Added background toggle functionality across multiple components (AgentflowCanvas, MarketplaceCanvas, Canvas) with new icons for enabling/disabling background.

.github/workflows/docker-image-dockerhub.yml

@@ -1,72 +0,0 @@
-name: Docker Image CI - Docker Hub
-
-on:
-    workflow_dispatch:
-        inputs:
-            node_version:
-                description: 'Node.js version to build this image with.'
-                type: choice
-                required: true
-                default: '20'
-                options:
-                    - '20'
-            tag_version:
-                description: 'Tag version of the image to be pushed.'
-                type: string
-                required: true
-                default: 'latest'
-
-jobs:
-    docker:
-        runs-on: ubuntu-latest
-        steps:
-            - name: Set default values
-              id: defaults
-              run: |
-                  echo "node_version=${{ github.event.inputs.node_version || '20' }}" >> $GITHUB_OUTPUT
-                  echo "tag_version=${{ github.event.inputs.tag_version || 'latest' }}" >> $GITHUB_OUTPUT
-
-            - name: Checkout
-              uses: actions/checkout@v4.1.1
-
-            - name: Set up QEMU
-              uses: docker/setup-qemu-action@v3.0.0
-
-            - name: Set up Docker Buildx
-              uses: docker/setup-buildx-action@v3.0.0
-
-            - name: Login to Docker Hub
-              uses: docker/login-action@v3
-              with:
-                  username: ${{ secrets.DOCKERHUB_USERNAME }}
-                  password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-            # -------------------------
-            # Build and push main image
-            # -------------------------
-            - name: Build and push main image
-              uses: docker/build-push-action@v5.3.0
-              with:
-                  context: .
-                  file: ./docker/Dockerfile
-                  build-args: |
-                      NODE_VERSION=${{ steps.defaults.outputs.node_version }}
-                  platforms: linux/amd64,linux/arm64
-                  push: true
-                  tags: |
-                      flowiseai/flowise:${{ steps.defaults.outputs.tag_version }}
-
-            # -------------------------
-            # Build and push worker image
-            # -------------------------
-            - name: Build and push worker image
-              uses: docker/build-push-action@v5.3.0
-              with:
-                  context: .
-                  file: docker/worker/Dockerfile
-                  build-args: |
-                      NODE_VERSION=${{ steps.defaults.outputs.node_version }}
-                  platforms: linux/amd64,linux/arm64
-                  push: true
-                  tags: |
-                      flowiseai/flowise-worker:${{ steps.defaults.outputs.tag_version }}

.github/workflows/docker-image-ecr.yml

@@ -1,73 +0,0 @@
-name: Docker Image CI - AWS ECR
-
-on:
-    workflow_dispatch:
-        inputs:
-            environment:
-                description: 'Environment to push the image to.'
-                required: true
-                default: 'dev'
-                type: choice
-                options:
-                    - dev
-                    - prod
-            node_version:
-                description: 'Node.js version to build this image with.'
-                type: choice
-                required: true
-                default: '20'
-                options:
-                    - '20'
-            tag_version:
-                description: 'Tag version of the image to be pushed.'
-                type: string
-                required: true
-                default: 'latest'
-
-jobs:
-    docker:
-        runs-on: ubuntu-latest
-        environment: ${{ github.event.inputs.environment }}
-        steps:
-            - name: Set default values
-              id: defaults
-              run: |
-                  echo "node_version=${{ github.event.inputs.node_version || '20' }}" >> $GITHUB_OUTPUT
-                  echo "tag_version=${{ github.event.inputs.tag_version || 'latest' }}" >> $GITHUB_OUTPUT
-
-            - name: Checkout
-              uses: actions/checkout@v4.1.1
-
-            - name: Set up QEMU
-              uses: docker/setup-qemu-action@v3.0.0
-
-            - name: Set up Docker Buildx
-              uses: docker/setup-buildx-action@v3.0.0
-
-            - name: Configure AWS Credentials
-              uses: aws-actions/configure-aws-credentials@v3
-              with:
-                  aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-                  aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-                  aws-region: ${{ secrets.AWS_REGION }}
-
-            - name: Login to Amazon ECR
-              uses: aws-actions/amazon-ecr-login@v1
-
-            # -------------------------
-            # Build and push main image
-            # -------------------------
-            - name: Build and push main image
-              uses: docker/build-push-action@v5.3.0
-              with:
-                  context: .
-                  file: Dockerfile
-                  build-args: |
-                      NODE_VERSION=${{ steps.defaults.outputs.node_version }}
-                  platforms: linux/amd64,linux/arm64
-                  push: true
-                  tags: |
-                      ${{ format('{0}.dkr.ecr.{1}.amazonaws.com/flowise:{2}', 
-                          secrets.AWS_ACCOUNT_ID, 
-                          secrets.AWS_REGION, 
-                          steps.defaults.outputs.tag_version) }}

.github/workflows/docker-image.yml

@@ -0,0 +1,114 @@
+name: Docker Image CI
+
+on:
+    workflow_dispatch:
+        inputs:
+            registry:
+                description: 'Container Registry to push the image to.'
+                type: choice
+                required: true
+                default: 'aws_ecr'
+                options:
+                    - 'docker_hub'
+                    - 'aws_ecr'
+            environment:
+                description: 'Environment to push the image to.'
+                required: true
+                default: 'dev'
+                type: choice
+                options:
+                    - dev
+                    - prod
+            image_type:
+                description: 'Type of image to build and push.'
+                type: choice
+                required: true
+                default: 'main'
+                options:
+                    - 'main'
+                    - 'worker'
+            node_version:
+                description: 'Node.js version to build this image with.'
+                type: choice
+                required: true
+                default: '20'
+                options:
+                    - '20'
+            tag_version:
+                description: 'Tag version of the image to be pushed.'
+                type: string
+                required: true
+                default: 'latest'
+
+jobs:
+    docker:
+        runs-on: ubuntu-latest
+        environment: ${{ github.event.inputs.environment }}
+        steps:
+            - name: Set default values
+              id: defaults
+              run: |
+                  echo "registry=${{ github.event.inputs.registry || 'aws_ecr' }}" >> $GITHUB_OUTPUT
+                  echo "image_type=${{ github.event.inputs.image_type || 'main' }}" >> $GITHUB_OUTPUT
+                  echo "node_version=${{ github.event.inputs.node_version || '20' }}" >> $GITHUB_OUTPUT
+                  echo "tag_version=${{ github.event.inputs.tag_version || 'latest' }}" >> $GITHUB_OUTPUT
+
+            - name: Checkout
+              uses: actions/checkout@v4.1.1
+
+            - name: Set up QEMU
+              uses: docker/setup-qemu-action@v3.0.0
+
+            - name: Set up Docker Buildx
+              uses: docker/setup-buildx-action@v3.0.0
+
+            # ------------------------
+            # Login Steps (conditional)
+            # ------------------------
+            - name: Login to Docker Hub
+              if: steps.defaults.outputs.registry == 'docker_hub'
+              uses: docker/login-action@v3
+              with:
+                  username: ${{ secrets.DOCKERHUB_USERNAME }}
+                  password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+            - name: Configure AWS Credentials
+              if: steps.defaults.outputs.registry == 'aws_ecr'
+              uses: aws-actions/configure-aws-credentials@v3
+              with:
+                  aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+                  aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+                  aws-region: ${{ secrets.AWS_REGION }}
+
+            - name: Login to Amazon ECR
+              if: steps.defaults.outputs.registry == 'aws_ecr'
+              uses: aws-actions/amazon-ecr-login@v1
+
+            # -------------------------
+            # Build and push (conditional tags)
+            # -------------------------
+            - name: Build and push
+              uses: docker/build-push-action@v5.3.0
+              with:
+                  context: .
+                  file: |
+                      ${{ 
+                        steps.defaults.outputs.image_type == 'worker' && 'docker/worker/Dockerfile' ||
+                        (steps.defaults.outputs.registry == 'docker_hub' && './docker/Dockerfile' || 'Dockerfile') 
+                      }}
+                  build-args: |
+                      NODE_VERSION=${{ steps.defaults.outputs.node_version }}
+                  platforms: linux/amd64,linux/arm64
+                  push: true
+                  tags: |
+                      ${{ 
+                          steps.defaults.outputs.registry == 'docker_hub' && 
+                          format('flowiseai/flowise{0}:{1}', 
+                              steps.defaults.outputs.image_type == 'worker' && '-worker' || '', 
+                              steps.defaults.outputs.tag_version) ||
+                          format('{0}.dkr.ecr.{1}.amazonaws.com/flowise{2}:{3}', 
+                              secrets.AWS_ACCOUNT_ID, 
+                              secrets.AWS_REGION, 
+                              steps.defaults.outputs.image_type == 'worker' && '-worker' || '',
+                              steps.defaults.outputs.tag_version) 
+                      }}

CONTRIBUTING.md

@@ -114,7 +114,7 @@ Flowise has 3 different modules in a single mono repository.
 
     to make sure everything works fine in production.
 
-11. Commit code and submit Pull Request from forked branch pointing to [Flowise main](https://github.com/FlowiseAI/Flowise/tree/main).
+11. Commit code and submit Pull Request from forked branch pointing to [Flowise master](https://github.com/FlowiseAI/Flowise/tree/master).
 
 ## 🌱 Env Variables
 

Dockerfile

@@ -5,41 +5,34 @@
 # docker run -d -p 3000:3000 flowise
 
 FROM node:20-alpine
+RUN apk add --update libc6-compat python3 make g++
+# needed for pdfjs-dist
+RUN apk add --no-cache build-base cairo-dev pango-dev
 
-# Install system dependencies and build tools
-RUN apk update && \
-    apk add --no-cache \
-        libc6-compat \
-        python3 \
-        make \
-        g++ \
-        build-base \
-        cairo-dev \
-        pango-dev \
-        chromium \
-        curl && \
-    npm install -g pnpm
+# Install Chromium
+RUN apk add --no-cache chromium
+
+# Install curl for container-level health checks
+# Fixes: https://github.com/FlowiseAI/Flowise/issues/4126
+RUN apk add --no-cache curl
+
+#install PNPM globaly
+RUN npm install -g pnpm
 
 ENV PUPPETEER_SKIP_DOWNLOAD=true
 ENV PUPPETEER_EXECUTABLE_PATH=/usr/bin/chromium-browser
 
 ENV NODE_OPTIONS=--max-old-space-size=8192
 
-WORKDIR /usr/src/flowise
+WORKDIR /usr/src
 
 # Copy app source
 COPY . .
 
-# Install dependencies and build
-RUN pnpm install && \
-    pnpm build
+RUN pnpm install
 
-# Give the node user ownership of the application files
-RUN chown -R node:node .
-
-# Switch to non-root user (node user already exists in node:20-alpine)
-USER node
+RUN pnpm build
 
 EXPOSE 3000
 
-CMD [ "pnpm", "start" ]
+CMD [ "pnpm", "start" ]

README.md

@@ -190,10 +190,6 @@ Deploy Flowise self-hosted in your existing infrastructure, we support various [
 
         [![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/pn4G8S?referralCode=WVNPD9)
 
-    -   [Northflank](https://northflank.com/stacks/deploy-flowiseai)
-
-        [![Deploy to Northflank](https://assets.northflank.com/deploy_to_northflank_smm_36700fb050.svg)](https://northflank.com/stacks/deploy-flowiseai)
-
     -   [Render](https://docs.flowiseai.com/configuration/deployment/render)
 
         [![Deploy to Render](https://render.com/images/deploy-to-render-button.svg)](https://docs.flowiseai.com/configuration/deployment/render)

SECURITY.md

@@ -1,38 +1,40 @@
-### Responsible Disclosure Policy
+### Responsible Disclosure Policy  
 
-At Flowise, we prioritize security and continuously work to safeguard our systems. However, vulnerabilities can still exist. If you identify a security issue, please report it to us so we can address it promptly. Your cooperation helps us better protect our platform and users.
+At Flowise, we prioritize security and continuously work to safeguard our systems. However, vulnerabilities can still exist. If you identify a security issue, please report it to us so we can address it promptly. Your cooperation helps us better protect our platform and users.  
 
