fix: tool info

Co-authored-by: xudong2.zhang <xudong2.zhang@zkh.com>
Co-authored-by: crazywoola <427733928@qq.com>

.github/workflows/api-tests.yml

@@ -4,7 +4,6 @@ on:
   pull_request:
     branches:
       - main
-      - plugins/beta
     paths:
       - api/**
       - docker/**
@@ -27,6 +26,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Setup Poetry and Python ${{ matrix.python-version }}
         uses: ./.github/actions/setup-poetry

.github/workflows/build-push.yml

@@ -5,8 +5,6 @@ on:
     branches:
       - "main"
       - "deploy/dev"
-      - "plugins/beta"
-      - "dev/plugin-deploy"
   release:
     types: [published]
 
@@ -81,10 +79,12 @@ jobs:
           cache-to: type=gha,mode=max,scope=${{ matrix.service_name }}
 
       - name: Export digest
+        env:
+          DIGEST: ${{ steps.build.outputs.digest }}
         run: |
           mkdir -p /tmp/digests
-          digest="${{ steps.build.outputs.digest }}"
-          touch "/tmp/digests/${digest#sha256:}"
+          sanitized_digest=${DIGEST#sha256:}
+          touch "/tmp/digests/${sanitized_digest}"
 
       - name: Upload digest
         uses: actions/upload-artifact@v4
@@ -134,23 +134,15 @@ jobs:
 
       - name: Create manifest list and push
         working-directory: /tmp/digests
+        env:
+          IMAGE_NAME: ${{ env[matrix.image_name_env] }}
         run: |
           docker buildx imagetools create $(jq -cr '.tags | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON") \
-            $(printf '${{ env[matrix.image_name_env] }}@sha256:%s ' *)
+            $(printf "$IMAGE_NAME@sha256:%s " *)
 
       - name: Inspect image
-        run: |
-          docker buildx imagetools inspect ${{ env[matrix.image_name_env] }}:${{ steps.meta.outputs.version }}
-
-      - name: print context var
-        uses: actions/checkout@v4
-
-      - name: deploy pod in plugin env
-        if: github.ref == 'refs/heads/dev/plugin-deploy'
         env:
-          IMAGEHASH: ${{ github.sha }}
-          APICMD: "${{ secrets.PLUGIN_CD_API_CURL }}"
-          WEBCMD: "${{ secrets.PLUGIN_CD_WEB_CURL }}"
+          IMAGE_NAME: ${{ env[matrix.image_name_env] }}
+          IMAGE_VERSION: ${{ steps.meta.outputs.version }}
         run: |
-          bash -c "${APICMD/yourNewVersion/$IMAGEHASH}"
-          bash -c "${WEBCMD/yourNewVersion/$IMAGEHASH}"
+          docker buildx imagetools inspect "$IMAGE_NAME:$IMAGE_VERSION"

.github/workflows/db-migration-test.yml

@@ -20,6 +20,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Setup Poetry and Python
         uses: ./.github/actions/setup-poetry

.github/workflows/deploy-plugin-dev.yml

@@ -1,23 +0,0 @@
-name: Deploy Plugin Dev
-
-on:
-  workflow_run:
-    workflows: ["Build and Push API & Web"]
-    branches:
-      - "dev/plugin-deploy"
-    types:
-      - completed
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    if: |
-      github.event.workflow_run.conclusion == 'success'
-    steps:
-      - name: Deploy to server
-        uses: appleboy/ssh-action@v0.1.8
-        with:
-          host: ${{ secrets.SSH_HOST }}
-          username: ${{ secrets.SSH_USER }}
-          key: ${{ secrets.SSH_PRIVATE_KEY }}
-          script: "echo 123"

.github/workflows/expose_service_ports.sh

@@ -9,6 +9,6 @@ yq eval '.services["pgvecto-rs"].ports += ["5431:5432"]' -i docker/docker-compos
 yq eval '.services["elasticsearch"].ports += ["9200:9200"]' -i docker/docker-compose.yaml
 yq eval '.services.couchbase-server.ports += ["8091-8096:8091-8096"]' -i docker/docker-compose.yaml
 yq eval '.services.couchbase-server.ports += ["11210:11210"]' -i docker/docker-compose.yaml
-yq eval '.services.tidb.ports += ["4000:4000"]' -i docker/docker-compose.yaml
+yq eval '.services.tidb.ports += ["4000:4000"]' -i docker/tidb/docker-compose.yaml
 
 echo "Ports exposed for sandbox, weaviate, tidb, qdrant, chroma, milvus, pgvector, pgvecto-rs, elasticsearch, couchbase"

.github/workflows/style.yml

@@ -4,7 +4,6 @@ on:
   pull_request:
     branches:
       - main
-      - plugins/beta
 
 concurrency:
   group: style-${{ github.head_ref || github.run_id }}
@@ -18,6 +17,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Check changed files
         id: changed-files
@@ -60,6 +62,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Check changed files
         id: changed-files
@@ -87,7 +92,7 @@ jobs:
 
       - name: Web style check
         if: steps.changed-files.outputs.any_changed == 'true'
-        run: yarn run lint
+        run: pnpm run lint
 
   docker-compose-template:
     name: Docker Compose Template
@@ -96,6 +101,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Check changed files
         id: changed-files
@@ -124,6 +132,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Check changed files
         id: changed-files
@@ -141,7 +152,7 @@ jobs:
         if: steps.changed-files.outputs.any_changed == 'true'
         env:
           BASH_SEVERITY: warning
-          DEFAULT_BRANCH: plugins/beta
+          DEFAULT_BRANCH: main
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           IGNORE_GENERATED_FILES: true
           IGNORE_GITIGNORED_FILES: true

.github/workflows/tool-test-sdks.yaml

@@ -26,6 +26,9 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Use Node.js ${{ matrix.node-version }}
         uses: actions/setup-node@v4
@@ -35,7 +38,7 @@ jobs:
           cache-dependency-path: 'pnpm-lock.yaml'
 
       - name: Install Dependencies
-        run: pnpm install
+        run: pnpm install --frozen-lockfile
 
       - name: Test
         run: pnpm test

.github/workflows/translate-i18n-base-on-english.yml

@@ -16,6 +16,7 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 2 # last 2 commits
+          persist-credentials: false
 
       - name: Check for file changes in i18n/en-US
         id: check_files

.github/workflows/vdb-tests.yml

@@ -28,6 +28,9 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Setup Poetry and Python ${{ matrix.python-version }}
         uses: ./.github/actions/setup-poetry
@@ -51,7 +54,15 @@ jobs:
       - name: Expose Service Ports
         run: sh .github/workflows/expose_service_ports.sh
 
-      - name: Set up Vector Stores (TiDB, Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale, ElasticSearch, Couchbase)
+      - name: Set up Vector Store (TiDB)
+        uses: hoverkraft-tech/compose-action@v2.0.2
+        with:
+          compose-file: docker/tidb/docker-compose.yaml
+          services: |
+            tidb
+            tiflash
+
+      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma, MyScale, ElasticSearch, Couchbase)
         uses: hoverkraft-tech/compose-action@v2.0.2
         with:
           compose-file: |
@@ -67,7 +78,9 @@ jobs:
             pgvector
             chroma
             elasticsearch
-            tidb
+
+      - name: Check TiDB Ready
+        run: poetry run -P api python api/tests/integration_tests/vdb/tidb_vector/check_tiflash_ready.py
 
       - name: Test Vector Stores
         run: poetry run -P api bash dev/pytest/pytest_vdb.sh

.github/workflows/web-tests.yml

@@ -22,25 +22,34 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
 
       - name: Check changed files
         id: changed-files
         uses: tj-actions/changed-files@v45
         with:
           files: web/**
+      # to run pnpm, should install package canvas, but it always install failed on amd64 under ubuntu-latest
+      # - name: Install pnpm
+      #   uses: pnpm/action-setup@v4
+      #   with:
+      #     version: 10
+      #     run_install: false
 
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        if: steps.changed-files.outputs.any_changed == 'true'
-        with:
-          node-version: 20
-          cache: pnpm
-          cache-dependency-path: ./web/package.json
+      # - name: Setup Node.js
+      #   uses: actions/setup-node@v4
+      #   if: steps.changed-files.outputs.any_changed == 'true'
+      #   with:
+      #     node-version: 20
+      #     cache: pnpm
+      #     cache-dependency-path: ./web/package.json
 
-      - name: Install dependencies
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: pnpm install --frozen-lockfile
+      # - name: Install dependencies
+      #   if: steps.changed-files.outputs.any_changed == 'true'
+      #   run: pnpm install --frozen-lockfile
 
-      - name: Run tests
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: pnpm test
+      # - name: Run tests
+      #   if: steps.changed-files.outputs.any_changed == 'true'
+      #   run: pnpm test

.gitignore

@@ -163,6 +163,7 @@ docker/volumes/db/data/*
 docker/volumes/redis/data/*
 docker/volumes/weaviate/*
 docker/volumes/qdrant/*
+docker/tidb/volumes/*
 docker/volumes/etcd/*
 docker/volumes/minio/*
 docker/volumes/milvus/*

README.md

@@ -108,6 +108,72 @@ Please refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-host
 **7. Backend-as-a-Service**: 
   All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
 
+## Feature Comparison
+<table style="width: 100%;">
+  <tr>
+    <th align="center">Feature</th>
+    <th align="center">Dify.AI</th>
+    <th align="center">LangChain</th>
+    <th align="center">Flowise</th>
+    <th align="center">OpenAI Assistants API</th>
+  </tr>
+  <tr>
+    <td align="center">Programming Approach</td>
+    <td align="center">API + App-oriented</td>
+    <td align="center">Python Code</td>
+    <td align="center">App-oriented</td>
+    <td align="center">API-oriented</td>
+  </tr>
+  <tr>
+    <td align="center">Supported LLMs</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">OpenAI-only</td>
+  </tr>
+  <tr>
+    <td align="center">RAG Engine</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">Agent</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">Workflow</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Observability</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Enterprise Feature (SSO/Access control)</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Local Deployment</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+</table>
 
 ## Using Dify
 

README_FR.md

@@ -55,7 +55,7 @@
 Dify est une plateforme de développement d'applications LLM open source. Son interface intuitive combine un flux de travail d'IA, un pipeline RAG, des capacités d'agent, une gestion de modèles, des fonctionnalités d'observabilité, et plus encore, vous permettant de passer rapidement du prototype à la production. Voici une liste des fonctionnalités principales:
 </br> </br>
 
-**1. Flux de travail**: 
+**1. Flux de travail** : 
   Construisez et testez des flux de travail d'IA puissants sur un canevas visuel, en utilisant toutes les fonctionnalités suivantes et plus encore.
 
 
@@ -63,27 +63,25 @@ Dify est une plateforme de développement d'applications LLM open source. Son in
 
 
 
-**2. Prise en charge complète des modèles**: 
+**2. Prise en charge complète des modèles** : 
   Intégration transparente avec des centaines de LLM propriétaires / open source provenant de dizaines de fournisseurs d'inférence et de solutions auto-hébergées, couvrant GPT, Mistral, Llama3, et tous les modèles compatibles avec l'API OpenAI. Une liste complète des fournisseurs de modèles pris en charge se trouve [ici](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
 
-**3. IDE de prompt**: 
+**3. IDE de prompt** : 
   Interface intuitive pour créer des prompts, comparer les performances des modèles et ajouter des fonctionnalités supplémentaires telles que la synthèse vocale à une application basée sur des chats. 
 
-**4. Pipeline RAG**: 
+**4. Pipeline RAG** : 
   Des capacités RAG étendues qui couvrent tout, de l'ingestion de documents à la récupération, avec un support prêt à l'emploi pour l'extraction de texte à partir de PDF, PPT et autres formats de document courants.
 
-**5. Capac
-
-ités d'agent**: 
+**5. Capacités d'agent** : 
   Vous pouvez définir des agents basés sur l'appel de fonction LLM ou ReAct, et ajouter des outils pré-construits ou personnalisés pour l'agent. Dify fournit plus de 50 outils intégrés pour les agents d'IA, tels que la recherche Google, DALL·E, Stable Diffusion et WolframAlpha.
 
-**6. LLMOps**: 
+**6. LLMOps** : 
   Surveillez et analysez les journaux d'application et les performances au fil du temps. Vous pouvez continuellement améliorer les prompts, les ensembles de données et les modèles en fonction des données de production et des annotations.
 
-**7. Backend-as-a-Service**: 
+**7. Backend-as-a-Service** : 
   Toutes les offres de Dify sont accompagnées d'API correspondantes, vous permettant d'intégrer facilement Dify dans votre propre logique métier.
 
 

README_JA.md

@@ -164,7 +164,7 @@ DifyはオープンソースのLLMアプリケーション開発プラットフ
 
 - **企業/組織向けのDify</br>**
 企業中心の機能を提供しています。[メールを送信](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry)して企業のニーズについて相談してください。 </br>
-  > AWSを使用しているスタートアップ企業や中小企業の場合は、[AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6)のDify Premiumをチェックして、ワンクリックで自分のAWS VPCにデプロイできます。さらに、手頃な価格のAMIオファリングどして、ロゴやブランディングをカスタマイズしてアプリケーションを作成するオプションがあります。
+  > AWSを使用しているスタートアップ企業や中小企業の場合は、[AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t23mebxzwjhu6)のDify Premiumをチェックして、ワンクリックで自分のAWS VPCにデプロイできます。さらに、手頃な価格のAMIオファリングとして、ロゴやブランディングをカスタマイズしてアプリケーションを作成するオプションがあります。
 
 
 ## 最新の情報を入手

README_KL.md

@@ -87,9 +87,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
 
 ## Feature Comparison
 <table style="width: 100%;">
-  <tr
-
->
+  <tr>
     <th align="center">Feature</th>
     <th align="center">Dify.AI</th>
     <th align="center">LangChain</th>

README_SI.md

@@ -106,6 +106,73 @@ Prosimo, glejte naša pogosta vprašanja [FAQ](https://docs.dify.ai/getting-star
 **7. Backend-as-a-Service**: 
   AVse ponudbe Difyja so opremljene z ustreznimi API-ji, tako da lahko Dify brez težav integrirate v svojo poslovno logiko.
 
+## Primerjava Funkcij
+
+<table style="width: 100%;">
+  <tr>
+    <th align="center">Funkcija</th>
+    <th align="center">Dify.AI</th>
+    <th align="center">LangChain</th>
+    <th align="center">Flowise</th>
+    <th align="center">OpenAI Assistants API</th>
+  </tr>
+  <tr>
+    <td align="center">Programski pristop</td>
+    <td align="center">API + usmerjeno v aplikacije</td>
+    <td align="center">Python koda</td>
+    <td align="center">Usmerjeno v aplikacije</td>
+    <td align="center">Usmerjeno v API</td>
+  </tr>
+  <tr>
+    <td align="center">Podprti LLM-ji</td>
+    <td align="center">Bogata izbira</td>
+    <td align="center">Bogata izbira</td>
+    <td align="center">Bogata izbira</td>
+    <td align="center">Samo OpenAI</td>
+  </tr>
+  <tr>
+    <td align="center">RAG pogon</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">Agent</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">Potek dela</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Spremljanje</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Funkcija za podjetja (SSO/nadzor dostopa)</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Lokalna namestitev</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+</table>
 
 ## Uporaba Dify
 
@@ -187,4 +254,4 @@ Zaradi zaščite vaše zasebnosti se izogibajte objavljanju varnostnih vprašanj
 
 ## Licenca
 
-To skladišče je na voljo pod [odprtokodno licenco Dify](LICENSE) , ki je v bistvu Apache 2.0 z nekaj dodatnimi omejitvami.
+To skladišče je na voljo pod [odprtokodno licenco Dify](LICENSE) , ki je v bistvu Apache 2.0 z nekaj dodatnimi omejitvami.

api/Dockerfile

@@ -55,9 +55,11 @@ RUN \
         # basic environment
         curl nodejs libgmp-dev libmpfr-dev libmpc-dev \
         # For Security
-        # expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
+        expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
         # install a chinese font to support the use of tools like matplotlib
         fonts-noto-cjk \
+        # install a package to improve the accuracy of guessing mime type and file extension
+        media-types \
         # install libmagic to support the use of python-magic guess MIMETYPE
         libmagic1 \
     && apt-get autoremove -y \

api/README.md

@@ -37,7 +37,13 @@
 
 4. Create environment.
 
-   Dify API service uses [Poetry](https://python-poetry.org/docs/) to manage dependencies. You can execute `poetry shell` to activate the environment.
+   Dify API service uses [Poetry](https://python-poetry.org/docs/) to manage dependencies. First, you need to add the poetry shell plugin, if you don't have it already, in order to run in a virtual environment. [Note: Poetry shell is no longer a native command so you need to install the poetry plugin beforehand]
+
+   ```bash
+   poetry self add poetry-plugin-shell
+   ```
+   
+   Then, You can execute `poetry shell` to activate the environment.
 
