdoc/parser/mineru_parser.py

@@ -190,7 +190,7 @@ class MinerUParser(RAGFlowPdfParser):
             self._run_mineru_executable(input_path, output_dir, method, backend, lang, server_url, callback)
 
     def _run_mineru_api(self, input_path: Path, output_dir: Path, method: str = "auto", backend: str = "pipeline", lang: Optional[str] = None, callback: Optional[Callable] = None):
-        OUTPUT_ZIP_PATH = os.path.join(str(output_dir), "output.zip")
+        output_zip_path = os.path.join(str(output_dir), "output.zip")
 
         pdf_file_path = str(input_path)
 
@@ -230,16 +230,16 @@ class MinerUParser(RAGFlowPdfParser):
 
             response.raise_for_status()
             if response.headers.get("Content-Type") == "application/zip":
-                self.logger.info(f"[MinerU] zip file returned, saving to {OUTPUT_ZIP_PATH}...")
+                self.logger.info(f"[MinerU] zip file returned, saving to {output_zip_path}...")
 
                 if callback:
-                    callback(0.30, f"[MinerU] zip file returned, saving to {OUTPUT_ZIP_PATH}...")
+                    callback(0.30, f"[MinerU] zip file returned, saving to {output_zip_path}...")
 
-                with open(OUTPUT_ZIP_PATH, "wb") as f:
+                with open(output_zip_path, "wb") as f:
                     f.write(response.content)
 
                 self.logger.info(f"[MinerU] Unzip to {output_path}...")
-                self._extract_zip_no_root(OUTPUT_ZIP_PATH, output_path, pdf_file_name + "/")
+                self._extract_zip_no_root(output_zip_path, output_path, pdf_file_name + "/")
 
                 if callback:
                     callback(0.40, f"[MinerU] Unzip to {output_path}...")
@@ -459,13 +459,36 @@ class MinerUParser(RAGFlowPdfParser):
         return poss
 
     def _read_output(self, output_dir: Path, file_stem: str, method: str = "auto", backend: str = "pipeline") -> list[dict[str, Any]]:
-        subdir = output_dir / file_stem / method
-        if backend.startswith("vlm-"):
-            subdir = output_dir / file_stem / "vlm"
-        json_file = subdir / f"{file_stem}_content_list.json"
+        candidates = []
+        seen = set()
 
-        if not json_file.exists():
-            raise FileNotFoundError(f"[MinerU] Missing output file: {json_file}")
+        def add_candidate_path(p: Path):
+            if p not in seen:
+                seen.add(p)
+                candidates.append(p)
+
+        if backend.startswith("vlm-"):
+            add_candidate_path(output_dir / file_stem / "vlm")
+            if method:
+                add_candidate_path(output_dir / file_stem / method)
+            add_candidate_path(output_dir / file_stem / "auto")
+        else:
+            if method:
+                add_candidate_path(output_dir / file_stem / method)
+            add_candidate_path(output_dir / file_stem / "vlm")
+            add_candidate_path(output_dir / file_stem / "auto")
+
+        json_file = None
+        subdir = None
+        for sub in candidates:
+            jf = sub / f"{file_stem}_content_list.json"
+            if jf.exists():
+                subdir = sub
+                json_file = jf
+                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)}")
 
         with open(json_file, "r", encoding="utf-8") as f:
             data = json.load(f)
@@ -520,7 +543,7 @@ class MinerUParser(RAGFlowPdfParser):
         method: str = "auto",
         server_url: Optional[str] = None,
         delete_output: bool = True,
-        parse_method: str = "raw"
+        parse_method: str = "raw",
     ) -> tuple:
         import shutil
 
@@ -570,7 +593,7 @@ class MinerUParser(RAGFlowPdfParser):
             self.logger.info(f"[MinerU] Parsed {len(outputs)} blocks from PDF.")
             if callback:
                 callback(0.75, f"[MinerU] Parsed {len(outputs)} blocks from PDF.")
-                
+
             return self._transfer_to_sections(outputs, parse_method), self._transfer_to_tables(outputs)
         finally:
             if temp_pdf and temp_pdf.exists():

docker/service_conf.yaml.template

@@ -38,6 +38,7 @@ oceanbase:
     port: ${OCEANBASE_PORT:-2881}
 redis:
   db: 1
+  username: '${REDIS_USERNAME:-}'
   password: '${REDIS_PASSWORD:-infini_rag_flow}'
   host: '${REDIS_HOST:-redis}:6379'
 user_default_llm:

docs/configurations.md

@@ -89,6 +89,8 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
 
 - `REDIS_PORT`
   The port used to expose the Redis service to the host machine, allowing **external** access to the Redis service running inside the Docker container. Defaults to `6379`.
+- `REDIS_USERNAME`
+  Optional Redis ACL username when using Redis 6+ authentication.
 - `REDIS_PASSWORD`
   The password for Redis.
 
