fix: eslint run failed

Co-authored-by: crazywoola <427733928@qq.com>

.github/actions/setup-poetry/action.yml

@@ -8,7 +8,7 @@ inputs:
   poetry-version:
     description: Poetry version to set up
     required: true
-    default: '1.8.4'
+    default: '2.0.1'
   poetry-lockfile:
     description: Path to the Poetry lockfile to restore cache from
     required: true

.github/workflows/api-tests.yml

@@ -42,25 +42,23 @@ jobs:
         run: poetry install -C api --with dev
 
       - name: Check dependencies in pyproject.toml
-        run: poetry run -C api bash dev/pytest/pytest_artifacts.sh
+        run: poetry run -P api bash dev/pytest/pytest_artifacts.sh
 
       - name: Run Unit tests
-        run: poetry run -C api bash dev/pytest/pytest_unit_tests.sh
+        run: poetry run -P api bash dev/pytest/pytest_unit_tests.sh
 
       - name: Run ModelRuntime
-        run: poetry run -C api bash dev/pytest/pytest_model_runtime.sh
+        run: poetry run -P api bash dev/pytest/pytest_model_runtime.sh
 
       - name: Run dify config tests
-        run: poetry run -C api python dev/pytest/pytest_config_tests.py
+        run: poetry run -P api python dev/pytest/pytest_config_tests.py
 
       - name: Run Tool
-        run: poetry run -C api bash dev/pytest/pytest_tools.sh
+        run: poetry run -P api bash dev/pytest/pytest_tools.sh
 
       - name: Run mypy
         run: |
-          pushd api
-          poetry run python -m mypy --install-types --non-interactive .
-          popd
+          poetry run -C api python -m mypy --install-types --non-interactive .
 
       - name: Set up dotenvs
         run: |
@@ -80,4 +78,4 @@ jobs:
             ssrf_proxy
 
       - name: Run Workflow
-        run: poetry run -C api bash dev/pytest/pytest_workflow.sh
+        run: poetry run -P api bash dev/pytest/pytest_workflow.sh

.github/workflows/style.yml

@@ -38,12 +38,12 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: |
           poetry run -C api ruff --version
-          poetry run -C api ruff check ./api
-          poetry run -C api ruff format --check ./api
+          poetry run -C api ruff check ./
+          poetry run -C api ruff format --check ./
 
       - name: Dotenv check
         if: steps.changed-files.outputs.any_changed == 'true'
-        run: poetry run -C api dotenv-linter ./api/.env.example ./web/.env.example
+        run: poetry run -P api dotenv-linter ./api/.env.example ./web/.env.example
 
       - name: Lint hints
         if: failure()
@@ -82,6 +82,33 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: yarn run lint
 
+  docker-compose-template:
+    name: Docker Compose Template
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Check changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v45
+        with:
+          files: |
+            docker/generate_docker_compose
+            docker/.env.example
+            docker/docker-compose-template.yaml
+            docker/docker-compose.yaml
+
+      - name: Generate Docker Compose
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: |
+          cd docker
+          ./generate_docker_compose
+
+      - name: Check for changes
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: git diff --exit-code
 
   superlinter:
     name: SuperLinter

.github/workflows/vdb-tests.yml

@@ -70,4 +70,4 @@ jobs:
             tidb
 
       - name: Test Vector Stores
-        run: poetry run -C api bash dev/pytest/pytest_vdb.sh
+        run: poetry run -P api bash dev/pytest/pytest_vdb.sh

api/.ruff.toml

@@ -53,10 +53,12 @@ ignore = [
     "FURB152", # math-constant
     "UP007", # non-pep604-annotation
     "UP032", # f-string
+    "UP045", # non-pep604-annotation-optional
     "B005", # strip-with-multi-characters
     "B006", # mutable-argument-default
     "B007", # unused-loop-control-variable
     "B026", # star-arg-unpacking-after-keyword-arg
+    "B903", # class-as-data-structure
     "B904", # raise-without-from-inside-except
     "B905", # zip-without-explicit-strict
     "N806", # non-lowercase-variable-in-function

api/Dockerfile

@@ -4,7 +4,7 @@ FROM python:3.12-slim-bookworm AS base
 WORKDIR /app/api
 
 # Install Poetry
-ENV POETRY_VERSION=1.8.4
+ENV POETRY_VERSION=2.0.1
 
 # if you located in China, you can use aliyun mirror to speed up
 # RUN pip install --no-cache-dir poetry==${POETRY_VERSION} -i https://mirrors.aliyun.com/pypi/simple/
@@ -52,12 +52,14 @@ RUN apt-get update \
     && apt-get install -y --no-install-recommends curl nodejs libgmp-dev libmpfr-dev libmpc-dev \
     # if you located in China, you can use aliyun mirror to speed up
     # && echo "deb http://mirrors.aliyun.com/debian testing main" > /etc/apt/sources.list \
-    && echo "deb http://deb.debian.org/debian testing main" > /etc/apt/sources.list \
+    && echo "deb http://deb.debian.org/debian bookworm main" > /etc/apt/sources.list \
     && apt-get update \
     # For Security
-    && apt-get install -y --no-install-recommends expat=2.6.4-1 libldap-2.5-0=2.5.19+dfsg-1 perl=5.40.0-8 libsqlite3-0=3.46.1-1 zlib1g=1:1.3.dfsg+really1.3.1-1+b1 \
+    && apt-get install -y --no-install-recommends expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
     # install a chinese font to support the use of tools like matplotlib
     && apt-get install -y fonts-noto-cjk \
+    # install libmagic to support the use of python-magic guess MIMETYPE
+    && apt-get install -y libmagic1 \
     && apt-get autoremove -y \
     && rm -rf /var/lib/apt/lists/*
 

api/README.md

@@ -79,5 +79,5 @@
 2. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`
 
    ```bash
-   poetry run -C api bash dev/pytest/pytest_all_tests.sh
+   poetry run -P api bash dev/pytest/pytest_all_tests.sh
    ```

api/configs/feature/__init__.py

@@ -146,7 +146,7 @@ class EndpointConfig(BaseSettings):
     )
 
     CONSOLE_WEB_URL: str = Field(
-        description="Base URL for the console web interface," "used for frontend references and CORS configuration",
+        description="Base URL for the console web interface,used for frontend references and CORS configuration",
         default="",
     )
 

api/configs/feature/hosted_service/__init__.py

@@ -181,7 +181,7 @@ class HostedFetchAppTemplateConfig(BaseSettings):
     """
 
     HOSTED_FETCH_APP_TEMPLATES_MODE: str = Field(
-        description="Mode for fetching app templates: remote, db, or builtin" " default to remote,",
+        description="Mode for fetching app templates: remote, db, or builtin default to remote,",
         default="remote",
     )
 

api/configs/middleware/vdb/milvus_config.py

@@ -33,3 +33,9 @@ class MilvusConfig(BaseSettings):
         description="Name of the Milvus database to connect to (default is 'default')",
         default="default",
     )
+
+    MILVUS_ENABLE_HYBRID_SEARCH: bool = Field(
+        description="Enable hybrid search features (requires Milvus >= 2.5.0). Set to false for compatibility with "
+        "older versions",
+        default=True,
+    )

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.14.2",
+        default="0.15.2",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/common/helpers.py

@@ -1,12 +1,32 @@
 import mimetypes
 import os
+import platform
 import re
 import urllib.parse
+import warnings
 from collections.abc import Mapping
 from typing import Any
 from uuid import uuid4
 
 import httpx
+
+try:
+    import magic
+except ImportError:
+    if platform.system() == "Windows":
+        warnings.warn(
+            "To use python-magic guess MIMETYPE, you need to run `pip install python-magic-bin`", stacklevel=2
+        )
+    elif platform.system() == "Darwin":
+        warnings.warn("To use python-magic guess MIMETYPE, you need to run `brew install libmagic`", stacklevel=2)
+    elif platform.system() == "Linux":
+        warnings.warn(
+            "To use python-magic guess MIMETYPE, you need to run `sudo apt-get install libmagic1`", stacklevel=2
+        )
+    else:
+        warnings.warn("To use python-magic guess MIMETYPE, you need to install `libmagic`", stacklevel=2)
+    magic = None  # type: ignore
+
 from pydantic import BaseModel
 
 from configs import dify_config
@@ -47,6 +67,13 @@ def guess_file_info_from_response(response: httpx.Response):
         # If guessing fails, use Content-Type from response headers
         mimetype = response.headers.get("Content-Type", "application/octet-stream")
 
+    # Use python-magic to guess MIME type if still unknown or generic
+    if mimetype == "application/octet-stream" and magic is not None:
+        try:
+            mimetype = magic.from_buffer(response.content[:1024], mime=True)
+        except magic.MagicException:
+            pass
+
     extension = os.path.splitext(filename)[1]
 
