feat: bump version to 0.3.12 (#674)

Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
Co-authored-by: JzoNg <jzongcode@gmail.com>

.github/workflows/check_no_chinese_comments.py

@@ -19,7 +19,7 @@ def check_file_for_chinese_comments(file_path):
 
 def main():
     has_chinese = False
-    excluded_files = ["model_template.py", 'stopwords.py', 'commands.py', 'indexing_runner.py']
+    excluded_files = ["model_template.py", 'stopwords.py', 'commands.py', 'indexing_runner.py', 'web_reader_tool.py']
 
     for root, _, files in os.walk("."):
         for file in files:

.github/workflows/stale.yml

@@ -20,8 +20,11 @@ jobs:
     steps:
     - uses: actions/stale@v5
       with:
+        days-before-issue-stale: 30
+        days-before-issue-close: 3
         repo-token: ${{ secrets.GITHUB_TOKEN }}
         stale-issue-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
         stale-pr-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
         stale-issue-label: 'no-issue-activity'
         stale-pr-label: 'no-pr-activity'
+        any-of-labels: 'duplicate,question,invalid,wontfix,no-issue-activity,no-pr-activity,enhancement'

.gitignore

@@ -109,6 +109,7 @@ venv/
 ENV/
 env.bak/
 venv.bak/
+.conda/
 
 # Spyder project settings
 .spyderproject
@@ -147,3 +148,5 @@ docker/volumes/weaviate/*
 sdks/python-client/build
 sdks/python-client/dist
 sdks/python-client/dify_client.egg-info
+
+.vscode/

CONTRIBUTING.md

@@ -54,3 +54,8 @@ Did you have an issue, like a merge conflict, or don't know how to open a pull r
 ## Community channels
 
 Stuck somewhere? Have any questions? Join the [Discord Community Server](https://discord.gg/AhzKf7dNgk). We are here to help!
+
+### i18n (Internationalization) Support
+
+We are looking for contributors to help with translations in other languages. If you are interested in helping, please join the [Discord Community Server](https://discord.gg/AhzKf7dNgk) and let us know.  
+Also check out the [Frontend i18n README]((web/i18n/README_EN.md)) for more information.

CONTRIBUTING_CN.md

@@ -51,3 +51,7 @@ git clone git@github.com:<github_username>/dify.git
 ## 社区渠道
 
 遇到困难了吗？有任何问题吗? 加入 [Discord Community Server](https://discord.gg/AhzKf7dNgk)，我们将为您提供帮助。
+
+### 多语言支持
+
+需要参与贡献翻译内容，请参阅[前端多语言翻译 README](web/i18n/README_CN.md)。

LICENSE

@@ -1,26 +1,26 @@
 # Dify Open Source License
 
-The Dify project uses a combination of the Apache License 2.0, MIT License, and an additional agreement to protect against direct competition with Dify Cloud services.
+The Dify project is licensed under the Apache License 2.0, with the following additional conditions:
 
-As a contributor, you should agree that your contributed code:
-a. Might be subject to a more permissive open source license in the future.
+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:
+
+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.
+
+Please contact business@dify.ai by email to inquire about licensing matters.
+
+2. As a contributor, you should agree that your contributed code:
+
+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.
 
-The following components are open source under the MIT license, allowing you to build and develop applications based on them:
-- WebApp elements, e.g., web/app/components/share
-- Derived WebApp Template projects
-
-The remaining parts of the project are open source under the Apache License 2.0.
-
-With the Apache License 2.0, MIT License, and this supplementary agreement, anyone can freely use, modify, and distribute Dify, provided that:
-
-- If you use Dify solely as a backend service for other applications, no authorization is needed for commercial or closed source purposes.
-- If you wish to use Dify for commercial and closed source SaaS services similar to Dify Cloud, please contact us for authorization.
+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.
 
 The interactive design of this product is protected by appearance patent.
 
 © 2023 LangGenius, Inc.
 
+
 ----------
 
 Licensed under the Apache License, Version 2.0 (the "License");
@@ -34,13 +34,3 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-
-----------
-The MIT License
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

@@ -17,9 +17,15 @@ A single API encompassing plugin capabilities, context enhancement, and more, sa
 Visual data analysis, log review, and annotation for applications
 Dify is compatible with Langchain, meaning we'll gradually support multiple LLMs, currently supported:
 
-- GPT 3 (text-davinci-003)
-- GPT 3.5 Turbo(ChatGPT)
-- GPT-4
+* **OpenAI** ：GPT4、GPT3.5-turbo、GPT3.5-turbo-16k、text-davinci-003
+
+* **Azure OpenAI**
+
+* **Antropic**：Claude2、Claude-instant
+> We've got 1000 free trial credits available for all cloud service users to try out the Claude model.Visit [Dify.ai](https://dify.ai) and
+try it now.
+
+* **hugging face Hub**：Coming soon.
 
 ## Use Cloud Services
 
@@ -45,6 +51,11 @@ docker compose up -d
 
 After running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization installation process.
 
+### Helm Chart
+
+A big thanks to @BorisPolonsky for providing us with a [Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
+You can go to https://github.com/BorisPolonsky/dify-helm for deployment information.
+
 ### Configuration
 
 If you need to customize the configuration, please refer to the comments in our [docker-compose.yml](docker/docker-compose.yaml) file and manually set the environment configuration. After making the changes, please run 'docker-compose up -d' again.

README_CN.md

@@ -17,11 +17,16 @@
 - 一套 API 即可包含插件、上下文增强等能力，替你省下了后端代码的编写工作
 - 可视化的对应用进行数据分析，查阅日志或进行标注
 
-Dify 兼容 Langchain，这意味着我们将逐步支持多种 LLMs ，目前已支持：
+Dify 兼容 Langchain，这意味着我们将逐步支持多种 LLMs ，目前支持的模型供应商：
 
-- GPT 3 (text-davinci-003)
-- GPT 3.5 Turbo(ChatGPT)
-- GPT-4
+* **OpenAI**：GPT4、GPT3.5-turbo、GPT3.5-turbo-16k、text-davinci-003 
+
+* **Azure OpenAI Service**
+* **Anthropic**：Claude2、Claude-instant
+
+> 我们为所有注册云端版的用户免费提供了 1000 次 Claude 模型的消息调用额度，登录 [dify.ai](https://cloud.dify.ai) 即可使用。
+
+* **Hugging Face Hub**（即将推出）
 
 ## 使用云服务
 
@@ -47,6 +52,11 @@ docker compose up -d
 
 运行后，可以在浏览器上访问 [http://localhost/install](http://localhost/install) 进入 Dify 控制台并开始初始化安装操作。
 
+### Helm Chart
+
+非常感谢 @BorisPolonsky 为我们提供了一个 [Helm Chart](https://helm.sh/) 版本，可以在 Kubernetes 上部署 Dify。
+您可以前往 https://github.com/BorisPolonsky/dify-helm 来获取部署信息。
+
 ### 配置
 
 需要自定义配置，请参考我们的 [docker-compose.yml](docker/docker-compose.yaml) 文件中的注释，并手动设置环境配置，修改完毕后，请再次执行 `docker-compose up -d`。

README_ES.md

@@ -46,6 +46,11 @@ docker compose up -d
 
 Después de ejecutarlo, puedes acceder al panel de control de Dify en tu navegador desde [http://localhost/install](http://localhost/install) y comenzar el proceso de instalación de inicialización.
 
+### Helm Chart
+
+Un gran agradecimiento a @BorisPolonsky por proporcionarnos una versión de [Helm Chart](https://helm.sh/), que permite desplegar Dify en Kubernetes.
+Puede ir a https://github.com/BorisPolonsky/dify-helm para obtener información de despliegue.
+
 ### Configuración
 
 Si necesitas personalizar la configuración, consulta los comentarios en nuestro archivo [docker-compose.yml](docker/docker-compose.yaml) y configura manualmente la configuración del entorno. Después de realizar los cambios, ejecuta nuevamente 'docker-compose up -d'.

README_JA.md

@@ -46,6 +46,11 @@ docker compose up -d
 
 実行後、ブラウザで [http://localhost/install](http://localhost/install) にアクセスし、初期化インストール作業を開始することができます。
 
+### Helm Chart
+
+@BorisPolonsky に大感謝します。彼は Dify を Kubernetes 上にデプロイするための [Helm Chart](https://helm.sh/) バージョンを提供してくれました。
+デプロイ情報については、https://github.com/BorisPolonsky/dify-helm をご覧ください。
+
 ### 構成
 
 カスタマイズが必要な場合は、[docker-compose.yml](docker/docker-compose.yaml) ファイルのコメントを参照し、手動で環境設定をお願いします。変更後、再度 'docker-compose up -d' を実行してください。

api/.env.example

@@ -8,13 +8,19 @@ EDITION=SELF_HOSTED
 SECRET_KEY=
 
 # Console API base URL
-CONSOLE_URL=http://127.0.0.1:5001
+CONSOLE_API_URL=http://127.0.0.1:5001
+
+# Console frontend web base URL
+CONSOLE_WEB_URL=http://127.0.0.1:3000
 
 # Service API base URL
-API_URL=http://127.0.0.1:5001
+SERVICE_API_URL=http://127.0.0.1:5001
 
-# Web APP base URL
-APP_URL=http://127.0.0.1:3000
+# Web APP API base URL
+APP_API_URL=http://127.0.0.1:5001
+
+# Web APP frontend web base URL
+APP_WEB_URL=http://127.0.0.1:3000
 
 # celery configuration
 CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
@@ -22,6 +28,7 @@ CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
 # redis configuration
 REDIS_HOST=localhost
 REDIS_PORT=6379
+REDIS_USERNAME=
 REDIS_PASSWORD=difyai123456
 REDIS_DB=0
 
@@ -78,9 +85,20 @@ WEAVIATE_BATCH_SIZE=100
 QDRANT_URL=path:storage/qdrant
 QDRANT_API_KEY=your-qdrant-api-key
 
+# Mail configuration, support: resend
+MAIL_TYPE=
+MAIL_DEFAULT_SEND_FROM=no-reply <no-reply@dify.ai>
+RESEND_API_KEY=
+
 # Sentry configuration
 SENTRY_DSN=
 
 # DEBUG
 DEBUG=false
 SQLALCHEMY_ECHO=false
+
+# Notion import configuration, support public and internal
+NOTION_INTEGRATION_TYPE=public
+NOTION_CLIENT_SECRET=you-client-secret
+NOTION_CLIENT_ID=you-client-id
+NOTION_INTERNAL_SECRET=you-internal-secret

api/Dockerfile

@@ -5,9 +5,11 @@ LABEL maintainer="takatost@gmail.com"
 ENV FLASK_APP app.py
 ENV EDITION SELF_HOSTED
 ENV DEPLOY_ENV PRODUCTION
-ENV CONSOLE_URL http://127.0.0.1:5001
-ENV API_URL http://127.0.0.1:5001
-ENV APP_URL http://127.0.0.1:5001
+ENV CONSOLE_API_URL http://127.0.0.1:5001
+ENV CONSOLE_WEB_URL http://127.0.0.1:3000
+ENV SERVICE_API_URL http://127.0.0.1:5001
+ENV APP_API_URL http://127.0.0.1:5001
+ENV APP_WEB_URL http://127.0.0.1:3000
 
 EXPOSE 5001
 
@@ -25,4 +27,4 @@ RUN chmod +x /entrypoint.sh
 ARG COMMIT_SHA
 ENV COMMIT_SHA ${COMMIT_SHA}
 
-ENTRYPOINT ["/entrypoint.sh"]
+ENTRYPOINT ["/bin/bash", "/entrypoint.sh"]

api/README.md

@@ -17,6 +17,11 @@
    ```bash
    openssl rand -base64 42
    ```
+3.5 If you use annaconda, create a new environment and activate it
+   ```bash
+   conda create --name dify python=3.10
+   conda activate dify
+   ```
 4. Install dependencies
    ```bash
    pip install -r requirements.txt

api/app.py

@@ -1,5 +1,9 @@
 # -*- coding:utf-8 -*-
 import os
+from datetime import datetime
+
+from werkzeug.exceptions import Forbidden
+
 if not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true':
     from gevent import monkey
     monkey.patch_all()
@@ -12,20 +16,20 @@ from flask import Flask, request, Response, session
 import flask_login
 from flask_cors import CORS
 
-from extensions import ext_session, ext_celery, ext_sentry, ext_redis, ext_login, ext_vector_store, ext_migrate, \
-    ext_database, ext_storage
+from extensions import ext_session, ext_celery, ext_sentry, ext_redis, ext_login, ext_migrate, \
+    ext_database, ext_storage, ext_mail
 from extensions.ext_database import db
 from extensions.ext_login import login_manager
 
 # DO NOT REMOVE BELOW
-from models import model, account, dataset, web, task
+from models import model, account, dataset, web, task, source, tool
 from events import event_handlers
 # DO NOT REMOVE ABOVE
 
 import core
 from config import Config, CloudEditionConfig
 from commands import register_commands
-from models.account import TenantAccountJoin
+from models.account import TenantAccountJoin, AccountStatus
 from models.model import Account, EndUser, App
 
 import warnings
@@ -77,11 +81,11 @@ def initialize_extensions(app):
     ext_database.init_app(app)
     ext_migrate.init(app, db)
     ext_redis.init_app(app)
-    ext_vector_store.init_app(app)
     ext_storage.init_app(app)
     ext_celery.init_app(app)
     ext_session.init_app(app)
     ext_login.init_app(app)
+    ext_mail.init_app(app)
     ext_sentry.init_app(app)
 
 
@@ -99,6 +103,9 @@ def load_user(user_id):
         account = db.session.query(Account).filter(Account.id == account_id).first()
 
         if account:
+            if account.status == AccountStatus.BANNED.value or account.status == AccountStatus.CLOSED.value:
+                raise Forbidden('Account is banned or closed.')
+
             workspace_id = session.get('workspace_id')
             if workspace_id:
                 tenant_account_join = db.session.query(TenantAccountJoin).filter(
@@ -122,6 +129,9 @@ def load_user(user_id):
                     account.current_tenant_id = tenant_account_join.tenant_id
                     session['workspace_id'] = account.current_tenant_id
 
+            account.last_active_at = datetime.utcnow()
+            db.session.commit()
+
             # Log in the user with the updated user_id
             flask_login.login_user(account, remember=True)
 
@@ -145,13 +155,17 @@ def register_blueprints(app):
     from controllers.web import bp as web_bp
     from controllers.console import bp as console_app_bp
 
+    CORS(service_api_bp,
+         allow_headers=['Content-Type', 'Authorization', 'X-App-Code'],
+         methods=['GET', 'PUT', 'POST', 'DELETE', 'OPTIONS', 'PATCH']
+         )
     app.register_blueprint(service_api_bp)
 
     CORS(web_bp,
          resources={
              r"/*": {"origins": app.config['WEB_API_CORS_ALLOW_ORIGINS']}},
          supports_credentials=True,
-         allow_headers=['Content-Type', 'Authorization'],
+         allow_headers=['Content-Type', 'Authorization', 'X-App-Code'],
          methods=['GET', 'PUT', 'POST', 'DELETE', 'OPTIONS', 'PATCH'],
          expose_headers=['X-Version', 'X-Env']
          )

api/commands.py

@@ -1,20 +1,26 @@
 import datetime
+import logging
 import random
 import string
+import time
 
 import click
 from flask import current_app
+from werkzeug.exceptions import NotFound
 
+from core.index.index import IndexBuilder
 from libs.password import password_pattern, valid_password, hash_password
 from libs.helper import email as email_validate
 from extensions.ext_database import db
 from libs.rsa import generate_key_pair
 from models.account import InvitationCode, Tenant
+from models.dataset import Dataset, DatasetQuery, Document, DocumentSegment
 from models.model import Account
 import secrets
 import base64
 
-from models.provider import Provider
+from models.provider import Provider, ProviderName
+from services.provider_service import ProviderService
 
 
 @click.command('reset-password', help='Reset the account password.')
@@ -159,8 +165,127 @@ def generate_upper_string():
     return result
 
 
+@click.command('recreate-all-dataset-indexes', help='Recreate all dataset indexes.')
+def recreate_all_dataset_indexes():
+    click.echo(click.style('Start recreate all dataset indexes.', fg='green'))
+    recreate_count = 0
+
+    page = 1
+    while True:
+        try:
+            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality') \
+                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
+        except NotFound:
+            break
+
+        page += 1
+        for dataset in datasets:
+            try:
+                click.echo('Recreating dataset index: {}'.format(dataset.id))
+                index = IndexBuilder.get_index(dataset, 'high_quality')
+                if index and index._is_origin():
+                    index.recreate_dataset(dataset)
+                    recreate_count += 1
+                else:
+                    click.echo('passed.')
+            except Exception as e:
+                click.echo(
+                    click.style('Recreate dataset index error: {} {}'.format(e.__class__.__name__, str(e)), fg='red'))
+                continue
+
+    click.echo(click.style('Congratulations! Recreate {} dataset indexes.'.format(recreate_count), fg='green'))
+
+
+@click.command('clean-unused-dataset-indexes', help='Clean unused dataset indexes.')
+def clean_unused_dataset_indexes():
+    click.echo(click.style('Start clean unused dataset indexes.', fg='green'))
+    clean_days = int(current_app.config.get('CLEAN_DAY_SETTING'))
+    start_at = time.perf_counter()
+    thirty_days_ago = datetime.datetime.now() - datetime.timedelta(days=clean_days)
+    page = 1
+    while True:
+        try:
+            datasets = db.session.query(Dataset).filter(Dataset.created_at < thirty_days_ago) \
+                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
+        except NotFound:
+            break
+        page += 1
+        for dataset in datasets:
+            dataset_query = db.session.query(DatasetQuery).filter(
+                DatasetQuery.created_at > thirty_days_ago,
+                DatasetQuery.dataset_id == dataset.id
+            ).all()
+            if not dataset_query or len(dataset_query) == 0:
+                documents = db.session.query(Document).filter(
+                    Document.dataset_id == dataset.id,
+                    Document.indexing_status == 'completed',
+                    Document.enabled == True,
+                    Document.archived == False,
+                    Document.updated_at > thirty_days_ago
+                ).all()
+                if not documents or len(documents) == 0:
+                    try:
+                        # remove index
+                        vector_index = IndexBuilder.get_index(dataset, 'high_quality')
+                        kw_index = IndexBuilder.get_index(dataset, 'economy')
+                        # delete from vector index
+                        if vector_index:
+                            vector_index.delete()
+                        kw_index.delete()
+                        # update document
+                        update_params = {
+                            Document.enabled: False
+                        }
+
+                        Document.query.filter_by(dataset_id=dataset.id).update(update_params)
+                        db.session.commit()
+                        click.echo(click.style('Cleaned unused dataset {} from db success!'.format(dataset.id),
+                                               fg='green'))
+                    except Exception as e:
+                        click.echo(
+                            click.style('clean dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                        fg='red'))
+    end_at = time.perf_counter()
+    click.echo(click.style('Cleaned unused dataset from db success latency: {}'.format(end_at - start_at), fg='green'))
+
+
+@click.command('sync-anthropic-hosted-providers', help='Sync anthropic hosted providers.')
+def sync_anthropic_hosted_providers():
+    click.echo(click.style('Start sync anthropic hosted providers.', fg='green'))
+    count = 0
+
+    page = 1
+    while True:
+        try:
+            tenants = db.session.query(Tenant).order_by(Tenant.created_at.desc()).paginate(page=page, per_page=50)
+        except NotFound:
+            break
+
+        page += 1
+        for tenant in tenants:
+            try:
+                click.echo('Syncing tenant anthropic hosted provider: {}'.format(tenant.id))
+                ProviderService.create_system_provider(
+                    tenant,
+                    ProviderName.ANTHROPIC.value,
+                    current_app.config['ANTHROPIC_HOSTED_QUOTA_LIMIT'],
+                    True
+                )
+                count += 1
+            except Exception as e:
+                click.echo(click.style(
+                    'Sync tenant anthropic hosted provider error: {} {}'.format(e.__class__.__name__, str(e)),
+                    fg='red'))
+                continue
+
+    click.echo(click.style('Congratulations! Synced {} anthropic hosted providers.'.format(count), fg='green'))
+
+
 def register_commands(app):
     app.cli.add_command(reset_password)
     app.cli.add_command(reset_email)
     app.cli.add_command(generate_invitation_codes)
     app.cli.add_command(reset_encrypt_key_pair)
+    app.cli.add_command(recreate_all_dataset_indexes)
+    app.cli.add_command(sync_anthropic_hosted_providers)
+    app.cli.add_command(clean_unused_dataset_indexes)

api/config.py

@@ -28,9 +28,11 @@ DEFAULTS = {
     'SESSION_REDIS_USE_SSL': 'False',
     'OAUTH_REDIRECT_PATH': '/console/api/oauth/authorize',
     'OAUTH_REDIRECT_INDEX_PATH': '/',
-    'CONSOLE_URL': 'https://cloud.dify.ai',
-    'API_URL': 'https://api.dify.ai',
-    'APP_URL': 'https://udify.app',
+    'CONSOLE_WEB_URL': 'https://cloud.dify.ai',
+    'CONSOLE_API_URL': 'https://cloud.dify.ai',
+    'SERVICE_API_URL': 'https://api.dify.ai',
+    'APP_WEB_URL': 'https://udify.app',
+    'APP_API_URL': 'https://udify.app',
     'STORAGE_TYPE': 'local',
     'STORAGE_LOCAL_PATH': 'storage',
     'CHECK_UPDATE_URL': 'https://updates.dify.ai',
@@ -48,7 +50,11 @@ DEFAULTS = {
     'PDF_PREVIEW': 'True',
     'LOG_LEVEL': 'INFO',
     'DISABLE_PROVIDER_CONFIG_VALIDATION': 'False',
-    'DEFAULT_LLM_PROVIDER': 'openai'
+    'DEFAULT_LLM_PROVIDER': 'openai',
+    'OPENAI_HOSTED_QUOTA_LIMIT': 200,
+    'ANTHROPIC_HOSTED_QUOTA_LIMIT': 1000,
+    'TENANT_DOCUMENT_COUNT': 100,
+    'CLEAN_DAY_SETTING': 30
 }
 
 
@@ -76,10 +82,15 @@ class Config:
 
     def __init__(self):
         # app settings
+        self.CONSOLE_API_URL = get_env('CONSOLE_URL') if get_env('CONSOLE_URL') else get_env('CONSOLE_API_URL')
+        self.CONSOLE_WEB_URL = get_env('CONSOLE_URL') if get_env('CONSOLE_URL') else get_env('CONSOLE_WEB_URL')
+        self.SERVICE_API_URL = get_env('API_URL') if get_env('API_URL') else get_env('SERVICE_API_URL')
+        self.APP_WEB_URL = get_env('APP_URL') if get_env('APP_URL') else get_env('APP_WEB_URL')
+        self.APP_API_URL = get_env('APP_URL') if get_env('APP_URL') else get_env('APP_API_URL')
         self.CONSOLE_URL = get_env('CONSOLE_URL')
         self.API_URL = get_env('API_URL')
         self.APP_URL = get_env('APP_URL')
-        self.CURRENT_VERSION = "0.3.3"
+        self.CURRENT_VERSION = "0.3.12"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -147,10 +158,15 @@ class Config:
 
         # cors settings
         self.CONSOLE_CORS_ALLOW_ORIGINS = get_cors_allow_origins(
-            'CONSOLE_CORS_ALLOW_ORIGINS', self.CONSOLE_URL)
+            'CONSOLE_CORS_ALLOW_ORIGINS', self.CONSOLE_WEB_URL)
         self.WEB_API_CORS_ALLOW_ORIGINS = get_cors_allow_origins(
             'WEB_API_CORS_ALLOW_ORIGINS', '*')
 
+        # mail settings
+        self.MAIL_TYPE = get_env('MAIL_TYPE')
+        self.MAIL_DEFAULT_SEND_FROM = get_env('MAIL_DEFAULT_SEND_FROM')
+        self.RESEND_API_KEY = get_env('RESEND_API_KEY')
+
         # sentry settings
         self.SENTRY_DSN = get_env('SENTRY_DSN')
         self.SENTRY_TRACES_SAMPLE_RATE = float(get_env('SENTRY_TRACES_SAMPLE_RATE'))
@@ -179,6 +195,10 @@ class Config:
 
         # hosted provider credentials
         self.OPENAI_API_KEY = get_env('OPENAI_API_KEY')
+        self.ANTHROPIC_API_KEY = get_env('ANTHROPIC_API_KEY')
+
+        self.OPENAI_HOSTED_QUOTA_LIMIT = get_env('OPENAI_HOSTED_QUOTA_LIMIT')
+        self.ANTHROPIC_HOSTED_QUOTA_LIMIT = get_env('ANTHROPIC_HOSTED_QUOTA_LIMIT')
 
         # By default it is False
         # You could disable it for compatibility with certain OpenAPI providers
@@ -188,6 +208,17 @@ class Config:
         # set default LLM provider, default is 'openai', support `azure_openai`
         self.DEFAULT_LLM_PROVIDER = get_env('DEFAULT_LLM_PROVIDER')
 
+        # notion import setting
+        self.NOTION_CLIENT_ID = get_env('NOTION_CLIENT_ID')
+        self.NOTION_CLIENT_SECRET = get_env('NOTION_CLIENT_SECRET')
+        self.NOTION_INTEGRATION_TYPE = get_env('NOTION_INTEGRATION_TYPE')
+        self.NOTION_INTERNAL_SECRET = get_env('NOTION_INTERNAL_SECRET')
+        self.NOTION_INTEGRATION_TOKEN = get_env('NOTION_INTEGRATION_TOKEN')
+
+        self.TENANT_DOCUMENT_COUNT = get_env('TENANT_DOCUMENT_COUNT')
+        self.CLEAN_DAY_SETTING = get_env('CLEAN_DAY_SETTING')
+
+
 class CloudEditionConfig(Config):
 
     def __init__(self):

api/controllers/console/__init__.py

@@ -9,16 +9,19 @@ api = ExternalApi(bp)
 from . import setup, version, apikey, admin
 
 # Import app controllers
-from .app import app, site, completion, model_config, statistic, conversation, message, generator
+from .app import app, site, completion, model_config, statistic, conversation, message, generator, audio
 
 # Import auth controllers
-from .auth import login, oauth
+from .auth import login, oauth, data_source_oauth, activate
 
 # Import datasets controllers
-from .datasets import datasets, datasets_document, datasets_segments, file, hit_testing
+from .datasets import datasets, datasets_document, datasets_segments, file, hit_testing, data_source
 
 # Import workspace controllers
-from .workspace import workspace, members, providers, account
+from .workspace import workspace, members, model_providers, account, tool_providers
 
