feat: bump version to 0.3.7 (#540)

Co-authored-by: jyong <718720800@qq.com>

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

@@ -0,0 +1,32 @@
+---
+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

@@ -0,0 +1,20 @@
+---
+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

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

.github/workflows/build-api-image.sh

@@ -1,61 +0,0 @@
-#!/usr/bin/env bash
-
-set -eo pipefail
-
-SHA=$(git rev-parse HEAD)
-REPO_NAME=langgenius/dify
-API_REPO_NAME="${REPO_NAME}-api"
-
-if [[ "${GITHUB_EVENT_NAME}" == "pull_request" ]]; then
-  REFSPEC=$(echo "${GITHUB_HEAD_REF}" | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
-  PR_NUM=$(echo "${GITHUB_REF}" | sed 's:refs/pull/::' | sed 's:/merge::')
-  LATEST_TAG="pr-${PR_NUM}"
-  CACHE_FROM_TAG="latest"
-elif [[ "${GITHUB_EVENT_NAME}" == "release" ]]; then
-  REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/tags/::' | head -c 40)
-  LATEST_TAG="${REFSPEC}"
-  CACHE_FROM_TAG="latest"
-else
-  REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/heads/::' | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
-  LATEST_TAG="${REFSPEC}"
-  CACHE_FROM_TAG="${REFSPEC}"
-fi
-
-if [[ "${REFSPEC}" == "main" ]]; then
-  LATEST_TAG="latest"
-  CACHE_FROM_TAG="latest"
-fi
-
-echo "Pulling cache image ${API_REPO_NAME}:${CACHE_FROM_TAG}"
-if docker pull "${API_REPO_NAME}:${CACHE_FROM_TAG}"; then
-  API_CACHE_FROM_SCRIPT="--cache-from ${API_REPO_NAME}:${CACHE_FROM_TAG}"
-else
-  echo "WARNING: Failed to pull ${API_REPO_NAME}:${CACHE_FROM_TAG}, disable build image cache."
-  API_CACHE_FROM_SCRIPT=""
-fi
-
-
-cat<<EOF
-  Rolling with tags:
-  - ${API_REPO_NAME}:${SHA}
-  - ${API_REPO_NAME}:${REFSPEC}
-  - ${API_REPO_NAME}:${LATEST_TAG}
-EOF
-
-#
-# Build image
-#
-cd api
-docker build \
-  ${API_CACHE_FROM_SCRIPT} \
-  --build-arg COMMIT_SHA=${SHA} \
-  -t "${API_REPO_NAME}:${SHA}" \
-  -t "${API_REPO_NAME}:${REFSPEC}" \
-  -t "${API_REPO_NAME}:${LATEST_TAG}" \
-  --label "sha=${SHA}" \
-  --label "built_at=$(date)" \
-  --label "build_actor=${GITHUB_ACTOR}" \
-  .
-
-# push
-docker push --all-tags "${API_REPO_NAME}"

.github/workflows/build-api-image.yml

@@ -5,18 +5,19 @@ on:
     branches:
       - 'main'
       - 'deploy/dev'
-  pull_request:
-    types: [synchronize, opened, reopened, ready_for_review]
+  release:
+    types: [published]
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
     steps:
-    - name: "Checkout ${{ github.ref }} ( ${{ github.sha }} )"
-      uses: actions/checkout@v2
-      with:
-        persist-credentials: false
+    - name: Set up QEMU
+      uses: docker/setup-qemu-action@v2
+
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v2
 
     - name: Login to Docker Hub
       uses: docker/login-action@v2
@@ -24,13 +25,29 @@ jobs:
         username: ${{ secrets.DOCKERHUB_USER }}
         password: ${{ secrets.DOCKERHUB_TOKEN }}
 
-    - name: Build and push Docker image
-      shell: bash
-      env:
-        DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USER }}
-        DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
-      run: |
-        /bin/bash .github/workflows/build-api-image.sh
+    - name: Extract metadata (tags, labels) for Docker
+      id: meta
+      uses: docker/metadata-action@v4
+      with:
+        images: langgenius/dify-api
+        tags: |
+          type=raw,value=latest,enable={{is_default_branch}}
+          type=ref,event=branch
+          type=sha,enable=true,priority=100,prefix=,suffix=,format=long
+          type=semver,pattern={{major}}.{{minor}}.{{patch}}
+          type=semver,pattern={{major}}.{{minor}}
+          type=semver,pattern={{major}}
+
+    - name: Build and push
+      uses: docker/build-push-action@v4
+      with:
+        context: "{{defaultContext}}:api"
+        platforms: linux/amd64,linux/arm64
+        build-args: |
+          COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
+        push: true
+        tags: ${{ steps.meta.outputs.tags }}
+        labels: ${{ steps.meta.outputs.labels }}
 
     - name: Deploy to server
       if: github.ref == 'refs/heads/deploy/dev'

.github/workflows/build-web-image.sh

@@ -1,60 +0,0 @@
-#!/usr/bin/env bash
-
-set -eo pipefail
-
-SHA=$(git rev-parse HEAD)
-REPO_NAME=langgenius/dify
-WEB_REPO_NAME="${REPO_NAME}-web"
-
-if [[ "${GITHUB_EVENT_NAME}" == "pull_request" ]]; then
-  REFSPEC=$(echo "${GITHUB_HEAD_REF}" | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
-  PR_NUM=$(echo "${GITHUB_REF}" | sed 's:refs/pull/::' | sed 's:/merge::')
-  LATEST_TAG="pr-${PR_NUM}"
-  CACHE_FROM_TAG="latest"
-elif [[ "${GITHUB_EVENT_NAME}" == "release" ]]; then
-  REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/tags/::' | head -c 40)
-  LATEST_TAG="${REFSPEC}"
-  CACHE_FROM_TAG="latest"
-else
-  REFSPEC=$(echo "${GITHUB_REF}" | sed 's:refs/heads/::' | sed 's/[^a-zA-Z0-9]/-/g' | head -c 40)
-  LATEST_TAG="${REFSPEC}"
-  CACHE_FROM_TAG="${REFSPEC}"
-fi
-
-if [[ "${REFSPEC}" == "main" ]]; then
-  LATEST_TAG="latest"
-  CACHE_FROM_TAG="latest"
-fi
-
-echo "Pulling cache image ${WEB_REPO_NAME}:${CACHE_FROM_TAG}"
-if docker pull "${WEB_REPO_NAME}:${CACHE_FROM_TAG}"; then
-  WEB_CACHE_FROM_SCRIPT="--cache-from ${WEB_REPO_NAME}:${CACHE_FROM_TAG}"
-else
-  echo "WARNING: Failed to pull ${WEB_REPO_NAME}:${CACHE_FROM_TAG}, disable build image cache."
-  WEB_CACHE_FROM_SCRIPT=""
-fi
-
-
-cat<<EOF
-  Rolling with tags:
-  - ${WEB_REPO_NAME}:${SHA}
-  - ${WEB_REPO_NAME}:${REFSPEC}
-  - ${WEB_REPO_NAME}:${LATEST_TAG}
-EOF
-
-#
-# Build image
-#
-cd web
-docker build \
-  ${WEB_CACHE_FROM_SCRIPT} \
-  --build-arg COMMIT_SHA=${SHA} \
-  -t "${WEB_REPO_NAME}:${SHA}" \
-  -t "${WEB_REPO_NAME}:${REFSPEC}" \
-  -t "${WEB_REPO_NAME}:${LATEST_TAG}" \
-  --label "sha=${SHA}" \
-  --label "built_at=$(date)" \
-  --label "build_actor=${GITHUB_ACTOR}" \
-  .
-
-docker push --all-tags "${WEB_REPO_NAME}"

.github/workflows/build-web-image.yml

@@ -5,18 +5,19 @@ on:
     branches:
       - 'main'
       - 'deploy/dev'
-  pull_request:
-    types: [synchronize, opened, reopened, ready_for_review]
+  release:
+    types: [published]
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
     steps:
-    - name: "Checkout ${{ github.ref }} ( ${{ github.sha }} )"
-      uses: actions/checkout@v2
-      with:
-        persist-credentials: false
+    - name: Set up QEMU
+      uses: docker/setup-qemu-action@v2
+
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v2
 
     - name: Login to Docker Hub
       uses: docker/login-action@v2
@@ -24,13 +25,29 @@ jobs:
         username: ${{ secrets.DOCKERHUB_USER }}
         password: ${{ secrets.DOCKERHUB_TOKEN }}
 
-    - name: Build and push Docker image
-      shell: bash
-      env:
-        DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USER }}
-        DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
-      run: |
-        /bin/bash .github/workflows/build-web-image.sh
+    - name: Extract metadata (tags, labels) for Docker
+      id: meta
+      uses: docker/metadata-action@v4
+      with:
+        images: langgenius/dify-web
+        tags: |
+          type=raw,value=latest,enable={{is_default_branch}}
+          type=ref,event=branch
+          type=sha,enable=true,priority=100,prefix=,suffix=,format=long
+          type=semver,pattern={{major}}.{{minor}}.{{patch}}
+          type=semver,pattern={{major}}.{{minor}}
+          type=semver,pattern={{major}}
+
+    - name: Build and push
+      uses: docker/build-push-action@v4
+      with:
+        context: "{{defaultContext}}:web"
+        platforms: linux/amd64,linux/arm64
+        build-args: |
+          COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
+        push: true
+        tags: ${{ steps.meta.outputs.tags }}
+        labels: ${{ steps.meta.outputs.labels }}
 
     - name: Deploy to server
       if: github.ref == 'refs/heads/deploy/dev'

.github/workflows/flake8.yml

@@ -1,19 +0,0 @@
-name: PEP8 Check
-on:
-  push:
-    branches: [main]
-  pull_request:
-    branches: [main]
-jobs:
-  pep8:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
-        with:
-          python-version: '3.10'
-      - name: Install flake8
-        run: pip install flake8
-      - name: Run flake8
-        run: flake8 --ignore=E501 .

.github/workflows/stale.yml

@@ -0,0 +1,30 @@
+# This workflow warns and then closes issues and PRs that have had no activity for a specified amount of time.
+#
+# You can adjust the behavior by modifying this file.
+# For more information, see:
+# https://github.com/actions/stale
+name: Mark stale issues and pull requests
+
+on:
+  schedule:
+  - cron: '0 3 * * *'
+
+jobs:
+  stale:
+
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+
+    steps:
+    - uses: actions/stale@v5
+      with:
+        days-before-issue-stale: 30
+        days-before-issue-close: 3
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        stale-issue-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
+        stale-pr-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
+        stale-issue-label: 'no-issue-activity'
+        stale-pr-label: 'no-pr-activity'
+        any-of-labels: 'duplicate,question,invalid,wontfix,no-issue-activity,no-pr-activity,enhancement'

.gitignore

@@ -130,7 +130,7 @@ dmypy.json
 .idea/'
 
 .DS_Store
-.vscode
+web/.vscode/settings.json
 
 # Intellij IDEA Files
 .idea/
@@ -139,7 +139,7 @@ dmypy.json
 api/.env
 api/storage/*
 
-docker/volumes/app/storage/privkeys/*
+docker/volumes/app/storage/*
 docker/volumes/db/data/*
 docker/volumes/redis/data/*
 docker/volumes/weaviate/*
@@ -147,3 +147,5 @@ docker/volumes/weaviate/*
 sdks/python-client/build
 sdks/python-client/dist
 sdks/python-client/dify_client.egg-info
+
+.vscode/

CONTRIBUTING.md

@@ -22,14 +22,14 @@ To set up a working development environment, just fork the project git repositor
 
 ### Fork the repository
 
-you need to fork the [repository](https://github.com/langgenius/langgenius-gateway).
+you need to fork the [repository](https://github.com/langgenius/dify).
 
 ### Clone the repo
 
 Clone your GitHub forked repository:
 
 ```
-git clone git@github.com:<github_username>/langgenius-gateway.git
+git clone git@github.com:<github_username>/dify.git
 ```
 
 ### Install backend
@@ -54,3 +54,8 @@ Did you have an issue, like a merge conflict, or don't know how to open a pull r
 ## Community channels
 
 Stuck somewhere? Have any questions? Join the [Discord Community Server](https://discord.gg/AhzKf7dNgk). We are here to help!
+
+### i18n (Internationalization) Support
+
+We are looking for contributors to help with translations in other languages. If you are interested in helping, please join the [Discord Community Server](https://discord.gg/AhzKf7dNgk) and let us know.  
+Also check out the [Frontend i18n README]((web/i18n/README_EN.md)) for more information.

CONTRIBUTING_CN.md

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

CONTRIBUTING_JA.md

@@ -0,0 +1,55 @@
+# コントリビュート
+
+[Dify](https://dify.ai) に興味を持ち、貢献したいと思うようになったことに感謝します！始める前に、
+[行動規範](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md)を読み、
+[既存の問題](https://github.com/langgenius/langgenius-gateway/issues)をチェックしてください。
+本ドキュメントは、[Dify](https://dify.ai) をビルドしてテストするための開発環境の構築方法を説明するものです。
+
+### 依存関係のインストール
+
+[Dify](https://dify.ai)をビルドするには、お使いのマシンに以下の依存関係をインストールし、設定する必要があります:
+
+- [Git](http://git-scm.com/)
+- [Docker](https://www.docker.com/)
+- [Docker Compose](https://docs.docker.com/compose/install/)
+- [Node.js v18.x (LTS)](http://nodejs.org)
+- [npm](https://www.npmjs.com/) バージョン 8.x.x もしくは [Yarn](https://yarnpkg.com/)
+- [Python](https://www.python.org/) バージョン 3.10.x
+
+## ローカル開発
+
+開発環境を構築するには、プロジェクトの git リポジトリをフォークし、適切なパッケージマネージャを使用してバックエンドとフロントエンドの依存関係をインストールし、docker-compose スタックを実行するように作成します。
+
+### リポジトリのフォーク
+
+[リポジトリ](https://github.com/langgenius/dify) をフォークする必要があります。
+
+### リポジトリのクローン
+
+GitHub でフォークしたリポジトリのクローンを作成する:
+
+```
+git clone git@github.com:<github_username>/dify.git
+```
+
+### バックエンドのインストール
+
+バックエンドアプリケーションのインストール方法については、[Backend README](api/README.md) を参照してください。
+
+### フロントエンドのインストール
+
+フロントエンドアプリケーションのインストール方法については、[Frontend README](web/README.md) を参照してください。
+
+### ブラウザで dify にアクセス
+
+[Dify](https://dify.ai) をローカル環境で見ることができるようになりました [http://localhost:3000](http://localhost:3000)。
+
+## プルリクエストの作成
+
+変更後、プルリクエスト (PR) をオープンしてください。プルリクエストを提出すると、Dify チーム/コミュニティの他の人があなたと一緒にそれをレビューします。
+
+マージコンフリクトなどの問題が発生したり、プルリクエストの開き方がわからなくなったりしませんでしたか？ [GitHub's pull request tutorial](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests) で、マージコンフリクトやその他の問題を解決する方法をチェックしてみてください。あなたの PR がマージされると、[コントリビュータチャート](https://github.com/langgenius/langgenius-gateway/graphs/contributors)にコントリビュータとして誇らしげに掲載されます。
+
+## 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！

README.md

@@ -1,10 +1,12 @@
 ![](./images/describe-en.png)
 <p align="center">
   <a href="./README.md">English</a> |
-  <a href="./README_CN.md">简体中文</a>
+  <a href="./README_CN.md">简体中文</a> |
+  <a href="./README_JA.md">日本語</a> |
+  <a href="./README_ES.md">Español</a>
 </p>
 
-[Website](http://dify.ai) • [Docs](https://docs.dify.ai) • [Twitter](https://twitter.com/dify_ai)
+[Website](https://dify.ai) • [Docs](https://docs.dify.ai) • [Twitter](https://twitter.com/dify_ai) • [Discord](https://discord.gg/FngNHpbcY7)
 
 **Dify** is an easy-to-use LLMOps platform designed to empower more people to create sustainable, AI-native applications. With visual orchestration for various application types, Dify offers out-of-the-box, ready-to-use applications that can also serve as Backend-as-a-Service APIs. Unify your development process with one API for plugins and datasets integration, and streamline your operations using a single interface for prompt engineering, visual analytics, and continuous improvement.
 
@@ -21,7 +23,7 @@ Dify is compatible with Langchain, meaning we'll gradually support multiple LLMs
 
 ## Use Cloud Services
 
-Visit [Dify.ai](http://dify.ai)
+Visit [Dify.ai](https://dify.ai)
 
 ## Install the Community Edition
 
@@ -38,10 +40,15 @@ The easiest way to start the Dify server is to run our [docker-compose.yml](dock
 
 ```bash
 cd docker
-docker-compose up -d
+docker compose up -d
 ```
 
-After running, you can access the Dify console in your browser at [http://localhost](http://localhost) and start the initialization operation.
+After running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization installation process.
+
+### Helm Chart
+
+A big thanks to @BorisPolonsky for providing us with a [Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
+You can go to https://github.com/BorisPolonsky/dify-helm for deployment information.
 
 ### Configuration
 
@@ -81,22 +88,42 @@ A: English and Chinese are currently supported, and you can contribute language
 
 [![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
 
+
+## Contributing 
+
+We welcome you to contribute to Dify to help make Dify better. We welcome contributions in various ways, submitting code, issues, new ideas, or sharing the interesting and useful AI applications you have created based on Dify. At the same time, we also welcome you to share Dify at different events, conferences, and social media.
+
+### Submit a Pull Request 
+
+To ensure proper review, all code contributions, including from contributors with direct commit access, must be submitted as PR requests and approved by core developers before merging branches. 
+We welcome PRs from everyone! If you're willing to help out, you can learn more about how to contribute code to the project in the [Contribution Guide](CONTRIBUTING.md).  
+
+### Submit issues or ideas  
+
+You can submit your issues or ideas by adding issues to the Dify repository. If you encounter issues, please describe the steps you took to encounter the issue as much as possible so we can better discover it. If you have any new ideas for our product, we also welcome your feedback. Please share your insights as much as possible so we can get more feedback and further discussion in the community.  
+
+### Share your applications
+
+We encourage all community members to share their AI applications built on Dify, which can be applied to different scenarios or different users. This will provide powerful inspiration for people who want to create AI capabilities! You can share your experience by [submitting an issue in the Dify-user-case repository](https://github.com/langgenius/dify-user-case/issues).  
+
+### Share Dify with others
+
+We encourage community contributors to actively demonstrate different aspects of using Dify. You can talk or share any feature of using Dify at  meetups and conferences, blogs or social media. We believe your unique sharing will be of great help to others!  Mention @Dify.AI on Twitter and/or communicate on [Discord](https://discord.gg/FngNHpbcY7) so we can give pointers and tips and help you spread the word by promoting your content on the different Dify communication channels.
+
+### Help others 
+You can also help people in need of help on Discord, GitHub issues or other social platforms, guide others to solve problems encountered during use and share usage experiences. This is also a great contribution! If you want to become a maintainer of the Dify community, please contact the official team via [Discord](https://discord.gg/FngNHpbcY7) or email us at support@dify.ai. 
+
+
 ## Contact Us
 
 If you have any questions, suggestions, or partnership inquiries, feel free to contact us through the following channels:
 
 - Submit an Issue or PR on our GitHub Repo
-- Join the discussion in our [Discord](https://discord.gg/AhzKf7dNgk) Community
+- Join the discussion in our [Discord](https://discord.gg/FngNHpbcY7) Community
 - Send an email to hello@dify.ai
 
 We're eager to assist you and together create more fun and useful AI applications!
 
-## Contributing
-
-To ensure proper review, all code contributions - including those from contributors with direct commit access - must be submitted via pull requests and approved by the core development team prior to being merged.
-
-We welcome all pull requests! If you'd like to help, check out the [Contribution Guide](CONTRIBUTING.md) for more information on how to get started.
-
 ## Security
 
 To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.

README_CN.md

@@ -1,11 +1,13 @@
 ![](./images/describe-cn.jpg)
 <p align="center">
   <a href="./README.md">English</a> |
-  <a href="./README_CN.md">简体中文</a>
+  <a href="./README_CN.md">简体中文</a> |
+  <a href="./README_JA.md">日本語</a> |
+  <a href="./README_ES.md">Español</a>
 </p>
 
 
-[官方网站](http://dify.ai) • [文档](https://docs.dify.ai/v/zh-hans) • [Twitter](https://twitter.com/dify_ai)
+[官方网站](https://dify.ai) • [文档](https://docs.dify.ai/v/zh-hans) • [Twitter](https://twitter.com/dify_ai) •  [Discord](https://discord.gg/FngNHpbcY7)
 
 **Dify** 是一个易用的 LLMOps 平台，旨在让更多人可以创建可持续运营的原生 AI 应用。Dify 提供多种类型应用的可视化编排，应用可开箱即用，也能以“后端即服务”的 API 提供服务。
 
@@ -23,7 +25,7 @@ Dify 兼容 Langchain，这意味着我们将逐步支持多种 LLMs ，目前
 
 ## 使用云服务
 
-访问 [Dify.ai](http://cloud.dify.ai)
+访问 [Dify.ai](https://cloud.dify.ai)
 
 ## 安装社区版
 
@@ -40,10 +42,15 @@ Dify 兼容 Langchain，这意味着我们将逐步支持多种 LLMs ，目前
 
 ```bash
 cd docker
-docker-compose up -d
+docker compose up -d
 ```
 
-运行后，可以在浏览器上访问 [http://localhost](http://localhost) 进入 Dify 控制台，并开始初始化操作。
+运行后，可以在浏览器上访问 [http://localhost/install](http://localhost/install) 进入 Dify 控制台并开始初始化安装操作。
+
+### Helm Chart
+
+非常感谢 @BorisPolonsky 为我们提供了一个 [Helm Chart](https://helm.sh/) 版本，可以在 Kubernetes 上部署 Dify。
+您可以前往 https://github.com/BorisPolonsky/dify-helm 来获取部署信息。
 
 ### 配置
 
@@ -82,20 +89,37 @@ A: 现已支持英文与中文，你可以为我们贡献语言包。
 
 [![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
 
+
+## 贡献
+
+我们欢迎你为 Dify 作出贡献帮助 Dify 变得更好。我们欢迎各种方式的贡献，提交代码、问题、新想法、或者分享你基于 Dify 创建出的各种有趣有用的 AI 应用。同时，我们也欢迎你在不同的活动、研讨会、社交媒体上分享 Dify。
+
+### 贡献代码
+为了确保正确审查，所有代码贡献 - 包括来自具有直接提交更改权限的贡献者 - 都必须提交 PR 请求并在合并分支之前得到核心开发人员的批准。
+
+我们欢迎所有人提交 PR！如果您愿意提供帮助，可以在 [贡献指南](CONTRIBUTING_CN.md) 中了解有关如何为项目做出代码贡献的更多信息。
+
+### 提交问题或想法
+你可以通过 Dify 代码仓库新增 issues 来提交你的问题或想法。如遇到问题，请尽可能描述你遇到问题的操作步骤，以便我们更好地发现它。如果你对我们的产品有任何新想法，也欢迎向我们反馈，请尽可能多地分享你的见解，以便我们在社区中获得更多反馈和进一步讨论。
+
+### 分享你的应用
+我们鼓励所有社区成员分享他们基于 Dify 创造出的 AI 应用，它们可以是应用于不同情景或不同用户，这将有助于为希望基于 AI 能力创造的人们提供强大灵感！你可以通过 [Dify-user-case 仓库项目提交 issue](https://github.com/langgenius/dify-user-case) 来分享你的应用案例。
+
+### 向别人分享 Dify
+我们鼓励社区贡献者们积极展示你使用 Dify 的不同角度。你可以通过线下研讨会、博客或社交媒体上谈论或分享你使用 Dify 的任意功能，相信你独特的使用分享会给别人带来非常大的帮助！如果你需要任何指导帮助，欢迎联系我们 support@dify.ai ,你也可以在 twitter @Dify.AI 或在 [Discord 社区](https://discord.gg/FngNHpbcY7)交流来帮助你传播信息。
+
+### 帮助别人
+你还可以在 Discord、GitHub issues或其他社交平台上帮助需要帮助的人，指导别人解决使用过程中遇到的问题和分享使用经验。这也是个非常了不起的贡献！如果你希望成为 Dify 社区的维护者，请通过[Discord 社区](https://discord.gg/FngNHpbcY7) 联系官方团队或邮件联系我们 support@dify.ai.
+
+
 ## 联系我们
 
 如果您有任何问题、建议或合作意向，欢迎通过以下方式联系我们：
 
 - 在我们的 [GitHub Repo](https://github.com/langgenius/dify) 上提交 Issue 或 PR
-- 在我们的 [Discord 社区](https://discord.gg/AhzKf7dNgk) 上加入讨论
+- 在我们的 [Discord 社区](https://discord.gg/FngNHpbcY7) 上加入讨论
 - 发送邮件至 hello@dify.ai
 
-## 贡献代码
-
-为了确保正确审查，所有代码贡献 - 包括来自具有直接提交更改权限的贡献者 - 都必须提交 PR 请求并在合并分支之前得到核心开发人员的批准。
-
-我们欢迎所有人提交 PR！如果您愿意提供帮助，可以在 [贡献指南](CONTRIBUTING_CN.md) 中了解有关如何为项目做出贡献的更多信息。
-
 ## 安全
 
 为了保护您的隐私，请避免在 GitHub 上发布安全问题。发送问题至 security@dify.ai，我们将为您做更细致的解答。

README_ES.md

@@ -0,0 +1,124 @@
+![](./images/describe-en.png)
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_CN.md">简体中文</a> |
+  <a href="./README_JA.md">日本語</a> |
+  <a href="./README_ES.md">Español</a>
+</p>
+
+[Sitio web](https://dify.ai) • [Documentación](https://docs.dify.ai) • [Twitter](https://twitter.com/dify_ai) • [Discord](https://discord.gg/FngNHpbcY7)
+
+**Dify** es una plataforma LLMOps fácil de usar diseñada para capacitar a más personas para que creen aplicaciones sostenibles basadas en IA. Con orquestación visual para varios tipos de aplicaciones, Dify ofrece aplicaciones listas para usar que también pueden funcionar como APIs de Backend-as-a-Service. Unifica tu proceso de desarrollo con una API para la integración de complementos y conjuntos de datos, y agiliza tus operaciones utilizando una interfaz única para la ingeniería de indicaciones, análisis visual y mejora continua.
+
+Las aplicaciones creadas con Dify incluyen:
+
+- Sitios web listos para usar que admiten el modo de formulario y el modo de conversación por chat.
+- Una API única que abarca capacidades de complementos, mejora de contexto y más, lo que te ahorra esfuerzo de programación en el backend.
+- Análisis visual de datos, revisión de registros y anotación para aplicaciones.
+
+Dify es compatible con Langchain, lo que significa que gradualmente admitiremos múltiples LLMs, actualmente compatibles con:
+
+- GPT 3 (text-davinci-003)
+- GPT 3.5 Turbo (ChatGPT)
+- GPT-4
+
+## Usar servicios en la nube
+
+Visita [Dify.ai](https://dify.ai)
+
+## Instalar la Edición Comunitaria
+
+### Requisitos del sistema
+
+Antes de instalar Dify, asegúrate de que tu máquina cumple con los siguientes requisitos mínimos del sistema:
+
+- CPU >= 1 Core
+- RAM >= 4GB
+
+### Inicio rápido
+
+La forma más sencilla de iniciar el servidor de Dify es ejecutar nuestro archivo [docker-compose.yml](docker/docker-compose.yaml). Antes de ejecutar el comando de instalación, asegúrate de que [Docker](https://docs.docker.com/get-docker/) y [Docker Compose](https://docs.docker.com/compose/install/) estén instalados en tu máquina:
+
+```bash
+cd docker
+docker compose up -d
+```
+
+Después de ejecutarlo, puedes acceder al panel de control de Dify en tu navegador desde [http://localhost/install](http://localhost/install) y comenzar el proceso de instalación de inicialización.
+
+### Helm Chart
+
+Un gran agradecimiento a @BorisPolonsky por proporcionarnos una versión de [Helm Chart](https://helm.sh/), que permite desplegar Dify en Kubernetes.
+Puede ir a https://github.com/BorisPolonsky/dify-helm para obtener información de despliegue.
+
+### Configuración
+
+Si necesitas personalizar la configuración, consulta los comentarios en nuestro archivo [docker-compose.yml](docker/docker-compose.yaml) y configura manualmente la configuración del entorno. Después de realizar los cambios, ejecuta nuevamente 'docker-compose up -d'.
+
+## Hoja de ruta
+
+Funciones en desarrollo:
+
+- **Conjuntos de datos**, admitiendo más conjuntos de datos, por ejemplo, sincronización de contenido desde Notion o páginas web.
+Admitiremos más conjuntos de datos, incluidos texto, páginas web e incluso contenido de Notion. Los usuarios pueden construir aplicaciones de IA basadas en sus propias fuentes de datos
+- **Complementos**, introduciendo complementos estándar de ChatGPT para aplicaciones, o utilizando complementos producidos por Dify.
+Lanzaremos complementos que cumplan con el estándar de ChatGPT, o nuestros propios complementos de Dify para habilitar más capacidades en las aplicaciones.
+- **Modelos de código abierto**, por ejemplo, adoptar Llama como proveedor de modelos o para un ajuste adicional.
+Trabajaremos con excelentes modelos de código abierto como Llama, proporcionándolos como opciones de modelos en nuestra plataforma o utilizándolos para un ajuste adicional.
+
+## Preguntas y respuestas
+
+**P: ¿Qué puedo hacer con Dify?**
+
+R: Dify es una herramienta de desarrollo y operaciones de LLM, simple pero poderosa. Puedes usarla para construir aplicaciones de calidad comercial y asistentes personales. Si deseas desarrollar tus propias aplicaciones, LangDifyGenius puede ahorrarte trabajo en el backend al integrar con OpenAI y ofrecer capacidades de operaciones visuales, lo que te permite mejorar y entrenar continuamente tu modelo GPT.
+
+**P: ¿Cómo uso Dify para "entrenar" mi propio modelo?**
+
+R: Una aplicación valiosa consta de Ingeniería de indicaciones, mejora de contexto y ajuste fino. Hemos creado un enfoque de programación híbrida que combina las indicaciones con lenguajes de programación (similar a un motor de plantillas), lo que facilita la incorporación de texto largo o la captura de subtítulos de un video de YouTube ingresado por el usuario, todo lo cual se enviará como contexto para que los LLM lo procesen. Damos gran importancia a la operabilidad de la aplicación, con los datos generados por los usuarios durante el uso de la aplicación disponibles para análisis, anotación y entrenamiento continuo. Sin las herramientas adecuadas, estos pasos pueden llevar mucho tiempo.
+
+**P: ¿Qué necesito preparar si quiero crear mi propia aplicación?**
+
+R: Suponemos que ya tienes una clave de API de OpenAI; si no la tienes, por favor regístrate. ¡Si ya tienes contenido que pueda servir como contexto de entrenamiento, eso es genial!
+
+**P: ¿Qué idiomas de interfaz están disponibles?**
+
+R: Actualmente se admiten inglés y chino, y puedes contribuir con paquetes de idiomas.
+
+## Historial de estrellas
+
+[![Gráfico de historial de estrellas](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+## Contáctanos
+
+Si tienes alguna pregunta, sugerencia o consulta sobre asociación, no dudes en contactarnos a través de los siguientes canales:
+
+- Presentar un problema o una solicitud de extracción en nuestro repositorio de GitHub.
+- Únete a la discusión en nuestra comunidad de [Discord](https://discord.gg/FngNHpbcY7).
+- Envía un correo electrónico a hello@dify.ai.
+
+¡Estamos ansiosos por ayudarte y crear juntos aplicaciones de IA más divertidas y útiles!
+
+## Contribuciones
+
+Para garantizar una revisión adecuada, todas las contribuciones de código, incluidas las de los colaboradores con acceso directo a los compromisos, deben enviarse mediante solicitudes de extracción y ser aprobadas por el equipo principal de
+
+ desarrollo antes de fusionarse.
+
+¡Agradecemos todas las solicitudes de extracción! Si deseas ayudar, consulta la [Guía de Contribución](CONTRIBUTING.md) para obtener más información sobre cómo comenzar.
+
+## Seguridad
+
+Para proteger tu privacidad, evita publicar problemas de seguridad en GitHub. En su lugar, envía tus preguntas a security@dify.ai y te proporcionaremos una respuesta más detallada.
+
+## Citación
+
+Este software utiliza el siguiente software de código abierto:
+
+- Chase, H. (2022). LangChain [Software de computadora]. https://github.com/hwchase17/langchain
+- Liu, J. (2022). LlamaIndex [Software de computadora]. doi: 10.5281/zenodo.1234.
+
+Para obtener más información, consulta el sitio web oficial o el texto de la licencia del software correspondiente.
+
+## Licencia
+
+Este repositorio está disponible bajo la [Licencia de código abierto de Dify](LICENSE).

