chore: bump version to 0.13.2 (#11489)

Signed-off-by: -LAN- <laipz8200@outlook.com>

.github/pull_request_template.md

@@ -8,16 +8,9 @@ Please include a summary of the change and which issue is fixed. Please also inc
 
 # Screenshots
 
-<table>
-  <tr>
-  <td>Before: </td>
-  <td>After: </td>
-  </tr>
-  <tr>
-  <td>...</td>
-  <td>...</td>
-  </tr>
-</table>
+| Before | After |
+|--------|-------|
+| ...    | ...   |
 
 # Checklist
 

.github/workflows/db-migration-test.yml

@@ -48,6 +48,8 @@ jobs:
           cp .env.example .env
 
       - name: Run DB Migration
+        env:
+          DEBUG: true
         run: |
           cd api
           poetry run python -m flask upgrade-db

README.md

@@ -147,6 +147,13 @@ Deploy Dify to Cloud Platform with a single click using [terraform](https://www.
 ##### Google Cloud
 - [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### Using AWS CDK for Deployment
+
+Deploy Dify to AWS with [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Contributing
 
 For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 

README_AR.md

@@ -190,6 +190,13 @@ docker compose up -d
 ##### Google Cloud
 - [Google Cloud Terraform بواسطة @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### استخدام AWS CDK للنشر
+
+انشر Dify على AWS باستخدام [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK بواسطة @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## المساهمة
 
 لأولئك الذين يرغبون في المساهمة، انظر إلى [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) لدينا. 
@@ -222,3 +229,10 @@ docker compose up -d
 ## الرخصة
 
 هذا المستودع متاح تحت [رخصة البرنامج الحر Dify](LICENSE)، والتي تعتبر بشكل أساسي Apache 2.0 مع بعض القيود الإضافية.
+## الكشف عن الأمان
+
+لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى security@dify.ai وسنقدم لك إجابة أكثر تفصيلاً.
+
+## الرخصة
+
+هذا المستودع متاح تحت [رخصة البرنامج الحر Dify](LICENSE)، والتي تعتبر بشكل أساسي Apache 2.0 مع بعض القيود الإضافية.

README_CN.md

@@ -213,6 +213,13 @@ docker compose up -d
 ##### Google Cloud
 - [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### 使用 AWS CDK 部署
+
+使用 [CDK](https://aws.amazon.com/cdk/) 将 Dify 部署到 AWS
+
+##### AWS 
+- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)

README_ES.md

@@ -215,6 +215,13 @@ Despliega Dify en una plataforma en la nube con un solo clic utilizando [terrafo
 ##### Google Cloud
 - [Google Cloud Terraform por @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### Usando AWS CDK para el Despliegue
+
+Despliegue Dify en AWS usando [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK por @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Contribuir
 
 Para aquellos que deseen contribuir con código, consulten nuestra [Guía de contribución](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 
@@ -248,3 +255,10 @@ Para proteger tu privacidad, evita publicar problemas de seguridad en GitHub. En
 ## Licencia
 
 Este repositorio está disponible bajo la [Licencia de Código Abierto de Dify](LICENSE), que es esencialmente Apache 2.0 con algunas restricciones adicionales.
+## Divulgación de Seguridad
+
+Para proteger tu privacidad, evita publicar problemas de seguridad en GitHub. En su lugar, envía tus preguntas a security@dify.ai y te proporcionaremos una respuesta más detallada.
+
+## Licencia
+
+Este repositorio está disponible bajo la [Licencia de Código Abierto de Dify](LICENSE), que es esencialmente Apache 2.0 con algunas restricciones adicionales.

README_FR.md

@@ -213,6 +213,13 @@ Déployez Dify sur une plateforme cloud en un clic en utilisant [terraform](http
 ##### Google Cloud
 - [Google Cloud Terraform par @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### Utilisation d'AWS CDK pour le déploiement
+
+Déployez Dify sur AWS en utilisant [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK par @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Contribuer
 
 Pour ceux qui souhaitent contribuer du code, consultez notre [Guide de contribution](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 
@@ -246,3 +253,10 @@ Pour protéger votre vie privée, veuillez éviter de publier des problèmes de
 ## Licence
 
 Ce référentiel est disponible sous la [Licence open source Dify](LICENSE), qui est essentiellement l'Apache 2.0 avec quelques restrictions supplémentaires.
+## Divulgation de sécurité
+
+Pour protéger votre vie privée, veuillez éviter de publier des problèmes de sécurité sur GitHub. Au lieu de cela, envoyez vos questions à security@dify.ai et nous vous fournirons une réponse plus détaillée.
+
+## Licence
+
+Ce référentiel est disponible sous la [Licence open source Dify](LICENSE), qui est essentiellement l'Apache 2.0 avec quelques restrictions supplémentaires.

README_JA.md

@@ -212,6 +212,13 @@ docker compose up -d
 ##### Google Cloud
 - [@sotazumによるGoogle Cloud Terraform](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### AWS CDK を使用したデプロイ
+
+[CDK](https://aws.amazon.com/cdk/) を使用して、DifyをAWSにデプロイします
+
+##### AWS 
+- [@KevinZhaoによるAWS CDK](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## 貢献
 
 コードに貢献したい方は、[Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)を参照してください。

README_KL.md

@@ -213,6 +213,13 @@ wa'logh nIqHom neH ghun deployment toy'wI' [terraform](https://www.terraform.io/
 ##### Google Cloud
 - [Google Cloud Terraform qachlot @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### AWS CDK atorlugh pilersitsineq
+
+wa'logh nIqHom neH ghun deployment toy'wI' [CDK](https://aws.amazon.com/cdk/) lo'laH.
+
+##### AWS 
+- [AWS CDK qachlot @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Contributing
 
 For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 

README_KR.md

@@ -205,6 +205,13 @@ Dify를 Kubernetes에 배포하고 프리미엄 스케일링 설정을 구성했
 ##### Google Cloud
 - [sotazum의 Google Cloud Terraform](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### AWS CDK를 사용한 배포
+
+[CDK](https://aws.amazon.com/cdk/)를 사용하여 AWS에 Dify 배포
+
+##### AWS 
+- [KevinZhao의 AWS CDK](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## 기여
 
 코드에 기여하고 싶은 분들은 [기여 가이드](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)를 참조하세요.

README_PT.md

@@ -211,6 +211,13 @@ Implante o Dify na Plataforma Cloud com um único clique usando [terraform](http
 ##### Google Cloud
 - [Google Cloud Terraform por @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### Usando AWS CDK para Implantação
+
+Implante o Dify na AWS usando [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK por @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Contribuindo
 
 Para aqueles que desejam contribuir com código, veja nosso [Guia de Contribuição](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 

README_SI.md

@@ -145,6 +145,13 @@ namestite Dify v Cloud Platform z enim klikom z uporabo [terraform](https://www.
 ##### Google Cloud
 - [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### Uporaba AWS CDK za uvajanje
+
+Uvedite Dify v AWS z uporabo [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Prispevam
 
 Za tiste, ki bi radi prispevali kodo, si oglejte naš vodnik za prispevke . Hkrati vas prosimo, da podprete Dify tako, da ga delite na družbenih medijih ter na dogodkih in konferencah. 

README_TR.md

@@ -211,6 +211,13 @@ Dify'ı bulut platformuna tek tıklamayla dağıtın [terraform](https://www.ter
 ##### Google Cloud
 - [Google Cloud Terraform tarafından @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### AWS CDK ile Dağıtım
+
+[CDK](https://aws.amazon.com/cdk/) kullanarak Dify'ı AWS'ye dağıtın
+
+##### AWS 
+- [AWS CDK tarafından @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Katkıda Bulunma
 
 Kod katkısında bulunmak isteyenler için [Katkı Kılavuzumuza](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) bakabilirsiniz.

README_VI.md

@@ -207,6 +207,13 @@ Triển khai Dify lên nền tảng đám mây với một cú nhấp chuột b
 ##### Google Cloud
 - [Google Cloud Terraform bởi @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
+#### Sử dụng AWS CDK để Triển khai
+
+Triển khai Dify trên AWS bằng [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK bởi @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
 ## Đóng góp
 
 Đối với những người muốn đóng góp mã, xem [Hướng dẫn Đóng góp](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) của chúng tôi. 

api/.env.example

@@ -329,6 +329,7 @@ NOTION_INTERNAL_SECRET=you-internal-secret
 ETL_TYPE=dify
 UNSTRUCTURED_API_URL=
 UNSTRUCTURED_API_KEY=
+SCARF_NO_ANALYTICS=true
 
 #ssrf
 SSRF_PROXY_HTTP_URL=
@@ -382,7 +383,7 @@ LOG_DATEFORMAT=%Y-%m-%d %H:%M:%S
 LOG_TZ=UTC
 
 # Indexing configuration
-INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=1000
+INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=4000
 
 # Workflow runtime configuration
 WORKFLOW_MAX_EXECUTION_STEPS=500
@@ -410,4 +411,5 @@ POSITION_PROVIDER_EXCLUDES=
 # Reset password token expiry minutes
 RESET_PASSWORD_TOKEN_EXPIRY_MINUTES=5
 
-CREATE_TIDB_SERVICE_JOB_ENABLED=false
+CREATE_TIDB_SERVICE_JOB_ENABLED=false
+

api/.ruff.toml

@@ -0,0 +1,96 @@
+exclude = [
+    "migrations/*",
+]
+line-length = 120
+
+[format]
+quote-style = "double"
+
+[lint]
+preview = true
+select = [
+    "B", # flake8-bugbear rules
+    "C4", # flake8-comprehensions
+    "E", # pycodestyle E rules
+    "F", # pyflakes rules
+    "FURB", # refurb rules
+    "I", # isort rules
+    "N", # pep8-naming
+    "PT", # flake8-pytest-style rules
+    "PLC0208", # iteration-over-set
+    "PLC2801", # unnecessary-dunder-call
+    "PLC0414", # useless-import-alias
+    "PLE0604", # invalid-all-object
+    "PLE0605", # invalid-all-format
+    "PLR0402", # manual-from-import
+    "PLR1711", # useless-return
+    "PLR1714", # repeated-equality-comparison
+    "RUF013", # implicit-optional
+    "RUF019", # unnecessary-key-check
+    "RUF100", # unused-noqa
+    "RUF101", # redirected-noqa
+    "RUF200", # invalid-pyproject-toml
+    "RUF022", # unsorted-dunder-all
+    "S506", # unsafe-yaml-load
+    "SIM", # flake8-simplify rules
+    "TRY400", # error-instead-of-exception
+    "TRY401", # verbose-log-message
+    "UP", # pyupgrade rules
+    "W191", # tab-indentation
+    "W605", # invalid-escape-sequence
+]
+
+ignore = [
+    "E402", # module-import-not-at-top-of-file
+    "E711", # none-comparison
+    "E712", # true-false-comparison
+    "E721", # type-comparison
+    "E722", # bare-except
+    "E731", # lambda-assignment
+    "F821", # undefined-name
+    "F841", # unused-variable
+    "FURB113", # repeated-append
+    "FURB152", # math-constant
+    "UP007", # non-pep604-annotation
+    "UP032", # f-string
+    "B005", # strip-with-multi-characters
+    "B006", # mutable-argument-default
+    "B007", # unused-loop-control-variable
+    "B026", # star-arg-unpacking-after-keyword-arg
+    "B904", # raise-without-from-inside-except
+    "B905", # zip-without-explicit-strict
+    "N806", # non-lowercase-variable-in-function
+    "N815", # mixed-case-variable-in-class-scope
+    "PT011", # pytest-raises-too-broad
+    "SIM102", # collapsible-if
+    "SIM103", # needless-bool
+    "SIM105", # suppressible-exception
+    "SIM107", # return-in-try-except-finally
+    "SIM108", # if-else-block-instead-of-if-exp
+    "SIM113", # eumerate-for-loop
+    "SIM117", # multiple-with-statements
+    "SIM210", # if-expr-with-true-false
+    "SIM300", # yoda-conditions,
+]
+
+[lint.per-file-ignores]
+"__init__.py" = [
+    "F401", # unused-import
+    "F811", # redefined-while-unused
+]
+"configs/*" = [
+    "N802", # invalid-function-name
+]
+"libs/gmpy2_pkcs10aep_cipher.py" = [
+    "N803", # invalid-argument-name
+]
+"tests/*" = [
+    "F811", # redefined-while-unused
+    "F401", # unused-import
+]
+
+[lint.pyflakes]
+extend-generics = [
+    "_pytest.monkeypatch",
+    "tests.integration_tests",
+]

