fix model runtime quota

.github/workflows/api-tests.yml

@@ -76,7 +76,7 @@ jobs:
       - name: Run Workflow
         run: poetry run -C api bash dev/pytest/pytest_workflow.sh
 
-      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale, ElasticSearch)
+      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale)
         uses: hoverkraft-tech/compose-action@v2.0.0
         with:
           compose-file: |
@@ -90,6 +90,5 @@ jobs:
             pgvecto-rs
             pgvector
             chroma
-            elasticsearch
       - name: Test Vector Stores
         run: poetry run -C api bash dev/pytest/pytest_vdb.sh

.github/workflows/expose_service_ports.sh

@@ -6,6 +6,5 @@ yq eval '.services.chroma.ports += ["8000:8000"]' -i docker/docker-compose.yaml
 yq eval '.services["milvus-standalone"].ports += ["19530:19530"]' -i docker/docker-compose.yaml
 yq eval '.services.pgvector.ports += ["5433:5432"]' -i docker/docker-compose.yaml
 yq eval '.services["pgvecto-rs"].ports += ["5431:5432"]' -i docker/docker-compose.yaml
-yq eval '.services["elasticsearch"].ports += ["9200:9200"]' -i docker/docker-compose.yaml
 
-echo "Ports exposed for sandbox, weaviate, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch"
+echo "Ports exposed for sandbox, weaviate, qdrant, chroma, milvus, pgvector, pgvecto-rs."

.github/workflows/style.yml

@@ -45,10 +45,6 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         run: poetry run -C api dotenv-linter ./api/.env.example ./web/.env.example
 
-      - name: Ruff formatter check
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: poetry run -C api ruff format --check ./api
-
       - name: Lint hints
         if: failure()
         run: echo "Please run 'dev/reformat' to fix the fixable linting errors."

README_VI.md

@@ -152,7 +152,7 @@ Nhanh chóng chạy Dify trong môi trường của bạn với [hướng dẫn
 Sử dụng [tài liệu](https://docs.dify.ai) của chúng tôi để tham khảo thêm và nhận hướng dẫn chi tiết hơn.
 
 - **Dify cho doanh nghiệp / tổ chức</br>**
-Chúng tôi cung cấp các tính năng bổ sung tập trung vào doanh nghiệp. [Ghi lại câu hỏi của bạn cho chúng tôi thông qua chatbot này](https://udify.app/chat/22L1zSxg6yW1cWQg) hoặc [gửi email cho chúng tôi](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) để thảo luận về nhu cầu doanh nghiệp. </br>
+Chúng tôi cung cấp các tính năng bổ sung tập trung vào doanh nghiệp. [Lên lịch cuộc họp với chúng tôi](https://cal.com/guchenhe/30min) hoặc [gửi email cho chúng tôi](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) để thảo luận về nhu cầu doanh nghiệp. </br>
   > Đối với các công ty khởi nghiệp và doanh nghiệp nhỏ sử dụng AWS, hãy xem [Dify Premium trên AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) và triển khai nó vào AWS VPC của riêng bạn chỉ với một cú nhấp chuột. Đây là một AMI giá cả phải chăng với tùy chọn tạo ứng dụng với logo và thương hiệu tùy chỉnh.
 
 
@@ -221,6 +221,23 @@ Triển khai Dify lên Azure chỉ với một cú nhấp chuột bằng cách s
 * [Discord](https://discord.gg/FngNHpbcY7). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.
 * [Twitter](https://twitter.com/dify_ai). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.
 
+Hoặc, lên lịch cuộc họp trực tiếp với một thành viên trong nhóm:
+
+<table>
+  <tr>
+    <th>Điểm liên hệ</th>
+    <th>Mục đích</th>
+  </tr>
+  <tr>
+    <td><a href='https://cal.com/guchenhe/15min' target='_blank'><img class="schedule-button" src='https://github.com/langgenius/dify/assets/13230914/9ebcd111-1205-4d71-83d5-948d70b809f5' alt='Git-Hub-README-Button-3x' style="width: 180px; height: auto; object-fit: contain;"/></a></td>
+    <td>Yêu cầu kinh doanh & phản hồi sản phẩm</td>
+  </tr>
+  <tr>
+    <td><a href='https://cal.com/pinkbanana' target='_blank'><img class="schedule-button" src='https://github.com/langgenius/dify/assets/13230914/d1edd00a-d7e4-4513-be6c-e57038e143fd' alt='Git-Hub-README-Button-2x' style="width: 180px; height: auto; object-fit: contain;"/></a></td>
+    <td>Đóng góp, vấn đề & yêu cầu tính năng</td>
+  </tr>
+</table>
+
 ## Lịch sử Yêu thích
 
 [![Biểu đồ Lịch sử Yêu thích](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)

api/.env.example

@@ -130,12 +130,6 @@ TENCENT_VECTOR_DB_DATABASE=dify
 TENCENT_VECTOR_DB_SHARD=1
 TENCENT_VECTOR_DB_REPLICAS=2
 
-# ElasticSearch configuration
-ELASTICSEARCH_HOST=127.0.0.1
-ELASTICSEARCH_PORT=9200
-ELASTICSEARCH_USERNAME=elastic
-ELASTICSEARCH_PASSWORD=elastic
-
 # PGVECTO_RS configuration
 PGVECTO_RS_HOST=localhost
 PGVECTO_RS_PORT=5431

api/app.py

@@ -1,6 +1,6 @@
 import os
 
-if os.environ.get("DEBUG", "false").lower() != "true":
+if os.environ.get("DEBUG", "false").lower() != 'true':
     from gevent import monkey
 
     monkey.patch_all()
@@ -57,7 +57,7 @@ warnings.simplefilter("ignore", ResourceWarning)
 if os.name == "nt":
     os.system('tzutil /s "UTC"')
 else:
-    os.environ["TZ"] = "UTC"
+    os.environ['TZ'] = 'UTC'
     time.tzset()
 
 
@@ -70,14 +70,13 @@ class DifyApp(Flask):
 # -------------
 
 
-config_type = os.getenv("EDITION", default="SELF_HOSTED")  # ce edition first
+config_type = os.getenv('EDITION', default='SELF_HOSTED')  # ce edition first
 
 
 # ----------------------------
 # Application Factory Function
 # ----------------------------
 
-
 def create_flask_app_with_configs() -> Flask:
     """
     create a raw flask app
@@ -93,7 +92,7 @@ def create_flask_app_with_configs() -> Flask:
         elif isinstance(value, int | float | bool):
             os.environ[key] = str(value)
         elif value is None:
-            os.environ[key] = ""
+            os.environ[key] = ''
 
     return dify_app
 
@@ -101,10 +100,10 @@ def create_flask_app_with_configs() -> Flask:
 def create_app() -> Flask:
     app = create_flask_app_with_configs()
 
-    app.secret_key = app.config["SECRET_KEY"]
+    app.secret_key = app.config['SECRET_KEY']
 
     log_handlers = None
-    log_file = app.config.get("LOG_FILE")
+    log_file = app.config.get('LOG_FILE')
     if log_file:
         log_dir = os.path.dirname(log_file)
         os.makedirs(log_dir, exist_ok=True)
@@ -112,24 +111,23 @@ def create_app() -> Flask:
             RotatingFileHandler(
                 filename=log_file,
                 maxBytes=1024 * 1024 * 1024,
-                backupCount=5,
+                backupCount=5
             ),
-            logging.StreamHandler(sys.stdout),
+            logging.StreamHandler(sys.stdout)
         ]
 
     logging.basicConfig(
-        level=app.config.get("LOG_LEVEL"),
-        format=app.config.get("LOG_FORMAT"),
-        datefmt=app.config.get("LOG_DATEFORMAT"),
+        level=app.config.get('LOG_LEVEL'),
+        format=app.config.get('LOG_FORMAT'),
+        datefmt=app.config.get('LOG_DATEFORMAT'),
         handlers=log_handlers,
-        force=True,
+        force=True
     )
-    log_tz = app.config.get("LOG_TZ")
+    log_tz = app.config.get('LOG_TZ')
     if log_tz:
         from datetime import datetime
 
         import pytz
-
         timezone = pytz.timezone(log_tz)
 
         def time_converter(seconds):
@@ -164,24 +162,24 @@ def initialize_extensions(app):
 @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"]:
+    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", "")
+    auth_header = request.headers.get('Authorization', '')
     if not auth_header:
-        auth_token = request.args.get("_token")
+        auth_token = request.args.get('_token')
         if not auth_token:
-            raise Unauthorized("Invalid Authorization token.")
+            raise Unauthorized('Invalid Authorization token.')
     else:
-        if " " not in auth_header:
-            raise Unauthorized("Invalid Authorization header format. Expected 'Bearer <api-key>' format.")
+        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.")
+        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")
+    user_id = decoded.get('user_id')
 
     account = AccountService.load_logged_in_account(account_id=user_id, token=auth_token)
     if account:
@@ -192,11 +190,10 @@ def load_user_from_request(request_from_flask_login):
 @login_manager.unauthorized_handler
 def unauthorized_handler():
     """Handle unauthorized requests."""
-    return Response(
-        json.dumps({"code": "unauthorized", "message": "Unauthorized."}),
-        status=401,
-        content_type="application/json",
-    )
+    return Response(json.dumps({
+        'code': 'unauthorized',
+        'message': "Unauthorized."
+    }), status=401, content_type="application/json")
 
 
 # register blueprint routers
@@ -207,36 +204,38 @@ def register_blueprints(app):
     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"],
-    )
+    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": app.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"],
-    )
+    CORS(web_bp,
+         resources={
+             r"/*": {"origins": app.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": app.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"],
-    )
+    CORS(console_app_bp,
+         resources={
+             r"/*": {"origins": app.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"])
+    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)
@@ -246,29 +245,29 @@ def register_blueprints(app):
 app = create_app()
 celery = app.extensions["celery"]
 
-if app.config.get("TESTING"):
+if app.config.get('TESTING'):
     print("App is running in TESTING mode")
 
 
 @app.after_request
 def after_request(response):
     """Add Version headers to the response."""
-    response.set_cookie("remember_token", "", expires=0)
-    response.headers.add("X-Version", app.config["CURRENT_VERSION"])
-    response.headers.add("X-Env", app.config["DEPLOY_ENV"])
+    response.set_cookie('remember_token', '', expires=0)
+    response.headers.add('X-Version', app.config['CURRENT_VERSION'])
+    response.headers.add('X-Env', app.config['DEPLOY_ENV'])
     return response
 
 
-@app.route("/health")
+@app.route('/health')
 def health():
-    return Response(
-        json.dumps({"pid": os.getpid(), "status": "ok", "version": app.config["CURRENT_VERSION"]}),
-        status=200,
-        content_type="application/json",
-    )
+    return Response(json.dumps({
+        'pid': os.getpid(),
+        'status': 'ok',
+        'version': app.config['CURRENT_VERSION']
+    }), status=200, content_type="application/json")
 
 
-@app.route("/threads")
+@app.route('/threads')
 def threads():
     num_threads = threading.active_count()
     threads = threading.enumerate()
@@ -279,34 +278,32 @@ def threads():
         thread_id = thread.ident
         is_alive = thread.is_alive()
 
-        thread_list.append(
-            {
-                "name": thread_name,
-                "id": thread_id,
-                "is_alive": 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,
+        'pid': os.getpid(),
+        'thread_num': num_threads,
+        'threads': thread_list
     }
 
 
-@app.route("/db-pool-stat")
+@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,
+        '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)
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5001)

api/commands.py

@@ -27,29 +27,32 @@ from models.provider import Provider, ProviderModel
 from services.account_service import RegisterService, TenantService
 
 
-@click.command("reset-password", help="Reset the account password.")
-@click.option("--email", prompt=True, help="The email address of the account whose password you need to reset")
-@click.option("--new-password", prompt=True, help="the new password.")
-@click.option("--password-confirm", prompt=True, help="the new password confirm.")
+@click.command('reset-password', help='Reset the account password.')
+@click.option('--email', prompt=True, help='The email address of the account whose password you need to reset')
+@click.option('--new-password', prompt=True, help='the new password.')
+@click.option('--password-confirm', prompt=True, help='the new password confirm.')
 def reset_password(email, new_password, password_confirm):
     """
     Reset password of owner account
     Only available in SELF_HOSTED mode
     """
     if str(new_password).strip() != str(password_confirm).strip():
-        click.echo(click.style("sorry. The two passwords do not match.", fg="red"))
+        click.echo(click.style('sorry. The two passwords do not match.', fg='red'))
         return
 
-    account = db.session.query(Account).filter(Account.email == email).one_or_none()
+    account = db.session.query(Account). \
+        filter(Account.email == email). \
+        one_or_none()
 
     if not account:
-        click.echo(click.style("sorry. the account: [{}] not exist .".format(email), fg="red"))
+        click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
         return
 
     try:
         valid_password(new_password)
     except:
-        click.echo(click.style("sorry. The passwords must match {} ".format(password_pattern), fg="red"))
+        click.echo(
+            click.style('sorry. The passwords must match {} '.format(password_pattern), fg='red'))
         return
 
     # generate password salt
@@ -62,87 +65,80 @@ def reset_password(email, new_password, password_confirm):
     account.password = base64_password_hashed
     account.password_salt = base64_salt
     db.session.commit()
-    click.echo(click.style("Congratulations! Password has been reset.", fg="green"))
+    click.echo(click.style('Congratulations! Password has been reset.', fg='green'))
 
 
-@click.command("reset-email", help="Reset the account email.")
-@click.option("--email", prompt=True, help="The old email address of the account whose email you need to reset")
-@click.option("--new-email", prompt=True, help="the new email.")
-@click.option("--email-confirm", prompt=True, help="the new email confirm.")
+@click.command('reset-email', help='Reset the account email.')
+@click.option('--email', prompt=True, help='The old email address of the account whose email you need to reset')
+@click.option('--new-email', prompt=True, help='the new email.')
+@click.option('--email-confirm', prompt=True, help='the new email confirm.')
 def reset_email(email, new_email, email_confirm):
     """
     Replace account email
     :return:
     """
     if str(new_email).strip() != str(email_confirm).strip():
-        click.echo(click.style("Sorry, new email and confirm email do not match.", fg="red"))
+        click.echo(click.style('Sorry, new email and confirm email do not match.', fg='red'))
         return
 
-    account = db.session.query(Account).filter(Account.email == email).one_or_none()
+    account = db.session.query(Account). \
+        filter(Account.email == email). \
+        one_or_none()
 
     if not account:
-        click.echo(click.style("sorry. the account: [{}] not exist .".format(email), fg="red"))
+        click.echo(click.style('sorry. the account: [{}] not exist .'.format(email), fg='red'))
         return
 
     try:
         email_validate(new_email)
     except:
-        click.echo(click.style("sorry. {} is not a valid email. ".format(email), fg="red"))
+        click.echo(
+            click.style('sorry. {} is not a valid email. '.format(email), fg='red'))
         return
 
     account.email = new_email
     db.session.commit()
-    click.echo(click.style("Congratulations!, email has been reset.", fg="green"))
+    click.echo(click.style('Congratulations!, email has been reset.', fg='green'))
 
 
-@click.command(
-    "reset-encrypt-key-pair",
-    help="Reset the asymmetric key pair of workspace for encrypt LLM credentials. "
-    "After the reset, all LLM credentials will become invalid, "
-    "requiring re-entry."
-    "Only support SELF_HOSTED mode.",
-)
-@click.confirmation_option(
-    prompt=click.style(
-        "Are you sure you want to reset encrypt key pair?" " this operation cannot be rolled back!", fg="red"
-    )
-)
+@click.command('reset-encrypt-key-pair', help='Reset the asymmetric key pair of workspace for encrypt LLM credentials. '
+                                              'After the reset, all LLM credentials will become invalid, '
+                                              'requiring re-entry.'
+                                              'Only support SELF_HOSTED mode.')
+@click.confirmation_option(prompt=click.style('Are you sure you want to reset encrypt key pair?'
+                                              ' this operation cannot be rolled back!', fg='red'))
 def reset_encrypt_key_pair():
     """
     Reset the encrypted key pair of workspace for encrypt LLM credentials.
     After the reset, all LLM credentials will become invalid, requiring re-entry.
     Only support SELF_HOSTED mode.
     """
-    if dify_config.EDITION != "SELF_HOSTED":
-        click.echo(click.style("Sorry, only support SELF_HOSTED mode.", fg="red"))
+    if dify_config.EDITION != 'SELF_HOSTED':
+        click.echo(click.style('Sorry, only support SELF_HOSTED mode.', fg='red'))
         return
 
     tenants = db.session.query(Tenant).all()
     for tenant in tenants:
         if not tenant:
-            click.echo(click.style("Sorry, no workspace found. Please enter /install to initialize.", fg="red"))
+            click.echo(click.style('Sorry, no workspace found. Please enter /install to initialize.', fg='red'))
             return
 
         tenant.encrypt_public_key = generate_key_pair(tenant.id)
 
-        db.session.query(Provider).filter(Provider.provider_type == "custom", Provider.tenant_id == tenant.id).delete()
+        db.session.query(Provider).filter(Provider.provider_type == 'custom', Provider.tenant_id == tenant.id).delete()
         db.session.query(ProviderModel).filter(ProviderModel.tenant_id == tenant.id).delete()
         db.session.commit()
 
-        click.echo(
-            click.style(
-                "Congratulations! " "the asymmetric key pair of workspace {} has been reset.".format(tenant.id),
-                fg="green",
-            )
-        )
+        click.echo(click.style('Congratulations! '
+                               'the asymmetric key pair of workspace {} has been reset.'.format(tenant.id), fg='green'))
 
 
-@click.command("vdb-migrate", help="migrate vector db.")
-@click.option("--scope", default="all", prompt=False, help="The scope of vector database to migrate, Default is All.")
+@click.command('vdb-migrate', help='migrate vector db.')
+@click.option('--scope', default='all', prompt=False, help='The scope of vector database to migrate, Default is All.')
 def vdb_migrate(scope: str):
-    if scope in ["knowledge", "all"]:
+    if scope in ['knowledge', 'all']:
         migrate_knowledge_vector_database()
-    if scope in ["annotation", "all"]:
+    if scope in ['annotation', 'all']:
         migrate_annotation_vector_database()
 