README_JA.md

@@ -0,0 +1,123 @@
+![](./images/describe-en.png)
+<p align="center">
+  <a href="./README.md">English</a> |
+  <a href="./README_CN.md">简体中文</a> |
+  <a href="./README_JA.md">日本語</a> |
+  <a href="./README_ES.md">Español</a>
+</p>
+
+[Web サイト](https://dify.ai) • [ドキュメント](https://docs.dify.ai) • [Twitter](https://twitter.com/dify_ai) • [Discord](https://discord.gg/FngNHpbcY7)
+
+
+**Dify** は、より多くの人々が持続可能な AI ネイティブアプリケーションを作成できるように設計された、使いやすい LLMOps プラットフォームです。様々なアプリケーションタイプに対応したビジュアルオーケストレーションにより Dify は Backend-as-a-Service API としても機能する、すぐに使えるアプリケーションを提供します。プラグインやデータセットを統合するための1つの API で開発プロセスを統一し、プロンプトエンジニアリング、ビジュアル分析、継続的な改善のための1つのインターフェイスを使って業務を合理化します。
+
+Difyで作成したアプリケーションは以下の通りです:
+
+フォームモードとチャット会話モードをサポートする、すぐに使える Web サイト
+プラグイン機能、コンテキストの強化などを網羅する単一の API により、バックエンドのコーディングの手間を省きます。
+アプリケーションの視覚的なデータ分析、ログレビュー、アノテーションが可能です。
+Dify は LangChain と互換性があり、複数の LLM を徐々にサポートします:
+
+- GPT 3 (text-davinci-003)
+- GPT 3.5 Turbo(ChatGPT)
+- GPT-4
+
+## クラウドサービスの利用
+
+[Dify.ai](https://dify.ai) をご覧ください
+
+## Community Edition のインストール
+
+### システム要件
+
+Dify をインストールする前に、お使いのマシンが以下の最低システム要件を満たしていることを確認してください:
+
+- CPU >= 1 Core
+- RAM >= 4GB
+
+### クイックスタート
+
+Dify サーバーを起動する最も簡単な方法は、[docker-compose.yml](docker/docker-compose.yaml) ファイルを実行することです。インストールコマンドを実行する前に、[Docker](https://docs.docker.com/get-docker/) と [Docker Compose](https://docs.docker.com/compose/install/) がお使いのマシンにインストールされていることを確認してください:
+
+```bash
+cd docker
+docker compose up -d
+```
+
+実行後、ブラウザで [http://localhost/install](http://localhost/install) にアクセスし、初期化インストール作業を開始することができます。
+
+### Helm Chart
+
+@BorisPolonsky に大感謝します。彼は Dify を Kubernetes 上にデプロイするための [Helm Chart](https://helm.sh/) バージョンを提供してくれました。
+デプロイ情報については、https://github.com/BorisPolonsky/dify-helm をご覧ください。
+
+### 構成
+
+カスタマイズが必要な場合は、[docker-compose.yml](docker/docker-compose.yaml) ファイルのコメントを参照し、手動で環境設定をお願いします。変更後、再度 'docker-compose up -d' を実行してください。
+
+## ロードマップ
+
+開発中の機能:
+
+- **データセット**, Notionやウェブページからのコンテンツ同期など、より多くのデータセットをサポートします
+テキスト、ウェブページ、さらには Notion コンテンツなど、より多くのデータセットをサポートする予定です。ユーザーは、自分のデータソースをもとに AI アプリケーションを構築することができます。
+- **プラグイン**, アプリケーションに ChatGPT プラグイン標準のプラグインを導入する、または Dify 制作のプラグインを利用する
+今後、ChatGPT 規格に準拠したプラグインや、ディファイ独自のプラグインを公開し、より多くの機能をアプリケーションで実現できるようにします。
+- **オープンソースモデル**, 例えばモデルプロバイダーとして Llama を採用したり、さらにファインチューニングを行う
+Llama のような優れたオープンソースモデルを、私たちのプラットフォームのモデルオプションとして提供したり、さらなる微調整のために使用したりすることで、協力していきます。
+
+
+## Q&A
+
+**Q: Dify で何ができるのか？**
+
+A: Dify はシンプルでパワフルな LLM 開発・運用ツールです。商用グレードのアプリケーション、パーソナルアシスタントを構築するために使用することができます。独自のアプリケーションを開発したい場合、LangDifyGenius は OpenAI と統合する際のバックエンド作業を省き、視覚的な操作機能を提供し、GPT モデルを継続的に改善・訓練することが可能です。
+
+**Q: Dify を使って、自分のモデルを「トレーニング」するにはどうすればいいのでしょうか？**
+
+A: プロンプトエンジニアリング、コンテキスト拡張、ファインチューニングからなる価値あるアプリケーションです。プロンプトとプログラミング言語を組み合わせたハイブリッドプログラミングアプローチ（テンプレートエンジンのようなもの）で、長文の埋め込みやユーザー入力の YouTube 動画からの字幕取り込みなどを簡単に実現し、これらはすべて LLM が処理するコンテキストとして提出される予定です。また、アプリケーションの操作性を重視し、ユーザーがアプリケーションを使用する際に生成したデータを分析、アノテーション、継続的なトレーニングに利用できるようにしました。適切なツールがなければ、これらのステップに時間がかかることがあります。
+
+**Q: 自分でアプリケーションを作りたい場合、何を準備すればよいですか？**
+
+A: すでに OpenAI API Key をお持ちだと思いますが、お持ちでない場合はご登録ください。もし、すでにトレーニングのコンテキストとなるコンテンツをお持ちでしたら、それは素晴らしいことです！
+
+**Q: インターフェイスにどの言語が使えますか？**
+
+A: 現在、英語と中国語に対応しており、言語パックを寄贈することも可能です。
+
+## Star ヒストリー
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+## お問合せ
+
+ご質問、ご提案、パートナーシップに関するお問い合わせは、以下のチャンネルからお気軽にご連絡ください:
+
+- GitHub Repo で Issue や PR を提出する
+- [Discord](https://discord.gg/FngNHpbcY7) コミュニティで議論に参加する。
+- hello@dify.ai にメールを送信します
+
+私たちは、皆様のお手伝いをさせていただき、より楽しく、より便利な AI アプリケーションを一緒に作っていきたいと思っています！
+
+## コントリビュート
+
+適切なレビューを行うため、コミットへの直接アクセスが可能なコントリビュータを含むすべてのコードコントリビュータは、プルリクエストで提出し、マージされる前にコア開発チームによって承認される必要があります。
+
+私たちはすべてのプルリクエストを歓迎します！協力したい方は、[コントリビューションガイド](CONTRIBUTING.md) をチェックしてみてください。
+
+## セキュリティ
+
+プライバシー保護のため、GitHub へのセキュリティ問題の投稿は避けてください。代わりに、あなたの質問を security@dify.ai に送ってください。より詳細な回答を提供します。
+
+## 引用
+
+本ソフトウェアは、以下のオープンソースソフトウェアを使用しています:
+
+- Chase, H. (2022). LangChain [Computer software]. https://github.com/hwchase17/langchain
+- Liu, J. (2022). LlamaIndex [Computer software]. doi: 10.5281/zenodo.1234.
+
+詳しくは、各ソフトウェアの公式サイトまたはライセンス文をご参照ください。
+
+## ライセンス
+
+このリポジトリは、[Dify Open Source License](LICENSE) のもとで利用できます。

api/.env.example

@@ -14,7 +14,7 @@ CONSOLE_URL=http://127.0.0.1:5001
 API_URL=http://127.0.0.1:5001
 
 # Web APP base URL
-APP_URL=http://127.0.0.1:5001
+APP_URL=http://127.0.0.1:3000
 
 # celery configuration
 CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
@@ -22,6 +22,7 @@ CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
 # redis configuration
 REDIS_HOST=localhost
 REDIS_PORT=6379
+REDIS_USERNAME=
 REDIS_PASSWORD=difyai123456
 REDIS_DB=0
 
@@ -72,6 +73,7 @@ VECTOR_STORE=weaviate
 WEAVIATE_ENDPOINT=http://localhost:8080
 WEAVIATE_API_KEY=WVF5YThaHlkYwhGUSmCRgsX3tD5ngdN8pkih
 WEAVIATE_GRPC_ENABLED=false
+WEAVIATE_BATCH_SIZE=100
 
 # Qdrant configuration, use `path:` prefix for local mode or `https://your-qdrant-cluster-url.qdrant.io` for remote mode
 QDRANT_URL=path:storage/qdrant
@@ -83,3 +85,9 @@ SENTRY_DSN=
 # DEBUG
 DEBUG=false
 SQLALCHEMY_ECHO=false
+
+# Notion import configuration, support public and internal
+NOTION_INTEGRATION_TYPE=public
+NOTION_CLIENT_SECRET=you-client-secret
+NOTION_CLIENT_ID=you-client-id
+NOTION_INTERNAL_SECRET=you-internal-secret

api/README.md

@@ -17,6 +17,11 @@
    ```bash
    openssl rand -base64 42
    ```
+3.5 If you use annaconda, create a new environment and activate it
+   ```bash
+   conda create --name dify python=3.10
+   conda activate dify
+   ```
 4. Install dependencies
    ```bash
    pip install -r requirements.txt
@@ -33,3 +38,4 @@
    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`, celery can do dataset importing and other async tasks.

api/app.py

@@ -1,5 +1,7 @@
 # -*- coding:utf-8 -*-
 import os
+from datetime import datetime
+
 if not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true':
     from gevent import monkey
     monkey.patch_all()
@@ -12,13 +14,13 @@ from flask import Flask, request, Response, session
 import flask_login
 from flask_cors import CORS
 
-from extensions import ext_session, ext_celery, ext_sentry, ext_redis, ext_login, ext_vector_store, ext_migrate, \
+from extensions import ext_session, ext_celery, ext_sentry, ext_redis, ext_login, ext_migrate, \
     ext_database, ext_storage
 from extensions.ext_database import db
 from extensions.ext_login import login_manager
 
 # DO NOT REMOVE BELOW
-from models import model, account, dataset, web, task
+from models import model, account, dataset, web, task, source
 from events import event_handlers
 # DO NOT REMOVE ABOVE
 
@@ -77,7 +79,6 @@ def initialize_extensions(app):
     ext_database.init_app(app)
     ext_migrate.init(app, db)
     ext_redis.init_app(app)
-    ext_vector_store.init_app(app)
     ext_storage.init_app(app)
     ext_celery.init_app(app)
     ext_session.init_app(app)
@@ -122,6 +123,9 @@ def load_user(user_id):
                     account.current_tenant_id = tenant_account_join.tenant_id
                     session['workspace_id'] = account.current_tenant_id
 
+            account.last_active_at = datetime.utcnow()
+            db.session.commit()
+
             # Log in the user with the updated user_id
             flask_login.login_user(account, remember=True)
 

api/commands.py

@@ -1,18 +1,25 @@
 import datetime
-import json
+import logging
 import random
 import string
 
 import click
+from flask import current_app
+from werkzeug.exceptions import NotFound
 
+from core.index.index import IndexBuilder
 from libs.password import password_pattern, valid_password, hash_password
 from libs.helper import email as email_validate
 from extensions.ext_database import db
-from models.account import InvitationCode
-from models.model import Account, AppModelConfig, ApiToken, Site, App, RecommendedApp
+from libs.rsa import generate_key_pair
+from models.account import InvitationCode, Tenant
+from models.dataset import Dataset
+from models.model import Account
 import secrets
 import base64
 
+from models.provider import Provider
+
 
 @click.command('reset-password', help='Reset the account password.')
 @click.option('--email', prompt=True, help='The email address of the account whose password you need to reset')
@@ -74,6 +81,31 @@ def reset_email(email, new_email, email_confirm):
     click.echo(click.style('Congratulations!, email has been reset.', fg='green'))
 
 
+@click.command('reset-encrypt-key-pair', help='Reset the asymmetric key pair of workspace for encrypt LLM credentials. '
+                                              'After the reset, all LLM credentials will become invalid, '
+                                              'requiring re-entry.'
+                                              'Only support SELF_HOSTED mode.')
+@click.confirmation_option(prompt=click.style('Are you sure you want to reset encrypt key pair?'
+                                              ' this operation cannot be rolled back!', fg='red'))
+def reset_encrypt_key_pair():
+    if current_app.config['EDITION'] != 'SELF_HOSTED':
+        click.echo(click.style('Sorry, only support SELF_HOSTED mode.', fg='red'))
+        return
+
+    tenant = db.session.query(Tenant).first()
+    if not tenant:
+        click.echo(click.style('Sorry, no workspace found. Please enter /install to initialize.', fg='red'))
+        return
+
+    tenant.encrypt_public_key = generate_key_pair(tenant.id)
+
+    db.session.query(Provider).filter(Provider.provider_type == 'custom').delete()
+    db.session.commit()
+
+    click.echo(click.style('Congratulations! '
+                           'the asymmetric key pair of workspace {} has been reset.'.format(tenant.id), fg='green'))
+
+
 @click.command('generate-invitation-codes', help='Generate invitation codes.')
 @click.option('--batch', help='The batch of invitation codes.')
 @click.option('--count', prompt=True, help='Invitation codes count.')
@@ -131,30 +163,39 @@ def generate_upper_string():
     return result
 
 
-@click.command('gen-recommended-apps', help='Number of records to generate')
-def generate_recommended_apps():
-    print('Generating recommended app data...')
-    apps = App.query.all()
-    for app in apps:
-        recommended_app = RecommendedApp(
-            app_id=app.id,
-            description={
-                'en': 'Description for ' + app.name,
-                'zh': '描述 ' + app.name
-            },
-            copyright='Copyright ' + str(random.randint(1990, 2020)),
-            privacy_policy='https://privacypolicy.example.com',
-            category=random.choice(['Games', 'News', 'Music', 'Sports']),
-            position=random.randint(1, 100),
-            install_count=random.randint(100, 100000)
-        )
-        db.session.add(recommended_app)
-    db.session.commit()
-    print('Done!')
+@click.command('recreate-all-dataset-indexes', help='Recreate all dataset indexes.')
+def recreate_all_dataset_indexes():
+    click.echo(click.style('Start recreate all dataset indexes.', fg='green'))
+    recreate_count = 0
+
+    page = 1
+    while True:
+        try:
+            datasets = db.session.query(Dataset).filter(Dataset.indexing_technique == 'high_quality')\
+                .order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
+        except NotFound:
+            break
+
+        page += 1
+        for dataset in datasets:
+            try:
+                click.echo('Recreating dataset index: {}'.format(dataset.id))
+                index = IndexBuilder.get_index(dataset, 'high_quality')
+                if index and index._is_origin():
+                    index.recreate_dataset(dataset)
+                    recreate_count += 1
+                else:
+                    click.echo('passed.')
+            except Exception as e:
+                click.echo(click.style('Recreate dataset index error: {} {}'.format(e.__class__.__name__, str(e)), fg='red'))
+                continue
+
+    click.echo(click.style('Congratulations! Recreate {} dataset indexes.'.format(recreate_count), fg='green'))
 
 
 def register_commands(app):
     app.cli.add_command(reset_password)
     app.cli.add_command(reset_email)
     app.cli.add_command(generate_invitation_codes)
-    app.cli.add_command(generate_recommended_apps)
+    app.cli.add_command(reset_encrypt_key_pair)
+    app.cli.add_command(recreate_all_dataset_indexes)

api/config.py

@@ -21,9 +21,11 @@ DEFAULTS = {
     'REDIS_HOST': 'localhost',
     'REDIS_PORT': '6379',
     'REDIS_DB': '0',
+    'REDIS_USE_SSL': 'False',
     'SESSION_REDIS_HOST': 'localhost',
     'SESSION_REDIS_PORT': '6379',
     'SESSION_REDIS_DB': '2',
+    'SESSION_REDIS_USE_SSL': 'False',
     'OAUTH_REDIRECT_PATH': '/console/api/oauth/authorize',
     'OAUTH_REDIRECT_INDEX_PATH': '/',
     'CONSOLE_URL': 'https://cloud.dify.ai',
@@ -41,9 +43,12 @@ DEFAULTS = {
     'SENTRY_TRACES_SAMPLE_RATE': 1.0,
     'SENTRY_PROFILES_SAMPLE_RATE': 1.0,
     'WEAVIATE_GRPC_ENABLED': 'True',
+    'WEAVIATE_BATCH_SIZE': 100,
     'CELERY_BACKEND': 'database',
     'PDF_PREVIEW': 'True',
     'LOG_LEVEL': 'INFO',
+    'DISABLE_PROVIDER_CONFIG_VALIDATION': 'False',
+    'DEFAULT_LLM_PROVIDER': 'openai'
 }
 
 
@@ -74,7 +79,7 @@ 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 = "0.2.0"
+        self.CURRENT_VERSION = "0.3.7"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -105,14 +110,18 @@ class Config:
         # redis settings
         self.REDIS_HOST = get_env('REDIS_HOST')
         self.REDIS_PORT = get_env('REDIS_PORT')
+        self.REDIS_USERNAME = get_env('REDIS_USERNAME')
         self.REDIS_PASSWORD = get_env('REDIS_PASSWORD')
         self.REDIS_DB = get_env('REDIS_DB')
+        self.REDIS_USE_SSL = get_bool_env('REDIS_USE_SSL')
 
         # session redis settings
         self.SESSION_REDIS_HOST = get_env('SESSION_REDIS_HOST')
         self.SESSION_REDIS_PORT = get_env('SESSION_REDIS_PORT')
+        self.SESSION_REDIS_USERNAME = get_env('SESSION_REDIS_USERNAME')
         self.SESSION_REDIS_PASSWORD = get_env('SESSION_REDIS_PASSWORD')
         self.SESSION_REDIS_DB = get_env('SESSION_REDIS_DB')
+        self.SESSION_REDIS_USE_SSL = get_bool_env('SESSION_REDIS_USE_SSL')
 
         # storage settings
         self.STORAGE_TYPE = get_env('STORAGE_TYPE')
@@ -130,6 +139,7 @@ class Config:
         self.WEAVIATE_ENDPOINT = get_env('WEAVIATE_ENDPOINT')
         self.WEAVIATE_API_KEY = get_env('WEAVIATE_API_KEY')
         self.WEAVIATE_GRPC_ENABLED = get_bool_env('WEAVIATE_GRPC_ENABLED')
+        self.WEAVIATE_BATCH_SIZE = int(get_env('WEAVIATE_BATCH_SIZE'))
 
         # qdrant settings
         self.QDRANT_URL = get_env('QDRANT_URL')
@@ -165,10 +175,26 @@ class Config:
         self.CELERY_BACKEND = get_env('CELERY_BACKEND')
         self.CELERY_RESULT_BACKEND = 'db+{}'.format(self.SQLALCHEMY_DATABASE_URI) \
             if self.CELERY_BACKEND == 'database' else self.CELERY_BROKER_URL
+        self.BROKER_USE_SSL = self.CELERY_BROKER_URL.startswith('rediss://')
 
         # hosted provider credentials
         self.OPENAI_API_KEY = get_env('OPENAI_API_KEY')
 
+        # By default it is False
+        # You could disable it for compatibility with certain OpenAPI providers
+        self.DISABLE_PROVIDER_CONFIG_VALIDATION = get_bool_env('DISABLE_PROVIDER_CONFIG_VALIDATION')
+
+        # For temp use only
+        # set default LLM provider, default is 'openai', support `azure_openai`
+        self.DEFAULT_LLM_PROVIDER = get_env('DEFAULT_LLM_PROVIDER')
+
+        # notion import setting
+        self.NOTION_CLIENT_ID = get_env('NOTION_CLIENT_ID')
+        self.NOTION_CLIENT_SECRET = get_env('NOTION_CLIENT_SECRET')
+        self.NOTION_INTEGRATION_TYPE = get_env('NOTION_INTEGRATION_TYPE')
+        self.NOTION_INTERNAL_SECRET = get_env('NOTION_INTERNAL_SECRET')
+        self.NOTION_INTEGRATION_TOKEN = get_env('NOTION_INTEGRATION_TOKEN')
+
 
 class CloudEditionConfig(Config):
 

api/controllers/console/__init__.py

@@ -5,16 +5,20 @@ from libs.external_api import ExternalApi
 bp = Blueprint('console', __name__, url_prefix='/console/api')
 api = ExternalApi(bp)
 
+# Import other controllers
+from . import setup, version, apikey, admin
+
 # Import app controllers
-from .app import app, site, explore, completion, model_config, statistic, conversation, message
+from .app import app, site, completion, model_config, statistic, conversation, message, generator, audio
 
 # Import auth controllers
-from .auth import login, oauth
+from .auth import login, oauth, data_source_oauth
 
 # Import datasets controllers
-from .datasets import datasets, datasets_document, datasets_segments, file, hit_testing
-
-# Import other controllers
-from . import setup, version, apikey
+from .datasets import datasets, datasets_document, datasets_segments, file, hit_testing, data_source
 
+# Import workspace controllers
 from .workspace import workspace, members, providers, account
+
+# Import explore controllers
+from .explore import installed_app, recommended_app, completion, conversation, message, parameter, saved_message, audio

api/controllers/console/admin.py

@@ -0,0 +1,132 @@
+import os
+from functools import wraps
+
+from flask import request
+from flask_restful import Resource, reqparse
+from werkzeug.exceptions import NotFound, Unauthorized
+
+from controllers.console import api
+from controllers.console.wraps import only_edition_cloud
+from extensions.ext_database import db
+from libs.helper import supported_language
+from models.model import RecommendedApp, App, InstalledApp
+
+
+def admin_required(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        if not os.getenv('ADMIN_API_KEY'):
+            raise Unauthorized('API key is invalid.')
+
+        auth_header = request.headers.get('Authorization')
+        if auth_header is None:
+            raise Unauthorized('Authorization header is missing.')
+
+        if ' ' not in auth_header:
+            raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
+
+        auth_scheme, auth_token = auth_header.split(None, 1)
+        auth_scheme = auth_scheme.lower()
+
+        if auth_scheme != 'bearer':
+            raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
+
+        if os.getenv('ADMIN_API_KEY') != auth_token:
+            raise Unauthorized('API key is invalid.')
+
+        return view(*args, **kwargs)
+
+    return decorated
+
+
+class InsertExploreAppListApi(Resource):
+    @only_edition_cloud
+    @admin_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('app_id', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('desc', type=str, location='json')
+        parser.add_argument('copyright', type=str, location='json')
+        parser.add_argument('privacy_policy', type=str, location='json')
+        parser.add_argument('language', type=supported_language, required=True, nullable=False, location='json')
+        parser.add_argument('category', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('position', type=int, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        app = App.query.filter(App.id == args['app_id']).first()
+        if not app:
+            raise NotFound('App not found')
+
+        site = app.site
+        if not site:
+            desc = args['desc'] if args['desc'] else ''
+            copy_right = args['copyright'] if args['copyright'] else ''
+            privacy_policy = args['privacy_policy'] if args['privacy_policy'] else ''
+        else:
+            desc = site.description if (site.description if not args['desc'] else args['desc']) else ''
+            copy_right = site.copyright if (site.copyright if not args['copyright'] else args['copyright']) else ''
+            privacy_policy = site.privacy_policy \
+                if (site.privacy_policy if not args['privacy_policy'] else args['privacy_policy']) else ''
+
+        recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == args['app_id']).first()
+
+        if not recommended_app:
+            recommended_app = RecommendedApp(
+                app_id=app.id,
+                description=desc,
+                copyright=copy_right,
+                privacy_policy=privacy_policy,
+                language=args['language'],
+                category=args['category'],
+                position=args['position']
+            )
+
+            db.session.add(recommended_app)
+
+            app.is_public = True
+            db.session.commit()
+
+            return {'result': 'success'}, 201
+        else:
+            recommended_app.description = desc
+            recommended_app.copyright = copy_right
+            recommended_app.privacy_policy = privacy_policy
+            recommended_app.language = args['language']
+            recommended_app.category = args['category']
+            recommended_app.position = args['position']
+
+            app.is_public = True
+
+            db.session.commit()
+
+            return {'result': 'success'}, 200
+
+
+class InsertExploreAppApi(Resource):
+    @only_edition_cloud
+    @admin_required
+    def delete(self, app_id):
+        recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == str(app_id)).first()
+        if not recommended_app:
+            return {'result': 'success'}, 204
+
+        app = App.query.filter(App.id == recommended_app.app_id).first()
+        if app:
+            app.is_public = False
+
+        installed_apps = InstalledApp.query.filter(
+            InstalledApp.app_id == recommended_app.app_id,
+            InstalledApp.tenant_id != InstalledApp.app_owner_tenant_id
+        ).all()
+
+        for installed_app in installed_apps:
+            db.session.delete(installed_app)
+
+        db.session.delete(recommended_app)
+        db.session.commit()
+
+        return {'result': 'success'}, 204
+
+
+api.add_resource(InsertExploreAppListApi, '/admin/insert-explore-apps')
+api.add_resource(InsertExploreAppApi, '/admin/insert-explore-apps/<uuid:app_id>')

api/controllers/console/app/__init__.py

@@ -17,6 +17,6 @@ def _get_app(app_id, mode=None):
         raise NotFound("App not found")
 
     if mode and app.mode != mode:
-        raise AppUnavailableError()
+        raise NotFound("The {} app not found".format(mode))
 
     return app

api/controllers/console/app/app.py

@@ -9,24 +9,20 @@ from werkzeug.exceptions import Unauthorized, Forbidden
 
 from constants.model_template import model_templates, demo_model_templates
 from controllers.console import api
-from controllers.console.app.error import AppNotFoundError, ProviderNotInitializeError, ProviderQuotaExceededError, \
-    CompletionRequestError, ProviderModelCurrentlyNotSupportError
+from controllers.console.app.error import AppNotFoundError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
-from core.generator.llm_generator import LLMGenerator
-from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, LLMBadRequestError, LLMAPIConnectionError, \
-    LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError, ModelCurrentlyNotSupportError
 from events.app_event import app_was_created, app_was_deleted
 from libs.helper import TimestampField
 from extensions.ext_database import db
-from models.model import App, AppModelConfig, Site, InstalledApp
-from services.account_service import TenantService
+from models.model import App, AppModelConfig, Site
 from services.app_model_config_service import AppModelConfigService
 
 model_config_fields = {
     'opening_statement': fields.String,
     'suggested_questions': fields.Raw(attribute='suggested_questions_list'),
     'suggested_questions_after_answer': fields.Raw(attribute='suggested_questions_after_answer_dict'),
+    'speech_to_text': fields.Raw(attribute='speech_to_text_dict'),
     'more_like_this': fields.Raw(attribute='more_like_this_dict'),
     'model': fields.Raw(attribute='model_dict'),
     'user_input_form': fields.Raw(attribute='user_input_form_list'),
@@ -149,6 +145,7 @@ class AppListApi(Resource):
                 opening_statement=model_configuration['opening_statement'],
                 suggested_questions=json.dumps(model_configuration['suggested_questions']),
                 suggested_questions_after_answer=json.dumps(model_configuration['suggested_questions_after_answer']),
+                speech_to_text=json.dumps(model_configuration['speech_to_text']),
                 more_like_this=json.dumps(model_configuration['more_like_this']),
                 model=json.dumps(model_configuration['model']),
                 user_input_form=json.dumps(model_configuration['user_input_form']),
@@ -220,7 +217,11 @@ class AppTemplateApi(Resource):
         account = current_user
         interface_language = account.interface_language
 