 # Import explore controllers
-from .explore import installed_app, recommended_app, completion, conversation, message, parameter, saved_message
+from .explore import installed_app, recommended_app, completion, conversation, message, parameter, saved_message, audio
+
+# Import universal chat controllers
+from .universal_chat import chat, conversation, message, parameter, audio

api/controllers/console/app/app.py

@@ -22,7 +22,9 @@ model_config_fields = {
     'opening_statement': fields.String,
     'suggested_questions': fields.Raw(attribute='suggested_questions_list'),
     'suggested_questions_after_answer': fields.Raw(attribute='suggested_questions_after_answer_dict'),
+    'speech_to_text': fields.Raw(attribute='speech_to_text_dict'),
     'more_like_this': fields.Raw(attribute='more_like_this_dict'),
+    'sensitive_word_avoidance': fields.Raw(attribute='sensitive_word_avoidance_dict'),
     'model': fields.Raw(attribute='model_dict'),
     'user_input_form': fields.Raw(attribute='user_input_form_list'),
     'pre_prompt': fields.String,
@@ -95,7 +97,8 @@ class AppListApi(Resource):
         args = parser.parse_args()
 
         app_models = db.paginate(
-            db.select(App).where(App.tenant_id == current_user.current_tenant_id).order_by(App.created_at.desc()),
+            db.select(App).where(App.tenant_id == current_user.current_tenant_id,
+                                 App.is_universal == False).order_by(App.created_at.desc()),
             page=args['page'],
             per_page=args['limit'],
             error_out=False)
@@ -144,7 +147,9 @@ class AppListApi(Resource):
                 opening_statement=model_configuration['opening_statement'],
                 suggested_questions=json.dumps(model_configuration['suggested_questions']),
                 suggested_questions_after_answer=json.dumps(model_configuration['suggested_questions_after_answer']),
+                speech_to_text=json.dumps(model_configuration['speech_to_text']),
                 more_like_this=json.dumps(model_configuration['more_like_this']),
+                sensitive_word_avoidance=json.dumps(model_configuration['sensitive_word_avoidance']),
                 model=json.dumps(model_configuration['model']),
                 user_input_form=json.dumps(model_configuration['user_input_form']),
                 pre_prompt=model_configuration['pre_prompt'],
@@ -434,7 +439,9 @@ class AppCopy(Resource):
             opening_statement=app_config.opening_statement,
             suggested_questions=app_config.suggested_questions,
             suggested_questions_after_answer=app_config.suggested_questions_after_answer,
+            speech_to_text=app_config.speech_to_text,
             more_like_this=app_config.more_like_this,
+            sensitive_word_avoidance=app_config.sensitive_word_avoidance,
             model=app_config.model,
             user_input_form=app_config.user_input_form,
             pre_prompt=app_config.pre_prompt,

api/controllers/console/app/audio.py

@@ -0,0 +1,69 @@
+# -*- coding:utf-8 -*-
+import logging
+
+from flask import request
+from flask_login import login_required
+from werkzeug.exceptions import InternalServerError, NotFound
+
+import services
+from controllers.console import api
+from controllers.console.app import _get_app
+from controllers.console.app.error import AppUnavailableError, \
+    ProviderNotInitializeError, CompletionRequestError, ProviderQuotaExceededError, \
+    ProviderModelCurrentlyNotSupportError, NoAudioUploadedError, AudioTooLargeError, \
+    UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from core.llm.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from flask_restful import Resource
+from services.audio_service import AudioService
+from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \
+    UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError
+
+
+class ChatMessageAudioApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, app_id):
+        app_id = str(app_id)
+        app_model = _get_app(app_id, 'chat')
+
+        file = request.files['file']
+
+        try:
+            response = AudioService.transcript(
+                tenant_id=app_model.tenant_id,
+                file=file,
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+        
+
+api.add_resource(ChatMessageAudioApi, '/apps/<uuid:app_id>/audio-to-text')

api/controllers/console/app/completion.py

@@ -63,8 +63,8 @@ class CompletionMessageApi(Resource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -133,8 +133,8 @@ class ChatMessageApi(Resource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -164,8 +164,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
             except services.errors.app_model_config.AppModelConfigBrokenError:
                 logging.exception("App model config broken.")
                 yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:

api/controllers/console/app/conversation.py

@@ -95,6 +95,7 @@ class CompletionConversationApi(Resource):
         'status': fields.String,
         'from_source': fields.String,
         'from_end_user_id': fields.String,
+        'from_end_user_session_id': fields.String(attribute='end_user.session_id'),
         'from_account_id': fields.String,
         'read_at': TimestampField,
         'created_at': TimestampField,
@@ -135,6 +136,8 @@ class CompletionConversationApi(Resource):
 
         query = db.select(Conversation).where(Conversation.app_id == app.id, Conversation.mode == 'completion')
 
+        query = query.options(joinedload(Conversation.end_user))
+
         if args['keyword']:
             query = query.join(
                 Message, Message.conversation_id == Conversation.id
@@ -160,7 +163,7 @@ class CompletionConversationApi(Resource):
 
         if args['end']:
             end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
-            end_datetime = end_datetime.replace(second=0)
+            end_datetime = end_datetime.replace(second=59)
 
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
@@ -209,6 +212,26 @@ class CompletionConversationDetailApi(Resource):
         conversation_id = str(conversation_id)
 
         return _get_conversation(app_id, conversation_id, 'completion')
+    
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, app_id, conversation_id):
+        app_id = str(app_id)
+        conversation_id = str(conversation_id)
+
+        app = _get_app(app_id, 'chat')
+
+        conversation = db.session.query(Conversation) \
+            .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+
+        if not conversation:
+            raise NotFound("Conversation Not Exists.")
+
+        conversation.is_deleted = True
+        db.session.commit()
+
+        return {'result': 'success'}, 204
 
 
 class ChatConversationApi(Resource):
@@ -226,6 +249,7 @@ class ChatConversationApi(Resource):
         'status': fields.String,
         'from_source': fields.String,
         'from_end_user_id': fields.String,
+        'from_end_user_session_id': fields.String(attribute='end_user.session_id'),
         'from_account_id': fields.String,
         'summary': fields.String(attribute='summary_or_query'),
         'read_at': TimestampField,
@@ -268,6 +292,8 @@ class ChatConversationApi(Resource):
 
         query = db.select(Conversation).where(Conversation.app_id == app.id, Conversation.mode == 'chat')
 
+        query = query.options(joinedload(Conversation.end_user))
+
         if args['keyword']:
             query = query.join(
                 Message, Message.conversation_id == Conversation.id
@@ -296,7 +322,7 @@ class ChatConversationApi(Resource):
 
         if args['end']:
             end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
-            end_datetime = end_datetime.replace(second=0)
+            end_datetime = end_datetime.replace(second=59)
 
             end_datetime_timezone = timezone.localize(end_datetime)
             end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
@@ -356,6 +382,27 @@ class ChatConversationDetailApi(Resource):
         conversation_id = str(conversation_id)
 
         return _get_conversation(app_id, conversation_id, 'chat')
+    
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, app_id, conversation_id):
+        app_id = str(app_id)
+        conversation_id = str(conversation_id)
+
+        # get app info
+        app = _get_app(app_id, 'chat')
+
+        conversation = db.session.query(Conversation) \
+            .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+
+        if not conversation:
+            raise NotFound("Conversation Not Exists.")
+
+        conversation.is_deleted = True
+        db.session.commit()
+
+        return {'result': 'success'}, 204
 
 
 

api/controllers/console/app/error.py

@@ -16,7 +16,7 @@ class ProviderNotInitializeError(BaseHTTPException):
 
 class ProviderQuotaExceededError(BaseHTTPException):
     error_code = 'provider_quota_exceeded'
-    description = "Your quota for Dify Hosted OpenAI has been exhausted. " \
+    description = "Your quota for Dify Hosted Model Provider has been exhausted. " \
                   "Please go to Settings -> Model Provider to complete your own provider credentials."
     code = 400
 
@@ -49,3 +49,27 @@ class AppMoreLikeThisDisabledError(BaseHTTPException):
     error_code = 'app_more_like_this_disabled'
     description = "The 'More like this' feature is disabled. Please refresh your page."
     code = 403
+
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = 'no_audio_uploaded'
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = 'audio_too_large'
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = 'unsupported_audio_type'
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = 'provider_not_support_speech_to_text'
+    description = "Provider not support speech to text."
+    code = 400

api/controllers/console/app/generator.py

@@ -27,8 +27,8 @@ class IntroductionGenerateApi(Resource):
                 account.current_tenant_id,
                 args['prompt_template']
             )
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -58,8 +58,8 @@ class RuleGenerateApi(Resource):
                 args['audiences'],
                 args['hoping_to_solve']
             )
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:

api/controllers/console/app/message.py

@@ -269,8 +269,8 @@ class MessageMoreLikeThisApi(Resource):
             raise NotFound("Message Not Exists.")
         except MoreLikeThisDisabledError:
             raise AppMoreLikeThisDisabledError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -297,8 +297,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
                 yield "data: " + json.dumps(api.handle_error(NotFound("Message Not Exists.")).get_json()) + "\n\n"
             except MoreLikeThisDisabledError:
                 yield "data: " + json.dumps(api.handle_error(AppMoreLikeThisDisabledError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:
@@ -339,8 +339,8 @@ class MessageSuggestedQuestionApi(Resource):
             raise NotFound("Message not found")
         except ConversationNotExistsError:
             raise NotFound("Conversation not found")
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:

api/controllers/console/app/model_config.py

@@ -41,7 +41,9 @@ class ModelConfigResource(Resource):
             opening_statement=model_configuration['opening_statement'],
             suggested_questions=json.dumps(model_configuration['suggested_questions']),
             suggested_questions_after_answer=json.dumps(model_configuration['suggested_questions_after_answer']),
+            speech_to_text=json.dumps(model_configuration['speech_to_text']),
             more_like_this=json.dumps(model_configuration['more_like_this']),
+            sensitive_word_avoidance=json.dumps(model_configuration['sensitive_word_avoidance']),
             model=json.dumps(model_configuration['model']),
             user_input_form=json.dumps(model_configuration['user_input_form']),
             pre_prompt=model_configuration['pre_prompt'],

api/controllers/console/auth/activate.py

@@ -0,0 +1,75 @@
+import base64
+import secrets
+from datetime import datetime
+
+from flask_restful import Resource, reqparse
+
+from controllers.console import api
+from controllers.console.error import AlreadyActivateError
+from extensions.ext_database import db
+from libs.helper import email, str_len, supported_language, timezone
+from libs.password import valid_password, hash_password
+from models.account import AccountStatus, Tenant
+from services.account_service import RegisterService
+
+
+class ActivateCheckApi(Resource):
+    def get(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('workspace_id', type=str, required=True, nullable=False, location='args')
+        parser.add_argument('email', type=email, required=True, nullable=False, location='args')
+        parser.add_argument('token', type=str, required=True, nullable=False, location='args')
+        args = parser.parse_args()
+
+        account = RegisterService.get_account_if_token_valid(args['workspace_id'], args['email'], args['token'])
+
+        tenant = db.session.query(Tenant).filter(
+            Tenant.id == args['workspace_id'],
+            Tenant.status == 'normal'
+        ).first()
+
+        return {'is_valid': account is not None, 'workspace_name': tenant.name}
+
+
+class ActivateApi(Resource):
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('workspace_id', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('email', type=email, required=True, nullable=False, location='json')
+        parser.add_argument('token', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('name', type=str_len(30), required=True, nullable=False, location='json')
+        parser.add_argument('password', type=valid_password, required=True, nullable=False, location='json')
+        parser.add_argument('interface_language', type=supported_language, required=True, nullable=False,
+                            location='json')
+        parser.add_argument('timezone', type=timezone, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        account = RegisterService.get_account_if_token_valid(args['workspace_id'], args['email'], args['token'])
+        if account is None:
+            raise AlreadyActivateError()
+
+        RegisterService.revoke_token(args['workspace_id'], args['email'], args['token'])
+
+        account.name = args['name']
+
+        # generate password salt
+        salt = secrets.token_bytes(16)
+        base64_salt = base64.b64encode(salt).decode()
+
+        # encrypt password with salt
+        password_hashed = hash_password(args['password'], salt)
+        base64_password_hashed = base64.b64encode(password_hashed).decode()
+        account.password = base64_password_hashed
+        account.password_salt = base64_salt
+        account.interface_language = args['interface_language']
+        account.timezone = args['timezone']
+        account.interface_theme = 'light'
+        account.status = AccountStatus.ACTIVE.value
+        account.initialized_at = datetime.utcnow()
+        db.session.commit()
+
+        return {'result': 'success'}
+
+
+api.add_resource(ActivateCheckApi, '/activate/check')
+api.add_resource(ActivateApi, '/activate')

api/controllers/console/auth/data_source_oauth.py

@@ -0,0 +1,101 @@
+import logging
+from datetime import datetime
+from typing import Optional
+
+import flask_login
+import requests
+from flask import request, redirect, current_app, session
+from flask_login import current_user, login_required
+from flask_restful import Resource
+from werkzeug.exceptions import Forbidden
+from libs.oauth_data_source import NotionOAuth
+from controllers.console import api
+from ..setup import setup_required
+from ..wraps import account_initialization_required
+
+
+def get_oauth_providers():
+    with current_app.app_context():
+        notion_oauth = NotionOAuth(client_id=current_app.config.get('NOTION_CLIENT_ID'),
+                                   client_secret=current_app.config.get(
+                                       'NOTION_CLIENT_SECRET'),
+                                   redirect_uri=current_app.config.get(
+                                       'CONSOLE_API_URL') + '/console/api/oauth/data-source/callback/notion')
+
+        OAUTH_PROVIDERS = {
+            'notion': notion_oauth
+        }
+        return OAUTH_PROVIDERS
+
+
+class OAuthDataSource(Resource):
+    def get(self, provider: str):
+        # The role of the current user in the table must be admin or owner
+        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+            raise Forbidden()
+        OAUTH_DATASOURCE_PROVIDERS = get_oauth_providers()
+        with current_app.app_context():
+            oauth_provider = OAUTH_DATASOURCE_PROVIDERS.get(provider)
+            print(vars(oauth_provider))
+        if not oauth_provider:
+            return {'error': 'Invalid provider'}, 400
+        if current_app.config.get('NOTION_INTEGRATION_TYPE') == 'internal':
+            internal_secret = current_app.config.get('NOTION_INTERNAL_SECRET')
+            oauth_provider.save_internal_access_token(internal_secret)
+            return redirect(f'{current_app.config.get("CONSOLE_WEB_URL")}?oauth_data_source=success')
+        else:
+            auth_url = oauth_provider.get_authorization_url()
+            return redirect(auth_url)
+
+
+
+
+class OAuthDataSourceCallback(Resource):
+    def get(self, provider: str):
+        OAUTH_DATASOURCE_PROVIDERS = get_oauth_providers()
+        with current_app.app_context():
+            oauth_provider = OAUTH_DATASOURCE_PROVIDERS.get(provider)
+        if not oauth_provider:
+            return {'error': 'Invalid provider'}, 400
+        if 'code' in request.args:
+            code = request.args.get('code')
+            try:
+                oauth_provider.get_access_token(code)
+            except requests.exceptions.HTTPError as e:
+                logging.exception(
+                    f"An error occurred during the OAuthCallback process with {provider}: {e.response.text}")
+                return {'error': 'OAuth data source process failed'}, 400
+
+            return redirect(f'{current_app.config.get("CONSOLE_WEB_URL")}?oauth_data_source=success')
+        elif 'error' in request.args:
+            error = request.args.get('error')
+            return redirect(f'{current_app.config.get("CONSOLE_WEB_URL")}?oauth_data_source={error}')
+        else:
+            return redirect(f'{current_app.config.get("CONSOLE_WEB_URL")}?oauth_data_source=access_denied')
+
+
+class OAuthDataSourceSync(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, provider, binding_id):
+        provider = str(provider)
+        binding_id = str(binding_id)
+        OAUTH_DATASOURCE_PROVIDERS = get_oauth_providers()
+        with current_app.app_context():
+            oauth_provider = OAUTH_DATASOURCE_PROVIDERS.get(provider)
+        if not oauth_provider:
+            return {'error': 'Invalid provider'}, 400
+        try:
+            oauth_provider.sync_data_source(binding_id)
+        except requests.exceptions.HTTPError as e:
+            logging.exception(
+                f"An error occurred during the OAuthCallback process with {provider}: {e.response.text}")
+            return {'error': 'OAuth data source process failed'}, 400
+
+        return {'result': 'success'}, 200
+
+
+api.add_resource(OAuthDataSource, '/oauth/data-source/<string:provider>')
+api.add_resource(OAuthDataSourceCallback, '/oauth/data-source/callback/<string:provider>')
+api.add_resource(OAuthDataSourceSync, '/oauth/data-source/<string:provider>/<uuid:binding_id>/sync')

api/controllers/console/auth/oauth.py

@@ -20,13 +20,13 @@ def get_oauth_providers():
                                    client_secret=current_app.config.get(
                                        'GITHUB_CLIENT_SECRET'),
                                    redirect_uri=current_app.config.get(
-                                       'CONSOLE_URL') + '/console/api/oauth/authorize/github')
+                                       'CONSOLE_API_URL') + '/console/api/oauth/authorize/github')
 
         google_oauth = GoogleOAuth(client_id=current_app.config.get('GOOGLE_CLIENT_ID'),
                                    client_secret=current_app.config.get(
                                        'GOOGLE_CLIENT_SECRET'),
                                    redirect_uri=current_app.config.get(
-                                       'CONSOLE_URL') + '/console/api/oauth/authorize/google')
+                                       'CONSOLE_API_URL') + '/console/api/oauth/authorize/google')
 
         OAUTH_PROVIDERS = {
             'github': github_oauth,
@@ -80,7 +80,7 @@ class OAuthCallback(Resource):
         flask_login.login_user(account, remember=True)
         AccountService.update_last_login(account, request)
 
-        return redirect(f'{current_app.config.get("CONSOLE_URL")}?oauth_login=success')
+        return redirect(f'{current_app.config.get("CONSOLE_WEB_URL")}?oauth_login=success')
 
 
 def _get_account_by_openid_or_email(provider: str, user_info: OAuthUserInfo) -> Optional[Account]:

api/controllers/console/datasets/data_source.py

@@ -0,0 +1,304 @@
+import datetime
+import json
+
+from cachetools import TTLCache
+from flask import request, current_app
+from flask_login import login_required, current_user
+from flask_restful import Resource, marshal_with, fields, reqparse, marshal
+from werkzeug.exceptions import NotFound
+
+from controllers.console import api
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from core.data_loader.loader.notion import NotionLoader
+from core.indexing_runner import IndexingRunner
+from extensions.ext_database import db
+from libs.helper import TimestampField
+from models.dataset import Document
+from models.source import DataSourceBinding
+from services.dataset_service import DatasetService, DocumentService
+from tasks.document_indexing_sync_task import document_indexing_sync_task
+
+cache = TTLCache(maxsize=None, ttl=30)
+
+FILE_SIZE_LIMIT = 15 * 1024 * 1024  # 15MB
+ALLOWED_EXTENSIONS = ['txt', 'markdown', 'md', 'pdf', 'html', 'htm']
+PREVIEW_WORDS_LIMIT = 3000
+
+
+class DataSourceApi(Resource):
+    integrate_icon_fields = {
+        'type': fields.String,
+        'url': fields.String,
+        'emoji': fields.String
+    }
+    integrate_page_fields = {
+        'page_name': fields.String,
+        'page_id': fields.String,
+        'page_icon': fields.Nested(integrate_icon_fields, allow_null=True),
+        'parent_id': fields.String,
+        'type': fields.String
+    }
+    integrate_workspace_fields = {
+        'workspace_name': fields.String,
+        'workspace_id': fields.String,
+        'workspace_icon': fields.String,
+        'pages': fields.List(fields.Nested(integrate_page_fields)),
+        'total': fields.Integer
+    }
+    integrate_fields = {
+        'id': fields.String,
+        'provider': fields.String,
+        'created_at': TimestampField,
+        'is_bound': fields.Boolean,
+        'disabled': fields.Boolean,
+        'link': fields.String,
+        'source_info': fields.Nested(integrate_workspace_fields)
+    }
+    integrate_list_fields = {
+        'data': fields.List(fields.Nested(integrate_fields)),
+    }
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(integrate_list_fields)
+    def get(self):
+        # get workspace data source integrates
+        data_source_integrates = db.session.query(DataSourceBinding).filter(
+            DataSourceBinding.tenant_id == current_user.current_tenant_id,
+            DataSourceBinding.disabled == False
+        ).all()
+
+        base_url = request.url_root.rstrip('/')
+        data_source_oauth_base_path = "/console/api/oauth/data-source"
+        providers = ["notion"]
+
+        integrate_data = []
+        for provider in providers:
+            # existing_integrate = next((ai for ai in data_source_integrates if ai.provider == provider), None)
+            existing_integrates = filter(lambda item: item.provider == provider, data_source_integrates)
+            if existing_integrates:
+                for existing_integrate in list(existing_integrates):
+                    integrate_data.append({
+                        'id': existing_integrate.id,
+                        'provider': provider,
+                        'created_at': existing_integrate.created_at,
+                        'is_bound': True,
+                        'disabled': existing_integrate.disabled,
+                        'source_info': existing_integrate.source_info,
+                        'link': f'{base_url}{data_source_oauth_base_path}/{provider}'
+                })
+            else:
+                integrate_data.append({
+                    'id': None,
+                    'provider': provider,
+                    'created_at': None,
+                    'source_info': None,
+                    'is_bound': False,
+                    'disabled': None,
+                    'link': f'{base_url}{data_source_oauth_base_path}/{provider}'
+                })
+        return {'data': integrate_data}, 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, binding_id, action):
+        binding_id = str(binding_id)
+        action = str(action)
+        data_source_binding = DataSourceBinding.query.filter_by(
+            id=binding_id
+        ).first()
+        if data_source_binding is None:
+            raise NotFound('Data source binding not found.')
+        # enable binding
+        if action == 'enable':
+            if data_source_binding.disabled:
+                data_source_binding.disabled = False
+                data_source_binding.updated_at = datetime.datetime.utcnow()
+                db.session.add(data_source_binding)
+                db.session.commit()
+            else:
+                raise ValueError('Data source is not disabled.')
+        # disable binding
+        if action == 'disable':
+            if not data_source_binding.disabled:
+                data_source_binding.disabled = True
+                data_source_binding.updated_at = datetime.datetime.utcnow()
+                db.session.add(data_source_binding)
+                db.session.commit()
+            else:
+                raise ValueError('Data source is disabled.')
+        return {'result': 'success'}, 200
+
+
+class DataSourceNotionListApi(Resource):
+    integrate_icon_fields = {
+        'type': fields.String,
+        'url': fields.String,
+        'emoji': fields.String
+    }
+    integrate_page_fields = {
+        'page_name': fields.String,
+        'page_id': fields.String,
+        'page_icon': fields.Nested(integrate_icon_fields, allow_null=True),
+        'is_bound': fields.Boolean,
+        'parent_id': fields.String,
+        'type': fields.String
+    }
+    integrate_workspace_fields = {
+        'workspace_name': fields.String,
+        'workspace_id': fields.String,
+        'workspace_icon': fields.String,
+        'pages': fields.List(fields.Nested(integrate_page_fields))
+    }
+    integrate_notion_info_list_fields = {
+        'notion_info': fields.List(fields.Nested(integrate_workspace_fields)),
+    }
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(integrate_notion_info_list_fields)
+    def get(self):
+        dataset_id = request.args.get('dataset_id', default=None, type=str)
+        exist_page_ids = []
+        # import notion in the exist dataset
+        if dataset_id:
+            dataset = DatasetService.get_dataset(dataset_id)
+            if not dataset:
+                raise NotFound('Dataset not found.')
+            if dataset.data_source_type != 'notion_import':
+                raise ValueError('Dataset is not notion type.')
+            documents = Document.query.filter_by(
+                dataset_id=dataset_id,
+                tenant_id=current_user.current_tenant_id,
+                data_source_type='notion_import',
+                enabled=True
+            ).all()
+            if documents:
+                for document in documents:
+                    data_source_info = json.loads(document.data_source_info)
+                    exist_page_ids.append(data_source_info['notion_page_id'])
+        # get all authorized pages
+        data_source_bindings = DataSourceBinding.query.filter_by(
+            tenant_id=current_user.current_tenant_id,
+            provider='notion',
+            disabled=False
+        ).all()
+        if not data_source_bindings:
+            return {
+                'notion_info': []
+            }, 200
+        pre_import_info_list = []
+        for data_source_binding in data_source_bindings:
+            source_info = data_source_binding.source_info
+            pages = source_info['pages']
+            # Filter out already bound pages
+            for page in pages:
+                if page['page_id'] in exist_page_ids:
+                    page['is_bound'] = True
+                else:
+                    page['is_bound'] = False
+            pre_import_info = {
+                'workspace_name': source_info['workspace_name'],
+                'workspace_icon': source_info['workspace_icon'],
+                'workspace_id': source_info['workspace_id'],
+                'pages': pages,
+            }
+            pre_import_info_list.append(pre_import_info)
+        return {
+            'notion_info': pre_import_info_list
+        }, 200
+
+
+class DataSourceNotionApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, workspace_id, page_id, page_type):
+        workspace_id = str(workspace_id)
+        page_id = str(page_id)
+        data_source_binding = DataSourceBinding.query.filter(
+            db.and_(
+                DataSourceBinding.tenant_id == current_user.current_tenant_id,
+                DataSourceBinding.provider == 'notion',
+                DataSourceBinding.disabled == False,
+                DataSourceBinding.source_info['workspace_id'] == f'"{workspace_id}"'
+            )
+        ).first()
+        if not data_source_binding:
+            raise NotFound('Data source binding not found.')
+
+        loader = NotionLoader(
+            notion_access_token=data_source_binding.access_token,
+            notion_workspace_id=workspace_id,
+            notion_obj_id=page_id,
+            notion_page_type=page_type
+        )
+
+        text_docs = loader.load()
+        return {
+            'content': "\n".join([doc.page_content for doc in text_docs])
+        }, 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('notion_info_list', type=list, required=True, nullable=True, location='json')
+        parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        args = parser.parse_args()
+        # validate args
+        DocumentService.estimate_args_validate(args)
+        indexing_runner = IndexingRunner()
+        response = indexing_runner.notion_indexing_estimate(args['notion_info_list'], args['process_rule'])
+        return response, 200
+
+
+class DataSourceNotionDatasetSyncApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id):
+        dataset_id_str = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id_str)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+
+        documents = DocumentService.get_document_by_dataset_id(dataset_id_str)
+        for document in documents:
+            document_indexing_sync_task.delay(dataset_id_str, document.id)
+        return 200
+
+
+class DataSourceNotionDocumentSyncApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, document_id):
+        dataset_id_str = str(dataset_id)
+        document_id_str = str(document_id)
+        dataset = DatasetService.get_dataset(dataset_id_str)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+
+        document = DocumentService.get_document(dataset_id_str, document_id_str)
+        if document is None:
+            raise NotFound("Document not found.")
+        document_indexing_sync_task.delay(dataset_id_str, document_id_str)
+        return 200
+
+
+api.add_resource(DataSourceApi, '/data-source/integrates', '/data-source/integrates/<uuid:binding_id>/<string:action>')
+api.add_resource(DataSourceNotionListApi, '/notion/pre-import/pages')
+api.add_resource(DataSourceNotionApi,
+                 '/notion/workspaces/<uuid:workspace_id>/pages/<uuid:page_id>/<string:page_type>/preview',
+                 '/datasets/notion-indexing-estimate')
+api.add_resource(DataSourceNotionDatasetSyncApi, '/datasets/<uuid:dataset_id>/notion/sync')
+api.add_resource(DataSourceNotionDocumentSyncApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/notion/sync')

api/controllers/console/datasets/datasets.py

@@ -3,7 +3,6 @@ from flask import request
 from flask_login import login_required, current_user
 from flask_restful import Resource, reqparse, fields, marshal, marshal_with
 from werkzeug.exceptions import NotFound, Forbidden
-
 import services
 from controllers.console import api
 from controllers.console.datasets.error import DatasetNameDuplicateError
@@ -12,8 +11,9 @@ from controllers.console.wraps import account_initialization_required
 from core.indexing_runner import IndexingRunner
 from libs.helper import TimestampField
 from extensions.ext_database import db
+from models.dataset import DocumentSegment, Document
 from models.model import UploadFile
-from services.dataset_service import DatasetService
+from services.dataset_service import DatasetService, DocumentService
 
 dataset_detail_fields = {
     'id': fields.String,
@@ -217,17 +217,32 @@ class DatasetIndexingEstimateApi(Resource):
     @login_required
     @account_initialization_required
     def post(self):
-        segment_rule = request.get_json()
-        file_detail = db.session.query(UploadFile).filter(
-            UploadFile.tenant_id == current_user.current_tenant_id,
-            UploadFile.id == segment_rule["file_id"]
-        ).first()
+        parser = reqparse.RequestParser()
+        parser.add_argument('info_list', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
+        args = parser.parse_args()
+        # validate args
+        DocumentService.estimate_args_validate(args)
+        if args['info_list']['data_source_type'] == 'upload_file':
+            file_ids = args['info_list']['file_info_list']['file_ids']
+            file_details = db.session.query(UploadFile).filter(
+                UploadFile.tenant_id == current_user.current_tenant_id,
+                UploadFile.id.in_(file_ids)
+            ).all()
 
-        if file_detail is None:
-            raise NotFound("File not found.")
+            if file_details is None:
+                raise NotFound("File not found.")
 