api/Dockerfile

@@ -55,7 +55,7 @@ RUN apt-get update \
     && echo "deb http://deb.debian.org/debian testing 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=2.5.18+dfsg-3+b1 perl=5.40.0-7 libsqlite3-0=3.46.1-1 zlib1g=1:1.3.dfsg+really1.3.1-1+b1 \
+    && apt-get install -y --no-install-recommends expat=2.6.4-1 libldap-2.5-0=2.5.18+dfsg-3+b1 perl=5.40.0-8 libsqlite3-0=3.46.1-1 zlib1g=1:1.3.dfsg+really1.3.1-1+b1 \
     # install a chinese font to support the use of tools like matplotlib
     && apt-get install -y fonts-noto-cjk \
     && apt-get autoremove -y \

api/app.py

@@ -1,113 +1,13 @@
-import os
-import sys
-
-python_version = sys.version_info
-if not ((3, 11) <= python_version < (3, 13)):
-    print(f"Python 3.11 or 3.12 is required, current version is {python_version.major}.{python_version.minor}")
-    raise SystemExit(1)
-
-from configs import dify_config
-
-if not dify_config.DEBUG:
-    from gevent import monkey
-
-    monkey.patch_all()
-
-    import grpc.experimental.gevent
-
-    grpc.experimental.gevent.init_gevent()
-
-import json
-import threading
-import time
-import warnings
-
-from flask import Response
-
 from app_factory import create_app
+from libs import threadings_utils, version_utils
 
-# DO NOT REMOVE BELOW
-from events import event_handlers  # noqa: F401
-from extensions.ext_database import db
-
-# TODO: Find a way to avoid importing models here
-from models import account, dataset, model, source, task, tool, tools, web  # noqa: F401
-
-# DO NOT REMOVE ABOVE
-
-
-warnings.simplefilter("ignore", ResourceWarning)
-
-os.environ["TZ"] = "UTC"
-# windows platform not support tzset
-if hasattr(time, "tzset"):
-    time.tzset()
-
+# preparation before creating app
+version_utils.check_supported_python_version()
+threadings_utils.apply_gevent_threading_patch()
 
 # create app
 app = create_app()
 celery = app.extensions["celery"]
 
-if dify_config.TESTING:
-    print("App is running in TESTING mode")
-
-
-@app.after_request
-def after_request(response):
-    """Add Version headers to the response."""
-    response.headers.add("X-Version", dify_config.CURRENT_VERSION)
-    response.headers.add("X-Env", dify_config.DEPLOY_ENV)
-    return response
-
-
-@app.route("/health")
-def health():
-    return Response(
-        json.dumps({"pid": os.getpid(), "status": "ok", "version": dify_config.CURRENT_VERSION}),
-        status=200,
-        content_type="application/json",
-    )
-
-
-@app.route("/threads")
-def threads():
-    num_threads = threading.active_count()
-    threads = threading.enumerate()
-
-    thread_list = []
-    for thread in threads:
-        thread_name = thread.name
-        thread_id = thread.ident
-        is_alive = thread.is_alive()
-
-        thread_list.append(
-            {
-                "name": thread_name,
-                "id": thread_id,
-                "is_alive": is_alive,
-            }
-        )
-
-    return {
-        "pid": os.getpid(),
-        "thread_num": num_threads,
-        "threads": thread_list,
-    }
-
-
-@app.route("/db-pool-stat")
-def pool_stat():
-    engine = db.engine
-    return {
-        "pid": os.getpid(),
-        "pool_size": engine.pool.size(),
-        "checked_in_connections": engine.pool.checkedin(),
-        "checked_out_connections": engine.pool.checkedout(),
-        "overflow_connections": engine.pool.overflow(),
-        "connection_timeout": engine.pool.timeout(),
-        "recycle_time": db.engine.pool._recycle,
-    }
-
-
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=5001)

api/app_factory.py

@@ -1,54 +1,15 @@
+import logging
 import os
+import time
 
 from configs import dify_config
-
-if not dify_config.DEBUG:
-    from gevent import monkey
-
-    monkey.patch_all()
-
-    import grpc.experimental.gevent
-
-    grpc.experimental.gevent.init_gevent()
-
-import json
-
-from flask import Flask, Response, request
-from flask_cors import CORS
-from werkzeug.exceptions import Unauthorized
-
-import contexts
-from commands import register_commands
-from configs import dify_config
-from extensions import (
-    ext_celery,
-    ext_code_based_extension,
-    ext_compress,
-    ext_database,
-    ext_hosting_provider,
-    ext_logging,
-    ext_login,
-    ext_mail,
-    ext_migrate,
-    ext_proxy_fix,
-    ext_redis,
-    ext_sentry,
-    ext_storage,
-)
-from extensions.ext_database import db
-from extensions.ext_login import login_manager
-from libs.passport import PassportService
-from services.account_service import AccountService
-
-
-class DifyApp(Flask):
-    pass
+from dify_app import DifyApp
 
 
 # ----------------------------
 # Application Factory Function
 # ----------------------------
-def create_flask_app_with_configs() -> Flask:
+def create_flask_app_with_configs() -> DifyApp:
     """
     create a raw flask app
     with configs loaded from .env file
@@ -68,111 +29,72 @@ def create_flask_app_with_configs() -> Flask:
     return dify_app
 
 
-def create_app() -> Flask:
+def create_app() -> DifyApp:
+    start_time = time.perf_counter()
     app = create_flask_app_with_configs()
-    app.secret_key = dify_config.SECRET_KEY
     initialize_extensions(app)
-    register_blueprints(app)
-    register_commands(app)
-
+    end_time = time.perf_counter()
+    if dify_config.DEBUG:
+        logging.info(f"Finished create_app ({round((end_time - start_time) * 1000, 2)} ms)")
     return app
 
 
-def initialize_extensions(app):
-    # Since the application instance is now created, pass it to each Flask
-    # extension instance to bind it to the Flask application instance (app)
-    ext_logging.init_app(app)
-    ext_compress.init_app(app)
-    ext_code_based_extension.init()
-    ext_database.init_app(app)
-    ext_migrate.init(app, db)
-    ext_redis.init_app(app)
-    ext_storage.init_app(app)
-    ext_celery.init_app(app)
-    ext_login.init_app(app)
-    ext_mail.init_app(app)
-    ext_hosting_provider.init_app(app)
-    ext_sentry.init_app(app)
-    ext_proxy_fix.init_app(app)
-
-
-# Flask-Login configuration
-@login_manager.request_loader
-def load_user_from_request(request_from_flask_login):
-    """Load user based on the request."""
-    if request.blueprint not in {"console", "inner_api"}:
-        return None
-    # Check if the user_id contains a dot, indicating the old format
-    auth_header = request.headers.get("Authorization", "")
-    if not auth_header:
-        auth_token = request.args.get("_token")
-        if not auth_token:
-            raise Unauthorized("Invalid Authorization token.")
-    else:
-        if " " not in auth_header:
-            raise Unauthorized("Invalid Authorization header format. Expected 'Bearer <api-key>' format.")
-        auth_scheme, auth_token = auth_header.split(None, 1)
-        auth_scheme = auth_scheme.lower()
-        if auth_scheme != "bearer":
-            raise Unauthorized("Invalid Authorization header format. Expected 'Bearer <api-key>' format.")
-
-    decoded = PassportService().verify(auth_token)
-    user_id = decoded.get("user_id")
-
-    logged_in_account = AccountService.load_logged_in_account(account_id=user_id)
-    if logged_in_account:
-        contexts.tenant_id.set(logged_in_account.current_tenant_id)
-    return logged_in_account
-
-
-@login_manager.unauthorized_handler
-def unauthorized_handler():
-    """Handle unauthorized requests."""
-    return Response(
-        json.dumps({"code": "unauthorized", "message": "Unauthorized."}),
-        status=401,
-        content_type="application/json",
+def initialize_extensions(app: DifyApp):
+    from extensions import (
+        ext_app_metrics,
+        ext_blueprints,
+        ext_celery,
+        ext_code_based_extension,
+        ext_commands,
+        ext_compress,
+        ext_database,
+        ext_hosting_provider,
+        ext_import_modules,
+        ext_logging,
+        ext_login,
+        ext_mail,
+        ext_migrate,
+        ext_proxy_fix,
+        ext_redis,
+        ext_sentry,
+        ext_set_secretkey,
+        ext_storage,
+        ext_timezone,
+        ext_warnings,
     )
 
+    extensions = [
+        ext_timezone,
+        ext_logging,
+        ext_warnings,
+        ext_import_modules,
+        ext_set_secretkey,
+        ext_compress,
+        ext_code_based_extension,
+        ext_database,
+        ext_app_metrics,
+        ext_migrate,
+        ext_redis,
+        ext_storage,
+        ext_celery,
+        ext_login,
+        ext_mail,
+        ext_hosting_provider,
+        ext_sentry,
+        ext_proxy_fix,
+        ext_blueprints,
+        ext_commands,
+    ]
+    for ext in extensions:
+        short_name = ext.__name__.split(".")[-1]
+        is_enabled = ext.is_enabled() if hasattr(ext, "is_enabled") else True
+        if not is_enabled:
+            if dify_config.DEBUG:
+                logging.info(f"Skipped {short_name}")
+            continue
 