 
@@ -150,7 +146,7 @@ def migrate_annotation_vector_database():
     """
     Migrate annotation datas to target vector database .
     """
-    click.echo(click.style("Start migrate annotation data.", fg="green"))
+    click.echo(click.style('Start migrate annotation data.', fg='green'))
     create_count = 0
     skipped_count = 0
     total_count = 0
@@ -158,103 +154,98 @@ def migrate_annotation_vector_database():
     while True:
         try:
             # get apps info
-            apps = (
-                db.session.query(App)
-                .filter(App.status == "normal")
-                .order_by(App.created_at.desc())
-                .paginate(page=page, per_page=50)
-            )
+            apps = db.session.query(App).filter(
+                App.status == 'normal'
+            ).order_by(App.created_at.desc()).paginate(page=page, per_page=50)
         except NotFound:
             break
 
         page += 1
         for app in apps:
             total_count = total_count + 1
-            click.echo(
-                f"Processing the {total_count} app {app.id}. " + f"{create_count} created, {skipped_count} skipped."
-            )
+            click.echo(f'Processing the {total_count} app {app.id}. '
+                       + f'{create_count} created, {skipped_count} skipped.')
             try:
-                click.echo("Create app annotation index: {}".format(app.id))
-                app_annotation_setting = (
-                    db.session.query(AppAnnotationSetting).filter(AppAnnotationSetting.app_id == app.id).first()
-                )
+                click.echo('Create app annotation index: {}'.format(app.id))
+                app_annotation_setting = db.session.query(AppAnnotationSetting).filter(
+                    AppAnnotationSetting.app_id == app.id
+                ).first()
 
                 if not app_annotation_setting:
                     skipped_count = skipped_count + 1
-                    click.echo("App annotation setting is disabled: {}".format(app.id))
+                    click.echo('App annotation setting is disabled: {}'.format(app.id))
                     continue
                 # get dataset_collection_binding info
-                dataset_collection_binding = (
-                    db.session.query(DatasetCollectionBinding)
-                    .filter(DatasetCollectionBinding.id == app_annotation_setting.collection_binding_id)
-                    .first()
-                )
+                dataset_collection_binding = db.session.query(DatasetCollectionBinding).filter(
+                    DatasetCollectionBinding.id == app_annotation_setting.collection_binding_id
+                ).first()
                 if not dataset_collection_binding:
-                    click.echo("App annotation collection binding is not exist: {}".format(app.id))
+                    click.echo('App annotation collection binding is not exist: {}'.format(app.id))
                     continue
                 annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app.id).all()
                 dataset = Dataset(
                     id=app.id,
                     tenant_id=app.tenant_id,
-                    indexing_technique="high_quality",
+                    indexing_technique='high_quality',
                     embedding_model_provider=dataset_collection_binding.provider_name,
                     embedding_model=dataset_collection_binding.model_name,
-                    collection_binding_id=dataset_collection_binding.id,
+                    collection_binding_id=dataset_collection_binding.id
                 )
                 documents = []
                 if annotations:
                     for annotation in annotations:
                         document = Document(
                             page_content=annotation.question,
-                            metadata={"annotation_id": annotation.id, "app_id": app.id, "doc_id": annotation.id},
+                            metadata={
+                                "annotation_id": annotation.id,
+                                "app_id": app.id,
+                                "doc_id": annotation.id
+                            }
                         )
                         documents.append(document)
 
-                vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"])
+                vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
                 click.echo(f"Start to migrate annotation, app_id: {app.id}.")
 
                 try:
                     vector.delete()
-                    click.echo(click.style(f"Successfully delete vector index for app: {app.id}.", fg="green"))
+                    click.echo(
+                        click.style(f'Successfully delete vector index for app: {app.id}.',
+                                    fg='green'))
                 except Exception as e:
-                    click.echo(click.style(f"Failed to delete vector index for app {app.id}.", fg="red"))
+                    click.echo(
+                        click.style(f'Failed to delete vector index for app {app.id}.',
+                                    fg='red'))
                     raise e
                 if documents:
                     try:
-                        click.echo(
-                            click.style(
-                                f"Start to created vector index with {len(documents)} annotations for app {app.id}.",
-                                fg="green",
-                            )
-                        )
+                        click.echo(click.style(
+                            f'Start to created vector index with {len(documents)} annotations for app {app.id}.',
+                            fg='green'))
                         vector.create(documents)
-                        click.echo(click.style(f"Successfully created vector index for app {app.id}.", fg="green"))
+                        click.echo(
+                            click.style(f'Successfully created vector index for app {app.id}.', fg='green'))
                     except Exception as e:
-                        click.echo(click.style(f"Failed to created vector index for app {app.id}.", fg="red"))
+                        click.echo(click.style(f'Failed to created vector index for app {app.id}.', fg='red'))
                         raise e
-                click.echo(f"Successfully migrated app annotation {app.id}.")
+                click.echo(f'Successfully migrated app annotation {app.id}.')
                 create_count += 1
             except Exception as e:
                 click.echo(
-                    click.style(
-                        "Create app annotation index error: {} {}".format(e.__class__.__name__, str(e)), fg="red"
-                    )
-                )
+                    click.style('Create app annotation index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                fg='red'))
                 continue
 
     click.echo(
-        click.style(
-            f"Congratulations! Create {create_count} app annotation indexes, and skipped {skipped_count} apps.",
-            fg="green",
-        )
-    )
+        click.style(f'Congratulations! Create {create_count} app annotation indexes, and skipped {skipped_count} apps.',
+                    fg='green'))
 
 
 def migrate_knowledge_vector_database():
     """
     Migrate vector database datas to target vector database .
     """
-    click.echo(click.style("Start migrate vector db.", fg="green"))
+    click.echo(click.style('Start migrate vector db.', fg='green'))
     create_count = 0
     skipped_count = 0
     total_count = 0
@@ -262,77 +253,87 @@ def migrate_knowledge_vector_database():
     page = 1
     while True:
         try:
-            datasets = (
-                db.session.query(Dataset)
-                .filter(Dataset.indexing_technique == "high_quality")
-                .order_by(Dataset.created_at.desc())
-                .paginate(page=page, per_page=50)
-            )
+            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \
+                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
         except NotFound:
             break
 
         page += 1
         for dataset in datasets:
             total_count = total_count + 1
-            click.echo(
-                f"Processing the {total_count} dataset {dataset.id}. "
-                + f"{create_count} created, {skipped_count} skipped."
-            )
+            click.echo(f'Processing the {total_count} dataset {dataset.id}. '
+                       + f'{create_count} created, {skipped_count} skipped.')
             try:
-                click.echo("Create dataset vdb index: {}".format(dataset.id))
+                click.echo('Create dataset vdb index: {}'.format(dataset.id))
                 if dataset.index_struct_dict:
-                    if dataset.index_struct_dict["type"] == vector_type:
+                    if dataset.index_struct_dict['type'] == vector_type:
                         skipped_count = skipped_count + 1
                         continue
-                collection_name = ""
+                collection_name = ''
                 if vector_type == VectorType.WEAVIATE:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": VectorType.WEAVIATE, "vector_store": {"class_prefix": collection_name}}
+                    index_struct_dict = {
+                        "type": VectorType.WEAVIATE,
+                        "vector_store": {"class_prefix": collection_name}
+                    }
                     dataset.index_struct = json.dumps(index_struct_dict)
                 elif vector_type == VectorType.QDRANT:
                     if dataset.collection_binding_id:
-                        dataset_collection_binding = (
-                            db.session.query(DatasetCollectionBinding)
-                            .filter(DatasetCollectionBinding.id == dataset.collection_binding_id)
-                            .one_or_none()
-                        )
+                        dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
+                            filter(DatasetCollectionBinding.id == dataset.collection_binding_id). \
+                            one_or_none()
                         if dataset_collection_binding:
                             collection_name = dataset_collection_binding.collection_name
                         else:
-                            raise ValueError("Dataset Collection Bindings is not exist!")
+                            raise ValueError('Dataset Collection Bindings is not exist!')
                     else:
                         dataset_id = dataset.id
                         collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": VectorType.QDRANT, "vector_store": {"class_prefix": collection_name}}
+                    index_struct_dict = {
+                        "type": VectorType.QDRANT,
+                        "vector_store": {"class_prefix": collection_name}
+                    }
                     dataset.index_struct = json.dumps(index_struct_dict)
 
                 elif vector_type == VectorType.MILVUS:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": VectorType.MILVUS, "vector_store": {"class_prefix": collection_name}}
+                    index_struct_dict = {
+                        "type": VectorType.MILVUS,
+                        "vector_store": {"class_prefix": collection_name}
+                    }
                     dataset.index_struct = json.dumps(index_struct_dict)
                 elif vector_type == VectorType.RELYT:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": "relyt", "vector_store": {"class_prefix": collection_name}}
+                    index_struct_dict = {
+                        "type": 'relyt',
+                        "vector_store": {"class_prefix": collection_name}
+                    }
                     dataset.index_struct = json.dumps(index_struct_dict)
                 elif vector_type == VectorType.TENCENT:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": VectorType.TENCENT, "vector_store": {"class_prefix": collection_name}}
+                    index_struct_dict = {
+                        "type": VectorType.TENCENT,
+                        "vector_store": {"class_prefix": collection_name}
+                    }
                     dataset.index_struct = json.dumps(index_struct_dict)
                 elif vector_type == VectorType.PGVECTOR:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": VectorType.PGVECTOR, "vector_store": {"class_prefix": collection_name}}
+                    index_struct_dict = {
+                        "type": VectorType.PGVECTOR,
+                        "vector_store": {"class_prefix": collection_name}
+                    }
                     dataset.index_struct = json.dumps(index_struct_dict)
                 elif vector_type == VectorType.OPENSEARCH:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": VectorType.OPENSEARCH,
-                        "vector_store": {"class_prefix": collection_name},
+                        "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
                 elif vector_type == VectorType.ANALYTICDB:
@@ -340,14 +341,9 @@ def migrate_knowledge_vector_database():
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": VectorType.ANALYTICDB,
-                        "vector_store": {"class_prefix": collection_name},
+                        "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
-                elif vector_type == VectorType.ELASTICSEARCH:
-                    dataset_id = dataset.id
-                    index_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {"type": "elasticsearch", "vector_store": {"class_prefix": index_name}}
-                    dataset.index_struct = json.dumps(index_struct_dict)
                 else:
                     raise ValueError(f"Vector store {vector_type} is not supported.")
 
@@ -357,41 +353,29 @@ def migrate_knowledge_vector_database():
                 try:
                     vector.delete()
                     click.echo(
-                        click.style(
-                            f"Successfully delete vector index {collection_name} for dataset {dataset.id}.", fg="green"
-                        )
-                    )
+                        click.style(f'Successfully delete vector index {collection_name} for dataset {dataset.id}.',
+                                    fg='green'))
                 except Exception as e:
                     click.echo(
-                        click.style(
-                            f"Failed to delete vector index {collection_name} for dataset {dataset.id}.", fg="red"
-                        )
-                    )
+                        click.style(f'Failed to delete vector index {collection_name} for dataset {dataset.id}.',
+                                    fg='red'))
                     raise e
 
-                dataset_documents = (
-                    db.session.query(DatasetDocument)
-                    .filter(
-                        DatasetDocument.dataset_id == dataset.id,
-                        DatasetDocument.indexing_status == "completed",
-                        DatasetDocument.enabled == True,
-                        DatasetDocument.archived == False,
-                    )
-                    .all()
-                )
+                dataset_documents = db.session.query(DatasetDocument).filter(
+                    DatasetDocument.dataset_id == dataset.id,
+                    DatasetDocument.indexing_status == 'completed',
+                    DatasetDocument.enabled == True,
+                    DatasetDocument.archived == False,
+                ).all()
 
                 documents = []
                 segments_count = 0
                 for dataset_document in dataset_documents:
-                    segments = (
-                        db.session.query(DocumentSegment)
-                        .filter(
-                            DocumentSegment.document_id == dataset_document.id,
-                            DocumentSegment.status == "completed",
-                            DocumentSegment.enabled == True,
-                        )
-                        .all()
-                    )
+                    segments = db.session.query(DocumentSegment).filter(
+                        DocumentSegment.document_id == dataset_document.id,
+                        DocumentSegment.status == 'completed',
+                        DocumentSegment.enabled == True
+                    ).all()
 
                     for segment in segments:
                         document = Document(
@@ -401,7 +385,7 @@ def migrate_knowledge_vector_database():
                                 "doc_hash": segment.index_node_hash,
                                 "document_id": segment.document_id,
                                 "dataset_id": segment.dataset_id,
-                            },
+                            }
                         )
 
                         documents.append(document)
@@ -409,43 +393,37 @@ def migrate_knowledge_vector_database():
 
                 if documents:
                     try:
-                        click.echo(
-                            click.style(
-                                f"Start to created vector index with {len(documents)} documents of {segments_count} segments for dataset {dataset.id}.",
-                                fg="green",
-                            )
-                        )
+                        click.echo(click.style(
+                            f'Start to created vector index with {len(documents)} documents of {segments_count} segments for dataset {dataset.id}.',
+                            fg='green'))
                         vector.create(documents)
                         click.echo(
-                            click.style(f"Successfully created vector index for dataset {dataset.id}.", fg="green")
-                        )
+                            click.style(f'Successfully created vector index for dataset {dataset.id}.', fg='green'))
                     except Exception as e:
-                        click.echo(click.style(f"Failed to created vector index for dataset {dataset.id}.", fg="red"))
+                        click.echo(click.style(f'Failed to created vector index for dataset {dataset.id}.', fg='red'))
                         raise e
                 db.session.add(dataset)
                 db.session.commit()
-                click.echo(f"Successfully migrated dataset {dataset.id}.")
+                click.echo(f'Successfully migrated dataset {dataset.id}.')
                 create_count += 1
             except Exception as e:
                 db.session.rollback()
                 click.echo(
-                    click.style("Create dataset index error: {} {}".format(e.__class__.__name__, str(e)), fg="red")
-                )
+                    click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                fg='red'))
                 continue
 
     click.echo(
-        click.style(
-            f"Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.", fg="green"
-        )
-    )
+        click.style(f'Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.',
+                    fg='green'))
 
 
-@click.command("convert-to-agent-apps", help="Convert Agent Assistant to Agent App.")
+@click.command('convert-to-agent-apps', help='Convert Agent Assistant to Agent App.')
 def convert_to_agent_apps():
     """
     Convert Agent Assistant to Agent App.
     """
-    click.echo(click.style("Start convert to agent apps.", fg="green"))
+    click.echo(click.style('Start convert to agent apps.', fg='green'))
 
     proceeded_app_ids = []
 
@@ -480,7 +458,7 @@ def convert_to_agent_apps():
                 break
 
         for app in apps:
-            click.echo("Converting app: {}".format(app.id))
+            click.echo('Converting app: {}'.format(app.id))
 
             try:
                 app.mode = AppMode.AGENT_CHAT.value
@@ -492,139 +470,137 @@ def convert_to_agent_apps():
                 )
 
                 db.session.commit()
-                click.echo(click.style("Converted app: {}".format(app.id), fg="green"))
+                click.echo(click.style('Converted app: {}'.format(app.id), fg='green'))
             except Exception as e:
-                click.echo(click.style("Convert app error: {} {}".format(e.__class__.__name__, str(e)), fg="red"))
+                click.echo(
+                    click.style('Convert app error: {} {}'.format(e.__class__.__name__,
+                                                                  str(e)), fg='red'))
 
-    click.echo(click.style("Congratulations! Converted {} agent apps.".format(len(proceeded_app_ids)), fg="green"))
+    click.echo(click.style('Congratulations! Converted {} agent apps.'.format(len(proceeded_app_ids)), fg='green'))
 
 
-@click.command("add-qdrant-doc-id-index", help="add qdrant doc_id index.")
-@click.option("--field", default="metadata.doc_id", prompt=False, help="index field , default is metadata.doc_id.")
+@click.command('add-qdrant-doc-id-index', help='add qdrant doc_id index.')
+@click.option('--field', default='metadata.doc_id', prompt=False, help='index field , default is metadata.doc_id.')
 def add_qdrant_doc_id_index(field: str):
-    click.echo(click.style("Start add qdrant doc_id index.", fg="green"))
+    click.echo(click.style('Start add qdrant doc_id index.', fg='green'))
     vector_type = dify_config.VECTOR_STORE
     if vector_type != "qdrant":
-        click.echo(click.style("Sorry, only support qdrant vector store.", fg="red"))
+        click.echo(click.style('Sorry, only support qdrant vector store.', fg='red'))
         return
     create_count = 0
 
     try:
         bindings = db.session.query(DatasetCollectionBinding).all()
         if not bindings:
-            click.echo(click.style("Sorry, no dataset collection bindings found.", fg="red"))
+            click.echo(click.style('Sorry, no dataset collection bindings found.', fg='red'))
             return
         import qdrant_client
         from qdrant_client.http.exceptions import UnexpectedResponse
         from qdrant_client.http.models import PayloadSchemaType
 
         from core.rag.datasource.vdb.qdrant.qdrant_vector import QdrantConfig
-
         for binding in bindings:
             if dify_config.QDRANT_URL is None:
-                raise ValueError("Qdrant url is required.")
+                raise ValueError('Qdrant url is required.')
             qdrant_config = QdrantConfig(
                 endpoint=dify_config.QDRANT_URL,
                 api_key=dify_config.QDRANT_API_KEY,
                 root_path=current_app.root_path,
                 timeout=dify_config.QDRANT_CLIENT_TIMEOUT,
                 grpc_port=dify_config.QDRANT_GRPC_PORT,
-                prefer_grpc=dify_config.QDRANT_GRPC_ENABLED,
+                prefer_grpc=dify_config.QDRANT_GRPC_ENABLED
             )
             try:
                 client = qdrant_client.QdrantClient(**qdrant_config.to_qdrant_params())
                 # create payload index
-                client.create_payload_index(binding.collection_name, field, field_schema=PayloadSchemaType.KEYWORD)
+                client.create_payload_index(binding.collection_name, field,
+                                            field_schema=PayloadSchemaType.KEYWORD)
                 create_count += 1
             except UnexpectedResponse as e:
                 # Collection does not exist, so return
                 if e.status_code == 404:
-                    click.echo(
-                        click.style(f"Collection not found, collection_name:{binding.collection_name}.", fg="red")
-                    )
+                    click.echo(click.style(f'Collection not found, collection_name:{binding.collection_name}.', fg='red'))
                     continue
                 # Some other error occurred, so re-raise the exception
                 else:
-                    click.echo(
-                        click.style(
-                            f"Failed to create qdrant index, collection_name:{binding.collection_name}.", fg="red"
-                        )
-                    )
+                    click.echo(click.style(f'Failed to create qdrant index, collection_name:{binding.collection_name}.', fg='red'))
 
     except Exception as e:
-        click.echo(click.style("Failed to create qdrant client.", fg="red"))
+        click.echo(click.style('Failed to create qdrant client.', fg='red'))
 
-    click.echo(click.style(f"Congratulations! Create {create_count} collection indexes.", fg="green"))
+    click.echo(
+        click.style(f'Congratulations! Create {create_count} collection indexes.',
+                    fg='green'))
 
 
-@click.command("create-tenant", help="Create account and tenant.")
-@click.option("--email", prompt=True, help="The email address of the tenant account.")
-@click.option("--language", prompt=True, help="Account language, default: en-US.")
+@click.command('create-tenant', help='Create account and tenant.')
+@click.option('--email', prompt=True, help='The email address of the tenant account.')
+@click.option('--language', prompt=True, help='Account language, default: en-US.')
 def create_tenant(email: str, language: Optional[str] = None):
     """
     Create tenant account
     """
     if not email:
-        click.echo(click.style("Sorry, email is required.", fg="red"))
+        click.echo(click.style('Sorry, email is required.', fg='red'))
         return
 