@@ -160,6 +162,13 @@ If you cannot download the RAGFlow Docker image, try the following mirrors.
 - `password`: The password for MinIO.
 - `host`: The MinIO serving IP *and* port inside the Docker container. Defaults to `minio:9000`.
 
+### `redis`
+
+- `host`: The Redis serving IP *and* port inside the Docker container. Defaults to `redis:6379`.
+- `db`: The Redis database index to use. Defaults to `1`.
+- `username`: Optional Redis ACL username (Redis 6+).
+- `password`: The password for the specified Redis user.
+
 ### `oauth`
 
 The OAuth configuration for signing up or signing in to RAGFlow using a third-party account.

docs/guides/models/deploy_local_llm.mdx

@@ -314,35 +314,3 @@ To enable IPEX-LLM accelerated Ollama in RAGFlow, you must also complete the con
 3. [Update System Model Settings](#6-update-system-model-settings)
 4. [Update Chat Configuration](#7-update-chat-configuration)
 
-## Deploy a local model using jina 
-
-To deploy a local model, e.g., **gpt2**, using jina:
-
-### 1. Check firewall settings
-
-Ensure that your host machine's firewall allows inbound connections on port 12345.
-
-```bash
-sudo ufw allow 12345/tcp
-```
-
-### 2. Install jina package
-
-```bash
-pip install jina
-```
-
-### 3. Deploy a local model
-
-Step 1: Navigate to the **rag/svr** directory.
-
-```bash
-cd rag/svr
-```
-
-Step 2: Run **jina_server.py**, specifying either the model's name or its local directory: 
-
-```bash
-python jina_server.py  --model_name gpt2
-```
-> The script only supports models downloaded from Hugging Face.

docs/references/http_api_reference.md

@@ -419,7 +419,15 @@ Creates a dataset.
   - `"embedding_model"`: `string`
   - `"permission"`: `string`
   - `"chunk_method"`: `string`
-  - `"parser_config"`: `object`
+  - "parser_config": `object`
+  - "parse_type": `int`
+  - "pipeline_id": `string`
+
+Note: Choose exactly one ingestion mode when creating a dataset.
+- Chunking method: provide `"chunk_method"` (optionally with `"parser_config"`).
+- Ingestion pipeline: provide both `"parse_type"` and `"pipeline_id"` and do not provide `"chunk_method"`.
+
+These options are mutually exclusive. If all three of `chunk_method`, `parse_type`, and `pipeline_id` are omitted, the system defaults to `chunk_method = "naive"`.
 
