bump version to 0.5.10 (#2902)

Co-authored-by: OSS-MAOLONGDONG\kaihong <maolongdong@kaihong.com>

.github/pull_request_template.md

@@ -12,6 +12,8 @@ Please delete options that are not relevant.
 - [ ] New feature (non-breaking change which adds functionality)
 - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
 - [ ] This change requires a documentation update, included: [Dify Document](https://github.com/langgenius/dify-docs)
+- [ ] Improvement，including but not limited to code refactoring, performance optimization, and UI/UX improvement
+- [ ] Dependency upgrade
 
 # How Has This Been Tested?
 

.github/workflows/build-push.yml

@@ -1,17 +1,32 @@
-name: Build and Push API Image
+name: Build and Push API & Web
 
 on:
   push:
     branches:
-      - 'main'
-      - 'deploy/dev'
+      - "main"
+      - "deploy/dev"
   release:
-    types: [ published ]
+    types: [published]
+
+env:
+  DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USER }}
+  DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
+  DIFY_WEB_IMAGE_NAME: ${{ vars.DIFY_WEB_IMAGE_NAME || 'langgenius/dify-web' }}
+  DIFY_API_IMAGE_NAME: ${{ vars.DIFY_API_IMAGE_NAME || 'langgenius/dify-api' }}
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
+    strategy:
+      matrix:
+        include:
+          - service_name: "web"
+            image_name_env: "DIFY_WEB_IMAGE_NAME"
+            context: "web"
+          - service_name: "api"
+            image_name_env: "DIFY_API_IMAGE_NAME"
+            context: "api"
     steps:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
@@ -22,14 +37,14 @@ jobs:
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
-          username: ${{ secrets.DOCKERHUB_USER }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
+          username: ${{ env.DOCKERHUB_USER }}
+          password: ${{ env.DOCKERHUB_TOKEN }}
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta
         uses: docker/metadata-action@v5
         with:
-          images: langgenius/dify-api
+          images: ${{ env[matrix.image_name_env] }}
           tags: |
             type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
             type=ref,event=branch
@@ -39,22 +54,11 @@ jobs:
       - name: Build and push
         uses: docker/build-push-action@v5
         with:
-          context: "{{defaultContext}}:api"
+          context: "{{defaultContext}}:${{ matrix.context }}"
           platforms: ${{ startsWith(github.ref, 'refs/tags/') && 'linux/amd64,linux/arm64' || 'linux/amd64' }}
-          build-args: |
-            COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
+          build-args: COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: type=gha
           cache-to: type=gha,mode=max
-
-      - name: Deploy to server
-        if: github.ref == 'refs/heads/deploy/dev'
-        uses: appleboy/ssh-action@v0.1.8
-        with:
-          host: ${{ secrets.SSH_HOST }}
-          username: ${{ secrets.SSH_USER }}
-          key: ${{ secrets.SSH_PRIVATE_KEY }}
-          script: |
-            ${{ secrets.SSH_SCRIPT }}

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

@@ -1,60 +0,0 @@
-name: Build and Push WEB Image
-
-on:
-  push:
-    branches:
-      - 'main'
-      - 'deploy/dev'
-  release:
-    types: [ published ]
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    if: github.event.pull_request.draft == false
-    steps:
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v2
-        with:
-          username: ${{ secrets.DOCKERHUB_USER }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: langgenius/dify-web
-          tags: |
-            type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
-            type=ref,event=branch
-            type=sha,enable=true,priority=100,prefix=,suffix=,format=long
-            type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }}
-
-      - name: Build and push
-        uses: docker/build-push-action@v5
-        with:
-          context: "{{defaultContext}}:web"
-          platforms: ${{ startsWith(github.ref, 'refs/tags/') && 'linux/amd64,linux/arm64' || 'linux/amd64' }}
-          build-args: |
-            COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-
-      - name: Deploy to server
-        if: github.ref == 'refs/heads/deploy/dev'
-        uses: appleboy/ssh-action@v0.1.8
-        with:
-          host: ${{ secrets.SSH_HOST }}
-          username: ${{ secrets.SSH_USER }}
-          key: ${{ secrets.SSH_PRIVATE_KEY }}
-          script: |
-            ${{ secrets.SSH_SCRIPT }}

.github/workflows/deploy-dev.yml

@@ -0,0 +1,24 @@
+name: Deploy Dev
+
+on:
+  workflow_run:
+    workflows: ["Build and Push API & Web"]
+    branches:
+      - "deploy/dev"
+    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: |
+            ${{ vars.SSH_SCRIPT || secrets.SSH_SCRIPT }}

.gitignore

@@ -145,10 +145,14 @@ docker/volumes/db/data/*
 docker/volumes/redis/data/*
 docker/volumes/weaviate/*
 docker/volumes/qdrant/*
+docker/volumes/etcd/*
+docker/volumes/minio/*
+docker/volumes/milvus/*
 
 sdks/python-client/build
 sdks/python-client/dist
 sdks/python-client/dify_client.egg-info
 
 .vscode/*
-!.vscode/launch.json
+!.vscode/launch.json
+pyrightconfig.json

Makefile

@@ -0,0 +1,43 @@
+# Variables
+DOCKER_REGISTRY=langgenius
+WEB_IMAGE=$(DOCKER_REGISTRY)/dify-web
+API_IMAGE=$(DOCKER_REGISTRY)/dify-api
+VERSION=latest
+
+# Build Docker images
+build-web:
+	@echo "Building web Docker image: $(WEB_IMAGE):$(VERSION)..."
+	docker build -t $(WEB_IMAGE):$(VERSION) ./web
+	@echo "Web Docker image built successfully: $(WEB_IMAGE):$(VERSION)"
+
+build-api:
+	@echo "Building API Docker image: $(API_IMAGE):$(VERSION)..."
+	docker build -t $(API_IMAGE):$(VERSION) ./api
+	@echo "API Docker image built successfully: $(API_IMAGE):$(VERSION)"
+
+# Push Docker images
+push-web:
+	@echo "Pushing web Docker image: $(WEB_IMAGE):$(VERSION)..."
+	docker push $(WEB_IMAGE):$(VERSION)
+	@echo "Web Docker image pushed successfully: $(WEB_IMAGE):$(VERSION)"
+
+push-api:
+	@echo "Pushing API Docker image: $(API_IMAGE):$(VERSION)..."
+	docker push $(API_IMAGE):$(VERSION)
+	@echo "API Docker image pushed successfully: $(API_IMAGE):$(VERSION)"
+
+# Build all images
+build-all: build-web build-api
+
+# Push all images
+push-all: push-web push-api
+
+build-push-api: build-api push-api
+build-push-web: build-web push-web
+
+# Build and push all images
+build-push-all: build-all push-all
+	@echo "All Docker images have been built and pushed."
+
+# Phony targets
+.PHONY: build-web build-api push-web push-api build-all push-all build-push-all

README.md

@@ -22,19 +22,8 @@
 </p>
 
 <p align="center">
-  <a href="https://discord.com/events/1082486657678311454/1211724120996188220" target="_blank">
-   Dify.AI Upcoming Meetup Event [👉 Click to Join the Event Here 👈]
-  </a>
-   <ul align="center" style="text-decoration: none; list-style: none;">
-      <li> US EST: 09:00 (9:00 AM)</li>
-      <li> CET: 15:00 (3:00 PM)</li>
-      <li> CST:  22:00 (10:00 PM)</li>
-   </ul>
-</p>
-
-<p align="center">
-   <a href="https://dify.ai/blog/dify-ai-unveils-ai-agent-creating-gpts-and-assistants-with-various-llms" target="_blank">
-   Dify.AI Unveils AI Agent: Creating GPTs and Assistants with Various LLMs
+   <a href="https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6" target="_blank">
+   📌 Check out Dify Premium on AWS and deploy it to your own AWS VPC with one-click.
   </a>
 </p>
 
@@ -48,6 +37,9 @@
 
 You can try out [Dify.AI Cloud](https://dify.ai) now. It provides all the capabilities of the self-deployed version, and includes 200 free requests to OpenAI GPT-3.5.
 
+### Looking to purchase via AWS?
+Check out [Dify Premium on AWS](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one-click. 
+
 ## Dify vs. LangChain vs. Assistants API
 
 | Feature | Dify.AI | Assistants API | LangChain |

api/README.md

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

api/app.py

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

api/config.py

@@ -90,7 +90,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.5.8"
+        self.CURRENT_VERSION = "0.5.10"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -293,6 +293,8 @@ class Config:
 
         self.BATCH_UPLOAD_LIMIT = get_env('BATCH_UPLOAD_LIMIT')
 
+        self.API_COMPRESSION_ENABLED = get_bool_env('API_COMPRESSION_ENABLED')
+
 
 class CloudEditionConfig(Config):
 

api/constants/languages.py

@@ -2,7 +2,7 @@ import json
 
 from models.model import AppModelConfig
 
-languages = ['en-US', 'zh-Hans', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT', 'uk-UA']
+languages = ['en-US', 'zh-Hans', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT', 'uk-UA', 'vi-VN']
 
 language_timezone_mapping = {
     'en-US': 'America/New_York',
@@ -16,6 +16,7 @@ language_timezone_mapping = {
     'ru-RU': 'Europe/Moscow',
     'it-IT': 'Europe/Rome',
     'uk-UA': 'Europe/Kyiv',
+    'vi-VN': 'Asia/Ho_Chi_Minh',
 }
 
 
@@ -79,6 +80,16 @@ user_input_form_template = {
             }
         }
     ],
+     "vi-VN": [
+        {
+            "paragraph": {
+                "label": "Nội dung truy vấn",
+                "variable": "default_input",
+                "required": False,
+                "default": ""
+            }
+        }
+    ],
 }
 