-# register blueprint routers
-def register_blueprints(app):
-    from controllers.console import bp as console_app_bp
-    from controllers.files import bp as files_bp
-    from controllers.inner_api import bp as inner_api_bp
-    from controllers.service_api import bp as service_api_bp
-    from controllers.web import bp as web_bp
-
-    CORS(
-        service_api_bp,
-        allow_headers=["Content-Type", "Authorization", "X-App-Code"],
-        methods=["GET", "PUT", "POST", "DELETE", "OPTIONS", "PATCH"],
-    )
-    app.register_blueprint(service_api_bp)
-
-    CORS(
-        web_bp,
-        resources={r"/*": {"origins": dify_config.WEB_API_CORS_ALLOW_ORIGINS}},
-        supports_credentials=True,
-        allow_headers=["Content-Type", "Authorization", "X-App-Code"],
-        methods=["GET", "PUT", "POST", "DELETE", "OPTIONS", "PATCH"],
-        expose_headers=["X-Version", "X-Env"],
-    )
-
-    app.register_blueprint(web_bp)
-
-    CORS(
-        console_app_bp,
-        resources={r"/*": {"origins": dify_config.CONSOLE_CORS_ALLOW_ORIGINS}},
-        supports_credentials=True,
-        allow_headers=["Content-Type", "Authorization"],
-        methods=["GET", "PUT", "POST", "DELETE", "OPTIONS", "PATCH"],
-        expose_headers=["X-Version", "X-Env"],
-    )
-
-    app.register_blueprint(console_app_bp)
-
-    CORS(files_bp, allow_headers=["Content-Type"], methods=["GET", "PUT", "POST", "DELETE", "OPTIONS", "PATCH"])
-    app.register_blueprint(files_bp)
-
-    app.register_blueprint(inner_api_bp)
+        start_time = time.perf_counter()
+        ext.init_app(app)
+        end_time = time.perf_counter()
+        if dify_config.DEBUG:
+            logging.info(f"Loaded {short_name} ({round((end_time - start_time) * 1000, 2)} ms)")

api/commands.py

@@ -259,7 +259,7 @@ def migrate_knowledge_vector_database():
     skipped_count = 0
     total_count = 0
     vector_type = dify_config.VECTOR_STORE
-    upper_colletion_vector_types = {
+    upper_collection_vector_types = {
         VectorType.MILVUS,
         VectorType.PGVECTOR,
         VectorType.RELYT,
@@ -267,7 +267,7 @@ def migrate_knowledge_vector_database():
         VectorType.ORACLE,
         VectorType.ELASTICSEARCH,
     }
-    lower_colletion_vector_types = {
+    lower_collection_vector_types = {
         VectorType.ANALYTICDB,
         VectorType.CHROMA,
         VectorType.MYSCALE,
@@ -307,7 +307,7 @@ def migrate_knowledge_vector_database():
                         continue
                 collection_name = ""
                 dataset_id = dataset.id
-                if vector_type in upper_colletion_vector_types:
+                if vector_type in upper_collection_vector_types:
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                 elif vector_type == VectorType.QDRANT:
                     if dataset.collection_binding_id:
@@ -323,7 +323,7 @@ def migrate_knowledge_vector_database():
                     else:
                         collection_name = Dataset.gen_collection_name_by_id(dataset_id)
 
-                elif vector_type in lower_colletion_vector_types:
+                elif vector_type in lower_collection_vector_types:
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
                 else:
                     raise ValueError(f"Vector store {vector_type} is not supported.")
@@ -640,15 +640,3 @@ where sites.id is null limit 1000"""
                 break
 
     click.echo(click.style("Fix for missing app-related sites completed successfully!", fg="green"))
-
-
-def register_commands(app):
-    app.cli.add_command(reset_password)
-    app.cli.add_command(reset_email)
-    app.cli.add_command(reset_encrypt_key_pair)
-    app.cli.add_command(vdb_migrate)
-    app.cli.add_command(convert_to_agent_apps)
-    app.cli.add_command(add_qdrant_doc_id_index)
-    app.cli.add_command(create_tenant)
-    app.cli.add_command(upgrade_db)
-    app.cli.add_command(fix_app_site_missing)

api/configs/deploy/__init__.py

@@ -17,11 +17,6 @@ class DeploymentConfig(BaseSettings):
         default=False,
     )
 
-    TESTING: bool = Field(
-        description="Enable testing mode for running automated tests",
-        default=False,
-    )
-
     EDITION: str = Field(
         description="Deployment edition of the application (e.g., 'SELF_HOSTED', 'CLOUD')",
         default="SELF_HOSTED",

api/configs/feature/__init__.py

@@ -585,6 +585,11 @@ class RagEtlConfig(BaseSettings):
         default=None,
     )
 
+    SCARF_NO_ANALYTICS: Optional[str] = Field(
+        description="This is about whether to disable Scarf analytics in Unstructured library.",
+        default="false",
+    )
+
 
 class DataSetConfig(BaseSettings):
     """
@@ -640,7 +645,7 @@ class IndexingConfig(BaseSettings):
 
     INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH: PositiveInt = Field(
         description="Maximum token length for text segmentation during indexing",
-        default=1000,
+        default=4000,
     )
 
 

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.12.1",
+        default="0.13.2",
     )
 
     COMMIT_SHA: str = Field(

api/constants/languages.py

@@ -18,6 +18,7 @@ language_timezone_mapping = {
     "tr-TR": "Europe/Istanbul",
     "fa-IR": "Asia/Tehran",
     "sl-SI": "Europe/Ljubljana",
+    "th-TH": "Asia/Bangkok",
 }
 
 languages = list(language_timezone_mapping.keys())

api/controllers/console/app/workflow.py

@@ -100,11 +100,11 @@ class DraftWorkflowApi(Resource):
         try:
             environment_variables_list = args.get("environment_variables") or []
             environment_variables = [
-                variable_factory.build_variable_from_mapping(obj) for obj in environment_variables_list
+                variable_factory.build_environment_variable_from_mapping(obj) for obj in environment_variables_list
             ]
             conversation_variables_list = args.get("conversation_variables") or []
             conversation_variables = [
-                variable_factory.build_variable_from_mapping(obj) for obj in conversation_variables_list
+                variable_factory.build_conversation_variable_from_mapping(obj) for obj in conversation_variables_list
             ]
             workflow = workflow_service.sync_draft_workflow(
                 app_model=app_model,
@@ -382,7 +382,7 @@ class DefaultBlockConfigApi(Resource):
         filters = None
         if args.get("q"):
             try:
-                filters = json.loads(args.get("q"))
+                filters = json.loads(args.get("q", ""))
             except json.JSONDecodeError:
                 raise ValueError("Invalid filters")
 

api/controllers/console/auth/data_source_oauth.py

@@ -34,7 +34,6 @@ class OAuthDataSource(Resource):
         OAUTH_DATASOURCE_PROVIDERS = get_oauth_providers()
         with current_app.app_context():
             oauth_provider = OAUTH_DATASOURCE_PROVIDERS.get(provider)
-            print(vars(oauth_provider))
         if not oauth_provider:
             return {"error": "Invalid provider"}, 400
         if dify_config.NOTION_INTEGRATION_TYPE == "internal":

api/controllers/console/auth/oauth.py

@@ -52,7 +52,6 @@ class OAuthLogin(Resource):
         OAUTH_PROVIDERS = get_oauth_providers()
         with current_app.app_context():
             oauth_provider = OAUTH_PROVIDERS.get(provider)
-            print(vars(oauth_provider))
         if not oauth_provider:
             return {"error": "Invalid provider"}, 400
 

api/controllers/console/datasets/datasets_document.py

@@ -106,6 +106,7 @@ class GetProcessRuleApi(Resource):
         # get default rules
         mode = DocumentService.DEFAULT_RULES["mode"]
         rules = DocumentService.DEFAULT_RULES["rules"]
+        limits = DocumentService.DEFAULT_RULES["limits"]
         if document_id:
             # get the latest process rule
             document = Document.query.get_or_404(document_id)
@@ -132,7 +133,7 @@ class GetProcessRuleApi(Resource):
                 mode = dataset_process_rule.mode
                 rules = dataset_process_rule.rules_dict
 
-        return {"mode": mode, "rules": rules}
+        return {"mode": mode, "rules": rules, "limits": limits}
 
 
 class DatasetDocumentListApi(Resource):

api/controllers/console/explore/installed_app.py

@@ -1,5 +1,6 @@
 from datetime import UTC, datetime
 
+from flask import request
 from flask_login import current_user
 from flask_restful import Resource, inputs, marshal_with, reqparse
 from sqlalchemy import and_
@@ -20,8 +21,17 @@ class InstalledAppsListApi(Resource):
     @account_initialization_required
     @marshal_with(installed_app_list_fields)
     def get(self):
+        app_id = request.args.get("app_id", default=None, type=str)
         current_tenant_id = current_user.current_tenant_id
-        installed_apps = db.session.query(InstalledApp).filter(InstalledApp.tenant_id == current_tenant_id).all()
+
+        if app_id:
+            installed_apps = (
+                db.session.query(InstalledApp)
+                .filter(and_(InstalledApp.tenant_id == current_tenant_id, InstalledApp.app_id == app_id))
+                .all()
+            )
+        else:
+            installed_apps = db.session.query(InstalledApp).filter(InstalledApp.tenant_id == current_tenant_id).all()
 
         current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
         installed_apps = [

api/controllers/console/workspace/tool_providers.py

@@ -368,6 +368,7 @@ class ToolWorkflowProviderCreateApi(Resource):
             description=args["description"],
             parameters=args["parameters"],
             privacy_policy=args["privacy_policy"],
+            labels=args["labels"],
         )
 
 

api/controllers/service_api/app/app.py

@@ -48,7 +48,8 @@ class AppInfoApi(Resource):
     @validate_app_token
     def get(self, app_model: App):
         """Get app information"""
-        return {"name": app_model.name, "description": app_model.description}
+        tags = [tag.name for tag in app_model.tags]
+        return {"name": app_model.name, "description": app_model.description, "tags": tags}
 
 
 api.add_resource(AppParameterApi, "/parameters")

api/core/app/app_config/easy_ui_based_app/model_config/manager.py

@@ -1,3 +1,6 @@
+from collections.abc import Mapping
+from typing import Any
+
 from core.app.app_config.entities import ModelConfigEntity
 from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.model_providers import model_provider_factory
@@ -36,7 +39,7 @@ class ModelConfigManager:
         )
 
     @classmethod
-    def validate_and_set_defaults(cls, tenant_id: str, config: dict) -> tuple[dict, list[str]]:
+    def validate_and_set_defaults(cls, tenant_id: str, config: Mapping[str, Any]) -> tuple[dict, list[str]]:
         """
         Validate and set defaults for model config
 

api/core/app/apps/README.md

@@ -2,7 +2,7 @@
 
 Due to the presence of tasks in App Runner that require long execution times, such as LLM generation and external requests, Flask-Sqlalchemy's strategy for database connection pooling is to allocate one connection (transaction) per request. This approach keeps a connection occupied even during non-DB tasks, leading to the inability to acquire new connections during high concurrency requests due to multiple long-running tasks.
 
-Therefore, the database operations in App Runner and Task Pipeline must ensure connections are closed immediately after use, and it's better to pass IDs rather than Model objects to avoid deattach errors.
+Therefore, the database operations in App Runner and Task Pipeline must ensure connections are closed immediately after use, and it's better to pass IDs rather than Model objects to avoid detach errors.
 