     # Ensure filename has an extension

api/controllers/console/admin.py

@@ -56,7 +56,7 @@ class InsertExploreAppListApi(Resource):
 
         app = App.query.filter(App.id == args["app_id"]).first()
         if not app:
-            raise NotFound(f'App \'{args["app_id"]}\' is not found')
+            raise NotFound(f"App '{args['app_id']}' is not found")
 
         site = app.site
         if not site:

api/controllers/console/app/audio.py

@@ -22,7 +22,7 @@ from controllers.console.wraps import account_initialization_required, setup_req
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
-from models.model import AppMode
+from models import App, AppMode
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -79,7 +79,7 @@ class ChatMessageTextApi(Resource):
     @login_required
     @account_initialization_required
     @get_app_model
-    def post(self, app_model):
+    def post(self, app_model: App):
         from werkzeug.exceptions import InternalServerError
 
         try:
@@ -98,9 +98,13 @@ class ChatMessageTextApi(Resource):
                 and app_model.workflow.features_dict
             ):
                 text_to_speech = app_model.workflow.features_dict.get("text_to_speech")
+                if text_to_speech is None:
+                    raise ValueError("TTS is not enabled")
                 voice = args.get("voice") or text_to_speech.get("voice")
             else:
                 try:
+                    if app_model.app_model_config is None:
+                        raise ValueError("AppModelConfig not found")
                     voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
                 except Exception:
                     voice = None

api/controllers/console/datasets/datasets.py

@@ -52,12 +52,12 @@ class DatasetListApi(Resource):
         # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
-
+        include_all = request.args.get("include_all", default="false").lower() == "true"
         if ids:
             datasets, total = DatasetService.get_datasets_by_ids(ids, current_user.current_tenant_id)
         else:
             datasets, total = DatasetService.get_datasets(
-                page, limit, current_user.current_tenant_id, current_user, search, tag_ids
+                page, limit, current_user.current_tenant_id, current_user, search, tag_ids, include_all
             )
 
         # check embedding setting
@@ -457,7 +457,7 @@ class DatasetIndexingEstimateApi(Resource):
             )
         except LLMBadRequestError:
             raise ProviderNotInitializeError(
-                "No Embedding Model available. Please configure a valid provider " "in the Settings -> Model Provider."
+                "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
             )
         except ProviderTokenNotInitError as ex:
             raise ProviderNotInitializeError(ex.description)
@@ -619,9 +619,7 @@ class DatasetRetrievalSettingApi(Resource):
         vector_type = dify_config.VECTOR_STORE
         match vector_type:
             case (
-                VectorType.MILVUS
-                | VectorType.RELYT
-                | VectorType.PGVECTOR
+                VectorType.RELYT
                 | VectorType.TIDB_VECTOR
                 | VectorType.CHROMA
                 | VectorType.TENCENT
@@ -640,10 +638,12 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.ELASTICSEARCH_JA
                 | VectorType.PGVECTOR
                 | VectorType.TIDB_ON_QDRANT
                 | VectorType.LINDORM
                 | VectorType.COUCHBASE
+                | VectorType.MILVUS
             ):
                 return {
                     "retrieval_method": [
@@ -683,6 +683,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.MYSCALE
                 | VectorType.ORACLE
                 | VectorType.ELASTICSEARCH
+                | VectorType.ELASTICSEARCH_JA
                 | VectorType.COUCHBASE
                 | VectorType.PGVECTOR
                 | VectorType.LINDORM

api/controllers/console/datasets/datasets_document.py

@@ -257,7 +257,8 @@ class DatasetDocumentListApi(Resource):
         parser.add_argument("original_document_id", type=str, required=False, location="json")
         parser.add_argument("doc_form", type=str, default="text_model", required=False, nullable=False, location="json")
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
-
+        parser.add_argument("embedding_model", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("embedding_model_provider", type=str, required=False, nullable=True, location="json")
         parser.add_argument(
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )
@@ -349,8 +350,7 @@ class DatasetInitApi(Resource):
                 )
             except InvokeAuthorizationError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -525,8 +525,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                 return response.model_dump(), 200
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/console/datasets/datasets_segments.py

@@ -168,8 +168,7 @@ class DatasetDocumentSegmentApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -217,8 +216,7 @@ class DatasetDocumentSegmentAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -267,8 +265,7 @@ class DatasetDocumentSegmentUpdateApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -368,9 +365,9 @@ class DatasetDocumentSegmentBatchImportApi(Resource):
             result = []
             for index, row in df.iterrows():
                 if document.doc_form == "qa_model":
-                    data = {"content": row[0], "answer": row[1]}
+                    data = {"content": row.iloc[0], "answer": row.iloc[1]}
                 else:
-                    data = {"content": row[0]}
+                    data = {"content": row.iloc[0]}
                 result.append(data)
             if len(result) == 0:
                 raise ValueError("The CSV file is empty.")
@@ -437,8 +434,7 @@ class ChildChunkAddApi(Resource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/console/explore/conversation.py

@@ -32,7 +32,7 @@ class ConversationListApi(InstalledAppResource):
 
         pinned = None
         if "pinned" in args and args["pinned"] is not None:
-            pinned = True if args["pinned"] == "true" else False
+            pinned = args["pinned"] == "true"
 
         try:
             with Session(db.engine) as session:

api/controllers/console/explore/message.py

@@ -50,7 +50,7 @@ class MessageListApi(InstalledAppResource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, current_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
+                app_model, current_user, args["conversation_id"], args["first_id"], args["limit"]
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

api/controllers/inner_api/workspace/workspace.py

@@ -1,3 +1,5 @@
+import json
+
 from flask_restful import Resource, reqparse  # type: ignore
 
 from controllers.console.wraps import setup_required
@@ -29,4 +31,34 @@ class EnterpriseWorkspace(Resource):
         return {"message": "enterprise workspace created."}
 
 
+class EnterpriseWorkspaceNoOwnerEmail(Resource):
+    @setup_required
+    @inner_api_only
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("name", type=str, required=True, location="json")
+        args = parser.parse_args()
+
+        tenant = TenantService.create_tenant(args["name"], is_from_dashboard=True)
+
+        tenant_was_created.send(tenant)
+
+        resp = {
+            "id": tenant.id,
+            "name": tenant.name,
+            "encrypt_public_key": tenant.encrypt_public_key,
+            "plan": tenant.plan,
+            "status": tenant.status,
+            "custom_config": json.loads(tenant.custom_config) if tenant.custom_config else {},
+            "created_at": tenant.created_at.isoformat() if tenant.created_at else None,
+            "updated_at": tenant.updated_at.isoformat() if tenant.updated_at else None,
+        }
+
+        return {
+            "message": "enterprise workspace created.",
+            "tenant": resp,
+        }
+
+
 api.add_resource(EnterpriseWorkspace, "/enterprise/workspace")
+api.add_resource(EnterpriseWorkspaceNoOwnerEmail, "/enterprise/workspace/ownerless")

api/controllers/service_api/__init__.py

@@ -7,4 +7,4 @@ api = ExternalApi(bp)
 
 from . import index
 from .app import app, audio, completion, conversation, file, message, workflow
-from .dataset import dataset, document, hit_testing, segment
+from .dataset import dataset, document, hit_testing, segment, upload_file

api/controllers/service_api/dataset/dataset.py

@@ -31,8 +31,11 @@ class DatasetListApi(DatasetApiResource):
         # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
+        include_all = request.args.get("include_all", default="false").lower() == "true"
 
-        datasets, total = DatasetService.get_datasets(page, limit, tenant_id, current_user, search, tag_ids)
+        datasets, total = DatasetService.get_datasets(
+            page, limit, tenant_id, current_user, search, tag_ids, include_all
+        )
         # check embedding setting
         provider_manager = ProviderManager()
         configurations = provider_manager.get_configurations(tenant_id=current_user.current_tenant_id)

api/controllers/service_api/dataset/document.py

@@ -18,6 +18,7 @@ from controllers.service_api.app.error import (
 from controllers.service_api.dataset.error import (
     ArchivedDocumentImmutableError,
     DocumentIndexingError,
+    InvalidMetadataError,
 )
 from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check
 from core.errors.error import ProviderTokenNotInitError
@@ -50,6 +51,9 @@ class DocumentAddByTextApi(DatasetApiResource):
             "indexing_technique", type=str, choices=Dataset.INDEXING_TECHNIQUE_LIST, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
+
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -61,6 +65,28 @@ class DocumentAddByTextApi(DatasetApiResource):
         if not dataset.indexing_technique and not args["indexing_technique"]:
             raise ValueError("indexing_technique is required.")
 
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         text = args.get("text")
         name = args.get("name")
         if text is None or name is None:
@@ -107,6 +133,8 @@ class DocumentUpdateByTextApi(DatasetApiResource):
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -115,6 +143,32 @@ class DocumentUpdateByTextApi(DatasetApiResource):
         if not dataset:
             raise ValueError("Dataset is not exist.")
 
+        # indexing_technique is already set in dataset since this is an update
+        args["indexing_technique"] = dataset.indexing_technique
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         if args["text"]:
             text = args.get("text")
             name = args.get("name")
@@ -161,6 +215,30 @@ class DocumentAddByFileApi(DatasetApiResource):
             args["doc_form"] = "text_model"
         if "doc_language" not in args:
             args["doc_language"] = "English"
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         # get dataset info
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -228,6 +306,29 @@ class DocumentUpdateByFileApi(DatasetApiResource):
         if "doc_language" not in args:
             args["doc_language"] = "English"
 