     # Create account
     email = email.strip()
 
-    if "@" not in email:
-        click.echo(click.style("Sorry, invalid email address.", fg="red"))
+    if '@' not in email:
+        click.echo(click.style('Sorry, invalid email address.', fg='red'))
         return
 
-    account_name = email.split("@")[0]
+    account_name = email.split('@')[0]
 
     if language not in languages:
-        language = "en-US"
+        language = 'en-US'
 
     # generate random password
     new_password = secrets.token_urlsafe(16)
 
     # register account
-    account = RegisterService.register(email=email, name=account_name, password=new_password, language=language)
+    account = RegisterService.register(
+        email=email,
+        name=account_name,
+        password=new_password,
+        language=language
+    )
 
     TenantService.create_owner_tenant_if_not_exist(account)
 
-    click.echo(
-        click.style(
-            "Congratulations! Account and tenant created.\n" "Account: {}\nPassword: {}".format(email, new_password),
-            fg="green",
-        )
-    )
+    click.echo(click.style('Congratulations! Account and tenant created.\n'
+                           'Account: {}\nPassword: {}'.format(email, new_password), fg='green'))
 
 
-@click.command("upgrade-db", help="upgrade the database")
+@click.command('upgrade-db', help='upgrade the database')
 def upgrade_db():
-    click.echo("Preparing database migration...")
-    lock = redis_client.lock(name="db_upgrade_lock", timeout=60)
+    click.echo('Preparing database migration...')
+    lock = redis_client.lock(name='db_upgrade_lock', timeout=60)
     if lock.acquire(blocking=False):
         try:
-            click.echo(click.style("Start database migration.", fg="green"))
+            click.echo(click.style('Start database migration.', fg='green'))
 
             # run db migration
             import flask_migrate
-
             flask_migrate.upgrade()
 
-            click.echo(click.style("Database migration successful!", fg="green"))
+            click.echo(click.style('Database migration successful!', fg='green'))
 
         except Exception as e:
-            logging.exception(f"Database migration failed, error: {e}")
+            logging.exception(f'Database migration failed, error: {e}')
         finally:
             lock.release()
     else:
-        click.echo("Database migration skipped")
+        click.echo('Database migration skipped')
 
 
-@click.command("fix-app-site-missing", help="Fix app related site missing issue.")
+@click.command('fix-app-site-missing', help='Fix app related site missing issue.')
 def fix_app_site_missing():
     """
     Fix app related site missing issue.
     """
-    click.echo(click.style("Start fix app related site missing issue.", fg="green"))
+    click.echo(click.style('Start fix app related site missing issue.', fg='green'))
 
     failed_app_ids = []
     while True:
@@ -655,14 +631,15 @@ where sites.id is null limit 1000"""
                         app_was_created.send(app, account=account)
                 except Exception as e:
                     failed_app_ids.append(app_id)
-                    click.echo(click.style("Fix app {} related site missing issue failed!".format(app_id), fg="red"))
-                    logging.exception(f"Fix app related site missing issue failed, error: {e}")
+                    click.echo(click.style('Fix app {} related site missing issue failed!'.format(app_id), fg='red'))
+                    logging.exception(f'Fix app related site missing issue failed, error: {e}')
                     continue
 
             if not processed_count:
                 break
 
-    click.echo(click.style("Congratulations! Fix app related site missing issue successful!", fg="green"))
+
+    click.echo(click.style('Congratulations! Fix app related site missing issue successful!', fg='green'))
 
 
 def register_commands(app):

api/configs/app_config.py

@@ -12,14 +12,19 @@ from configs.packaging import PackagingInfo
 class DifyConfig(
     # Packaging info
     PackagingInfo,
+
     # Deployment configs
     DeploymentConfig,
+
     # Feature configs
     FeatureConfig,
+
     # Middleware configs
     MiddlewareConfig,
+
     # Extra service configs
     ExtraServiceConfig,
+
     # Enterprise feature configs
     # **Before using, please contact business@dify.ai by email to inquire about licensing matters.**
     EnterpriseFeatureConfig,
@@ -31,6 +36,7 @@ class DifyConfig(
         env_file='.env',
         env_file_encoding='utf-8',
         frozen=True,
+
         # ignore extra attributes
         extra='ignore',
     )
@@ -61,5 +67,3 @@ class DifyConfig(
     SSRF_PROXY_HTTPS_URL: str | None = None
 
     MODERATION_BUFFER_SIZE: int = Field(default=300, description='The buffer size for moderation.')
-
-    MAX_VARIABLE_SIZE: int = Field(default=5 * 1024, description='The maximum size of a variable. default is 5KB.')

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description='Dify version',
-        default='0.7.0',
+        default='0.6.16',
     )
 
     COMMIT_SHA: str = Field(

api/constants/__init__.py

@@ -1 +1 @@
-HIDDEN_VALUE = "[__HIDDEN__]"
+HIDDEN_VALUE = '[__HIDDEN__]'

api/constants/languages.py

@@ -1,22 +1,22 @@
 language_timezone_mapping = {
-    "en-US": "America/New_York",
-    "zh-Hans": "Asia/Shanghai",
-    "zh-Hant": "Asia/Taipei",
-    "pt-BR": "America/Sao_Paulo",
-    "es-ES": "Europe/Madrid",
-    "fr-FR": "Europe/Paris",
-    "de-DE": "Europe/Berlin",
-    "ja-JP": "Asia/Tokyo",
-    "ko-KR": "Asia/Seoul",
-    "ru-RU": "Europe/Moscow",
-    "it-IT": "Europe/Rome",
-    "uk-UA": "Europe/Kyiv",
-    "vi-VN": "Asia/Ho_Chi_Minh",
-    "ro-RO": "Europe/Bucharest",
-    "pl-PL": "Europe/Warsaw",
-    "hi-IN": "Asia/Kolkata",
-    "tr-TR": "Europe/Istanbul",
-    "fa-IR": "Asia/Tehran",
+    'en-US': 'America/New_York',
+    'zh-Hans': 'Asia/Shanghai',
+    'zh-Hant': 'Asia/Taipei',
+    'pt-BR': 'America/Sao_Paulo',
+    'es-ES': 'Europe/Madrid',
+    'fr-FR': 'Europe/Paris',
+    'de-DE': 'Europe/Berlin',
+    'ja-JP': 'Asia/Tokyo',
+    'ko-KR': 'Asia/Seoul',
+    'ru-RU': 'Europe/Moscow',
+    'it-IT': 'Europe/Rome',
+    'uk-UA': 'Europe/Kyiv',
+    'vi-VN': 'Asia/Ho_Chi_Minh',
+    'ro-RO': 'Europe/Bucharest',
+    'pl-PL': 'Europe/Warsaw',
+    'hi-IN': 'Asia/Kolkata',
+    'tr-TR': 'Europe/Istanbul',
+    'fa-IR': 'Asia/Tehran',
 }
 
 languages = list(language_timezone_mapping.keys())
@@ -26,5 +26,6 @@ def supported_language(lang):
     if lang in languages:
         return lang
 
-    error = "{lang} is not a valid language.".format(lang=lang)
+    error = ('{lang} is not a valid language.'
+             .format(lang=lang))
     raise ValueError(error)

api/constants/model_template.py

@@ -5,79 +5,82 @@ from models.model import AppMode
 default_app_templates = {
     # workflow default mode
     AppMode.WORKFLOW: {
-        "app": {
-            "mode": AppMode.WORKFLOW.value,
-            "enable_site": True,
-            "enable_api": True,
+        'app': {
+            'mode': AppMode.WORKFLOW.value,
+            'enable_site': True,
+            'enable_api': True
         }
     },
+
     # completion default mode
     AppMode.COMPLETION: {
-        "app": {
-            "mode": AppMode.COMPLETION.value,
-            "enable_site": True,
-            "enable_api": True,
+        'app': {
+            'mode': AppMode.COMPLETION.value,
+            'enable_site': True,
+            'enable_api': True
         },
-        "model_config": {
-            "model": {
+        'model_config': {
+            'model': {
                 "provider": "openai",
                 "name": "gpt-4o",
                 "mode": "chat",
-                "completion_params": {},
+                "completion_params": {}
             },
-            "user_input_form": json.dumps(
-                [
-                    {
-                        "paragraph": {
-                            "label": "Query",
-                            "variable": "query",
-                            "required": True,
-                            "default": "",
-                        },
-                    },
-                ]
-            ),
-            "pre_prompt": "{{query}}",
+            'user_input_form': json.dumps([
+                {
+                    "paragraph": {
+                        "label": "Query",
+                        "variable": "query",
+                        "required": True,
+                        "default": ""
+                    }
+                }
+            ]),
+            'pre_prompt': '{{query}}'
         },
+
     },
+
     # chat default mode
     AppMode.CHAT: {
-        "app": {
-            "mode": AppMode.CHAT.value,
-            "enable_site": True,
-            "enable_api": True,
+        'app': {
+            'mode': AppMode.CHAT.value,
+            'enable_site': True,
+            'enable_api': True
         },
-        "model_config": {
-            "model": {
+        'model_config': {
+            'model': {
                 "provider": "openai",
                 "name": "gpt-4o",
                 "mode": "chat",
-                "completion_params": {},
-            },
-        },
+                "completion_params": {}
+            }
+        }
     },
+
     # advanced-chat default mode
     AppMode.ADVANCED_CHAT: {
-        "app": {
-            "mode": AppMode.ADVANCED_CHAT.value,
-            "enable_site": True,
-            "enable_api": True,
-        },
+        'app': {
+            'mode': AppMode.ADVANCED_CHAT.value,
+            'enable_site': True,
+            'enable_api': True
+        }
     },
+
     # agent-chat default mode
     AppMode.AGENT_CHAT: {
-        "app": {
-            "mode": AppMode.AGENT_CHAT.value,
-            "enable_site": True,
-            "enable_api": True,
+        'app': {
+            'mode': AppMode.AGENT_CHAT.value,
+            'enable_site': True,
+            'enable_api': True
         },
-        "model_config": {
-            "model": {
+        'model_config': {
+            'model': {
                 "provider": "openai",
                 "name": "gpt-4o",
                 "mode": "chat",
-                "completion_params": {},
-            },
-        },
-    },
+                "completion_params": {}
+            }
+        }
+    }
 }

api/contexts/__init__.py

@@ -1,7 +1,3 @@
 from contextvars import ContextVar
 
-from core.workflow.entities.variable_pool import VariablePool
-
-tenant_id: ContextVar[str] = ContextVar("tenant_id")
-
-workflow_variable_pool: ContextVar[VariablePool] = ContextVar("workflow_variable_pool")
+tenant_id: ContextVar[str] = ContextVar('tenant_id')

api/controllers/console/__init__.py

@@ -17,7 +17,6 @@ from .app import (
     audio,
     completion,
     conversation,
-    conversation_variables,
     generator,
     message,
     model_config,

api/controllers/console/app/app.py

@@ -61,7 +61,6 @@ class AppListApi(Resource):
         parser.add_argument('name', type=str, required=True, location='json')
         parser.add_argument('description', type=str, location='json')
         parser.add_argument('mode', type=str, choices=ALLOW_CREATE_APP_MODES, location='json')
-        parser.add_argument('icon_type', type=str, location='json')
         parser.add_argument('icon', type=str, location='json')
         parser.add_argument('icon_background', type=str, location='json')
         args = parser.parse_args()
@@ -95,7 +94,6 @@ class AppImportApi(Resource):
         parser.add_argument('data', type=str, required=True, nullable=False, location='json')
         parser.add_argument('name', type=str, location='json')
         parser.add_argument('description', type=str, location='json')
-        parser.add_argument('icon_type', type=str, location='json')
         parser.add_argument('icon', type=str, location='json')
         parser.add_argument('icon_background', type=str, location='json')
         args = parser.parse_args()
@@ -169,7 +167,6 @@ class AppApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('name', type=str, required=True, nullable=False, location='json')
         parser.add_argument('description', type=str, location='json')
-        parser.add_argument('icon_type', type=str, location='json')
         parser.add_argument('icon', type=str, location='json')
         parser.add_argument('icon_background', type=str, location='json')
         parser.add_argument('max_active_requests', type=int, location='json')
@@ -211,7 +208,6 @@ class AppCopyApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('name', type=str, location='json')
         parser.add_argument('description', type=str, location='json')
-        parser.add_argument('icon_type', type=str, location='json')
         parser.add_argument('icon', type=str, location='json')
         parser.add_argument('icon_background', type=str, location='json')
         args = parser.parse_args()

api/controllers/console/app/conversation.py

@@ -33,7 +33,7 @@ class CompletionConversationApi(Resource):
     @get_app_model(mode=AppMode.COMPLETION)
     @marshal_with(conversation_pagination_fields)
     def get(self, app_model):
-        if not current_user.is_editor:
+        if not current_user.is_admin_or_owner:
             raise Forbidden()
         parser = reqparse.RequestParser()
         parser.add_argument('keyword', type=str, location='args')
@@ -108,7 +108,7 @@ class CompletionConversationDetailApi(Resource):
     @get_app_model(mode=AppMode.COMPLETION)
     @marshal_with(conversation_message_detail_fields)
     def get(self, app_model, conversation_id):
-        if not current_user.is_editor:
+        if not current_user.is_admin_or_owner:
             raise Forbidden()
         conversation_id = str(conversation_id)
 
@@ -119,7 +119,7 @@ class CompletionConversationDetailApi(Resource):
     @account_initialization_required
     @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
     def delete(self, app_model, conversation_id):
-        if not current_user.is_editor:
+        if not current_user.is_admin_or_owner:
             raise Forbidden()
         conversation_id = str(conversation_id)
 
@@ -256,7 +256,7 @@ class ChatConversationDetailApi(Resource):
     @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
     @account_initialization_required
     def delete(self, app_model, conversation_id):
-        if not current_user.is_editor:
+        if not current_user.is_admin_or_owner:
             raise Forbidden()
         conversation_id = str(conversation_id)
 

api/controllers/console/app/conversation_variables.py

@@ -1,61 +0,0 @@
-from flask_restful import Resource, marshal_with, reqparse
-from sqlalchemy import select
-from sqlalchemy.orm import Session
-
-from controllers.console import api
-from controllers.console.app.wraps import get_app_model
-from controllers.console.setup import setup_required
-from controllers.console.wraps import account_initialization_required
-from extensions.ext_database import db
-from fields.conversation_variable_fields import paginated_conversation_variable_fields
-from libs.login import login_required
-from models import ConversationVariable
-from models.model import AppMode
-
-
-class ConversationVariablesApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @get_app_model(mode=AppMode.ADVANCED_CHAT)
-    @marshal_with(paginated_conversation_variable_fields)
-    def get(self, app_model):
-        parser = reqparse.RequestParser()
-        parser.add_argument('conversation_id', type=str, location='args')
-        args = parser.parse_args()
-
-        stmt = (
-            select(ConversationVariable)
-            .where(ConversationVariable.app_id == app_model.id)
-            .order_by(ConversationVariable.created_at)
-        )
-        if args['conversation_id']:
-            stmt = stmt.where(ConversationVariable.conversation_id == args['conversation_id'])
-        else:
-            raise ValueError('conversation_id is required')
-
-        # NOTE: This is a temporary solution to avoid performance issues.
-        page = 1
-        page_size = 100
-        stmt = stmt.limit(page_size).offset((page - 1) * page_size)
-
-        with Session(db.engine) as session:
-            rows = session.scalars(stmt).all()
-
-        return {
-            'page': page,
-            'limit': page_size,
-            'total': len(rows),
-            'has_more': False,
-            'data': [
-                {
-                    'created_at': row.created_at,
-                    'updated_at': row.updated_at,
-                    **row.to_variable().model_dump(),
-                }
-                for row in rows
-            ],
-        }
-
-
-api.add_resource(ConversationVariablesApi, '/apps/<uuid:app_id>/conversation-variables')

api/controllers/console/app/site.py

@@ -16,7 +16,6 @@ from models.model import Site
 def parse_app_site_args():
     parser = reqparse.RequestParser()
     parser.add_argument('title', type=str, required=False, location='json')
-    parser.add_argument('icon_type', type=str, required=False, location='json')
     parser.add_argument('icon', type=str, required=False, location='json')
     parser.add_argument('icon_background', type=str, required=False, location='json')
     parser.add_argument('description', type=str, required=False, location='json')
@@ -54,7 +53,6 @@ class AppSite(Resource):
 
         for attr_name in [
             'title',
-            'icon_type',
             'icon',
             'icon_background',
             'description',

api/controllers/console/app/workflow.py

@@ -74,7 +74,6 @@ class DraftWorkflowApi(Resource):
             parser.add_argument('hash', type=str, required=False, location='json')
             # TODO: set this to required=True after frontend is updated
             parser.add_argument('environment_variables', type=list, required=False, location='json')
-            parser.add_argument('conversation_variables', type=list, required=False, location='json')
             args = parser.parse_args()
         elif 'text/plain' in content_type:
             try:
@@ -89,8 +88,7 @@ class DraftWorkflowApi(Resource):
                     'graph': data.get('graph'),
                     'features': data.get('features'),
                     'hash': data.get('hash'),
-                    'environment_variables': data.get('environment_variables'),
-                    'conversation_variables': data.get('conversation_variables'),
+                    'environment_variables': data.get('environment_variables')
                 }
             except json.JSONDecodeError:
                 return {'message': 'Invalid JSON data'}, 400
@@ -102,8 +100,6 @@ class DraftWorkflowApi(Resource):
         try:
             environment_variables_list = args.get('environment_variables') or []
             environment_variables = [factory.build_variable_from_mapping(obj) for obj in environment_variables_list]
-            conversation_variables_list = args.get('conversation_variables') or []
-            conversation_variables = [factory.build_variable_from_mapping(obj) for obj in conversation_variables_list]
             workflow = workflow_service.sync_draft_workflow(
                 app_model=app_model,
                 graph=args['graph'],
@@ -111,7 +107,6 @@ class DraftWorkflowApi(Resource):
                 unique_hash=args.get('hash'),
                 account=current_user,
                 environment_variables=environment_variables,
-                conversation_variables=conversation_variables,
             )
         except WorkflowHashNotEqualError:
             raise DraftWorkflowNotSync()
@@ -459,7 +454,6 @@ class ConvertToWorkflowApi(Resource):
         if request.data:
             parser = reqparse.RequestParser()
             parser.add_argument('name', type=str, required=False, nullable=True, location='json')
-            parser.add_argument('icon_type', type=str, required=False, nullable=True, location='json')
             parser.add_argument('icon', type=str, required=False, nullable=True, location='json')
             parser.add_argument('icon_background', type=str, required=False, nullable=True, location='json')
             args = parser.parse_args()

api/controllers/console/datasets/datasets.py

@@ -555,7 +555,7 @@ class DatasetRetrievalSettingApi(Resource):
                         RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
-            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE | VectorType.ELASTICSEARCH:
+            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH | VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value,
@@ -579,7 +579,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                         RetrievalMethod.SEMANTIC_SEARCH.value
                     ]
                 }
-            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE | VectorType.ELASTICSEARCH:
+            case VectorType.QDRANT | VectorType.WEAVIATE | VectorType.OPENSEARCH| VectorType.ANALYTICDB | VectorType.MYSCALE | VectorType.ORACLE:
                 return {
                     'retrieval_method': [
                         RetrievalMethod.SEMANTIC_SEARCH.value,

api/controllers/console/datasets/datasets_document.py

@@ -178,20 +178,11 @@ class DatasetDocumentListApi(Resource):
                 .subquery()
 
             query = query.outerjoin(sub_query, sub_query.c.document_id == Document.id) \
-                .order_by(
-                    sort_logic(db.func.coalesce(sub_query.c.total_hit_count, 0)),
-                    sort_logic(Document.position),
-                )
+                .order_by(sort_logic(db.func.coalesce(sub_query.c.total_hit_count, 0)))
         elif sort == 'created_at':
-            query = query.order_by(
-                sort_logic(Document.created_at),
-                sort_logic(Document.position),
-            )
+            query = query.order_by(sort_logic(Document.created_at))
         else:
-            query = query.order_by(
-                desc(Document.created_at),
-                desc(Document.position),
-            )
+            query = query.order_by(desc(Document.created_at))
 
         paginated_documents = query.paginate(
             page=page, per_page=limit, max_per_page=100, error_out=False)

api/controllers/service_api/app/message.py

@@ -131,7 +131,7 @@ class MessageSuggestedApi(Resource):
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
         except SuggestedQuestionsAfterAnswerDisabledError:
-            raise BadRequest("Suggested Questions Is Disabled.")
+            raise BadRequest("Message Not Exists.")
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/service_api/dataset/segment.py