-        return {'data': demo_model_templates.get(interface_language)}
+        templates = demo_model_templates.get(interface_language)
+        if not templates:
+            templates = demo_model_templates.get('en-US')
+
+        return {'data': templates}
 
 
 class AppApi(Resource):
@@ -435,6 +436,7 @@ class AppCopy(Resource):
             opening_statement=app_config.opening_statement,
             suggested_questions=app_config.suggested_questions,
             suggested_questions_after_answer=app_config.suggested_questions_after_answer,
+            speech_to_text=app_config.speech_to_text,
             more_like_this=app_config.more_like_this,
             model=app_config.model,
             user_input_form=app_config.user_input_form,
@@ -478,35 +480,6 @@ class AppExport(Resource):
         pass
 
 
-class IntroductionGenerateApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def post(self):
-        parser = reqparse.RequestParser()
-        parser.add_argument('prompt_template', type=str, required=True, location='json')
-        args = parser.parse_args()
-
-        account = current_user
-
-        try:
-            answer = LLMGenerator.generate_introduction(
-                account.current_tenant_id,
-                args['prompt_template']
-            )
-        except ProviderTokenNotInitError:
-            raise ProviderNotInitializeError()
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
-                LLMRateLimitError, LLMAuthorizationError) as e:
-            raise CompletionRequestError(str(e))
-
-        return {'introduction': answer}
-
-
 api.add_resource(AppListApi, '/apps')
 api.add_resource(AppTemplateApi, '/app-templates')
 api.add_resource(AppApi, '/apps/<uuid:app_id>')
@@ -515,4 +488,3 @@ api.add_resource(AppNameApi, '/apps/<uuid:app_id>/name')
 api.add_resource(AppSiteStatus, '/apps/<uuid:app_id>/site-enable')
 api.add_resource(AppApiStatus, '/apps/<uuid:app_id>/api-enable')
 api.add_resource(AppRateLimit, '/apps/<uuid:app_id>/rate-limit')
-api.add_resource(IntroductionGenerateApi, '/introduction-generate')

api/controllers/console/app/audio.py

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

api/controllers/console/app/conversation.py

@@ -45,7 +45,7 @@ message_detail_fields = {
     'message_tokens': fields.Integer,
     'answer': fields.String,
     'answer_tokens': fields.Integer,
-    'provider_response_latency': fields.Integer,
+    'provider_response_latency': fields.Float,
     'from_source': fields.String,
     'from_end_user_id': fields.String,
     'from_account_id': fields.String,
@@ -209,6 +209,26 @@ class CompletionConversationDetailApi(Resource):
         conversation_id = str(conversation_id)
 
         return _get_conversation(app_id, conversation_id, 'completion')
+    
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, app_id, conversation_id):
+        app_id = str(app_id)
+        conversation_id = str(conversation_id)
+
+        app = _get_app(app_id, 'chat')
+
+        conversation = db.session.query(Conversation) \
+            .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+
+        if not conversation:
+            raise NotFound("Conversation Not Exists.")
+
+        conversation.is_deleted = True
+        db.session.commit()
+
+        return {'result': 'success'}, 204
 
 
 class ChatConversationApi(Resource):
@@ -356,6 +376,27 @@ class ChatConversationDetailApi(Resource):
         conversation_id = str(conversation_id)
 
         return _get_conversation(app_id, conversation_id, 'chat')
+    
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, app_id, conversation_id):
+        app_id = str(app_id)
+        conversation_id = str(conversation_id)
+
+        # get app info
+        app = _get_app(app_id, 'chat')
+
+        conversation = db.session.query(Conversation) \
+            .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+
+        if not conversation:
+            raise NotFound("Conversation Not Exists.")
+
+        conversation.is_deleted = True
+        db.session.commit()
+
+        return {'result': 'success'}, 204
 
 
 

api/controllers/console/app/error.py

@@ -9,31 +9,33 @@ class AppNotFoundError(BaseHTTPException):
 
 class ProviderNotInitializeError(BaseHTTPException):
     error_code = 'provider_not_initialize'
-    description = "Provider Token not initialize."
+    description = "No valid model provider credentials found. " \
+                  "Please go to Settings -> Model Provider to complete your provider credentials."
     code = 400
 
 
 class ProviderQuotaExceededError(BaseHTTPException):
     error_code = 'provider_quota_exceeded'
-    description = "Provider quota exceeded."
+    description = "Your quota for Dify Hosted OpenAI has been exhausted. " \
+                  "Please go to Settings -> Model Provider to complete your own provider credentials."
     code = 400
 
 
 class ProviderModelCurrentlyNotSupportError(BaseHTTPException):
     error_code = 'model_currently_not_support'
-    description = "GPT-4 currently not support."
+    description = "Dify Hosted OpenAI trial currently not support the GPT-4 model."
     code = 400
 
 
 class ConversationCompletedError(BaseHTTPException):
     error_code = 'conversation_completed'
-    description = "Conversation was completed."
+    description = "The conversation has ended. Please start a new conversation."
     code = 400
 
 
 class AppUnavailableError(BaseHTTPException):
     error_code = 'app_unavailable'
-    description = "App unavailable."
+    description = "App unavailable, please check your app configurations."
     code = 400
 
 
@@ -45,5 +47,29 @@ class CompletionRequestError(BaseHTTPException):
 
 class AppMoreLikeThisDisabledError(BaseHTTPException):
     error_code = 'app_more_like_this_disabled'
-    description = "More like this disabled."
+    description = "The 'More like this' feature is disabled. Please refresh your page."
     code = 403
+
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = 'no_audio_uploaded'
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = 'audio_too_large'
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = 'unsupported_audio_type'
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = 'provider_not_support_speech_to_text'
+    description = "Provider not support speech to text."
+    code = 400

api/controllers/console/app/explore.py

@@ -1,209 +0,0 @@
-# -*- coding:utf-8 -*-
-from datetime import datetime
-
-from flask_login import login_required, current_user
-from flask_restful import Resource, reqparse, fields, marshal_with, abort, inputs
-from sqlalchemy import and_
-
-from controllers.console import api
-from extensions.ext_database import db
-from models.model import Tenant, App, InstalledApp, RecommendedApp
-from services.account_service import TenantService
-
-app_fields = {
-    'id': fields.String,
-    'name': fields.String,
-    'mode': fields.String,
-    'icon': fields.String,
-    'icon_background': fields.String
-}
-
-installed_app_fields = {
-    'id': fields.String,
-    'app': fields.Nested(app_fields, attribute='app'),
-    'app_owner_tenant_id': fields.String,
-    'is_pinned': fields.Boolean,
-    'last_used_at': fields.DateTime,
-    'editable': fields.Boolean
-}
-
-installed_app_list_fields = {
-    'installed_apps': fields.List(fields.Nested(installed_app_fields))
-}
-
-recommended_app_fields = {
-    'app': fields.Nested(app_fields, attribute='app'),
-    'app_id': fields.String,
-    'description': fields.String(attribute='description'),
-    'copyright': fields.String,
-    'privacy_policy': fields.String,
-    'category': fields.String,
-    'position': fields.Integer,
-    'is_listed': fields.Boolean,
-    'install_count': fields.Integer,
-    'installed': fields.Boolean,
-    'editable': fields.Boolean
-}
-
-recommended_app_list_fields = {
-    'recommended_apps': fields.List(fields.Nested(recommended_app_fields)),
-    'categories': fields.List(fields.String)
-}
-
-
-class InstalledAppsListResource(Resource):
-    @login_required
-    @marshal_with(installed_app_list_fields)
-    def get(self):
-        current_tenant_id = Tenant.query.first().id
-        installed_apps = db.session.query(InstalledApp).filter(
-            InstalledApp.tenant_id == current_tenant_id
-        ).all()
-
-        current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
-        installed_apps = [
-            {
-                **installed_app,
-                "editable": current_user.role in ["owner", "admin"],
-            }
-            for installed_app in installed_apps
-        ]
-        installed_apps.sort(key=lambda app: (-app.is_pinned, app.last_used_at))
-
-        return {'installed_apps': installed_apps}
-
-    @login_required
-    def post(self):
-        parser = reqparse.RequestParser()
-        parser.add_argument('app_id', type=str, required=True, help='Invalid app_id')
-        args = parser.parse_args()
-
-        current_tenant_id = Tenant.query.first().id
-        app = App.query.get(args['app_id'])
-        if app is None:
-            abort(404, message='App not found')
-        recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == args['app_id']).first()
-        if recommended_app is None:
-            abort(404, message='App not found')
-        if not app.is_public:
-            abort(403, message="You can't install a non-public app")
-
-        installed_app = InstalledApp.query.filter(and_(
-            InstalledApp.app_id == args['app_id'],
-            InstalledApp.tenant_id == current_tenant_id
-        )).first()
-
-        if installed_app is None:
-            # todo: position
-            recommended_app.install_count += 1
-
-            new_installed_app = InstalledApp(
-                app_id=args['app_id'],
-                tenant_id=current_tenant_id,
-                is_pinned=False,
-                last_used_at=datetime.utcnow()
-            )
-            db.session.add(new_installed_app)
-            db.session.commit()
-
-        return {'message': 'App installed successfully'}
-
-
-class InstalledAppResource(Resource):
-
-    @login_required
-    def delete(self, installed_app_id):
-
-        installed_app = InstalledApp.query.filter(and_(
-            InstalledApp.id == str(installed_app_id),
-            InstalledApp.tenant_id == current_user.current_tenant_id
-        )).first()
-
-        if installed_app is None:
-            abort(404, message='App not found')
-
-        if installed_app.app_owner_tenant_id == current_user.current_tenant_id:
-            abort(400, message="You can't uninstall an app owned by the current tenant")
-
-        db.session.delete(installed_app)
-        db.session.commit()
-
-        return {'result': 'success', 'message': 'App uninstalled successfully'}
-
-    @login_required
-    def patch(self, installed_app_id):
-        parser = reqparse.RequestParser()
-        parser.add_argument('is_pinned', type=inputs.boolean)
-        args = parser.parse_args()
-
-        current_tenant_id = Tenant.query.first().id
-        installed_app = InstalledApp.query.filter(and_(
-            InstalledApp.id == str(installed_app_id),
-            InstalledApp.tenant_id == current_tenant_id
-        )).first()
-
-        if installed_app is None:
-            abort(404, message='Installed app not found')
-
-        commit_args = False
-        if 'is_pinned' in args:
-            installed_app.is_pinned = args['is_pinned']
-            commit_args = True
-
-        if commit_args:
-            db.session.commit()
-
-        return {'result': 'success', 'message': 'App info updated successfully'}
-
-
-class RecommendedAppsResource(Resource):
-    @login_required
-    @marshal_with(recommended_app_list_fields)
-    def get(self):
-        recommended_apps = db.session.query(RecommendedApp).filter(
-            RecommendedApp.is_listed == True
-        ).all()
-
-        categories = set()
-        current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
-        recommended_apps_result = []
-        for recommended_app in recommended_apps:
-            installed = db.session.query(InstalledApp).filter(
-                and_(
-                    InstalledApp.app_id == recommended_app.app_id,
-                    InstalledApp.tenant_id == current_user.current_tenant_id
-                )
-            ).first() is not None
-
-            language_prefix = current_user.interface_language.split('-')[0]
-            desc = None
-            if recommended_app.description:
-                if language_prefix in recommended_app.description:
-                    desc = recommended_app.description[language_prefix]
-                elif 'en' in recommended_app.description:
-                    desc = recommended_app.description['en']
-
-            recommended_app_result = {
-                'id': recommended_app.id,
-                'app': recommended_app.app,
-                'app_id': recommended_app.app_id,
-                'description': desc,
-                'copyright': recommended_app.copyright,
-                'privacy_policy': recommended_app.privacy_policy,
-                'category': recommended_app.category,
-                'position': recommended_app.position,
-                'is_listed': recommended_app.is_listed,
-                'install_count': recommended_app.install_count,
-                'installed': installed,
-                'editable': current_user.role in ['owner', 'admin'],
-            }
-            recommended_apps_result.append(recommended_app_result)
-
-            categories.add(recommended_app.category)  # add category to categories
-
-        return {'recommended_apps': recommended_apps_result, 'categories': list(categories)}
-
-
-api.add_resource(InstalledAppsListResource, '/installed-apps')
-api.add_resource(InstalledAppResource, '/installed-apps/<uuid:installed_app_id>')
-api.add_resource(RecommendedAppsResource, '/explore/apps')

api/controllers/console/app/generator.py

@@ -0,0 +1,75 @@
+from flask_login import login_required, current_user
+from flask_restful import Resource, reqparse
+
+from controllers.console import api
+from controllers.console.app.error import ProviderNotInitializeError, ProviderQuotaExceededError, \
+    CompletionRequestError, ProviderModelCurrentlyNotSupportError
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from core.generator.llm_generator import LLMGenerator
+from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, LLMBadRequestError, LLMAPIConnectionError, \
+    LLMAPIUnavailableError, LLMRateLimitError, LLMAuthorizationError, ModelCurrentlyNotSupportError
+
+
+class IntroductionGenerateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('prompt_template', type=str, required=True, location='json')
+        args = parser.parse_args()
+
+        account = current_user
+
+        try:
+            answer = LLMGenerator.generate_introduction(
+                account.current_tenant_id,
+                args['prompt_template']
+            )
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+
+        return {'introduction': answer}
+
+
+class RuleGenerateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('audiences', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('hoping_to_solve', type=str, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        account = current_user
+
+        try:
+            rules = LLMGenerator.generate_rule_config(
+                account.current_tenant_id,
+                args['audiences'],
+                args['hoping_to_solve']
+            )
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+
+        return rules
+
+
+api.add_resource(IntroductionGenerateApi, '/introduction-generate')
+api.add_resource(RuleGenerateApi, '/rule-generate')

api/controllers/console/app/message.py

@@ -26,46 +26,46 @@ from services.errors.conversation import ConversationNotExistsError
 from services.errors.message import MessageNotExistsError
 from services.message_service import MessageService
 
+account_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'email': fields.String
+}
 
-class ChatMessageApi(Resource):
-    account_fields = {
-        'id': fields.String,
-        'name': fields.String,
-        'email': fields.String
-    }
+feedback_fields = {
+    'rating': fields.String,
+    'content': fields.String,
+    'from_source': fields.String,
+    'from_end_user_id': fields.String,
+    'from_account': fields.Nested(account_fields, allow_null=True),
+}
 
-    feedback_fields = {
-        'rating': fields.String,
-        'content': fields.String,
-        'from_source': fields.String,
-        'from_end_user_id': fields.String,
-        'from_account': fields.Nested(account_fields, allow_null=True),
-    }
+annotation_fields = {
+    'content': fields.String,
+    'account': fields.Nested(account_fields, allow_null=True),
+    'created_at': TimestampField
+}
 
-    annotation_fields = {
-        'content': fields.String,
-        'account': fields.Nested(account_fields, allow_null=True),
-        'created_at': TimestampField
-    }
+message_detail_fields = {
+    'id': fields.String,
+    'conversation_id': fields.String,
+    'inputs': fields.Raw,
+    'query': fields.String,
+    'message': fields.Raw,
+    'message_tokens': fields.Integer,
+    'answer': fields.String,
+    'answer_tokens': fields.Integer,
+    'provider_response_latency': fields.Float,
+    'from_source': fields.String,
+    'from_end_user_id': fields.String,
+    'from_account_id': fields.String,
+    'feedbacks': fields.List(fields.Nested(feedback_fields)),
+    'annotation': fields.Nested(annotation_fields, allow_null=True),
+    'created_at': TimestampField
+}
 
-    message_detail_fields = {
-        'id': fields.String,
-        'conversation_id': fields.String,
-        'inputs': fields.Raw,
-        'query': fields.String,
-        'message': fields.Raw,
-        'message_tokens': fields.Integer,
-        'answer': fields.String,
-        'answer_tokens': fields.Integer,
-        'provider_response_latency': fields.Integer,
-        'from_source': fields.String,
-        'from_end_user_id': fields.String,
-        'from_account_id': fields.String,
-        'feedbacks': fields.List(fields.Nested(feedback_fields)),
-        'annotation': fields.Nested(annotation_fields, allow_null=True),
-        'created_at': TimestampField
-    }
 
+class ChatMessageListApi(Resource):
     message_infinite_scroll_pagination_fields = {
         'limit': fields.Integer,
         'has_more': fields.Boolean,
@@ -253,7 +253,8 @@ class MessageMoreLikeThisApi(Resource):
         message_id = str(message_id)
 
         parser = reqparse.RequestParser()
-        parser.add_argument('response_mode', type=str, required=True, choices=['blocking', 'streaming'], location='args')
+        parser.add_argument('response_mode', type=str, required=True, choices=['blocking', 'streaming'],
+                            location='args')
         args = parser.parse_args()
 
         streaming = args['response_mode'] == 'streaming'
@@ -301,7 +302,8 @@ def compact_response(response: Union[dict | Generator]) -> Response:
             except QuotaExceededError:
                 yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
             except ModelCurrentlyNotSupportError:
-                yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
+                yield "data: " + json.dumps(
+                    api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
                     LLMRateLimitError, LLMAuthorizationError) as e:
                 yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
@@ -353,9 +355,33 @@ class MessageSuggestedQuestionApi(Resource):
         return {'data': questions}
 
 
+class MessageApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(message_detail_fields)
+    def get(self, app_id, message_id):
+        app_id = str(app_id)
+        message_id = str(message_id)
+
+        # get app info
+        app_model = _get_app(app_id, 'chat')
+
+        message = db.session.query(Message).filter(
+            Message.id == message_id,
+            Message.app_id == app_model.id
+        ).first()
+
+        if not message:
+            raise NotFound("Message Not Exists.")
+
+        return message
+
+
 api.add_resource(MessageMoreLikeThisApi, '/apps/<uuid:app_id>/completion-messages/<uuid:message_id>/more-like-this')
 api.add_resource(MessageSuggestedQuestionApi, '/apps/<uuid:app_id>/chat-messages/<uuid:message_id>/suggested-questions')
-api.add_resource(ChatMessageApi, '/apps/<uuid:app_id>/chat-messages', endpoint='chat_messages')
+api.add_resource(ChatMessageListApi, '/apps/<uuid:app_id>/chat-messages', endpoint='console_chat_messages')
 api.add_resource(MessageFeedbackApi, '/apps/<uuid:app_id>/feedbacks')
 api.add_resource(MessageAnnotationApi, '/apps/<uuid:app_id>/annotations')
 api.add_resource(MessageAnnotationCountApi, '/apps/<uuid:app_id>/annotations/count')
+api.add_resource(MessageApi, '/apps/<uuid:app_id>/messages/<uuid:message_id>', endpoint='console_message')

api/controllers/console/app/model_config.py

@@ -41,6 +41,7 @@ class ModelConfigResource(Resource):
             opening_statement=model_configuration['opening_statement'],
             suggested_questions=json.dumps(model_configuration['suggested_questions']),
             suggested_questions_after_answer=json.dumps(model_configuration['suggested_questions_after_answer']),
+            speech_to_text=json.dumps(model_configuration['speech_to_text']),
             more_like_this=json.dumps(model_configuration['more_like_this']),
             model=json.dumps(model_configuration['model']),
             user_input_form=json.dumps(model_configuration['user_input_form']),

api/controllers/console/app/statistic.py

@@ -1,4 +1,5 @@
 # -*- coding:utf-8 -*-
+from decimal import Decimal
 from datetime import datetime
 
 import pytz
@@ -59,18 +60,20 @@ class DailyConversationStatistic(Resource):
             arg_dict['end'] = end_datetime_utc
 
         sql_query += ' GROUP BY date order by date'
-        rs = db.session.execute(sql_query, arg_dict)
 
-        response_date = []
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+
+        response_data = []
 
         for i in rs:
-            response_date.append({
+            response_data.append({
                 'date': str(i.date),
                 'conversation_count': i.conversation_count
             })
 
         return jsonify({
-            'data': response_date
+            'data': response_data
         })
 
 
@@ -119,18 +122,20 @@ class DailyTerminalsStatistic(Resource):
             arg_dict['end'] = end_datetime_utc
 
         sql_query += ' GROUP BY date order by date'
-        rs = db.session.execute(sql_query, arg_dict)
 
-        response_date = []
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+
+        response_data = []
 
         for i in rs:
-            response_date.append({
+            response_data.append({
                 'date': str(i.date),
                 'terminal_count': i.terminal_count
             })
 
         return jsonify({
-            'data': response_date
+            'data': response_data
         })
 
 
@@ -180,12 +185,14 @@ class DailyTokenCostStatistic(Resource):
             arg_dict['end'] = end_datetime_utc
 
         sql_query += ' GROUP BY date order by date'
-        rs = db.session.execute(sql_query, arg_dict)
 
-        response_date = []
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+
+        response_data = []
 
         for i in rs:
-            response_date.append({
+            response_data.append({
                 'date': str(i.date),
                 'token_count': i.token_count,
                 'total_price': i.total_price,
@@ -193,10 +200,207 @@ class DailyTokenCostStatistic(Resource):
             })
 
         return jsonify({
-            'data': response_date
+            'data': response_data
+        })
+
+
+class AverageSessionInteractionStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, app_id):
+        account = current_user
+        app_id = str(app_id)
+        app_model = _get_app(app_id, 'chat')
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = """SELECT date(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
+AVG(subquery.message_count) AS interactions
+FROM (SELECT m.conversation_id, COUNT(m.id) AS message_count
+    FROM conversations c
+    JOIN messages m ON c.id = m.conversation_id
+    WHERE c.override_model_configs IS NULL AND c.app_id = :app_id"""
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and c.created_at >= :start'
+            arg_dict['start'] = start_datetime_utc
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and c.created_at < :end'
+            arg_dict['end'] = end_datetime_utc
+
+        sql_query += """
+        GROUP BY m.conversation_id) subquery
+LEFT JOIN conversations c on c.id=subquery.conversation_id
+GROUP BY date
+ORDER BY date"""
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+
+        response_data = []
+
+        for i in rs:
+            response_data.append({
+                'date': str(i.date),
+                'interactions': float(i.interactions.quantize(Decimal('0.01')))
+            })
+
+        return jsonify({
+            'data': response_data
+        })
+
+
+class UserSatisfactionRateStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, app_id):
+        account = current_user
+        app_id = str(app_id)
+        app_model = _get_app(app_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = '''
+                        SELECT date(DATE_TRUNC('day', m.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
+                            COUNT(m.id) as message_count, COUNT(mf.id) as feedback_count 
+                            FROM messages m
+                            LEFT JOIN message_feedbacks mf on mf.message_id=m.id
+                            WHERE m.app_id = :app_id 
+                        '''
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and m.created_at >= :start'
+            arg_dict['start'] = start_datetime_utc
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and m.created_at < :end'
+            arg_dict['end'] = end_datetime_utc
+
+        sql_query += ' GROUP BY date order by date'
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+
+        response_data = []
+
+        for i in rs:
+            response_data.append({
+                'date': str(i.date),
+                'rate': round((i.feedback_count * 1000 / i.message_count) if i.message_count > 0 else 0, 2),
+            })
+
+        return jsonify({
+                'data': response_data
+            })
+
+
+class AverageResponseTimeStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, app_id):
+        account = current_user
+        app_id = str(app_id)
+        app_model = _get_app(app_id, 'completion')
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = '''
+                SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
+                    AVG(provider_response_latency) as latency
+                    FROM messages
+                    WHERE app_id = :app_id
+                '''
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at >= :start'
+            arg_dict['start'] = start_datetime_utc
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at < :end'
+            arg_dict['end'] = end_datetime_utc
+
+        sql_query += ' GROUP BY date order by date'
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+
+        response_data = []
+
+        for i in rs:
+            response_data.append({
+                'date': str(i.date),
+                'latency': round(i.latency * 1000, 4)
+            })
+
+        return jsonify({
+            'data': response_data
         })
 
 
 api.add_resource(DailyConversationStatistic, '/apps/<uuid:app_id>/statistics/daily-conversations')
 api.add_resource(DailyTerminalsStatistic, '/apps/<uuid:app_id>/statistics/daily-end-users')
 api.add_resource(DailyTokenCostStatistic, '/apps/<uuid:app_id>/statistics/token-costs')
+api.add_resource(AverageSessionInteractionStatistic, '/apps/<uuid:app_id>/statistics/average-session-interactions')
+api.add_resource(UserSatisfactionRateStatistic, '/apps/<uuid:app_id>/statistics/user-satisfaction-rate')
+api.add_resource(AverageResponseTimeStatistic, '/apps/<uuid:app_id>/statistics/average-response-time')

api/controllers/console/auth/data_source_oauth.py

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

api/controllers/console/datasets/data_source.py

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

api/controllers/console/datasets/datasets.py

@@ -12,8 +12,9 @@ from controllers.console.wraps import account_initialization_required
 from core.indexing_runner import IndexingRunner
 from libs.helper import TimestampField
 from extensions.ext_database import db
+from models.dataset import DocumentSegment, Document
 from models.model import UploadFile
-from services.dataset_service import DatasetService
+from services.dataset_service import DatasetService, DocumentService
 
 dataset_detail_fields = {
     'id': fields.String,
@@ -50,8 +51,8 @@ def _validate_name(name):
 
 
 def _validate_description_length(description):
-    if len(description) > 200:
-        raise ValueError('Description cannot exceed 200 characters.')
+    if len(description) > 400:
+        raise ValueError('Description cannot exceed 400 characters.')
     return description
 
 
@@ -217,17 +218,31 @@ class DatasetIndexingEstimateApi(Resource):
     @login_required
     @account_initialization_required
     def post(self):
-        segment_rule = request.get_json()
-        file_detail = db.session.query(UploadFile).filter(
-            UploadFile.tenant_id == current_user.current_tenant_id,
-            UploadFile.id == segment_rule["file_id"]
-        ).first()
+        parser = reqparse.RequestParser()
+        parser.add_argument('info_list', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        args = parser.parse_args()
+        # validate args
+        DocumentService.estimate_args_validate(args)
+        if args['info_list']['data_source_type'] == 'upload_file':
+            file_ids = args['info_list']['file_info_list']['file_ids']
+            file_details = db.session.query(UploadFile).filter(
+                UploadFile.tenant_id == current_user.current_tenant_id,
+                UploadFile.id.in_(file_ids)
+            ).all()
 
-        if file_detail is None:
-            raise NotFound("File not found.")
+            if file_details is None:
+                raise NotFound("File not found.")
 