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         # get dataset info
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)

api/controllers/service_api/dataset/segment.py

@@ -53,8 +53,7 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -95,8 +94,7 @@ class SegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
@@ -175,8 +173,7 @@ class DatasetSegmentApi(DatasetApiResource):
                 )
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
-                    "No Embedding Model available. Please configure a valid provider "
-                    "in the Settings -> Model Provider."
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
                 )
             except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)

api/controllers/service_api/dataset/upload_file.py

@@ -0,0 +1,54 @@
+from werkzeug.exceptions import NotFound
+
+from controllers.service_api import api
+from controllers.service_api.wraps import (
+    DatasetApiResource,
+)
+from core.file import helpers as file_helpers
+from extensions.ext_database import db
+from models.dataset import Dataset
+from models.model import UploadFile
+from services.dataset_service import DocumentService
+
+
+class UploadFileApi(DatasetApiResource):
+    def get(self, tenant_id, dataset_id, document_id):
+        """Get upload file."""
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset.id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        # check upload file
+        if document.data_source_type != "upload_file":
+            raise ValueError(f"Document data source type ({document.data_source_type}) is not upload_file.")
+        data_source_info = document.data_source_info_dict
+        if data_source_info and "upload_file_id" in data_source_info:
+            file_id = data_source_info["upload_file_id"]
+            upload_file = db.session.query(UploadFile).filter(UploadFile.id == file_id).first()
+            if not upload_file:
+                raise NotFound("UploadFile not found.")
+        else:
+            raise ValueError("Upload file id not found in document data source info.")
+
+        url = file_helpers.get_signed_file_url(upload_file_id=upload_file.id)
+        return {
+            "id": upload_file.id,
+            "name": upload_file.name,
+            "size": upload_file.size,
+            "extension": upload_file.extension,
+            "url": url,
+            "download_url": f"{url}&as_attachment=true",
+            "mime_type": upload_file.mime_type,
+            "created_by": upload_file.created_by,
+            "created_at": upload_file.created_at.timestamp(),
+        }, 200
+
+
+api.add_resource(UploadFileApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/upload-file")

api/controllers/service_api/wraps.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from datetime import UTC, datetime
+from datetime import UTC, datetime, timedelta
 from enum import Enum
 from functools import wraps
 from typing import Optional
@@ -8,6 +8,8 @@ from flask import current_app, request
 from flask_login import user_logged_in  # type: ignore
 from flask_restful import Resource  # type: ignore
 from pydantic import BaseModel
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, Unauthorized
 
 from extensions.ext_database import db
@@ -174,7 +176,7 @@ def validate_dataset_token(view=None):
     return decorator
 
 
-def validate_and_get_api_token(scope=None):
+def validate_and_get_api_token(scope: str | None = None):
     """
     Validate and get API token.
     """
@@ -188,20 +190,29 @@ def validate_and_get_api_token(scope=None):
     if auth_scheme != "bearer":
         raise Unauthorized("Authorization scheme must be 'Bearer'")
 
-    api_token = (
-        db.session.query(ApiToken)
-        .filter(
-            ApiToken.token == auth_token,
-            ApiToken.type == scope,
+    current_time = datetime.now(UTC).replace(tzinfo=None)
+    cutoff_time = current_time - timedelta(minutes=1)
+    with Session(db.engine, expire_on_commit=False) as session:
+        update_stmt = (
+            update(ApiToken)
+            .where(
+                ApiToken.token == auth_token,
+                (ApiToken.last_used_at.is_(None) | (ApiToken.last_used_at < cutoff_time)),
+                ApiToken.type == scope,
+            )
+            .values(last_used_at=current_time)
+            .returning(ApiToken)
         )
-        .first()
-    )
+        result = session.execute(update_stmt)
+        api_token = result.scalar_one_or_none()
 
-    if not api_token:
-        raise Unauthorized("Access token is invalid")
-
-    api_token.last_used_at = datetime.now(UTC).replace(tzinfo=None)
-    db.session.commit()
+        if not api_token:
+            stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
+            api_token = session.scalar(stmt)
+            if not api_token:
+                raise Unauthorized("Access token is invalid")
+        else:
+            session.commit()
 
     return api_token
 
@@ -229,7 +240,7 @@ def create_or_update_end_user_for_user_id(app_model: App, user_id: Optional[str]
             tenant_id=app_model.tenant_id,
             app_id=app_model.id,
             type="service_api",
-            is_anonymous=True if user_id == "DEFAULT-USER" else False,
+            is_anonymous=user_id == "DEFAULT-USER",
             session_id=user_id,
         )
         db.session.add(end_user)

api/controllers/web/conversation.py

@@ -39,7 +39,7 @@ class ConversationListApi(WebApiResource):
 
         pinned = None
         if "pinned" in args and args["pinned"] is not None:
-            pinned = True if args["pinned"] == "true" else False
+            pinned = args["pinned"] == "true"
 
         try:
             with Session(db.engine) as session:

api/controllers/web/message.py

@@ -91,7 +91,7 @@ class MessageListApi(WebApiResource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
+                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

api/core/agent/cot_agent_runner.py

@@ -172,7 +172,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
 
             self.save_agent_thought(
                 agent_thought=agent_thought,
-                tool_name=scratchpad.action.action_name if scratchpad.action else "",
+                tool_name=(scratchpad.action.action_name if scratchpad.action and not scratchpad.is_final() else ""),
                 tool_input={scratchpad.action.action_name: scratchpad.action.action_input} if scratchpad.action else {},
                 tool_invoke_meta={},
                 thought=scratchpad.thought or "",

api/core/app/apps/agent_chat/app_runner.py

@@ -202,7 +202,7 @@ class AgentChatAppRunner(AppRunner):
         # change function call strategy based on LLM model
         llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
         model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
-        if not model_schema or not model_schema.features:
+        if not model_schema:
             raise ValueError("Model schema not found")
 
         if {ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL}.intersection(model_schema.features or []):

api/core/app/apps/base_app_queue_manager.py

@@ -167,8 +167,7 @@ class AppQueueManager:
         else:
             if isinstance(data, DeclarativeMeta) or hasattr(data, "_sa_instance_state"):
                 raise TypeError(
-                    "Critical Error: Passing SQLAlchemy Model instances "
-                    "that cause thread safety issues is not allowed."
+                    "Critical Error: Passing SQLAlchemy Model instances that cause thread safety issues is not allowed."
                 )
 
 

api/core/app/apps/message_based_app_generator.py

@@ -89,6 +89,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             Conversation.id == conversation_id,
             Conversation.app_id == app_model.id,
             Conversation.status == "normal",
+            Conversation.is_deleted.is_(False),
         ]
 
         if isinstance(user, Account):

api/core/app/task_pipeline/message_cycle_manage.py

@@ -145,7 +145,7 @@ class MessageCycleManage:
 
             # get extension
             if "." in message_file.url:
-                extension = f'.{message_file.url.split(".")[-1]}'
+                extension = f".{message_file.url.split('.')[-1]}"
                 if len(extension) > 10:
                     extension = ".bin"
             else:

api/core/external_data_tool/api/api.py

@@ -62,8 +62,9 @@ class ApiExternalDataTool(ExternalDataTool):
 
         if not api_based_extension:
             raise ValueError(
-                "[External data tool] API query failed, variable: {}, "
-                "error: api_based_extension_id is invalid".format(self.variable)
+                "[External data tool] API query failed, variable: {}, error: api_based_extension_id is invalid".format(
+                    self.variable
+                )
             )
 
         # decrypt api_key

api/core/file/models.py

@@ -90,7 +90,7 @@ class File(BaseModel):
     def markdown(self) -> str:
         url = self.generate_url()
         if self.type == FileType.IMAGE:
-            text = f'![{self.filename or ""}]({url})'
+            text = f"![{self.filename or ''}]({url})"
         else:
             text = f"[{self.filename or url}]({url})"
 

api/core/indexing_runner.py

@@ -530,7 +530,6 @@ class IndexingRunner:
         # chunk nodes by chunk size
         indexing_start_at = time.perf_counter()
         tokens = 0
-        chunk_size = 10
         if dataset_document.doc_form != IndexType.PARENT_CHILD_INDEX:
             # create keyword index
             create_keyword_thread = threading.Thread(
@@ -539,11 +538,22 @@ class IndexingRunner:
             )
             create_keyword_thread.start()
 
+        max_workers = 10
         if dataset.indexing_technique == "high_quality":
-            with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
+            with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
                 futures = []
-                for i in range(0, len(documents), chunk_size):
-                    chunk_documents = documents[i : i + chunk_size]
+
+                # Distribute documents into multiple groups based on the hash values of page_content
+                # This is done to prevent multiple threads from processing the same document,
+                # Thereby avoiding potential database insertion deadlocks
+                document_groups: list[list[Document]] = [[] for _ in range(max_workers)]
+                for document in documents:
+                    hash = helper.generate_text_hash(document.page_content)
+                    group_index = int(hash, 16) % max_workers
+                    document_groups[group_index].append(document)
+                for chunk_documents in document_groups:
+                    if len(chunk_documents) == 0:
+                        continue
                     futures.append(
                         executor.submit(
                             self._process_chunk,

api/core/llm_generator/prompts.py

@@ -131,7 +131,7 @@ JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE = (
 SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
     "Please help me predict the three most likely questions that human would ask, "
     "and keeping each question under 20 characters.\n"
-    "MAKE SURE your output is the SAME language as the Assistant's latest response"
+    "MAKE SURE your output is the SAME language as the Assistant's latest response. "
     "The output must be an array in JSON format following the specified schema:\n"
     '["question1","question2","question3"]\n'
 )

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -221,13 +221,12 @@ class AIModel(ABC):
         :param credentials: model credentials
         :return: model schema
         """
-        # get predefined models (predefined_models)
-        models = self.predefined_models()
-
-        model_map = {model.model: model for model in models}
-        if model in model_map:
-            return model_map[model]
+        # Try to get model schema from predefined models
+        for predefined_model in self.predefined_models():
+            if model == predefined_model.model:
+                return predefined_model
 
+        # Try to get model schema from credentials
         if credentials:
             model_schema = self.get_customizable_model_schema_from_credentials(model, credentials)
             if model_schema:

api/core/model_runtime/model_providers/__base/tokenizers/gpt2_tokenzier.py

@@ -1,13 +1,11 @@
-from concurrent.futures import ProcessPoolExecutor
-from os.path import abspath, dirname, join
+import logging
 from threading import Lock
-from typing import Any, cast
+from typing import Any
 
-from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
+logger = logging.getLogger(__name__)
 
 _tokenizer: Any = None
 _lock = Lock()
-_executor = ProcessPoolExecutor(max_workers=1)
 
 
 class GPT2Tokenizer:
@@ -17,22 +15,37 @@ class GPT2Tokenizer:
         use gpt2 tokenizer to get num tokens
         """
         _tokenizer = GPT2Tokenizer.get_encoder()
-        tokens = _tokenizer.encode(text, verbose=False)
+        tokens = _tokenizer.encode(text)
         return len(tokens)
 