 demo_model_templates = {
@@ -208,7 +219,6 @@ demo_model_templates = {
             )
         }
     ],
-
     'zh-Hans': [
         {
             'name': '翻译助手',
@@ -335,91 +345,92 @@ demo_model_templates = {
             )
         }
     ],
-    'uk-UA': [{
-        "name": "Помічник перекладу",
-        "icon": "",
-        "icon_background": "",
-        "description": "Багатомовний перекладач, який надає можливості перекладу різними мовами, перекладаючи введені користувачем дані на потрібну мову.",
-        "mode": "completion",
-        "model_config": AppModelConfig(
-            provider="openai",
-            model_id="gpt-3.5-turbo-instruct",
-            configs={
-                "prompt_template": "Будь ласка, перекладіть наступний текст на {{target_language}}:\n",
-                "prompt_variables": [
-                    {
-                        "key": "target_language",
-                        "name": "Цільова мова",
-                        "description": "Мова, на яку ви хочете перекласти.",
-                        "type": "select",
-                        "default": "Ukrainian",
-                        "options": [
-                            "Chinese",
-                            "English",
-                            "Japanese",
-                            "French",
-                            "Russian",
-                            "German",
-                            "Spanish",
-                            "Korean",
-                            "Italian",
-                        ],
+    'uk-UA': [
+        {
+            "name": "Помічник перекладу",
+            "icon": "",
+            "icon_background": "",
+            "description": "Багатомовний перекладач, який надає можливості перекладу різними мовами, перекладаючи введені користувачем дані на потрібну мову.",
+            "mode": "completion",
+            "model_config": AppModelConfig(
+                provider="openai",
+                model_id="gpt-3.5-turbo-instruct",
+                configs={
+                    "prompt_template": "Будь ласка, перекладіть наступний текст на {{target_language}}:\n",
+                    "prompt_variables": [
+                        {
+                            "key": "target_language",
+                            "name": "Цільова мова",
+                            "description": "Мова, на яку ви хочете перекласти.",
+                            "type": "select",
+                            "default": "Ukrainian",
+                            "options": [
+                                "Chinese",
+                                "English",
+                                "Japanese",
+                                "French",
+                                "Russian",
+                                "German",
+                                "Spanish",
+                                "Korean",
+                                "Italian",
+                            ],
+                        },
+                    ],
+                    "completion_params": {
+                        "max_token": 1000,
+                        "temperature": 0,
+                        "top_p": 0,
+                        "presence_penalty": 0.1,
+                        "frequency_penalty": 0.1,
                     },
-                ],
-                "completion_params": {
-                    "max_token": 1000,
-                    "temperature": 0,
-                    "top_p": 0,
-                    "presence_penalty": 0.1,
-                    "frequency_penalty": 0.1,
                 },
-            },
-            opening_statement="",
-            suggested_questions=None,
-            pre_prompt="Будь ласка, перекладіть наступний текст на {{target_language}}:\n{{query}}\ntranslate:",
-            model=json.dumps({
-                "provider": "openai",
-                "name": "gpt-3.5-turbo-instruct",
-                "mode": "completion",
-                "completion_params": {
-                    "max_tokens": 1000,
-                    "temperature": 0,
-                    "top_p": 0,
-                    "presence_penalty": 0.1,
-                    "frequency_penalty": 0.1,
-                },
-            }),
-            user_input_form=json.dumps([
-                {
-                    "select": {
-                        "label": "Цільова мова",
-                        "variable": "target_language",
-                        "description": "Мова, на яку ви хочете перекласти.",
-                        "default": "Chinese",
-                        "required": True,
-                        'options': [
-                            'Chinese',
-                            'English',
-                            'Japanese',
-                            'French',
-                            'Russian',
-                            'German',
-                            'Spanish',
-                            'Korean',
-                            'Italian',
-                        ]
+                opening_statement="",
+                suggested_questions=None,
+                pre_prompt="Будь ласка, перекладіть наступний текст на {{target_language}}:\n{{query}}\ntranslate:",
+                model=json.dumps({
+                    "provider": "openai",
+                    "name": "gpt-3.5-turbo-instruct",
+                    "mode": "completion",
+                    "completion_params": {
+                        "max_tokens": 1000,
+                        "temperature": 0,
+                        "top_p": 0,
+                        "presence_penalty": 0.1,
+                        "frequency_penalty": 0.1,
+                    },
+                }),
+                user_input_form=json.dumps([
+                    {
+                        "select": {
+                            "label": "Цільова мова",
+                            "variable": "target_language",
+                            "description": "Мова, на яку ви хочете перекласти.",
+                            "default": "Chinese",
+                            "required": True,
+                            'options': [
+                                'Chinese',
+                                'English',
+                                'Japanese',
+                                'French',
+                                'Russian',
+                                'German',
+                                'Spanish',
+                                'Korean',
+                                'Italian',
+                            ]
+                        }
+                    }, {
+                        "paragraph": {
+                            "label": "Запит",
+                            "variable": "query",
+                            "required": True,
+                            "default": ""
+                        }
                     }
-                }, {
-                    "paragraph": {
-                        "label": "Запит",
-                        "variable": "query",
-                        "required": True,
-                        "default": ""
-                    }
-                }
-            ])
-        )
-    },
+                ])
+            )
+        },
         {
             "name": "AI інтерв’юер фронтенду",
             "icon": "",
@@ -460,5 +471,132 @@ demo_model_templates = {
             ),
         }
     ],
-
+    'vi-VN': [
+        {
+            'name': 'Trợ lý dịch thuật',
+            'icon': '',
+            'icon_background': '',
+            'description': 'Trình dịch đa ngôn ngữ cung cấp khả năng dịch bằng nhiều ngôn ngữ, dịch thông tin đầu vào của người dùng sang ngôn ngữ họ cần.',
+            'mode': 'completion',
+            'model_config': AppModelConfig(
+                provider='openai',
+                model_id='gpt-3.5-turbo-instruct',
+                configs={
+                    'prompt_template': "Hãy dịch đoạn văn bản sau sang ngôn ngữ {{target_language}}:\n",
+                    'prompt_variables': [
+                        {
+                            "key": "target_language",
+                            "name": "Ngôn ngữ đích",
+                            "description": "Ngôn ngữ bạn muốn dịch sang.",
+                            "type": "select",
+                            "default": "Vietnamese",
+                            'options': [
+                                'Chinese',
+                                'English',
+                                'Japanese',
+                                'French',
+                                'Russian',
+                                'German',
+                                'Spanish',
+                                'Korean',
+                                'Italian',
+                                'Vietnamese',
+                            ]
+                        }
+                    ],
+                    'completion_params': {
+                        'max_token': 1000,
+                        'temperature': 0,
+                        'top_p': 0,
+                        'presence_penalty': 0.1,
+                        'frequency_penalty': 0.1,
+                    }
+                },
+                opening_statement='',
+                suggested_questions=None,
+                pre_prompt="Hãy dịch đoạn văn bản sau sang {{target_language}}:\n{{query}}\ndịch:",
+                model=json.dumps({
+                    "provider": "openai",
+                    "name": "gpt-3.5-turbo-instruct",
+                    "mode": "completion",
+                    "completion_params": {
+                        "max_tokens": 1000,
+                        "temperature": 0,
+                        "top_p": 0,
+                        "presence_penalty": 0.1,
+                        "frequency_penalty": 0.1
+                    }
+                }),
+                user_input_form=json.dumps([
+                    {
+                        "select": {
+                            "label": "Ngôn ngữ đích",
+                            "variable": "target_language",
+                            "description": "Ngôn ngữ bạn muốn dịch sang.",
+                            "default": "Vietnamese",
+                            "required": True,
+                            'options': [
+                                'Chinese',
+                                'English',
+                                'Japanese',
+                                'French',
+                                'Russian',
+                                'German',
+                                'Spanish',
+                                'Korean',
+                                'Italian',
+                                'Vietnamese',
+                            ]
+                        }
+                    }, {
+                        "paragraph": {
+                            "label": "Query",
+                            "variable": "query",
+                            "required": True,
+                            "default": ""
+                        }
+                    }
+                ])
+            )
+        },
+        {
+            'name': 'Phỏng vấn front-end AI',
+            'icon': '',
+            'icon_background': '',
+            'description': 'Một người phỏng vấn front-end mô phỏng để kiểm tra mức độ kỹ năng phát triển front-end thông qua việc đặt câu hỏi.',
+            'mode': 'chat',
+            'model_config': AppModelConfig(
+                provider='openai',
+                model_id='gpt-3.5-turbo',
+                configs={
+                    'introduction': 'Xin chào, chào mừng đến với cuộc phỏng vấn của chúng tôi. Tôi là người phỏng vấn cho công ty công nghệ này và tôi sẽ kiểm tra kỹ năng phát triển web front-end của bạn. Tiếp theo, tôi sẽ hỏi bạn một số câu hỏi kỹ thuật. Hãy trả lời chúng càng kỹ lưỡng càng tốt. ',
+                    'prompt_template': "Bạn sẽ đóng vai người phỏng vấn cho một công ty công nghệ, kiểm tra kỹ năng phát triển web front-end của người dùng và đặt ra 5-10 câu hỏi kỹ thuật sắc bén.\n\nXin lưu ý:\n- Mỗi lần chỉ hỏi một câu hỏi.\n - Sau khi người dùng trả lời một câu hỏi, hãy hỏi trực tiếp câu hỏi tiếp theo mà không cố gắng sửa bất kỳ lỗi nào mà thí sinh mắc phải.\n- Nếu bạn cho rằng người dùng đã không trả lời đúng cho một số câu hỏi liên tiếp, hãy hỏi ít câu hỏi hơn.\n- Sau đặt câu hỏi cuối cùng, bạn có thể hỏi câu hỏi này: Tại sao bạn lại rời bỏ công việc cuối cùng của mình? Sau khi người dùng trả lời câu hỏi này, vui lòng bày tỏ sự hiểu biết và ủng hộ của bạn.\n",
+                    'prompt_variables': [],
+                    'completion_params': {
+                        'max_token': 300,
+                        'temperature': 0.8,
+                        'top_p': 0.9,
+                        'presence_penalty': 0.1,
+                        'frequency_penalty': 0.1,
+                    }
+                },
+                opening_statement='Xin chào, chào mừng đến với cuộc phỏng vấn của chúng tôi. Tôi là người phỏng vấn cho công ty công nghệ này và tôi sẽ kiểm tra kỹ năng phát triển web front-end của bạn. Tiếp theo, tôi sẽ hỏi bạn một số câu hỏi kỹ thuật. Hãy trả lời chúng càng kỹ lưỡng càng tốt. ',
+                suggested_questions=None,
+                pre_prompt="Bạn sẽ đóng vai người phỏng vấn cho một công ty công nghệ, kiểm tra kỹ năng phát triển web front-end của người dùng và đặt ra 5-10 câu hỏi kỹ thuật sắc bén.\n\nXin lưu ý:\n- Mỗi lần chỉ hỏi một câu hỏi.\n - Sau khi người dùng trả lời một câu hỏi, hãy hỏi trực tiếp câu hỏi tiếp theo mà không cố gắng sửa bất kỳ lỗi nào mà thí sinh mắc phải.\n- Nếu bạn cho rằng người dùng đã không trả lời đúng cho một số câu hỏi liên tiếp, hãy hỏi ít câu hỏi hơn.\n- Sau đặt câu hỏi cuối cùng, bạn có thể hỏi câu hỏi này: Tại sao bạn lại rời bỏ công việc cuối cùng của mình? Sau khi người dùng trả lời câu hỏi này, vui lòng bày tỏ sự hiểu biết và ủng hộ của bạn.\n",
+                model=json.dumps({
+                    "provider": "openai",
+                    "name": "gpt-3.5-turbo",
+                    "mode": "chat",
+                    "completion_params": {
+                        "max_tokens": 300,
+                        "temperature": 0.8,
+                        "top_p": 0.9,
+                        "presence_penalty": 0.1,
+                        "frequency_penalty": 0.1
+                    }
+                }),
+                user_input_form=None
+            )
+        }
+    ],
 }

api/controllers/console/app/app.py

@@ -27,7 +27,9 @@ from fields.app_fields import (
 from libs.login import login_required
 from models.model import App, AppModelConfig, Site
 from services.app_model_config_service import AppModelConfigService
-
+from core.tools.utils.configuration import ToolParameterConfigurationManager
+from core.tools.tool_manager import ToolManager
+from core.entities.application_entities import AgentToolEntity
 
 def _get_app(app_id, tenant_id):
     app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id).first()
@@ -236,7 +238,44 @@ class AppApi(Resource):
     def get(self, app_id):
         """Get app detail"""
         app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
+        app: App = _get_app(app_id, current_user.current_tenant_id)
+
+        # get original app model config
+        model_config: AppModelConfig = app.app_model_config
+        agent_mode = model_config.agent_mode_dict
+        # decrypt agent tool parameters if it's secret-input
+        for tool in agent_mode.get('tools') or []:
+            if not isinstance(tool, dict) or len(tool.keys()) <= 3:
+                continue
+            agent_tool_entity = AgentToolEntity(**tool)
+            # get tool
+            try:
+                tool_runtime = ToolManager.get_agent_tool_runtime(
+                    tenant_id=current_user.current_tenant_id,
+                    agent_tool=agent_tool_entity,
+                    agent_callback=None
+                )
+                manager = ToolParameterConfigurationManager(
+                    tenant_id=current_user.current_tenant_id,
+                    tool_runtime=tool_runtime,
+                    provider_name=agent_tool_entity.provider_id,
+                    provider_type=agent_tool_entity.provider_type,
+                )
+
+                # get decrypted parameters
+                if agent_tool_entity.tool_parameters:
+                    parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+                    masked_parameter = manager.mask_tool_parameters(parameters or {})
+                else:
+                    masked_parameter = {}
+
+                # override tool parameters
+                tool['tool_parameters'] = masked_parameter
+            except Exception as e:
+                pass
+
+        # override agent mode
+        model_config.agent_mode = json.dumps(agent_mode)
 
         return app
 

api/controllers/console/app/model_config.py

@@ -1,3 +1,4 @@
+import json
 
 from flask import request
 from flask_login import current_user
@@ -7,6 +8,9 @@ from controllers.console import api
 from controllers.console.app import _get_app
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
+from core.entities.application_entities import AgentToolEntity
+from core.tools.tool_manager import ToolManager
+from core.tools.utils.configuration import ToolParameterConfigurationManager
 from events.app_event import app_model_config_was_updated
 from extensions.ext_database import db
 from libs.login import login_required
@@ -38,6 +42,91 @@ class ModelConfigResource(Resource):
         )
         new_app_model_config = new_app_model_config.from_model_config_dict(model_configuration)
 
