feat: bump version to 0.3.23 (#1198)

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,49 @@
+name: "🕷️ Bug report"
+description: Report errors or unexpected behavior
+labels:
+- bug
+body:
+- type: markdown
+  attributes:
+    value: Please make sure to [search for existing issues](https://github.com/langgenius/dify/issues) before filing a new one!
+- type: input
+  attributes:
+    label: Dify version
+    placeholder: 0.3.21
+    description: See about section in Dify console
+  validations:
+    required: true
+
+- type: dropdown
+  attributes:
+    label: Cloud or Self Hosted
+    description: How / Where was Dify installed from?
+    multiple: true
+    options:
+      - Cloud
+      - Self Hosted
+      - Other (please specify in "Steps to Reproduce")
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Steps to reproduce
+    description: We highly suggest including screenshots and a bug report log.
+    placeholder: Having detailed steps helps us reproduce the bug. 
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: ✔️ Expected Behavior
+    placeholder: What were you expecting?
+  validations:
+    required: false
+
+- type: textarea
+  attributes:
+    label: ❌ Actual Behavior
+    placeholder: What happened instead?
+  validations:
+    required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,8 @@
+blank_issues_enabled: false
+contact_links:
+  - name: "\U0001F4DA Dify user documentation"
+    url: https://docs.dify.ai/getting-started/readme
+    about: Documentation for users of Dify
+  - name: "\U0001F4DA Dify dev documentation"
+    url: https://docs.dify.ai/getting-started/install-self-hosted
+    about: Documentation for people interested in developing and contributing for Dify

.github/ISSUE_TEMPLATE/document_issue.yml

@@ -0,0 +1,11 @@
+name: "📚 Documentation Issue"
+description: Report issues in our documentation
+labels: 
+- ducumentation
+body:
+- type: textarea
+  attributes: 
+    label: Provide a description of requested docs changes
+    placeholder: Briefly describe which document needs to be corrected and why.
+  validations:
+    required: true

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,26 @@
+name: "⭐ Feature or enhancement request"
+description: Propose something new.
+labels:
+- enhancement
+body:
+- type: textarea
+  attributes: 
+    label: Description of the new feature / enhancement
+    placeholder: What is the expected behavior of the proposed feature?
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Scenario when this would be used?
+    placeholder: What is the scenario this would be used? Why is this important to your workflow as a dify user?
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: Supporting information
+    placeholder: "Having additional evidence, data, tweets, blog posts, research, ... anything is extremely helpful. This information provides context to the scenario that may otherwise be lost."
+  validations:
+    required: false
+- type: markdown
+  attributes:
+    value: Please limit one request per issue.

.github/ISSUE_TEMPLATE/translation_issue.yml

@@ -0,0 +1,46 @@
+name: "🌐 Localization/Translation issue"
+description: Report incorrect translations.
+labels:
+- translation
+body:
+- type: markdown
+  attributes:
+    value: Please make sure to [search for existing issues](https://github.com/langgenius/dify/issues) before filing a new one!
+- type: input
+  attributes:
+    label: Dify version
+    placeholder: 0.3.21
+    description: Hover over system tray icon or look at Settings
+  validations:
+    required: true
+- type: input
+  attributes:
+    label: Utility with translation issue
+    placeholder: Some area
+    description: Please input here the utility with the translation issue
+  validations:
+    required: true
+- type: input
+  attributes:
+    label: 🌐 Language affected
+    placeholder: "German"
+  validations:
+    required: true
+- type: textarea
+  attributes: 
+    label: ❌ Actual phrase(s)
+    placeholder: What is there? Please include a screenshot as that is extremely helpful.
+  validations:
+    required: true
+- type: textarea
+  attributes: 
+    label: ✔️ Expected phrase(s)
+    placeholder: What was expected?
+  validations:
+    required: true
+- type: textarea
+  attributes:
+    label: ℹ Why is the current translation wrong
+    placeholder: Why do you feel this is incorrect?
+  validations:
+    required: true

.github/ISSUE_TEMPLATE/🐛-bug-report.md

@@ -1,32 +0,0 @@
----
-name: "\U0001F41B Bug report"
-about: Create a report to help us improve
-title: ''
-labels: bug
-assignees: ''
-
----
-
-<!--
-  Please provide a clear and concise description of what the bug is. Include
-  screenshots if needed. Please test using the latest version of the relevant
-  Dify packages to make sure your issue has not already been fixed.
--->
-
-Dify version: Cloud | Self Host
-
-## Steps To Reproduce
-<!--
-  Your bug will get fixed much faster if we can run your code and it doesn't
-  have dependencies other than Dify. Issues without reproduction steps or
-  code examples may be immediately closed as not actionable.
--->
-
-1.
-2.
-
-
-## The current behavior
-
-
-## The expected behavior

.github/ISSUE_TEMPLATE/🚀-feature-request.md

@@ -1,20 +0,0 @@
----
-name: "\U0001F680 Feature request"
-about: Suggest an idea for this project
-title: ''
-labels: enhancement
-assignees: ''
-
----
-
-**Is your feature request related to a problem? Please describe.**
-A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
-
-**Describe the solution you'd like**
-A clear and concise description of what you want to happen.
-
-**Describe alternatives you've considered**
-A clear and concise description of any alternative solutions or features you've considered.
-
-**Additional context**
-Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/🤔-questions-and-help.md

@@ -1,10 +0,0 @@
----
-name: "\U0001F914 Questions and Help"
-about: Ask a usage or consultation question
-title: ''
-labels: ''
-assignees: ''
-
----
-
-

.github/workflows/check_no_chinese_comments.py

@@ -20,7 +20,8 @@ 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', 'web_reader_tool.py', 'spark_provider.py']
+                      'indexing_runner.py', 'web_reader_tool.py', 'spark_provider.py',
+                      'prompts.py']
 
     for root, _, files in os.walk("."):
         for file in files:

.gitignore

@@ -149,4 +149,5 @@ sdks/python-client/build
 sdks/python-client/dist
 sdks/python-client/dify_client.egg-info
 
-.vscode/
+.vscode/*
+!.vscode/launch.json

.vscode/launch.json

@@ -0,0 +1,27 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: Flask",
+            "type": "python",
+            "request": "launch",
+            "module": "flask",
+            "env": {
+                "FLASK_APP": "api/app.py",
+                "FLASK_DEBUG": "1",
+                "GEVENT_SUPPORT": "True"
+            },
+            "args": [
+                "run",
+                "--host=0.0.0.0",
+                "--port=5001",
+                "--debug"
+            ],
+            "jinja": true,
+            "justMyCode": true
+        }
+    ]
+}

CONTRIBUTING.md

@@ -53,9 +53,9 @@ 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!
+Stuck somewhere? Have any questions? Join the [Discord Community Server](https://discord.gg/j3XRWSPBf7). 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.
+Also check out the [Frontend i18n README]((web/i18n/README_EN.md)) for more information.

CONTRIBUTING_CN.md

@@ -16,15 +16,15 @@
 
 ## 本地开发
 
-要设置一个可工作的开发环境，只需 fork 项目的 git 存储库，并使用适当的软件包管理器安装后端和前端依赖项，然后创建并运行 docker-compose 堆栈。
+要设置一个可工作的开发环境，只需 fork 项目的 git 存储库，并使用适当的软件包管理器安装后端和前端依赖项，然后创建并运行 docker-compose。
 
 ### Fork存储库
 
-您需要 fork [存储库](https://github.com/langgenius/dify)。
+您需要 fork [Git 仓库](https://github.com/langgenius/dify)。
 