 Examples:
 

api/core/app/apps/advanced_chat/app_generator.py

@@ -2,8 +2,8 @@ import contextvars
 import logging
 import threading
 import uuid
-from collections.abc import Generator
-from typing import Any, Literal, Optional, Union, overload
+from collections.abc import Generator, Mapping
+from typing import Any, Optional, Union
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -23,6 +23,7 @@ from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity,
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
+from core.prompt.utils.get_thread_messages_length import get_thread_messages_length
 from extensions.ext_database import db
 from factories import file_factory
 from models.account import Account
@@ -33,37 +34,17 @@ logger = logging.getLogger(__name__)
 
 
 class AdvancedChatAppGenerator(MessageBasedAppGenerator):
-    @overload
-    def generate(
-        self,
-        app_model: App,
-        workflow: Workflow,
-        user: Union[Account, EndUser],
-        args: dict,
-        invoke_from: InvokeFrom,
-        stream: Literal[True] = True,
-    ) -> Generator[str, None, None]: ...
-
-    @overload
-    def generate(
-        self,
-        app_model: App,
-        workflow: Workflow,
-        user: Union[Account, EndUser],
-        args: dict,
-        invoke_from: InvokeFrom,
-        stream: Literal[False] = False,
-    ) -> dict: ...
+    _dialogue_count: int
 
     def generate(
         self,
         app_model: App,
         workflow: Workflow,
         user: Union[Account, EndUser],
-        args: dict,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: bool = True,
-    ) -> dict[str, Any] | Generator[str, Any, None]:
+        streaming: bool = True,
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         """
         Generate App response.
 
@@ -134,7 +115,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             files=file_objs,
             parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,
             user_id=user.id,
-            stream=stream,
+            stream=streaming,
             invoke_from=invoke_from,
             extras=extras,
             trace_manager=trace_manager,
@@ -148,12 +129,12 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=invoke_from,
             application_generate_entity=application_generate_entity,
             conversation=conversation,
-            stream=stream,
+            stream=streaming,
         )
 
     def single_iteration_generate(
-        self, app_model: App, workflow: Workflow, node_id: str, user: Account, args: dict, stream: bool = True
-    ) -> dict[str, Any] | Generator[str, Any, None]:
+        self, app_model: App, workflow: Workflow, node_id: str, user: Account, args: dict, streaming: bool = True
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         """
         Generate App response.
 
@@ -182,7 +163,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             query="",
             files=[],
             user_id=user.id,
-            stream=stream,
+            stream=streaming,
             invoke_from=InvokeFrom.DEBUGGER,
             extras={"auto_generate_conversation_name": False},
             single_iteration_run=AdvancedChatAppGenerateEntity.SingleIterationRunEntity(
@@ -197,7 +178,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=InvokeFrom.DEBUGGER,
             application_generate_entity=application_generate_entity,
             conversation=None,
-            stream=stream,
+            stream=streaming,
         )
 
     def _generate(
@@ -209,7 +190,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         application_generate_entity: AdvancedChatAppGenerateEntity,
         conversation: Optional[Conversation] = None,
         stream: bool = True,
-    ) -> dict[str, Any] | Generator[str, Any, None]:
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         """
         Generate App response.
 
@@ -233,6 +214,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             db.session.commit()
             db.session.refresh(conversation)
 
+        # get conversation dialogue count
+        self._dialogue_count = get_thread_messages_length(conversation.id)
+
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
             task_id=application_generate_entity.task_id,
@@ -303,6 +287,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                     queue_manager=queue_manager,
                     conversation=conversation,
                     message=message,
+                    dialogue_count=self._dialogue_count,
                 )
 
                 runner.run()
@@ -356,6 +341,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             message=message,
             user=user,
             stream=stream,
+            dialogue_count=self._dialogue_count,
         )
 
         try:

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

@@ -39,12 +39,14 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
         queue_manager: AppQueueManager,
         conversation: Conversation,
         message: Message,
+        dialogue_count: int,
     ) -> None:
         super().__init__(queue_manager)
 
         self.application_generate_entity = application_generate_entity
         self.conversation = conversation
         self.message = message
+        self._dialogue_count = dialogue_count
 
     def run(self) -> None:
         app_config = self.application_generate_entity.app_config
@@ -122,19 +124,13 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
 
                 session.commit()
 
-            # Increment dialogue count.
-            self.conversation.dialogue_count += 1
-
-            conversation_dialogue_count = self.conversation.dialogue_count
-            db.session.commit()
-
             # Create a variable pool.
             system_inputs = {
                 SystemVariableKey.QUERY: query,
                 SystemVariableKey.FILES: files,
                 SystemVariableKey.CONVERSATION_ID: self.conversation.id,
                 SystemVariableKey.USER_ID: user_id,
-                SystemVariableKey.DIALOGUE_COUNT: conversation_dialogue_count,
+                SystemVariableKey.DIALOGUE_COUNT: self._dialogue_count,
                 SystemVariableKey.APP_ID: app_config.app_id,
                 SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
                 SystemVariableKey.WORKFLOW_RUN_ID: self.application_generate_entity.workflow_run_id,

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -88,6 +88,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         message: Message,
         user: Union[Account, EndUser],
         stream: bool,
+        dialogue_count: int,
     ) -> None:
         """
         Initialize AdvancedChatAppGenerateTaskPipeline.
@@ -98,6 +99,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         :param message: message
         :param user: user
         :param stream: stream
+        :param dialogue_count: dialogue count
         """
         super().__init__(application_generate_entity, queue_manager, user, stream)
 
@@ -114,7 +116,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             SystemVariableKey.FILES: application_generate_entity.files,
             SystemVariableKey.CONVERSATION_ID: conversation.id,
             SystemVariableKey.USER_ID: user_id,
-            SystemVariableKey.DIALOGUE_COUNT: conversation.dialogue_count,
+            SystemVariableKey.DIALOGUE_COUNT: dialogue_count,
             SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
             SystemVariableKey.WORKFLOW_ID: workflow.id,
             SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
@@ -125,6 +127,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         self._conversation_name_generate_thread = None
         self._recorded_files: list[Mapping[str, Any]] = []
+        self.total_tokens: int = 0
 
     def process(self):
         """
@@ -358,6 +361,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 if not workflow_run:
                     raise Exception("Workflow run not initialized.")
 
+                # FIXME for issue #11221 quick fix maybe have a better solution
+                self.total_tokens += event.metadata.get("total_tokens", 0) if event.metadata else 0
                 yield self._workflow_iteration_completed_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
@@ -371,7 +376,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 workflow_run = self._handle_workflow_run_success(
                     workflow_run=workflow_run,
                     start_at=graph_runtime_state.start_at,
-                    total_tokens=graph_runtime_state.total_tokens,
+                    total_tokens=graph_runtime_state.total_tokens or self.total_tokens,
                     total_steps=graph_runtime_state.node_run_steps,
                     outputs=event.outputs,
                     conversation_id=self._conversation.id,

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

@@ -1,5 +1,6 @@
 import uuid
-from typing import Optional
+from collections.abc import Mapping
+from typing import Any, Optional
 
 from core.agent.entities import AgentEntity
 from core.app.app_config.base_app_config_manager import BaseAppConfigManager
@@ -85,7 +86,7 @@ class AgentChatAppConfigManager(BaseAppConfigManager):
         return app_config
 
     @classmethod
-    def config_validate(cls, tenant_id: str, config: dict) -> dict:
+    def config_validate(cls, tenant_id: str, config: Mapping[str, Any]) -> dict:
         """
         Validate for agent chat app model config
 

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

@@ -1,8 +1,8 @@
 import logging
 import threading
 import uuid
-from collections.abc import Generator
-from typing import Any, Literal, Union, overload
+from collections.abc import Generator, Mapping
+from typing import Any, Union
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -28,34 +28,15 @@ logger = logging.getLogger(__name__)
 
 
 class AgentChatAppGenerator(MessageBasedAppGenerator):
-    @overload
     def generate(
         self,
+        *,
         app_model: App,
         user: Union[Account, EndUser],
-        args: dict,
+        args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: Literal[True] = True,
-    ) -> Generator[dict, None, None]: ...
-
-    @overload
-    def generate(
-        self,
-        app_model: App,
-        user: Union[Account, EndUser],
-        args: dict,
-        invoke_from: InvokeFrom,
-        stream: Literal[False] = False,
-    ) -> dict: ...
-
-    def generate(
-        self,
-        app_model: App,
-        user: Union[Account, EndUser],
-        args: Any,
-        invoke_from: InvokeFrom,
-        stream: bool = True,
-    ) -> Union[dict, Generator[dict, None, None]]:
+        streaming: bool = True,
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         """
         Generate App response.
 
@@ -65,7 +46,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
         :param invoke_from: invoke from source
         :param stream: is stream
         """
-        if not stream:
+        if not streaming:
             raise ValueError("Agent Chat App does not support blocking mode")
 
         if not args.get("query"):
@@ -96,7 +77,8 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
 
             # validate config
             override_model_config_dict = AgentChatAppConfigManager.config_validate(
-                tenant_id=app_model.tenant_id, config=args.get("model_config")
+                tenant_id=app_model.tenant_id,
+                config=args["model_config"],
             )
 
             # always enable retriever resource in debugger mode
@@ -141,7 +123,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             files=file_objs,
             parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,
             user_id=user.id,
-            stream=stream,
+            stream=streaming,
             invoke_from=invoke_from,
             extras=extras,
             call_depth=0,
@@ -182,7 +164,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream,
+            stream=streaming,
         )
 
         return AgentChatAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)

api/core/app/apps/base_app_generate_response_converter.py

@@ -1,6 +1,6 @@
 import logging
 from abc import ABC, abstractmethod
-from collections.abc import Generator
+from collections.abc import Generator, Mapping
 from typing import Any, Union
 
 from core.app.entities.app_invoke_entities import InvokeFrom
@@ -14,8 +14,10 @@ class AppGenerateResponseConverter(ABC):
 
     @classmethod
     def convert(
-        cls, response: Union[AppBlockingResponse, Generator[AppStreamResponse, Any, None]], invoke_from: InvokeFrom
-    ) -> dict[str, Any] | Generator[str, Any, None]:
+        cls,
+        response: Union[AppBlockingResponse, Generator[AppStreamResponse, Any, None]],
+        invoke_from: InvokeFrom,
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         if invoke_from in {InvokeFrom.DEBUGGER, InvokeFrom.SERVICE_API}:
             if isinstance(response, AppBlockingResponse):
                 return cls.convert_blocking_full_response(response)
@@ -80,7 +82,7 @@ class AppGenerateResponseConverter(ABC):
             for resource in metadata["retriever_resources"]:
                 updated_resources.append(
                     {
-                        "segment_id": resource["segment_id"],
+                        "segment_id": resource.get("segment_id", ""),
                         "position": resource["position"],
                         "document_name": resource["document_name"],
                         "score": resource["score"],

api/core/app/apps/chat/app_generator.py

@@ -55,7 +55,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
         user: Union[Account, EndUser],
         args: Any,
         invoke_from: InvokeFrom,
-        stream: bool = True,
+        streaming: bool = True,
     ) -> Union[dict, Generator[str, None, None]]:
         """
         Generate App response.
@@ -142,7 +142,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             invoke_from=invoke_from,
             extras=extras,
             trace_manager=trace_manager,
-            stream=stream,
+            stream=streaming,
         )
 