 5. Install dependencies
 

api/commands.py

@@ -707,12 +707,13 @@ def extract_unique_plugins(output_file: str, input_file: str):
 @click.option(
     "--output_file", prompt=True, help="The file to store the installed plugins.", default="installed_plugins.jsonl"
 )
-def install_plugins(input_file: str, output_file: str):
+@click.option("--workers", prompt=True, help="The number of workers to install plugins.", default=100)
+def install_plugins(input_file: str, output_file: str, workers: int):
     """
     Install plugins.
     """
     click.echo(click.style("Starting install plugins.", fg="white"))
 
-    PluginMigration.install_plugins(input_file, output_file)
+    PluginMigration.install_plugins(input_file, output_file, workers)
 
     click.echo(click.style("Install plugins completed.", fg="green"))

api/configs/feature/__init__.py

@@ -373,8 +373,8 @@ class HttpConfig(BaseSettings):
     )
 
     RESPECT_XFORWARD_HEADERS_ENABLED: bool = Field(
-        description="Enable or disable the X-Forwarded-For Proxy Fix middleware from Werkzeug"
-        " to respect X-* headers to redirect clients",
+        description="Enable handling of X-Forwarded-For, X-Forwarded-Proto, and X-Forwarded-Port headers"
+        " when the app is behind a single trusted reverse proxy.",
         default=False,
     )
 

api/configs/middleware/__init__.py

@@ -1,3 +1,4 @@
+import os
 from typing import Any, Literal, Optional
 from urllib.parse import quote_plus
 
@@ -166,6 +167,11 @@ class DatabaseConfig(BaseSettings):
         default=False,
     )
 
+    RETRIEVAL_SERVICE_EXECUTORS: NonNegativeInt = Field(
+        description="Number of processes for the retrieval service, default to CPU cores.",
+        default=os.cpu_count(),
+    )
+
     @computed_field
     def SQLALCHEMY_ENGINE_OPTIONS(self) -> dict[str, Any]:
         return {

api/constants/__init__.py

@@ -15,7 +15,7 @@ AUDIO_EXTENSIONS.extend([ext.upper() for ext in AUDIO_EXTENSIONS])
 
 if dify_config.ETL_TYPE == "Unstructured":
     DOCUMENT_EXTENSIONS = ["txt", "markdown", "md", "mdx", "pdf", "html", "htm", "xlsx", "xls"]
-    DOCUMENT_EXTENSIONS.extend(("docx", "csv", "eml", "msg", "pptx", "xml", "epub"))
+    DOCUMENT_EXTENSIONS.extend(("doc", "docx", "csv", "eml", "msg", "pptx", "xml", "epub"))
     if dify_config.UNSTRUCTURED_API_URL:
         DOCUMENT_EXTENSIONS.append("ppt")
     DOCUMENT_EXTENSIONS.extend([ext.upper() for ext in DOCUMENT_EXTENSIONS])

api/controllers/console/datasets/datasets.py

@@ -14,6 +14,7 @@ from controllers.console.wraps import account_initialization_required, enterpris
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.indexing_runner import IndexingRunner
 from core.model_runtime.entities.model_entities import ModelType
+from core.plugin.entities.plugin import ModelProviderID
 from core.provider_manager import ProviderManager
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.extractor.entity.extract_setting import ExtractSetting
@@ -72,7 +73,9 @@ class DatasetListApi(Resource):
 
         data = marshal(datasets, dataset_detail_fields)
         for item in data:
+            # convert embedding_model_provider to plugin standard format
             if item["indexing_technique"] == "high_quality":
+                item["embedding_model_provider"] = str(ModelProviderID(item["embedding_model_provider"]))
                 item_model = f"{item['embedding_model']}:{item['embedding_model_provider']}"
                 if item_model in model_names:
                     item["embedding_available"] = True
@@ -620,7 +623,6 @@ class DatasetRetrievalSettingApi(Resource):
         match vector_type:
             case (
                 VectorType.RELYT
-                | VectorType.PGVECTOR
                 | VectorType.TIDB_VECTOR
                 | VectorType.CHROMA
                 | VectorType.TENCENT

api/controllers/files/image_preview.py

@@ -1,3 +1,5 @@
+from urllib.parse import quote
+
 from flask import Response, request
 from flask_restful import Resource, reqparse  # type: ignore
 from werkzeug.exceptions import NotFound
@@ -71,7 +73,8 @@ class FilePreviewApi(Resource):
         if upload_file.size > 0:
             response.headers["Content-Length"] = str(upload_file.size)
         if args["as_attachment"]:
-            response.headers["Content-Disposition"] = f"attachment; filename={upload_file.name}"
+            encoded_filename = quote(upload_file.name)
+            response.headers["Content-Disposition"] = f"attachment; filename*=UTF-8''{encoded_filename}"
 
         return response
 

api/controllers/inner_api/workspace/workspace.py

@@ -50,8 +50,8 @@ class EnterpriseWorkspaceNoOwnerEmail(Resource):
             "plan": tenant.plan,
             "status": tenant.status,
             "custom_config": json.loads(tenant.custom_config) if tenant.custom_config else {},
-            "created_at": tenant.created_at.isoformat() if tenant.created_at else None,
-            "updated_at": tenant.updated_at.isoformat() if tenant.updated_at else None,
+            "created_at": tenant.created_at.isoformat() + "Z" if tenant.created_at else None,
+            "updated_at": tenant.updated_at.isoformat() + "Z" if tenant.updated_at else None,
         }
 
         return {

api/controllers/service_api/app/message.py

@@ -10,6 +10,7 @@ from controllers.service_api.app.error import NotChatAppError
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
 from core.app.entities.app_invoke_entities import InvokeFrom
 from fields.conversation_fields import message_file_fields
+from fields.message_fields import feedback_fields, retriever_resource_fields
 from fields.raws import FilesContainedField
 from libs.helper import TimestampField, uuid_value
 from models.model import App, AppMode, EndUser
@@ -18,26 +19,6 @@ from services.message_service import MessageService
 
 
 class MessageListApi(Resource):
-    feedback_fields = {"rating": fields.String}
-    retriever_resource_fields = {
-        "id": fields.String,
-        "message_id": fields.String,
-        "position": fields.Integer,
-        "dataset_id": fields.String,
-        "dataset_name": fields.String,
-        "document_id": fields.String,
-        "document_name": fields.String,
-        "data_source_type": fields.String,
-        "segment_id": fields.String,
-        "score": fields.Float,
-        "hit_count": fields.Integer,
-        "word_count": fields.Integer,
-        "segment_position": fields.Integer,
-        "index_node_hash": fields.String,
-        "content": fields.String,
-        "created_at": TimestampField,
-    }
-
     agent_thought_fields = {
         "id": fields.String,
         "chain_id": fields.String,
@@ -89,7 +70,7 @@ class MessageListApi(Resource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
+                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

api/controllers/service_api/dataset/document.py

@@ -336,6 +336,10 @@ class DocumentUpdateByFileApi(DatasetApiResource):
 
         if not dataset:
             raise ValueError("Dataset is not exist.")
+
+        # indexing_technique is already set in dataset since this is an update
+        args["indexing_technique"] = dataset.indexing_technique
+
         if "file" in request.files:
             # save file info
             file = request.files["file"]

api/controllers/web/message.py

@@ -21,7 +21,7 @@ from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from fields.conversation_fields import message_file_fields
-from fields.message_fields import agent_thought_fields
+from fields.message_fields import agent_thought_fields, feedback_fields, retriever_resource_fields
 from fields.raws import FilesContainedField
 from libs import helper
 from libs.helper import TimestampField, uuid_value
@@ -34,27 +34,6 @@ from services.message_service import MessageService
 
 
 class MessageListApi(WebApiResource):
-    feedback_fields = {"rating": fields.String}
-
-    retriever_resource_fields = {
-        "id": fields.String,
-        "message_id": fields.String,
-        "position": fields.Integer,
-        "dataset_id": fields.String,
-        "dataset_name": fields.String,
-        "document_id": fields.String,
-        "document_name": fields.String,
-        "data_source_type": fields.String,
-        "segment_id": fields.String,
-        "score": fields.Float,
-        "hit_count": fields.Integer,
-        "word_count": fields.Integer,
-        "segment_position": fields.Integer,
-        "index_node_hash": fields.String,
-        "content": fields.String,
-        "created_at": TimestampField,
-    }
-
     message_fields = {
         "id": fields.String,
         "conversation_id": fields.String,

api/core/agent/base_agent_runner.py

@@ -329,6 +329,7 @@ class BaseAgentRunner(AppRunner):
         )
         if not updated_agent_thought:
             raise ValueError("agent thought not found")
+        agent_thought = updated_agent_thought
 
         if thought:
             agent_thought.thought = thought

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

@@ -140,9 +140,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             app_config=app_config,
             file_upload_config=file_extra_config,
             conversation_id=conversation.id if conversation else None,
-            inputs=conversation.inputs
-            if conversation
-            else self._prepare_user_inputs(
+            inputs=self._prepare_user_inputs(
                 user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
             ),
             query=query,

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

@@ -384,6 +384,7 @@ class AdvancedChatAppGenerateTaskPipeline:
                         task_id=self._application_generate_entity.task_id,
                         workflow_node_execution=workflow_node_execution,
                     )
+                    session.commit()
 
                 if node_finish_resp:
                     yield node_finish_resp

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

@@ -149,9 +149,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             model_conf=ModelConfigConverter.convert(app_config),
             file_upload_config=file_extra_config,
             conversation_id=conversation.id if conversation else None,
-            inputs=conversation.inputs
-            if conversation
-            else self._prepare_user_inputs(
+            inputs=self._prepare_user_inputs(
                 user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
             ),
             query=query,

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

@@ -141,9 +141,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             model_conf=ModelConfigConverter.convert(app_config),
             file_upload_config=file_extra_config,
             conversation_id=conversation.id if conversation else None,
-            inputs=conversation.inputs
-            if conversation
-            else self._prepare_user_inputs(
+            inputs=self._prepare_user_inputs(
                 user_inputs=inputs, variables=app_config.variables, tenant_id=app_model.tenant_id
             ),
             query=query,

api/core/app/apps/message_based_app_generator.py

@@ -42,7 +42,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             ChatAppGenerateEntity,
             CompletionAppGenerateEntity,
             AgentChatAppGenerateEntity,
-            AgentChatAppGenerateEntity,
         ],
         queue_manager: AppQueueManager,
         conversation: Conversation,

api/core/app/apps/workflow_app_runner.py

@@ -387,7 +387,6 @@ class WorkflowBasedAppRunner(AppRunner):
                     status=event.status,
                     data=event.data,
                     metadata=event.metadata,
-                    node_id=event.node_id,
                 )
             )
         elif isinstance(event, ParallelBranchRunStartedEvent):

api/core/app/entities/queue_entities.py

@@ -331,7 +331,6 @@ class QueueAgentLogEvent(AppQueueEvent):
     status: str
     data: Mapping[str, Any]
     metadata: Optional[Mapping[str, Any]] = None
-    node_id: str
 
 
 class QueueNodeRetryEvent(QueueNodeStartedEvent):

api/core/app/entities/task_entities.py

@@ -719,7 +719,6 @@ class AgentLogStreamResponse(StreamResponse):
         status: str
         data: Mapping[str, Any]
         metadata: Optional[Mapping[str, Any]] = None
-        node_id: str
 
     event: StreamEvent = StreamEvent.AGENT_LOG
     data: Data

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -844,7 +844,7 @@ class WorkflowCycleManage:
         if node_execution_id not in self._workflow_node_executions:
             raise ValueError(f"Workflow node execution not found: {node_execution_id}")
         cached_workflow_node_execution = self._workflow_node_executions[node_execution_id]
-        return cached_workflow_node_execution
+        return session.merge(cached_workflow_node_execution)
 
     def _handle_agent_log(self, task_id: str, event: QueueAgentLogEvent) -> AgentLogStreamResponse:
         """
@@ -864,6 +864,5 @@ class WorkflowCycleManage:
                 status=event.status,
                 data=event.data,
                 metadata=event.metadata,
-                node_id=event.node_id,
             ),
         )

api/core/entities/provider_configuration.py

@@ -7,6 +7,7 @@ from json import JSONDecodeError
 from typing import Optional
 
 from pydantic import BaseModel, ConfigDict
+from sqlalchemy import or_
 
 from constants import HIDDEN_VALUE
 from core.entities import DEFAULT_PLUGIN_ID
@@ -28,6 +29,7 @@ from core.model_runtime.entities.provider_entities import (
 )
 from core.model_runtime.model_providers.__base.ai_model import AIModel
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from core.plugin.entities.plugin import ModelProviderID
 from extensions.ext_database import db
 from models.provider import (
     LoadBalancingModelConfig,
@@ -190,8 +192,11 @@ class ProviderConfiguration(BaseModel):
             db.session.query(Provider)
             .filter(
                 Provider.tenant_id == self.tenant_id,
-                Provider.provider_name == self.provider.provider,
                 Provider.provider_type == ProviderType.CUSTOM.value,
+                or_(
+                    Provider.provider_name == ModelProviderID(self.provider.provider).plugin_name,
+                    Provider.provider_name == self.provider.provider,
+                ),
             )
             .first()
         )
@@ -279,7 +284,10 @@ class ProviderConfiguration(BaseModel):
             db.session.query(Provider)
             .filter(
                 Provider.tenant_id == self.tenant_id,
-                Provider.provider_name == self.provider.provider,
+                or_(
+                    Provider.provider_name == ModelProviderID(self.provider.provider).plugin_name,
+                    Provider.provider_name == self.provider.provider,
+                ),
                 Provider.provider_type == ProviderType.CUSTOM.value,
             )
             .first()

api/core/helper/ssrf_proxy.py

@@ -65,8 +65,7 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
         retries += 1
         if retries <= max_retries:
             time.sleep(BACKOFF_FACTOR * (2 ** (retries - 1)))
-    raise MaxRetriesExceededError(
-        f"Reached maximum retries ({max_retries}) for URL {url}")
+    raise MaxRetriesExceededError(f"Reached maximum retries ({max_retries}) for URL {url}")
 
 
 def get(url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):

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

@@ -228,7 +228,7 @@ class LargeLanguageModel(AIModel):
         :return: result generator
         """
         callbacks = callbacks or []
-        prompt_message = AssistantPromptMessage(content="")
+        assistant_message = AssistantPromptMessage(content="")
         usage = None
         system_fingerprint = None
         real_model = model
@@ -250,7 +250,7 @@ class LargeLanguageModel(AIModel):
                     callbacks=callbacks,
                 )
 
-                prompt_message.content += chunk.delta.message.content
+                assistant_message.content += chunk.delta.message.content
                 real_model = chunk.model
                 if chunk.delta.usage:
                     usage = chunk.delta.usage
@@ -265,7 +265,7 @@ class LargeLanguageModel(AIModel):
             result=LLMResult(
                 model=real_model,
                 prompt_messages=prompt_messages,
-                message=prompt_message,
+                message=assistant_message,
                 usage=usage or LLMUsage.empty_usage(),
                 system_fingerprint=system_fingerprint,
             ),

api/core/model_runtime/model_providers/model_provider_factory.py

@@ -20,6 +20,7 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
 from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.model_runtime.schema_validators.model_credential_schema_validator import ModelCredentialSchemaValidator
 from core.model_runtime.schema_validators.provider_credential_schema_validator import ProviderCredentialSchemaValidator
+from core.plugin.entities.plugin import ModelProviderID
 from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
 from core.plugin.manager.asset import PluginAssetManager
 from core.plugin.manager.model import PluginModelManager
@@ -112,6 +113,9 @@ class ModelProviderFactory:
         :param provider: provider name
         :return: provider schema
         """
+        if "/" not in provider:
+            provider = str(ModelProviderID(provider))
+
         # fetch plugin model providers
         plugin_model_provider_entities = self.get_plugin_model_providers()
 