-        indexing_runner = IndexingRunner()
-        response = indexing_runner.indexing_estimate(file_detail, segment_rule['process_rule'])
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.file_indexing_estimate(file_details, args['process_rule'])
+        elif args['info_list']['data_source_type'] == 'notion_import':
+
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.notion_indexing_estimate(args['info_list']['notion_info_list'],
+                                                                args['process_rule'])
+        else:
+            raise ValueError('Data source type not support')
         return response, 200
 
 
@@ -274,8 +289,54 @@ class DatasetRelatedAppListApi(Resource):
         }, 200
 
 
+class DatasetIndexingStatusApi(Resource):
+    document_status_fields = {
+        'id': fields.String,
+        'indexing_status': fields.String,
+        'processing_started_at': TimestampField,
+        'parsing_completed_at': TimestampField,
+        'cleaning_completed_at': TimestampField,
+        'splitting_completed_at': TimestampField,
+        'completed_at': TimestampField,
+        'paused_at': TimestampField,
+        'error': fields.String,
+        'stopped_at': TimestampField,
+        'completed_segments': fields.Integer,
+        'total_segments': fields.Integer,
+    }
+
+    document_status_fields_list = {
+        'data': fields.List(fields.Nested(document_status_fields))
+    }
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id):
+        dataset_id = str(dataset_id)
+        documents = db.session.query(Document).filter(
+            Document.dataset_id == dataset_id,
+            Document.tenant_id == current_user.current_tenant_id
+        ).all()
+        documents_status = []
+        for document in documents:
+            completed_segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                              DocumentSegment.document_id == str(document.id),
+                                                              DocumentSegment.status != 're_segment').count()
+            total_segments = DocumentSegment.query.filter(DocumentSegment.document_id == str(document.id),
+                                                          DocumentSegment.status != 're_segment').count()
+            document.completed_segments = completed_segments
+            document.total_segments = total_segments
+            documents_status.append(marshal(document, self.document_status_fields))
+        data = {
+            'data': documents_status
+        }
+        return data
+
+
 api.add_resource(DatasetListApi, '/datasets')
 api.add_resource(DatasetApi, '/datasets/<uuid:dataset_id>')
 api.add_resource(DatasetQueryApi, '/datasets/<uuid:dataset_id>/queries')
-api.add_resource(DatasetIndexingEstimateApi, '/datasets/file-indexing-estimate')
+api.add_resource(DatasetIndexingEstimateApi, '/datasets/indexing-estimate')
 api.add_resource(DatasetRelatedAppListApi, '/datasets/<uuid:dataset_id>/related-apps')
+api.add_resource(DatasetIndexingStatusApi, '/datasets/<uuid:dataset_id>/indexing-status')

api/controllers/console/datasets/datasets_document.py

@@ -1,6 +1,7 @@
 # -*- coding:utf-8 -*-
 import random
 from datetime import datetime
+from typing import List
 
 from flask import request
 from flask_login import login_required, current_user
@@ -10,13 +11,14 @@ from werkzeug.exceptions import NotFound, Forbidden
 
 import services
 from controllers.console import api
-from controllers.console.app.error import ProviderNotInitializeError
+from controllers.console.app.error import ProviderNotInitializeError, ProviderQuotaExceededError, \
+    ProviderModelCurrentlyNotSupportError
 from controllers.console.datasets.error import DocumentAlreadyFinishedError, InvalidActionError, DocumentIndexingError, \
     InvalidMetadataError, ArchivedDocumentImmutableError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.indexing_runner import IndexingRunner
-from core.llm.error import ProviderTokenNotInitError
+from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
 from extensions.ext_redis import redis_client
 from libs.helper import TimestampField
 from extensions.ext_database import db
@@ -60,6 +62,29 @@ document_fields = {
     'hit_count': fields.Integer,
 }
 
+document_with_segments_fields = {
+    'id': fields.String,
+    'position': fields.Integer,
+    'data_source_type': fields.String,
+    'data_source_info': fields.Raw(attribute='data_source_info_dict'),
+    'dataset_process_rule_id': fields.String,
+    'name': fields.String,
+    'created_from': fields.String,
+    'created_by': fields.String,
+    'created_at': TimestampField,
+    'tokens': fields.Integer,
+    'indexing_status': fields.String,
+    'error': fields.String,
+    'enabled': fields.Boolean,
+    'disabled_at': TimestampField,
+    'disabled_by': fields.String,
+    'archived': fields.Boolean,
+    'display_status': fields.String,
+    'word_count': fields.Integer,
+    'hit_count': fields.Integer,
+    'completed_segments': fields.Integer,
+    'total_segments': fields.Integer
+}
 
 class DocumentResource(Resource):
     def get_document(self, dataset_id: str, document_id: str) -> Document:
@@ -82,6 +107,23 @@ class DocumentResource(Resource):
 
         return document
 
+    def get_batch_documents(self, dataset_id: str, batch: str) -> List[Document]:
+        dataset = DatasetService.get_dataset(dataset_id)
+        if not dataset:
+            raise NotFound('Dataset not found.')
+
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        documents = DocumentService.get_batch_documents(dataset_id, batch)
+
+        if not documents:
+            raise NotFound('Documents not found.')
+
+        return documents
+
 
 class GetProcessRuleApi(Resource):
     @setup_required
@@ -131,9 +173,9 @@ class DatasetDocumentListApi(Resource):
         dataset_id = str(dataset_id)
         page = request.args.get('page', default=1, type=int)
         limit = request.args.get('limit', default=20, type=int)
-        search = request.args.get('search', default=None, type=str)
+        search = request.args.get('keyword', default=None, type=str)
         sort = request.args.get('sort', default='-created_at', type=str)
-
+        fetch = request.args.get('fetch', default=False, type=bool)
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')
@@ -172,9 +214,20 @@ class DatasetDocumentListApi(Resource):
         paginated_documents = query.paginate(
             page=page, per_page=limit, max_per_page=100, error_out=False)
         documents = paginated_documents.items
-
+        if fetch:
+            for document in documents:
+                completed_segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                                  DocumentSegment.document_id == str(document.id),
+                                                                  DocumentSegment.status != 're_segment').count()
+                total_segments = DocumentSegment.query.filter(DocumentSegment.document_id == str(document.id),
+                                                              DocumentSegment.status != 're_segment').count()
+                document.completed_segments = completed_segments
+                document.total_segments = total_segments
+            data = marshal(documents, document_with_segments_fields)
+        else:
+            data = marshal(documents, document_fields)
         response = {
-            'data': marshal(documents, document_fields),
+            'data': data,
             'has_more': len(documents) == limit,
             'limit': limit,
             'total': paginated_documents.total,
@@ -183,10 +236,15 @@ class DatasetDocumentListApi(Resource):
 
         return response
 
+    documents_and_batch_fields = {
+        'documents': fields.List(fields.Nested(document_fields)),
+        'batch': fields.String
+    }
+
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(document_fields)
+    @marshal_with(documents_and_batch_fields)
     def post(self, dataset_id):
         dataset_id = str(dataset_id)
 
@@ -207,9 +265,10 @@ class DatasetDocumentListApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('indexing_technique', type=str, choices=Dataset.INDEXING_TECHNIQUE_LIST, nullable=False,
                             location='json')
-        parser.add_argument('data_source', type=dict, required=True, nullable=True, location='json')
-        parser.add_argument('process_rule', type=dict, required=True, nullable=True, location='json')
+        parser.add_argument('data_source', type=dict, required=False, location='json')
+        parser.add_argument('process_rule', type=dict, required=False, location='json')
         parser.add_argument('duplicate', type=bool, nullable=False, location='json')
+        parser.add_argument('original_document_id', type=str, required=False, location='json')
         args = parser.parse_args()
 
         if not dataset.indexing_technique and not args['indexing_technique']:
@@ -219,17 +278,25 @@ class DatasetDocumentListApi(Resource):
         DocumentService.document_create_args_validate(args)
 
         try:
-            document = DocumentService.save_document_with_dataset_id(dataset, args, current_user)
+            documents, batch = DocumentService.save_document_with_dataset_id(dataset, args, current_user)
         except ProviderTokenNotInitError:
             raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
 
-        return document
+        return {
+            'documents': documents,
+            'batch': batch
+        }
 
 
 class DatasetInitApi(Resource):
     dataset_and_document_fields = {
         'dataset': fields.Nested(dataset_fields),
-        'document': fields.Nested(document_fields)
+        'documents': fields.List(fields.Nested(document_fields)),
+        'batch': fields.String
     }
 
     @setup_required
@@ -252,17 +319,22 @@ class DatasetInitApi(Resource):
         DocumentService.document_create_args_validate(args)
 
         try:
-            dataset, document = DocumentService.save_document_without_dataset_id(
+            dataset, documents, batch = DocumentService.save_document_without_dataset_id(
                 tenant_id=current_user.current_tenant_id,
                 document_data=args,
                 account=current_user
             )
         except ProviderTokenNotInitError:
             raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
 
         response = {
             'dataset': dataset,
-            'document': document
+            'documents': documents,
+            'batch': batch
         }
 
         return response
@@ -307,11 +379,122 @@ class DocumentIndexingEstimateApi(DocumentResource):
                     raise NotFound('File not found.')
 
                 indexing_runner = IndexingRunner()
-                response = indexing_runner.indexing_estimate(file, data_process_rule_dict)
+
+                response = indexing_runner.file_indexing_estimate([file], data_process_rule_dict)
 
         return response
 
 
+class DocumentBatchIndexingEstimateApi(DocumentResource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, batch):
+        dataset_id = str(dataset_id)
+        batch = str(batch)
+        dataset = DatasetService.get_dataset(dataset_id)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+        documents = self.get_batch_documents(dataset_id, batch)
+        response = {
+            "tokens": 0,
+            "total_price": 0,
+            "currency": "USD",
+            "total_segments": 0,
+            "preview": []
+        }
+        if not documents:
+            return response
+        data_process_rule = documents[0].dataset_process_rule
+        data_process_rule_dict = data_process_rule.to_dict()
+        info_list = []
+        for document in documents:
+            if document.indexing_status in ['completed', 'error']:
+                raise DocumentAlreadyFinishedError()
+            data_source_info = document.data_source_info_dict
+            # format document files info
+            if data_source_info and 'upload_file_id' in data_source_info:
+                file_id = data_source_info['upload_file_id']
+                info_list.append(file_id)
+            # format document notion info
+            elif data_source_info and 'notion_workspace_id' in data_source_info and 'notion_page_id' in data_source_info:
+                pages = []
+                page = {
+                    'page_id': data_source_info['notion_page_id'],
+                    'type': data_source_info['type']
+                }
+                pages.append(page)
+                notion_info = {
+                    'workspace_id': data_source_info['notion_workspace_id'],
+                    'pages': pages
+                }
+                info_list.append(notion_info)
+
+        if dataset.data_source_type == 'upload_file':
+            file_details = db.session.query(UploadFile).filter(
+                UploadFile.tenant_id == current_user.current_tenant_id,
+                UploadFile.id in info_list
+            ).all()
+
+            if file_details is None:
+                raise NotFound("File not found.")
+
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.file_indexing_estimate(file_details, data_process_rule_dict)
+        elif dataset.data_source_type:
+
+            indexing_runner = IndexingRunner()
+            response = indexing_runner.notion_indexing_estimate(info_list,
+                                                                data_process_rule_dict)
+        else:
+            raise ValueError('Data source type not support')
+        return response
+
+
+class DocumentBatchIndexingStatusApi(DocumentResource):
+    document_status_fields = {
+        'id': fields.String,
+        'indexing_status': fields.String,
+        'processing_started_at': TimestampField,
+        'parsing_completed_at': TimestampField,
+        'cleaning_completed_at': TimestampField,
+        'splitting_completed_at': TimestampField,
+        'completed_at': TimestampField,
+        'paused_at': TimestampField,
+        'error': fields.String,
+        'stopped_at': TimestampField,
+        'completed_segments': fields.Integer,
+        'total_segments': fields.Integer,
+    }
+
+    document_status_fields_list = {
+        'data': fields.List(fields.Nested(document_status_fields))
+    }
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id, batch):
+        dataset_id = str(dataset_id)
+        batch = str(batch)
+        documents = self.get_batch_documents(dataset_id, batch)
+        documents_status = []
+        for document in documents:
+            completed_segments = DocumentSegment.query.filter(DocumentSegment.completed_at.isnot(None),
+                                                              DocumentSegment.document_id == str(document.id),
+                                                              DocumentSegment.status != 're_segment').count()
+            total_segments = DocumentSegment.query.filter(DocumentSegment.document_id == str(document.id),
+                                                          DocumentSegment.status != 're_segment').count()
+            document.completed_segments = completed_segments
+            document.total_segments = total_segments
+            documents_status.append(marshal(document, self.document_status_fields))
+        data = {
+            'data': documents_status
+        }
+        return data
+
+
 class DocumentIndexingStatusApi(DocumentResource):
     document_status_fields = {
         'id': fields.String,
@@ -338,10 +521,12 @@ class DocumentIndexingStatusApi(DocumentResource):
 
         completed_segments = DocumentSegment.query \
             .filter(DocumentSegment.completed_at.isnot(None),
-                    DocumentSegment.document_id == str(document_id)) \
+                    DocumentSegment.document_id == str(document_id),
+                    DocumentSegment.status != 're_segment') \
             .count()
         total_segments = DocumentSegment.query \
-            .filter_by(document_id=str(document_id)) \
+            .filter(DocumentSegment.document_id == str(document_id),
+                    DocumentSegment.status != 're_segment') \
             .count()
 
         document.completed_segments = completed_segments
@@ -396,7 +581,7 @@ class DocumentDetailApi(DocumentResource):
                 'disabled_by': document.disabled_by,
                 'archived': document.archived,
                 'segment_count': document.segment_count,
-                'average_segment_length':   document.average_segment_length,
+                'average_segment_length': document.average_segment_length,
                 'hit_count': document.hit_count,
                 'display_status': document.display_status
             }
@@ -416,7 +601,7 @@ class DocumentDetailApi(DocumentResource):
                 'created_at': document.created_at.timestamp(),
                 'tokens': document.tokens,
                 'indexing_status': document.indexing_status,
-                'completed_at': int(document.completed_at.timestamp())if document.completed_at else None,
+                'completed_at': int(document.completed_at.timestamp()) if document.completed_at else None,
                 'updated_at': int(document.updated_at.timestamp()) if document.updated_at else None,
                 'indexing_latency': document.indexing_latency,
                 'error': document.error,
@@ -567,6 +752,8 @@ class DocumentStatusApi(DocumentResource):
             return {'result': 'success'}, 200
 
         elif action == "disable":
+            if not document.completed_at or document.indexing_status != 'completed':
+                raise InvalidActionError('Document is not completed.')
             if not document.enabled:
                 raise InvalidActionError('Document already disabled.')
 
@@ -666,6 +853,10 @@ api.add_resource(DatasetInitApi,
                  '/datasets/init')
 api.add_resource(DocumentIndexingEstimateApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/indexing-estimate')
+api.add_resource(DocumentBatchIndexingEstimateApi,
+                 '/datasets/<uuid:dataset_id>/batch/<string:batch>/indexing-estimate')
+api.add_resource(DocumentBatchIndexingStatusApi,
+                 '/datasets/<uuid:dataset_id>/batch/<string:batch>/indexing-status')
 api.add_resource(DocumentIndexingStatusApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/indexing-status')
 api.add_resource(DocumentDetailApi,

api/controllers/console/datasets/datasets_segments.py

@@ -78,12 +78,14 @@ class DatasetDocumentSegmentListApi(Resource):
         parser.add_argument('hit_count_gte', type=int,
                             default=None, location='args')
         parser.add_argument('enabled', type=str, default='all', location='args')
+        parser.add_argument('keyword', type=str, default=None, location='args')
         args = parser.parse_args()
 
         last_id = args['last_id']
         limit = min(args['limit'], 100)
         status_list = args['status']
         hit_count_gte = args['hit_count_gte']
+        keyword = args['keyword']
 
         query = DocumentSegment.query.filter(
             DocumentSegment.document_id == str(document_id),
@@ -104,6 +106,9 @@ class DatasetDocumentSegmentListApi(Resource):
         if hit_count_gte is not None:
             query = query.filter(DocumentSegment.hit_count >= hit_count_gte)
 
+        if keyword:
+            query = query.where(DocumentSegment.content.ilike(f'%{keyword}%'))
+
         if args['enabled'].lower() != 'all':
             if args['enabled'].lower() == 'true':
                 query = query.filter(DocumentSegment.enabled == True)

api/controllers/console/datasets/error.py

@@ -3,7 +3,7 @@ from libs.exception import BaseHTTPException
 
 class NoFileUploadedError(BaseHTTPException):
     error_code = 'no_file_uploaded'
-    description = "No file uploaded."
+    description = "Please upload your file."
     code = 400
 
 
@@ -27,25 +27,25 @@ class UnsupportedFileTypeError(BaseHTTPException):
 
 class HighQualityDatasetOnlyError(BaseHTTPException):
     error_code = 'high_quality_dataset_only'
-    description = "High quality dataset only."
+    description = "Current operation only supports 'high-quality' datasets."
     code = 400
 
 
 class DatasetNotInitializedError(BaseHTTPException):
     error_code = 'dataset_not_initialized'
-    description = "Dataset not initialized."
+    description = "The dataset is still being initialized or indexing. Please wait a moment."
     code = 400
 
 
 class ArchivedDocumentImmutableError(BaseHTTPException):
     error_code = 'archived_document_immutable'
-    description = "Cannot process an archived document."
+    description = "The archived document is not editable."
     code = 403
 
 
 class DatasetNameDuplicateError(BaseHTTPException):
     error_code = 'dataset_name_duplicate'
-    description = "Dataset name already exists."
+    description = "The dataset name already exists. Please modify your dataset name."
     code = 409
 
 
@@ -57,17 +57,17 @@ class InvalidActionError(BaseHTTPException):
 
 class DocumentAlreadyFinishedError(BaseHTTPException):
     error_code = 'document_already_finished'
-    description = "Document already finished."
+    description = "The document has been processed. Please refresh the page or go to the document details."
     code = 400
 
 
 class DocumentIndexingError(BaseHTTPException):
     error_code = 'document_indexing'
-    description = "Document indexing."
+    description = "The document is being processed and cannot be edited."
     code = 400
 
 
 class InvalidMetadataError(BaseHTTPException):
     error_code = 'invalid_metadata'
-    description = "Invalid metadata."
+    description = "The metadata content is incorrect. Please check and verify."
     code = 400

api/controllers/console/datasets/file.py

@@ -1,6 +1,7 @@
 import datetime
 import hashlib
 import tempfile
+import chardet
 import time
 import uuid
 from pathlib import Path
@@ -16,8 +17,7 @@ from controllers.console.datasets.error import NoFileUploadedError, TooManyFiles
     UnsupportedFileTypeError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
-from core.index.readers.html_parser import HTMLParser
-from core.index.readers.pdf_parser import PDFParser
+from core.data_loader.file_extractor import FileExtractor
 from extensions.ext_storage import storage
 from libs.helper import TimestampField
 from extensions.ext_database import db
@@ -26,7 +26,7 @@ from models.model import UploadFile
 cache = TTLCache(maxsize=None, ttl=30)
 
 FILE_SIZE_LIMIT = 15 * 1024 * 1024  # 15MB
-ALLOWED_EXTENSIONS = ['txt', 'markdown', 'md', 'pdf', 'html', 'htm']
+ALLOWED_EXTENSIONS = ['txt', 'markdown', 'md', 'pdf', 'html', 'htm', 'xlsx']
 PREVIEW_WORDS_LIMIT = 3000
 
 
@@ -121,24 +121,7 @@ class FilePreviewApi(Resource):
         if extension not in ALLOWED_EXTENSIONS:
             raise UnsupportedFileTypeError()
 
-        with tempfile.TemporaryDirectory() as temp_dir:
-            suffix = Path(upload_file.key).suffix
-            filepath = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
-            storage.download(upload_file.key, filepath)
-
-            if extension == 'pdf':
-                parser = PDFParser({'upload_file': upload_file})
-                text = parser.parse_file(Path(filepath))
-            elif extension in ['html', 'htm']:
-                # Use BeautifulSoup to extract text
-                parser = HTMLParser()
-                text = parser.parse_file(Path(filepath))
-            else:
-                # ['txt', 'markdown', 'md']
-                with open(filepath, "rb") as fp:
-                    data = fp.read()
-                    text = data.decode(encoding='utf-8').strip() if data else ''
-
+        text = FileExtractor.load(upload_file, return_text=True)
         text = text[0:PREVIEW_WORDS_LIMIT] if text else ''
         return {'content': text}
 

api/controllers/console/datasets/hit_testing.py

@@ -6,9 +6,12 @@ from werkzeug.exceptions import InternalServerError, NotFound, Forbidden
 
 import services
 from controllers.console import api
+from controllers.console.app.error import ProviderNotInitializeError, ProviderQuotaExceededError, \
+    ProviderModelCurrentlyNotSupportError
 from controllers.console.datasets.error import HighQualityDatasetOnlyError, DatasetNotInitializedError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
+from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
 from libs.helper import TimestampField
 from services.dataset_service import DatasetService
 from services.hit_testing_service import HitTestingService
@@ -92,6 +95,12 @@ class HitTestingApi(Resource):
             return {"query": response['query'], 'records': marshal(response['records'], hit_testing_record_fields)}
         except services.errors.index.IndexNotInitializedError:
             raise DatasetNotInitializedError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
         except Exception as e:
             logging.exception("Hit testing failed.")
             raise InternalServerError(str(e))

api/controllers/console/error.py

@@ -3,13 +3,14 @@ from libs.exception import BaseHTTPException
 
 class AlreadySetupError(BaseHTTPException):
     error_code = 'already_setup'
-    description = "Application already setup."
+    description = "Dify has been successfully installed. Please refresh the page or return to the dashboard homepage."
     code = 403
 
 
 class NotSetupError(BaseHTTPException):
     error_code = 'not_setup'
-    description = "Application not setup."
+    description = "Dify has not been initialized and installed yet. " \
+                  "Please proceed with the initialization and installation process first."
     code = 401
 
 

api/controllers/console/explore/audio.py

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

api/controllers/console/explore/completion.py

@@ -0,0 +1,180 @@
+# -*- coding:utf-8 -*-
+import json
+import logging
+from typing import Generator, Union
+
+from flask import Response, stream_with_context
+from flask_login import current_user
+from flask_restful import reqparse
+from werkzeug.exceptions import InternalServerError, NotFound
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ConversationCompletedError, AppUnavailableError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError
+from controllers.console.explore.error import NotCompletionAppError, NotChatAppError
+from controllers.console.explore.wraps import InstalledAppResource
+from core.conversation_message_task import PubHandler
+from core.llm.error import LLMBadRequestError, LLMAPIUnavailableError, LLMAuthorizationError, LLMAPIConnectionError, \
+    LLMRateLimitError, ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
+from libs.helper import uuid_value
+from services.completion_service import CompletionService
+
+
+# define completion api for user
+class CompletionApi(InstalledAppResource):
+
+    def post(self, installed_app):
+        app_model = installed_app.app
+        if app_model.mode != 'completion':
+            raise NotCompletionAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, location='json')
+        parser.add_argument('query', type=str, location='json')
+        parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
+        args = parser.parse_args()
+
+        streaming = args['response_mode'] == 'streaming'
+
+        try:
+            response = CompletionService.completion(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                from_source='console',
+                streaming=streaming
+            )
+
+            return compact_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class CompletionStopApi(InstalledAppResource):
+    def post(self, installed_app, task_id):
+        app_model = installed_app.app
+        if app_model.mode != 'completion':
+            raise NotCompletionAppError()
+
+        PubHandler.stop(current_user, task_id)
+
+        return {'result': 'success'}, 200
+
+
+class ChatApi(InstalledAppResource):
+    def post(self, installed_app):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, location='json')
+        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')
+        args = parser.parse_args()
+
+        streaming = args['response_mode'] == 'streaming'
+
+        try:
+            response = CompletionService.completion(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                from_source='console',
+                streaming=streaming
+            )
+
+            return compact_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class ChatStopApi(InstalledAppResource):
+    def post(self, installed_app, task_id):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        PubHandler.stop(current_user, task_id)
+
+        return {'result': 'success'}, 200
+
+
+def compact_response(response: Union[dict | Generator]) -> Response:
+    if isinstance(response, dict):
+        return Response(response=json.dumps(response), status=200, mimetype='application/json')
+    else:
+        def generate() -> Generator:
+            try:
+                for chunk in response:
+                    yield chunk
+            except services.errors.conversation.ConversationNotExistsError:
+                yield "data: " + json.dumps(api.handle_error(NotFound("Conversation Not Exists.")).get_json()) + "\n\n"
+            except services.errors.conversation.ConversationCompletedError:
+                yield "data: " + json.dumps(api.handle_error(ConversationCompletedError()).get_json()) + "\n\n"
+            except services.errors.app_model_config.AppModelConfigBrokenError:
+                logging.exception("App model config broken.")
+                yield "data: " + json.dumps(api.handle_error(AppUnavailableError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except QuotaExceededError:
+                yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
+            except ModelCurrentlyNotSupportError:
+                yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
+            except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                    LLMRateLimitError, LLMAuthorizationError) as e:
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+            except ValueError as e:
+                yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
+            except Exception:
+                logging.exception("internal server error.")
+                yield "data: " + json.dumps(api.handle_error(InternalServerError()).get_json()) + "\n\n"
+
+        return Response(stream_with_context(generate()), status=200,
+                        mimetype='text/event-stream')
+
+
+api.add_resource(CompletionApi, '/installed-apps/<uuid:installed_app_id>/completion-messages', endpoint='installed_app_completion')
+api.add_resource(CompletionStopApi, '/installed-apps/<uuid:installed_app_id>/completion-messages/<string:task_id>/stop', endpoint='installed_app_stop_completion')
+api.add_resource(ChatApi, '/installed-apps/<uuid:installed_app_id>/chat-messages', endpoint='installed_app_chat_completion')
+api.add_resource(ChatStopApi, '/installed-apps/<uuid:installed_app_id>/chat-messages/<string:task_id>/stop', endpoint='installed_app_stop_chat_completion')

api/controllers/console/explore/conversation.py

@@ -0,0 +1,127 @@
+# -*- coding:utf-8 -*-
+from flask_login import current_user
+from flask_restful import fields, reqparse, marshal_with
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound
+
+from controllers.console import api
+from controllers.console.explore.error import NotChatAppError
+from controllers.console.explore.wraps import InstalledAppResource
+from libs.helper import TimestampField, uuid_value
+from services.conversation_service import ConversationService
+from services.errors.conversation import LastConversationNotExistsError, ConversationNotExistsError
+from services.web_conversation_service import WebConversationService
+
+conversation_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'inputs': fields.Raw,
+    'status': fields.String,
+    'introduction': fields.String,
+    'created_at': TimestampField
+}
+
+conversation_infinite_scroll_pagination_fields = {
+    'limit': fields.Integer,
+    'has_more': fields.Boolean,
+    'data': fields.List(fields.Nested(conversation_fields))
+}
+
+
+class ConversationListApi(InstalledAppResource):
+
+    @marshal_with(conversation_infinite_scroll_pagination_fields)
+    def get(self, installed_app):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('last_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        parser.add_argument('pinned', type=str, choices=['true', 'false', None], location='args')
+        args = parser.parse_args()
+
+        pinned = None
+        if 'pinned' in args and args['pinned'] is not None:
+            pinned = True if args['pinned'] == 'true' else False
+
+        try:
+            return WebConversationService.pagination_by_last_id(
+                app_model=app_model,
+                user=current_user,
+                last_id=args['last_id'],
+                limit=args['limit'],
+                pinned=pinned
+            )
+        except LastConversationNotExistsError:
+            raise NotFound("Last Conversation Not Exists.")
+
+
+class ConversationApi(InstalledAppResource):
+    def delete(self, installed_app, c_id):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+        ConversationService.delete(app_model, conversation_id, current_user)
+        WebConversationService.unpin(app_model, conversation_id, current_user)
+
+        return {"result": "success"}, 204
+
+
+class ConversationRenameApi(InstalledAppResource):
+
+    @marshal_with(conversation_fields)
+    def post(self, installed_app, c_id):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('name', type=str, required=True, location='json')
+        args = parser.parse_args()
+
+        try:
+            return ConversationService.rename(app_model, conversation_id, current_user, args['name'])
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+
+
+class ConversationPinApi(InstalledAppResource):
+
+    def patch(self, installed_app, c_id):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+
+        try:
+            WebConversationService.pin(app_model, conversation_id, current_user)
+        except ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+
+        return {"result": "success"}
+
+
+class ConversationUnPinApi(InstalledAppResource):
+    def patch(self, installed_app, c_id):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+        WebConversationService.unpin(app_model, conversation_id, current_user)
+
+        return {"result": "success"}
+
+
+api.add_resource(ConversationRenameApi, '/installed-apps/<uuid:installed_app_id>/conversations/<uuid:c_id>/name', endpoint='installed_app_conversation_rename')
+api.add_resource(ConversationListApi, '/installed-apps/<uuid:installed_app_id>/conversations', endpoint='installed_app_conversations')
+api.add_resource(ConversationApi, '/installed-apps/<uuid:installed_app_id>/conversations/<uuid:c_id>', endpoint='installed_app_conversation')
+api.add_resource(ConversationPinApi, '/installed-apps/<uuid:installed_app_id>/conversations/<uuid:c_id>/pin', endpoint='installed_app_conversation_pin')
+api.add_resource(ConversationUnPinApi, '/installed-apps/<uuid:installed_app_id>/conversations/<uuid:c_id>/unpin', endpoint='installed_app_conversation_unpin')