-        indexing_runner = IndexingRunner()
-        response = indexing_runner.indexing_estimate(file_detail, segment_rule['process_rule'])
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.file_indexing_estimate(file_details, args['process_rule'], args['doc_form'])
+        elif args['info_list']['data_source_type'] == 'notion_import':
+
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.notion_indexing_estimate(args['info_list']['notion_info_list'],
+                                                                args['process_rule'], args['doc_form'])
+        else:
+            raise ValueError('Data source type not support')
         return response, 200
 
 
@@ -274,8 +289,54 @@ class DatasetRelatedAppListApi(Resource):
         }, 200
 
 
+class DatasetIndexingStatusApi(Resource):
+    document_status_fields = {
+        'id': fields.String,
+        'indexing_status': fields.String,
+        'processing_started_at': TimestampField,
+        'parsing_completed_at': TimestampField,
+        'cleaning_completed_at': TimestampField,
+        'splitting_completed_at': TimestampField,
+        'completed_at': TimestampField,
+        'paused_at': TimestampField,
+        'error': fields.String,
+        'stopped_at': TimestampField,
+        'completed_segments': fields.Integer,
+        'total_segments': fields.Integer,
+    }
+
+    document_status_fields_list = {
+        'data': fields.List(fields.Nested(document_status_fields))
+    }
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id):
+        dataset_id = str(dataset_id)
+        documents = db.session.query(Document).filter(
+            Document.dataset_id == dataset_id,
+            Document.tenant_id == current_user.current_tenant_id
+        ).all()
+        documents_status = []
+        for document in documents:
+            completed_segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                              DocumentSegment.document_id == str(document.id),
+                                                              DocumentSegment.status != 're_segment').count()
+            total_segments = DocumentSegment.query.filter(DocumentSegment.document_id == str(document.id),
+                                                          DocumentSegment.status != 're_segment').count()
+            document.completed_segments = completed_segments
+            document.total_segments = total_segments
+            documents_status.append(marshal(document, self.document_status_fields))
+        data = {
+            'data': documents_status
+        }
+        return data
+
+
 api.add_resource(DatasetListApi, '/datasets')
 api.add_resource(DatasetApi, '/datasets/<uuid:dataset_id>')
 api.add_resource(DatasetQueryApi, '/datasets/<uuid:dataset_id>/queries')
-api.add_resource(DatasetIndexingEstimateApi, '/datasets/file-indexing-estimate')
+api.add_resource(DatasetIndexingEstimateApi, '/datasets/indexing-estimate')
 api.add_resource(DatasetRelatedAppListApi, '/datasets/<uuid:dataset_id>/related-apps')
+api.add_resource(DatasetIndexingStatusApi, '/datasets/<uuid:dataset_id>/indexing-status')

api/controllers/console/datasets/datasets_document.py

@@ -1,6 +1,7 @@
 # -*- coding:utf-8 -*-
 import random
 from datetime import datetime
+from typing import List
 
 from flask import request
 from flask_login import login_required, current_user
@@ -59,6 +60,31 @@ document_fields = {
     'display_status': fields.String,
     'word_count': fields.Integer,
     'hit_count': fields.Integer,
+    'doc_form': fields.String,
+}
+
+document_with_segments_fields = {
+    'id': fields.String,
+    'position': fields.Integer,
+    'data_source_type': fields.String,
+    'data_source_info': fields.Raw(attribute='data_source_info_dict'),
+    'dataset_process_rule_id': fields.String,
+    'name': fields.String,
+    'created_from': fields.String,
+    'created_by': fields.String,
+    'created_at': TimestampField,
+    'tokens': fields.Integer,
+    'indexing_status': fields.String,
+    'error': fields.String,
+    'enabled': fields.Boolean,
+    'disabled_at': TimestampField,
+    'disabled_by': fields.String,
+    'archived': fields.Boolean,
+    'display_status': fields.String,
+    'word_count': fields.Integer,
+    'hit_count': fields.Integer,
+    'completed_segments': fields.Integer,
+    'total_segments': fields.Integer
 }
 
 
@@ -83,6 +109,23 @@ class DocumentResource(Resource):
 
         return document
 
+    def get_batch_documents(self, dataset_id: str, batch: str) -> List[Document]:
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound('Dataset not found.')
+
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        documents = DocumentService.get_batch_documents(dataset_id, batch)
+
+        if not documents:
+            raise NotFound('Documents not found.')
+
+        return documents
+
 
 class GetProcessRuleApi(Resource):
     @setup_required
@@ -132,9 +175,9 @@ class DatasetDocumentListApi(Resource):
         dataset_id = str(dataset_id)
         page = request.args.get('page', default=1, type=int)
         limit = request.args.get('limit', default=20, type=int)
-        search = request.args.get('search', default=None, type=str)
+        search = request.args.get('keyword', default=None, type=str)
         sort = request.args.get('sort', default='-created_at', type=str)
-
+        fetch = request.args.get('fetch', default=False, type=bool)
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')
@@ -173,9 +216,20 @@ class DatasetDocumentListApi(Resource):
         paginated_documents = query.paginate(
             page=page, per_page=limit, max_per_page=100, error_out=False)
         documents = paginated_documents.items
-
+        if fetch:
+            for document in documents:
+                completed_segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                                  DocumentSegment.document_id == str(document.id),
+                                                                  DocumentSegment.status != 're_segment').count()
+                total_segments = DocumentSegment.query.filter(DocumentSegment.document_id == str(document.id),
+                                                              DocumentSegment.status != 're_segment').count()
+                document.completed_segments = completed_segments
+                document.total_segments = total_segments
+            data = marshal(documents, document_with_segments_fields)
+        else:
+            data = marshal(documents, document_fields)
         response = {
-            'data': marshal(documents, document_fields),
+            'data': data,
             'has_more': len(documents) == limit,
             'limit': limit,
             'total': paginated_documents.total,
@@ -184,10 +238,15 @@ class DatasetDocumentListApi(Resource):
 
         return response
 
+    documents_and_batch_fields = {
+        'documents': fields.List(fields.Nested(document_fields)),
+        'batch': fields.String
+    }
+
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(document_fields)
+    @marshal_with(documents_and_batch_fields)
     def post(self, dataset_id):
         dataset_id = str(dataset_id)
 
@@ -212,6 +271,7 @@ class DatasetDocumentListApi(Resource):
         parser.add_argument('process_rule', type=dict, required=False, location='json')
         parser.add_argument('duplicate', type=bool, nullable=False, location='json')
         parser.add_argument('original_document_id', type=str, required=False, location='json')
+        parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         args = parser.parse_args()
 
         if not dataset.indexing_technique and not args['indexing_technique']:
@@ -221,21 +281,25 @@ class DatasetDocumentListApi(Resource):
         DocumentService.document_create_args_validate(args)
 
         try:
-            document = DocumentService.save_document_with_dataset_id(dataset, args, current_user)
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+            documents, batch = DocumentService.save_document_with_dataset_id(dataset, args, current_user)
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
 
-        return document
+        return {
+            'documents': documents,
+            'batch': batch
+        }
 
 
 class DatasetInitApi(Resource):
     dataset_and_document_fields = {
         'dataset': fields.Nested(dataset_fields),
-        'document': fields.Nested(document_fields)
+        'documents': fields.List(fields.Nested(document_fields)),
+        'batch': fields.String
     }
 
     @setup_required
@@ -252,19 +316,20 @@ class DatasetInitApi(Resource):
                             nullable=False, location='json')
         parser.add_argument('data_source', type=dict, required=True, nullable=True, location='json')
         parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         args = parser.parse_args()
 
         # validate args
         DocumentService.document_create_args_validate(args)
 
         try:
-            dataset, document = DocumentService.save_document_without_dataset_id(
+            dataset, documents, batch = DocumentService.save_document_without_dataset_id(
                 tenant_id=current_user.current_tenant_id,
                 document_data=args,
                 account=current_user
             )
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -272,7 +337,8 @@ class DatasetInitApi(Resource):
 
         response = {
             'dataset': dataset,
-            'document': document
+            'documents': documents,
+            'batch': batch
         }
 
         return response
@@ -317,11 +383,124 @@ class DocumentIndexingEstimateApi(DocumentResource):
                     raise NotFound('File not found.')
 
                 indexing_runner = IndexingRunner()
-                response = indexing_runner.indexing_estimate(file, data_process_rule_dict)
+
+                response = indexing_runner.file_indexing_estimate([file], data_process_rule_dict)
 
         return response
 
 
+class DocumentBatchIndexingEstimateApi(DocumentResource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, batch):
+        dataset_id = str(dataset_id)
+        batch = str(batch)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+        documents = self.get_batch_documents(dataset_id, batch)
+        response = {
+            "tokens": 0,
+            "total_price": 0,
+            "currency": "USD",
+            "total_segments": 0,
+            "preview": []
+        }
+        if not documents:
+            return response
+        data_process_rule = documents[0].dataset_process_rule
+        data_process_rule_dict = data_process_rule.to_dict()
+        info_list = []
+        for document in documents:
+            if document.indexing_status in ['completed', 'error']:
+                raise DocumentAlreadyFinishedError()
+            data_source_info = document.data_source_info_dict
+            # format document files info
+            if data_source_info and 'upload_file_id' in data_source_info:
+                file_id = data_source_info['upload_file_id']
+                info_list.append(file_id)
+            # format document notion info
+            elif data_source_info and 'notion_workspace_id' in data_source_info and 'notion_page_id' in data_source_info:
+                pages = []
+                page = {
+                    'page_id': data_source_info['notion_page_id'],
+                    'type': data_source_info['type']
+                }
+                pages.append(page)
+                notion_info = {
+                    'workspace_id': data_source_info['notion_workspace_id'],
+                    'pages': pages
+                }
+                info_list.append(notion_info)
+
+        if dataset.data_source_type == 'upload_file':
+            file_details = db.session.query(UploadFile).filter(
+                UploadFile.tenant_id == current_user.current_tenant_id,
+                UploadFile.id in info_list
+            ).all()
+
+            if file_details is None:
+                raise NotFound("File not found.")
+
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.file_indexing_estimate(file_details, data_process_rule_dict)
+        elif dataset.data_source_type:
+
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.notion_indexing_estimate(info_list,
+                                                                data_process_rule_dict)
+        else:
+            raise ValueError('Data source type not support')
+        return response
+
+
+class DocumentBatchIndexingStatusApi(DocumentResource):
+    document_status_fields = {
+        'id': fields.String,
+        'indexing_status': fields.String,
+        'processing_started_at': TimestampField,
+        'parsing_completed_at': TimestampField,
+        'cleaning_completed_at': TimestampField,
+        'splitting_completed_at': TimestampField,
+        'completed_at': TimestampField,
+        'paused_at': TimestampField,
+        'error': fields.String,
+        'stopped_at': TimestampField,
+        'completed_segments': fields.Integer,
+        'total_segments': fields.Integer,
+    }
+
+    document_status_fields_list = {
+        'data': fields.List(fields.Nested(document_status_fields))
+    }
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, batch):
+        dataset_id = str(dataset_id)
+        batch = str(batch)
+        documents = self.get_batch_documents(dataset_id, batch)
+        documents_status = []
+        for document in documents:
+            completed_segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                              DocumentSegment.document_id == str(document.id),
+                                                              DocumentSegment.status != 're_segment').count()
+            total_segments = DocumentSegment.query.filter(DocumentSegment.document_id == str(document.id),
+                                                          DocumentSegment.status != 're_segment').count()
+            document.completed_segments = completed_segments
+            document.total_segments = total_segments
+            if document.is_paused:
+                document.indexing_status = 'paused'
+            documents_status.append(marshal(document, self.document_status_fields))
+        data = {
+            'data': documents_status
+        }
+        return data
+
+
 class DocumentIndexingStatusApi(DocumentResource):
     document_status_fields = {
         'id': fields.String,
@@ -408,9 +587,10 @@ class DocumentDetailApi(DocumentResource):
                 'disabled_by': document.disabled_by,
                 'archived': document.archived,
                 'segment_count': document.segment_count,
-                'average_segment_length':   document.average_segment_length,
+                'average_segment_length': document.average_segment_length,
                 'hit_count': document.hit_count,
-                'display_status': document.display_status
+                'display_status': document.display_status,
+                'doc_form': document.doc_form
             }
         else:
             process_rules = DatasetService.get_process_rules(dataset_id)
@@ -428,7 +608,7 @@ class DocumentDetailApi(DocumentResource):
                 'created_at': document.created_at.timestamp(),
                 'tokens': document.tokens,
                 'indexing_status': document.indexing_status,
-                'completed_at': int(document.completed_at.timestamp())if document.completed_at else None,
+                'completed_at': int(document.completed_at.timestamp()) if document.completed_at else None,
                 'updated_at': int(document.updated_at.timestamp()) if document.updated_at else None,
                 'indexing_latency': document.indexing_latency,
                 'error': document.error,
@@ -441,7 +621,8 @@ class DocumentDetailApi(DocumentResource):
                 'segment_count': document.segment_count,
                 'average_segment_length': document.average_segment_length,
                 'hit_count': document.hit_count,
-                'display_status': document.display_status
+                'display_status': document.display_status,
+                'doc_form': document.doc_form
             }
 
         return response, 200
@@ -579,6 +760,8 @@ class DocumentStatusApi(DocumentResource):
             return {'result': 'success'}, 200
 
         elif action == "disable":
+            if not document.completed_at or document.indexing_status != 'completed':
+                raise InvalidActionError('Document is not completed.')
             if not document.enabled:
                 raise InvalidActionError('Document already disabled.')
 
@@ -678,6 +861,10 @@ api.add_resource(DatasetInitApi,
                  '/datasets/init')
 api.add_resource(DocumentIndexingEstimateApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/indexing-estimate')
+api.add_resource(DocumentBatchIndexingEstimateApi,
+                 '/datasets/<uuid:dataset_id>/batch/<string:batch>/indexing-estimate')
+api.add_resource(DocumentBatchIndexingStatusApi,
+                 '/datasets/<uuid:dataset_id>/batch/<string:batch>/indexing-status')
 api.add_resource(DocumentIndexingStatusApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/indexing-status')
 api.add_resource(DocumentDetailApi,

api/controllers/console/datasets/datasets_segments.py

@@ -15,8 +15,8 @@ from extensions.ext_redis import redis_client
 from models.dataset import DocumentSegment
 
 from libs.helper import TimestampField
-from services.dataset_service import DatasetService, DocumentService
-from tasks.add_segment_to_index_task import add_segment_to_index_task
+from services.dataset_service import DatasetService, DocumentService, SegmentService
+from tasks.enable_segment_to_index_task import enable_segment_to_index_task
 from tasks.remove_segment_from_index_task import remove_segment_from_index_task
 
 segment_fields = {
@@ -24,6 +24,7 @@ segment_fields = {
     'position': fields.Integer,
     'document_id': fields.String,
     'content': fields.String,
+    'answer': fields.String,
     'word_count': fields.Integer,
     'tokens': fields.Integer,
     'keywords': fields.List(fields.String),
@@ -125,6 +126,7 @@ class DatasetDocumentSegmentListApi(Resource):
 
         return {
             'data': marshal(segments, segment_fields),
+            'doc_form': document.doc_form,
             'has_more': has_more,
             'limit': limit,
             'total': total
@@ -180,7 +182,7 @@ class DatasetDocumentSegmentApi(Resource):
             # Set cache to prevent indexing the same segment multiple times
             redis_client.setex(indexing_cache_key, 600, 1)
 
-            add_segment_to_index_task.delay(segment.id)
+            enable_segment_to_index_task.delay(segment.id)
 
             return {'result': 'success'}, 200
         elif action == "disable":
@@ -202,7 +204,91 @@ class DatasetDocumentSegmentApi(Resource):
             raise InvalidActionError()
 
 
+class DatasetDocumentSegmentAddApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, dataset_id, document_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound('Dataset not found.')
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound('Document not found.')
+        # The role of the current user in the ta table must be admin or owner
+        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+            raise Forbidden()
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+        # validate args
+        parser = reqparse.RequestParser()
+        parser.add_argument('content', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('answer', type=str, required=False, nullable=True, location='json')
+        parser.add_argument('keywords', type=list, required=False, nullable=True, location='json')
+        args = parser.parse_args()
+        SegmentService.segment_create_args_validate(args, document)
+        segment = SegmentService.create_segment(args, document)
+        return {
+            'data': marshal(segment, segment_fields),
+            'doc_form': document.doc_form
+        }, 200
+
+
+class DatasetDocumentSegmentUpdateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, dataset_id, document_id, segment_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound('Dataset not found.')
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound('Document not found.')
+        # check segment
+        segment_id = str(segment_id)
+        segment = DocumentSegment.query.filter(
+            DocumentSegment.id == str(segment_id),
+            DocumentSegment.tenant_id == current_user.current_tenant_id
+        ).first()
+        if not segment:
+            raise NotFound('Segment not found.')
+        # The role of the current user in the ta table must be admin or owner
+        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+            raise Forbidden()
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+        # validate args
+        parser = reqparse.RequestParser()
+        parser.add_argument('content', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('answer', type=str, required=False, nullable=True, location='json')
+        parser.add_argument('keywords', type=list, required=False, nullable=True, location='json')
+        args = parser.parse_args()
+        SegmentService.segment_create_args_validate(args, document)
+        segment = SegmentService.update_segment(args, segment, document)
+        return {
+            'data': marshal(segment, segment_fields),
+            'doc_form': document.doc_form
+        }, 200
+
+
 api.add_resource(DatasetDocumentSegmentListApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments')
 api.add_resource(DatasetDocumentSegmentApi,
                  '/datasets/<uuid:dataset_id>/segments/<uuid:segment_id>/<string:action>')
+api.add_resource(DatasetDocumentSegmentAddApi,
+                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segment')
+api.add_resource(DatasetDocumentSegmentUpdateApi,
+                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>')

api/controllers/console/datasets/file.py

@@ -1,6 +1,7 @@
 import datetime
 import hashlib
 import tempfile
+import chardet
 import time
 import uuid
 from pathlib import Path
@@ -16,9 +17,7 @@ from controllers.console.datasets.error import NoFileUploadedError, TooManyFiles
     UnsupportedFileTypeError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
-from core.index.readers.html_parser import HTMLParser
-from core.index.readers.pdf_parser import PDFParser
-from core.index.readers.xlsx_parser import XLSXParser
+from core.data_loader.file_extractor import FileExtractor
 from extensions.ext_storage import storage
 from libs.helper import TimestampField
 from extensions.ext_database import db
@@ -122,27 +121,7 @@ class FilePreviewApi(Resource):
         if extension not in ALLOWED_EXTENSIONS:
             raise UnsupportedFileTypeError()
 
-        with tempfile.TemporaryDirectory() as temp_dir:
-            suffix = Path(upload_file.key).suffix
-            filepath = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
-            storage.download(upload_file.key, filepath)
-
-            if extension == 'pdf':
-                parser = PDFParser({'upload_file': upload_file})
-                text = parser.parse_file(Path(filepath))
-            elif extension in ['html', 'htm']:
-                # Use BeautifulSoup to extract text
-                parser = HTMLParser()
-                text = parser.parse_file(Path(filepath))
-            elif extension == 'xlsx':
-                parser = XLSXParser()
-                text = parser.parse_file(filepath)
-            else:
-                # ['txt', 'markdown', 'md']
-                with open(filepath, "rb") as fp:
-                    data = fp.read()
-                    text = data.decode(encoding='utf-8').strip() if data else ''
-
+        text = FileExtractor.load(upload_file, return_text=True)
         text = text[0:PREVIEW_WORDS_LIMIT] if text else ''
         return {'content': text}
 

api/controllers/console/datasets/hit_testing.py

@@ -28,6 +28,7 @@ segment_fields = {
     'position': fields.Integer,
     'document_id': fields.String,
     'content': fields.String,
+    'answer': fields.String,
     'word_count': fields.Integer,
     'tokens': fields.Integer,
     'keywords': fields.List(fields.String),
@@ -95,8 +96,8 @@ class HitTestingApi(Resource):
             return {"query": response['query'], 'records': marshal(response['records'], hit_testing_record_fields)}
         except services.errors.index.IndexNotInitializedError:
             raise DatasetNotInitializedError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:

api/controllers/console/error.py

@@ -18,3 +18,9 @@ class AccountNotLinkTenantError(BaseHTTPException):
     error_code = 'account_not_link_tenant'
     description = "Account not link tenant."
     code = 403
+
+
+class AlreadyActivateError(BaseHTTPException):
+    error_code = 'already_activate'
+    description = "Auth Token is invalid or account already activated, please check again."
+    code = 403

api/controllers/console/explore/audio.py

@@ -0,0 +1,66 @@
+# -*- coding:utf-8 -*-
+import logging
+
+from flask import request
+from werkzeug.exceptions import InternalServerError
+
+import services
+from controllers.console import api
+from controllers.console.app.error import AppUnavailableError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError, \
+    NoAudioUploadedError, AudioTooLargeError, \
+    UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError
+from controllers.console.explore.wraps import InstalledAppResource
+from core.llm.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from services.audio_service import AudioService
+from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \
+    UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError
+from models.model import AppModelConfig
+
+
+class ChatAudioApi(InstalledAppResource):
+    def post(self, installed_app):
+        app_model = installed_app.app
+        app_model_config: AppModelConfig = app_model.app_model_config
+
+        if not app_model_config.speech_to_text_dict['enabled']:
+            raise AppUnavailableError()
+
+        file = request.files['file']
+
+        try:
+            response = AudioService.transcript(
+                tenant_id=app_model.tenant_id,
+                file=file,
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+        
+
+api.add_resource(ChatAudioApi, '/installed-apps/<uuid:installed_app_id>/audio-to-text', endpoint='installed_app_audio')

api/controllers/console/explore/completion.py

@@ -54,8 +54,8 @@ class CompletionApi(InstalledAppResource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -113,8 +113,8 @@ class ChatApi(InstalledAppResource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -155,8 +155,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
             except services.errors.app_model_config.AppModelConfigBrokenError:
                 logging.exception("App model config broken.")
                 yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:

api/controllers/console/explore/conversation.py

@@ -65,7 +65,10 @@ class ConversationApi(InstalledAppResource):
             raise NotChatAppError()
 
         conversation_id = str(c_id)
-        ConversationService.delete(app_model, conversation_id, current_user)
+        try:
+            ConversationService.delete(app_model, conversation_id, current_user)
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
         WebConversationService.unpin(app_model, conversation_id, current_user)
 
         return {"result": "success"}, 204

api/controllers/console/explore/message.py

@@ -107,8 +107,8 @@ class MessageMoreLikeThisApi(InstalledAppResource):
             raise NotFound("Message Not Exists.")
         except MoreLikeThisDisabledError:
             raise AppMoreLikeThisDisabledError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -135,8 +135,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
                 yield "data: " + json.dumps(api.handle_error(NotFound("Message Not Exists.")).get_json()) + "\n\n"
             except MoreLikeThisDisabledError:
                 yield "data: " + json.dumps(api.handle_error(AppMoreLikeThisDisabledError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:
@@ -174,8 +174,8 @@ class MessageSuggestedQuestionApi(InstalledAppResource):
             raise NotFound("Conversation not found")
         except SuggestedQuestionsAfterAnswerDisabledError:
             raise AppSuggestedQuestionsAfterAnswerDisabledError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:

api/controllers/console/explore/parameter.py

@@ -4,6 +4,10 @@ from flask_restful import marshal_with, fields
 from controllers.console import api
 from controllers.console.explore.wraps import InstalledAppResource
 
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import InstalledApp
+
 
 class AppParameterApi(InstalledAppResource):
     """Resource for app variables."""
@@ -21,20 +25,23 @@ class AppParameterApi(InstalledAppResource):
         'opening_statement': fields.String,
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
 
     @marshal_with(parameters_fields)
-    def get(self, installed_app):
+    def get(self, installed_app: InstalledApp):
         """Retrieve app parameters."""
         app_model = installed_app.app
         app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(installed_app.tenant_id, 'whisper-1')
 
         return {
             'opening_statement': app_model_config.opening_statement,
             'suggested_questions': app_model_config.suggested_questions_list,
             'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/console/universal_chat/audio.py