 ### 克隆存储库
 
-克隆您在 GitHub 上 fork 的存储库：
+克隆您在 GitHub 上 fork 的仓库：
 
 ```
 git clone git@github.com:<github_username>/dify.git

CONTRIBUTING_JA.md

@@ -52,4 +52,4 @@ git clone git@github.com:<github_username>/dify.git
 
 ## コミュニティチャンネル
 
-お困りですか？何か質問がありますか？ [Discord Community サーバ](https://discord.gg/AhzKf7dNgk)に参加してください。私たちがお手伝いします！
+お困りですか？何か質問がありますか？ [Discord Community サーバ](https://discord.gg/j3XRWSPBf7) に参加してください。私たちがお手伝いします！

README.md

@@ -16,6 +16,10 @@ Out-of-the-box web sites supporting form mode and chat conversation mode
 A single API encompassing plugin capabilities, context enhancement, and more, saving you backend coding effort
 Visual data analysis, log review, and annotation for applications
 
+
+https://github.com/langgenius/dify/assets/100913391/f6e658d5-31b3-4c16-a0af-9e191da4d0f6
+
+
 ## Highlighted Features
 **1. LLMs support:** Choose capabilities based on different models when building your Dify AI apps. Dify is compatible with Langchain, meaning it will support various LLMs. Currently supported:
 

README_CN.md

@@ -17,7 +17,7 @@
 - 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
 
-
+https://github.com/langgenius/dify/assets/100913391/f6e658d5-31b3-4c16-a0af-9e191da4d0f6
 
 ## 核心能力
 1. **模型支持：** 你可以在 Dify 上选择基于不同模型的能力来开发你的 AI 应用。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：

api/Dockerfile

@@ -1,7 +1,18 @@
-FROM python:3.10-slim
+# packages install stage
+FROM python:3.10-slim AS base
 
 LABEL maintainer="takatost@gmail.com"
 
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends gcc g++ python3-dev libc-dev libffi-dev
+
+COPY requirements.txt /requirements.txt
+
+RUN pip install --prefix=/pkg -r requirements.txt
+
+# build stage
+FROM python:3.10-slim AS builder
+
 ENV FLASK_APP app.py
 ENV EDITION SELF_HOSTED
 ENV DEPLOY_ENV PRODUCTION
@@ -15,15 +26,17 @@ EXPOSE 5001
 
 WORKDIR /app/api
 
-RUN apt-get update && \
-    apt-get install -y bash curl wget vim gcc g++ python3-dev libc-dev libffi-dev nodejs
-
-COPY requirements.txt /app/api/requirements.txt
-
-RUN pip install -r requirements.txt
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends bash curl wget vim nodejs \
+    && apt-get autoremove \
+    && rm -rf /var/lib/apt/lists/*
 
+COPY --from=base /pkg /usr/local
 COPY . /app/api/
 
+RUN python -c "from transformers import GPT2TokenizerFast; GPT2TokenizerFast.from_pretrained('gpt2')"
+ENV TRANSFORMERS_OFFLINE true
+
 COPY docker/entrypoint.sh /entrypoint.sh
 RUN chmod +x /entrypoint.sh
 

api/README.md

@@ -52,11 +52,13 @@
    flask run --host 0.0.0.0 --port=5001 --debug
    ```
 7. Setup your application by visiting http://localhost:5001/console/api/setup or other apis...
-8. If you need to debug local async processing, you can run `celery -A app.celery worker -Q dataset,generation,mail`, celery can do dataset importing and other async tasks.
+8. If you need to debug local async processing, you can run `celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail`, celery can do dataset importing and other async tasks.
 
-8. Start frontend:
+8. Start frontend
+
+   You can start the frontend by running `npm install && npm run dev` in web/ folder, or you can use docker to start the frontend, for example:
 
    ```
-   docker run -it -d --platform linux/amd64 -p 3000:3000 -e EDITION=SELF_HOSTED -e CONSOLE_URL=http://127.0.0.1:5000 --name web-self-hosted langgenius/dify-web:latest
+   docker run -it -d --platform linux/amd64 -p 3000:3000 -e EDITION=SELF_HOSTED -e CONSOLE_URL=http://127.0.0.1:5001 --name web-self-hosted langgenius/dify-web:latest
    ```
    This will start a dify frontend, now you are all set, happy coding!

api/app.py

@@ -1,6 +1,6 @@
 # -*- coding:utf-8 -*-
 import os
-from datetime import datetime
+from datetime import datetime, timedelta
 
 from werkzeug.exceptions import Forbidden
 
@@ -145,8 +145,12 @@ def load_user(user_id):
                     _create_tenant_for_account(account)
                 session['workspace_id'] = account.current_tenant_id
 
-            account.last_active_at = datetime.utcnow()
-            db.session.commit()
+            current_time = datetime.utcnow()
+
+            # update last_active_at when last_active_at is more than 10 minutes ago
+            if current_time - account.last_active_at > timedelta(minutes=10):
+                account.last_active_at = current_time
+                db.session.commit()
 
             # Log in the user with the updated user_id
             flask_login.login_user(account, remember=True)

api/commands.py

@@ -1,22 +1,31 @@
 import datetime
+import json
 import math
 import random
 import string
 import time
+import uuid
 
 import click
+from tqdm import tqdm
 from flask import current_app
+from langchain.embeddings import OpenAIEmbeddings
 from werkzeug.exceptions import NotFound
 
+from core.embedding.cached_embedding import CacheEmbedding
 from core.index.index import IndexBuilder
+from core.model_providers.model_factory import ModelFactory
+from core.model_providers.models.embedding.openai_embedding import OpenAIEmbedding
+from core.model_providers.models.entity.model_params import ModelType
 from core.model_providers.providers.hosted import hosted_model_providers
+from core.model_providers.providers.openai_provider import OpenAIProvider
 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
-from models.model import Account
+from models.account import InvitationCode, Tenant, TenantAccountJoin
+from models.dataset import Dataset, DatasetQuery, Document, DatasetCollectionBinding
+from models.model import Account, AppModelConfig, App
 import secrets
 import base64
 
@@ -231,7 +240,13 @@ def clean_unused_dataset_indexes():
                         kw_index = IndexBuilder.get_index(dataset, 'economy')
                         # delete from vector index
                         if vector_index:
-                            vector_index.delete()
+                            if dataset.collection_binding_id:
+                                vector_index.delete_by_group_id(dataset.id)
+                            else:
+                                if dataset.collection_binding_id:
+                                    vector_index.delete_by_group_id(dataset.id)
+                                else:
+                                    vector_index.delete()
                         kw_index.delete()
                         # update document
                         update_params = {
@@ -296,6 +311,342 @@ def sync_anthropic_hosted_providers():
     click.echo(click.style('Congratulations! Synced {} anthropic hosted providers.'.format(count), fg='green'))
 
 
+@click.command('create-qdrant-indexes', help='Create qdrant indexes.')
+def create_qdrant_indexes():
+    click.echo(click.style('Start create qdrant indexes.', fg='green'))
+    create_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:
+            if dataset.index_struct_dict:
+                if dataset.index_struct_dict['type'] != 'qdrant':
+                    try:
+                        click.echo('Create dataset qdrant index: {}'.format(dataset.id))
+                        try:
+                            embedding_model = ModelFactory.get_embedding_model(
+                                tenant_id=dataset.tenant_id,
+                                model_provider_name=dataset.embedding_model_provider,
+                                model_name=dataset.embedding_model
+                            )
+                        except Exception:
+                            try:
+                                embedding_model = ModelFactory.get_embedding_model(
+                                    tenant_id=dataset.tenant_id
+                                )
+                                dataset.embedding_model = embedding_model.name
+                                dataset.embedding_model_provider = embedding_model.model_provider.provider_name
+                            except Exception:
+                                provider = Provider(
+                                    id='provider_id',
+                                    tenant_id=dataset.tenant_id,
+                                    provider_name='openai',
+                                    provider_type=ProviderType.SYSTEM.value,
+                                    encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
+                                    is_valid=True,
+                                )
+                                model_provider = OpenAIProvider(provider=provider)
+                                embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
+                                                                  model_provider=model_provider)
+                        embeddings = CacheEmbedding(embedding_model)
+
+                        from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig
+
+                        index = QdrantVectorIndex(
+                            dataset=dataset,
+                            config=QdrantConfig(
+                                endpoint=current_app.config.get('QDRANT_URL'),
+                                api_key=current_app.config.get('QDRANT_API_KEY'),
+                                root_path=current_app.root_path
+                            ),
+                            embeddings=embeddings
+                        )
+                        if index:
+                            index.create_qdrant_dataset(dataset)
+                            index_struct = {
+                                "type": 'qdrant',
+                                "vector_store": {
+                                    "class_prefix": dataset.index_struct_dict['vector_store']['class_prefix']}
+                            }
+                            dataset.index_struct = json.dumps(index_struct)
+                            db.session.commit()
+                            create_count += 1
+                        else:
+                            click.echo('passed.')
+                    except Exception as e:
+                        click.echo(
+                            click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                        fg='red'))
+                        continue
+
+    click.echo(click.style('Congratulations! Create {} dataset indexes.'.format(create_count), fg='green'))
+
+
+@click.command('update-qdrant-indexes', help='Update qdrant indexes.')
+def update_qdrant_indexes():
+    click.echo(click.style('Start Update qdrant indexes.', fg='green'))
+    create_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:
+            if dataset.index_struct_dict:
+                if dataset.index_struct_dict['type'] != 'qdrant':
+                    try:
+                        click.echo('Update dataset qdrant index: {}'.format(dataset.id))
+                        try:
+                            embedding_model = ModelFactory.get_embedding_model(
+                                tenant_id=dataset.tenant_id,
+                                model_provider_name=dataset.embedding_model_provider,
+                                model_name=dataset.embedding_model
+                            )
+                        except Exception:
+                            provider = Provider(
+                                id='provider_id',
+                                tenant_id=dataset.tenant_id,
+                                provider_name='openai',
+                                provider_type=ProviderType.CUSTOM.value,
+                                encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
+                                is_valid=True,
+                            )
+                            model_provider = OpenAIProvider(provider=provider)
+                            embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
+                                                              model_provider=model_provider)
+                        embeddings = CacheEmbedding(embedding_model)
+
+                        from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig
+
+                        index = QdrantVectorIndex(
+                            dataset=dataset,
+                            config=QdrantConfig(
+                                endpoint=current_app.config.get('QDRANT_URL'),
+                                api_key=current_app.config.get('QDRANT_API_KEY'),
+                                root_path=current_app.root_path
+                            ),
+                            embeddings=embeddings
+                        )
+                        if index:
+                            index.update_qdrant_dataset(dataset)
+                            create_count += 1
+                        else:
+                            click.echo('passed.')
+                    except Exception as e:
+                        click.echo(
+                            click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                        fg='red'))
+                        continue
+
+    click.echo(click.style('Congratulations! Update {} dataset indexes.'.format(create_count), fg='green'))
+
+
+@click.command('normalization-collections', help='restore all collections in one')
+def normalization_collections():
+    click.echo(click.style('Start normalization collections.', fg='green'))
+    normalization_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:
+            if not dataset.collection_binding_id:
+                try:
+                    click.echo('restore dataset index: {}'.format(dataset.id))
+                    try:
+                        embedding_model = ModelFactory.get_embedding_model(
+                            tenant_id=dataset.tenant_id,
+                            model_provider_name=dataset.embedding_model_provider,
+                            model_name=dataset.embedding_model
+                        )
+                    except Exception:
+                        provider = Provider(
+                            id='provider_id',
+                            tenant_id=dataset.tenant_id,
+                            provider_name='openai',
+                            provider_type=ProviderType.CUSTOM.value,
+                            encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
+                            is_valid=True,
+                        )
+                        model_provider = OpenAIProvider(provider=provider)
+                        embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
+                                                          model_provider=model_provider)
+                    embeddings = CacheEmbedding(embedding_model)
+                    dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
+                        filter(DatasetCollectionBinding.provider_name == embedding_model.model_provider.provider_name,
+                               DatasetCollectionBinding.model_name == embedding_model.name). \
+                        order_by(DatasetCollectionBinding.created_at). \
+                        first()
+
+                    if not dataset_collection_binding:
+                        dataset_collection_binding = DatasetCollectionBinding(
+                            provider_name=embedding_model.model_provider.provider_name,
+                            model_name=embedding_model.name,
+                            collection_name="Vector_index_" + str(uuid.uuid4()).replace("-", "_") + '_Node'
+                        )
+                        db.session.add(dataset_collection_binding)
+                        db.session.commit()
+
+                    from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig
+
+                    index = QdrantVectorIndex(
+                        dataset=dataset,
+                        config=QdrantConfig(
+                            endpoint=current_app.config.get('QDRANT_URL'),
+                            api_key=current_app.config.get('QDRANT_API_KEY'),
+                            root_path=current_app.root_path
+                        ),
+                        embeddings=embeddings
+                    )
+                    if index:
+                        index.restore_dataset_in_one(dataset, dataset_collection_binding)
+                    else:
+                        click.echo('passed.')
+
+                    original_index = QdrantVectorIndex(
+                        dataset=dataset,
+                        config=QdrantConfig(
+                            endpoint=current_app.config.get('QDRANT_URL'),
+                            api_key=current_app.config.get('QDRANT_API_KEY'),
+                            root_path=current_app.root_path
+                        ),
+                        embeddings=embeddings
+                    )
+                    if original_index:
+                        original_index.delete_original_collection(dataset, dataset_collection_binding)
+                        normalization_count += 1
+                    else:
+                        click.echo('passed.')
+                except Exception as e:
+                    click.echo(
+                        click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                    fg='red'))
+                    continue
+
+    click.echo(click.style('Congratulations! restore {} dataset indexes.'.format(normalization_count), fg='green'))
+
+
+@click.command('update_app_model_configs', help='Migrate data to support paragraph variable.')
+@click.option("--batch-size", default=500, help="Number of records to migrate in each batch.")
+def update_app_model_configs(batch_size):
+    pre_prompt_template = '{{default_input}}'
+    user_input_form_template = {
+        "en-US": [
+            {
+                "paragraph": {
+                    "label": "Query",
+                    "variable": "default_input",
+                    "required": False,
+                    "default": ""
+                }
+            }
+        ],
+        "zh-Hans": [
+            {
+                "paragraph": {
+                    "label": "查询内容",
+                    "variable": "default_input",
+                    "required": False,
+                    "default": ""
+                }
+            }
+        ]
+    }
+
+    click.secho("Start migrate old data that the text generator can support paragraph variable.", fg='green')
+
+    total_records = db.session.query(AppModelConfig) \
+        .join(App, App.app_model_config_id == AppModelConfig.id) \
+        .filter(App.mode == 'completion') \
+        .count()
+
+    if total_records == 0:
+        click.secho("No data to migrate.", fg='green')
+        return
+
+    num_batches = (total_records + batch_size - 1) // batch_size
+
+    with tqdm(total=total_records, desc="Migrating Data") as pbar:
+        for i in range(num_batches):
+            offset = i * batch_size
+            limit = min(batch_size, total_records - offset)
+
+            click.secho(f"Fetching batch {i + 1}/{num_batches} from source database...", fg='green')
+
+            data_batch = db.session.query(AppModelConfig) \
+                .join(App, App.app_model_config_id == AppModelConfig.id) \
+                .filter(App.mode == 'completion') \
+                .order_by(App.created_at) \
+                .offset(offset).limit(limit).all()
+
+            if not data_batch:
+                click.secho("No more data to migrate.", fg='green')
+                break
+
+            try:
+                click.secho(f"Migrating {len(data_batch)} records...", fg='green')
+                for data in data_batch:
+                    # click.secho(f"Migrating data {data.id}, pre_prompt: {data.pre_prompt}, user_input_form: {data.user_input_form}", fg='green')
+
+                    if data.pre_prompt is None:
+                        data.pre_prompt = pre_prompt_template
+                    else:
+                        if pre_prompt_template in data.pre_prompt:
+                            continue
+                        data.pre_prompt += pre_prompt_template
+
+                    app_data = db.session.query(App) \
+                        .filter(App.id == data.app_id) \
+                        .one()
+
+                    account_data = db.session.query(Account) \
+                        .join(TenantAccountJoin, Account.id == TenantAccountJoin.account_id) \
+                        .filter(TenantAccountJoin.role == 'owner') \
+                        .filter(TenantAccountJoin.tenant_id == app_data.tenant_id) \
+                        .one_or_none()
+
+                    if not account_data:
+                        continue
+
+                    if data.user_input_form is None or data.user_input_form == 'null':
+                        data.user_input_form = json.dumps(user_input_form_template[account_data.interface_language])
+                    else:
+                        raw_json_data = json.loads(data.user_input_form)
+                        raw_json_data.append(user_input_form_template[account_data.interface_language][0])
+                        data.user_input_form = json.dumps(raw_json_data)
+
+                    # click.secho(f"Updated data {data.id}, pre_prompt: {data.pre_prompt}, user_input_form: {data.user_input_form}", fg='green')
+
+                db.session.commit()
+
+            except Exception as e:
+                click.secho(f"Error while migrating data: {e}, app_id: {data.app_id}, app_model_config_id: {data.id}",
+                            fg='red')
+                continue
+
+            click.secho(f"Successfully migrated batch {i + 1}/{num_batches}.", fg='green')
+
+            pbar.update(len(data_batch))
+
+
 def register_commands(app):
     app.cli.add_command(reset_password)
     app.cli.add_command(reset_email)
@@ -304,3 +655,7 @@ def register_commands(app):
     app.cli.add_command(recreate_all_dataset_indexes)
     app.cli.add_command(sync_anthropic_hosted_providers)
     app.cli.add_command(clean_unused_dataset_indexes)
+    app.cli.add_command(create_qdrant_indexes)
+    app.cli.add_command(update_qdrant_indexes)
+    app.cli.add_command(update_app_model_configs)
+    app.cli.add_command(normalization_collections)

api/config.py

@@ -61,6 +61,8 @@ DEFAULTS = {
     'HOSTED_ANTHROPIC_PAID_INCREASE_QUOTA': 1000000,
     'HOSTED_ANTHROPIC_PAID_MIN_QUANTITY': 20,
     'HOSTED_ANTHROPIC_PAID_MAX_QUANTITY': 100,
+    'HOSTED_MODERATION_ENABLED': 'False',
+    'HOSTED_MODERATION_PROVIDERS': '',
     'TENANT_DOCUMENT_COUNT': 100,
     'CLEAN_DAY_SETTING': 30,
     'UPLOAD_FILE_SIZE_LIMIT': 15,
@@ -100,7 +102,7 @@ class Config:
         self.CONSOLE_URL = get_env('CONSOLE_URL')
         self.API_URL = get_env('API_URL')
         self.APP_URL = get_env('APP_URL')
-        self.CURRENT_VERSION = "0.3.18"
+        self.CURRENT_VERSION = "0.3.23"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -230,6 +232,9 @@ class Config:
         self.HOSTED_ANTHROPIC_PAID_MIN_QUANTITY = int(get_env('HOSTED_ANTHROPIC_PAID_MIN_QUANTITY'))
         self.HOSTED_ANTHROPIC_PAID_MAX_QUANTITY = int(get_env('HOSTED_ANTHROPIC_PAID_MAX_QUANTITY'))
 
+        self.HOSTED_MODERATION_ENABLED = get_bool_env('HOSTED_MODERATION_ENABLED')
+        self.HOSTED_MODERATION_PROVIDERS = get_env('HOSTED_MODERATION_PROVIDERS')
+
         self.STRIPE_API_KEY = get_env('STRIPE_API_KEY')
         self.STRIPE_WEBHOOK_SECRET = get_env('STRIPE_WEBHOOK_SECRET')
 

api/constants/model_template.py

@@ -16,7 +16,7 @@ model_templates = {
         },
         'model_config': {
             'provider': 'openai',
-            'model_id': 'text-davinci-003',
+            'model_id': 'gpt-3.5-turbo-instruct',
             'configs': {
                 'prompt_template': '',
                 'prompt_variables': [],
@@ -30,7 +30,7 @@ model_templates = {
             },
             'model': json.dumps({
                 "provider": "openai",
-                "name": "text-davinci-003",
+                "name": "gpt-3.5-turbo-instruct",
                 "completion_params": {
                     "max_tokens": 512,
                     "temperature": 1,
@@ -38,7 +38,18 @@ model_templates = {
                     "presence_penalty": 0,
                     "frequency_penalty": 0