api/controllers/console/explore/error.py

@@ -0,0 +1,20 @@
+# -*- coding:utf-8 -*-
+from libs.exception import BaseHTTPException
+
+
+class NotCompletionAppError(BaseHTTPException):
+    error_code = 'not_completion_app'
+    description = "Not Completion App"
+    code = 400
+
+
+class NotChatAppError(BaseHTTPException):
+    error_code = 'not_chat_app'
+    description = "Not Chat App"
+    code = 400
+
+
+class AppSuggestedQuestionsAfterAnswerDisabledError(BaseHTTPException):
+    error_code = 'app_suggested_questions_after_answer_disabled'
+    description = "Function Suggested questions after answer disabled."
+    code = 403

api/controllers/console/explore/installed_app.py

@@ -0,0 +1,143 @@
+# -*- coding:utf-8 -*-
+from datetime import datetime
+
+from flask_login import login_required, current_user
+from flask_restful import Resource, reqparse, fields, marshal_with, inputs
+from sqlalchemy import and_
+from werkzeug.exceptions import NotFound, Forbidden, BadRequest
+
+from controllers.console import api
+from controllers.console.explore.wraps import InstalledAppResource
+from controllers.console.wraps import account_initialization_required
+from extensions.ext_database import db
+from libs.helper import TimestampField
+from models.model import App, InstalledApp, RecommendedApp
+from services.account_service import TenantService
+
+app_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'mode': fields.String,
+    'icon': fields.String,
+    'icon_background': fields.String
+}
+
+installed_app_fields = {
+    'id': fields.String,
+    'app': fields.Nested(app_fields),
+    'app_owner_tenant_id': fields.String,
+    'is_pinned': fields.Boolean,
+    'last_used_at': TimestampField,
+    'editable': fields.Boolean,
+    'uninstallable': fields.Boolean,
+}
+
+installed_app_list_fields = {
+    'installed_apps': fields.List(fields.Nested(installed_app_fields))
+}
+
+
+class InstalledAppsListApi(Resource):
+    @login_required
+    @account_initialization_required
+    @marshal_with(installed_app_list_fields)
+    def get(self):
+        current_tenant_id = current_user.current_tenant_id
+        installed_apps = db.session.query(InstalledApp).filter(
+            InstalledApp.tenant_id == current_tenant_id
+        ).all()
+
+        current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
+        installed_apps = [
+            {
+                'id': installed_app.id,
+                'app': installed_app.app,
+                'app_owner_tenant_id': installed_app.app_owner_tenant_id,
+                'is_pinned': installed_app.is_pinned,
+                'last_used_at': installed_app.last_used_at,
+                "editable": current_user.role in ["owner", "admin"],
+                "uninstallable": current_tenant_id == installed_app.app_owner_tenant_id
+            }
+            for installed_app in installed_apps
+        ]
+        installed_apps.sort(key=lambda app: (-app['is_pinned'], app['last_used_at']
+                            if app['last_used_at'] is not None else datetime.min))
+
+        return {'installed_apps': installed_apps}
+
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument('app_id', type=str, required=True, help='Invalid app_id')
+        args = parser.parse_args()
+
+        recommended_app = RecommendedApp.query.filter(RecommendedApp.app_id == args['app_id']).first()
+        if recommended_app is None:
+            raise NotFound('App not found')
+
+        current_tenant_id = current_user.current_tenant_id
+        app = db.session.query(App).filter(
+            App.id == args['app_id']
+        ).first()
+
+        if app is None:
+            raise NotFound('App not found')
+
+        if not app.is_public:
+            raise Forbidden('You can\'t install a non-public app')
+
+        installed_app = InstalledApp.query.filter(and_(
+            InstalledApp.app_id == args['app_id'],
+            InstalledApp.tenant_id == current_tenant_id
+        )).first()
+
+        if installed_app is None:
+            # todo: position
+            recommended_app.install_count += 1
+
+            new_installed_app = InstalledApp(
+                app_id=args['app_id'],
+                tenant_id=current_tenant_id,
+                app_owner_tenant_id=app.tenant_id,
+                is_pinned=False,
+                last_used_at=datetime.utcnow()
+            )
+            db.session.add(new_installed_app)
+            db.session.commit()
+
+        return {'message': 'App installed successfully'}
+
+
+class InstalledAppApi(InstalledAppResource):
+    """
+    update and delete an installed app
+    use InstalledAppResource to apply default decorators and get installed_app
+    """
+    def delete(self, installed_app):
+        if installed_app.app_owner_tenant_id == current_user.current_tenant_id:
+            raise BadRequest('You can\'t uninstall an app owned by the current tenant')
+
+        db.session.delete(installed_app)
+        db.session.commit()
+
+        return {'result': 'success', 'message': 'App uninstalled successfully'}
+
+    def patch(self, installed_app):
+        parser = reqparse.RequestParser()
+        parser.add_argument('is_pinned', type=inputs.boolean)
+        args = parser.parse_args()
+
+        commit_args = False
+        if 'is_pinned' in args:
+            installed_app.is_pinned = args['is_pinned']
+            commit_args = True
+
+        if commit_args:
+            db.session.commit()
+
+        return {'result': 'success', 'message': 'App info updated successfully'}
+
+
+api.add_resource(InstalledAppsListApi, '/installed-apps')
+api.add_resource(InstalledAppApi, '/installed-apps/<uuid:installed_app_id>')

api/controllers/console/explore/message.py

@@ -0,0 +1,196 @@
+# -*- coding:utf-8 -*-
+import json
+import logging
+from typing import Generator, Union
+
+from flask import stream_with_context, Response
+from flask_login import current_user
+from flask_restful import reqparse, fields, marshal_with
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound, InternalServerError
+
+import services
+from controllers.console import api
+from controllers.console.app.error import AppMoreLikeThisDisabledError, ProviderNotInitializeError, \
+    ProviderQuotaExceededError, ProviderModelCurrentlyNotSupportError, CompletionRequestError
+from controllers.console.explore.error import NotCompletionAppError, AppSuggestedQuestionsAfterAnswerDisabledError
+from controllers.console.explore.wraps import InstalledAppResource
+from core.llm.error import LLMRateLimitError, LLMBadRequestError, LLMAuthorizationError, LLMAPIConnectionError, \
+    ProviderTokenNotInitError, LLMAPIUnavailableError, QuotaExceededError, ModelCurrentlyNotSupportError
+from libs.helper import uuid_value, TimestampField
+from services.completion_service import CompletionService
+from services.errors.app import MoreLikeThisDisabledError
+from services.errors.conversation import ConversationNotExistsError
+from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
+from services.message_service import MessageService
+
+
+class MessageListApi(InstalledAppResource):
+    feedback_fields = {
+        'rating': fields.String
+    }
+
+    message_fields = {
+        'id': fields.String,
+        'conversation_id': fields.String,
+        'inputs': fields.Raw,
+        'query': fields.String,
+        'answer': fields.String,
+        'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+        'created_at': TimestampField
+    }
+
+    message_infinite_scroll_pagination_fields = {
+        'limit': fields.Integer,
+        'has_more': fields.Boolean,
+        'data': fields.List(fields.Nested(message_fields))
+    }
+
+    @marshal_with(message_infinite_scroll_pagination_fields)
+    def get(self, installed_app):
+        app_model = installed_app.app
+
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('conversation_id', required=True, type=uuid_value, location='args')
+        parser.add_argument('first_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        args = parser.parse_args()
+
+        try:
+            return MessageService.pagination_by_first_id(app_model, current_user,
+                                                     args['conversation_id'], args['first_id'], args['limit'])
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.message.FirstMessageNotExistsError:
+            raise NotFound("First Message Not Exists.")
+
+
+class MessageFeedbackApi(InstalledAppResource):
+    def post(self, installed_app, message_id):
+        app_model = installed_app.app
+
+        message_id = str(message_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('rating', type=str, choices=['like', 'dislike', None], location='json')
+        args = parser.parse_args()
+
+        try:
+            MessageService.create_feedback(app_model, message_id, current_user, args['rating'])
+        except services.errors.message.MessageNotExistsError:
+            raise NotFound("Message Not Exists.")
+
+        return {'result': 'success'}
+
+
+class MessageMoreLikeThisApi(InstalledAppResource):
+    def get(self, installed_app, message_id):
+        app_model = installed_app.app
+        if app_model.mode != 'completion':
+            raise NotCompletionAppError()
+
+        message_id = str(message_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('response_mode', type=str, required=True, choices=['blocking', 'streaming'], location='args')
+        args = parser.parse_args()
+
+        streaming = args['response_mode'] == 'streaming'
+
+        try:
+            response = CompletionService.generate_more_like_this(app_model, current_user, message_id, streaming)
+            return compact_response(response)
+        except MessageNotExistsError:
+            raise NotFound("Message Not Exists.")
+        except MoreLikeThisDisabledError:
+            raise AppMoreLikeThisDisabledError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except ValueError as e:
+            raise e
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+def compact_response(response: Union[dict | Generator]) -> Response:
+    if isinstance(response, dict):
+        return Response(response=json.dumps(response), status=200, mimetype='application/json')
+    else:
+        def generate() -> Generator:
+            try:
+                for chunk in response:
+                    yield chunk
+            except MessageNotExistsError:
+                yield "data: " + json.dumps(api.handle_error(NotFound("Message Not Exists.")).get_json()) + "\n\n"
+            except MoreLikeThisDisabledError:
+                yield "data: " + json.dumps(api.handle_error(AppMoreLikeThisDisabledError()).get_json()) + "\n\n"
+            except ProviderTokenNotInitError:
+                yield "data: " + json.dumps(api.handle_error(ProviderNotInitializeError()).get_json()) + "\n\n"
+            except QuotaExceededError:
+                yield "data: " + json.dumps(api.handle_error(ProviderQuotaExceededError()).get_json()) + "\n\n"
+            except ModelCurrentlyNotSupportError:
+                yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
+            except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                    LLMRateLimitError, LLMAuthorizationError) as e:
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+            except ValueError as e:
+                yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
+            except Exception:
+                logging.exception("internal server error.")
+                yield "data: " + json.dumps(api.handle_error(InternalServerError()).get_json()) + "\n\n"
+
+        return Response(stream_with_context(generate()), status=200,
+                        mimetype='text/event-stream')
+
+
+class MessageSuggestedQuestionApi(InstalledAppResource):
+    def get(self, installed_app, message_id):
+        app_model = installed_app.app
+        if app_model.mode != 'chat':
+            raise NotCompletionAppError()
+
+        message_id = str(message_id)
+
+        try:
+            questions = MessageService.get_suggested_questions_after_answer(
+                app_model=app_model,
+                user=current_user,
+                message_id=message_id
+            )
+        except MessageNotExistsError:
+            raise NotFound("Message not found")
+        except ConversationNotExistsError:
+            raise NotFound("Conversation not found")
+        except SuggestedQuestionsAfterAnswerDisabledError:
+            raise AppSuggestedQuestionsAfterAnswerDisabledError()
+        except ProviderTokenNotInitError:
+            raise ProviderNotInitializeError()
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except (LLMBadRequestError, LLMAPIConnectionError, LLMAPIUnavailableError,
+                LLMRateLimitError, LLMAuthorizationError) as e:
+            raise CompletionRequestError(str(e))
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+        return {'data': questions}
+
+
+api.add_resource(MessageListApi, '/installed-apps/<uuid:installed_app_id>/messages', endpoint='installed_app_messages')
+api.add_resource(MessageFeedbackApi, '/installed-apps/<uuid:installed_app_id>/messages/<uuid:message_id>/feedbacks', endpoint='installed_app_message_feedback')
+api.add_resource(MessageMoreLikeThisApi, '/installed-apps/<uuid:installed_app_id>/messages/<uuid:message_id>/more-like-this', endpoint='installed_app_more_like_this')
+api.add_resource(MessageSuggestedQuestionApi, '/installed-apps/<uuid:installed_app_id>/messages/<uuid:message_id>/suggested-questions', endpoint='installed_app_suggested_question')

api/controllers/console/explore/parameter.py

@@ -0,0 +1,45 @@
+# -*- coding:utf-8 -*-
+from flask_restful import marshal_with, fields
+
+from controllers.console import api
+from controllers.console.explore.wraps import InstalledAppResource
+
+
+class AppParameterApi(InstalledAppResource):
+    """Resource for app variables."""
+    variable_fields = {
+        'key': fields.String,
+        'name': fields.String,
+        'description': fields.String,
+        'type': fields.String,
+        'default': fields.String,
+        'max_length': fields.Integer,
+        'options': fields.List(fields.String)
+    }
+
+    parameters_fields = {
+        'opening_statement': fields.String,
+        'suggested_questions': fields.Raw,
+        'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
+        'more_like_this': fields.Raw,
+        'user_input_form': fields.Raw,
+    }
+
+    @marshal_with(parameters_fields)
+    def get(self, installed_app):
+        """Retrieve app parameters."""
+        app_model = installed_app.app
+        app_model_config = app_model.app_model_config
+
+        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,
+            'more_like_this': app_model_config.more_like_this_dict,
+            'user_input_form': app_model_config.user_input_form_list
+        }
+
+
+api.add_resource(AppParameterApi, '/installed-apps/<uuid:installed_app_id>/parameters', endpoint='installed_app_parameters')

api/controllers/console/explore/recommended_app.py

@@ -0,0 +1,138 @@
+# -*- coding:utf-8 -*-
+from flask_login import login_required, current_user
+from flask_restful import Resource, fields, marshal_with
+from sqlalchemy import and_
+
+from controllers.console import api
+from controllers.console.app.error import AppNotFoundError
+from controllers.console.wraps import account_initialization_required
+from extensions.ext_database import db
+from models.model import App, InstalledApp, RecommendedApp
+from services.account_service import TenantService
+
+app_fields = {
+    'id': fields.String,
+    'name': fields.String,
+    'mode': fields.String,
+    'icon': fields.String,
+    'icon_background': fields.String
+}
+
+recommended_app_fields = {
+    'app': fields.Nested(app_fields, attribute='app'),
+    'app_id': fields.String,
+    'description': fields.String(attribute='description'),
+    'copyright': fields.String,
+    'privacy_policy': fields.String,
+    'category': fields.String,
+    'position': fields.Integer,
+    'is_listed': fields.Boolean,
+    'install_count': fields.Integer,
+    'installed': fields.Boolean,
+    'editable': fields.Boolean
+}
+
+recommended_app_list_fields = {
+    'recommended_apps': fields.List(fields.Nested(recommended_app_fields)),
+    'categories': fields.List(fields.String)
+}
+
+
+class RecommendedAppListApi(Resource):
+    @login_required
+    @account_initialization_required
+    @marshal_with(recommended_app_list_fields)
+    def get(self):
+        language_prefix = current_user.interface_language if current_user.interface_language else 'en-US'
+
+        recommended_apps = db.session.query(RecommendedApp).filter(
+            RecommendedApp.is_listed == True,
+            RecommendedApp.language == language_prefix
+        ).all()
+
+        categories = set()
+        current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
+        recommended_apps_result = []
+        for recommended_app in recommended_apps:
+            installed = db.session.query(InstalledApp).filter(
+                and_(
+                    InstalledApp.app_id == recommended_app.app_id,
+                    InstalledApp.tenant_id == current_user.current_tenant_id
+                )
+            ).first() is not None
+
+            app = recommended_app.app
+            if not app or not app.is_public:
+                continue
+
+            site = app.site
+            if not site:
+                continue
+
+            recommended_app_result = {
+                'id': recommended_app.id,
+                'app': app,
+                'app_id': recommended_app.app_id,
+                'description': site.description,
+                'copyright': site.copyright,
+                'privacy_policy': site.privacy_policy,
+                'category': recommended_app.category,
+                'position': recommended_app.position,
+                'is_listed': recommended_app.is_listed,
+                'install_count': recommended_app.install_count,
+                'installed': installed,
+                'editable': current_user.role in ['owner', 'admin'],
+            }
+            recommended_apps_result.append(recommended_app_result)
+
+            categories.add(recommended_app.category)  # add category to categories
+
+        return {'recommended_apps': recommended_apps_result, 'categories': list(categories)}
+
+
+class RecommendedAppApi(Resource):
+    model_config_fields = {
+        'opening_statement': fields.String,
+        'suggested_questions': fields.Raw(attribute='suggested_questions_list'),
+        'suggested_questions_after_answer': fields.Raw(attribute='suggested_questions_after_answer_dict'),
+        'more_like_this': fields.Raw(attribute='more_like_this_dict'),
+        'model': fields.Raw(attribute='model_dict'),
+        'user_input_form': fields.Raw(attribute='user_input_form_list'),
+        'pre_prompt': fields.String,
+        'agent_mode': fields.Raw(attribute='agent_mode_dict'),
+    }
+
+    app_simple_detail_fields = {
+        'id': fields.String,
+        'name': fields.String,
+        'icon': fields.String,
+        'icon_background': fields.String,
+        'mode': fields.String,
+        'app_model_config': fields.Nested(model_config_fields),
+    }
+
+    @login_required
+    @account_initialization_required
+    @marshal_with(app_simple_detail_fields)
+    def get(self, app_id):
+        app_id = str(app_id)
+
+        # is in public recommended list
+        recommended_app = db.session.query(RecommendedApp).filter(
+            RecommendedApp.is_listed == True,
+            RecommendedApp.app_id == app_id
+        ).first()
+
+        if not recommended_app:
+            raise AppNotFoundError
+
+        # get app detail
+        app = db.session.query(App).filter(App.id == app_id).first()
+        if not app or not app.is_public:
+            raise AppNotFoundError
+
+        return app
+
+
+api.add_resource(RecommendedAppListApi, '/explore/apps')
+api.add_resource(RecommendedAppApi, '/explore/apps/<uuid:app_id>')

api/controllers/console/explore/saved_message.py

@@ -0,0 +1,79 @@
+from flask_login import current_user
+from flask_restful import reqparse, marshal_with, fields
+from flask_restful.inputs import int_range
+from werkzeug.exceptions import NotFound
+
+from controllers.console import api
+from controllers.console.explore.error import NotCompletionAppError
+from controllers.console.explore.wraps import InstalledAppResource
+from libs.helper import uuid_value, TimestampField
+from services.errors.message import MessageNotExistsError
+from services.saved_message_service import SavedMessageService
+
+feedback_fields = {
+    'rating': fields.String
+}
+
+message_fields = {
+    'id': fields.String,
+    'inputs': fields.Raw,
+    'query': fields.String,
+    'answer': fields.String,
+    'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
+    'created_at': TimestampField
+}
+
+
+class SavedMessageListApi(InstalledAppResource):
+    saved_message_infinite_scroll_pagination_fields = {
+        'limit': fields.Integer,
+        'has_more': fields.Boolean,
+        'data': fields.List(fields.Nested(message_fields))
+    }
+
+    @marshal_with(saved_message_infinite_scroll_pagination_fields)
+    def get(self, installed_app):
+        app_model = installed_app.app
+        if app_model.mode != 'completion':
+            raise NotCompletionAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('last_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        args = parser.parse_args()
+
+        return SavedMessageService.pagination_by_last_id(app_model, current_user, args['last_id'], args['limit'])
+
+    def post(self, installed_app):
+        app_model = installed_app.app
+        if app_model.mode != 'completion':
+            raise NotCompletionAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('message_id', type=uuid_value, required=True, location='json')
+        args = parser.parse_args()
+
+        try:
+            SavedMessageService.save(app_model, current_user, args['message_id'])
+        except MessageNotExistsError:
+            raise NotFound("Message Not Exists.")
+
+        return {'result': 'success'}
+
+
+class SavedMessageApi(InstalledAppResource):
+    def delete(self, installed_app, message_id):
+        app_model = installed_app.app
+
+        message_id = str(message_id)
+
+        if app_model.mode != 'completion':
+            raise NotCompletionAppError()
+
+        SavedMessageService.delete(app_model, current_user, message_id)
+
+        return {'result': 'success'}
+
+
+api.add_resource(SavedMessageListApi, '/installed-apps/<uuid:installed_app_id>/saved-messages', endpoint='installed_app_saved_messages')
+api.add_resource(SavedMessageApi, '/installed-apps/<uuid:installed_app_id>/saved-messages/<uuid:message_id>', endpoint='installed_app_saved_message')

api/controllers/console/explore/wraps.py

@@ -0,0 +1,48 @@
+from flask_login import login_required, current_user
+from flask_restful import Resource
+from functools import wraps
+
+from werkzeug.exceptions import NotFound
+
+from controllers.console.wraps import account_initialization_required
+from extensions.ext_database import db
+from models.model import InstalledApp
+
+
+def installed_app_required(view=None):
+    def decorator(view):
+        @wraps(view)
+        def decorated(*args, **kwargs):
+            if not kwargs.get('installed_app_id'):
+                raise ValueError('missing installed_app_id in path parameters')
+
+            installed_app_id = kwargs.get('installed_app_id')
+            installed_app_id = str(installed_app_id)
+
+            del kwargs['installed_app_id']
+
+            installed_app = db.session.query(InstalledApp).filter(
+                InstalledApp.id == str(installed_app_id),
+                InstalledApp.tenant_id == current_user.current_tenant_id
+            ).first()
+
+            if installed_app is None:
+                raise NotFound('Installed app not found')
+
+            if not installed_app.app:
+                db.session.delete(installed_app)
+                db.session.commit()
+
+                raise NotFound('Installed app not found')
+
+            return view(installed_app, *args, **kwargs)
+        return decorated
+
+    if view:
+        return decorator(view)
+    return decorator
+
+
+class InstalledAppResource(Resource):
+    # must be reversed if there are multiple decorators
+    method_decorators = [installed_app_required, account_initialization_required, login_required]

api/controllers/console/version.py

@@ -19,13 +19,26 @@ class VersionApi(Resource):
         args = parser.parse_args()
         check_update_url = current_app.config['CHECK_UPDATE_URL']
 
+        if not check_update_url:
+            return {
+                'version': '0.0.0',
+                'release_date': '',
+                'release_notes': '',
+                'can_auto_update': False
+            }
+
         try:
             response = requests.get(check_update_url, {
                 'current_version': args.get('current_version')
             })
         except Exception as error:
-            logging.exception("Check update error.")
-            raise InternalServerError()
+            logging.warning("Check update version error: {}.".format(str(error)))
+            return {
+                'version': args.get('current_version'),
+                'release_date': '',
+                'release_notes': '',
+                'can_auto_update': False
+            }
 
         content = json.loads(response.content)
         return {

api/controllers/console/workspace/error.py

@@ -21,11 +21,11 @@ class InvalidInvitationCodeError(BaseHTTPException):
 
 class AccountAlreadyInitedError(BaseHTTPException):
     error_code = 'account_already_inited'
-    description = "Account already inited."
+    description = "The account has been initialized. Please refresh the page."
     code = 400
 
 
 class AccountNotInitializedError(BaseHTTPException):
     error_code = 'account_not_initialized'
-    description = "Account not initialized."
+    description = "The account has not been initialized yet. Please proceed with the initialization process first."
     code = 400

api/controllers/console/workspace/providers.py

@@ -82,29 +82,33 @@ class ProviderTokenApi(Resource):
 
         args = parser.parse_args()
 
-        if not args['token']:
-            raise ValueError('Token is empty')
+        if args['token']:
+            try:
+                ProviderService.validate_provider_configs(
+                    tenant=current_user.current_tenant,
+                    provider_name=ProviderName(provider),
+                    configs=args['token']
+                )
+                token_is_valid = True
+            except ValidateFailedError as ex:
+                raise ValueError(str(ex))
 
-        try:
-            ProviderService.validate_provider_configs(
+            base64_encrypted_token = ProviderService.get_encrypted_token(
                 tenant=current_user.current_tenant,
                 provider_name=ProviderName(provider),
                 configs=args['token']
             )
-            token_is_valid = True
-        except ValidateFailedError:
+        else:
+            base64_encrypted_token = None
             token_is_valid = False
 
         tenant = current_user.current_tenant
 
-        base64_encrypted_token = ProviderService.get_encrypted_token(
-            tenant=current_user.current_tenant,
-            provider_name=ProviderName(provider),
-            configs=args['token']
-        )
-
-        provider_model = Provider.query.filter_by(tenant_id=tenant.id, provider_name=provider,
-                                                  provider_type=ProviderType.CUSTOM.value).first()
+        provider_model = db.session.query(Provider).filter(
+                Provider.tenant_id == tenant.id,
+                Provider.provider_name == provider,
+                Provider.provider_type == ProviderType.CUSTOM.value
+            ).first()
 
         # Only allow updating token for CUSTOM provider type
         if provider_model:
@@ -117,6 +121,16 @@ class ProviderTokenApi(Resource):
                                       is_valid=token_is_valid)
             db.session.add(provider_model)
 
+        if provider_model.is_valid:
+            other_providers = db.session.query(Provider).filter(
+                Provider.tenant_id == tenant.id,
+                Provider.provider_name != provider,
+                Provider.provider_type == ProviderType.CUSTOM.value
+            ).all()
+
+            for other_provider in other_providers:
+                other_provider.is_valid = False
+
         db.session.commit()
 
         if provider in [ProviderName.ANTHROPIC.value, ProviderName.AZURE_OPENAI.value, ProviderName.COHERE.value,
@@ -143,7 +157,7 @@ class ProviderTokenValidateApi(Resource):
         args = parser.parse_args()
 
         # todo: remove this when the provider is supported
-        if provider in [ProviderName.ANTHROPIC.value, ProviderName.AZURE_OPENAI.value, ProviderName.COHERE.value,
+        if provider in [ProviderName.ANTHROPIC.value, ProviderName.COHERE.value,
                         ProviderName.HUGGINGFACEHUB.value]:
             return {'result': 'success', 'warning': 'MOCK: This provider is not supported yet.'}
 

api/controllers/service_api/__init__.py

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

api/controllers/service_api/app/app.py

@@ -22,6 +22,7 @@ class AppParameterApi(AppApiResource):
         'opening_statement': fields.String,
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
@@ -35,6 +36,7 @@ class AppParameterApi(AppApiResource):
             '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,
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/service_api/app/audio.py

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

api/controllers/service_api/app/conversation.py

@@ -48,6 +48,26 @@ class ConversationApi(AppApiResource):
         except services.errors.conversation.LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 
+class ConversationDetailApi(AppApiResource):
+    @marshal_with(conversation_fields)
+    def delete(self, app_model, end_user, c_id):
+        if app_model.mode != 'chat':
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('user', type=str, location='args')
+        args = parser.parse_args()
+
+        if end_user is None and args['user'] is not None:
+            end_user = create_or_update_end_user_for_user_id(app_model, args['user'])
+
+        try:
+            ConversationService.delete(app_model, conversation_id, end_user)
+            return {"result": "success"}, 204
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
 
 class ConversationRenameApi(AppApiResource):
 
@@ -74,3 +94,4 @@ class ConversationRenameApi(AppApiResource):
 
 api.add_resource(ConversationRenameApi, '/conversations/<uuid:c_id>/name', endpoint='conversation_name')
 api.add_resource(ConversationApi, '/conversations')
+api.add_resource(ConversationApi, '/conversations/<uuid:c_id>', endpoint='conversation')

api/controllers/service_api/app/error.py

@@ -4,43 +4,45 @@ from libs.exception import BaseHTTPException
 
 class AppUnavailableError(BaseHTTPException):
     error_code = 'app_unavailable'
-    description = "App unavailable."
+    description = "App unavailable, please check your app configurations."
     code = 400
 
 
 class NotCompletionAppError(BaseHTTPException):
     error_code = 'not_completion_app'
-    description = "Not Completion App"
+    description = "Please check if your Completion app mode matches the right API route."
     code = 400
 
 
 class NotChatAppError(BaseHTTPException):
     error_code = 'not_chat_app'
-    description = "Not Chat App"
+    description = "Please check if your Chat app mode matches the right API route."
     code = 400
 
 
 class ConversationCompletedError(BaseHTTPException):
     error_code = 'conversation_completed'
-    description = "Conversation Completed."
+    description = "The conversation has ended. Please start a new conversation."
     code = 400
 
 
 class ProviderNotInitializeError(BaseHTTPException):
     error_code = 'provider_not_initialize'
-    description = "Provider Token not initialize."
+    description = "No valid model provider credentials found. " \
+                  "Please go to Settings -> Model Provider to complete your provider credentials."
     code = 400
 
 
 class ProviderQuotaExceededError(BaseHTTPException):
     error_code = 'provider_quota_exceeded'
-    description = "Provider quota exceeded."
+    description = "Your quota for Dify Hosted OpenAI has been exhausted. " \
+                  "Please go to Settings -> Model Provider to complete your own provider credentials."
     code = 400
 
 
 class ProviderModelCurrentlyNotSupportError(BaseHTTPException):
     error_code = 'model_currently_not_support'
-    description = "GPT-4 currently not support."
+    description = "Dify Hosted OpenAI trial currently not support the GPT-4 model."
     code = 400
 