     @staticmethod
     def get_num_tokens(text: str) -> int:
-        future = _executor.submit(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
-        result = future.result()
-        return cast(int, result)
+        # Because this process needs more cpu resource, we turn this back before we find a better way to handle it.
+        #
+        # future = _executor.submit(GPT2Tokenizer._get_num_tokens_by_gpt2, text)
+        # result = future.result()
+        # return cast(int, result)
+        return GPT2Tokenizer._get_num_tokens_by_gpt2(text)
 
     @staticmethod
     def get_encoder() -> Any:
         global _tokenizer, _lock
         with _lock:
             if _tokenizer is None:
-                base_path = abspath(__file__)
-                gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
-                _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
+                # Try to use tiktoken to get the tokenizer because it is faster
+                #
+                try:
+                    import tiktoken
+
+                    _tokenizer = tiktoken.get_encoding("gpt2")
+                except Exception:
+                    from os.path import abspath, dirname, join
+
+                    from transformers import GPT2Tokenizer as TransformerGPT2Tokenizer  # type: ignore
+
+                    base_path = abspath(__file__)
+                    gpt2_tokenizer_path = join(dirname(base_path), "gpt2")
+                    _tokenizer = TransformerGPT2Tokenizer.from_pretrained(gpt2_tokenizer_path)
+                    logger.info("Fallback to Transformers' GPT-2 tokenizer from tiktoken")
 
             return _tokenizer

api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml

@@ -53,6 +53,9 @@ model_credential_schema:
       type: select
       required: true
       options:
+        - label:
+            en_US: 2024-12-01-preview
+          value: 2024-12-01-preview
         - label:
             en_US: 2024-10-01-preview
           value: 2024-10-01-preview

api/core/model_runtime/model_providers/azure_openai/llm/llm.py

@@ -108,7 +108,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         ai_model_entity = self._get_ai_model_entity(base_model_name=base_model_name, model=model)
 
         if not ai_model_entity:
-            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
+            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
 
         try:
             client = AzureOpenAI(**self._to_credential_kwargs(credentials))

api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py

@@ -130,7 +130,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             raise CredentialsValidateFailedError("Base Model Name is required")
 
         if not self._get_ai_model_entity(credentials["base_model_name"], model):
-            raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
+            raise CredentialsValidateFailedError(f"Base Model Name {credentials['base_model_name']} is invalid")
 
         try:
             credentials_kwargs = self._to_credential_kwargs(credentials)

api/core/model_runtime/model_providers/bedrock/bedrock.yaml

@@ -44,6 +44,7 @@ provider_credential_schema:
       label:
         en_US: AWS Region
         zh_Hans: AWS 地区
+        ja_JP: AWS リージョン
       type: select
       default: us-east-1
       options:
@@ -51,62 +52,77 @@ provider_credential_schema:
           label:
             en_US: US East (N. Virginia)
             zh_Hans: 美国东部 (弗吉尼亚北部)
+            ja_JP: 米国 (バージニア北部)
         - value: us-east-2
           label:
             en_US: US East (Ohio)
-            zh_Hans: 美国东部 (弗吉尼亚北部)
+            zh_Hans: 美国东部 (俄亥俄)
+            ja_JP: 米国 (オハイオ)
         - value: us-west-2
           label:
             en_US: US West (Oregon)
             zh_Hans: 美国西部 (俄勒冈州)
+            ja_JP: 米国 (オレゴン)
         - value: ap-south-1
           label:
             en_US: Asia Pacific (Mumbai)
             zh_Hans: 亚太地区（孟买）
+            ja_JP: アジアパシフィック (ムンバイ)
         - value: ap-southeast-1
           label:
             en_US: Asia Pacific (Singapore)
             zh_Hans: 亚太地区 (新加坡)
+            ja_JP: アジアパシフィック (シンガポール)
         - value: ap-southeast-2
           label:
             en_US: Asia Pacific (Sydney)
             zh_Hans: 亚太地区 (悉尼)
+            ja_JP: アジアパシフィック (シドニー)
         - value: ap-northeast-1
           label:
             en_US: Asia Pacific (Tokyo)
             zh_Hans: 亚太地区 (东京)
+            ja_JP: アジアパシフィック (東京)
         - value: ap-northeast-2
           label:
             en_US: Asia Pacific (Seoul)
             zh_Hans: 亚太地区（首尔）
+            ja_JP: アジアパシフィック (ソウル)
         - value: ca-central-1
           label:
             en_US: Canada (Central)
             zh_Hans: 加拿大（中部）
+            ja_JP: カナダ (中部)
         - value: eu-central-1
           label:
             en_US: Europe (Frankfurt)
             zh_Hans: 欧洲 (法兰克福)
+            ja_JP: 欧州 (フランクフルト)
         - value: eu-west-1
           label:
             en_US: Europe (Ireland)
             zh_Hans: 欧洲（爱尔兰）
+            ja_JP: 欧州 (アイルランド)
         - value: eu-west-2
           label:
             en_US: Europe (London)
             zh_Hans: 欧洲西部 (伦敦)
+            ja_JP: 欧州 (ロンドン)
         - value: eu-west-3
           label:
             en_US: Europe (Paris)
             zh_Hans: 欧洲（巴黎）
+            ja_JP: 欧州 (パリ)
         - value: sa-east-1
           label:
             en_US: South America (São Paulo)
             zh_Hans: 南美洲（圣保罗）
+            ja_JP: 南米 (サンパウロ)
         - value: us-gov-west-1
           label:
             en_US: AWS GovCloud (US-West)
             zh_Hans: AWS GovCloud (US-West)
+            ja_JP: AWS GovCloud (米国西部)
     - variable: model_for_validation
       required: false
       label:

api/core/model_runtime/model_providers/bedrock/rerank/rerank.py

@@ -70,7 +70,7 @@ class BedrockRerankModel(RerankModel):
         rerankingConfiguration = {
             "type": "BEDROCK_RERANKING_MODEL",
             "bedrockRerankingConfiguration": {
-                "numberOfResults": top_n,
+                "numberOfResults": min(top_n, len(text_sources)),
                 "modelConfiguration": {
                     "modelArn": model_package_arn,
                 },

api/core/model_runtime/model_providers/cohere/llm/llm.py

@@ -677,16 +677,17 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         :return: model schema
         """
-        # get model schema
-        models = self.predefined_models()
-        model_map = {model.model: model for model in models}
-
         mode = credentials.get("mode")
+        base_model_schema = None
+        for predefined_model in self.predefined_models():
+            if (
+                mode == "chat" and predefined_model.model == "command-light-chat"
+            ) or predefined_model.model == "command-light":
+                base_model_schema = predefined_model
+                break
 
-        if mode == "chat":
-            base_model_schema = model_map["command-light-chat"]
-        else:
-            base_model_schema = model_map["command-light"]
+        if not base_model_schema:
+            raise ValueError("Model not found")
 
         base_model_schema = cast(AIModelEntity, base_model_schema)
 

api/core/model_runtime/model_providers/deepseek/llm/_position.yaml

@@ -1,2 +1,3 @@
 - deepseek-chat
 - deepseek-coder
+- deepseek-reasoner

api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml

@@ -10,7 +10,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 128000
+  context_size: 64000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/deepseek/llm/deepseek-coder.yaml

@@ -10,7 +10,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 128000
+  context_size: 64000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/deepseek/llm/deepseek-reasoner.yaml

@@ -0,0 +1,21 @@
+model: deepseek-reasoner
+label:
+  zh_Hans: deepseek-reasoner
+  en_US: deepseek-reasoner
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 8192
+    default: 4096
+pricing:
+  input: "4"
+  output: "16"
+  unit: "0.000001"
+  currency: RMB

api/core/model_runtime/model_providers/deepseek/llm/llm.py

@@ -1,10 +1,13 @@
+import json
 from collections.abc import Generator
 from typing import Optional, Union
 