-### Out of scope vulnerabilities
+### Vulnerabilities  
 
--   Clickjacking on pages without sensitive actions
--   CSRF on unauthenticated/logout/login pages
--   Attacks requiring MITM (Man-in-the-Middle) or physical device access
--   Social engineering attacks
--   Activities that cause service disruption (DoS)
--   Content spoofing and text injection without a valid attack vector
--   Email spoofing
--   Absence of DNSSEC, CAA, CSP headers
--   Missing Secure or HTTP-only flag on non-sensitive cookies
--   Deadlinks
--   User enumeration
+The following types of issues are some of the most common vulnerabilities:
 
-### Reporting Guidelines
+- Clickjacking on pages without sensitive actions  
+- CSRF on unauthenticated/logout/login pages  
+- Attacks requiring MITM (Man-in-the-Middle) or physical device access  
+- Social engineering attacks  
+- Activities that cause service disruption (DoS)  
+- Content spoofing and text injection without a valid attack vector  
+- Email spoofing  
+- Absence of DNSSEC, CAA, CSP headers  
+- Missing Secure or HTTP-only flag on non-sensitive cookies  
+- Deadlinks  
+- User enumeration  
 
--   Submit your findings to https://github.com/FlowiseAI/Flowise/security
--   Provide clear details to help us reproduce and fix the issue quickly.
+### Reporting Guidelines  
 
-### Disclosure Guidelines
+- Submit your findings to https://github.com/FlowiseAI/Flowise/security
+- Provide clear details to help us reproduce and fix the issue quickly.  
 
--   Do not publicly disclose vulnerabilities until we have assessed, resolved, and notified affected users.
--   If you plan to present your research (e.g., at a conference or in a blog), share a draft with us at least **30 days in advance** for review.
--   Avoid including:
-    -   Data from any Flowise customer projects
-    -   Flowise user/customer information
-    -   Details about Flowise employees, contractors, or partners
+### Disclosure Guidelines  
 
-### Response to Reports
+- Do not publicly disclose vulnerabilities until we have assessed, resolved, and notified affected users.  
+- If you plan to present your research (e.g., at a conference or in a blog), share a draft with us at least **30 days in advance** for review.  
+- Avoid including:  
+  - Data from any Flowise customer projects  
+  - Flowise user/customer information  
+  - Details about Flowise employees, contractors, or partners  
 
--   We will acknowledge your report within **5 business days** and provide an estimated resolution timeline.
--   Your report will be kept **confidential**, and your details will not be shared without your consent.
+### Response to Reports  
 
-We appreciate your efforts in helping us maintain a secure platform and look forward to working together to resolve any issues responsibly.
+- We will acknowledge your report within **5 business days** and provide an estimated resolution timeline.  
+- Your report will be kept **confidential**, and your details will not be shared without your consent.
+  
+We appreciate your efforts in helping us maintain a secure platform and look forward to working together to resolve any issues responsibly.  

docker/.env.example

@@ -38,8 +38,6 @@ SECRETKEY_PATH=/root/.flowise
 # DEBUG=true
 LOG_PATH=/root/.flowise/logs
 # LOG_LEVEL=info #(error | warn | info | verbose | debug)
-# LOG_SANITIZE_BODY_FIELDS=password,pwd,pass,secret,token,apikey,api_key,accesstoken,access_token,refreshtoken,refresh_token,clientsecret,client_secret,privatekey,private_key,secretkey,secret_key,auth,authorization,credential,credentials
-# LOG_SANITIZE_HEADER_FIELDS=authorization,x-api-key,x-auth-token,cookie
 # TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
 # TOOL_FUNCTION_EXTERNAL_DEP=moment,lodash
 # ALLOW_BUILTIN_DEP=false

docker/docker-compose-queue-prebuilt.yml

@@ -46,8 +46,6 @@ services:
             - DEBUG=${DEBUG}
             - LOG_PATH=${LOG_PATH}
             - LOG_LEVEL=${LOG_LEVEL}
-            - LOG_SANITIZE_BODY_FIELDS=${LOG_SANITIZE_BODY_FIELDS}
-            - LOG_SANITIZE_HEADER_FIELDS=${LOG_SANITIZE_HEADER_FIELDS}
 
             # CUSTOM TOOL/FUNCTION DEPENDENCIES
             - TOOL_FUNCTION_BUILTIN_DEP=${TOOL_FUNCTION_BUILTIN_DEP}
@@ -192,8 +190,6 @@ services:
             - DEBUG=${DEBUG}
             - LOG_PATH=${LOG_PATH}
             - LOG_LEVEL=${LOG_LEVEL}
-            - LOG_SANITIZE_BODY_FIELDS=${LOG_SANITIZE_BODY_FIELDS}
-            - LOG_SANITIZE_HEADER_FIELDS=${LOG_SANITIZE_HEADER_FIELDS}
 
             # CUSTOM TOOL/FUNCTION DEPENDENCIES
             - TOOL_FUNCTION_BUILTIN_DEP=${TOOL_FUNCTION_BUILTIN_DEP}

docker/docker-compose.yml

@@ -31,8 +31,6 @@ services:
             - DEBUG=${DEBUG}
             - LOG_PATH=${LOG_PATH}
             - LOG_LEVEL=${LOG_LEVEL}
-            - LOG_SANITIZE_BODY_FIELDS=${LOG_SANITIZE_BODY_FIELDS}
-            - LOG_SANITIZE_HEADER_FIELDS=${LOG_SANITIZE_HEADER_FIELDS}
 
             # CUSTOM TOOL/FUNCTION DEPENDENCIES
             - TOOL_FUNCTION_BUILTIN_DEP=${TOOL_FUNCTION_BUILTIN_DEP}

docker/worker/.env.example

@@ -38,8 +38,6 @@ SECRETKEY_PATH=/root/.flowise
 # DEBUG=true
 LOG_PATH=/root/.flowise/logs
 # LOG_LEVEL=info #(error | warn | info | verbose | debug)
-# LOG_SANITIZE_BODY_FIELDS=password,pwd,pass,secret,token,apikey,api_key,accesstoken,access_token,refreshtoken,refresh_token,clientsecret,client_secret,privatekey,private_key,secretkey,secret_key,auth,authorization,credential,credentials
-# LOG_SANITIZE_HEADER_FIELDS=authorization,x-api-key,x-auth-token,cookie
 # TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
 # TOOL_FUNCTION_EXTERNAL_DEP=moment,lodash
 # ALLOW_BUILTIN_DEP=false

docker/worker/Dockerfile

@@ -7,7 +7,7 @@ RUN apk add --no-cache build-base cairo-dev pango-dev
 # Install Chromium and curl for container-level health checks
 RUN apk add --no-cache chromium curl
 
-#install PNPM globally
+#install PNPM globaly
 RUN npm install -g pnpm
 
 ENV PUPPETEER_SKIP_DOWNLOAD=true

docker/worker/docker-compose.yml

@@ -31,8 +31,6 @@ services:
             - DEBUG=${DEBUG}
             - LOG_PATH=${LOG_PATH}
             - LOG_LEVEL=${LOG_LEVEL}
-            - LOG_SANITIZE_BODY_FIELDS=${LOG_SANITIZE_BODY_FIELDS}
-            - LOG_SANITIZE_HEADER_FIELDS=${LOG_SANITIZE_HEADER_FIELDS}
 
             # CUSTOM TOOL/FUNCTION DEPENDENCIES
             - TOOL_FUNCTION_BUILTIN_DEP=${TOOL_FUNCTION_BUILTIN_DEP}

i18n/CONTRIBUTING-ZH.md

@@ -112,7 +112,7 @@ Flowise 在一个单一的单体存储库中有 3 个不同的模块。
     pnpm start
     ```
 
-11. 提交代码并从指向 [Flowise 主分支](https://github.com/FlowiseAI/Flowise/tree/main) 的分叉分支上提交 Pull Request。
+11. 提交代码并从指向 [Flowise 主分支](https://github.com/FlowiseAI/Flowise/tree/master) 的分叉分支上提交 Pull Request。
 
 ## 🌱 环境变量
 

metrics/otel/compose.yaml

@@ -1,7 +1,6 @@
 version: "2"
 services:
   otel-collector:
-    read_only: true
     image: otel/opentelemetry-collector-contrib
     command: ["--config=/etc/otelcol-contrib/config.yaml", "--feature-gates=-exporter.datadogexporter.DisableAPMStats", "${OTELCOL_ARGS}"]
     volumes:

package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "3.0.11",
+    "version": "3.0.7",
     "private": true,
     "homepage": "https://flowiseai.com",
     "workspaces": [
@@ -51,7 +51,7 @@
         "eslint-plugin-react-hooks": "^4.6.0",
         "eslint-plugin-unused-imports": "^2.0.0",
         "husky": "^8.0.1",
-        "kill-port": "2.0.1",
+        "kill-port": "^2.0.1",
         "lint-staged": "^13.0.3",
         "prettier": "^2.7.1",
         "pretty-quick": "^3.1.3",

packages/components/credentials/TeradataVectorStoreApi.credential.ts

@@ -1,47 +0,0 @@
-import { INodeParams, INodeCredential } from '../src/Interface'
-
-class TeradataVectorStoreApiCredentials implements INodeCredential {
-    label: string
-    name: string
-    version: number
-    inputs: INodeParams[]
-
-    constructor() {
-        this.label = 'Teradata Vector Store API Credentials'
-        this.name = 'teradataVectorStoreApiCredentials'
-        this.version = 1.0
-        this.inputs = [
-            {
-                label: 'Teradata Host IP',
-                name: 'tdHostIp',
-                type: 'string'
-            },
-            {
-                label: 'Username',
-                name: 'tdUsername',
-                type: 'string'
-            },
-            {
-                label: 'Password',
-                name: 'tdPassword',
-                type: 'password'
-            },
-            {
-                label: 'Vector_Store_Base_URL',
-                name: 'baseURL',
-                description: 'Teradata Vector Store Base URL',
-                placeholder: `Base_URL`,
-                type: 'string'
-            },
-            {
-                label: 'JWT Token',
-                name: 'jwtToken',
-                type: 'password',
-                description: 'Bearer token for JWT authentication',
-                optional: true
-            }
-        ]
-    }
-}
-
-module.exports = { credClass: TeradataVectorStoreApiCredentials }