@@ -0,0 +1,66 @@
+# -*- coding:utf-8 -*-
+import logging
+
+from flask import request
+from werkzeug.exceptions import InternalServerError
+
+import services
+from controllers.console import api
+from controllers.console.app.error import AppUnavailableError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError, \
+    NoAudioUploadedError, AudioTooLargeError, \
+    UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from core.llm.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from services.audio_service import AudioService
+from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \
+    UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError
+from models.model import AppModelConfig
+
+
+class UniversalChatAudioApi(UniversalChatResource):
+    def post(self, universal_app):
+        app_model = universal_app
+        app_model_config: AppModelConfig = app_model.app_model_config
+
+        if not app_model_config.speech_to_text_dict['enabled']:
+            raise AppUnavailableError()
+
+        file = request.files['file']
+
+        try:
+            response = AudioService.transcript(
+                tenant_id=app_model.tenant_id,
+                file=file,
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+        
+
+api.add_resource(UniversalChatAudioApi, '/universal-chat/audio-to-text')

api/controllers/console/universal_chat/chat.py

@@ -0,0 +1,142 @@
+import json
+import logging
+from typing import Generator, Union
+
+from flask import Response, stream_with_context
+from flask_login import current_user
+from flask_restful import reqparse
+from werkzeug.exceptions import InternalServerError, NotFound
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ConversationCompletedError, AppUnavailableError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from core.constant import llm_constant
+from core.conversation_message_task import PubHandler
+from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \
+    LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError
+from libs.helper import uuid_value
+from services.completion_service import CompletionService
+
+
+class UniversalChatApi(UniversalChatResource):
+    def post(self, universal_app):
+        app_model = universal_app
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('query', type=str, required=True, location='json')
+        parser.add_argument('conversation_id', type=uuid_value, location='json')
+        parser.add_argument('model', type=str, required=True, location='json')
+        parser.add_argument('tools', type=list, required=True, location='json')
+        args = parser.parse_args()
+
+        app_model_config = app_model.app_model_config
+
+        # update app model config
+        args['model_config'] = app_model_config.to_dict()
+        args['model_config']['model']['name'] = args['model']
+
+        if not llm_constant.models[args['model']]:
+            raise ValueError("Model not exists.")
+
+        args['model_config']['model']['provider'] = llm_constant.models[args['model']]
+        args['model_config']['agent_mode']['tools'] = args['tools']
+
+        if not args['model_config']['agent_mode']['tools']:
+            args['model_config']['agent_mode']['tools'] = [
+                {
+                    "current_datetime": {
+                        "enabled": True
+                    }
+                }
+            ]
+        else:
+            args['model_config']['agent_mode']['tools'].append({
+                    "current_datetime": {
+                        "enabled": True
+                    }
+                })
+
+        args['inputs'] = {}
+
+        del args['model']
+        del args['tools']
+
+        try:
+            response = CompletionService.completion(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                from_source='console',
+                streaming=True,
+                is_model_config_override=True,
+            )
+
+            return compact_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class UniversalChatStopApi(UniversalChatResource):
+    def post(self, universal_app, task_id):
+        PubHandler.stop(current_user, task_id)
+
+        return {'result': 'success'}, 200
+
+
+def compact_response(response: Union[dict | Generator]) -> Response:
+    if isinstance(response, dict):
+        return Response(response=json.dumps(response), status=200, mimetype='application/json')
+    else:
+        def generate() -> Generator:
+            try:
+                for chunk in response:
+                    yield chunk
+            except services.errors.conversation.ConversationNotExistsError:
+                yield "data: " + json.dumps(api.handle_error(NotFound("Conversation Not Exists.")).get_json()) + "\n\n"
+            except services.errors.conversation.ConversationCompletedError:
+                yield "data: " + json.dumps(api.handle_error(ConversationCompletedError()).get_json()) + "\n\n"
+            except services.errors.app_model_config.AppModelConfigBrokenError:
+                logging.exception("App model config broken.")
+                yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except QuotaExceededError:
+                yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
+            except ModelCurrentlyNotSupportError:
+                yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
+            except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                    LLMRateLimitError, LLMAuthorizationError) as e:
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+            except ValueError as e:
+                yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
+            except Exception:
+                logging.exception("internal server error.")
+                yield "data: " + json.dumps(api.handle_error(InternalServerError()).get_json()) + "\n\n"
+
+        return Response(stream_with_context(generate()), status=200,
+                        mimetype='text/event-stream')
+
+
+api.add_resource(UniversalChatApi, '/universal-chat/messages')
+api.add_resource(UniversalChatStopApi, '/universal-chat/messages/<string:task_id>/stop')

api/controllers/console/universal_chat/conversation.py

@@ -0,0 +1,118 @@
+# -*- coding:utf-8 -*-
+from flask_login import current_user
+from flask_restful import fields, reqparse, marshal_with
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound
+
+from controllers.console import api
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from libs.helper import TimestampField, uuid_value
+from services.conversation_service import ConversationService
+from services.errors.conversation import LastConversationNotExistsError, ConversationNotExistsError
+from services.web_conversation_service import WebConversationService
+
+conversation_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'inputs': fields.Raw,
+    'status': fields.String,
+    'introduction': fields.String,
+    'created_at': TimestampField,
+    'model_config': fields.Raw,
+}
+
+conversation_infinite_scroll_pagination_fields = {
+    'limit': fields.Integer,
+    'has_more': fields.Boolean,
+    'data': fields.List(fields.Nested(conversation_fields))
+}
+
+
+class UniversalChatConversationListApi(UniversalChatResource):
+
+    @marshal_with(conversation_infinite_scroll_pagination_fields)
+    def get(self, universal_app):
+        app_model = universal_app
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('last_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        parser.add_argument('pinned', type=str, choices=['true', 'false', None], location='args')
+        args = parser.parse_args()
+
+        pinned = None
+        if 'pinned' in args and args['pinned'] is not None:
+            pinned = True if args['pinned'] == 'true' else False
+
+        try:
+            return WebConversationService.pagination_by_last_id(
+                app_model=app_model,
+                user=current_user,
+                last_id=args['last_id'],
+                limit=args['limit'],
+                pinned=pinned
+            )
+        except LastConversationNotExistsError:
+            raise NotFound("Last Conversation Not Exists.")
+
+
+class UniversalChatConversationApi(UniversalChatResource):
+    def delete(self, universal_app, c_id):
+        app_model = universal_app
+        conversation_id = str(c_id)
+
+        try:
+            ConversationService.delete(app_model, conversation_id, current_user)
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+
+        WebConversationService.unpin(app_model, conversation_id, current_user)
+
+        return {"result": "success"}, 204
+
+
+class UniversalChatConversationRenameApi(UniversalChatResource):
+
+    @marshal_with(conversation_fields)
+    def post(self, universal_app, c_id):
+        app_model = universal_app
+        conversation_id = str(c_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('name', type=str, required=True, location='json')
+        args = parser.parse_args()
+
+        try:
+            return ConversationService.rename(app_model, conversation_id, current_user, args['name'])
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+
+
+class UniversalChatConversationPinApi(UniversalChatResource):
+
+    def patch(self, universal_app, c_id):
+        app_model = universal_app
+        conversation_id = str(c_id)
+
+        try:
+            WebConversationService.pin(app_model, conversation_id, current_user)
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+
+        return {"result": "success"}
+
+
+class UniversalChatConversationUnPinApi(UniversalChatResource):
+    def patch(self, universal_app, c_id):
+        app_model = universal_app
+        conversation_id = str(c_id)
+        WebConversationService.unpin(app_model, conversation_id, current_user)
+
+        return {"result": "success"}
+
+
+api.add_resource(UniversalChatConversationRenameApi, '/universal-chat/conversations/<uuid:c_id>/name')
+api.add_resource(UniversalChatConversationListApi, '/universal-chat/conversations')
+api.add_resource(UniversalChatConversationApi, '/universal-chat/conversations/<uuid:c_id>')
+api.add_resource(UniversalChatConversationPinApi, '/universal-chat/conversations/<uuid:c_id>/pin')
+api.add_resource(UniversalChatConversationUnPinApi, '/universal-chat/conversations/<uuid:c_id>/unpin')

api/controllers/console/universal_chat/message.py

@@ -0,0 +1,127 @@
+# -*- coding:utf-8 -*-
+import logging
+
+from flask_login import current_user
+from flask_restful import reqparse, fields, marshal_with
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound, InternalServerError
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError
+from controllers.console.explore.error import AppSuggestedQuestionsAfterAnswerDisabledError
+from controllers.console.universal_chat.wraps import UniversalChatResource
+from core.llm.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \
+    ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError
+from libs.helper import uuid_value, TimestampField
+from services.errors.conversation import ConversationNotExistsError
+from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
+from services.message_service import MessageService
+
+
+class UniversalChatMessageListApi(UniversalChatResource):
+    feedback_fields = {
+        'rating': fields.String
+    }
+
+    agent_thought_fields = {
+        'id': fields.String,
+        'chain_id': fields.String,
+        'message_id': fields.String,
+        'position': fields.Integer,
+        'thought': fields.String,
+        'tool': fields.String,
+        'tool_input': fields.String,
+        'created_at': TimestampField
+    }
+
+    message_fields = {
+        'id': fields.String,
+        'conversation_id': fields.String,
+        'inputs': fields.Raw,
+        'query': fields.String,
+        'answer': fields.String,
+        'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+        'created_at': TimestampField,
+        'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
+    }
+
+    message_infinite_scroll_pagination_fields = {
+        'limit': fields.Integer,
+        'has_more': fields.Boolean,
+        'data': fields.List(fields.Nested(message_fields))
+    }
+
+    @marshal_with(message_infinite_scroll_pagination_fields)
+    def get(self, universal_app):
+        app_model = universal_app
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('conversation_id', required=True, type=uuid_value, location='args')
+        parser.add_argument('first_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        args = parser.parse_args()
+
+        try:
+            return MessageService.pagination_by_first_id(app_model, current_user,
+                                                     args['conversation_id'], args['first_id'], args['limit'])
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.message.FirstMessageNotExistsError:
+            raise NotFound("First Message Not Exists.")
+
+
+class UniversalChatMessageFeedbackApi(UniversalChatResource):
+    def post(self, universal_app, message_id):
+        app_model = universal_app
+        message_id = str(message_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('rating', type=str, choices=['like', 'dislike', None], location='json')
+        args = parser.parse_args()
+
+        try:
+            MessageService.create_feedback(app_model, message_id, current_user, args['rating'])
+        except services.errors.message.MessageNotExistsError:
+            raise NotFound("Message Not Exists.")
+
+        return {'result': 'success'}
+
+
+class UniversalChatMessageSuggestedQuestionApi(UniversalChatResource):
+    def get(self, universal_app, message_id):
+        app_model = universal_app
+        message_id = str(message_id)
+
+        try:
+            questions = MessageService.get_suggested_questions_after_answer(
+                app_model=app_model,
+                user=current_user,
+                message_id=message_id
+            )
+        except MessageNotExistsError:
+            raise NotFound("Message not found")
+        except ConversationNotExistsError:
+            raise NotFound("Conversation not found")
+        except SuggestedQuestionsAfterAnswerDisabledError:
+            raise AppSuggestedQuestionsAfterAnswerDisabledError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+        return {'data': questions}
+
+
+api.add_resource(UniversalChatMessageListApi, '/universal-chat/messages')
+api.add_resource(UniversalChatMessageFeedbackApi, '/universal-chat/messages/<uuid:message_id>/feedbacks')
+api.add_resource(UniversalChatMessageSuggestedQuestionApi, '/universal-chat/messages/<uuid:message_id>/suggested-questions')

api/controllers/console/universal_chat/parameter.py

@@ -0,0 +1,36 @@
+# -*- coding:utf-8 -*-
+from flask_restful import marshal_with, fields
+
+from controllers.console import api
+from controllers.console.universal_chat.wraps import UniversalChatResource
+
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import App
+
+
+class UniversalChatParameterApi(UniversalChatResource):
+    """Resource for app variables."""
+    parameters_fields = {
+        'opening_statement': fields.String,
+        'suggested_questions': fields.Raw,
+        'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
+    }
+
+    @marshal_with(parameters_fields)
+    def get(self, universal_app: App):
+        """Retrieve app parameters."""
+        app_model = universal_app
+        app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(universal_app.tenant_id, 'whisper-1')
+
+        return {
+            'opening_statement': app_model_config.opening_statement,
+            'suggested_questions': app_model_config.suggested_questions_list,
+            'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
+        }
+
+
+api.add_resource(UniversalChatParameterApi, '/universal-chat/parameters')

api/controllers/console/universal_chat/wraps.py

@@ -0,0 +1,84 @@
+import json
+from functools import wraps
+
+from flask_login import login_required, current_user
+from flask_restful import Resource
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from extensions.ext_database import db
+from models.model import App, AppModelConfig
+
+
+def universal_chat_app_required(view=None):
+    def decorator(view):
+        @wraps(view)
+        def decorated(*args, **kwargs):
+            # get universal chat app
+            universal_app = db.session.query(App).filter(
+                App.tenant_id == current_user.current_tenant_id,
+                App.is_universal == True
+            ).first()
+
+            if universal_app is None:
+                # create universal app if not exists
+                universal_app = App(
+                    tenant_id=current_user.current_tenant_id,
+                    name='Universal Chat',
+                    mode='chat',
+                    is_universal=True,
+                    icon='',
+                    icon_background='',
+                    api_rpm=0,
+                    api_rph=0,
+                    enable_site=False,
+                    enable_api=False,
+                    status='normal'
+                )
+
+                db.session.add(universal_app)
+                db.session.flush()
+
+                app_model_config = AppModelConfig(
+                    provider="",
+                    model_id="",
+                    configs={},
+                    opening_statement='',
+                    suggested_questions=json.dumps([]),
+                    suggested_questions_after_answer=json.dumps({'enabled': True}),
+                    speech_to_text=json.dumps({'enabled': True}),
+                    more_like_this=None,
+                    sensitive_word_avoidance=None,
+                    model=json.dumps({
+                        "provider": "openai",
+                        "name": "gpt-3.5-turbo-16k",
+                        "completion_params": {
+                            "max_tokens": 800,
+                            "temperature": 0.8,
+                            "top_p": 1,
+                            "presence_penalty": 0,
+                            "frequency_penalty": 0
+                        }
+                    }),
+                    user_input_form=json.dumps([]),
+                    pre_prompt='',
+                    agent_mode=json.dumps({"enabled": True, "strategy": "function_call", "tools": []}),
+                )
+
+                app_model_config.app_id = universal_app.id
+                db.session.add(app_model_config)
+                db.session.flush()
+
+                universal_app.app_model_config_id = app_model_config.id
+                db.session.commit()
+
+            return view(universal_app, *args, **kwargs)
+        return decorated
+
+    if view:
+        return decorator(view)
+    return decorator
+
+
+class UniversalChatResource(Resource):
+    # must be reversed if there are multiple decorators
+    method_decorators = [universal_chat_app_required, account_initialization_required, login_required, setup_required]

api/controllers/console/version.py

@@ -32,8 +32,13 @@ class VersionApi(Resource):
                 'current_version': args.get('current_version')
             })
         except Exception as error:
-            logging.exception("Check update error.")
-            raise InternalServerError()
+            logging.warning("Check update version error: {}.".format(str(error)))
+            return {
+                'version': args.get('current_version'),
+                'release_date': '',
+                'release_notes': '',
+                'can_auto_update': False
+            }
 
         content = json.loads(response.content)
         return {

api/controllers/console/workspace/account.py

@@ -6,22 +6,23 @@ from flask import current_app, request
 from flask_login import login_required, current_user
 from flask_restful import Resource, reqparse, fields, marshal_with
 
+from services.errors.account import CurrentPasswordIncorrectError as ServiceCurrentPasswordIncorrectError
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.workspace.error import AccountAlreadyInitedError, InvalidInvitationCodeError, \
-    RepeatPasswordNotMatchError
+    RepeatPasswordNotMatchError, CurrentPasswordIncorrectError
 from controllers.console.wraps import account_initialization_required
 from libs.helper import TimestampField, supported_language, timezone
 from extensions.ext_database import db
 from models.account import InvitationCode, AccountIntegrate
 from services.account_service import AccountService
 
-
 account_fields = {
     'id': fields.String,
     'name': fields.String,
     'avatar': fields.String,
     'email': fields.String,
+    'is_password_set': fields.Boolean,
     'interface_language': fields.String,
     'interface_theme': fields.String,
     'timezone': fields.String,
@@ -194,8 +195,11 @@ class AccountPasswordApi(Resource):
         if args['new_password'] != args['repeat_new_password']:
             raise RepeatPasswordNotMatchError()
 
-        AccountService.update_account_password(
-            current_user, args['password'], args['new_password'])
+        try:
+            AccountService.update_account_password(
+                current_user, args['password'], args['new_password'])
+        except ServiceCurrentPasswordIncorrectError:
+            raise CurrentPasswordIncorrectError()
 
         return {"result": "success"}
 

api/controllers/console/workspace/error.py

@@ -7,6 +7,12 @@ class RepeatPasswordNotMatchError(BaseHTTPException):
     code = 400
 
 
+class CurrentPasswordIncorrectError(BaseHTTPException):
+    error_code = 'current_password_incorrect'
+    description = "Current password is incorrect."
+    code = 400
+
+
 class ProviderRequestFailedError(BaseHTTPException):
     error_code = 'provider_request_failed'
     description = None

api/controllers/console/workspace/members.py

@@ -1,5 +1,5 @@
 # -*- coding:utf-8 -*-
-
+from flask import current_app
 from flask_login import login_required, current_user
 from flask_restful import Resource, reqparse, marshal_with, abort, fields, marshal
 
@@ -60,7 +60,8 @@ class MemberInviteEmailApi(Resource):
         inviter = current_user
 
         try:
-            RegisterService.invite_new_member(inviter.current_tenant, invitee_email, role=invitee_role, inviter=inviter)
+            token = RegisterService.invite_new_member(inviter.current_tenant, invitee_email, role=invitee_role,
+                                                      inviter=inviter)
             account = db.session.query(Account, TenantAccountJoin.role).join(
                 TenantAccountJoin, Account.id == TenantAccountJoin.account_id
             ).filter(Account.email == args['email']).first()
@@ -78,7 +79,16 @@ class MemberInviteEmailApi(Resource):
 
         # todo:413
 
-        return {'result': 'success', 'account': account}, 201
+        return {
+            'result': 'success',
+            'account': account,
+            'invite_url': '{}/activate?workspace_id={}&email={}&token={}'.format(
+                current_app.config.get("CONSOLE_WEB_URL"),
+                str(current_user.current_tenant_id),
+                invitee_email,
+                token
+            )
+        }, 201
 
 
 class MemberCancelInviteApi(Resource):
@@ -88,7 +98,7 @@ class MemberCancelInviteApi(Resource):
     @login_required
     @account_initialization_required
     def delete(self, member_id):
-        member = Account.query.get(str(member_id))
+        member = db.session.query(Account).filter(Account.id == str(member_id)).first()
         if not member:
             abort(404)
 

api/controllers/console/workspace/model_providers.py

@@ -3,6 +3,7 @@ import base64
 import json
 import logging
 
+from flask import current_app
 from flask_login import login_required, current_user
 from flask_restful import Resource, reqparse, abort
 from werkzeug.exceptions import Forbidden
@@ -34,7 +35,7 @@ class ProviderListApi(Resource):
         plaintext, the rest is replaced by * and the last two bits are displayed in plaintext
         """
 
-        ProviderService.init_supported_provider(current_user.current_tenant, "cloud")
+        ProviderService.init_supported_provider(current_user.current_tenant)
         providers = Provider.query.filter_by(tenant_id=tenant_id).all()
 
         provider_list = [
@@ -50,7 +51,8 @@ class ProviderListApi(Resource):
                        'quota_used': p.quota_used
                    } if p.provider_type == ProviderType.SYSTEM.value else {}),
                 'token': ProviderService.get_obfuscated_api_key(current_user.current_tenant,
-                                                                ProviderName(p.provider_name))
+                                                                ProviderName(p.provider_name), only_custom=True)
+                if p.provider_type == ProviderType.CUSTOM.value else None
             }
             for p in providers
         ]
@@ -121,9 +123,10 @@ class ProviderTokenApi(Resource):
                                       is_valid=token_is_valid)
             db.session.add(provider_model)
 
-        if provider_model.is_valid:
+        if provider in [ProviderName.OPENAI.value, ProviderName.AZURE_OPENAI.value] and provider_model.is_valid:
             other_providers = db.session.query(Provider).filter(
                 Provider.tenant_id == tenant.id,
+                Provider.provider_name.in_([ProviderName.OPENAI.value, ProviderName.AZURE_OPENAI.value]),
                 Provider.provider_name != provider,
                 Provider.provider_type == ProviderType.CUSTOM.value
             ).all()
@@ -133,7 +136,7 @@ class ProviderTokenApi(Resource):
 
         db.session.commit()
 
-        if provider in [ProviderName.ANTHROPIC.value, ProviderName.AZURE_OPENAI.value, ProviderName.COHERE.value,
+        if provider in [ProviderName.AZURE_OPENAI.value, ProviderName.COHERE.value,
                         ProviderName.HUGGINGFACEHUB.value]:
             return {'result': 'success', 'warning': 'MOCK: This provider is not supported yet.'}, 201
 
@@ -157,7 +160,7 @@ class ProviderTokenValidateApi(Resource):
         args = parser.parse_args()
 
         # todo: remove this when the provider is supported
-        if provider in [ProviderName.ANTHROPIC.value, ProviderName.COHERE.value,
+        if provider in [ProviderName.COHERE.value,
                         ProviderName.HUGGINGFACEHUB.value]:
             return {'result': 'success', 'warning': 'MOCK: This provider is not supported yet.'}
 
@@ -203,7 +206,19 @@ class ProviderSystemApi(Resource):
             provider_model.is_valid = args['is_enabled']
             db.session.commit()
         elif not provider_model:
-            ProviderService.create_system_provider(tenant, provider, args['is_enabled'])
+            if provider == ProviderName.OPENAI.value:
+                quota_limit = current_app.config['OPENAI_HOSTED_QUOTA_LIMIT']
+            elif provider == ProviderName.ANTHROPIC.value:
+                quota_limit = current_app.config['ANTHROPIC_HOSTED_QUOTA_LIMIT']
+            else:
+                quota_limit = 0
+
+            ProviderService.create_system_provider(
+                tenant,
+                provider,
+                quota_limit,
+                args['is_enabled']
+            )
         else:
             abort(403)
 

api/controllers/console/workspace/tool_providers.py

@@ -0,0 +1,136 @@
+import json
+
+from flask_login import login_required, current_user
+from flask_restful import Resource, abort, reqparse
+from werkzeug.exceptions import Forbidden
+
+from controllers.console import api
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from core.tool.provider.errors import ToolValidateFailedError
+from core.tool.provider.tool_provider_service import ToolProviderService
+from extensions.ext_database import db
+from models.tool import ToolProvider, ToolProviderName
+
+
+class ToolProviderListApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        tenant_id = current_user.current_tenant_id
+
+        tool_credential_dict = {}
+        for tool_name in ToolProviderName:
+            tool_credential_dict[tool_name.value] = {
+                'tool_name': tool_name.value,
+                'is_enabled': False,
+                'credentials': None
+            }
+
+        tool_providers = db.session.query(ToolProvider).filter(ToolProvider.tenant_id == tenant_id).all()
+
+        for p in tool_providers:
+            if p.is_enabled:
+                tool_credential_dict[p.tool_name] = {
+                    'tool_name': p.tool_name,
+                    'is_enabled': p.is_enabled,
+                    'credentials': ToolProviderService(tenant_id, p.tool_name).get_credentials(obfuscated=True)
+                }
+
+        return list(tool_credential_dict.values())
+
+
+class ToolProviderCredentialsApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider):
+        if provider not in [p.value for p in ToolProviderName]:
+            abort(404)
+
+        # The role of the current user in the ta table must be admin or owner
+        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+            raise Forbidden(f'User {current_user.id} is not authorized to update provider token, '
+                            f'current_role is {current_user.current_tenant.current_role}')
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        tenant_id = current_user.current_tenant_id
+
+        tool_provider_service = ToolProviderService(tenant_id, provider)
+
+        try:
+            tool_provider_service.credentials_validate(args['credentials'])
+        except ToolValidateFailedError as ex:
+            raise ValueError(str(ex))
+
+        encrypted_credentials = json.dumps(tool_provider_service.encrypt_credentials(args['credentials']))
+
+        tenant = current_user.current_tenant
+
+        tool_provider_model = db.session.query(ToolProvider).filter(
+                ToolProvider.tenant_id == tenant.id,
+                ToolProvider.tool_name == provider,
+            ).first()
+
+        # Only allow updating token for CUSTOM provider type
+        if tool_provider_model:
+            tool_provider_model.encrypted_credentials = encrypted_credentials
+            tool_provider_model.is_enabled = True
+        else:
+            tool_provider_model = ToolProvider(
+                tenant_id=tenant.id,
+                tool_name=provider,
+                encrypted_credentials=encrypted_credentials,
+                is_enabled=True
+            )
+            db.session.add(tool_provider_model)
+
+        db.session.commit()
+
+        return {'result': 'success'}, 201
+
+
+class ToolProviderCredentialsValidateApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider):
+        if provider not in [p.value for p in ToolProviderName]:
+            abort(404)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        result = True
+        error = None
+
+        tenant_id = current_user.current_tenant_id
+
+        tool_provider_service = ToolProviderService(tenant_id, provider)
+
+        try:
+            tool_provider_service.credentials_validate(args['credentials'])
+        except ToolValidateFailedError as ex:
+            result = False
+            error = str(ex)
+
+        response = {'result': 'success' if result else 'error'}
+
+        if not result:
+            response['error'] = error
+
+        return response
+
+
+api.add_resource(ToolProviderListApi, '/workspaces/current/tool-providers')
+api.add_resource(ToolProviderCredentialsApi, '/workspaces/current/tool-providers/<provider>/credentials')
+api.add_resource(ToolProviderCredentialsValidateApi,
+                 '/workspaces/current/tool-providers/<provider>/credentials-validate')

api/controllers/service_api/__init__.py

@@ -7,6 +7,6 @@ bp = Blueprint('service_api', __name__, url_prefix='/v1')
 api = ExternalApi(bp)
 
 
-from .app import completion, app, conversation, message
+from .app import completion, app, conversation, message, audio
 
 from .dataset import document

api/controllers/service_api/app/app.py

@@ -4,6 +4,10 @@ from flask_restful import fields, marshal_with
 from controllers.service_api import api
 from controllers.service_api.wraps import AppApiResource
 
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import App
+
 
 class AppParameterApi(AppApiResource):
     """Resource for app variables."""
@@ -22,19 +26,22 @@ class AppParameterApi(AppApiResource):
         'opening_statement': fields.String,
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
 
     @marshal_with(parameters_fields)
-    def get(self, app_model, end_user):
+    def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
         app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(app_model.tenant_id, 'whisper-1')
 
         return {
             'opening_statement': app_model_config.opening_statement,
             'suggested_questions': app_model_config.suggested_questions_list,
             'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/service_api/app/audio.py

@@ -0,0 +1,61 @@
+import logging
+
+from flask import request
+from werkzeug.exceptions import InternalServerError
+
+import services
+from controllers.service_api import api
+from controllers.service_api.app.error import AppUnavailableError, ProviderNotInitializeError, CompletionRequestError, ProviderQuotaExceededError, \
+    ProviderModelCurrentlyNotSupportError, NoAudioUploadedError, AudioTooLargeError, UnsupportedAudioTypeError, \
+    ProviderNotSupportSpeechToTextError
+from controllers.service_api.wraps import AppApiResource
+from core.llm.error import LLMBadRequestError, LLMAuthorizationError, LLMAPIUnavailableError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from models.model import App, AppModelConfig
+from services.audio_service import AudioService
+from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \
+    UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError
+
+class AudioApi(AppApiResource):
+    def post(self, app_model: App, end_user):
+        app_model_config: AppModelConfig = app_model.app_model_config
+
+        if not app_model_config.speech_to_text_dict['enabled']:
+            raise AppUnavailableError() 
+
+        file = request.files['file']
+
+        try:
+            response = AudioService.transcript(
+                tenant_id=app_model.tenant_id,
+                file=file,
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+        
+api.add_resource(AudioApi, '/audio-to-text')

api/controllers/service_api/app/completion.py

@@ -54,8 +54,8 @@ class CompletionApi(AppApiResource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -115,8 +115,8 @@ class ChatApi(AppApiResource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -156,8 +156,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
             except services.errors.app_model_config.AppModelConfigBrokenError:
                 logging.exception("App model config broken.")
                 yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:

api/controllers/service_api/app/conversation.py

@@ -1,4 +1,5 @@
 # -*- coding:utf-8 -*-
+from flask import request
 from flask_restful import fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
 from werkzeug.exceptions import NotFound
@@ -48,6 +49,24 @@ class ConversationApi(AppApiResource):
         except services.errors.conversation.LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 
+class ConversationDetailApi(AppApiResource):
+    @marshal_with(conversation_fields)
+    def delete(self, app_model, end_user, c_id):
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+
+        user = request.get_json().get('user')
+
+        if end_user is None and user is not None:
+            end_user = create_or_update_end_user_for_user_id(app_model, user)
+
+        try:
+            ConversationService.delete(app_model, conversation_id, end_user)
+            return {"result": "success"}
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
 
 class ConversationRenameApi(AppApiResource):
 
@@ -74,3 +93,5 @@ class ConversationRenameApi(AppApiResource):
 
 api.add_resource(ConversationRenameApi, '/conversations/<uuid:c_id>/name', endpoint='conversation_name')
 api.add_resource(ConversationApi, '/conversations')
+api.add_resource(ConversationApi, '/conversations/<uuid:c_id>', endpoint='conversation')
+api.add_resource(ConversationDetailApi, '/conversations/<uuid:c_id>', endpoint='conversation_detail')

api/controllers/service_api/app/error.py

@@ -51,3 +51,27 @@ class CompletionRequestError(BaseHTTPException):
     description = "Completion request failed."
     code = 400
 
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = 'no_audio_uploaded'
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = 'audio_too_large'
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = 'unsupported_audio_type'
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = 'provider_not_support_speech_to_text'
+    description = "Provider not support speech to text."
+    code = 400
+

api/controllers/service_api/dataset/document.py

@@ -69,21 +69,25 @@ class DocumentListApi(DatasetApiResource):
         document_data = {
             'data_source': {
                 'type': 'upload_file',
-                'info': upload_file.id
+                'info': [
+                    {
+                        'upload_file_id': upload_file.id
+                    }
+                ]
             }
         }
 
         try:
-            document = DocumentService.save_document_with_dataset_id(
+            documents, batch = DocumentService.save_document_with_dataset_id(
                 dataset=dataset,
                 document_data=document_data,
                 account=dataset.created_by_account,
                 dataset_process_rule=dataset.latest_process_rule,
                 created_from='api'
             )
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
-
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        document = documents[0]
         if doc_type and doc_metadata:
             metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[doc_type]
 

api/controllers/web/__init__.py

@@ -7,4 +7,4 @@ bp = Blueprint('web', __name__, url_prefix='/api')
 api = ExternalApi(bp)
 
 
-from . import completion, app, conversation, message, site, saved_message
+from . import completion, app, conversation, message, site, saved_message, audio, passport

api/controllers/web/app.py

@@ -4,6 +4,10 @@ from flask_restful import marshal_with, fields
 from controllers.web import api
 from controllers.web.wraps import WebApiResource
 
+from core.llm.llm_builder import LLMBuilder
+from models.provider import ProviderName
+from models.model import App
+
 
 class AppParameterApi(WebApiResource):
     """Resource for app variables."""
@@ -21,19 +25,22 @@ class AppParameterApi(WebApiResource):
         'opening_statement': fields.String,
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
 
     @marshal_with(parameters_fields)
-    def get(self, app_model, end_user):
+    def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
         app_model_config = app_model.app_model_config
+        provider_name = LLMBuilder.get_default_provider(app_model.tenant_id, 'whisper-1')
 
         return {
             'opening_statement': app_model_config.opening_statement,
             'suggested_questions': app_model_config.suggested_questions_list,
             'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
+            'speech_to_text': app_model_config.speech_to_text_dict if provider_name == ProviderName.OPENAI.value else { 'enabled': False },
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/web/audio.py