@@ -363,4 +367,4 @@ class ModelProviderFactory:
             plugin_id = "/".join(provider.split("/")[:-1])
             provider_name = provider.split("/")[-1]
 
-        return plugin_id, provider_name
+        return str(plugin_id), provider_name

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

@@ -0,0 +1,22 @@
+- claude-3-haiku@20240307
+- claude-3-opus@20240229
+- claude-3-sonnet@20240229
+- claude-3-5-sonnet-v2@20241022
+- claude-3-5-sonnet@20240620
+- gemini-1.0-pro-vision-001
+- gemini-1.0-pro-002
+- gemini-1.5-flash-001
+- gemini-1.5-flash-002
+- gemini-1.5-pro-001
+- gemini-1.5-pro-002
+- gemini-2.0-flash-001
+- gemini-2.0-flash-exp
+- gemini-2.0-flash-lite-preview-02-05
+- gemini-2.0-flash-thinking-exp-01-21
+- gemini-2.0-flash-thinking-exp-1219
+- gemini-2.0-pro-exp-02-05
+- gemini-exp-1114
+- gemini-exp-1121
+- gemini-exp-1206
+- gemini-flash-experimental
+- gemini-pro-experimental

api/core/plugin/entities/plugin.py

@@ -159,7 +159,7 @@ class GenericProviderID:
             if re.match(r"^[a-z0-9_-]+$", value):
                 value = f"langgenius/{value}/{value}"
             else:
-                raise ValueError("Invalid plugin id")
+                raise ValueError(f"Invalid plugin id {value}")
 
         self.organization, self.plugin_name, self.provider_name = value.split("/")
         self.is_hardcoded = is_hardcoded
@@ -169,6 +169,21 @@ class GenericProviderID:
         return f"{self.organization}/{self.plugin_name}"
 
 
+class ModelProviderID(GenericProviderID):
+    def __init__(self, value: str, is_hardcoded: bool = False) -> None:
+        super().__init__(value, is_hardcoded)
+        if self.organization == "langgenius" and self.provider_name == "google":
+            self.plugin_name = "gemini"
+
+
+class ToolProviderID(GenericProviderID):
+    def __init__(self, value: str, is_hardcoded: bool = False) -> None:
+        super().__init__(value, is_hardcoded)
+        if self.organization == "langgenius":
+            if self.provider_name in ["jina", "siliconflow", "stepfun"]:
+                self.plugin_name = f"{self.provider_name}_tool"
+
+
 class PluginDependency(BaseModel):
     class Type(enum.StrEnum):
         Github = PluginInstallationSource.Github.value

api/core/plugin/manager/tool.py

@@ -3,7 +3,7 @@ from typing import Any, Optional
 
 from pydantic import BaseModel
 
-from core.plugin.entities.plugin import GenericProviderID
+from core.plugin.entities.plugin import GenericProviderID, ToolProviderID
 from core.plugin.entities.plugin_daemon import PluginBasicBooleanResponse, PluginToolProviderEntity
 from core.plugin.manager.base import BasePluginManager
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolParameter
@@ -45,7 +45,7 @@ class PluginToolManager(BasePluginManager):
         """
         Fetch tool provider for the given tenant and plugin.
         """
-        tool_provider_id = GenericProviderID(provider)
+        tool_provider_id = ToolProviderID(provider)
 
         def transformer(json_response: dict[str, Any]) -> dict:
             data = json_response.get("data")

api/core/provider_manager.py

@@ -30,6 +30,7 @@ from core.model_runtime.entities.provider_entities import (
     ProviderEntity,
 )
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from core.plugin.entities.plugin import ModelProviderID
 from extensions import ext_hosting_provider
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -99,6 +100,15 @@ class ProviderManager:
             tenant_id, provider_name_to_provider_records_dict
         )
 
+        # append providers with langgenius/openai/openai
+        provider_name_list = list(provider_name_to_provider_records_dict.keys())
+        for provider_name in provider_name_list:
+            provider_id = ModelProviderID(provider_name)
+            if str(provider_id) not in provider_name_list:
+                provider_name_to_provider_records_dict[str(provider_id)] = provider_name_to_provider_records_dict[
+                    provider_name
+                ]
+
         # Get all provider model records of the workspace
         provider_name_to_provider_model_records_dict = self._get_all_provider_models(tenant_id)
 
@@ -191,7 +201,7 @@ class ProviderManager:
                 model_settings=model_settings,
             )
 
-            provider_configurations[provider_name] = provider_configuration
+            provider_configurations[str(ModelProviderID(provider_name))] = provider_configuration
 
         # Return the encapsulated object
         return provider_configurations
@@ -453,11 +463,9 @@ class ProviderManager:
 
         provider_name_to_provider_load_balancing_model_configs_dict = defaultdict(list)
         for provider_load_balancing_config in provider_load_balancing_configs:
-            (
-                provider_name_to_provider_load_balancing_model_configs_dict[
-                    provider_load_balancing_config.provider_name
-                ].append(provider_load_balancing_config)
-            )
+            provider_name_to_provider_load_balancing_model_configs_dict[
+                provider_load_balancing_config.provider_name
+            ].append(provider_load_balancing_config)
 
         return provider_name_to_provider_load_balancing_model_configs_dict
 

api/core/rag/datasource/retrieval_service.py

@@ -1,8 +1,12 @@
-import threading
+import concurrent.futures
+import json
+from concurrent.futures import ThreadPoolExecutor
 from typing import Optional
 
 from flask import Flask, current_app
+from sqlalchemy.orm import load_only
 
+from configs import dify_config
 from core.rag.data_post_processor.data_post_processor import DataPostProcessor
 from core.rag.datasource.keyword.keyword_factory import Keyword
 from core.rag.datasource.vdb.vector_factory import Vector
@@ -26,6 +30,7 @@ default_retrieval_model = {
 
 
 class RetrievalService:
+    # Cache precompiled regular expressions to avoid repeated compilation
     @classmethod
     def retrieve(
         cls,
@@ -40,74 +45,62 @@ class RetrievalService:
     ):
         if not query:
             return []
-        dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
-        if not dataset:
-            return []
-
+        dataset = cls._get_dataset(dataset_id)
         if not dataset or dataset.available_document_count == 0 or dataset.available_segment_count == 0:
             return []
+
         all_documents: list[Document] = []
-        threads: list[threading.Thread] = []
         exceptions: list[str] = []
-        # retrieval_model source with keyword
-        if retrieval_method == "keyword_search":
-            keyword_thread = threading.Thread(
-                target=RetrievalService.keyword_search,
-                kwargs={
-                    "flask_app": current_app._get_current_object(),  # type: ignore
-                    "dataset_id": dataset_id,
-                    "query": query,
-                    "top_k": top_k,
-                    "all_documents": all_documents,
-                    "exceptions": exceptions,
-                },
-            )
-            threads.append(keyword_thread)
-            keyword_thread.start()
-        # retrieval_model source with semantic
-        if RetrievalMethod.is_support_semantic_search(retrieval_method):
-            embedding_thread = threading.Thread(
-                target=RetrievalService.embedding_search,
-                kwargs={
-                    "flask_app": current_app._get_current_object(),  # type: ignore
-                    "dataset_id": dataset_id,
-                    "query": query,
-                    "top_k": top_k,
-                    "score_threshold": score_threshold,
-                    "reranking_model": reranking_model,
-                    "all_documents": all_documents,
-                    "retrieval_method": retrieval_method,
-                    "exceptions": exceptions,
-                },
-            )
-            threads.append(embedding_thread)
-            embedding_thread.start()
 
-        # retrieval source with full text
-        if RetrievalMethod.is_support_fulltext_search(retrieval_method):
-            full_text_index_thread = threading.Thread(
-                target=RetrievalService.full_text_index_search,
-                kwargs={
-                    "flask_app": current_app._get_current_object(),  # type: ignore
-                    "dataset_id": dataset_id,
-                    "query": query,
-                    "retrieval_method": retrieval_method,
-                    "score_threshold": score_threshold,
-                    "top_k": top_k,
-                    "reranking_model": reranking_model,
-                    "all_documents": all_documents,
-                    "exceptions": exceptions,
-                },
-            )
-            threads.append(full_text_index_thread)
-            full_text_index_thread.start()
-
-        for thread in threads:
-            thread.join()
+        # Optimize multithreading with thread pools
+        with ThreadPoolExecutor(max_workers=dify_config.RETRIEVAL_SERVICE_EXECUTORS) as executor:  # type: ignore
+            futures = []
+            if retrieval_method == "keyword_search":
+                futures.append(
+                    executor.submit(
+                        cls.keyword_search,
+                        flask_app=current_app._get_current_object(),  # type: ignore
+                        dataset_id=dataset_id,
+                        query=query,
+                        top_k=top_k,
+                        all_documents=all_documents,
+                        exceptions=exceptions,
+                    )
+                )
+            if RetrievalMethod.is_support_semantic_search(retrieval_method):
+                futures.append(
+                    executor.submit(
+                        cls.embedding_search,
+                        flask_app=current_app._get_current_object(),  # type: ignore
+                        dataset_id=dataset_id,
+                        query=query,
+                        top_k=top_k,
+                        score_threshold=score_threshold,
+                        reranking_model=reranking_model,
+                        all_documents=all_documents,
+                        retrieval_method=retrieval_method,
+                        exceptions=exceptions,
+                    )
+                )
+            if RetrievalMethod.is_support_fulltext_search(retrieval_method):
+                futures.append(
+                    executor.submit(
+                        cls.full_text_index_search,
+                        flask_app=current_app._get_current_object(),  # type: ignore
+                        dataset_id=dataset_id,
+                        query=query,
+                        top_k=top_k,
+                        score_threshold=score_threshold,
+                        reranking_model=reranking_model,
+                        all_documents=all_documents,
+                        retrieval_method=retrieval_method,
+                        exceptions=exceptions,
+                    )
+                )
+            concurrent.futures.wait(futures, timeout=30, return_when=concurrent.futures.ALL_COMPLETED)
 
         if exceptions:
-            exception_message = ";\n".join(exceptions)
-            raise ValueError(exception_message)
+            raise ValueError(";\n".join(exceptions))
 
         if retrieval_method == RetrievalMethod.HYBRID_SEARCH.value:
             data_post_processor = DataPostProcessor(
@@ -132,18 +125,21 @@ class RetrievalService:
         )
         return all_documents
 
+    @classmethod
+    def _get_dataset(cls, dataset_id: str) -> Optional[Dataset]:
+        return db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+
     @classmethod
     def keyword_search(
         cls, flask_app: Flask, dataset_id: str, query: str, top_k: int, all_documents: list, exceptions: list
     ):
         with flask_app.app_context():
             try:
-                dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+                dataset = cls._get_dataset(dataset_id)
                 if not dataset:
                     raise ValueError("dataset not found")
 
                 keyword = Keyword(dataset=dataset)
-
                 documents = keyword.search(cls.escape_query_for_search(query), top_k=top_k)
                 all_documents.extend(documents)
             except Exception as e:
@@ -164,14 +160,13 @@ class RetrievalService:
     ):
         with flask_app.app_context():
             try:
-                dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+                dataset = cls._get_dataset(dataset_id)
                 if not dataset:
                     raise ValueError("dataset not found")
 
                 vector = Vector(dataset=dataset)
-
                 documents = vector.search_by_vector(
-                    cls.escape_query_for_search(query),
+                    query,
                     search_type="similarity_score_threshold",
                     top_k=top_k,
                     score_threshold=score_threshold,
@@ -186,7 +181,7 @@ class RetrievalService:
                         and retrieval_method == RetrievalMethod.SEMANTIC_SEARCH.value
                     ):
                         data_post_processor = DataPostProcessor(
-                            str(dataset.tenant_id), RerankMode.RERANKING_MODEL.value, reranking_model, None, False
+                            str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, False
                         )
                         all_documents.extend(
                             data_post_processor.invoke(
@@ -216,13 +211,11 @@ class RetrievalService:
     ):
         with flask_app.app_context():
             try:
-                dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
+                dataset = cls._get_dataset(dataset_id)
                 if not dataset:
                     raise ValueError("dataset not found")
 
-                vector_processor = Vector(
-                    dataset=dataset,
-                )
+                vector_processor = Vector(dataset=dataset)
 
                 documents = vector_processor.search_by_full_text(cls.escape_query_for_search(query), top_k=top_k)
                 if documents:
@@ -233,7 +226,7 @@ class RetrievalService:
                         and retrieval_method == RetrievalMethod.FULL_TEXT_SEARCH.value
                     ):
                         data_post_processor = DataPostProcessor(
-                            str(dataset.tenant_id), RerankMode.RERANKING_MODEL.value, reranking_model, None, False
+                            str(dataset.tenant_id), str(RerankMode.RERANKING_MODEL.value), reranking_model, None, False
                         )
                         all_documents.extend(
                             data_post_processor.invoke(
@@ -250,66 +243,106 @@ class RetrievalService:
 
     @staticmethod
     def escape_query_for_search(query: str) -> str:
-        return query.replace('"', '\\"')
+        return json.dumps(query).strip('"')
+
+    @classmethod
+    def format_retrieval_documents(cls, documents: list[Document]) -> list[RetrievalSegments]:
+        """Format retrieval documents with optimized batch processing"""
+        if not documents:
+            return []
+
+        try:
+            # Collect document IDs
+            document_ids = {doc.metadata.get("document_id") for doc in documents if "document_id" in doc.metadata}
+            if not document_ids:
+                return []
+
+            # Batch query dataset documents
+            dataset_documents = {
+                doc.id: doc
+                for doc in db.session.query(DatasetDocument)
+                .filter(DatasetDocument.id.in_(document_ids))
+                .options(load_only(DatasetDocument.id, DatasetDocument.doc_form, DatasetDocument.dataset_id))
+                .all()
+            }
+
+            records = []
+            include_segment_ids = set()
+            segment_child_map = {}
+
+            # Process documents
+            for document in documents:
+                document_id = document.metadata.get("document_id")
+                if document_id not in dataset_documents:
+                    continue
+
+                dataset_document = dataset_documents[document_id]
 
