bump version to 0.5.9 (#2794)

Co-authored-by: jyong <jyong@dify.ai>

.github/workflows/style.yml

@@ -41,6 +41,8 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
       - name: Setup NodeJS
         uses: actions/setup-node@v4
@@ -60,11 +62,10 @@ jobs:
           yarn run lint
 
       - name: Super-linter
-        uses: super-linter/super-linter/slim@v5
+        uses: super-linter/super-linter/slim@v6
         env:
           BASH_SEVERITY: warning
           DEFAULT_BRANCH: main
-          ERROR_ON_MISSING_EXEC_BIT: true
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           IGNORE_GENERATED_FILES: true
           IGNORE_GITIGNORED_FILES: true

.gitignore

@@ -145,6 +145,9 @@ docker/volumes/db/data/*
 docker/volumes/redis/data/*
 docker/volumes/weaviate/*
 docker/volumes/qdrant/*
+docker/volumes/etcd/*
+docker/volumes/minio/*
+docker/volumes/milvus/*
 
 sdks/python-client/build
 sdks/python-client/dist

LICENSE

@@ -1,24 +1,26 @@
-# Dify Open Source License
+# Open Source License
 
-The Dify project is licensed under the Apache License 2.0, with the following additional conditions:
+Dify is licensed under the Apache License 2.0, with the following additional conditions:
 
-1. Dify is permitted to be used for commercialization, such as using Dify as a "backend-as-a-service" for your other applications, or delivering it to enterprises as an application development platform. However, when the following conditions are met, you must contact the producer to obtain a commercial license:
+1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer:
 
-a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify.AI source code to operate a multi-tenant SaaS service that is similar to the Dify.AI service edition.
-b. LOGO and copyright information: In the process of using Dify, you may not remove or modify the LOGO or copyright information in the Dify console.
+a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. 
+    - Tenant Definition: Within the context of Dify, one tenant corresponds to one workspace. The workspace provides a separated area for each tenant's data and configurations.
+
+b. LOGO and copyright information: In the process of using Dify's frontend components, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend components.
 
 Please contact business@dify.ai by email to inquire about licensing matters.
 
-2. As a contributor, you should agree that your contributed code:
+2. As a contributor, you should agree that:
 
-a. The producer can adjust the open-source agreement to be more strict or relaxed.
-b. Can be used for commercial purposes, such as Dify's cloud business.
+a. The producer can adjust the open-source agreement to be more strict or relaxed as deemed necessary.
+b. Your contributed code may be used for commercial purposes, including but not limited to its cloud business operations.
 
-Apart from this, all other rights and restrictions follow the Apache License 2.0. If you need more detailed information, you can refer to the full version of Apache License 2.0.
+Apart from the specific conditions mentioned above, all other rights and restrictions follow the Apache License 2.0. Detailed information about the Apache License 2.0 can be found at http://www.apache.org/licenses/LICENSE-2.0.
 
 The interactive design of this product is protected by appearance patent.
 
-© 2023 LangGenius, Inc.
+© 2024 LangGenius, Inc.
 
 
 ----------

README.md

@@ -21,17 +21,6 @@
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web"></a>
 </p>
 
-<p align="center">
-  <a href="https://discord.com/events/1082486657678311454/1211724120996188220" target="_blank">
-   Dify.AI Upcoming Meetup Event [👉 Click to Join the Event Here 👈]
-  </a>
-   <ul align="center" style="text-decoration: none; list-style: none;">
-      <li> US EST: 09:00 (9:00 AM)</li>
-      <li> CET: 15:00 (3:00 PM)</li>
-      <li> CST:  22:00 (10:00 PM)</li>
-   </ul>
-</p>
-
 <p align="center">
    <a href="https://dify.ai/blog/dify-ai-unveils-ai-agent-creating-gpts-and-assistants-with-various-llms" target="_blank">
    Dify.AI Unveils AI Agent: Creating GPTs and Assistants with Various LLMs

api/.env.example

@@ -82,7 +82,7 @@ UPLOAD_IMAGE_FILE_SIZE_LIMIT=10
 MULTIMODAL_SEND_IMAGE_FORMAT=base64
 
 # Mail configuration, support: resend, smtp
-MAIL_TYPE=resend
+MAIL_TYPE=
 MAIL_DEFAULT_SEND_FROM=no-reply <no-reply@dify.ai>
 RESEND_API_KEY=
 RESEND_API_URL=https://api.resend.com
@@ -131,4 +131,4 @@ UNSTRUCTURED_API_URL=
 SSRF_PROXY_HTTP_URL=
 SSRF_PROXY_HTTPS_URL=
 