@@ -0,0 +1,63 @@
+# -*- coding:utf-8 -*-
+import logging
+
+from flask import request
+from werkzeug.exceptions import InternalServerError
+
+import services
+from controllers.web import api
+from controllers.web.error import AppUnavailableError, ProviderNotInitializeError, CompletionRequestError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, NoAudioUploadedError, AudioTooLargeError, \
+    UnsupportedAudioTypeError, ProviderNotSupportSpeechToTextError
+from controllers.web.wraps import WebApiResource
+from core.llm.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from services.audio_service import AudioService
+from services.errors.audio import NoAudioUploadedServiceError, AudioTooLargeServiceError, \
+    UnsupportedAudioTypeServiceError, ProviderNotSupportSpeechToTextServiceError
+from models.model import App, AppModelConfig
+
+
+class AudioApi(WebApiResource):
+    def post(self, app_model: App, end_user):
+        app_model_config: AppModelConfig = app_model.app_model_config
+
+        if not app_model_config.speech_to_text_dict['enabled']:
+            raise AppUnavailableError()
+
+        file = request.files['file']
+
+        try:
+            response = AudioService.transcript(
+                tenant_id=app_model.tenant_id,
+                file=file,
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+api.add_resource(AudioApi, '/audio-to-text')

api/controllers/web/completion.py

@@ -52,8 +52,8 @@ class CompletionApi(WebApiResource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -109,8 +109,8 @@ class ChatApi(WebApiResource):
         except services.errors.app_model_config.AppModelConfigBrokenError:
             logging.exception("App model config broken.")
             raise AppUnavailableError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -150,8 +150,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
             except services.errors.app_model_config.AppModelConfigBrokenError:
                 logging.exception("App model config broken.")
                 yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:

api/controllers/web/conversation.py

@@ -62,7 +62,10 @@ class ConversationApi(WebApiResource):
             raise NotChatAppError()
 
         conversation_id = str(c_id)
-        ConversationService.delete(app_model, conversation_id, end_user)
+        try:
+            ConversationService.delete(app_model, conversation_id, end_user)
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
         WebConversationService.unpin(app_model, conversation_id, end_user)
 
         return {"result": "success"}, 204

api/controllers/web/error.py

@@ -62,3 +62,27 @@ class AppSuggestedQuestionsAfterAnswerDisabledError(BaseHTTPException):
     error_code = 'app_suggested_questions_after_answer_disabled'
     description = "The 'Suggested Questions After Answer' feature is disabled. Please refresh your page."
     code = 403
+
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = 'no_audio_uploaded'
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = 'audio_too_large'
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = 'unsupported_audio_type'
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = 'provider_not_support_speech_to_text'
+    description = "Provider not support speech to text."
+    code = 400

api/controllers/web/message.py

@@ -101,8 +101,8 @@ class MessageMoreLikeThisApi(WebApiResource):
             raise NotFound("Message Not Exists.")
         except MoreLikeThisDisabledError:
             raise AppMoreLikeThisDisabledError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:
@@ -129,8 +129,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
                 yield "data: " + json.dumps(api.handle_error(NotFound("Message Not Exists.")).get_json()) + "\n\n"
             except MoreLikeThisDisabledError:
                 yield "data: " + json.dumps(api.handle_error(AppMoreLikeThisDisabledError()).get_json()) + "\n\n"
-            except ProviderTokenNotInitError:
-                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError as ex:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError(ex.description)).get_json()) + "\n\n"
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:
@@ -167,8 +167,8 @@ class MessageSuggestedQuestionApi(WebApiResource):
             raise NotFound("Conversation not found")
         except SuggestedQuestionsAfterAnswerDisabledError:
             raise AppSuggestedQuestionsAfterAnswerDisabledError()
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
         except QuotaExceededError:
             raise ProviderQuotaExceededError()
         except ModelCurrentlyNotSupportError:

api/controllers/web/passport.py

@@ -0,0 +1,64 @@
+# -*- coding:utf-8 -*-
+import uuid
+from controllers.web import api
+from flask_restful import Resource
+from flask import request
+from werkzeug.exceptions import Unauthorized, NotFound
+from models.model import Site, EndUser, App
+from extensions.ext_database import db
+from libs.passport import PassportService
+
+class PassportResource(Resource):
+    """Base resource for passport."""
+    def get(self):
+        app_id = request.headers.get('X-App-Code')
+        if app_id is None:
+            raise Unauthorized('X-App-Code header is missing.')
+
+        # get site from db and check if it is normal
+        site = db.session.query(Site).filter(
+            Site.code == app_id,
+            Site.status == 'normal'
+        ).first()
+        if not site:
+            raise NotFound()
+        # get app from db and check if it is normal and enable_site
+        app_model = db.session.query(App).filter(App.id == site.app_id).first()
+        if not app_model or app_model.status != 'normal' or not app_model.enable_site:
+            raise NotFound()
+        
+        end_user = EndUser(
+            tenant_id=app_model.tenant_id,
+            app_id=app_model.id,
+            type='browser',
+            is_anonymous=True,
+            session_id=generate_session_id(),
+        )
+        db.session.add(end_user)
+        db.session.commit()
+
+        payload = {
+            "iss": site.app_id,
+            'sub': 'Web API Passport',
+            'app_id': site.app_id,
+            'end_user_id': end_user.id,
+        }
+
+        tk = PassportService().issue(payload)
+
+        return {
+            'access_token': tk,
+        }
+
+api.add_resource(PassportResource, '/passport')
+
+def generate_session_id():
+    """
+    Generate a unique session ID.
+    """
+    while True:
+        session_id = str(uuid.uuid4())
+        existing_count = db.session.query(EndUser) \
+            .filter(EndUser.session_id == session_id).count()
+        if existing_count == 0:
+            return session_id

api/controllers/web/wraps.py

@@ -1,110 +1,50 @@
 # -*- coding:utf-8 -*-
-import uuid
 from functools import wraps
 
-from flask import request, session
+from flask import request
 from flask_restful import Resource
 from werkzeug.exceptions import NotFound, Unauthorized
 
 from extensions.ext_database import db
-from models.model import App, Site, EndUser
+from models.model import App, EndUser
+from libs.passport import PassportService
 
-
-def validate_token(view=None):
+def validate_jwt_token(view=None):
     def decorator(view):
         @wraps(view)
         def decorated(*args, **kwargs):
-            site = validate_and_get_site()
-
-            app_model = db.session.query(App).filter(App.id == site.app_id).first()
-            if not app_model:
-                raise NotFound()
-
-            if app_model.status != 'normal':
-                raise NotFound()
-
-            if not app_model.enable_site:
-                raise NotFound()
-
-            end_user = create_or_update_end_user_for_session(app_model)
+            app_model, end_user = decode_jwt_token()
 
             return view(app_model, end_user, *args, **kwargs)
         return decorated
-
     if view:
         return decorator(view)
     return decorator
 
-
-def validate_and_get_site():
-    """
-    Validate and get API token.
-    """
+def decode_jwt_token():
     auth_header = request.headers.get('Authorization')
     if auth_header is None:
         raise Unauthorized('Authorization header is missing.')
 
     if ' ' not in auth_header:
         raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
-
-    auth_scheme, auth_token = auth_header.split(None, 1)
+    
+    auth_scheme, tk = auth_header.split(None, 1)
     auth_scheme = auth_scheme.lower()
 
     if auth_scheme != 'bearer':
         raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
-
-    site = db.session.query(Site).filter(
-        Site.code == auth_token,
-        Site.status == 'normal'
-    ).first()
-
-    if not site:
+    decoded = PassportService().verify(tk)
+    app_model = db.session.query(App).filter(App.id == decoded['app_id']).first()
+    if not app_model:
+        raise NotFound()
+    if app_model.enable_site is False:
+        raise Unauthorized('Site is disabled.')
+    end_user = db.session.query(EndUser).filter(EndUser.id == decoded['end_user_id']).first()
+    if not end_user:
         raise NotFound()
 
-    return site
-
-
-def create_or_update_end_user_for_session(app_model):
-    """
-    Create or update session terminal based on session ID.
-    """
-    if 'session_id' not in session:
-        session['session_id'] = generate_session_id()
-
-    session_id = session.get('session_id')
-    end_user = db.session.query(EndUser) \
-        .filter(
-        EndUser.session_id == session_id,
-        EndUser.type == 'browser'
-    ).first()
-
-    if end_user is None:
-        end_user = EndUser(
-            tenant_id=app_model.tenant_id,
-            app_id=app_model.id,
-            type='browser',
-            is_anonymous=True,
-            session_id=session_id
-        )
-        db.session.add(end_user)
-        db.session.commit()
-
-    return end_user
-
-
-def generate_session_id():
-    """
-    Generate a unique session ID.
-    """
-    count = 1
-    session_id = ''
-    while count != 0:
-        session_id = str(uuid.uuid4())
-        count = db.session.query(EndUser) \
-            .filter(EndUser.session_id == session_id).count()
-
-    return session_id
-
+    return app_model, end_user
 
 class WebApiResource(Resource):
-    method_decorators = [validate_token]
+    method_decorators = [validate_jwt_token]

api/core/__init__.py

@@ -3,50 +3,34 @@ from typing import Optional
 
 import langchain
 from flask import Flask
-from jieba.analyse import default_tfidf
-from langchain import set_handler
-from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING
-from llama_index import IndexStructType, QueryMode
-from llama_index.indices.registry import INDEX_STRUT_TYPE_TO_QUERY_MAP
 from pydantic import BaseModel
 
 from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.index.keyword_table.jieba_keyword_table import GPTJIEBAKeywordTableIndex
-from core.index.keyword_table.stopwords import STOPWORDS
 from core.prompt.prompt_template import OneLineFormatter
-from core.vector_store.vector_store import VectorStore
-from core.vector_store.vector_store_index_query import EnhanceGPTVectorStoreIndexQuery
 
 
 class HostedOpenAICredential(BaseModel):
     api_key: str
 
 
+class HostedAnthropicCredential(BaseModel):
+    api_key: str
+
+
 class HostedLLMCredentials(BaseModel):
     openai: Optional[HostedOpenAICredential] = None
+    anthropic: Optional[HostedAnthropicCredential] = None
 
 
 hosted_llm_credentials = HostedLLMCredentials()
 
 
 def init_app(app: Flask):
-    formatter = OneLineFormatter()
-    DEFAULT_FORMATTER_MAPPING['f-string'] = formatter.format
-    INDEX_STRUT_TYPE_TO_QUERY_MAP[IndexStructType.KEYWORD_TABLE] = GPTJIEBAKeywordTableIndex.get_query_map()
-    INDEX_STRUT_TYPE_TO_QUERY_MAP[IndexStructType.WEAVIATE] = {
-        QueryMode.DEFAULT: EnhanceGPTVectorStoreIndexQuery,
-        QueryMode.EMBEDDING: EnhanceGPTVectorStoreIndexQuery,
-    }
-    INDEX_STRUT_TYPE_TO_QUERY_MAP[IndexStructType.QDRANT] = {
-        QueryMode.DEFAULT: EnhanceGPTVectorStoreIndexQuery,
-        QueryMode.EMBEDDING: EnhanceGPTVectorStoreIndexQuery,
-    }
-
-    default_tfidf.stop_words = STOPWORDS
-
     if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
         langchain.verbose = True
-        set_handler(DifyStdOutCallbackHandler())
 
     if app.config.get("OPENAI_API_KEY"):
         hosted_llm_credentials.openai = HostedOpenAICredential(api_key=app.config.get("OPENAI_API_KEY"))
+
+    if app.config.get("ANTHROPIC_API_KEY"):
+        hosted_llm_credentials.anthropic = HostedAnthropicCredential(api_key=app.config.get("ANTHROPIC_API_KEY"))

api/core/agent/agent/calc_token_mixin.py

@@ -0,0 +1,35 @@
+from typing import cast, List
+
+from langchain import OpenAI
+from langchain.base_language import BaseLanguageModel
+from langchain.chat_models.openai import ChatOpenAI
+from langchain.schema import BaseMessage
+
+from core.constant import llm_constant
+
+
+class CalcTokenMixin:
+
+    def get_num_tokens_from_messages(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
+        llm = cast(ChatOpenAI, llm)
+        return llm.get_num_tokens_from_messages(messages)
+
+    def get_message_rest_tokens(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> int:
+        """
+        Got the rest tokens available for the model after excluding messages tokens and completion max tokens
+
+        :param llm:
+        :param messages:
+        :return:
+        """
+        llm = cast(ChatOpenAI, llm)
+        llm_max_tokens = llm_constant.max_context_token_length[llm.model_name]
+        completion_max_tokens = llm.max_tokens
+        used_tokens = self.get_num_tokens_from_messages(llm, messages, **kwargs)
+        rest_tokens = llm_max_tokens - completion_max_tokens - used_tokens
+
+        return rest_tokens
+
+
+class ExceededLLMTokensLimitError(Exception):
+    pass

api/core/agent/agent/multi_dataset_router_agent.py

@@ -0,0 +1,83 @@
+from typing import Tuple, List, Any, Union, Sequence, Optional, cast
+
+from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, BaseLanguageModel, SystemMessage
+from langchain.tools import BaseTool
+
+from core.tool.dataset_retriever_tool import DatasetRetrieverTool
+
+
+class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
+    """
+    An Multi Dataset Retrieve Agent driven by Router.
+    """
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+
+        :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 observations
+            **kwargs: User inputs.
+
+        Returns:
+            Action specifying what tool to use.
+        """
+        if len(self.tools) == 0:
+            return AgentFinish(return_values={"output": ''}, log='')
+        elif len(self.tools) == 1:
+            tool = next(iter(self.tools))
+            tool = cast(DatasetRetrieverTool, tool)
+            rst = tool.run(tool_input={'dataset_id': tool.dataset_id, 'query': kwargs['input']})
+            return AgentFinish(return_values={"output": rst}, log=rst)
+
+        if intermediate_steps:
+            _, observation = intermediate_steps[-1]
+            return AgentFinish(return_values={"output": observation}, log=observation)
+
+        return super().plan(intermediate_steps, callbacks, **kwargs)
+
+    async def aplan(
+            self,
+            intermediate_steps: List[Tuple[AgentAction, str]],
+            callbacks: Callbacks = None,
+            **kwargs: Any,
+    ) -> Union[AgentAction, AgentFinish]:
+        raise NotImplementedError()
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            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."
+            ),
+            **kwargs: Any,
+    ) -> BaseSingleActionAgent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            extra_prompt_messages=extra_prompt_messages,
+            system_message=system_message,
+            **kwargs,
+        )

api/core/agent/agent/openai_function_call.py

@@ -0,0 +1,112 @@
+from typing import List, Tuple, Any, Union, Sequence, Optional
+
+from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
+from langchain.agents.openai_functions_agent.base import _parse_ai_message, \
+    _format_intermediate_steps
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, SystemMessage, BaseLanguageModel
+from langchain.tools import BaseTool
+
+from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
+from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
+
+
+class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            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."
+            ),
+            **kwargs: Any,
+    ) -> BaseSingleActionAgent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            extra_prompt_messages=extra_prompt_messages,
+            system_message=cls.get_system_message(),
+            **kwargs,
+        )
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+
+        :param query:
+        :return:
+        """
+        original_max_tokens = self.llm.max_tokens
+        self.llm.max_tokens = 15
+
+        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
+        messages = prompt.to_messages()
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=None
+        )
+
+        function_call = predicted_message.additional_kwargs.get("function_call", {})
+
+        self.llm.max_tokens = original_max_tokens
+
+        return True if function_call 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()
+
+        # summarize messages if rest_tokens < 0
+        try:
+            messages = self.summarize_messages_if_needed(self.llm, messages, functions=self.functions)
+        except ExceededLLMTokensLimitError as e:
+            return AgentFinish(return_values={"output": str(e)}, log=str(e))
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=callbacks
+        )
+        agent_decision = _parse_ai_message(predicted_message)
+        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."}, "")

api/core/agent/agent/openai_function_call_summarize_mixin.py

@@ -0,0 +1,132 @@
+from typing import cast, List
+
+from langchain.chat_models import ChatOpenAI
+from langchain.chat_models.openai import _convert_message_to_dict
+from langchain.memory.summary import SummarizerMixin
+from langchain.schema import SystemMessage, HumanMessage, BaseMessage, AIMessage, BaseLanguageModel
+from pydantic import BaseModel
+
+from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError, CalcTokenMixin
+
+
+class OpenAIFunctionCallSummarizeMixin(BaseModel, CalcTokenMixin):
+    moving_summary_buffer: str = ""
+    moving_summary_index: int = 0
+    summary_llm: BaseLanguageModel
+
+    def summarize_messages_if_needed(self, llm: BaseLanguageModel, messages: List[BaseMessage], **kwargs) -> List[BaseMessage]:
+        # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
+        rest_tokens = self.get_message_rest_tokens(llm, 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)
+
+        summary_handler = SummarizerMixin(llm=self.summary_llm)
+        self.moving_summary_buffer = summary_handler.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 get_num_tokens_from_messages(self, llm: BaseLanguageModel, 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"""
+        llm = cast(ChatOpenAI, llm)
+        model, encoding = llm._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/openai_multi_function_call.py

@@ -0,0 +1,102 @@
+from typing import List, Tuple, Any, Union, Sequence, Optional
+
+from langchain.agents import BaseMultiActionAgent
+from langchain.agents.openai_functions_multi_agent.base import OpenAIMultiFunctionsAgent, _format_intermediate_steps, \
+    _parse_ai_message
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, SystemMessage, BaseLanguageModel
+from langchain.tools import BaseTool
+
+from core.agent.agent.calc_token_mixin import ExceededLLMTokensLimitError
+from core.agent.agent.openai_function_call_summarize_mixin import OpenAIFunctionCallSummarizeMixin
+
+
+class AutoSummarizingOpenMultiAIFunctionCallAgent(OpenAIMultiFunctionsAgent, OpenAIFunctionCallSummarizeMixin):
+
+    @classmethod
+    def from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            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."
+            ),
+            **kwargs: Any,
+    ) -> BaseMultiActionAgent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            extra_prompt_messages=extra_prompt_messages,
+            system_message=cls.get_system_message(),
+            **kwargs,
+        )
+
+    def should_use_agent(self, query: str):
+        """
+        return should use agent
+
+        :param query:
+        :return:
+        """
+        original_max_tokens = self.llm.max_tokens
+        self.llm.max_tokens = 15
+
+        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
+        messages = prompt.to_messages()
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=None
+        )
+
+        function_call = predicted_message.additional_kwargs.get("function_call", {})
+
+        self.llm.max_tokens = original_max_tokens
+
+        return True if function_call 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()
+
+        # summarize messages if rest_tokens < 0
+        try:
+            messages = self.summarize_messages_if_needed(self.llm, messages, functions=self.functions)
+        except ExceededLLMTokensLimitError as e:
+            return AgentFinish(return_values={"output": str(e)}, log=str(e))
+
+        predicted_message = self.llm.predict_messages(
+            messages, functions=self.functions, callbacks=callbacks
+        )
+        agent_decision = _parse_ai_message(predicted_message)
+        return agent_decision
+
+    @classmethod
+    def get_system_message(cls):
+        # get current time
+        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.")

api/core/agent/agent/output_parser/structured_chat.py

@@ -0,0 +1,29 @@
+import json
+import re
+from typing import Union
+
+from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser as LCStructuredChatOutputParser, \
+    logger
+from langchain.schema import AgentAction, AgentFinish, OutputParserException
+
+
+class StructuredChatOutputParser(LCStructuredChatOutputParser):
+    def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
+        try:
+            action_match = re.search(r"```(.*?)\n(.*?)```?", text, re.DOTALL)
+            if action_match is not None:
+                response = json.loads(action_match.group(2).strip(), strict=False)
+                if isinstance(response, list):
+                    # gpt turbo frequently ignores the directive to emit a single action
+                    logger.warning("Got multiple action responses: %s", response)
+                    response = response[0]
+                if response["action"] == "Final Answer":
+                    return AgentFinish({"output": response["action_input"]}, text)
+                else:
+                    return AgentAction(
+                        response["action"], response.get("action_input", {}), text
+                    )
+            else:
+                return AgentFinish({"output": text}, text)
+        except Exception as e:
+            raise OutputParserException(f"Could not parse LLM output: {text}") from e

api/core/agent/agent/structured_chat.py

@@ -0,0 +1,187 @@
+import re
+from typing import List, Tuple, Any, Union, Sequence, Optional
+
+from langchain import BasePromptTemplate
+from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
+from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.memory.summary import SummarizerMixin
+from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
+from langchain.schema import AgentAction, AgentFinish, AIMessage, HumanMessage, OutputParserException
+from langchain.tools import BaseTool
+from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+
+from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
+
+
+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_llm: BaseLanguageModel
+
+    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 observations
+            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()
+
+        rest_tokens = self.get_message_rest_tokens(self.llm_chain.llm, messages)
+        if rest_tokens < 0:
+            full_inputs = self.summarize_messages(intermediate_steps, **kwargs)
+
+        full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
+
+        try:
+            return self.output_parser.parse(full_output)
+        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:
+            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)
+
+        summary_handler = SummarizerMixin(llm=self.summary_llm)
+        if self.moving_summary_buffer and 'chat_history' in kwargs:
+            kwargs["chat_history"].pop()
+
+        self.moving_summary_buffer = summary_handler.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)
+
+    @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 from_llm_and_tools(
+            cls,
+            llm: BaseLanguageModel,
+            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,
+            **kwargs: Any,
+    ) -> Agent:
+        return super().from_llm_and_tools(
+            llm=llm,
+            tools=tools,
+            callback_manager=callback_manager,
+            output_parser=output_parser,
+            prefix=prefix,
+            suffix=suffix,
+            human_message_template=human_message_template,
+            format_instructions=format_instructions,
+            input_variables=input_variables,
+            memory_prompts=memory_prompts,
+            **kwargs,
+        )

api/core/agent/agent_builder.py

@@ -1,89 +0,0 @@
-from typing import Optional
-
-from langchain import LLMChain
-from langchain.agents import ZeroShotAgent, AgentExecutor, ConversationalAgent
-from langchain.callbacks import CallbackManager
-from langchain.memory.chat_memory import BaseChatMemory
-
-from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
-from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.llm.llm_builder import LLMBuilder
-
-
-class AgentBuilder:
-    @classmethod
-    def to_agent_chain(cls, tenant_id: str, tools, memory: Optional[BaseChatMemory],
-                       dataset_tool_callback_handler: DatasetToolCallbackHandler,
-                       agent_loop_gather_callback_handler: AgentLoopGatherCallbackHandler):
-        llm_callback_manager = CallbackManager([agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()])
-        llm = LLMBuilder.to_llm(
-            tenant_id=tenant_id,
-            model_name=agent_loop_gather_callback_handler.model_name,
-            temperature=0,
-            max_tokens=1024,
-            callback_manager=llm_callback_manager
-        )
-
-        tool_callback_manager = CallbackManager([
-            agent_loop_gather_callback_handler,
-            dataset_tool_callback_handler,
-            DifyStdOutCallbackHandler()
-        ])
-
-        for tool in tools:
-            tool.callback_manager = tool_callback_manager
-
-        prompt = cls.build_agent_prompt_template(
-            tools=tools,
-            memory=memory,
-        )
-
-        agent_llm_chain = LLMChain(
-            llm=llm,
-            prompt=prompt,
-        )
-
-        agent = cls.build_agent(agent_llm_chain=agent_llm_chain, memory=memory)
-
-        agent_callback_manager = CallbackManager(
-            [agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()]
-        )
-
-        agent_chain = AgentExecutor.from_agent_and_tools(
-            tools=tools,
-            agent=agent,
-            memory=memory,
-            callback_manager=agent_callback_manager,
-            max_iterations=6,
-            early_stopping_method="generate",
-            # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit
-        )
-
-        return agent_chain
-
-    @classmethod
-    def build_agent_prompt_template(cls, tools, memory: Optional[BaseChatMemory]):
-        if memory:
-            prompt = ConversationalAgent.create_prompt(
-                tools=tools,
-            )
-        else:
-            prompt = ZeroShotAgent.create_prompt(
-                tools=tools,
-            )
-
-        return prompt
-
-    @classmethod
-    def build_agent(cls, agent_llm_chain: LLMChain, memory: Optional[BaseChatMemory]):
-        if memory:
-            agent = ConversationalAgent(
-                llm_chain=agent_llm_chain
-            )
-        else:
-            agent = ZeroShotAgent(
-                llm_chain=agent_llm_chain
-            )
-
-        return agent

api/core/agent/agent_executor.py

@@ -0,0 +1,122 @@
+import enum
+import logging
+from typing import Union, Optional
+
+from langchain.agents import BaseSingleActionAgent, BaseMultiActionAgent
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import Callbacks
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.tools import BaseTool
+from pydantic import BaseModel, Extra
+
+from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
+from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent
+from core.agent.agent.openai_multi_function_call import AutoSummarizingOpenMultiAIFunctionCallAgent
+from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
+from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
+from langchain.agents import AgentExecutor as LCAgentExecutor
+
+from core.tool.dataset_retriever_tool import DatasetRetrieverTool
+
+
+class PlanningStrategy(str, enum.Enum):
+    ROUTER = 'router'
+    REACT = 'react'
+    FUNCTION_CALL = 'function_call'
+    MULTI_FUNCTION_CALL = 'multi_function_call'
+
+
+class AgentConfiguration(BaseModel):
+    strategy: PlanningStrategy
+    llm: BaseLanguageModel
+    tools: list[BaseTool]
+    summary_llm: BaseLanguageModel
+    dataset_llm: BaseLanguageModel
+    memory: Optional[BaseChatMemory] = None
+    callbacks: Callbacks = None
+    max_iterations: int = 6
+    max_execution_time: Optional[float] = None
+    early_stopping_method: str = "generate"
+    # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+
+class AgentExecuteResult(BaseModel):
+    strategy: PlanningStrategy
+    output: Optional[str]
+    configuration: AgentConfiguration
+
+
+class AgentExecutor:
+    def __init__(self, configuration: AgentConfiguration):
+        self.configuration = configuration
+        self.agent = self._init_agent()
+
+    def _init_agent(self) -> Union[BaseSingleActionAgent | BaseMultiActionAgent]:
+        if self.configuration.strategy == PlanningStrategy.REACT:
+            agent = AutoSummarizingStructuredChatAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                output_parser=StructuredChatOutputParser(),
+                summary_llm=self.configuration.summary_llm,
+                verbose=True
+            )
+        elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL:
+            agent = AutoSummarizingOpenAIFunctionCallAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,  # used for read chat histories memory
+                summary_llm=self.configuration.summary_llm,
+                verbose=True
+            )
+        elif self.configuration.strategy == PlanningStrategy.MULTI_FUNCTION_CALL:
+            agent = AutoSummarizingOpenMultiAIFunctionCallAgent.from_llm_and_tools(
+                llm=self.configuration.llm,
+                tools=self.configuration.tools,
+                extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,  # used for read chat histories memory
+                summary_llm=self.configuration.summary_llm,
+                verbose=True
+            )
+        elif self.configuration.strategy == PlanningStrategy.ROUTER:
+            self.configuration.tools = [t for t in self.configuration.tools if isinstance(t, DatasetRetrieverTool)]
+            agent = MultiDatasetRouterAgent.from_llm_and_tools(
+                llm=self.configuration.dataset_llm,
+                tools=self.configuration.tools,
+                extra_prompt_messages=self.configuration.memory.buffer if self.configuration.memory else None,
+                verbose=True
+            )
+        else:
+            raise NotImplementedError(f"Unknown Agent Strategy: {self.configuration.strategy}")
+
+        return agent
+
+    def should_use_agent(self, query: str) -> bool:
+        return self.agent.should_use_agent(query)
+
+    def run(self, query: str) -> AgentExecuteResult:
+        agent_executor = LCAgentExecutor.from_agent_and_tools(
+            agent=self.agent,
+            tools=self.configuration.tools,
+            memory=self.configuration.memory,
+            max_iterations=self.configuration.max_iterations,
+            max_execution_time=self.configuration.max_execution_time,
+            early_stopping_method=self.configuration.early_stopping_method,
+            callbacks=self.configuration.callbacks
+        )
+
+        try:
+            output = agent_executor.run(query)
+        except Exception:
+            logging.exception("agent_executor run failed")
+            output = None
+
+        return AgentExecuteResult(
+            output=output,
+            strategy=self.configuration.strategy,
+            configuration=self.configuration
+        )