packages/components/models.json

@@ -3,13 +3,6 @@
         {
             "name": "awsChatBedrock",
             "models": [
-                {
-                    "label": "anthropic.claude-opus-4-5-20251101-v1:0",
-                    "name": "anthropic.claude-opus-4-5-20251101-v1:0",
-                    "description": "Claude 4.5 Opus",
-                    "input_cost": 0.000005,
-                    "output_cost": 0.000025
-                },
                 {
                     "label": "anthropic.claude-sonnet-4-5-20250929-v1:0",
                     "name": "anthropic.claude-sonnet-4-5-20250929-v1:0",
@@ -17,13 +10,6 @@
                     "input_cost": 0.000003,
                     "output_cost": 0.000015
                 },
-                {
-                    "label": "anthropic.claude-haiku-4-5-20251001-v1:0",
-                    "name": "anthropic.claude-haiku-4-5-20251001-v1:0",
-                    "description": "Claude 4.5 Haiku",
-                    "input_cost": 0.000001,
-                    "output_cost": 0.000005
-                },
                 {
                     "label": "openai.gpt-oss-20b-1:0",
                     "name": "openai.gpt-oss-20b-1:0",
@@ -322,12 +308,6 @@
         {
             "name": "azureChatOpenAI",
             "models": [
-                {
-                    "label": "gpt-5.1",
-                    "name": "gpt-5.1",
-                    "input_cost": 0.00000125,
-                    "output_cost": 0.00001
-                },
                 {
                     "label": "gpt-5",
                     "name": "gpt-5",
@@ -512,13 +492,6 @@
         {
             "name": "chatAnthropic",
             "models": [
-                {
-                    "label": "claude-opus-4-5",
-                    "name": "claude-opus-4-5",
-                    "description": "Claude 4.5 Opus",
-                    "input_cost": 0.000005,
-                    "output_cost": 0.000025
-                },
                 {
                     "label": "claude-sonnet-4-5",
                     "name": "claude-sonnet-4-5",
@@ -526,13 +499,6 @@
                     "input_cost": 0.000003,
                     "output_cost": 0.000015
                 },
-                {
-                    "label": "claude-haiku-4-5",
-                    "name": "claude-haiku-4-5",
-                    "description": "Claude 4.5 Haiku",
-                    "input_cost": 0.000001,
-                    "output_cost": 0.000005
-                },
                 {
                     "label": "claude-sonnet-4-0",
                     "name": "claude-sonnet-4-0",
@@ -641,18 +607,6 @@
         {
             "name": "chatGoogleGenerativeAI",
             "models": [
-                {
-                    "label": "gemini-3-pro-preview",
-                    "name": "gemini-3-pro-preview",
-                    "input_cost": 0.00002,
-                    "output_cost": 0.00012
-                },
-                {
-                    "label": "gemini-3-pro-image-preview",
-                    "name": "gemini-3-pro-image-preview",
-                    "input_cost": 0.00002,
-                    "output_cost": 0.00012
-                },
                 {
                     "label": "gemini-2.5-pro",
                     "name": "gemini-2.5-pro",
@@ -665,12 +619,6 @@
                     "input_cost": 1.25e-6,
                     "output_cost": 0.00001
                 },
-                {
-                    "label": "gemini-2.5-flash-image",
-                    "name": "gemini-2.5-flash-image",
-                    "input_cost": 1.25e-6,
-                    "output_cost": 0.00001
-                },
                 {
                     "label": "gemini-2.5-flash-lite",
                     "name": "gemini-2.5-flash-lite",
@@ -723,12 +671,6 @@
         {
             "name": "chatGoogleVertexAI",
             "models": [
-                {
-                    "label": "gemini-3-pro-preview",
-                    "name": "gemini-3-pro-preview",
-                    "input_cost": 0.00002,
-                    "output_cost": 0.00012
-                },
                 {
                     "label": "gemini-2.5-pro",
                     "name": "gemini-2.5-pro",
@@ -795,13 +737,6 @@
                     "input_cost": 1.25e-7,
                     "output_cost": 3.75e-7
                 },
-                {
-                    "label": "claude-opus-4-5@20251101",
-                    "name": "claude-opus-4-5@20251101",
-                    "description": "Claude 4.5 Opus",
-                    "input_cost": 0.000005,
-                    "output_cost": 0.000025
-                },
                 {
                     "label": "claude-sonnet-4-5@20250929",
                     "name": "claude-sonnet-4-5@20250929",
@@ -809,13 +744,6 @@
                     "input_cost": 0.000003,
                     "output_cost": 0.000015
                 },
-                {
-                    "label": "claude-haiku-4-5@20251001",
-                    "name": "claude-haiku-4-5@20251001",
-                    "description": "Claude 4.5 Haiku",
-                    "input_cost": 0.000001,
-                    "output_cost": 0.000005
-                },
                 {
                     "label": "claude-opus-4-1@20250805",
                     "name": "claude-opus-4-1@20250805",
@@ -1047,12 +975,6 @@
         {
             "name": "chatOpenAI",
             "models": [
-                {
-                    "label": "gpt-5.1",
-                    "name": "gpt-5.1",
-                    "input_cost": 0.00000125,
-                    "output_cost": 0.00001
-                },
                 {
                     "label": "gpt-5",
                     "name": "gpt-5",

packages/components/nodes/agentflow/Agent/Agent.ts

@@ -22,16 +22,15 @@ import zodToJsonSchema from 'zod-to-json-schema'
 import { getErrorMessage } from '../../../src/error'
 import { DataSource } from 'typeorm'
 import {
-    addImageArtifactsToMessages,
-    extractArtifactsFromResponse,
     getPastChatHistoryImageMessages,
     getUniqueImageMessages,
     processMessagesWithImages,
     replaceBase64ImagesWithFileReferences,
-    replaceInlineDataWithFileReferences,
     updateFlowState
 } from '../utils'
-import { convertMultiOptionsToStringArray, processTemplateVariables, configureStructuredOutput } from '../../../src/utils'
+import { convertMultiOptionsToStringArray, getCredentialData, getCredentialParam, processTemplateVariables } from '../../../src/utils'
+import { addSingleFileToStorage } from '../../../src/storageUtils'
+import fetch from 'node-fetch'
 
 interface ITool {
     agentSelectedTool: string
@@ -82,7 +81,7 @@ class Agent_Agentflow implements INode {
     constructor() {
         this.label = 'Agent'
         this.name = 'agentAgentflow'
-        this.version = 3.2
+        this.version = 2.2
         this.type = 'Agent'
         this.category = 'Agent Flows'
         this.description = 'Dynamically choose and utilize tools during runtime, enabling multi-step reasoning'
@@ -177,11 +176,6 @@ class Agent_Agentflow implements INode {
                         label: 'Google Search',
                         name: 'googleSearch',
                         description: 'Search real-time web content'
-                    },
-                    {
-                        label: 'Code Execution',
-                        name: 'codeExecution',
-                        description: 'Write and run Python code in a sandboxed environment'
                     }
                 ],
                 show: {
@@ -400,108 +394,6 @@ class Agent_Agentflow implements INode {
                 ],
                 default: 'userMessage'
             },
-            {
-                label: 'JSON Structured Output',
-                name: 'agentStructuredOutput',
-                description: 'Instruct the Agent to give output in a JSON structured schema',
-                type: 'array',
-                optional: true,
-                acceptVariable: true,
-                array: [
-                    {
-                        label: 'Key',
-                        name: 'key',
-                        type: 'string'
-                    },
-                    {
-                        label: 'Type',
-                        name: 'type',
-                        type: 'options',
-                        options: [
-                            {
-                                label: 'String',
-                                name: 'string'
-                            },
-                            {
-                                label: 'String Array',
-                                name: 'stringArray'
-                            },
-                            {
-                                label: 'Number',
-                                name: 'number'
-                            },
-                            {
-                                label: 'Boolean',
-                                name: 'boolean'
-                            },
-                            {
-                                label: 'Enum',
-                                name: 'enum'
-                            },
-                            {
-                                label: 'JSON Array',
-                                name: 'jsonArray'
-                            }
-                        ]
-                    },
-                    {
-                        label: 'Enum Values',
-                        name: 'enumValues',
-                        type: 'string',
-                        placeholder: 'value1, value2, value3',
-                        description: 'Enum values. Separated by comma',
-                        optional: true,
-                        show: {
-                            'agentStructuredOutput[$index].type': 'enum'
-                        }
-                    },
-                    {
-                        label: 'JSON Schema',
-                        name: 'jsonSchema',
-                        type: 'code',
-                        placeholder: `{
-    "answer": {
-        "type": "string",
-        "description": "Value of the answer"
-    },
-    "reason": {
-        "type": "string",
-        "description": "Reason for the answer"
-    },
-    "optional": {
-        "type": "boolean"
-    },
-    "count": {
-        "type": "number"
-    },
-    "children": {
-        "type": "array",
-        "items": {
-            "type": "object",
-            "properties": {
-                "value": {
-                    "type": "string",
-                    "description": "Value of the children's answer"
-                }
-            }
-        }
-    }
-}`,
-                        description: 'JSON schema for the structured output',
-                        optional: true,
-                        hideCodeExecute: true,
-                        show: {
-                            'agentStructuredOutput[$index].type': 'jsonArray'
-                        }
-                    },
-                    {
-                        label: 'Description',
-                        name: 'description',
-                        type: 'string',
-                        placeholder: 'Description of the key'
-                    }
-                ]
-            },
             {
                 label: 'Update Flow State',
                 name: 'agentUpdateState',
@@ -514,7 +406,8 @@ class Agent_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',
@@ -877,7 +770,6 @@ class Agent_Agentflow implements INode {
             const memoryType = nodeData.inputs?.agentMemoryType as string
             const userMessage = nodeData.inputs?.agentUserMessage as string
             const _agentUpdateState = nodeData.inputs?.agentUpdateState
-            const _agentStructuredOutput = nodeData.inputs?.agentStructuredOutput
             const agentMessages = (nodeData.inputs?.agentMessages as unknown as ILLMMessage[]) ?? []
 
             // Extract runtime state and history
@@ -903,8 +795,6 @@ class Agent_Agentflow implements INode {
             const llmWithoutToolsBind = (await newLLMNodeInstance.init(newNodeData, '', options)) as BaseChatModel
             let llmNodeInstance = llmWithoutToolsBind
 