@@ -53,22 +53,19 @@ class SegmentApi(DatasetApiResource):
                 raise ProviderNotInitializeError(
                     "No Embedding Model available. Please configure a valid provider "
                     "in the Settings -> Model Provider.")
-            except ProviderTokenNotInitError as ex:   
+            except ProviderTokenNotInitError as ex:
                 raise ProviderNotInitializeError(ex.description)
         # validate args
         parser = reqparse.RequestParser()
         parser.add_argument('segments', type=list, required=False, nullable=True, location='json')
         args = parser.parse_args()
-        if args['segments'] is not None:
-            for args_item in args['segments']:
-                SegmentService.segment_create_args_validate(args_item, document)
-            segments = SegmentService.multi_create_segment(args['segments'], document, dataset)
-            return {
-                'data': marshal(segments, segment_fields),
-                'doc_form': document.doc_form
-            }, 200
-        else:
-            return {"error": "Segemtns is required"}, 400
+        for args_item in args['segments']:
+            SegmentService.segment_create_args_validate(args_item, document)
+        segments = SegmentService.multi_create_segment(args['segments'], document, dataset)
+        return {
+            'data': marshal(segments, segment_fields),
+            'doc_form': document.doc_form
+        }, 200
 
     def get(self, tenant_id, dataset_id, document_id):
         """Create single segment."""

api/controllers/web/site.py

@@ -6,7 +6,6 @@ from configs import dify_config
 from controllers.web import api
 from controllers.web.wraps import WebApiResource
 from extensions.ext_database import db
-from libs.helper import AppIconUrlField
 from models.account import TenantStatus
 from models.model import Site
 from services.feature_service import FeatureService
@@ -29,10 +28,8 @@ class AppSiteApi(WebApiResource):
         'title': fields.String,
         'chat_color_theme': fields.String,
         'chat_color_theme_inverted': fields.Boolean,
-        'icon_type': fields.String,
         'icon': fields.String,
         'icon_background': fields.String,
-        'icon_url': AppIconUrlField,
         'description': fields.String,
         'copyright': fields.String,
         'privacy_policy': fields.String,

api/core/agent/base_agent_runner.py

@@ -64,19 +64,15 @@ class BaseAgentRunner(AppRunner):
         """
         Agent runner
         :param tenant_id: tenant id
-        :param application_generate_entity: application generate entity
-        :param conversation: conversation
         :param app_config: app generate entity
         :param model_config: model config
         :param config: dataset config
         :param queue_manager: queue manager
         :param message: message
         :param user_id: user id
+        :param agent_llm_callback: agent llm callback
+        :param callback: callback
         :param memory: memory
-        :param prompt_messages: prompt messages
-        :param variables_pool: variables pool
-        :param db_variables: db variables
-        :param model_instance: model instance
         """
         self.tenant_id = tenant_id
         self.application_generate_entity = application_generate_entity
@@ -449,7 +445,7 @@ class BaseAgentRunner(AppRunner):
                         try:
                             tool_responses = json.loads(agent_thought.observation)
                         except Exception as e:
-                            tool_responses = dict.fromkeys(tools, agent_thought.observation)
+                            tool_responses = { tool: agent_thought.observation for tool in tools }
 
                         for tool in tools:
                             # generate a uuid for tool call

api/core/agent/cot_agent_runner.py

@@ -292,8 +292,6 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         handle invoke action
         :param action: action
         :param tool_instances: tool instances
-        :param message_file_ids: message file ids
-        :param trace_manager: trace manager
         :return: observation, meta
         """
         # action is tool call, invoke tool

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

@@ -93,7 +93,6 @@ class DatasetConfigManager:
                     reranking_model=dataset_configs.get('reranking_model'),
                     weights=dataset_configs.get('weights'),
                     reranking_enabled=dataset_configs.get('reranking_enabled', True),
-                    rerank_mode=dataset_configs["reranking_mode"],
                 )
             )
 

api/core/app/app_config/entities.py

@@ -3,9 +3,8 @@ from typing import Any, Optional
 
 from pydantic import BaseModel
 
-from core.file.file_obj import FileExtraConfig
 from core.model_runtime.entities.message_entities import PromptMessageRole
-from models import AppMode
+from models.model import AppMode
 
 
 class ModelConfigEntity(BaseModel):
@@ -201,6 +200,11 @@ class TracingConfigEntity(BaseModel):
     tracing_provider: str
 
 
+class FileExtraConfig(BaseModel):
+    """
+    File Upload Entity.
+    """
+    image_config: Optional[dict[str, Any]] = None
 
 
 class AppAdditionalFeatures(BaseModel):

api/core/app/app_config/features/file_upload/manager.py

@@ -1,7 +1,7 @@
 from collections.abc import Mapping
 from typing import Any, Optional
 
-from core.file.file_obj import FileExtraConfig
+from core.app.app_config.entities import FileExtraConfig
 
 
 class FileUploadConfigManager:

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

@@ -8,8 +8,6 @@ from typing import Union
 
 from flask import Flask, current_app
 from pydantic import ValidationError
-from sqlalchemy import select
-from sqlalchemy.orm import Session
 
 import contexts
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
@@ -20,20 +18,15 @@ from core.app.apps.advanced_chat.generate_task_pipeline import AdvancedChatAppGe
 from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedException, PublishFrom
 from core.app.apps.message_based_app_generator import MessageBasedAppGenerator
 from core.app.apps.message_based_app_queue_manager import MessageBasedAppQueueManager
-from core.app.entities.app_invoke_entities import (
-    AdvancedChatAppGenerateEntity,
-    InvokeFrom,
-)
+from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom
 from core.app.entities.task_entities import ChatbotAppBlockingResponse, ChatbotAppStreamResponse
 from core.file.message_file_parser import MessageFileParser
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.enums import SystemVariable
 from extensions.ext_database import db
 from models.account import Account
 from models.model import App, Conversation, EndUser, Message
-from models.workflow import ConversationVariable, Workflow
+from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -120,6 +113,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
+            app_model=app_model,
             workflow=workflow,
             user=user,
             invoke_from=invoke_from,
@@ -127,7 +121,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             stream=stream
         )
-
+    
     def single_iteration_generate(self, app_model: App,
                                   workflow: Workflow,
                                   node_id: str,
@@ -147,10 +141,10 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         """
         if not node_id:
             raise ValueError('node_id is required')
-
+        
         if args.get('inputs') is None:
             raise ValueError('inputs is required')
-
+        
         extras = {
             "auto_generate_conversation_name": False
         }
@@ -186,6 +180,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
 
         return self._generate(
+            app_model=app_model,
             workflow=workflow,
             user=user,
             invoke_from=InvokeFrom.DEBUGGER,
@@ -194,12 +189,12 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             stream=stream
         )
 
-    def _generate(self, *,
+    def _generate(self, app_model: App,
                  workflow: Workflow,
                  user: Union[Account, EndUser],
                  invoke_from: InvokeFrom,
                  application_generate_entity: AdvancedChatAppGenerateEntity,
-                 conversation: Conversation | None = None,
+                 conversation: Conversation = None,
                  stream: bool = True) \
             -> Union[dict, Generator[dict, None, None]]:
         is_first_conversation = False
@@ -216,7 +211,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             # update conversation features
             conversation.override_model_configs = workflow.features
             db.session.commit()
-            # db.session.refresh(conversation)
+            db.session.refresh(conversation)
 
         # init queue manager
         queue_manager = MessageBasedAppQueueManager(
@@ -228,69 +223,15 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             message_id=message.id
         )
 
-        # Init conversation variables
-        stmt = select(ConversationVariable).where(
-            ConversationVariable.app_id == conversation.app_id, ConversationVariable.conversation_id == conversation.id
-        )
-        with Session(db.engine) as session:
-            conversation_variables = session.scalars(stmt).all()
-            if not conversation_variables:
-                # Create conversation variables if they don't exist.
-                conversation_variables = [
-                    ConversationVariable.from_variable(
-                        app_id=conversation.app_id, conversation_id=conversation.id, variable=variable
-                    )
-                    for variable in workflow.conversation_variables
-                ]
-                session.add_all(conversation_variables)
-            # Convert database entities to variables.
-            conversation_variables = [item.to_variable() for item in conversation_variables]
-
-            session.commit()
-
-            # Increment dialogue count.
-            conversation.dialogue_count += 1
-
-            conversation_id = conversation.id
-            conversation_dialogue_count = conversation.dialogue_count
-            db.session.commit()
-            db.session.refresh(conversation)
-
-        inputs = application_generate_entity.inputs
-        query = application_generate_entity.query
-        files = application_generate_entity.files
-
-        user_id = None
-        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
-            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
-            if end_user:
-                user_id = end_user.session_id
-        else:
-            user_id = application_generate_entity.user_id
-
-        # Create a variable pool.
-        system_inputs = {
-            SystemVariable.QUERY: query,
-            SystemVariable.FILES: files,
-            SystemVariable.CONVERSATION_ID: conversation_id,
-            SystemVariable.USER_ID: user_id,
-            SystemVariable.DIALOGUE_COUNT: conversation_dialogue_count,
-        }
-        variable_pool = VariablePool(
-            system_variables=system_inputs,
-            user_inputs=inputs,
-            environment_variables=workflow.environment_variables,
-            conversation_variables=conversation_variables,
-        )
-        contexts.workflow_variable_pool.set(variable_pool)
-
         # new thread
         worker_thread = threading.Thread(target=self._generate_worker, kwargs={
             'flask_app': current_app._get_current_object(),
             'application_generate_entity': application_generate_entity,
             'queue_manager': queue_manager,
+            'conversation_id': conversation.id,
             'message_id': message.id,
-            'context': contextvars.copy_context(),
+            'user': user,
+            'context': contextvars.copy_context()
         })
 
         worker_thread.start()
@@ -303,7 +244,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream,
+            stream=stream
         )
 
         return AdvancedChatAppGenerateResponseConverter.convert(
@@ -314,7 +255,9 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
     def _generate_worker(self, flask_app: Flask,
                          application_generate_entity: AdvancedChatAppGenerateEntity,
                          queue_manager: AppQueueManager,
+                         conversation_id: str,
                          message_id: str,
+                         user: Account,
                          context: contextvars.Context) -> None:
         """
         Generate worker in a new thread.
@@ -341,7 +284,8 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                         user_id=application_generate_entity.user_id
                     )
                 else:
-                    # get message
+                    # get conversation and message
+                    conversation = self._get_conversation(conversation_id)
                     message = self._get_message(message_id)
 
                     # chatbot app
@@ -349,6 +293,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
                     runner.run(
                         application_generate_entity=application_generate_entity,
                         queue_manager=queue_manager,
+                        conversation=conversation,
                         message=message
                     )
             except GenerateTaskStoppedException:
@@ -371,17 +316,14 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             finally:
                 db.session.close()
 
-    def _handle_advanced_chat_response(
-        self,
-        *,
-        application_generate_entity: AdvancedChatAppGenerateEntity,
-        workflow: Workflow,
-        queue_manager: AppQueueManager,
-        conversation: Conversation,
-        message: Message,
-        user: Union[Account, EndUser],
-        stream: bool = False,
-    ) -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
+    def _handle_advanced_chat_response(self, application_generate_entity: AdvancedChatAppGenerateEntity,
+                                       workflow: Workflow,
+                                       queue_manager: AppQueueManager,
+                                       conversation: Conversation,
+                                       message: Message,
+                                       user: Union[Account, EndUser],
+                                       stream: bool = False) \
+            -> Union[ChatbotAppBlockingResponse, Generator[ChatbotAppStreamResponse, None, None]]:
         """
         Handle response.
         :param application_generate_entity: application generate entity
@@ -401,7 +343,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             conversation=conversation,
             message=message,
             user=user,
-            stream=stream,
+            stream=stream
         )
 
         try:

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

@@ -16,10 +16,12 @@ from core.app.entities.app_invoke_entities import (
 from core.app.entities.queue_entities import QueueAnnotationReplyEvent, QueueStopEvent, QueueTextChunkEvent
 from core.moderation.base import ModerationException
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
+from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
-from models import App, Message, Workflow
+from models.model import App, Conversation, EndUser, Message
+from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
 
@@ -29,12 +31,10 @@ class AdvancedChatAppRunner(AppRunner):
     AdvancedChat Application Runner
     """
 
-    def run(
-        self,
-        application_generate_entity: AdvancedChatAppGenerateEntity,
-        queue_manager: AppQueueManager,
-        message: Message,
-    ) -> None:
+    def run(self, application_generate_entity: AdvancedChatAppGenerateEntity,
+            queue_manager: AppQueueManager,
+            conversation: Conversation,
+            message: Message) -> None:
         """
         Run application
         :param application_generate_entity: application generate entity
@@ -48,43 +48,53 @@ class AdvancedChatAppRunner(AppRunner):
 
         app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
         if not app_record:
-            raise ValueError('App not found')
+            raise ValueError("App not found")
 
         workflow = self.get_workflow(app_model=app_record, workflow_id=app_config.workflow_id)
         if not workflow:
-            raise ValueError('Workflow not initialized')
+            raise ValueError("Workflow not initialized")
 
         inputs = application_generate_entity.inputs
         query = application_generate_entity.query
+        files = application_generate_entity.files
+
+        user_id = None
+        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
+            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
+            if end_user:
+                user_id = end_user.session_id
+        else:
+            user_id = application_generate_entity.user_id
 
         # moderation
         if self.handle_input_moderation(
-            queue_manager=queue_manager,
-            app_record=app_record,
-            app_generate_entity=application_generate_entity,
-            inputs=inputs,
-            query=query,
-            message_id=message.id,
+                queue_manager=queue_manager,
+                app_record=app_record,
+                app_generate_entity=application_generate_entity,
+                inputs=inputs,
+                query=query,
+                message_id=message.id
         ):
             return
 
         # annotation reply
         if self.handle_annotation_reply(
-            app_record=app_record,
-            message=message,
-            query=query,
-            queue_manager=queue_manager,
-            app_generate_entity=application_generate_entity,
+                app_record=app_record,
+                message=message,
+                query=query,
+                queue_manager=queue_manager,
+                app_generate_entity=application_generate_entity
         ):
             return
 
         db.session.close()
 
-        workflow_callbacks: list[WorkflowCallback] = [
-            WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)
-        ]
+        workflow_callbacks: list[WorkflowCallback] = [WorkflowEventTriggerCallback(
+            queue_manager=queue_manager,
+            workflow=workflow
+        )]
 
-        if bool(os.environ.get('DEBUG', 'False').lower() == 'true'):
+        if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
             workflow_callbacks.append(WorkflowLoggingCallback())
 
         # RUN WORKFLOW
@@ -96,29 +106,43 @@ class AdvancedChatAppRunner(AppRunner):
             if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
             else UserFrom.END_USER,
             invoke_from=application_generate_entity.invoke_from,
+            user_inputs=inputs,
+            system_inputs={
+                SystemVariable.QUERY: query,
+                SystemVariable.FILES: files,
+                SystemVariable.CONVERSATION_ID: conversation.id,
+                SystemVariable.USER_ID: user_id
+            },
             callbacks=workflow_callbacks,
-            call_depth=application_generate_entity.call_depth,
+            call_depth=application_generate_entity.call_depth
         )
 
-    def single_iteration_run(
-        self, app_id: str, workflow_id: str, queue_manager: AppQueueManager, inputs: dict, node_id: str, user_id: str
-    ) -> None:
+    def single_iteration_run(self, app_id: str, workflow_id: str,
+                             queue_manager: AppQueueManager,
+                             inputs: dict, node_id: str, user_id: str) -> None:
         """
         Single iteration run
         """
-        app_record = db.session.query(App).filter(App.id == app_id).first()
+        app_record: App = db.session.query(App).filter(App.id == app_id).first()
         if not app_record:
-            raise ValueError('App not found')
-
+            raise ValueError("App not found")
+        
         workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
         if not workflow:
-            raise ValueError('Workflow not initialized')
-
-        workflow_callbacks = [WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)]
+            raise ValueError("Workflow not initialized")
+        
+        workflow_callbacks = [WorkflowEventTriggerCallback(
+            queue_manager=queue_manager,
+            workflow=workflow
+        )]
 
         workflow_engine_manager = WorkflowEngineManager()
         workflow_engine_manager.single_step_run_iteration_workflow_node(
-            workflow=workflow, node_id=node_id, user_id=user_id, user_inputs=inputs, callbacks=workflow_callbacks
+            workflow=workflow,
+            node_id=node_id,
+            user_id=user_id,
+            user_inputs=inputs,
+            callbacks=workflow_callbacks
         )
 
     def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
@@ -126,25 +150,22 @@ class AdvancedChatAppRunner(AppRunner):
         Get workflow
         """
         # fetch workflow by workflow_id
-        workflow = (
-            db.session.query(Workflow)
-            .filter(
-                Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
-            )
-            .first()
-        )
+        workflow = db.session.query(Workflow).filter(
+            Workflow.tenant_id == app_model.tenant_id,
+            Workflow.app_id == app_model.id,
+            Workflow.id == workflow_id
+        ).first()
 