                 }
-            })
+            }),
+            'user_input_form': json.dumps([
+                {
+                    "paragraph": {
+                        "label": "Query",
+                        "variable": "query",
+                        "required": True,
+                        "default": ""
+                    }
+                }
+            ]),
+            'pre_prompt': '{{query}}'
         }
     },
 
@@ -93,7 +104,7 @@ demo_model_templates = {
             'mode': 'completion',
             'model_config': AppModelConfig(
                 provider='openai',
-                model_id='text-davinci-003',
+                model_id='gpt-3.5-turbo-instruct',
                 configs={
                     'prompt_template': "Please translate the following text into {{target_language}}:\n",
                     'prompt_variables': [
@@ -129,7 +140,7 @@ demo_model_templates = {
                 pre_prompt="Please translate the following text into {{target_language}}:\n",
                 model=json.dumps({
                     "provider": "openai",
-                    "name": "text-davinci-003",
+                    "name": "gpt-3.5-turbo-instruct",
                     "completion_params": {
                         "max_tokens": 1000,
                         "temperature": 0,
@@ -211,7 +222,7 @@ demo_model_templates = {
             'mode': 'completion',
             'model_config': AppModelConfig(
                 provider='openai',
-                model_id='text-davinci-003',
+                model_id='gpt-3.5-turbo-instruct',
                 configs={
                     'prompt_template': "请将以下文本翻译为{{target_language}}:\n",
                     'prompt_variables': [
@@ -247,7 +258,7 @@ demo_model_templates = {
                 pre_prompt="请将以下文本翻译为{{target_language}}:\n",
                 model=json.dumps({
                     "provider": "openai",
-                    "name": "text-davinci-003",
+                    "name": "gpt-3.5-turbo-instruct",
                     "completion_params": {
                         "max_tokens": 1000,
                         "temperature": 0,

api/controllers/console/app/app.py

@@ -29,6 +29,7 @@ model_config_fields = {
     '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'),
+    'retriever_resource': fields.Raw(attribute='retriever_resource_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'),

api/controllers/console/app/completion.py

@@ -39,9 +39,10 @@ class CompletionMessageApi(Resource):
 
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
-        parser.add_argument('query', type=str, location='json')
+        parser.add_argument('query', type=str, location='json', default='')
         parser.add_argument('model_config', type=dict, required=True, location='json')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
         args = parser.parse_args()
 
         streaming = args['response_mode'] != 'blocking'
@@ -115,6 +116,7 @@ class ChatMessageApi(Resource):
         parser.add_argument('model_config', type=dict, required=True, location='json')
         parser.add_argument('conversation_id', type=uuid_value, location='json')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
         args = parser.parse_args()
 
         streaming = args['response_mode'] != 'blocking'

api/controllers/console/auth/activate.py

@@ -16,26 +16,25 @@ 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('workspace_id', type=str, required=False, nullable=True, location='args')
+        parser.add_argument('email', type=email, required=False, nullable=True, 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'])
+        workspaceId = args['workspace_id']
+        reg_email = args['email']
+        token = args['token']
 
-        tenant = db.session.query(Tenant).filter(
-            Tenant.id == args['workspace_id'],
-            Tenant.status == 'normal'
-        ).first()
+        invitation = RegisterService.get_invitation_if_token_valid(workspaceId, reg_email, token)
 
-        return {'is_valid': account is not None, 'workspace_name': tenant.name}
+        return {'is_valid': invitation is not None, 'workspace_name': invitation['tenant'].name if invitation else None}
 
 
 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('workspace_id', type=str, required=False, nullable=True, location='json')
+        parser.add_argument('email', type=email, required=False, nullable=True, 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')
@@ -44,12 +43,13 @@ class ActivateApi(Resource):
         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:
+        invitation = RegisterService.get_invitation_if_token_valid(args['workspace_id'], args['email'], args['token'])
+        if invitation is None:
             raise AlreadyActivateError()
 
         RegisterService.revoke_token(args['workspace_id'], args['email'], args['token'])
 
+        account = invitation['account']
         account.name = args['name']
 
         # generate password salt

api/controllers/console/datasets/datasets.py

@@ -87,13 +87,19 @@ class DatasetListApi(Resource):
         #     raise ProviderNotInitializeError(
         #         f"No Embedding Model available. Please configure a valid provider "
         #         f"in the Settings -> Model Provider.")
-        model_names = [item['model_name'] for item in valid_model_list]
+        model_names = []
+        for valid_model in valid_model_list:
+            model_names.append(f"{valid_model['model_name']}:{valid_model['model_provider']['provider_name']}")
         data = marshal(datasets, dataset_detail_fields)
         for item in data:
-            if item['embedding_model'] in model_names:
-                item['embedding_available'] = True
+            if item['indexing_technique'] == 'high_quality':
+                item_model = f"{item['embedding_model']}:{item['embedding_model_provider']}"
+                if item_model in model_names:
+                    item['embedding_available'] = True
+                else:
+                    item['embedding_available'] = False
             else:
-                item['embedding_available'] = False
+                item['embedding_available'] = True
         response = {
             'data': data,
             'has_more': len(datasets) == limit,
@@ -119,14 +125,6 @@ class DatasetListApi(Resource):
         # 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:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
 
         try:
             dataset = DatasetService.create_empty_dataset(
@@ -150,20 +148,39 @@ class DatasetApi(Resource):
         dataset = DatasetService.get_dataset(dataset_id_str)
         if dataset is None:
             raise NotFound("Dataset not found.")
-
         try:
             DatasetService.check_dataset_permission(
                 dataset, current_user)
         except services.errors.account.NoPermissionError as e:
             raise Forbidden(str(e))
-
-        return marshal(dataset, dataset_detail_fields), 200
+        data = marshal(dataset, dataset_detail_fields)
+        # check embedding setting
+        provider_service = ProviderService()
+        # get valid model list
+        valid_model_list = provider_service.get_valid_model_list(current_user.current_tenant_id, ModelType.EMBEDDINGS.value)
+        model_names = []
+        for valid_model in valid_model_list:
+            model_names.append(f"{valid_model['model_name']}:{valid_model['model_provider']['provider_name']}")
+        if data['indexing_technique'] == 'high_quality':
+            item_model = f"{data['embedding_model']}:{data['embedding_model_provider']}"
+            if item_model in model_names:
+                data['embedding_available'] = True
+            else:
+                data['embedding_available'] = False
+        else:
+            data['embedding_available'] = True
+        return data, 200
 
     @setup_required
     @login_required
     @account_initialization_required
     def patch(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.")
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
 
         parser = reqparse.RequestParser()
         parser.add_argument('name', nullable=False,
@@ -251,6 +268,7 @@ class DatasetIndexingEstimateApi(Resource):
         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('indexing_technique', type=str, required=True, nullable=True, location='json')
         parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         parser.add_argument('dataset_id', type=str, required=False, nullable=False, location='json')
         parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False, location='json')
@@ -272,7 +290,8 @@ class DatasetIndexingEstimateApi(Resource):
             try:
                 response = indexing_runner.file_indexing_estimate(current_user.current_tenant_id, file_details,
                                                                   args['process_rule'], args['doc_form'],
-                                                                  args['doc_language'], args['dataset_id'])
+                                                                  args['doc_language'], args['dataset_id'],
+                                                                  args['indexing_technique'])
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
                     f"No Embedding Model available. Please configure a valid provider "
@@ -287,7 +306,8 @@ class DatasetIndexingEstimateApi(Resource):
                 response = indexing_runner.notion_indexing_estimate(current_user.current_tenant_id,
                                                                     args['info_list']['notion_info_list'],
                                                                     args['process_rule'], args['doc_form'],
-                                                                    args['doc_language'], args['dataset_id'])
+                                                                    args['doc_language'], args['dataset_id'],
+                                                                    args['indexing_technique'])
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
                     f"No Embedding Model available. Please configure a valid provider "

api/controllers/console/datasets/datasets_document.py

@@ -3,7 +3,7 @@ import random
 from datetime import datetime
 from typing import List
 
-from flask import request
+from flask import request, current_app
 from flask_login import current_user
 from core.login.login import login_required
 from flask_restful import Resource, fields, marshal, marshal_with, reqparse
@@ -138,6 +138,10 @@ class GetProcessRuleApi(Resource):
         req_data = request.args
 
         document_id = req_data.get('document_id')
+        
+        # get default rules
+        mode = DocumentService.DEFAULT_RULES['mode']
+        rules = DocumentService.DEFAULT_RULES['rules']
         if document_id:
             # get the latest process rule
             document = Document.query.get_or_404(document_id)
@@ -158,11 +162,9 @@ class GetProcessRuleApi(Resource):
                 order_by(DatasetProcessRule.created_at.desc()). \
                 limit(1). \
                 one_or_none()
-            mode = dataset_process_rule.mode
-            rules = dataset_process_rule.rules_dict
-        else:
-            mode = DocumentService.DEFAULT_RULES['mode']
-            rules = DocumentService.DEFAULT_RULES['rules']
+            if dataset_process_rule:
+                mode = dataset_process_rule.mode
+                rules = dataset_process_rule.rules_dict
 
         return {
             'mode': mode,
@@ -275,7 +277,8 @@ class DatasetDocumentListApi(Resource):
         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')
-        parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False, location='json')
+        parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False,
+                            location='json')
         args = parser.parse_args()
 
         if not dataset.indexing_technique and not args['indexing_technique']:
@@ -284,20 +287,6 @@ class DatasetDocumentListApi(Resource):
         # validate args
         DocumentService.document_create_args_validate(args)
 
-        # check embedding model setting
-        try:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-
         try:
             documents, batch = DocumentService.save_document_with_dataset_id(dataset, args, current_user)
         except ProviderTokenNotInitError as ex:
@@ -335,17 +324,20 @@ class DatasetInitApi(Resource):
         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')
-        parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False, location='json')
+        parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False,
+                            location='json')
         args = parser.parse_args()
-
-        try:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
+        if args['indexing_technique'] == 'high_quality':
+            try:
+                ModelFactory.get_embedding_model(
+                    tenant_id=current_user.current_tenant_id
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    f"No Embedding Model available. Please configure a valid provider "
+                    f"in the Settings -> Model Provider.")
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
 
         # validate args
         DocumentService.document_create_args_validate(args)
@@ -414,7 +406,8 @@ class DocumentIndexingEstimateApi(DocumentResource):
 
                 try:
                     response = indexing_runner.file_indexing_estimate(current_user.current_tenant_id, [file],
-                                                                      data_process_rule_dict, None, dataset_id)
+                                                                      data_process_rule_dict, None,
+                                                                      'English', dataset_id)
                 except LLMBadRequestError:
                     raise ProviderNotInitializeError(
                         f"No Embedding Model available. Please configure a valid provider "
@@ -483,7 +476,8 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
             indexing_runner = IndexingRunner()
             try:
                 response = indexing_runner.file_indexing_estimate(current_user.current_tenant_id, file_details,
-                                                                  data_process_rule_dict,  None, dataset_id)
+                                                                  data_process_rule_dict, None,
+                                                                  'English', dataset_id)
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
                     f"No Embedding Model available. Please configure a valid provider "
@@ -497,7 +491,7 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
                 response = indexing_runner.notion_indexing_estimate(current_user.current_tenant_id,
                                                                     info_list,
                                                                     data_process_rule_dict,
-                                                                    None, dataset_id)
+                                                                    None, 'English', dataset_id)
             except LLMBadRequestError:
                 raise ProviderNotInitializeError(
                     f"No Embedding Model available. Please configure a valid provider "
@@ -725,6 +719,12 @@ class DocumentDeleteApi(DocumentResource):
     def delete(self, dataset_id, document_id):
         dataset_id = str(dataset_id)
         document_id = str(document_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
+
         document = self.get_document(dataset_id, document_id)
 
         try:
@@ -787,6 +787,12 @@ class DocumentStatusApi(DocumentResource):
     def patch(self, dataset_id, document_id, action):
         dataset_id = str(dataset_id)
         document_id = str(document_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
+
         document = self.get_document(dataset_id, document_id)
 
         # The role of the current user in the ta table must be admin or owner
@@ -855,6 +861,14 @@ class DocumentStatusApi(DocumentResource):
             if not document.archived:
                 raise InvalidActionError('Document is not archived.')
 
+            # check document limit
+            if current_app.config['EDITION'] == 'CLOUD':
+                documents_count = DocumentService.get_tenant_documents_count()
+                total_count = documents_count + 1
+                tenant_document_count = int(current_app.config['TENANT_DOCUMENT_COUNT'])
+                if total_count > tenant_document_count:
+                    raise ValueError(f"All your documents have overed limit {tenant_document_count}.")
+
             document.archived = False
             document.archived_at = None
             document.archived_by = None
@@ -872,6 +886,10 @@ class DocumentStatusApi(DocumentResource):
 
 
 class DocumentPauseApi(DocumentResource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
     def patch(self, dataset_id, document_id):
         """pause document."""
         dataset_id = str(dataset_id)
@@ -901,6 +919,9 @@ class DocumentPauseApi(DocumentResource):
 
 
 class DocumentRecoverApi(DocumentResource):
+    @setup_required
+    @login_required
+    @account_initialization_required
     def patch(self, dataset_id, document_id):
         """recover document."""
         dataset_id = str(dataset_id)
@@ -926,6 +947,21 @@ class DocumentRecoverApi(DocumentResource):
         return {'result': 'success'}, 204
 
 
+class DocumentLimitApi(DocumentResource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        """get document limit"""
+        documents_count = DocumentService.get_tenant_documents_count()
+        tenant_document_count = int(current_app.config['TENANT_DOCUMENT_COUNT'])
+
+        return {
+            'documents_count': documents_count,
+            'documents_limit': tenant_document_count
+                }, 200
+
+
 api.add_resource(GetProcessRuleApi, '/datasets/process-rule')
 api.add_resource(DatasetDocumentListApi,
                  '/datasets/<uuid:dataset_id>/documents')
@@ -951,3 +987,4 @@ api.add_resource(DocumentStatusApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/status/<string:action>')
 api.add_resource(DocumentPauseApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/processing/pause')
 api.add_resource(DocumentRecoverApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/processing/resume')
+api.add_resource(DocumentLimitApi, '/datasets/limit')

api/controllers/console/datasets/datasets_segments.py

@@ -149,7 +149,8 @@ class DatasetDocumentSegmentApi(Resource):
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')
-
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
         # 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()
@@ -158,20 +159,20 @@ class DatasetDocumentSegmentApi(Resource):
             DatasetService.check_dataset_permission(dataset, current_user)
         except services.errors.account.NoPermissionError as e:
             raise Forbidden(str(e))
-
-        # check embedding model setting
-        try:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
+        if dataset.indexing_technique == 'high_quality':
+            # check embedding model setting
+            try:
+                ModelFactory.get_embedding_model(
+                    tenant_id=current_user.current_tenant_id,
+                    model_provider_name=dataset.embedding_model_provider,
+                    model_name=dataset.embedding_model
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    f"No Embedding Model available. Please configure a valid provider "
+                    f"in the Settings -> Model Provider.")
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
 
         segment = DocumentSegment.query.filter(
             DocumentSegment.id == str(segment_id),
@@ -244,18 +245,19 @@ class DatasetDocumentSegmentAddApi(Resource):
         if current_user.current_tenant.current_role not in ['admin', 'owner']:
             raise Forbidden()
         # check embedding model setting
-        try:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
+        if dataset.indexing_technique == 'high_quality':
+            try:
+                ModelFactory.get_embedding_model(
+                    tenant_id=current_user.current_tenant_id,
+                    model_provider_name=dataset.embedding_model_provider,
+                    model_name=dataset.embedding_model
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    f"No Embedding Model available. Please configure a valid provider "
+                    f"in the Settings -> Model Provider.")
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
         try:
             DatasetService.check_dataset_permission(dataset, current_user)
         except services.errors.account.NoPermissionError as e:
@@ -284,25 +286,28 @@ class DatasetDocumentSegmentUpdateApi(Resource):
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
         # check document
         document_id = str(document_id)
         document = DocumentService.get_document(dataset_id, document_id)
         if not document:
             raise NotFound('Document not found.')
-        # check embedding model setting
-        try:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        # check segment
+        if dataset.indexing_technique == 'high_quality':
+            # check embedding model setting
+            try:
+                ModelFactory.get_embedding_model(
+                    tenant_id=current_user.current_tenant_id,
+                    model_provider_name=dataset.embedding_model_provider,
+                    model_name=dataset.embedding_model
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    f"No Embedding Model available. Please configure a valid provider "
+                    f"in the Settings -> Model Provider.")
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
+            # check segment
         segment_id = str(segment_id)
         segment = DocumentSegment.query.filter(
             DocumentSegment.id == str(segment_id),
@@ -339,6 +344,8 @@ class DatasetDocumentSegmentUpdateApi(Resource):
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
         # check document
         document_id = str(document_id)
         document = DocumentService.get_document(dataset_id, document_id)
@@ -378,18 +385,6 @@ class DatasetDocumentSegmentBatchImportApi(Resource):
         document = DocumentService.get_document(dataset_id, document_id)
         if not document:
             raise NotFound('Document not found.')
-        try:
-            ModelFactory.get_embedding_model(
-                tenant_id=current_user.current_tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                f"No Embedding Model available. Please configure a valid provider "
-                f"in the Settings -> Model Provider.")
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
         # get file from request
         file = request.files['file']
         # check file

api/controllers/console/datasets/file.py

@@ -26,7 +26,7 @@ from models.model import UploadFile
 
 cache = TTLCache(maxsize=None, ttl=30)
 
-ALLOWED_EXTENSIONS = ['txt', 'markdown', 'md', 'pdf', 'html', 'htm', 'xlsx']
+ALLOWED_EXTENSIONS = ['txt', 'markdown', 'md', 'pdf', 'html', 'htm', 'xlsx', 'docx', 'csv']
 PREVIEW_WORDS_LIMIT = 3000
 
 
@@ -83,7 +83,7 @@ class FileApi(Resource):
             raise FileTooLargeError(message)
 
         extension = file.filename.split('.')[-1]
-        if extension not in ALLOWED_EXTENSIONS:
+        if extension.lower() not in ALLOWED_EXTENSIONS:
             raise UnsupportedFileTypeError()
 
         # user uuid as file name
@@ -136,7 +136,7 @@ class FilePreviewApi(Resource):
 
         # extract text from file
         extension = upload_file.extension
-        if extension not in ALLOWED_EXTENSIONS:
+        if extension.lower() not in ALLOWED_EXTENSIONS:
             raise UnsupportedFileTypeError()
 