 
@@ -49,3 +51,27 @@ class CompletionRequestError(BaseHTTPException):
     description = "Completion request failed."
     code = 400
 
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = 'no_audio_uploaded'
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = 'audio_too_large'
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = 'unsupported_audio_type'
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = 'provider_not_support_speech_to_text'
+    description = "Provider not support speech to text."
+    code = 400
+

api/controllers/service_api/dataset/document.py

@@ -69,12 +69,16 @@ class DocumentListApi(DatasetApiResource):
         document_data = {
             'data_source': {
                 'type': 'upload_file',
-                'info': upload_file.id
+                'info': [
+                    {
+                        'upload_file_id': upload_file.id
+                    }
+                ]
             }
         }
 
         try:
-            document = DocumentService.save_document_with_dataset_id(
+            documents, batch = DocumentService.save_document_with_dataset_id(
                 dataset=dataset,
                 document_data=document_data,
                 account=dataset.created_by_account,
@@ -83,7 +87,7 @@ class DocumentListApi(DatasetApiResource):
             )
         except ProviderTokenNotInitError:
             raise ProviderNotInitializeError()
-
+        document = documents[0]
         if doc_type and doc_metadata:
             metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[doc_type]
 

api/controllers/service_api/dataset/error.py

@@ -16,5 +16,5 @@ class DocumentIndexingError(BaseHTTPException):
 
 class DatasetNotInitedError(BaseHTTPException):
     error_code = 'dataset_not_inited'
-    description = "Dataset not inited."
+    description = "The dataset is still being initialized or indexing. Please wait a moment."
     code = 403

api/controllers/web/__init__.py

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

api/controllers/web/app.py

@@ -21,6 +21,7 @@ class AppParameterApi(WebApiResource):
         'opening_statement': fields.String,
         'suggested_questions': fields.Raw,
         'suggested_questions_after_answer': fields.Raw,
+        'speech_to_text': fields.Raw,
         'more_like_this': fields.Raw,
         'user_input_form': fields.Raw,
     }
@@ -34,6 +35,7 @@ class AppParameterApi(WebApiResource):
             '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,
             'more_like_this': app_model_config.more_like_this_dict,
             'user_input_form': app_model_config.user_input_form_list
         }

api/controllers/web/audio.py

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

api/controllers/web/conversation.py

@@ -47,7 +47,7 @@ class ConversationListApi(WebApiResource):
         try:
             return WebConversationService.pagination_by_last_id(
                 app_model=app_model,
-                end_user=end_user,
+                user=end_user,
                 last_id=args['last_id'],
                 limit=args['limit'],
                 pinned=pinned

api/controllers/web/error.py

@@ -4,43 +4,45 @@ from libs.exception import BaseHTTPException
 
 class AppUnavailableError(BaseHTTPException):
     error_code = 'app_unavailable'
-    description = "App unavailable."
+    description = "App unavailable, please check your app configurations."
     code = 400
 
 
 class NotCompletionAppError(BaseHTTPException):
     error_code = 'not_completion_app'
-    description = "Not Completion App"
+    description = "Please check if your Completion app mode matches the right API route."
     code = 400
 
 
 class NotChatAppError(BaseHTTPException):
     error_code = 'not_chat_app'
-    description = "Not Chat App"
+    description = "Please check if your Chat app mode matches the right API route."
     code = 400
 
 
 class ConversationCompletedError(BaseHTTPException):
     error_code = 'conversation_completed'
-    description = "Conversation Completed."
+    description = "The conversation has ended. Please start a new conversation."
     code = 400
 
 
 class ProviderNotInitializeError(BaseHTTPException):
     error_code = 'provider_not_initialize'
-    description = "Provider Token not initialize."
+    description = "No valid model provider credentials found. " \
+                  "Please go to Settings -> Model Provider to complete your provider credentials."
     code = 400
 
 
 class ProviderQuotaExceededError(BaseHTTPException):
     error_code = 'provider_quota_exceeded'
-    description = "Provider quota exceeded."
+    description = "Your quota for Dify Hosted OpenAI has been exhausted. " \
+                  "Please go to Settings -> Model Provider to complete your own provider credentials."
     code = 400
 
 
 class ProviderModelCurrentlyNotSupportError(BaseHTTPException):
     error_code = 'model_currently_not_support'
-    description = "GPT-4 currently not support."
+    description = "Dify Hosted OpenAI trial currently not support the GPT-4 model."
     code = 400
 
 
@@ -52,11 +54,35 @@ class CompletionRequestError(BaseHTTPException):
 
 class AppMoreLikeThisDisabledError(BaseHTTPException):
     error_code = 'app_more_like_this_disabled'
-    description = "More like this disabled."
+    description = "The 'More like this' feature is disabled. Please refresh your page."
     code = 403
 
 
 class AppSuggestedQuestionsAfterAnswerDisabledError(BaseHTTPException):
     error_code = 'app_suggested_questions_after_answer_disabled'
-    description = "Function Suggested questions after answer disabled."
+    description = "The 'Suggested Questions After Answer' feature is disabled. Please refresh your page."
     code = 403
+
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = 'no_audio_uploaded'
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = 'audio_too_large'
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = 'unsupported_audio_type'
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = 'provider_not_support_speech_to_text'
+    description = "Provider not support speech to text."
+    code = 400

api/controllers/web/wraps.py

@@ -16,7 +16,7 @@ def validate_token(view=None):
         def decorated(*args, **kwargs):
             site = validate_and_get_site()
 
-            app_model = db.session.query(App).get(site.app_id)
+            app_model = db.session.query(App).filter(App.id == site.app_id).first()
             if not app_model:
                 raise NotFound()
 
@@ -42,13 +42,16 @@ def validate_and_get_site():
     """
     auth_header = request.headers.get('Authorization')
     if auth_header is None:
-        raise Unauthorized()
+        raise Unauthorized('Authorization header is missing.')
+
+    if ' ' not in auth_header:
+        raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
 
     auth_scheme, auth_token = auth_header.split(None, 1)
     auth_scheme = auth_scheme.lower()
 
     if auth_scheme != 'bearer':
-        raise Unauthorized()
+        raise Unauthorized('Invalid Authorization header format. Expected \'Bearer <api-key>\' format.')
 
     site = db.session.query(Site).filter(
         Site.code == auth_token,

api/core/__init__.py

@@ -3,19 +3,10 @@ from typing import Optional
 
 import langchain
 from flask import Flask
-from jieba.analyse import default_tfidf
-from langchain import set_handler
-from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING
-from llama_index import IndexStructType, QueryMode
-from llama_index.indices.registry import INDEX_STRUT_TYPE_TO_QUERY_MAP
 from pydantic import BaseModel
 
 from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
-from core.index.keyword_table.jieba_keyword_table import GPTJIEBAKeywordTableIndex
-from core.index.keyword_table.stopwords import STOPWORDS
 from core.prompt.prompt_template import OneLineFormatter
-from core.vector_store.vector_store import VectorStore
-from core.vector_store.vector_store_index_query import EnhanceGPTVectorStoreIndexQuery
 
 
 class HostedOpenAICredential(BaseModel):
@@ -30,23 +21,8 @@ hosted_llm_credentials = HostedLLMCredentials()
 
 
 def init_app(app: Flask):
-    formatter = OneLineFormatter()
-    DEFAULT_FORMATTER_MAPPING['f-string'] = formatter.format
-    INDEX_STRUT_TYPE_TO_QUERY_MAP[IndexStructType.KEYWORD_TABLE] = GPTJIEBAKeywordTableIndex.get_query_map()
-    INDEX_STRUT_TYPE_TO_QUERY_MAP[IndexStructType.WEAVIATE] = {
-        QueryMode.DEFAULT: EnhanceGPTVectorStoreIndexQuery,
-        QueryMode.EMBEDDING: EnhanceGPTVectorStoreIndexQuery,
-    }
-    INDEX_STRUT_TYPE_TO_QUERY_MAP[IndexStructType.QDRANT] = {
-        QueryMode.DEFAULT: EnhanceGPTVectorStoreIndexQuery,
-        QueryMode.EMBEDDING: EnhanceGPTVectorStoreIndexQuery,
-    }
-
-    default_tfidf.stop_words = STOPWORDS
-
     if os.environ.get("DEBUG") and os.environ.get("DEBUG").lower() == 'true':
         langchain.verbose = True
-        set_handler(DifyStdOutCallbackHandler())
 
     if app.config.get("OPENAI_API_KEY"):
         hosted_llm_credentials.openai = HostedOpenAICredential(api_key=app.config.get("OPENAI_API_KEY"))

api/core/agent/agent_builder.py

@@ -2,7 +2,7 @@ from typing import Optional
 
 from langchain import LLMChain
 from langchain.agents import ZeroShotAgent, AgentExecutor, ConversationalAgent
-from langchain.callbacks import CallbackManager
+from langchain.callbacks.manager import CallbackManager
 from langchain.memory.chat_memory import BaseChatMemory
 
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
@@ -16,23 +16,20 @@ class AgentBuilder:
     def to_agent_chain(cls, tenant_id: str, tools, memory: Optional[BaseChatMemory],
                        dataset_tool_callback_handler: DatasetToolCallbackHandler,
                        agent_loop_gather_callback_handler: AgentLoopGatherCallbackHandler):
-        llm_callback_manager = CallbackManager([agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()])
         llm = LLMBuilder.to_llm(
             tenant_id=tenant_id,
             model_name=agent_loop_gather_callback_handler.model_name,
             temperature=0,
             max_tokens=1024,
-            callback_manager=llm_callback_manager
+            callbacks=[agent_loop_gather_callback_handler, DifyStdOutCallbackHandler()]
         )
 
-        tool_callback_manager = CallbackManager([
-            agent_loop_gather_callback_handler,
-            dataset_tool_callback_handler,
-            DifyStdOutCallbackHandler()
-        ])
-
         for tool in tools:
-            tool.callback_manager = tool_callback_manager
+            tool.callbacks = [
+                agent_loop_gather_callback_handler,
+                dataset_tool_callback_handler,
+                DifyStdOutCallbackHandler()
+            ]
 
         prompt = cls.build_agent_prompt_template(
             tools=tools,
@@ -54,7 +51,7 @@ class AgentBuilder:
             tools=tools,
             agent=agent,
             memory=memory,
-            callback_manager=agent_callback_manager,
+            callbacks=agent_callback_manager,
             max_iterations=6,
             early_stopping_method="generate",
             # `generate` will continue to complete the last inference after reaching the iteration limit or request time limit

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -12,6 +12,7 @@ from core.conversation_message_task import ConversationMessageTask
 
 class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
+    raise_error: bool = True
 
     def __init__(self, model_name, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
@@ -64,10 +65,6 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
             self._current_loop.completion = response.generations[0][0].text
             self._current_loop.completion_tokens = response.llm_output['token_usage']['completion_tokens']
 
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Do nothing."""
-        pass
-
     def on_llm_error(
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:
@@ -75,21 +72,6 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         self._agent_loops = []
         self._current_loop = None
 
-    def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        """Print out that we are entering a chain."""
-        pass
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        """Print out that we finished a chain."""
-        pass
-
-    def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        logging.error(error)
-
     def on_tool_start(
         self,
         serialized: Dict[str, Any],
@@ -151,16 +133,6 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         self._agent_loops = []
         self._current_loop = None
 
-    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Optional[str],
-    ) -> None:
-        """Run on additional input from chains and agents."""
-        pass
-
     def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
         """Run on agent end."""
         # Final Answer

api/core/callback_handler/dataset_tool_callback_handler.py

@@ -3,7 +3,6 @@ import logging
 from typing import Any, Dict, List, Union, Optional
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 from core.callback_handler.entity.dataset_query import DatasetQueryObj
 from core.conversation_message_task import ConversationMessageTask
@@ -11,6 +10,7 @@ from core.conversation_message_task import ConversationMessageTask
 
 class DatasetToolCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
+    raise_error: bool = True
 
     def __init__(self, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
@@ -66,52 +66,3 @@ class DatasetToolCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """Do nothing."""
         logging.error(error)
-
-    def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        pass
-
-    def on_chain_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        pass
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Do nothing."""
-        pass
-
-    def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        logging.error(error)
-
-    def on_agent_action(
-        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
-    ) -> Any:
-        pass
-
-    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Optional[str],
-    ) -> None:
-        """Run on additional input from chains and agents."""
-        pass
-
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
-        """Run on agent end."""
-        pass

api/core/callback_handler/index_tool_callback_handler.py

@@ -1,38 +1,29 @@
-from llama_index import Response
+from typing import List
+
+from langchain.schema import Document
 
 from extensions.ext_database import db
 from models.dataset import DocumentSegment
 
 
-class IndexToolCallbackHandler:
-
-    def __init__(self) -> None:
-        self._response = None
-
-    @property
-    def response(self) -> Response:
-        return self._response
-
-    def on_tool_end(self, response: Response) -> None:
-        """Handle tool end."""
-        self._response = response
-
-
-class DatasetIndexToolCallbackHandler(IndexToolCallbackHandler):
+class DatasetIndexToolCallbackHandler:
     """Callback handler for dataset tool."""
 
     def __init__(self, dataset_id: str) -> None:
-        super().__init__()
         self.dataset_id = dataset_id
 
-    def on_tool_end(self, response: Response) -> None:
+    def on_tool_end(self, documents: List[Document]) -> None:
         """Handle tool end."""
-        for node in response.source_nodes:
-            index_node_id = node.node.doc_id
+        for document in documents:
+            doc_id = document.metadata['doc_id']
 
             # add hit count to document segment
             db.session.query(DocumentSegment).filter(
                 DocumentSegment.dataset_id == self.dataset_id,
-                DocumentSegment.index_node_id == index_node_id
-            ).update({DocumentSegment.hit_count: DocumentSegment.hit_count + 1}, synchronize_session=False)
+                DocumentSegment.index_node_id == doc_id
+            ).update(
+                {DocumentSegment.hit_count: DocumentSegment.hit_count + 1},
+                synchronize_session=False
+            )
 
+            db.session.commit()

api/core/callback_handler/llm_callback_handler.py

@@ -3,7 +3,7 @@ import time
 from typing import Any, Dict, List, Union, Optional
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult, HumanMessage, AIMessage, SystemMessage
+from langchain.schema import AgentAction, AgentFinish, LLMResult, HumanMessage, AIMessage, SystemMessage, BaseMessage
 
 from core.callback_handler.entity.llm_message import LLMMessage
 from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
@@ -12,6 +12,7 @@ from core.llm.streamable_open_ai import StreamableOpenAI
 
 
 class LLMCallbackHandler(BaseCallbackHandler):
+    raise_error: bool = True
 
     def __init__(self, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
                  conversation_message_task: ConversationMessageTask):
@@ -25,41 +26,41 @@ class LLMCallbackHandler(BaseCallbackHandler):
         """Whether to call verbose callbacks even if verbose is False."""
         return True
 
+    def on_chat_model_start(
+            self,
+            serialized: Dict[str, Any],
+            messages: List[List[BaseMessage]],
+            **kwargs: Any
+    ) -> Any:
+        self.start_at = time.perf_counter()
+        real_prompts = []
+        for message in messages[0]:
+            if message.type == 'human':
+                role = 'user'
+            elif message.type == 'ai':
+                role = 'assistant'
+            else:
+                role = 'system'
+
+            real_prompts.append({
+                "role": role,
+                "text": message.content
+            })
+
+        self.llm_message.prompt = real_prompts
+        self.llm_message.prompt_tokens = self.llm.get_messages_tokens(messages[0])
+
     def on_llm_start(
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
         self.start_at = time.perf_counter()
 
-        if 'Chat' in serialized['name']:
-            real_prompts = []
-            messages = []
-            for prompt in prompts:
-                role, content = prompt.split(': ', maxsplit=1)
-                if role == 'human':
-                    role = 'user'
-                    message = HumanMessage(content=content)
-                elif role == 'ai':
-                    role = 'assistant'
-                    message = AIMessage(content=content)
-                else:
-                    message = SystemMessage(content=content)
+        self.llm_message.prompt = [{
+            "role": 'user',
+            "text": prompts[0]
+        }]
 
-                real_prompt = {
-                    "role": role,
-                    "text": content
-                }
-                real_prompts.append(real_prompt)
-                messages.append(message)
-
-            self.llm_message.prompt = real_prompts
-            self.llm_message.prompt_tokens = self.llm.get_messages_tokens(messages)
-        else:
-            self.llm_message.prompt = [{
-                "role": 'user',
-                "text": prompts[0]
-            }]
-
-            self.llm_message.prompt_tokens = self.llm.get_num_tokens(prompts[0])
+        self.llm_message.prompt_tokens = self.llm.get_num_tokens(prompts[0])
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         end_at = time.perf_counter()
@@ -75,7 +76,12 @@ class LLMCallbackHandler(BaseCallbackHandler):
         self.conversation_message_task.save_message(self.llm_message)
 
     def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        self.conversation_message_task.append_message_text(token)
+        try:
+            self.conversation_message_task.append_message_text(token)
+        except ConversationTaskStoppedException as ex:
+            self.on_llm_error(error=ex)
+            raise ex
+
         self.llm_message.completion += token
 
     def on_llm_error(
@@ -90,58 +96,3 @@ class LLMCallbackHandler(BaseCallbackHandler):
                 self.conversation_message_task.save_message(llm_message=self.llm_message, by_stopped=True)
         else:
             logging.error(error)
-
-    def on_chain_start(
-            self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
-        pass
-
-    def on_chain_error(
-            self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_tool_start(
-            self,
-            serialized: Dict[str, Any],
-            input_str: str,
-            **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_agent_action(
-            self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
-    ) -> Any:
-        pass
-
-    def on_tool_end(
-            self,
-            output: str,
-            color: Optional[str] = None,
-            observation_prefix: Optional[str] = None,
-            llm_prefix: Optional[str] = None,
-            **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_tool_error(
-            self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_text(
-            self,
-            text: str,
-            color: Optional[str] = None,
-            end: str = "",
-            **kwargs: Optional[str],
-    ) -> None:
-        pass
-
-    def on_agent_finish(
-            self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
-    ) -> None:
-        pass

api/core/callback_handler/main_chain_gather_callback_handler.py

@@ -1,10 +1,9 @@
 import logging
 import time
 
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.callback_handler.entity.chain_result import ChainResult
@@ -14,6 +13,7 @@ from core.conversation_message_task import ConversationMessageTask
 
 class MainChainGatherCallbackHandler(BaseCallbackHandler):
     """Callback Handler that prints to std out."""
+    raise_error: bool = True
 
     def __init__(self, conversation_message_task: ConversationMessageTask) -> None:
         """Initialize callback handler."""
@@ -50,13 +50,15 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
     ) -> None:
         """Print out that we are entering a chain."""
         if not self._current_chain_result:
-            self._current_chain_result = ChainResult(
-                type=serialized['name'],
-                prompt=inputs,
-                started_at=time.perf_counter()
-            )
-            self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
-            self.agent_loop_gather_callback_handler.current_chain = self._current_chain_message
+            chain_type = serialized['id'][-1]
+            if chain_type:
+                self._current_chain_result = ChainResult(
+                    type=chain_type,
+                    prompt=inputs,
+                    started_at=time.perf_counter()
+                )
+                self._current_chain_message = self.conversation_message_task.init_chain(self._current_chain_result)
+                self.agent_loop_gather_callback_handler.current_chain = self._current_chain_message
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""
@@ -74,64 +76,4 @@ class MainChainGatherCallbackHandler(BaseCallbackHandler):
         self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
     ) -> None:
         logging.error(error)
-        self.clear_chain_results()
-
-    def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
-    ) -> None:
-        pass
-
-    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        pass
-
-    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
-        """Do nothing."""
-        pass
-
-    def on_llm_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        logging.error(error)
-
-    def on_tool_start(
-        self,
-        serialized: Dict[str, Any],
-        input_str: str,
-        **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_agent_action(
-        self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
-    ) -> Any:
-        pass
-
-    def on_tool_end(
-        self,
-        output: str,
-        color: Optional[str] = None,
-        observation_prefix: Optional[str] = None,
-        llm_prefix: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        pass
-
-    def on_tool_error(
-        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
-    ) -> None:
-        """Do nothing."""
-        logging.error(error)
-
-    def on_text(
-        self,
-        text: str,
-        color: Optional[str] = None,
-        end: str = "",
-        **kwargs: Optional[str],
-    ) -> None:
-        """Run on additional input from chains and agents."""
-        pass
-
-    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
-        """Run on agent end."""
-        pass
+        self.clear_chain_results()

api/core/callback_handler/std_out_callback_handler.py

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

api/core/chain/chain_builder.py

@@ -1,7 +1,5 @@
 from typing import Optional
 
-from langchain.callbacks import CallbackManager
-
 from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
 from core.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceChain
 from core.chain.tool_chain import ToolChain
@@ -14,7 +12,7 @@ class ChainBuilder:
             tool=tool,
             input_key=kwargs.get('input_key', 'input'),
             output_key=kwargs.get('output_key', 'tool_output'),
-            callback_manager=CallbackManager([DifyStdOutCallbackHandler()])
+            callbacks=[DifyStdOutCallbackHandler()]
         )
 
     @classmethod
@@ -27,7 +25,7 @@ class ChainBuilder:
                 sensitive_words=sensitive_words.split(","),
                 canned_response=tool_config.get("canned_response", ''),
                 output_key="sensitive_word_avoidance_output",
-                callback_manager=CallbackManager([DifyStdOutCallbackHandler()]),
+                callbacks=[DifyStdOutCallbackHandler()],
                 **kwargs
             )
 

api/core/chain/llm_router_chain.py

@@ -0,0 +1,111 @@
+"""Base classes for LLM-powered router chains."""
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Type, cast, NamedTuple
+
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.base import Chain
+from pydantic import root_validator
+
+from langchain.chains import LLMChain
+from langchain.prompts import BasePromptTemplate
+from langchain.schema import BaseOutputParser, OutputParserException
+
+from libs.json_in_md_parser import parse_and_check_json_markdown
+
+
+class Route(NamedTuple):
+    destination: Optional[str]
+    next_inputs: Dict[str, Any]
+
+
+class LLMRouterChain(Chain):
+    """A router chain that uses an LLM chain to perform routing."""
+
+    llm_chain: LLMChain
+    """LLM chain used to perform routing"""
+
+    @root_validator()
+    def validate_prompt(cls, values: dict) -> dict:
+        prompt = values["llm_chain"].prompt
+        if prompt.output_parser is None:
+            raise ValueError(
+                "LLMRouterChain requires base llm_chain prompt to have an output"
+                " parser that converts LLM text output to a dictionary with keys"
+                " 'destination' and 'next_inputs'. Received a prompt with no output"
+                " parser."
+            )
+        return values
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the LLM chain prompt expects.
+
+        :meta private:
+        """
+        return self.llm_chain.input_keys
+
+    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
+        super()._validate_outputs(outputs)
+        if not isinstance(outputs["next_inputs"], dict):
+            raise ValueError
+
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        output = cast(
+            Dict[str, Any],
+            self.llm_chain.predict_and_parse(**inputs),
+        )
+        return output
+
+    @classmethod
+    def from_llm(
+        cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
+    ) -> LLMRouterChain:
+        """Convenience constructor."""
+        llm_chain = LLMChain(llm=llm, prompt=prompt)
+        return cls(llm_chain=llm_chain, **kwargs)
+
+    @property
+    def output_keys(self) -> List[str]:
+        return ["destination", "next_inputs"]
+
+    def route(self, inputs: Dict[str, Any]) -> Route:
+        result = self(inputs)
+        return Route(result["destination"], result["next_inputs"])
+
+
+class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
+    """Parser for output of router chain int he multi-prompt chain."""
+
+    default_destination: str = "DEFAULT"
+    next_inputs_type: Type = str
+    next_inputs_inner_key: str = "input"
+
+    def parse(self, text: str) -> Dict[str, Any]:
+        try:
+            expected_keys = ["destination", "next_inputs"]
+            parsed = parse_and_check_json_markdown(text, expected_keys)
+            if not isinstance(parsed["destination"], str):
+                raise ValueError("Expected 'destination' to be a string.")
+            if not isinstance(parsed["next_inputs"], self.next_inputs_type):
+                raise ValueError(
+                    f"Expected 'next_inputs' to be {self.next_inputs_type}."
+                )
+            parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
+            if (
+                parsed["destination"].strip().lower()
+                == self.default_destination.lower()
+            ):
+                parsed["destination"] = None
+            else:
+                parsed["destination"] = parsed["destination"].strip()
+            return parsed
+        except Exception as e:
+            raise OutputParserException(
+                f"Parsing text\n{text}\n of llm router raised following error:\n{e}"
+            )

api/core/chain/main_chain_builder.py

@@ -1,23 +1,22 @@
-from typing import Optional, List
+from typing import Optional, List, cast
 
-from langchain.callbacks import SharedCallbackManager
 from langchain.chains import SequentialChain
 from langchain.chains.base import Chain
 from langchain.memory.chat_memory import BaseChatMemory
 
-from core.agent.agent_builder import AgentBuilder
-from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
-from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
 from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
+from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
 from core.chain.chain_builder import ChainBuilder
-from core.constant import llm_constant
+from core.chain.multi_dataset_router_chain import MultiDatasetRouterChain
 from core.conversation_message_task import ConversationMessageTask
-from core.tool.dataset_tool_builder import DatasetToolBuilder
+from extensions.ext_database import db
+from models.dataset import Dataset
 
 
 class MainChainBuilder:
     @classmethod
     def to_langchain_components(cls, tenant_id: str, agent_mode: dict, memory: Optional[BaseChatMemory],
+                                rest_tokens: int,
                                 conversation_message_task: ConversationMessageTask):
         first_input_key = "input"
         final_output_key = "output"
@@ -30,9 +29,9 @@ class MainChainBuilder:
         tool_chains, chains_output_key = cls.get_agent_chains(
             tenant_id=tenant_id,
             agent_mode=agent_mode,
+            rest_tokens=rest_tokens,
             memory=memory,
-            dataset_tool_callback_handler=DatasetToolCallbackHandler(conversation_message_task),
-            agent_loop_gather_callback_handler=chain_callback_handler.agent_loop_gather_callback_handler
+            conversation_message_task=conversation_message_task
         )
         chains += tool_chains
 
@@ -43,9 +42,8 @@ class MainChainBuilder:
             return None
 
         for chain in chains:
-            # do not add handler into singleton callback manager
-            if not isinstance(chain.callback_manager, SharedCallbackManager):
-                chain.callback_manager.add_handler(chain_callback_handler)
+            chain = cast(Chain, chain)
+            chain.callbacks.append(chain_callback_handler)
 
         # build main chain
         overall_chain = SequentialChain(
@@ -58,16 +56,18 @@ class MainChainBuilder:
         return overall_chain
 
     @classmethod
-    def get_agent_chains(cls, tenant_id: str, agent_mode: dict, memory: Optional[BaseChatMemory],
-                         dataset_tool_callback_handler: DatasetToolCallbackHandler,
-                         agent_loop_gather_callback_handler: AgentLoopGatherCallbackHandler):
+    def get_agent_chains(cls, tenant_id: str, agent_mode: dict,
+                         rest_tokens: int,
+                         memory: Optional[BaseChatMemory],
+                         conversation_message_task: ConversationMessageTask):
         # agent mode
         chains = []
         if agent_mode and agent_mode.get('enabled'):
             tools = agent_mode.get('tools', [])
 
             pre_fixed_chains = []
-            agent_tools = []
+            # agent_tools = []
+            datasets = []
             for tool in tools:
                 tool_type = list(tool.keys())[0]
                 tool_config = list(tool.values())[0]
@@ -76,34 +76,28 @@ class MainChainBuilder:
                     if chain:
                         pre_fixed_chains.append(chain)
                 elif tool_type == "dataset":
-                    dataset_tool = DatasetToolBuilder.build_dataset_tool(
-                        tenant_id=tenant_id,
-                        dataset_id=tool_config.get("id"),
-                        response_mode='no_synthesizer',  # "compact"
-                        callback_handler=dataset_tool_callback_handler
-                    )
+                    # get dataset from dataset id
+                    dataset = db.session.query(Dataset).filter(
+                        Dataset.tenant_id == tenant_id,
+                        Dataset.id == tool_config.get("id")
+                    ).first()
 
-                    if dataset_tool:
-                        agent_tools.append(dataset_tool)
+                    if dataset:
+                        datasets.append(dataset)
 