-    @staticmethod
-    def format_retrieval_documents(documents: list[Document]) -> list[RetrievalSegments]:
-        records = []
-        include_segment_ids = []
-        segment_child_map = {}
-        for document in documents:
-            document_id = document.metadata.get("document_id")
-            dataset_document = db.session.query(DatasetDocument).filter(DatasetDocument.id == document_id).first()
-            if dataset_document:
                 if dataset_document.doc_form == IndexType.PARENT_CHILD_INDEX:
+                    # Handle parent-child documents
                     child_index_node_id = document.metadata.get("doc_id")
-                    result = (
-                        db.session.query(ChildChunk, DocumentSegment)
-                        .join(DocumentSegment, ChildChunk.segment_id == DocumentSegment.id)
+
+                    child_chunk = (
+                        db.session.query(ChildChunk).filter(ChildChunk.index_node_id == child_index_node_id).first()
+                    )
+
+                    if not child_chunk:
+                        continue
+
+                    segment = (
+                        db.session.query(DocumentSegment)
                         .filter(
-                            ChildChunk.index_node_id == child_index_node_id,
                             DocumentSegment.dataset_id == dataset_document.dataset_id,
                             DocumentSegment.enabled == True,
                             DocumentSegment.status == "completed",
+                            DocumentSegment.id == child_chunk.segment_id,
+                        )
+                        .options(
+                            load_only(
+                                DocumentSegment.id,
+                                DocumentSegment.content,
+                                DocumentSegment.answer,
+                            )
                         )
                         .first()
                     )
-                    if result:
-                        child_chunk, segment = result
-                        if not segment:
-                            continue
-                        if segment.id not in include_segment_ids:
-                            include_segment_ids.append(segment.id)
-                            child_chunk_detail = {
-                                "id": child_chunk.id,
-                                "content": child_chunk.content,
-                                "position": child_chunk.position,
-                                "score": document.metadata.get("score", 0.0),
-                            }
-                            map_detail = {
-                                "max_score": document.metadata.get("score", 0.0),
-                                "child_chunks": [child_chunk_detail],
-                            }
-                            segment_child_map[segment.id] = map_detail
-                            record = {
-                                "segment": segment,
-                            }
-                            records.append(record)
-                        else:
-                            child_chunk_detail = {
-                                "id": child_chunk.id,
-                                "content": child_chunk.content,
-                                "position": child_chunk.position,
-                                "score": document.metadata.get("score", 0.0),
-                            }
-                            segment_child_map[segment.id]["child_chunks"].append(child_chunk_detail)
-                            segment_child_map[segment.id]["max_score"] = max(
-                                segment_child_map[segment.id]["max_score"], document.metadata.get("score", 0.0)
-                            )
-                    else:
+
+                    if not segment:
                         continue
+
+                    if segment.id not in include_segment_ids:
+                        include_segment_ids.add(segment.id)
+                        child_chunk_detail = {
+                            "id": child_chunk.id,
+                            "content": child_chunk.content,
+                            "position": child_chunk.position,
+                            "score": document.metadata.get("score", 0.0),
+                        }
+                        map_detail = {
+                            "max_score": document.metadata.get("score", 0.0),
+                            "child_chunks": [child_chunk_detail],
+                        }
+                        segment_child_map[segment.id] = map_detail
+                        record = {
+                            "segment": segment,
+                        }
+                        records.append(record)
+                    else:
+                        child_chunk_detail = {
+                            "id": child_chunk.id,
+                            "content": child_chunk.content,
+                            "position": child_chunk.position,
+                            "score": document.metadata.get("score", 0.0),
+                        }
+                        segment_child_map[segment.id]["child_chunks"].append(child_chunk_detail)
+                        segment_child_map[segment.id]["max_score"] = max(
+                            segment_child_map[segment.id]["max_score"], document.metadata.get("score", 0.0)
+                        )
                 else:
-                    index_node_id = document.metadata["doc_id"]
+                    # Handle normal documents
+                    index_node_id = document.metadata.get("doc_id")
+                    if not index_node_id:
+                        continue
 
                     segment = (
                         db.session.query(DocumentSegment)
@@ -324,16 +357,21 @@ class RetrievalService:
 
                     if not segment:
                         continue
-                    include_segment_ids.append(segment.id)
+
+                    include_segment_ids.add(segment.id)
                     record = {
                         "segment": segment,
-                        "score": document.metadata.get("score", None),
+                        "score": document.metadata.get("score"),  # type: ignore
                     }
-
                     records.append(record)
+
+            # Add child chunks information to records
             for record in records:
                 if record["segment"].id in segment_child_map:
-                    record["child_chunks"] = segment_child_map[record["segment"].id].get("child_chunks", None)
+                    record["child_chunks"] = segment_child_map[record["segment"].id].get("child_chunks")  # type: ignore
                     record["score"] = segment_child_map[record["segment"].id]["max_score"]
 
-        return [RetrievalSegments(**record) for record in records]
+            return [RetrievalSegments(**record) for record in records]
+        except Exception as e:
+            db.session.rollback()
+            raise e

api/core/rag/datasource/vdb/chroma/chroma_vector.py

@@ -111,8 +111,9 @@ class ChromaVector(BaseVector):
         for index in range(len(ids)):
             distance = distances[index]
             metadata = dict(metadatas[index])
-            if distance >= score_threshold:
-                metadata["score"] = distance
+            score = 1 - distance
+            if score > score_threshold:
+                metadata["score"] = score
                 doc = Document(
                     page_content=documents[index],
                     metadata=metadata,

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -9,6 +9,7 @@ from sqlalchemy import text as sql_text
 from sqlalchemy.orm import Session, declarative_base
 
 from configs import dify_config
+from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
 from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
 from core.rag.datasource.vdb.vector_type import VectorType
@@ -54,14 +55,13 @@ class TiDBVector(BaseVector):
         return Table(
             self._collection_name,
             self._orm_base.metadata,
-            Column("id", String(36), primary_key=True, nullable=False),
+            Column(Field.PRIMARY_KEY.value, String(36), primary_key=True, nullable=False),
             Column(
-                "vector",
+                Field.VECTOR.value,
                 VectorType(dim),
                 nullable=False,
-                comment="" if self._distance_func is None else f"hnsw(distance={self._distance_func})",
             ),
-            Column("text", TEXT, nullable=False),
+            Column(Field.TEXT_KEY.value, TEXT, nullable=False),
             Column("meta", JSON, nullable=False),
             Column("create_time", DateTime, server_default=sqlalchemy.text("CURRENT_TIMESTAMP")),
             Column(
@@ -96,6 +96,7 @@ class TiDBVector(BaseVector):
             collection_exist_cache_key = "vector_indexing_{}".format(self._collection_name)
             if redis_client.get(collection_exist_cache_key):
                 return
+            tidb_dist_func = self._get_distance_func()
             with Session(self._engine) as session:
                 session.begin()
                 create_statement = sql_text(f"""
@@ -104,14 +105,14 @@ class TiDBVector(BaseVector):
                         text TEXT NOT NULL,
                         meta JSON NOT NULL,
                         doc_id VARCHAR(64) AS (JSON_UNQUOTE(JSON_EXTRACT(meta, '$.doc_id'))) STORED,
-                        KEY (doc_id),
-                        vector VECTOR<FLOAT>({dimension}) NOT NULL COMMENT "hnsw(distance={self._distance_func})",
+                        vector VECTOR<FLOAT>({dimension}) NOT NULL,
                         create_time DATETIME DEFAULT CURRENT_TIMESTAMP,
-                        update_time DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP
+                        update_time DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
+                        KEY (doc_id),
+                        VECTOR INDEX idx_vector (({tidb_dist_func}(vector))) USING HNSW
                     );
                 """)
                 session.execute(create_statement)
-                # tidb vector not support 'CREATE/ADD INDEX' now
                 session.commit()
             redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
@@ -194,23 +195,30 @@ class TiDBVector(BaseVector):
         )
 
         docs = []
-        if self._distance_func == "l2":
-            tidb_func = "Vec_l2_distance"
-        elif self._distance_func == "cosine":
-            tidb_func = "Vec_Cosine_distance"
-        else:
-            tidb_func = "Vec_Cosine_distance"
+        tidb_dist_func = self._get_distance_func()
 
         with Session(self._engine) as session:
-            select_statement = sql_text(
-                f"""SELECT meta, text, distance FROM (
-                        SELECT meta, text, {tidb_func}(vector, "{query_vector_str}")  as distance
-                        FROM {self._collection_name}
-                        ORDER BY distance
-                        LIMIT {top_k}
-                    ) t WHERE distance < {distance};"""
+            select_statement = sql_text(f"""
+                SELECT meta, text, distance 
+                FROM (
+                  SELECT 
+                    meta,
+                    text,
+                    {tidb_dist_func}(vector, :query_vector_str) AS distance
+                  FROM {self._collection_name}
+                  ORDER BY distance ASC
+                  LIMIT :top_k
+                ) t
+                WHERE distance <= :distance
+                """)
+            res = session.execute(
+                select_statement,
+                params={
+                    "query_vector_str": query_vector_str,
+                    "distance": distance,
+                    "top_k": top_k,
+                },
             )
-            res = session.execute(select_statement)
             results = [(row[0], row[1], row[2]) for row in res]
             for meta, text, distance in results:
                 metadata = json.loads(meta)
@@ -227,6 +235,16 @@ class TiDBVector(BaseVector):
             session.execute(sql_text(f"""DROP TABLE IF EXISTS {self._collection_name};"""))
             session.commit()
 
+    def _get_distance_func(self) -> str:
+        match self._distance_func:
+            case "l2":
+                tidb_dist_func = "VEC_L2_DISTANCE"
+            case "cosine":
+                tidb_dist_func = "VEC_COSINE_DISTANCE"
+            case _:
+                tidb_dist_func = "VEC_COSINE_DISTANCE"
+        return tidb_dist_func
+
 
 class TiDBVectorFactory(AbstractVectorFactory):
     def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> TiDBVector:

api/core/tools/custom_tool/tool.py

@@ -105,10 +105,10 @@ class ApiTool(Tool):
         needed_parameters = [parameter for parameter in (self.api_bundle.parameters or []) if parameter.required]
         for parameter in needed_parameters:
             if parameter.required and parameter.name not in parameters:
-                raise ToolParameterValidationError(f"Missing required parameter {parameter.name}")
-
-            if parameter.default is not None and parameter.name not in parameters:
-                parameters[parameter.name] = parameter.default
+                if parameter.default is not None:
+                    parameters[parameter.name] = parameter.default
+                else:
+                    raise ToolParameterValidationError(f"Missing required parameter {parameter.name}")
 
         return headers
 

api/core/tools/tool_engine.py

@@ -246,10 +246,11 @@ class ToolEngine:
                     + "you do not need to create it, just tell the user to check it now."
                 )
             elif response.type == ToolInvokeMessage.MessageType.JSON:
-                text = json.dumps(cast(ToolInvokeMessage.JsonMessage, response.message).json_object, ensure_ascii=False)
-                result += f"tool response: {text}."
+                result = json.dumps(
+                    cast(ToolInvokeMessage.JsonMessage, response.message).json_object, ensure_ascii=False
+                )
             else:
-                result += f"tool response: {response.message!r}."
+                result += str(response.message)
 
         return result
 

api/core/tools/tool_manager.py

@@ -160,8 +160,8 @@ class ToolManager:
         """
         get the tool runtime
 
-        :param provider_type: the type of  the provider
-        :param provider_name: the name of  the provider
+        :param provider_type: the type of the provider
+        :param provider_name: the name of the provider
         :param tool_name: the name of the tool
 
         :return: the tool

api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py

@@ -3,11 +3,13 @@ from typing import Any
 from pydantic import BaseModel, Field
 
 from core.rag.datasource.retrieval_service import RetrievalService
+from core.rag.entities.context_entities import DocumentContext
 from core.rag.models.document import Document as RetrievalDocument
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
 from extensions.ext_database import db
-from models.dataset import Dataset, Document, DocumentSegment
+from models.dataset import Dataset
+from models.dataset import Document as DatasetDocument
 from services.external_knowledge_service import ExternalDatasetService
 
 default_retrieval_model = {
@@ -54,7 +56,6 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
 
         if not dataset:
             return ""
-
         for hit_callback in self.hit_callbacks:
             hit_callback.on_query(query, dataset.id)
         if dataset.provider == "external":
@@ -125,7 +126,6 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                     )
                 else:
                     documents = []
-
                 for hit_callback in self.hit_callbacks:
                     hit_callback.on_tool_end(documents)
                 document_score_list = {}
@@ -134,50 +134,46 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                         if item.metadata is not None and item.metadata.get("score"):
                             document_score_list[item.metadata["doc_id"]] = item.metadata["score"]
                 document_context_list = []
-                index_node_ids = [document.metadata["doc_id"] for document in documents]
-                segments = DocumentSegment.query.filter(
-                    DocumentSegment.dataset_id == self.dataset_id,
-                    DocumentSegment.completed_at.isnot(None),
-                    DocumentSegment.status == "completed",
-                    DocumentSegment.enabled == True,
-                    DocumentSegment.index_node_id.in_(index_node_ids),
-                ).all()
-
-                if segments:
-                    index_node_id_to_position = {id: position for position, id in enumerate(index_node_ids)}
-                    sorted_segments = sorted(
-                        segments, key=lambda segment: index_node_id_to_position.get(segment.index_node_id, float("inf"))
-                    )
-                    for segment in sorted_segments:
+                records = RetrievalService.format_retrieval_documents(documents)
+                if records:
+                    for record in records:
+                        segment = record.segment
                         if segment.answer:
                             document_context_list.append(
-                                f"question:{segment.get_sign_content()} answer:{segment.answer}"
+                                DocumentContext(
+                                    content=f"question:{segment.get_sign_content()} answer:{segment.answer}",
+                                    score=record.score,
+                                )
                             )
                         else:
-                            document_context_list.append(segment.get_sign_content())
+                            document_context_list.append(
+                                DocumentContext(
+                                    content=segment.get_sign_content(),
+                                    score=record.score,
+                                )
+                            )
+                    retrieval_resource_list = []
                     if self.return_resource:
-                        context_list = []
-                        resource_number = 1
-                        for segment in sorted_segments:
-                            document_segment = Document.query.filter(
-                                Document.id == segment.document_id,
-                                Document.enabled == True,
-                                Document.archived == False,
+                        for record in records:
+                            segment = record.segment
+                            dataset = Dataset.query.filter_by(id=segment.dataset_id).first()
+                            document = DatasetDocument.query.filter(
+                                DatasetDocument.id == segment.document_id,
+                                DatasetDocument.enabled == True,
+                                DatasetDocument.archived == False,
                             ).first()
-                            if not document_segment:
-                                continue
-                            if dataset and document_segment:
+                            if dataset and document:
                                 source = {
-                                    "position": resource_number,
                                     "dataset_id": dataset.id,
                                     "dataset_name": dataset.name,
-                                    "document_id": document_segment.id,
-                                    "document_name": document_segment.name,
-                                    "data_source_type": document_segment.data_source_type,
+                                    "document_id": document.id,  # type: ignore
+                                    "document_name": document.name,  # type: ignore
+                                    "data_source_type": document.data_source_type,  # type: ignore
                                     "segment_id": segment.id,
                                     "retriever_from": self.retriever_from,
-                                    "score": document_score_list.get(segment.index_node_id, None),
+                                    "score": record.score or 0.0,
                                 }
+
                                 if self.retriever_from == "dev":
                                     source["hit_count"] = segment.hit_count
                                     source["word_count"] = segment.word_count
@@ -187,10 +183,19 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                                     source["content"] = f"question:{segment.content} \nanswer:{segment.answer}"
                                 else:
                                     source["content"] = segment.content
-                                context_list.append(source)
-                            resource_number += 1
+                                retrieval_resource_list.append(source)
 
-                        for hit_callback in self.hit_callbacks:
-                            hit_callback.return_retriever_resource_info(context_list)
-
-                return str("\n".join(document_context_list))
+            if self.return_resource and retrieval_resource_list:
+                retrieval_resource_list = sorted(
+                    retrieval_resource_list,
+                    key=lambda x: x.get("score") or 0.0,
+                    reverse=True,
+                )
+                for position, item in enumerate(retrieval_resource_list, start=1):  # type: ignore
+                    item["position"] = position  # type: ignore
+                for hit_callback in self.hit_callbacks:
+                    hit_callback.return_retriever_resource_info(retrieval_resource_list)
+            if document_context_list:
+                document_context_list = sorted(document_context_list, key=lambda x: x.score or 0.0, reverse=True)
+                return str("\n".join([document_context.content for document_context in document_context_list]))
+            return ""

api/core/workflow/graph_engine/entities/event.py

@@ -207,7 +207,6 @@ class AgentLogEvent(BaseAgentEvent):
     status: str = Field(..., description="status")
     data: Mapping[str, Any] = Field(..., description="data")
     metadata: Optional[Mapping[str, Any]] = Field(default=None, description="metadata")
-    node_id: str = Field(..., description="agent node id")
 
 
 InNodeEvent = BaseNodeEvent | BaseParallelBranchEvent | BaseIterationEvent | BaseAgentEvent

api/core/workflow/graph_engine/graph_engine.py

@@ -18,7 +18,6 @@ from core.workflow.entities.node_entities import AgentNodeStrategyInit, NodeRunM
 from core.workflow.entities.variable_pool import VariablePool, VariableValue
 from core.workflow.graph_engine.condition_handlers.condition_manager import ConditionManager
 from core.workflow.graph_engine.entities.event import (
-    BaseAgentEvent,
     BaseIterationEvent,
     GraphEngineEvent,
     GraphRunFailedEvent,
@@ -502,7 +501,7 @@ class GraphEngine:
                     break
 
                 yield event
-                if not isinstance(event, BaseAgentEvent) and event.parallel_id == parallel_id:
+                if event.parallel_id == parallel_id:
                     if isinstance(event, ParallelBranchRunSucceededEvent):
                         succeeded_count += 1
                         if succeeded_count == len(futures):
@@ -666,7 +665,7 @@ class GraphEngine:
                                     retries += 1
                                     route_node_state.node_run_result = run_result
                                     yield NodeRunRetryEvent(
-                                        id=node_instance.id,
+                                        id=str(uuid.uuid4()),
                                         node_id=node_instance.node_id,
                                         node_type=node_instance.node_type,
                                         node_data=node_instance.node_data,
@@ -681,7 +680,7 @@ class GraphEngine:
                                         start_at=retry_start_at,
                                     )
                                     time.sleep(retry_interval)
-                                    continue
+                                    break
                             route_node_state.set_finished(run_result=run_result)
 