-BATCH_UPLOAD_LIMIT=10
+BATCH_UPLOAD_LIMIT=10

api/README.md

@@ -5,7 +5,7 @@
 1. Start the docker-compose stack
 
    The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
-   
+
    ```bash
    cd ../docker
    docker-compose -f docker-compose.middleware.yaml -p dify up -d
@@ -15,7 +15,7 @@
 3. Generate a `SECRET_KEY` in the `.env` file.
 
    ```bash
-   openssl rand -base64 42
+   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
    ```
 3.5 If you use annaconda, create a new environment and activate it
    ```bash
@@ -46,7 +46,7 @@
    ```
    pip install -r requirements.txt --upgrade --force-reinstall
    ```
-   
+
 6. Start backend:
    ```bash
    flask run --host 0.0.0.0 --port=5001 --debug

api/app.py

@@ -26,6 +26,7 @@ from config import CloudEditionConfig, Config
 from extensions import (
     ext_celery,
     ext_code_based_extension,
+    ext_compress,
     ext_database,
     ext_hosting_provider,
     ext_login,
@@ -96,6 +97,7 @@ def create_app(test_config=None) -> Flask:
 def initialize_extensions(app):
     # Since the application instance is now created, pass it to each Flask
     # extension instance to bind it to the Flask application instance (app)
+    ext_compress.init_app(app)
     ext_code_based_extension.init()
     ext_database.init_app(app)
     ext_migrate.init(app, db)

api/commands.py

@@ -15,7 +15,7 @@ from libs.rsa import generate_key_pair
 from models.account import Tenant
 from models.dataset import Dataset, DatasetCollectionBinding, DocumentSegment
 from models.dataset import Document as DatasetDocument
-from models.model import Account
+from models.model import Account, App, AppAnnotationSetting, MessageAnnotation
 from models.provider import Provider, ProviderModel
 
 
@@ -125,12 +125,121 @@ def reset_encrypt_key_pair():
 
 
 @click.command('vdb-migrate', help='migrate vector db.')
-def vdb_migrate():
+@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']:
+        migrate_knowledge_vector_database()
+    if scope in ['annotation', 'all']:
+        migrate_annotation_vector_database()
+
+
+def migrate_annotation_vector_database():
+    """
+    Migrate annotation datas to target vector database .
+    """
+    click.echo(click.style('Start migrate annotation data.', fg='green'))
+    create_count = 0
+    skipped_count = 0
+    total_count = 0
+    page = 1
+    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)
+        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.')
+            try:
+                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))
+                    continue
+                # get dataset_collection_binding info
+                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))
+                    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',
+                    embedding_model_provider=dataset_collection_binding.provider_name,
+                    embedding_model=dataset_collection_binding.model_name,
+                    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
+                            }
+                        )
+                        documents.append(document)
+
+                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'))
+                except Exception as e:
+                    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'))
+                        vector.create(documents)
+                        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'))
+                        raise e
+                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'))
+                continue
+
+    click.echo(
+        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'))
     create_count = 0
+    skipped_count = 0
+    total_count = 0
     config = current_app.config
     vector_type = config.get('VECTOR_STORE')
     page = 1
@@ -143,14 +252,19 @@ def vdb_migrate():
 
         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.')
             try:
                 click.echo('Create dataset vdb index: {}'.format(dataset.id))
                 if dataset.index_struct_dict:
                     if dataset.index_struct_dict['type'] == vector_type:
+                        skipped_count = skipped_count + 1
                         continue
+                collection_name = ''
                 if vector_type == "weaviate":
                     dataset_id = dataset.id
-                    collection_name = "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": 'weaviate',
                         "vector_store": {"class_prefix": collection_name}
@@ -167,7 +281,7 @@ def vdb_migrate():
                             raise ValueError('Dataset Collection Bindings is not exist!')
                     else:
                         dataset_id = dataset.id
-                        collection_name = "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+                        collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": 'qdrant',
                         "vector_store": {"class_prefix": collection_name}
@@ -176,7 +290,7 @@ def vdb_migrate():
 
                 elif vector_type == "milvus":
                     dataset_id = dataset.id
-                    collection_name = "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": 'milvus',
                         "vector_store": {"class_prefix": collection_name}
@@ -186,11 +300,17 @@ def vdb_migrate():
                     raise ValueError(f"Vector store {config.get('VECTOR_STORE')} is not supported.")
 
                 vector = Vector(dataset)
-                click.echo(f"vdb_migrate {dataset.id}")
+                click.echo(f"Start to migrate dataset {dataset.id}.")
 
                 try:
                     vector.delete()
+                    click.echo(
+                        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'))
                     raise e
 
                 dataset_documents = db.session.query(DatasetDocument).filter(
@@ -201,6 +321,7 @@ def vdb_migrate():
                 ).all()
 
                 documents = []
+                segments_count = 0
                 for dataset_document in dataset_documents:
                     segments = db.session.query(DocumentSegment).filter(
                         DocumentSegment.document_id == dataset_document.id,
@@ -220,15 +341,22 @@ def vdb_migrate():
                         )
 
                         documents.append(document)
+                        segments_count = segments_count + 1
 
                 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'))
                         vector.create(documents)
+                        click.echo(
+                            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'))
                         raise e
-                click.echo(f"Dataset {dataset.id} create successfully.")
                 db.session.add(dataset)
                 db.session.commit()
+                click.echo(f'Successfully migrated dataset {dataset.id}.')
                 create_count += 1
             except Exception as e:
                 db.session.rollback()
@@ -237,7 +365,9 @@ def vdb_migrate():
                                 fg='red'))
                 continue
 
-    click.echo(click.style('Congratulations! Create {} dataset indexes.'.format(create_count), fg='green'))
+    click.echo(
+        click.style(f'Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.',
+                    fg='green'))
 
 
 def register_commands(app):

api/config.py

@@ -90,7 +90,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.5.7"
+        self.CURRENT_VERSION = "0.5.9"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -293,6 +293,8 @@ class Config:
 
         self.BATCH_UPLOAD_LIMIT = get_env('BATCH_UPLOAD_LIMIT')
 
+        self.API_COMPRESSION_ENABLED = get_bool_env('API_COMPRESSION_ENABLED')
+
 
 class CloudEditionConfig(Config):
 

api/constants/model_template.py

@@ -13,30 +13,14 @@ model_templates = {
             'status': 'normal'
         },
         'model_config': {
-            'provider': 'openai',
-            'model_id': 'gpt-3.5-turbo-instruct',
-            'configs': {
-                'prompt_template': '',
-                'prompt_variables': [],
-                'completion_params': {
-                    'max_token': 512,
-                    'temperature': 1,
-                    'top_p': 1,
-                    'presence_penalty': 0,
-                    'frequency_penalty': 0,
-                }
-            },
+            'provider': '',
+            'model_id': '',
+            'configs': {},
             'model': json.dumps({
                 "provider": "openai",
                 "name": "gpt-3.5-turbo-instruct",
                 "mode": "completion",
-                "completion_params": {
-                    "max_tokens": 512,
-                    "temperature": 1,
-                    "top_p": 1,
-                    "presence_penalty": 0,
-                    "frequency_penalty": 0
-                }
+                "completion_params": {}
             }),
             'user_input_form': json.dumps([
                 {
@@ -64,30 +48,14 @@ model_templates = {
             'status': 'normal'
         },
         'model_config': {
-            'provider': 'openai',
-            'model_id': 'gpt-3.5-turbo',
-            'configs': {
-                'prompt_template': '',
-                'prompt_variables': [],
-                'completion_params': {
-                    'max_token': 512,
-                    'temperature': 1,
-                    'top_p': 1,
-                    'presence_penalty': 0,
-                    'frequency_penalty': 0,
-                }
-            },
+            'provider': '',
+            'model_id': '',
+            'configs': {},
             'model': json.dumps({
                 "provider": "openai",
                 "name": "gpt-3.5-turbo",
                 "mode": "chat",
-                "completion_params": {
-                    "max_tokens": 512,
-                    "temperature": 1,
-                    "top_p": 1,
-                    "presence_penalty": 0,
-                    "frequency_penalty": 0
-                }
+                "completion_params": {}
             })
         }
     },

api/controllers/console/app/app.py

@@ -27,7 +27,9 @@ from fields.app_fields import (
 from libs.login import login_required
 from models.model import App, AppModelConfig, Site
 from services.app_model_config_service import AppModelConfigService
-
+from core.tools.utils.configuration import ToolParameterConfigurationManager
+from core.tools.tool_manager import ToolManager
+from core.entities.application_entities import AgentToolEntity
 
 def _get_app(app_id, tenant_id):
     app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id).first()
@@ -129,7 +131,7 @@ class AppListApi(Resource):
                         "No Default System Reasoning Model available. Please configure "
                         "in the Settings -> Model Provider.")
                 else:
-                    model_config_dict["model"]["provider"] = default_model_entity.provider
+                    model_config_dict["model"]["provider"] = default_model_entity.provider.provider
                     model_config_dict["model"]["name"] = default_model_entity.model
 
             model_configuration = AppModelConfigService.validate_configuration(
@@ -236,7 +238,42 @@ class AppApi(Resource):
     def get(self, app_id):
         """Get app detail"""
         app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
+        app: App = _get_app(app_id, current_user.current_tenant_id)
+
+        # get original app model config
+        model_config: AppModelConfig = app.app_model_config
+        agent_mode = model_config.agent_mode_dict
+        # decrypt agent tool parameters if it's secret-input
+        for tool in agent_mode.get('tools') or []:
+            agent_tool_entity = AgentToolEntity(**tool)
+            # get tool
+            try:
+                tool_runtime = ToolManager.get_agent_tool_runtime(
+                    tenant_id=current_user.current_tenant_id,
+                    agent_tool=agent_tool_entity,
+                    agent_callback=None
+                )
+                manager = ToolParameterConfigurationManager(
+                    tenant_id=current_user.current_tenant_id,
+                    tool_runtime=tool_runtime,
+                    provider_name=agent_tool_entity.provider_id,
+                    provider_type=agent_tool_entity.provider_type,
+                )
+
+                # get decrypted parameters
+                if agent_tool_entity.tool_parameters:
+                    parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+                    masked_parameter = manager.mask_tool_parameters(parameters or {})
+                else:
+                    masked_parameter = {}
+
+                # override tool parameters
+                tool['tool_parameters'] = masked_parameter
+            except Exception as e:
+                pass
+
+        # override agent mode
+        model_config.agent_mode = json.dumps(agent_mode)
 
         return app
 

api/controllers/console/app/audio.py

@@ -88,7 +88,7 @@ class ChatMessageTextApi(Resource):
             response = AudioService.transcript_tts(
                 tenant_id=app_model.tenant_id,
                 text=request.form['text'],
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form['voice'] if request.form['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
 

api/controllers/console/app/model_config.py

@@ -1,3 +1,4 @@
+import json
 
 from flask import request
 from flask_login import current_user
@@ -7,6 +8,9 @@ from controllers.console import api
 from controllers.console.app import _get_app
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
+from core.entities.application_entities import AgentToolEntity
+from core.tools.tool_manager import ToolManager
+from core.tools.utils.configuration import ToolParameterConfigurationManager
 from events.app_event import app_model_config_was_updated
 from extensions.ext_database import db
 from libs.login import login_required
@@ -38,6 +42,88 @@ class ModelConfigResource(Resource):
         )
         new_app_model_config = new_app_model_config.from_model_config_dict(model_configuration)
 
+        # get original app model config
+        original_app_model_config: AppModelConfig = db.session.query(AppModelConfig).filter(
+            AppModelConfig.id == app.app_model_config_id
+        ).first()
+        agent_mode = original_app_model_config.agent_mode_dict
+        # decrypt agent tool parameters if it's secret-input
+        parameter_map = {}
+        masked_parameter_map = {}
+        tool_map = {}
+        for tool in agent_mode.get('tools') or []:
+            agent_tool_entity = AgentToolEntity(**tool)
+            # get tool
+            try:
+                tool_runtime = ToolManager.get_agent_tool_runtime(
+                    tenant_id=current_user.current_tenant_id,
+                    agent_tool=agent_tool_entity,
+                    agent_callback=None
+                )
+                manager = ToolParameterConfigurationManager(
+                    tenant_id=current_user.current_tenant_id,
+                    tool_runtime=tool_runtime,
+                    provider_name=agent_tool_entity.provider_id,
+                    provider_type=agent_tool_entity.provider_type,
+                )
+            except Exception as e:
+                continue
+
+            # get decrypted parameters
+            if agent_tool_entity.tool_parameters:
+                parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+                masked_parameter = manager.mask_tool_parameters(parameters or {})
+            else:
+                parameters = {}
+                masked_parameter = {}
+
+            key = f'{agent_tool_entity.provider_id}.{agent_tool_entity.provider_type}.{agent_tool_entity.tool_name}'
+            masked_parameter_map[key] = masked_parameter
+            parameter_map[key] = parameters
+            tool_map[key] = tool_runtime
+
+        # encrypt agent tool parameters if it's secret-input
+        agent_mode = new_app_model_config.agent_mode_dict
+        for tool in agent_mode.get('tools') or []:
+            agent_tool_entity = AgentToolEntity(**tool)
+            
+            # get tool
+            key = f'{agent_tool_entity.provider_id}.{agent_tool_entity.provider_type}.{agent_tool_entity.tool_name}'
+            if key in tool_map:
+                tool_runtime = tool_map[key]
+            else:
+                try:
+                    tool_runtime = ToolManager.get_agent_tool_runtime(
+                        tenant_id=current_user.current_tenant_id,
+                        agent_tool=agent_tool_entity,
+                        agent_callback=None
+                    )
+                except Exception as e:
+                    continue
+            
+            manager = ToolParameterConfigurationManager(
+                tenant_id=current_user.current_tenant_id,
+                tool_runtime=tool_runtime,
+                provider_name=agent_tool_entity.provider_id,
+                provider_type=agent_tool_entity.provider_type,
+            )
+            manager.delete_tool_parameters_cache()
+
+            # override parameters if it equals to masked parameters
+            if agent_tool_entity.tool_parameters:
+                if key not in masked_parameter_map:
+                    continue
+
+                if agent_tool_entity.tool_parameters == masked_parameter_map[key]:
+                    agent_tool_entity.tool_parameters = parameter_map[key]
+
+            # encrypt parameters
+            if agent_tool_entity.tool_parameters:
+                tool['tool_parameters'] = manager.encrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+
+        # update app model config
+        new_app_model_config.agent_mode = json.dumps(agent_mode)
+
         db.session.add(new_app_model_config)
         db.session.flush()
 

api/controllers/console/datasets/file.py

@@ -11,7 +11,7 @@ from controllers.console.datasets.error import (
     UnsupportedFileTypeError,
 )
 from controllers.console.setup import setup_required
-from controllers.console.wraps import account_initialization_required
+from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
 from fields.file_fields import file_fields, upload_config_fields
 from libs.login import login_required
 from services.file_service import ALLOWED_EXTENSIONS, UNSTRUSTURED_ALLOWED_EXTENSIONS, FileService
@@ -39,6 +39,7 @@ class FileApi(Resource):
     @login_required
     @account_initialization_required
     @marshal_with(file_fields)
+    @cloud_edition_billing_resource_check(resource='documents')
     def post(self):
 
         # get file from request

api/controllers/console/explore/audio.py

@@ -85,7 +85,7 @@ class ChatTextApi(InstalledAppResource):
             response = AudioService.transcript_tts(
                 tenant_id=app_model.tenant_id,
                 text=request.form['text'],
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form['voice'] if request.form['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
             return {'data': response.data.decode('latin1')}

api/controllers/console/workspace/tool_providers.py

@@ -82,6 +82,30 @@ class ToolBuiltinProviderIconApi(Resource):
         icon_bytes, minetype = ToolManageService.get_builtin_tool_provider_icon(provider)
         return send_file(io.BytesIO(icon_bytes), mimetype=minetype)
 
+class ToolModelProviderIconApi(Resource):
+    @setup_required
+    def get(self, provider):
+        icon_bytes, mimetype = ToolManageService.get_model_tool_provider_icon(provider)
+        return send_file(io.BytesIO(icon_bytes), mimetype=mimetype)
+    
+class ToolModelProviderListToolsApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('provider', type=str, required=True, nullable=False, location='args')
+
+        args = parser.parse_args()
+
+        return ToolManageService.list_model_tool_provider_tools(
+            user_id,
+            tenant_id,
+            args['provider'],
+        )
 
 class ToolApiProviderAddApi(Resource):
     @setup_required
@@ -259,6 +283,7 @@ class ToolApiProviderPreviousTestApi(Resource):
         parser = reqparse.RequestParser()
 
         parser.add_argument('tool_name', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('provider_name', type=str, required=False, nullable=False, location='json')
         parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('parameters', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
@@ -268,6 +293,7 @@ class ToolApiProviderPreviousTestApi(Resource):
 
         return ToolManageService.test_api_tool_preview(
             current_user.current_tenant_id,
+            args['provider_name'] if args['provider_name'] else '',
             args['tool_name'],
             args['credentials'],
             args['parameters'],
@@ -281,6 +307,8 @@ api.add_resource(ToolBuiltinProviderDeleteApi, '/workspaces/current/tool-provide
 api.add_resource(ToolBuiltinProviderUpdateApi, '/workspaces/current/tool-provider/builtin/<provider>/update')
 api.add_resource(ToolBuiltinProviderCredentialsSchemaApi, '/workspaces/current/tool-provider/builtin/<provider>/credentials_schema')
 api.add_resource(ToolBuiltinProviderIconApi, '/workspaces/current/tool-provider/builtin/<provider>/icon')
+api.add_resource(ToolModelProviderIconApi, '/workspaces/current/tool-provider/model/<provider>/icon')
+api.add_resource(ToolModelProviderListToolsApi, '/workspaces/current/tool-provider/model/tools')
 api.add_resource(ToolApiProviderAddApi, '/workspaces/current/tool-provider/api/add')
 api.add_resource(ToolApiProviderGetRemoteSchemaApi, '/workspaces/current/tool-provider/api/remote')
 api.add_resource(ToolApiProviderListToolsApi, '/workspaces/current/tool-provider/api/tools')

api/controllers/console/wraps.py

@@ -56,6 +56,7 @@ def cloud_edition_billing_resource_check(resource: str,
                 members = features.members
                 apps = features.apps
                 vector_space = features.vector_space
+                documents_upload_quota = features.documents_upload_quota
                 annotation_quota_limit = features.annotation_quota_limit
 
                 if resource == 'members' and 0 < members.limit <= members.size:
@@ -64,6 +65,13 @@ def cloud_edition_billing_resource_check(resource: str,
                     abort(403, error_msg)
                 elif resource == 'vector_space' and 0 < vector_space.limit <= vector_space.size:
                     abort(403, error_msg)
+                elif resource == 'documents' and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
+                    # The api of file upload is used in the multiple places, so we need to check the source of the request from datasets
+                    source = request.args.get('source')
+                    if source == 'datasets':