             # add pre-fixed chains
             chains += pre_fixed_chains
 
-            if len(agent_tools) == 1:
+            if len(datasets) > 0:
                 # tool to chain
-                tool_chain = ChainBuilder.to_tool_chain(tool=agent_tools[0], output_key='tool_output')
-                chains.append(tool_chain)
-            elif len(agent_tools) > 1:
-                # build agent config
-                agent_chain = AgentBuilder.to_agent_chain(
+                multi_dataset_router_chain = MultiDatasetRouterChain.from_datasets(
                     tenant_id=tenant_id,
-                    tools=agent_tools,
-                    memory=memory,
-                    dataset_tool_callback_handler=dataset_tool_callback_handler,
-                    agent_loop_gather_callback_handler=agent_loop_gather_callback_handler
+                    datasets=datasets,
+                    conversation_message_task=conversation_message_task,
+                    rest_tokens=rest_tokens,
+                    callbacks=[DifyStdOutCallbackHandler()]
                 )
-
-                chains.append(agent_chain)
+                chains.append(multi_dataset_router_chain)
 
         final_output_key = cls.get_chains_output_key(chains)
 

api/core/chain/multi_dataset_router_chain.py

@@ -0,0 +1,198 @@
+import math
+import re
+from typing import Mapping, List, Dict, Any, Optional
+
+from langchain import PromptTemplate
+from langchain.callbacks.manager import CallbackManagerForChainRun
+from langchain.chains.base import Chain
+from pydantic import Extra
+
+from core.callback_handler.dataset_tool_callback_handler import DatasetToolCallbackHandler
+from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
+from core.chain.llm_router_chain import LLMRouterChain, RouterOutputParser
+from core.conversation_message_task import ConversationMessageTask
+from core.llm.llm_builder import LLMBuilder
+from core.tool.dataset_index_tool import DatasetTool
+from models.dataset import Dataset, DatasetProcessRule
+
+DEFAULT_K = 2
+CONTEXT_TOKENS_PERCENT = 0.3
+MULTI_PROMPT_ROUTER_TEMPLATE = """
+Given a raw text input to a language model select the model prompt best suited for \
+the input. You will be given the names of the available prompts and a description of \
+what the prompt is best suited for. You may also revise the original input if you \
+think that revising it will ultimately lead to a better response from the language \
+model.
+
+<< FORMATTING >>
+Return a markdown code snippet with a JSON object formatted to look like, \
+no any other string out of markdown code snippet:
+```json
+{{{{
+    "destination": string \\ name of the prompt to use or "DEFAULT"
+    "next_inputs": string \\ a potentially modified version of the original input
+}}}}
+```
+
+REMEMBER: "destination" MUST be one of the candidate prompt names specified below OR \
+it can be "DEFAULT" if the input is not well suited for any of the candidate prompts.
+REMEMBER: "next_inputs" can just be the original input if you don't think any \
+modifications are needed.
+
+<< CANDIDATE PROMPTS >>
+{destinations}
+
+<< INPUT >>
+{{input}}
+
+<< OUTPUT >>
+"""
+
+
+class MultiDatasetRouterChain(Chain):
+    """Use a single chain to route an input to one of multiple candidate chains."""
+
+    router_chain: LLMRouterChain
+    """Chain for deciding a destination chain and the input to it."""
+    dataset_tools: Mapping[str, DatasetTool]
+    """Map of name to candidate chains that inputs can be routed to."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the router chain prompt expects.
+
+        :meta private:
+        """
+        return self.router_chain.input_keys
+
+    @property
+    def output_keys(self) -> List[str]:
+        return ["text"]
+
+    @classmethod
+    def from_datasets(
+            cls,
+            tenant_id: str,
+            datasets: List[Dataset],
+            conversation_message_task: ConversationMessageTask,
+            rest_tokens: int,
+            **kwargs: Any,
+    ):
+        """Convenience constructor for instantiating from destination prompts."""
+        llm = LLMBuilder.to_llm(
+            tenant_id=tenant_id,
+            model_name='gpt-3.5-turbo',
+            temperature=0,
+            max_tokens=1024,
+            callbacks=[DifyStdOutCallbackHandler()]
+        )
+
+        destinations = ["[[{}]]: {}".format(d.id, d.description.replace('\n', ' ') if d.description
+                        else ('useful for when you want to answer queries about the ' + d.name))
+                        for d in datasets]
+        destinations_str = "\n".join(destinations)
+        router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
+            destinations=destinations_str
+        )
+
+        router_prompt = PromptTemplate(
+            template=router_template,
+            input_variables=["input"],
+            output_parser=RouterOutputParser(),
+        )
+
+        router_chain = LLMRouterChain.from_llm(llm, router_prompt)
+        dataset_tools = {}
+        for dataset in datasets:
+            # fulfill description when it is empty
+            if dataset.available_document_count == 0 or dataset.available_document_count == 0:
+                continue
+
+            description = dataset.description
+            if not description:
+                description = 'useful for when you want to answer queries about the ' + dataset.name
+
+            k = cls._dynamic_calc_retrieve_k(dataset, rest_tokens)
+            if k == 0:
+                continue
+
+            dataset_tool = DatasetTool(
+                name=f"dataset-{dataset.id}",
+                description=description,
+                k=k,
+                dataset=dataset,
+                callbacks=[DatasetToolCallbackHandler(conversation_message_task), DifyStdOutCallbackHandler()]
+            )
+
+            dataset_tools[str(dataset.id)] = dataset_tool
+
+        return cls(
+            router_chain=router_chain,
+            dataset_tools=dataset_tools,
+            **kwargs,
+        )
+
+    @classmethod
+    def _dynamic_calc_retrieve_k(cls, dataset: Dataset, rest_tokens: int) -> int:
+        processing_rule = dataset.latest_process_rule
+        if not processing_rule:
+            return DEFAULT_K
+
+        if processing_rule.mode == "custom":
+            rules = processing_rule.rules_dict
+            if not rules:
+                return DEFAULT_K
+
+            segmentation = rules["segmentation"]
+            segment_max_tokens = segmentation["max_tokens"]
+        else:
+            segment_max_tokens = DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens']
+
+        # when rest_tokens is less than default context tokens
+        if rest_tokens < segment_max_tokens * DEFAULT_K:
+            return rest_tokens // segment_max_tokens
+
+        context_limit_tokens = math.floor(rest_tokens * CONTEXT_TOKENS_PERCENT)
+
+        # when context_limit_tokens is less than default context tokens, use default_k
+        if context_limit_tokens <= segment_max_tokens * DEFAULT_K:
+            return DEFAULT_K
+
+        # Expand the k value when there's still some room left in the 30% rest tokens space
+        return context_limit_tokens // segment_max_tokens
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        if len(self.dataset_tools) == 0:
+            return {"text": ''}
+        elif len(self.dataset_tools) == 1:
+            return {"text": next(iter(self.dataset_tools.values())).run(inputs['input'])}
+
+        route = self.router_chain.route(inputs)
+
+        destination = ''
+        if route.destination:
+            pattern = r'\b[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}\b'
+            match = re.search(pattern, route.destination, re.IGNORECASE)
+            if match:
+                destination = match.group()
+
+        if not destination:
+            return {"text": ''}
+        elif destination in self.dataset_tools:
+            return {"text": self.dataset_tools[destination].run(
+                route.next_inputs['input']
+            )}
+        else:
+            raise ValueError(
+                f"Received invalid destination chain name '{destination}'"
+            )

api/core/chain/sensitive_word_avoidance_chain.py

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

api/core/chain/tool_chain.py

@@ -1,5 +1,6 @@
-from typing import List, Dict
+from typing import List, Dict, Optional, Any
 
+from langchain.callbacks.manager import CallbackManagerForChainRun, AsyncCallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.tools import BaseTool
 
@@ -30,12 +31,20 @@ class ToolChain(Chain):
         """
         return [self.output_key]
 
-    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
+    def _call(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
         input = inputs[self.input_key]
         output = self.tool.run(input, self.verbose)
         return {self.output_key: output}
 
-    async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
+    async def _acall(
+            self,
+            inputs: Dict[str, Any],
+            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
         """Run the logic of this chain and return the output."""
         input = inputs[self.input_key]
         output = await self.tool.arun(input, self.verbose)

api/core/completion.py

@@ -1,9 +1,13 @@
-from typing import Optional, List, Union
+import logging
+from typing import Optional, List, Union, Tuple
 
-from langchain.callbacks import CallbackManager
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.base import BaseCallbackHandler
 from langchain.chat_models.base import BaseChatModel
 from langchain.llms import BaseLLM
-from langchain.schema import BaseMessage, BaseLanguageModel, HumanMessage
+from langchain.schema import BaseMessage, HumanMessage
+from requests.exceptions import ChunkedEncodingError
+
 from core.constant import llm_constant
 from core.callback_handler.llm_callback_handler import LLMCallbackHandler
 from core.callback_handler.std_out_callback_handler import DifyStreamingStdOutCallbackHandler, \
@@ -19,7 +23,7 @@ from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
 from core.memory.read_only_conversation_token_db_string_buffer_shared_memory import \
     ReadOnlyConversationTokenDBStringBufferSharedMemory
 from core.prompt.prompt_builder import PromptBuilder
-from core.prompt.prompt_template import OutLinePromptTemplate
+from core.prompt.prompt_template import JinjaPromptTemplate
 from core.prompt.prompts import MORE_LIKE_THIS_GENERATE_PROMPT
 from models.model import App, AppModelConfig, Account, Conversation, Message
 
@@ -31,7 +35,7 @@ class Completion:
         """
         errors: ProviderTokenNotInitError
         """
-        cls.validate_query_tokens(app.tenant_id, app_model_config, query)
+        query = PromptBuilder.process_template(query)
 
         memory = None
         if conversation:
@@ -39,11 +43,20 @@ class Completion:
             memory = cls.get_memory_from_conversation(
                 tenant_id=app.tenant_id,
                 app_model_config=app_model_config,
-                conversation=conversation
+                conversation=conversation,
+                return_messages=False
             )
 
             inputs = conversation.inputs
 
+        rest_tokens_for_context_and_memory = cls.get_validate_rest_tokens(
+            mode=app.mode,
+            tenant_id=app.tenant_id,
+            app_model_config=app_model_config,
+            query=query,
+            inputs=inputs
+        )
+
         conversation_message_task = ConversationMessageTask(
             task_id=task_id,
             app=app,
@@ -60,6 +73,7 @@ class Completion:
         main_chain = MainChainBuilder.to_langchain_components(
             tenant_id=app.tenant_id,
             agent_mode=app_model_config.agent_mode_dict,
+            rest_tokens=rest_tokens_for_context_and_memory,
             memory=ReadOnlyConversationTokenDBStringBufferSharedMemory(memory=memory) if memory else None,
             conversation_message_task=conversation_message_task
         )
@@ -83,6 +97,11 @@ class Completion:
             )
         except ConversationTaskStoppedException:
             return
+        except ChunkedEncodingError as e:
+            # Interrupt by LLM (like OpenAI), handle it.
+            logging.warning(f'ChunkedEncodingError: {e}')
+            conversation_message_task.end()
+            return
 
     @classmethod
     def run_final_llm(cls, tenant_id: str, mode: str, app_model_config: AppModelConfig, query: str, inputs: dict,
@@ -96,7 +115,7 @@ class Completion:
         )
 
         # get llm prompt
-        prompt = cls.get_main_llm_prompt(
+        prompt, stop_words = cls.get_main_llm_prompt(
             mode=mode,
             llm=final_llm,
             pre_prompt=app_model_config.pre_prompt,
@@ -106,7 +125,7 @@ class Completion:
             memory=memory
         )
 
-        final_llm.callback_manager = cls.get_llm_callback_manager(final_llm, streaming, conversation_message_task)
+        final_llm.callbacks = cls.get_llm_callbacks(final_llm, streaming, conversation_message_task)
 
         cls.recale_llm_max_tokens(
             final_llm=final_llm,
@@ -114,30 +133,46 @@ class Completion:
             mode=mode
         )
 
-        response = final_llm.generate([prompt])
+        response = final_llm.generate([prompt], stop_words)
 
         return response
 
     @classmethod
-    def get_main_llm_prompt(cls, mode: str, llm: BaseLanguageModel, pre_prompt: str, query: str, inputs: dict, chain_output: Optional[str],
+    def get_main_llm_prompt(cls, mode: str, llm: BaseLanguageModel, pre_prompt: str, query: str, inputs: dict,
+                            chain_output: Optional[str],
                             memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]) -> \
-            Union[str | List[BaseMessage]]:
-        pre_prompt = PromptBuilder.process_template(pre_prompt) if pre_prompt else pre_prompt
+            Tuple[Union[str | List[BaseMessage]], Optional[List[str]]]:
+        # disable template string in query
+        # query_params = JinjaPromptTemplate.from_template(template=query).input_variables
+        # if query_params:
+        #     for query_param in query_params:
+        #         if query_param not in inputs:
+        #             inputs[query_param] = '{{' + query_param + '}}'
+
         if mode == 'completion':
-            prompt_template = OutLinePromptTemplate.from_template(
-                template=("Use the following pieces of [CONTEXT] to answer the question at the end. "
-                          "If you don't know the answer, "
-                          "just say that you don't know, don't try to make up an answer. \n"
-                          "```\n"
-                          "[CONTEXT]\n"
-                          "{context}\n"
-                          "```\n" if chain_output else "")
+            prompt_template = JinjaPromptTemplate.from_template(
+                template=("""Use the following CONTEXT as your learned knowledge:
+[CONTEXT]
+{{context}}
+[END CONTEXT]
+
+When answer to user:
+- If you don't know, just say that you don't know.
+- If you don't know when you are not sure, ask for clarification. 
+Avoid mentioning that you obtained the information from the context.
+And answer according to the language of the user's question.
+""" if chain_output else "")
                          + (pre_prompt + "\n" if pre_prompt else "")
-                         + "{query}\n"
+                         + "{{query}}\n"
             )
 
             if chain_output:
                 inputs['context'] = chain_output
+                # context_params = JinjaPromptTemplate.from_template(template=chain_output).input_variables
+                # if context_params:
+                #     for context_param in context_params:
+                #         if context_param not in inputs:
+                #             inputs[context_param] = '{{' + context_param + '}}'
 
             prompt_inputs = {k: inputs[k] for k in prompt_template.input_variables if k in inputs}
             prompt_content = prompt_template.format(
@@ -147,76 +182,93 @@ class Completion:
 
             if isinstance(llm, BaseChatModel):
                 # use chat llm as completion model
-                return [HumanMessage(content=prompt_content)]
+                return [HumanMessage(content=prompt_content)], None
             else:
-                return prompt_content
+                return prompt_content, None
         else:
             messages: List[BaseMessage] = []
 
-            system_message = None
-            if pre_prompt:
-                # append pre prompt as system message
-                system_message = PromptBuilder.to_system_message(pre_prompt, inputs)
-
-            if chain_output:
-                # append context as system message, currently only use simple stuff prompt
-                context_message = PromptBuilder.to_system_message(
-                    """Use the following pieces of [CONTEXT] to answer the users question. 
-If you don't know the answer, just say that you don't know, don't try to make up an answer.
-```
-[CONTEXT]
-{context}
-```""",
-                    {'context': chain_output}
-                )
-
-                if not system_message:
-                    system_message = context_message
-                else:
-                    system_message.content = context_message.content + "\n\n" + system_message.content
-
-            if system_message:
-                messages.append(system_message)
-
             human_inputs = {
                 "query": query
             }
 
-            # construct main prompt
-            human_message = PromptBuilder.to_human_message(
-                prompt_content="{query}",
-                inputs=human_inputs
-            )
+            human_message_prompt = ""
+
+            if pre_prompt:
+                pre_prompt_inputs = {k: inputs[k] for k in
+                                     JinjaPromptTemplate.from_template(template=pre_prompt).input_variables
+                                     if k in inputs}
+
+                if pre_prompt_inputs:
+                    human_inputs.update(pre_prompt_inputs)
+
+            if chain_output:
+                human_inputs['context'] = chain_output
+                human_message_prompt += """Use the following CONTEXT as your learned knowledge.
+[CONTEXT]
+{{context}}
+[END CONTEXT]
+
+When answer to user:
+- If you don't know, just say that you don't know.
+- If you don't know when you are not sure, ask for clarification. 
+Avoid mentioning that you obtained the information from the context.
+And answer according to the language of the user's question.
+"""
+
+            if pre_prompt:
+                human_message_prompt += pre_prompt
+
+            query_prompt = "\nHuman: {{query}}\nAI: "
 
             if memory:
                 # append chat histories
-                tmp_messages = messages.copy() + [human_message]
-                curr_message_tokens = memory.llm.get_messages_tokens(tmp_messages)
-                rest_tokens = llm_constant.max_context_token_length[
-                                  memory.llm.model_name] - memory.llm.max_tokens - curr_message_tokens
+                tmp_human_message = PromptBuilder.to_human_message(
+                    prompt_content=human_message_prompt + query_prompt,
+                    inputs=human_inputs
+                )
+
+                curr_message_tokens = memory.llm.get_messages_tokens([tmp_human_message])
+                rest_tokens = llm_constant.max_context_token_length[memory.llm.model_name] \
+                              - memory.llm.max_tokens - curr_message_tokens
                 rest_tokens = max(rest_tokens, 0)
-                history_messages = cls.get_history_messages_from_memory(memory, rest_tokens)
-                messages += history_messages
+                histories = cls.get_history_messages_from_memory(memory, rest_tokens)
+
+                # disable template string in query
+                # histories_params = JinjaPromptTemplate.from_template(template=histories).input_variables
+                # if histories_params:
+                #     for histories_param in histories_params:
+                #         if histories_param not in human_inputs:
+                #             human_inputs[histories_param] = '{{' + histories_param + '}}'
+
+                human_message_prompt += "\n\n" + histories
+
+            human_message_prompt += query_prompt
+
+            # construct main prompt
+            human_message = PromptBuilder.to_human_message(
+                prompt_content=human_message_prompt,
+                inputs=human_inputs
+            )
 
             messages.append(human_message)
 
-            return messages
+            return messages, ['\nHuman:']
 
     @classmethod
-    def get_llm_callback_manager(cls, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
-                                 streaming: bool, conversation_message_task: ConversationMessageTask) -> CallbackManager:
+    def get_llm_callbacks(cls, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
+                          streaming: bool,
+                          conversation_message_task: ConversationMessageTask) -> List[BaseCallbackHandler]:
         llm_callback_handler = LLMCallbackHandler(llm, conversation_message_task)
         if streaming:
-            callback_handlers = [llm_callback_handler, DifyStreamingStdOutCallbackHandler()]
+            return [llm_callback_handler, DifyStreamingStdOutCallbackHandler()]
         else:
-            callback_handlers = [llm_callback_handler, DifyStdOutCallbackHandler()]
-
-        return CallbackManager(callback_handlers)
+            return [llm_callback_handler, DifyStdOutCallbackHandler()]
 
     @classmethod
     def get_history_messages_from_memory(cls, memory: ReadOnlyConversationTokenDBBufferSharedMemory,
                                          max_token_limit: int) -> \
-            List[BaseMessage]:
+            str:
         """Get memory messages."""
         memory.max_token_limit = max_token_limit
         memory_key = memory.memory_variables[0]
@@ -248,7 +300,8 @@ If you don't know the answer, just say that you don't know, don't try to make up
         return memory
 
     @classmethod
-    def validate_query_tokens(cls, tenant_id: str, app_model_config: AppModelConfig, query: str):
+    def get_validate_rest_tokens(cls, mode: str, tenant_id: str, app_model_config: AppModelConfig,
+                                 query: str, inputs: dict) -> int:
         llm = LLMBuilder.to_llm_from_model(
             tenant_id=tenant_id,
             model=app_model_config.model_dict
@@ -257,8 +310,26 @@ If you don't know the answer, just say that you don't know, don't try to make up
         model_limited_tokens = llm_constant.max_context_token_length[llm.model_name]
         max_tokens = llm.max_tokens
 
-        if model_limited_tokens - max_tokens - llm.get_num_tokens(query) < 0:
-            raise LLMBadRequestError("Query is too long")
+        # get prompt without memory and context
+        prompt, _ = cls.get_main_llm_prompt(
+            mode=mode,
+            llm=llm,
+            pre_prompt=app_model_config.pre_prompt,
+            query=query,
+            inputs=inputs,
+            chain_output=None,
+            memory=None
+        )
+
+        prompt_tokens = llm.get_num_tokens(prompt) if isinstance(prompt, str) \
+            else llm.get_num_tokens_from_messages(prompt)
+
+        rest_tokens = model_limited_tokens - max_tokens - prompt_tokens
+        if rest_tokens < 0:
+            raise LLMBadRequestError("Query or prefix prompt is too long, you can reduce the prefix prompt, "
+                                     "or shrink the max token, or switch to a llm with a larger token limit size.")
+
+        return rest_tokens
 
     @classmethod
     def recale_llm_max_tokens(cls, final_llm: Union[StreamableOpenAI, StreamableChatOpenAI],
@@ -286,7 +357,7 @@ If you don't know the answer, just say that you don't know, don't try to make up
         )
 
         # get llm prompt
-        original_prompt = cls.get_main_llm_prompt(
+        original_prompt, _ = cls.get_main_llm_prompt(
             mode="completion",
             llm=llm,
             pre_prompt=pre_prompt,
@@ -315,7 +386,7 @@ If you don't know the answer, just say that you don't know, don't try to make up
             streaming=streaming
         )
 
-        llm.callback_manager = cls.get_llm_callback_manager(llm, streaming, conversation_message_task)
+        llm.callbacks = cls.get_llm_callbacks(llm, streaming, conversation_message_task)
 
         cls.recale_llm_max_tokens(
             final_llm=llm,

api/core/constant/llm_constant.py

@@ -4,6 +4,7 @@ models = {
     'gpt-4': 'openai',  # 8,192 tokens
     'gpt-4-32k': 'openai',  # 32,768 tokens
     'gpt-3.5-turbo': 'openai',  # 4,096 tokens
+    'gpt-3.5-turbo-16k': 'openai',  # 16384 tokens
     'text-davinci-003': 'openai',  # 4,097 tokens
     'text-davinci-002': 'openai',  # 4,097 tokens
     'text-curie-001': 'openai',  # 2,049 tokens
@@ -16,6 +17,7 @@ max_context_token_length = {
     'gpt-4': 8192,
     'gpt-4-32k': 32768,
     'gpt-3.5-turbo': 4096,
+    'gpt-3.5-turbo-16k': 16384,
     'text-davinci-003': 4097,
     'text-davinci-002': 4097,
     'text-curie-001': 2049,
@@ -29,11 +31,13 @@ models_by_mode = {
         'gpt-4',  # 8,192 tokens
         'gpt-4-32k',  # 32,768 tokens
         'gpt-3.5-turbo',  # 4,096 tokens
+        'gpt-3.5-turbo-16k',  # 16,384 tokens
     ],
     'completion': [
         'gpt-4',  # 8,192 tokens
         'gpt-4-32k',  # 32,768 tokens
         'gpt-3.5-turbo',  # 4,096 tokens
+        'gpt-3.5-turbo-16k',  # 16,384 tokens
         'text-davinci-003',  # 4,097 tokens
         'text-davinci-002'  # 4,097 tokens
         'text-curie-001',  # 2,049 tokens
@@ -57,9 +61,13 @@ model_prices = {
         'completion': Decimal('0.12')
     },
     'gpt-3.5-turbo': {
-        'prompt': Decimal('0.002'),
+        'prompt': Decimal('0.0015'),
         'completion': Decimal('0.002')
     },
+    'gpt-3.5-turbo-16k': {
+        'prompt': Decimal('0.003'),
+        'completion': Decimal('0.004')
+    },
     'text-davinci-003': {
         'prompt': Decimal('0.02'),
         'completion': Decimal('0.02')
@@ -77,7 +85,7 @@ model_prices = {
         'completion': Decimal('0.0004')
     },
     'text-embedding-ada-002': {
-        'usage': Decimal('0.0004'),
+        'usage': Decimal('0.0001'),
     }
 }
 

api/core/conversation_message_task.py

@@ -2,8 +2,6 @@ import decimal
 import json
 from typing import Optional, Union
 
-from gunicorn.config import User
-
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.callback_handler.entity.dataset_query import DatasetQueryObj
 from core.callback_handler.entity.llm_message import LLMMessage
@@ -12,7 +10,7 @@ from core.constant import llm_constant
 from core.llm.llm_builder import LLMBuilder
 from core.llm.provider.llm_provider_service import LLMProviderService
 from core.prompt.prompt_builder import PromptBuilder
-from core.prompt.prompt_template import OutLinePromptTemplate
+from core.prompt.prompt_template import JinjaPromptTemplate
 from events.message_event import message_was_created
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -58,6 +56,9 @@ class ConversationMessageTask:
         )
 
     def init(self):
+        provider_name = LLMBuilder.get_default_provider(self.app.tenant_id)
+        self.model_dict['provider'] = provider_name
+
         override_model_configs = None
         if self.is_override:
             override_model_configs = {
@@ -77,13 +78,15 @@ class ConversationMessageTask:
         if self.mode == 'chat':
             introduction = self.app_model_config.opening_statement
             if introduction:
-                prompt_template = OutLinePromptTemplate.from_template(template=PromptBuilder.process_template(introduction))
+                prompt_template = JinjaPromptTemplate.from_template(template=introduction)
                 prompt_inputs = {k: self.inputs[k] for k in prompt_template.input_variables if k in self.inputs}
-                introduction = prompt_template.format(**prompt_inputs)
+                try:
+                    introduction = prompt_template.format(**prompt_inputs)
+                except KeyError:
+                    pass
 
             if self.app_model_config.pre_prompt:
-                pre_prompt = PromptBuilder.process_template(self.app_model_config.pre_prompt)
-                system_message = PromptBuilder.to_system_message(pre_prompt, self.inputs)
+                system_message = PromptBuilder.to_system_message(self.app_model_config.pre_prompt, self.inputs)
                 system_instruction = system_message.content
                 llm = LLMBuilder.to_llm(self.tenant_id, self.model_name)
                 system_instruction_tokens = llm.get_messages_tokens([system_message])
@@ -153,7 +156,7 @@ class ConversationMessageTask:
         self.message.message = llm_message.prompt
         self.message.message_tokens = message_tokens
         self.message.message_unit_price = message_unit_price
-        self.message.answer = llm_message.completion.strip() if llm_message.completion else ''
+        self.message.answer = PromptBuilder.process_template(llm_message.completion.strip()) if llm_message.completion else ''
         self.message.answer_tokens = answer_tokens
         self.message.answer_unit_price = answer_unit_price
         self.message.provider_response_latency = llm_message.latency
@@ -170,7 +173,7 @@ class ConversationMessageTask:
         )
 
         if not by_stopped:
-            self._pub_handler.pub_end()
+            self.end()
 
     def update_provider_quota(self):
         llm_provider_service = LLMProviderService(
@@ -267,9 +270,12 @@ class ConversationMessageTask:
         total_price = message_tokens_per_1k * message_unit_price + answer_tokens_per_1k * answer_unit_price
         return total_price.quantize(decimal.Decimal('0.0000001'), rounding=decimal.ROUND_HALF_UP)
 
+    def end(self):
+        self._pub_handler.pub_end()
+
 
 class PubHandler:
-    def __init__(self, user: Union[Account | User], task_id: str,
+    def __init__(self, user: Union[Account | EndUser], task_id: str,
                  message: Message, conversation: Conversation,
                  chain_pub: bool = False, agent_thought_pub: bool = False):
         self._channel = PubHandler.generate_channel_name(user, task_id)
@@ -282,13 +288,16 @@ class PubHandler:
         self._agent_thought_pub = agent_thought_pub
 
     @classmethod
-    def generate_channel_name(cls, user: Union[Account | User], task_id: str):
-        user_str = 'account-' + user.id if isinstance(user, Account) else 'end-user-' + user.id
+    def generate_channel_name(cls, user: Union[Account | EndUser], task_id: str):
+        if not user:
+            raise ValueError("user is required")
+
+        user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id)
         return "generate_result:{}-{}".format(user_str, task_id)
 
     @classmethod
-    def generate_stopped_cache_key(cls, user: Union[Account | User], task_id: str):
-        user_str = 'account-' + user.id if isinstance(user, Account) else 'end-user-' + user.id
+    def generate_stopped_cache_key(cls, user: Union[Account | EndUser], task_id: str):
+        user_str = 'account-' + str(user.id) if isinstance(user, Account) else 'end-user-' + str(user.id)
         return "generate_result_stopped:{}-{}".format(user_str, task_id)
 
     def pub_text(self, text: str):
@@ -296,10 +305,10 @@ class PubHandler:
             'event': 'message',
             'data': {
                 'task_id': self._task_id,
-                'message_id': self._message.id,
+                'message_id': str(self._message.id),
                 'text': text,
                 'mode': self._conversation.mode,
-                'conversation_id': self._conversation.id
+                'conversation_id': str(self._conversation.id)
             }
         }
 
@@ -366,7 +375,7 @@ class PubHandler:
         redis_client.publish(self._channel, json.dumps(content))
 