 ##### Request example
 
@@ -433,6 +441,26 @@ curl --request POST \
       }'
 ```
 
+##### Request example (ingestion pipeline)
+
+Use this form when specifying an ingestion pipeline (do not include `chunk_method`).
+
+```bash
+curl --request POST \
+  --url http://{address}/api/v1/datasets \
+  --header 'Content-Type: application/json' \
+  --header 'Authorization: Bearer <YOUR_API_KEY>' \
+  --data '{
+   "name": "test-sdk",
+   "parse_type": <NUMBER_OF_FORMATS_IN_PARSE>,
+   "pipeline_id": "<PIPELINE_ID_32_HEX>"
+  }'
+```
+
+Notes:
+- `parse_type` is an integer. Replace `<NUMBER_OF_FORMATS_IN_PARSE>` with your pipeline's parse-type value.
+- `pipeline_id` must be a 32-character lowercase hexadecimal string.
+
 ##### Request parameters
 
 - `"name"`: (*Body parameter*), `string`, *Required*  
@@ -473,6 +501,7 @@ curl --request POST \
   - `"qa"`: Q&A
   - `"table"`: Table
   - `"tag"`: Tag
+  - Mutually exclusive with `parse_type` and `pipeline_id`. If you set `chunk_method`, do not include `parse_type` or `pipeline_id`.
 
 - `"parser_config"`: (*Body parameter*), `object`  
   The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected `"chunk_method"`:  
@@ -509,6 +538,15 @@ curl --request POST \
       - Defaults to: `{"use_raptor": false}`.
   - If `"chunk_method"` is `"table"`, `"picture"`, `"one"`, or `"email"`, `"parser_config"` is an empty JSON object.
 
+- "parse_type": (*Body parameter*), `int`  
+  The ingestion pipeline parse type identifier. Required if and only if you are using an ingestion pipeline (together with `"pipeline_id"`). Must not be provided when `"chunk_method"` is set.
+
+- "pipeline_id": (*Body parameter*), `string`  
+  The ingestion pipeline ID. Required if and only if you are using an ingestion pipeline (together with `"parse_type"`).  
+  - Must not be provided when `"chunk_method"` is set.
+
+Note: If none of `chunk_method`, `parse_type`, and `pipeline_id` are provided, the system will default to `chunk_method = "naive"`.
+
 #### Response
 
 Success:

rag/llm/chat_model.py

@@ -33,9 +33,9 @@ from openai.lib.azure import AzureOpenAI
 from strenum import StrEnum
 from zhipuai import ZhipuAI
 
+from common.token_utils import num_tokens_from_string, total_token_count_from_response
 from rag.llm import FACTORY_DEFAULT_BASE_URL, LITELLM_PROVIDER_PREFIX, SupportedLiteLLMProvider
 from rag.nlp import is_chinese, is_english
-from common.token_utils import num_tokens_from_string, total_token_count_from_response
 
 
 # Error message constants
@@ -66,7 +66,7 @@ LENGTH_NOTIFICATION_EN = "...\nThe answer is truncated by your chosen LLM due to
 
 class Base(ABC):
     def __init__(self, key, model_name, base_url, **kwargs):
-        timeout = int(os.environ.get("LM_TIMEOUT_SECONDS", 600))
+        timeout = int(os.environ.get("LLM_TIMEOUT_SECONDS", 600))
         self.client = OpenAI(api_key=key, base_url=base_url, timeout=timeout)
         self.model_name = model_name
         # Configure retry parameters
@@ -127,7 +127,7 @@ class Base(ABC):
             "tool_choice",
             "logprobs",
             "top_logprobs",
-            "extra_headers"
+            "extra_headers",
         }
 
         gen_conf = {k: v for k, v in gen_conf.items() if k in allowed_conf}
@@ -1213,7 +1213,7 @@ class GoogleChat(Base):
 
         # Build GenerateContentConfig
         try:
-            from google.genai.types import GenerateContentConfig, ThinkingConfig, Content, Part
+            from google.genai.types import Content, GenerateContentConfig, Part, ThinkingConfig
         except ImportError as e:
             logging.error(f"[GoogleChat] Failed to import google-genai: {e}. Please install: pip install google-genai>=1.41.0")
             raise
@@ -1242,14 +1242,14 @@ class GoogleChat(Base):
             role = "model" if item["role"] == "assistant" else item["role"]
             content = Content(
                 role=role,
-                parts=[Part(text=item["content"])]
+                parts=[Part(text=item["content"])],
             )
             contents.append(content)
 
         response = self.client.models.generate_content(
             model=self.model_name,
             contents=contents,
-            config=config
+            config=config,
         )
 
         ans = response.text
@@ -1299,7 +1299,7 @@ class GoogleChat(Base):
 
             # Build GenerateContentConfig
             try:
-                from google.genai.types import GenerateContentConfig, ThinkingConfig, Content, Part
+                from google.genai.types import Content, GenerateContentConfig, Part, ThinkingConfig
             except ImportError as e:
                 logging.error(f"[GoogleChat] Failed to import google-genai: {e}. Please install: pip install google-genai>=1.41.0")