+                        abort(403, error_msg)
+                    else:
+                        return view(*args, **kwargs)
                 elif resource == 'workspace_custom' and not features.can_replace_logo:
                     abort(403, error_msg)
                 elif resource == 'annotation' and 0 < annotation_quota_limit.limit < annotation_quota_limit.size:

api/controllers/service_api/app/audio.py

@@ -87,7 +87,7 @@ class TextApi(Resource):
                 tenant_id=app_model.tenant_id,
                 text=args['text'],
                 end_user=end_user,
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=args['voice'] if args['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=args['streaming']
             )
 

api/controllers/service_api/dataset/document.py

@@ -28,6 +28,7 @@ class DocumentAddByTextApi(DatasetApiResource):
     """Resource for documents."""
 
     @cloud_edition_billing_resource_check('vector_space', 'dataset')
+    @cloud_edition_billing_resource_check('documents', 'dataset')
     def post(self, tenant_id, dataset_id):
         """Create document by text."""
         parser = reqparse.RequestParser()
@@ -153,6 +154,7 @@ class DocumentUpdateByTextApi(DatasetApiResource):
 class DocumentAddByFileApi(DatasetApiResource):
     """Resource for documents."""
     @cloud_edition_billing_resource_check('vector_space', 'dataset')
+    @cloud_edition_billing_resource_check('documents', 'dataset')
     def post(self, tenant_id, dataset_id):
         """Create document by upload file."""
         args = {}

api/controllers/service_api/dataset/segment.py

@@ -200,8 +200,8 @@ class DatasetSegmentApi(DatasetApiResource):
         parser.add_argument('segments', type=dict, required=False, nullable=True, location='json')
         args = parser.parse_args()
 
-        SegmentService.segment_create_args_validate(args['segments'], document)
-        segment = SegmentService.update_segment(args['segments'], segment, document, dataset)
+        SegmentService.segment_create_args_validate(args, document)
+        segment = SegmentService.update_segment(args, segment, document, dataset)
         return {
             'data': marshal(segment, segment_fields),
             'doc_form': document.doc_form

api/controllers/service_api/wraps.py

@@ -89,6 +89,7 @@ def cloud_edition_billing_resource_check(resource: str,
                 members = features.members
                 apps = features.apps
                 vector_space = features.vector_space
+                documents_upload_quota = features.documents_upload_quota
 
                 if resource == 'members' and 0 < members.limit <= members.size:
                     raise Unauthorized(error_msg)
@@ -96,6 +97,8 @@ def cloud_edition_billing_resource_check(resource: str,
                     raise Unauthorized(error_msg)
                 elif resource == 'vector_space' and 0 < vector_space.limit <= vector_space.size:
                     raise Unauthorized(error_msg)
+                elif resource == 'documents' and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
+                    raise Unauthorized(error_msg)
                 else:
                     return view(*args, **kwargs)
 

api/controllers/web/audio.py

@@ -84,7 +84,7 @@ class TextApi(WebApiResource):
                 tenant_id=app_model.tenant_id,
                 text=request.form['text'],
                 end_user=end_user.external_user_id,
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form['voice'] if request.form['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
 

api/core/agent/agent/calc_token_mixin.py

@@ -1,49 +0,0 @@
-from typing import cast
-
-from core.entities.application_entities import ModelConfigEntity
-from core.model_runtime.entities.message_entities import PromptMessage
-from core.model_runtime.entities.model_entities import ModelPropertyKey
-from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-
-
-class CalcTokenMixin:
-
-    def get_message_rest_tokens(self, model_config: ModelConfigEntity, messages: list[PromptMessage], **kwargs) -> int:
-        """
-        Got the rest tokens available for the model after excluding messages tokens and completion max tokens
-
-        :param model_config:
-        :param messages:
-        :return:
-        """
-        model_type_instance = model_config.provider_model_bundle.model_type_instance
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
-
-        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
-
-        max_tokens = 0
-        for parameter_rule in model_config.model_schema.parameter_rules:
-            if (parameter_rule.name == 'max_tokens'
-                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                max_tokens = (model_config.parameters.get(parameter_rule.name)
-                              or model_config.parameters.get(parameter_rule.use_template)) or 0
-
-        if model_context_tokens is None:
-            return 0
-
-        if max_tokens is None:
-            max_tokens = 0
-
-        prompt_tokens = model_type_instance.get_num_tokens(
-            model_config.model,
-            model_config.credentials,
-            messages
-        )
-
-        rest_tokens = model_context_tokens - max_tokens - prompt_tokens
-
-        return rest_tokens
-
-
-class ExceededLLMTokensLimitError(Exception):
-    pass

api/core/agent/agent/openai_function_call.py

@@ -1,361 +0,0 @@
-from collections.abc import Sequence
-from typing import Any, Optional, Union
-
-from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
-from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
-from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.prompts.chat import BaseMessagePromptTemplate
-from langchain.schema import (
-    AgentAction,
-    AgentFinish,
-    AIMessage,
-    BaseMessage,
-    HumanMessage,
-    SystemMessage,
-    get_buffer_string,
-)
-from langchain.tools import BaseTool
-from pydantic import root_validator
-
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
-from core.model_manager import ModelInstance
-from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
-from core.third_party.langchain.llms.fake import FakeLLM
-
-
-class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin):
-    moving_summary_buffer: str = ""
-    moving_summary_index: int = 0
-    summary_model_config: ModelConfigEntity = None
-    model_config: ModelConfigEntity
-    agent_llm_callback: Optional[AgentLLMCallback] = None
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-    @root_validator
-    def validate_llm(cls, values: dict) -> dict:
-        return values
-
-    @classmethod
-    def from_llm_and_tools(
-            cls,
-            model_config: ModelConfigEntity,
-            tools: Sequence[BaseTool],
-            callback_manager: Optional[BaseCallbackManager] = None,
-            extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
-            system_message: Optional[SystemMessage] = SystemMessage(
-                content="You are a helpful AI assistant."
-            ),
-            agent_llm_callback: Optional[AgentLLMCallback] = None,
-            **kwargs: Any,
-    ) -> BaseSingleActionAgent:
-        prompt = cls.create_prompt(
-            extra_prompt_messages=extra_prompt_messages,
-            system_message=system_message,
-        )
-        return cls(
-            model_config=model_config,
-            llm=FakeLLM(response=''),
-            prompt=prompt,
-            tools=tools,
-            callback_manager=callback_manager,
-            agent_llm_callback=agent_llm_callback,
-            **kwargs,
-        )
-
-    def should_use_agent(self, query: str):
-        """
-        return should use agent
-
-        :param query:
-        :return:
-        """
-        original_max_tokens = 0
-        for parameter_rule in self.model_config.model_schema.parameter_rules:
-            if (parameter_rule.name == 'max_tokens'
-                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                original_max_tokens = (self.model_config.parameters.get(parameter_rule.name)
-                              or self.model_config.parameters.get(parameter_rule.use_template)) or 0
-
-        self.model_config.parameters['max_tokens'] = 40
-
-        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
-        messages = prompt.to_messages()
-
-        try:
-            prompt_messages = lc_messages_to_prompt_messages(messages)
-            model_instance = ModelInstance(
-                provider_model_bundle=self.model_config.provider_model_bundle,
-                model=self.model_config.model,
-            )
-
-            tools = []
-            for function in self.functions:
-                tool = PromptMessageTool(
-                    **function
-                )
-
-                tools.append(tool)
-
-            result = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                tools=tools,
-                stream=False,
-                model_parameters={
-                    'temperature': 0.2,
-                    'top_p': 0.3,
-                    'max_tokens': 1500
-                }
-            )
-        except Exception as e:
-            raise e
-
-        self.model_config.parameters['max_tokens'] = original_max_tokens
-
-        return True if result.message.tool_calls else False
-
-    def plan(
-            self,
-            intermediate_steps: list[tuple[AgentAction, str]],
-            callbacks: Callbacks = None,
-            **kwargs: Any,
-    ) -> Union[AgentAction, AgentFinish]:
-        """Given input, decided what to do.
-
-        Args:
-            intermediate_steps: Steps the LLM has taken to date, along with observations
-            **kwargs: User inputs.
-
-        Returns:
-            Action specifying what tool to use.
-        """
-        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
-        selected_inputs = {
-            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
-        }
-        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
-        prompt = self.prompt.format_prompt(**full_inputs)
-        messages = prompt.to_messages()
-
-        prompt_messages = lc_messages_to_prompt_messages(messages)
-
-        # summarize messages if rest_tokens < 0
-        try:
-            prompt_messages = self.summarize_messages_if_needed(prompt_messages, functions=self.functions)
-        except ExceededLLMTokensLimitError as e:
-            return AgentFinish(return_values={"output": str(e)}, log=str(e))
-
-        model_instance = ModelInstance(
-            provider_model_bundle=self.model_config.provider_model_bundle,
-            model=self.model_config.model,
-        )
-
-        tools = []
-        for function in self.functions:
-            tool = PromptMessageTool(
-                **function
-            )
-
-            tools.append(tool)
-
-        result = model_instance.invoke_llm(
-            prompt_messages=prompt_messages,
-            tools=tools,
-            stream=False,
-            callbacks=[self.agent_llm_callback] if self.agent_llm_callback else [],
-            model_parameters={
-                'temperature': 0.2,
-                'top_p': 0.3,
-                'max_tokens': 1500
-            }
-        )
-
-        ai_message = AIMessage(
-            content=result.message.content or "",
-            additional_kwargs={
-                'function_call': {
-                    'id': result.message.tool_calls[0].id,
-                    **result.message.tool_calls[0].function.dict()
-                } if result.message.tool_calls else None
-            }
-        )
-        agent_decision = _parse_ai_message(ai_message)
-
-        if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
-            tool_inputs = agent_decision.tool_input
-            if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
-                tool_inputs['query'] = kwargs['input']
-                agent_decision.tool_input = tool_inputs
-
-        return agent_decision
-
-    @classmethod
-    def get_system_message(cls):
-        return SystemMessage(content="You are a helpful AI assistant.\n"
-                                     "The current date or current time you know is wrong.\n"
-                                     "Respond directly if appropriate.")
-
-    def return_stopped_response(
-            self,
-            early_stopping_method: str,
-            intermediate_steps: list[tuple[AgentAction, str]],
-            **kwargs: Any,
-    ) -> AgentFinish:
-        try:
-            return super().return_stopped_response(early_stopping_method, intermediate_steps, **kwargs)
-        except ValueError:
-            return AgentFinish({"output": "I'm sorry, I don't know how to respond to that."}, "")
-
-    def summarize_messages_if_needed(self, messages: list[PromptMessage], **kwargs) -> list[PromptMessage]:
-        # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
-        rest_tokens = self.get_message_rest_tokens(
-            self.model_config,
-            messages,
-            **kwargs
-        )
-
-        rest_tokens = rest_tokens - 20  # to deal with the inaccuracy of rest_tokens
-        if rest_tokens >= 0:
-            return messages
-
-        system_message = None
-        human_message = None
-        should_summary_messages = []
-        for message in messages:
-            if isinstance(message, SystemMessage):
-                system_message = message
-            elif isinstance(message, HumanMessage):
-                human_message = message
-            else:
-                should_summary_messages.append(message)
-
-        if len(should_summary_messages) > 2:
-            ai_message = should_summary_messages[-2]
-            function_message = should_summary_messages[-1]
-            should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
-            self.moving_summary_index = len(should_summary_messages)
-        else:
-            error_msg = "Exceeded LLM tokens limit, stopped."
-            raise ExceededLLMTokensLimitError(error_msg)
-
-        new_messages = [system_message, human_message]
-
-        if self.moving_summary_index == 0:
-            should_summary_messages.insert(0, human_message)
-
-        self.moving_summary_buffer = self.predict_new_summary(
-            messages=should_summary_messages,
-            existing_summary=self.moving_summary_buffer
-        )
-
-        new_messages.append(AIMessage(content=self.moving_summary_buffer))
-        new_messages.append(ai_message)
-        new_messages.append(function_message)
-
-        return new_messages
-
-    def predict_new_summary(
-        self, messages: list[BaseMessage], existing_summary: str
-    ) -> str:
-        new_lines = get_buffer_string(
-            messages,
-            human_prefix="Human",
-            ai_prefix="AI",
-        )
-
-        chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
-        return chain.predict(summary=existing_summary, new_lines=new_lines)
-
-    def get_num_tokens_from_messages(self, model_config: ModelConfigEntity, messages: list[BaseMessage], **kwargs) -> 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"""
-        if model_config.provider == 'azure_openai':
-            model = model_config.model
-            model = model.replace("gpt-35", "gpt-3.5")
-        else:
-            model = model_config.credentials.get("base_model_name")
-
-        tiktoken_ = _import_tiktoken()
-        try:
-            encoding = tiktoken_.encoding_for_model(model)
-        except KeyError:
-            model = "cl100k_base"
-            encoding = tiktoken_.get_encoding(model)
-
-        if model.startswith("gpt-3.5-turbo"):
-            # every message follows <im_start>{role/name}\n{content}<im_end>\n
-            tokens_per_message = 4
-            # if there's a name, the role is omitted
-            tokens_per_name = -1
-        elif model.startswith("gpt-4"):
-            tokens_per_message = 3
-            tokens_per_name = 1
-        else:
-            raise NotImplementedError(
-                f"get_num_tokens_from_messages() is not presently implemented "
-                f"for model {model}."
-                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
-                "information on how messages are converted to tokens."
-            )
-        num_tokens = 0
-        for m in messages:
-            message = _convert_message_to_dict(m)
-            num_tokens += tokens_per_message
-            for key, value in message.items():
-                if key == "function_call":
-                    for f_key, f_value in value.items():
-                        num_tokens += len(encoding.encode(f_key))
-                        num_tokens += len(encoding.encode(f_value))
-                else:
-                    num_tokens += len(encoding.encode(value))
-
-                if key == "name":
-                    num_tokens += tokens_per_name
-        # every reply is primed with <im_start>assistant
-        num_tokens += 3
-
-        if kwargs.get('functions'):
-            for function in kwargs.get('functions'):
-                num_tokens += len(encoding.encode('name'))
-                num_tokens += len(encoding.encode(function.get("name")))
-                num_tokens += len(encoding.encode('description'))
-                num_tokens += len(encoding.encode(function.get("description")))
-                parameters = function.get("parameters")
-                num_tokens += len(encoding.encode('parameters'))
-                if 'title' in parameters:
-                    num_tokens += len(encoding.encode('title'))
-                    num_tokens += len(encoding.encode(parameters.get("title")))
-                num_tokens += len(encoding.encode('type'))
-                num_tokens += len(encoding.encode(parameters.get("type")))
-                if 'properties' in parameters:
-                    num_tokens += len(encoding.encode('properties'))
-                    for key, value in parameters.get('properties').items():
-                        num_tokens += len(encoding.encode(key))
-                        for field_key, field_value in value.items():
-                            num_tokens += len(encoding.encode(field_key))
-                            if field_key == 'enum':
-                                for enum_field in field_value:
-                                    num_tokens += 3
-                                    num_tokens += len(encoding.encode(enum_field))
-                            else:
-                                num_tokens += len(encoding.encode(field_key))
-                                num_tokens += len(encoding.encode(str(field_value)))
-                if 'required' in parameters:
-                    num_tokens += len(encoding.encode('required'))
-                    for required_field in parameters['required']:
-                        num_tokens += 3
-                        num_tokens += len(encoding.encode(required_field))
-
-        return num_tokens

api/core/agent/agent/structured_chat.py

@@ -1,306 +0,0 @@
-import re
-from collections.abc import Sequence
-from typing import Any, Optional, Union, cast
-
-from langchain import BasePromptTemplate, PromptTemplate
-from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
-from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
-from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
-from langchain.schema import (
-    AgentAction,
-    AgentFinish,
-    AIMessage,
-    BaseMessage,
-    HumanMessage,
-    OutputParserException,
-    get_buffer_string,
-)
-from langchain.tools import BaseTool
-
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
-
-FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
-The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
-Valid "action" values: "Final Answer" or {tool_names}
-
-Provide only ONE action per $JSON_BLOB, as shown:
-
-```
-{{{{
-  "action": $TOOL_NAME,
-  "action_input": $INPUT
-}}}}
-```
-
-Follow this format:
-
-Question: input question to answer
-Thought: consider previous and subsequent steps
-Action:
-```
-$JSON_BLOB
-```
-Observation: action result
-... (repeat Thought/Action/Observation N times)
-Thought: I know what to respond
-Action:
-```
-{{{{
-  "action": "Final Answer",
-  "action_input": "Final response to human"
-}}}}
-```"""
-
-
-class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
-    moving_summary_buffer: str = ""
-    moving_summary_index: int = 0
-    summary_model_config: ModelConfigEntity = None
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-    def should_use_agent(self, query: str):
-        """
-        return should use agent
-        Using the ReACT mode to determine whether an agent is needed is costly,
-        so it's better to just use an Agent for reasoning, which is cheaper.
-
-        :param query:
-        :return:
-        """
-        return True
-
-    def plan(
-        self,
-        intermediate_steps: list[tuple[AgentAction, str]],
-        callbacks: Callbacks = None,
-        **kwargs: Any,
-    ) -> Union[AgentAction, AgentFinish]:
-        """Given input, decided what to do.
-
-        Args:
-            intermediate_steps: Steps the LLM has taken to date,
-                along with observatons
-            callbacks: Callbacks to run.
-            **kwargs: User inputs.
-
-        Returns:
-            Action specifying what tool to use.
-        """
-        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
-        prompts, _ = self.llm_chain.prep_prompts(input_list=[self.llm_chain.prep_inputs(full_inputs)])
-
-        messages = []
-        if prompts:
-            messages = prompts[0].to_messages()
-
-        prompt_messages = lc_messages_to_prompt_messages(messages)
-
-        rest_tokens = self.get_message_rest_tokens(self.llm_chain.model_config, prompt_messages)
-        if rest_tokens < 0:
-            full_inputs = self.summarize_messages(intermediate_steps, **kwargs)
-
-        try:
-            full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
-        except Exception as e:
-            raise e
-
-        try:
-            agent_decision = self.output_parser.parse(full_output)
-            if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
-                tool_inputs = agent_decision.tool_input
-                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
-                    tool_inputs['query'] = kwargs['input']
-                    agent_decision.tool_input = tool_inputs
-            return agent_decision
-        except OutputParserException:
-            return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
-                                          "I don't know how to respond to that."}, "")
-
-    def summarize_messages(self, intermediate_steps: list[tuple[AgentAction, str]], **kwargs):
-        if len(intermediate_steps) >= 2 and self.summary_model_config:
-            should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1]