                             if run_result.status == WorkflowNodeExecutionStatus.FAILED:

api/core/workflow/nodes/agent/agent_node.py

@@ -8,12 +8,12 @@ from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
 from core.plugin.manager.exc import PluginDaemonClientSideError
 from core.plugin.manager.plugin import PluginInstallationManager
-from core.tools.entities.tool_entities import ToolParameter, ToolProviderType
+from core.tools.entities.tool_entities import ToolProviderType
 from core.tools.tool_manager import ToolManager
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.enums import SystemVariableKey
-from core.workflow.nodes.agent.entities import AgentNodeData, ParamsAutoGenerated
+from core.workflow.nodes.agent.entities import AgentNodeData
 from core.workflow.nodes.base.entities import BaseNodeData
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event.event import RunCompletedEvent
@@ -161,29 +161,11 @@ class AgentNode(ToolNode):
                     value = cast(list[dict[str, Any]], value)
                     tool_value = []
                     for tool in value:
-                        provider_type = ToolProviderType(tool.get("type", ToolProviderType.BUILT_IN.value))
-                        # handle the original settings
-                        original_parameters = tool.get("parameters", {})
-                        setting_params = tool.get("settings", {})
-                        manual_input_params = []
-                        # handle legacy data compatibility
-                        if not all(isinstance(v, dict) for _, v in original_parameters.items()):
-                            parameters = original_parameters
-                        else:
-                            params = {}
-                            for key, param in original_parameters.items():
-                                if param.get("auto", ParamsAutoGenerated.OPEN.value) == ParamsAutoGenerated.CLOSE.value:
-                                    params[key] = param.get("value", "")
-                                    manual_input_params.append(key)
-                                else:
-                                    params[key] = None
-                            settings = {k: v.get("value", None) for k, v in setting_params.items()}
-                            parameters = {**params, **settings}
                         entity = AgentToolEntity(
                             provider_id=tool.get("provider_name", ""),
-                            provider_type=provider_type,
+                            provider_type=ToolProviderType.BUILT_IN,
                             tool_name=tool.get("tool_name", ""),
-                            tool_parameters=parameters,
+                            tool_parameters=tool.get("parameters", {}),
                             plugin_unique_identifier=tool.get("plugin_unique_identifier", None),
                         )
 
@@ -196,27 +178,13 @@ class AgentNode(ToolNode):
                             tool_runtime.entity.description.llm = (
                                 extra.get("descrption", "") or tool_runtime.entity.description.llm
                             )
-                        for params in tool_runtime.entity.parameters:
-                            params.form = (
-                                ToolParameter.ToolParameterForm.FORM
-                                if params.name in manual_input_params
-                                else params.form
-                            )
-                        if tool_runtime.entity.parameters:
-                            manual_input_value = {
-                                key: value for key, value in parameters.items() if key in manual_input_params
+
+                        tool_value.append(
+                            {
+                                **tool_runtime.entity.model_dump(mode="json"),
+                                "runtime_parameters": tool_runtime.runtime.runtime_parameters,
                             }
-                            runtime_parameters = {
-                                **tool_runtime.runtime.runtime_parameters,
-                                **manual_input_value,
-                            }
-                            tool_value.append(
-                                {
-                                    **tool_runtime.entity.model_dump(mode="json"),
-                                    "runtime_parameters": runtime_parameters,
-                                    "provider_type": provider_type.value,
-                                }
-                            )
+                        )
                     value = tool_value
                 if parameter.type == "model-selector":
                     value = cast(dict[str, Any], value)

api/core/workflow/nodes/agent/entities.py

@@ -1,4 +1,3 @@
-from enum import Enum
 from typing import Any, Literal, Union
 
 from pydantic import BaseModel
@@ -17,8 +16,3 @@ class AgentNodeData(BaseNodeData):
         type: Literal["mixed", "variable", "constant"]
 
     agent_parameters: dict[str, AgentInput]
-
-
-class ParamsAutoGenerated(Enum):
-    CLOSE = 0
-    OPEN = 1

api/core/workflow/nodes/document_extractor/node.py

@@ -107,8 +107,10 @@ def _extract_text_by_mime_type(*, file_content: bytes, mime_type: str) -> str:
             return _extract_text_from_plain_text(file_content)
         case "application/pdf":
             return _extract_text_from_pdf(file_content)
-        case "application/vnd.openxmlformats-officedocument.wordprocessingml.document" | "application/msword":
+        case "application/msword":
             return _extract_text_from_doc(file_content)
+        case "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
+            return _extract_text_from_docx(file_content)
         case "text/csv":
             return _extract_text_from_csv(file_content)
         case "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" | "application/vnd.ms-excel":
@@ -142,8 +144,10 @@ def _extract_text_by_file_extension(*, file_content: bytes, file_extension: str)
             return _extract_text_from_yaml(file_content)
         case ".pdf":
             return _extract_text_from_pdf(file_content)
-        case ".doc" | ".docx":
+        case ".doc":
             return _extract_text_from_doc(file_content)
+        case ".docx":
+            return _extract_text_from_docx(file_content)
         case ".csv":
             return _extract_text_from_csv(file_content)
         case ".xls" | ".xlsx":
@@ -203,7 +207,33 @@ def _extract_text_from_pdf(file_content: bytes) -> str:
 
 def _extract_text_from_doc(file_content: bytes) -> str:
     """
-    Extract text from a DOC/DOCX file.
+    Extract text from a DOC file.
+    """
+    from unstructured.partition.api import partition_via_api
+
+    if not (dify_config.UNSTRUCTURED_API_URL and dify_config.UNSTRUCTURED_API_KEY):
+        raise TextExtractionError("UNSTRUCTURED_API_URL and UNSTRUCTURED_API_KEY must be set")
+
+    try:
+        with tempfile.NamedTemporaryFile(suffix=".doc", delete=False) as temp_file:
+            temp_file.write(file_content)
+            temp_file.flush()
+            with open(temp_file.name, "rb") as file:
+                elements = partition_via_api(
+                    file=file,
+                    metadata_filename=temp_file.name,
+                    api_url=dify_config.UNSTRUCTURED_API_URL,
+                    api_key=dify_config.UNSTRUCTURED_API_KEY,
+                )
+            os.unlink(temp_file.name)
+        return "\n".join([getattr(element, "text", "") for element in elements])
+    except Exception as e:
+        raise TextExtractionError(f"Failed to extract text from DOC: {str(e)}") from e
+
+
+def _extract_text_from_docx(file_content: bytes) -> str:
+    """
+    Extract text from a DOCX file.
     For now support only paragraph and table add more if needed
     """
     try:
@@ -255,13 +285,13 @@ def _extract_text_from_doc(file_content: bytes) -> str:
 
                         text.append(markdown_table)
                 except Exception as e:
-                    logger.warning(f"Failed to extract table from DOC/DOCX: {e}")
+                    logger.warning(f"Failed to extract table from DOC: {e}")
                     continue
 
         return "\n".join(text)
 
     except Exception as e:
-        raise TextExtractionError(f"Failed to extract text from DOC/DOCX: {str(e)}") from e
+        raise TextExtractionError(f"Failed to extract text from DOCX: {str(e)}") from e
 
 
 def _download_file_content(file: File) -> bytes:
@@ -329,14 +359,29 @@ def _extract_text_from_excel(file_content: bytes) -> str:
 
 
 def _extract_text_from_ppt(file_content: bytes) -> str:
+    from unstructured.partition.api import partition_via_api
     from unstructured.partition.ppt import partition_ppt
 
     try:
-        with io.BytesIO(file_content) as file:
-            elements = partition_ppt(file=file)
+        if dify_config.UNSTRUCTURED_API_URL and dify_config.UNSTRUCTURED_API_KEY:
+            with tempfile.NamedTemporaryFile(suffix=".ppt", delete=False) as temp_file:
+                temp_file.write(file_content)
+                temp_file.flush()
+                with open(temp_file.name, "rb") as file:
+                    elements = partition_via_api(
+                        file=file,
+                        metadata_filename=temp_file.name,
+                        api_url=dify_config.UNSTRUCTURED_API_URL,
+                        api_key=dify_config.UNSTRUCTURED_API_KEY,
+                    )
+                os.unlink(temp_file.name)
+        else:
+            with io.BytesIO(file_content) as file:
+                elements = partition_ppt(file=file)
         return "\n".join([getattr(element, "text", "") for element in elements])
+
     except Exception as e:
-        raise TextExtractionError(f"Failed to extract text from PPT: {str(e)}") from e
+        raise TextExtractionError(f"Failed to extract text from PPTX: {str(e)}") from e
 
 
 def _extract_text_from_pptx(file_content: bytes) -> str:

api/core/workflow/nodes/end/end_stream_generate_router.py

@@ -64,7 +64,7 @@ class EndStreamGeneratorRouter:
                 node_type = node.get("data", {}).get("type")
                 if (
                     variable_selector.value_selector not in value_selectors
-                    and (node_type in (NodeType.LLM.value, NodeType.AGENT.value))
+                    and node_type == NodeType.LLM.value
                     and variable_selector.value_selector[1] == "text"
                 ):
                     value_selectors.append(list(variable_selector.value_selector))

api/core/workflow/nodes/iteration/iteration_node.py

@@ -590,6 +590,7 @@ class IterationNode(BaseNode[IterationNodeData]):
         with flask_app.app_context():
             parallel_mode_run_id = uuid.uuid4().hex
             graph_engine_copy = graph_engine.create_copy()
+            graph_engine_copy.graph_runtime_state.total_tokens = 0
             variable_pool_copy = graph_engine_copy.graph_runtime_state.variable_pool
             variable_pool_copy.add([self.node_id, "index"], index)
             variable_pool_copy.add([self.node_id, "item"], item)

api/core/workflow/nodes/llm/node.py

@@ -247,6 +247,24 @@ class LLMNode(BaseNode[LLMNodeData]):
 
     def _handle_invoke_result(self, invoke_result: LLMResult | Generator) -> Generator[NodeEvent, None, None]:
         if isinstance(invoke_result, LLMResult):
+            content = invoke_result.message.content
+            if content is None:
+                message_text = ""
+            elif isinstance(content, str):
+                message_text = content
+            elif isinstance(content, list):
+                # Assuming the list contains PromptMessageContent objects with a "data" attribute
+                message_text = "".join(
+                    item.data if hasattr(item, "data") and isinstance(item.data, str) else str(item) for item in content
+                )
+            else:
+                message_text = str(content)
+
+            yield ModelInvokeCompletedEvent(
+                text=message_text,
+                usage=invoke_result.usage,
+                finish_reason=None,
+            )
             return
 
         model = None

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -44,13 +44,11 @@ class QuestionClassifierNode(LLMNode):
         variable_pool = self.graph_runtime_state.variable_pool
 
         # extract variables
-        variable = variable_pool.get(
-            node_data.query_variable_selector) if node_data.query_variable_selector else None
+        variable = variable_pool.get(node_data.query_variable_selector) if node_data.query_variable_selector else None
         query = variable.value if variable else None
         variables = {"query": query}
         # fetch model config
-        model_instance, model_config = self._fetch_model_config(
-            node_data.model)
+        model_instance, model_config = self._fetch_model_config(node_data.model)
         # fetch memory
         memory = self._fetch_memory(
             node_data_memory=node_data.memory,
@@ -58,8 +56,7 @@ class QuestionClassifierNode(LLMNode):
         )
         # fetch instruction
         node_data.instruction = node_data.instruction or ""
-        node_data.instruction = variable_pool.convert_template(
-            node_data.instruction).text
+        node_data.instruction = variable_pool.convert_template(node_data.instruction).text
 
         files = (
             self._fetch_files(
@@ -181,15 +178,12 @@ class QuestionClassifierNode(LLMNode):
         variable_mapping = {"query": node_data.query_variable_selector}
         variable_selectors = []
         if node_data.instruction:
-            variable_template_parser = VariableTemplateParser(
-                template=node_data.instruction)
-            variable_selectors.extend(
-                variable_template_parser.extract_variable_selectors())
+            variable_template_parser = VariableTemplateParser(template=node_data.instruction)
+            variable_selectors.extend(variable_template_parser.extract_variable_selectors())
         for variable_selector in variable_selectors:
             variable_mapping[variable_selector.variable] = variable_selector.value_selector
 
-        variable_mapping = {node_id + "." + key: value for key,
-                            value in variable_mapping.items()}
+        variable_mapping = {node_id + "." + key: value for key, value in variable_mapping.items()}
 
         return variable_mapping
 
@@ -210,8 +204,7 @@ class QuestionClassifierNode(LLMNode):
         context: Optional[str],
     ) -> int:
         prompt_transform = AdvancedPromptTransform(with_variable_tmpl=True)
-        prompt_template = self._get_prompt_template(
-            node_data, query, None, 2000)
+        prompt_template = self._get_prompt_template(node_data, query, None, 2000)
         prompt_messages = prompt_transform.get_prompt(
             prompt_template=prompt_template,
             inputs={},
@@ -224,15 +217,13 @@ class QuestionClassifierNode(LLMNode):
         )
         rest_tokens = 2000
 
-        model_context_tokens = model_config.model_schema.model_properties.get(
-            ModelPropertyKey.CONTEXT_SIZE)
+        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
         if model_context_tokens:
             model_instance = ModelInstance(
                 provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
             )
 
-            curr_message_tokens = model_instance.get_llm_num_tokens(
-                prompt_messages)
+            curr_message_tokens = model_instance.get_llm_num_tokens(prompt_messages)
 
             max_tokens = 0
             for parameter_rule in model_config.model_schema.parameter_rules:
@@ -273,8 +264,7 @@ class QuestionClassifierNode(LLMNode):
         prompt_messages: list[LLMNodeChatModelMessage] = []
         if model_mode == ModelMode.CHAT:
             system_prompt_messages = LLMNodeChatModelMessage(
-                role=PromptMessageRole.SYSTEM, text=QUESTION_CLASSIFIER_SYSTEM_PROMPT.format(
-                    histories=memory_str)
+                role=PromptMessageRole.SYSTEM, text=QUESTION_CLASSIFIER_SYSTEM_PROMPT.format(histories=memory_str)
             )
             prompt_messages.append(system_prompt_messages)
             user_prompt_message_1 = LLMNodeChatModelMessage(
@@ -315,5 +305,4 @@ class QuestionClassifierNode(LLMNode):
             )
 
         else:
-            raise InvalidModelTypeError(
-                f"Model mode {model_mode} not support.")
+            raise InvalidModelTypeError(f"Model mode {model_mode} not support.")

api/core/workflow/nodes/tool/tool_node.py

@@ -338,7 +338,6 @@ class ToolNode(BaseNode[ToolNodeData]):
                     data=message.message.data,
                     label=message.message.label,
                     metadata=message.message.metadata,
-                    node_id=self.node_id,
                 )
 
                 # check if the agent log is already in the list

api/core/workflow/nodes/variable_aggregator/variable_aggregator_node.py

@@ -1,6 +1,3 @@
-from collections.abc import Mapping, Sequence
-from typing import Any
-
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
@@ -36,16 +33,3 @@ class VariableAggregatorNode(BaseNode[VariableAssignerNodeData]):
                         break
 
         return NodeRunResult(status=WorkflowNodeExecutionStatus.SUCCEEDED, outputs=outputs, inputs=inputs)
-
-    @classmethod
-    def _extract_variable_selector_to_variable_mapping(
-        cls, *, graph_config: Mapping[str, Any], node_id: str, node_data: VariableAssignerNodeData
-    ) -> Mapping[str, Sequence[str]]:
-        """
-        Extract variable selector to variable mapping
-        :param graph_config: graph config
-        :param node_id: node id
-        :param node_data: node data
-        :return:
-        """
-        return {}

api/core/workflow/utils/condition/processor.py

@@ -64,6 +64,10 @@ class ConditionProcessor:
                     expected=expected_value,
                 )
             group_results.append(result)
+            # Implemented short-circuit evaluation for logical conditions
+            if (operator == "and" and not result) or (operator == "or" and result):
+                final_result = result
+                return input_conditions, group_results, final_result
 
         final_result = all(group_results) if operator == "and" else any(group_results)
         return input_conditions, group_results, final_result

api/extensions/ext_proxy_fix.py

@@ -6,4 +6,4 @@ def init_app(app: DifyApp):
     if dify_config.RESPECT_XFORWARD_HEADERS_ENABLED:
         from werkzeug.middleware.proxy_fix import ProxyFix
 