                 raise
@@ -1326,7 +1326,7 @@ class GoogleChat(Base):
                 role = "model" if item["role"] == "assistant" else item["role"]
                 content = Content(
                     role=role,
-                    parts=[Part(text=item["content"])]
+                    parts=[Part(text=item["content"])],
                 )
                 contents.append(content)
 
@@ -1334,7 +1334,7 @@ class GoogleChat(Base):
                 for chunk in self.client.models.generate_content_stream(
                     model=self.model_name,
                     contents=contents,
-                    config=config
+                    config=config,
                 ):
                     text = chunk.text
                     ans = text
@@ -1406,7 +1406,7 @@ class LiteLLMBase(ABC):
     ]
 
     def __init__(self, key, model_name, base_url=None, **kwargs):
-        self.timeout = int(os.environ.get("LM_TIMEOUT_SECONDS", 600))
+        self.timeout = int(os.environ.get("LLM_TIMEOUT_SECONDS", 600))
         self.provider = kwargs.get("provider", "")
         self.prefix = LITELLM_PROVIDER_PREFIX.get(self.provider, "")
         self.model_name = f"{self.prefix}{model_name}"
@@ -1625,6 +1625,7 @@ class LiteLLMBase(ABC):
 
         if self.provider == SupportedLiteLLMProvider.OpenRouter:
             if self.provider_order:
+
                 def _to_order_list(x):
                     if x is None:
                         return []
@@ -1633,6 +1634,7 @@ class LiteLLMBase(ABC):
                     if isinstance(x, (list, tuple)):
                         return [str(s).strip() for s in x if str(s).strip()]
                     return []
+
                 extra_body = {}
                 provider_cfg = {}
                 provider_order = _to_order_list(self.provider_order)

rag/prompts/citation_prompt.md

@@ -106,4 +106,4 @@ REMEMBER:
 - Each citation supports the ENTIRE sentence
 - When in doubt, ask: "Would a fact-checker need to verify this?"
 - Place citations at sentence end, before punctuation
-- Format likes this is FORBIDDEN: [ID:0, ID:5, ID:...]. It MUST be seperated like, [ID:0][ID:5]...
+- Format likes this is FORBIDDEN: [ID:0, ID:5, ID:...]. It MUST be separated like, [ID:0][ID:5]...

rag/utils/es_conn.py

@@ -575,9 +575,9 @@ class ESConnection(DocStoreConnection):
                 time.sleep(3)
                 self._connect()
                 continue
-            except Exception:
-                logger.exception("ESConnection.sql got exception")
-                break
+            except Exception as e:
+                logger.exception(f"ESConnection.sql got exception. SQL:\n{sql}")
+                raise Exception(f"SQL error: {e}\n\nSQL: {sql}")
         logger.error(f"ESConnection.sql timeout for {ATTEMPT_TIME} times!")
         return None
 

rag/utils/redis_conn.py

@@ -86,6 +86,9 @@ class RedisDB:
                 "db": int(self.config.get("db", 1)),
                 "decode_responses": True,
             }
+            username = self.config.get("username")
+            if username:
+                conn_params["username"] = username
             password = self.config.get("password")
             if password:
                 conn_params["password"] = password

web/src/components/embed-dialog/index.tsx

@@ -22,6 +22,7 @@ import { SharedFrom } from '@/constants/chat';
 import {
   LanguageAbbreviation,
   LanguageAbbreviationMap,
+  ThemeEnum,
 } from '@/constants/common';
 import { useTranslate } from '@/hooks/common-hooks';
 import { IModalProps } from '@/interfaces/common';
@@ -36,6 +37,7 @@ const FormSchema = z.object({
   locale: z.string(),
   embedType: z.enum(['fullscreen', 'widget']),
   enableStreaming: z.boolean(),
+  theme: z.enum([ThemeEnum.Light, ThemeEnum.Dark]),
 });
 
 type IProps = IModalProps<any> & {
@@ -61,6 +63,7 @@ function EmbedDialog({
       locale: '',
       embedType: 'fullscreen' as const,
       enableStreaming: false,
+      theme: ThemeEnum.Light,
     },
   });
 
@@ -74,7 +77,7 @@ function EmbedDialog({
   }, []);
 
   const generateIframeSrc = useCallback(() => {
-    const { visibleAvatar, locale, embedType, enableStreaming } = values;
+    const { visibleAvatar, locale, embedType, enableStreaming, theme } = values;
     const baseRoute =
       embedType === 'widget'
         ? Routes.ChatWidget
@@ -91,6 +94,9 @@ function EmbedDialog({
     if (enableStreaming) {
       src += '&streaming=true';
     }
+    if (theme && embedType === 'fullscreen') {
+      src += `&theme=${theme}`;
+    }
     return src;
   }, [beta, from, token, values]);
 
@@ -181,6 +187,41 @@ function EmbedDialog({
                   </FormItem>
                 )}
               />
+              {values.embedType === 'fullscreen' && (