+        # get original app model config
+        original_app_model_config: AppModelConfig = db.session.query(AppModelConfig).filter(
+            AppModelConfig.id == app.app_model_config_id
+        ).first()
+        agent_mode = original_app_model_config.agent_mode_dict
+        # decrypt agent tool parameters if it's secret-input
+        parameter_map = {}
+        masked_parameter_map = {}
+        tool_map = {}
+        for tool in agent_mode.get('tools') or []:
+            if not isinstance(tool, dict) or len(tool.keys()) <= 3:
+                continue
+            
+            agent_tool_entity = AgentToolEntity(**tool)
+            # get tool
+            try:
+                tool_runtime = ToolManager.get_agent_tool_runtime(
+                    tenant_id=current_user.current_tenant_id,
+                    agent_tool=agent_tool_entity,
+                    agent_callback=None
+                )
+                manager = ToolParameterConfigurationManager(
+                    tenant_id=current_user.current_tenant_id,
+                    tool_runtime=tool_runtime,
+                    provider_name=agent_tool_entity.provider_id,
+                    provider_type=agent_tool_entity.provider_type,
+                )
+            except Exception as e:
+                continue
+
+            # get decrypted parameters
+            if agent_tool_entity.tool_parameters:
+                parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+                masked_parameter = manager.mask_tool_parameters(parameters or {})
+            else:
+                parameters = {}
+                masked_parameter = {}
+
+            key = f'{agent_tool_entity.provider_id}.{agent_tool_entity.provider_type}.{agent_tool_entity.tool_name}'
+            masked_parameter_map[key] = masked_parameter
+            parameter_map[key] = parameters
+            tool_map[key] = tool_runtime
+
+        # encrypt agent tool parameters if it's secret-input
+        agent_mode = new_app_model_config.agent_mode_dict
+        for tool in agent_mode.get('tools') or []:
+            agent_tool_entity = AgentToolEntity(**tool)
+            
+            # get tool
+            key = f'{agent_tool_entity.provider_id}.{agent_tool_entity.provider_type}.{agent_tool_entity.tool_name}'
+            if key in tool_map:
+                tool_runtime = tool_map[key]
+            else:
+                try:
+                    tool_runtime = ToolManager.get_agent_tool_runtime(
+                        tenant_id=current_user.current_tenant_id,
+                        agent_tool=agent_tool_entity,
+                        agent_callback=None
+                    )
+                except Exception as e:
+                    continue
+            
+            manager = ToolParameterConfigurationManager(
+                tenant_id=current_user.current_tenant_id,
+                tool_runtime=tool_runtime,
+                provider_name=agent_tool_entity.provider_id,
+                provider_type=agent_tool_entity.provider_type,
+            )
+            manager.delete_tool_parameters_cache()
+
+            # override parameters if it equals to masked parameters
+            if agent_tool_entity.tool_parameters:
+                if key not in masked_parameter_map:
+                    continue
+
+                if agent_tool_entity.tool_parameters == masked_parameter_map[key]:
+                    agent_tool_entity.tool_parameters = parameter_map[key]
+
+            # encrypt parameters
+            if agent_tool_entity.tool_parameters:
+                tool['tool_parameters'] = manager.encrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+
+        # update app model config
+        new_app_model_config.agent_mode = json.dumps(agent_mode)
+
         db.session.add(new_app_model_config)
         db.session.flush()
 

api/controllers/console/workspace/tool_providers.py

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

api/controllers/service_api/app/audio.py

@@ -44,7 +44,7 @@ class AudioApi(Resource):
             response = AudioService.transcript_asr(
                 tenant_id=app_model.tenant_id,
                 file=file,
-                end_user=end_user
+                end_user=end_user.get_id()
             )
 
             return response
@@ -75,7 +75,7 @@ class AudioApi(Resource):
 
 
 class TextApi(Resource):
-    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
     def post(self, app_model: App, end_user: EndUser):
         parser = reqparse.RequestParser()
         parser.add_argument('text', type=str, required=True, nullable=False, location='json')
@@ -86,8 +86,8 @@ class TextApi(Resource):
             response = AudioService.transcript_tts(
                 tenant_id=app_model.tenant_id,
                 text=args['text'],
-                end_user=end_user,
-                voice=args['voice'] if args['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
+                end_user=end_user.get_id(),
+                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=args['streaming']
             )
 

api/controllers/service_api/dataset/segment.py

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

api/core/app_runner/assistant_app_runner.py

@@ -195,6 +195,10 @@ class AssistantApplicationRunner(AppRunner):
         if set([ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL]).intersection(model_schema.features or []):
             agent_entity.strategy = AgentEntity.Strategy.FUNCTION_CALLING
 
+        db.session.refresh(conversation)
+        db.session.refresh(message)
+        db.session.close()
+
         # start agent runner
         if agent_entity.strategy == AgentEntity.Strategy.CHAIN_OF_THOUGHT:
             assistant_cot_runner = AssistantCotApplicationRunner(

api/core/app_runner/basic_app_runner.py

@@ -192,6 +192,8 @@ class BasicApplicationRunner(AppRunner):
             model=app_orchestration_config.model_config.model
         )
 
+        db.session.close()
+
         invoke_result = model_instance.invoke_llm(
             prompt_messages=prompt_messages,
             model_parameters=app_orchestration_config.model_config.parameters,

api/core/app_runner/generate_task_pipeline.py

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

api/core/application_manager.py

@@ -35,7 +35,7 @@ from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotIni
 from core.file.file_obj import FileObj
 from core.model_runtime.entities.message_entities import PromptMessageRole
 from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
+from core.model_runtime.errors.invoke import InvokeAuthorizationError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.prompt.prompt_template import PromptTemplateParser
 from core.provider_manager import ProviderManager
@@ -195,13 +195,11 @@ class ApplicationManager:
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
-            except (ValueError, InvokeError) as e:
-                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
                 logger.exception("Unknown Error when generating")
                 queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             finally:
-                db.session.remove()
+                db.session.close()
 
     def _handle_response(self, application_generate_entity: ApplicationGenerateEntity,
                          queue_manager: ApplicationQueueManager,
@@ -233,8 +231,6 @@ class ApplicationManager:
             else:
                 logger.exception(e)
                 raise e
-        finally:
-            db.session.remove()
 
     def _convert_from_app_model_config_dict(self, tenant_id: str, app_model_config_dict: dict) \
             -> AppOrchestrationConfigEntity:
@@ -651,6 +647,7 @@ class ApplicationManager:
 
             db.session.add(conversation)
             db.session.commit()
+            db.session.refresh(conversation)
         else:
             conversation = (
                 db.session.query(Conversation)
@@ -689,6 +686,7 @@ class ApplicationManager:
 
         db.session.add(message)
         db.session.commit()
+        db.session.refresh(message)
 
         for file in application_generate_entity.files:
             message_file = MessageFile(

api/core/extension/extensible.py

@@ -3,11 +3,12 @@ import importlib.util
 import json
 import logging
 import os
-from collections import OrderedDict
 from typing import Any, Optional
 
 from pydantic import BaseModel
 
+from core.utils.position_helper import sort_to_dict_by_position_map
+
 
 class ExtensionModule(enum.Enum):
     MODERATION = 'moderation'
@@ -36,7 +37,8 @@ class Extensible:
 
     @classmethod
     def scan_extensions(cls):
-        extensions = {}
+        extensions: list[ModuleExtension] = []
+        position_map = {}
 
         # get the path of the current class
         current_path = os.path.abspath(cls.__module__.replace(".", os.path.sep) + '.py')
@@ -63,6 +65,7 @@ class Extensible:
                     if os.path.exists(builtin_file_path):
                         with open(builtin_file_path, encoding='utf-8') as f:
                             position = int(f.read().strip())
+                position_map[extension_name] = position
 
                 if (extension_name + '.py') not in file_names:
                     logging.warning(f"Missing {extension_name}.py file in {subdir_path}, Skip.")
@@ -96,16 +99,15 @@ class Extensible:
                         with open(json_path, encoding='utf-8') as f:
                             json_data = json.load(f)
 
-                extensions[extension_name] = ModuleExtension(
+                extensions.append(ModuleExtension(
                     extension_class=extension_class,
                     name=extension_name,
                     label=json_data.get('label'),
                     form_schema=json_data.get('form_schema'),
                     builtin=builtin,
                     position=position
-                )
+                ))
 
-        sorted_items = sorted(extensions.items(), key=lambda x: (x[1].position is None, x[1].position))
-        sorted_extensions = OrderedDict(sorted_items)
+        sorted_extensions = sort_to_dict_by_position_map(position_map, extensions, lambda x: x.name)
 
         return sorted_extensions

api/core/features/assistant_base_runner.py

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

api/core/features/assistant_cot_runner.py

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

api/core/helper/tool_parameter_cache.py

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

api/core/hosting_configuration.py

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

api/core/indexing_runner.py

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

api/core/memory/token_buffer_memory.py

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

api/core/model_runtime/entities/model_entities.py

@@ -17,7 +17,7 @@ class ModelType(Enum):
     SPEECH2TEXT = "speech2text"
     MODERATION = "moderation"
     TTS = "tts"
-    # TEXT2IMG = "text2img"
+    TEXT2IMG = "text2img"
 
     @classmethod
     def value_of(cls, origin_model_type: str) -> "ModelType":
@@ -36,6 +36,8 @@ class ModelType(Enum):
             return cls.SPEECH2TEXT
         elif origin_model_type == 'tts' or origin_model_type == cls.TTS.value:
             return cls.TTS
+        elif origin_model_type == 'text2img' or origin_model_type == cls.TEXT2IMG.value:
+            return cls.TEXT2IMG
         elif origin_model_type == cls.MODERATION.value:
             return cls.MODERATION
         else:
@@ -59,10 +61,11 @@ class ModelType(Enum):
             return 'tts'
         elif self == self.MODERATION:
             return 'moderation'
+        elif self == self.TEXT2IMG:
+            return 'text2img'
         else:
             raise ValueError(f'invalid model type {self}')
 
-
 class FetchFrom(Enum):
     """
     Enum class for fetch from.
@@ -130,7 +133,7 @@ class ModelPropertyKey(Enum):
     DEFAULT_VOICE = "default_voice"
     VOICES = "voices"
     WORD_LIMIT = "word_limit"
-    AUDOI_TYPE = "audio_type"
+    AUDIO_TYPE = "audio_type"
     MAX_WORKERS = "max_workers"
 
 

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

@@ -18,6 +18,7 @@ from core.model_runtime.entities.model_entities import (
 )
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
+from core.utils.position_helper import get_position_map, sort_by_position_map
 
 
 class AIModel(ABC):
@@ -148,15 +149,7 @@ class AIModel(ABC):
         ]
 
         # get _position.yaml file path
-        position_file_path = os.path.join(provider_model_type_path, '_position.yaml')
-
-        # read _position.yaml file
-        position_map = {}
-        if os.path.exists(position_file_path):
-            with open(position_file_path, encoding='utf-8') as f:
-                positions = yaml.safe_load(f)
-                # convert list to dict with key as model provider name, value as index
-                position_map = {position: index for index, position in enumerate(positions)}
+        position_map = get_position_map(provider_model_type_path)
 
         # traverse all model_schema_yaml_paths
         for model_schema_yaml_path in model_schema_yaml_paths:
@@ -206,8 +199,7 @@ class AIModel(ABC):
             model_schemas.append(model_schema)
 
         # resort model schemas by position
-        if position_map:
-            model_schemas.sort(key=lambda x: position_map.get(x.model, 999))
+        model_schemas = sort_by_position_map(position_map, model_schemas, lambda x: x.model)
 
         # cache model schemas
         self.model_schemas = model_schemas

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

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

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

@@ -94,8 +94,8 @@ class TTSModel(AIModel):
         """
         model_schema = self.get_model_schema(model, credentials)
 
-        if model_schema and ModelPropertyKey.AUDOI_TYPE in model_schema.model_properties:
-            return model_schema.model_properties[ModelPropertyKey.AUDOI_TYPE]
+        if model_schema and ModelPropertyKey.AUDIO_TYPE in model_schema.model_properties:
+            return model_schema.model_properties[ModelPropertyKey.AUDIO_TYPE]
 
     def _get_model_word_limit(self, model: str, credentials: dict) -> int:
         """

api/core/model_runtime/model_providers/_position.yaml

@@ -2,11 +2,13 @@
 - anthropic
 - azure_openai
 - google
+- nvidia
 - cohere
 - bedrock
 - togetherai
 - ollama
 - mistralai
+- groq
 - replicate
 - huggingface_hub
 - zhipuai
@@ -19,6 +21,7 @@
 - jina
 - chatglm
 - xinference
+- yi
 - openllm
 - localai
 - openai_api_compatible

api/core/model_runtime/model_providers/anthropic/llm/claude-3-haiku-20240307.yaml

@@ -0,0 +1,37 @@
+model: claude-3-haiku-20240307
+label:
+  en_US: claude-3-haiku-20240307
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.25'
+  output: '1.25'
+  unit: '0.000001'
+  currency: USD

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

@@ -342,12 +342,20 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         Convert prompt messages to dict list and system
         """
         system = ""
-        prompt_message_dicts = []
-
+        first_loop = True
         for message in prompt_messages:
             if isinstance(message, SystemPromptMessage):
-                system += message.content + ("\n" if not system else "")
-            else:
+                message.content=message.content.strip()
+                if first_loop:
+                    system=message.content
+                    first_loop=False
+                else:
+                    system+="\n"
+                    system+=message.content
+
+        prompt_message_dicts = []
+        for message in prompt_messages:
+            if not isinstance(message, SystemPromptMessage):
                 prompt_message_dicts.append(self._convert_prompt_message_to_dict(message))
 
         return system, prompt_message_dicts
@@ -424,8 +432,25 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         if isinstance(message, UserPromptMessage):
             message_text = f"{human_prompt} {content}"
+            if not isinstance(message.content, list):
+                message_text = f"{ai_prompt} {content}"
+            else:
+                message_text = ""
+                for sub_message in message.content:
+                    if sub_message.type == PromptMessageContentType.TEXT:
+                        message_text += f"{human_prompt} {sub_message.data}"
+                    elif sub_message.type == PromptMessageContentType.IMAGE:
+                        message_text += f"{human_prompt} [IMAGE]"
         elif isinstance(message, AssistantPromptMessage):
-            message_text = f"{ai_prompt} {content}"
+            if not isinstance(message.content, list):
+                message_text = f"{ai_prompt} {content}"
+            else:
+                message_text = ""
+                for sub_message in message.content:
+                    if sub_message.type == PromptMessageContentType.TEXT:
+                        message_text += f"{ai_prompt} {sub_message.data}"
+                    elif sub_message.type == PromptMessageContentType.IMAGE:
+                        message_text += f"{ai_prompt} [IMAGE]"
         elif isinstance(message, SystemPromptMessage):
             message_text = content
         else:

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

@@ -15,10 +15,11 @@ from core.model_runtime.model_providers.azure_openai._constant import AZURE_OPEN
 class _CommonAzureOpenAI:
     @staticmethod
     def _to_credential_kwargs(credentials: dict) -> dict:
+        api_version = credentials.get('openai_api_version', AZURE_OPENAI_API_VERSION)
         credentials_kwargs = {
             "api_key": credentials['openai_api_key'],
             "azure_endpoint": credentials['openai_api_base'],
-            "api_version": AZURE_OPENAI_API_VERSION,
+            "api_version": api_version,
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "max_retries": 1,
         }

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

@@ -14,8 +14,7 @@ from core.model_runtime.entities.model_entities import (
     PriceConfig,
 )
 
-AZURE_OPENAI_API_VERSION = '2023-12-01-preview'
-
+AZURE_OPENAI_API_VERSION = '2024-02-15-preview'
 
 def _get_max_tokens(default: int, min_val: int, max_val: int) -> ParameterRule:
     rule = ParameterRule(
@@ -124,6 +123,65 @@ LLM_BASE_MODELS = [
             )
         )
     ),
+    AzureBaseModel(
+        base_model_name='gpt-35-turbo-0125',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label',
+            ),
+            model_type=ModelType.LLM,
+            features=[
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
+            ],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 16385,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name='temperature',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name='top_p',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name='presence_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name='frequency_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                ParameterRule(
+                    name='response_format',
+                    label=I18nObject(
+                        zh_Hans='回复格式',
+                        en_US='response_format'
+                    ),
+                    type='string',
+                    help=I18nObject(
+                        zh_Hans='指定模型必须输出的格式',
+                        en_US='specifying the format that the model must output'
+                    ),
+                    required=False,
+                    options=['text', 'json_object']
+                ),
+            ],
+            pricing=PriceConfig(
+                input=0.0005,
+                output=0.0015,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
     AzureBaseModel(
         base_model_name='gpt-4',
         entity=AIModelEntity(
@@ -274,6 +332,81 @@ LLM_BASE_MODELS = [
             )
         )
     ),
+    AzureBaseModel(
+        base_model_name='gpt-4-0125-preview',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label',
+            ),
+            model_type=ModelType.LLM,
+            features=[
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
+            ],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name='temperature',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name='top_p',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name='presence_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name='frequency_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                ParameterRule(
+                    name='seed',
+                    label=I18nObject(
+                        zh_Hans='种子',
+                        en_US='Seed'
+                    ),
+                    type='int',
+                    help=I18nObject(
+                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
+                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
+                    ),
+                    required=False,
+                    precision=2,
+                    min=0,
+                    max=1,
+                ),
+                ParameterRule(
+                    name='response_format',
+                    label=I18nObject(
+                        zh_Hans='回复格式',
+                        en_US='response_format'
+                    ),
+                    type='string',
+                    help=I18nObject(
+                        zh_Hans='指定模型必须输出的格式',
+                        en_US='specifying the format that the model must output'
+                    ),
+                    required=False,
+                    options=['text', 'json_object']
+                ),
+            ],
+            pricing=PriceConfig(
+                input=0.01,
+                output=0.03,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
     AzureBaseModel(
         base_model_name='gpt-4-1106-preview',
         entity=AIModelEntity(
@@ -524,5 +657,172 @@ EMBEDDING_BASE_MODELS = [
                 currency='USD',
             )
         )
+    ),
+    AzureBaseModel(
+        base_model_name='text-embedding-3-small',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TEXT_EMBEDDING,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: 8191,
+                ModelPropertyKey.MAX_CHUNKS: 32,
+            },
+            pricing=PriceConfig(
+                input=0.00002,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
+    AzureBaseModel(
+        base_model_name='text-embedding-3-large',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TEXT_EMBEDDING,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: 8191,
+                ModelPropertyKey.MAX_CHUNKS: 32,
+            },
+            pricing=PriceConfig(
+                input=0.00013,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    )
+]
+SPEECH2TEXT_BASE_MODELS = [
+    AzureBaseModel(
+        base_model_name='whisper-1',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.SPEECH2TEXT,
+            model_properties={
+                ModelPropertyKey.FILE_UPLOAD_LIMIT: 25,
+                ModelPropertyKey.SUPPORTED_FILE_EXTENSIONS: 'flac,mp3,mp4,mpeg,mpga,m4a,ogg,wav,webm'
+            }
+        )
+    )
+]
+TTS_BASE_MODELS = [
+    AzureBaseModel(
+        base_model_name='tts-1',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TTS,
+            model_properties={
+                ModelPropertyKey.DEFAULT_VOICE: 'alloy',
+                ModelPropertyKey.VOICES: [
+                    {
+                        'mode': 'alloy',
+                        'name': 'Alloy',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'echo',
+                        'name': 'Echo',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'fable',
+                        'name': 'Fable',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'onyx',
+                        'name': 'Onyx',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'nova',
+                        'name': 'Nova',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'shimmer',
+                        'name': 'Shimmer',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                ],
+                ModelPropertyKey.WORD_LIMIT: 120,
+                ModelPropertyKey.AUDIO_TYPE: 'mp3',
+                ModelPropertyKey.MAX_WORKERS: 5
+            },
+            pricing=PriceConfig(
+                input=0.015,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
+    AzureBaseModel(
+        base_model_name='tts-1-hd',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label'
+            ),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.TTS,
+            model_properties={
+                ModelPropertyKey.DEFAULT_VOICE: 'alloy',
+                ModelPropertyKey.VOICES: [
+                    {
+                        'mode': 'alloy',
+                        'name': 'Alloy',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'echo',
+                        'name': 'Echo',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'fable',
+                        'name': 'Fable',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'onyx',
+                        'name': 'Onyx',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'nova',
+                        'name': 'Nova',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                    {
+                        'mode': 'shimmer',
+                        'name': 'Shimmer',
+                        'language': ['zh-Hans', 'en-US', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'th-TH', 'id-ID']
+                    },
+                ],
+                ModelPropertyKey.WORD_LIMIT: 120,
+                ModelPropertyKey.AUDIO_TYPE: 'mp3',
+                ModelPropertyKey.MAX_WORKERS: 5
+            },
+            pricing=PriceConfig(
+                input=0.03,
+                unit=0.001,
+                currency='USD',
+            )
+        )
     )
 ]

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

@@ -15,6 +15,8 @@ help:
 supported_model_types:
   - llm
   - text-embedding
+  - speech2text
+  - tts
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -44,6 +46,22 @@ model_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API key here
+    - variable: openai_api_version
+      label:
+        zh_Hans: API 版本
+        en_US: API Version
+      type: select
+      required: true
+      options:
+        - label:
+            en_US: 2024-02-15-preview
+          value: 2024-02-15-preview
+        - label:
+            en_US: 2023-12-01-preview
+          value: 2023-12-01-preview
+      placeholder:
+        zh_Hans: 在此选择您的 API 版本
+        en_US: Select your API Version here
     - variable: base_model_name
       label:
         en_US: Base Model
@@ -57,6 +75,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-35-turbo-0125
+          value: gpt-35-turbo-0125
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-35-turbo-16k
           value: gpt-35-turbo-16k
@@ -75,6 +99,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-4-0125-preview
+          value: gpt-4-0125-preview
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-4-1106-preview
           value: gpt-4-1106-preview
@@ -99,6 +129,36 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: text-embedding
+        - label:
+            en_US: text-embedding-3-small
+          value: text-embedding-3-small
+          show_on:
+            - variable: __model_type
+              value: text-embedding
+        - label:
+            en_US: text-embedding-3-large
+          value: text-embedding-3-large
+          show_on:
+            - variable: __model_type
+              value: text-embedding
+        - label:
+            en_US: whisper-1
+          value: whisper-1
+          show_on:
+            - variable: __model_type
+              value: speech2text
+        - label:
+            en_US: tts-1
+          value: tts-1
+          show_on:
+            - variable: __model_type
+              value: tts
+        - label:
+            en_US: tts-1-hd
+          value: tts-1-hd
+          show_on:
+            - variable: __model_type
+              value: tts
       placeholder:
         zh_Hans: 在此输入您的模型版本
         en_US: Enter your model version

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

@@ -0,0 +1,82 @@
+import copy
+from typing import IO, Optional
+
+from openai import AzureOpenAI
+
+from core.model_runtime.entities.model_entities import AIModelEntity
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
+from core.model_runtime.model_providers.azure_openai._common import _CommonAzureOpenAI
+from core.model_runtime.model_providers.azure_openai._constant import SPEECH2TEXT_BASE_MODELS, AzureBaseModel
+
+
+class AzureOpenAISpeech2TextModel(_CommonAzureOpenAI, Speech2TextModel):
+    """
+    Model class for OpenAI Speech to text model.
+    """
+
+    def _invoke(self, model: str, credentials: dict,
+                file: IO[bytes], user: Optional[str] = None) \
+            -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :param user: unique user id
+        :return: text for given audio file
+        """
+        return self._speech2text_invoke(model, credentials, file)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            audio_file_path = self._get_demo_file_path()
+
+            with open(audio_file_path, 'rb') as audio_file:
+                self._speech2text_invoke(model, credentials, audio_file)
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _speech2text_invoke(self, model: str, credentials: dict, file: IO[bytes]) -> str:
+        """
+        Invoke speech2text model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param file: audio file
+        :return: text for given audio file
+        """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+
+        # init model client
+        client = AzureOpenAI(**credentials_kwargs)
+
+        response = client.audio.transcriptions.create(model=model, file=file)
+
+        return response.text
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
+        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        return ai_model_entity.entity
+
+
+    @staticmethod
+    def _get_ai_model_entity(base_model_name: str, model: str) -> AzureBaseModel:
+        for ai_model_entity in SPEECH2TEXT_BASE_MODELS:
+            if ai_model_entity.base_model_name == base_model_name:
+                ai_model_entity_copy = copy.deepcopy(ai_model_entity)
+                ai_model_entity_copy.entity.model = model
+                ai_model_entity_copy.entity.label.en_US = model
+                ai_model_entity_copy.entity.label.zh_Hans = model
+                return ai_model_entity_copy
+
+        return None

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

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

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

@@ -108,7 +108,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         try:
             response = post(url, headers=headers, data=dumps(data))
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             try:
@@ -124,7 +124,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
             elif err == 'insufficient_quota':
                 raise InsufficientAccountBalance(msg)
             elif err == 'invalid_authentication':
-                raise InvalidAuthenticationError(msg)
+                raise InvalidAuthenticationError(msg) 
             elif err and 'rate' in err:
                 raise RateLimitReachedError(msg)
             elif err and 'internal' in err:

api/core/model_runtime/model_providers/bedrock/bedrock.py

@@ -17,10 +17,9 @@ class BedrockProvider(ModelProvider):
         """
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
-
-            # Use `gemini-pro` model for validate,
+            bedrock_validate_model_name = credentials.get('model_for_validation', 'amazon.titan-text-lite-v1')
             model_instance.validate_credentials(
-                model='amazon.titan-text-lite-v1',
+                model=bedrock_validate_model_name,
                 credentials=credentials
             )
         except CredentialsValidateFailedError as ex:

api/core/model_runtime/model_providers/bedrock/bedrock.yaml

@@ -48,24 +48,33 @@ provider_credential_schema:
         - value: us-east-1
           label:
             en_US: US East (N. Virginia)
-            zh_Hans: US East (N. Virginia)
+            zh_Hans: 美国东部 (弗吉尼亚北部)
         - value: us-west-2
           label:
             en_US: US West (Oregon)
-            zh_Hans: US West (Oregon)
+            zh_Hans: 美国西部 (俄勒冈州)
         - value: ap-southeast-1
           label:
             en_US: Asia Pacific (Singapore)
-            zh_Hans: Asia Pacific (Singapore)
+            zh_Hans: 亚太地区 (新加坡)
         - value: ap-northeast-1
           label:
             en_US: Asia Pacific (Tokyo)
-            zh_Hans: Asia Pacific (Tokyo)
+            zh_Hans: 亚太地区 (东京)
         - value: eu-central-1
           label:
             en_US: Europe (Frankfurt)
-            zh_Hans: Europe (Frankfurt)
+            zh_Hans: 欧洲 (法兰克福)
         - value: us-gov-west-1
           label:
             en_US: AWS GovCloud (US-West)
             zh_Hans: AWS GovCloud (US-West)
+    - variable: model_for_validation
+      required: false
+      label:
+        en_US: Available Model Name
+        zh_Hans: 可用模型名称
+      type: text-input
+      placeholder:
+        en_US: A model you have access to (e.g. amazon.titan-text-lite-v1) for validation.
+        zh_Hans: 为了进行验证，请输入一个您可用的模型名称 (例如：amazon.titan-text-lite-v1)