-        app.wsgi_app = ProxyFix(app.wsgi_app)  # type: ignore
+        app.wsgi_app = ProxyFix(app.wsgi_app, x_port=1)  # type: ignore

api/fields/conversation_fields.py

@@ -95,10 +95,6 @@ model_config_fields = {
     "agent_mode": fields.Raw,
 }
 
-simple_configs_fields = {
-    "prompt_template": fields.String,
-}
-
 simple_model_config_fields = {
     "model": fields.Raw(attribute="model_dict"),
     "pre_prompt": fields.String,
@@ -212,14 +208,3 @@ conversation_infinite_scroll_pagination_fields = {
     "has_more": fields.Boolean,
     "data": fields.List(fields.Nested(simple_conversation_fields)),
 }
-
-conversation_with_model_config_fields = {
-    **simple_conversation_fields,
-    "model_config": fields.Raw,
-}
-
-conversation_with_model_config_infinite_scroll_pagination_fields = {
-    "limit": fields.Integer,
-    "has_more": fields.Boolean,
-    "data": fields.List(fields.Nested(conversation_with_model_config_fields)),
-}

api/fields/message_fields.py

@@ -7,27 +7,6 @@ from .raws import FilesContainedField
 
 feedback_fields = {"rating": fields.String}
 
-retriever_resource_fields = {
-    "id": fields.String,
-    "message_id": fields.String,
-    "position": fields.Integer,
-    "dataset_id": fields.String,
-    "dataset_name": fields.String,
-    "document_id": fields.String,
-    "document_name": fields.String,
-    "data_source_type": fields.String,
-    "segment_id": fields.String,
-    "score": fields.Float,
-    "hit_count": fields.Integer,
-    "word_count": fields.Integer,
-    "segment_position": fields.Integer,
-    "index_node_hash": fields.String,
-    "content": fields.String,
-    "created_at": TimestampField,
-}
-
-feedback_fields = {"rating": fields.String}
-
 agent_thought_fields = {
     "id": fields.String,
     "chain_id": fields.String,

api/models/dataset.py

@@ -748,7 +748,7 @@ class DatasetKeywordTable(db.Model):  # type: ignore[name-defined]
                 if keyword_table_text:
                     return json.loads(keyword_table_text.decode("utf-8"), cls=SetDecoder)
                 return None
-            except Exception:
+            except Exception as e:
                 logging.exception(f"Failed to load keyword table from file: {file_key}")
                 return None
 

api/models/model.py

@@ -1289,7 +1289,7 @@ class MessageAnnotation(Base):
         return account
 
 
-class AppAnnotationHitHistory(Base):
+class AppAnnotationHitHistory(db.Model):  # type: ignore[name-defined]
     __tablename__ = "app_annotation_hit_histories"
     __table_args__ = (
         db.PrimaryKeyConstraint("id", name="app_annotation_hit_histories_pkey"),

api/models/staging.py

@@ -1,27 +0,0 @@
-from datetime import datetime
-
-from sqlalchemy.orm import Mapped
-
-from extensions.ext_database import db
-from models.base import Base
-
-from .types import StringUUID
-
-
-class StagingAccountWhitelist(Base):
-    __tablename__ = "staging_account_whitelists"
-
-    __table_args__ = (
-        db.PrimaryKeyConstraint("id", name="staging_account_whitelist_pkey"),
-        db.Index("account_email_idx", "email"),
-    )
-
-    id: Mapped[str] = db.Column(StringUUID, server_default=db.text("uuid_generate_v4()"))
-    email: Mapped[str] = db.Column(db.String(255), nullable=False)
-    disabled: Mapped[bool] = db.Column(db.Boolean, nullable=False, server_default=db.text("false"))
-    created_at: Mapped[datetime] = db.Column(
-        db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)")
-    )
-    updated_at: Mapped[datetime] = db.Column(
-        db.DateTime, nullable=False, server_default=db.text("CURRENT_TIMESTAMP(0)")
-    )

api/poetry.lock

@@ -644,15 +644,15 @@ files = [
 
 [[package]]
 name = "bce-python-sdk"
-version = "0.9.25"
+version = "0.9.29"
 description = "BCE SDK for python"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,<4,>=2.7"
 groups = ["storage"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "bce_python_sdk-0.9.25-py3-none-any.whl", hash = "sha256:cd1ab4c887e163adba6bfb3cd40465a365e5f4255705a015b0cdbe768e649877"},
-    {file = "bce_python_sdk-0.9.25.tar.gz", hash = "sha256:93a0623fbb1bf3a58b4f2d7bdbd799a3b342a538f0c72950c77168e431470e86"},
+    {file = "bce_python_sdk-0.9.29-py3-none-any.whl", hash = "sha256:6518dc0ada422acd1841eeabcb7f89cfc07e3bb1a4be3c75945cab953907b555"},
+    {file = "bce_python_sdk-0.9.29.tar.gz", hash = "sha256:326fbd50d57bf6d2fc21d58f589b069e0e84fc0a8733be9575c109293ab08cc4"},
 ]
 
 [package.dependencies]
@@ -769,15 +769,15 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.36.16"
+version = "1.36.21"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "botocore-1.36.16-py3-none-any.whl", hash = "sha256:aca0348ccd730332082489b6817fdf89e1526049adcf6e9c8c11c96dd9f42c03"},
-    {file = "botocore-1.36.16.tar.gz", hash = "sha256:10c6aa386ba1a9a0faef6bb5dbfc58fc2563a3c6b95352e86a583cd5f14b11f3"},
+    {file = "botocore-1.36.21-py3-none-any.whl", hash = "sha256:24a7052e792639dc2726001bd474cd0aaa959c1e18ddd92c17f3adc6efa1b132"},
+    {file = "botocore-1.36.21.tar.gz", hash = "sha256:da746240e2ad64fd4997f7f3664a0a8e303d18075fc1d473727cb6375080ea16"},
 ]
 
 [package.dependencies]
@@ -860,10 +860,6 @@ files = [
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:a37b8f0391212d29b3a91a799c8e4a2855e0576911cdfb2515487e30e322253d"},
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e84799f09591700a4154154cab9787452925578841a94321d5ee8fb9a9a328f0"},
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 [package.extras]
 cssselect = ["cssselect (>=0.7)"]
-html-clean = ["lxml-html-clean"]
+html-clean = ["lxml_html_clean"]
 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
-source = ["Cython (>=3.0.11)"]
+source = ["Cython (>=3.0.11,<3.1.0)"]
 
 [[package]]
 name = "lz4"
@@ -6074,15 +6036,15 @@ pydantic = ">=1.9,<3.0"
 
 [[package]]
 name = "posthog"
-version = "3.11.0"
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 description = "Integrate PostHog into any python application."
 optional = false
 python-versions = "*"
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 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
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 [package.dependencies]
@@ -6093,10 +6055,10 @@ requests = ">=2.7,<3.0"
 six = ">=1.5"
 
 [package.extras]
-dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
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+test = ["anthropic", "coverage", "django", "flake8", "freezegun (==1.5.1)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "langgraph", "mock (>=2.0.0)", "openai", "pydantic", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
 
 [[package]]
 name = "prompt-toolkit"
@@ -6250,34 +6212,27 @@ files = [
 
 [[package]]
 name = "psutil"
-version = "6.1.1"
-description = "Cross-platform lib for process and system monitoring in Python."
+version = "7.0.0"
+description = "Cross-platform lib for process and system monitoring in Python.  NOTE: the syntax of this script MUST be kept compatible with Python 2.7."
 optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+python-versions = ">=3.6"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
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+    {file = "psutil-7.0.0.tar.gz", hash = "sha256:7be9c3eba38beccb6495ea33afd982a44074b78f28c434a1f51cc07fd315c456"},
 ]
 
 [package.extras]
-dev = ["abi3audit", "black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest-cov", "requests", "rstcheck", "ruff", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
+dev = ["abi3audit", "black (==24.10.0)", "check-manifest", "coverage", "packaging", "pylint", "pyperf", "pypinfo", "pytest", "pytest-cov", "pytest-xdist", "requests", "rstcheck", "ruff", "setuptools", "sphinx", "sphinx_rtd_theme", "toml-sort", "twine", "virtualenv", "vulture", "wheel"]
 test = ["pytest", "pytest-xdist", "setuptools"]
 
 [[package]]
@@ -6822,15 +6777,15 @@ diagrams = ["jinja2", "railroad-diagrams"]
 
 [[package]]
 name = "pypdf"
-version = "5.2.0"
+version = "5.3.0"
 description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "pypdf-5.2.0-py3-none-any.whl", hash = "sha256:d107962ec45e65e3bd10c1d9242bdbbedaa38193c9e3a6617bd6d996e5747b19"},
-    {file = "pypdf-5.2.0.tar.gz", hash = "sha256:7c38e68420f038f2c4998fd9d6717b6db4f6cef1642e9cf384d519c9cf094663"},
+    {file = "pypdf-5.3.0-py3-none-any.whl", hash = "sha256:d7b6db242f5f8fdb4990ae11815c394b8e1b955feda0befcce862efd8559c181"},
+    {file = "pypdf-5.3.0.tar.gz", hash = "sha256:08393660dfea25b27ec6fe863fb2f2248e6270da5103fae49e9dea8178741951"},
 ]
 
 [package.extras]
@@ -7153,19 +7108,19 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-iso639"
-version = "2025.1.28"
+version = "2025.2.8"
 description = "ISO 639 language codes, names, and other associated information"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "python_iso639-2025.1.28-py3-none-any.whl", hash = "sha256:66bcd88838727bc8ed1dfc9b5a354a27ed5c4bab8322473da81071fae265228b"},
-    {file = "python_iso639-2025.1.28.tar.gz", hash = "sha256:42b18bf52ad6ce5882c0e4acec9ae528310c7ef2966b776fc49d154c654580c5"},
+    {file = "python_iso639-2025.2.8-py3-none-any.whl", hash = "sha256:fc072f1f2007eae4a877778a73d7653c51020973e719b502e0d31e95a92c99d0"},
+    {file = "python_iso639-2025.2.8.tar.gz", hash = "sha256:94f27c0286fc81ff0e033d7b63d63a5967eef584b97f13289366bf178953f5d7"},
 ]
 
 [package.extras]
-dev = ["black (==24.10.0)", "build (==1.2.2)", "flake8 (==7.1.1)", "mypy (==1.14.1)", "pytest (==8.3.4)", "requests (==2.32.3)", "twine (==6.1.0)"]
+dev = ["black (==25.1.0)", "build (==1.2.2)", "flake8 (==7.1.1)", "mypy (==1.15.0)", "pytest (==8.3.4)", "requests (==2.32.3)", "twine (==6.1.0)"]
 
 [[package]]
 name = "python-magic"
@@ -7916,31 +7871,31 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.9.5"
+version = "0.9.6"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["lint"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
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+    {file = "ruff-0.9.6-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:19f505b643228b417c1111a2a536424ddde0db4ef9023b9e04a46ed8a1cb4656"},
+    {file = "ruff-0.9.6-py3-none-win32.whl", hash = "sha256:194d8402bceef1b31164909540a597e0d913c0e4952015a5b40e28c146121b5d"},
+    {file = "ruff-0.9.6-py3-none-win_amd64.whl", hash = "sha256:03482d5c09d90d4ee3f40d97578423698ad895c87314c4de39ed2af945633caa"},
+    {file = "ruff-0.9.6-py3-none-win_arm64.whl", hash = "sha256:0e2bb706a2be7ddfea4a4af918562fdc1bcb16df255e5fa595bbd800ce322a5a"},
+    {file = "ruff-0.9.6.tar.gz", hash = "sha256:81761592f72b620ec8fa1068a6fd00e98a5ebee342a3642efd84454f3031dca9"},
 ]
 
 [[package]]
@@ -9812,6 +9767,27 @@ files = [
     {file = "wrapt-1.17.2.tar.gz", hash = "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3"},
 ]
 
+[[package]]
+name = "xinference-client"
+version = "1.2.2"
+description = "Client for Xinference"
+optional = false
+python-versions = "*"
+groups = ["vdb"]
+markers = "python_version == \"3.11\" or python_version >= \"3.12\""
+files = [
+    {file = "xinference-client-1.2.2.tar.gz", hash = "sha256:85d2ba0fcbaae616b06719c422364123cbac97f3e3c82e614095fe6d0e630ed0"},
+    {file = "xinference_client-1.2.2-py3-none-any.whl", hash = "sha256:6941d87cf61283a9d6e81cee6cb2609a183d34c6b7d808c6ba0c33437520518f"},
+]
+
+[package.dependencies]
+pydantic = "*"
+requests = "*"
+typing-extensions = "*"
+
+[package.extras]
+dev = ["black", "cython (>=0.29)", "flake8 (>=3.8.0)", "ipython (>=6.5.0)", "pytest (>=3.5.0)", "pytest-asyncio (>=0.14.0)", "pytest-cov (>=2.5.0)", "pytest-forked (>=1.0)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=1.2.0)"]
+
 [[package]]
 name = "xlrd"
 version = "2.0.1"
@@ -10168,4 +10144,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<3.13"
-content-hash = "7bb979b6667b099ad0ba75a9cad287409c6d7739b595c05197581ad1c0d702ce"
+content-hash = "a61e69fbd8c0306ed676d791b3783f8ba9914f871855543b265c4e7b5d2af0f9"

api/pyproject.toml

@@ -134,6 +134,7 @@ tidb-vector = "0.0.9"
 upstash-vector = "0.6.0"
 volcengine-compat = "~1.0.156"
 weaviate-client = "~3.21.0"
+xinference-client = "~1.2.2"
 
 ############################################################
 # [ Dev ] dependency group

api/pytest.ini

@@ -1,4 +1,5 @@
 [pytest]
+continue-on-collection-errors = true
 env =
     ANTHROPIC_API_KEY = sk-ant-api11-IamNotARealKeyJustForMockTestKawaiiiiiiiiii-NotBaka-ASkksz
     AZURE_OPENAI_API_BASE = https://difyai-openai.openai.azure.com

api/services/account_service.py

@@ -33,7 +33,6 @@ from models.account import (
     TenantStatus,
 )
 from models.model import DifySetup
-from models.staging import StagingAccountWhitelist
 from services.billing_service import BillingService
 from services.errors.account import (
     AccountAlreadyInTenantError,
@@ -349,9 +348,6 @@ class AccountService:
 
     @staticmethod
     def login(account: Account, *, ip_address: Optional[str] = None) -> TokenPair:
-        if not AccountService.verify_account_whitelist(account.email):
-            raise ValueError("Account is not whitelisted")
-
         if ip_address:
             AccountService.update_login_info(account=account, ip_address=ip_address)
 
@@ -383,9 +379,6 @@ class AccountService:
         if not account:
             raise ValueError("Invalid account")
 
-        if not AccountService.verify_account_whitelist(account.email):
-            raise ValueError("Account is not whitelisted")
-
         # Generate new access token and refresh token
         new_access_token = AccountService.get_account_jwt_token(account)
         new_refresh_token = _generate_refresh_token()
@@ -395,16 +388,6 @@ class AccountService:
 
         return TokenPair(access_token=new_access_token, refresh_token=new_refresh_token)
 
-    @staticmethod
-    def verify_account_whitelist(email: str) -> bool:
-        with Session(db.engine) as session:
-            return (
-                session.query(StagingAccountWhitelist)
-                .filter(StagingAccountWhitelist.email == email, StagingAccountWhitelist.disabled == False)
-                .first()
-                is not None
-            )
-
     @staticmethod
     def load_logged_in_account(*, account_id: str):
         return AccountService.load_user(account_id)
@@ -449,13 +432,9 @@ class AccountService:
     def send_email_code_login_email(
         cls, account: Optional[Account] = None, email: Optional[str] = None, language: Optional[str] = "en-US"
     ):
-        if email:
-            if not AccountService.verify_account_whitelist(email):
-                raise ValueError("Account is not whitelisted")
-        elif account:
-            if not AccountService.verify_account_whitelist(account.email):
-                raise ValueError("Account is not whitelisted")
-
+        email = account.email if account else email
+        if email is None:
+            raise ValueError("Email must be provided.")
         if cls.email_code_login_rate_limiter.is_rate_limited(email):
             from controllers.console.auth.error import EmailCodeLoginRateLimitExceededError
 

api/services/hit_testing_service.py

@@ -47,7 +47,7 @@ class HitTestingService:
         all_documents = RetrievalService.retrieve(
             retrieval_method=retrieval_model.get("search_method", "semantic_search"),
             dataset_id=dataset.id,
-            query=cls.escape_query_for_search(query),
+            query=query,
             top_k=retrieval_model.get("top_k", 2),
             score_threshold=retrieval_model.get("score_threshold", 0.0)
             if retrieval_model["score_threshold_enabled"]

api/services/message_service.py

@@ -46,6 +46,8 @@ class MessageService:
             app_model=app_model, user=user, conversation_id=conversation_id
         )
 