-            should_summary_messages = [AIMessage(content=observation)
-                                       for _, observation in should_summary_intermediate_steps]
-            if self.moving_summary_index == 0:
-                should_summary_messages.insert(0, HumanMessage(content=kwargs.get("input")))
-
-            self.moving_summary_index = len(intermediate_steps)
-        else:
-            error_msg = "Exceeded LLM tokens limit, stopped."
-            raise ExceededLLMTokensLimitError(error_msg)
-
-        if self.moving_summary_buffer and 'chat_history' in kwargs:
-            kwargs["chat_history"].pop()
-
-        self.moving_summary_buffer = self.predict_new_summary(
-            messages=should_summary_messages,
-            existing_summary=self.moving_summary_buffer
-        )
-
-        if 'chat_history' in kwargs:
-            kwargs["chat_history"].append(AIMessage(content=self.moving_summary_buffer))
-
-        return self.get_full_inputs([intermediate_steps[-1]], **kwargs)
-
-    def predict_new_summary(
-        self, messages: list[BaseMessage], existing_summary: str
-    ) -> str:
-        new_lines = get_buffer_string(
-            messages,
-            human_prefix="Human",
-            ai_prefix="AI",
-        )
-
-        chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
-        return chain.predict(summary=existing_summary, new_lines=new_lines)
-
-    @classmethod
-    def create_prompt(
-            cls,
-            tools: Sequence[BaseTool],
-            prefix: str = PREFIX,
-            suffix: str = SUFFIX,
-            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[list[str]] = None,
-            memory_prompts: Optional[list[BasePromptTemplate]] = None,
-    ) -> BasePromptTemplate:
-        tool_strings = []
-        for tool in tools:
-            args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
-            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
-        formatted_tools = "\n".join(tool_strings)
-        tool_names = ", ".join([('"' + tool.name + '"') for tool in tools])
-        format_instructions = format_instructions.format(tool_names=tool_names)
-        template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
-        if input_variables is None:
-            input_variables = ["input", "agent_scratchpad"]
-        _memory_prompts = memory_prompts or []
-        messages = [
-            SystemMessagePromptTemplate.from_template(template),
-            *_memory_prompts,
-            HumanMessagePromptTemplate.from_template(human_message_template),
-        ]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
-
-    @classmethod
-    def create_completion_prompt(
-            cls,
-            tools: Sequence[BaseTool],
-            prefix: str = PREFIX,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[list[str]] = None,
-    ) -> PromptTemplate:
-        """Create prompt in the style of the zero shot agent.
-
-        Args:
-            tools: List of tools the agent will have access to, used to format the
-                prompt.
-            prefix: String to put before the list of tools.
-            input_variables: List of input variables the final prompt will expect.
-
-        Returns:
-            A PromptTemplate with the template assembled from the pieces here.
-        """
-        suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
-Question: {input}
-Thought: {agent_scratchpad}
-"""
-
-        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
-        tool_names = ", ".join([tool.name for tool in tools])
-        format_instructions = format_instructions.format(tool_names=tool_names)
-        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
-        if input_variables is None:
-            input_variables = ["input", "agent_scratchpad"]
-        return PromptTemplate(template=template, input_variables=input_variables)
-
-    def _construct_scratchpad(
-        self, intermediate_steps: list[tuple[AgentAction, str]]
-    ) -> str:
-        agent_scratchpad = ""
-        for action, observation in intermediate_steps:
-            agent_scratchpad += action.log
-            agent_scratchpad += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
-
-        if not isinstance(agent_scratchpad, str):
-            raise ValueError("agent_scratchpad should be of type string.")
-        if agent_scratchpad:
-            llm_chain = cast(LLMChain, self.llm_chain)
-            if llm_chain.model_config.mode == "chat":
-                return (
-                    f"This was your previous work "
-                    f"(but I haven't seen any of it! I only see what "
-                    f"you return as final answer):\n{agent_scratchpad}"
-                )
-            else:
-                return agent_scratchpad
-        else:
-            return agent_scratchpad
-
-    @classmethod
-    def from_llm_and_tools(
-            cls,
-            model_config: ModelConfigEntity,
-            tools: Sequence[BaseTool],
-            callback_manager: Optional[BaseCallbackManager] = None,
-            output_parser: Optional[AgentOutputParser] = None,
-            prefix: str = PREFIX,
-            suffix: str = SUFFIX,
-            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[list[str]] = None,
-            memory_prompts: Optional[list[BasePromptTemplate]] = None,
-            agent_llm_callback: Optional[AgentLLMCallback] = None,
-            **kwargs: Any,
-    ) -> Agent:
-        """Construct an agent from an LLM and tools."""
-        cls._validate_tools(tools)
-        if model_config.mode == "chat":
-            prompt = cls.create_prompt(
-                tools,
-                prefix=prefix,
-                suffix=suffix,
-                human_message_template=human_message_template,
-                format_instructions=format_instructions,
-                input_variables=input_variables,
-                memory_prompts=memory_prompts,
-            )
-        else:
-            prompt = cls.create_completion_prompt(
-                tools,
-                prefix=prefix,
-                format_instructions=format_instructions,
-                input_variables=input_variables,
-            )
-        llm_chain = LLMChain(
-            model_config=model_config,
-            prompt=prompt,
-            callback_manager=callback_manager,
-            agent_llm_callback=agent_llm_callback,
-            parameters={
-                'temperature': 0.2,
-                'top_p': 0.3,
-                'max_tokens': 1500
-            }
-        )
-        tool_names = [tool.name for tool in tools]
-        _output_parser = output_parser
-        return cls(
-            llm_chain=llm_chain,
-            allowed_tools=tool_names,
-            output_parser=_output_parser,
-            **kwargs,
-        )

api/core/app_runner/app_runner.py

@@ -84,7 +84,7 @@ class AppRunner:
 
         return rest_tokens
 
-    def recale_llm_max_tokens(self, model_config: ModelConfigEntity,
+    def recalc_llm_max_tokens(self, model_config: ModelConfigEntity,
                               prompt_messages: list[PromptMessage]):
         # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
         model_type_instance = model_config.provider_model_bundle.model_type_instance

api/core/app_runner/assistant_app_runner.py

@@ -1,4 +1,3 @@
-import json
 import logging
 from typing import cast
 
@@ -15,7 +14,7 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.moderation.base import ModerationException
 from core.tools.entities.tool_entities import ToolRuntimeVariablePool
 from extensions.ext_database import db
-from models.model import App, Conversation, Message, MessageAgentThought, MessageChain
+from models.model import App, Conversation, Message, MessageAgentThought
 from models.tools import ToolConversationVariables
 
 logger = logging.getLogger(__name__)
@@ -173,11 +172,6 @@ class AssistantApplicationRunner(AppRunner):
 
         # convert db variables to tool variables
         tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
-        
-        message_chain = self._init_message_chain(
-            message=message,
-            query=query
-        )
 
         # init model instance
         model_instance = ModelInstance(
@@ -201,6 +195,10 @@ class AssistantApplicationRunner(AppRunner):
         if set([ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL]).intersection(model_schema.features or []):
             agent_entity.strategy = AgentEntity.Strategy.FUNCTION_CALLING
 
+        db.session.refresh(conversation)
+        db.session.refresh(message)
+        db.session.close()
+
         # start agent runner
         if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
             assistant_cot_runner = AssistantCotApplicationRunner(
@@ -290,38 +288,6 @@ class AssistantApplicationRunner(AppRunner):
             'pool': db_variables.variables
         })
 
-    def _init_message_chain(self, message: Message, query: str) -> MessageChain:
-        """
-        Init MessageChain
-        :param message: message
-        :param query: query
-        :return:
-        """
-        message_chain = MessageChain(
-            message_id=message.id,
-            type="AgentExecutor",
-            input=json.dumps({
-                "input": query
-            })
-        )
-
-        db.session.add(message_chain)
-        db.session.commit()
-
-        return message_chain
-
-    def _save_message_chain(self, message_chain: MessageChain, output_text: str) -> None:
-        """
-        Save MessageChain
-        :param message_chain: message chain
-        :param output_text: output text
-        :return:
-        """
-        message_chain.output = json.dumps({
-            "output": output_text
-        })
-        db.session.commit()
-
     def _get_usage_of_all_agent_thoughts(self, model_config: ModelConfigEntity,
                                          message: Message) -> LLMUsage:
         """

api/core/app_runner/basic_app_runner.py

@@ -5,7 +5,7 @@ from core.app_runner.app_runner import AppRunner
 from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.entities.application_entities import ApplicationGenerateEntity, DatasetEntity, InvokeFrom, ModelConfigEntity
-from core.features.dataset_retrieval import DatasetRetrievalFeature
+from core.features.dataset_retrieval.dataset_retrieval import DatasetRetrievalFeature
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.moderation.base import ModerationException
@@ -181,7 +181,7 @@ class BasicApplicationRunner(AppRunner):
             return
 
         # Re-calculate the max tokens if sum(prompt_token +  max_tokens) over model token limit
-        self.recale_llm_max_tokens(
+        self.recalc_llm_max_tokens(
             model_config=app_orchestration_config.model_config,
             prompt_messages=prompt_messages
         )
@@ -192,6 +192,8 @@ class BasicApplicationRunner(AppRunner):
             model=app_orchestration_config.model_config.model
         )
 
+        db.session.close()
+
         invoke_result = model_instance.invoke_llm(
             prompt_messages=prompt_messages,
             model_parameters=app_orchestration_config.model_config.parameters,

api/core/app_runner/generate_task_pipeline.py

@@ -89,6 +89,10 @@ class GenerateTaskPipeline:
         Process generate task pipeline.
         :return:
         """
+        db.session.refresh(self._conversation)
+        db.session.refresh(self._message)
+        db.session.close()
+
         if stream:
             return self._process_stream_response()
         else:
@@ -303,6 +307,7 @@ class GenerateTaskPipeline:
                     .first()
                 )
                 db.session.refresh(agent_thought)
+                db.session.close()
 
                 if agent_thought:
                     response = {
@@ -330,6 +335,8 @@ class GenerateTaskPipeline:
                     .filter(MessageFile.id == event.message_file_id)
                     .first()
                 )
+                db.session.close()
+
                 # get extension
                 if '.' in message_file.url:
                     extension = f'.{message_file.url.split(".")[-1]}'
@@ -413,6 +420,7 @@ class GenerateTaskPipeline:
         usage = llm_result.usage
 
         self._message = db.session.query(Message).filter(Message.id == self._message.id).first()
+        self._conversation = db.session.query(Conversation).filter(Conversation.id == self._conversation.id).first()
 
         self._message.message = self._prompt_messages_to_prompt_for_saving(self._task_state.llm_result.prompt_messages)
         self._message.message_tokens = usage.prompt_tokens

api/core/application_manager.py

@@ -201,7 +201,7 @@ class ApplicationManager:
                 logger.exception("Unknown Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             finally:
-                db.session.remove()
+                db.session.close()
 
     def _handle_response(self, application_generate_entity: ApplicationGenerateEntity,
                          queue_manager: ApplicationQueueManager,
@@ -233,8 +233,6 @@ class ApplicationManager:
             else:
                 logger.exception(e)
                 raise e
-        finally:
-            db.session.remove()
 
     def _convert_from_app_model_config_dict(self, tenant_id: str, app_model_config_dict: dict) \
             -> AppOrchestrationConfigEntity:
@@ -651,6 +649,7 @@ class ApplicationManager:
 
             db.session.add(conversation)
             db.session.commit()
+            db.session.refresh(conversation)
         else:
             conversation = (
                 db.session.query(Conversation)
@@ -689,6 +688,7 @@ class ApplicationManager:
 
         db.session.add(message)
         db.session.commit()
+        db.session.refresh(message)
 
         for file in application_generate_entity.files:
             message_file = MessageFile(

api/core/entities/agent_entities.py

@@ -0,0 +1,8 @@
+from enum import Enum
+
+
+class PlanningStrategy(Enum):
+    ROUTER = 'router'
+    REACT_ROUTER = 'react_router'
+    REACT = 'react'
+    FUNCTION_CALL = 'function_call'

api/core/features/agent_runner.py

@@ -1,199 +0,0 @@
-import logging
-from typing import Optional, cast
-
-from langchain.tools import BaseTool
-
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
-from core.application_queue_manager import ApplicationQueueManager
-from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
-from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.entities.application_entities import (
-    AgentEntity,
-    AppOrchestrationConfigEntity,
-    InvokeFrom,
-    ModelConfigEntity,
-)
-from core.memory.token_buffer_memory import TokenBufferMemory
-from core.model_runtime.entities.model_entities import ModelFeature, ModelType
-from core.model_runtime.model_providers import model_provider_factory
-from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
-from extensions.ext_database import db
-from models.dataset import Dataset
-from models.model import Message
-
-logger = logging.getLogger(__name__)
-
-
-class AgentRunnerFeature:
-    def __init__(self, tenant_id: str,
-                 app_orchestration_config: AppOrchestrationConfigEntity,
-                 model_config: ModelConfigEntity,
-                 config: AgentEntity,
-                 queue_manager: ApplicationQueueManager,
-                 message: Message,
-                 user_id: str,
-                 agent_llm_callback: AgentLLMCallback,
-                 callback: AgentLoopGatherCallbackHandler,
-                 memory: Optional[TokenBufferMemory] = None,) -> None:
-        """
-        Agent runner
-        :param tenant_id: tenant id
-        :param app_orchestration_config: app orchestration config
-        :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
-        """
-        self.tenant_id = tenant_id
-        self.app_orchestration_config = app_orchestration_config
-        self.model_config = model_config
-        self.config = config
-        self.queue_manager = queue_manager
-        self.message = message
-        self.user_id = user_id
-        self.agent_llm_callback = agent_llm_callback
-        self.callback = callback
-        self.memory = memory
-
-    def run(self, query: str,
-            invoke_from: InvokeFrom) -> Optional[str]:
-        """
-        Retrieve agent loop result.
-        :param query: query
-        :param invoke_from: invoke from
-        :return:
-        """
-        provider = self.config.provider
-        model = self.config.model
-        tool_configs = self.config.tools
-
-        # check model is support tool calling
-        provider_instance = model_provider_factory.get_provider_instance(provider=provider)
-        model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
-
-        # get model schema
-        model_schema = model_type_instance.get_model_schema(
-            model=model,
-            credentials=self.model_config.credentials
-        )
-
-        if not model_schema:
-            return None
-
-        planning_strategy = PlanningStrategy.REACT
-        features = model_schema.features
-        if features:
-            if ModelFeature.TOOL_CALL in features \
-                    or ModelFeature.MULTI_TOOL_CALL in features:
-                planning_strategy = PlanningStrategy.FUNCTION_CALL
-
-        tools = self.to_tools(
-            tool_configs=tool_configs,
-            invoke_from=invoke_from,
-            callbacks=[self.callback, DifyStdOutCallbackHandler()],
-        )
-
-        if len(tools) == 0:
-            return None
-
-        agent_configuration = AgentConfiguration(
-            strategy=planning_strategy,
-            model_config=self.model_config,
-            tools=tools,
-            memory=self.memory,
-            max_iterations=10,
-            max_execution_time=400.0,
-            early_stopping_method="generate",
-            agent_llm_callback=self.agent_llm_callback,
-            callbacks=[self.callback, DifyStdOutCallbackHandler()]
-        )
-
-        agent_executor = AgentExecutor(agent_configuration)
-
-        try:
-            # check if should use agent
-            should_use_agent = agent_executor.should_use_agent(query)
-            if not should_use_agent:
-                return None
-
-            result = agent_executor.run(query)
-            return result.output
-        except Exception as ex:
-            logger.exception("agent_executor run failed")
-            return None
-
-    def to_dataset_retriever_tool(self, tool_config: dict,
-                                  invoke_from: InvokeFrom) \
-            -> Optional[BaseTool]:
-        """
-        A dataset tool is a tool that can be used to retrieve information from a dataset
-        :param tool_config: tool config
-        :param invoke_from: invoke from
-        """
-        show_retrieve_source = self.app_orchestration_config.show_retrieve_source
-
-        hit_callback = DatasetIndexToolCallbackHandler(
-            queue_manager=self.queue_manager,
-            app_id=self.message.app_id,
-            message_id=self.message.id,
-            user_id=self.user_id,
-            invoke_from=invoke_from
-        )
-
-        # get dataset from dataset id
-        dataset = db.session.query(Dataset).filter(
-            Dataset.tenant_id == self.tenant_id,
-            Dataset.id == tool_config.get("id")
-        ).first()
-
-        # pass if dataset is not available
-        if not dataset:
-            return None
-
-        # pass if dataset is not available
-        if (dataset and dataset.available_document_count == 0
-                and dataset.available_document_count == 0):
-            return None
-
-        # get retrieval model config
-        default_retrieval_model = {
-            'search_method': 'semantic_search',
-            'reranking_enable': False,
-            'reranking_model': {
-                'reranking_provider_name': '',
-                'reranking_model_name': ''
-            },
-            'top_k': 2,
-            'score_threshold_enabled': False
-        }
-
-        retrieval_model_config = dataset.retrieval_model \
-            if dataset.retrieval_model else default_retrieval_model
-
-        # get top k
-        top_k = retrieval_model_config['top_k']
-
-        # get score threshold
-        score_threshold = None
-        score_threshold_enabled = retrieval_model_config.get("score_threshold_enabled")
-        if score_threshold_enabled:
-            score_threshold = retrieval_model_config.get("score_threshold")
-
-        tool = DatasetRetrieverTool.from_dataset(
-            dataset=dataset,
-            top_k=top_k,
-            score_threshold=score_threshold,
-            hit_callbacks=[hit_callback],
-            return_resource=show_retrieve_source,
-            retriever_from=invoke_from.to_source()
-        )
-
-        return tool

api/core/features/assistant_base_runner.py

@@ -114,6 +114,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         self.agent_thought_count = db.session.query(MessageAgentThought).filter(
             MessageAgentThought.message_id == self.message.id,
         ).count()
+        db.session.close()
 
         # check if model supports stream tool call
         llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
@@ -154,9 +155,9 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
             convert tool to prompt message tool
         """
-        tool_entity = ToolManager.get_tool_runtime(
-            provider_type=tool.provider_type, provider_name=tool.provider_id, tool_name=tool.tool_name, 
-            tenant_id=self.application_generate_entity.tenant_id,
+        tool_entity = ToolManager.get_agent_tool_runtime(
+            tenant_id=self.tenant_id,
+            agent_tool=tool,
             agent_callback=self.agent_callback
         )
         tool_entity.load_variables(self.variables_pool)
@@ -171,33 +172,11 @@ class BaseAssistantApplicationRunner(AppRunner):
             }
         )
 
-        runtime_parameters = {}
-
-        parameters = tool_entity.parameters or []
-        user_parameters = tool_entity.get_runtime_parameters() or []
-
-        # override parameters
-        for parameter in user_parameters:
-            # check if parameter in tool parameters
-            found = False
-            for tool_parameter in parameters:
-                if tool_parameter.name == parameter.name:
-                    found = True
-                    break
-
-            if found:
-                # override parameter
-                tool_parameter.type = parameter.type
-                tool_parameter.form = parameter.form
-                tool_parameter.required = parameter.required
-                tool_parameter.default = parameter.default
-                tool_parameter.options = parameter.options
-                tool_parameter.llm_description = parameter.llm_description
-            else:
-                # add new parameter
-                parameters.append(parameter)
-
+        parameters = tool_entity.get_all_runtime_parameters()
         for parameter in parameters:
+            if parameter.form != ToolParameter.ToolParameterForm.LLM:
+                continue
+
             parameter_type = 'string'
             enum = []
             if parameter.type == ToolParameter.ToolParameterType.STRING:
@@ -213,59 +192,16 @@ class BaseAssistantApplicationRunner(AppRunner):
             else:
                 raise ValueError(f"parameter type {parameter.type} is not supported")
             
-            if parameter.form == ToolParameter.ToolParameterForm.FORM:
-                # get tool parameter from form
-                tool_parameter_config = tool.tool_parameters.get(parameter.name)
-                if not tool_parameter_config:
-                    # get default value
-                    tool_parameter_config = parameter.default
-                    if not tool_parameter_config and parameter.required:
-                        raise ValueError(f"tool parameter {parameter.name} not found in tool config")
-                    
-                if parameter.type == ToolParameter.ToolParameterType.SELECT:
-                    # check if tool_parameter_config in options
-                    options = list(map(lambda x: x.value, parameter.options))
-                    if tool_parameter_config not in options:
-                        raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} not in options {options}")
-                    
-                # convert tool parameter config to correct type
-                try:
-                    if parameter.type == ToolParameter.ToolParameterType.NUMBER:
-                        # check if tool parameter is integer
-                        if isinstance(tool_parameter_config, int):
-                            tool_parameter_config = tool_parameter_config
-                        elif isinstance(tool_parameter_config, float):
-                            tool_parameter_config = tool_parameter_config
-                        elif isinstance(tool_parameter_config, str):
-                            if '.' in tool_parameter_config:
-                                tool_parameter_config = float(tool_parameter_config)
-                            else:
-                                tool_parameter_config = int(tool_parameter_config)
-                    elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
-                        tool_parameter_config = bool(tool_parameter_config)
-                    elif parameter.type not in [ToolParameter.ToolParameterType.SELECT, ToolParameter.ToolParameterType.STRING]:
-                        tool_parameter_config = str(tool_parameter_config)
-                    elif parameter.type == ToolParameter.ToolParameterType:
-                        tool_parameter_config = str(tool_parameter_config)
-                except Exception as e:
-                    raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} is not correct type")
-                
-                # save tool parameter to tool entity memory
-                runtime_parameters[parameter.name] = tool_parameter_config
-            
-            elif parameter.form == ToolParameter.ToolParameterForm.LLM:
-                message_tool.parameters['properties'][parameter.name] = {
-                    "type": parameter_type,
-                    "description": parameter.llm_description or '',
-                }
+            message_tool.parameters['properties'][parameter.name] = {
+                "type": parameter_type,
+                "description": parameter.llm_description or '',
+            }
 
-                if len(enum) > 0:
-                    message_tool.parameters['properties'][parameter.name]['enum'] = enum
+            if len(enum) > 0:
+                message_tool.parameters['properties'][parameter.name]['enum'] = enum
 
-                if parameter.required:
-                    message_tool.parameters['required'].append(parameter.name)
-
-        tool_entity.runtime.runtime_parameters.update(runtime_parameters)
+            if parameter.required:
+                message_tool.parameters['required'].append(parameter.name)
 
         return message_tool, tool_entity
     
@@ -305,6 +241,9 @@ class BaseAssistantApplicationRunner(AppRunner):
         tool_runtime_parameters = tool.get_runtime_parameters() or []
 
         for parameter in tool_runtime_parameters:
+            if parameter.form != ToolParameter.ToolParameterForm.LLM:
+                continue
+
             parameter_type = 'string'
             enum = []
             if parameter.type == ToolParameter.ToolParameterType.STRING:
@@ -320,18 +259,17 @@ class BaseAssistantApplicationRunner(AppRunner):
             else:
                 raise ValueError(f"parameter type {parameter.type} is not supported")
         
-            if parameter.form == ToolParameter.ToolParameterForm.LLM:
-                prompt_tool.parameters['properties'][parameter.name] = {
-                    "type": parameter_type,
-                    "description": parameter.llm_description or '',
-                }
+            prompt_tool.parameters['properties'][parameter.name] = {
+                "type": parameter_type,
+                "description": parameter.llm_description or '',
+            }
 
-                if len(enum) > 0:
-                    prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
+            if len(enum) > 0:
+                prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
 
-                if parameter.required:
-                    if parameter.name not in prompt_tool.parameters['required']:
-                        prompt_tool.parameters['required'].append(parameter.name)
+            if parameter.required:
+                if parameter.name not in prompt_tool.parameters['required']:
+                    prompt_tool.parameters['required'].append(parameter.name)
 
         return prompt_tool
     
@@ -404,13 +342,16 @@ class BaseAssistantApplicationRunner(AppRunner):
                 created_by=self.user_id,
             )
             db.session.add(message_file)
+            db.session.commit()
+            db.session.refresh(message_file)
+
             result.append((
                 message_file,
                 message.save_as
             ))
-            
-        db.session.commit()
 
+        db.session.close()
+            
         return result
         
     def create_agent_thought(self, message_id: str, message: str, 
@@ -447,6 +388,8 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         db.session.add(thought)
         db.session.commit()
+        db.session.refresh(thought)
+        db.session.close()
 
         self.agent_thought_count += 1
 
@@ -464,6 +407,10 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
         Save agent thought
         """
+        agent_thought = db.session.query(MessageAgentThought).filter(
+            MessageAgentThought.id == agent_thought.id
+        ).first()
+
         if thought is not None:
             agent_thought.thought = thought
 
@@ -514,6 +461,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         agent_thought.tool_labels_str = json.dumps(labels)
 
         db.session.commit()
+        db.session.close()
     
     def transform_tool_invoke_messages(self, messages: list[ToolInvokeMessage]) -> list[ToolInvokeMessage]:
         """
@@ -586,9 +534,14 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
         convert tool variables to db variables
         """
+        db_variables = db.session.query(ToolConversationVariables).filter(
+            ToolConversationVariables.conversation_id == self.message.conversation_id,
+        ).first()
+
         db_variables.updated_at = datetime.utcnow()
         db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
         db.session.commit()
+        db.session.close()
 
     def organize_agent_history(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
         """
@@ -644,4 +597,6 @@ class BaseAssistantApplicationRunner(AppRunner):
                 if message.answer:
                     result.append(AssistantPromptMessage(content=message.answer))
 
+        db.session.close()
+
         return result

api/core/features/assistant_cot_runner.py

@@ -28,6 +28,9 @@ from models.model import Conversation, Message
 
 
 class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
+    _is_first_iteration = True
+    _ignore_observation_providers = ['wenxin']
+
     def run(self, conversation: Conversation,
         message: Message,
         query: str,
@@ -42,10 +45,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         agent_scratchpad: list[AgentScratchpadUnit] = []
         self._init_agent_scratchpad(agent_scratchpad, self.history_prompt_messages)
 
-        # check model mode
-        if self.app_orchestration_config.model_config.mode == "completion":
-            # TODO: stop words
-            if 'Observation' not in app_orchestration_config.model_config.stop:
+        if 'Observation' not in app_orchestration_config.model_config.stop:
+            if app_orchestration_config.model_config.provider not in self._ignore_observation_providers:
                 app_orchestration_config.model_config.stop.append('Observation')
 
         # override inputs
@@ -130,8 +131,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 input=query
             )
 
-            # recale llm max tokens
-            self.recale_llm_max_tokens(self.model_config, prompt_messages)
+            # recalc llm max tokens
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
@@ -202,6 +203,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         )
                     )
 
+            scratchpad.thought = scratchpad.thought.strip() or 'I am thinking about how to help you'
             agent_scratchpad.append(scratchpad)
                         
             # get llm usage
@@ -255,9 +257,15 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         # invoke tool
                         error_response = None
                         try:
+                            if isinstance(tool_call_args, str):
+                                try:
+                                    tool_call_args = json.loads(tool_call_args)
+                                except json.JSONDecodeError:
+                                    pass
+                            
                             tool_response = tool_instance.invoke(
                                 user_id=self.user_id, 
-                                tool_parameters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
+                                tool_parameters=tool_call_args
                             )
                             # transform tool response to llm friendly response
                             tool_response = self.transform_tool_invoke_messages(tool_response)
@@ -466,7 +474,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             if isinstance(message, AssistantPromptMessage):
                 current_scratchpad = AgentScratchpadUnit(
                     agent_response=message.content,
-                    thought=message.content,
+                    thought=message.content or 'I am thinking about how to help you',
                     action_str='',
                     action=None,
                     observation=None,
@@ -546,7 +554,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
 
         result = ''
         for scratchpad in agent_scratchpad:
-            result += scratchpad.thought + next_iteration.replace("{{observation}}", scratchpad.observation or '') + "\n"
+            result += (scratchpad.thought or '') + (scratchpad.action_str or '') + \
+                next_iteration.replace("{{observation}}", scratchpad.observation or 'It seems that no response is available')
 
         return result
     
@@ -621,21 +630,24 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 ))
 
             # add assistant message
-            if len(agent_scratchpad) > 0:
+            if len(agent_scratchpad) > 0 and not self._is_first_iteration:
                 prompt_messages.append(AssistantPromptMessage(
-                    content=(agent_scratchpad[-1].thought or '')
+                    content=(agent_scratchpad[-1].thought or '') + (agent_scratchpad[-1].action_str or ''),
                 ))
             
             # add user message
-            if len(agent_scratchpad) > 0:
+            if len(agent_scratchpad) > 0 and not self._is_first_iteration:
                 prompt_messages.append(UserPromptMessage(
-                    content=(agent_scratchpad[-1].observation or ''),
+                    content=(agent_scratchpad[-1].observation or 'It seems that no response is available'),
                 ))
 
+            self._is_first_iteration = False
+
             return prompt_messages
         elif mode == "completion":
             # parse agent scratchpad
             agent_scratchpad_str = self._convert_scratchpad_list_to_str(agent_scratchpad)
+            self._is_first_iteration = False
             # parse prompt messages
             return [UserPromptMessage(
                 content=first_prompt.replace("{{instruction}}", instruction)

api/core/features/assistant_fc_runner.py

@@ -105,8 +105,8 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 messages_ids=message_file_ids
             )
 
-            # recale llm max tokens
-            self.recale_llm_max_tokens(self.model_config, prompt_messages)
+            # recalc llm max tokens
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,

api/core/features/dataset_retrieval/agent/llm_chain.py

@@ -5,11 +5,11 @@ from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.schema import Generation, LLMResult
 from langchain.schema.language_model import BaseLanguageModel
 
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
+from core.features.dataset_retrieval.agent.agent_llm_callback import AgentLLMCallback
+from core.features.dataset_retrieval.agent.fake_llm import FakeLLM
 from core.model_manager import ModelInstance
-from core.third_party.langchain.llms.fake import FakeLLM
 
 
 class LLMChain(LCLLMChain):

api/core/features/dataset_retrieval/agent/multi_dataset_router_agent.py

@@ -12,9 +12,9 @@ from pydantic import root_validator
 
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
+from core.features.dataset_retrieval.agent.fake_llm import FakeLLM
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessageTool
-from core.third_party.langchain.llms.fake import FakeLLM
 
 
 class MultiDatasetRouterAgent(OpenAIFunctionsAgent):

api/core/features/dataset_retrieval/agent/structed_multi_dataset_router_agent.py

@@ -12,8 +12,8 @@ from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, Sy
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 from langchain.tools import BaseTool
 
-from core.chain.llm_chain import LLMChain
 from core.entities.application_entities import ModelConfigEntity
+from core.features.dataset_retrieval.agent.llm_chain import LLMChain
 
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.

api/core/features/dataset_retrieval/agent_based_dataset_executor.py

@@ -1,4 +1,3 @@
-import enum
 import logging
 from typing import Optional, Union
 
@@ -8,14 +7,13 @@ from langchain.callbacks.manager import Callbacks
 from langchain.tools import BaseTool
 from pydantic import BaseModel, Extra
 
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
-from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent
-from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
-from core.agent.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
-from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
+from core.entities.agent_entities import PlanningStrategy
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import prompt_messages_to_lc_messages
+from core.features.dataset_retrieval.agent.agent_llm_callback import AgentLLMCallback
+from core.features.dataset_retrieval.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
+from core.features.dataset_retrieval.agent.output_parser.structured_chat import StructuredChatOutputParser
+from core.features.dataset_retrieval.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
 from core.helper import moderation
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.errors.invoke import InvokeError
@@ -23,13 +21,6 @@ from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import Datas
 from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
 
 
-class PlanningStrategy(str, enum.Enum):
-    ROUTER = 'router'
-    REACT_ROUTER = 'react_router'
-    REACT = 'react'
-    FUNCTION_CALL = 'function_call'
-
-
 class AgentConfiguration(BaseModel):
     strategy: PlanningStrategy
     model_config: ModelConfigEntity
@@ -62,28 +53,7 @@ class AgentExecutor:
         self.agent = self._init_agent()
 
     def _init_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
-        if self.configuration.strategy == PlanningStrategy.REACT:
-            agent = AutoSummarizingStructuredChatAgent.from_llm_and_tools(
-                model_config=self.configuration.model_config,
-                tools=self.configuration.tools,
-                output_parser=StructuredChatOutputParser(),
-                summary_model_config=self.configuration.summary_model_config
-                if self.configuration.summary_model_config else None,
-                agent_llm_callback=self.configuration.agent_llm_callback,
-                verbose=True
-            )
-        elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL:
-            agent = AutoSummarizingOpenAIFunctionCallAgent.from_llm_and_tools(
-                model_config=self.configuration.model_config,
-                tools=self.configuration.tools,
-                extra_prompt_messages=prompt_messages_to_lc_messages(self.configuration.memory.get_history_prompt_messages())
-                if self.configuration.memory else None,  # used for read chat histories memory
-                summary_model_config=self.configuration.summary_model_config
-                if self.configuration.summary_model_config else None,
-                agent_llm_callback=self.configuration.agent_llm_callback,
-                verbose=True
-            )
-        elif self.configuration.strategy == PlanningStrategy.ROUTER:
+        if self.configuration.strategy == PlanningStrategy.ROUTER:
             self.configuration.tools = [t for t in self.configuration.tools
                                         if isinstance(t, DatasetRetrieverTool)
                                         or isinstance(t, DatasetMultiRetrieverTool)]

api/core/features/dataset_retrieval/dataset_retrieval.py

@@ -2,9 +2,10 @@ from typing import Optional, cast
 
 from langchain.tools import BaseTool
 
-from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
+from core.entities.agent_entities import PlanningStrategy
 from core.entities.application_entities import DatasetEntity, DatasetRetrieveConfigEntity, InvokeFrom, ModelConfigEntity
+from core.features.dataset_retrieval.agent_based_dataset_executor import AgentConfiguration, AgentExecutor
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities.model_entities import ModelFeature
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel

api/core/helper/tool_parameter_cache.py

@@ -0,0 +1,54 @@
+import json
+from enum import Enum
+from json import JSONDecodeError
+from typing import Optional
+
+from extensions.ext_redis import redis_client
+
+
+class ToolParameterCacheType(Enum):
+    PARAMETER = "tool_parameter"
+
+class ToolParameterCache:
+    def __init__(self, 
+                 tenant_id: str, 
+                 provider: str, 
+                 tool_name: str, 
+                 cache_type: ToolParameterCacheType
+        ):
+        self.cache_key = f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}"
+
+    def get(self) -> Optional[dict]:
+        """
+        Get cached model provider credentials.
+
+        :return:
+        """
+        cached_tool_parameter = redis_client.get(self.cache_key)
+        if cached_tool_parameter:
+            try:
+                cached_tool_parameter = cached_tool_parameter.decode('utf-8')
+                cached_tool_parameter = json.loads(cached_tool_parameter)
+            except JSONDecodeError:
+                return None
+
+            return cached_tool_parameter
+        else:
+            return None
+
+    def set(self, parameters: dict) -> None:
+        """
+        Cache model provider credentials.
+
+        :param credentials: provider credentials
+        :return:
+        """
+        redis_client.setex(self.cache_key, 86400, json.dumps(parameters))
+
+    def delete(self) -> None:
+        """
+        Delete cached model provider credentials.
+
+        :return:
+        """
+        redis_client.delete(self.cache_key)

api/core/hosting_configuration.py

@@ -82,6 +82,8 @@ class HostingConfiguration:
                     RestrictModel(model="gpt-35-turbo-16k", base_model_name="gpt-35-turbo-16k", model_type=ModelType.LLM),
                     RestrictModel(model="text-davinci-003", base_model_name="text-davinci-003", model_type=ModelType.LLM),
                     RestrictModel(model="text-embedding-ada-002", base_model_name="text-embedding-ada-002", model_type=ModelType.TEXT_EMBEDDING),
+                    RestrictModel(model="text-embedding-3-small", base_model_name="text-embedding-3-small", model_type=ModelType.TEXT_EMBEDDING),
+                    RestrictModel(model="text-embedding-3-large", base_model_name="text-embedding-3-large", model_type=ModelType.TEXT_EMBEDDING),
                 ]
             )
             quotas.append(trial_quota)

api/core/indexing_runner.py

@@ -62,7 +62,8 @@ class IndexingRunner:
                 text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict())
 
                 # transform
-                documents = self._transform(index_processor, dataset, text_docs, processing_rule.to_dict())
+                documents = self._transform(index_processor, dataset, text_docs, dataset_document.doc_language,
+                                            processing_rule.to_dict())
                 # save segment
                 self._load_segments(dataset, dataset_document, documents)
 
@@ -120,7 +121,8 @@ class IndexingRunner:
             text_docs = self._extract(index_processor, dataset_document, processing_rule.to_dict())
 
             # transform
-            documents = self._transform(index_processor, dataset, text_docs, processing_rule.to_dict())
+            documents = self._transform(index_processor, dataset, text_docs, dataset_document.doc_language,
+                                        processing_rule.to_dict())
             # save segment
             self._load_segments(dataset, dataset_document, documents)
 
@@ -186,7 +188,7 @@ class IndexingRunner:
                 first()
 