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

@@ -4,6 +4,8 @@
 - anthropic.claude-v1
 - anthropic.claude-v2
 - anthropic.claude-v2:1
+- anthropic.claude-3-sonnet-v1:0
+- anthropic.claude-3-haiku-v1:0
 - cohere.command-light-text-v14
 - cohere.command-text-v14
 - meta.llama2-13b-chat-v1

api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-haiku-v1.yaml

@@ -0,0 +1,57 @@
+model: anthropic.claude-3-haiku-20240307-v1:0
+label:
+  en_US: Claude 3 Haiku
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  # docs: https://docs.anthropic.com/claude/docs/system-prompts
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.003'
+  output: '0.015'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.yaml

@@ -0,0 +1,56 @@
+model: anthropic.claude-3-sonnet-20240229-v1:0
+label:
+  en_US: Claude 3 Sonnet
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
+      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
+  - name: temperature
+    use_template: temperature
+    required: false
+    type: float
+    default: 1
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 生成内容的随机性。
+      en_US: The amount of randomness injected into the response.
+  - name: top_p
+    required: false
+    type: float
+    default: 0.999
+    min: 0.000
+    max: 1.000
+    help:
+      zh_Hans: 在核采样中，Anthropic Claude 按概率递减顺序计算每个后续标记的所有选项的累积分布，并在达到 top_p 指定的特定概率时将其切断。您应该更改温度或top_p，但不能同时更改两者。
+      en_US: In nucleus sampling, Anthropic Claude computes the cumulative distribution over all the options for each subsequent token in decreasing probability order and cuts it off once it reaches a particular probability specified by top_p. You should alter either temperature or top_p, but not both.
+  - name: top_k
+    required: false
+    type: int
+    default: 0
+    min: 0
+    # tip docs from aws has error, max value is 500
+    max: 500
+    help:
+      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
+      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
+pricing:
+  input: '0.00025'
+  output: '0.00125'
+  unit: '0.001'
+  currency: USD

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

@@ -1,9 +1,22 @@
+import base64
 import json
 import logging
+import mimetypes
+import time
 from collections.abc import Generator
-from typing import Optional, Union
+from typing import Optional, Union, cast
 
 import boto3
+import requests
+from anthropic import AnthropicBedrock, Stream
+from anthropic.types import (
+    ContentBlockDeltaEvent,
+    Message,
+    MessageDeltaEvent,
+    MessageStartEvent,
+    MessageStopEvent,
+    MessageStreamEvent,
+)
 from botocore.config import Config
 from botocore.exceptions import (
     ClientError,
@@ -13,14 +26,18 @@ from botocore.exceptions import (
     UnknownServiceError,
 )
 
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
+    ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
+    TextPromptMessageContent,
     UserPromptMessage,
 )
+from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.errors.invoke import (
     InvokeAuthorizationError,
     InvokeBadRequestError,
@@ -54,9 +71,293 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param user: unique user id
         :return: full response or stream response chunk generator result
         """
+
+        # invoke claude 3 models via anthropic official SDK
+        if "anthropic.claude-3" in model:
+            return self._invoke_claude3(model, credentials, prompt_messages, model_parameters, stop, stream, user)
         # invoke model
         return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
 
+    def _invoke_claude3(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
+                stop: Optional[list[str]] = None, stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+        """
+        Invoke Claude3 large language model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param stop: stop words
+        :param stream: is stream response
+        :return: full response or stream response chunk generator result
+        """
+        # use Anthropic official SDK references
+        # - https://docs.anthropic.com/claude/reference/claude-on-amazon-bedrock
+        # - https://github.com/anthropics/anthropic-sdk-python
+        client = AnthropicBedrock(
+            aws_access_key=credentials["aws_access_key_id"],
+            aws_secret_key=credentials["aws_secret_access_key"],
+            aws_region=credentials["aws_region"],
+        )
+
+        extra_model_kwargs = {}
+        if stop:
+            extra_model_kwargs['stop_sequences'] = stop
+
+        # Notice: If you request the current version of the SDK to the bedrock server,
+        #         you will get the following error message and you need to wait for the service or SDK to be updated.
+        #         Response:  Error code: 400
+        #                    {'message': 'Malformed input request: #: subject must not be valid against schema
+        #                        {"required":["messages"]}#: extraneous key [metadata] is not permitted, please reformat your input and try again.'}
+        # TODO: Open in the future when the interface is properly supported
+        # if user:
+            # ref: https://github.com/anthropics/anthropic-sdk-python/blob/e84645b07ca5267066700a104b4d8d6a8da1383d/src/anthropic/resources/messages.py#L465
+            # extra_model_kwargs['metadata'] = message_create_params.Metadata(user_id=user)
+
+        system, prompt_message_dicts = self._convert_claude3_prompt_messages(prompt_messages)
+
+        if system:
+            extra_model_kwargs['system'] = system
+
+        response = client.messages.create(
+            model=model,
+            messages=prompt_message_dicts,
+            stream=stream,
+            **model_parameters,
+            **extra_model_kwargs
+        )
+
+        if stream:
+            return self._handle_claude3_stream_response(model, credentials, response, prompt_messages)
+
+        return self._handle_claude3_response(model, credentials, response, prompt_messages)
+
+    def _handle_claude3_response(self, model: str, credentials: dict, response: Message,
+                                prompt_messages: list[PromptMessage]) -> LLMResult:
+        """
+        Handle llm chat response
+
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :return: full response chunk generator result
+        """
+
+        # transform assistant message to prompt message
+        assistant_prompt_message = AssistantPromptMessage(
+            content=response.content[0].text
+        )
+
+        # calculate num tokens
+        if response.usage:
+            # transform usage
+            prompt_tokens = response.usage.input_tokens
+            completion_tokens = response.usage.output_tokens
+        else:
+            # calculate num tokens
+            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+
+        # transform usage
+        usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+        # transform response
+        response = LLMResult(
+            model=response.model,
+            prompt_messages=prompt_messages,
+            message=assistant_prompt_message,
+            usage=usage
+        )
+
+        return response
+
+    def _handle_claude3_stream_response(self, model: str, credentials: dict, response: Stream[MessageStreamEvent],
+                                        prompt_messages: list[PromptMessage], ) -> Generator:
+        """
+        Handle llm chat stream response
+
+        :param model: model name
+        :param credentials: credentials
+        :param response: response
+        :param prompt_messages: prompt messages
+        :return: full response or stream response chunk generator result
+        """
+
+        try:
+            full_assistant_content = ''
+            return_model = None
+            input_tokens = 0
+            output_tokens = 0
+            finish_reason = None
+            index = 0
+
+            for chunk in response:
+                if isinstance(chunk, MessageStartEvent):
+                    return_model = chunk.message.model
+                    input_tokens = chunk.message.usage.input_tokens
+                elif isinstance(chunk, MessageDeltaEvent):
+                    output_tokens = chunk.usage.output_tokens
+                    finish_reason = chunk.delta.stop_reason
+                elif isinstance(chunk, MessageStopEvent):
+                    usage = self._calc_response_usage(model, credentials, input_tokens, output_tokens)
+                    yield LLMResultChunk(
+                        model=return_model,
+                        prompt_messages=prompt_messages,
+                        delta=LLMResultChunkDelta(
+                            index=index + 1,
+                            message=AssistantPromptMessage(
+                                content=''
+                            ),
+                            finish_reason=finish_reason,
+                            usage=usage
+                        )
+                    )
+                elif isinstance(chunk, ContentBlockDeltaEvent):
+                    chunk_text = chunk.delta.text if chunk.delta.text else ''
+                    full_assistant_content += chunk_text
+                    assistant_prompt_message = AssistantPromptMessage(
+                        content=chunk_text if chunk_text else '',
+                    )
+                    index = chunk.index
+                    yield LLMResultChunk(
+                        model=model,
+                        prompt_messages=prompt_messages,
+                        delta=LLMResultChunkDelta(
+                            index=index,
+                            message=assistant_prompt_message,
+                        )
+                    )
+        except Exception as ex:
+            raise InvokeError(str(ex))
+
+    def _calc_claude3_response_usage(self, model: str, credentials: dict, prompt_tokens: int, completion_tokens: int) -> LLMUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_tokens: prompt tokens
+        :param completion_tokens: completion tokens
+        :return: usage
+        """
+        # get prompt price info
+        prompt_price_info = self.get_price(
+            model=model,
+            credentials=credentials,
+            price_type=PriceType.INPUT,
+            tokens=prompt_tokens,
+        )
+
+        # get completion price info
+        completion_price_info = self.get_price(
+            model=model,
+            credentials=credentials,
+            price_type=PriceType.OUTPUT,
+            tokens=completion_tokens
+        )
+
+        # transform usage
+        usage = LLMUsage(
+            prompt_tokens=prompt_tokens,
+            prompt_unit_price=prompt_price_info.unit_price,
+            prompt_price_unit=prompt_price_info.unit,
+            prompt_price=prompt_price_info.total_amount,
+            completion_tokens=completion_tokens,
+            completion_unit_price=completion_price_info.unit_price,
+            completion_price_unit=completion_price_info.unit,
+            completion_price=completion_price_info.total_amount,
+            total_tokens=prompt_tokens + completion_tokens,
+            total_price=prompt_price_info.total_amount + completion_price_info.total_amount,
+            currency=prompt_price_info.currency,
+            latency=time.perf_counter() - self.started_at
+        )
+
+        return usage
+
+    def _convert_claude3_prompt_messages(self, prompt_messages: list[PromptMessage]) -> tuple[str, list[dict]]:
+        """
+        Convert prompt messages to dict list and system
+        """
+
+        system = ""
+        first_loop = True
+        for message in prompt_messages:
+            if isinstance(message, SystemPromptMessage):
+                message.content=message.content.strip()
+                if first_loop:
+                    system=message.content
+                    first_loop=False
+                else:
+                    system+="\n"
+                    system+=message.content
+
+        prompt_message_dicts = []
+        for message in prompt_messages:
+            if not isinstance(message, SystemPromptMessage):
+                prompt_message_dicts.append(self._convert_claude3_prompt_message_to_dict(message))
+
+        return system, prompt_message_dicts
+
+    def _convert_claude3_prompt_message_to_dict(self, message: PromptMessage) -> dict:
+        """
+        Convert PromptMessage to dict
+        """
+        if isinstance(message, UserPromptMessage):
+            message = cast(UserPromptMessage, message)
+            if isinstance(message.content, str):
+                message_dict = {"role": "user", "content": message.content}
+            else:
+                sub_messages = []
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_content = cast(TextPromptMessageContent, message_content)
+                        sub_message_dict = {
+                            "type": "text",
+                            "text": message_content.data
+                        }
+                        sub_messages.append(sub_message_dict)
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        if not message_content.data.startswith("data:"):
+                            # fetch image data from url
+                            try:
+                                image_content = requests.get(message_content.data).content
+                                mime_type, _ = mimetypes.guess_type(message_content.data)
+                                base64_data = base64.b64encode(image_content).decode('utf-8')
+                            except Exception as ex:
+                                raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
+                        else:
+                            data_split = message_content.data.split(";base64,")
+                            mime_type = data_split[0].replace("data:", "")
+                            base64_data = data_split[1]
+
+                        if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
+                            raise ValueError(f"Unsupported image type {mime_type}, "
+                                             f"only support image/jpeg, image/png, image/gif, and image/webp")
+
+                        sub_message_dict = {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": mime_type,
+                                "data": base64_data
+                            }
+                        }
+                        sub_messages.append(sub_message_dict)
+
+                message_dict = {"role": "user", "content": sub_messages}
+        elif isinstance(message, AssistantPromptMessage):
+            message = cast(AssistantPromptMessage, message)
+            message_dict = {"role": "assistant", "content": message.content}
+        elif isinstance(message, SystemPromptMessage):
+            message = cast(SystemPromptMessage, message)
+            message_dict = {"role": "system", "content": message.content}
+        else:
+            raise ValueError(f"Got unknown type {message}")
+
+        return message_dict
+
     def get_num_tokens(self, model: str, credentials: dict, messages: list[PromptMessage] | str,
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
@@ -101,7 +402,19 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :param credentials: model credentials
         :return:
         """
-        
+
+        if "anthropic.claude-3" in model:
+            try:
+                self._invoke_claude3(model=model,
+                                        credentials=credentials,
+                                        prompt_messages=[{"role": "user", "content": "ping"}],
+                                        model_parameters={},
+                                        stop=None,
+                                        stream=False)
+
+            except Exception as ex:
+                raise CredentialsValidateFailedError(str(ex))
+
         try:
             ping_message = UserPromptMessage(content="ping")
             self._generate(model=model,