-            const isStructuredOutput = _agentStructuredOutput && Array.isArray(_agentStructuredOutput) && _agentStructuredOutput.length > 0
-
             const agentToolsBuiltInOpenAI = convertMultiOptionsToStringArray(nodeData.inputs?.agentToolsBuiltInOpenAI)
             if (agentToolsBuiltInOpenAI && agentToolsBuiltInOpenAI.length > 0) {
                 for (const tool of agentToolsBuiltInOpenAI) {
@@ -1063,7 +953,7 @@ class Agent_Agentflow implements INode {
             // Initialize response and determine if streaming is possible
             let response: AIMessageChunk = new AIMessageChunk('')
             const isLastNode = options.isLastNode as boolean
-            const isStreamable = isLastNode && options.sseStreamer !== undefined && modelConfig?.streaming !== false && !isStructuredOutput
+            const isStreamable = isLastNode && options.sseStreamer !== undefined && modelConfig?.streaming !== false
 
             // Start analytics
             if (analyticHandlers && options.parentTraceIds) {
@@ -1071,6 +961,12 @@ class Agent_Agentflow implements INode {
                 llmIds = await analyticHandlers.onLLMStart(llmLabel, messages, options.parentTraceIds)
             }
 
+            // Track execution time
+            const startTime = Date.now()
+
+            // Get initial response from LLM
+            const sseStreamer: IServerSideEventStreamer | undefined = options.sseStreamer
+
             // Handle tool calls with support for recursion
             let usedTools: IUsedTool[] = []
             let sourceDocuments: Array<any> = []
@@ -1083,24 +979,12 @@ class Agent_Agentflow implements INode {
             const messagesBeforeToolCalls = [...messages]
             let _toolCallMessages: BaseMessageLike[] = []
 
-            /**
-             * Add image artifacts from previous assistant responses as user messages
-             * Images are converted from FILE-STORAGE::<image_path> to base 64 image_url format
-             */
-            await addImageArtifactsToMessages(messages, options)
-
             // Check if this is hummanInput for tool calls
             const _humanInput = nodeData.inputs?.humanInput
             const humanInput: IHumanInput = typeof _humanInput === 'string' ? JSON.parse(_humanInput) : _humanInput
             const humanInputAction = options.humanInputAction
             const iterationContext = options.iterationContext
 
-            // Track execution time
-            const startTime = Date.now()
-
-            // Get initial response from LLM
-            const sseStreamer: IServerSideEventStreamer | undefined = options.sseStreamer
-
             if (humanInput) {
                 if (humanInput.type !== 'proceed' && humanInput.type !== 'reject') {
                     throw new Error(`Invalid human input type. Expected 'proceed' or 'reject', but got '${humanInput.type}'`)
@@ -1118,8 +1002,7 @@ class Agent_Agentflow implements INode {
                     llmWithoutToolsBind,
                     isStreamable,
                     isLastNode,
-                    iterationContext,
-                    isStructuredOutput
+                    iterationContext
                 })
 
                 response = result.response
@@ -1148,14 +1031,7 @@ class Agent_Agentflow implements INode {
                 }
             } else {
                 if (isStreamable) {
-                    response = await this.handleStreamingResponse(
-                        sseStreamer,
-                        llmNodeInstance,
-                        messages,
-                        chatId,
-                        abortController,
-                        isStructuredOutput
-                    )
+                    response = await this.handleStreamingResponse(sseStreamer, llmNodeInstance, messages, chatId, abortController)
                 } else {
                     response = await llmNodeInstance.invoke(messages, { signal: abortController?.signal })
                 }
@@ -1177,8 +1053,7 @@ class Agent_Agentflow implements INode {
                     llmNodeInstance,
                     isStreamable,
                     isLastNode,
-                    iterationContext,
-                    isStructuredOutput
+                    iterationContext
                 })
 
                 response = result.response
@@ -1205,20 +1080,11 @@ class Agent_Agentflow implements INode {
                         sseStreamer.streamArtifactsEvent(chatId, flatten(artifacts))
                     }
                 }
-            } else if (!humanInput && !isStreamable && isLastNode && sseStreamer && !isStructuredOutput) {
+            } else if (!humanInput && !isStreamable && isLastNode && sseStreamer) {
                 // Stream whole response back to UI if not streaming and no tool calls
-                // Skip this if structured output is enabled - it will be streamed after conversion
                 let finalResponse = ''
                 if (response.content && Array.isArray(response.content)) {
-                    finalResponse = response.content
-                        .map((item: any) => {
-                            if ((item.text && !item.type) || (item.type === 'text' && item.text)) {
-                                return item.text
-                            }
-                            return ''
-                        })
-                        .filter((text: string) => text)
-                        .join('\n')
+                    finalResponse = response.content.map((item: any) => item.text).join('\n')
                 } else if (response.content && typeof response.content === 'string') {
                     finalResponse = response.content
                 } else {
@@ -1247,53 +1113,9 @@ class Agent_Agentflow implements INode {
             // Prepare final response and output object
             let finalResponse = ''
             if (response.content && Array.isArray(response.content)) {
-                // Process items and concatenate consecutive text items
-                const processedParts: string[] = []
-                let currentTextBuffer = ''
-
-                for (const item of response.content) {
-                    const itemAny = item as any
-                    const isTextItem = (itemAny.text && !itemAny.type) || (itemAny.type === 'text' && itemAny.text)
-
-                    if (isTextItem) {
-                        // Accumulate consecutive text items
-                        currentTextBuffer += itemAny.text
-                    } else {
-                        // Flush accumulated text before processing other types
-                        if (currentTextBuffer) {
-                            processedParts.push(currentTextBuffer)
-                            currentTextBuffer = ''
-                        }
-
-                        // Process non-text items
-                        if (itemAny.type === 'executableCode' && itemAny.executableCode) {
-                            // Format executable code as a code block
-                            const language = itemAny.executableCode.language?.toLowerCase() || 'python'
-                            processedParts.push(`\n\`\`\`${language}\n${itemAny.executableCode.code}\n\`\`\`\n`)
-                        } else if (itemAny.type === 'codeExecutionResult' && itemAny.codeExecutionResult) {
-                            // Format code execution result
-                            const outcome = itemAny.codeExecutionResult.outcome || 'OUTCOME_OK'
-                            const output = itemAny.codeExecutionResult.output || ''
-                            if (outcome === 'OUTCOME_OK' && output) {
-                                processedParts.push(`**Code Output:**\n\`\`\`\n${output}\n\`\`\`\n`)
-                            } else if (outcome !== 'OUTCOME_OK') {
-                                processedParts.push(`**Code Execution Error:**\n\`\`\`\n${output}\n\`\`\`\n`)
-                            }
-                        }
-                    }
-                }
-
-                // Flush any remaining text
-                if (currentTextBuffer) {
-                    processedParts.push(currentTextBuffer)
-                }
-
-                finalResponse = processedParts.filter((text) => text).join('\n')
+                finalResponse = response.content.map((item: any) => item.text).join('\n')
             } else if (response.content && typeof response.content === 'string') {
                 finalResponse = response.content
-            } else if (response.content === '') {
-                // Empty response content, this could happen when there is only image data
-                finalResponse = ''
             } else {
                 finalResponse = JSON.stringify(response, null, 2)
             }
@@ -1309,13 +1131,10 @@ class Agent_Agentflow implements INode {
                 }
             }
 
-            // Extract artifacts from annotations in response metadata and replace inline data
+            // Extract artifacts from annotations in response metadata
             if (response.response_metadata) {
-                const {
-                    artifacts: extractedArtifacts,
-                    fileAnnotations: extractedFileAnnotations,
-                    savedInlineImages
-                } = await extractArtifactsFromResponse(response.response_metadata, newNodeData, options)
+                const { artifacts: extractedArtifacts, fileAnnotations: extractedFileAnnotations } =
+                    await this.extractArtifactsFromResponse(response.response_metadata, newNodeData, options)
                 if (extractedArtifacts.length > 0) {
                     artifacts = [...artifacts, ...extractedArtifacts]
 
@@ -1333,11 +1152,6 @@ class Agent_Agentflow implements INode {
                         sseStreamer.streamFileAnnotationsEvent(chatId, fileAnnotations)
                     }
                 }
-
-                // Replace inlineData base64 with file references in the response
-                if (savedInlineImages && savedInlineImages.length > 0) {
-                    replaceInlineDataWithFileReferences(response, savedInlineImages)
-                }
             }
 
             // Replace sandbox links with proper download URLs. Example: [Download the script](sandbox:/mnt/data/dummy_bar_graph.py)
@@ -1345,23 +1159,6 @@ class Agent_Agentflow implements INode {
                 finalResponse = await this.processSandboxLinks(finalResponse, options.baseURL, options.chatflowid, chatId)
             }
 
-            // If is structured output, then invoke LLM again with structured output at the very end after all tool calls
-            if (isStructuredOutput) {
-                llmNodeInstance = configureStructuredOutput(llmNodeInstance, _agentStructuredOutput)
-                const prompt = 'Convert the following response to the structured output format: ' + finalResponse
-                response = await llmNodeInstance.invoke(prompt, { signal: abortController?.signal })
-
-                if (typeof response === 'object') {
-                    finalResponse = '```json\n' + JSON.stringify(response, null, 2) + '\n```'
-                } else {
-                    finalResponse = response
-                }
-
-                if (isLastNode && sseStreamer) {
-                    sseStreamer.streamTokenEvent(chatId, finalResponse)
-                }
-            }
-
             const output = this.prepareOutputObject(
                 response,
                 availableTools,
@@ -1374,8 +1171,7 @@ class Agent_Agentflow implements INode {
                 artifacts,
                 additionalTokens,
                 isWaitingForHumanInput,
-                fileAnnotations,
-                isStructuredOutput
+                fileAnnotations
             )
 
             // End analytics tracking
@@ -1396,15 +1192,9 @@ class Agent_Agentflow implements INode {
             // Process template variables in state
             newState = processTemplateVariables(newState, finalResponse)
 
-            /**
-             * Remove the temporarily added image artifact messages before storing
-             * This is to avoid storing the actual base64 data into database
-             */
-            const messagesToStore = messages.filter((msg: any) => !msg._isTemporaryImageMessage)
-
             // Replace the actual messages array with one that includes the file references for images instead of base64 data
             const messagesWithFileReferences = replaceBase64ImagesWithFileReferences(
-                messagesToStore,
+                messages,
                 runtimeImageMessagesWithFileRef,
                 pastImageMessagesWithFileRef
             )
@@ -1543,12 +1333,7 @@ class Agent_Agentflow implements INode {
         // Handle Gemini googleSearch tool
         if (groundingMetadata && groundingMetadata.webSearchQueries && Array.isArray(groundingMetadata.webSearchQueries)) {
             // Check for duplicates
-            const isDuplicate = builtInUsedTools.find(
-                (tool) =>
-                    tool.tool === 'googleSearch' &&
-                    JSON.stringify((tool.toolInput as any)?.queries) === JSON.stringify(groundingMetadata.webSearchQueries)
-            )
-            if (!isDuplicate) {
+            if (!builtInUsedTools.find((tool) => tool.tool === 'googleSearch')) {
                 builtInUsedTools.push({
                     tool: 'googleSearch',
                     toolInput: {
@@ -1562,12 +1347,7 @@ class Agent_Agentflow implements INode {
         // Handle Gemini urlContext tool
         if (urlContextMetadata && urlContextMetadata.urlMetadata && Array.isArray(urlContextMetadata.urlMetadata)) {
             // Check for duplicates
-            const isDuplicate = builtInUsedTools.find(
-                (tool) =>
-                    tool.tool === 'urlContext' &&
-                    JSON.stringify((tool.toolInput as any)?.urlMetadata) === JSON.stringify(urlContextMetadata.urlMetadata)
-            )
-            if (!isDuplicate) {
+            if (!builtInUsedTools.find((tool) => tool.tool === 'urlContext')) {
                 builtInUsedTools.push({
                     tool: 'urlContext',
                     toolInput: {
@@ -1578,55 +1358,47 @@ class Agent_Agentflow implements INode {
             }
         }
 