     @classmethod
-    def pub_error(cls, user: Union[Account | User], task_id: str, e):
+    def pub_error(cls, user: Union[Account | EndUser], task_id: str, e):
         content = {
             'error': type(e).__name__,
             'description': e.description if getattr(e, 'description', None) is not None else str(e)
@@ -379,7 +388,7 @@ class PubHandler:
         return redis_client.get(self._stopped_cache_key) is not None
 
     @classmethod
-    def stop(cls, user: Union[Account | User], task_id: str):
+    def stop(cls, user: Union[Account | EndUser], task_id: str):
         stopped_cache_key = cls.generate_stopped_cache_key(user, task_id)
         redis_client.setex(stopped_cache_key, 600, 1)
 

api/core/data_loader/file_extractor.py

@@ -0,0 +1,43 @@
+import tempfile
+from pathlib import Path
+from typing import List, Union
+
+from langchain.document_loaders import TextLoader, Docx2txtLoader
+from langchain.schema import Document
+
+from core.data_loader.loader.csv import CSVLoader
+from core.data_loader.loader.excel import ExcelLoader
+from core.data_loader.loader.html import HTMLLoader
+from core.data_loader.loader.markdown import MarkdownLoader
+from core.data_loader.loader.pdf import PdfLoader
+from extensions.ext_storage import storage
+from models.model import UploadFile
+
+
+class FileExtractor:
+    @classmethod
+    def load(cls, upload_file: UploadFile, return_text: bool = False) -> Union[List[Document] | str]:
+        with tempfile.TemporaryDirectory() as temp_dir:
+            suffix = Path(upload_file.key).suffix
+            file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
+            storage.download(upload_file.key, file_path)
+
+            input_file = Path(file_path)
+            delimiter = '\n'
+            if input_file.suffix == '.xlsx':
+                loader = ExcelLoader(file_path)
+            elif input_file.suffix == '.pdf':
+                loader = PdfLoader(file_path, upload_file=upload_file)
+            elif input_file.suffix in ['.md', '.markdown']:
+                loader = MarkdownLoader(file_path, autodetect_encoding=True)
+            elif input_file.suffix in ['.htm', '.html']:
+                loader = HTMLLoader(file_path)
+            elif input_file.suffix == '.docx':
+                loader = Docx2txtLoader(file_path)
+            elif input_file.suffix == '.csv':
+                loader = CSVLoader(file_path, autodetect_encoding=True)
+            else:
+                # txt
+                loader = TextLoader(file_path, autodetect_encoding=True)
+
+            return delimiter.join([document.page_content for document in loader.load()]) if return_text else loader.load()

api/core/data_loader/loader/csv.py

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

api/core/data_loader/loader/excel.py

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

api/core/data_loader/loader/html.py

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

api/core/data_loader/loader/markdown.py

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

api/core/data_loader/loader/notion.py

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

api/core/data_loader/loader/pdf.py

@@ -0,0 +1,55 @@
+import logging
+from typing import List, Optional
+
+from langchain.document_loaders import PyPDFium2Loader
+from langchain.document_loaders.base import BaseLoader
+from langchain.schema import Document
+
+from extensions.ext_storage import storage
+from models.model import UploadFile
+
+logger = logging.getLogger(__name__)
+
+
+class PdfLoader(BaseLoader):
+    """Load pdf files.
+
+
+    Args:
+        file_path: Path to the file to load.
+    """
+
+    def __init__(
+        self,
+        file_path: str,
+        upload_file: Optional[UploadFile] = None
+    ):
+        """Initialize with file path."""
+        self._file_path = file_path
+        self._upload_file = upload_file
+
+    def load(self) -> List[Document]:
+        plaintext_file_key = ''
+        plaintext_file_exists = False
+        if self._upload_file:
+            if self._upload_file.hash:
+                plaintext_file_key = 'upload_files/' + self._upload_file.tenant_id + '/' \
+                                     + self._upload_file.hash + '.0625.plaintext'
+                try:
+                    text = storage.load(plaintext_file_key).decode('utf-8')
+                    plaintext_file_exists = True
+                    return [Document(page_content=text)]
+                except FileNotFoundError:
+                    pass
+        documents = PyPDFium2Loader(file_path=self._file_path).load()
+        text_list = []
+        for document in documents:
+            text_list.append(document.page_content)
+        text = "\n\n".join(text_list)
+
+        # save plaintext file for caching
+        if not plaintext_file_exists and plaintext_file_key:
+            storage.save(plaintext_file_key, text.encode('utf-8'))
+
+        return documents
+

api/core/docstore/dataset_docstore.py

@@ -1,10 +1,6 @@
 from typing import Any, Dict, Optional, Sequence
 
-import tiktoken
-from llama_index.data_structs import Node
-from llama_index.docstore.types import BaseDocumentStore
-from llama_index.docstore.utils import json_to_doc
-from llama_index.schema import BaseDocument
+from langchain.schema import Document
 from sqlalchemy import func
 
 from core.llm.token_calculator import TokenCalculator
@@ -12,7 +8,7 @@ from extensions.ext_database import db
 from models.dataset import Dataset, DocumentSegment
 
 
-class DatesetDocumentStore(BaseDocumentStore):
+class DatesetDocumentStore:
     def __init__(
         self,
         dataset: Dataset,
@@ -48,7 +44,7 @@ class DatesetDocumentStore(BaseDocumentStore):
         return self._embedding_model_name
 
     @property
-    def docs(self) -> Dict[str, BaseDocument]:
+    def docs(self) -> Dict[str, Document]:
         document_segments = db.session.query(DocumentSegment).filter(
             DocumentSegment.dataset_id == self._dataset.id
         ).all()
@@ -56,13 +52,20 @@ class DatesetDocumentStore(BaseDocumentStore):
         output = {}
         for document_segment in document_segments:
             doc_id = document_segment.index_node_id
-            result = self.segment_to_dict(document_segment)
-            output[doc_id] = json_to_doc(result)
+            output[doc_id] = Document(
+                page_content=document_segment.content,
+                metadata={
+                    "doc_id": document_segment.index_node_id,
+                    "doc_hash": document_segment.index_node_hash,
+                    "document_id": document_segment.document_id,
+                    "dataset_id": document_segment.dataset_id,
+                }
+            )
 
         return output
 
     def add_documents(
-        self, docs: Sequence[BaseDocument], allow_update: bool = True
+        self, docs: Sequence[Document], allow_update: bool = True
     ) -> None:
         max_position = db.session.query(func.max(DocumentSegment.position)).filter(
             DocumentSegment.document == self._document_id
@@ -72,23 +75,20 @@ class DatesetDocumentStore(BaseDocumentStore):
             max_position = 0
 
         for doc in docs:
-            if doc.is_doc_id_none:
-                raise ValueError("doc_id not set")
+            if not isinstance(doc, Document):
+                raise ValueError("doc must be a Document")
 
-            if not isinstance(doc, Node):
-                raise ValueError("doc must be a Node")
-
-            segment_document = self.get_document(doc_id=doc.get_doc_id(), raise_error=False)
+            segment_document = self.get_document(doc_id=doc.metadata['doc_id'], raise_error=False)
 
             # NOTE: doc could already exist in the store, but we overwrite it
             if not allow_update and segment_document:
                 raise ValueError(
-                    f"doc_id {doc.get_doc_id()} already exists. "
+                    f"doc_id {doc.metadata['doc_id']} already exists. "
                     "Set allow_update to True to overwrite."
                 )
 
             # calc embedding use tokens
-            tokens = TokenCalculator.get_num_tokens(self._embedding_model_name, doc.get_text())
+            tokens = TokenCalculator.get_num_tokens(self._embedding_model_name, doc.page_content)
 
             if not segment_document:
                 max_position += 1
@@ -97,19 +97,19 @@ class DatesetDocumentStore(BaseDocumentStore):
                     tenant_id=self._dataset.tenant_id,
                     dataset_id=self._dataset.id,
                     document_id=self._document_id,
-                    index_node_id=doc.get_doc_id(),
-                    index_node_hash=doc.get_doc_hash(),
+                    index_node_id=doc.metadata['doc_id'],
+                    index_node_hash=doc.metadata['doc_hash'],
                     position=max_position,
-                    content=doc.get_text(),
-                    word_count=len(doc.get_text()),
+                    content=doc.page_content,
+                    word_count=len(doc.page_content),
                     tokens=tokens,
                     created_by=self._user_id,
                 )
                 db.session.add(segment_document)
             else:
-                segment_document.content = doc.get_text()
-                segment_document.index_node_hash = doc.get_doc_hash()
-                segment_document.word_count = len(doc.get_text())
+                segment_document.content = doc.page_content
+                segment_document.index_node_hash = doc.metadata['doc_hash']
+                segment_document.word_count = len(doc.page_content)
                 segment_document.tokens = tokens
 
             db.session.commit()
@@ -121,7 +121,7 @@ class DatesetDocumentStore(BaseDocumentStore):
 
     def get_document(
         self, doc_id: str, raise_error: bool = True
-    ) -> Optional[BaseDocument]:
+    ) -> Optional[Document]:
         document_segment = self.get_document_segment(doc_id)
 
         if document_segment is None:
@@ -130,8 +130,15 @@ class DatesetDocumentStore(BaseDocumentStore):
             else:
                 return None
 
-        result = self.segment_to_dict(document_segment)
-        return json_to_doc(result)
+        return Document(
+            page_content=document_segment.content,
+            metadata={
+                "doc_id": document_segment.index_node_id,
+                "doc_hash": document_segment.index_node_hash,
+                "document_id": document_segment.document_id,
+                "dataset_id": document_segment.dataset_id,
+            }
+        )
 
     def delete_document(self, doc_id: str, raise_error: bool = True) -> None:
         document_segment = self.get_document_segment(doc_id)
@@ -164,15 +171,6 @@ class DatesetDocumentStore(BaseDocumentStore):
 
         return document_segment.index_node_hash
 
-    def update_docstore(self, other: "BaseDocumentStore") -> None:
-        """Update docstore.
-
-        Args:
-            other (BaseDocumentStore): docstore to update from
-
-        """
-        self.add_documents(list(other.docs.values()))
-
     def get_document_segment(self, doc_id: str) -> DocumentSegment:
         document_segment = db.session.query(DocumentSegment).filter(
             DocumentSegment.dataset_id == self._dataset.id,
@@ -180,11 +178,3 @@ class DatesetDocumentStore(BaseDocumentStore):
         ).first()
 
         return document_segment
-
-    def segment_to_dict(self, segment: DocumentSegment) -> Dict[str, Any]:
-        return {
-            "doc_id": segment.index_node_id,
-            "doc_hash": segment.index_node_hash,
-            "text": segment.content,
-            "__type__": Node.get_type()
-        }

api/core/docstore/empty_docstore.py

@@ -1,51 +0,0 @@
-from typing import Any, Dict, Optional, Sequence
-from llama_index.docstore.types import BaseDocumentStore
-from llama_index.schema import BaseDocument
-
-
-class EmptyDocumentStore(BaseDocumentStore):
-    @classmethod
-    def from_dict(cls, config_dict: Dict[str, Any]) -> "EmptyDocumentStore":
-        return cls()
-
-    def to_dict(self) -> Dict[str, Any]:
-        """Serialize to dict."""
-        return {}
-
-    @property
-    def docs(self) -> Dict[str, BaseDocument]:
-        return {}
-
-    def add_documents(
-        self, docs: Sequence[BaseDocument], allow_update: bool = True
-    ) -> None:
-        pass
-
-    def document_exists(self, doc_id: str) -> bool:
-        """Check if document exists."""
-        return False
-
-    def get_document(
-        self, doc_id: str, raise_error: bool = True
-    ) -> Optional[BaseDocument]:
-        return None
-
-    def delete_document(self, doc_id: str, raise_error: bool = True) -> None:
-        pass
-
-    def set_document_hash(self, doc_id: str, doc_hash: str) -> None:
-        """Set the hash for a given doc_id."""
-        pass
-
-    def get_document_hash(self, doc_id: str) -> Optional[str]:
-        """Get the stored hash for a document, if it exists."""
-        return None
-
-    def update_docstore(self, other: "BaseDocumentStore") -> None:
-        """Update docstore.
-
-        Args:
-            other (BaseDocumentStore): docstore to update from
-
-        """
-        self.add_documents(list(other.docs.values()))

api/core/embedding/cached_embedding.py

@@ -0,0 +1,72 @@
+import logging
+from typing import List
+
+from langchain.embeddings.base import Embeddings
+from sqlalchemy.exc import IntegrityError
+
+from extensions.ext_database import db
+from libs import helper
+from models.dataset import Embedding
+
+
+class CacheEmbedding(Embeddings):
+    def __init__(self, embeddings: Embeddings):
+        self._embeddings = embeddings
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Embed search docs."""
+        # use doc embedding cache or store if not exists
+        text_embeddings = []
+        embedding_queue_texts = []
+        for text in texts:
+            hash = helper.generate_text_hash(text)
+            embedding = db.session.query(Embedding).filter_by(hash=hash).first()
+            if embedding:
+                text_embeddings.append(embedding.get_embedding())
+            else:
+                embedding_queue_texts.append(text)
+
+        embedding_results = self._embeddings.embed_documents(embedding_queue_texts)
+
+        i = 0
+        for text in embedding_queue_texts:
+            hash = helper.generate_text_hash(text)
+
+            try:
+                embedding = Embedding(hash=hash)
+                embedding.set_embedding(embedding_results[i])
+                db.session.add(embedding)
+                db.session.commit()
+            except IntegrityError:
+                db.session.rollback()
+                continue
+            except:
+                logging.exception('Failed to add embedding to db')
+                continue
+
+            i += 1
+
+        text_embeddings.extend(embedding_results)
+        return text_embeddings
+
+    def embed_query(self, text: str) -> List[float]:
+        """Embed query text."""
+        # use doc embedding cache or store if not exists
+        hash = helper.generate_text_hash(text)
+        embedding = db.session.query(Embedding).filter_by(hash=hash).first()
+        if embedding:
+            return embedding.get_embedding()
+
+        embedding_results = self._embeddings.embed_query(text)
+
+        try:
+            embedding = Embedding(hash=hash)
+            embedding.set_embedding(embedding_results)
+            db.session.add(embedding)
+            db.session.commit()
+        except IntegrityError:
+            db.session.rollback()
+        except:
+            logging.exception('Failed to add embedding to db')
+
+        return embedding_results

api/core/embedding/openai_embedding.py

@@ -1,176 +0,0 @@
-from typing import Optional, Any, List
-
-import openai
-from llama_index.embeddings.base import BaseEmbedding
-from llama_index.embeddings.openai import OpenAIEmbeddingMode, OpenAIEmbeddingModelType, _QUERY_MODE_MODEL_DICT, \
-    _TEXT_MODE_MODEL_DICT
-from tenacity import wait_random_exponential, retry, stop_after_attempt
-
-from core.llm.error_handle_wraps import handle_llm_exceptions, handle_llm_exceptions_async
-
-
-@retry(reraise=True, wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
-def get_embedding(
-    text: str,
-    engine: Optional[str] = None,
-    openai_api_key: Optional[str] = None,
-) -> List[float]:
-    """Get embedding.
-
-    NOTE: Copied from OpenAI's embedding utils:
-    https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
-
-    Copied here to avoid importing unnecessary dependencies
-    like matplotlib, plotly, scipy, sklearn.
-
-    """
-    text = text.replace("\n", " ")
-    return openai.Embedding.create(input=[text], engine=engine, api_key=openai_api_key)["data"][0]["embedding"]
-
-
-@retry(reraise=True, wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
-async def aget_embedding(text: str, engine: Optional[str] = None, openai_api_key: Optional[str] = None) -> List[float]:
-    """Asynchronously get embedding.
-
-    NOTE: Copied from OpenAI's embedding utils:
-    https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
-
-    Copied here to avoid importing unnecessary dependencies
-    like matplotlib, plotly, scipy, sklearn.
-
-    """
-    # replace newlines, which can negatively affect performance.
-    text = text.replace("\n", " ")
-
-    return (await openai.Embedding.acreate(input=[text], engine=engine, api_key=openai_api_key))["data"][0][
-        "embedding"
-    ]
-
-
-@retry(reraise=True, wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
-def get_embeddings(
-    list_of_text: List[str],
-    engine: Optional[str] = None,
-    openai_api_key: Optional[str] = None
-) -> List[List[float]]:
-    """Get embeddings.
-
-    NOTE: Copied from OpenAI's embedding utils:
-    https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
-
-    Copied here to avoid importing unnecessary dependencies
-    like matplotlib, plotly, scipy, sklearn.
-
-    """
-    assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
-
-    # replace newlines, which can negatively affect performance.
-    list_of_text = [text.replace("\n", " ") for text in list_of_text]
-
-    data = openai.Embedding.create(input=list_of_text, engine=engine, api_key=openai_api_key).data
-    data = sorted(data, key=lambda x: x["index"])  # maintain the same order as input.
-    return [d["embedding"] for d in data]
-
-
-@retry(reraise=True, wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
-async def aget_embeddings(
-    list_of_text: List[str], engine: Optional[str] = None, openai_api_key: Optional[str] = None
-) -> List[List[float]]:
-    """Asynchronously get embeddings.
-
-    NOTE: Copied from OpenAI's embedding utils:
-    https://github.com/openai/openai-python/blob/main/openai/embeddings_utils.py
-
-    Copied here to avoid importing unnecessary dependencies
-    like matplotlib, plotly, scipy, sklearn.
-
-    """
-    assert len(list_of_text) <= 2048, "The batch size should not be larger than 2048."
-
-    # replace newlines, which can negatively affect performance.
-    list_of_text = [text.replace("\n", " ") for text in list_of_text]
-
-    data = (await openai.Embedding.acreate(input=list_of_text, engine=engine, api_key=openai_api_key)).data
-    data = sorted(data, key=lambda x: x["index"])  # maintain the same order as input.
-    return [d["embedding"] for d in data]
-
-
-class OpenAIEmbedding(BaseEmbedding):
-
-    def __init__(
-        self,
-        mode: str = OpenAIEmbeddingMode.TEXT_SEARCH_MODE,
-        model: str = OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002,
-        deployment_name: Optional[str] = None,
-        openai_api_key: Optional[str] = None,
-        **kwargs: Any,
-    ) -> None:
-        """Init params."""
-        super().__init__(**kwargs)
-        self.mode = OpenAIEmbeddingMode(mode)
-        self.model = OpenAIEmbeddingModelType(model)
-        self.deployment_name = deployment_name
-        self.openai_api_key = openai_api_key
-
-    @handle_llm_exceptions
-    def _get_query_embedding(self, query: str) -> List[float]:
-        """Get query embedding."""
-        if self.deployment_name is not None:
-            engine = self.deployment_name
-        else:
-            key = (self.mode, self.model)
-            if key not in _QUERY_MODE_MODEL_DICT:
-                raise ValueError(f"Invalid mode, model combination: {key}")
-            engine = _QUERY_MODE_MODEL_DICT[key]
-        return get_embedding(query, engine=engine, openai_api_key=self.openai_api_key)
-
-    def _get_text_embedding(self, text: str) -> List[float]:
-        """Get text embedding."""
-        if self.deployment_name is not None:
-            engine = self.deployment_name
-        else:
-            key = (self.mode, self.model)
-            if key not in _TEXT_MODE_MODEL_DICT:
-                raise ValueError(f"Invalid mode, model combination: {key}")
-            engine = _TEXT_MODE_MODEL_DICT[key]
-        return get_embedding(text, engine=engine, openai_api_key=self.openai_api_key)
-
-    async def _aget_text_embedding(self, text: str) -> List[float]:
-        """Asynchronously get text embedding."""
-        if self.deployment_name is not None:
-            engine = self.deployment_name
-        else:
-            key = (self.mode, self.model)
-            if key not in _TEXT_MODE_MODEL_DICT:
-                raise ValueError(f"Invalid mode, model combination: {key}")
-            engine = _TEXT_MODE_MODEL_DICT[key]
-        return await aget_embedding(text, engine=engine, openai_api_key=self.openai_api_key)
-
-    def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
-        """Get text embeddings.
-
-        By default, this is a wrapper around _get_text_embedding.
-        Can be overriden for batch queries.
-
-        """
-        if self.deployment_name is not None:
-            engine = self.deployment_name
-        else:
-            key = (self.mode, self.model)
-            if key not in _TEXT_MODE_MODEL_DICT:
-                raise ValueError(f"Invalid mode, model combination: {key}")
-            engine = _TEXT_MODE_MODEL_DICT[key]
-        embeddings = get_embeddings(texts, engine=engine, openai_api_key=self.openai_api_key)
-        return embeddings
-
-    async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
-        """Asynchronously get text embeddings."""
-        if self.deployment_name is not None:
-            engine = self.deployment_name
-        else:
-            key = (self.mode, self.model)
-            if key not in _TEXT_MODE_MODEL_DICT:
-                raise ValueError(f"Invalid mode, model combination: {key}")
-            engine = _TEXT_MODE_MODEL_DICT[key]
-        embeddings = await aget_embeddings(texts, engine=engine, openai_api_key=self.openai_api_key)
-        return embeddings

api/core/generator/llm_generator.py

@@ -1,15 +1,17 @@
 import logging
 
+from langchain import PromptTemplate
 from langchain.chat_models.base import BaseChatModel
-from langchain.schema import HumanMessage
+from langchain.schema import HumanMessage, OutputParserException, BaseMessage
 
 from core.constant import llm_constant
 from core.llm.llm_builder import LLMBuilder
 from core.llm.streamable_open_ai import StreamableOpenAI
 from core.llm.token_calculator import TokenCalculator
+from core.prompt.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
 
 from core.prompt.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
-from core.prompt.prompt_template import OutLinePromptTemplate
+from core.prompt.prompt_template import JinjaPromptTemplate, OutLinePromptTemplate
 from core.prompt.prompts import CONVERSATION_TITLE_PROMPT, CONVERSATION_SUMMARY_PROMPT, INTRODUCTION_GENERATE_PROMPT
 
 
@@ -21,10 +23,10 @@ class LLMGenerator:
     @classmethod
     def generate_conversation_name(cls, tenant_id: str, query, answer):
         prompt = CONVERSATION_TITLE_PROMPT
-        prompt = prompt.format(query=query, answer=answer)
+        prompt = prompt.format(query=query)
         llm: StreamableOpenAI = LLMBuilder.to_llm(
             tenant_id=tenant_id,
-            model_name=generate_base_model,
+            model_name='gpt-3.5-turbo',
             max_tokens=50
         )
 
@@ -38,11 +40,12 @@ class LLMGenerator:
     @classmethod
     def generate_conversation_summary(cls, tenant_id: str, messages):
         max_tokens = 200
+        model = 'gpt-3.5-turbo'
 
         prompt = CONVERSATION_SUMMARY_PROMPT
         prompt_with_empty_context = prompt.format(context='')
-        prompt_tokens = TokenCalculator.get_num_tokens(generate_base_model, prompt_with_empty_context)
-        rest_tokens = llm_constant.max_context_token_length[generate_base_model] - prompt_tokens - max_tokens
+        prompt_tokens = TokenCalculator.get_num_tokens(model, prompt_with_empty_context)
+        rest_tokens = llm_constant.max_context_token_length[model] - prompt_tokens - max_tokens - 1
 
         context = ''
         for message in messages:
@@ -50,14 +53,17 @@ class LLMGenerator:
                 continue
 
             message_qa_text = "Human:" + message.query + "\nAI:" + message.answer + "\n"
-            if rest_tokens - TokenCalculator.get_num_tokens(generate_base_model, context + message_qa_text) > 0:
+            if rest_tokens - TokenCalculator.get_num_tokens(model, context + message_qa_text) > 0:
                 context += message_qa_text
 
+        if not context:
+            return '[message too long, no summary]'
+
         prompt = prompt.format(context=context)
 
         llm: StreamableOpenAI = LLMBuilder.to_llm(
             tenant_id=tenant_id,
-            model_name=generate_base_model,
+            model_name=model,
             max_tokens=max_tokens
         )
 
@@ -90,8 +96,8 @@ class LLMGenerator:
         output_parser = SuggestedQuestionsAfterAnswerOutputParser()
         format_instructions = output_parser.get_format_instructions()
 
-        prompt = OutLinePromptTemplate(
-            template="{histories}\n{format_instructions}\nquestions:\n",
+        prompt = JinjaPromptTemplate(
+            template="{{histories}}\n{{format_instructions}}\nquestions:\n",
             input_variables=["histories"],
             partial_variables={"format_instructions": format_instructions}
         )
@@ -100,7 +106,7 @@ class LLMGenerator:
 
         llm: StreamableOpenAI = LLMBuilder.to_llm(
             tenant_id=tenant_id,
-            model_name=generate_base_model,
+            model_name='gpt-3.5-turbo',
             temperature=0,
             max_tokens=256
         )
@@ -112,9 +118,56 @@ class LLMGenerator:
 
         try:
             output = llm(query)
+            if isinstance(output, BaseMessage):
+                output = output.content
             questions = output_parser.parse(output)
         except Exception:
             logging.exception("Error generating suggested questions after answer")
             questions = []
 
         return questions
+
+    @classmethod
+    def generate_rule_config(cls, tenant_id: str, audiences: str, hoping_to_solve: str) -> dict:
+        output_parser = RuleConfigGeneratorOutputParser()
+
+        prompt = OutLinePromptTemplate(
+            template=output_parser.get_format_instructions(),
+            input_variables=["audiences", "hoping_to_solve"],
+            partial_variables={
+                "variable": '{variable}',
+                "lanA": '{lanA}',
+                "lanB": '{lanB}',
+                "topic": '{topic}'
+            },
+            validate_template=False
+        )
+
+        _input = prompt.format_prompt(audiences=audiences, hoping_to_solve=hoping_to_solve)
+
+        llm: StreamableOpenAI = LLMBuilder.to_llm(
+            tenant_id=tenant_id,
+            model_name=generate_base_model,
+            temperature=0,
+            max_tokens=512
+        )
+
+        if isinstance(llm, BaseChatModel):
+            query = [HumanMessage(content=_input.to_string())]
+        else:
+            query = _input.to_string()
+
+        try:
+            output = llm(query)
+            rule_config = output_parser.parse(output)
+        except OutputParserException:
+            raise ValueError('Please give a valid input for intended audience or hoping to solve problems.')
+        except Exception:
+            logging.exception("Error generating prompt")
+            rule_config = {
+                "prompt": "",
+                "variables": [],
+                "opening_statement": ""
+            }
+
+        return rule_config

api/core/index/base.py

@@ -0,0 +1,59 @@
+from __future__ import annotations
+from abc import abstractmethod, ABC
+from typing import List, Any
+
+from langchain.schema import Document, BaseRetriever
+
+from models.dataset import Dataset
+
+
+class BaseIndex(ABC):
+
+    def __init__(self, dataset: Dataset):
+        self.dataset = dataset
+
+    @abstractmethod
+    def create(self, texts: list[Document], **kwargs) -> BaseIndex:
+        raise NotImplementedError
+
+    @abstractmethod
+    def add_texts(self, texts: list[Document], **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def text_exists(self, id: str) -> bool:
+        raise NotImplementedError
+
+    @abstractmethod
+    def delete_by_ids(self, ids: list[str]) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def delete_by_document_id(self, document_id: str):
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_retriever(self, **kwargs: Any) -> BaseRetriever:
+        raise NotImplementedError
+
+    @abstractmethod
+    def search(
+            self, query: str,
+            **kwargs: Any
+    ) -> List[Document]:
+        raise NotImplementedError
+
+    def delete(self) -> None:
+        raise NotImplementedError
+
+    def _filter_duplicate_texts(self, texts: list[Document]) -> list[Document]:
+        for text in texts:
+            doc_id = text.metadata['doc_id']
+            exists_duplicate_node = self.text_exists(doc_id)
+            if exists_duplicate_node:
+                texts.remove(text)
+
+        return texts
+
+    def _get_uuids(self, texts: list[Document]) -> list[str]:
+        return [text.metadata['doc_id'] for text in texts]

api/core/index/index.py

@@ -0,0 +1,41 @@
+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.llm.llm_builder import LLMBuilder
+from models.dataset import Dataset
+
+
+class IndexBuilder:
+    @classmethod
+    def get_index(cls, dataset: Dataset, indexing_technique: str, ignore_high_quality_check: bool = False):
+        if indexing_technique == "high_quality":
+            if not ignore_high_quality_check and dataset.indexing_technique != 'high_quality':
+                return None
+
+            model_credentials = LLMBuilder.get_model_credentials(
+                tenant_id=dataset.tenant_id,
+                model_provider=LLMBuilder.get_default_provider(dataset.tenant_id),
+                model_name='text-embedding-ada-002'
+            )
+
+            embeddings = CacheEmbedding(OpenAIEmbeddings(
+                **model_credentials
+            ))
+
+            return VectorIndex(
+                dataset=dataset,
+                config=current_app.config,
+                embeddings=embeddings
+            )
+        elif indexing_technique == "economy":
+            return KeywordTableIndex(
+                dataset=dataset,
+                config=KeywordTableConfig(
+                    max_keywords_per_chunk=10
+                )
+            )
+        else:
+            raise ValueError('Unknown indexing technique')