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

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

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

@@ -0,0 +1,11 @@
+<svg width="112" height="24" viewBox="0 0 112 24" fill="none" xmlns="http://www.w3.org/2000/svg">
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+</svg>

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

@@ -0,0 +1,4 @@
+<svg width="24" height="24" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg">
+<rect width="24" height="24" rx="12" fill="#F55036"/>
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+</svg>

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

@@ -1,6 +1,5 @@
 from collections.abc import Generator
 from typing import cast
-from urllib.parse import urljoin
 
 from httpx import Timeout
 from openai import (
@@ -19,6 +18,7 @@ from openai import (
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion_message import FunctionCall
 from openai.types.completion import Completion
+from yarl import URL
 
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
@@ -181,7 +181,7 @@ class LocalAILarguageModel(LargeLanguageModel):
                 UserPromptMessage(content='ping')
             ], model_parameters={
                 'max_tokens': 10,
-            }, stop=[])
+            }, stop=[], stream=False)
         except Exception as ex:
             raise CredentialsValidateFailedError(f'Invalid credentials {str(ex)}')
 
@@ -227,6 +227,12 @@ class LocalAILarguageModel(LargeLanguageModel):
             )
         ]
 
+        model_properties = { 
+            ModelPropertyKey.MODE: completion_model,
+        } if completion_model else {}
+
+        model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(credentials.get('context_size', '2048'))
+
         entity = AIModelEntity(
             model=model,
             label=I18nObject(
@@ -234,7 +240,7 @@ class LocalAILarguageModel(LargeLanguageModel):
             ),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
-            model_properties={ ModelPropertyKey.MODE: completion_model } if completion_model else {},
+            model_properties=model_properties,
             parameter_rules=rules
         )
 
@@ -319,7 +325,7 @@ class LocalAILarguageModel(LargeLanguageModel):
         client_kwargs = {
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "api_key": "1",
-            "base_url": urljoin(credentials['server_url'], 'v1'),
+            "base_url": str(URL(credentials['server_url']) / 'v1'),
         }
 
         return client_kwargs

api/core/model_runtime/model_providers/localai/localai.yaml

@@ -56,3 +56,12 @@ model_credential_schema:
       placeholder:
         zh_Hans: 在此输入LocalAI的服务器地址，如 http://192.168.1.100:8080
         en_US: Enter the url of your LocalAI, e.g. http://192.168.1.100:8080
+    - variable: context_size
+      label:
+        zh_Hans: 上下文大小
+        en_US: Context size
+      placeholder:
+        zh_Hans: 输入上下文大小
+        en_US: Enter context size
+      required: false
+      type: text-input

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

@@ -1,11 +1,12 @@
 import time
 from json import JSONDecodeError, dumps
-from os.path import join
 from typing import Optional
 
 from requests import post
+from yarl import URL
 
-from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
     InvokeAuthorizationError,
@@ -57,9 +58,9 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
         }
 
         try:
-            response = post(join(url, 'embeddings'), headers=headers, data=dumps(data), timeout=10)
+            response = post(str(URL(url) / 'embeddings'), headers=headers, data=dumps(data), timeout=10)
         except Exception as e:
-            raise InvokeConnectionError(e)
+            raise InvokeConnectionError(str(e))
         
         if response.status_code != 200:
             try:
@@ -113,6 +114,27 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
             # use GPT2Tokenizer to get num tokens
             num_tokens += self._get_num_tokens_by_gpt2(text)
         return num_tokens
+    
+    def _get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        Get customizable model schema
+
+        :param model: model name
+        :param credentials: model credentials
+        :return: model schema
+        """
+        return AIModelEntity(
+            model=model,
+            label=I18nObject(zh_Hans=model, en_US=model),
+            model_type=ModelType.TEXT_EMBEDDING,
+            features=[],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', '512')),
+                ModelPropertyKey.MAX_CHUNKS: 1,
+            },
+            parameter_rules=[]
+        )
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """

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

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

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

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

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

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

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

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

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

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

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

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

api/core/model_runtime/model_providers/model_provider_factory.py

@@ -1,10 +1,8 @@
 import importlib
 import logging
 import os
-from collections import OrderedDict
 from typing import Optional
 
-import yaml
 from pydantic import BaseModel
 
 from core.model_runtime.entities.model_entities import ModelType
@@ -12,6 +10,7 @@ from core.model_runtime.entities.provider_entities import ProviderConfig, Provid
 from core.model_runtime.model_providers.__base.model_provider import ModelProvider
 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.utils.position_helper import get_position_map, sort_to_dict_by_position_map
 
 logger = logging.getLogger(__name__)
 
@@ -200,7 +199,6 @@ class ModelProviderFactory:
         if self.model_provider_extensions:
             return self.model_provider_extensions
 
-        model_providers = {}
 
         # get the path of current classes
         current_path = os.path.abspath(__file__)
@@ -215,17 +213,10 @@ class ModelProviderFactory:
         ]
 
         # get _position.yaml file path
-        position_file_path = os.path.join(model_providers_path, '_position.yaml')
-
-        # read _position.yaml file
-        position_map = {}
-        if os.path.exists(position_file_path):
-            with open(position_file_path, encoding='utf-8') as f:
-                positions = yaml.safe_load(f)
-                # convert list to dict with key as model provider name, value as index
-                position_map = {position: index for index, position in enumerate(positions)}
+        position_map = get_position_map(model_providers_path)
 
         # traverse all model_provider_dir_paths
+        model_providers: list[ModelProviderExtension] = []
         for model_provider_dir_path in model_provider_dir_paths:
             # get model_provider dir name
             model_provider_name = os.path.basename(model_provider_dir_path)
@@ -256,14 +247,13 @@ class ModelProviderFactory:
                 logger.warning(f"Missing {model_provider_name}.yaml file in {model_provider_dir_path}, Skip.")
                 continue
 
-            model_providers[model_provider_name] = ModelProviderExtension(
+            model_providers.append(ModelProviderExtension(
                 name=model_provider_name,
                 provider_instance=model_provider_class(),
                 position=position_map.get(model_provider_name)
-            )
+            ))
 
-        sorted_items = sorted(model_providers.items(), key=lambda x: (x[1].position is None, x[1].position))
-        sorted_extensions = OrderedDict(sorted_items)
+        sorted_extensions = sort_to_dict_by_position_map(position_map, model_providers, lambda x: x.name)
 
         self.model_provider_extensions = sorted_extensions
 

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

@@ -0,0 +1,3 @@
+<svg width="567" height="376" viewBox="0 0 567 376" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M58.0366 161.868C58.0366 161.868 109.261 86.2912 211.538 78.4724V51.053C98.2528 60.1511 0.152344 156.098 0.152344 156.098C0.152344 156.098 55.7148 316.717 211.538 331.426V302.282C97.1876 287.896 58.0366 161.868 58.0366 161.868ZM211.538 244.32V271.013C125.114 255.603 101.125 165.768 101.125 165.768C101.125 165.768 142.621 119.799 211.538 112.345V141.633C211.486 141.633 211.449 141.617 211.406 141.617C175.235 137.276 146.978 171.067 146.978 171.067C146.978 171.067 162.816 227.949 211.538 244.32ZM211.538 0.47998V51.053C214.864 50.7981 218.189 50.5818 221.533 50.468C350.326 46.1273 434.243 156.098 434.243 156.098C434.243 156.098 337.861 273.296 237.448 273.296C228.245 273.296 219.63 272.443 211.538 271.009V302.282C218.695 303.201 225.903 303.667 233.119 303.675C326.56 303.675 394.134 255.954 459.566 199.474C470.415 208.162 514.828 229.299 523.958 238.55C461.745 290.639 316.752 332.626 234.551 332.626C226.627 332.626 219.018 332.148 211.538 331.426V375.369H566.701V0.47998H211.538ZM211.538 112.345V78.4724C214.829 78.2425 218.146 78.0672 221.533 77.9602C314.148 75.0512 374.909 157.548 374.909 157.548C374.909 157.548 309.281 248.693 238.914 248.693C228.787 248.693 219.707 247.065 211.536 244.318V141.631C247.591 145.987 254.848 161.914 276.524 198.049L324.737 157.398C324.737 157.398 289.544 111.243 230.219 111.243C223.768 111.241 217.597 111.696 211.538 112.345Z" fill="#77B900"/>
+</svg>

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

@@ -0,0 +1,4 @@
+- google/gemma-7b
+- meta/llama2-70b
+- mistralai/mixtral-8x7b-instruct-v0.1
+- fuyu-8b

api/core/model_runtime/model_providers/nvidia/llm/fuyu-8b.yaml

@@ -0,0 +1,27 @@
+model: fuyu-8b
+label:
+  zh_Hans: fuyu-8b
+  en_US: fuyu-8b
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 16000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.2
+    min: 0.1
+    max: 1
+  - name: top_p
+    use_template: top_p
+    default: 0.7
+    min: 0.1
+    max: 1
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/nvidia/llm/gemma-7b.yaml

@@ -0,0 +1,30 @@
+model: google/gemma-7b
+label:
+  zh_Hans: google/gemma-7b
+  en_US: google/gemma-7b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 1024
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0

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

@@ -0,0 +1,30 @@
+model: meta/llama2-70b
+label:
+  zh_Hans: meta/llama2-70b
+  en_US: meta/llama2-70b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 1024
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0