-        // Handle Gemini codeExecution tool
-        if (response.content && Array.isArray(response.content)) {
-            for (let i = 0; i < response.content.length; i++) {
-                const item = response.content[i]
-
-                if (item.type === 'executableCode' && item.executableCode) {
-                    const language = item.executableCode.language || 'PYTHON'
-                    const code = item.executableCode.code || ''
-                    let toolOutput = ''
-
-                    // Check for duplicates
-                    const isDuplicate = builtInUsedTools.find(
-                        (tool) =>
-                            tool.tool === 'codeExecution' &&
-                            (tool.toolInput as any)?.language === language &&
-                            (tool.toolInput as any)?.code === code
-                    )
-                    if (isDuplicate) {
-                        continue
-                    }
-
-                    // Check the next item for the output
-                    const nextItem = i + 1 < response.content.length ? response.content[i + 1] : null
-
-                    if (nextItem) {
-                        if (nextItem.type === 'codeExecutionResult' && nextItem.codeExecutionResult) {
-                            const outcome = nextItem.codeExecutionResult.outcome
-                            const output = nextItem.codeExecutionResult.output || ''
-                            toolOutput = outcome === 'OUTCOME_OK' ? output : `Error: ${output}`
-                        } else if (nextItem.type === 'inlineData') {
-                            toolOutput = 'Generated image data'
-                        }
-                    }
-
-                    builtInUsedTools.push({
-                        tool: 'codeExecution',
-                        toolInput: {
-                            language,
-                            code
-                        },
-                        toolOutput
-                    })
-                }
-            }
-        }
-
         return builtInUsedTools
     }
 
+    /**
+     * Saves base64 image data to storage and returns file information
+     */
+    private async saveBase64Image(
+        outputItem: any,
+        options: ICommonObject
+    ): Promise<{ filePath: string; fileName: string; totalSize: number } | null> {
+        try {
+            if (!outputItem.result) {
+                return null
+            }
+
+            // Extract base64 data and create buffer
+            const base64Data = outputItem.result
+            const imageBuffer = Buffer.from(base64Data, 'base64')
+
+            // Determine file extension and MIME type
+            const outputFormat = outputItem.output_format || 'png'
+            const fileName = `generated_image_${outputItem.id || Date.now()}.${outputFormat}`
+            const mimeType = outputFormat === 'png' ? 'image/png' : 'image/jpeg'
+
+            // Save the image using the existing storage utility
+            const { path, totalSize } = await addSingleFileToStorage(
+                mimeType,
+                imageBuffer,
+                fileName,
+                options.orgId,
+                options.chatflowid,
+                options.chatId
+            )
+
+            return { filePath: path, fileName, totalSize }
+        } catch (error) {
+            console.error('Error saving base64 image:', error)
+            return null
+        }
+    }
+
     /**
      * Handles memory management based on the specified memory type
      */
@@ -1789,62 +1561,32 @@ class Agent_Agentflow implements INode {
         llmNodeInstance: BaseChatModel,
         messages: BaseMessageLike[],
         chatId: string,
-        abortController: AbortController,
-        isStructuredOutput: boolean = false
+        abortController: AbortController
     ): Promise<AIMessageChunk> {
         let response = new AIMessageChunk('')
 
         try {
             for await (const chunk of await llmNodeInstance.stream(messages, { signal: abortController?.signal })) {
-                if (sseStreamer && !isStructuredOutput) {
+                if (sseStreamer) {
                     let content = ''
-
-                    if (typeof chunk === 'string') {
-                        content = chunk
-                    } else if (Array.isArray(chunk.content) && chunk.content.length > 0) {
-                        content = chunk.content
-                            .map((item: any) => {
-                                if ((item.text && !item.type) || (item.type === 'text' && item.text)) {
-                                    return item.text
-                                } else if (item.type === 'executableCode' && item.executableCode) {
-                                    const language = item.executableCode.language?.toLowerCase() || 'python'
-                                    return `\n\`\`\`${language}\n${item.executableCode.code}\n\`\`\`\n`
-                                } else if (item.type === 'codeExecutionResult' && item.codeExecutionResult) {
-                                    const outcome = item.codeExecutionResult.outcome || 'OUTCOME_OK'
-                                    const output = item.codeExecutionResult.output || ''
-                                    if (outcome === 'OUTCOME_OK' && output) {
-                                        return `**Code Output:**\n\`\`\`\n${output}\n\`\`\`\n`
-                                    } else if (outcome !== 'OUTCOME_OK') {
-                                        return `**Code Execution Error:**\n\`\`\`\n${output}\n\`\`\`\n`
-                                    }
-                                }
-                                return ''
-                            })
-                            .filter((text: string) => text)
-                            .join('')
-                    } else if (chunk.content) {
+                    if (Array.isArray(chunk.content) && chunk.content.length > 0) {
+                        const contents = chunk.content as MessageContentText[]
+                        content = contents.map((item) => item.text).join('')
+                    } else {
                         content = chunk.content.toString()
                     }
                     sseStreamer.streamTokenEvent(chatId, content)
                 }
 
-                const messageChunk = typeof chunk === 'string' ? new AIMessageChunk(chunk) : chunk
-                response = response.concat(messageChunk)
+                response = response.concat(chunk)
             }
         } catch (error) {
             console.error('Error during streaming:', error)
             throw error
         }
-
-        // Only convert to string if all content items are text (no inlineData or other special types)
         if (Array.isArray(response.content) && response.content.length > 0) {
-            const hasNonTextContent = response.content.some(
-                (item: any) => item.type === 'inlineData' || item.type === 'executableCode' || item.type === 'codeExecutionResult'
-            )
-            if (!hasNonTextContent) {
-                const responseContents = response.content as MessageContentText[]
-                response.content = responseContents.map((item) => item.text).join('')
-            }
+            const responseContents = response.content as MessageContentText[]
+            response.content = responseContents.map((item) => item.text).join('')
         }
         return response
     }
@@ -1864,8 +1606,7 @@ class Agent_Agentflow implements INode {
         artifacts: any[],
         additionalTokens: number = 0,
         isWaitingForHumanInput: boolean = false,
-        fileAnnotations: any[] = [],
-        isStructuredOutput: boolean = false
+        fileAnnotations: any[] = []
     ): any {
         const output: any = {
             content: finalResponse,
@@ -1900,15 +1641,6 @@ class Agent_Agentflow implements INode {
             output.responseMetadata = response.response_metadata
         }
 
-        if (isStructuredOutput && typeof response === 'object') {
-            const structuredOutput = response as Record<string, any>
-            for (const key in structuredOutput) {
-                if (structuredOutput[key] !== undefined && structuredOutput[key] !== null) {
-                    output[key] = structuredOutput[key]
-                }
-            }
-        }
-
         // Add used tools, source documents and artifacts to output
         if (usedTools && usedTools.length > 0) {
             output.usedTools = flatten(usedTools)
@@ -1944,12 +1676,7 @@ class Agent_Agentflow implements INode {
         const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
 
         if (response.tool_calls) {
-            const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
-                tool: toolCall.name || 'tool',
-                toolInput: toolCall.args,
-                toolOutput: ''
-            }))
-            sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
+            sseStreamer.streamCalledToolsEvent(chatId, response.tool_calls)
         }
 
         if (response.usage_metadata) {
@@ -1974,8 +1701,7 @@ class Agent_Agentflow implements INode {
         llmNodeInstance,
         isStreamable,
         isLastNode,
-        iterationContext,
-        isStructuredOutput = false
+        iterationContext
     }: {
         response: AIMessageChunk
         messages: BaseMessageLike[]
@@ -1989,7 +1715,6 @@ class Agent_Agentflow implements INode {
         isStreamable: boolean
         isLastNode: boolean
         iterationContext: ICommonObject
-        isStructuredOutput?: boolean
     }): Promise<{
         response: AIMessageChunk
         usedTools: IUsedTool[]
@@ -2011,12 +1736,7 @@ class Agent_Agentflow implements INode {
 
         // Stream tool calls if available
         if (sseStreamer) {
-            const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
-                tool: toolCall.name || 'tool',
-                toolInput: toolCall.args,
-                toolOutput: ''
-            }))
-            sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
+            sseStreamer.streamCalledToolsEvent(chatId, JSON.stringify(response.tool_calls))
         }
 
         // Remove tool calls with no id
@@ -2069,9 +1789,7 @@ class Agent_Agentflow implements INode {
                     const toolCallDetails = '```json\n' + JSON.stringify(toolCall, null, 2) + '\n```'
                     const responseContent = response.content + `\nAttempting to use tool:\n${toolCallDetails}`
                     response.content = responseContent
-                    if (!isStructuredOutput) {
-                        sseStreamer?.streamTokenEvent(chatId, responseContent)
-                    }
+                    sseStreamer?.streamTokenEvent(chatId, responseContent)
                     return { response, usedTools, sourceDocuments, artifacts, totalTokens, isWaitingForHumanInput: true }
                 }
 
@@ -2177,7 +1895,7 @@ class Agent_Agentflow implements INode {
                 const lastToolOutput = usedTools[0]?.toolOutput || ''
                 const lastToolOutputString = typeof lastToolOutput === 'string' ? lastToolOutput : JSON.stringify(lastToolOutput, null, 2)
 
-                if (sseStreamer && !isStructuredOutput) {
+                if (sseStreamer) {
                     sseStreamer.streamTokenEvent(chatId, lastToolOutputString)
                 }
 
@@ -2206,19 +1924,12 @@ class Agent_Agentflow implements INode {
         let newResponse: AIMessageChunk
 
         if (isStreamable) {
-            newResponse = await this.handleStreamingResponse(
-                sseStreamer,
-                llmNodeInstance,
-                messages,
-                chatId,
-                abortController,
-                isStructuredOutput
-            )
+            newResponse = await this.handleStreamingResponse(sseStreamer, llmNodeInstance, messages, chatId, abortController)
         } else {
             newResponse = await llmNodeInstance.invoke(messages, { signal: abortController?.signal })
 
             // Stream non-streaming response if this is the last node
-            if (isLastNode && sseStreamer && !isStructuredOutput) {
+            if (isLastNode && sseStreamer) {
                 let responseContent = JSON.stringify(newResponse, null, 2)
                 if (typeof newResponse.content === 'string') {
                     responseContent = newResponse.content
@@ -2253,8 +1964,7 @@ class Agent_Agentflow implements INode {
                 llmNodeInstance,
                 isStreamable,
                 isLastNode,
-                iterationContext,
-                isStructuredOutput
+                iterationContext
             })
 