             index_type = dataset_document.doc_form
-            index_processor = IndexProcessorFactory(index_type, processing_rule.to_dict()).init_index_processor()
+            index_processor = IndexProcessorFactory(index_type).init_index_processor()
             self._load(
                 index_processor=index_processor,
                 dataset=dataset,
@@ -414,9 +416,14 @@ class IndexingRunner:
             if separator:
                 separator = separator.replace('\\n', '\n')
 
+            if 'chunk_overlap' in segmentation and segmentation['chunk_overlap']:
+                chunk_overlap = segmentation['chunk_overlap']
+            else:
+                chunk_overlap = 0
+
             character_splitter = FixedRecursiveCharacterTextSplitter.from_encoder(
                 chunk_size=segmentation["max_tokens"],
-                chunk_overlap=segmentation.get('chunk_overlap', 0),
+                chunk_overlap=chunk_overlap,
                 fixed_separator=separator,
                 separators=["\n\n", "。", ".", " ", ""],
                 embedding_model_instance=embedding_model_instance
@@ -750,7 +757,7 @@ class IndexingRunner:
         index_processor.load(dataset, documents)
 
     def _transform(self, index_processor: BaseIndexProcessor, dataset: Dataset,
-                   text_docs: list[Document], process_rule: dict) -> list[Document]:
+                   text_docs: list[Document], doc_language: str, process_rule: dict) -> list[Document]:
         # get embedding model instance
         embedding_model_instance = None
         if dataset.indexing_technique == 'high_quality':
@@ -768,7 +775,8 @@ class IndexingRunner:
                 )
 
         documents = index_processor.transform(text_docs, embedding_model_instance=embedding_model_instance,
-                                              process_rule=process_rule)
+                                              process_rule=process_rule, tenant_id=dataset.tenant_id,
+                                              doc_language=doc_language)
 
         return documents
 

api/core/memory/token_buffer_memory.py

@@ -47,11 +47,14 @@ class TokenBufferMemory:
                     files, message.app_model_config
                 )
 
-                prompt_message_contents = [TextPromptMessageContent(data=message.query)]
-                for file_obj in file_objs:
-                    prompt_message_contents.append(file_obj.prompt_message_content)
+                if not file_objs:
+                    prompt_messages.append(UserPromptMessage(content=message.query))
+                else:
+                    prompt_message_contents = [TextPromptMessageContent(data=message.query)]
+                    for file_obj in file_objs:
+                        prompt_message_contents.append(file_obj.prompt_message_content)
 
-                prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
+                    prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
             else:
                 prompt_messages.append(UserPromptMessage(content=message.query))
 

api/core/model_runtime/entities/model_entities.py

@@ -17,7 +17,7 @@ class ModelType(Enum):
     SPEECH2TEXT = "speech2text"
     MODERATION = "moderation"
     TTS = "tts"
-    # TEXT2IMG = "text2img"
+    TEXT2IMG = "text2img"
 
     @classmethod
     def value_of(cls, origin_model_type: str) -> "ModelType":
@@ -36,6 +36,8 @@ class ModelType(Enum):
             return cls.SPEECH2TEXT
         elif origin_model_type == 'tts' or origin_model_type == cls.TTS.value:
             return cls.TTS
+        elif origin_model_type == 'text2img' or origin_model_type == cls.TEXT2IMG.value:
+            return cls.TEXT2IMG
         elif origin_model_type == cls.MODERATION.value:
             return cls.MODERATION
         else:
@@ -59,10 +61,11 @@ class ModelType(Enum):
             return 'tts'
         elif self == self.MODERATION:
             return 'moderation'
+        elif self == self.TEXT2IMG:
+            return 'text2img'
         else:
             raise ValueError(f'invalid model type {self}')
 
-
 class FetchFrom(Enum):
     """
     Enum class for fetch from.

api/core/model_runtime/model_providers/__base/text2img_model.py

@@ -0,0 +1,48 @@
+from abc import abstractmethod
+from typing import IO, Optional
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.model_providers.__base.ai_model import AIModel
+
+
+class Text2ImageModel(AIModel):
+    """
+    Model class for text2img model.
+    """
+    model_type: ModelType = ModelType.TEXT2IMG
+
+    def invoke(self, model: str, credentials: dict, prompt: str, 
+               model_parameters: dict, user: Optional[str] = None) \
+            -> list[IO[bytes]]:
+        """
+        Invoke Text2Image model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt: prompt for image generation
+        :param model_parameters: model parameters
+        :param user: unique user id
+
+        :return: image bytes
+        """
+        try:
+            return self._invoke(model, credentials, prompt, model_parameters, user)
+        except Exception as e:
+            raise self._transform_invoke_error(e)
+
+    @abstractmethod
+    def _invoke(self, model: str, credentials: dict, prompt: str, 
+                model_parameters: dict, user: Optional[str] = None) \
+            -> list[IO[bytes]]:
+        """
+        Invoke Text2Image model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt: prompt for image generation
+        :param model_parameters: model parameters
+        :param user: unique user id
+
+        :return: image bytes
+        """
+        raise NotImplementedError

api/core/model_runtime/model_providers/_position.yaml

@@ -7,6 +7,7 @@
 - togetherai
 - ollama
 - mistralai
+- groq
 - replicate
 - huggingface_hub
 - zhipuai

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

@@ -21,7 +21,7 @@ class AnthropicProvider(ModelProvider):
 
             # Use `claude-instant-1` model for validate,
             model_instance.validate_credentials(
-                model='claude-instant-1',
+                model='claude-instant-1.2',
                 credentials=credentials
             )
         except CredentialsValidateFailedError as ex:

api/core/model_runtime/model_providers/anthropic/anthropic.yaml

@@ -2,8 +2,8 @@ provider: anthropic
 label:
   en_US: Anthropic
 description:
-  en_US: Anthropic’s powerful models, such as Claude 2 and Claude Instant.
-  zh_Hans: Anthropic 的强大模型，例如 Claude 2 和 Claude Instant。
+  en_US: Anthropic’s powerful models, such as Claude 3.
+  zh_Hans: Anthropic 的强大模型，例如 Claude 3。
 icon_small:
   en_US: icon_s_en.svg
 icon_large:

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

@@ -0,0 +1,6 @@
+- claude-3-opus-20240229
+- claude-3-sonnet-20240229
+- claude-2.1
+- claude-instant-1.2
+- claude-2
+- claude-instant-1

api/core/model_runtime/model_providers/anthropic/llm/claude-2.yaml

@@ -34,3 +34,4 @@ pricing:
   output: '24.00'
   unit: '0.000001'
   currency: USD
+deprecated: true

api/core/model_runtime/model_providers/anthropic/llm/claude-3-opus-20240229.yaml

@@ -0,0 +1,37 @@
+model: claude-3-opus-20240229
+label:
+  en_US: claude-3-opus-20240229
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '15.00'
+  output: '75.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-3-sonnet-20240229.yaml

@@ -0,0 +1,37 @@
+model: claude-3-sonnet-20240229
+label:
+  en_US: claude-3-sonnet-20240229
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '3.00'
+  output: '15.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.2.yaml

@@ -0,0 +1,35 @@
+model: claude-instant-1.2
+label:
+  en_US: claude-instant-1.2
+model_type: llm
+features: [ ]
+model_properties:
+  mode: chat
+  context_size: 100000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '1.63'
+  output: '5.51'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.yaml

@@ -33,3 +33,4 @@ pricing:
   output: '5.51'
   unit: '0.000001'
   currency: USD
+deprecated: true

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

@@ -1,18 +1,32 @@
+import base64
+import mimetypes
 from collections.abc import Generator
-from typing import Optional, Union
+from typing import Optional, Union, cast
 
 import anthropic
+import requests
 from anthropic import Anthropic, Stream
-from anthropic.types import Completion, completion_create_params
+from anthropic.types import (
+    ContentBlockDeltaEvent,
+    Message,
+    MessageDeltaEvent,
+    MessageStartEvent,
+    MessageStopEvent,
+    MessageStreamEvent,
+    completion_create_params,
+)
 from httpx import Timeout
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
+    ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
+    TextPromptMessageContent,
     UserPromptMessage,
 )
 from core.model_runtime.errors.invoke import (
@@ -35,6 +49,7 @@ if you are not sure about the structure.
 </instructions>
 """
 
+
 class AnthropicLargeLanguageModel(LargeLanguageModel):
     def _invoke(self, model: str, credentials: dict,
                 prompt_messages: list[PromptMessage], model_parameters: dict,
@@ -55,54 +70,114 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         # invoke model
-        return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
-    
+        return self._chat_generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
+
+    def _chat_generate(self, model: str, credentials: dict,
+                       prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None,
+                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+        """
+        Invoke llm chat model
+
+        :param model: model name
+        :param credentials: credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+
+        # transform model parameters from completion api of anthropic to chat api
+        if 'max_tokens_to_sample' in model_parameters:
+            model_parameters['max_tokens'] = model_parameters.pop('max_tokens_to_sample')
+
+        # init model client
+        client = Anthropic(**credentials_kwargs)
+
+        extra_model_kwargs = {}
+        if stop:
+            extra_model_kwargs['stop_sequences'] = stop
+
+        if user:
+            extra_model_kwargs['metadata'] = completion_create_params.Metadata(user_id=user)
+
+        system, prompt_message_dicts = self._convert_prompt_messages(prompt_messages)
+
+        if system:
+            extra_model_kwargs['system'] = system
+
+        # chat model
+        response = client.messages.create(
+            model=model,
+            messages=prompt_message_dicts,
+            stream=stream,
+            **model_parameters,
+            **extra_model_kwargs
+        )
+
+        if stream:
+            return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages)
+
+        return self._handle_chat_generate_response(model, credentials, response, prompt_messages)
+
     def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                           model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
-                           stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
-                           callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
+                                 model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
+                                 stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None,
+                                 callbacks: list[Callback] = None) -> Union[LLMResult, Generator]:
         """
         Code block mode wrapper for invoking large language model
         """
         if 'response_format' in model_parameters and model_parameters['response_format']:
             stop = stop or []
-            self._transform_json_prompts(
-                model, credentials, prompt_messages, model_parameters, tools, stop, stream, user, model_parameters['response_format']
+            # chat model
+            self._transform_chat_json_prompts(
+                model=model,
+                credentials=credentials,
+                prompt_messages=prompt_messages,
+                model_parameters=model_parameters,
+                tools=tools,
+                stop=stop,
+                stream=stream,
+                user=user,
+                response_format=model_parameters['response_format']
             )
             model_parameters.pop('response_format')
 
         return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    def _transform_json_prompts(self, model: str, credentials: dict, 
-                               prompt_messages: list[PromptMessage], model_parameters: dict, 
-                               tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
-                               stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
-                            -> None:
+    def _transform_chat_json_prompts(self, model: str, credentials: dict,
+                                     prompt_messages: list[PromptMessage], model_parameters: dict,
+                                     tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
+                                     stream: bool = True, user: str | None = None, response_format: str = 'JSON') \
+            -> None:
         """
         Transform json prompts
         """
         if "```\n" not in stop:
             stop.append("```\n")
+        if "\n```" not in stop:
+            stop.append("\n```")
 
         # check if there is a system message
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
             # override the system message
             prompt_messages[0] = SystemPromptMessage(
                 content=ANTHROPIC_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", prompt_messages[0].content)
-                    .replace("{{block}}", response_format)
+                .replace("{{instructions}}", prompt_messages[0].content)
+                .replace("{{block}}", response_format)
             )
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
         else:
             # insert the system message
             prompt_messages.insert(0, SystemPromptMessage(
                 content=ANTHROPIC_BLOCK_MODE_PROMPT
-                    .replace("{{instructions}}", f"Please output a valid {response_format} object.")
-                    .replace("{{block}}", response_format)
+                .replace("{{instructions}}", f"Please output a valid {response_format} object.")
+                .replace("{{block}}", response_format)
             ))
-
-        prompt_messages.append(AssistantPromptMessage(
-            content=f"```{response_format}\n"
-        ))
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
 
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
@@ -129,7 +204,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         try:
-            self._generate(
+            self._chat_generate(
                 model=model,
                 credentials=credentials,
                 prompt_messages=[
@@ -137,58 +212,17 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 ],
                 model_parameters={
                     "temperature": 0,
-                    "max_tokens_to_sample": 20,
+                    "max_tokens": 20,
                 },
                 stream=False
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  stop: Optional[list[str]] = None, stream: bool = True,
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+    def _handle_chat_generate_response(self, model: str, credentials: dict, response: Message,
+                                       prompt_messages: list[PromptMessage]) -> LLMResult:
         """
-        Invoke large language model
-
-        :param model: model name
-        :param credentials: credentials kwargs
-        :param prompt_messages: prompt messages
-        :param model_parameters: model parameters
-        :param stop: stop words
-        :param stream: is stream response
-        :param user: unique user id
-        :return: full response or stream response chunk generator result
-        """
-        # transform credentials to kwargs for model instance
-        credentials_kwargs = self._to_credential_kwargs(credentials)
-
-        client = Anthropic(**credentials_kwargs)
-
-        extra_model_kwargs = {}
-        if stop:
-            extra_model_kwargs['stop_sequences'] = stop
-
-        if user:
-            extra_model_kwargs['metadata'] = completion_create_params.Metadata(user_id=user)
-
-        response = client.completions.create(
-            model=model,
-            prompt=self._convert_messages_to_prompt_anthropic(prompt_messages),
-            stream=stream,
-            **model_parameters,
-            **extra_model_kwargs
-        )
-
-        if stream:
-            return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
-
-        return self._handle_generate_response(model, credentials, response, prompt_messages)
-
-    def _handle_generate_response(self, model: str, credentials: dict, response: Completion,
-                                  prompt_messages: list[PromptMessage]) -> LLMResult:
-        """
-        Handle llm response
+        Handle llm chat response
 
         :param model: model name
         :param credentials: credentials
@@ -198,75 +232,89 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         """
         # transform assistant message to prompt message
         assistant_prompt_message = AssistantPromptMessage(
-            content=response.completion
+            content=response.content[0].text
         )
 
         # calculate num tokens
-        prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-        completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+        if response.usage:
+            # transform usage
+            prompt_tokens = response.usage.input_tokens
+            completion_tokens = response.usage.output_tokens
+        else:
+            # calculate num tokens
+            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
         # transform usage
         usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
         # transform response
-        result = LLMResult(
+        response = LLMResult(
             model=response.model,
             prompt_messages=prompt_messages,
             message=assistant_prompt_message,
-            usage=usage,
+            usage=usage
         )
 
-        return result
+        return response
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: Stream[Completion],
-                                         prompt_messages: list[PromptMessage]) -> Generator:
+    def _handle_chat_generate_stream_response(self, model: str, credentials: dict,
+                                              response: Stream[MessageStreamEvent],
+                                              prompt_messages: list[PromptMessage]) -> Generator:
         """
-        Handle llm stream response
+        Handle llm chat stream response
 
         :param model: model name
-        :param credentials: credentials
         :param response: response
         :param prompt_messages: prompt messages
-        :return: llm response chunk generator result
+        :return: llm response chunk generator
         """
-        index = -1
+        full_assistant_content = ''
+        return_model = None
+        input_tokens = 0
+        output_tokens = 0
+        finish_reason = None
+        index = 0
         for chunk in response:
-            content = chunk.completion
-            if chunk.stop_reason is None and (content is None or content == ''):
-                continue
-
-            # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=content if content else '',
-            )
-
-            index += 1
-
-            if chunk.stop_reason is not None:
-                # calculate num tokens
-                prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-                completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
-
+            if isinstance(chunk, MessageStartEvent):
+                return_model = chunk.message.model
+                input_tokens = chunk.message.usage.input_tokens
+            elif isinstance(chunk, MessageDeltaEvent):
+                output_tokens = chunk.usage.output_tokens
+                finish_reason = chunk.delta.stop_reason
+            elif isinstance(chunk, MessageStopEvent):
                 # transform usage
-                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+                usage = self._calc_response_usage(model, credentials, input_tokens, output_tokens)
 
                 yield LLMResultChunk(
-                    model=chunk.model,
+                    model=return_model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message,
-                        finish_reason=chunk.stop_reason,
+                        index=index + 1,
+                        message=AssistantPromptMessage(
+                            content=''
+                        ),
+                        finish_reason=finish_reason,
                         usage=usage
                     )
                 )
-            else:
+            elif isinstance(chunk, ContentBlockDeltaEvent):
+                chunk_text = chunk.delta.text if chunk.delta.text else ''
+                full_assistant_content += chunk_text
+
+                # transform assistant message to prompt message
+                assistant_prompt_message = AssistantPromptMessage(
+                    content=chunk_text
+                )
+
+                index = chunk.index
+
                 yield LLMResultChunk(
-                    model=chunk.model,
+                    model=return_model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message
+                        index=chunk.index,
+                        message=assistant_prompt_message,
                     )
                 )
 
@@ -289,6 +337,80 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         return credentials_kwargs
 
+    def _convert_prompt_messages(self, prompt_messages: list[PromptMessage]) -> tuple[str, list[dict]]:
+        """
+        Convert prompt messages to dict list and system
+        """
+        system = ""
+        prompt_message_dicts = []
+
+        for message in prompt_messages:
+            if isinstance(message, SystemPromptMessage):
+                system += message.content + ("\n" if not system else "")
+            else:
+                prompt_message_dicts.append(self._convert_prompt_message_to_dict(message))
+
+        return system, prompt_message_dicts
+
+    def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
+        """
+        Convert PromptMessage to dict
+        """
+        if isinstance(message, UserPromptMessage):
+            message = cast(UserPromptMessage, message)
+            if isinstance(message.content, str):
+                message_dict = {"role": "user", "content": message.content}
+            else:
+                sub_messages = []
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_content = cast(TextPromptMessageContent, message_content)