         text = FileExtractor.load(upload_file, return_text=True)

api/controllers/console/explore/completion.py

@@ -31,8 +31,9 @@ class CompletionApi(InstalledAppResource):
 
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
-        parser.add_argument('query', type=str, location='json')
+        parser.add_argument('query', type=str, location='json', default='')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='explore_app', location='json')
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'
@@ -92,6 +93,7 @@ class ChatApi(InstalledAppResource):
         parser.add_argument('query', type=str, required=True, location='json')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
         parser.add_argument('conversation_id', type=uuid_value, location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='explore_app', location='json')
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'

api/controllers/console/explore/message.py

@@ -30,6 +30,25 @@ class MessageListApi(InstalledAppResource):
         'rating': fields.String
     }
 
+    retriever_resource_fields = {
+        'id': fields.String,
+        'message_id': fields.String,
+        'position': fields.Integer,
+        'dataset_id': fields.String,
+        'dataset_name': fields.String,
+        'document_id': fields.String,
+        'document_name': fields.String,
+        'data_source_type': fields.String,
+        'segment_id': fields.String,
+        'score': fields.Float,
+        'hit_count': fields.Integer,
+        'word_count': fields.Integer,
+        'segment_position': fields.Integer,
+        'index_node_hash': fields.String,
+        'content': fields.String,
+        'created_at': TimestampField
+    }
+
     message_fields = {
         'id': fields.String,
         'conversation_id': fields.String,
@@ -37,6 +56,7 @@ class MessageListApi(InstalledAppResource):
         'query': fields.String,
         'answer': fields.String,
         'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+        'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
         'created_at': TimestampField
     }
 

api/controllers/console/explore/parameter.py

@@ -24,6 +24,7 @@ class AppParameterApi(InstalledAppResource):
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
         'speech_to_text': fields.Raw,
+        'retriever_resource': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
@@ -39,6 +40,7 @@ class AppParameterApi(InstalledAppResource):
             '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,
+            'retriever_resource': app_model_config.retriever_resource_dict,
             '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/chat.py

@@ -29,6 +29,7 @@ class UniversalChatApi(UniversalChatResource):
         parser.add_argument('provider', type=str, required=True, location='json')
         parser.add_argument('model', type=str, required=True, location='json')
         parser.add_argument('tools', type=list, required=True, location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='universal_app', location='json')
         args = parser.parse_args()
 
         app_model_config = app_model.app_model_config

api/controllers/console/universal_chat/message.py

@@ -36,6 +36,25 @@ class UniversalChatMessageListApi(UniversalChatResource):
         'created_at': TimestampField
     }
 
+    retriever_resource_fields = {
+        'id': fields.String,
+        'message_id': fields.String,
+        'position': fields.Integer,
+        'dataset_id': fields.String,
+        'dataset_name': fields.String,
+        'document_id': fields.String,
+        'document_name': fields.String,
+        'data_source_type': fields.String,
+        'segment_id': fields.String,
+        'score': fields.Float,
+        'hit_count': fields.Integer,
+        'word_count': fields.Integer,
+        'segment_position': fields.Integer,
+        'index_node_hash': fields.String,
+        'content': fields.String,
+        'created_at': TimestampField
+    }
+
     message_fields = {
         'id': fields.String,
         'conversation_id': fields.String,
@@ -43,6 +62,7 @@ class UniversalChatMessageListApi(UniversalChatResource):
         'query': fields.String,
         'answer': fields.String,
         'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+        'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
         'created_at': TimestampField,
         'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
     }

api/controllers/console/universal_chat/parameter.py

@@ -1,4 +1,6 @@
 # -*- coding:utf-8 -*-
+import json
+
 from flask_restful import marshal_with, fields
 
 from controllers.console import api
@@ -14,6 +16,7 @@ class UniversalChatParameterApi(UniversalChatResource):
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
         'speech_to_text': fields.Raw,
+        'retriever_resource': fields.Raw,
     }
 
     @marshal_with(parameters_fields)
@@ -21,12 +24,14 @@ class UniversalChatParameterApi(UniversalChatResource):
         """Retrieve app parameters."""
         app_model = universal_app
         app_model_config = app_model.app_model_config
+        app_model_config.retriever_resource = json.dumps({'enabled': True})
 
         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,
+            'retriever_resource': app_model_config.retriever_resource_dict,
         }
 
 

api/controllers/console/universal_chat/wraps.py

@@ -47,6 +47,7 @@ def universal_chat_app_required(view=None):
                     suggested_questions=json.dumps([]),
                     suggested_questions_after_answer=json.dumps({'enabled': True}),
                     speech_to_text=json.dumps({'enabled': True}),
+                    retriever_resource=json.dumps({'enabled': True}),
                     more_like_this=None,
                     sensitive_word_avoidance=None,
                     model=json.dumps({

api/controllers/console/workspace/members.py

@@ -49,46 +49,43 @@ class MemberInviteEmailApi(Resource):
     @account_initialization_required
     def post(self):
         parser = reqparse.RequestParser()
-        parser.add_argument('email', type=str, required=True, location='json')
+        parser.add_argument('emails', type=str, required=True, location='json', action='append')
         parser.add_argument('role', type=str, required=True, default='admin', location='json')
         args = parser.parse_args()
 
-        invitee_email = args['email']
+        invitee_emails = args['emails']
         invitee_role = args['role']
         if invitee_role not in ['admin', 'normal']:
             return {'code': 'invalid-role', 'message': 'Invalid role'}, 400
 
         inviter = current_user
-
-        try:
-            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()
-            account, role = account
-            account = marshal(account, account_fields)
-            account['role'] = role
-        except services.errors.account.CannotOperateSelfError as e:
-            return {'code': 'cannot-operate-self', 'message': str(e)}, 400
-        except services.errors.account.NoPermissionError as e:
-            return {'code': 'forbidden', 'message': str(e)}, 403
-        except services.errors.account.AccountAlreadyInTenantError as e:
-            return {'code': 'email-taken', 'message': str(e)}, 409
-        except Exception as e:
-            return {'code': 'unexpected-error', 'message': str(e)}, 500
-
-        # todo:413
+        invitation_results = []
+        console_web_url = current_app.config.get("CONSOLE_WEB_URL")
+        for invitee_email in invitee_emails:
+            try:
+                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 == invitee_email).first()
+                account, role = account
+                invitation_results.append({
+                    'status': 'success',
+                    'email': invitee_email,
+                    'url': f'{console_web_url}/activate?email={invitee_email}&token={token}'
+                })
+                account = marshal(account, account_fields)
+                account['role'] = role
+            except Exception as e:
+                invitation_results.append({
+                    'status': 'failed',
+                    'email': invitee_email,
+                    'message': str(e)
+                })
 
         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
-            )
+            'invitation_results': invitation_results,
         }, 201
 
 

api/controllers/console/workspace/model_providers.py

@@ -246,7 +246,8 @@ class ModelProviderModelParameterRuleApi(Resource):
                 'enabled': v.enabled,
                 'min': v.min,
                 'max': v.max,
-                'default': v.default
+                'default': v.default,
+                'precision': v.precision
             }
             for k, v in vars(parameter_rules).items()
         }
@@ -285,6 +286,25 @@ class ModelProviderFreeQuotaSubmitApi(Resource):
         return result
 
 
+class ModelProviderFreeQuotaQualificationVerifyApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, provider_name: str):
+        parser = reqparse.RequestParser()
+        parser.add_argument('token', type=str, required=False, nullable=True, location='args')
+        args = parser.parse_args()
+
+        provider_service = ProviderService()
+        result = provider_service.free_quota_qualification_verify(
+            tenant_id=current_user.current_tenant_id,
+            provider_name=provider_name,
+            token=args['token']
+        )
+
+        return result
+
+
 api.add_resource(ModelProviderListApi, '/workspaces/current/model-providers')
 api.add_resource(ModelProviderValidateApi, '/workspaces/current/model-providers/<string:provider_name>/validate')
 api.add_resource(ModelProviderUpdateApi, '/workspaces/current/model-providers/<string:provider_name>')
@@ -300,3 +320,5 @@ api.add_resource(ModelProviderPaymentCheckoutUrlApi,
                  '/workspaces/current/model-providers/<string:provider_name>/checkout-url')
 api.add_resource(ModelProviderFreeQuotaSubmitApi,
                  '/workspaces/current/model-providers/<string:provider_name>/free-quota-submit')
+api.add_resource(ModelProviderFreeQuotaQualificationVerifyApi,
+                 '/workspaces/current/model-providers/<string:provider_name>/free-quota-qualification-verify')

api/controllers/service_api/app/app.py

@@ -25,6 +25,7 @@ class AppParameterApi(AppApiResource):
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
         'speech_to_text': fields.Raw,
+        'retriever_resource': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
@@ -39,6 +40,7 @@ class AppParameterApi(AppApiResource):
             '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,
+            'retriever_resource': app_model_config.retriever_resource_dict,
             '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/completion.py

@@ -27,9 +27,11 @@ class CompletionApi(AppApiResource):
 
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
-        parser.add_argument('query', type=str, location='json')
+        parser.add_argument('query', type=str, location='json', default='')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
         parser.add_argument('user', type=str, location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
+
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'
@@ -91,6 +93,8 @@ class ChatApi(AppApiResource):
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
         parser.add_argument('conversation_id', type=uuid_value, location='json')
         parser.add_argument('user', type=str, location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
+
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'

api/controllers/service_api/app/message.py

@@ -16,6 +16,24 @@ class MessageListApi(AppApiResource):
     feedback_fields = {
         'rating': fields.String
     }
+    retriever_resource_fields = {
+        'id': fields.String,
+        'message_id': fields.String,
+        'position': fields.Integer,
+        'dataset_id': fields.String,
+        'dataset_name': fields.String,
+        'document_id': fields.String,
+        'document_name': fields.String,
+        'data_source_type': fields.String,
+        'segment_id': fields.String,
+        'score': fields.Float,
+        'hit_count': fields.Integer,
+        'word_count': fields.Integer,
+        'segment_position': fields.Integer,
+        'index_node_hash': fields.String,
+        'content': fields.String,
+        'created_at': TimestampField
+    }
 
     message_fields = {
         'id': fields.String,
@@ -24,6 +42,7 @@ class MessageListApi(AppApiResource):
         'query': fields.String,
         'answer': fields.String,
         'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+        'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
         'created_at': TimestampField
     }
 

api/controllers/web/app.py

@@ -24,6 +24,7 @@ class AppParameterApi(WebApiResource):
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
         'speech_to_text': fields.Raw,
+        'retriever_resource': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
@@ -38,6 +39,7 @@ class AppParameterApi(WebApiResource):
             '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,
+            'retriever_resource': app_model_config.retriever_resource_dict,
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/web/completion.py

@@ -29,8 +29,10 @@ class CompletionApi(WebApiResource):
 
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
-        parser.add_argument('query', type=str, location='json')
+        parser.add_argument('query', type=str, location='json', default='')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='web_app', location='json')
+
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'
@@ -88,6 +90,8 @@ class ChatApi(WebApiResource):
         parser.add_argument('query', type=str, required=True, location='json')
         parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
         parser.add_argument('conversation_id', type=uuid_value, location='json')
+        parser.add_argument('retriever_from', type=str, required=False, default='web_app', location='json')
+
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'

api/controllers/web/message.py

@@ -29,6 +29,25 @@ class MessageListApi(WebApiResource):
         'rating': fields.String
     }
 
+    retriever_resource_fields = {
+        'id': fields.String,
+        'message_id': fields.String,
+        'position': fields.Integer,
+        'dataset_id': fields.String,
+        'dataset_name': fields.String,
+        'document_id': fields.String,
+        'document_name': fields.String,
+        'data_source_type': fields.String,
+        'segment_id': fields.String,
+        'score': fields.Float,
+        'hit_count': fields.Integer,
+        'word_count': fields.Integer,
+        'segment_position': fields.Integer,
+        'index_node_hash': fields.String,
+        'content': fields.String,
+        'created_at': TimestampField
+    }
+
     message_fields = {
         'id': fields.String,
         'conversation_id': fields.String,
@@ -36,6 +55,7 @@ class MessageListApi(WebApiResource):
         'query': fields.String,
         'answer': fields.String,
         'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+        'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
         'created_at': TimestampField
     }
 

api/core/agent/agent/multi_dataset_router_agent.py

@@ -1,3 +1,4 @@
+import json
 from typing import Tuple, List, Any, Union, Sequence, Optional, cast
 
 from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
@@ -52,7 +53,11 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
         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']})
+            rst = tool.run(tool_input={'query': kwargs['input']})
+            # output = ''
+            # rst_json = json.loads(rst)
+            # for item in rst_json:
+            #     output += f'{item["content"]}\n'
             return AgentFinish(return_values={"output": rst}, log=rst)
 
         if intermediate_steps:
@@ -60,7 +65,13 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
             return AgentFinish(return_values={"output": observation}, log=observation)
 
         try:
-            return super().plan(intermediate_steps, callbacks, **kwargs)
+            agent_decision = super().plan(intermediate_steps, callbacks, **kwargs)
+            if isinstance(agent_decision, AgentAction):
+                tool_inputs = agent_decision.tool_input
+                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
+                    tool_inputs['query'] = kwargs['input']
+                    agent_decision.tool_input = tool_inputs
+            return agent_decision
         except Exception as e:
             new_exception = self.model_instance.handle_exceptions(e)
             raise new_exception

api/core/agent/agent/openai_function_call.py

@@ -45,7 +45,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, OpenAIFunctio
         :return:
         """
         original_max_tokens = self.llm.max_tokens
-        self.llm.max_tokens = 15
+        self.llm.max_tokens = 40
 
         prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
         messages = prompt.to_messages()
@@ -97,6 +97,13 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, OpenAIFunctio
             messages, functions=self.functions, callbacks=callbacks
         )
         agent_decision = _parse_ai_message(predicted_message)
+
+        if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
+            tool_inputs = agent_decision.tool_input
+            if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
+                tool_inputs['query'] = kwargs['input']
+                agent_decision.tool_input = tool_inputs
+
         return agent_decision
 
     @classmethod

api/core/agent/agent/structed_multi_dataset_router_agent.py

@@ -90,7 +90,7 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
         elif len(self.dataset_tools) == 1:
             tool = next(iter(self.dataset_tools))
             tool = cast(DatasetRetrieverTool, tool)
-            rst = tool.run(tool_input={'dataset_id': tool.dataset_id, 'query': kwargs['input']})
+            rst = tool.run(tool_input={'query': kwargs['input']})
             return AgentFinish(return_values={"output": rst}, log=rst)
 
         full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
@@ -102,7 +102,13 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
             raise new_exception
 
         try:
-            return self.output_parser.parse(full_output)
+            agent_decision = self.output_parser.parse(full_output)
+            if isinstance(agent_decision, AgentAction):
+                tool_inputs = agent_decision.tool_input
+                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
+                    tool_inputs['query'] = kwargs['input']
+                    agent_decision.tool_input = tool_inputs
+            return agent_decision
         except OutputParserException:
             return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
                                           "I don't know how to respond to that."}, "")

api/core/agent/agent/structured_chat.py

@@ -106,7 +106,13 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
             raise new_exception
 
         try:
-            return self.output_parser.parse(full_output)
+            agent_decision = self.output_parser.parse(full_output)
+            if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
+                tool_inputs = agent_decision.tool_input
+                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
+                    tool_inputs['query'] = kwargs['input']
+                    agent_decision.tool_input = tool_inputs
+            return agent_decision
         except OutputParserException:
             return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
                                           "I don't know how to respond to that."}, "")

api/core/agent/agent_executor.py

@@ -16,6 +16,8 @@ from core.agent.agent.structed_multi_dataset_router_agent import StructuredMulti
 from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
 from langchain.agents import AgentExecutor as LCAgentExecutor
 
+from core.helper import moderation
+from core.model_providers.error import LLMError
 from core.model_providers.models.llm.base import BaseLLM
 from core.tool.dataset_retriever_tool import DatasetRetrieverTool
 
@@ -116,6 +118,18 @@ class AgentExecutor:
         return self.agent.should_use_agent(query)
 
     def run(self, query: str) -> AgentExecuteResult:
+        moderation_result = moderation.check_moderation(
+            self.configuration.model_instance.model_provider,
+            query
+        )
+
+        if not moderation_result:
+            return AgentExecuteResult(
+                output="I apologize for any confusion, but I'm an AI assistant to be helpful, harmless, and honest.",
+                strategy=self.configuration.strategy,
+                configuration=self.configuration
+            )
+
         agent_executor = LCAgentExecutor.from_agent_and_tools(
             agent=self.agent,
             tools=self.configuration.tools,
@@ -128,7 +142,9 @@ class AgentExecutor:
 
         try:
             output = agent_executor.run(query)
-        except Exception:
+        except LLMError as ex:
+            raise ex
+        except Exception as ex:
             logging.exception("agent_executor run failed")
             output = None
 

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -6,7 +6,7 @@ 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, ChatGeneration
+from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration, BaseMessage
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.conversation_message_task import ConversationMessageTask
@@ -18,9 +18,9 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
     raise_error: bool = True
 
-    def __init__(self, model_instant: BaseLLM, conversation_message_task: ConversationMessageTask) -> None:
+    def __init__(self, model_instance: BaseLLM, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
-        self.model_instant = model_instant
+        self.model_instance = model_instance
         self.conversation_message_task = conversation_message_task
         self._agent_loops = []
         self._current_loop = None
@@ -46,6 +46,21 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         """Whether to ignore chain callbacks."""
         return True
 
+    def on_chat_model_start(
+            self,
+            serialized: Dict[str, Any],
+            messages: List[List[BaseMessage]],
+            **kwargs: Any
+    ) -> Any:
+        if not self._current_loop:
+            # Agent start with a LLM query
+            self._current_loop = AgentLoop(
+                position=len(self._agent_loops) + 1,
+                prompt="\n".join([message.content for message in messages[0]]),
+                status='llm_started',
+                started_at=time.perf_counter()
+            )
+
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
@@ -70,7 +85,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             if response.llm_output:
                 self._current_loop.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
             else:
-                self._current_loop.prompt_tokens = self.model_instant.get_num_tokens(
+                self._current_loop.prompt_tokens = self.model_instance.get_num_tokens(
                     [PromptMessage(content=self._current_loop.prompt)]
                 )
             completion_generation = response.generations[0][0]
@@ -87,7 +102,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             if response.llm_output:
                 self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
             else:
-                self._current_loop.completion_tokens = self.model_instant.get_num_tokens(
+                self._current_loop.completion_tokens = self.model_instance.get_num_tokens(
                     [PromptMessage(content=self._current_loop.completion)]