             // Merge results from recursive tool calls
@@ -2285,8 +1995,7 @@ class Agent_Agentflow implements INode {
         llmWithoutToolsBind,
         isStreamable,
         isLastNode,
-        iterationContext,
-        isStructuredOutput = false
+        iterationContext
     }: {
         humanInput: IHumanInput
         humanInputAction: Record<string, any> | undefined
@@ -2301,7 +2010,6 @@ class Agent_Agentflow implements INode {
         isStreamable: boolean
         isLastNode: boolean
         iterationContext: ICommonObject
-        isStructuredOutput?: boolean
     }): Promise<{
         response: AIMessageChunk
         usedTools: IUsedTool[]
@@ -2337,12 +2045,7 @@ class Agent_Agentflow implements INode {
 
         // Stream tool calls if available
         if (sseStreamer) {
-            const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
-                tool: toolCall.name || 'tool',
-                toolInput: toolCall.args,
-                toolOutput: ''
-            }))
-            sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
+            sseStreamer.streamCalledToolsEvent(chatId, JSON.stringify(response.tool_calls))
         }
 
         // Remove tool calls with no id
@@ -2504,7 +2207,7 @@ class Agent_Agentflow implements INode {
                 const lastToolOutput = usedTools[0]?.toolOutput || ''
                 const lastToolOutputString = typeof lastToolOutput === 'string' ? lastToolOutput : JSON.stringify(lastToolOutput, null, 2)
 
-                if (sseStreamer && !isStructuredOutput) {
+                if (sseStreamer) {
                     sseStreamer.streamTokenEvent(chatId, lastToolOutputString)
                 }
 
@@ -2535,19 +2238,12 @@ class Agent_Agentflow implements INode {
         }
 
         if (isStreamable) {
-            newResponse = await this.handleStreamingResponse(
-                sseStreamer,
-                llmNodeInstance,
-                messages,
-                chatId,
-                abortController,
-                isStructuredOutput
-            )
+            newResponse = await this.handleStreamingResponse(sseStreamer, llmNodeInstance, messages, chatId, abortController)
         } else {
             newResponse = await llmNodeInstance.invoke(messages, { signal: abortController?.signal })
 
             // Stream non-streaming response if this is the last node
-            if (isLastNode && sseStreamer && !isStructuredOutput) {
+            if (isLastNode && sseStreamer) {
                 let responseContent = JSON.stringify(newResponse, null, 2)
                 if (typeof newResponse.content === 'string') {
                     responseContent = newResponse.content
@@ -2582,8 +2278,7 @@ class Agent_Agentflow implements INode {
                 llmNodeInstance,
                 isStreamable,
                 isLastNode,
-                iterationContext,
-                isStructuredOutput
+                iterationContext
             })
 
             // Merge results from recursive tool calls
@@ -2598,6 +2293,190 @@ class Agent_Agentflow implements INode {
         return { response: newResponse, usedTools, sourceDocuments, artifacts, totalTokens, isWaitingForHumanInput }
     }
 
+    /**
+     * Extracts artifacts from response metadata (both annotations and built-in tools)
+     */
+    private async extractArtifactsFromResponse(
+        responseMetadata: any,
+        modelNodeData: INodeData,
+        options: ICommonObject
+    ): Promise<{ artifacts: any[]; fileAnnotations: any[] }> {
+        const artifacts: any[] = []
+        const fileAnnotations: any[] = []
+
+        if (!responseMetadata?.output || !Array.isArray(responseMetadata.output)) {
+            return { artifacts, fileAnnotations }
+        }
+
+        for (const outputItem of responseMetadata.output) {
+            // Handle container file citations from annotations
+            if (outputItem.type === 'message' && outputItem.content && Array.isArray(outputItem.content)) {
+                for (const contentItem of outputItem.content) {
+                    if (contentItem.annotations && Array.isArray(contentItem.annotations)) {
+                        for (const annotation of contentItem.annotations) {
+                            if (annotation.type === 'container_file_citation' && annotation.file_id && annotation.filename) {
+                                try {
+                                    // Download and store the file content
+                                    const downloadResult = await this.downloadContainerFile(
+                                        annotation.container_id,
+                                        annotation.file_id,
+                                        annotation.filename,
+                                        modelNodeData,
+                                        options
+                                    )
+
+                                    if (downloadResult) {
+                                        const fileType = this.getArtifactTypeFromFilename(annotation.filename)
+
+                                        if (fileType === 'png' || fileType === 'jpeg' || fileType === 'jpg') {
+                                            const artifact = {
+                                                type: fileType,
+                                                data: downloadResult.filePath
+                                            }
+
+                                            artifacts.push(artifact)
+                                        } else {
+                                            fileAnnotations.push({
+                                                filePath: downloadResult.filePath,
+                                                fileName: annotation.filename
+                                            })
+                                        }
+                                    }
+                                } catch (error) {
+                                    console.error('Error processing annotation:', error)
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+
+            // Handle built-in tool artifacts (like image generation)
+            if (outputItem.type === 'image_generation_call' && outputItem.result) {
+                try {
+                    const savedImageResult = await this.saveBase64Image(outputItem, options)
+                    if (savedImageResult) {
+                        // Replace the base64 result with the file path in the response metadata
+                        outputItem.result = savedImageResult.filePath
+
+                        // Create artifact in the same format as other image artifacts
+                        const fileType = this.getArtifactTypeFromFilename(savedImageResult.fileName)
+                        artifacts.push({
+                            type: fileType,
+                            data: savedImageResult.filePath
+                        })
+                    }
+                } catch (error) {
+                    console.error('Error processing image generation artifact:', error)
+                }
+            }
+        }
+
+        return { artifacts, fileAnnotations }
+    }
+
+    /**
+     * Downloads file content from container file citation
+     */
+    private async downloadContainerFile(
+        containerId: string,
+        fileId: string,
+        filename: string,
+        modelNodeData: INodeData,
+        options: ICommonObject
+    ): Promise<{ filePath: string; totalSize: number } | null> {
+        try {
+            const credentialData = await getCredentialData(modelNodeData.credential ?? '', options)
+            const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, modelNodeData)
+
+            if (!openAIApiKey) {
+                console.warn('No OpenAI API key available for downloading container file')
+                return null
+            }
+
+            // Download the file using OpenAI Container API
+            const response = await fetch(`https://api.openai.com/v1/containers/${containerId}/files/${fileId}/content`, {
+                method: 'GET',
+                headers: {
+                    Accept: '*/*',
+                    Authorization: `Bearer ${openAIApiKey}`
+                }
+            })
+
+            if (!response.ok) {
+                console.warn(
+                    `Failed to download container file ${fileId} from container ${containerId}: ${response.status} ${response.statusText}`
+                )
+                return null
+            }
+
+            // Extract the binary data from the Response object
+            const data = await response.arrayBuffer()
+            const dataBuffer = Buffer.from(data)
+            const mimeType = this.getMimeTypeFromFilename(filename)
+
+            // Store the file using the same storage utility as OpenAIAssistant
+            const { path, totalSize } = await addSingleFileToStorage(
+                mimeType,
+                dataBuffer,
+                filename,
+                options.orgId,
+                options.chatflowid,
+                options.chatId
+            )
+
+            return { filePath: path, totalSize }
+        } catch (error) {
+            console.error('Error downloading container file:', error)
+            return null
+        }
+    }
+
+    /**
+     * Gets MIME type from filename extension
+     */
+    private getMimeTypeFromFilename(filename: string): string {
+        const extension = filename.toLowerCase().split('.').pop()
+        const mimeTypes: { [key: string]: string } = {
+            png: 'image/png',
+            jpg: 'image/jpeg',
+            jpeg: 'image/jpeg',
+            gif: 'image/gif',
+            pdf: 'application/pdf',
+            txt: 'text/plain',
+            csv: 'text/csv',
+            json: 'application/json',
+            html: 'text/html',
+            xml: 'application/xml'
+        }
+        return mimeTypes[extension || ''] || 'application/octet-stream'
+    }
+
+    /**
+     * Gets artifact type from filename extension for UI rendering
+     */
+    private getArtifactTypeFromFilename(filename: string): string {
+        const extension = filename.toLowerCase().split('.').pop()
+        const artifactTypes: { [key: string]: string } = {
+            png: 'png',
+            jpg: 'jpeg',
+            jpeg: 'jpeg',
+            html: 'html',
+            htm: 'html',
+            md: 'markdown',
+            markdown: 'markdown',
+            json: 'json',
+            js: 'javascript',
+            javascript: 'javascript',
+            tex: 'latex',
+            latex: 'latex',
+            txt: 'text',
+            csv: 'text',
+            pdf: 'text'
+        }
+        return artifactTypes[extension || ''] || 'text'
+    }
+
     /**
      * Processes sandbox links in the response text and converts them to file annotations
      */

packages/components/nodes/agentflow/Condition/Condition.ts

@@ -317,7 +317,7 @@ class Condition_Agentflow implements INode {
             }
         }
 
-        // If no condition is fulfilled, add isFulfilled to the ELSE condition
+        // If no condition is fullfilled, add isFulfilled to the ELSE condition
         const dummyElseConditionData = {
             type: 'string',
             value1: '',

packages/components/nodes/agentflow/CustomFunction/CustomFunction.ts

@@ -60,7 +60,7 @@ class CustomFunction_Agentflow implements INode {
     constructor() {
         this.label = 'Custom Function'
         this.name = 'customFunctionAgentflow'
-        this.version = 1.1
+        this.version = 1.0
         this.type = 'CustomFunction'
         this.category = 'Agent Flows'
         this.description = 'Execute custom function'
@@ -107,7 +107,8 @@ class CustomFunction_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',
@@ -133,7 +134,7 @@ class CustomFunction_Agentflow implements INode {
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
         const javascriptFunction = nodeData.inputs?.customFunctionJavascriptFunction as string
-        const functionInputVariables = (nodeData.inputs?.customFunctionInputVariables as ICustomFunctionInputVariables[]) ?? []
+        const functionInputVariables = nodeData.inputs?.customFunctionInputVariables as ICustomFunctionInputVariables[]
         const _customFunctionUpdateState = nodeData.inputs?.customFunctionUpdateState
 
         const state = options.agentflowRuntime?.state as ICommonObject
@@ -146,17 +147,11 @@ class CustomFunction_Agentflow implements INode {
 
         const variables = await getVars(appDataSource, databaseEntities, nodeData, options)
         const flow = {
-            input,
-            state,
             chatflowId: options.chatflowid,
             sessionId: options.sessionId,
             chatId: options.chatId,
-            rawOutput: options.postProcessing?.rawOutput || '',
-            chatHistory: options.postProcessing?.chatHistory || [],
-            sourceDocuments: options.postProcessing?.sourceDocuments,
-            usedTools: options.postProcessing?.usedTools,
-            artifacts: options.postProcessing?.artifacts,
-            fileAnnotations: options.postProcessing?.fileAnnotations
+            input,
+            state
         }
 