+                        sub_message_dict = {
+                            "type": "text",
+                            "text": message_content.data
+                        }
+                        sub_messages.append(sub_message_dict)
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        if not message_content.data.startswith("data:"):
+                            # fetch image data from url
+                            try:
+                                image_content = requests.get(message_content.data).content
+                                mime_type, _ = mimetypes.guess_type(message_content.data)
+                                base64_data = base64.b64encode(image_content).decode('utf-8')
+                            except Exception as ex:
+                                raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
+                        else:
+                            data_split = message_content.data.split(";base64,")
+                            mime_type = data_split[0].replace("data:", "")
+                            base64_data = data_split[1]
+
+                        if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
+                            raise ValueError(f"Unsupported image type {mime_type}, "
+                                             f"only support image/jpeg, image/png, image/gif, and image/webp")
+
+                        sub_message_dict = {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": mime_type,
+                                "data": base64_data
+                            }
+                        }
+                        sub_messages.append(sub_message_dict)
+
+                message_dict = {"role": "user", "content": sub_messages}
+        elif isinstance(message, AssistantPromptMessage):
+            message = cast(AssistantPromptMessage, message)
+            message_dict = {"role": "assistant", "content": message.content}
+        elif isinstance(message, SystemPromptMessage):
+            message = cast(SystemPromptMessage, message)
+            message_dict = {"role": "system", "content": message.content}
+        else:
+            raise ValueError(f"Got unknown type {message}")
+
+        return message_dict
+
     def _convert_one_message_to_text(self, message: PromptMessage) -> str:
         """
         Convert a single message to a string.
@@ -302,8 +424,25 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
+            if not isinstance(message.content, list):
+                message_text = f"{ai_prompt} {content}"
+            else:
+                message_text = ""
+                for sub_message in message.content:
+                    if sub_message.type == PromptMessageContentType.TEXT:
+                        message_text += f"{human_prompt} {sub_message.data}"
+                    elif sub_message.type == PromptMessageContentType.IMAGE:
+                        message_text += f"{human_prompt} [IMAGE]"
         elif isinstance(message, AssistantPromptMessage):
-            message_text = f"{ai_prompt} {content}"
+            if not isinstance(message.content, list):
+                message_text = f"{ai_prompt} {content}"
+            else:
+                message_text = ""
+                for sub_message in message.content:
+                    if sub_message.type == PromptMessageContentType.TEXT:
+                        message_text += f"{ai_prompt} {sub_message.data}"
+                    elif sub_message.type == PromptMessageContentType.IMAGE:
+                        message_text += f"{ai_prompt} [IMAGE]"
         elif isinstance(message, SystemPromptMessage):
             message_text = content
         else:

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

@@ -524,5 +524,172 @@ EMBEDDING_BASE_MODELS = [
                 currency='USD',
             )
         )
+    ),
+    AzureBaseModel(
+        base_model_name='text-embedding-3-small',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TEXT_EMBEDDING,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: 8191,
+                ModelPropertyKey.MAX_CHUNKS: 32,
+            },
+            pricing=PriceConfig(
+                input=0.00002,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
+    AzureBaseModel(
+        base_model_name='text-embedding-3-large',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TEXT_EMBEDDING,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: 8191,
+                ModelPropertyKey.MAX_CHUNKS: 32,
+            },
+            pricing=PriceConfig(
+                input=0.00013,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    )
+]
+SPEECH2TEXT_BASE_MODELS = [
+    AzureBaseModel(
+        base_model_name='whisper-1',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.SPEECH2TEXT,
+            model_properties={
+                ModelPropertyKey.FILE_UPLOAD_LIMIT: 25,
+                ModelPropertyKey.SUPPORTED_FILE_EXTENSIONS: 'flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm'
+            }
+        )
+    )
+]
+TTS_BASE_MODELS = [
+    AzureBaseModel(
+        base_model_name='tts-1',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TTS,
+            model_properties={
+                ModelPropertyKey.DEFAULT_VOICE: 'alloy',
+                ModelPropertyKey.VOICES: [
+                    {
+                        'mode': 'alloy',
+                        'name': 'Alloy',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'echo',
+                        'name': 'Echo',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'fable',
+                        'name': 'Fable',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'onyx',
+                        'name': 'Onyx',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'nova',
+                        'name': 'Nova',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'shimmer',
+                        'name': 'Shimmer',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                ],
+                ModelPropertyKey.WORD_LIMIT: 120,
+                ModelPropertyKey.AUDOI_TYPE: 'mp3',
+                ModelPropertyKey.MAX_WORKERS: 5
+            },
+            pricing=PriceConfig(
+                input=0.015,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
+    AzureBaseModel(
+        base_model_name='tts-1-hd',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TTS,
+            model_properties={
+                ModelPropertyKey.DEFAULT_VOICE: 'alloy',
+                ModelPropertyKey.VOICES: [
+                    {
+                        'mode': 'alloy',
+                        'name': 'Alloy',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'echo',
+                        'name': 'Echo',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'fable',
+                        'name': 'Fable',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'onyx',
+                        'name': 'Onyx',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'nova',
+                        'name': 'Nova',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'shimmer',
+                        'name': 'Shimmer',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                ],
+                ModelPropertyKey.WORD_LIMIT: 120,
+                ModelPropertyKey.AUDOI_TYPE: 'mp3',
+                ModelPropertyKey.MAX_WORKERS: 5
+            },
+            pricing=PriceConfig(
+                input=0.03,
+                unit=0.001,
+                currency='USD',
+            )
+        )
     )
 ]

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

@@ -15,6 +15,8 @@ help:
 supported_model_types:
   - llm
   - text-embedding
+  - speech2text
+  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -99,6 +101,36 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: text-embedding
+        - label:
+            en_US: text-embedding-3-small
+          value: text-embedding-3-small
+          show_on:
+            - variable: __model_type
+              value: text-embedding
+        - label:
+            en_US: text-embedding-3-large
+          value: text-embedding-3-large
+          show_on:
+            - variable: __model_type
+              value: text-embedding
+        - label:
+            en_US: whisper-1
+          value: whisper-1
+          show_on:
+            - variable: __model_type
+              value: speech2text
+        - label:
+            en_US: tts-1
+          value: tts-1
+          show_on:
+            - variable: __model_type
+              value: tts
+        - label:
+            en_US: tts-1-hd
+          value: tts-1-hd
+          show_on:
+            - variable: __model_type
+              value: tts
       placeholder:
         zh_Hans: 在此输入您的模型版本
         en_US: Enter your model version

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

@@ -0,0 +1,82 @@
+import copy
+from typing import IO, Optional
+
+from openai import AzureOpenAI
+
+from core.model_runtime.entities.model_entities import AIModelEntity
+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.azure_openai._common import _CommonAzureOpenAI
+from core.model_runtime.model_providers.azure_openai._constant import SPEECH2TEXT_BASE_MODELS, AzureBaseModel
+
+
+class AzureOpenAISpeech2TextModel(_CommonAzureOpenAI, Speech2TextModel):
+    """
+    Model class for OpenAI 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
+        """
+        return self._speech2text_invoke(model, credentials, file)
+
+    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._speech2text_invoke(model, credentials, audio_file)
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _speech2text_invoke(self, model: str, credentials: dict, file: IO[bytes]) -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :return: text for given audio file
+        """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+
+        # init model client
+        client = AzureOpenAI(**credentials_kwargs)
+
+        response = client.audio.transcriptions.create(model=model, file=file)
+
+        return response.text
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
+        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        return ai_model_entity.entity
+
+
+    @staticmethod
+    def _get_ai_model_entity(base_model_name: str, model: str) -> AzureBaseModel:
+        for ai_model_entity in SPEECH2TEXT_BASE_MODELS:
+            if ai_model_entity.base_model_name == base_model_name:
+                ai_model_entity_copy = copy.deepcopy(ai_model_entity)
+                ai_model_entity_copy.entity.model = model
+                ai_model_entity_copy.entity.label.en_US = model
+                ai_model_entity_copy.entity.label.zh_Hans = model
+                return ai_model_entity_copy
+
+        return None

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

@@ -0,0 +1,174 @@
+import concurrent.futures
+import copy
+from functools import reduce
+from io import BytesIO
+from typing import Optional
+
+from flask import Response, stream_with_context
+from openai import AzureOpenAI
+from pydub import AudioSegment
+
+from core.model_runtime.entities.model_entities import AIModelEntity
+from core.model_runtime.errors.invoke import InvokeBadRequestError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.tts_model import TTSModel
+from core.model_runtime.model_providers.azure_openai._common import _CommonAzureOpenAI
+from core.model_runtime.model_providers.azure_openai._constant import TTS_BASE_MODELS, AzureBaseModel
+from extensions.ext_storage import storage
+
+
+class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
+    """
+    Model class for OpenAI Speech to text model.
+    """
+
+    def _invoke(self, model: str, tenant_id: str, credentials: dict,
+                content_text: str, voice: str, streaming: bool, user: Optional[str] = None) -> any:
+        """
+        _invoke text2speech model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :param streaming: output is streaming
+        :param user: unique user id
+        :return: text translated to audio file
+        """
+        audio_type = self._get_model_audio_type(model, credentials)
+        if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
+            voice = self._get_model_default_voice(model, credentials)
+        if streaming:
+            return Response(stream_with_context(self._tts_invoke_streaming(model=model,
+                                                                           credentials=credentials,
+                                                                           content_text=content_text,
+                                                                           tenant_id=tenant_id,
+                                                                           voice=voice)),
+                            status=200, mimetype=f'audio/{audio_type}')
+        else:
+            return self._tts_invoke(model=model, credentials=credentials, content_text=content_text, voice=voice)
+
+    def validate_credentials(self, model: str, credentials: dict, user: Optional[str] = None) -> None:
+        """
+        validate credentials text2speech model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param user: unique user id
+        :return: text translated to audio file
+        """
+        try:
+            self._tts_invoke(
+                model=model,
+                credentials=credentials,
+                content_text='Hello Dify!',
+                voice=self._get_model_default_voice(model, credentials),
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _tts_invoke(self, model: str, credentials: dict, content_text: str, voice: str) -> Response:
+        """
+        _tts_invoke text2speech model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :return: text translated to audio file
+        """
+        audio_type = self._get_model_audio_type(model, credentials)
+        word_limit = self._get_model_word_limit(model, credentials)
+        max_workers = self._get_model_workers_limit(model, credentials)
+        try:
+            sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
+            audio_bytes_list = list()
+
+            # Create a thread pool and map the function to the list of sentences
+            with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
+                futures = [executor.submit(self._process_sentence, sentence=sentence, model=model, voice=voice,
+                                           credentials=credentials) for sentence in sentences]
+                for future in futures:
+                    try:
+                        if future.result():
+                            audio_bytes_list.append(future.result())
+                    except Exception as ex:
+                        raise InvokeBadRequestError(str(ex))
+
+            if len(audio_bytes_list) > 0:
+                audio_segments = [AudioSegment.from_file(BytesIO(audio_bytes), format=audio_type) for audio_bytes in
+                                  audio_bytes_list if audio_bytes]
+                combined_segment = reduce(lambda x, y: x + y, audio_segments)
+                buffer: BytesIO = BytesIO()
+                combined_segment.export(buffer, format=audio_type)
+                buffer.seek(0)
+                return Response(buffer.read(), status=200, mimetype=f"audio/{audio_type}")
+        except Exception as ex:
+            raise InvokeBadRequestError(str(ex))
+
+    # Todo: To improve the streaming function
+    def _tts_invoke_streaming(self, model: str, tenant_id: str, credentials: dict, content_text: str,
+                              voice: str) -> any:
+        """
+        _tts_invoke_streaming text2speech model
+
+        :param model: model name
+        :param tenant_id: user tenant id
+        :param credentials: model credentials
+        :param content_text: text content to be translated
+        :param voice: model timbre
+        :return: text translated to audio file
+        """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+        if not voice or voice not in self.get_tts_model_voices(model=model, credentials=credentials):
+            voice = self._get_model_default_voice(model, credentials)
+        word_limit = self._get_model_word_limit(model, credentials)
+        audio_type = self._get_model_audio_type(model, credentials)
+        tts_file_id = self._get_file_name(content_text)
+        file_path = f'generate_files/audio/{tenant_id}/{tts_file_id}.{audio_type}'
+        try:
+            client = AzureOpenAI(**credentials_kwargs)
+            sentences = list(self._split_text_into_sentences(text=content_text, limit=word_limit))
+            for sentence in sentences:
+                response = client.audio.speech.create(model=model, voice=voice, input=sentence.strip())
+                # response.stream_to_file(file_path)
+                storage.save(file_path, response.read())
+        except Exception as ex:
+            raise InvokeBadRequestError(str(ex))
+
+    def _process_sentence(self, sentence: str, model: str,
+                          voice, credentials: dict):
+        """
+        _tts_invoke openai text2speech model api
+
+        :param model: model name
+        :param credentials: model credentials
+        :param voice: model timbre
+        :param sentence: text content to be translated
+        :return: text translated to audio file
+        """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+        client = AzureOpenAI(**credentials_kwargs)
+        response = client.audio.speech.create(model=model, voice=voice, input=sentence.strip())
+        if isinstance(response.read(), bytes):
+            return response.read()
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
+        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        return ai_model_entity.entity
+
+
+    @staticmethod
+    def _get_ai_model_entity(base_model_name: str, model: str) -> AzureBaseModel:
+        for ai_model_entity in TTS_BASE_MODELS:
+            if ai_model_entity.base_model_name == base_model_name:
+                ai_model_entity_copy = copy.deepcopy(ai_model_entity)
+                ai_model_entity_copy.entity.model = model
+                ai_model_entity_copy.entity.label.en_US = model
+                ai_model_entity_copy.entity.label.zh_Hans = model
+                return ai_model_entity_copy
+
+        return None

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

@@ -108,7 +108,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             try:

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

@@ -472,7 +472,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         else:
             raise ValueError(f"Got unknown type {message}")
 
-        if message.name is not None:
+        if message.name:
             message_dict["user_name"] = message.name
 
         return message_dict

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

@@ -0,0 +1,11 @@
+<svg width="112" height="24" viewBox="0 0 112 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M57.4336 17.092C56.4746 16.5453 55.7216 15.7924 55.1749 14.8244C54.6283 13.8564 54.3594 12.763 54.3594 11.544C54.3594 10.3251 54.6283 9.2137 55.1749 8.24571C55.7216 7.27772 56.4746 6.52485 57.4336 5.98708C58.3926 5.4493 59.4861 5.18042 60.6961 5.18042C61.6999 5.18042 62.623 5.3776 63.4476 5.77197C64.2722 6.16633 64.9445 6.73995 65.4554 7.49284L64.568 8.13816C64.1199 7.51076 63.5642 7.04469 62.9009 6.731C62.2377 6.41729 61.5027 6.26492 60.705 6.26492C59.7281 6.26492 58.8498 6.48899 58.0789 6.92818C57.2992 7.36736 56.6986 7.98579 56.2505 8.79244C55.8113 9.59014 55.5872 10.5133 55.5872 11.553C55.5872 12.5926 55.8113 13.5159 56.2505 14.3136C56.6896 15.1112 57.2992 15.7297 58.0789 16.1778C58.8587 16.617 59.7281 16.8411 60.705 16.8411C61.5027 16.8411 62.2377 16.6888 62.9009 16.375C63.5642 16.0613 64.1199 15.5953 64.568 14.9678L65.4554 15.6132C64.9445 16.366 64.2722 16.9396 63.4476 17.334C62.623 17.7284 61.7089 17.9255 60.6961 17.9255C59.4771 17.9255 58.3926 17.6568 57.4336 17.11V17.092Z" fill="#F55036"/>
+<path d="M67.2754 0H68.4763V17.8181H67.2754V0Z" fill="#F55036"/>
+<path d="M73.6754 17.092C72.7254 16.5454 71.9725 15.7924 71.4347 14.8244C70.888 13.8564 70.6191 12.763 70.6191 11.544C70.6191 10.3251 70.888 9.23163 71.4347 8.26364C71.9814 7.29566 72.7254 6.54277 73.6754 5.99604C74.6255 5.4493 75.6921 5.18042 76.8841 5.18042C78.0762 5.18042 79.1338 5.4493 80.0928 5.99604C81.0429 6.54277 81.7957 7.29566 82.3335 8.26364C82.8803 9.23163 83.1492 10.3251 83.1492 11.544C83.1492 12.763 82.8803 13.8564 82.3335 14.8244C81.7868 15.7924 81.0429 16.5454 80.0928 17.092C79.1427 17.6387 78.0673 17.9076 76.8841 17.9076C75.7011 17.9076 74.6344 17.6387 73.6754 17.092ZM79.4655 16.1599C80.2273 15.7118 80.8277 15.0843 81.2669 14.2867C81.7062 13.489 81.9302 12.5747 81.9302 11.553C81.9302 10.5312 81.7062 9.61703 81.2669 8.81933C80.8277 8.02164 80.2273 7.39425 79.4655 6.9461C78.7036 6.49796 77.8431 6.27389 76.8841 6.27389C75.9251 6.27389 75.0646 6.49796 74.3028 6.9461C73.5409 7.39425 72.9405 8.02164 72.5013 8.81933C72.0621 9.61703 71.838 10.5312 71.838 11.553C71.838 12.5747 72.0621 13.489 72.5013 14.2867C72.9405 15.0843 73.5409 15.7118 74.3028 16.1599C75.0646 16.608 75.9251 16.8322 76.8841 16.8322C77.8431 16.8322 78.7036 16.608 79.4655 16.1599Z" fill="#F55036"/>
+<path d="M96.2799 5.27905V17.8091H95.1237V15.1203C94.7114 15.9986 94.0929 16.6887 93.2774 17.1728C92.4618 17.6567 91.5027 17.9077 90.4003 17.9077C88.769 17.9077 87.4873 17.4506 86.5553 16.5364C85.6231 15.6222 85.166 14.3136 85.166 12.6017V5.27905H86.367V12.5031C86.367 13.9102 86.7255 14.9858 87.4515 15.7207C88.1775 16.4557 89.1903 16.8232 90.4989 16.8232C91.9061 16.8232 93.0264 16.384 93.851 15.5057C94.6756 14.6272 95.0878 13.4442 95.0878 11.9563V5.27905H96.2889H96.2799Z" fill="#F55036"/>
+<path d="M110.952 0V17.8181H109.777V14.8604C109.284 15.8374 108.585 16.5902 107.689 17.119C106.793 17.6479 105.78 17.9077 104.642 17.9077C103.503 17.9077 102.419 17.6389 101.469 17.0922C100.528 16.5454 99.7838 15.7925 99.246 14.8336C98.7083 13.8745 98.4395 12.781 98.4395 11.5441C98.4395 10.3073 98.7083 9.2138 99.246 8.24582C99.7838 7.27783 100.519 6.52496 101.469 5.98718C102.41 5.44941 103.468 5.18053 104.642 5.18053C105.816 5.18053 106.766 5.44044 107.653 5.96925C108.541 6.49807 109.24 7.23301 109.75 8.17411V0H110.952ZM107.295 16.16C108.057 15.7119 108.657 15.0844 109.096 14.2868C109.535 13.4891 109.759 12.5749 109.759 11.5531C109.759 10.5313 109.535 9.61713 109.096 8.81944C108.657 8.02174 108.057 7.39434 107.295 6.9462C106.533 6.49807 105.672 6.27399 104.713 6.27399C103.754 6.27399 102.894 6.49807 102.132 6.9462C101.37 7.39434 100.77 8.02174 100.331 8.81944C99.8914 9.61713 99.6673 10.5313 99.6673 11.5531C99.6673 12.5749 99.8914 13.4891 100.331 14.2868C100.77 15.0844 101.37 15.7119 102.132 16.16C102.894 16.6081 103.754 16.8322 104.713 16.8322C105.672 16.8322 106.533 16.6081 107.295 16.16Z" fill="#F55036"/>