+import requests
 from yarl import URL
 
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
     PromptMessage,
     PromptMessageTool,
 )
@@ -24,9 +27,6 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
-        # {"response_format": "xx"} need convert to {"response_format": {"type": "xx"}}
-        if "response_format" in model_parameters:
-            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -39,3 +39,208 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         credentials["mode"] = LLMMode.CHAT.value
         credentials["function_calling_type"] = "tool_call"
         credentials["stream_function_calling"] = "support"
+
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
+    ) -> Generator:
+        """
+        Handle llm stream response
+
+        :param model: model name
+        :param credentials: model credentials
+        :param response: streamed response
+        :param prompt_messages: prompt messages
+        :return: llm response chunk generator
+        """
+        full_assistant_content = ""
+        chunk_index = 0
+        is_reasoning_started = False  # Add flag to track reasoning state
+
+        def create_final_llm_result_chunk(
+            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
+        ) -> LLMResultChunk:
+            # calculate num tokens
+            prompt_tokens = usage and usage.get("prompt_tokens")
+            if prompt_tokens is None:
+                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
+            completion_tokens = usage and usage.get("completion_tokens")
+            if completion_tokens is None:
+                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
+
+            # transform usage
+            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+            return LLMResultChunk(
+                id=id,
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
+            )
+
+        # delimiter for stream response, need unicode_escape
+        import codecs
+
+        delimiter = credentials.get("stream_mode_delimiter", "\n\n")
+        delimiter = codecs.decode(delimiter, "unicode_escape")
+
+        tools_calls: list[AssistantPromptMessage.ToolCall] = []
+
+        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
+            def get_tool_call(tool_call_id: str):
+                if not tool_call_id:
+                    return tools_calls[-1]
+
+                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
+                if tool_call is None:
+                    tool_call = AssistantPromptMessage.ToolCall(
+                        id=tool_call_id,
+                        type="function",
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
+                    )
+                    tools_calls.append(tool_call)
+
+                return tool_call
+
+            for new_tool_call in new_tool_calls:
+                # get tool call
+                tool_call = get_tool_call(new_tool_call.function.name)
+                # update tool call
+                if new_tool_call.id:
+                    tool_call.id = new_tool_call.id
+                if new_tool_call.type:
+                    tool_call.type = new_tool_call.type
+                if new_tool_call.function.name:
+                    tool_call.function.name = new_tool_call.function.name
+                if new_tool_call.function.arguments:
+                    tool_call.function.arguments += new_tool_call.function.arguments
+
+        finish_reason = None  # The default value of finish_reason is None
+        message_id, usage = None, None
+        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
+            chunk = chunk.strip()
+            if chunk:
+                # ignore sse comments
+                if chunk.startswith(":"):
+                    continue
+                decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
+                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
+                    continue
+
+                try:
+                    chunk_json: dict = json.loads(decoded_chunk)
+                # stream ended
+                except json.JSONDecodeError as e:
+                    yield create_final_llm_result_chunk(
+                        id=message_id,
+                        index=chunk_index + 1,
+                        message=AssistantPromptMessage(content=""),
+                        finish_reason="Non-JSON encountered.",
+                        usage=usage,
+                    )
+                    break
+                # handle the error here. for issue #11629
+                if chunk_json.get("error") and chunk_json.get("choices") is None:
+                    raise ValueError(chunk_json.get("error"))
+
+                if chunk_json:
+                    if u := chunk_json.get("usage"):
+                        usage = u
+                if not chunk_json or len(chunk_json["choices"]) == 0:
+                    continue
+
+                choice = chunk_json["choices"][0]
+                finish_reason = chunk_json["choices"][0].get("finish_reason")
+                message_id = chunk_json.get("id")
+                chunk_index += 1
+
+                if "delta" in choice:
+                    delta = choice["delta"]
+                    is_reasoning = delta.get("reasoning_content")
+                    delta_content = delta.get("content") or delta.get("reasoning_content")
+
+                    assistant_message_tool_calls = None
+
+                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
+                        assistant_message_tool_calls = delta.get("tool_calls", None)
+                    elif (
+                        "function_call" in delta
+                        and credentials.get("function_calling_type", "no_call") == "function_call"
+                    ):
+                        assistant_message_tool_calls = [
+                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
+                        ]
+
+                    # assistant_message_function_call = delta.delta.function_call
+
+                    # extract tool calls from response
+                    if assistant_message_tool_calls:
+                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
+                        increase_tool_call(tool_calls)
+
+                    if delta_content is None or delta_content == "":
+                        continue
+
+                    # Add markdown quote markers for reasoning content
+                    if is_reasoning:
+                        if not is_reasoning_started:
+                            delta_content = "> 💭 " + delta_content
+                            is_reasoning_started = True
+                        elif "\n\n" in delta_content:
+                            delta_content = delta_content.replace("\n\n", "\n> ")
+                        elif "\n" in delta_content:
+                            delta_content = delta_content.replace("\n", "\n> ")
+                    elif is_reasoning_started:
+                        # If we were in reasoning mode but now getting regular content,
+                        # add \n\n to close the reasoning block
+                        delta_content = "\n\n" + delta_content
+                        is_reasoning_started = False
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(
+                        content=delta_content,
+                    )
+
+                    # reset tool calls
+                    tool_calls = []
+                    full_assistant_content += delta_content
+                elif "text" in choice:
+                    choice_text = choice.get("text", "")
+                    if choice_text == "":
+                        continue
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(content=choice_text)
+                    full_assistant_content += choice_text
+                else:
+                    continue
+
+                yield LLMResultChunk(
+                    id=message_id,
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=chunk_index,
+                        message=assistant_prompt_message,
+                    ),
+                )
+
+            chunk_index += 1
+
+        if tools_calls:
+            yield LLMResultChunk(
+                id=message_id,
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(
+                    index=chunk_index,
+                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
+                ),
+            )
+
+        yield create_final_llm_result_chunk(
+            id=message_id,
+            index=chunk_index,
+            message=AssistantPromptMessage(content=""),
+            finish_reason=finish_reason,
+            usage=usage,
+        )

api/core/model_runtime/model_providers/google/llm/_position.yaml

@@ -1,5 +1,6 @@
 - gemini-2.0-flash-exp
 - gemini-2.0-flash-thinking-exp-1219
+- gemini-2.0-flash-thinking-exp-01-21
 - gemini-1.5-pro
 - gemini-1.5-pro-latest
 - gemini-1.5-pro-001

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-thinking-exp-01-21.yaml

@@ -0,0 +1,39 @@
+model: gemini-2.0-flash-thinking-exp-01-21
+label:
+  en_US: Gemini 2.0 Flash Thinking Exp 01-21
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py

@@ -162,9 +162,9 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel
     @staticmethod
     def _check_endpoint_url_model_repository_name(credentials: dict, model_name: str):
         try:
-            url = f'{HUGGINGFACE_ENDPOINT_API}{credentials["huggingface_namespace"]}'
+            url = f"{HUGGINGFACE_ENDPOINT_API}{credentials['huggingface_namespace']}"
             headers = {
-                "Authorization": f'Bearer {credentials["huggingfacehub_api_token"]}',
+                "Authorization": f"Bearer {credentials['huggingfacehub_api_token']}",
                 "Content-Type": "application/json",
             }
 

api/core/model_runtime/model_providers/minimax/llm/llm.py

@@ -34,6 +34,7 @@ from core.model_runtime.model_providers.minimax.llm.types import MinimaxMessage
 
 class MinimaxLargeLanguageModel(LargeLanguageModel):
     model_apis = {
+        "minimax-text-01": MinimaxChatCompletionPro,
         "abab7-chat-preview": MinimaxChatCompletionPro,
         "abab6.5t-chat": MinimaxChatCompletionPro,
         "abab6.5s-chat": MinimaxChatCompletionPro,

api/core/model_runtime/model_providers/minimax/llm/minimax-text-01.yaml

@@ -0,0 +1,46 @@
+model: minimax-text-01
+label:
+  en_US: Minimax-Text-01
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1000192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0.01
+    max: 1
+    default: 0.1
+  - name: top_p
+    use_template: top_p
+    min: 0.01
+    max: 1
+    default: 0.95
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 1000192
+  - name: mask_sensitive_info
+    type: boolean
+    default: true
+    label:
+      zh_Hans: 隐私保护
+      en_US: Moderate
+    help:
+      zh_Hans: 对输出中易涉及隐私问题的文本信息进行打码，目前包括但不限于邮箱、域名、链接、证件号、家庭住址等，默认true，即开启打码
+      en_US: Mask the sensitive info of the generated content, such as email/domain/link/address/phone/id..
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+pricing:
+  input: '0.001'
+  output: '0.008'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/moonshot/llm/llm.py

@@ -44,9 +44,6 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         self._add_custom_parameters(credentials)
         self._add_function_call(model, credentials)
         user = user[:32] if user else None
-        # {"response_format": "json_object"} need convert to {"response_format": {"type": "json_object"}}
-        if "response_format" in model_parameters:
-            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

api/core/model_runtime/model_providers/novita/_assets/icon_l_en.svg

@@ -1,19 +1,11 @@
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api/core/model_runtime/model_providers/novita/llm/L3-8B-Stheno-v3.2.yaml

@@ -0,0 +1,41 @@
+model: Sao10K/L3-8B-Stheno-v3.2
+label:
+  zh_Hans: L3 8B Stheno V3.2
+  en_US: L3 8B Stheno V3.2
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0005'
+  output: '0.0005'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/_position.yaml

@@ -0,0 +1,41 @@
+# Deepseek Models
+- deepseek/deepseek-r1
+- deepseek/deepseek_v3
+
+# LLaMA Models
+- meta-llama/llama-3.3-70b-instruct
+- meta-llama/llama-3.2-11b-vision-instruct
+- meta-llama/llama-3.2-3b-instruct
+- meta-llama/llama-3.2-1b-instruct
+- meta-llama/llama-3.1-70b-instruct
+- meta-llama/llama-3.1-8b-instruct
+- meta-llama/llama-3.1-8b-instruct-max
+- meta-llama/llama-3.1-8b-instruct-bf16
+- meta-llama/llama-3-70b-instruct
+- meta-llama/llama-3-8b-instruct
+
+# Mistral Models
+- mistralai/mistral-nemo
+- mistralai/mistral-7b-instruct
+
+# Qwen Models
+- qwen/qwen-2.5-72b-instruct
+- qwen/qwen-2-72b-instruct
+- qwen/qwen-2-vl-72b-instruct
+- qwen/qwen-2-7b-instruct
+
+# Other Models
+- sao10k/L3-8B-Stheno-v3.2
+- sao10k/l3-70b-euryale-v2.1
+- sao10k/l31-70b-euryale-v2.2
+- sao10k/l3-8b-lunaris
+- jondurbin/airoboros-l2-70b
+- cognitivecomputations/dolphin-mixtral-8x22b
+- google/gemma-2-9b-it
+- nousresearch/hermes-2-pro-llama-3-8b
+- sophosympatheia/midnight-rose-70b
+- gryphe/mythomax-l2-13b
+- nousresearch/nous-hermes-llama2-13b
+- openchat/openchat-7b
+- teknium/openhermes-2.5-mistral-7b
+- microsoft/wizardlm-2-8x22b

api/core/model_runtime/model_providers/novita/llm/airoboros-l2-70b.yaml

@@ -1,7 +1,7 @@
 model: jondurbin/airoboros-l2-70b
 label:
-  zh_Hans: jondurbin/airoboros-l2-70b
-  en_US: jondurbin/airoboros-l2-70b
+  zh_Hans: Airoboros L2 70B
+  en_US: Airoboros L2 70B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/deepseek-r1.yaml