         # init generate records
@@ -179,7 +179,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream,
+            stream=streaming,
         )
 
         return ChatAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)

api/core/app/apps/completion/app_generator.py

@@ -50,7 +50,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
     ) -> dict: ...
 
     def generate(
-        self, app_model: App, user: Union[Account, EndUser], args: Any, invoke_from: InvokeFrom, stream: bool = True
+        self, app_model: App, user: Union[Account, EndUser], args: Any, invoke_from: InvokeFrom, streaming: bool = True
     ) -> Union[dict, Generator[str, None, None]]:
         """
         Generate App response.
@@ -119,7 +119,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             query=query,
             files=file_objs,
             user_id=user.id,
-            stream=stream,
+            stream=streaming,
             invoke_from=invoke_from,
             extras=extras,
             trace_manager=trace_manager,
@@ -158,7 +158,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream,
+            stream=streaming,
         )
 
         return CompletionAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)

api/core/app/apps/workflow/app_generator.py

@@ -3,7 +3,7 @@ import logging
 import threading
 import uuid
 from collections.abc import Generator, Mapping, Sequence
-from typing import Any, Literal, Optional, Union, overload
+from typing import Any, Optional, Union
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -30,43 +30,18 @@ logger = logging.getLogger(__name__)
 
 
 class WorkflowAppGenerator(BaseAppGenerator):
-    @overload
     def generate(
         self,
+        *,
         app_model: App,
         workflow: Workflow,
-        user: Union[Account, EndUser],
-        args: dict,
-        invoke_from: InvokeFrom,
-        stream: Literal[True] = True,
-        call_depth: int = 0,
-        workflow_thread_pool_id: Optional[str] = None,
-    ) -> Generator[str, None, None]: ...
-
-    @overload
-    def generate(
-        self,
-        app_model: App,
-        workflow: Workflow,
-        user: Union[Account, EndUser],
-        args: dict,
-        invoke_from: InvokeFrom,
-        stream: Literal[False] = False,
-        call_depth: int = 0,
-        workflow_thread_pool_id: Optional[str] = None,
-    ) -> dict: ...
-
-    def generate(
-        self,
-        app_model: App,
-        workflow: Workflow,
-        user: Union[Account, EndUser],
+        user: Account | EndUser,
         args: Mapping[str, Any],
         invoke_from: InvokeFrom,
-        stream: bool = True,
+        streaming: bool = True,
         call_depth: int = 0,
         workflow_thread_pool_id: Optional[str] = None,
-    ):
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         files: Sequence[Mapping[str, Any]] = args.get("files") or []
 
         # parse files
@@ -101,7 +76,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             ),
             files=system_files,
             user_id=user.id,
-            stream=stream,
+            stream=streaming,
             invoke_from=invoke_from,
             call_depth=call_depth,
             trace_manager=trace_manager,
@@ -115,7 +90,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             user=user,
             application_generate_entity=application_generate_entity,
             invoke_from=invoke_from,
-            stream=stream,
+            streaming=streaming,
             workflow_thread_pool_id=workflow_thread_pool_id,
         )
 
@@ -127,20 +102,9 @@ class WorkflowAppGenerator(BaseAppGenerator):
         user: Union[Account, EndUser],
         application_generate_entity: WorkflowAppGenerateEntity,
         invoke_from: InvokeFrom,
-        stream: bool = True,
+        streaming: bool = True,
         workflow_thread_pool_id: Optional[str] = None,
-    ) -> dict[str, Any] | Generator[str, None, None]:
-        """
-        Generate App response.
-
-        :param app_model: App
-        :param workflow: Workflow
-        :param user: account or end user
-        :param application_generate_entity: application generate entity
-        :param invoke_from: invoke from source
-        :param stream: is stream
-        :param workflow_thread_pool_id: workflow thread pool id
-        """
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         # init queue manager
         queue_manager = WorkflowAppQueueManager(
             task_id=application_generate_entity.task_id,
@@ -169,14 +133,20 @@ class WorkflowAppGenerator(BaseAppGenerator):
             workflow=workflow,
             queue_manager=queue_manager,
             user=user,
-            stream=stream,
+            stream=streaming,
         )
 
         return WorkflowAppGenerateResponseConverter.convert(response=response, invoke_from=invoke_from)
 
     def single_iteration_generate(
-        self, app_model: App, workflow: Workflow, node_id: str, user: Account, args: dict, stream: bool = True
-    ) -> dict[str, Any] | Generator[str, Any, None]:
+        self,
+        app_model: App,
+        workflow: Workflow,
+        node_id: str,
+        user: Account,
+        args: Mapping[str, Any],
+        streaming: bool = True,
+    ) -> Mapping[str, Any] | Generator[str, None, None]:
         """
         Generate App response.
 
@@ -203,7 +173,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             inputs={},
             files=[],
             user_id=user.id,
-            stream=stream,
+            stream=streaming,
             invoke_from=InvokeFrom.DEBUGGER,
             extras={"auto_generate_conversation_name": False},
             single_iteration_run=WorkflowAppGenerateEntity.SingleIterationRunEntity(
@@ -218,7 +188,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             user=user,
             invoke_from=InvokeFrom.DEBUGGER,
             application_generate_entity=application_generate_entity,
-            stream=stream,
+            streaming=streaming,
         )
 
     def _generate_worker(

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -106,6 +106,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
 
         self._task_state = WorkflowTaskState()
         self._wip_workflow_node_executions = {}
+        self.total_tokens: int = 0
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
         """
@@ -319,6 +320,8 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 if not workflow_run:
                     raise Exception("Workflow run not initialized.")
 
+                # FIXME for issue #11221 quick fix maybe have a better solution
+                self.total_tokens += event.metadata.get("total_tokens", 0) if event.metadata else 0
                 yield self._workflow_iteration_completed_to_stream_response(
                     task_id=self._application_generate_entity.task_id, workflow_run=workflow_run, event=event
                 )
@@ -332,7 +335,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 workflow_run = self._handle_workflow_run_success(
                     workflow_run=workflow_run,
                     start_at=graph_runtime_state.start_at,
-                    total_tokens=graph_runtime_state.total_tokens,
+                    total_tokens=graph_runtime_state.total_tokens or self.total_tokens,
                     total_steps=graph_runtime_state.node_run_steps,
                     outputs=event.outputs,
                     conversation_id=None,

api/core/app/apps/workflow_app_runner.py

@@ -43,7 +43,7 @@ from core.workflow.graph_engine.entities.event import (
 )
 from core.workflow.graph_engine.entities.graph import Graph
 from core.workflow.nodes import NodeType
-from core.workflow.nodes.node_mapping import node_type_classes_mapping
+from core.workflow.nodes.node_mapping import NODE_TYPE_CLASSES_MAPPING
 from core.workflow.workflow_entry import WorkflowEntry
 from extensions.ext_database import db
 from models.model import App
@@ -138,7 +138,8 @@ class WorkflowBasedAppRunner(AppRunner):
 
         # Get node class
         node_type = NodeType(iteration_node_config.get("data", {}).get("type"))
-        node_cls = node_type_classes_mapping[node_type]
+        node_version = iteration_node_config.get("data", {}).get("version", "1")
+        node_cls = NODE_TYPE_CLASSES_MAPPING[node_type][node_version]
 
         # init variable pool
         variable_pool = VariablePool(

api/core/app/entities/queue_entities.py

@@ -2,7 +2,7 @@ from datetime import datetime
 from enum import Enum, StrEnum
 from typing import Any, Optional
 
-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.workflow.entities.node_entities import NodeRunMetadataKey
@@ -113,18 +113,6 @@ class QueueIterationNextEvent(AppQueueEvent):
     output: Optional[Any] = None  # output for the current iteration
     duration: Optional[float] = None
 
-    @field_validator("output", mode="before")
-    @classmethod
-    def set_output(cls, v):
-        """
-        Set output
-        """
-        if v is None:
-            return None
-        if isinstance(v, int | float | str | bool | dict | list):
-            return v
-        raise ValueError("output must be a valid type")
-
 
 class QueueIterationCompletedEvent(AppQueueEvent):
     """

api/core/app/features/rate_limiting/rate_limit.py

@@ -1,9 +1,9 @@
 import logging
 import time
 import uuid
-from collections.abc import Generator
+from collections.abc import Generator, Mapping
 from datetime import timedelta
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 from core.errors.error import AppInvokeQuotaExceededError
 from extensions.ext_redis import redis_client
@@ -88,20 +88,17 @@ class RateLimit:
     def gen_request_key() -> str:
         return str(uuid.uuid4())
 
-    def generate(self, generator: Union[Generator, callable, dict], request_id: str):
-        if isinstance(generator, dict):
+    def generate(self, generator: Union[Generator[str, None, None], Mapping[str, Any]], request_id: str):
+        if isinstance(generator, Mapping):
             return generator
         else:
-            return RateLimitGenerator(self, generator, request_id)
+            return RateLimitGenerator(rate_limit=self, generator=generator, request_id=request_id)
 
 
 class RateLimitGenerator:
-    def __init__(self, rate_limit: RateLimit, generator: Union[Generator, callable], request_id: str):
+    def __init__(self, rate_limit: RateLimit, generator: Generator[str, None, None], request_id: str):
         self.rate_limit = rate_limit
-        if callable(generator):
-            self.generator = generator()
-        else:
-            self.generator = generator
+        self.generator = generator
         self.request_id = request_id
         self.closed = False
 

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -340,7 +340,7 @@ class WorkflowCycleManage:
                 WorkflowNodeExecution.status: WorkflowNodeExecutionStatus.FAILED.value,
                 WorkflowNodeExecution.error: event.error,
                 WorkflowNodeExecution.inputs: json.dumps(inputs) if inputs else None,
-                WorkflowNodeExecution.process_data: json.dumps(event.process_data) if event.process_data else None,
+                WorkflowNodeExecution.process_data: json.dumps(process_data) if process_data else None,
                 WorkflowNodeExecution.outputs: json.dumps(outputs) if outputs else None,
                 WorkflowNodeExecution.finished_at: finished_at,
                 WorkflowNodeExecution.elapsed_time: elapsed_time,

api/core/file/__init__.py

@@ -7,13 +7,13 @@ from .models import (
 )
 