         # return workflow
         return workflow
 
     def handle_input_moderation(
-        self,
-        queue_manager: AppQueueManager,
-        app_record: App,
-        app_generate_entity: AdvancedChatAppGenerateEntity,
-        inputs: Mapping[str, Any],
-        query: str,
-        message_id: str,
+            self, queue_manager: AppQueueManager,
+            app_record: App,
+            app_generate_entity: AdvancedChatAppGenerateEntity,
+            inputs: Mapping[str, Any],
+            query: str,
+            message_id: str
     ) -> bool:
         """
         Handle input moderation
@@ -171,20 +192,17 @@ class AdvancedChatAppRunner(AppRunner):
                 queue_manager=queue_manager,
                 text=str(e),
                 stream=app_generate_entity.stream,
-                stopped_by=QueueStopEvent.StopBy.INPUT_MODERATION,
+                stopped_by=QueueStopEvent.StopBy.INPUT_MODERATION
             )
             return True
 
         return False
 
-    def handle_annotation_reply(
-        self,
-        app_record: App,
-        message: Message,
-        query: str,
-        queue_manager: AppQueueManager,
-        app_generate_entity: AdvancedChatAppGenerateEntity,
-    ) -> bool:
+    def handle_annotation_reply(self, app_record: App,
+                                message: Message,
+                                query: str,
+                                queue_manager: AppQueueManager,
+                                app_generate_entity: AdvancedChatAppGenerateEntity) -> bool:
         """
         Handle annotation reply
         :param app_record: app record
@@ -199,27 +217,29 @@ class AdvancedChatAppRunner(AppRunner):
             message=message,
             query=query,
             user_id=app_generate_entity.user_id,
-            invoke_from=app_generate_entity.invoke_from,
+            invoke_from=app_generate_entity.invoke_from
         )
 
         if annotation_reply:
             queue_manager.publish(
-                QueueAnnotationReplyEvent(message_annotation_id=annotation_reply.id), PublishFrom.APPLICATION_MANAGER
+                QueueAnnotationReplyEvent(message_annotation_id=annotation_reply.id),
+                PublishFrom.APPLICATION_MANAGER
             )
 
             self._stream_output(
                 queue_manager=queue_manager,
                 text=annotation_reply.content,
                 stream=app_generate_entity.stream,
-                stopped_by=QueueStopEvent.StopBy.ANNOTATION_REPLY,
+                stopped_by=QueueStopEvent.StopBy.ANNOTATION_REPLY
             )
             return True
 
         return False
 
-    def _stream_output(
-        self, queue_manager: AppQueueManager, text: str, stream: bool, stopped_by: QueueStopEvent.StopBy
-    ) -> None:
+    def _stream_output(self, queue_manager: AppQueueManager,
+                       text: str,
+                       stream: bool,
+                       stopped_by: QueueStopEvent.StopBy) -> None:
         """
         Direct output
         :param queue_manager: application queue manager
@@ -230,10 +250,21 @@ class AdvancedChatAppRunner(AppRunner):
         if stream:
             index = 0
             for token in text:
-                queue_manager.publish(QueueTextChunkEvent(text=token), PublishFrom.APPLICATION_MANAGER)
+                queue_manager.publish(
+                    QueueTextChunkEvent(
+                        text=token
+                    ), PublishFrom.APPLICATION_MANAGER
+                )
                 index += 1
                 time.sleep(0.01)
         else:
-            queue_manager.publish(QueueTextChunkEvent(text=text), PublishFrom.APPLICATION_MANAGER)
+            queue_manager.publish(
+                QueueTextChunkEvent(
+                    text=text
+                ), PublishFrom.APPLICATION_MANAGER
+            )
 
-        queue_manager.publish(QueueStopEvent(stopped_by=stopped_by), PublishFrom.APPLICATION_MANAGER)
+        queue_manager.publish(
+            QueueStopEvent(stopped_by=stopped_by),
+            PublishFrom.APPLICATION_MANAGER
+        )

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

@@ -4,7 +4,6 @@ import time
 from collections.abc import Generator
 from typing import Any, Optional, Union, cast
 
-import contexts
 from constants.tts_auto_play_timeout import TTS_AUTO_PLAY_TIMEOUT, TTS_AUTO_PLAY_YIELD_CPU_TIME
 from core.app.apps.advanced_chat.app_generator_tts_publisher import AppGeneratorTTSPublisher, AudioTrunk
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -48,8 +47,7 @@ from core.file.file_obj import FileVar
 from core.model_runtime.entities.llm_entities import LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.node_entities import NodeType
-from core.workflow.enums import SystemVariable
+from core.workflow.entities.node_entities import NodeType, SystemVariable
 from core.workflow.nodes.answer.answer_node import AnswerNode
 from core.workflow.nodes.answer.entities import TextGenerateRouteChunk, VarGenerateRouteChunk
 from events.message_event import message_was_created
@@ -73,7 +71,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
     _application_generate_entity: AdvancedChatAppGenerateEntity
     _workflow: Workflow
     _user: Union[Account, EndUser]
-    # Deprecated
     _workflow_system_variables: dict[SystemVariable, Any]
     _iteration_nested_relations: dict[str, list[str]]
 
@@ -84,7 +81,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             conversation: Conversation,
             message: Message,
             user: Union[Account, EndUser],
-            stream: bool,
+            stream: bool
     ) -> None:
         """
         Initialize AdvancedChatAppGenerateTaskPipeline.
@@ -106,12 +103,11 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         self._workflow = workflow
         self._conversation = conversation
         self._message = message
-        # Deprecated
         self._workflow_system_variables = {
             SystemVariable.QUERY: message.query,
             SystemVariable.FILES: application_generate_entity.files,
             SystemVariable.CONVERSATION_ID: conversation.id,
-            SystemVariable.USER_ID: user_id,
+            SystemVariable.USER_ID: user_id
         }
 
         self._task_state = AdvancedChatTaskState(
@@ -249,7 +245,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         """
         for message in self._queue_manager.listen():
             if (message.event
-                    and getattr(message.event, 'metadata', None)
+                    and hasattr(message.event, 'metadata')
+                    and message.event.metadata
                     and message.event.metadata.get('is_answer_previous_node', False)
                     and publisher):
                 publisher.publish(message=message)
@@ -616,9 +613,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
                 if route_chunk_node_id == 'sys':
                     # system variable
-                    value = contexts.workflow_variable_pool.get().get(value_selector)
-                    if value:
-                        value = value.text
+                    value = self._workflow_system_variables.get(SystemVariable.value_of(value_selector[1]))
                 elif route_chunk_node_id in self._iteration_nested_relations:
                     # it's a iteration variable
                     if not self._iteration_state or route_chunk_node_id not in self._iteration_state.current_iterations:

api/core/app/apps/base_app_runner.py

@@ -1,6 +1,6 @@
 import time
 from collections.abc import Generator
-from typing import TYPE_CHECKING, Optional, Union
+from typing import Optional, Union
 
 from core.app.app_config.entities import ExternalDataVariableEntity, PromptTemplateEntity
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -14,6 +14,7 @@ from core.app.entities.queue_entities import QueueAgentMessageEvent, QueueLLMChu
 from core.app.features.annotation_reply.annotation_reply import AnnotationReplyFeature
 from core.app.features.hosting_moderation.hosting_moderation import HostingModerationFeature
 from core.external_data_tool.external_data_fetch import ExternalDataFetch
+from core.file.file_obj import FileVar
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
@@ -26,16 +27,13 @@ from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, Comp
 from core.prompt.simple_prompt_transform import ModelMode, SimplePromptTransform
 from models.model import App, AppMode, Message, MessageAnnotation
 
-if TYPE_CHECKING:
-    from core.file.file_obj import FileVar
-
 
 class AppRunner:
     def get_pre_calculate_rest_tokens(self, app_record: App,
                                       model_config: ModelConfigWithCredentialsEntity,
                                       prompt_template_entity: PromptTemplateEntity,
                                       inputs: dict[str, str],
-                                      files: list["FileVar"],
+                                      files: list[FileVar],
                                       query: Optional[str] = None) -> int:
         """
         Get pre calculate rest tokens
@@ -128,7 +126,7 @@ class AppRunner:
                                  model_config: ModelConfigWithCredentialsEntity,
                                  prompt_template_entity: PromptTemplateEntity,
                                  inputs: dict[str, str],
-                                 files: list["FileVar"],
+                                 files: list[FileVar],
                                  query: Optional[str] = None,
                                  context: Optional[str] = None,
                                  memory: Optional[TokenBufferMemory] = None) \
@@ -256,7 +254,6 @@ class AppRunner:
         :param invoke_result: invoke result
         :param queue_manager: application queue manager
         :param stream: stream
-        :param agent: agent
         :return:
         """
         if not stream:
@@ -279,7 +276,6 @@ class AppRunner:
         Handle invoke result direct
         :param invoke_result: invoke result
         :param queue_manager: application queue manager
-        :param agent: agent
         :return:
         """
         queue_manager.publish(
@@ -295,7 +291,6 @@ class AppRunner:
         Handle invoke result
         :param invoke_result: invoke result
         :param queue_manager: application queue manager
-        :param agent: agent
         :return:
         """
         model = None
@@ -371,7 +366,7 @@ class AppRunner:
             message_id=message_id,
             trace_manager=app_generate_entity.trace_manager
         )
-
+    
     def check_hosting_moderation(self, application_generate_entity: EasyUIBasedAppGenerateEntity,
                                  queue_manager: AppQueueManager,
                                  prompt_messages: list[PromptMessage]) -> bool:
@@ -423,7 +418,7 @@ class AppRunner:
             inputs=inputs,
             query=query
         )
-
+    
     def query_app_annotations_to_reply(self, app_record: App,
                                        message: Message,
                                        query: str,

api/core/app/apps/message_based_app_generator.py

@@ -138,7 +138,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
         """
         Initialize generate records
         :param application_generate_entity: application generate entity
-        :conversation conversation
         :return:
         """
         app_config = application_generate_entity.app_config
@@ -259,7 +258,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
 
         return introduction
 
-    def _get_conversation(self, conversation_id: str):
+    def _get_conversation(self, conversation_id: str) -> Conversation:
         """
         Get conversation by conversation id
         :param conversation_id: conversation id
@@ -271,9 +270,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             .first()
         )
 
-        if not conversation:
-            raise ConversationNotExistsError()
-
         return conversation
 
     def _get_message(self, message_id: str) -> Message:

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

@@ -11,8 +11,7 @@ from core.app.entities.app_invoke_entities import (
     WorkflowAppGenerateEntity,
 )
 from core.workflow.callbacks.base_workflow_callback import WorkflowCallback
-from core.workflow.entities.variable_pool import VariablePool
-from core.workflow.enums import SystemVariable
+from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
@@ -27,7 +26,8 @@ class WorkflowAppRunner:
     Workflow Application Runner
     """
 
-    def run(self, application_generate_entity: WorkflowAppGenerateEntity, queue_manager: AppQueueManager) -> None:
+    def run(self, application_generate_entity: WorkflowAppGenerateEntity,
+            queue_manager: AppQueueManager) -> None:
         """
         Run application
         :param application_generate_entity: application generate entity
@@ -47,36 +47,25 @@ class WorkflowAppRunner:
 
         app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
         if not app_record:
-            raise ValueError('App not found')
+            raise ValueError("App not found")
 
         workflow = self.get_workflow(app_model=app_record, workflow_id=app_config.workflow_id)
         if not workflow:
-            raise ValueError('Workflow not initialized')
+            raise ValueError("Workflow not initialized")
 
         inputs = application_generate_entity.inputs
         files = application_generate_entity.files
 
         db.session.close()
 
-        workflow_callbacks: list[WorkflowCallback] = [
-            WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)
-        ]
+        workflow_callbacks: list[WorkflowCallback] = [WorkflowEventTriggerCallback(
+            queue_manager=queue_manager,
+            workflow=workflow
+        )]
 
-        if bool(os.environ.get('DEBUG', 'False').lower() == 'true'):
+        if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
             workflow_callbacks.append(WorkflowLoggingCallback())
 
-        # Create a variable pool.
-        system_inputs = {
-            SystemVariable.FILES: files,
-            SystemVariable.USER_ID: user_id,
-        }
-        variable_pool = VariablePool(
-            system_variables=system_inputs,
-            user_inputs=inputs,
-            environment_variables=workflow.environment_variables,
-            conversation_variables=[],
-        )
-
         # RUN WORKFLOW
         workflow_engine_manager = WorkflowEngineManager()
         workflow_engine_manager.run_workflow(
@@ -86,33 +75,44 @@ class WorkflowAppRunner:
             if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
             else UserFrom.END_USER,
             invoke_from=application_generate_entity.invoke_from,
+            user_inputs=inputs,
+            system_inputs={
+                SystemVariable.FILES: files,
+                SystemVariable.USER_ID: user_id
+            },
             callbacks=workflow_callbacks,
-            call_depth=application_generate_entity.call_depth,
-            variable_pool=variable_pool,
+            call_depth=application_generate_entity.call_depth
         )
 
-    def single_iteration_run(
-        self, app_id: str, workflow_id: str, queue_manager: AppQueueManager, inputs: dict, node_id: str, user_id: str
-    ) -> None:
+    def single_iteration_run(self, app_id: str, workflow_id: str,
+                             queue_manager: AppQueueManager,
+                             inputs: dict, node_id: str, user_id: str) -> None:
         """
         Single iteration run
         """
-        app_record = db.session.query(App).filter(App.id == app_id).first()
+        app_record: App = db.session.query(App).filter(App.id == app_id).first()
         if not app_record:
-            raise ValueError('App not found')
-
+            raise ValueError("App not found")
+        
         if not app_record.workflow_id:
-            raise ValueError('Workflow not initialized')
+            raise ValueError("Workflow not initialized")
 
         workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
         if not workflow:
-            raise ValueError('Workflow not initialized')
-
-        workflow_callbacks = [WorkflowEventTriggerCallback(queue_manager=queue_manager, workflow=workflow)]
+            raise ValueError("Workflow not initialized")
+        
+        workflow_callbacks = [WorkflowEventTriggerCallback(
+            queue_manager=queue_manager,
+            workflow=workflow
+        )]
 
         workflow_engine_manager = WorkflowEngineManager()
         workflow_engine_manager.single_step_run_iteration_workflow_node(
-            workflow=workflow, node_id=node_id, user_id=user_id, user_inputs=inputs, callbacks=workflow_callbacks
+            workflow=workflow,
+            node_id=node_id,
+            user_id=user_id,
+            user_inputs=inputs,
+            callbacks=workflow_callbacks
         )
 
     def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
@@ -120,13 +120,11 @@ class WorkflowAppRunner:
         Get workflow
         """
         # fetch workflow by workflow_id
-        workflow = (
-            db.session.query(Workflow)
-            .filter(
-                Workflow.tenant_id == app_model.tenant_id, Workflow.app_id == app_model.id, Workflow.id == workflow_id
-            )
-            .first()
-        )
+        workflow = db.session.query(Workflow).filter(
+            Workflow.tenant_id == app_model.tenant_id,
+            Workflow.app_id == app_model.id,
+            Workflow.id == workflow_id
+        ).first()
 
         # return workflow
         return workflow

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

@@ -42,8 +42,7 @@ from core.app.entities.task_entities import (
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.workflow_cycle_manage import WorkflowCycleManage
 from core.ops.ops_trace_manager import TraceQueueManager
-from core.workflow.entities.node_entities import NodeType
-from core.workflow.enums import SystemVariable
+from core.workflow.entities.node_entities import NodeType, SystemVariable
 from core.workflow.nodes.end.end_node import EndNode
 from extensions.ext_database import db
 from models.account import Account
@@ -520,7 +519,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
         """
         nodes = graph.get('nodes')
 
-        iteration_ids = [node.get('id') for node in nodes
+        iteration_ids = [node.get('id') for node in nodes 
                          if node.get('data', {}).get('type') in [
                              NodeType.ITERATION.value,
                              NodeType.LOOP.value,
@@ -531,3 +530,4 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 node.get('id') for node in nodes if node.get('data', {}).get('iteration_id') == iteration_id
             ] for iteration_id in iteration_ids
         }
+    

api/core/app/entities/app_invoke_entities.py

@@ -166,4 +166,4 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         node_id: str
         inputs: dict
 
-    single_iteration_run: Optional[SingleIterationRunEntity] = None
+    single_iteration_run: Optional[SingleIterationRunEntity] = None

api/core/app/segments/__init__.py

@@ -1,7 +1,7 @@
 from .segment_group import SegmentGroup
 from .segments import (
     ArrayAnySegment,
-    ArraySegment,
+    FileSegment,
     FloatSegment,
     IntegerSegment,
     NoneSegment,
@@ -12,9 +12,11 @@ from .segments import (
 from .types import SegmentType
 from .variables import (
     ArrayAnyVariable,
+    ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
     ArrayStringVariable,
+    FileVariable,
     FloatVariable,
     IntegerVariable,
     NoneVariable,
@@ -29,6 +31,7 @@ __all__ = [
     'FloatVariable',
     'ObjectVariable',
     'SecretVariable',
+    'FileVariable',
     'StringVariable',
     'ArrayAnyVariable',
     'Variable',
@@ -41,9 +44,10 @@ __all__ = [
     'FloatSegment',
     'ObjectSegment',
     'ArrayAnySegment',
+    'FileSegment',
     'StringSegment',
     'ArrayStringVariable',
     'ArrayNumberVariable',
     'ArrayObjectVariable',
-    'ArraySegment',
+    'ArrayFileVariable',
 ]

api/core/app/segments/exc.py

@@ -1,2 +0,0 @@
-class VariableError(Exception):
-    pass

api/core/app/segments/factory.py

@@ -1,11 +1,11 @@
 from collections.abc import Mapping
 from typing import Any
 
-from configs import dify_config
+from core.file.file_obj import FileVar
 
-from .exc import VariableError
 from .segments import (
     ArrayAnySegment,
+    FileSegment,
     FloatSegment,
     IntegerSegment,
     NoneSegment,
@@ -15,9 +15,11 @@ from .segments import (
 )
 from .types import SegmentType
 from .variables import (
+    ArrayFileVariable,
     ArrayNumberVariable,
     ArrayObjectVariable,
     ArrayStringVariable,
+    FileVariable,
     FloatVariable,
     IntegerVariable,
     ObjectVariable,
@@ -27,37 +29,39 @@ from .variables import (
 )
 