         // Create additional sandbox variables for custom function inputs

packages/components/nodes/agentflow/ExecuteFlow/ExecuteFlow.ts

@@ -30,7 +30,7 @@ class ExecuteFlow_Agentflow implements INode {
     constructor() {
         this.label = 'Execute Flow'
         this.name = 'executeFlowAgentflow'
-        this.version = 1.2
+        this.version = 1.1
         this.type = 'ExecuteFlow'
         this.category = 'Agent Flows'
         this.description = 'Execute another flow'
@@ -102,7 +102,8 @@ class ExecuteFlow_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',

packages/components/nodes/agentflow/HumanInput/HumanInput.ts

@@ -241,11 +241,8 @@ class HumanInput_Agentflow implements INode {
                     if (isStreamable) {
                         const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
                         for await (const chunk of await llmNodeInstance.stream(messages)) {
-                            const content = typeof chunk === 'string' ? chunk : chunk.content.toString()
-                            sseStreamer.streamTokenEvent(chatId, content)
-
-                            const messageChunk = typeof chunk === 'string' ? new AIMessageChunk(chunk) : chunk
-                            response = response.concat(messageChunk)
+                            sseStreamer.streamTokenEvent(chatId, chunk.content.toString())
+                            response = response.concat(chunk)
                         }
                         humanInputDescription = response.content as string
                     } else {

packages/components/nodes/agentflow/LLM/LLM.ts

@@ -2,20 +2,17 @@ import { BaseChatModel } from '@langchain/core/language_models/chat_models'
 import { ICommonObject, IMessage, INode, INodeData, INodeOptionsValue, INodeParams, IServerSideEventStreamer } from '../../../src/Interface'
 import { AIMessageChunk, BaseMessageLike, MessageContentText } from '@langchain/core/messages'
 import { DEFAULT_SUMMARIZER_TEMPLATE } from '../prompt'
+import { z } from 'zod'
 import { AnalyticHandler } from '../../../src/handler'
-import { ILLMMessage } from '../Interface.Agentflow'
+import { ILLMMessage, IStructuredOutput } from '../Interface.Agentflow'
 import {
-    addImageArtifactsToMessages,
-    extractArtifactsFromResponse,
     getPastChatHistoryImageMessages,
     getUniqueImageMessages,
     processMessagesWithImages,
     replaceBase64ImagesWithFileReferences,
-    replaceInlineDataWithFileReferences,
     updateFlowState
 } from '../utils'
-import { processTemplateVariables, configureStructuredOutput } from '../../../src/utils'
-import { flatten } from 'lodash'
+import { processTemplateVariables } from '../../../src/utils'
 
 class LLM_Agentflow implements INode {
     label: string
@@ -34,7 +31,7 @@ class LLM_Agentflow implements INode {
     constructor() {
         this.label = 'LLM'
         this.name = 'llmAgentflow'
-        this.version = 1.1
+        this.version = 1.0
         this.type = 'LLM'
         this.category = 'Agent Flows'
         this.description = 'Large language models to analyze user-provided inputs and generate responses'
@@ -290,7 +287,8 @@ class LLM_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',
@@ -450,16 +448,10 @@ class LLM_Agentflow implements INode {
             }
             delete nodeData.inputs?.llmMessages
 
-            /**
-             * Add image artifacts from previous assistant responses as user messages
-             * Images are converted from FILE-STORAGE::<image_path> to base 64 image_url format
-             */
-            await addImageArtifactsToMessages(messages, options)
-
             // Configure structured output if specified
             const isStructuredOutput = _llmStructuredOutput && Array.isArray(_llmStructuredOutput) && _llmStructuredOutput.length > 0
             if (isStructuredOutput) {
-                llmNodeInstance = configureStructuredOutput(llmNodeInstance, _llmStructuredOutput)
+                llmNodeInstance = this.configureStructuredOutput(llmNodeInstance, _llmStructuredOutput)
             }
 
             // Initialize response and determine if streaming is possible
@@ -475,11 +467,9 @@ class LLM_Agentflow implements INode {
 
             // Track execution time
             const startTime = Date.now()
+
             const sseStreamer: IServerSideEventStreamer | undefined = options.sseStreamer
 
-            /*
-             * Invoke LLM
-             */
             if (isStreamable) {
                 response = await this.handleStreamingResponse(sseStreamer, llmNodeInstance, messages, chatId, abortController)
             } else {
@@ -504,40 +494,6 @@ class LLM_Agentflow implements INode {
             const endTime = Date.now()
             const timeDelta = endTime - startTime
 
-            // Extract artifacts and file annotations from response metadata
-            let artifacts: any[] = []
-            let fileAnnotations: any[] = []
-            if (response.response_metadata) {
-                const {
-                    artifacts: extractedArtifacts,
-                    fileAnnotations: extractedFileAnnotations,
-                    savedInlineImages
-                } = await extractArtifactsFromResponse(response.response_metadata, newNodeData, options)
-
-                if (extractedArtifacts.length > 0) {
-                    artifacts = extractedArtifacts
-
-                    // Stream artifacts if this is the last node
-                    if (isLastNode && sseStreamer) {
-                        sseStreamer.streamArtifactsEvent(chatId, artifacts)
-                    }
-                }
-
-                if (extractedFileAnnotations.length > 0) {
-                    fileAnnotations = extractedFileAnnotations
-
-                    // Stream file annotations if this is the last node
-                    if (isLastNode && sseStreamer) {
-                        sseStreamer.streamFileAnnotationsEvent(chatId, fileAnnotations)
-                    }
-                }
-
-                // Replace inlineData base64 with file references in the response
-                if (savedInlineImages && savedInlineImages.length > 0) {
-                    replaceInlineDataWithFileReferences(response, savedInlineImages)
-                }
-            }
-
             // Update flow state if needed
             let newState = { ...state }
             if (_llmUpdateState && Array.isArray(_llmUpdateState) && _llmUpdateState.length > 0) {
@@ -557,22 +513,10 @@ class LLM_Agentflow implements INode {
                 finalResponse = response.content.map((item: any) => item.text).join('\n')
             } else if (response.content && typeof response.content === 'string') {
                 finalResponse = response.content
-            } else if (response.content === '') {
-                // Empty response content, this could happen when there is only image data
-                finalResponse = ''
             } else {
                 finalResponse = JSON.stringify(response, null, 2)
             }
-            const output = this.prepareOutputObject(
-                response,
-                finalResponse,
-                startTime,
-                endTime,
-                timeDelta,
-                isStructuredOutput,
-                artifacts,
-                fileAnnotations
-            )
+            const output = this.prepareOutputObject(response, finalResponse, startTime, endTime, timeDelta, isStructuredOutput)
 
             // End analytics tracking
             if (analyticHandlers && llmIds) {
@@ -584,23 +528,12 @@ class LLM_Agentflow implements INode {
                 this.sendStreamingEvents(options, chatId, response)
             }
 
-            // Stream file annotations if any were extracted
-            if (fileAnnotations.length > 0 && isLastNode && sseStreamer) {
-                sseStreamer.streamFileAnnotationsEvent(chatId, fileAnnotations)
-            }
-
             // Process template variables in state
             newState = processTemplateVariables(newState, finalResponse)
 
-            /**
-             * Remove the temporarily added image artifact messages before storing
-             * This is to avoid storing the actual base64 data into database
-             */
-            const messagesToStore = messages.filter((msg: any) => !msg._isTemporaryImageMessage)
-
             // Replace the actual messages array with one that includes the file references for images instead of base64 data
             const messagesWithFileReferences = replaceBase64ImagesWithFileReferences(
-                messagesToStore,
+                messages,
                 runtimeImageMessagesWithFileRef,
                 pastImageMessagesWithFileRef
             )
@@ -651,13 +584,7 @@ class LLM_Agentflow implements INode {
                     {
                         role: returnRole,
                         content: finalResponse,
-                        name: nodeData?.label ? nodeData?.label.toLowerCase().replace(/\s/g, '_').trim() : nodeData?.id,
-                        ...(((artifacts && artifacts.length > 0) || (fileAnnotations && fileAnnotations.length > 0)) && {
-                            additional_kwargs: {
-                                ...(artifacts && artifacts.length > 0 && { artifacts }),
-                                ...(fileAnnotations && fileAnnotations.length > 0 && { fileAnnotations })
-                            }
-                        })
+                        name: nodeData?.label ? nodeData?.label.toLowerCase().replace(/\s/g, '_').trim() : nodeData?.id
                     }
                 ]
             }
@@ -827,6 +754,59 @@ class LLM_Agentflow implements INode {
         }
     }
 
+    /**
+     * Configures structured output for the LLM
+     */
+    private configureStructuredOutput(llmNodeInstance: BaseChatModel, llmStructuredOutput: IStructuredOutput[]): BaseChatModel {
+        try {
+            const zodObj: ICommonObject = {}
+            for (const sch of llmStructuredOutput) {
+                if (sch.type === 'string') {
+                    zodObj[sch.key] = z.string().describe(sch.description || '')
+                } else if (sch.type === 'stringArray') {
+                    zodObj[sch.key] = z.array(z.string()).describe(sch.description || '')
+                } else if (sch.type === 'number') {
+                    zodObj[sch.key] = z.number().describe(sch.description || '')
+                } else if (sch.type === 'boolean') {
+                    zodObj[sch.key] = z.boolean().describe(sch.description || '')
+                } else if (sch.type === 'enum') {
+                    const enumValues = sch.enumValues?.split(',').map((item: string) => item.trim()) || []
+                    zodObj[sch.key] = z
+                        .enum(enumValues.length ? (enumValues as [string, ...string[]]) : ['default'])
+                        .describe(sch.description || '')
+                } else if (sch.type === 'jsonArray') {
+                    const jsonSchema = sch.jsonSchema
+                    if (jsonSchema) {
+                        try {
+                            // Parse the JSON schema
+                            const schemaObj = JSON.parse(jsonSchema)
+
+                            // Create a Zod schema from the JSON schema
+                            const itemSchema = this.createZodSchemaFromJSON(schemaObj)
+
+                            // Create an array schema of the item schema
+                            zodObj[sch.key] = z.array(itemSchema).describe(sch.description || '')
+                        } catch (err) {
+                            console.error(`Error parsing JSON schema for ${sch.key}:`, err)
+                            // Fallback to generic array of records
+                            zodObj[sch.key] = z.array(z.record(z.any())).describe(sch.description || '')
+                        }
+                    } else {
+                        // If no schema provided, use generic array of records
+                        zodObj[sch.key] = z.array(z.record(z.any())).describe(sch.description || '')
+                    }
+                }
+            }
+            const structuredOutput = z.object(zodObj)
+
+            // @ts-ignore
+            return llmNodeInstance.withStructuredOutput(structuredOutput)
+        } catch (exception) {
+            console.error(exception)
+            return llmNodeInstance
+        }
+    }
+
     /**
      * Handles streaming response from the LLM
      */
@@ -843,20 +823,16 @@ class LLM_Agentflow implements INode {
             for await (const chunk of await llmNodeInstance.stream(messages, { signal: abortController?.signal })) {
                 if (sseStreamer) {
                     let content = ''
-
-                    if (typeof chunk === 'string') {
-                        content = chunk
-                    } else if (Array.isArray(chunk.content) && chunk.content.length > 0) {
+                    if (Array.isArray(chunk.content) && chunk.content.length > 0) {
                         const contents = chunk.content as MessageContentText[]
                         content = contents.map((item) => item.text).join('')
-                    } else if (chunk.content) {
+                    } else {
                         content = chunk.content.toString()
                     }
                     sseStreamer.streamTokenEvent(chatId, content)
                 }
 