 __all__ = [
+    "FILE_MODEL_IDENTITY",
+    "ArrayFileAttribute",
+    "File",
+    "FileAttribute",
+    "FileBelongsTo",
+    "FileTransferMethod",
     "FileType",
     "FileUploadConfig",
-    "FileTransferMethod",
-    "FileBelongsTo",
-    "File",
     "ImageConfig",
-    "FileAttribute",
-    "ArrayFileAttribute",
-    "FILE_MODEL_IDENTITY",
 ]

api/core/helper/ssrf_proxy.py

@@ -53,8 +53,6 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
                     response = client.request(method=method, url=url, **kwargs)
 
             if response.status_code not in STATUS_FORCELIST:
-                if stream:
-                    return response.iter_bytes()
                 return response
             else:
                 logging.warning(f"Received status code {response.status_code} for URL {url} which is in the force list")

api/core/llm_generator/output_parser/suggested_questions_after_answer.py

@@ -15,6 +15,5 @@ class SuggestedQuestionsAfterAnswerOutputParser:
             json_obj = json.loads(action_match.group(0).strip())
         else:
             json_obj = []
-            print(f"Could not parse LLM output: {text}")
 
         return json_obj

api/core/model_runtime/docs/zh_Hans/interfaces.md

@@ -91,7 +91,7 @@ class XinferenceProvider(Provider):
       """
   ```
 
-  也可以直接抛出对应Erros，并做如下定义，这样在之后的调用中可以直接抛出`InvokeConnectionError`等异常。
+  也可以直接抛出对应 Errors，并做如下定义，这样在之后的调用中可以直接抛出`InvokeConnectionError`等异常。
   
     ```python
     @property

api/core/model_runtime/entities/__init__.py

@@ -18,25 +18,25 @@ from .message_entities import (
 from .model_entities import ModelPropertyKey
 
 __all__ = [
-    "ImagePromptMessageContent",
-    "VideoPromptMessageContent",
-    "PromptMessage",
-    "PromptMessageRole",
-    "LLMUsage",
-    "ModelPropertyKey",
     "AssistantPromptMessage",
-    "PromptMessage",
-    "PromptMessageContent",
-    "PromptMessageRole",
-    "SystemPromptMessage",
-    "TextPromptMessageContent",
-    "UserPromptMessage",
-    "PromptMessageTool",
-    "ToolPromptMessage",
-    "PromptMessageContentType",
+    "AudioPromptMessageContent",
+    "DocumentPromptMessageContent",
+    "ImagePromptMessageContent",
     "LLMResult",
     "LLMResultChunk",
     "LLMResultChunkDelta",
-    "AudioPromptMessageContent",
-    "DocumentPromptMessageContent",
+    "LLMUsage",
+    "ModelPropertyKey",
+    "PromptMessage",
+    "PromptMessage",
+    "PromptMessageContent",
+    "PromptMessageContentType",
+    "PromptMessageRole",
+    "PromptMessageRole",
+    "PromptMessageTool",
+    "SystemPromptMessage",
+    "TextPromptMessageContent",
+    "ToolPromptMessage",
+    "UserPromptMessage",
+    "VideoPromptMessageContent",
 ]

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

@@ -483,6 +483,10 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 if isinstance(message, UserPromptMessage):
                     message = cast(UserPromptMessage, message)
                     if isinstance(message.content, str):
+                        # handle empty user prompt see #10013 #10520
+                        # responses, ignore user prompts containing only whitespace, the Claude API can't handle it.
+                        if not message.content.strip():
+                            continue
                         message_dict = {"role": "user", "content": message.content}
                         prompt_message_dicts.append(message_dict)
                     else:

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

@@ -598,6 +598,9 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             # message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}
             if message.tool_calls:
+                # fix azure when enable json schema cant process content = "" in assistant fix with None
+                if not message.content:
+                    message_dict["content"] = None
                 message_dict["tool_calls"] = [helper.dump_model(tool_call) for tool_call in message.tool_calls]
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)

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

@@ -16,6 +16,7 @@ help:
 supported_model_types:
   - llm
   - text-embedding
+  - rerank
 configurate_methods:
   - predefined-model
 provider_credential_schema:

api/core/model_runtime/model_providers/bedrock/llm/amazon.nova-lite-v1.yaml

@@ -0,0 +1,53 @@
+model: amazon.nova-lite-v1:0
+label:
+  en_US: Nova Lite V1
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00006'
+  output: '0.00024'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/amazon.nova-micro-v1.yaml

@@ -0,0 +1,52 @@
+model: amazon.nova-micro-v1:0
+label:
+  en_US: Nova Micro V1
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.000035'
+  output: '0.00014'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/amazon.nova-pro-v1.yaml

@@ -0,0 +1,53 @@
+model: amazon.nova-pro-v1:0
+label:
+  en_US: Nova Pro V1
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.0008'
+  output: '0.0032'
+  unit: '0.001'
+  currency: USD

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

@@ -70,6 +70,8 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         {"prefix": "cohere.command-r", "support_system_prompts": True, "support_tool_use": True},
         {"prefix": "amazon.titan", "support_system_prompts": False, "support_tool_use": False},
         {"prefix": "ai21.jamba-1-5", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "amazon.nova", "support_system_prompts": True, "support_tool_use": False},
+        {"prefix": "us.amazon.nova", "support_system_prompts": True, "support_tool_use": False},
     ]
 
     @staticmethod
@@ -194,6 +196,13 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         if model_info["support_tool_use"] and tools:
             parameters["toolConfig"] = self._convert_converse_tool_config(tools=tools)
         try:
+            # for issue #10976
+            conversations_list = parameters["messages"]
+            # if two consecutive user messages found, combine them into one message
+            for i in range(len(conversations_list) - 2, -1, -1):
+                if conversations_list[i]["role"] == conversations_list[i + 1]["role"]:
+                    conversations_list[i]["content"].extend(conversations_list.pop(i + 1)["content"])
+
             if stream:
                 response = bedrock_client.converse_stream(**parameters)
                 return self._handle_converse_stream_response(

api/core/model_runtime/model_providers/bedrock/llm/us.amazon.nova-lite-v1.yaml

@@ -0,0 +1,53 @@
+model: us.amazon.nova-lite-v1:0
+label:
+  en_US: Nova Lite V1 (US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00006'
+  output: '0.00024'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/us.amazon.nova-micro-v1.yaml

@@ -0,0 +1,52 @@
+model: us.amazon.nova-micro-v1:0
+label:
+  en_US: Nova Micro V1 (US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.000035'
+  output: '0.00014'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/us.amazon.nova-pro-v1.yaml

@@ -0,0 +1,53 @@
+model: us.amazon.nova-pro-v1:0
+label:
+  en_US: Nova Pro V1 (US.Cross Region Inference)
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+  - vision
+model_properties:
+  mode: chat
+  context_size: 300000
+parameter_rules:
+  - name: max_new_tokens
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 5000
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.0008'
+  output: '0.0032'
+  unit: '0.001'
+  currency: USD

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

@@ -0,0 +1,2 @@
+- amazon.rerank-v1
+- cohere.rerank-v3-5

api/core/model_runtime/model_providers/bedrock/rerank/amazon.rerank-v1.yaml

@@ -0,0 +1,4 @@
+model: amazon.rerank-v1:0
+model_type: rerank
+model_properties:
+  context_size: 5120

api/core/model_runtime/model_providers/bedrock/rerank/cohere.rerank-v3-5.yaml

@@ -0,0 +1,4 @@
+model: cohere.rerank-v3-5:0
+model_type: rerank
+model_properties:
+  context_size: 5120

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

@@ -0,0 +1,147 @@
+from typing import Optional
+
+import boto3
+from botocore.config import Config
+
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class BedrockRerankModel(RerankModel):
+    """
+    Model class for Cohere rerank model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n
+        :param user: unique user id
+        :return: rerank result
+        """
+
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=docs)
+
+        # initialize client
+        client_config = Config(region_name=credentials["aws_region"])
+        bedrock_runtime = boto3.client(
+            service_name="bedrock-agent-runtime",
+            config=client_config,
+            aws_access_key_id=credentials.get("aws_access_key_id", ""),
+            aws_secret_access_key=credentials.get("aws_secret_access_key"),
+        )
+        queries = [{"type": "TEXT", "textQuery": {"text": query}}]
+        text_sources = []
+        for text in docs:
+            text_sources.append(
+                {
+                    "type": "INLINE",
+                    "inlineDocumentSource": {
+                        "type": "TEXT",
+                        "textDocument": {
+                            "text": text,
+                        },
+                    },
+                }
+            )
+        modelId = model
+        region = credentials["aws_region"]
+        model_package_arn = f"arn:aws:bedrock:{region}::foundation-model/{modelId}"
+        rerankingConfiguration = {
+            "type": "BEDROCK_RERANKING_MODEL",
+            "bedrockRerankingConfiguration": {
+                "numberOfResults": top_n,
+                "modelConfiguration": {
+                    "modelArn": model_package_arn,
+                },
+            },
+        }
+        response = bedrock_runtime.rerank(
+            queries=queries, sources=text_sources, rerankingConfiguration=rerankingConfiguration
+        )
+
+        rerank_documents = []
+        for idx, result in enumerate(response["results"]):
+            # format document
+            index = result["index"]
+            rerank_document = RerankDocument(
+                index=index,
+                text=docs[index],
+                score=result["relevanceScore"],
+            )
+
+            # score threshold check
+            if score_threshold is not None:
+                if rerank_document.score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+            else:
+                rerank_documents.append(rerank_document)
+
+        return RerankResult(model=model, docs=rerank_documents)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self.invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller
+        The value is the md = genai.GenerativeModel(model) error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke emd = genai.GenerativeModel(model) error mapping
+        """
+        return {
+            InvokeConnectionError: [],
+            InvokeServerUnavailableError: [],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [],
+            InvokeBadRequestError: [],
+        }

api/core/model_runtime/model_providers/cohere/rerank/_position.yaml

@@ -2,3 +2,4 @@
 - rerank-english-v3.0
 - rerank-multilingual-v2.0
 - rerank-multilingual-v3.0
+- rerank-v3.5

api/core/model_runtime/model_providers/cohere/rerank/rerank-v3.5.yaml

@@ -0,0 +1,4 @@
+model: rerank-v3.5
+model_type: rerank
+model_properties:
+  context_size: 5120

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

@@ -32,12 +32,12 @@ class GiteeAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
         return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials, model, None)
+        self._add_custom_parameters(credentials, None)
         super().validate_credentials(model, credentials)
 
-    def _add_custom_parameters(self, credentials: dict, model: str, model_parameters: dict) -> None:
+    def _add_custom_parameters(self, credentials: dict, model: Optional[str]) -> None:
         if model is None:
-            model = "bge-large-zh-v1.5"
+            model = "Qwen2-72B-Instruct"
 
         model_identity = GiteeAILargeLanguageModel.MODEL_TO_IDENTITY.get(model, model)
         credentials["endpoint_url"] = f"https://ai.gitee.com/api/serverless/{model_identity}/"
@@ -47,5 +47,7 @@ class GiteeAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
             credentials["mode"] = LLMMode.CHAT.value
 
         schema = self.get_model_schema(model, credentials)
+        assert schema is not None, f"Model schema not found for model {model}"
+        assert schema.features is not None, f"Model features not found for model {model}"
         if ModelFeature.TOOL_CALL in schema.features or ModelFeature.MULTI_TOOL_CALL in schema.features:
             credentials["function_calling_type"] = "tool_call"

api/core/model_runtime/model_providers/google/llm/gemini-exp-1206.yaml

@@ -0,0 +1,38 @@
+model: gemini-exp-1206
+label:
+  en_US: Gemini exp 1206
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+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/google/llm/llm.py

@@ -254,8 +254,12 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         assistant_prompt_message = AssistantPromptMessage(content=response.text)
 
         # calculate num tokens
-        prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-        completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+        if response.usage_metadata:
+            prompt_tokens = response.usage_metadata.prompt_token_count
+            completion_tokens = response.usage_metadata.candidates_token_count
+        else:
+            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
         # transform usage
         usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)

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