                 )
 
@@ -162,7 +177,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
 
             self.conversation_message_task.on_agent_end(
-                self._message_agent_thought, self.model_instant, self._current_loop
+                self._message_agent_thought, self.model_instance, self._current_loop
             )
 
             self._agent_loops.append(self._current_loop)
@@ -193,7 +208,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             )
 
             self.conversation_message_task.on_agent_end(
-                self._message_agent_thought, self.model_instant, self._current_loop
+                self._message_agent_thought, self.model_instance, self._current_loop
             )
 
             self._agent_loops.append(self._current_loop)

api/core/callback_handler/dataset_tool_callback_handler.py

@@ -1,5 +1,6 @@
 import json
 import logging
+from json import JSONDecodeError
 
 from typing import Any, Dict, List, Union, Optional
 
@@ -44,10 +45,15 @@ class DatasetToolCallbackHandler(BaseCallbackHandler):
         input_str: str,
         **kwargs: Any,
     ) -> None:
-        # tool_name = serialized.get('name')
-        input_dict = json.loads(input_str.replace("'", "\""))
-        dataset_id = input_dict.get('dataset_id')
-        query = input_dict.get('query')
+        tool_name: str = serialized.get('name')
+        dataset_id = tool_name.removeprefix('dataset-')
+
+        try:
+            input_dict = json.loads(input_str.replace("'", "\""))
+            query = input_dict.get('query')
+        except JSONDecodeError:
+            query = input_str
+
         self.conversation_message_task.on_dataset_query_end(DatasetQueryObj(dataset_id=dataset_id, query=query))
 
     def on_tool_end(
@@ -58,12 +64,9 @@ class DatasetToolCallbackHandler(BaseCallbackHandler):
         llm_prefix: Optional[str] = None,
         **kwargs: Any,
     ) -> None:
-        # kwargs={'name': 'Search'}
-        # llm_prefix='Thought:'
-        # observation_prefix='Observation: '
-        # output='53 years'
         pass
 
+
     def on_tool_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:

api/core/callback_handler/entity/llm_message.py

@@ -6,4 +6,3 @@ class LLMMessage(BaseModel):
     prompt_tokens: int = 0
     completion: str = ''
     completion_tokens: int = 0
-    latency: float = 0.0

api/core/callback_handler/index_tool_callback_handler.py

@@ -2,6 +2,7 @@ from typing import List
 
 from langchain.schema import Document
 
+from core.conversation_message_task import ConversationMessageTask
 from extensions.ext_database import db
 from models.dataset import DocumentSegment
 
@@ -9,8 +10,9 @@ from models.dataset import DocumentSegment
 class DatasetIndexToolCallbackHandler:
     """Callback handler for dataset tool."""
 
-    def __init__(self, dataset_id: str) -> None:
+    def __init__(self, dataset_id: str, conversation_message_task: ConversationMessageTask) -> None:
         self.dataset_id = dataset_id
+        self.conversation_message_task = conversation_message_task
 
     def on_tool_end(self, documents: List[Document]) -> None:
         """Handle tool end."""
@@ -27,3 +29,7 @@ class DatasetIndexToolCallbackHandler:
             )
 
             db.session.commit()
+
+    def return_retriever_resource_info(self, resource: List):
+        """Handle return_retriever_resource_info."""
+        self.conversation_message_task.on_dataset_query_finish(resource)

api/core/callback_handler/llm_callback_handler.py

@@ -1,5 +1,4 @@
 import logging
-import time
 from typing import Any, Dict, List, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
@@ -32,7 +31,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
             messages: List[List[BaseMessage]],
             **kwargs: Any
     ) -> Any:
-        self.start_at = time.perf_counter()
         real_prompts = []
         for message in messages[0]:
             if message.type == 'human':
@@ -53,8 +51,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
-        self.start_at = time.perf_counter()
-
         self.llm_message.prompt = [{
             "role": 'user',
             "text": prompts[0]
@@ -63,14 +59,22 @@ class LLMCallbackHandler(BaseCallbackHandler):
         self.llm_message.prompt_tokens = self.model_instance.get_num_tokens([PromptMessage(content=prompts[0])])
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        end_at = time.perf_counter()
-        self.llm_message.latency = end_at - self.start_at
-
         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 = self.model_instance.get_num_tokens([PromptMessage(content=self.llm_message.completion)])
+        if response.llm_output and 'token_usage' in response.llm_output:
+            if 'prompt_tokens' in response.llm_output['token_usage']:
+                self.llm_message.prompt_tokens = response.llm_output['token_usage']['prompt_tokens']
+
+            if 'completion_tokens' in response.llm_output['token_usage']:
+                self.llm_message.completion_tokens = response.llm_output['token_usage']['completion_tokens']
+            else:
+                self.llm_message.completion_tokens = self.model_instance.get_num_tokens(
+                    [PromptMessage(content=self.llm_message.completion)])
+        else:
+            self.llm_message.completion_tokens = self.model_instance.get_num_tokens(
+                [PromptMessage(content=self.llm_message.completion)])
 
         self.conversation_message_task.save_message(self.llm_message)
 
@@ -89,8 +93,6 @@ class LLMCallbackHandler(BaseCallbackHandler):
         """Do nothing."""
         if isinstance(error, ConversationTaskStoppedException):
             if self.conversation_message_task.streaming:
-                end_at = time.perf_counter()
-                self.llm_message.latency = end_at - self.start_at
                 self.llm_message.completion_tokens = self.model_instance.get_num_tokens(
                     [PromptMessage(content=self.llm_message.completion)]
                 )

api/core/chain/sensitive_word_avoidance_chain.py

@@ -1,15 +1,33 @@
+import enum
+import logging
 from typing import List, Dict, Optional, Any
 
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
+from pydantic import BaseModel
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.model_factory import ModelFactory
+from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.moderation import openai_moderation
+
+
+class SensitiveWordAvoidanceRule(BaseModel):
+    class Type(enum.Enum):
+        MODERATION = "moderation"
+        KEYWORDS = "keywords"
+
+    type: Type
+    canned_response: str = 'Your content violates our usage policy. Please revise and try again.'
+    extra_params: dict = {}
 
 
 class SensitiveWordAvoidanceChain(Chain):
     input_key: str = "input"  #: :meta private:
     output_key: str = "output"  #: :meta private:
 
-    sensitive_words: List[str] = []
-    canned_response: str = None
+    model_instance: BaseLLM
+    sensitive_word_avoidance_rule: SensitiveWordAvoidanceRule
 
     @property
     def _chain_type(self) -> str:
@@ -31,11 +49,24 @@ class SensitiveWordAvoidanceChain(Chain):
         """
         return [self.output_key]
 
-    def _check_sensitive_word(self, text: str) -> str:
-        for word in self.sensitive_words:
+    def _check_sensitive_word(self, text: str) -> bool:
+        for word in self.sensitive_word_avoidance_rule.extra_params.get('sensitive_words', []):
             if word in text:
-                return self.canned_response
-        return text
+                return False
+        return True
+
+    def _check_moderation(self, text: str) -> bool:
+        moderation_model_instance = ModelFactory.get_moderation_model(
+            tenant_id=self.model_instance.model_provider.provider.tenant_id,
+            model_provider_name='openai',
+            model_name=openai_moderation.DEFAULT_MODEL
+        )
+
+        try:
+            return moderation_model_instance.run(text=text)
+        except Exception as ex:
+            logging.exception(ex)
+            raise LLMBadRequestError('Rate limit exceeded, please try again later.')
 
     def _call(
             self,
@@ -43,5 +74,19 @@ class SensitiveWordAvoidanceChain(Chain):
             run_manager: Optional[CallbackManagerForChainRun] = None,
     ) -> Dict[str, Any]:
         text = inputs[self.input_key]
-        output = self._check_sensitive_word(text)
-        return {self.output_key: output}
+
+        if self.sensitive_word_avoidance_rule.type == SensitiveWordAvoidanceRule.Type.KEYWORDS:
+            result = self._check_sensitive_word(text)
+        else:
+            result = self._check_moderation(text)
+
+        if not result:
+            raise SensitiveWordAvoidanceError(self.sensitive_word_avoidance_rule.canned_response)
+
+        return {self.output_key: text}
+
+
+class SensitiveWordAvoidanceError(Exception):
+    def __init__(self, message):
+        super().__init__(message)
+        self.message = message

api/core/completion.py

@@ -1,31 +1,32 @@
+import json
 import logging
-import re
-from typing import Optional, List, Union, Tuple
+from typing import Optional, List, Union
 
-from langchain.schema import BaseMessage
 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.callback_handler.llm_callback_handler import LLMCallbackHandler
+from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceError
 from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
 from core.model_providers.error import LLMBadRequestError
 from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
     ReadOnlyConversationTokenDBBufferSharedMemory
 from core.model_providers.model_factory import ModelFactory
-from core.model_providers.models.entity.message import PromptMessage, to_prompt_messages
+from core.model_providers.models.entity.message import PromptMessage
 from core.model_providers.models.llm.base import BaseLLM
 from core.orchestrator_rule_parser import OrchestratorRuleParser
 from core.prompt.prompt_builder import PromptBuilder
-from core.prompt.prompt_template import JinjaPromptTemplate
 from core.prompt.prompts import MORE_LIKE_THIS_GENERATE_PROMPT
+from models.dataset import DocumentSegment, Dataset, Document
 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: Union[Account, EndUser], conversation: Optional[Conversation], streaming: bool, is_override: bool = False):
+                 user: Union[Account, EndUser], conversation: Optional[Conversation], streaming: bool,
+                 is_override: bool = False, retriever_from: str = 'dev'):
         """
         errors: ProviderTokenNotInitError
         """
@@ -76,29 +77,53 @@ class Completion:
             app_model_config=app_model_config
         )
 
-        # parse sensitive_word_avoidance_chain
-        chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
-        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:
+            # parse sensitive_word_avoidance_chain
+            chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
+            sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain(
+                final_model_instance, [chain_callback])
+            if sensitive_word_avoidance_chain:
+                try:
+                    query = sensitive_word_avoidance_chain.run(query)
+                except SensitiveWordAvoidanceError as ex:
+                    cls.run_final_llm(
+                        model_instance=final_model_instance,
+                        mode=app.mode,
+                        app_model_config=app_model_config,
+                        query=query,
+                        inputs=inputs,
+                        agent_execute_result=None,
+                        conversation_message_task=conversation_message_task,
+                        memory=memory,
+                        fake_response=ex.message
+                    )
+                    return
+
+            # 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,
+                retriever_from=retriever_from
+            )
+
+            # 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)
+
+            # 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
+            fake_response = None
+            if not app_model_config.pre_prompt and agent_execute_result and agent_execute_result.output \
+                    and agent_execute_result.strategy not in [PlanningStrategy.ROUTER,
+                                                              PlanningStrategy.REACT_ROUTER]:
+                fake_response = agent_execute_result.output
+
+            # run the final llm
             cls.run_final_llm(
                 model_instance=final_model_instance,
                 mode=app.mode,
@@ -107,7 +132,8 @@ class Completion:
                 inputs=inputs,
                 agent_execute_result=agent_execute_result,
                 conversation_message_task=conversation_message_task,
-                memory=memory
+                memory=memory,
+                fake_response=fake_response
             )
         except ConversationTaskStoppedException:
             return
@@ -118,17 +144,12 @@ class Completion:
             return
 
     @classmethod
-    def run_final_llm(cls, model_instance: BaseLLM, mode: str, app_model_config: AppModelConfig, query: str, inputs: dict,
+    def run_final_llm(cls, model_instance: BaseLLM, mode: str, app_model_config: AppModelConfig, query: str,
+                      inputs: dict,
                       agent_execute_result: Optional[AgentExecuteResult],
                       conversation_message_task: ConversationMessageTask,
-                      memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]):
-        # 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
-        fake_response = None
-        if not app_model_config.pre_prompt and agent_execute_result and agent_execute_result.output \
-                and agent_execute_result.strategy not in [PlanningStrategy.ROUTER, PlanningStrategy.REACT_ROUTER]:
-            fake_response = agent_execute_result.output
-
+                      memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory],
+                      fake_response: Optional[str]):
         # get llm prompt
         prompt_messages, stop_words = model_instance.get_prompt(
             mode=mode,
@@ -150,7 +171,6 @@ class Completion:
             callbacks=[LLMCallbackHandler(model_instance, conversation_message_task)],
             fake_response=fake_response
         )
-
         return response
 
     @classmethod

api/core/conversation_message_task.py

@@ -1,6 +1,6 @@
-import decimal
 import json
-from typing import Optional, Union
+import time
+from typing import Optional, Union, List
 
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.callback_handler.entity.dataset_query import DatasetQueryObj
@@ -15,13 +15,16 @@ from events.message_event import message_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from models.dataset import DatasetQuery
-from models.model import AppModelConfig, Conversation, Account, Message, EndUser, App, MessageAgentThought, MessageChain
+from models.model import AppModelConfig, Conversation, Account, Message, EndUser, App, MessageAgentThought, \
+    MessageChain, DatasetRetrieverResource
 
 
 class ConversationMessageTask:
     def __init__(self, task_id: str, app: App, app_model_config: AppModelConfig, user: Account,
                  inputs: dict, query: str, streaming: bool, model_instance: BaseLLM,
                  conversation: Optional[Conversation] = None, is_override: bool = False):
+        self.start_at = time.perf_counter()
+
         self.task_id = task_id
 
         self.app = app
@@ -41,6 +44,8 @@ class ConversationMessageTask:
 
         self.message = None
 
+        self.retriever_resource = None
+
         self.model_dict = self.app_model_config.model_dict
         self.provider_name = self.model_dict.get('provider')
         self.model_name = self.model_dict.get('name')
@@ -58,6 +63,7 @@ class ConversationMessageTask:
         )
 
     def init(self):
+
         override_model_configs = None
         if self.is_override:
             override_model_configs = self.app_model_config.to_dict()
@@ -137,7 +143,8 @@ class ConversationMessageTask:
         db.session.flush()
 
     def append_message_text(self, text: str):
-        self._pub_handler.pub_text(text)
+        if text is not None:
+            self._pub_handler.pub_text(text)
 
     def save_message(self, llm_message: LLMMessage, by_stopped: bool = False):
         message_tokens = llm_message.prompt_tokens
@@ -156,11 +163,12 @@ class ConversationMessageTask:
         self.message.message_tokens = message_tokens
         self.message.message_unit_price = message_unit_price
         self.message.message_price_unit = message_price_unit
-        self.message.answer = PromptBuilder.process_template(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.answer_price_unit = answer_price_unit
-        self.message.provider_response_latency = llm_message.latency
+        self.message.provider_response_latency = time.perf_counter() - self.start_at
         self.message.total_price = total_price
 
         db.session.commit()
@@ -215,18 +223,18 @@ class ConversationMessageTask:
 
         return message_agent_thought
 
-    def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instant: BaseLLM,
+    def on_agent_end(self, message_agent_thought: MessageAgentThought, agent_model_instance: BaseLLM,
                      agent_loop: AgentLoop):
-        agent_message_unit_price = agent_model_instant.get_tokens_unit_price(MessageType.HUMAN)
-        agent_message_price_unit = agent_model_instant.get_price_unit(MessageType.HUMAN)
-        agent_answer_unit_price = agent_model_instant.get_tokens_unit_price(MessageType.ASSISTANT)
-        agent_answer_price_unit = agent_model_instant.get_price_unit(MessageType.ASSISTANT)
+        agent_message_unit_price = agent_model_instance.get_tokens_unit_price(MessageType.HUMAN)
+        agent_message_price_unit = agent_model_instance.get_price_unit(MessageType.HUMAN)
+        agent_answer_unit_price = agent_model_instance.get_tokens_unit_price(MessageType.ASSISTANT)
+        agent_answer_price_unit = agent_model_instance.get_price_unit(MessageType.ASSISTANT)
 
         loop_message_tokens = agent_loop.prompt_tokens
         loop_answer_tokens = agent_loop.completion_tokens
 
-        loop_message_total_price = agent_model_instant.calc_tokens_price(loop_message_tokens, MessageType.HUMAN)
-        loop_answer_total_price = agent_model_instant.calc_tokens_price(loop_answer_tokens, MessageType.ASSISTANT)
+        loop_message_total_price = agent_model_instance.calc_tokens_price(loop_message_tokens, MessageType.HUMAN)
+        loop_answer_total_price = agent_model_instance.calc_tokens_price(loop_answer_tokens, MessageType.ASSISTANT)
         loop_total_price = loop_message_total_price + loop_answer_total_price
 
         message_agent_thought.observation = agent_loop.tool_output
@@ -240,7 +248,7 @@ class ConversationMessageTask:
         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 = agent_model_instant.get_currency()
+        message_agent_thought.currency = agent_model_instance.get_currency()
         db.session.flush()
 
     def on_dataset_query_end(self, dataset_query_obj: DatasetQueryObj):
@@ -255,7 +263,36 @@ class ConversationMessageTask:
 
         db.session.add(dataset_query)
 
+    def on_dataset_query_finish(self, resource: List):
+        if resource and len(resource) > 0:
+            for item in resource:
+                dataset_retriever_resource = DatasetRetrieverResource(
+                    message_id=self.message.id,
+                    position=item.get('position'),
+                    dataset_id=item.get('dataset_id'),
+                    dataset_name=item.get('dataset_name'),
+                    document_id=item.get('document_id'),
+                    document_name=item.get('document_name'),
+                    data_source_type=item.get('data_source_type'),
+                    segment_id=item.get('segment_id'),
+                    score=item.get('score') if 'score' in item else None,
+                    hit_count=item.get('hit_count') if 'hit_count' else None,
+                    word_count=item.get('word_count') if 'word_count' in item else None,
+                    segment_position=item.get('segment_position') if 'segment_position' in item else None,
+                    index_node_hash=item.get('index_node_hash') if 'index_node_hash' in item else None,
+                    content=item.get('content'),
+                    retriever_from=item.get('retriever_from'),
+                    created_by=self.user.id
+                )
+                db.session.add(dataset_retriever_resource)
+                db.session.flush()
+            self.retriever_resource = resource
+
+    def message_end(self):
+        self._pub_handler.pub_message_end(self.retriever_resource)
+
     def end(self):
+        self._pub_handler.pub_message_end(self.retriever_resource)
         self._pub_handler.pub_end()
 
 
@@ -349,6 +386,23 @@ class PubHandler:
             self.pub_end()
             raise ConversationTaskStoppedException()
 
+    def pub_message_end(self, retriever_resource: List):
+        content = {
+            'event': 'message_end',
+            'data': {
+                'task_id': self._task_id,
+                'message_id': self._message.id,
+                'mode': self._conversation.mode,
+                'conversation_id': self._conversation.id
+            }
+        }
+        if retriever_resource:
+            content['data']['retriever_resources'] = retriever_resource
+        redis_client.publish(self._channel, json.dumps(content))
+
+        if self._is_stopped():
+            self.pub_end()
+            raise ConversationTaskStoppedException()
 
     def pub_end(self):
         content = {

api/core/data_loader/file_extractor.py

@@ -6,7 +6,7 @@ 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.csv_loader 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
@@ -47,17 +47,18 @@ class FileExtractor:
                        upload_file: Optional[UploadFile] = None) -> Union[List[Document] | str]:
         input_file = Path(file_path)
         delimiter = '\n'
-        if input_file.suffix == '.xlsx':
+        file_extension = input_file.suffix.lower()
+        if file_extension == '.xlsx':
             loader = ExcelLoader(file_path)
-        elif input_file.suffix == '.pdf':
+        elif file_extension == '.pdf':
             loader = PdfLoader(file_path, upload_file=upload_file)
-        elif input_file.suffix in ['.md', '.markdown']:
+        elif file_extension in ['.md', '.markdown']:
             loader = MarkdownLoader(file_path, autodetect_encoding=True)
-        elif input_file.suffix in ['.htm', '.html']:
+        elif file_extension in ['.htm', '.html']:
             loader = HTMLLoader(file_path)
-        elif input_file.suffix == '.docx':
+        elif file_extension == '.docx':
             loader = Docx2txtLoader(file_path)
-        elif input_file.suffix == '.csv':
+        elif file_extension == '.csv':
             loader = CSVLoader(file_path, autodetect_encoding=True)
         else:
             # txt

api/core/data_loader/loader/csv_loader.py

@@ -1,10 +1,10 @@
 import logging
+import csv
 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
+from langchain.schema import Document
 
 logger = logging.getLogger(__name__)
 

api/core/data_loader/loader/excel.py

@@ -30,6 +30,8 @@ class ExcelLoader(BaseLoader):
         wb = load_workbook(filename=self._file_path, read_only=True)
         # loop over all sheets
         for sheet in wb:
+            if 'A1:A1' == sheet.calculate_dimension():
+                sheet.reset_dimensions()
             for row in sheet.iter_rows(values_only=True):
                 if all(v is None for v in row):
                     continue
@@ -38,7 +40,7 @@ class ExcelLoader(BaseLoader):
                 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())
+                    item = ''.join(f'{k}:{v};' for k, v in row_dict.items())
                     document = Document(page_content=item, metadata={'source': self._file_path})