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -1,10 +1,12 @@
+import json
 import logging
 import time
 
 from typing import Any, Dict, List, Union, Optional
 
+from langchain.agents import openai_functions_agent, openai_functions_multi_agent
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
+from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.conversation_message_task import ConversationMessageTask
@@ -12,6 +14,7 @@ from core.conversation_message_task import ConversationMessageTask
 
 class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
+    raise_error: bool = True
 
     def __init__(self, model_name, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
@@ -19,6 +22,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         self.conversation_message_task = conversation_message_task
         self._agent_loops = []
         self._current_loop = None
+        self._message_agent_thought = None
         self.current_chain = None
 
     @property
@@ -28,6 +32,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     def clear_agent_loops(self) -> None:
         self._agent_loops = []
         self._current_loop = None
+        self._message_agent_thought = None
 
     @property
     def always_verbose(self) -> bool:
@@ -60,13 +65,21 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         # kwargs={}
         if self._current_loop and self._current_loop.status == 'llm_started':
             self._current_loop.status = 'llm_end'
-            self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
-            self._current_loop.completion = response.generations[0][0].text
-            self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
+            if response.llm_output:
+                self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
+            completion_generation = response.generations[0][0]
+            if isinstance(completion_generation, ChatGeneration):
+                completion_message = completion_generation.message
+                if 'function_call' in completion_message.additional_kwargs:
+                    self._current_loop.completion \
+                        = json.dumps({'function_call': completion_message.additional_kwargs['function_call']})
+                else:
+                    self._current_loop.completion = response.generations[0][0].text
+            else:
+                self._current_loop.completion = completion_generation.text
 
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Do nothing."""
-        pass
+            if response.llm_output:
+                self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
 
     def on_llm_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
@@ -74,21 +87,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         logging.error(error)
         self._agent_loops = []
         self._current_loop = None
-
-    def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        """Print out that we are entering a chain."""
-        pass
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        """Print out that we finished a chain."""
-        pass
-
-    def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        logging.error(error)
+        self._message_agent_thought = None
 
     def on_tool_start(
         self,
@@ -107,15 +106,29 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     ) -> Any:
         """Run on agent action."""
         tool = action.tool
-        tool_input = action.tool_input
-        action_name_position = action.log.index("\nAction:") + 1 if action.log else -1
-        thought = action.log[:action_name_position].strip() if action.log else ''
+        tool_input = json.dumps({"query": action.tool_input}
+                                if isinstance(action.tool_input, str) else action.tool_input)
+        completion = None
+        if isinstance(action, openai_functions_agent.base._FunctionsAgentAction) \
+                or isinstance(action, openai_functions_multi_agent.base._FunctionsAgentAction):
+            thought = action.log.strip()
+            completion = json.dumps({'function_call': action.message_log[0].additional_kwargs['function_call']})
+        else:
+            action_name_position = action.log.index("Action:") if action.log else -1
+            thought = action.log[:action_name_position].strip() if action.log else ''
 
         if self._current_loop and self._current_loop.status == 'llm_end':
             self._current_loop.status = 'agent_action'
             self._current_loop.thought = thought
             self._current_loop.tool_name = tool
             self._current_loop.tool_input = tool_input
+            if completion is not None:
+                self._current_loop.completion = completion
+
+            self._message_agent_thought = self.conversation_message_task.on_agent_start(
+                self.current_chain,
+                self._current_loop
+            )
 
     def on_tool_end(
         self,
@@ -138,10 +151,13 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.completed_at = time.perf_counter()
             self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
 
-            self.conversation_message_task.on_agent_end(self.current_chain, self.model_name, self._current_loop)
+            self.conversation_message_task.on_agent_end(
+                self._message_agent_thought, self.model_name, self._current_loop
+            )
 
             self._agent_loops.append(self._current_loop)
             self._current_loop = None
+            self._message_agent_thought = None
 
     def on_tool_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
@@ -150,16 +166,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         logging.error(error)
         self._agent_loops = []
         self._current_loop = None
-
-    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Optional[str],
-    ) -> None:
-        """Run on additional input from chains and agents."""
-        pass
+        self._message_agent_thought = None
 
     def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
         """Run on agent end."""
@@ -169,10 +176,18 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.completed = True
             self._current_loop.completed_at = time.perf_counter()
             self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
+            self._current_loop.thought = '[DONE]'
+            self._message_agent_thought = self.conversation_message_task.on_agent_start(
+                self.current_chain,
+                self._current_loop
+            )
 
-            self.conversation_message_task.on_agent_end(self.current_chain, self.model_name, self._current_loop)
+            self.conversation_message_task.on_agent_end(
+                self._message_agent_thought, self.model_name, self._current_loop
+            )
 
             self._agent_loops.append(self._current_loop)
             self._current_loop = None
+            self._message_agent_thought = None
         elif not self._current_loop and self._agent_loops:
             self._agent_loops[-1].status = 'agent_finish'

api/core/callback_handler/dataset_tool_callback_handler.py

@@ -1,9 +1,9 @@
+import json
 import logging
 
 from typing import Any, Dict, List, Union, Optional
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 from core.callback_handler.entity.dataset_query import DatasetQueryObj
 from core.conversation_message_task import ConversationMessageTask
@@ -11,6 +11,7 @@ from core.conversation_message_task import ConversationMessageTask
 
 class DatasetToolCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
+    raise_error: bool = True
 
     def __init__(self, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
@@ -43,9 +44,11 @@ class DatasetToolCallbackHandler(BaseCallbackHandler):
         input_str: str,
         **kwargs: Any,
     ) -> None:
-        tool_name = serialized.get('name')
-        dataset_id = tool_name[len("dataset-"):]
-        self.conversation_message_task.on_dataset_query_end(DatasetQueryObj(dataset_id=dataset_id, query=input_str))
+        # tool_name = serialized.get('name')
+        input_dict = json.loads(input_str.replace("'", "\""))
+        dataset_id = input_dict.get('dataset_id')
+        query = input_dict.get('query')
+        self.conversation_message_task.on_dataset_query_end(DatasetQueryObj(dataset_id=dataset_id, query=query))
 
     def on_tool_end(
         self,
@@ -66,52 +69,3 @@ class DatasetToolCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """Do nothing."""
         logging.error(error)
-
-    def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        pass
-
-    def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        pass
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Do nothing."""
-        pass
-
-    def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        logging.error(error)
-
-    def on_agent_action(
-        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
-    ) -> Any:
-        pass
-
-    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Optional[str],
-    ) -> None:
-        """Run on additional input from chains and agents."""
-        pass
-
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
-        """Run on agent end."""
-        pass

api/core/callback_handler/entity/agent_loop.py

@@ -10,9 +10,9 @@ class AgentLoop(BaseModel):
     tool_output: str = None
 
     prompt: str = None
-    prompt_tokens: int = None
+    prompt_tokens: int = 0
     completion: str = None
-    completion_tokens: int = None
+    completion_tokens: int = 0
 
     latency: float = None
 

api/core/callback_handler/index_tool_callback_handler.py

@@ -1,39 +1,26 @@
-from llama_index import Response
+from typing import List
+
+from langchain.schema import Document
 
 from extensions.ext_database import db
 from models.dataset import DocumentSegment
 
 
-class IndexToolCallbackHandler:
-
-    def __init__(self) -> None:
-        self._response = None
-
-    @property
-    def response(self) -> Response:
-        return self._response
-
-    def on_tool_end(self, response: Response) -> None:
-        """Handle tool end."""
-        self._response = response
-
-
-class DatasetIndexToolCallbackHandler(IndexToolCallbackHandler):
+class DatasetIndexToolCallbackHandler:
     """Callback handler for dataset tool."""
 
     def __init__(self, dataset_id: str) -> None:
-        super().__init__()
         self.dataset_id = dataset_id
 
-    def on_tool_end(self, response: Response) -> None:
+    def on_tool_end(self, documents: List[Document]) -> None:
         """Handle tool end."""
-        for node in response.source_nodes:
-            index_node_id = node.node.doc_id
+        for document in documents:
+            doc_id = document.metadata['doc_id']
 
             # add hit count to document segment
             db.session.query(DocumentSegment).filter(
                 DocumentSegment.dataset_id == self.dataset_id,
-                DocumentSegment.index_node_id == index_node_id
+                DocumentSegment.index_node_id == doc_id
             ).update(
                 {DocumentSegment.hit_count: DocumentSegment.hit_count + 1},
                 synchronize_session=False

api/core/callback_handler/llm_callback_handler.py

@@ -1,19 +1,18 @@
 import logging
 import time
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, List, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult, HumanMessage, AIMessage, SystemMessage
+from langchain.schema import LLMResult, BaseMessage, BaseLanguageModel
 
 from core.callback_handler.entity.llm_message import LLMMessage
 from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
-from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
-from core.llm.streamable_open_ai import StreamableOpenAI
 
 
 class LLMCallbackHandler(BaseCallbackHandler):
+    raise_error: bool = True
 
-    def __init__(self, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
+    def __init__(self, llm: BaseLanguageModel,
                  conversation_message_task: ConversationMessageTask):
         self.llm = llm
         self.llm_message = LLMMessage()
@@ -25,41 +24,41 @@ class LLMCallbackHandler(BaseCallbackHandler):
         """Whether to call verbose callbacks even if verbose is False."""
         return True
 
+    def on_chat_model_start(
+            self,
+            serialized: Dict[str, Any],
+            messages: List[List[BaseMessage]],
+            **kwargs: Any
+    ) -> Any:
+        self.start_at = time.perf_counter()
+        real_prompts = []
+        for message in messages[0]:
+            if message.type == 'human':
+                role = 'user'
+            elif message.type == 'ai':
+                role = 'assistant'
+            else:
+                role = 'system'
+
+            real_prompts.append({
+                "role": role,
+                "text": message.content
+            })
+
+        self.llm_message.prompt = real_prompts
+        self.llm_message.prompt_tokens = self.llm.get_num_tokens_from_messages(messages[0])
+
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
         self.start_at = time.perf_counter()
 
-        if 'Chat' in serialized['name']:
-            real_prompts = []
-            messages = []
-            for prompt in prompts:
-                role, content = prompt.split(': ', maxsplit=1)
-                if role == 'human':
-                    role = 'user'
-                    message = HumanMessage(content=content)
-                elif role == 'ai':
-                    role = 'assistant'
-                    message = AIMessage(content=content)
-                else:
-                    message = SystemMessage(content=content)
+        self.llm_message.prompt = [{
+            "role": 'user',
+            "text": prompts[0]
+        }]
 
-                real_prompt = {
-                    "role": role,
-                    "text": content
-                }
-                real_prompts.append(real_prompt)
-                messages.append(message)
-
-            self.llm_message.prompt = real_prompts
-            self.llm_message.prompt_tokens = self.llm.get_messages_tokens(messages)
-        else:
-            self.llm_message.prompt = [{
-                "role": 'user',
-                "text": prompts[0]
-            }]
-
-            self.llm_message.prompt_tokens = self.llm.get_num_tokens(prompts[0])
+        self.llm_message.prompt_tokens = self.llm.get_num_tokens(prompts[0])
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         end_at = time.perf_counter()
@@ -68,9 +67,8 @@ class LLMCallbackHandler(BaseCallbackHandler):
         if not self.conversation_message_task.streaming:
             self.conversation_message_task.append_message_text(response.generations[0][0].text)
             self.llm_message.completion = response.generations[0][0].text
-            self.llm_message.completion_tokens = response.llm_output['token_usage']['completion_tokens']
-        else:
-            self.llm_message.completion_tokens = self.llm.get_num_tokens(self.llm_message.completion)
+
+        self.llm_message.completion_tokens = self.llm.get_num_tokens(self.llm_message.completion)
 
         self.conversation_message_task.save_message(self.llm_message)
 
@@ -95,58 +93,3 @@ class LLMCallbackHandler(BaseCallbackHandler):
                 self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True)
         else:
             logging.error(error)
-
-    def on_chain_start(
-            self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        pass
-
-    def on_chain_error(
-            self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_tool_start(
-            self,
-            serialized: Dict[str, Any],
-            input_str: str,
-            **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_agent_action(
-            self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
-    ) -> Any:
-        pass
-
-    def on_tool_end(
-            self,
-            output: str,
-            color: Optional[str] = None,
-            observation_prefix: Optional[str] = None,
-            llm_prefix: Optional[str] = None,
-            **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_tool_error(
-            self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_text(
-            self,
-            text: str,
-            color: Optional[str] = None,
-            end: str = "",
-            **kwargs: Optional[str],
-    ) -> None:
-        pass
-
-    def on_agent_finish(
-            self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
-    ) -> None:
-        pass

api/core/callback_handler/main_chain_gather_callback_handler.py

@@ -1,10 +1,9 @@
 import logging
 import time
 
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.callback_handler.entity.chain_result import ChainResult
@@ -14,21 +13,20 @@ from core.conversation_message_task import ConversationMessageTask
 
 class MainChainGatherCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
+    raise_error: bool = True
 
     def __init__(self, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
         self._current_chain_result = None
         self._current_chain_message = None
         self.conversation_message_task = conversation_message_task
-        self.agent_loop_gather_callback_handler = AgentLoopGatherCallbackHandler(
-            llm_constant.agent_model_name,
-            conversation_message_task
-        )
+        self.agent_callback = None
 
     def clear_chain_results(self) -> None:
         self._current_chain_result = None
         self._current_chain_message = None
-        self.agent_loop_gather_callback_handler.current_chain = None
+        if self.agent_callback:
+            self.agent_callback.current_chain = None
 
     @property
     def always_verbose(self) -> bool:
@@ -50,13 +48,16 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """Print out that we are entering a chain."""
         if not self._current_chain_result:
-            self._current_chain_result = ChainResult(
-                type=serialized['name'],
-                prompt=inputs,
-                started_at=time.perf_counter()
-            )
-            self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
-            self.agent_loop_gather_callback_handler.current_chain = self._current_chain_message
+            chain_type = serialized['id'][-1]
+            if chain_type:
+                self._current_chain_result = ChainResult(
+                    type=chain_type,
+                    prompt=inputs,
+                    started_at=time.perf_counter()
+                )
+                self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
+                if self.agent_callback:
+                    self.agent_callback.current_chain = self._current_chain_message
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""
@@ -74,64 +75,4 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:
         logging.error(error)
-        self.clear_chain_results()
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        pass
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Do nothing."""
-        pass
-
-    def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        logging.error(error)
-
-    def on_tool_start(
-        self,
-        serialized: Dict[str, Any],
-        input_str: str,
-        **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_agent_action(
-        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
-    ) -> Any:
-        pass
-
-    def on_tool_end(
-        self,
-        output: str,
-        color: Optional[str] = None,
-        observation_prefix: Optional[str] = None,
-        llm_prefix: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_tool_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        """Do nothing."""
-        logging.error(error)
-
-    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Optional[str],
-    ) -> None:
-        """Run on additional input from chains and agents."""
-        pass
-
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
-        """Run on agent end."""
-        pass
+        self.clear_chain_results()

api/core/callback_handler/std_out_callback_handler.py

@@ -1,9 +1,10 @@
+import os
 import sys
 from typing import Any, Dict, List, Optional, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.input import print_text
-from langchain.schema import AgentAction, AgentFinish, LLMResult
+from langchain.schema import AgentAction, AgentFinish, LLMResult, BaseMessage
 
 
 class DifyStdOutCallbackHandler(BaseCallbackHandler):
@@ -13,17 +14,23 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
         """Initialize callback handler."""
         self.color = color
 
+    def on_chat_model_start(
+            self,
+            serialized: Dict[str, Any],
+            messages: List[List[BaseMessage]],
+            **kwargs: Any
+    ) -> Any:
+        print_text("\n[on_chat_model_start]\n", color='blue')
+        for sub_messages in messages:
+            for sub_message in sub_messages:
+                print_text(str(sub_message) + "\n", color='blue')
+
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
         """Print out the prompts."""
         print_text("\n[on_llm_start]\n", color='blue')
-
-        if 'Chat' in serialized['name']:
-            for prompt in prompts:
-                print_text(prompt + "\n", color='blue')
-        else:
-            print_text(prompts[0] + "\n", color='blue')
+        print_text(prompts[0] + "\n", color='blue')
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Do nothing."""
@@ -44,8 +51,8 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
         self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
     ) -> None:
         """Print out that we are entering a chain."""
-        class_name = serialized["name"]
-        print_text("\n[on_chain_start]\nChain: " + class_name + "\nInputs: " + str(inputs) + "\n", color='pink')
+        chain_type = serialized['id'][-1]
+        print_text("\n[on_chain_start]\nChain: " + chain_type + "\nInputs: " + str(inputs) + "\n", color='pink')
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""
@@ -117,6 +124,26 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
         """Run on agent end."""
         print_text("[on_agent_finish] " + finish.return_values['output'] + "\n", color='green', end="\n")
 
+    @property
+    def ignore_llm(self) -> bool:
+        """Whether to ignore LLM callbacks."""
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
+
+    @property
+    def ignore_chain(self) -> bool:
+        """Whether to ignore chain callbacks."""
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
+
+    @property
+    def ignore_agent(self) -> bool:
+        """Whether to ignore agent callbacks."""
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
+
+    @property
+    def ignore_chat_model(self) -> bool:
+        """Whether to ignore chat model callbacks."""
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
+
 
 class DifyStreamingStdOutCallbackHandler(DifyStdOutCallbackHandler):
     """Callback handler for streaming. Only works with LLMs that support streaming."""

api/core/chain/chain_builder.py

@@ -1,34 +0,0 @@
-from typing import Optional
-
-from langchain.callbacks import CallbackManager
-
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceChain
-from core.chain.tool_chain import ToolChain
-
-
-class ChainBuilder:
-    @classmethod
-    def to_tool_chain(cls, tool, **kwargs) -> ToolChain:
-        return ToolChain(
-            tool=tool,
-            input_key=kwargs.get('input_key', 'input'),
-            output_key=kwargs.get('output_key', 'tool_output'),
-            callback_manager=CallbackManager([DifyStdOutCallbackHandler()])
-        )
-
-    @classmethod
-    def to_sensitive_word_avoidance_chain(cls, tool_config: dict, **kwargs) -> Optional[
-        SensitiveWordAvoidanceChain]:
-        sensitive_words = tool_config.get("words", "")
-        if tool_config.get("enabled", False) \
-                and sensitive_words:
-            return SensitiveWordAvoidanceChain(
-                sensitive_words=sensitive_words.split(","),
-                canned_response=tool_config.get("canned_response", ''),
-                output_key="sensitive_word_avoidance_output",
-                callback_manager=CallbackManager([DifyStdOutCallbackHandler()]),
-                **kwargs
-            )
-
-        return None

api/core/chain/llm_router_chain.py

@@ -1,109 +0,0 @@
-"""Base classes for LLM-powered router chains."""
-from __future__ import annotations
-
-import json
-from typing import Any, Dict, List, Optional, Type, cast, NamedTuple
-
-from langchain.chains.base import Chain
-from pydantic import root_validator
-
-from langchain.chains import LLMChain
-from langchain.prompts import BasePromptTemplate
-from langchain.schema import BaseOutputParser, OutputParserException, BaseLanguageModel
-
-from libs.json_in_md_parser import parse_and_check_json_markdown
-
-
-class Route(NamedTuple):
-    destination: Optional[str]
-    next_inputs: Dict[str, Any]
-
-
-class LLMRouterChain(Chain):
-    """A router chain that uses an LLM chain to perform routing."""
-
-    llm_chain: LLMChain
-    """LLM chain used to perform routing"""
-
-    @root_validator()
-    def validate_prompt(cls, values: dict) -> dict:
-        prompt = values["llm_chain"].prompt
-        if prompt.output_parser is None:
-            raise ValueError(
-                "LLMRouterChain requires base llm_chain prompt to have an output"
-                " parser that converts LLM text output to a dictionary with keys"
-                " 'destination' and 'next_inputs'. Received a prompt with no output"
-                " parser."
-            )
-        return values
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Will be whatever keys the LLM chain prompt expects.
-
-        :meta private:
-        """
-        return self.llm_chain.input_keys
-
-    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
-        super()._validate_outputs(outputs)
-        if not isinstance(outputs["next_inputs"], dict):
-            raise ValueError
-
-    def _call(
-        self,
-        inputs: Dict[str, Any]
-    ) -> Dict[str, Any]:
-        output = cast(
-            Dict[str, Any],
-            self.llm_chain.predict_and_parse(**inputs),
-        )
-        return output
-
-    @classmethod
-    def from_llm(
-        cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
-    ) -> LLMRouterChain:
-        """Convenience constructor."""
-        llm_chain = LLMChain(llm=llm, prompt=prompt)
-        return cls(llm_chain=llm_chain, **kwargs)
-
-    @property
-    def output_keys(self) -> List[str]:
-        return ["destination", "next_inputs"]
-
-    def route(self, inputs: Dict[str, Any]) -> Route:
-        result = self(inputs)
-        return Route(result["destination"], result["next_inputs"])
-
-
-class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
-    """Parser for output of router chain int he multi-prompt chain."""
-
-    default_destination: str = "DEFAULT"
-    next_inputs_type: Type = str
-    next_inputs_inner_key: str = "input"
-
-    def parse(self, text: str) -> Dict[str, Any]:
-        try:
-            expected_keys = ["destination", "next_inputs"]
-            parsed = parse_and_check_json_markdown(text, expected_keys)
-            if not isinstance(parsed["destination"], str):
-                raise ValueError("Expected 'destination' to be a string.")
-            if not isinstance(parsed["next_inputs"], self.next_inputs_type):
-                raise ValueError(
-                    f"Expected 'next_inputs' to be {self.next_inputs_type}."
-                )
-            parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
-            if (
-                parsed["destination"].strip().lower()
-                == self.default_destination.lower()
-            ):
-                parsed["destination"] = None
-            else:
-                parsed["destination"] = parsed["destination"].strip()
-            return parsed
-        except Exception as e:
-            raise OutputParserException(
-                f"Parsing text\n{text}\n of llm router raised following error:\n{e}"
-            )

api/core/chain/main_chain_builder.py

@@ -1,108 +0,0 @@
-from typing import Optional, List
-
-from langchain.callbacks import SharedCallbackManager, CallbackManager
-from langchain.chains import SequentialChain
-from langchain.chains.base import Chain
-from langchain.memory.chat_memory import BaseChatMemory
-
-from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
-from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.chain.chain_builder import ChainBuilder
-from core.chain.multi_dataset_router_chain import MultiDatasetRouterChain
-from core.conversation_message_task import ConversationMessageTask
-from extensions.ext_database import db
-from models.dataset import Dataset
-
-
-class MainChainBuilder:
-    @classmethod
-    def to_langchain_components(cls, tenant_id: str, agent_mode: dict, memory: Optional[BaseChatMemory],
-                                conversation_message_task: ConversationMessageTask):
-        first_input_key = "input"
-        final_output_key = "output"
-
-        chains = []
-
-        chain_callback_handler = MainChainGatherCallbackHandler(conversation_message_task)
-
-        # agent mode
-        tool_chains, chains_output_key = cls.get_agent_chains(
-            tenant_id=tenant_id,
-            agent_mode=agent_mode,
-            memory=memory,
-            conversation_message_task=conversation_message_task
-        )
-        chains += tool_chains
-
-        if chains_output_key:
-            final_output_key = chains_output_key
-
-        if len(chains) == 0:
-            return None
-
-        for chain in chains:
-            # do not add handler into singleton callback manager
-            if not isinstance(chain.callback_manager, SharedCallbackManager):
-                chain.callback_manager.add_handler(chain_callback_handler)
-
-        # build main chain
-        overall_chain = SequentialChain(
-            chains=chains,
-            input_variables=[first_input_key],
-            output_variables=[final_output_key],
-            memory=memory,  # only for use the memory prompt input key
-        )
-
-        return overall_chain
-
-    @classmethod
-    def get_agent_chains(cls, tenant_id: str, agent_mode: dict, memory: Optional[BaseChatMemory],
-                         conversation_message_task: ConversationMessageTask):
-        # agent mode
-        chains = []
-        if agent_mode and agent_mode.get('enabled'):
-            tools = agent_mode.get('tools', [])
-
-            pre_fixed_chains = []
-            # agent_tools = []
-            datasets = []
-            for tool in tools:
-                tool_type = list(tool.keys())[0]
-                tool_config = list(tool.values())[0]
-                if tool_type == 'sensitive-word-avoidance':
-                    chain = ChainBuilder.to_sensitive_word_avoidance_chain(tool_config)
-                    if chain:
-                        pre_fixed_chains.append(chain)
-                elif tool_type == "dataset":
-                    # get dataset from dataset id
-                    dataset = db.session.query(Dataset).filter(
-                        Dataset.tenant_id == tenant_id,
-                        Dataset.id == tool_config.get("id")
-                    ).first()
-
-                    if dataset:
-                        datasets.append(dataset)
-
-            # add pre-fixed chains
-            chains += pre_fixed_chains
-
-            if len(datasets) > 0:
-                # tool to chain
-                multi_dataset_router_chain = MultiDatasetRouterChain.from_datasets(
-                    tenant_id=tenant_id,
-                    datasets=datasets,
-                    conversation_message_task=conversation_message_task,
-                    callback_manager=CallbackManager([DifyStdOutCallbackHandler()])
-                )
-                chains.append(multi_dataset_router_chain)
-
-        final_output_key = cls.get_chains_output_key(chains)
-
-        return chains, final_output_key
-
-    @classmethod
-    def get_chains_output_key(cls, chains: List[Chain]):
-        if len(chains) > 0:
-            return chains[-1].output_keys[0]
-        return None

api/core/chain/multi_dataset_router_chain.py

@@ -1,144 +0,0 @@
-from typing import Mapping, List, Dict, Any, Optional
-
-from langchain import LLMChain, PromptTemplate, ConversationChain
-from langchain.callbacks import CallbackManager
-from langchain.chains.base import Chain
-from langchain.schema import BaseLanguageModel
-from pydantic import Extra
-
-from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
-from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.chain.llm_router_chain import LLMRouterChain, RouterOutputParser
-from core.conversation_message_task import ConversationMessageTask
-from core.llm.llm_builder import LLMBuilder
-from core.tool.dataset_tool_builder import DatasetToolBuilder
-from core.tool.llama_index_tool import EnhanceLlamaIndexTool
-from models.dataset import Dataset
-
-MULTI_PROMPT_ROUTER_TEMPLATE = """
-Given a raw text input to a language model select the model prompt best suited for \
-the input. You will be given the names of the available prompts and a description of \
-what the prompt is best suited for. You may also revise the original input if you \
-think that revising it will ultimately lead to a better response from the language \
-model.
-
-<< FORMATTING >>
-Return a markdown code snippet with a JSON object formatted to look like, \
-no any other string out of markdown code snippet:
-```json
-{{{{
-    "destination": string \\ name of the prompt to use or "DEFAULT"
-    "next_inputs": string \\ a potentially modified version of the original input
-}}}}
-```
-
-REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR \
-it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
-REMEMBER: "next_inputs" can just be the original input if you don't think any \
-modifications are needed.
-
-<< CANDIDATE PROMPTS >>
-{destinations}
-
-<< INPUT >>
-{{input}}
-
-<< OUTPUT >>
-"""
-
-
-class MultiDatasetRouterChain(Chain):
-    """Use a single chain to route an input to one of multiple candidate chains."""
-
-    router_chain: LLMRouterChain
-    """Chain for deciding a destination chain and the input to it."""
-    dataset_tools: Mapping[str, EnhanceLlamaIndexTool]
-    """Map of name to candidate chains that inputs can be routed to."""
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-        arbitrary_types_allowed = True
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Will be whatever keys the router chain prompt expects.
-
-        :meta private:
-        """
-        return self.router_chain.input_keys
-
-    @property
-    def output_keys(self) -> List[str]:
-        return ["text"]
-
-    @classmethod
-    def from_datasets(
-            cls,
-            tenant_id: str,
-            datasets: List[Dataset],
-            conversation_message_task: ConversationMessageTask,
-            **kwargs: Any,
-    ):
-        """Convenience constructor for instantiating from destination prompts."""
-        llm_callback_manager = CallbackManager([DifyStdOutCallbackHandler()])
-        llm = LLMBuilder.to_llm(
-            tenant_id=tenant_id,
-            model_name='gpt-3.5-turbo',
-            temperature=0,
-            max_tokens=1024,
-            callback_manager=llm_callback_manager
-        )
-
-        destinations = ["{}: {}".format(d.id, d.description.replace('\n', ' ') if d.description
-                        else ('useful for when you want to answer queries about the ' + d.name))
-                        for d in datasets]
-        destinations_str = "\n".join(destinations)
-        router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
-            destinations=destinations_str
-        )
-        router_prompt = PromptTemplate(
-            template=router_template,
-            input_variables=["input"],
-            output_parser=RouterOutputParser(),
-        )
-        router_chain = LLMRouterChain.from_llm(llm, router_prompt)
-        dataset_tools = {}
-        for dataset in datasets:
-            dataset_tool = DatasetToolBuilder.build_dataset_tool(
-                dataset=dataset,
-                response_mode='no_synthesizer',  # "compact"
-                callback_handler=DatasetToolCallbackHandler(conversation_message_task)
-            )
-
-            if dataset_tool:
-                dataset_tools[dataset.id] = dataset_tool
-
-        return cls(
-            router_chain=router_chain,
-            dataset_tools=dataset_tools,
-            **kwargs,
-        )
-
-    def _call(
-        self,
-        inputs: Dict[str, Any]
-    ) -> Dict[str, Any]:
-        if len(self.dataset_tools) == 0:
-            return {"text": ''}
-        elif len(self.dataset_tools) == 1:
-            return {"text": next(iter(self.dataset_tools.values())).run(inputs['input'])}
-
-        route = self.router_chain.route(inputs)
-
-        if not route.destination:
-            return {"text": ''}
-        elif route.destination in self.dataset_tools:
-            return {"text": self.dataset_tools[route.destination].run(
-                route.next_inputs['input']
-            )}
-        else:
-            raise ValueError(
-                f"Received invalid destination chain name '{route.destination}'"
-            )

api/core/chain/sensitive_word_avoidance_chain.py

@@ -1,5 +1,6 @@
-from typing import List, Dict
+from typing import List, Dict, Optional, Any
 