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

@@ -0,0 +1,247 @@
+import json
+from collections.abc import Generator
+from typing import Optional, Union
+
+import requests
+from yarl import URL
+
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
+from core.model_runtime.entities.message_entities import (
+    PromptMessage,
+    PromptMessageContentType,
+    PromptMessageFunction,
+    PromptMessageTool,
+    UserPromptMessage,
+)
+from core.model_runtime.errors.invoke import InvokeError
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
+from core.model_runtime.utils import helper
+
+
+class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
+    MODEL_SUFFIX_MAP = {
+        'fuyu-8b': 'vlm/adept/fuyu-8b',
+        'mistralai/mixtral-8x7b-instruct-v0.1': '',
+        'google/gemma-7b': '',
+        'meta/llama2-70b': ''
+    }
+
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                stream: bool = True, user: Optional[str] = None) \
+            -> Union[LLMResult, Generator]:
+        
+        self._add_custom_parameters(credentials, model)
+        prompt_messages = self._transform_prompt_messages(prompt_messages)
+        stop = []
+        user = None
+
+        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def _transform_prompt_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+        """
+        Handle Image transform
+        """
+        for i, p in enumerate(prompt_messages):
+            if isinstance(p, UserPromptMessage) and isinstance(p.content, list):
+                content = p.content
+                content_text = ''
+                for prompt_content in content:
+                    if prompt_content.type == PromptMessageContentType.TEXT:
+                        content_text += prompt_content.data
+                    else:
+                        content_text += f' <img src="{prompt_content.data}" />'
+
+                prompt_message = UserPromptMessage(
+                    content=content_text
+                )
+                prompt_messages[i] = prompt_message
+        return prompt_messages
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials, model)
+        self._validate_credentials(model, credentials)
+
+    def _add_custom_parameters(self, credentials: dict, model: str) -> None:
+        credentials['mode'] = 'chat'
+        
+        if self.MODEL_SUFFIX_MAP[model]:
+            credentials['server_url'] = f'https://ai.api.nvidia.com/v1/{self.MODEL_SUFFIX_MAP[model]}'
+            credentials.pop('endpoint_url')
+        else:
+            credentials['endpoint_url'] = 'https://integrate.api.nvidia.com/v1'
+
+        credentials['stream_mode_delimiter'] = '\n'
+
+    def _validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials using requests to ensure compatibility with all providers following OpenAI's API standard.
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            headers = {
+                'Content-Type': 'application/json'
+            }
+
+            api_key = credentials.get('api_key')
+            if api_key:
+                headers["Authorization"] = f"Bearer {api_key}"
+
+            endpoint_url = credentials['endpoint_url'] if 'endpoint_url' in credentials else None
+            if endpoint_url and not endpoint_url.endswith('/'):
+                endpoint_url += '/'
+            server_url = credentials['server_url'] if 'server_url' in credentials else None
+
+            # prepare the payload for a simple ping to the model
+            data = {
+                'model': model,
+                'max_tokens': 5
+            }
+
+            completion_type = LLMMode.value_of(credentials['mode'])
+
+            if completion_type is LLMMode.CHAT:
+                data['messages'] = [
+                    {
+                        "role": "user",
+                        "content": "ping"
+                    },
+                ]
+                if 'endpoint_url' in credentials:
+                    endpoint_url = str(URL(endpoint_url) / 'chat' / 'completions')
+                elif 'server_url' in credentials:
+                    endpoint_url = server_url
+            elif completion_type is LLMMode.COMPLETION:
+                data['prompt'] = 'ping'
+                if 'endpoint_url' in credentials:
+                    endpoint_url = str(URL(endpoint_url) / 'completions')
+                elif 'server_url' in credentials:
+                    endpoint_url = server_url
+            else:
+                raise ValueError("Unsupported completion type for model configuration.")
+
+            # send a post request to validate the credentials
+            response = requests.post(
+                endpoint_url,
+                headers=headers,
+                json=data,
+                timeout=(10, 60)
+            )
+
+            if response.status_code != 200:
+                raise CredentialsValidateFailedError(
+                    f'Credentials validation failed with status code {response.status_code}')
+
+            try:
+                json_result = response.json()
+            except json.JSONDecodeError as e:
+                raise CredentialsValidateFailedError('Credentials validation failed: JSON decode error')
+        except CredentialsValidateFailedError:
+            raise
+        except Exception as ex:
+            raise CredentialsValidateFailedError(f'An error occurred during credentials validation: {str(ex)}')
+
+    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], model_parameters: dict,
+                  tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                  stream: bool = True, \
+                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
+        """
+        Invoke llm completion model
+
+        :param model: model name
+        :param credentials: credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+        headers = {
+            'Content-Type': 'application/json',
+            'Accept-Charset': 'utf-8',
+        }
+
+        api_key = credentials.get('api_key')
+        if api_key:
+            headers['Authorization'] = f'Bearer {api_key}'
+
+        if stream:
+            headers['Accept'] = 'text/event-stream'
+
+        endpoint_url = credentials['endpoint_url'] if 'endpoint_url' in credentials else None
+        if endpoint_url and not endpoint_url.endswith('/'):
+            endpoint_url += '/'
+        server_url = credentials['server_url'] if 'server_url' in credentials else None
+
+        data = {
+            "model": model,
+            "stream": stream,
+            **model_parameters
+        }
+
+        completion_type = LLMMode.value_of(credentials['mode'])
+
+        if completion_type is LLMMode.CHAT:
+            if 'endpoint_url' in credentials:
+                endpoint_url = str(URL(endpoint_url) / 'chat' / 'completions')
+            elif 'server_url' in credentials:
+                endpoint_url = server_url
+            data['messages'] = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
+        elif completion_type is LLMMode.COMPLETION:
+            data['prompt'] = 'ping'
+            if 'endpoint_url' in credentials:
+                endpoint_url = str(URL(endpoint_url) / 'completions')
+            elif 'server_url' in credentials:
+                endpoint_url = server_url
+        else:
+            raise ValueError("Unsupported completion type for model configuration.")
+
+
+        # annotate tools with names, descriptions, etc.
+        function_calling_type = credentials.get('function_calling_type', 'no_call')
+        formatted_tools = []
+        if tools:
+            if function_calling_type == 'function_call':
+                data['functions'] = [{
+                    "name": tool.name,
+                    "description": tool.description,
+                    "parameters": tool.parameters
+                } for tool in tools]
+            elif function_calling_type == 'tool_call':
+                data["tool_choice"] = "auto"
+
+                for tool in tools:
+                    formatted_tools.append(helper.dump_model(PromptMessageFunction(function=tool)))
+
+                data["tools"] = formatted_tools
+
+        if stop:
+            data["stop"] = stop
+
+        if user:
+            data["user"] = user
+
+        response = requests.post(
+            endpoint_url,
+            headers=headers,
+            json=data,
+            timeout=(10, 60),
+            stream=stream
+        )
+
+        if response.encoding is None or response.encoding == 'ISO-8859-1':
+            response.encoding = 'utf-8'
+
+        if not response.ok:
+            raise InvokeError(f"API request failed with status code {response.status_code}: {response.text}")
+
+        if stream:
+            return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
+
+        return self._handle_generate_response(model, credentials, response, prompt_messages)

api/core/model_runtime/model_providers/nvidia/llm/mistralai_mixtral-8x7b-instruct-v0.1.yaml

@@ -0,0 +1,30 @@
+model: mistralai/mixtral-8x7b-instruct-v0.1
+label:
+  zh_Hans: mistralai/mixtral-8x7b-instruct-v0.1
+  en_US: mistralai/mixtral-8x7b-instruct-v0.1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 1024
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0

api/core/model_runtime/model_providers/nvidia/nvidia.py

@@ -0,0 +1,30 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class MistralAIProvider(ModelProvider):
+
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+
+            model_instance.validate_credentials(
+                model='mistralai/mixtral-8x7b-instruct-v0.1',
+                credentials=credentials
+            )
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            raise ex

api/core/model_runtime/model_providers/nvidia/nvidia.yaml

@@ -0,0 +1,30 @@
+provider: nvidia
+label:
+  en_US: NVIDIA
+icon_small:
+  en_US: icon_s_en.svg
+icon_large:
+  en_US: icon_l_en.png
+background: "#FFFFFF"
+help:
+  title:
+    en_US: Get your API Key from NVIDIA
+    zh_Hans: 从 NVIDIA 获取 API Key
+  url:
+    en_US: https://build.nvidia.com/explore/discover
+supported_model_types:
+  - llm
+  - text-embedding
+  - rerank
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/nvidia/rerank/rerank-qa-mistral-4b.yaml

@@ -0,0 +1,4 @@
+model: nv-rerank-qa-mistral-4b:1
+model_type: rerank
+model_properties:
+  context_size: 8192

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

@@ -0,0 +1,112 @@
+from math import exp
+from typing import Optional
+
+import requests
+
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class NvidiaRerankModel(RerankModel):
+    """
+    Model class for NVIDIA rerank model.
+    """
+
+    def _sigmoid(self, logit: float) -> float:
+        return 1/(1+exp(-logit))
+
+    def _invoke(self, model: str, credentials: dict,
+                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
+                user: Optional[str] = None) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        try:
+            invoke_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/reranking"
+
+            headers = {
+                "Authorization": f"Bearer {credentials.get('api_key')}",
+                "Accept": "application/json",
+            }
+            payload = {
+                "model": model,
+                "query": {"text": query},
+                "passages": [{"text": doc} for doc in docs],
+            }
+
+            session = requests.Session()
+            response = session.post(invoke_url, headers=headers, json=payload)
+            response.raise_for_status()
+            results = response.json()
+
+            rerank_documents = []
+            for result in results['rankings']:
+                index = result['index']
+                logit = result['logit']
+                rerank_document = RerankDocument(
+                    index=index,
+                    text=docs[index],
+                    score=self._sigmoid(logit),
+                )
+
+                rerank_documents.append(rerank_document)
+
+            return RerankResult(model=model, docs=rerank_documents)
+        except requests.HTTPError as e:
+            raise InvokeServerUnavailableError(str(e))
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the GPU memory bandwidth of H100 SXM?",
+                docs=[
+                    "Example doc 1",
+                    "Example doc 2",
+                    "Example doc 3",
+                ],
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [requests.ConnectionError],
+            InvokeServerUnavailableError: [requests.HTTPError],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [requests.HTTPError],
+            InvokeBadRequestError: [requests.RequestException]
+        }

api/core/model_runtime/model_providers/nvidia/text_embedding/embed-qa-4.yaml

@@ -0,0 +1,5 @@
+model: NV-Embed-QA
+model_type: text-embedding
+model_properties:
+  context_size: 512
+  max_chunks: 1

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

@@ -0,0 +1,172 @@
+import time
+from json import JSONDecodeError, dumps
+from typing import Optional
+
+from requests import post
+
+from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+
+
+class NvidiaTextEmbeddingModel(TextEmbeddingModel):
+    """
+    Model class for Nvidia text embedding model.
+    """
+    api_base: str = 'https://ai.api.nvidia.com/v1/retrieval/nvidia/embeddings'
+    models: list[str] = ['NV-Embed-QA']
+
+    def _invoke(self, model: str, credentials: dict,
+                texts: list[str], user: Optional[str] = None) \
+            -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
+        api_key = credentials['api_key']
+        if model not in self.models:
+            raise InvokeBadRequestError('Invalid model name')
+        if not api_key:
+            raise CredentialsValidateFailedError('api_key is required')
+        url = self.api_base
+        headers = {
+            'Authorization': 'Bearer ' + api_key,
+            'Content-Type': 'application/json'
+        }
+
+        data = {
+            'model': model,
+            'input': texts[0], 
+            'input_type': 'query'
+        }
+
+        try:
+            response = post(url, headers=headers, data=dumps(data))
+        except Exception as e:
+            raise InvokeConnectionError(str(e))
+        
+        if response.status_code != 200:
+            try:
+                resp = response.json()
+                msg = resp['detail']
+                if response.status_code == 401:
+                    raise InvokeAuthorizationError(msg)
+                elif response.status_code == 429:
+                    raise InvokeRateLimitError(msg)
+                elif response.status_code == 500:
+                    raise InvokeServerUnavailableError(msg)
+                else:
+                    raise InvokeError(msg)
+            except JSONDecodeError as e:
+                raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
+
+        try:
+            resp = response.json()
+            embeddings = resp['data']
+            usage = resp['usage']
+        except Exception as e:
+            raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
+
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
+
+        result = TextEmbeddingResult(
+            model=model,
+            embeddings=[[
+                float(data) for data in x['embedding']
+            ] for x in embeddings],
+            usage=usage
+        )
+
+        return result
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        num_tokens = 0
+        for text in texts:
+            # use JinaTokenizer to get num tokens
+            num_tokens += self._get_num_tokens_by_gpt2(text)
+        return num_tokens
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(model=model, credentials=credentials, texts=['ping'])
+        except InvokeAuthorizationError:
+            raise CredentialsValidateFailedError('Invalid api key')
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        return {
+            InvokeConnectionError: [
+                InvokeConnectionError
+            ],
+            InvokeServerUnavailableError: [
+                InvokeServerUnavailableError
+            ],
+            InvokeRateLimitError: [
+                InvokeRateLimitError
+            ],
+            InvokeAuthorizationError: [
+                InvokeAuthorizationError
+            ],
+            InvokeBadRequestError: [
+                KeyError
+            ]
+        }
+    
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: input tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model,
+            credentials=credentials,
+            price_type=PriceType.INPUT,
+            tokens=tokens
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at
+        )
+
+        return usage

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

@@ -449,7 +449,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     help=I18nObject(en_US="The temperature of the model. "
                                           "Increasing the temperature will make the model answer "
                                           "more creatively. (Default: 0.8)"),
-                    default=0.8,
+                    default=0.1,
                     min=0,
                     max=2
                 ),
@@ -472,7 +472,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     help=I18nObject(en_US="Reduces the probability of generating nonsense. "
                                           "A higher value (e.g. 100) will give more diverse answers, "
                                           "while a lower value (e.g. 10) will be more conservative. (Default: 40)"),
-                    default=40,
                     min=1,
                     max=100
                 ),
@@ -483,7 +482,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     help=I18nObject(en_US="Sets how strongly to penalize repetitions. "
                                           "A higher value (e.g., 1.5) will penalize repetitions more strongly, "
                                           "while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)"),
-                    default=1.1,
                     min=-2,
                     max=2
                 ),
@@ -494,7 +492,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(en_US="Maximum number of tokens to predict when generating text. "
                                           "(Default: 128, -1 = infinite generation, -2 = fill context)"),
-                    default=128,
+                    default=512 if int(credentials.get('max_tokens', 4096)) >= 768 else 128,
                     min=-2,
                     max=int(credentials.get('max_tokens', 4096)),
                 ),
@@ -504,7 +502,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(en_US="Enable Mirostat sampling for controlling perplexity. "
                                           "(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)"),
-                    default=0,
                     min=0,
                     max=2
                 ),
@@ -516,7 +513,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                                           "the generated text. A lower learning rate will result in slower adjustments, "
                                           "while a higher learning rate will make the algorithm more responsive. "
                                           "(Default: 0.1)"),
-                    default=0.1,
                     precision=1
                 ),
                 ParameterRule(
@@ -525,7 +521,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     type=ParameterType.FLOAT,
                     help=I18nObject(en_US="Controls the balance between coherence and diversity of the output. "
                                           "A lower value will result in more focused and coherent text. (Default: 5.0)"),
-                    default=5.0,
                     precision=1
                 ),
                 ParameterRule(
@@ -543,7 +538,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(en_US="The number of layers to send to the GPU(s). "
                                           "On macOS it defaults to 1 to enable metal support, 0 to disable."),
-                    default=1,
                     min=0,
                     max=1
                 ),
@@ -563,7 +557,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     type=ParameterType.INT,
                     help=I18nObject(en_US="Sets how far back for the model to look back to prevent repetition. "
                                           "(Default: 64, 0 = disabled, -1 = num_ctx)"),
-                    default=64,
                     min=-1
                 ),
                 ParameterRule(
@@ -573,7 +566,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     help=I18nObject(en_US="Tail free sampling is used to reduce the impact of less probable tokens "
                                           "from the output. A higher value (e.g., 2.0) will reduce the impact more, "
                                           "while a value of 1.0 disables this setting. (default: 1)"),
-                    default=1,
                     precision=1
                 ),
                 ParameterRule(
@@ -583,7 +575,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
                     help=I18nObject(en_US="Sets the random number seed to use for generation. Setting this to "
                                           "a specific number will make the model generate the same text for "
                                           "the same prompt. (Default: 0)"),
-                    default=0
                 ),
                 ParameterRule(
                     name='format',