                     data.append(document)
 

api/core/docstore/dataset_docstore.py

@@ -67,12 +67,13 @@ class DatesetDocumentStore:
 
         if max_position is None:
             max_position = 0
-
-        embedding_model = ModelFactory.get_embedding_model(
-            tenant_id=self._dataset.tenant_id,
-            model_provider_name=self._dataset.embedding_model_provider,
-            model_name=self._dataset.embedding_model
-        )
+        embedding_model = None
+        if self._dataset.indexing_technique == 'high_quality':
+            embedding_model = ModelFactory.get_embedding_model(
+                tenant_id=self._dataset.tenant_id,
+                model_provider_name=self._dataset.embedding_model_provider,
+                model_name=self._dataset.embedding_model
+            )
 
         for doc in docs:
             if not isinstance(doc, Document):
@@ -88,7 +89,7 @@ class DatesetDocumentStore:
                 )
 
             # calc embedding use tokens
-            tokens = embedding_model.get_num_tokens(doc.page_content)
+            tokens = embedding_model.get_num_tokens(doc.page_content) if embedding_model else 0
 
             if not segment_document:
                 max_position += 1

api/core/generator/llm_generator.py

@@ -1,3 +1,4 @@
+import json
 import logging
 
 from langchain.schema import OutputParserException
@@ -22,18 +23,25 @@ class LLMGenerator:
         if len(query) > 2000:
             query = query[:300] + "...[TRUNCATED]..." + query[-300:]
 
-        prompt = prompt.format(query=query)
+        query = query.replace("\n", " ")
+
+        prompt += query + "\n"
 
         model_instance = ModelFactory.get_text_generation_model(
             tenant_id=tenant_id,
             model_kwargs=ModelKwargs(
-                max_tokens=50
+                temperature=1,
+                max_tokens=100
             )
         )
 
         prompts = [PromptMessage(content=prompt)]
         response = model_instance.run(prompts)
         answer = response.content
+
+        result_dict = json.loads(answer)
+        answer = result_dict['Your Output']
+
         return answer.strip()
 
     @classmethod

api/core/helper/moderation.py

@@ -0,0 +1,34 @@
+import logging
+
+import openai
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.providers.base import BaseModelProvider
+from core.model_providers.providers.hosted import hosted_config, hosted_model_providers
+from models.provider import ProviderType
+
+
+def check_moderation(model_provider: BaseModelProvider, text: str) -> bool:
+    if hosted_config.moderation.enabled is True and hosted_model_providers.openai:
+        if model_provider.provider.provider_type == ProviderType.SYSTEM.value \
+                and model_provider.provider_name in hosted_config.moderation.providers:
+            # 2000 text per chunk
+            length = 2000
+            text_chunks = [text[i:i + length] for i in range(0, len(text), length)]
+
+            max_text_chunks = 32
+            chunks = [text_chunks[i:i + max_text_chunks] for i in range(0, len(text_chunks), max_text_chunks)]
+
+            for text_chunk in chunks:
+                try:
+                    moderation_result = openai.Moderation.create(input=text_chunk,
+                                                                 api_key=hosted_model_providers.openai.api_key)
+                except Exception as ex:
+                    logging.exception(ex)
+                    raise LLMBadRequestError('Rate limit exceeded, please try again later.')
+
+                for result in moderation_result.results:
+                    if result['flagged'] is True:
+                        return False
+
+    return True

api/core/index/base.py

@@ -16,6 +16,10 @@ class BaseIndex(ABC):
     def create(self, texts: list[Document], **kwargs) -> BaseIndex:
         raise NotImplementedError
 
+    @abstractmethod
+    def create_with_collection_name(self, texts: list[Document], collection_name: str, **kwargs) -> BaseIndex:
+        raise NotImplementedError
+
     @abstractmethod
     def add_texts(self, texts: list[Document], **kwargs):
         raise NotImplementedError
@@ -28,6 +32,10 @@ class BaseIndex(ABC):
     def delete_by_ids(self, ids: list[str]) -> None:
         raise NotImplementedError
 
+    @abstractmethod
+    def delete_by_group_id(self, group_id: str) -> None:
+        raise NotImplementedError
+
     @abstractmethod
     def delete_by_document_id(self, document_id: str):
         raise NotImplementedError

api/core/index/index.py

@@ -1,10 +1,18 @@
+import json
+
 from flask import current_app
+from langchain.embeddings import OpenAIEmbeddings
 
 from core.embedding.cached_embedding import CacheEmbedding
 from core.index.keyword_table_index.keyword_table_index import KeywordTableIndex, KeywordTableConfig
 from core.index.vector_index.vector_index import VectorIndex
 from core.model_providers.model_factory import ModelFactory
+from core.model_providers.models.embedding.openai_embedding import OpenAIEmbedding
+from core.model_providers.models.entity.model_params import ModelKwargs
+from core.model_providers.models.llm.openai_model import OpenAIModel
+from core.model_providers.providers.openai_provider import OpenAIProvider
 from models.dataset import Dataset
+from models.provider import Provider, ProviderType
 
 
 class IndexBuilder:
@@ -35,4 +43,13 @@ class IndexBuilder:
                 )
             )
         else:
-            raise ValueError('Unknown indexing technique')
+            raise ValueError('Unknown indexing technique')
+
+    @classmethod
+    def get_default_high_quality_index(cls, dataset: Dataset):
+        embeddings = OpenAIEmbeddings(openai_api_key=' ')
+        return VectorIndex(
+            dataset=dataset,
+            config=current_app.config,
+            embeddings=embeddings
+        )

api/core/index/keyword_table_index/keyword_table_index.py

@@ -25,7 +25,33 @@ class KeywordTableIndex(BaseIndex):
         keyword_table = {}
         for text in texts:
             keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
-            self._update_segment_keywords(text.metadata['doc_id'], list(keywords))
+            self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
+            keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
+
+        dataset_keyword_table = DatasetKeywordTable(
+            dataset_id=self.dataset.id,
+            keyword_table=json.dumps({
+                '__type__': 'keyword_table',
+                '__data__': {
+                    "index_id": self.dataset.id,
+                    "summary": None,
+                    "table": {}
+                }
+            }, cls=SetEncoder)
+        )
+        db.session.add(dataset_keyword_table)
+        db.session.commit()
+
+        self._save_dataset_keyword_table(keyword_table)
+
+        return self
+
+    def create_with_collection_name(self, texts: list[Document], collection_name: str, **kwargs) -> BaseIndex:
+        keyword_table_handler = JiebaKeywordTableHandler()
+        keyword_table = {}
+        for text in texts:
+            keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
+            self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
             keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
 
         dataset_keyword_table = DatasetKeywordTable(
@@ -52,7 +78,7 @@ class KeywordTableIndex(BaseIndex):
         keyword_table = self._get_dataset_keyword_table()
         for text in texts:
             keywords = keyword_table_handler.extract_keywords(text.page_content, self._config.max_keywords_per_chunk)
-            self._update_segment_keywords(text.metadata['doc_id'], list(keywords))
+            self._update_segment_keywords(self.dataset.id, text.metadata['doc_id'], list(keywords))
             keyword_table = self._add_text_to_keyword_table(keyword_table, text.metadata['doc_id'], list(keywords))
 
         self._save_dataset_keyword_table(keyword_table)
@@ -74,7 +100,7 @@ class KeywordTableIndex(BaseIndex):
             DocumentSegment.document_id == document_id
         ).all()
 
-        ids = [segment.id for segment in segments]
+        ids = [segment.index_node_id for segment in segments]
 
         keyword_table = self._get_dataset_keyword_table()
         keyword_table = self._delete_ids_from_keyword_table(keyword_table, ids)
@@ -120,6 +146,12 @@ class KeywordTableIndex(BaseIndex):
             db.session.delete(dataset_keyword_table)
             db.session.commit()
 
+    def delete_by_group_id(self, group_id: str) -> None:
+        dataset_keyword_table = self.dataset.dataset_keyword_table
+        if dataset_keyword_table:
+            db.session.delete(dataset_keyword_table)
+            db.session.commit()
+
     def _save_dataset_keyword_table(self, keyword_table):
         keyword_table_dict = {
             '__type__': 'keyword_table',
@@ -199,15 +231,18 @@ class KeywordTableIndex(BaseIndex):
 
         return sorted_chunk_indices[: k]
 
-    def _update_segment_keywords(self, node_id: str, keywords: List[str]):
-        document_segment = db.session.query(DocumentSegment).filter(DocumentSegment.index_node_id == node_id).first()
+    def _update_segment_keywords(self, dataset_id: str, node_id: str, keywords: List[str]):
+        document_segment = db.session.query(DocumentSegment).filter(
+            DocumentSegment.dataset_id == dataset_id,
+            DocumentSegment.index_node_id == node_id
+        ).first()
         if document_segment:
             document_segment.keywords = keywords
             db.session.commit()
 
     def create_segment_keywords(self, node_id: str, keywords: List[str]):
         keyword_table = self._get_dataset_keyword_table()
-        self._update_segment_keywords(node_id, keywords)
+        self._update_segment_keywords(self.dataset.id, node_id, keywords)
         keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
         self._save_dataset_keyword_table(keyword_table)
 

api/core/index/vector_index/base.py

@@ -10,17 +10,17 @@ from weaviate import UnexpectedStatusCodeException
 
 from core.index.base import BaseIndex
 from extensions.ext_database import db
-from models.dataset import Dataset, DocumentSegment
+from models.dataset import Dataset, DocumentSegment, DatasetCollectionBinding
 from models.dataset import Document as DatasetDocument
 
 
 class BaseVectorIndex(BaseIndex):
-    
+
     def __init__(self, dataset: Dataset, embeddings: Embeddings):
         super().__init__(dataset)
         self._embeddings = embeddings
         self._vector_store = None
-        
+
     def get_type(self) -> str:
         raise NotImplementedError
 
@@ -110,6 +110,12 @@ class BaseVectorIndex(BaseIndex):
         for node_id in ids:
             vector_store.del_text(node_id)
 
+    def delete_by_group_id(self, group_id: str) -> None:
+        vector_store = self._get_vector_store()
+        vector_store = cast(self._get_vector_store_class(), vector_store)
+
+        vector_store.delete()
+
     def delete(self) -> None:
         vector_store = self._get_vector_store()
         vector_store = cast(self._get_vector_store_class(), vector_store)
@@ -143,7 +149,7 @@ class BaseVectorIndex(BaseIndex):
                 DocumentSegment.status == 'completed',
                 DocumentSegment.enabled == True
             ).all()
-            
+
             for segment in segments:
                 document = Document(
                     page_content=segment.content,
@@ -173,3 +179,123 @@ class BaseVectorIndex(BaseIndex):
 
         self.dataset = dataset
         logging.info(f"Dataset {dataset.id} recreate successfully.")
+
+    def create_qdrant_dataset(self, dataset: Dataset):
+        logging.info(f"create_qdrant_dataset {dataset.id}")
+
+        try:
+            self.delete()
+        except UnexpectedStatusCodeException as e:
+            if e.status_code != 400:
+                # 400 means index not exists
+                raise e
+
+        dataset_documents = db.session.query(DatasetDocument).filter(
+            DatasetDocument.dataset_id == dataset.id,
+            DatasetDocument.indexing_status == 'completed',
+            DatasetDocument.enabled == True,
+            DatasetDocument.archived == False,
+        ).all()
+
+        documents = []
+        for dataset_document in dataset_documents:
+            segments = db.session.query(DocumentSegment).filter(
+                DocumentSegment.document_id == dataset_document.id,
+                DocumentSegment.status == 'completed',
+                DocumentSegment.enabled == True
+            ).all()
+
+            for segment in segments:
+                document = Document(
+                    page_content=segment.content,
+                    metadata={
+                        "doc_id": segment.index_node_id,
+                        "doc_hash": segment.index_node_hash,
+                        "document_id": segment.document_id,
+                        "dataset_id": segment.dataset_id,
+                    }
+                )
+
+                documents.append(document)
+
+        if documents:
+            try:
+                self.create(documents)
+            except Exception as e:
+                raise e
+
+        logging.info(f"Dataset {dataset.id} recreate successfully.")
+
+    def update_qdrant_dataset(self, dataset: Dataset):
+        logging.info(f"update_qdrant_dataset {dataset.id}")
+
+        segment = db.session.query(DocumentSegment).filter(
+            DocumentSegment.dataset_id == dataset.id,
+            DocumentSegment.status == 'completed',
+            DocumentSegment.enabled == True
+        ).first()
+
+        if segment:
+            try:
+                exist = self.text_exists(segment.index_node_id)
+                if exist:
+                    index_struct = {
+                        "type": 'qdrant',
+                        "vector_store": {"class_prefix": dataset.index_struct_dict['vector_store']['class_prefix']}
+                    }
+                    dataset.index_struct = json.dumps(index_struct)
+                    db.session.commit()
+            except Exception as e:
+                raise e
+
+        logging.info(f"Dataset {dataset.id} recreate successfully.")
+
+    def restore_dataset_in_one(self, dataset: Dataset, dataset_collection_binding: DatasetCollectionBinding):
+        logging.info(f"restore dataset in_one,_dataset {dataset.id}")
+
+        dataset_documents = db.session.query(DatasetDocument).filter(
+            DatasetDocument.dataset_id == dataset.id,
+            DatasetDocument.indexing_status == 'completed',
+            DatasetDocument.enabled == True,
+            DatasetDocument.archived == False,
+        ).all()
+
+        documents = []
+        for dataset_document in dataset_documents:
+            segments = db.session.query(DocumentSegment).filter(
+                DocumentSegment.document_id == dataset_document.id,
+                DocumentSegment.status == 'completed',
+                DocumentSegment.enabled == True
+            ).all()
+
+            for segment in segments:
+                document = Document(
+                    page_content=segment.content,
+                    metadata={
+                        "doc_id": segment.index_node_id,
+                        "doc_hash": segment.index_node_hash,
+                        "document_id": segment.document_id,
+                        "dataset_id": segment.dataset_id,
+                    }
+                )
+
+                documents.append(document)
+
+        if documents:
+            try:
+                self.create_with_collection_name(documents, dataset_collection_binding.collection_name)
+            except Exception as e:
+                raise e
+
+        logging.info(f"Dataset {dataset.id} recreate successfully.")
+
+    def delete_original_collection(self, dataset: Dataset, dataset_collection_binding: DatasetCollectionBinding):
+        logging.info(f"delete original collection: {dataset.id}")
+
+        self.delete()
+
+        dataset.collection_binding_id = dataset_collection_binding.id
+        db.session.add(dataset)
+        db.session.commit()
+
+        logging.info(f"Dataset {dataset.id} recreate successfully.")

api/core/index/vector_index/milvus_vector_index.py

@@ -0,0 +1,127 @@
+from typing import Optional, cast
+
+from langchain.embeddings.base import Embeddings
+from langchain.schema import Document, BaseRetriever
+from langchain.vectorstores import VectorStore, milvus
+from pydantic import BaseModel, root_validator
+
+from core.index.base import BaseIndex
+from core.index.vector_index.base import BaseVectorIndex
+from core.vector_store.milvus_vector_store import MilvusVectorStore
+from core.vector_store.weaviate_vector_store import WeaviateVectorStore
+from models.dataset import Dataset
+
+
+class MilvusConfig(BaseModel):
+    endpoint: str
+    user: str
+    password: str
+    batch_size: int = 100
+
+    @root_validator()
+    def validate_config(cls, values: dict) -> dict:
+        if not values['endpoint']:
+            raise ValueError("config MILVUS_ENDPOINT is required")
+        if not values['user']:
+            raise ValueError("config MILVUS_USER is required")
+        if not values['password']:
+            raise ValueError("config MILVUS_PASSWORD is required")
+        return values
+
+
+class MilvusVectorIndex(BaseVectorIndex):
+    def __init__(self, dataset: Dataset, config: MilvusConfig, embeddings: Embeddings):
+        super().__init__(dataset, embeddings)
+        self._client = self._init_client(config)
+
+    def get_type(self) -> str:
+        return 'milvus'
+
+    def get_index_name(self, dataset: Dataset) -> str:
+        if self.dataset.index_struct_dict:
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
+                # original class_prefix
+                class_prefix += '_Node'
+
+            return class_prefix
+
+        dataset_id = dataset.id
+        return "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+
+
+    def to_index_struct(self) -> dict:
+        return {
+            "type": self.get_type(),
+            "vector_store": {"class_prefix": self.get_index_name(self.dataset)}
+        }
+
+    def create(self, texts: list[Document], **kwargs) -> BaseIndex:
+        uuids = self._get_uuids(texts)
+        self._vector_store = WeaviateVectorStore.from_documents(
+            texts,
+            self._embeddings,
+            client=self._client,
+            index_name=self.get_index_name(self.dataset),
+            uuids=uuids,
+            by_text=False
+        )
+
+        return self
+
+    def create_with_collection_name(self, texts: list[Document], collection_name: str, **kwargs) -> BaseIndex:
+        uuids = self._get_uuids(texts)
+        self._vector_store = WeaviateVectorStore.from_documents(
+            texts,
+            self._embeddings,
+            client=self._client,
+            index_name=collection_name,
+            uuids=uuids,
+            by_text=False
+        )
+
+        return self
+
+    def _get_vector_store(self) -> VectorStore:
+        """Only for created index."""
+        if self._vector_store:
+            return self._vector_store
+
+        attributes = ['doc_id', 'dataset_id', 'document_id']
+        if self._is_origin():
+            attributes = ['doc_id']
+
+        return WeaviateVectorStore(
+            client=self._client,
+            index_name=self.get_index_name(self.dataset),
+            text_key='text',
+            embedding=self._embeddings,
+            attributes=attributes,
+            by_text=False
+        )
+
+    def _get_vector_store_class(self) -> type:
+        return MilvusVectorStore
+
+    def delete_by_document_id(self, document_id: str):
+        if self._is_origin():
+            self.recreate_dataset(self.dataset)
+            return
+
+        vector_store = self._get_vector_store()
+        vector_store = cast(self._get_vector_store_class(), vector_store)
+
+        vector_store.del_texts({
+            "operator": "Equal",
+            "path": ["document_id"],
+            "valueText": document_id
+        })
+
+    def _is_origin(self):
+        if self.dataset.index_struct_dict:
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
+                # original class_prefix
+                return True
+
+        return False

api/core/index/vector_index/qdrant_vector_index.py

@@ -6,18 +6,20 @@ from langchain.embeddings.base import Embeddings
 from langchain.schema import Document, BaseRetriever
 from langchain.vectorstores import VectorStore
 from pydantic import BaseModel
+from qdrant_client.http.models import HnswConfigDiff
 
 from core.index.base import BaseIndex
 from core.index.vector_index.base import BaseVectorIndex
 from core.vector_store.qdrant_vector_store import QdrantVectorStore
-from models.dataset import Dataset
+from extensions.ext_database import db
+from models.dataset import Dataset, DatasetCollectionBinding
 
 
 class QdrantConfig(BaseModel):
     endpoint: str
     api_key: Optional[str]
     root_path: Optional[str]
-    
+
     def to_qdrant_params(self):
         if self.endpoint and self.endpoint.startswith('path:'):
             path = self.endpoint.replace('path:', '')
@@ -43,16 +45,26 @@ class QdrantVectorIndex(BaseVectorIndex):
         return 'qdrant'
 
     def get_index_name(self, dataset: Dataset) -> str:
-        if self.dataset.index_struct_dict:
-            return self.dataset.index_struct_dict['vector_store']['collection_name']
+        if dataset.collection_binding_id:
+            dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
+                filter(DatasetCollectionBinding.id == dataset.collection_binding_id). \
+                one_or_none()
+            if dataset_collection_binding:
+                return dataset_collection_binding.collection_name
+            else:
+                raise ValueError('Dataset Collection Bindings is not exist!')
+        else:
+            if self.dataset.index_struct_dict:
+                class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+                return class_prefix
 
-        dataset_id = dataset.id
-        return "Index_" + dataset_id.replace("-", "_")
+            dataset_id = dataset.id
+            return "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
 
     def to_index_struct(self) -> dict:
         return {
             "type": self.get_type(),
-            "vector_store": {"collection_name": self.get_index_name(self.dataset)}
+            "vector_store": {"class_prefix": self.get_index_name(self.dataset)}
         }
 
     def create(self, texts: list[Document], **kwargs) -> BaseIndex:
@@ -62,7 +74,28 @@ class QdrantVectorIndex(BaseVectorIndex):
             self._embeddings,
             collection_name=self.get_index_name(self.dataset),
             ids=uuids,
-            content_payload_key='text',
+            content_payload_key='page_content',
+            group_id=self.dataset.id,
+            group_payload_key='group_id',
+            hnsw_config=HnswConfigDiff(m=0, payload_m=16, ef_construct=100, full_scan_threshold=10000,
+                                       max_indexing_threads=0, on_disk=False),
+            **self._client_config.to_qdrant_params()
+        )
+
+        return self
+
+    def create_with_collection_name(self, texts: list[Document], collection_name: str, **kwargs) -> BaseIndex:
+        uuids = self._get_uuids(texts)
+        self._vector_store = QdrantVectorStore.from_documents(
+            texts,
+            self._embeddings,
+            collection_name=collection_name,
+            ids=uuids,
+            content_payload_key='page_content',
+            group_id=self.dataset.id,
+            group_payload_key='group_id',
+            hnsw_config=HnswConfigDiff(m=0, payload_m=16, ef_construct=100, full_scan_threshold=10000,
+                                       max_indexing_threads=0, on_disk=False),
             **self._client_config.to_qdrant_params()
         )
 
@@ -72,7 +105,7 @@ class QdrantVectorIndex(BaseVectorIndex):
         """Only for created index."""
         if self._vector_store:
             return self._vector_store