@@ -252,7 +252,7 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 # ignore sse comments
                 if chunk.startswith(":"):
                     continue
-                decoded_chunk = chunk.strip().lstrip("data: ").lstrip()
+                decoded_chunk = chunk.strip().removeprefix("data: ")
                 chunk_json = None
                 try:
                     chunk_json = json.loads(decoded_chunk)

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

@@ -181,9 +181,11 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         # prepare the payload for a simple ping to the model
         data = {"model": model, "stream": stream}
 
-        if "format" in model_parameters:
-            data["format"] = model_parameters["format"]
-            del model_parameters["format"]
+        if format_schema := model_parameters.pop("format", None):
+            try:
+                data["format"] = format_schema if format_schema == "json" else json.loads(format_schema)
+            except json.JSONDecodeError as e:
+                raise InvokeBadRequestError(f"Invalid format schema: {str(e)}")
 
         if "keep_alive" in model_parameters:
             data["keep_alive"] = model_parameters["keep_alive"]
@@ -733,12 +735,12 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                 ParameterRule(
                     name="format",
                     label=I18nObject(en_US="Format", zh_Hans="返回格式"),
-                    type=ParameterType.STRING,
+                    type=ParameterType.TEXT,
+                    default="json",
                     help=I18nObject(
-                        en_US="the format to return a response in. Currently the only accepted value is json.",
-                        zh_Hans="返回响应的格式。目前唯一接受的值是json。",
+                        en_US="the format to return a response in. Format can be `json` or a JSON schema.",
+                        zh_Hans="返回响应的格式。目前接受的值是字符串`json`或JSON schema.",
                     ),
-                    options=["json"],
                 ),
             ],
             pricing=PriceConfig(

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

@@ -462,7 +462,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                 # ignore sse comments
                 if chunk.startswith(":"):
                     continue
-                decoded_chunk = chunk.strip().lstrip("data: ").lstrip()
+                decoded_chunk = chunk.strip().removeprefix("data: ")
                 if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
                     continue
 

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

@@ -139,13 +139,17 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
             if api_key:
                 headers["Authorization"] = f"Bearer {api_key}"
 
-            endpoint_url = credentials.get("endpoint_url")
+            endpoint_url = credentials.get("endpoint_url", "")
             if not endpoint_url.endswith("/"):
                 endpoint_url += "/"
 
             endpoint_url = urljoin(endpoint_url, "embeddings")
 
             payload = {"input": "ping", "model": model}
+            # For nvidia models, the "input_type":"query" need in the payload
+            # more to check issue #11193 or NvidiaTextEmbeddingModel
+            if model.startswith("nvidia/"):
+                payload["input_type"] = "query"
 
             response = requests.post(url=endpoint_url, headers=headers, data=json.dumps(payload), timeout=(10, 300))
 

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

@@ -250,7 +250,7 @@ class StepfunLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 # ignore sse comments
                 if chunk.startswith(":"):
                     continue
-                decoded_chunk = chunk.strip().lstrip("data: ").lstrip()
+                decoded_chunk = chunk.strip().removeprefix("data: ")
                 chunk_json = None
                 try:
                     chunk_json = json.loads(decoded_chunk)

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

@@ -104,13 +104,14 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         """
         # use Anthropic official SDK references
         # - https://github.com/anthropics/anthropic-sdk-python
-        service_account_info = json.loads(base64.b64decode(credentials["vertex_service_account_key"]))
+        service_account_key = credentials.get("vertex_service_account_key", "")
         project_id = credentials["vertex_project_id"]
         SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]
         token = ""
 
         # get access token from service account credential
-        if service_account_info:
+        if service_account_key:
+            service_account_info = json.loads(base64.b64decode(service_account_key))
             credentials = service_account.Credentials.from_service_account_info(service_account_info, scopes=SCOPES)
             request = google.auth.transport.requests.Request()
             credentials.refresh(request)
@@ -478,10 +479,11 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
         if stop:
             config_kwargs["stop_sequences"] = stop
 
-        service_account_info = json.loads(base64.b64decode(credentials["vertex_service_account_key"]))
+        service_account_key = credentials.get("vertex_service_account_key", "")
         project_id = credentials["vertex_project_id"]
         location = credentials["vertex_location"]
-        if service_account_info:
+        if service_account_key:
+            service_account_info = json.loads(base64.b64decode(service_account_key))
             service_accountSA = service_account.Credentials.from_service_account_info(service_account_info)
             aiplatform.init(credentials=service_accountSA, project=project_id, location=location)
         else:

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

@@ -48,10 +48,11 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
         :param input_type: input type
         :return: embeddings result
         """
-        service_account_info = json.loads(base64.b64decode(credentials["vertex_service_account_key"]))
+        service_account_key = credentials.get("vertex_service_account_key", "")
         project_id = credentials["vertex_project_id"]
         location = credentials["vertex_location"]
-        if service_account_info:
+        if service_account_key:
+            service_account_info = json.loads(base64.b64decode(service_account_key))
             service_accountSA = service_account.Credentials.from_service_account_info(service_account_info)
             aiplatform.init(credentials=service_accountSA, project=project_id, location=location)
         else:
@@ -100,10 +101,11 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
         :return:
         """
         try:
-            service_account_info = json.loads(base64.b64decode(credentials["vertex_service_account_key"]))
+            service_account_key = credentials.get("vertex_service_account_key", "")
             project_id = credentials["vertex_project_id"]
             location = credentials["vertex_location"]
-            if service_account_info:
+            if service_account_key:
+                service_account_info = json.loads(base64.b64decode(service_account_key))
                 service_accountSA = service_account.Credentials.from_service_account_info(service_account_info)
                 aiplatform.init(credentials=service_accountSA, project=project_id, location=location)
             else:

api/core/model_runtime/model_providers/volcengine_maas/legacy/volc_sdk/__init__.py

@@ -1,4 +1,4 @@
 from .common import ChatRole
 from .maas import MaasError, MaasService
 
-__all__ = ["MaasService", "ChatRole", "MaasError"]
+__all__ = ["ChatRole", "MaasError", "MaasService"]

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

@@ -17,7 +17,13 @@ class WenxinRerank(_CommonWenxin):
     def rerank(self, model: str, query: str, docs: list[str], top_n: Optional[int] = None):
         access_token = self._get_access_token()
         url = f"{self.api_bases[model]}?access_token={access_token}"
-
+        # For issue #11252
+        # for wenxin Rerank model top_n length should be equal or less than docs length
+        if top_n is not None and top_n > len(docs):
+            top_n = len(docs)
+        # for wenxin Rerank model, query should not be an empty string
+        if query == "":
+            query = " "  # FIXME: this is a workaround for wenxin rerank model for better user experience.
         try:
             response = httpx.post(
                 url,
@@ -25,7 +31,11 @@ class WenxinRerank(_CommonWenxin):
                 headers={"Content-Type": "application/json"},
             )
             response.raise_for_status()
-            return response.json()
+            data = response.json()
+            # wenxin error handling
+            if "error_code" in data:
+                raise InternalServerError(data["error_msg"])
+            return data
         except httpx.HTTPStatusError as e:
             raise InternalServerError(str(e))
 
@@ -69,6 +79,9 @@ class WenxinRerankModel(RerankModel):
             results = wenxin_rerank.rerank(model, query, docs, top_n)
 
             rerank_documents = []
+            if "results" not in results:
+                raise ValueError("results key not found in response")
+
             for result in results["results"]:
                 index = result["index"]
                 if "document" in result:

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 8192
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flashx.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml

@@ -8,7 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 10240
+  context_size: 1048576
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml

@@ -8,6 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
+  context_size: 131072
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml

@@ -4,6 +4,7 @@ label:
 model_type: llm
 model_properties:
   mode: chat
+  context_size: 2048
 features:
   - vision
 parameter_rules:

api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_flash.yaml

@@ -0,0 +1,52 @@
+model: glm-4v-flash
+label:
+  en_US: glm-4v-flash
+model_type: llm
+model_properties:
+  mode: chat
+  context_size: 2048
+features:
+  - vision
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.95
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 采样温度，控制输出的随机性，必须为正数取值范围是：(0.0,1.0]，不能等于 0,默认值为 0.95 值越大，会使输出更随机，更具创造性；值越小，输出会更加稳定或确定建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
+      en_US: Sampling temperature, controls the randomness of the output, must be a positive number. The value range is (0.0,1.0], which cannot be equal to 0. The default value is 0.95. The larger the value, the more random and creative the output will be; the smaller the value, The output will be more stable or certain. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
+  - name: top_p
+    use_template: top_p
+    default: 0.6
+    help:
+      zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
+      en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
+  - name: do_sample
+    label:
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
+    type: boolean
+    help:
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 1024
+  - name: web_search
+    type: boolean
+    label:
+      zh_Hans: 联网搜索
+      en_US: Web Search
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml

@@ -4,6 +4,7 @@ label:
 model_type: llm
 model_properties:
   mode: chat
+  context_size: 8192
 features:
   - vision
   - video

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

@@ -22,18 +22,6 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.model_runtime.model_providers.zhipuai._common import _CommonZhipuaiAI
 from core.model_runtime.utils import helper
 
-GLM_JSON_MODE_PROMPT = """You should always follow the instructions and output a valid JSON object.
-The structure of the JSON object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
-if you are not sure about the structure.
-
-And you should always end the block with a "```" to indicate the end of the JSON object.
-
-<instructions>
-{{instructions}}
-</instructions>
-
-```JSON"""  # noqa: E501
-
 
 class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
     def _invoke(
@@ -64,42 +52,8 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         credentials_kwargs = self._to_credential_kwargs(credentials)
 
         # invoke model
-        # stop = stop or []
-        # self._transform_json_prompts(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
         return self._generate(model, credentials_kwargs, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    # def _transform_json_prompts(self, model: str, credentials: dict,
-    #                             prompt_messages: list[PromptMessage], model_parameters: dict,
-    #                             tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
-    #                             stream: bool = True, user: str | None = None) \
-    #                         -> None:
-    #     """
-    #     Transform json prompts to model prompts
-    #     """
-    #     if "}\n\n" not in stop:
-    #         stop.append("}\n\n")
-
-    #     # check if there is a system message
-    #     if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
-    #         # override the system message
-    #         prompt_messages[0] = SystemPromptMessage(
-    #             content=GLM_JSON_MODE_PROMPT.replace("{{instructions}}", prompt_messages[0].content)
-    #         )
-    #     else:
-    #         # insert the system message
-    #         prompt_messages.insert(0, SystemPromptMessage(
-    #             content=GLM_JSON_MODE_PROMPT.replace("{{instructions}}", "Please output a valid JSON object.")
-    #         ))