@@ -0,0 +1,41 @@
+model: deepseek/deepseek-r1
+label:
+  zh_Hans: DeepSeek R1
+  en_US: DeepSeek R1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.04'
+  output: '0.04'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/deepseek_v3.yaml

@@ -0,0 +1,41 @@
+model: deepseek/deepseek_v3
+label:
+  zh_Hans: DeepSeek V3
+  en_US: DeepSeek V3
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0089'
+  output: '0.0089'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/dolphin-mixtral-8x22b.yaml

@@ -1,7 +1,7 @@
 model: cognitivecomputations/dolphin-mixtral-8x22b
 label:
-  zh_Hans: cognitivecomputations/dolphin-mixtral-8x22b
-  en_US: cognitivecomputations/dolphin-mixtral-8x22b
+  zh_Hans: Dolphin Mixtral 8x22B
+  en_US: Dolphin Mixtral 8x22B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/gemma-2-9b-it.yaml

@@ -1,7 +1,7 @@
 model: google/gemma-2-9b-it
 label:
-  zh_Hans: google/gemma-2-9b-it
-  en_US: google/gemma-2-9b-it
+  zh_Hans: Gemma 2 9B
+  en_US: Gemma 2 9B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/hermes-2-pro-llama-3-8b.yaml

@@ -1,7 +1,7 @@
 model: nousresearch/hermes-2-pro-llama-3-8b
 label:
-  zh_Hans: nousresearch/hermes-2-pro-llama-3-8b
-  en_US: nousresearch/hermes-2-pro-llama-3-8b
+  zh_Hans: Hermes 2 Pro Llama 3 8B
+  en_US: Hermes 2 Pro Llama 3 8B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/l3-70b-euryale-v2.1.yaml

@@ -1,7 +1,7 @@
 model: sao10k/l3-70b-euryale-v2.1
 label:
-  zh_Hans: sao10k/l3-70b-euryale-v2.1
-  en_US: sao10k/l3-70b-euryale-v2.1
+  zh_Hans: "L3 70B Euryale V2.1\t"
+  en_US: "L3 70B Euryale V2.1\t"
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/l3-8b-lunaris.yaml

@@ -0,0 +1,41 @@
+model: sao10k/l3-8b-lunaris
+label:
+  zh_Hans: "Sao10k L3 8B Lunaris"
+  en_US: "Sao10k L3 8B Lunaris"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0005'
+  output: '0.0005'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/l31-70b-euryale-v2.2.yaml

@@ -0,0 +1,41 @@
+model: sao10k/l31-70b-euryale-v2.2
+label:
+  zh_Hans: L31 70B Euryale V2.2
+  en_US: L31 70B Euryale V2.2
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 16000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0148'
+  output: '0.0148'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3-70b-instruct.yaml

@@ -1,7 +1,7 @@
 model: meta-llama/llama-3-70b-instruct
 label:
-  zh_Hans: meta-llama/llama-3-70b-instruct
-  en_US: meta-llama/llama-3-70b-instruct
+  zh_Hans: Llama3 70b Instruct
+  en_US: Llama3 70b Instruct
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/llama-3-8b-instruct.yaml

@@ -1,7 +1,7 @@
 model: meta-llama/llama-3-8b-instruct
 label:
-  zh_Hans: meta-llama/llama-3-8b-instruct
-  en_US: meta-llama/llama-3-8b-instruct
+  zh_Hans: Llama 3 8B Instruct
+  en_US: Llama 3 8B Instruct
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.00063'
-  output: '0.00063'
+  input: '0.0004'
+  output: '0.0004'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-70b-instruct.yaml

@@ -1,13 +1,13 @@
 model: meta-llama/llama-3.1-70b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.1-70b-instruct
-  en_US: meta-llama/llama-3.1-70b-instruct
+  zh_Hans: Llama 3.1 70B Instruct
+  en_US: Llama 3.1 70B Instruct
 model_type: llm
 features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 8192
+  context_size: 32768
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0055'
-  output: '0.0076'
+  input: '0.0034'
+  output: '0.0039'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-bf16.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.1-8b-instruct-bf16
+label:
+  zh_Hans: Llama 3.1 8B Instruct BF16
+  en_US: Llama 3.1 8B Instruct BF16
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0006'
+  output: '0.0006'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-max.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.1-8b-instruct-max
+label:
+  zh_Hans: "Llama3.1 8B Instruct Max\t"
+  en_US: "Llama3.1 8B Instruct Max\t"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 16384
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0005'
+  output: '0.0005'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct.yaml

@@ -1,13 +1,13 @@
 model: meta-llama/llama-3.1-8b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.1-8b-instruct
-  en_US: meta-llama/llama-3.1-8b-instruct
+  zh_Hans: Llama 3.1 8B Instruct
+  en_US: Llama 3.1 8B Instruct
 model_type: llm
 features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 8192
+  context_size: 16384
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.001'
-  output: '0.001'
+  input: '0.0005'
+  output: '0.0005'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.2-11b-vision-instruct.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.2-11b-vision-instruct
+label:
+  zh_Hans: "Llama 3.2 11B Vision Instruct\t"
+  en_US: "Llama 3.2 11B Vision Instruct\t"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0006'
+  output: '0.0006'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.2-1b-instruct.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.2-1b-instruct
+label:
+  zh_Hans: "Llama 3.2 1B Instruct\t"
+  en_US: "Llama 3.2 1B Instruct\t"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0002'
+  output: '0.0002'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.2-3b-instruct.yaml

@@ -1,7 +1,7 @@
-model: Nous-Hermes-2-Mixtral-8x7B-DPO
+model: meta-llama/llama-3.2-3b-instruct
 label:
-  zh_Hans: Nous-Hermes-2-Mixtral-8x7B-DPO
-  en_US: Nous-Hermes-2-Mixtral-8x7B-DPO
+  zh_Hans: Llama 3.2 3B Instruct
+  en_US: Llama 3.2 3B Instruct
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0027'
-  output: '0.0027'
+  input: '0.0003'
+  output: '0.0005'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.3-70b-instruct.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.3-70b-instruct
+label:
+  zh_Hans: Llama 3.3 70B Instruct
+  en_US: Llama 3.3 70B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0039'
+  output: '0.0039'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/midnight-rose-70b.yaml

@@ -1,7 +1,7 @@
 model: sophosympatheia/midnight-rose-70b
 label:
-  zh_Hans: sophosympatheia/midnight-rose-70b
-  en_US: sophosympatheia/midnight-rose-70b
+  zh_Hans: Midnight Rose 70B
+  en_US: Midnight Rose 70B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/mistral-7b-instruct.yaml

@@ -1,7 +1,7 @@
 model: mistralai/mistral-7b-instruct
 label:
-  zh_Hans: mistralai/mistral-7b-instruct
-  en_US: mistralai/mistral-7b-instruct
+  zh_Hans: Mistral 7B Instruct
+  en_US: Mistral 7B Instruct
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/mistral-nemo.yaml

@@ -0,0 +1,41 @@
+model: mistralai/mistral-nemo
+label:
+  zh_Hans: Mistral Nemo
+  en_US: Mistral Nemo
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0017'
+  output: '0.0017'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/mythomax-l2-13b.yaml

@@ -1,7 +1,7 @@
 model: gryphe/mythomax-l2-13b
 label:
-  zh_Hans: gryphe/mythomax-l2-13b
-  en_US: gryphe/mythomax-l2-13b
+  zh_Hans: Mythomax L2 13B
+  en_US: Mythomax L2 13B
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.00119'
-  output: '0.00119'
+  input: '0.0009'
+  output: '0.0009'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/nous-hermes-llama2-13b.yaml

@@ -1,7 +1,7 @@
 model: nousresearch/nous-hermes-llama2-13b
 label:
-  zh_Hans: nousresearch/nous-hermes-llama2-13b
-  en_US: nousresearch/nous-hermes-llama2-13b
+  zh_Hans: Nous Hermes Llama2 13B
+  en_US: Nous Hermes Llama2 13B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/openchat-7b.yaml

@@ -1,7 +1,7 @@
-model: lzlv_70b
+model: openchat/openchat-7b
 label:
-  zh_Hans: lzlv_70b
-  en_US: lzlv_70b
+  zh_Hans: OpenChat 7B
+  en_US: OpenChat 7B
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0058'
-  output: '0.0078'
+  input: '0.0006'
+  output: '0.0006'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/openhermes-2.5-mistral-7b.yaml

@@ -1,7 +1,7 @@
 model: teknium/openhermes-2.5-mistral-7b
 label:
-  zh_Hans: teknium/openhermes-2.5-mistral-7b
-  en_US: teknium/openhermes-2.5-mistral-7b
+  zh_Hans: Openhermes2.5 Mistral 7B
+  en_US: Openhermes2.5 Mistral 7B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/qwen-2-72b-instruct.yaml

@@ -1,7 +1,7 @@
-model: meta-llama/llama-3.1-405b-instruct
+model: qwen/qwen-2-72b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.1-405b-instruct
-  en_US: meta-llama/llama-3.1-405b-instruct
+  zh_Hans: Qwen2 72B Instruct
+  en_US: Qwen2 72B Instruct
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.03'
-  output: '0.05'
+  input: '0.0034'
+  output: '0.0039'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/qwen-2-7b-instruct.yaml

@@ -0,0 +1,41 @@
+model: qwen/qwen-2-7b-instruct
+label:
+  zh_Hans: Qwen 2 7B Instruct
+  en_US: Qwen 2 7B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.00054'
+  output: '0.00054'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/qwen-2-vl-72b-instruct.yaml