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

@@ -656,6 +656,8 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                 if assistant_message_function_call:
                     # start of stream function call
                     delta_assistant_message_function_call_storage = assistant_message_function_call
+                    if delta_assistant_message_function_call_storage.arguments is None:
+                        delta_assistant_message_function_call_storage.arguments = ''
                     if not has_finish_reason:
                         continue
 

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

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

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

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

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

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

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

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

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml

@@ -8,54 +8,70 @@ model_properties:
 parameter_rules:
   - name: temperature
     use_template: temperature
-    default: 1.0
+    type: float
+    default: 0.85
     min: 0.0
     max: 2.0
     help:
       zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
       en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 2000
+    min: 1
+    max: 2000
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
   - name: top_p
     use_template: top_p
+    type: float
     default: 0.8
     min: 0.1
     max: 0.9
     help:
       zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
       en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: max_tokens
-    use_template: max_tokens
-    default: 1500
-    min: 1
-    max: 6000
-    help:
-      zh_Hans: 用于限制模型生成token的数量，max_tokens设置的是生成上限，并不表示一定会生成这么多的token数量。
-      en_US: It is used to limit the number of tokens generated by the model. max_tokens sets the upper limit of generation, which does not mean that so many tokens will be generated.
   - name: top_k
+    type: int
+    min: 0
+    max: 99
     label:
       zh_Hans: 取样数量
       en_US: Top k
-    type: int
     help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。默认不传递该参数，取值为None或当top_k大于100时，表示不启用top_k策略，此时，仅有top_p策略生效。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. This parameter is not passed by default. The value is None or when top_k is greater than 100, it means that the top_k policy is not enabled. At this time, only the top_p policy takes effect.
-    required: false
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
   - name: seed
+    required: false
+    type: int
+    default: 1234
     label:
       zh_Hans: 随机种子
       en_US: Random seed
-    type: int
     help:
-      zh_Hans: 生成时，随机数的种子，用于控制模型生成的随机性。如果使用相同的种子，每次运行生成的结果都将相同；当需要复现模型的生成结果时，可以使用相同的种子。seed参数支持无符号64位整数类型。
-      en_US: When generating, the random number seed is used to control the randomness of model generation. If you use the same seed, the results generated by each run will be the same; when you need to reproduce the results of the model, you can use the same seed. The seed parameter supports unsigned 64-bit integer types.
-    required: false
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
   - name: repetition_penalty
-    label:
-      en_US: Repetition penalty
+    required: false
     type: float
     default: 1.1
+    label:
+      en_US: Repetition penalty
     help:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
-    required: false
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: enable_search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
   - name: response_format
     use_template: response_format
+pricing:
+  input: '0.12'
+  output: '0.12'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml

@@ -4,58 +4,74 @@ label:
 model_type: llm
 model_properties:
   mode: chat
-  context_size: 30000
+  context_size: 32768
 parameter_rules:
   - name: temperature
     use_template: temperature
-    default: 1.0
+    type: float
+    default: 0.85
     min: 0.0
     max: 2.0
     help:
       zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
       en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 2000
+    min: 1
+    max: 2000
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
   - name: top_p
     use_template: top_p
+    type: float
     default: 0.8
     min: 0.1
     max: 0.9
     help:
       zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
       en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: max_tokens
-    use_template: max_tokens
-    default: 2000
-    min: 1
-    max: 28000
-    help:
-      zh_Hans: 用于限制模型生成token的数量，max_tokens设置的是生成上限，并不表示一定会生成这么多的token数量。
-      en_US: It is used to limit the number of tokens generated by the model. max_tokens sets the upper limit of generation, which does not mean that so many tokens will be generated.
   - name: top_k
+    type: int
+    min: 0
+    max: 99
     label:
       zh_Hans: 取样数量
       en_US: Top k
-    type: int
     help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。默认不传递该参数，取值为None或当top_k大于100时，表示不启用top_k策略，此时，仅有top_p策略生效。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. This parameter is not passed by default. The value is None or when top_k is greater than 100, it means that the top_k policy is not enabled. At this time, only the top_p policy takes effect.
-    required: false
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
   - name: seed
+    required: false
+    type: int
+    default: 1234
     label:
       zh_Hans: 随机种子
       en_US: Random seed
-    type: int
     help:
-      zh_Hans: 生成时，随机数的种子，用于控制模型生成的随机性。如果使用相同的种子，每次运行生成的结果都将相同；当需要复现模型的生成结果时，可以使用相同的种子。seed参数支持无符号64位整数类型。
-      en_US: When generating, the random number seed is used to control the randomness of model generation. If you use the same seed, the results generated by each run will be the same; when you need to reproduce the results of the model, you can use the same seed. The seed parameter supports unsigned 64-bit integer types.
-    required: false
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
   - name: repetition_penalty
-    label:
-      en_US: Repetition penalty
+    required: false
     type: float
     default: 1.1
+    label:
+      en_US: Repetition penalty
     help:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
-    required: false
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: enable_search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
   - name: response_format
     use_template: response_format
+pricing:
+  input: '0.12'
+  output: '0.12'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml

@@ -8,54 +8,70 @@ model_properties:
 parameter_rules:
   - name: temperature
     use_template: temperature
-    default: 1.0
+    type: float
+    default: 0.85
     min: 0.0
     max: 2.0
     help:
       zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
       en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 2000
+    min: 1
+    max: 2000
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
   - name: top_p
     use_template: top_p
+    type: float
     default: 0.8
     min: 0.1
     max: 0.9
     help:
       zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
       en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: max_tokens
-    use_template: max_tokens
-    default: 1500
-    min: 1
-    max: 6000
-    help:
-      zh_Hans: 用于限制模型生成token的数量，max_tokens设置的是生成上限，并不表示一定会生成这么多的token数量。
-      en_US: It is used to limit the number of tokens generated by the model. max_tokens sets the upper limit of generation, which does not mean that so many tokens will be generated.
   - name: top_k
+    type: int
+    min: 0
+    max: 99
     label:
       zh_Hans: 取样数量
       en_US: Top k
-    type: int
     help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。默认不传递该参数，取值为None或当top_k大于100时，表示不启用top_k策略，此时，仅有top_p策略生效。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. This parameter is not passed by default. The value is None or when top_k is greater than 100, it means that the top_k policy is not enabled. At this time, only the top_p policy takes effect.
-    required: false
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
   - name: seed
+    required: false
+    type: int
+    default: 1234
     label:
       zh_Hans: 随机种子
       en_US: Random seed
-    type: int
     help:
-      zh_Hans: 生成时，随机数的种子，用于控制模型生成的随机性。如果使用相同的种子，每次运行生成的结果都将相同；当需要复现模型的生成结果时，可以使用相同的种子。seed参数支持无符号64位整数类型。
-      en_US: When generating, the random number seed is used to control the randomness of model generation. If you use the same seed, the results generated by each run will be the same; when you need to reproduce the results of the model, you can use the same seed. The seed parameter supports unsigned 64-bit integer types.
-    required: false
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
   - name: repetition_penalty
-    label:
-      en_US: Repetition penalty
+    required: false
     type: float
     default: 1.1
+    label:
+      en_US: Repetition penalty
     help:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
-    required: false
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: enable_search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
   - name: response_format
     use_template: response_format
+pricing:
+  input: '0.12'
+  output: '0.12'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml

@@ -4,58 +4,70 @@ label:
 model_type: llm
 model_properties:
   mode: completion
-  context_size: 32000
+  context_size: 32768
 parameter_rules:
   - name: temperature
     use_template: temperature
-    default: 1.0
+    type: float
+    default: 0.85
     min: 0.0
     max: 2.0
     help:
       zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
       en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 1500
+    min: 1
+    max: 1500
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
   - name: top_p
     use_template: top_p
+    type: float
     default: 0.8
     min: 0.1
     max: 0.9
     help:
       zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
       en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: max_tokens
-    use_template: max_tokens
-    default: 2000
-    min: 1
-    max: 30000
-    help:
-      zh_Hans: 用于限制模型生成token的数量，max_tokens设置的是生成上限，并不表示一定会生成这么多的token数量。
-      en_US: It is used to limit the number of tokens generated by the model. max_tokens sets the upper limit of generation, which does not mean that so many tokens will be generated.
   - name: top_k
+    type: int
+    min: 0
+    max: 99
     label:
       zh_Hans: 取样数量
       en_US: Top k
-    type: int
     help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。默认不传递该参数，取值为None或当top_k大于100时，表示不启用top_k策略，此时，仅有top_p策略生效。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. This parameter is not passed by default. The value is None or when top_k is greater than 100, it means that the top_k policy is not enabled. At this time, only the top_p policy takes effect.
-    required: false
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
   - name: seed
+    required: false
+    type: int
+    default: 1234
     label:
       zh_Hans: 随机种子
       en_US: Random seed
-    type: int
     help:
-      zh_Hans: 生成时，随机数的种子，用于控制模型生成的随机性。如果使用相同的种子，每次运行生成的结果都将相同；当需要复现模型的生成结果时，可以使用相同的种子。seed参数支持无符号64位整数类型。
-      en_US: When generating, the random number seed is used to control the randomness of model generation. If you use the same seed, the results generated by each run will be the same; when you need to reproduce the results of the model, you can use the same seed. The seed parameter supports unsigned 64-bit integer types.
-    required: false
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
   - name: repetition_penalty
-    label:
-      en_US: Repetition penalty
+    required: false
     type: float
     default: 1.1
+    label:
+      en_US: Repetition penalty
     help:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: enable_search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
   - name: response_format
     use_template: response_format
 pricing:

api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml

@@ -8,55 +8,66 @@ model_properties:
 parameter_rules:
   - name: temperature
     use_template: temperature
-    default: 1.0
+    type: float
+    default: 0.85
     min: 0.0
     max: 2.0
     help:
       zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。
       en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 1500
+    min: 1
+    max: 1500
+    help:
+      zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。
+      en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time.
   - name: top_p
     use_template: top_p
+    type: float
     default: 0.8
     min: 0.1
     max: 0.9
     help:
       zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。
       en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated.
-  - name: max_tokens
-    use_template: max_tokens
-    default: 1500
-    min: 1
-    max: 6000
-    help:
-      zh_Hans: 用于限制模型生成token的数量，max_tokens设置的是生成上限，并不表示一定会生成这么多的token数量。
-      en_US: It is used to limit the number of tokens generated by the model. max_tokens sets the upper limit of generation, which does not mean that so many tokens will be generated.
   - name: top_k
+    type: int
+    min: 0
+    max: 99
     label:
       zh_Hans: 取样数量
       en_US: Top k
-    type: int
     help:
-      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。默认不传递该参数，取值为None或当top_k大于100时，表示不启用top_k策略，此时，仅有top_p策略生效。
-      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. This parameter is not passed by default. The value is None or when top_k is greater than 100, it means that the top_k policy is not enabled. At this time, only the top_p policy takes effect.
-    required: false
+      zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。
+      en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated.
   - name: seed
+    required: false
+    type: int
+    default: 1234
     label:
       zh_Hans: 随机种子
       en_US: Random seed
-    type: int
     help:
-      zh_Hans: 生成时，随机数的种子，用于控制模型生成的随机性。如果使用相同的种子，每次运行生成的结果都将相同；当需要复现模型的生成结果时，可以使用相同的种子。seed参数支持无符号64位整数类型。
-      en_US: When generating, the random number seed is used to control the randomness of model generation. If you use the same seed, the results generated by each run will be the same; when you need to reproduce the results of the model, you can use the same seed. The seed parameter supports unsigned 64-bit integer types.
-    required: false
+      zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。
+      en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time.
   - name: repetition_penalty
-    label:
-      en_US: Repetition penalty
+    required: false
     type: float
     default: 1.1
+    label:
+      en_US: Repetition penalty
     help:
       zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。
-      en_US: Used to control the repetition of model generation. Increasing the repetition_penalty can reduce the repetition of model generation. 1.0 means no punishment.
-    required: false
+      en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment.
+  - name: enable_search
+    type: boolean
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
   - name: response_format
     use_template: response_format
 pricing:

api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml

@@ -0,0 +1,4 @@
+model: text-embedding-v1
+model_type: text-embedding
+model_properties:
+  context_size: 2048