+        fetch_limit = limit + 1
+
         if first_id:
             first_message = (
                 db.session.query(Message)
@@ -64,7 +66,7 @@ class MessageService:
                     Message.id != first_message.id,
                 )
                 .order_by(Message.created_at.desc())
-                .limit(limit)
+                .limit(fetch_limit)
                 .all()
             )
         else:
@@ -72,25 +74,14 @@ class MessageService:
                 db.session.query(Message)
                 .filter(Message.conversation_id == conversation.id)
                 .order_by(Message.created_at.desc())
-                .limit(limit)
+                .limit(fetch_limit)
                 .all()
             )
 
         has_more = False
-        if len(history_messages) == limit:
-            current_page_first_message = history_messages[-1]
-            rest_count = (
-                db.session.query(Message)
-                .filter(
-                    Message.conversation_id == conversation.id,
-                    Message.created_at < current_page_first_message.created_at,
-                    Message.id != current_page_first_message.id,
-                )
-                .count()
-            )
-
-            if rest_count > 0:
-                has_more = True
+        if len(history_messages) > limit:
+            has_more = True
+            history_messages = history_messages[:-1]
 
         if order == "asc":
             history_messages = list(reversed(history_messages))
@@ -112,6 +103,8 @@ class MessageService:
 
         base_query = db.session.query(Message)
 
+        fetch_limit = limit + 1
+
         if conversation_id is not None:
             conversation = ConversationService.get_conversation(
                 app_model=app_model, user=user, conversation_id=conversation_id
@@ -131,21 +124,16 @@ class MessageService:
             history_messages = (
                 base_query.filter(Message.created_at < last_message.created_at, Message.id != last_message.id)
                 .order_by(Message.created_at.desc())
-                .limit(limit)
+                .limit(fetch_limit)
                 .all()
             )
         else:
-            history_messages = base_query.order_by(Message.created_at.desc()).limit(limit).all()
+            history_messages = base_query.order_by(Message.created_at.desc()).limit(fetch_limit).all()
 
         has_more = False
-        if len(history_messages) == limit:
-            current_page_first_message = history_messages[-1]
-            rest_count = base_query.filter(
-                Message.created_at < current_page_first_message.created_at, Message.id != current_page_first_message.id
-            ).count()
-
-            if rest_count > 0:
-                has_more = True
+        if len(history_messages) > limit:
+            has_more = True
+            history_messages = history_messages[:-1]
 
         return InfiniteScrollPagination(data=history_messages, limit=limit, has_more=has_more)
 

api/services/plugin/dependencies_analysis.py

@@ -1,5 +1,5 @@
 from core.helper import marketplace
-from core.plugin.entities.plugin import GenericProviderID, PluginDependency, PluginInstallationSource
+from core.plugin.entities.plugin import ModelProviderID, PluginDependency, PluginInstallationSource, ToolProviderID
 from core.plugin.manager.plugin import PluginInstallationManager
 
 
@@ -12,10 +12,7 @@ class DependenciesAnalysisService:
         Convert the tool id to the plugin_id
         """
         try:
-            tool_provider_id = GenericProviderID(tool_id)
-            if tool_id in ["jina", "siliconflow"]:
-                tool_provider_id.plugin_name = tool_provider_id.plugin_name + "_tool"
-            return tool_provider_id.plugin_id
+            return ToolProviderID(tool_id).plugin_id
         except Exception as e:
             raise e
 
@@ -27,11 +24,7 @@ class DependenciesAnalysisService:
         Convert the model provider id to the plugin_id
         """
         try:
-            generic_provider_id = GenericProviderID(model_provider_id)
-            if model_provider_id == "google":
-                generic_provider_id.plugin_name = "gemini"
-
-            return generic_provider_id.plugin_id
+            return ModelProviderID(model_provider_id).plugin_id
         except Exception as e:
             raise e
 

api/services/plugin/plugin_migration.py

@@ -1,7 +1,6 @@
 import datetime
 import json
 import logging
-import sys
 import time
 from collections.abc import Mapping, Sequence
 from concurrent.futures import ThreadPoolExecutor
@@ -15,9 +14,8 @@ from flask import Flask, current_app
 from sqlalchemy.orm import Session
 
 from core.agent.entities import AgentToolEntity
-from core.entities import DEFAULT_PLUGIN_ID
 from core.helper import marketplace
-from core.plugin.entities.plugin import PluginInstallationSource
+from core.plugin.entities.plugin import ModelProviderID, PluginInstallationSource, ToolProviderID
 from core.plugin.entities.plugin_daemon import PluginInstallTaskStatus
 from core.plugin.manager.plugin import PluginInstallationManager
 from core.tools.entities.tool_entities import ToolProviderType
@@ -38,7 +36,6 @@ class PluginMigration:
         """
         Migrate plugin.
         """
-        import concurrent.futures
         from threading import Lock
 
         click.echo(click.style("Migrating models/tools to new plugin Mechanism", fg="white"))
@@ -55,7 +52,7 @@ class PluginMigration:
         file_lock = Lock()
         counter_lock = Lock()
 
-        thread_pool = concurrent.futures.ThreadPoolExecutor(max_workers=workers)
+        thread_pool = ThreadPoolExecutor(max_workers=workers)
 
         def process_tenant(flask_app: Flask, tenant_id: str) -> None:
             with flask_app.app_context():
@@ -205,13 +202,7 @@ class PluginMigration:
             result = []
             for row in rs:
                 provider_name = str(row[0])
-                if provider_name and "/" not in provider_name:
-                    if provider_name == "google":
-                        provider_name = "gemini"
-
-                    result.append(DEFAULT_PLUGIN_ID + "/" + provider_name)
-                elif provider_name:
-                    result.append(provider_name)
+                result.append(ModelProviderID(provider_name).plugin_id)
 
             return result
 
@@ -224,30 +215,10 @@ class PluginMigration:
             rs = session.query(BuiltinToolProvider).filter(BuiltinToolProvider.tenant_id == tenant_id).all()
             result = []
             for row in rs:
-                if "/" not in row.provider:
-                    result.append(DEFAULT_PLUGIN_ID + "/" + row.provider)
-                else:
-                    result.append(row.provider)
+                result.append(ToolProviderID(row.provider).plugin_id)
 
             return result
 
-    @classmethod
-    def _handle_builtin_tool_provider(cls, provider_name: str) -> str:
-        """
-        Handle builtin tool provider.
-        """
-        if provider_name == "jina":
-            provider_name = "jina_tool"
-        elif provider_name == "siliconflow":
-            provider_name = "siliconflow_tool"
-        elif provider_name == "stepfun":
-            provider_name = "stepfun_tool"
-
-        if "/" not in provider_name:
-            return DEFAULT_PLUGIN_ID + "/" + provider_name
-        else:
-            return provider_name
-
     @classmethod
     def extract_workflow_tables(cls, tenant_id: str) -> Sequence[str]:
         """
@@ -268,8 +239,7 @@ class PluginMigration:
                         provider_name = data.get("provider_name")
                         provider_type = data.get("provider_type")
                         if provider_name not in excluded_providers and provider_type == ToolProviderType.BUILT_IN.value:
-                            provider_name = cls._handle_builtin_tool_provider(provider_name)
-                            result.append(provider_name)
+                            result.append(ToolProviderID(provider_name).plugin_id)
 
             return result
 
@@ -300,7 +270,7 @@ class PluginMigration:
                                     tool_entity.provider_type == ToolProviderType.BUILT_IN.value
                                     and tool_entity.provider_id not in excluded_providers
                                 ):
-                                    result.append(cls._handle_builtin_tool_provider(tool_entity.provider_id))
+                                    result.append(ToolProviderID(tool_entity.provider_id).plugin_id)
 
                             except Exception:
                                 logger.exception(f"Failed to process tool {tool}")
@@ -341,10 +311,14 @@ class PluginMigration:
                         plugin_ids.append(plugin_id)
 
         def fetch_plugin(plugin_id):
-            unique_identifier = cls._fetch_plugin_unique_identifier(plugin_id)
-            if unique_identifier:
-                plugins[plugin_id] = unique_identifier
-            else:
+            try:
+                unique_identifier = cls._fetch_plugin_unique_identifier(plugin_id)
+                if unique_identifier:
+                    plugins[plugin_id] = unique_identifier
+                else:
+                    plugin_not_exist.append(plugin_id)
+            except Exception:
+                logger.exception(f"Failed to fetch plugin unique identifier for {plugin_id}")
                 plugin_not_exist.append(plugin_id)
 
         with ThreadPoolExecutor(max_workers=10) as executor:
@@ -353,7 +327,7 @@ class PluginMigration:
         return {"plugins": plugins, "plugin_not_exist": plugin_not_exist}
 
     @classmethod
-    def install_plugins(cls, extracted_plugins: str, output_file: str) -> None:
+    def install_plugins(cls, extracted_plugins: str, output_file: str, workers: int = 100) -> None:
         """
         Install plugins.
         """
@@ -367,7 +341,7 @@ class PluginMigration:
         fake_tenant_id = uuid4().hex
         logger.info(f"Installing {len(plugins['plugins'])} plugin instances for fake tenant {fake_tenant_id}")
 
-        thread_pool = ThreadPoolExecutor(max_workers=40)
+        thread_pool = ThreadPoolExecutor(max_workers=workers)
 
         response = cls.handle_plugin_instance_install(fake_tenant_id, plugins["plugins"])
         if response.get("failed"):
@@ -375,10 +349,17 @@ class PluginMigration:
 
         def install(tenant_id: str, plugin_ids: list[str]) -> None:
             logger.info(f"Installing {len(plugin_ids)} plugins for tenant {tenant_id}")
+            # fetch plugin already installed
+            installed_plugins = manager.list_plugins(tenant_id)
+            installed_plugins_ids = [plugin.plugin_id for plugin in installed_plugins]
             # at most 64 plugins one batch
             for i in range(0, len(plugin_ids), 64):
                 batch_plugin_ids = plugin_ids[i : i + 64]
-                batch_plugin_identifiers = [plugins["plugins"][plugin_id] for plugin_id in batch_plugin_ids]
+                batch_plugin_identifiers = [
+                    plugins["plugins"][plugin_id]
+                    for plugin_id in batch_plugin_ids
+                    if plugin_id not in installed_plugins_ids and plugin_id in plugins["plugins"]
+                ]
                 manager.install_from_identifiers(
                     tenant_id,
                     batch_plugin_identifiers,
@@ -418,8 +399,6 @@ class PluginMigration:
 
         logger.info("Uninstall plugins")
 
-        sys.exit(-1)
-
         # get installation
         try:
             installation = manager.list_plugins(fake_tenant_id)
@@ -475,9 +454,9 @@ class PluginMigration:
 
         reverse_map = {v: k for k, v in plugin_identifiers_map.items()}
 
-        # at most 64 plugins one batch
-        for i in range(0, len(plugin_identifiers_map), 64):
-            batch_plugin_ids = list(plugin_identifiers_map.keys())[i : i + 64]
+        # at most 8 plugins one batch
+        for i in range(0, len(plugin_identifiers_map), 8):
+            batch_plugin_ids = list(plugin_identifiers_map.keys())[i : i + 8]
             batch_plugin_identifiers = [plugin_identifiers_map[plugin_id] for plugin_id in batch_plugin_ids]
 
             try:

api/services/tools/builtin_tools_manage_service.py

@@ -7,7 +7,7 @@ from sqlalchemy.orm import Session
 from configs import dify_config
 from core.helper.position_helper import is_filtered
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.plugin.entities.plugin import GenericProviderID
+from core.plugin.entities.plugin import GenericProviderID, ToolProviderID
 from core.plugin.manager.exc import PluginDaemonClientSideError
 from core.tools.builtin_tool.providers._positions import BuiltinToolProviderSort
 from core.tools.entities.api_entities import ToolApiEntity, ToolProviderApiEntity
@@ -240,10 +240,7 @@ class BuiltinToolManageService:
 
         # rewrite db_providers
         for db_provider in db_providers:
-            try:
-                GenericProviderID(db_provider.provider)
-            except Exception:
-                db_provider.provider = f"langgenius/{db_provider.provider}/{db_provider.provider}"
+            db_provider.provider = str(ToolProviderID(db_provider.provider))
 
         # find provider
         def find_provider(provider):
@@ -258,7 +255,7 @@ class BuiltinToolManageService:
                     include_set=dify_config.POSITION_TOOL_INCLUDES_SET,  # type: ignore
                     exclude_set=dify_config.POSITION_TOOL_EXCLUDES_SET,  # type: ignore
                     data=provider_controller,
-                    name_func=lambda x: x.entity.identity.name,
+                    name_func=lambda x: x.identity.name,
                 ):
                     continue
 

api/services/tools/tools_transform_service.py

@@ -54,7 +54,7 @@ class ToolTransformService:
     @staticmethod
     def repack_provider(tenant_id: str, provider: Union[dict, ToolProviderApiEntity]):
         """
-        repack  provider
+        repack provider
 
         :param provider: the provider dict
         """

api/tests/integration_tests/vdb/tidb_vector/check_tiflash_ready.py

@@ -0,0 +1,59 @@
+import time
+
+import pymysql
+
+
+def check_tiflash_ready() -> bool:
+    try:
+        connection = pymysql.connect(
+            host="localhost",
+            port=4000,
+            user="root",
+            password="",
+        )
+
+        with connection.cursor() as cursor:
+            # Doc reference:
+            # https://docs.pingcap.com/zh/tidb/stable/information-schema-cluster-hardware
+            select_tiflash_query = """
+            SELECT * FROM information_schema.cluster_hardware
+            WHERE TYPE='tiflash'
+            LIMIT 1;
+            """
+            cursor.execute(select_tiflash_query)
+            result = cursor.fetchall()
+            return result is not None and len(result) > 0
+    except Exception as e:
+        print(f"TiFlash is not ready. Exception: {e}")
+        return False
+    finally:
+        if connection:
+            connection.close()
+
+
+def main():
+    max_attempts = 30
+    retry_interval_seconds = 2
+    is_tiflash_ready = False
+    for attempt in range(max_attempts):
+        try:
+            is_tiflash_ready = check_tiflash_ready()
+        except Exception as e:
+            print(f"TiFlash is not ready. Exception: {e}")
+            is_tiflash_ready = False
+
+        if is_tiflash_ready:
+            break
+        else:
+            print(f"Attempt {attempt + 1} failed，retry in {retry_interval_seconds} seconds...")
+            time.sleep(retry_interval_seconds)
+
+    if is_tiflash_ready:
+        print("TiFlash is ready in TiDB.")
+    else:
+        print(f"TiFlash is not ready in TiDB after {max_attempts} attempting checks.")
+        exit(1)
+
+
+if __name__ == "__main__":
+    main()

api/tests/unit_tests/core/workflow/nodes/http_request/test_http_request_executor.py

@@ -68,8 +68,7 @@ def test_executor_with_json_body_and_object_variable():
         system_variables={},
         user_inputs={},
     )
-    variable_pool.add(["pre_node_id", "object"], {
-                      "name": "John Doe", "age": 30, "email": "john@example.com"})
+    variable_pool.add(["pre_node_id", "object"], {"name": "John Doe", "age": 30, "email": "john@example.com"})
 
     # Prepare the node data
     node_data = HttpRequestNodeData(
@@ -124,8 +123,7 @@ def test_executor_with_json_body_and_nested_object_variable():
         system_variables={},
         user_inputs={},
     )
-    variable_pool.add(["pre_node_id", "object"], {
-                      "name": "John Doe", "age": 30, "email": "john@example.com"})
+    variable_pool.add(["pre_node_id", "object"], {"name": "John Doe", "age": 30, "email": "john@example.com"})
 
     # Prepare the node data
     node_data = HttpRequestNodeData(

api/tests/unit_tests/core/workflow/nodes/http_request/test_http_request_node.py

@@ -18,14 +18,6 @@ from models.enums import UserFrom
 from models.workflow import WorkflowNodeExecutionStatus, WorkflowType
 