 
-def build_variable_from_mapping(mapping: Mapping[str, Any], /) -> Variable:
-    if (value_type := mapping.get('value_type')) is None:
-        raise VariableError('missing value type')
-    if not mapping.get('name'):
-        raise VariableError('missing name')
-    if (value := mapping.get('value')) is None:
-        raise VariableError('missing value')
+def build_variable_from_mapping(m: Mapping[str, Any], /) -> Variable:
+    if (value_type := m.get('value_type')) is None:
+        raise ValueError('missing value type')
+    if not m.get('name'):
+        raise ValueError('missing name')
+    if (value := m.get('value')) is None:
+        raise ValueError('missing value')
     match value_type:
         case SegmentType.STRING:
-            result = StringVariable.model_validate(mapping)
+            return StringVariable.model_validate(m)
         case SegmentType.SECRET:
-            result = SecretVariable.model_validate(mapping)
+            return SecretVariable.model_validate(m)
         case SegmentType.NUMBER if isinstance(value, int):
-            result = IntegerVariable.model_validate(mapping)
+            return IntegerVariable.model_validate(m)
         case SegmentType.NUMBER if isinstance(value, float):
-            result = FloatVariable.model_validate(mapping)
+            return FloatVariable.model_validate(m)
         case SegmentType.NUMBER if not isinstance(value, float | int):
-            raise VariableError(f'invalid number value {value}')
+            raise ValueError(f'invalid number value {value}')
+        case SegmentType.FILE:
+            return FileVariable.model_validate(m)
         case SegmentType.OBJECT if isinstance(value, dict):
-            result = ObjectVariable.model_validate(mapping)
+            return ObjectVariable.model_validate(
+                {**m, 'value': {k: build_variable_from_mapping(v) for k, v in value.items()}}
+            )
         case SegmentType.ARRAY_STRING if isinstance(value, list):
-            result = ArrayStringVariable.model_validate(mapping)
+            return ArrayStringVariable.model_validate({**m, 'value': [build_variable_from_mapping(v) for v in value]})
         case SegmentType.ARRAY_NUMBER if isinstance(value, list):
-            result = ArrayNumberVariable.model_validate(mapping)
+            return ArrayNumberVariable.model_validate({**m, 'value': [build_variable_from_mapping(v) for v in value]})
         case SegmentType.ARRAY_OBJECT if isinstance(value, list):
-            result = ArrayObjectVariable.model_validate(mapping)
-        case _:
-            raise VariableError(f'not supported value type {value_type}')
-    if result.size > dify_config.MAX_VARIABLE_SIZE:
-        raise VariableError(f'variable size {result.size} exceeds limit {dify_config.MAX_VARIABLE_SIZE}')
-    return result
+            return ArrayObjectVariable.model_validate({**m, 'value': [build_variable_from_mapping(v) for v in value]})
+        case SegmentType.ARRAY_FILE if isinstance(value, list):
+            return ArrayFileVariable.model_validate({**m, 'value': [build_variable_from_mapping(v) for v in value]})
+    raise ValueError(f'not supported value type {value_type}')
 
 
 def build_segment(value: Any, /) -> Segment:
@@ -70,7 +74,12 @@ def build_segment(value: Any, /) -> Segment:
     if isinstance(value, float):
         return FloatSegment(value=value)
     if isinstance(value, dict):
+        # TODO: Limit the depth of the object
         return ObjectSegment(value=value)
     if isinstance(value, list):
-        return ArrayAnySegment(value=value)
+        # TODO: Limit the depth of the array
+        elements = [build_segment(v) for v in value]
+        return ArrayAnySegment(value=elements)
+    if isinstance(value, FileVar):
+        return FileSegment(value=value)
     raise ValueError(f'not supported value {value}')

api/core/app/segments/segments.py

@@ -1,10 +1,11 @@
 import json
-import sys
 from collections.abc import Mapping, Sequence
 from typing import Any
 
 from pydantic import BaseModel, ConfigDict, field_validator
 
+from core.file.file_obj import FileVar
+
 from .types import SegmentType
 
 
@@ -36,10 +37,6 @@ class Segment(BaseModel):
     def markdown(self) -> str:
         return str(self.value)
 
-    @property
-    def size(self) -> int:
-        return sys.getsizeof(self.value)
-
     def to_object(self) -> Any:
         return self.value
 
@@ -76,7 +73,14 @@ class IntegerSegment(Segment):
     value: int
 
 
+class FileSegment(Segment):
+    value_type: SegmentType = SegmentType.FILE
+    # TODO: embed FileVar in this model.
+    value: FileVar
 
+    @property
+    def markdown(self) -> str:
+        return self.value.to_markdown()
 
 
 class ObjectSegment(Segment):
@@ -99,31 +103,32 @@ class ObjectSegment(Segment):
 class ArraySegment(Segment):
     @property
     def markdown(self) -> str:
-        items = []
-        for item in self.value:
-            if hasattr(item, 'to_markdown'):
-                items.append(item.to_markdown())
-            else:
-                items.append(str(item))
-        return '\n'.join(items)
+        return '\n'.join(['- ' + item.markdown for item in self.value])
+
+    def to_object(self):
+        return [v.to_object() for v in self.value]
 
 
 class ArrayAnySegment(ArraySegment):
     value_type: SegmentType = SegmentType.ARRAY_ANY
-    value: Sequence[Any]
+    value: Sequence[Segment]
 
 
 class ArrayStringSegment(ArraySegment):
     value_type: SegmentType = SegmentType.ARRAY_STRING
-    value: Sequence[str]
+    value: Sequence[StringSegment]
 
 
 class ArrayNumberSegment(ArraySegment):
     value_type: SegmentType = SegmentType.ARRAY_NUMBER
-    value: Sequence[float | int]
+    value: Sequence[FloatSegment | IntegerSegment]
 
 
 class ArrayObjectSegment(ArraySegment):
     value_type: SegmentType = SegmentType.ARRAY_OBJECT
-    value: Sequence[Mapping[str, Any]]
+    value: Sequence[ObjectSegment]
 
+
+class ArrayFileSegment(ArraySegment):
+    value_type: SegmentType = SegmentType.ARRAY_FILE
+    value: Sequence[FileSegment]

api/core/app/segments/types.py

@@ -10,6 +10,8 @@ class SegmentType(str, Enum):
     ARRAY_STRING = 'array[string]'
     ARRAY_NUMBER = 'array[number]'
     ARRAY_OBJECT = 'array[object]'
+    ARRAY_FILE = 'array[file]'
     OBJECT = 'object'
+    FILE = 'file'
 
     GROUP = 'group'

api/core/app/segments/variables.py

@@ -4,9 +4,11 @@ from core.helper import encrypter
 
 from .segments import (
     ArrayAnySegment,
+    ArrayFileSegment,
     ArrayNumberSegment,
     ArrayObjectSegment,
     ArrayStringSegment,
+    FileSegment,
     FloatSegment,
     IntegerSegment,
     NoneSegment,
@@ -42,6 +44,10 @@ class IntegerVariable(IntegerSegment, Variable):
     pass
 
 
+class FileVariable(FileSegment, Variable):
+    pass
+
+
 class ObjectVariable(ObjectSegment, Variable):
     pass
 
@@ -62,6 +68,9 @@ class ArrayObjectVariable(ArrayObjectSegment, Variable):
     pass
 
 
+class ArrayFileVariable(ArrayFileSegment, Variable):
+    pass
+
 
 class SecretVariable(StringVariable):
     value_type: SegmentType = SegmentType.SECRET

api/core/app/task_pipeline/workflow_cycle_state_manager.py

@@ -2,7 +2,7 @@ from typing import Any, Union
 
 from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity
 from core.app.entities.task_entities import AdvancedChatTaskState, WorkflowTaskState
-from core.workflow.enums import SystemVariable
+from core.workflow.entities.node_entities import SystemVariable
 from models.account import Account
 from models.model import EndUser
 from models.workflow import Workflow
@@ -13,4 +13,4 @@ class WorkflowCycleStateManager:
     _workflow: Workflow
     _user: Union[Account, EndUser]
     _task_state: Union[AdvancedChatTaskState, WorkflowTaskState]
-    _workflow_system_variables: dict[SystemVariable, Any]
+    _workflow_system_variables: dict[SystemVariable, Any]

api/core/file/file_obj.py

@@ -1,19 +1,14 @@
 import enum
-from typing import Any, Optional
+from typing import Optional
 
 from pydantic import BaseModel
 
+from core.app.app_config.entities import FileExtraConfig
 from core.file.tool_file_parser import ToolFileParser
 from core.file.upload_file_parser import UploadFileParser
 from core.model_runtime.entities.message_entities import ImagePromptMessageContent
 from extensions.ext_database import db
-
-
-class FileExtraConfig(BaseModel):
-    """
-    File Upload Entity.
-    """
-    image_config: Optional[dict[str, Any]] = None
+from models.model import UploadFile
 
 
 class FileType(enum.Enum):
@@ -119,7 +114,6 @@ class FileVar(BaseModel):
             )
 
     def _get_data(self, force_url: bool = False) -> Optional[str]:
-        from models.model import UploadFile
         if self.type == FileType.IMAGE:
             if self.transfer_method == FileTransferMethod.REMOTE_URL:
                 return self.url

api/core/file/message_file_parser.py

@@ -5,7 +5,8 @@ from urllib.parse import parse_qs, urlparse
 
 import requests
 
-from core.file.file_obj import FileBelongsTo, FileExtraConfig, FileTransferMethod, FileType, FileVar
+from core.app.app_config.entities import FileExtraConfig
+from core.file.file_obj import FileBelongsTo, FileTransferMethod, FileType, FileVar
 from extensions.ext_database import db
 from models.account import Account
 from models.model import EndUser, MessageFile, UploadFile
@@ -99,7 +100,7 @@ class MessageFileParser:
         # return all file objs
         return new_files
 
-    def transform_message_files(self, files: list[MessageFile], file_extra_config: FileExtraConfig):
+    def transform_message_files(self, files: list[MessageFile], file_extra_config: FileExtraConfig) -> list[FileVar]:
         """
         transform message files
 
@@ -144,7 +145,7 @@ class MessageFileParser:
 
         return type_file_objs
 
-    def _to_file_obj(self, file: Union[dict, MessageFile], file_extra_config: FileExtraConfig):
+    def _to_file_obj(self, file: Union[dict, MessageFile], file_extra_config: FileExtraConfig) -> FileVar:
         """
         transform file to file obj
 

api/core/helper/encrypter.py

@@ -2,6 +2,7 @@ import base64
 
 from extensions.ext_database import db
 from libs import rsa
+from models.account import Tenant
 
 
 def obfuscated_token(token: str):
@@ -13,7 +14,6 @@ def obfuscated_token(token: str):
 
 
 def encrypt_token(tenant_id: str, token: str):
-    from models.account import Tenant
     if not (tenant := db.session.query(Tenant).filter(Tenant.id == tenant_id).first()):
         raise ValueError(f'Tenant with id {tenant_id} not found')
     encrypted_token = rsa.encrypt(token, tenant.encrypt_public_key)

api/core/model_manager.py

@@ -271,8 +271,9 @@ class ModelInstance:
 
         :param content_text: text content to be translated
         :param tenant_id: user tenant id
-        :param voice: model timbre
         :param user: unique user id
+        :param voice: model timbre
+        :param streaming: output is streaming
         :return: text for given audio file
         """
         if not isinstance(self.model_type_instance, TTSModel):
@@ -400,10 +401,6 @@ class LBModelManager:
                  managed_credentials: Optional[dict] = None) -> None:
         """
         Load balancing model manager
-        :param tenant_id: tenant_id
-        :param provider: provider
-        :param model_type: model_type
-        :param model: model name
         :param load_balancing_configs: all load balancing configurations
         :param managed_credentials: credentials if load balancing configuration name is __inherit__
         """

api/core/model_runtime/entities/defaults.py

@@ -1,3 +1,4 @@
+
 from core.model_runtime.entities.model_entities import DefaultParameterName
 
 PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
@@ -93,16 +94,5 @@ PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
         },
         'required': False,
         'options': ['JSON', 'XML'],
-    },
-    DefaultParameterName.JSON_SCHEMA: {
-        'label': {
-            'en_US': 'JSON Schema',
-        },
-        'type': 'text',
-        'help': {
-            'en_US': 'Set a response json schema will ensure LLM to adhere it.',
-            'zh_Hans': '设置返回的json schema，llm将按照它返回',
-        },
-        'required': False,
-    },
-}
+    }
+}

api/core/model_runtime/entities/model_entities.py

@@ -95,7 +95,6 @@ class DefaultParameterName(Enum):
     FREQUENCY_PENALTY = "frequency_penalty"
     MAX_TOKENS = "max_tokens"
     RESPONSE_FORMAT = "response_format"
-    JSON_SCHEMA = "json_schema"
 
     @classmethod
     def value_of(cls, value: Any) -> 'DefaultParameterName':
@@ -119,7 +118,6 @@ class ParameterType(Enum):
     INT = "int"
     STRING = "string"
     BOOLEAN = "boolean"
-    TEXT = "text"
 
 
 class ModelPropertyKey(Enum):

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

@@ -84,8 +84,7 @@ class MoonshotLargeLanguageModel(OAIAPICompatLargeLanguageModel):
 
     def _add_custom_parameters(self, credentials: dict) -> None:
         credentials['mode'] = 'chat'
-        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
-            credentials['endpoint_url'] = 'https://api.moonshot.cn/v1'
+        credentials['endpoint_url'] = 'https://api.moonshot.cn/v1'
 
     def _add_function_call(self, model: str, credentials: dict) -> None:
         model_schema = self.get_model_schema(model, credentials)

api/core/model_runtime/model_providers/moonshot/moonshot.yaml

@@ -31,14 +31,6 @@ provider_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
-    - variable: endpoint_url
-      label:
-        en_US: API Base
-      type: text-input
-      required: false
-      placeholder:
-        zh_Hans: Base URL, 如：https://api.moonshot.cn/v1
-        en_US: Base URL, e.g. https://api.moonshot.cn/v1
 model_credential_schema:
   model:
     label:

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

@@ -2,7 +2,6 @@
 - gpt-4o
 - gpt-4o-2024-05-13
 - gpt-4o-2024-08-06
-- chatgpt-4o-latest
 - gpt-4o-mini
 - gpt-4o-mini-2024-07-18
 - gpt-4-turbo

api/core/model_runtime/model_providers/openai/llm/chatgpt-4o-latest.yaml

@@ -1,44 +0,0 @@
-model: chatgpt-4o-latest
-label:
-  zh_Hans: chatgpt-4o-latest
-  en_US: chatgpt-4o-latest
-model_type: llm
-features:
-  - multi-tool-call
-  - agent-thought
-  - stream-tool-call
-  - vision
-model_properties:
-  mode: chat
-  context_size: 128000
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: presence_penalty
-    use_template: presence_penalty
-  - name: frequency_penalty
-    use_template: frequency_penalty
-  - name: max_tokens
-    use_template: max_tokens
-    default: 512
-    min: 1
-    max: 16384
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: response_format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
-pricing:
-  input: '2.50'
-  output: '10.00'
-  unit: '0.000001'
-  currency: USD

api/core/model_runtime/model_providers/openai/llm/gpt-4o-2024-08-06.yaml

@@ -37,9 +37,6 @@ parameter_rules:
     options:
       - text
       - json_object
-      - json_schema
-  - name: json_schema
-    use_template: json_schema
 pricing:
   input: '2.50'
   output: '10.00'

api/core/model_runtime/model_providers/openai/llm/gpt-4o-mini.yaml

@@ -37,9 +37,6 @@ parameter_rules:
     options:
       - text
       - json_object
-      - json_schema
-  - name: json_schema
-    use_template: json_schema
 pricing:
   input: '0.15'
   output: '0.60'

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

@@ -1,4 +1,3 @@
-import json
 import logging
 from collections.abc import Generator
 from typing import Optional, Union, cast
@@ -545,18 +544,13 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
 
         response_format = model_parameters.get("response_format")
         if response_format:
-            if response_format == "json_schema":
-                json_schema = model_parameters.get("json_schema")
-                if not json_schema:
-                    raise ValueError("Must define JSON Schema when the response format is json_schema")
-                try:
-                    schema = json.loads(json_schema)
-                except:
-                    raise ValueError(f"not currect json_schema format: {json_schema}")
-                model_parameters.pop("json_schema")
-                model_parameters["response_format"] = {"type": "json_schema", "json_schema": schema}
+            if response_format == "json_object":
+                response_format = {"type": "json_object"}
             else:
-                model_parameters["response_format"] = {"type": response_format}
+                response_format = {"type": "text"}
+
+            model_parameters["response_format"] = response_format
+
 
         extra_model_kwargs = {}
 
@@ -928,14 +922,11 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                                   tools: Optional[list[PromptMessageTool]] = None) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
-        Official documentation: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
+        Official documentation: https://github.com/openai/openai-cookbook/blob/
+        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
         if model.startswith('ft:'):
             model = model.split(':')[1]
 
-        # Currently, we can use gpt4o to calculate chatgpt-4o-latest's token.
-        if model == "chatgpt-4o-latest":
-            model = "gpt-4o"
-
         try:
             encoding = tiktoken.encoding_for_model(model)
         except KeyError:
@@ -955,7 +946,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             raise NotImplementedError(
                 f"get_num_tokens_from_messages() is not presently implemented "
                 f"for model {model}."
-                "See https://platform.openai.com/docs/advanced-usage/managing-tokens for "
+                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
                 "information on how messages are converted to tokens."
             )
         num_tokens = 0

api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.yaml

@@ -7,7 +7,6 @@ description:
 supported_model_types:
   - llm
   - text-embedding
-  - speech2text
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -62,22 +61,6 @@ model_credential_schema:
         zh_Hans: 模型上下文长度
         en_US: Model context size
       required: true
-      show_on:
-        - variable: __model_type
-          value: llm
-      type: text-input
-      default: '4096'
-      placeholder:
-        zh_Hans: 在此输入您的模型上下文长度
-        en_US: Enter your Model context size
-    - variable: context_size
-      label:
-        zh_Hans: 模型上下文长度
-        en_US: Model context size
-      required: true
-      show_on:
-        - variable: __model_type
-          value: text-embedding
       type: text-input
       default: '4096'
       placeholder:

api/core/model_runtime/model_providers/openai_api_compatible/speech2text/speech2text.py