+from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 
 
@@ -36,7 +37,11 @@ class SensitiveWordAvoidanceChain(Chain):
                 return self.canned_response
         return text
 
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
         text = inputs[self.input_key]
         output = self._check_sensitive_word(text)
         return {self.output_key: output}

api/core/chain/tool_chain.py

@@ -1,42 +0,0 @@
-from typing import List, Dict
-
-from langchain.chains.base import Chain
-from langchain.tools import BaseTool
-
-
-class ToolChain(Chain):
-    input_key: str = "input"  #: :meta private:
-    output_key: str = "output"  #: :meta private:
-
-    tool: BaseTool
-
-    @property
-    def _chain_type(self) -> str:
-        return "tool_chain"
-
-    @property
-    def input_keys(self) -> List[str]:
-        """Expect input key.
-
-        :meta private:
-        """
-        return [self.input_key]
-
-    @property
-    def output_keys(self) -> List[str]:
-        """Return output key.
-
-        :meta private:
-        """
-        return [self.output_key]
-
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
-        input = inputs[self.input_key]
-        output = self.tool.run(input, self.verbose)
-        return {self.output_key: output}
-
-    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
-        """Run the logic of this chain and return the output."""
-        input = inputs[self.input_key]
-        output = await self.tool.arun(input, self.verbose)
-        return {self.output_key: output}

api/core/completion.py

@@ -1,40 +1,43 @@
 import logging
+import re
 from typing import Optional, List, Union, Tuple
 
-from langchain.callbacks import CallbackManager
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.base import BaseCallbackHandler
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms import BaseLLM
-from langchain.schema import BaseMessage, BaseLanguageModel, HumanMessage
+from langchain.schema import BaseMessage, HumanMessage
 from requests.exceptions import ChunkedEncodingError
 
+from core.agent.agent_executor import AgentExecuteResult, PlanningStrategy
+from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
 from core.constant import llm_constant
 from core.callback_handler.llm_callback_handler import LLMCallbackHandler
 from core.callback_handler.std_out_callback_handler import DifyStreamingStdOutCallbackHandler, \
     DifyStdOutCallbackHandler
-from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException, PubHandler
+from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
 from core.llm.error import LLMBadRequestError
+from core.llm.fake import FakeLLM
 from core.llm.llm_builder import LLMBuilder
-from core.chain.main_chain_builder import MainChainBuilder
 from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
 from core.llm.streamable_open_ai import StreamableOpenAI
 from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
     ReadOnlyConversationTokenDBBufferSharedMemory
-from core.memory.read_only_conversation_token_db_string_buffer_shared_memory import \
-    ReadOnlyConversationTokenDBStringBufferSharedMemory
+from core.orchestrator_rule_parser import OrchestratorRuleParser
 from core.prompt.prompt_builder import PromptBuilder
-from core.prompt.prompt_template import OutLinePromptTemplate
+from core.prompt.prompt_template import JinjaPromptTemplate
 from core.prompt.prompts import MORE_LIKE_THIS_GENERATE_PROMPT
-from models.model import App, AppModelConfig, Account, Conversation, Message
+from models.model import App, AppModelConfig, Account, Conversation, Message, EndUser
 
 
 class Completion:
     @classmethod
     def generate(cls, task_id: str, app: App, app_model_config: AppModelConfig, query: str, inputs: dict,
-                 user: Account, conversation: Optional[Conversation], streaming: bool, is_override: bool = False):
+                 user: Union[Account, EndUser], conversation: Optional[Conversation], streaming: bool, is_override: bool = False):
         """
         errors: ProviderTokenNotInitError
         """
-        cls.validate_query_tokens(app.tenant_id, app_model_config, query)
+        query = PromptBuilder.process_template(query)
 
         memory = None
         if conversation:
@@ -48,6 +51,14 @@ class Completion:
 
             inputs = conversation.inputs
 
+        rest_tokens_for_context_and_memory = cls.get_validate_rest_tokens(
+            mode=app.mode,
+            tenant_id=app.tenant_id,
+            app_model_config=app_model_config,
+            query=query,
+            inputs=inputs
+        )
+
         conversation_message_task = ConversationMessageTask(
             task_id=task_id,
             app=app,
@@ -60,17 +71,33 @@ class Completion:
             streaming=streaming
         )
 
-        # build main chain include agent
-        main_chain = MainChainBuilder.to_langchain_components(
+        chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
+
+        # init orchestrator rule parser
+        orchestrator_rule_parser = OrchestratorRuleParser(
             tenant_id=app.tenant_id,
-            agent_mode=app_model_config.agent_mode_dict,
-            memory=ReadOnlyConversationTokenDBStringBufferSharedMemory(memory=memory) if memory else None,
-            conversation_message_task=conversation_message_task
+            app_model_config=app_model_config
         )
 
-        chain_output = ''
-        if main_chain:
-            chain_output = main_chain.run(query)
+        # parse sensitive_word_avoidance_chain
+        sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain([chain_callback])
+        if sensitive_word_avoidance_chain:
+            query = sensitive_word_avoidance_chain.run(query)
+
+        # get agent executor
+        agent_executor = orchestrator_rule_parser.to_agent_executor(
+            conversation_message_task=conversation_message_task,
+            memory=memory,
+            rest_tokens=rest_tokens_for_context_and_memory,
+            chain_callback=chain_callback
+        )
+
+        # run agent executor
+        agent_execute_result = None
+        if agent_executor:
+            should_use_agent = agent_executor.should_use_agent(query)
+            if should_use_agent:
+                agent_execute_result = agent_executor.run(query)
 
         # run the final llm
         try:
@@ -80,7 +107,7 @@ class Completion:
                 app_model_config=app_model_config,
                 query=query,
                 inputs=inputs,
-                chain_output=chain_output,
+                agent_execute_result=agent_execute_result,
                 conversation_message_task=conversation_message_task,
                 memory=memory,
                 streaming=streaming
@@ -95,9 +122,20 @@ class Completion:
 
     @classmethod
     def run_final_llm(cls, tenant_id: str, mode: str, app_model_config: AppModelConfig, query: str, inputs: dict,
-                      chain_output: str,
+                      agent_execute_result: Optional[AgentExecuteResult],
                       conversation_message_task: ConversationMessageTask,
                       memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory], streaming: bool):
+        # When no extra pre prompt is specified,
+        # the output of the agent can be used directly as the main output content without calling LLM again
+        if not app_model_config.pre_prompt and agent_execute_result and agent_execute_result.output \
+                and agent_execute_result.strategy != PlanningStrategy.ROUTER:
+            final_llm = FakeLLM(response=agent_execute_result.output,
+                                origin_llm=agent_execute_result.configuration.llm,
+                                streaming=streaming)
+            final_llm.callbacks = cls.get_llm_callbacks(final_llm, streaming, conversation_message_task)
+            response = final_llm.generate([[HumanMessage(content=query)]])
+            return response
+
         final_llm = LLMBuilder.to_llm_from_model(
             tenant_id=tenant_id,
             model=app_model_config.model_dict,
@@ -108,17 +146,19 @@ class Completion:
         prompt, stop_words = cls.get_main_llm_prompt(
             mode=mode,
             llm=final_llm,
+            model=app_model_config.model_dict,
             pre_prompt=app_model_config.pre_prompt,
             query=query,
             inputs=inputs,
-            chain_output=chain_output,
+            agent_execute_result=agent_execute_result,
             memory=memory
         )
 
-        final_llm.callback_manager = cls.get_llm_callback_manager(final_llm, streaming, conversation_message_task)
+        final_llm.callbacks = cls.get_llm_callbacks(final_llm, streaming, conversation_message_task)
 
         cls.recale_llm_max_tokens(
             final_llm=final_llm,
+            model=app_model_config.model_dict,
             prompt=prompt,
             mode=mode
         )
@@ -128,42 +168,31 @@ class Completion:
         return response
 
     @classmethod
-    def get_main_llm_prompt(cls, mode: str, llm: BaseLanguageModel, pre_prompt: str, query: str, inputs: dict,
-                            chain_output: Optional[str],
+    def get_main_llm_prompt(cls, mode: str, llm: BaseLanguageModel, model: dict,
+                            pre_prompt: str, query: str, inputs: dict,
+                            agent_execute_result: Optional[AgentExecuteResult],
                             memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]) -> \
             Tuple[Union[str | List[BaseMessage]], Optional[List[str]]]:
-        # disable template string in query
-        query_params = OutLinePromptTemplate.from_template(template=query).input_variables
-        if query_params:
-            for query_param in query_params:
-                if query_param not in inputs:
-                    inputs[query_param] = '{' + query_param + '}'
-
-        pre_prompt = PromptBuilder.process_template(pre_prompt) if pre_prompt else pre_prompt
         if mode == 'completion':
-            prompt_template = OutLinePromptTemplate.from_template(
-                template=("""Use the following CONTEXT as your learned knowledge:
-[CONTEXT]
-{context}
-[END CONTEXT]
+            prompt_template = JinjaPromptTemplate.from_template(
+                template=("""Use the following context as your learned knowledge, inside <context></context> XML tags.
+
+<context>
+{{context}}
+</context>
 
 When answer to user:
 - If you don't know, just say that you don't know.
 - If you don't know when you are not sure, ask for clarification. 
 Avoid mentioning that you obtained the information from the context.
 And answer according to the language of the user's question.
-""" if chain_output else "")
+""" if agent_execute_result else "")
                          + (pre_prompt + "\n" if pre_prompt else "")
-                         + "{query}\n"
+                         + "{{query}}\n"
             )
 
-            if chain_output:
-                inputs['context'] = chain_output
-                context_params = OutLinePromptTemplate.from_template(template=chain_output).input_variables
-                if context_params:
-                    for context_param in context_params:
-                        if context_param not in inputs:
-                            inputs[context_param] = '{' + context_param + '}'
+            if agent_execute_result:
+                inputs['context'] = agent_execute_result.output
 
             prompt_inputs = {k: inputs[k] for k in prompt_template.input_variables if k in inputs}
             prompt_content = prompt_template.format(
@@ -187,18 +216,19 @@ And answer according to the language of the user's question.
 
             if pre_prompt:
                 pre_prompt_inputs = {k: inputs[k] for k in
-                                     OutLinePromptTemplate.from_template(template=pre_prompt).input_variables
+                                     JinjaPromptTemplate.from_template(template=pre_prompt).input_variables
                                      if k in inputs}
 
                 if pre_prompt_inputs:
                     human_inputs.update(pre_prompt_inputs)
 
-            if chain_output:
-                human_inputs['context'] = chain_output
-                human_message_prompt += """Use the following CONTEXT as your learned knowledge.
-[CONTEXT]
-{context}
-[END CONTEXT]
+            if agent_execute_result:
+                human_inputs['context'] = agent_execute_result.output
+                human_message_prompt += """Use the following context as your learned knowledge, inside <context></context> XML tags.
+
+<context>
+{{context}}
+</context>
 
 When answer to user:
 - If you don't know, just say that you don't know.
@@ -210,7 +240,7 @@ And answer according to the language of the user's question.
             if pre_prompt:
                 human_message_prompt += pre_prompt
 
-            query_prompt = "\nHuman: {query}\nAI: "
+            query_prompt = "\n\nHuman: {{query}}\n\nAssistant: "
 
             if memory:
                 # append chat histories
@@ -219,20 +249,17 @@ And answer according to the language of the user's question.
                     inputs=human_inputs
                 )
 
-                curr_message_tokens = memory.llm.get_messages_tokens([tmp_human_message])
-                rest_tokens = llm_constant.max_context_token_length[memory.llm.model_name] \
-                              - memory.llm.max_tokens - curr_message_tokens
+                curr_message_tokens = memory.llm.get_num_tokens_from_messages([tmp_human_message])
+                model_name = model['name']
+                max_tokens = model.get("completion_params").get('max_tokens')
+                rest_tokens = llm_constant.max_context_token_length[model_name] \
+                              - max_tokens - curr_message_tokens
                 rest_tokens = max(rest_tokens, 0)
                 histories = cls.get_history_messages_from_memory(memory, rest_tokens)
-
-                # disable template string in query
-                histories_params = OutLinePromptTemplate.from_template(template=histories).input_variables
-                if histories_params:
-                    for histories_param in histories_params:
-                        if histories_param not in human_inputs:
-                            human_inputs[histories_param] = '{' + histories_param + '}'
-
-                human_message_prompt += "\n\n" + histories
+                human_message_prompt += "\n\n" if human_message_prompt else ""
+                human_message_prompt += "Here is the chat histories between human and assistant, " \
+                                        "inside <histories></histories> XML tags.\n\n<histories>\n"
+                human_message_prompt += histories + "\n</histories>"
 
             human_message_prompt += query_prompt
 
@@ -244,24 +271,24 @@ And answer according to the language of the user's question.
 
             messages.append(human_message)
 
-            return messages, ['\nHuman:']
+            for message in messages:
+                message.content = re.sub(r'<\|.*?\|>', '', message.content)
+
+            return messages, ['\nHuman:', '</histories>']
 
     @classmethod
-    def get_llm_callback_manager(cls, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
-                                 streaming: bool,
-                                 conversation_message_task: ConversationMessageTask) -> CallbackManager:
+    def get_llm_callbacks(cls, llm: BaseLanguageModel,
+                          streaming: bool,
+                          conversation_message_task: ConversationMessageTask) -> List[BaseCallbackHandler]:
         llm_callback_handler = LLMCallbackHandler(llm, conversation_message_task)
         if streaming:
-            callback_handlers = [llm_callback_handler, DifyStreamingStdOutCallbackHandler()]
+            return [llm_callback_handler, DifyStreamingStdOutCallbackHandler()]
         else:
-            callback_handlers = [llm_callback_handler, DifyStdOutCallbackHandler()]
-
-        return CallbackManager(callback_handlers)
+            return [llm_callback_handler, DifyStdOutCallbackHandler()]
 
     @classmethod
     def get_history_messages_from_memory(cls, memory: ReadOnlyConversationTokenDBBufferSharedMemory,
-                                         max_token_limit: int) -> \
-            str:
+                                         max_token_limit: int) -> str:
         """Get memory messages."""
         memory.max_token_limit = max_token_limit
         memory_key = memory.memory_variables[0]
@@ -293,29 +320,51 @@ And answer according to the language of the user's question.
         return memory
 
     @classmethod
-    def validate_query_tokens(cls, tenant_id: str, app_model_config: AppModelConfig, query: str):
+    def get_validate_rest_tokens(cls, mode: str, tenant_id: str, app_model_config: AppModelConfig,
+                                 query: str, inputs: dict) -> int:
         llm = LLMBuilder.to_llm_from_model(
             tenant_id=tenant_id,
             model=app_model_config.model_dict
         )
 
-        model_limited_tokens = llm_constant.max_context_token_length[llm.model_name]
-        max_tokens = llm.max_tokens
+        model_name = app_model_config.model_dict.get("name")
+        model_limited_tokens = llm_constant.max_context_token_length[model_name]
+        max_tokens = app_model_config.model_dict.get("completion_params").get('max_tokens')
 
-        if model_limited_tokens - max_tokens - llm.get_num_tokens(query) < 0:
-            raise LLMBadRequestError("Query is too long")
+        # get prompt without memory and context
+        prompt, _ = cls.get_main_llm_prompt(
+            mode=mode,
+            llm=llm,
+            model=app_model_config.model_dict,
+            pre_prompt=app_model_config.pre_prompt,
+            query=query,
+            inputs=inputs,
+            agent_execute_result=None,
+            memory=None
+        )
+
+        prompt_tokens = llm.get_num_tokens(prompt) if isinstance(prompt, str) \
+            else llm.get_num_tokens_from_messages(prompt)
+
+        rest_tokens = model_limited_tokens - max_tokens - prompt_tokens
+        if rest_tokens < 0:
+            raise LLMBadRequestError("Query or prefix prompt is too long, you can reduce the prefix prompt, "
+                                     "or shrink the max token, or switch to a llm with a larger token limit size.")
+
+        return rest_tokens
 
     @classmethod
-    def recale_llm_max_tokens(cls, final_llm: Union[StreamableOpenAI, StreamableChatOpenAI],
+    def recale_llm_max_tokens(cls, final_llm: BaseLanguageModel, model: dict,
                               prompt: Union[str, List[BaseMessage]], mode: str):
         # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
-        model_limited_tokens = llm_constant.max_context_token_length[final_llm.model_name]
-        max_tokens = final_llm.max_tokens
+        model_name = model.get("name")
+        model_limited_tokens = llm_constant.max_context_token_length[model_name]
+        max_tokens = model.get("completion_params").get('max_tokens')
 
         if mode == 'completion' and isinstance(final_llm, BaseLLM):
             prompt_tokens = final_llm.get_num_tokens(prompt)
         else:
-            prompt_tokens = final_llm.get_messages_tokens(prompt)
+            prompt_tokens = final_llm.get_num_tokens_from_messages(prompt)
 
         if prompt_tokens + max_tokens > model_limited_tokens:
             max_tokens = max(model_limited_tokens - prompt_tokens, 16)
@@ -324,9 +373,10 @@ And answer according to the language of the user's question.
     @classmethod
     def generate_more_like_this(cls, task_id: str, app: App, message: Message, pre_prompt: str,
                                 app_model_config: AppModelConfig, user: Account, streaming: bool):
-        llm: StreamableOpenAI = LLMBuilder.to_llm(
+
+        llm = LLMBuilder.to_llm_from_model(
             tenant_id=app.tenant_id,
-            model_name='gpt-3.5-turbo',
+            model=app_model_config.model_dict,
             streaming=streaming
         )
 
@@ -334,10 +384,12 @@ And answer according to the language of the user's question.
         original_prompt, _ = cls.get_main_llm_prompt(
             mode="completion",
             llm=llm,
+            model=app_model_config.model_dict,
             pre_prompt=pre_prompt,
             query=message.query,
             inputs=message.inputs,
             chain_output=None,
+            agent_execute_result=None,
             memory=None
         )
 
@@ -360,10 +412,11 @@ And answer according to the language of the user's question.
             streaming=streaming
         )
 
-        llm.callback_manager = cls.get_llm_callback_manager(llm, streaming, conversation_message_task)
+        llm.callbacks = cls.get_llm_callbacks(llm, streaming, conversation_message_task)
 
         cls.recale_llm_max_tokens(
             final_llm=llm,
+            model=app_model_config.model_dict,
             prompt=prompt,
             mode='completion'
         )

api/core/constant/llm_constant.py

@@ -1,6 +1,8 @@
 from _decimal import Decimal
 
 models = {
+    'claude-instant-1': 'anthropic',  # 100,000 tokens
+    'claude-2': 'anthropic',  # 100,000 tokens
     'gpt-4': 'openai',  # 8,192 tokens
     'gpt-4-32k': 'openai',  # 32,768 tokens
     'gpt-3.5-turbo': 'openai',  # 4,096 tokens
@@ -10,10 +12,13 @@ models = {
     'text-curie-001': 'openai',  # 2,049 tokens
     'text-babbage-001': 'openai',  # 2,049 tokens
     'text-ada-001': 'openai',  # 2,049 tokens
-    'text-embedding-ada-002': 'openai'  # 8191 tokens, 1536 dimensions
+    'text-embedding-ada-002': 'openai',  # 8191 tokens, 1536 dimensions
+    'whisper-1': 'openai'
 }
 
 max_context_token_length = {
+    'claude-instant-1': 100000,
+    'claude-2': 100000,
     'gpt-4': 8192,
     'gpt-4-32k': 32768,
     'gpt-3.5-turbo': 4096,
@@ -23,17 +28,21 @@ max_context_token_length = {
     'text-curie-001': 2049,
     'text-babbage-001': 2049,
     'text-ada-001': 2049,
-    'text-embedding-ada-002': 8191
+    'text-embedding-ada-002': 8191,
 }
 
 models_by_mode = {
     'chat': [
+        'claude-instant-1',  # 100,000 tokens
+        'claude-2',  # 100,000 tokens
         'gpt-4',  # 8,192 tokens
         'gpt-4-32k',  # 32,768 tokens
         'gpt-3.5-turbo',  # 4,096 tokens
         'gpt-3.5-turbo-16k',  # 16,384 tokens
     ],
     'completion': [
+        'claude-instant-1',  # 100,000 tokens
+        'claude-2',  # 100,000 tokens
         'gpt-4',  # 8,192 tokens
         'gpt-4-32k',  # 32,768 tokens
         'gpt-3.5-turbo',  # 4,096 tokens
@@ -52,6 +61,14 @@ models_by_mode = {
 model_currency = 'USD'
 
 model_prices = {
+    'claude-instant-1': {
+        'prompt': Decimal('0.00163'),
+        'completion': Decimal('0.00551'),
+    },
+    'claude-2': {
+        'prompt': Decimal('0.01102'),
+        'completion': Decimal('0.03268'),
+    },
     'gpt-4': {
         'prompt': Decimal('0.03'),
         'completion': Decimal('0.06'),

api/core/conversation_message_task.py

@@ -10,7 +10,7 @@ from core.constant import llm_constant
 from core.llm.llm_builder import LLMBuilder
 from core.llm.provider.llm_provider_service import LLMProviderService
 from core.prompt.prompt_builder import PromptBuilder
-from core.prompt.prompt_template import OutLinePromptTemplate
+from core.prompt.prompt_template import JinjaPromptTemplate
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -52,11 +52,11 @@ class ConversationMessageTask:
             message=self.message,
             conversation=self.conversation,
             chain_pub=False,  # disabled currently
-            agent_thought_pub=False  # disabled currently
+            agent_thought_pub=True
         )
 
     def init(self):
-        provider_name = LLMBuilder.get_default_provider(self.app.tenant_id)
+        provider_name = LLMBuilder.get_default_provider(self.app.tenant_id, self.model_name)
         self.model_dict['provider'] = provider_name
 
         override_model_configs = None
@@ -69,6 +69,7 @@ class ConversationMessageTask:
                 "suggested_questions": self.app_model_config.suggested_questions_list,
                 "suggested_questions_after_answer": self.app_model_config.suggested_questions_after_answer_dict,
                 "more_like_this": self.app_model_config.more_like_this_dict,
+                "sensitive_word_avoidance": self.app_model_config.sensitive_word_avoidance_dict,
                 "user_input_form": self.app_model_config.user_input_form_list,
             }
 
@@ -78,7 +79,7 @@ class ConversationMessageTask:
         if self.mode == 'chat':
             introduction = self.app_model_config.opening_statement
             if introduction:
-                prompt_template = OutLinePromptTemplate.from_template(template=PromptBuilder.process_template(introduction))
+                prompt_template = JinjaPromptTemplate.from_template(template=introduction)
                 prompt_inputs = {k: self.inputs[k] for k in prompt_template.input_variables if k in self.inputs}
                 try:
                     introduction = prompt_template.format(**prompt_inputs)
@@ -86,11 +87,10 @@ class ConversationMessageTask:
                     pass
 
             if self.app_model_config.pre_prompt:
-                pre_prompt = PromptBuilder.process_template(self.app_model_config.pre_prompt)
-                system_message = PromptBuilder.to_system_message(pre_prompt, self.inputs)
+                system_message = PromptBuilder.to_system_message(self.app_model_config.pre_prompt, self.inputs)
                 system_instruction = system_message.content
                 llm = LLMBuilder.to_llm(self.tenant_id, self.model_name)
-                system_instruction_tokens = llm.get_messages_tokens([system_message])
+                system_instruction_tokens = llm.get_num_tokens_from_messages([system_message])
 
         if not self.conversation:
             self.is_new_conversation = True
@@ -157,7 +157,7 @@ class ConversationMessageTask:
         self.message.message = llm_message.prompt
         self.message.message_tokens = message_tokens
         self.message.message_unit_price = message_unit_price
-        self.message.answer = llm_message.completion.strip() if llm_message.completion else ''
+        self.message.answer = PromptBuilder.process_template(llm_message.completion.strip()) if llm_message.completion else ''
         self.message.answer_tokens = answer_tokens
         self.message.answer_unit_price = answer_unit_price
         self.message.provider_response_latency = llm_message.latency
@@ -186,6 +186,7 @@ class ConversationMessageTask:
         if provider and provider.provider_type == ProviderType.SYSTEM.value:
             db.session.query(Provider).filter(
                 Provider.tenant_id == self.app.tenant_id,
+                Provider.provider_name == provider.provider_name,
                 Provider.quota_limit > Provider.quota_used
             ).update({'quota_used': Provider.quota_used + 1})
 
@@ -207,7 +208,28 @@ class ConversationMessageTask:
 
         self._pub_handler.pub_chain(message_chain)
 
-    def on_agent_end(self, message_chain: MessageChain, agent_model_name: str,
+    def on_agent_start(self, message_chain: MessageChain, agent_loop: AgentLoop) -> MessageAgentThought:
+        message_agent_thought = MessageAgentThought(
+            message_id=self.message.id,
+            message_chain_id=message_chain.id,
+            position=agent_loop.position,
+            thought=agent_loop.thought,
+            tool=agent_loop.tool_name,
+            tool_input=agent_loop.tool_input,
+            message=agent_loop.prompt,
+            answer=agent_loop.completion,
+            created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
+            created_by=self.user.id
+        )
+
+        db.session.add(message_agent_thought)
+        db.session.flush()
+
+        self._pub_handler.pub_agent_thought(message_agent_thought)
+
+        return message_agent_thought
+
+    def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_name: str,
                      agent_loop: AgentLoop):
         agent_message_unit_price = llm_constant.model_prices[agent_model_name]['prompt']
         agent_answer_unit_price = llm_constant.model_prices[agent_model_name]['completion']
@@ -222,34 +244,18 @@ class ConversationMessageTask:
             agent_answer_unit_price
         )
 