 
-def test_plain_text_to_dict():
-    assert _plain_text_to_dict("aa\n cc:") == {"aa": "", "cc": ""}
-    assert _plain_text_to_dict("aa:bb\n cc:dd") == {"aa": "bb", "cc": "dd"}
-    assert _plain_text_to_dict("aa:bb\n cc:dd\n") == {"aa": "bb", "cc": "dd"}
-    assert _plain_text_to_dict("aa:bb\n\n cc : dd\n\n") == {
-        "aa": "bb", "cc": "dd"}
-
-
 def test_http_request_node_binary_file(monkeypatch):
     data = HttpRequestNodeData(
         title="test",
@@ -191,8 +183,7 @@ def test_http_request_node_form_with_file(monkeypatch):
 
     def attr_checker(*args, **kwargs):
         assert kwargs["data"] == {"name": "test"}
-        assert kwargs["files"] == {
-            "file": (None, b"test", "application/octet-stream")}
+        assert kwargs["files"] == {"file": (None, b"test", "application/octet-stream")}
         return httpx.Response(200, content=b"")
 
     monkeypatch.setattr(

api/tests/unit_tests/core/workflow/nodes/test_document_extractor_node.py

@@ -8,7 +8,7 @@ from core.variables.variables import StringVariable
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.nodes.document_extractor import DocumentExtractorNode, DocumentExtractorNodeData
 from core.workflow.nodes.document_extractor.node import (
-    _extract_text_from_doc,
+    _extract_text_from_docx,
     _extract_text_from_pdf,
     _extract_text_from_plain_text,
 )
@@ -120,7 +120,7 @@ def test_run_extract_text(
         monkeypatch.setattr("core.workflow.nodes.document_extractor.node._extract_text_from_pdf", mock_pdf_extract)
     elif mime_type.startswith("application/vnd.openxmlformats"):
         mock_docx_extract = Mock(return_value=expected_text[0])
-        monkeypatch.setattr("core.workflow.nodes.document_extractor.node._extract_text_from_doc", mock_docx_extract)
+        monkeypatch.setattr("core.workflow.nodes.document_extractor.node._extract_text_from_docx", mock_docx_extract)
 
     result = document_extractor_node._run()
 
@@ -163,14 +163,14 @@ def test_extract_text_from_pdf(mock_pdf_document):
 
 
 @patch("docx.Document")
-def test_extract_text_from_doc(mock_document):
+def test_extract_text_from_docx(mock_document):
     mock_paragraph1 = Mock()
     mock_paragraph1.text = "Paragraph 1"
     mock_paragraph2 = Mock()
     mock_paragraph2.text = "Paragraph 2"
     mock_document.return_value.paragraphs = [mock_paragraph1, mock_paragraph2]
 
-    text = _extract_text_from_doc(b"PK\x03\x04")
+    text = _extract_text_from_docx(b"PK\x03\x04")
     assert text == "Paragraph 1\nParagraph 2"
 
 

docker/.env.example

@@ -887,6 +887,10 @@ SSRF_HTTP_PORT=3128
 SSRF_COREDUMP_DIR=/var/spool/squid
 SSRF_REVERSE_PROXY_PORT=8194
 SSRF_SANDBOX_HOST=sandbox
+SSRF_DEFAULT_TIME_OUT=5
+SSRF_DEFAULT_CONNECT_TIME_OUT=5
+SSRF_DEFAULT_READ_TIME_OUT=5
+SSRF_DEFAULT_WRITE_TIME_OUT=5
 
 # ------------------------------
 # docker env var for specifying vector db type at startup

docker/docker-compose-template.yaml

@@ -2,7 +2,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:1.0.0
+    image: langgenius/dify-api:0.15.3
     restart: always
     environment:
       # Use the shared environment variables.
@@ -27,7 +27,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:1.0.0
+    image: langgenius/dify-api:0.15.3
     restart: always
     environment:
       # Use the shared environment variables.
@@ -51,7 +51,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:1.0.0
+    image: langgenius/dify-web:0.15.3
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}
@@ -64,6 +64,7 @@ services:
       MARKETPLACE_URL: ${MARKETPLACE_URL:-https://marketplace.dify.ai}
       TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
       INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH: ${INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH:-}
+      PM2_INSTANCES: ${PM2_INSTANCES:-2}
 
   # The postgres database.
   db:
@@ -128,7 +129,7 @@ services:
 
   # plugin daemon
   plugin_daemon:
-    image: langgenius/dify-plugin-daemon:0.0.1-local
+    image: langgenius/dify-plugin-daemon:0.0.2-local
     restart: always
     environment:
       # Use the shared environment variables.
@@ -231,16 +232,6 @@ services:
       - '${EXPOSE_NGINX_PORT:-80}:${NGINX_PORT:-80}'
       - '${EXPOSE_NGINX_SSL_PORT:-443}:${NGINX_SSL_PORT:-443}'
 
-  # The TiDB vector store.
-  # For production use, please refer to https://github.com/pingcap/tidb-docker-compose
-  tidb:
-    image: pingcap/tidb:v8.4.0
-    profiles:
-      - tidb
-    command:
-      - --store=unistore
-    restart: always
-
   # The Weaviate vector store.
   weaviate:
     image: semitechnologies/weaviate:1.19.0

docker/docker-compose.middleware.yaml

@@ -66,7 +66,7 @@ services:
 
   # plugin daemon
   plugin_daemon:
-    image: langgenius/dify-plugin-daemon:0.0.1-local
+    image: langgenius/dify-plugin-daemon:0.0.2-local
     restart: always
     environment:
       # Use the shared environment variables.

docker/docker-compose.yaml

@@ -377,6 +377,10 @@ x-shared-env: &shared-api-worker-env
   SSRF_COREDUMP_DIR: ${SSRF_COREDUMP_DIR:-/var/spool/squid}
   SSRF_REVERSE_PROXY_PORT: ${SSRF_REVERSE_PROXY_PORT:-8194}
   SSRF_SANDBOX_HOST: ${SSRF_SANDBOX_HOST:-sandbox}
+  SSRF_DEFAULT_TIME_OUT: ${SSRF_DEFAULT_TIME_OUT:-5}
+  SSRF_DEFAULT_CONNECT_TIME_OUT: ${SSRF_DEFAULT_CONNECT_TIME_OUT:-5}
+  SSRF_DEFAULT_READ_TIME_OUT: ${SSRF_DEFAULT_READ_TIME_OUT:-5}
+  SSRF_DEFAULT_WRITE_TIME_OUT: ${SSRF_DEFAULT_WRITE_TIME_OUT:-5}
   EXPOSE_NGINX_PORT: ${EXPOSE_NGINX_PORT:-80}
   EXPOSE_NGINX_SSL_PORT: ${EXPOSE_NGINX_SSL_PORT:-443}
   POSITION_TOOL_PINS: ${POSITION_TOOL_PINS:-}
@@ -410,7 +414,7 @@ x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:1.0.0
+    image: langgenius/dify-api:0.15.3
     restart: always
     environment:
       # Use the shared environment variables.
@@ -435,7 +439,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:1.0.0
+    image: langgenius/dify-api:0.15.3
     restart: always
     environment:
       # Use the shared environment variables.
@@ -459,13 +463,11 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:1.0.0
+    image: langgenius/dify-web:0.15.3
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}
       APP_API_URL: ${APP_API_URL:-}
-      MARKETPLACE_API_URL: ${MARKETPLACE_API_URL:-}
-      MARKETPLACE_URL: ${MARKETPLACE_URL:-}
       SENTRY_DSN: ${WEB_SENTRY_DSN:-}
       NEXT_TELEMETRY_DISABLED: ${NEXT_TELEMETRY_DISABLED:-0}
       TEXT_GENERATION_TIMEOUT_MS: ${TEXT_GENERATION_TIMEOUT_MS:-60000}
@@ -474,6 +476,7 @@ services:
       MARKETPLACE_URL: ${MARKETPLACE_URL:-https://marketplace.dify.ai}
       TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
       INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH: ${INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH:-}
+      PM2_INSTANCES: ${PM2_INSTANCES:-2}
 
   # The postgres database.
   db:
@@ -538,7 +541,7 @@ services:
 
   # plugin daemon
   plugin_daemon:
-    image: langgenius/dify-plugin-daemon:0.0.1-local
+    image: langgenius/dify-plugin-daemon:0.0.2-local
     restart: always
     environment:
       # Use the shared environment variables.
@@ -641,16 +644,6 @@ services:
       - '${EXPOSE_NGINX_PORT:-80}:${NGINX_PORT:-80}'
       - '${EXPOSE_NGINX_SSL_PORT:-443}:${NGINX_SSL_PORT:-443}'
 
-  # The TiDB vector store.
-  # For production use, please refer to https://github.com/pingcap/tidb-docker-compose
-  tidb:
-    image: pingcap/tidb:v8.4.0
-    profiles:
-      - tidb
-    command:
-      - --store=unistore
-    restart: always
-
   # The Weaviate vector store.
   weaviate:
     image: semitechnologies/weaviate:1.19.0

docker/nginx/conf.d/default.conf.template

@@ -31,6 +31,7 @@ server {
 
     location /e {
       proxy_pass http://plugin_daemon:5002;
+      proxy_set_header Dify-Hook-Url $scheme://$host$request_uri;
       include proxy.conf;
     }
 

docker/nginx/proxy.conf.template

@@ -3,6 +3,7 @@
 proxy_set_header Host $host;
 proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
 proxy_set_header X-Forwarded-Proto $scheme;
+proxy_set_header X-Forwarded-Port $server_port;
 proxy_http_version 1.1;
 proxy_set_header Connection "";
 proxy_buffering off;

docker/tidb/config/pd.toml

@@ -0,0 +1,4 @@
+# PD Configuration File reference:
+# https://docs.pingcap.com/tidb/stable/pd-configuration-file#pd-configuration-file
+[replication]
+max-replicas = 1

docker/tidb/config/tiflash-learner.toml

@@ -0,0 +1,13 @@
+# TiFlash tiflash-learner.toml Configuration File reference:
+# https://docs.pingcap.com/tidb/stable/tiflash-configuration#configure-the-tiflash-learnertoml-file
+
+log-file = "/logs/tiflash_tikv.log"
+
+[server]
+engine-addr = "tiflash:4030"
+addr = "0.0.0.0:20280"
+advertise-addr = "tiflash:20280"
+status-addr = "tiflash:20292"
+
+[storage]
+data-dir = "/data/flash"

docker/tidb/config/tiflash.toml

@@ -0,0 +1,19 @@
+# TiFlash tiflash.toml Configuration File reference:
+# https://docs.pingcap.com/tidb/stable/tiflash-configuration#configure-the-tiflashtoml-file
+
+listen_host = "0.0.0.0"
+path = "/data"
+
+[flash]
+tidb_status_addr = "tidb:10080"
+service_addr = "tiflash:4030"
+
+[flash.proxy]
+config = "/tiflash-learner.toml"
+
+[logger]
+errorlog = "/logs/tiflash_error.log"
+log = "/logs/tiflash.log"
+
+[raft]
+pd_addr = "pd0:2379"

docker/tidb/docker-compose.yaml

@@ -0,0 +1,62 @@
+services:
+  pd0:
+    image: pingcap/pd:v8.5.1
+    # ports:
+    #  - "2379"
+    volumes:
+      - ./config/pd.toml:/pd.toml:ro
+      - ./volumes/data:/data
+      - ./volumes/logs:/logs
+    command:
+      - --name=pd0
+      - --client-urls=http://0.0.0.0:2379
+      - --peer-urls=http://0.0.0.0:2380
+      - --advertise-client-urls=http://pd0:2379
+      - --advertise-peer-urls=http://pd0:2380
+      - --initial-cluster=pd0=http://pd0:2380
+      - --data-dir=/data/pd
+      - --config=/pd.toml
+      - --log-file=/logs/pd.log
+    restart: on-failure
+  tikv:
+    image: pingcap/tikv:v8.5.1
+    volumes:
+      - ./volumes/data:/data
+      - ./volumes/logs:/logs
+    command:
+      - --addr=0.0.0.0:20160
+      - --advertise-addr=tikv:20160
+      - --status-addr=tikv:20180
+      - --data-dir=/data/tikv
+      - --pd=pd0:2379
+      - --log-file=/logs/tikv.log
+    depends_on:
+      - "pd0"
+    restart: on-failure
+  tidb:
+    image: pingcap/tidb:v8.5.1
+    # ports:
+    #   - "4000:4000"
+    volumes:
+      - ./volumes/logs:/logs
+    command:
+      - --advertise-address=tidb
+      - --store=tikv
+      - --path=pd0:2379
+      - --log-file=/logs/tidb.log
+    depends_on:
+      - "tikv"
+    restart: on-failure
+  tiflash:
+    image: pingcap/tiflash:v8.5.1
+    volumes:
+      - ./config/tiflash.toml:/tiflash.toml:ro
+      - ./config/tiflash-learner.toml:/tiflash-learner.toml:ro
+      - ./volumes/data:/data
+      - ./volumes/logs:/logs
+    command:
+      - --config=/tiflash.toml
+    depends_on:
+      - "tikv"
+      - "tidb"
+    restart: on-failure

web/.env.example

@@ -10,10 +10,10 @@ NEXT_PUBLIC_API_PREFIX=http://localhost:5001/console/api
 # console or api domain.
 # example: http://udify.app/api
 NEXT_PUBLIC_PUBLIC_API_PREFIX=http://localhost:5001/api
-# The APIFREX for MARKETPLACE
-NEXT_PUBLIC_MARKETPLACE_API_PREFIX=http://localhost:5002/api
+# The API PREFIX for MARKETPLACE
+NEXT_PUBLIC_MARKETPLACE_API_PREFIX=https://marketplace.dify.ai/api/v1
 # The URL for MARKETPLACE
-NEXT_PUBLIC_MARKETPLACE_URL_PREFIX=
+NEXT_PUBLIC_MARKETPLACE_URL_PREFIX=https://marketplace.dify.ai
 
 # SENTRY
 NEXT_PUBLIC_SENTRY_DSN=

web/Dockerfile

@@ -46,6 +46,7 @@ ENV MARKETPLACE_API_URL=http://127.0.0.1:5001
 ENV MARKETPLACE_URL=http://127.0.0.1:5001
 ENV PORT=3000
 ENV NEXT_TELEMETRY_DISABLED=1
+ENV PM2_INSTANCES=2
 
 # set timezone
 ENV TZ=UTC
@@ -58,7 +59,6 @@ COPY --from=builder /app/web/public ./public
 COPY --from=builder /app/web/.next/standalone ./
 COPY --from=builder /app/web/.next/static ./.next/static
 
-COPY docker/pm2.json ./pm2.json
 COPY docker/entrypoint.sh ./entrypoint.sh
 
 

web/README.md

@@ -70,6 +70,8 @@ If you want to customize the host and port:
 pnpm run start --port=3001 --host=0.0.0.0
 ```
 
+If you want to customize the number of instances launched by PM2, you can configure `PM2_INSTANCES` in `docker-compose.yaml` or `Dockerfile`.
+
 ## Storybook
 
 This project uses [Storybook](https://storybook.js.org/) for UI component development.

web/app/(commonLayout)/app/(appDetailLayout)/[appId]/layout.tsx

@@ -147,7 +147,8 @@ const AppDetailLayout: FC<IAppDetailLayoutProps> = (props) => {
         })
       }
     }
-  }, [appDetailRes, appId, getNavigations, isCurrentWorkspaceEditor, isLoadingAppDetail, isLoadingCurrentWorkspace, pathname, router, setAppDetail, systemFeatures.enable_web_sso_switch_component])
+  // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [appDetailRes, appId, getNavigations, isCurrentWorkspaceEditor, isLoadingAppDetail, isLoadingCurrentWorkspace, router, setAppDetail, systemFeatures.enable_web_sso_switch_component])
 
   useUnmount(() => {
     setAppDetail()

web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/chartView.tsx

@@ -53,7 +53,7 @@ export default function ChartView({ appId }: IChartViewProps) {
           className='mt-0 !w-40'
           onSelect={(item) => {
             const id = item.value
-            const value = TIME_PERIOD_MAPPING[id]?.value || '-1'
+            const value = TIME_PERIOD_MAPPING[id]?.value ?? '-1'
             const name = item.name || t('appLog.filter.period.allTime')
             onSelect({ value, name })
           }}