-        
+        attributes = ['doc_id', 'dataset_id', 'document_id']
         client = qdrant_client.QdrantClient(
             **self._client_config.to_qdrant_params()
         )
@@ -81,16 +114,15 @@ class QdrantVectorIndex(BaseVectorIndex):
             client=client,
             collection_name=self.get_index_name(self.dataset),
             embeddings=self._embeddings,
-            content_payload_key='text'
+            content_payload_key='page_content',
+            group_id=self.dataset.id,
+            group_payload_key='group_id'
         )
 
     def _get_vector_store_class(self) -> type:
         return QdrantVectorStore
 
     def delete_by_document_id(self, document_id: str):
-        if self._is_origin():
-            self.recreate_dataset(self.dataset)
-            return
 
         vector_store = self._get_vector_store()
         vector_store = cast(self._get_vector_store_class(), vector_store)
@@ -106,10 +138,42 @@ class QdrantVectorIndex(BaseVectorIndex):
             ],
         ))
 
+    def delete_by_ids(self, ids: list[str]) -> None:
+
+        vector_store = self._get_vector_store()
+        vector_store = cast(self._get_vector_store_class(), vector_store)
+
+        from qdrant_client.http import models
+        for node_id in ids:
+            vector_store.del_texts(models.Filter(
+                must=[
+                    models.FieldCondition(
+                        key="metadata.doc_id",
+                        match=models.MatchValue(value=node_id),
+                    ),
+                ],
+            ))
+
+    def delete_by_group_id(self, group_id: str) -> None:
+
+        vector_store = self._get_vector_store()
+        vector_store = cast(self._get_vector_store_class(), vector_store)
+
+        from qdrant_client.http import models
+        vector_store.del_texts(models.Filter(
+            must=[
+                models.FieldCondition(
+                    key="group_id",
+                    match=models.MatchValue(value=group_id),
+                ),
+            ],
+        ))
+
+
     def _is_origin(self):
         if self.dataset.index_struct_dict:
-            class_prefix: str = self.dataset.index_struct_dict['vector_store']['collection_name']
-            if class_prefix.startswith('Vector_'):
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
                 # original class_prefix
                 return True
 

api/core/index/vector_index/weaviate_vector_index.py

@@ -91,6 +91,20 @@ class WeaviateVectorIndex(BaseVectorIndex):
 
         return self
 
+    def create_with_collection_name(self, texts: list[Document], collection_name: str, **kwargs) -> BaseIndex:
+        uuids = self._get_uuids(texts)
+        self._vector_store = WeaviateVectorStore.from_documents(
+            texts,
+            self._embeddings,
+            client=self._client,
+            index_name=self.get_index_name(self.dataset),
+            uuids=uuids,
+            by_text=False
+        )
+
+        return self
+
+
     def _get_vector_store(self) -> VectorStore:
         """Only for created index."""
         if self._vector_store:

api/core/indexing_runner.py

@@ -217,25 +217,29 @@ class IndexingRunner:
             db.session.commit()
 
     def file_indexing_estimate(self, tenant_id: str, file_details: List[UploadFile], tmp_processing_rule: dict,
-                               doc_form: str = None, doc_language: str = 'English', dataset_id: str = None) -> dict:
+                               doc_form: str = None, doc_language: str = 'English', dataset_id: str = None,
+                               indexing_technique: str = 'economy') -> dict:
         """
         Estimate the indexing for the document.
         """
+        embedding_model = None
         if dataset_id:
             dataset = Dataset.query.filter_by(
                 id=dataset_id
             ).first()
             if not dataset:
                 raise ValueError('Dataset not found.')
-            embedding_model = ModelFactory.get_embedding_model(
-                tenant_id=dataset.tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
+            if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality':
+                embedding_model = ModelFactory.get_embedding_model(
+                    tenant_id=dataset.tenant_id,
+                    model_provider_name=dataset.embedding_model_provider,
+                    model_name=dataset.embedding_model
+                )
         else:
-            embedding_model = ModelFactory.get_embedding_model(
-                tenant_id=tenant_id
-            )
+            if indexing_technique == 'high_quality':
+                embedding_model = ModelFactory.get_embedding_model(
+                    tenant_id=tenant_id
+                )
         tokens = 0
         preview_texts = []
         total_segments = 0
@@ -263,8 +267,8 @@ class IndexingRunner:
             for document in documents:
                 if len(preview_texts) < 5:
                     preview_texts.append(document.page_content)
-
-                tokens += embedding_model.get_num_tokens(self.filter_string(document.page_content))
+                if indexing_technique == 'high_quality' or embedding_model:
+                    tokens += embedding_model.get_num_tokens(self.filter_string(document.page_content))
 
         if doc_form and doc_form == 'qa_model':
             text_generation_model = ModelFactory.get_text_generation_model(
@@ -286,32 +290,35 @@ class IndexingRunner:
         return {
             "total_segments": total_segments,
             "tokens": tokens,
-            "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)),
-            "currency": embedding_model.get_currency(),
+            "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)) if embedding_model else 0,
+            "currency": embedding_model.get_currency() if embedding_model else 'USD',
             "preview": preview_texts
         }
 
     def notion_indexing_estimate(self, tenant_id: str, notion_info_list: list, tmp_processing_rule: dict,
-                                 doc_form: str = None, doc_language: str = 'English', dataset_id: str = None) -> dict:
+                                 doc_form: str = None, doc_language: str = 'English', dataset_id: str = None,
+                                 indexing_technique: str = 'economy') -> dict:
         """
         Estimate the indexing for the document.
         """
+        embedding_model = None
         if dataset_id:
             dataset = Dataset.query.filter_by(
                 id=dataset_id
             ).first()
             if not dataset:
                 raise ValueError('Dataset not found.')
-            embedding_model = ModelFactory.get_embedding_model(
-                tenant_id=dataset.tenant_id,
-                model_provider_name=dataset.embedding_model_provider,
-                model_name=dataset.embedding_model
-            )
+            if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality':
+                embedding_model = ModelFactory.get_embedding_model(
+                    tenant_id=dataset.tenant_id,
+                    model_provider_name=dataset.embedding_model_provider,
+                    model_name=dataset.embedding_model
+                )
         else:
-            embedding_model = ModelFactory.get_embedding_model(
-                tenant_id=tenant_id
-            )
-
+            if indexing_technique == 'high_quality':
+                embedding_model = ModelFactory.get_embedding_model(
+                    tenant_id=tenant_id
+                )
         # load data from notion
         tokens = 0
         preview_texts = []
@@ -356,8 +363,8 @@ class IndexingRunner:
                 for document in documents:
                     if len(preview_texts) < 5:
                         preview_texts.append(document.page_content)
-
-                    tokens += embedding_model.get_num_tokens(document.page_content)
+                    if indexing_technique == 'high_quality' or embedding_model:
+                        tokens += embedding_model.get_num_tokens(document.page_content)
 
         if doc_form and doc_form == 'qa_model':
             text_generation_model = ModelFactory.get_text_generation_model(
@@ -379,8 +386,8 @@ class IndexingRunner:
         return {
             "total_segments": total_segments,
             "tokens": tokens,
-            "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)),
-            "currency": embedding_model.get_currency(),
+            "total_price": '{:f}'.format(embedding_model.calc_tokens_price(tokens)) if embedding_model else 0,
+            "currency": embedding_model.get_currency() if embedding_model else 'USD',
             "preview": preview_texts
         }
 
@@ -399,7 +406,8 @@ class IndexingRunner:
                 filter(UploadFile.id == data_source_info['upload_file_id']). \
                 one_or_none()
 
-            text_docs = FileExtractor.load(file_detail)
+            if file_detail:
+                text_docs = FileExtractor.load(file_detail)
         elif dataset_document.data_source_type == 'notion_import':
             loader = NotionLoader.from_document(dataset_document)
             text_docs = loader.load()
@@ -525,12 +533,13 @@ class IndexingRunner:
             documents = splitter.split_documents([text_doc])
             split_documents = []
             for document_node in documents:
-                doc_id = str(uuid.uuid4())
-                hash = helper.generate_text_hash(document_node.page_content)
-                document_node.metadata['doc_id'] = doc_id
-                document_node.metadata['doc_hash'] = hash
 
-                split_documents.append(document_node)
+                if document_node.page_content.strip():
+                    doc_id = str(uuid.uuid4())
+                    hash = helper.generate_text_hash(document_node.page_content)
+                    document_node.metadata['doc_id'] = doc_id
+                    document_node.metadata['doc_hash'] = hash
+                    split_documents.append(document_node)
             all_documents.extend(split_documents)
         # processing qa document
         if document_form == 'qa_model':
@@ -656,12 +665,13 @@ class IndexingRunner:
         """
         vector_index = IndexBuilder.get_index(dataset, 'high_quality')
         keyword_table_index = IndexBuilder.get_index(dataset, 'economy')
-
-        embedding_model = ModelFactory.get_embedding_model(
-            tenant_id=dataset.tenant_id,
-            model_provider_name=dataset.embedding_model_provider,
-            model_name=dataset.embedding_model
-        )
+        embedding_model = None
+        if dataset.indexing_technique == 'high_quality':
+            embedding_model = ModelFactory.get_embedding_model(
+                tenant_id=dataset.tenant_id,
+                model_provider_name=dataset.embedding_model_provider,
+                model_name=dataset.embedding_model
+            )
 
         # chunk nodes by chunk size
         indexing_start_at = time.perf_counter()
@@ -671,11 +681,11 @@ class IndexingRunner:
             # check document is paused
             self._check_document_paused_status(dataset_document.id)
             chunk_documents = documents[i:i + chunk_size]
-
-            tokens += sum(
-                embedding_model.get_num_tokens(document.page_content)
-                for document in chunk_documents
-            )
+            if dataset.indexing_technique == 'high_quality' or embedding_model:
+                tokens += sum(
+                    embedding_model.get_num_tokens(document.page_content)
+                    for document in chunk_documents
+                )
 
             # save vector index
             if vector_index:

api/core/model_providers/model_factory.py

@@ -8,6 +8,7 @@ from core.model_providers.models.base import BaseProviderModel
 from core.model_providers.models.embedding.base import BaseEmbedding
 from core.model_providers.models.entity.model_params import ModelKwargs, ModelType
 from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.moderation.base import BaseModeration
 from core.model_providers.models.speech2text.base import BaseSpeech2Text
 from extensions.ext_database import db
 from models.provider import TenantDefaultModel
@@ -180,7 +181,7 @@ class ModelFactory:
     def get_moderation_model(cls,
                              tenant_id: str,
                              model_provider_name: str,
-                             model_name: str) -> Optional[BaseProviderModel]:
+                             model_name: str) -> Optional[BaseModeration]:
         """
         get moderation model.
 

api/core/model_providers/model_provider_factory.py

@@ -45,6 +45,9 @@ class ModelProviderFactory:
         elif provider_name == 'wenxin':
             from core.model_providers.providers.wenxin_provider import WenxinProvider
             return WenxinProvider
+        elif provider_name == 'zhipuai':
+            from core.model_providers.providers.zhipuai_provider import ZhipuAIProvider
+            return ZhipuAIProvider
         elif provider_name == 'chatglm':
             from core.model_providers.providers.chatglm_provider import ChatGLMProvider
             return ChatGLMProvider
@@ -63,6 +66,9 @@ class ModelProviderFactory:
         elif provider_name == 'openllm':
             from core.model_providers.providers.openllm_provider import OpenLLMProvider
             return OpenLLMProvider
+        elif provider_name == 'localai':
+            from core.model_providers.providers.localai_provider import LocalAIProvider
+            return LocalAIProvider
         else:
             raise NotImplementedError
 

api/core/model_providers/models/embedding/localai_embedding.py

@@ -0,0 +1,29 @@
+from langchain.embeddings import LocalAIEmbeddings
+
+from replicate.exceptions import ModelError, ReplicateError
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.providers.base import BaseModelProvider
+from core.model_providers.models.embedding.base import BaseEmbedding
+
+
+class LocalAIEmbedding(BaseEmbedding):
+    def __init__(self, model_provider: BaseModelProvider, name: str):
+        credentials = model_provider.get_model_credentials(
+            model_name=name,
+            model_type=self.type
+        )
+
+        client = LocalAIEmbeddings(
+            model=name,
+            openai_api_key="1",
+            openai_api_base=credentials['server_url'],
+        )
+
+        super().__init__(model_provider, client, name)
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        if isinstance(ex, (ModelError, ReplicateError)):
+            return LLMBadRequestError(f"LocalAI embedding: {str(ex)}")
+        else:
+            return ex

api/core/model_providers/models/embedding/zhipuai_embedding.py

@@ -0,0 +1,22 @@
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.providers.base import BaseModelProvider
+from core.model_providers.models.embedding.base import BaseEmbedding
+from core.third_party.langchain.embeddings.zhipuai_embedding import ZhipuAIEmbeddings
+
+
+class ZhipuAIEmbedding(BaseEmbedding):
+    def __init__(self, model_provider: BaseModelProvider, name: str):
+        credentials = model_provider.get_model_credentials(
+            model_name=name,
+            model_type=self.type
+        )
+
+        client = ZhipuAIEmbeddings(
+            model=name,
+            **credentials,
+        )
+
+        super().__init__(model_provider, client, name)
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"ZhipuAI embedding: {str(ex)}")

api/core/model_providers/models/entity/message.py

@@ -8,6 +8,7 @@ class LLMRunResult(BaseModel):
     content: str
     prompt_tokens: int
     completion_tokens: int
+    source: list = None
 
 
 class MessageType(enum.Enum):

api/core/model_providers/models/entity/model_params.py

@@ -49,6 +49,7 @@ class KwargRule(Generic[T], BaseModel):
     max: Optional[T] = None
     default: Optional[T] = None
     alias: Optional[str] = None
+    precision: Optional[int] = None
 
 
 class ModelKwargsRules(BaseModel):

api/core/model_providers/models/llm/anthropic_model.py

@@ -1,11 +1,8 @@
-import decimal
 import logging
-from functools import wraps
 from typing import List, Optional, Any
 
 import anthropic
 from langchain.callbacks.manager import Callbacks
-from langchain.chat_models import ChatAnthropic
 from langchain.schema import LLMResult
 
 from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \
@@ -13,6 +10,7 @@ from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError
 from core.model_providers.models.llm.base import BaseLLM
 from core.model_providers.models.entity.message import PromptMessage, MessageType
 from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+from core.third_party.langchain.llms.anthropic_llm import AnthropicLLM
 
 
 class AnthropicModel(BaseLLM):
@@ -20,7 +18,7 @@ class AnthropicModel(BaseLLM):
 
     def _init_client(self) -> Any:
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
-        return ChatAnthropic(
+        return AnthropicLLM(
             model=self.name,
             streaming=self.streaming,
             callbacks=self.callbacks,
@@ -75,7 +73,7 @@ class AnthropicModel(BaseLLM):
         else:
             return ex
 
-    @classmethod
-    def support_streaming(cls):
+    @property
+    def support_streaming(self):
         return True
 

api/core/model_providers/models/llm/azure_openai_model.py

@@ -141,6 +141,6 @@ class AzureOpenAIModel(BaseLLM):
         else:
             return ex
 
-    @classmethod
-    def support_streaming(cls):
-        return True
+    @property
+    def support_streaming(self):
+        return True

api/core/model_providers/models/llm/base.py

@@ -10,6 +10,7 @@ from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema import LLMResult, SystemMessage, AIMessage, HumanMessage, BaseMessage, ChatGeneration
 
 from core.callback_handler.std_out_callback_handler import DifyStreamingStdOutCallbackHandler, DifyStdOutCallbackHandler
+from core.helper import moderation
 from core.model_providers.models.base import BaseProviderModel
 from core.model_providers.models.entity.message import PromptMessage, MessageType, LLMRunResult, to_prompt_messages
 from core.model_providers.models.entity.model_params import ModelType, ModelKwargs, ModelMode, ModelKwargsRules
@@ -116,6 +117,15 @@ class BaseLLM(BaseProviderModel):
         :param callbacks:
         :return:
         """
+        moderation_result = moderation.check_moderation(
+            self.model_provider,
+            "\n".join([message.content for message in messages])
+        )
+
+        if not moderation_result:
+            kwargs['fake_response'] = "I apologize for any confusion, " \
+                                      "but I'm an AI assistant to be helpful, harmless, and honest."
+
         if self.deduct_quota:
             self.model_provider.check_quota_over_limit()
 
@@ -138,7 +148,7 @@ class BaseLLM(BaseProviderModel):
                 result = self._run(
                     messages=messages,
                     stop=stop,
-                    callbacks=callbacks if not (self.streaming and not self.support_streaming()) else None,
+                    callbacks=callbacks if not (self.streaming and not self.support_streaming) else None,
                     **kwargs
                 )
             except Exception as ex:
@@ -149,7 +159,7 @@ class BaseLLM(BaseProviderModel):
         else:
             completion_content = result.generations[0][0].text
 
-        if self.streaming and not self.support_streaming():
+        if self.streaming and not self.support_streaming:
             # use FakeLLM to simulate streaming when current model not support streaming but streaming is True
             prompts = self._get_prompt_from_messages(messages, ModelMode.CHAT)
             fake_llm = FakeLLM(
@@ -298,8 +308,8 @@ class BaseLLM(BaseProviderModel):
         else:
             self.client.callbacks.extend(callbacks)
 
-    @classmethod
-    def support_streaming(cls):
+    @property
+    def support_streaming(self):
         return False
 
     def get_prompt(self, mode: str,
@@ -342,7 +352,7 @@ class BaseLLM(BaseProviderModel):
             if order == 'context_prompt':
                 prompt += context_prompt_content
             elif order == 'pre_prompt':
-                prompt += (pre_prompt_content + '\n\n') if pre_prompt_content else ''
+                prompt += pre_prompt_content
 
         query_prompt = prompt_rules['query_prompt'] if 'query_prompt' in prompt_rules else '{{query}}'
 

api/core/model_providers/models/llm/chatglm_model.py

@@ -61,7 +61,3 @@ class ChatGLMModel(BaseLLM):
             return LLMBadRequestError(f"ChatGLM: {str(ex)}")
         else:
             return ex
-
-    @classmethod
-    def support_streaming(cls):
-        return False

api/core/model_providers/models/llm/huggingface_hub_model.py

@@ -1,6 +1,5 @@
 from typing import List, Optional, Any
 
-from langchain import HuggingFaceHub
 from langchain.callbacks.manager import Callbacks
 from langchain.schema import LLMResult
 
@@ -9,6 +8,7 @@ from core.model_providers.models.llm.base import BaseLLM
 from core.model_providers.models.entity.message import PromptMessage
 from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
 from core.third_party.langchain.llms.huggingface_endpoint_llm import HuggingFaceEndpointLLM
+from core.third_party.langchain.llms.huggingface_hub_llm import HuggingFaceHubLLM
 
 
 class HuggingfaceHubModel(BaseLLM):
@@ -17,15 +17,21 @@ class HuggingfaceHubModel(BaseLLM):
     def _init_client(self) -> Any:
         provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
         if self.credentials['huggingfacehub_api_type'] == 'inference_endpoints':
+            streaming = self.streaming
+
+            if 'baichuan' in self.name.lower():
+                streaming = False
+
             client = HuggingFaceEndpointLLM(
                 endpoint_url=self.credentials['huggingfacehub_endpoint_url'],
                 task=self.credentials['task_type'],
                 model_kwargs=provider_model_kwargs,
                 huggingfacehub_api_token=self.credentials['huggingfacehub_api_token'],
-                callbacks=self.callbacks
+                callbacks=self.callbacks,
+                streaming=streaming
             )
         else:
-            client = HuggingFaceHub(
+            client = HuggingFaceHubLLM(
                 repo_id=self.name,
                 task=self.credentials['task_type'],
                 model_kwargs=provider_model_kwargs,
@@ -76,7 +82,12 @@ class HuggingfaceHubModel(BaseLLM):
     def handle_exceptions(self, ex: Exception) -> Exception:
         return LLMBadRequestError(f"Huggingface Hub: {str(ex)}")
 
-    @classmethod
-    def support_streaming(cls):
-        return False
+    @property
+    def support_streaming(self):
+        if self.credentials['huggingfacehub_api_type'] == 'inference_endpoints':
+            if 'baichuan' in self.name.lower():
+                return False
 
+            return True
+        else:
+            return False

api/core/model_providers/models/llm/localai_model.py

@@ -0,0 +1,131 @@
+import logging
+from typing import List, Optional, Any
+
+import openai
+from langchain.callbacks.manager import Callbacks
+from langchain.schema import LLMResult, get_buffer_string
+
+from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \
+    LLMRateLimitError, LLMAuthorizationError
+from core.model_providers.providers.base import BaseModelProvider
+from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI
+from core.third_party.langchain.llms.open_ai import EnhanceOpenAI
+from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.entity.message import PromptMessage
+from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+
+
+class LocalAIModel(BaseLLM):
+    def __init__(self, model_provider: BaseModelProvider,
+                 name: str,
+                 model_kwargs: ModelKwargs,
+                 streaming: bool = False,
+                 callbacks: Callbacks = None):
+        credentials = model_provider.get_model_credentials(
+            model_name=name,
+            model_type=self.type