@@ -0,0 +1,41 @@
+model: qwen/qwen-2-vl-72b-instruct
+label:
+  zh_Hans: Qwen 2 VL 72B Instruct
+  en_US: Qwen 2 VL 72B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0045'
+  output: '0.0045'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/qwen-2.5-72b-instruct.yaml

@@ -0,0 +1,41 @@
+model: qwen/qwen-2.5-72b-instruct
+label:
+  zh_Hans: Qwen 2.5 72B Instruct
+  en_US: Qwen 2.5 72B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+pricing:
+  input: '0.0038'
+  output: '0.004'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/wizardlm-2-8x22b.yaml

@@ -1,7 +1,7 @@
 model: microsoft/wizardlm-2-8x22b
 label:
-  zh_Hans: microsoft/wizardlm-2-8x22b
-  en_US: microsoft/wizardlm-2-8x22b
+  zh_Hans: Wizardlm 2 8x22B
+  en_US: Wizardlm 2 8x22B
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0064'
-  output: '0.0064'
+  input: '0.0062'
+  output: '0.0062'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/novita.yaml

@@ -1,6 +1,6 @@
 provider: novita
 label:
-  en_US: novita.ai
+  en_US: Novita AI
 description:
   en_US: An LLM API that matches various application scenarios with high cost-effectiveness.
   zh_Hans: 适配多种海外应用场景的高性价比 LLM API
@@ -8,13 +8,13 @@ icon_small:
   en_US: icon_s_en.svg
 icon_large:
   en_US: icon_l_en.svg
-background: "#eadeff"
+background: "#c7fce2"
 help:
   title:
-    en_US: Get your API key from novita.ai
-    zh_Hans: 从 novita.ai 获取 API Key
+    en_US: Get your API key from Novita AI
+    zh_Hans: 从 Novita AI 获取 API Key
   url:
-    en_US: https://novita.ai/settings#key-management?utm_source=dify&utm_medium=ch&utm_campaign=api
+    en_US: https://novita.ai/settings/key-management?utm_source=dify&utm_medium=ch&utm_campaign=api
 supported_model_types:
   - llm
 configurate_methods:

api/core/model_runtime/model_providers/openai/llm/llm.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import re
 from collections.abc import Generator
 from typing import Any, Optional, Union, cast
 
@@ -340,9 +341,6 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         :param credentials: provider credentials
         :return:
         """
-        # get predefined models
-        predefined_models = self.predefined_models()
-        predefined_models_map = {model.model: model for model in predefined_models}
 
         # transform credentials to kwargs for model instance
         credentials_kwargs = self._to_credential_kwargs(credentials)
@@ -358,9 +356,10 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             base_model = model.id.split(":")[1]
 
             base_model_schema = None
-            for predefined_model_name, predefined_model in predefined_models_map.items():
-                if predefined_model_name in base_model:
+            for predefined_model in self.predefined_models():
+                if predefined_model.model in base_model:
                     base_model_schema = predefined_model
+                    break
 
             if not base_model_schema:
                 continue
@@ -621,11 +620,19 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
 
         # o1 compatibility
+        block_as_stream = False
         if model.startswith("o1"):
             if "max_tokens" in model_parameters:
                 model_parameters["max_completion_tokens"] = model_parameters["max_tokens"]
                 del model_parameters["max_tokens"]
 
+            if re.match(r"^o1(-\d{4}-\d{2}-\d{2})?$", model):
+                if stream:
+                    block_as_stream = True
+                    stream = False
+                    if "stream_options" in extra_model_kwargs:
+                        del extra_model_kwargs["stream_options"]
+
             if "stop" in extra_model_kwargs:
                 del extra_model_kwargs["stop"]
 
@@ -642,7 +649,45 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         if stream:
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
 
-        return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
+        block_result = self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
+
+        if block_as_stream:
+            return self._handle_chat_block_as_stream_response(block_result, prompt_messages, stop)
+
+        return block_result
+
+    def _handle_chat_block_as_stream_response(
+        self,
+        block_result: LLMResult,
+        prompt_messages: list[PromptMessage],
+        stop: Optional[list[str]] = None,
+    ) -> Generator[LLMResultChunk, None, None]:
+        """
+        Handle llm chat response
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return: llm response chunk generator
+        """
+        text = block_result.message.content
+        text = cast(str, text)
+
+        if stop:
+            text = self.enforce_stop_tokens(text, stop)
+
+        yield LLMResultChunk(
+            model=block_result.model,
+            prompt_messages=prompt_messages,
+            system_fingerprint=block_result.system_fingerprint,
+            delta=LLMResultChunkDelta(
+                index=0,
+                message=block_result.message,
+                finish_reason="stop",
+                usage=block_result.usage,
+            ),
+        )
 
     def _handle_chat_generate_response(
         self,
@@ -1139,12 +1184,14 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             base_model = model.split(":")[1]
 
         # get model schema
-        models = self.predefined_models()
-        model_map = {model.model: model for model in models}
-        if base_model not in model_map:
-            raise ValueError(f"Base model {base_model} not found")
+        base_model_schema = None
+        for predefined_model in self.predefined_models():
+            if base_model == predefined_model.model:
+                base_model_schema = predefined_model
+                break
 
-        base_model_schema = model_map[base_model]
+        if not base_model_schema:
+            raise ValueError(f"Base model {base_model} not found")
 
         base_model_schema_features = base_model_schema.features or []
         base_model_schema_model_properties = base_model_schema.model_properties

api/core/model_runtime/model_providers/openrouter/llm/claude-3-5-sonnet.yaml

@@ -7,6 +7,7 @@ features:
   - vision
   - tool-call
   - stream-tool-call
+  - document
 model_properties:
   mode: chat
   context_size: 200000

api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py

@@ -1,29 +1,13 @@
-import json
-import time
-from decimal import Decimal
 from typing import Optional
-from urllib.parse import urljoin
-
-import numpy as np
-import requests
 
 from core.entities.embedding_type import EmbeddingInputType
-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import (
-    AIModelEntity,
-    FetchFrom,
-    ModelPropertyKey,
-    ModelType,
-    PriceConfig,
-    PriceType,
+from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.model_providers.openai_api_compatible.text_embedding.text_embedding import (
+    OAICompatEmbeddingModel,
 )
-from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
 
 
-class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
+class PerfXCloudEmbeddingModel(OAICompatEmbeddingModel):
     """
     Model class for an OpenAI API-compatible text embedding model.
     """
@@ -47,86 +31,10 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
         :return: embeddings result
         """
 
-        # Prepare headers and payload for the request
-        headers = {"Content-Type": "application/json"}
-
-        api_key = credentials.get("api_key")
-        if api_key:
-            headers["Authorization"] = f"Bearer {api_key}"
-        endpoint_url: Optional[str]
         if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
-            endpoint_url = "https://cloud.perfxlab.cn/v1/"
-        else:
-            endpoint_url = credentials.get("endpoint_url")
-            assert endpoint_url is not None, "endpoint_url is required in credentials"
-            if not endpoint_url.endswith("/"):
-                endpoint_url += "/"
+            credentials["endpoint_url"] = "https://cloud.perfxlab.cn/v1/"
 
-        assert isinstance(endpoint_url, str)
-        endpoint_url = urljoin(endpoint_url, "embeddings")
-
-        extra_model_kwargs = {}
-        if user:
-            extra_model_kwargs["user"] = user
-
-        extra_model_kwargs["encoding_format"] = "float"
-
-        # get model properties
-        context_size = self._get_context_size(model, credentials)
-        max_chunks = self._get_max_chunks(model, credentials)
-
-        inputs = []
-        indices = []
-        used_tokens = 0
-
-        for i, text in enumerate(texts):
-            # Here token count is only an approximation based on the GPT2 tokenizer
-            # TODO: Optimize for better token estimation and chunking
-            num_tokens = self._get_num_tokens_by_gpt2(text)
-
-            if num_tokens >= context_size:
-                cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
-                # if num tokens is larger than context length, only use the start
-                inputs.append(text[0:cutoff])
-            else:
-                inputs.append(text)
-            indices += [i]
-
-        batched_embeddings = []
-        _iter = range(0, len(inputs), max_chunks)
-
-        for i in _iter:
-            # Prepare the payload for the request
-            payload = {"input": inputs[i : i + max_chunks], "model": model, **extra_model_kwargs}
-
-            # Make the request to the OpenAI API
-            response = requests.post(endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
-
-            response.raise_for_status()  # Raise an exception for HTTP errors
-            response_data = response.json()
-
-            # Extract embeddings and used tokens from the response
-            embeddings_batch = [data["embedding"] for data in response_data["data"]]
-            embedding_used_tokens = response_data["usage"]["total_tokens"]
-
-            used_tokens += embedding_used_tokens
-            batched_embeddings += embeddings_batch
-
-        # calc usage
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
-
-        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
-
-    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
-        """
-        Approximate number of tokens for given messages using GPT2 tokenizer
-
-        :param model: model name
-        :param credentials: model credentials
-        :param texts: texts to embed
-        :return:
-        """
-        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+        return OAICompatEmbeddingModel._invoke(self, model, credentials, texts, user, input_type)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -136,93 +44,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
         :param credentials: model credentials
         :return:
         """
-        try:
-            headers = {"Content-Type": "application/json"}
+        if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
+            credentials["endpoint_url"] = "https://cloud.perfxlab.cn/v1/"
 