+<path d="M30.6085 5.27024C27.077 5.27024 24.209 8.13835 24.209 11.6697C24.209 15.201 27.077 18.0692 30.6085 18.0692C34.1399 18.0692 37.0079 15.201 37.0079 11.6697C37.0079 8.13835 34.1399 5.27921 30.6085 5.27024ZM30.6085 15.6672C28.4036 15.6672 26.611 13.8746 26.611 11.6697C26.611 9.46486 28.4036 7.67228 30.6085 7.67228C32.8133 7.67228 34.6059 9.46486 34.6059 11.6697C34.6059 13.8746 32.8133 15.6672 30.6085 15.6672Z" fill="black"/>
+<path d="M6.45358 5.23422C2.92222 5.19837 0.036187 8.0396 0.000335591 11.571C-0.0355158 15.1023 2.80571 17.9974 6.33706 18.0242C6.37292 18.0242 6.41773 18.0242 6.45358 18.0242H8.55986V15.6311H6.45358C4.24873 15.658 2.43823 13.8923 2.41134 11.6785C2.38445 9.47365 4.15014 7.66315 6.36395 7.63626C6.39084 7.63626 6.4267 7.63626 6.45358 7.63626C8.65844 7.63626 10.46 9.42884 10.46 11.6337V17.5222C10.46 19.7092 8.67637 21.4929 6.48943 21.5197C5.44078 21.5197 4.44591 21.0895 3.71095 20.3455L2.01698 22.0395C3.1911 23.2227 4.7865 23.8949 6.45358 23.9128H6.54321C10.0298 23.859 12.8351 21.0357 12.853 17.5491V11.4724C12.7635 8.00374 9.93116 5.23422 6.46254 5.23422H6.45358Z" fill="black"/>
+<path d="M51.2406 11.5082C51.151 8.03961 48.3187 5.27009 44.8501 5.27009C41.3187 5.23423 38.4237 8.07545 38.3968 11.6068C38.361 15.1382 41.2022 18.0331 44.7335 18.0601C44.7694 18.0601 44.8143 18.0601 44.8501 18.0601H46.9563V15.667H44.8501C42.6452 15.6939 40.8347 13.9282 40.8078 11.7144C40.7809 9.5095 42.5467 7.69902 44.7604 7.67213C44.7874 7.67213 44.8232 7.67213 44.8501 7.67213C47.055 7.67213 48.8565 9.46469 48.8565 11.6696V23.626L51.2406 23.6528V11.5082Z" fill="black"/>
+<path d="M14.6808 18.0602H17.0649V11.6607C17.0649 9.45589 18.8575 7.66332 21.0623 7.66332C21.7883 7.66332 22.4695 7.8605 23.0611 8.2011L24.2621 6.12172C23.3209 5.57498 22.2276 5.27024 21.0713 5.27024C17.5399 5.27024 14.6719 8.13835 14.6719 11.6697V18.0692L14.6808 18.0602Z" fill="black"/>
+</svg>

api/core/model_runtime/model_providers/groq/_assets/icon_s_en.svg

@@ -0,0 +1,4 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="24" height="24" rx="12" fill="#F55036"/>
+<path d="M12.146 6.00022C9.87734 5.97718 8.02325 7.80249 8.00022 10.0712C7.97718 12.3398 9.80249 14.1997 12.0712 14.217C12.0942 14.217 12.123 14.217 12.146 14.217H13.4992V12.6796H12.146C10.7295 12.6968 9.56641 11.5625 9.54913 10.1403C9.53186 8.72377 10.6662 7.56065 12.0884 7.54337C12.1057 7.54337 12.1287 7.54337 12.146 7.54337C13.5625 7.54337 14.7199 8.69498 14.7199 10.1115V13.8945C14.7199 15.2995 13.574 16.4453 12.169 16.4626C11.4953 16.4626 10.8562 16.1862 10.384 15.7083L9.29578 16.7965C10.0501 17.5566 11.075 17.9885 12.146 18H12.2036C14.4435 17.9654 16.2457 16.1516 16.2572 13.9117V10.0078C16.1997 7.77945 14.3801 6.00022 12.1518 6.00022H12.146Z" fill="white"/>
+</svg>

api/core/model_runtime/model_providers/groq/groq.py

@@ -0,0 +1,29 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+class GroqProvider(ModelProvider):
+
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+
+            model_instance.validate_credentials(
+                model='llama2-70b-4096',
+                credentials=credentials
+            )
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            raise ex

api/core/model_runtime/model_providers/groq/groq.yaml

@@ -0,0 +1,32 @@
+provider: groq
+label:
+  zh_Hans: GroqCloud
+  en_US: GroqCloud
+description:
+  en_US: GroqCloud provides access to the Groq Cloud API, which hosts models like LLama2 and Mixtral.
+  zh_Hans: GroqCloud 提供对 Groq Cloud API 的访问，其中托管了 LLama2 和 Mixtral 等模型。
+icon_small:
+  en_US: icon_s_en.svg
+icon_large:
+  en_US: icon_l_en.svg
+background: "#F5F5F4"
+help:
+  title:
+    en_US: Get your API Key from GroqCloud
+    zh_Hans: 从 GroqCloud 获取 API Key
+  url:
+    en_US: https://console.groq.com/
+supported_model_types:
+  - llm
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/groq/llm/llama2-70b-4096.yaml

@@ -0,0 +1,25 @@
+model: llama2-70b-4096
+label:
+  zh_Hans: Llama-2-70B-4096
+  en_US: Llama-2-70B-4096
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 4096
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 4096
+pricing:
+  input: '0.7'
+  output: '0.8'
+  unit: '0.000001'
+  currency: USD

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

@@ -0,0 +1,26 @@
+from collections.abc import Generator
+from typing import Optional, Union
+
+from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
+from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
+
+
+class GroqLargeLanguageModel(OAIAPICompatLargeLanguageModel):
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                stream: bool = True, user: Optional[str] = None) \
+            -> Union[LLMResult, Generator]:
+        self._add_custom_parameters(credentials)
+        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials)
+        super().validate_credentials(model, credentials)
+
+    @staticmethod
+    def _add_custom_parameters(credentials: dict) -> None:
+        credentials['mode'] = 'chat'
+        credentials['endpoint_url'] = 'https://api.groq.com/openai/v1'
+    

api/core/model_runtime/model_providers/groq/llm/mixtral-8x7b-instruct-v0.1.yaml

@@ -0,0 +1,25 @@
+model: mixtral-8x7b-32768
+label:
+  zh_Hans: Mixtral-8x7b-Instruct-v0.1
+  en_US: Mixtral-8x7b-Instruct-v0.1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 20480
+pricing:
+  input: '0.27'
+  output: '0.27'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/jina/jina.yaml

@@ -2,7 +2,7 @@ provider: jina
 label:
   en_US: Jina
 description:
-  en_US: Embedding Model Supported
+  en_US: Embedding and Rerank Model Supported
 icon_small:
   en_US: icon_s_en.svg
 icon_large:
@@ -13,9 +13,10 @@ help:
     en_US: Get your API key from Jina AI
     zh_Hans: 从 Jina 获取 API Key
   url:
-    en_US: https://jina.ai/embeddings/
+    en_US: https://jina.ai/
 supported_model_types:
   - text-embedding
+  - rerank
 configurate_methods:
   - predefined-model
 provider_credential_schema:

api/core/model_runtime/model_providers/jina/rerank/jina-reranker-v1-base-en.yaml

@@ -0,0 +1,4 @@
+model: jina-reranker-v1-base-en
+model_type: rerank
+model_properties:
+  context_size: 8192

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

@@ -0,0 +1,105 @@
+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 JinaRerankModel(RerankModel):
+    """
+    Model class for Jina rerank model.
+    """
+
+    def _invoke(self, model: str, credentials: dict,
+                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
+                user: Optional[str] = None) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        try:
+            response = httpx.post(
+                "https://api.jina.ai/v1/rerank",
+                json={
+                    "model": model,
+                    "query": query,
+                    "documents": docs,
+                    "top_n": top_n
+                },
+                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:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [httpx.ConnectError],
+            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
+            InvokeRateLimitError: [], 
+            InvokeAuthorizationError: [httpx.HTTPStatusError],  
+            InvokeBadRequestError: [httpx.RequestError]
+        }

api/core/model_runtime/model_providers/jina/text_embedding/jina_tokenizer.py

@@ -1,20 +1,32 @@
 from os.path import abspath, dirname, join
+from threading import Lock
 
 from transformers import AutoTokenizer
 
 
 class JinaTokenizer:
-    @staticmethod
-    def _get_num_tokens_by_jina_base(text: str) -> int:
+    _tokenizer = None
+    _lock = Lock()
+
+    @classmethod
+    def _get_tokenizer(cls):
+        if cls._tokenizer is None:
+            with cls._lock:
+                if cls._tokenizer is None:
+                    base_path = abspath(__file__)
+                    gpt2_tokenizer_path = join(dirname(base_path), 'tokenizer')
+                    cls._tokenizer = AutoTokenizer.from_pretrained(gpt2_tokenizer_path)
+        return cls._tokenizer
+
+    @classmethod
+    def _get_num_tokens_by_jina_base(cls, text: str) -> int:
         """
             use jina tokenizer to get num tokens
         """
-        base_path = abspath(__file__)
-        gpt2_tokenizer_path = join(dirname(base_path), 'tokenizer')
-        tokenizer = AutoTokenizer.from_pretrained(gpt2_tokenizer_path)
+        tokenizer = cls._get_tokenizer()
         tokens = tokenizer.encode(text)
         return len(tokens)
     
-    @staticmethod
-    def get_num_tokens(text: str) -> int:
-        return JinaTokenizer._get_num_tokens_by_jina_base(text)
+    @classmethod
+    def get_num_tokens(cls, text: str) -> int:
+        return cls._get_num_tokens_by_jina_base(text)

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

@@ -57,7 +57,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             try:

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

@@ -1,6 +1,6 @@
 from collections.abc import Generator
-from os.path import join
 from typing import cast
+from urllib.parse import urljoin
 
 from httpx import Timeout
 from openai import (
@@ -313,10 +313,13 @@ class LocalAILarguageModel(LargeLanguageModel):
         :param credentials: credentials dict
         :return: client kwargs
         """
+        if not credentials['server_url'].endswith('/'):
+            credentials['server_url'] += '/'
+            
         client_kwargs = {
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "api_key": "1",
-            "base_url": join(credentials['server_url'], 'v1'),
+            "base_url": urljoin(credentials['server_url'], 'v1'),
         }
 
         return client_kwargs

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

@@ -59,7 +59,7 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
         try:
             response = post(join(url, 'embeddings'), headers=headers, data=dumps(data), timeout=10)
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             try:

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

@@ -65,7 +65,7 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             raise InvokeServerUnavailableError(response.text)

api/core/model_runtime/model_providers/mistralai/llm/mistral-large-latest.yaml

@@ -24,7 +24,7 @@ parameter_rules:
     min: 1
     max: 8000
   - name: safe_prompt
-    defulat: false
+    default: false
     type: boolean
     help:
       en_US: Whether to inject a safety prompt before all conversations.

api/core/model_runtime/model_providers/mistralai/llm/mistral-medium-latest.yaml

@@ -24,7 +24,7 @@ parameter_rules:
     min: 1
     max: 8000
   - name: safe_prompt
-    defulat: false
+    default: false
     type: boolean
     help:
       en_US: Whether to inject a safety prompt before all conversations.

api/core/model_runtime/model_providers/mistralai/llm/mistral-small-latest.yaml

@@ -24,7 +24,7 @@ parameter_rules:
     min: 1
     max: 8000
   - name: safe_prompt
-    defulat: false
+    default: false
     type: boolean
     help:
       en_US: Whether to inject a safety prompt before all conversations.

api/core/model_runtime/model_providers/mistralai/llm/open-mistral-7b.yaml

@@ -24,7 +24,7 @@ parameter_rules:
     min: 1
     max: 2048
   - name: safe_prompt
-    defulat: false
+    default: false
     type: boolean
     help:
       en_US: Whether to inject a safety prompt before all conversations.

api/core/model_runtime/model_providers/mistralai/llm/open-mixtral-8x7b.yaml

@@ -24,7 +24,7 @@ parameter_rules:
     min: 1
     max: 8000
   - name: safe_prompt
-    defulat: false
+    default: false
     type: boolean
     help:
       en_US: Whether to inject a safety prompt before all conversations.

api/core/model_runtime/model_providers/openai/speech2text/whisper-1.yaml

@@ -2,4 +2,4 @@ model: whisper-1
 model_type: speech2text
 model_properties:
   file_upload_limit: 25
-  supported_file_extensions: mp3,mp4,mpeg,mpga,m4a,wav,webm
+  supported_file_extensions: flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm

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

@@ -34,7 +34,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
         :return: text translated to audio file
         """
         audio_type = self._get_model_audio_type(model, credentials)
-        if not voice:
+        if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
             voice = self._get_model_default_voice(model, credentials)
         if streaming:
             return Response(stream_with_context(self._tts_invoke_streaming(model=model,

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

@@ -25,6 +25,7 @@ from core.model_runtime.entities.model_entities import (
     AIModelEntity,
     DefaultParameterName,
     FetchFrom,
+    ModelFeature,
     ModelPropertyKey,
     ModelType,
     ParameterRule,
@@ -166,11 +167,23 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         """
             generate custom model entities from credentials
         """
+        support_function_call = False
+        features = []
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
+        if function_calling_type == 'function_call':
+            features = [ModelFeature.TOOL_CALL]
+            support_function_call = True
+        endpoint_url = credentials["endpoint_url"]
+        # if not endpoint_url.endswith('/'):
+        #     endpoint_url += '/'
+        # if 'https://api.openai.com/v1/' == endpoint_url:
+        #     features = [ModelFeature.STREAM_TOOL_CALL]
         entity = AIModelEntity(
             model=model,
             label=I18nObject(en_US=model),
             model_type=ModelType.LLM,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            features=features if support_function_call else [],
             model_properties={
                 ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', "4096")),
                 ModelPropertyKey.MODE: credentials.get('mode'),
@@ -194,14 +207,6 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                     max=1,
                     precision=2
                 ),
-                ParameterRule(
-                    name="top_k",
-                    label=I18nObject(en_US="Top K"),
-                    type=ParameterType.INT,
-                    default=int(credentials.get('top_k', 1)),
-                    min=1,
-                    max=100
-                ),
                 ParameterRule(
                     name=DefaultParameterName.FREQUENCY_PENALTY.value,
                     label=I18nObject(en_US="Frequency Penalty"),
@@ -232,7 +237,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                 output=Decimal(credentials.get('output_price', 0)),
                 unit=Decimal(credentials.get('unit', 0)),
                 currency=credentials.get('currency', "USD")
-            )
+            ),
         )
 
         if credentials['mode'] == 'chat':
@@ -292,14 +297,22 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             raise ValueError("Unsupported completion type for model configuration.")
 
         # annotate tools with names, descriptions, etc.
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
         formatted_tools = []
         if tools:
-            data["tool_choice"] = "auto"
+            if function_calling_type == 'function_call':
+                data['functions'] = [{
+                    "name": tool.name,
+                    "description": tool.description,
+                    "parameters": tool.parameters
+                } for tool in tools]
+            elif function_calling_type == 'tool_call':
+                data["tool_choice"] = "auto"
 
-            for tool in tools:
-                formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
+                for tool in tools:
+                    formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
 
-            data["tools"] = formatted_tools
+                data["tools"] = formatted_tools
 
         if stop:
             data["stop"] = stop
@@ -367,9 +380,9 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
             if chunk:
-                #ignore sse comments
+                # ignore sse comments
                 if chunk.startswith(':'):
-                    continue                 
+                    continue
                 decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
                 chunk_json = None
                 try:
@@ -452,10 +465,13 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
 
         response_content = ''
         tool_calls = None
-
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
         if completion_type is LLMMode.CHAT:
             response_content = output.get('message', {})['content']
-            tool_calls = output.get('message', {}).get('tool_calls')
+            if function_calling_type == 'tool_call':
+                tool_calls = output.get('message', {}).get('tool_calls')
+            elif function_calling_type == 'function_call':
+                tool_calls = output.get('message', {}).get('function_call')
 
         elif completion_type is LLMMode.COMPLETION:
             response_content = output['text']
@@ -463,7 +479,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
         assistant_message = AssistantPromptMessage(content=response_content, tool_calls=[])
 
         if tool_calls:
-            assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
+            if function_calling_type == 'tool_call':
+                assistant_message.tool_calls = self._extract_response_tool_calls(tool_calls)
+            elif function_calling_type == 'function_call':
+                assistant_message.tool_calls = [self._extract_response_function_call(tool_calls)]
 
         usage = response_json.get("usage")
         if usage:
@@ -522,33 +541,34 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
             message = cast(AssistantPromptMessage, message)
             message_dict = {"role": "assistant", "content": message.content}
             if message.tool_calls:
-                message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
-                                              in
-                                              message.tool_calls]
-                # function_call = message.tool_calls[0]
-                # message_dict["function_call"] = {
-                #     "name": function_call.function.name,
-                #     "arguments": function_call.function.arguments,
-                # }
+                # message_dict["tool_calls"] = [helper.dump_model(PromptMessageFunction(function=tool_call)) for tool_call
+                #                               in
+                #                               message.tool_calls]
+
+                function_call = message.tool_calls[0]
+                message_dict["function_call"] = {
+                    "name": function_call.function.name,
+                    "arguments": function_call.function.arguments,
+                }
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
             message_dict = {"role": "system", "content": message.content}
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
-            message_dict = {
-                "role": "tool",
-                "content": message.content,
-                "tool_call_id": message.tool_call_id
-            }
             # message_dict = {
-            #     "role": "function",
+            #     "role": "tool",
             #     "content": message.content,
-            #     "name": message.tool_call_id
+            #     "tool_call_id": message.tool_call_id
             # }
+            message_dict = {
+                "role": "function",
+                "content": message.content,
+                "name": message.tool_call_id
+            }
         else:
             raise ValueError(f"Got unknown type {message}")
 
-        if message.name is not None:
+        if message.name:
             message_dict["name"] = message.name
 
         return message_dict
@@ -693,3 +713,26 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
                 tool_calls.append(tool_call)
 
         return tool_calls
+
+    def _extract_response_function_call(self, response_function_call) \
+            -> AssistantPromptMessage.ToolCall:
+        """
+        Extract function call from response
+
+        :param response_function_call: response function call
+        :return: tool call
+        """
+        tool_call = None
+        if response_function_call:
+            function = AssistantPromptMessage.ToolCall.ToolCallFunction(
+                name=response_function_call['name'],
+                arguments=response_function_call['arguments']
+            )
+
+            tool_call = AssistantPromptMessage.ToolCall(
+                id=response_function_call['name'],
+                type="function",
+                function=function
+            )
+
+        return tool_call

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

@@ -75,6 +75,28 @@ model_credential_schema:
           value: llm
       default: '4096'
       type: text-input
+    - variable: function_calling_type
+      show_on:
+        - variable: __model_type
+          value: llm
+      label:
+        en_US: Function calling
+      type: select
+      required: false
+      default: no_call
+      options:
+        - value: function_call
+          label:
+            en_US: Support
+            zh_Hans: 支持
+#        - value: tool_call
+#          label:
+#            en_US: Tool Call
+#            zh_Hans: Tool Call
+        - value: no_call
+          label:
+            en_US: Not Support
+            zh_Hans: 不支持
     - variable: stream_mode_delimiter
       label:
         zh_Hans: 流模式返回结果的分隔符

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

@@ -53,7 +53,7 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
             # cloud not connect to the server
             raise InvokeAuthorizationError(f"Invalid server URL: {e}")
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             if response.status_code == 400:

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

@@ -34,7 +34,7 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
         :return: text translated to audio file
         """
         audio_type = self._get_model_audio_type(model, credentials)
-        if not voice or voice not in self.get_tts_model_voices(model=model, credentials=credentials):
+        if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
             voice = self._get_model_default_voice(model, credentials)
         if streaming:
             return Response(stream_with_context(self._tts_invoke_streaming(model=model,

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

@@ -308,6 +308,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                 type=ParameterType.INT,
                 use_template='max_tokens',
                 min=1,
+                max=credentials.get('context_length', 2048),
                 default=512,
                 label=I18nObject(
                     zh_Hans='最大生成长度',