@@ -1,63 +0,0 @@
-from typing import IO, Optional
-from urllib.parse import urljoin
-
-import requests
-
-from core.model_runtime.errors.invoke import InvokeBadRequestError
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
-from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOAI_API_Compat
-
-
-class OAICompatSpeech2TextModel(_CommonOAI_API_Compat, Speech2TextModel):
-    """
-    Model class for OpenAI Compatible Speech to text model.
-    """
-
-    def _invoke(
-            self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None
-    ) -> str:
-        """
-        Invoke speech2text model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param file: audio file
-        :param user: unique user id
-        :return: text for given audio file
-        """
-        headers = {}
-
-        api_key = credentials.get("api_key")
-        if api_key:
-            headers["Authorization"] = f"Bearer {api_key}"
-
-        endpoint_url = credentials.get("endpoint_url")
-        if not endpoint_url.endswith("/"):
-            endpoint_url += "/"
-        endpoint_url = urljoin(endpoint_url, "audio/transcriptions")
-
-        payload = {"model": model}
-        files = [("file", file)]
-        response = requests.post(endpoint_url, headers=headers, data=payload, files=files)
-
-        if response.status_code != 200:
-            raise InvokeBadRequestError(response.text)
-        response_data = response.json()
-        return response_data["text"]
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            audio_file_path = self._get_demo_file_path()
-
-            with open(audio_file_path, "rb") as audio_file:
-                self._invoke(model, credentials, audio_file)
-        except Exception as ex:
-            raise CredentialsValidateFailedError(str(ex))

api/core/model_runtime/model_providers/perfxcloud/llm/Llama3-Chinese_v2.yaml

@@ -1,61 +0,0 @@
-model: Llama3-Chinese_v2
-label:
-  en_US: Llama3-Chinese_v2
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 8192
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Meta-Llama-3-70B-Instruct-GPTQ-Int4.yaml

@@ -1,61 +0,0 @@
-model: Meta-Llama-3-70B-Instruct-GPTQ-Int4
-label:
-  en_US: Meta-Llama-3-70B-Instruct-GPTQ-Int4
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 1024
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Meta-Llama-3-8B-Instruct.yaml

@@ -1,61 +0,0 @@
-model: Meta-Llama-3-8B-Instruct
-label:
-  en_US: Meta-Llama-3-8B-Instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 8192
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Meta-Llama-3.1-405B-Instruct-AWQ-INT4.yaml

@@ -1,61 +0,0 @@
-model: Meta-Llama-3.1-405B-Instruct-AWQ-INT4
-label:
-  en_US: Meta-Llama-3.1-405B-Instruct-AWQ-INT4
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 410960
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Meta-Llama-3.1-8B-Instruct.yaml

@@ -1,61 +0,0 @@
-model: Meta-Llama-3.1-8B-Instruct
-label:
-  en_US: Meta-Llama-3.1-8B-Instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 4096
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.1
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen-14B-Chat-Int4.yaml

@@ -55,8 +55,7 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
 pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
+  input: '0.000'
+  output: '0.000'
+  unit: '0.000'
   currency: RMB
-deprecated: true

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen1.5-110B-Chat-GPTQ-Int4.yaml

@@ -55,8 +55,7 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
 pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
+  input: '0.000'
+  output: '0.000'
+  unit: '0.000'
   currency: RMB
-deprecated: true

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen1.5-72B-Chat-GPTQ-Int4.yaml

@@ -6,7 +6,7 @@ features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 2048
+  context_size: 8192
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -55,7 +55,7 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
 pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
+  input: '0.000'
+  output: '0.000'
+  unit: '0.000'
   currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen1.5-7B.yaml

@@ -6,7 +6,7 @@ features:
   - agent-thought
 model_properties:
   mode: completion
-  context_size: 32768
+  context_size: 8192
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -55,7 +55,7 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
 pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
+  input: '0.000'
+  output: '0.000'
+  unit: '0.000'
   currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen2-72B-Instruct-GPTQ-Int4.yaml

@@ -8,12 +8,12 @@ features:
   - stream-tool-call
 model_properties:
   mode: chat
-  context_size: 2048
+  context_size: 8192
 parameter_rules:
   - name: temperature
     use_template: temperature
     type: float
-    default: 0.7
+    default: 0.3
     min: 0.0
     max: 2.0
     help:
@@ -57,7 +57,7 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
 pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
+  input: '0.000'
+  output: '0.000'
+  unit: '0.000'
   currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen2-72B-Instruct.yaml

@@ -1,61 +0,0 @@
-model: Qwen2-72B-Instruct
-label:
-  en_US: Qwen2-72B-Instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/Qwen2-7B.yaml

@@ -8,7 +8,7 @@ features:
   - stream-tool-call
 model_properties:
   mode: completion
-  context_size: 32768
+  context_size: 8192
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -57,7 +57,7 @@ parameter_rules:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
       en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
 pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
+  input: '0.000'
+  output: '0.000'
+  unit: '0.000'
   currency: RMB

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

@@ -1,15 +1,6 @@
-- Meta-Llama-3.1-405B-Instruct-AWQ-INT4
-- Meta-Llama-3.1-8B-Instruct
-- Meta-Llama-3-70B-Instruct-GPTQ-Int4
-- Meta-Llama-3-8B-Instruct
 - Qwen2-72B-Instruct-GPTQ-Int4
-- Qwen2-72B-Instruct
 - Qwen2-7B
-- Qwen-14B-Chat-Int4
+- Qwen1.5-110B-Chat-GPTQ-Int4
 - Qwen1.5-72B-Chat-GPTQ-Int4
 - Qwen1.5-7B
-- Qwen1.5-110B-Chat-GPTQ-Int4
-- deepseek-v2-chat
-- deepseek-v2-lite-chat
-- Llama3-Chinese_v2
-- chatglm3-6b
+- Qwen-14B-Chat-Int4

api/core/model_runtime/model_providers/perfxcloud/llm/chatglm3-6b.yaml

@@ -1,61 +0,0 @@
-model: chatglm3-6b
-label:
-  en_US: chatglm3-6b
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 8192
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/deepseek-v2-chat.yaml

@@ -1,61 +0,0 @@
-model: deepseek-v2-chat
-label:
-  en_US: deepseek-v2-chat
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 4096
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/llm/deepseek-v2-lite-chat.yaml

@@ -1,61 +0,0 @@
-model: deepseek-v2-lite-chat
-label:
-  en_US: deepseek-v2-lite-chat
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 2048
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.5
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 600
-    min: 1
-    max: 1248
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-pricing:
-  input: "0.000"
-  output: "0.000"
-  unit: "0.000"
-  currency: RMB

api/core/model_runtime/model_providers/perfxcloud/text_embedding/BAAI-bge-large-en-v1.5.yaml

@@ -1,4 +0,0 @@
-model: BAAI/bge-large-en-v1.5
-model_type: text-embedding
-model_properties:
-  context_size: 32768

api/core/model_runtime/model_providers/perfxcloud/text_embedding/BAAI-bge-large-zh-v1.5.yaml

@@ -1,4 +0,0 @@
-model: BAAI/bge-large-zh-v1.5
-model_type: text-embedding
-model_properties:
-  context_size: 32768

api/core/model_runtime/model_providers/siliconflow/rerank/bce-reranker-base_v1.yaml

@@ -1,4 +0,0 @@
-model: netease-youdao/bce-reranker-base_v1
-model_type: rerank
-model_properties:
-  context_size: 512

api/core/model_runtime/model_providers/siliconflow/rerank/bge-reranker-v2-m3.yaml

@@ -1,4 +0,0 @@
-model: BAAI/bge-reranker-v2-m3
-model_type: rerank
-model_properties:
-  context_size: 8192

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

@@ -1,87 +0,0 @@
-from typing import Optional
-
-import httpx
-
-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 SiliconflowRerankModel(RerankModel):
-
-    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:
-        if len(docs) == 0:
-            return RerankResult(model=model, docs=[])
-
-        base_url = credentials.get('base_url', 'https://api.siliconflow.cn/v1')
-        if base_url.endswith('/'):
-            base_url = base_url[:-1]
-        try:
-            response = httpx.post(
-                base_url + '/rerank',
-                json={
-                    "model": model,
-                    "query": query,
-                    "documents": docs,
-                    "top_n": top_n,
-                    "return_documents": True
-                },
-                headers={"Authorization": f"Bearer {credentials.get('api_key')}"}
-            )
-            response.raise_for_status()
-            results = response.json()
-
-            rerank_documents = []
-            for result in results['results']:
-                rerank_document = RerankDocument(
-                    index=result['index'],
-                    text=result['document']['text'],
-                    score=result['relevance_score'],
-                )
-                if score_threshold is None or result['relevance_score'] >= score_threshold:
-                    rerank_documents.append(rerank_document)
-
-            return RerankResult(model=model, docs=rerank_documents)
-        except httpx.HTTPStatusError as e:
-            raise InvokeServerUnavailableError(str(e))
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        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
-        """
-        return {
-            InvokeConnectionError: [httpx.ConnectError],
-            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
-            InvokeRateLimitError: [],
-            InvokeAuthorizationError: [httpx.HTTPStatusError],
-            InvokeBadRequestError: [httpx.RequestError]
-        }

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

@@ -6,7 +6,6 @@ from core.model_runtime.model_providers.__base.model_provider import ModelProvid
 
 logger = logging.getLogger(__name__)
 
-
 class SiliconflowProvider(ModelProvider):
 
     def validate_provider_credentials(self, credentials: dict) -> None:

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

@@ -12,12 +12,10 @@ help:
     en_US: Get your API Key from SiliconFlow
     zh_Hans: 从 SiliconFlow 获取 API Key
   url:
-    en_US: https://cloud.siliconflow.cn/account/ak
+    en_US: https://cloud.siliconflow.cn/keys
 supported_model_types:
   - llm
   - text-embedding
-  - rerank
-  - speech2text
 configurate_methods:
   - predefined-model
 provider_credential_schema:

api/core/model_runtime/model_providers/siliconflow/speech2text/sense-voice-small.yaml

@@ -1,5 +0,0 @@
-model: iic/SenseVoiceSmall
-model_type: speech2text
-model_properties:
-  file_upload_limit: 1
-  supported_file_extensions: mp3,wav

api/core/model_runtime/model_providers/siliconflow/speech2text/speech2text.py

@@ -1,32 +0,0 @@
-from typing import IO, Optional
-
-from core.model_runtime.model_providers.openai_api_compatible.speech2text.speech2text import OAICompatSpeech2TextModel
-
-
-class SiliconflowSpeech2TextModel(OAICompatSpeech2TextModel):
-    """
-    Model class for Siliconflow Speech to text model.
-    """
-
-    def _invoke(
-            self, model: str, credentials: dict, file: IO[bytes], user: Optional[str] = None
-    ) -> str:
-        """
-        Invoke speech2text model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param file: audio file
-        :param user: unique user id
-        :return: text for given audio file
-        """
-        self._add_custom_parameters(credentials)
-        return super()._invoke(model, credentials, file)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        self._add_custom_parameters(credentials)
-        return super().validate_credentials(model, credentials)
-
-    @classmethod
-    def _add_custom_parameters(cls, credentials: dict) -> None:
-        credentials["endpoint_url"] = "https://api.siliconflow.cn/v1"

api/core/model_runtime/model_providers/siliconflow/text_embedding/bce-embedding-base-v1.yaml

@@ -1,5 +0,0 @@
-model: netease-youdao/bce-embedding-base_v1
-model_type: text-embedding
-model_properties:
-  context_size: 512
-  max_chunks: 1

api/core/model_runtime/model_providers/siliconflow/text_embedding/bge-m3.yaml

@@ -1,5 +0,0 @@
-model: BAAI/bge-m3
-model_type: text-embedding
-model_properties:
-  context_size: 8192
-  max_chunks: 1

api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml

@@ -1,81 +0,0 @@
-model: farui-plus
-label:
-  en_US: farui-plus
-model_type: llm
-features:
-  - multi-tool-call
-  - agent-thought
-  - stream-tool-call
-model_properties:
-  mode: chat
-  context_size: 12288
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-    type: float
-    default: 0.3
-    min: 0.0
-    max: 2.0
-    help:
-      zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
-      en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 2000
-    min: 1
-    max: 2000
-    help:
-      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
-      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
-  - name: top_p
-    use_template: top_p
-    type: float
-    default: 0.8
-    min: 0.1
-    max: 0.9
-    help:
-      zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
-      en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: top_k
-    type: int
-    min: 0
-    max: 99
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
-  - name: seed
-    required: false
-    type: int
-    default: 1234
-    label:
-      zh_Hans: 随机种子
-      en_US: Random seed
-    help:
-      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
-      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
-  - name: repetition_penalty
-    required: false
-    type: float
-    default: 1.1
-    label:
-      en_US: Repetition penalty
-    help:
-      zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
-  - name: enable_search
-    type: boolean
-    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.
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.02'
-  output: '0.02'
-  unit: '0.001'
-  currency: RMB

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

@@ -159,8 +159,6 @@ You should also complete the text started with ``` but not tell ``` directly.
         """
         if model in ['qwen-turbo-chat', 'qwen-plus-chat']:
             model = model.replace('-chat', '')
-        if model == 'farui-plus':
-            model = 'qwen-farui-plus'
 
         if model in self.tokenizers:
             tokenizer = self.tokenizers[model]

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml

@@ -2,8 +2,3 @@ model: text-embedding-v1
 model_type: text-embedding
 model_properties:
   context_size: 2048
-  max_chunks: 25
-pricing:
-  input: "0.0007"
-  unit: "0.001"
-  currency: RMB

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml

@@ -2,8 +2,3 @@ model: text-embedding-v2
 model_type: text-embedding
 model_properties:
   context_size: 2048
-  max_chunks: 25
-pricing:
-  input: "0.0007"
-  unit: "0.001"
-  currency: RMB

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

@@ -2,7 +2,6 @@ import time
 from typing import Optional
 
 import dashscope
-import numpy as np
 
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import (
@@ -22,11 +21,11 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
     """
 
     def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        texts: list[str],
-        user: Optional[str] = None,
+            self,
+            model: str,
+            credentials: dict,
+            texts: list[str],
+            user: Optional[str] = None,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -38,44 +37,16 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
         :return: embeddings result
         """
         credentials_kwargs = self._to_credential_kwargs(credentials)
-
-        context_size = self._get_context_size(model, credentials)
-        max_chunks = self._get_max_chunks(model, credentials)
-        inputs = []
-        indices = []
-        used_tokens = 0
-
-        for i, text in enumerate(texts):
-
-            # Here token count is only an approximation based on the GPT2 tokenizer
-            num_tokens = self._get_num_tokens_by_gpt2(text)
-
-            if num_tokens >= context_size:
-                cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
-                # if num tokens is larger than context length, only use the start
-                inputs.append(text[0:cutoff])
-            else:
-                inputs.append(text)
-            indices += [i]
-
-        batched_embeddings = []
-        _iter = range(0, len(inputs), max_chunks)
-
-        for i in _iter:
-            embeddings_batch, embedding_used_tokens = self.embed_documents(
-                credentials_kwargs=credentials_kwargs,
-                model=model,
-                texts=inputs[i : i + max_chunks],
-            )
-            used_tokens += embedding_used_tokens
-            batched_embeddings += embeddings_batch
-
-        # calc usage
-        usage = self._calc_response_usage(
-            model=model, credentials=credentials, tokens=used_tokens
+        embeddings, embedding_used_tokens = self.embed_documents(
+            credentials_kwargs=credentials_kwargs,
+            model=model,
+            texts=texts
         )
+
         return TextEmbeddingResult(
-            embeddings=batched_embeddings, usage=usage, model=model
+            embeddings=embeddings,
+            usage=self._calc_response_usage(model, credentials_kwargs, embedding_used_tokens),
+            model=model
         )
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
@@ -108,16 +79,12 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             credentials_kwargs = self._to_credential_kwargs(credentials)
 
             # call embedding model
-            self.embed_documents(
-                credentials_kwargs=credentials_kwargs, model=model, texts=["ping"]
-            )
+            self.embed_documents(credentials_kwargs=credentials_kwargs, model=model, texts=["ping"])
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
     @staticmethod
-    def embed_documents(
-        credentials_kwargs: dict, model: str, texts: list[str]
-    ) -> tuple[list[list[float]], int]:
+    def embed_documents(credentials_kwargs: dict, model: str, texts: list[str]) -> tuple[list[list[float]], int]:
         """Call out to Tongyi's embedding endpoint.
 
         Args:
@@ -135,7 +102,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
                 api_key=credentials_kwargs["dashscope_api_key"],
                 model=model,
                 input=text,
-                text_type="document",
+                text_type="document"
             )
             data = response.output["embeddings"][0]
             embeddings.append(data["embedding"])
@@ -144,7 +111,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
 
     def _calc_response_usage(
-        self, model: str, credentials: dict, tokens: int
+            self, model: str, credentials: dict, tokens: int
     ) -> EmbeddingUsage:
         """
         Calculate response usage
@@ -158,7 +125,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             model=model,
             credentials=credentials,
             price_type=PriceType.INPUT,
-            tokens=tokens,
+            tokens=tokens
         )
 
         # transform usage
@@ -169,7 +136,7 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
             price_unit=input_price_info.unit,
             total_price=input_price_info.total_amount,
             currency=input_price_info.currency,
-            latency=time.perf_counter() - self.started_at,
+            latency=time.perf_counter() - self.started_at
         )
 
         return usage

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

@@ -1 +1 @@
-- solar-1-mini-chat
+- soloar-1-mini-chat

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

@@ -35,10 +35,7 @@ from core.model_runtime.model_providers.volcengine_maas.errors import (
     RateLimitErrors,
     ServerUnavailableErrors,
 )
-from core.model_runtime.model_providers.volcengine_maas.llm.models import (
-    get_model_config,
-    get_v2_req_params,
-)
+from core.model_runtime.model_providers.volcengine_maas.llm.models import ModelConfigs
 from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
 
 logger = logging.getLogger(__name__)
@@ -98,12 +95,37 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
             -> LLMResult | Generator:
 
         client = MaaSClient.from_credential(credentials)
-        req_params = get_v2_req_params(credentials, model_parameters, stop)
+
+        req_params = ModelConfigs.get(
+            credentials['base_model_name'], {}).get('req_params', {}).copy()
+        if credentials.get('context_size'):
+            req_params['max_prompt_tokens'] = credentials.get('context_size')
+        if credentials.get('max_tokens'):
+            req_params['max_new_tokens'] = credentials.get('max_tokens')
+        if model_parameters.get('max_tokens'):
+            req_params['max_new_tokens'] = model_parameters.get('max_tokens')
+        if model_parameters.get('temperature'):
+            req_params['temperature'] = model_parameters.get('temperature')
+        if model_parameters.get('top_p'):
+            req_params['top_p'] = model_parameters.get('top_p')
+        if model_parameters.get('top_k'):
+            req_params['top_k'] = model_parameters.get('top_k')
+        if model_parameters.get('presence_penalty'):
+            req_params['presence_penalty'] = model_parameters.get(
+                'presence_penalty')
+        if model_parameters.get('frequency_penalty'):
+            req_params['frequency_penalty'] = model_parameters.get(
+                'frequency_penalty')
+        if stop:
+            req_params['stop'] = stop
+
         extra_model_kwargs = {}
+        
         if tools:
             extra_model_kwargs['tools'] = [
                 MaaSClient.transform_tool_prompt_to_maas_config(tool) for tool in tools
             ]
+
         resp = MaaSClient.wrap_exception(
             lambda: client.chat(req_params, prompt_messages, stream, **extra_model_kwargs))
         if not stream:
@@ -175,8 +197,10 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
         """
             used to define customizable model schema
         """
-        model_config = get_model_config(credentials)
-    
+        max_tokens = ModelConfigs.get(
+            credentials['base_model_name'], {}).get('req_params', {}).get('max_new_tokens')
+        if credentials.get('max_tokens'):
+            max_tokens = int(credentials.get('max_tokens'))
         rules = [
             ParameterRule(
                 name='temperature',
@@ -210,10 +234,10 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 name='presence_penalty',
                 type=ParameterType.FLOAT,
                 use_template='presence_penalty',
-                label=I18nObject(
-                    en_US='Presence Penalty',
-                    zh_Hans= '存在惩罚',
-                ),
+                label={
+                    'en_US': 'Presence Penalty',
+                    'zh_Hans': '存在惩罚',
+                },
                 min=-2.0,
                 max=2.0,
             ),
@@ -221,10 +245,10 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 name='frequency_penalty',
                 type=ParameterType.FLOAT,
                 use_template='frequency_penalty',
-                label=I18nObject(
-                    en_US= 'Frequency Penalty',
-                    zh_Hans= '频率惩罚',
-                ),
+                label={
+                    'en_US': 'Frequency Penalty',
+                    'zh_Hans': '频率惩罚',
+                },
                 min=-2.0,
                 max=2.0,
             ),
@@ -233,7 +257,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 type=ParameterType.INT,
                 use_template='max_tokens',
                 min=1,
-                max=model_config.properties.max_tokens,
+                max=max_tokens,
                 default=512,
                 label=I18nObject(
                     zh_Hans='最大生成长度',
@@ -242,10 +266,17 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
             ),
         ]
 