-            api_key = credentials.get("api_key")
-
-            if api_key:
-                headers["Authorization"] = f"Bearer {api_key}"
-
-            endpoint_url: Optional[str]
-            if "endpoint_url" not in credentials or credentials["endpoint_url"] == "":
-                endpoint_url = "https://cloud.perfxlab.cn/v1/"
-            else:
-                endpoint_url = credentials.get("endpoint_url")
-                assert endpoint_url is not None, "endpoint_url is required in credentials"
-                if not endpoint_url.endswith("/"):
-                    endpoint_url += "/"
-
-            assert isinstance(endpoint_url, str)
-            endpoint_url = urljoin(endpoint_url, "embeddings")
-
-            payload = {"input": "ping", "model": model}
-
-            response = requests.post(url=endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
-
-            if response.status_code != 200:
-                raise CredentialsValidateFailedError(
-                    f"Credentials validation failed with status code {response.status_code}"
-                )
-
-            try:
-                json_result = response.json()
-            except json.JSONDecodeError as e:
-                raise CredentialsValidateFailedError("Credentials validation failed: JSON decode error")
-
-            if "model" not in json_result:
-                raise CredentialsValidateFailedError("Credentials validation failed: invalid response")
-        except CredentialsValidateFailedError:
-            raise
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
-        """
-        generate custom model entities from credentials
-        """
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            model_type=ModelType.TEXT_EMBEDDING,
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
-                ModelPropertyKey.MAX_CHUNKS: 1,
-            },
-            parameter_rules=[],
-            pricing=PriceConfig(
-                input=Decimal(credentials.get("input_price", 0)),
-                unit=Decimal(credentials.get("unit", 0)),
-                currency=credentials.get("currency", "USD"),
-            ),
-        )
-
-        return entity
-
-    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
-        """
-        Calculate response usage
-
-        :param model: model name
-        :param credentials: model credentials
-        :param tokens: input tokens
-        :return: usage
-        """
-        # get input price info
-        input_price_info = self.get_price(
-            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
-        )
-
-        # transform usage
-        usage = EmbeddingUsage(
-            tokens=tokens,
-            total_tokens=tokens,
-            unit_price=input_price_info.unit_price,
-            price_unit=input_price_info.unit,
-            total_price=input_price_info.total_amount,
-            currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at,
-        )
-
-        return usage
+        OAICompatEmbeddingModel.validate_credentials(self, model, credentials)

api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml

@@ -7,6 +7,8 @@
 - Qwen/Qwen2.5-Coder-7B-Instruct
 - Qwen/Qwen2-VL-72B-Instruct
 - Qwen/Qwen2-1.5B-Instruct
+- Qwen/Qwen2.5-72B-Instruct-128K
+- Vendor-A/Qwen/Qwen2.5-72B-Instruct
 - Pro/Qwen/Qwen2-VL-7B-Instruct
 - OpenGVLab/InternVL2-26B
 - Pro/OpenGVLab/InternVL2-8B

api/core/model_runtime/model_providers/siliconflow/llm/llm.py

@@ -1,9 +1,16 @@
+import json
 from collections.abc import Generator
 from typing import Optional, Union
 
+import requests
+
 from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
-from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+)
 from core.model_runtime.entities.model_entities import (
     AIModelEntity,
     FetchFrom,
@@ -29,9 +36,6 @@ class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._add_custom_parameters(credentials)
-        # {"response_format": "json_object"} need convert to {"response_format": {"type": "json_object"}}
-        if "response_format" in model_parameters:
-            model_parameters["response_format"] = {"type": model_parameters.get("response_format")}
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -92,3 +96,208 @@ class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 ),
             ],
         )
+
+    def _handle_generate_stream_response(
+        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
+    ) -> Generator:
+        """
+        Handle llm stream response
+
+        :param model: model name
+        :param credentials: model credentials
+        :param response: streamed response
+        :param prompt_messages: prompt messages
+        :return: llm response chunk generator
+        """
+        full_assistant_content = ""
+        chunk_index = 0
+        is_reasoning_started = False  # Add flag to track reasoning state
+
+        def create_final_llm_result_chunk(
+            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
+        ) -> LLMResultChunk:
+            # calculate num tokens
+            prompt_tokens = usage and usage.get("prompt_tokens")
+            if prompt_tokens is None:
+                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
+            completion_tokens = usage and usage.get("completion_tokens")
+            if completion_tokens is None:
+                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
+
+            # transform usage
+            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+            return LLMResultChunk(
+                id=id,
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
+            )
+
+        # delimiter for stream response, need unicode_escape
+        import codecs
+
+        delimiter = credentials.get("stream_mode_delimiter", "\n\n")
+        delimiter = codecs.decode(delimiter, "unicode_escape")
+
+        tools_calls: list[AssistantPromptMessage.ToolCall] = []
+
+        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
+            def get_tool_call(tool_call_id: str):
+                if not tool_call_id:
+                    return tools_calls[-1]
+
+                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
+                if tool_call is None:
+                    tool_call = AssistantPromptMessage.ToolCall(
+                        id=tool_call_id,
+                        type="function",
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
+                    )
+                    tools_calls.append(tool_call)
+
+                return tool_call
+
+            for new_tool_call in new_tool_calls:
+                # get tool call
+                tool_call = get_tool_call(new_tool_call.function.name)
+                # update tool call
+                if new_tool_call.id:
+                    tool_call.id = new_tool_call.id
+                if new_tool_call.type:
+                    tool_call.type = new_tool_call.type
+                if new_tool_call.function.name:
+                    tool_call.function.name = new_tool_call.function.name
+                if new_tool_call.function.arguments:
+                    tool_call.function.arguments += new_tool_call.function.arguments
+
+        finish_reason = None  # The default value of finish_reason is None
+        message_id, usage = None, None
+        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
+            chunk = chunk.strip()
+            if chunk:
+                # ignore sse comments
+                if chunk.startswith(":"):
+                    continue
+                decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
+                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
+                    continue
+
+                try:
+                    chunk_json: dict = json.loads(decoded_chunk)
+                # stream ended
+                except json.JSONDecodeError as e:
+                    yield create_final_llm_result_chunk(
+                        id=message_id,
+                        index=chunk_index + 1,
+                        message=AssistantPromptMessage(content=""),
+                        finish_reason="Non-JSON encountered.",
+                        usage=usage,
+                    )
+                    break
+                # handle the error here. for issue #11629
+                if chunk_json.get("error") and chunk_json.get("choices") is None:
+                    raise ValueError(chunk_json.get("error"))
+
+                if chunk_json:
+                    if u := chunk_json.get("usage"):
+                        usage = u
+                if not chunk_json or len(chunk_json["choices"]) == 0:
+                    continue
+
+                choice = chunk_json["choices"][0]
+                finish_reason = chunk_json["choices"][0].get("finish_reason")
+                message_id = chunk_json.get("id")
+                chunk_index += 1
+
+                if "delta" in choice:
+                    delta = choice["delta"]
+                    delta_content = delta.get("content")
+
+                    assistant_message_tool_calls = None
+
+                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
+                        assistant_message_tool_calls = delta.get("tool_calls", None)
+                    elif (
+                        "function_call" in delta
+                        and credentials.get("function_calling_type", "no_call") == "function_call"
+                    ):
+                        assistant_message_tool_calls = [
+                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
+                        ]
+
+                    # assistant_message_function_call = delta.delta.function_call
+
+                    # extract tool calls from response
+                    if assistant_message_tool_calls:
+                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
+                        increase_tool_call(tool_calls)
+
+                    if delta_content is None or delta_content == "":
+                        continue
+
+                    # Check for think tags
+                    if "<think>" in delta_content:
+                        is_reasoning_started = True
+                        # Remove <think> tag and add markdown quote
+                        delta_content = "> 💭 " + delta_content.replace("<think>", "")
+                    elif "</think>" in delta_content:
+                        # Remove </think> tag and add newlines to end quote block
+                        delta_content = delta_content.replace("</think>", "") + "\n\n"
+                        is_reasoning_started = False
+                    elif is_reasoning_started:
+                        # Add quote markers for content within thinking block
+                        if "\n\n" in delta_content:
+                            delta_content = delta_content.replace("\n\n", "\n> ")
+                        elif "\n" in delta_content:
+                            delta_content = delta_content.replace("\n", "\n> ")
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(
+                        content=delta_content,
+                    )
+
+                    # reset tool calls
+                    tool_calls = []
+                    full_assistant_content += delta_content
+                elif "text" in choice:
+                    choice_text = choice.get("text", "")
+                    if choice_text == "":
+                        continue
+
+                    # transform assistant message to prompt message
+                    assistant_prompt_message = AssistantPromptMessage(content=choice_text)
+                    full_assistant_content += choice_text
+                else:
+                    continue
+
+                yield LLMResultChunk(
+                    id=message_id,
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=chunk_index,
+                        message=assistant_prompt_message,
+                    ),
+                )
+
+            chunk_index += 1
+
+        if tools_calls:
+            yield LLMResultChunk(
+                id=message_id,
+                model=model,
+                prompt_messages=prompt_messages,
+                delta=LLMResultChunkDelta(
+                    index=chunk_index,
+                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
+                ),
+            )
+
+        yield create_final_llm_result_chunk(
+            id=message_id,
+            index=chunk_index,
+            message=AssistantPromptMessage(content=""),
+            finish_reason=finish_reason,
+            usage=usage,
+        )

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-72b-instruct-128k.yaml

@@ -0,0 +1,51 @@
+model: Qwen/Qwen2.5-72B-Instruct-128K
+label:
+  en_US: Qwen/Qwen2.5-72B-Instruct-128K
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 512
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: Response Format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '4.13'
+  output: '4.13'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-72b-instruct-vendorA.yaml

@@ -0,0 +1,51 @@
+model: Vendor-A/Qwen/Qwen2.5-72B-Instruct
+label:
+  en_US: Vendor-A/Qwen/Qwen2.5-72B-Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 512
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: Response Format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '1.00'
+  output: '1.00'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-72b-instruct.yaml

@@ -15,7 +15,7 @@ parameter_rules:
     type: int
     default: 512
     min: 1
-    max: 8192
+    max: 4096
     help:
       zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
       en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.