+                <FormField
+                  control={form.control}
+                  name="theme"
+                  render={({ field }) => (
+                    <FormItem>
+                      <FormLabel>Theme</FormLabel>
+                      <FormControl>
+                        <RadioGroup
+                          onValueChange={field.onChange}
+                          value={field.value}
+                          className="flex flex-row space-x-4"
+                        >
+                          <div className="flex items-center space-x-2">
+                            <RadioGroupItem
+                              value={ThemeEnum.Light}
+                              id="light"
+                            />
+                            <Label htmlFor="light" className="text-sm">
+                              Light
+                            </Label>
+                          </div>
+                          <div className="flex items-center space-x-2">
+                            <RadioGroupItem value={ThemeEnum.Dark} id="dark" />
+                            <Label htmlFor="dark" className="text-sm">
+                              Dark
+                            </Label>
+                          </div>
+                        </RadioGroup>
+                      </FormControl>
+                      <FormMessage />
+                    </FormItem>
+                  )}
+                />
+              )}
               <FormField
                 control={form.control}
                 name="visibleAvatar"

web/src/components/theme-provider.tsx

@@ -71,3 +71,13 @@ export function useSwitchToDarkThemeOnMount() {
     setTheme(ThemeEnum.Dark);
   }, [setTheme]);
 }
+
+export function useSyncThemeFromParams(theme: string | null) {
+  const { setTheme } = useTheme();
+
+  useEffect(() => {
+    if (theme && (theme === ThemeEnum.Light || theme === ThemeEnum.Dark)) {
+      setTheme(theme as ThemeEnum);
+    }
+  }, [theme, setTheme]);
+}

web/src/pages/agent/hooks/use-send-shared-message.ts

@@ -29,6 +29,7 @@ export const useGetSharedChatSearchParams = () => {
     from: searchParams.get('from') as SharedFrom,
     sharedId: searchParams.get('shared_id'),
     locale: searchParams.get('locale'),
+    theme: searchParams.get('theme'),
     data: data,
     visibleAvatar: searchParams.get('visible_avatar')
       ? searchParams.get('visible_avatar') !== '1'

web/src/pages/agent/share/index.tsx

@@ -4,6 +4,7 @@ import { NextMessageInput } from '@/components/message-input/next';
 import MessageItem from '@/components/next-message-item';
 import PdfSheet from '@/components/pdf-drawer';
 import { useClickDrawer } from '@/components/pdf-drawer/hooks';
+import { useSyncThemeFromParams } from '@/components/theme-provider';
 import { MessageType } from '@/constants/chat';
 import { useUploadCanvasFileWithProgress } from '@/hooks/use-agent-request';
 import { cn } from '@/lib/utils';
@@ -25,8 +26,10 @@ const ChatContainer = () => {
   const {
     sharedId: conversationId,
     locale,
+    theme,
     visibleAvatar,
   } = useGetSharedChatSearchParams();
+  useSyncThemeFromParams(theme);
   const { visible, hideModal, documentId, selectedChunk, clickDocumentButton } =
     useClickDrawer();
 

web/src/pages/next-chats/hooks/use-send-shared-message.ts

@@ -33,6 +33,7 @@ export const useGetSharedChatSearchParams = () => {
     from: searchParams.get('from') as SharedFrom,
     sharedId: searchParams.get('shared_id'),
     locale: searchParams.get('locale'),
+    theme: searchParams.get('theme'),
     data: data,
     visibleAvatar: searchParams.get('visible_avatar')
       ? searchParams.get('visible_avatar') !== '1'

web/src/pages/next-chats/share/index.tsx

@@ -3,6 +3,7 @@ import { NextMessageInput } from '@/components/message-input/next';
 import MessageItem from '@/components/message-item';
 import PdfSheet from '@/components/pdf-drawer';
 import { useClickDrawer } from '@/components/pdf-drawer/hooks';
+import { useSyncThemeFromParams } from '@/components/theme-provider';
 import { MessageType, SharedFrom } from '@/constants/chat';
 import { useFetchNextConversationSSE } from '@/hooks/chat-hooks';
 import { useFetchFlowSSE } from '@/hooks/flow-hooks';
@@ -22,8 +23,10 @@ const ChatContainer = () => {
     sharedId: conversationId,
     from,
     locale,
+    theme,
     visibleAvatar,
   } = useGetSharedChatSearchParams();
+  useSyncThemeFromParams(theme);
   const { visible, hideModal, documentId, selectedChunk, clickDocumentButton } =
     useClickDrawer();
 
@@ -52,6 +55,7 @@ const ChatContainer = () => {
       i18n.changeLanguage(locale);
     }
   }, [locale, visibleAvatar]);
+
   const { data: avatarData } = useFetchAvatar();
 
   if (!conversationId) {