+        )
+
+        if credentials['completion_type'] == 'chat_completion':
+            self.model_mode = ModelMode.CHAT
+        else:
+            self.model_mode = ModelMode.COMPLETION
+
+        super().__init__(model_provider, name, model_kwargs, streaming, callbacks)
+
+    def _init_client(self) -> Any:
+        provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
+        if self.model_mode == ModelMode.COMPLETION:
+            client = EnhanceOpenAI(
+                model_name=self.name,
+                streaming=self.streaming,
+                callbacks=self.callbacks,
+                request_timeout=60,
+                openai_api_key="1",
+                openai_api_base=self.credentials['server_url'] + '/v1',
+                **provider_model_kwargs
+            )
+        else:
+            extra_model_kwargs = {
+                'top_p': provider_model_kwargs.get('top_p')
+            }
+
+            client = EnhanceChatOpenAI(
+                model_name=self.name,
+                temperature=provider_model_kwargs.get('temperature'),
+                max_tokens=provider_model_kwargs.get('max_tokens'),
+                model_kwargs=extra_model_kwargs,
+                streaming=self.streaming,
+                callbacks=self.callbacks,
+                request_timeout=60,
+                openai_api_key="1",
+                openai_api_base=self.credentials['server_url'] + '/v1'
+            )
+
+        return client
+
+    def _run(self, messages: List[PromptMessage],
+             stop: Optional[List[str]] = None,
+             callbacks: Callbacks = None,
+             **kwargs) -> LLMResult:
+        """
+        run predict by prompt messages and stop words.
+
+        :param messages:
+        :param stop:
+        :param callbacks:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return self._client.generate([prompts], stop, callbacks)
+
+    def get_num_tokens(self, messages: List[PromptMessage]) -> int:
+        """
+        get num tokens of prompt messages.
+
+        :param messages:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        if isinstance(prompts, str):
+            return self._client.get_num_tokens(prompts)
+        else:
+            return max(sum([self._client.get_num_tokens(get_buffer_string([m])) for m in prompts]) - len(prompts), 0)
+
+    def _set_model_kwargs(self, model_kwargs: ModelKwargs):
+        provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
+        if self.model_mode == ModelMode.COMPLETION:
+            for k, v in provider_model_kwargs.items():
+                if hasattr(self.client, k):
+                    setattr(self.client, k, v)
+        else:
+            extra_model_kwargs = {
+                'top_p': provider_model_kwargs.get('top_p')
+            }
+
+            self.client.temperature = provider_model_kwargs.get('temperature')
+            self.client.max_tokens = provider_model_kwargs.get('max_tokens')
+            self.client.model_kwargs = extra_model_kwargs
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        if isinstance(ex, openai.error.InvalidRequestError):
+            logging.warning("Invalid request to LocalAI API.")
+            return LLMBadRequestError(str(ex))
+        elif isinstance(ex, openai.error.APIConnectionError):
+            logging.warning("Failed to connect to LocalAI API.")
+            return LLMAPIConnectionError(ex.__class__.__name__ + ":" + str(ex))
+        elif isinstance(ex, (openai.error.APIError, openai.error.ServiceUnavailableError, openai.error.Timeout)):
+            logging.warning("LocalAI service unavailable.")
+            return LLMAPIUnavailableError(ex.__class__.__name__ + ":" + str(ex))
+        elif isinstance(ex, openai.error.RateLimitError):
+            return LLMRateLimitError(str(ex))
+        elif isinstance(ex, openai.error.AuthenticationError):
+            return LLMAuthorizationError(str(ex))
+        elif isinstance(ex, openai.error.OpenAIError):
+            return LLMBadRequestError(ex.__class__.__name__ + ":" + str(ex))
+        else:
+            return ex
+
+    @classmethod
+    def support_streaming(cls):
+        return True

api/core/model_providers/models/llm/openai_model.py

@@ -17,6 +17,7 @@ from core.model_providers.models.entity.model_params import ModelMode, ModelKwar
 from models.provider import ProviderType, ProviderQuotaType
 
 COMPLETION_MODELS = [
+    'gpt-3.5-turbo-instruct',  # 4,096 tokens
     'text-davinci-003',  # 4,097 tokens
 ]
 
@@ -31,6 +32,7 @@ MODEL_MAX_TOKENS = {
     'gpt-4': 8192,
     'gpt-4-32k': 32768,
     'gpt-3.5-turbo': 4096,
+    'gpt-3.5-turbo-instruct': 8192,
     'gpt-3.5-turbo-16k': 16384,
     'text-davinci-003': 4097,
 }
@@ -154,8 +156,8 @@ class OpenAIModel(BaseLLM):
         else:
             return ex
 
-    @classmethod
-    def support_streaming(cls):
+    @property
+    def support_streaming(self):
         return True
 
     # def is_model_valid_or_raise(self):

api/core/model_providers/models/llm/openllm_model.py

@@ -63,7 +63,3 @@ class OpenLLMModel(BaseLLM):
 
     def handle_exceptions(self, ex: Exception) -> Exception:
         return LLMBadRequestError(f"OpenLLM: {str(ex)}")
-
-    @classmethod
-    def support_streaming(cls):
-        return False

api/core/model_providers/models/llm/replicate_model.py

@@ -91,6 +91,6 @@ class ReplicateModel(BaseLLM):
         else:
             return ex
 
-    @classmethod
-    def support_streaming(cls):
-        return True
+    @property
+    def support_streaming(self):
+        return True

api/core/model_providers/models/llm/spark_model.py

@@ -65,6 +65,6 @@ class SparkModel(BaseLLM):
         else:
             return ex
 
-    @classmethod
-    def support_streaming(cls):
-        return True
+    @property
+    def support_streaming(self):
+        return True

api/core/model_providers/models/llm/tongyi_model.py

@@ -69,6 +69,6 @@ class TongyiModel(BaseLLM):
         else:
             return ex
 
-    @classmethod
-    def support_streaming(cls):
+    @property
+    def support_streaming(self):
         return True

api/core/model_providers/models/llm/wenxin_model.py

@@ -57,7 +57,3 @@ class WenxinModel(BaseLLM):
 
     def handle_exceptions(self, ex: Exception) -> Exception:
         return LLMBadRequestError(f"Wenxin: {str(ex)}")
-
-    @classmethod
-    def support_streaming(cls):
-        return False

api/core/model_providers/models/llm/xinference_model.py

@@ -74,6 +74,6 @@ class XinferenceModel(BaseLLM):
     def handle_exceptions(self, ex: Exception) -> Exception:
         return LLMBadRequestError(f"Xinference: {str(ex)}")
 
-    @classmethod
-    def support_streaming(cls):
+    @property
+    def support_streaming(self):
         return True

api/core/model_providers/models/llm/zhipuai_model.py

@@ -0,0 +1,61 @@
+from typing import List, Optional, Any
+
+from langchain.callbacks.manager import Callbacks
+from langchain.schema import LLMResult
+
+from core.model_providers.error import LLMBadRequestError
+from core.model_providers.models.llm.base import BaseLLM
+from core.model_providers.models.entity.message import PromptMessage
+from core.model_providers.models.entity.model_params import ModelMode, ModelKwargs
+from core.third_party.langchain.llms.zhipuai_llm import ZhipuAIChatLLM
+
+
+class ZhipuAIModel(BaseLLM):
+    model_mode: ModelMode = ModelMode.CHAT
+
+    def _init_client(self) -> Any:
+        provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, self.model_kwargs)
+        return ZhipuAIChatLLM(
+            streaming=self.streaming,
+            callbacks=self.callbacks,
+            **self.credentials,
+            **provider_model_kwargs
+        )
+
+    def _run(self, messages: List[PromptMessage],
+             stop: Optional[List[str]] = None,
+             callbacks: Callbacks = None,
+             **kwargs) -> LLMResult:
+        """
+        run predict by prompt messages and stop words.
+
+        :param messages:
+        :param stop:
+        :param callbacks:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return self._client.generate([prompts], stop, callbacks)
+
+    def get_num_tokens(self, messages: List[PromptMessage]) -> int:
+        """
+        get num tokens of prompt messages.
+
+        :param messages:
+        :return:
+        """
+        prompts = self._get_prompt_from_messages(messages)
+        return max(self._client.get_num_tokens_from_messages(prompts), 0)
+
+    def _set_model_kwargs(self, model_kwargs: ModelKwargs):
+        provider_model_kwargs = self._to_model_kwargs_input(self.model_rules, model_kwargs)
+        for k, v in provider_model_kwargs.items():
+            if hasattr(self.client, k):
+                setattr(self.client, k, v)
+
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        return LLMBadRequestError(f"ZhipuAI: {str(ex)}")
+
+    @property
+    def support_streaming(self):
+        return True

api/core/model_providers/models/moderation/base.py

@@ -0,0 +1,29 @@
+from abc import abstractmethod
+from typing import Any
+
+from core.model_providers.models.base import BaseProviderModel
+from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.providers.base import BaseModelProvider
+
+
+class BaseModeration(BaseProviderModel):
+    name: str
+    type: ModelType = ModelType.MODERATION
+
+    def __init__(self, model_provider: BaseModelProvider, client: Any, name: str):
+        super().__init__(model_provider, client)
+        self.name = name
+
+    def run(self, text: str) -> bool:
+        try:
+            return self._run(text)
+        except Exception as ex:
+            raise self.handle_exceptions(ex)
+
+    @abstractmethod
+    def _run(self, text: str) -> bool:
+        raise NotImplementedError
+
+    @abstractmethod
+    def handle_exceptions(self, ex: Exception) -> Exception:
+        raise NotImplementedError

api/core/model_providers/models/moderation/openai_moderation.py

@@ -4,29 +4,39 @@ import openai
 
 from core.model_providers.error import LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError, \
     LLMRateLimitError, LLMAuthorizationError
-from core.model_providers.models.base import BaseProviderModel
-from core.model_providers.models.entity.model_params import ModelType
+from core.model_providers.models.moderation.base import BaseModeration
 from core.model_providers.providers.base import BaseModelProvider
 
-DEFAULT_AUDIO_MODEL = 'whisper-1'
+DEFAULT_MODEL = 'whisper-1'
 
 
-class OpenAIModeration(BaseProviderModel):
-    type: ModelType = ModelType.MODERATION
+class OpenAIModeration(BaseModeration):
 
     def __init__(self, model_provider: BaseModelProvider, name: str):
-        super().__init__(model_provider, openai.Moderation)
+        super().__init__(model_provider, openai.Moderation, name)
 
-    def run(self, text):
+    def _run(self, text: str) -> bool:
         credentials = self.model_provider.get_model_credentials(
-            model_name=DEFAULT_AUDIO_MODEL,
+            model_name=self.name,
             model_type=self.type
         )
 
-        try:
-            return self._client.create(input=text, api_key=credentials['openai_api_key'])
-        except Exception as ex:
-            raise self.handle_exceptions(ex)
+        # 2000 text per chunk
+        length = 2000
+        text_chunks = [text[i:i + length] for i in range(0, len(text), length)]
+
+        max_text_chunks = 32
+        chunks = [text_chunks[i:i + max_text_chunks] for i in range(0, len(text_chunks), max_text_chunks)]
+
+        for text_chunk in chunks:
+            moderation_result = self._client.create(input=text_chunk,
+                                                    api_key=credentials['openai_api_key'])
+
+            for result in moderation_result.results:
+                if result['flagged'] is True:
+                    return False
+
+        return True
 
     def handle_exceptions(self, ex: Exception) -> Exception:
         if isinstance(ex, openai.error.InvalidRequestError):

api/core/model_providers/providers/anthropic_provider.py

@@ -5,7 +5,6 @@ from typing import Type, Optional
 
 import anthropic
 from flask import current_app
-from langchain.chat_models import ChatAnthropic
 from langchain.schema import HumanMessage
 
 from core.helper import encrypter
@@ -16,6 +15,7 @@ from core.model_providers.models.llm.anthropic_model import AnthropicModel
 from core.model_providers.models.llm.base import ModelType
 from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
 from core.model_providers.providers.hosted import hosted_model_providers
+from core.third_party.langchain.llms.anthropic_llm import AnthropicLLM
 from models.provider import ProviderType
 
 
@@ -69,11 +69,11 @@ class AnthropicProvider(BaseModelProvider):
         :return:
         """
         return ModelKwargsRules(
-            temperature=KwargRule[float](min=0, max=1, default=1),
-            top_p=KwargRule[float](min=0, max=1, default=0.7),
+            temperature=KwargRule[float](min=0, max=1, default=1, precision=2),
+            top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2),
             presence_penalty=KwargRule[float](enabled=False),
             frequency_penalty=KwargRule[float](enabled=False),
-            max_tokens=KwargRule[int](alias="max_tokens_to_sample", min=10, max=100000, default=256),
+            max_tokens=KwargRule[int](alias="max_tokens_to_sample", min=10, max=100000, default=256, precision=0),
         )
 
     @classmethod
@@ -92,7 +92,7 @@ class AnthropicProvider(BaseModelProvider):
             if 'anthropic_api_url' in credentials:
                 credential_kwargs['anthropic_api_url'] = credentials['anthropic_api_url']
 
-            chat_llm = ChatAnthropic(
+            chat_llm = AnthropicLLM(
                 model='claude-instant-1',
                 max_tokens_to_sample=10,
                 temperature=0,

api/core/model_providers/providers/azure_openai_provider.py

@@ -164,14 +164,14 @@ class AzureOpenAIProvider(BaseModelProvider):
         model_credentials = self.get_model_credentials(model_name, model_type)
 
         return ModelKwargsRules(
-            temperature=KwargRule[float](min=0, max=2, default=1),
-            top_p=KwargRule[float](min=0, max=1, default=1),
-            presence_penalty=KwargRule[float](min=-2, max=2, default=0),
-            frequency_penalty=KwargRule[float](min=-2, max=2, default=0),
+            temperature=KwargRule[float](min=0, max=2, default=1, precision=2),
+            top_p=KwargRule[float](min=0, max=1, default=1, precision=2),
+            presence_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2),
+            frequency_penalty=KwargRule[float](min=-2, max=2, default=0, precision=2),
             max_tokens=KwargRule[int](min=10, max=base_model_max_tokens.get(
                 model_credentials['base_model_name'],
                 4097
-            ), default=16),
+            ), default=16, precision=0),
         )
 
     @classmethod

api/core/model_providers/providers/chatglm_provider.py

@@ -64,11 +64,11 @@ class ChatGLMProvider(BaseModelProvider):
         }
 
         return ModelKwargsRules(
-            temperature=KwargRule[float](min=0, max=2, default=1),
-            top_p=KwargRule[float](min=0, max=1, default=0.7),
+            temperature=KwargRule[float](min=0, max=2, default=1, precision=2),
+            top_p=KwargRule[float](min=0, max=1, default=0.7, precision=2),
             presence_penalty=KwargRule[float](enabled=False),
             frequency_penalty=KwargRule[float](enabled=False),
-            max_tokens=KwargRule[int](alias='max_token', min=10, max=model_max_tokens.get(model_name), default=2048),
+            max_tokens=KwargRule[int](alias='max_token', min=10, max=model_max_tokens.get(model_name), default=2048, precision=0),
         )
 
     @classmethod

api/core/model_providers/providers/hosted.py

@@ -45,6 +45,18 @@ class HostedModelProviders(BaseModel):
 hosted_model_providers = HostedModelProviders()
 
 
+class HostedModerationConfig(BaseModel):
+    enabled: bool = False
+    providers: list[str] = []
+
+
+class HostedConfig(BaseModel):
+    moderation = HostedModerationConfig()
+
+
+hosted_config = HostedConfig()
+
+
 def init_app(app: Flask):
     if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
         langchain.verbose = True
@@ -78,3 +90,9 @@ def init_app(app: Flask):
             paid_min_quantity=app.config.get("HOSTED_ANTHROPIC_PAID_MIN_QUANTITY"),
             paid_max_quantity=app.config.get("HOSTED_ANTHROPIC_PAID_MAX_QUANTITY"),
         )
+
+    if app.config.get("HOSTED_MODERATION_ENABLED") and app.config.get("HOSTED_MODERATION_PROVIDERS"):
+        hosted_config.moderation = HostedModerationConfig(
+            enabled=app.config.get("HOSTED_MODERATION_ENABLED"),
+            providers=app.config.get("HOSTED_MODERATION_PROVIDERS").split(',')
+        )

api/core/model_providers/providers/huggingface_hub_provider.py

@@ -47,11 +47,11 @@ class HuggingfaceHubProvider(BaseModelProvider):
         :return:
         """
         return ModelKwargsRules(
-            temperature=KwargRule[float](min=0, max=2, default=1),
-            top_p=KwargRule[float](min=0.01, max=0.99, default=0.7),
+            temperature=KwargRule[float](min=0, max=2, default=1, precision=2),
+            top_p=KwargRule[float](min=0.01, max=0.99, default=0.7, precision=2),
             presence_penalty=KwargRule[float](enabled=False),
             frequency_penalty=KwargRule[float](enabled=False),
-            max_tokens=KwargRule[int](alias='max_new_tokens', min=10, max=4000, default=200),
+            max_tokens=KwargRule[int](alias='max_new_tokens', min=10, max=4000, default=200, precision=0),
         )
 
     @classmethod
@@ -89,7 +89,8 @@ class HuggingfaceHubProvider(BaseModelProvider):
                 raise CredentialsValidateFailedError('Task Type must be provided.')
 
             if credentials['task_type'] not in ("text2text-generation", "text-generation", "summarization"):
-                raise CredentialsValidateFailedError('Task Type must be one of text2text-generation, text-generation, summarization.')
+                raise CredentialsValidateFailedError('Task Type must be one of text2text-generation, '
+                                                     'text-generation, summarization.')
 
             try:
                 llm = HuggingFaceEndpointLLM(

api/core/model_providers/providers/localai_provider.py

@@ -0,0 +1,164 @@
+import json
+from typing import Type
+
+from langchain.embeddings import LocalAIEmbeddings
+from langchain.schema import HumanMessage
+
+from core.helper import encrypter
+from core.model_providers.models.embedding.localai_embedding import LocalAIEmbedding
+from core.model_providers.models.entity.model_params import ModelKwargsRules, ModelType, KwargRule
+from core.model_providers.models.llm.localai_model import LocalAIModel
+from core.model_providers.providers.base import BaseModelProvider, CredentialsValidateFailedError
+
+from core.model_providers.models.base import BaseProviderModel
+from core.third_party.langchain.llms.chat_open_ai import EnhanceChatOpenAI
+from core.third_party.langchain.llms.open_ai import EnhanceOpenAI
+from models.provider import ProviderType
+
+
+class LocalAIProvider(BaseModelProvider):
+    @property
+    def provider_name(self):
+        """
+        Returns the name of a provider.
+        """
+        return 'localai'
+
+    def _get_fixed_model_list(self, model_type: ModelType) -> list[dict]:
+        return []
+
+    def get_model_class(self, model_type: ModelType) -> Type[BaseProviderModel]:
+        """
+        Returns the model class.
+
+        :param model_type:
+        :return:
+        """
+        if model_type == ModelType.TEXT_GENERATION:
+            model_class = LocalAIModel
+        elif model_type == ModelType.EMBEDDINGS:
+            model_class = LocalAIEmbedding
+        else:
+            raise NotImplementedError
+
+        return model_class
+
+    def get_model_parameter_rules(self, model_name: str, model_type: ModelType) -> ModelKwargsRules:
+        """
+        get model parameter rules.
+
+        :param model_name:
+        :param model_type:
+        :return:
+        """
+        return ModelKwargsRules(
+            temperature=KwargRule[float](min=0, max=2, default=0.7, precision=2),
+            top_p=KwargRule[float](min=0, max=1, default=1, precision=2),
+            max_tokens=KwargRule[int](min=10, max=4097, default=16, precision=0),
+        )
+
+    @classmethod
+    def is_model_credentials_valid_or_raise(cls, model_name: str, model_type: ModelType, credentials: dict):
+        """
+        check model credentials valid.
+
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        """
+        if 'server_url' not in credentials:
+            raise CredentialsValidateFailedError('LocalAI Server URL must be provided.')
+
+        try:
+            if model_type == ModelType.EMBEDDINGS:
+                model = LocalAIEmbeddings(
+                    model=model_name,
+                    openai_api_key='1',
+                    openai_api_base=credentials['server_url']
+                )
+
+                model.embed_query("ping")
+            else:
+                if ('completion_type' not in credentials
+                        or credentials['completion_type'] not in ['completion', 'chat_completion']):
+                    raise CredentialsValidateFailedError('LocalAI Completion Type must be provided.')
+
+                if credentials['completion_type'] == 'chat_completion':
+                    model = EnhanceChatOpenAI(
+                        model_name=model_name,
+                        openai_api_key='1',
+                        openai_api_base=credentials['server_url'] + '/v1',
+                        max_tokens=10,
+                        request_timeout=60,
+                    )
+
+                    model([HumanMessage(content='ping')])
+                else:
+                    model = EnhanceOpenAI(
+                        model_name=model_name,
+                        openai_api_key='1',
+                        openai_api_base=credentials['server_url'] + '/v1',
+                        max_tokens=10,
+                        request_timeout=60,
+                    )
+
+                    model('ping')
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @classmethod
+    def encrypt_model_credentials(cls, tenant_id: str, model_name: str, model_type: ModelType,
+                                  credentials: dict) -> dict:
+        """
+        encrypt model credentials for save.
+
+        :param tenant_id:
+        :param model_name:
+        :param model_type:
+        :param credentials:
+        :return:
+        """
+        credentials['server_url'] = encrypter.encrypt_token(tenant_id, credentials['server_url'])
+        return credentials
+
+    def get_model_credentials(self, model_name: str, model_type: ModelType, obfuscated: bool = False) -> dict:
+        """
+        get credentials for llm use.
+
+        :param model_name:
+        :param model_type:
+        :param obfuscated:
+        :return:
+        """
+        if self.provider.provider_type != ProviderType.CUSTOM.value:
+            raise NotImplementedError
+
+        provider_model = self._get_provider_model(model_name, model_type)
+
+        if not provider_model.encrypted_config:
+            return {
+                'server_url': None,
+            }
+
+        credentials = json.loads(provider_model.encrypted_config)
+        if credentials['server_url']:
+            credentials['server_url'] = encrypter.decrypt_token(
+                self.provider.tenant_id,
+                credentials['server_url']
+            )
+
+            if obfuscated:
+                credentials['server_url'] = encrypter.obfuscated_token(credentials['server_url'])
+
+        return credentials
+
+    @classmethod
+    def is_provider_credentials_valid_or_raise(cls, credentials: dict):
+        return
+
+    @classmethod
+    def encrypt_provider_credentials(cls, tenant_id: str, credentials: dict) -> dict:
+        return {}
+
+    def get_provider_credentials(self, obfuscated: bool = False) -> dict:
+        return {}