-        message_agent_loop = MessageAgentThought(
-            message_id=self.message.id,
-            message_chain_id=message_chain.id,
-            position=agent_loop.position,
-            thought=agent_loop.thought,
-            tool=agent_loop.tool_name,
-            tool_input=agent_loop.tool_input,
-            observation=agent_loop.tool_output,
-            tool_process_data='',  # currently not support
-            message=agent_loop.prompt,
-            message_token=loop_message_tokens,
-            message_unit_price=agent_message_unit_price,
-            answer=agent_loop.completion,
-            answer_token=loop_answer_tokens,
-            answer_unit_price=agent_answer_unit_price,
-            latency=agent_loop.latency,
-            tokens=agent_loop.prompt_tokens + agent_loop.completion_tokens,
-            total_price=loop_total_price,
-            currency=llm_constant.model_currency,
-            created_by_role=('account' if isinstance(self.user, Account) else 'end_user'),
-            created_by=self.user.id
-        )
-
-        db.session.add(message_agent_loop)
+        message_agent_thought.observation = agent_loop.tool_output
+        message_agent_thought.tool_process_data = ''  # currently not support
+        message_agent_thought.message_token = loop_message_tokens
+        message_agent_thought.message_unit_price = agent_message_unit_price
+        message_agent_thought.answer_token = loop_answer_tokens
+        message_agent_thought.answer_unit_price = agent_answer_unit_price
+        message_agent_thought.latency = agent_loop.latency
+        message_agent_thought.tokens = agent_loop.prompt_tokens + agent_loop.completion_tokens
+        message_agent_thought.total_price = loop_total_price
+        message_agent_thought.currency = llm_constant.model_currency
         db.session.flush()
 
-        self._pub_handler.pub_agent_thought(message_agent_loop)
-
     def on_dataset_query_end(self, dataset_query_obj: DatasetQueryObj):
         dataset_query = DatasetQuery(
             dataset_id=dataset_query_obj.dataset_id,
@@ -293,12 +299,12 @@ class PubHandler:
         if not user:
             raise ValueError("user is required")
 
-        user_str = 'account-' + user.id if isinstance(user, Account) else 'end-user-' + user.id
+        user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id)
         return "generate_result:{}-{}".format(user_str, task_id)
 
     @classmethod
     def generate_stopped_cache_key(cls, user: Union[Account | EndUser], task_id: str):
-        user_str = 'account-' + user.id if isinstance(user, Account) else 'end-user-' + user.id
+        user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id)
         return "generate_result_stopped:{}-{}".format(user_str, task_id)
 
     def pub_text(self, text: str):
@@ -306,10 +312,10 @@ class PubHandler:
             'event': 'message',
             'data': {
                 'task_id': self._task_id,
-                'message_id': self._message.id,
+                'message_id': str(self._message.id),
                 'text': text,
                 'mode': self._conversation.mode,
-                'conversation_id': self._conversation.id
+                'conversation_id': str(self._conversation.id)
             }
         }
 
@@ -346,16 +352,14 @@ class PubHandler:
             content = {
                 'event': 'agent_thought',
                 'data': {
+                    'id': message_agent_thought.id,
                     'task_id': self._task_id,
                     'message_id': self._message.id,
                     'chain_id': message_agent_thought.message_chain_id,
-                    'agent_thought_id': message_agent_thought.id,
                     'position': message_agent_thought.position,
                     'thought': message_agent_thought.thought,
                     'tool': message_agent_thought.tool,
                     'tool_input': message_agent_thought.tool_input,
-                    'observation': message_agent_thought.observation,
-                    'answer': message_agent_thought.answer,
                     'mode': self._conversation.mode,
                     'conversation_id': self._conversation.id
                 }
@@ -388,6 +392,15 @@ class PubHandler:
     def _is_stopped(self):
         return redis_client.get(self._stopped_cache_key) is not None
 
+    @classmethod
+    def ping(cls, user: Union[Account | EndUser], task_id: str):
+        content = {
+            'event': 'ping'
+        }
+
+        channel = cls.generate_channel_name(user, task_id)
+        redis_client.publish(channel, json.dumps(content))
+
     @classmethod
     def stop(cls, user: Union[Account | EndUser], task_id: str):
         stopped_cache_key = cls.generate_stopped_cache_key(user, task_id)

api/core/data_loader/file_extractor.py

@@ -0,0 +1,66 @@
+import tempfile
+from pathlib import Path
+from typing import List, Union, Optional
+
+import requests
+from langchain.document_loaders import TextLoader, Docx2txtLoader
+from langchain.schema import Document
+
+from core.data_loader.loader.csv import CSVLoader
+from core.data_loader.loader.excel import ExcelLoader
+from core.data_loader.loader.html import HTMLLoader
+from core.data_loader.loader.markdown import MarkdownLoader
+from core.data_loader.loader.pdf import PdfLoader
+from extensions.ext_storage import storage
+from models.model import UploadFile
+
+SUPPORT_URL_CONTENT_TYPES = ['application/pdf', 'text/plain']
+USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+
+
+class FileExtractor:
+    @classmethod
+    def load(cls, upload_file: UploadFile, return_text: bool = False) -> Union[List[Document] | str]:
+        with tempfile.TemporaryDirectory() as temp_dir:
+            suffix = Path(upload_file.key).suffix
+            file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
+            storage.download(upload_file.key, file_path)
+
+            return cls.load_from_file(file_path, return_text, upload_file)
+
+    @classmethod
+    def load_from_url(cls, url: str, return_text: bool = False) -> Union[List[Document] | str]:
+        response = requests.get(url, headers={
+            "User-Agent": USER_AGENT
+        })
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            suffix = Path(url).suffix
+            file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
+            with open(file_path, 'wb') as file:
+                file.write(response.content)
+
+            return cls.load_from_file(file_path, return_text)
+
+    @classmethod
+    def load_from_file(cls, file_path: str, return_text: bool = False,
+                       upload_file: Optional[UploadFile] = None) -> Union[List[Document] | str]:
+        input_file = Path(file_path)
+        delimiter = '\n'
+        if input_file.suffix == '.xlsx':
+            loader = ExcelLoader(file_path)
+        elif input_file.suffix == '.pdf':
+            loader = PdfLoader(file_path, upload_file=upload_file)
+        elif input_file.suffix in ['.md', '.markdown']:
+            loader = MarkdownLoader(file_path, autodetect_encoding=True)
+        elif input_file.suffix in ['.htm', '.html']:
+            loader = HTMLLoader(file_path)
+        elif input_file.suffix == '.docx':
+            loader = Docx2txtLoader(file_path)
+        elif input_file.suffix == '.csv':
+            loader = CSVLoader(file_path, autodetect_encoding=True)
+        else:
+            # txt
+            loader = TextLoader(file_path, autodetect_encoding=True)
+
+        return delimiter.join([document.page_content for document in loader.load()]) if return_text else loader.load()

api/core/data_loader/loader/csv.py

@@ -0,0 +1,67 @@
+import logging
+from typing import Optional, Dict, List
+
+from langchain.document_loaders import CSVLoader as LCCSVLoader
+from langchain.document_loaders.helpers import detect_file_encodings
+
+from models.dataset import Document
+
+logger = logging.getLogger(__name__)
+
+
+class CSVLoader(LCCSVLoader):
+    def __init__(
+            self,
+            file_path: str,
+            source_column: Optional[str] = None,
+            csv_args: Optional[Dict] = None,
+            encoding: Optional[str] = None,
+            autodetect_encoding: bool = True,
+    ):
+        self.file_path = file_path
+        self.source_column = source_column
+        self.encoding = encoding
+        self.csv_args = csv_args or {}
+        self.autodetect_encoding = autodetect_encoding
+
+    def load(self) -> List[Document]:
+        """Load data into document objects."""
+        try:
+            with open(self.file_path, newline="", encoding=self.encoding) as csvfile:
+                docs = self._read_from_file(csvfile)
+        except UnicodeDecodeError as e:
+            if self.autodetect_encoding:
+                detected_encodings = detect_file_encodings(self.file_path)
+                for encoding in detected_encodings:
+                    logger.debug("Trying encoding: ", encoding.encoding)
+                    try:
+                        with open(self.file_path, newline="", encoding=encoding.encoding) as csvfile:
+                            docs = self._read_from_file(csvfile)
+                        break
+                    except UnicodeDecodeError:
+                        continue
+            else:
+                raise RuntimeError(f"Error loading {self.file_path}") from e
+
+        return docs
+
+    def _read_from_file(self, csvfile):
+        docs = []
+        csv_reader = csv.DictReader(csvfile, **self.csv_args)  # type: ignore
+        for i, row in enumerate(csv_reader):
+            content = "\n".join(f"{k.strip()}: {v.strip()}" for k, v in row.items())
+            try:
+                source = (
+                    row[self.source_column]
+                    if self.source_column is not None
+                    else ''
+                )
+            except KeyError:
+                raise ValueError(
+                    f"Source column '{self.source_column}' not found in CSV file."
+                )
+            metadata = {"source": source, "row": i}
+            doc = Document(page_content=content, metadata=metadata)
+            docs.append(doc)
+
+        return docs

api/core/data_loader/loader/excel.py

@@ -0,0 +1,45 @@
+import json
+import logging
+from typing import List
+
+from langchain.document_loaders.base import BaseLoader
+from langchain.schema import Document
+from openpyxl.reader.excel import load_workbook
+
+logger = logging.getLogger(__name__)
+
+
+class ExcelLoader(BaseLoader):
+    """Load xlxs files.
+
+
+    Args:
+        file_path: Path to the file to load.
+    """
+
+    def __init__(
+        self,
+        file_path: str
+    ):
+        """Initialize with file path."""
+        self._file_path = file_path
+
+    def load(self) -> List[Document]:
+        data = []
+        keys = []
+        wb = load_workbook(filename=self._file_path, read_only=True)
+        # loop over all sheets
+        for sheet in wb:
+            for row in sheet.iter_rows(values_only=True):
+                if all(v is None for v in row):
+                    continue
+                if keys == []:
+                    keys = list(map(str, row))
+                else:
+                    row_dict = dict(zip(keys, list(map(str, row))))
+                    row_dict = {k: v for k, v in row_dict.items() if v}
+                    item = ''.join(f'{k}:{v}\n' for k, v in row_dict.items())
+                    document = Document(page_content=item, metadata={'source': self._file_path})
+                    data.append(document)
+
+        return data

api/core/data_loader/loader/html.py

@@ -0,0 +1,35 @@
+import logging
+from typing import List
+
+from bs4 import BeautifulSoup
+from langchain.document_loaders.base import BaseLoader
+from langchain.schema import Document
+
+logger = logging.getLogger(__name__)
+
+
+class HTMLLoader(BaseLoader):
+    """Load html files.
+
+
+    Args:
+        file_path: Path to the file to load.
+    """
+
+    def __init__(
+        self,
+        file_path: str
+    ):
+        """Initialize with file path."""
+        self._file_path = file_path
+
+    def load(self) -> List[Document]:
+        return [Document(page_content=self._load_as_text())]
+
+    def _load_as_text(self) -> str:
+        with open(self._file_path, "rb") as fp:
+            soup = BeautifulSoup(fp, 'html.parser')
+            text = soup.get_text()
+            text = text.strip() if text else ''
+
+        return text

api/core/data_loader/loader/markdown.py

@@ -0,0 +1,134 @@
+import logging
+import re
+from typing import Optional, List, Tuple, cast
+
+from langchain.document_loaders.base import BaseLoader
+from langchain.document_loaders.helpers import detect_file_encodings
+from langchain.schema import Document
+
+logger = logging.getLogger(__name__)
+
+
+class MarkdownLoader(BaseLoader):
+    """Load md files.
+
+
+    Args:
+        file_path: Path to the file to load.
+
+        remove_hyperlinks: Whether to remove hyperlinks from the text.
+
+        remove_images: Whether to remove images from the text.
+
+        encoding: File encoding to use. If `None`, the file will be loaded
+        with the default system encoding.
+
+        autodetect_encoding: Whether to try to autodetect the file encoding
+            if the specified encoding fails.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        remove_hyperlinks: bool = True,
+        remove_images: bool = True,
+        encoding: Optional[str] = None,
+        autodetect_encoding: bool = True,
+    ):
+        """Initialize with file path."""
+        self._file_path = file_path
+        self._remove_hyperlinks = remove_hyperlinks
+        self._remove_images = remove_images
+        self._encoding = encoding
+        self._autodetect_encoding = autodetect_encoding
+
+    def load(self) -> List[Document]:
+        tups = self.parse_tups(self._file_path)
+        documents = []
+        for header, value in tups:
+            value = value.strip()
+            if header is None:
+                documents.append(Document(page_content=value))
+            else:
+                documents.append(Document(page_content=f"\n\n{header}\n{value}"))
+
+        return documents
+
+    def markdown_to_tups(self, markdown_text: str) -> List[Tuple[Optional[str], str]]:
+        """Convert a markdown file to a dictionary.
+
+        The keys are the headers and the values are the text under each header.
+
+        """
+        markdown_tups: List[Tuple[Optional[str], str]] = []
+        lines = markdown_text.split("\n")
+
+        current_header = None
+        current_text = ""
+
+        for line in lines:
+            header_match = re.match(r"^#+\s", line)
+            if header_match:
+                if current_header is not None:
+                    markdown_tups.append((current_header, current_text))
+
+                current_header = line
+                current_text = ""
+            else:
+                current_text += line + "\n"
+        markdown_tups.append((current_header, current_text))
+
+        if current_header is not None:
+            # pass linting, assert keys are defined
+            markdown_tups = [
+                (re.sub(r"#", "", cast(str, key)).strip(), re.sub(r"<.*?>", "", value))
+                for key, value in markdown_tups
+            ]
+        else:
+            markdown_tups = [
+                (key, re.sub("\n", "", value)) for key, value in markdown_tups
+            ]
+
+        return markdown_tups
+
+    def remove_images(self, content: str) -> str:
+        """Get a dictionary of a markdown file from its path."""
+        pattern = r"!{1}\[\[(.*)\]\]"
+        content = re.sub(pattern, "", content)
+        return content
+
+    def remove_hyperlinks(self, content: str) -> str:
+        """Get a dictionary of a markdown file from its path."""
+        pattern = r"\[(.*?)\]\((.*?)\)"
+        content = re.sub(pattern, r"\1", content)
+        return content
+
+    def parse_tups(self, filepath: str) -> List[Tuple[Optional[str], str]]:
+        """Parse file into tuples."""
+        content = ""
+        try:
+            with open(filepath, "r", encoding=self._encoding) as f:
+                content = f.read()
+        except UnicodeDecodeError as e:
+            if self._autodetect_encoding:
+                detected_encodings = detect_file_encodings(filepath)
+                for encoding in detected_encodings:
+                    logger.debug("Trying encoding: ", encoding.encoding)
+                    try:
+                        with open(filepath, encoding=encoding.encoding) as f:
+                            content = f.read()
+                        break
+                    except UnicodeDecodeError:
+                        continue
+            else:
+                raise RuntimeError(f"Error loading {filepath}") from e
+        except Exception as e:
+            raise RuntimeError(f"Error loading {filepath}") from e
+
+        if self._remove_hyperlinks:
+            content = self.remove_hyperlinks(content)
+
+        if self._remove_images:
+            content = self.remove_images(content)
+
+        return self.markdown_to_tups(content)

api/core/data_loader/loader/notion.py

@@ -0,0 +1,379 @@
+import json
+import logging
+from typing import List, Dict, Any, Optional
+
+import requests
+from flask import current_app
+from langchain.document_loaders.base import BaseLoader
+from langchain.schema import Document
+
+from extensions.ext_database import db
+from models.dataset import Document as DocumentModel
+from models.source import DataSourceBinding
+
+logger = logging.getLogger(__name__)
+
+BLOCK_CHILD_URL_TMPL = "https://api.notion.com/v1/blocks/{block_id}/children"
+DATABASE_URL_TMPL = "https://api.notion.com/v1/databases/{database_id}/query"
+SEARCH_URL = "https://api.notion.com/v1/search"
+RETRIEVE_PAGE_URL_TMPL = "https://api.notion.com/v1/pages/{page_id}"
+RETRIEVE_DATABASE_URL_TMPL = "https://api.notion.com/v1/databases/{database_id}"
+HEADING_TYPE = ['heading_1', 'heading_2', 'heading_3']
+
+
+class NotionLoader(BaseLoader):
+    def __init__(
+            self,
+            notion_access_token: str,
+            notion_workspace_id: str,
+            notion_obj_id: str,
+            notion_page_type: str,
+            document_model: Optional[DocumentModel] = None
+    ):
+        self._document_model = document_model
+        self._notion_workspace_id = notion_workspace_id
+        self._notion_obj_id = notion_obj_id
+        self._notion_page_type = notion_page_type
+        self._notion_access_token = notion_access_token
+
+        if not self._notion_access_token:
+            integration_token = current_app.config.get('NOTION_INTEGRATION_TOKEN')
+            if integration_token is None:
+                raise ValueError(
+                    "Must specify `integration_token` or set environment "
+                    "variable `NOTION_INTEGRATION_TOKEN`."
+                )
+
+            self._notion_access_token = integration_token
+
+    @classmethod
+    def from_document(cls, document_model: DocumentModel):
+        data_source_info = document_model.data_source_info_dict
+        if not data_source_info or 'notion_page_id' not in data_source_info \
+                or 'notion_workspace_id' not in data_source_info:
+            raise ValueError("no notion page found")
+
+        notion_workspace_id = data_source_info['notion_workspace_id']
+        notion_obj_id = data_source_info['notion_page_id']
+        notion_page_type = data_source_info['type']
+        notion_access_token = cls._get_access_token(document_model.tenant_id, notion_workspace_id)
+
+        return cls(
+            notion_access_token=notion_access_token,
+            notion_workspace_id=notion_workspace_id,
+            notion_obj_id=notion_obj_id,
+            notion_page_type=notion_page_type,
+            document_model=document_model
+        )
+
+    def load(self) -> List[Document]:
+        self.update_last_edited_time(
+            self._document_model
+        )
+
+        text_docs = self._load_data_as_documents(self._notion_obj_id, self._notion_page_type)
+
+        return text_docs
+
+    def _load_data_as_documents(
+            self, notion_obj_id: str, notion_page_type: str
+    ) -> List[Document]:
+        docs = []
+        if notion_page_type == 'database':
+            # get all the pages in the database
+            page_text_documents = self._get_notion_database_data(notion_obj_id)
+            docs.extend(page_text_documents)
+        elif notion_page_type == 'page':
+            page_text_list = self._get_notion_block_data(notion_obj_id)
+            for page_text in page_text_list:
+                docs.append(Document(page_content=page_text))
+        else:
+            raise ValueError("notion page type not supported")
+
+        return docs
+
+    def _get_notion_database_data(
+            self, database_id: str, query_dict: Dict[str, Any] = {}
+    ) -> List[Document]:
+        """Get all the pages from a Notion database."""
+        res = requests.post(
+            DATABASE_URL_TMPL.format(database_id=database_id),
+            headers={
+                "Authorization": "Bearer " + self._notion_access_token,
+                "Content-Type": "application/json",
+                "Notion-Version": "2022-06-28",
+            },
+            json=query_dict,
+        )
+
+        data = res.json()
+
+        database_content_list = []
+        if 'results' not in data or data["results"] is None:
+            return []
+        for result in data["results"]:
+            properties = result['properties']
+            data = {}
+            for property_name, property_value in properties.items():
+                type = property_value['type']
+                if type == 'multi_select':
+                    value = []
+                    multi_select_list = property_value[type]
+                    for multi_select in multi_select_list:
+                        value.append(multi_select['name'])
+                elif type == 'rich_text' or type == 'title':
+                    if len(property_value[type]) > 0:
+                        value = property_value[type][0]['plain_text']
+                    else:
+                        value = ''
+                elif type == 'select' or type == 'status':
+                    if property_value[type]:
+                        value = property_value[type]['name']
+                    else:
+                        value = ''
+                else:
+                    value = property_value[type]
+                data[property_name] = value
+            row_dict = {k: v for k, v in data.items() if v}
+            row_content = ''
+            for key, value in row_dict.items():
+                if isinstance(value, dict):
+                    value_dict = {k: v for k, v in value.items() if v}
+                    value_content = ''.join(f'{k}:{v} ' for k, v in value_dict.items())
+                    row_content = row_content + f'{key}:{value_content}\n'
+                else:
+                    row_content = row_content + f'{key}:{value}\n'
+            document = Document(page_content=row_content)
+            database_content_list.append(document)
+
+        return database_content_list
+
+    def _get_notion_block_data(self, page_id: str) -> List[str]:
+        result_lines_arr = []
+        cur_block_id = page_id
+        while True:
+            block_url = BLOCK_CHILD_URL_TMPL.format(block_id=cur_block_id)
+            query_dict: Dict[str, Any] = {}
+
+            res = requests.request(
+                "GET",
+                block_url,
+                headers={
+                    "Authorization": "Bearer " + self._notion_access_token,
+                    "Content-Type": "application/json",
+                    "Notion-Version": "2022-06-28",
+                },
+                json=query_dict
+            )
+            data = res.json()
+            # current block's heading
+            heading = ''
+            for result in data["results"]:
+                result_type = result["type"]
+                result_obj = result[result_type]
+                cur_result_text_arr = []
+                if result_type == 'table':
+                    result_block_id = result["id"]
+                    text = self._read_table_rows(result_block_id)
+                    text += "\n\n"
+                    result_lines_arr.append(text)
+                else:
+                    if "rich_text" in result_obj:
+                        for rich_text in result_obj["rich_text"]:
+                            # skip if doesn't have text object
+                            if "text" in rich_text:
+                                text = rich_text["text"]["content"]
+                                cur_result_text_arr.append(text)
+                                if result_type in HEADING_TYPE:
+                                    heading = text
+
+                    result_block_id = result["id"]
+                    has_children = result["has_children"]
+                    block_type = result["type"]
+                    if has_children and block_type != 'child_page':
+                        children_text = self._read_block(
+                            result_block_id, num_tabs=1
+                        )
+                        cur_result_text_arr.append(children_text)
+
+                    cur_result_text = "\n".join(cur_result_text_arr)
+                    cur_result_text += "\n\n"
+                    if result_type in HEADING_TYPE:
+                        result_lines_arr.append(cur_result_text)
+                    else:
+                        result_lines_arr.append(f'{heading}\n{cur_result_text}')
+
+            if data["next_cursor"] is None:
+                break
+            else:
+                cur_block_id = data["next_cursor"]
+        return result_lines_arr
+
+    def _read_block(self, block_id: str, num_tabs: int = 0) -> str:
+        """Read a block."""
+        result_lines_arr = []
+        cur_block_id = block_id
+        while True:
+            block_url = BLOCK_CHILD_URL_TMPL.format(block_id=cur_block_id)
+            query_dict: Dict[str, Any] = {}
+
+            res = requests.request(
+                "GET",
+                block_url,
+                headers={
+                    "Authorization": "Bearer " + self._notion_access_token,
+                    "Content-Type": "application/json",
+                    "Notion-Version": "2022-06-28",
+                },
+                json=query_dict
+            )
+            data = res.json()
+            if 'results' not in data or data["results"] is None:
+                break
+            heading = ''
+            for result in data["results"]:
+                result_type = result["type"]
+                result_obj = result[result_type]
+                cur_result_text_arr = []
+                if result_type == 'table':
+                    result_block_id = result["id"]
+                    text = self._read_table_rows(result_block_id)
+                    result_lines_arr.append(text)
+                else:
+                    if "rich_text" in result_obj:
+                        for rich_text in result_obj["rich_text"]:
+                            # skip if doesn't have text object
+                            if "text" in rich_text:
+                                text = rich_text["text"]["content"]
+                                prefix = "\t" * num_tabs
+                                cur_result_text_arr.append(prefix + text)
+                                if result_type in HEADING_TYPE:
+                                    heading = text
+                    result_block_id = result["id"]
+                    has_children = result["has_children"]
+                    block_type = result["type"]
+                    if has_children and block_type != 'child_page':
+                        children_text = self._read_block(
+                            result_block_id, num_tabs=num_tabs + 1
+                        )
+                        cur_result_text_arr.append(children_text)
+
+                    cur_result_text = "\n".join(cur_result_text_arr)
+                    if result_type in HEADING_TYPE:
+                        result_lines_arr.append(cur_result_text)
+                    else:
+                        result_lines_arr.append(f'{heading}\n{cur_result_text}')
+
+            if data["next_cursor"] is None:
+                break
+            else:
+                cur_block_id = data["next_cursor"]
+
+        result_lines = "\n".join(result_lines_arr)
+        return result_lines
+
+    def _read_table_rows(self, block_id: str) -> str:
+        """Read table rows."""
+        done = False
+        result_lines_arr = []
+        cur_block_id = block_id
+        while not done:
+            block_url = BLOCK_CHILD_URL_TMPL.format(block_id=cur_block_id)
+            query_dict: Dict[str, Any] = {}
+
+            res = requests.request(
+                "GET",
+                block_url,
+                headers={
+                    "Authorization": "Bearer " + self._notion_access_token,
+                    "Content-Type": "application/json",
+                    "Notion-Version": "2022-06-28",
+                },
+                json=query_dict
+            )
+            data = res.json()
+            # get table headers text
+            table_header_cell_texts = []
+            tabel_header_cells = data["results"][0]['table_row']['cells']
+            for tabel_header_cell in tabel_header_cells:
+                if tabel_header_cell:
+                    for table_header_cell_text in tabel_header_cell:
+                        text = table_header_cell_text["text"]["content"]
+                        table_header_cell_texts.append(text)
+            # get table columns text and format
+            results = data["results"]
+            for i in range(len(results) - 1):
+                column_texts = []
+                tabel_column_cells = data["results"][i + 1]['table_row']['cells']
+                for j in range(len(tabel_column_cells)):
+                    if tabel_column_cells[j]:
+                        for table_column_cell_text in tabel_column_cells[j]:
+                            column_text = table_column_cell_text["text"]["content"]
+                            column_texts.append(f'{table_header_cell_texts[j]}:{column_text}')
+
+                cur_result_text = "\n".join(column_texts)
+                result_lines_arr.append(cur_result_text)
+
+            if data["next_cursor"] is None:
+                done = True
+                break
+            else:
+                cur_block_id = data["next_cursor"]
+
+        result_lines = "\n".join(result_lines_arr)
+        return result_lines
+
+    def update_last_edited_time(self, document_model: DocumentModel):
+        if not document_model:
+            return
+
+        last_edited_time = self.get_notion_last_edited_time()
+        data_source_info = document_model.data_source_info_dict
+        data_source_info['last_edited_time'] = last_edited_time
+        update_params = {
+            DocumentModel.data_source_info: json.dumps(data_source_info)
+        }
+
+        DocumentModel.query.filter_by(id=document_model.id).update(update_params)
+        db.session.commit()
+
+    def get_notion_last_edited_time(self) -> str:
+        obj_id = self._notion_obj_id
+        page_type = self._notion_page_type
+        if page_type == 'database':
+            retrieve_page_url = RETRIEVE_DATABASE_URL_TMPL.format(database_id=obj_id)
+        else:
+            retrieve_page_url = RETRIEVE_PAGE_URL_TMPL.format(page_id=obj_id)
+
+        query_dict: Dict[str, Any] = {}
+
+        res = requests.request(
+            "GET",
+            retrieve_page_url,
+            headers={
+                "Authorization": "Bearer " + self._notion_access_token,
+                "Content-Type": "application/json",
+                "Notion-Version": "2022-06-28",
+            },
+            json=query_dict
+        )
+
+        data = res.json()
+        return data["last_edited_time"]
+
+    @classmethod
+    def _get_access_token(cls, tenant_id: str, notion_workspace_id: str) -> str:
+        data_source_binding = DataSourceBinding.query.filter(
+            db.and_(
+                DataSourceBinding.tenant_id == tenant_id,
+                DataSourceBinding.provider == 'notion',
+                DataSourceBinding.disabled == False,
+                DataSourceBinding.source_info['workspace_id'] == f'"{notion_workspace_id}"'
+            )
+        ).first()
+
+        if not data_source_binding:
+            raise Exception(f'No notion data source binding found for tenant {tenant_id} '
+                            f'and notion workspace {notion_workspace_id}')
+
+        return data_source_binding.access_token