-        model_properties = {}
-        model_properties[ModelPropertyKey.CONTEXT_SIZE] = model_config.properties.context_size
-        model_properties[ModelPropertyKey.MODE] = model_config.properties.mode.value
-       
+        model_properties = ModelConfigs.get(
+            credentials['base_model_name'], {}).get('model_properties', {}).copy()
+        if credentials.get('mode'):
+            model_properties[ModelPropertyKey.MODE] = credentials.get('mode')
+        if credentials.get('context_size'):
+            model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(
+                credentials.get('context_size', 4096))
+
+        model_features = ModelConfigs.get(
+            credentials['base_model_name'], {}).get('features', [])
+
         entity = AIModelEntity(
             model=model,
             label=I18nObject(
@@ -255,7 +286,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
             model_type=ModelType.LLM,
             model_properties=model_properties,
             parameter_rules=rules,
-            features=model_config.features,
+            features=model_features,
         )
 
         return entity

api/core/model_runtime/model_providers/volcengine_maas/llm/models.py

@@ -1,123 +1,181 @@
-from pydantic import BaseModel
-
-from core.model_runtime.entities.llm_entities import LLMMode
 from core.model_runtime.entities.model_entities import ModelFeature
 
-
-class ModelProperties(BaseModel):
-    context_size: int 
-    max_tokens: int 
-    mode: LLMMode
-
-class ModelConfig(BaseModel):
-    properties: ModelProperties
-    features: list[ModelFeature]
-
-
-configs: dict[str, ModelConfig] = {
-    'Doubao-pro-4k': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
-    ),
-    'Doubao-lite-4k': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
-    ),
-    'Doubao-pro-32k': ModelConfig(
-        properties=ModelProperties(context_size=32768, max_tokens=32768, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
-    ),
-    'Doubao-lite-32k': ModelConfig(
-        properties=ModelProperties(context_size=32768, max_tokens=32768, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
-    ),
-    'Doubao-pro-128k': ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=131072, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
-    ),
-    'Doubao-lite-128k': ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=131072, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
-    ),
-    'Skylark2-pro-4k': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_tokens=4000, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'Llama3-8B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'Llama3-70B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=8192, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'Moonshot-v1-8k': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'Moonshot-v1-32k': ModelConfig(
-        properties=ModelProperties(context_size=32768, max_tokens=16384, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'Moonshot-v1-128k': ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=65536, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'GLM3-130B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'GLM3-130B-Fin': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
-    ),
-    'Mistral-7B': ModelConfig(
-        properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT),
-        features=[]
-    )
+ModelConfigs = {
+    'Doubao-pro-4k': {
+        'req_params': {
+            'max_prompt_tokens': 4096,
+            'max_new_tokens': 4096,
+        },
+        'model_properties': {
+            'context_size': 4096,
+            'mode': 'chat',
+        },
+        'features': [
+            ModelFeature.TOOL_CALL
+        ],
+    },
+    'Doubao-lite-4k': {
+        'req_params': {
+            'max_prompt_tokens': 4096,
+            'max_new_tokens': 4096,
+        },
+        'model_properties': {
+            'context_size': 4096,
+            'mode': 'chat',
+        },
+        'features': [
+            ModelFeature.TOOL_CALL
+        ],
+    },
+    'Doubao-pro-32k': {
+        'req_params': {
+            'max_prompt_tokens': 32768,
+            'max_new_tokens': 32768,
+        },
+        'model_properties': {
+            'context_size': 32768,
+            'mode': 'chat',
+        },
+        'features': [
+            ModelFeature.TOOL_CALL
+        ],
+    },
+    'Doubao-lite-32k': {
+        'req_params': {
+            'max_prompt_tokens': 32768,
+            'max_new_tokens': 32768,
+        },
+        'model_properties': {
+            'context_size': 32768,
+            'mode': 'chat',
+        },
+        'features': [
+            ModelFeature.TOOL_CALL
+        ],
+    },
+    'Doubao-pro-128k': {
+        'req_params': {
+            'max_prompt_tokens': 131072,
+            'max_new_tokens': 131072,
+        },
+        'model_properties': {
+            'context_size': 131072,
+            'mode': 'chat',
+        },
+        'features': [
+            ModelFeature.TOOL_CALL
+        ],
+    },
+    'Doubao-lite-128k': {
+        'req_params': {
+            'max_prompt_tokens': 131072,
+            'max_new_tokens': 131072,
+        },
+        'model_properties': {
+            'context_size': 131072,
+            'mode': 'chat',
+        },
+        'features': [
+            ModelFeature.TOOL_CALL
+        ],
+    },
+    'Skylark2-pro-4k': {
+        'req_params': {
+            'max_prompt_tokens': 4096,
+            'max_new_tokens': 4000,
+        },
+        'model_properties': {
+            'context_size': 4096,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'Llama3-8B': {
+        'req_params': {
+            'max_prompt_tokens': 8192,
+            'max_new_tokens': 8192,
+        },
+        'model_properties': {
+            'context_size': 8192,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'Llama3-70B': {
+        'req_params': {
+            'max_prompt_tokens': 8192,
+            'max_new_tokens': 8192,
+        },
+        'model_properties': {
+            'context_size': 8192,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'Moonshot-v1-8k': {
+        'req_params': {
+            'max_prompt_tokens': 8192,
+            'max_new_tokens': 4096,
+        },
+        'model_properties': {
+            'context_size': 8192,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'Moonshot-v1-32k': {
+        'req_params': {
+            'max_prompt_tokens': 32768,
+            'max_new_tokens': 16384,
+        },
+        'model_properties': {
+            'context_size': 32768,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'Moonshot-v1-128k': {
+        'req_params': {
+            'max_prompt_tokens': 131072,
+            'max_new_tokens': 65536,
+        },
+        'model_properties': {
+            'context_size': 131072,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'GLM3-130B': {
+        'req_params': {
+            'max_prompt_tokens': 8192,
+            'max_new_tokens': 4096,
+        },
+        'model_properties': {
+            'context_size': 8192,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'GLM3-130B-Fin': {
+        'req_params': {
+            'max_prompt_tokens': 8192,
+            'max_new_tokens': 4096,
+        },
+        'model_properties': {
+            'context_size': 8192,
+            'mode': 'chat',
+        },
+        'features': [],
+    },
+    'Mistral-7B': {
+        'req_params': {
+            'max_prompt_tokens': 8192,
+            'max_new_tokens': 2048,
+        },
+        'model_properties': {
+            'context_size': 8192,
+            'mode': 'chat',
+        },
+        'features': [],
+    }
 }
-
-def get_model_config(credentials: dict)->ModelConfig:
-    base_model = credentials.get('base_model_name', '')
-    model_configs = configs.get(base_model)
-    if not model_configs:
-        return ModelConfig(
-                properties=ModelProperties(
-                context_size=int(credentials.get('context_size', 0)),
-                max_tokens=int(credentials.get('max_tokens', 0)),
-                mode= LLMMode.value_of(credentials.get('mode', 'chat')),
-            ),
-            features=[]
-        )
-    return model_configs
-
-
-def get_v2_req_params(credentials: dict, model_parameters: dict, 
-                      stop: list[str] | None=None):
-    req_params = {}
-    # predefined properties
-    model_configs = get_model_config(credentials)
-    if model_configs:
-        req_params['max_prompt_tokens'] = model_configs.properties.context_size
-        req_params['max_new_tokens'] = model_configs.properties.max_tokens
-
-    # model parameters
-    if model_parameters.get('max_tokens'):
-        req_params['max_new_tokens'] = model_parameters.get('max_tokens')
-    if model_parameters.get('temperature'):
-        req_params['temperature'] = model_parameters.get('temperature')
-    if model_parameters.get('top_p'):
-        req_params['top_p'] = model_parameters.get('top_p')
-    if model_parameters.get('top_k'):
-        req_params['top_k'] = model_parameters.get('top_k')
-    if model_parameters.get('presence_penalty'):
-        req_params['presence_penalty'] = model_parameters.get(
-            'presence_penalty')
-    if model_parameters.get('frequency_penalty'):
-        req_params['frequency_penalty'] = model_parameters.get(
-            'frequency_penalty')
-            
-    if stop:
-        req_params['stop'] = stop
-
-    return req_params

api/core/model_runtime/model_providers/volcengine_maas/text_embedding/models.py

@@ -1,27 +1,9 @@
-from pydantic import BaseModel
-
-
-class ModelProperties(BaseModel):
-    context_size: int 
-    max_chunks: int 
-
-class ModelConfig(BaseModel):
-    properties: ModelProperties
-
 ModelConfigs = {
-    'Doubao-embedding': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_chunks=1)
-    ),
+    'Doubao-embedding': {
+        'req_params': {},
+        'model_properties': {
+            'context_size': 4096,
+            'max_chunks': 1,
+        }
+    },
 }
-
-def get_model_config(credentials: dict)->ModelConfig:
-    base_model = credentials.get('base_model_name', '')
-    model_configs = ModelConfigs.get(base_model)
-    if not model_configs:
-        return ModelConfig(
-                properties=ModelProperties(
-                context_size=int(credentials.get('context_size', 0)),
-                max_chunks=int(credentials.get('max_chunks', 0)),
-            )
-        )
-    return model_configs

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

@@ -30,7 +30,7 @@ from core.model_runtime.model_providers.volcengine_maas.errors import (
     RateLimitErrors,
     ServerUnavailableErrors,
 )
-from core.model_runtime.model_providers.volcengine_maas.text_embedding.models import get_model_config
+from core.model_runtime.model_providers.volcengine_maas.text_embedding.models import ModelConfigs
 from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
 
 
@@ -115,10 +115,14 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
         """
             generate custom model entities from credentials
         """
-        model_config = get_model_config(credentials)
-        model_properties = {}
-        model_properties[ModelPropertyKey.CONTEXT_SIZE] = model_config.properties.context_size
-        model_properties[ModelPropertyKey.MAX_CHUNKS] = model_config.properties.max_chunks
+        model_properties = ModelConfigs.get(
+            credentials['base_model_name'], {}).get('model_properties', {}).copy()
+        if credentials.get('context_size'):
+            model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(
+                credentials.get('context_size', 4096))
+        if credentials.get('max_chunks'):
+            model_properties[ModelPropertyKey.MAX_CHUNKS] = int(
+                credentials.get('max_chunks', 4096))
         entity = AIModelEntity(
             model=model,
             label=I18nObject(en_US=model),

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

@@ -1,195 +0,0 @@
-from datetime import datetime, timedelta
-from threading import Lock
-
-from requests import post
-
-from core.model_runtime.model_providers.wenxin.wenxin_errors import (
-    BadRequestError,
-    InternalServerError,
-    InvalidAPIKeyError,
-    InvalidAuthenticationError,
-    RateLimitReachedError,
-)
-
-baidu_access_tokens: dict[str, 'BaiduAccessToken'] = {}
-baidu_access_tokens_lock = Lock()
-
-
-class BaiduAccessToken:
-    api_key: str
-    access_token: str
-    expires: datetime
-
-    def __init__(self, api_key: str) -> None:
-        self.api_key = api_key
-        self.access_token = ''
-        self.expires = datetime.now() + timedelta(days=3)
-
-    @staticmethod
-    def _get_access_token(api_key: str, secret_key: str) -> str:
-        """
-            request access token from Baidu
-        """
-        try:
-            response = post(
-                url=f'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={api_key}&client_secret={secret_key}',
-                headers={
-                    'Content-Type': 'application/json',
-                    'Accept': 'application/json'
-                },
-            )
-        except Exception as e:
-            raise InvalidAuthenticationError(f'Failed to get access token from Baidu: {e}')
-
-        resp = response.json()
-        if 'error' in resp:
-            if resp['error'] == 'invalid_client':
-                raise InvalidAPIKeyError(f'Invalid API key or secret key: {resp["error_description"]}')
-            elif resp['error'] == 'unknown_error':
-                raise InternalServerError(f'Internal server error: {resp["error_description"]}')
-            elif resp['error'] == 'invalid_request':
-                raise BadRequestError(f'Bad request: {resp["error_description"]}')
-            elif resp['error'] == 'rate_limit_exceeded':
-                raise RateLimitReachedError(f'Rate limit reached: {resp["error_description"]}')
-            else:
-                raise Exception(f'Unknown error: {resp["error_description"]}')
-
-        return resp['access_token']
-
-    @staticmethod
-    def get_access_token(api_key: str, secret_key: str) -> 'BaiduAccessToken':
-        """
-            LLM from Baidu requires access token to invoke the API.
-            however, we have api_key and secret_key, and access token is valid for 30 days.
-            so we can cache the access token for 3 days. (avoid memory leak)
-
-            it may be more efficient to use a ticker to refresh access token, but it will cause
-            more complexity, so we just refresh access tokens when get_access_token is called.
-        """
-
-        # loop up cache, remove expired access token
-        baidu_access_tokens_lock.acquire()
-        now = datetime.now()
-        for key in list(baidu_access_tokens.keys()):
-            token = baidu_access_tokens[key]
-            if token.expires < now:
-                baidu_access_tokens.pop(key)
-
-        if api_key not in baidu_access_tokens:
-            # if access token not in cache, request it
-            token = BaiduAccessToken(api_key)
-            baidu_access_tokens[api_key] = token
-            # release it to enhance performance
-            # btw, _get_access_token will raise exception if failed, release lock here to avoid deadlock
-            baidu_access_tokens_lock.release()
-            # try to get access token
-            token_str = BaiduAccessToken._get_access_token(api_key, secret_key)
-            token.access_token = token_str
-            token.expires = now + timedelta(days=3)
-            return token
-        else:
-            # if access token in cache, return it
-            token = baidu_access_tokens[api_key]
-            baidu_access_tokens_lock.release()
-            return token
-
-
-class _CommonWenxin:
-    api_bases = {
-        'ernie-bot': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-4k-0205',
-        'ernie-bot-4': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro',
-        'ernie-bot-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions',
-        'ernie-bot-turbo': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant',
-        'ernie-3.5-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions',
-        'ernie-3.5-8k-0205': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-0205',
-        'ernie-3.5-8k-1222': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-8k-1222',
-        'ernie-3.5-4k-0205': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-4k-0205',
-        'ernie-3.5-128k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-3.5-128k',
-        'ernie-4.0-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro',
-        'ernie-4.0-8k-latest': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions_pro',
-        'ernie-speed-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie_speed',
-        'ernie-speed-128k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k',
-        'ernie-speed-appbuilder': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ai_apaas',
-        'ernie-lite-8k-0922': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant',
-        'ernie-lite-8k-0308': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-lite-8k',
-        'ernie-character-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k',
-        'ernie-character-8k-0321': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-char-8k',
-        'ernie-4.0-turbo-8k': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k',
-        'ernie-4.0-turbo-8k-preview': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-turbo-8k-preview',
-        'yi_34b_chat': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/yi_34b_chat',
-        'embedding-v1': 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/embeddings/embedding-v1',
-    }
-
-    function_calling_supports = [
-        'ernie-bot',
-        'ernie-bot-8k',
-        'ernie-3.5-8k',
-        'ernie-3.5-8k-0205',
-        'ernie-3.5-8k-1222',
-        'ernie-3.5-4k-0205',
-        'ernie-3.5-128k',
-        'ernie-4.0-8k',
-        'ernie-4.0-turbo-8k',
-        'ernie-4.0-turbo-8k-preview',
-        'yi_34b_chat'
-    ]
-
-    api_key: str = ''
-    secret_key: str = ''
-
-    def __init__(self, api_key: str, secret_key: str):
-        self.api_key = api_key
-        self.secret_key = secret_key
-
-    @staticmethod
-    def _to_credential_kwargs(credentials: dict) -> dict:
-        credentials_kwargs = {
-            "api_key": credentials['api_key'],
-            "secret_key": credentials['secret_key']
-        }
-        return credentials_kwargs
-
-    def _handle_error(self, code: int, msg: str):
-        error_map = {
-            1: InternalServerError,
-            2: InternalServerError,
-            3: BadRequestError,
-            4: RateLimitReachedError,
-            6: InvalidAuthenticationError,
-            13: InvalidAPIKeyError,
-            14: InvalidAPIKeyError,
-            15: InvalidAPIKeyError,
-            17: RateLimitReachedError,
-            18: RateLimitReachedError,
-            19: RateLimitReachedError,
-            100: InvalidAPIKeyError,
-            111: InvalidAPIKeyError,
-            200: InternalServerError,
-            336000: InternalServerError,
-            336001: BadRequestError,
-            336002: BadRequestError,
-            336003: BadRequestError,
-            336004: InvalidAuthenticationError,
-            336005: InvalidAPIKeyError,
-            336006: BadRequestError,
-            336007: BadRequestError,
-            336008: BadRequestError,
-            336100: InternalServerError,
-            336101: BadRequestError,
-            336102: BadRequestError,
-            336103: BadRequestError,
-            336104: BadRequestError,
-            336105: BadRequestError,
-            336200: InternalServerError,
-            336303: BadRequestError,
-            337006: BadRequestError
-        }
-
-        if code in error_map:
-            raise error_map[code](msg)
-        else:
-            raise InternalServerError(f'Unknown error: {msg}')
-
-    def _get_access_token(self) -> str:
-        token = BaiduAccessToken.get_access_token(self.api_key, self.secret_key)
-        return token.access_token