-    #     # check if the last message is a user message
-    #     if len(prompt_messages) > 0 and isinstance(prompt_messages[-1], UserPromptMessage):
-    #         # add ```JSON\n to the last message
-    #         prompt_messages[-1].content += "\n```JSON\n"
-    #     else:
-    #         # append a user message
-    #         prompt_messages.append(UserPromptMessage(
-    #             content="```JSON\n"
-    #         ))
-
     def get_num_tokens(
         self,
         model: str,
@@ -170,7 +124,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         extra_model_kwargs = {}
-        # request to glm-4v-plus with stop words will always response "finish_reason":"network_error"
+        # request to glm-4v-plus with stop words will always respond "finish_reason":"network_error"
         if stop and model != "glm-4v-plus":
             extra_model_kwargs["stop"] = stop
 
@@ -186,11 +140,11 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         # resolve zhipuai model not support system message and user message, assistant message must be in sequence
         new_prompt_messages: list[PromptMessage] = []
         for prompt_message in prompt_messages:
-            copy_prompt_message = prompt_message.copy()
+            copy_prompt_message = prompt_message.model_copy()
             if copy_prompt_message.role in {PromptMessageRole.USER, PromptMessageRole.SYSTEM, PromptMessageRole.TOOL}:
                 if isinstance(copy_prompt_message.content, list):
                     # check if model is 'glm-4v'
-                    if model not in {"glm-4v", "glm-4v-plus"}:
+                    if not model.startswith("glm-4v"):
                         # not support list message
                         continue
                     # get image and
@@ -234,63 +188,42 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
             else:
                 model_parameters["tools"] = [web_search_params]
 
-        if model in {"glm-4v", "glm-4v-plus"}:
+        if model.startswith("glm-4v"):
             params = self._construct_glm_4v_parameter(model, new_prompt_messages, model_parameters)
         else:
             params = {"model": model, "messages": [], **model_parameters}
-            # glm model
-            if not model.startswith("chatglm"):
-                for prompt_message in new_prompt_messages:
-                    if prompt_message.role == PromptMessageRole.TOOL:
+            for prompt_message in new_prompt_messages:
+                if prompt_message.role == PromptMessageRole.TOOL:
+                    params["messages"].append(
+                        {
+                            "role": "tool",
+                            "content": prompt_message.content,
+                            "tool_call_id": prompt_message.tool_call_id,
+                        }
+                    )
+                elif isinstance(prompt_message, AssistantPromptMessage):
+                    if prompt_message.tool_calls:
                         params["messages"].append(
                             {
-                                "role": "tool",
+                                "role": "assistant",
                                 "content": prompt_message.content,
-                                "tool_call_id": prompt_message.tool_call_id,
+                                "tool_calls": [
+                                    {
+                                        "id": tool_call.id,
+                                        "type": tool_call.type,
+                                        "function": {
+                                            "name": tool_call.function.name,
+                                            "arguments": tool_call.function.arguments,
+                                        },
+                                    }
+                                    for tool_call in prompt_message.tool_calls
+                                ],
                             }
                         )
-                    elif isinstance(prompt_message, AssistantPromptMessage):
-                        if prompt_message.tool_calls:
-                            params["messages"].append(
-                                {
-                                    "role": "assistant",
-                                    "content": prompt_message.content,
-                                    "tool_calls": [
-                                        {
-                                            "id": tool_call.id,
-                                            "type": tool_call.type,
-                                            "function": {
-                                                "name": tool_call.function.name,
-                                                "arguments": tool_call.function.arguments,
-                                            },
-                                        }
-                                        for tool_call in prompt_message.tool_calls
-                                    ],
-                                }
-                            )
-                        else:
-                            params["messages"].append({"role": "assistant", "content": prompt_message.content})
                     else:
-                        params["messages"].append(
-                            {"role": prompt_message.role.value, "content": prompt_message.content}
-                        )
-            else:
-                # chatglm model
-                for prompt_message in new_prompt_messages:
-                    # merge system message to user message
-                    if prompt_message.role in {
-                        PromptMessageRole.SYSTEM,
-                        PromptMessageRole.TOOL,
-                        PromptMessageRole.USER,
-                    }:
-                        if len(params["messages"]) > 0 and params["messages"][-1]["role"] == "user":
-                            params["messages"][-1]["content"] += "\n\n" + prompt_message.content
-                        else:
-                            params["messages"].append({"role": "user", "content": prompt_message.content})
-                    else:
-                        params["messages"].append(
-                            {"role": prompt_message.role.value, "content": prompt_message.content}
-                        )
+                        params["messages"].append({"role": "assistant", "content": prompt_message.content})
+                else:
+                    params["messages"].append({"role": prompt_message.role.value, "content": prompt_message.content})
 
         if tools and len(tools) > 0:
             params["tools"] = [{"type": "function", "function": helper.dump_model(tool)} for tool in tools]
@@ -406,7 +339,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         Handle llm stream response
 
         :param model: model name
-        :param response: response
+        :param responses: response
         :param prompt_messages: prompt messages
         :return: llm response chunk generator result
         """
@@ -479,6 +412,8 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         human_prompt = "\n\nHuman:"
         ai_prompt = "\n\nAssistant:"
         content = message.content
+        if isinstance(content, list):
+            content = "".join(c.data for c in content if c.type == PromptMessageContentType.TEXT)
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
@@ -505,7 +440,7 @@ class ZhipuAILargeLanguageModel(_CommonZhipuaiAI, LargeLanguageModel):
         if tools and len(tools) > 0:
             text += "\n\nTools:"
             for tool in tools:
-                text += f"\n{tool.json()}"
+                text += f"\n{tool.model_dump_json()}"
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()

api/core/prompt/prompt_templates/advanced_prompt_templates.py

@@ -5,7 +5,7 @@ BAICHUAN_CONTEXT = "用户在与一个客观的助手对话。助手会尊重找
 CHAT_APP_COMPLETION_PROMPT_CONFIG = {
     "completion_prompt_config": {
         "prompt": {
-            "text": "{{#pre_prompt#}}\nHere is the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: "  # noqa: E501
+            "text": "{{#pre_prompt#}}\nHere are the chat histories between human and assistant, inside <histories></histories> XML tags.\n\n<histories>\n{{#histories#}}\n</histories>\n\n\nHuman: {{#query#}}\n\nAssistant: "  # noqa: E501
         },
         "conversation_histories_role": {"user_prefix": "Human", "assistant_prefix": "Assistant"},
     },

api/core/prompt/utils/get_thread_messages_length.py

@@ -0,0 +1,32 @@
+from core.prompt.utils.extract_thread_messages import extract_thread_messages
+from extensions.ext_database import db
+from models.model import Message
+
+
+def get_thread_messages_length(conversation_id: str) -> int:
+    """
+    Get the number of thread messages based on the parent message id.
+    """
+    # Fetch all messages related to the conversation
+    query = (
+        db.session.query(
+            Message.id,
+            Message.parent_message_id,
+            Message.answer,
+        )
+        .filter(
+            Message.conversation_id == conversation_id,
+        )
+        .order_by(Message.created_at.desc())
+    )
+
+    messages = query.all()
+
+    # Extract thread messages
+    thread_messages = extract_thread_messages(messages)
+
+    # Exclude the newly created message with an empty answer
+    if thread_messages and not thread_messages[0].answer:
+        thread_messages.pop(0)
+
+    return len(thread_messages)

api/core/rag/datasource/retrieval_service.py

@@ -110,8 +110,12 @@ class RetrievalService:
                 str(dataset.tenant_id), reranking_mode, reranking_model, weights, False
             )
             all_documents = data_post_processor.invoke(
-                query=query, documents=all_documents, score_threshold=score_threshold, top_n=top_k
+                query=query,
+                documents=all_documents,
+                score_threshold=score_threshold,
+                top_n=top_k,
             )
+
         return all_documents
 
     @classmethod
@@ -178,7 +182,10 @@ class RetrievalService:
                         )
                         all_documents.extend(
                             data_post_processor.invoke(
-                                query=query, documents=documents, score_threshold=score_threshold, top_n=len(documents)
+                                query=query,
+                                documents=documents,
+                                score_threshold=score_threshold,
+                                top_n=len(documents),
                             )
                         )
                     else:
@@ -220,7 +227,10 @@ class RetrievalService:
                         )
                         all_documents.extend(
                             data_post_processor.invoke(
-                                query=query, documents=documents, score_threshold=score_threshold, top_n=len(documents)
+                                query=query,
+                                documents=documents,
+                                score_threshold=score_threshold,
+                                top_n=len(documents),
                             )
                         )
                     else:

api/core/rag/datasource/vdb/oceanbase/oceanbase_vector.py

@@ -104,8 +104,7 @@ class OceanBaseVector(BaseVector):
                 val = int(row[6])
                 vals.append(val)
             if len(vals) == 0:
-                print("ob_vector_memory_limit_percentage not found in parameters.")
-                exit(1)
+                raise ValueError("ob_vector_memory_limit_percentage not found in parameters.")
             if any(val == 0 for val in vals):
                 try:
                     self._client.perform_raw_text_sql("ALTER SYSTEM SET ob_vector_memory_limit_percentage = 30")
@@ -200,10 +199,10 @@ class OceanBaseVectorFactory(AbstractVectorFactory):
         return OceanBaseVector(
             collection_name,
             OceanBaseVectorConfig(
-                host=dify_config.OCEANBASE_VECTOR_HOST,
-                port=dify_config.OCEANBASE_VECTOR_PORT,
-                user=dify_config.OCEANBASE_VECTOR_USER,
+                host=dify_config.OCEANBASE_VECTOR_HOST or "",
+                port=dify_config.OCEANBASE_VECTOR_PORT or 0,
+                user=dify_config.OCEANBASE_VECTOR_USER or "",
                 password=(dify_config.OCEANBASE_VECTOR_PASSWORD or ""),
-                database=dify_config.OCEANBASE_VECTOR_DATABASE,
+                database=dify_config.OCEANBASE_VECTOR_DATABASE or "",
             ),
         )

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -230,7 +230,6 @@ class OracleVector(BaseVector):
                 except LookupError:
                     nltk.download("punkt")
                     nltk.download("stopwords")
-                    print("run download")
                 e_str = re.sub(r"[^\w ]", "", query)
                 all_tokens = nltk.word_tokenize(e_str)
                 stop_words = stopwords.words("english")

api/core/rag/datasource/vdb/tidb_on_qdrant/tidb_on_qdrant_vector.py

@@ -375,7 +375,6 @@ class TidbOnQdrantVector(BaseVector):
         for result in results:
             if result:
                 document = self._document_from_scored_point(result, Field.CONTENT_KEY.value, Field.METADATA_KEY.value)
-                document.metadata["vector"] = result.vector
                 documents.append(document)
 
         return documents
@@ -394,6 +393,7 @@ class TidbOnQdrantVector(BaseVector):
     ) -> Document:
         return Document(
             page_content=scored_point.payload.get(content_payload_key),
+            vector=scored_point.vector,
             metadata=scored_point.payload.get(metadata_payload_key) or {},
         )