-                const messageChunk = typeof chunk === 'string' ? new AIMessageChunk(chunk) : chunk
-                response = response.concat(messageChunk)
+                response = response.concat(chunk)
             }
         } catch (error) {
             console.error('Error during streaming:', error)
@@ -878,9 +854,7 @@ class LLM_Agentflow implements INode {
         startTime: number,
         endTime: number,
         timeDelta: number,
-        isStructuredOutput: boolean,
-        artifacts: any[] = [],
-        fileAnnotations: any[] = []
+        isStructuredOutput: boolean
     ): any {
         const output: any = {
             content: finalResponse,
@@ -899,10 +873,6 @@ class LLM_Agentflow implements INode {
             output.usageMetadata = response.usage_metadata
         }
 
-        if (response.response_metadata) {
-            output.responseMetadata = response.response_metadata
-        }
-
         if (isStructuredOutput && typeof response === 'object') {
             const structuredOutput = response as Record<string, any>
             for (const key in structuredOutput) {
@@ -912,14 +882,6 @@ class LLM_Agentflow implements INode {
             }
         }
 
-        if (artifacts && artifacts.length > 0) {
-            output.artifacts = flatten(artifacts)
-        }
-
-        if (fileAnnotations && fileAnnotations.length > 0) {
-            output.fileAnnotations = fileAnnotations
-        }
-
         return output
     }
 
@@ -930,12 +892,7 @@ class LLM_Agentflow implements INode {
         const sseStreamer: IServerSideEventStreamer = options.sseStreamer as IServerSideEventStreamer
 
         if (response.tool_calls) {
-            const formattedToolCalls = response.tool_calls.map((toolCall: any) => ({
-                tool: toolCall.name || 'tool',
-                toolInput: toolCall.args,
-                toolOutput: ''
-            }))
-            sseStreamer.streamCalledToolsEvent(chatId, flatten(formattedToolCalls))
+            sseStreamer.streamCalledToolsEvent(chatId, response.tool_calls)
         }
 
         if (response.usage_metadata) {
@@ -944,6 +901,107 @@ class LLM_Agentflow implements INode {
 
         sseStreamer.streamEndEvent(chatId)
     }
+
+    /**
+     * Creates a Zod schema from a JSON schema object
+     * @param jsonSchema The JSON schema object
+     * @returns A Zod schema
+     */
+    private createZodSchemaFromJSON(jsonSchema: any): z.ZodTypeAny {
+        // If the schema is an object with properties, create an object schema
+        if (typeof jsonSchema === 'object' && jsonSchema !== null) {
+            const schemaObj: Record<string, z.ZodTypeAny> = {}
+
+            // Process each property in the schema
+            for (const [key, value] of Object.entries(jsonSchema)) {
+                if (value === null) {
+                    // Handle null values
+                    schemaObj[key] = z.null()
+                } else if (typeof value === 'object' && !Array.isArray(value)) {
+                    // Check if the property has a type definition
+                    if ('type' in value) {
+                        const type = value.type as string
+                        const description = ('description' in value ? (value.description as string) : '') || ''
+
+                        // Create the appropriate Zod type based on the type property
+                        if (type === 'string') {
+                            schemaObj[key] = z.string().describe(description)
+                        } else if (type === 'number') {
+                            schemaObj[key] = z.number().describe(description)
+                        } else if (type === 'boolean') {
+                            schemaObj[key] = z.boolean().describe(description)
+                        } else if (type === 'array') {
+                            // If it's an array type, check if items is defined
+                            if ('items' in value && value.items) {
+                                const itemSchema = this.createZodSchemaFromJSON(value.items)
+                                schemaObj[key] = z.array(itemSchema).describe(description)
+                            } else {
+                                // Default to array of any if items not specified
+                                schemaObj[key] = z.array(z.any()).describe(description)
+                            }
+                        } else if (type === 'object') {
+                            // If it's an object type, check if properties is defined
+                            if ('properties' in value && value.properties) {
+                                const nestedSchema = this.createZodSchemaFromJSON(value.properties)
+                                schemaObj[key] = nestedSchema.describe(description)
+                            } else {
+                                // Default to record of any if properties not specified
+                                schemaObj[key] = z.record(z.any()).describe(description)
+                            }
+                        } else {
+                            // Default to any for unknown types
+                            schemaObj[key] = z.any().describe(description)
+                        }
+
+                        // Check if the property is optional
+                        if ('optional' in value && value.optional === true) {
+                            schemaObj[key] = schemaObj[key].optional()
+                        }
+                    } else if (Array.isArray(value)) {
+                        // Array values without a type property
+                        if (value.length > 0) {
+                            // If the array has items, recursively create a schema for the first item
+                            const itemSchema = this.createZodSchemaFromJSON(value[0])
+                            schemaObj[key] = z.array(itemSchema)
+                        } else {
+                            // Empty array, allow any array
+                            schemaObj[key] = z.array(z.any())
+                        }
+                    } else {
+                        // It's a nested object without a type property, recursively create schema
+                        schemaObj[key] = this.createZodSchemaFromJSON(value)
+                    }
+                } else if (Array.isArray(value)) {
+                    // Array values
+                    if (value.length > 0) {
+                        // If the array has items, recursively create a schema for the first item
+                        const itemSchema = this.createZodSchemaFromJSON(value[0])
+                        schemaObj[key] = z.array(itemSchema)
+                    } else {
+                        // Empty array, allow any array
+                        schemaObj[key] = z.array(z.any())
+                    }
+                } else {
+                    // For primitive values (which shouldn't be in the schema directly)
+                    // Use the corresponding Zod type
+                    if (typeof value === 'string') {
+                        schemaObj[key] = z.string()
+                    } else if (typeof value === 'number') {
+                        schemaObj[key] = z.number()
+                    } else if (typeof value === 'boolean') {
+                        schemaObj[key] = z.boolean()
+                    } else {
+                        schemaObj[key] = z.any()
+                    }
+                }
+            }
+
+            return z.object(schemaObj)
+        }
+
+        // Fallback to any for unknown types
+        return z.any()
+    }
 }
 
 module.exports = { nodeClass: LLM_Agentflow }

packages/components/nodes/agentflow/Loop/Loop.ts

@@ -20,7 +20,7 @@ class Loop_Agentflow implements INode {
     constructor() {
         this.label = 'Loop'
         this.name = 'loopAgentflow'
-        this.version = 1.2
+        this.version = 1.1
         this.type = 'Loop'
         this.category = 'Agent Flows'
         this.description = 'Loop back to a previous node'
@@ -64,7 +64,8 @@ class Loop_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',

packages/components/nodes/agentflow/Retriever/Retriever.ts

@@ -36,7 +36,7 @@ class Retriever_Agentflow implements INode {
     constructor() {
         this.label = 'Retriever'
         this.name = 'retrieverAgentflow'
-        this.version = 1.1
+        this.version = 1.0
         this.type = 'Retriever'
         this.category = 'Agent Flows'
         this.description = 'Retrieve information from vector database'
@@ -87,7 +87,8 @@ class Retriever_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',

packages/components/nodes/agentflow/Tool/Tool.ts

@@ -29,7 +29,7 @@ class Tool_Agentflow implements INode {
     constructor() {
         this.label = 'Tool'
         this.name = 'toolAgentflow'
-        this.version = 1.2
+        this.version = 1.1
         this.type = 'Tool'
         this.category = 'Agent Flows'
         this.description = 'Tools allow LLM to interact with external systems'
@@ -80,7 +80,8 @@ class Tool_Agentflow implements INode {
                         label: 'Key',
                         name: 'key',
                         type: 'asyncOptions',
-                        loadMethod: 'listRuntimeStateKeys'
+                        loadMethod: 'listRuntimeStateKeys',
+                        freeSolo: true
                     },
                     {
                         label: 'Value',

packages/components/nodes/agentflow/utils.ts

@@ -1,11 +1,10 @@
-import { BaseMessage, MessageContentImageUrl, AIMessageChunk } from '@langchain/core/messages'
+import { BaseMessage, MessageContentImageUrl } from '@langchain/core/messages'
 import { getImageUploads } from '../../src/multiModalUtils'
-import { addSingleFileToStorage, getFileFromStorage } from '../../src/storageUtils'
-import { ICommonObject, IFileUpload, INodeData } from '../../src/Interface'
+import { getFileFromStorage } from '../../src/storageUtils'
+import { ICommonObject, IFileUpload } from '../../src/Interface'
 import { BaseMessageLike } from '@langchain/core/messages'
 import { IFlowState } from './Interface.Agentflow'
-import { getCredentialData, getCredentialParam, handleEscapeCharacters, mapMimeTypeToInputField } from '../../src/utils'
-import fetch from 'node-fetch'
+import { handleEscapeCharacters, mapMimeTypeToInputField } from '../../src/utils'
 
 export const addImagesToMessages = async (
     options: ICommonObject,
@@ -19,8 +18,7 @@ export const addImagesToMessages = async (
         for (const upload of imageUploads) {
             let bf = upload.data
             if (upload.type == 'stored-file') {
-                const fileName = upload.name.replace(/^FILE-STORAGE::/, '')
-                const contents = await getFileFromStorage(fileName, options.orgId, options.chatflowid, options.chatId)
+                const contents = await getFileFromStorage(upload.name, options.orgId, options.chatflowid, options.chatId)
                 // as the image is stored in the server, read the file and convert it to base64
                 bf = 'data:' + upload.mime + ';base64,' + contents.toString('base64')
 
@@ -91,9 +89,8 @@ export const processMessagesWithImages = async (
                 if (item.type === 'stored-file' && item.name && item.mime.startsWith('image/')) {
                     hasImageReferences = true
                     try {
-                        const fileName = item.name.replace(/^FILE-STORAGE::/, '')
                         // Get file contents from storage
-                        const contents = await getFileFromStorage(fileName, options.orgId, options.chatflowid, options.chatId)
+                        const contents = await getFileFromStorage(item.name, options.orgId, options.chatflowid, options.chatId)
 
                         // Create base64 data URL
                         const base64Data = 'data:' + item.mime + ';base64,' + contents.toString('base64')
@@ -325,8 +322,7 @@ export const getPastChatHistoryImageMessages = async (
                 const imageContents: MessageContentImageUrl[] = []
                 for (const upload of uploads) {
                     if (upload.type === 'stored-file' && upload.mime.startsWith('image/')) {
-                        const fileName = upload.name.replace(/^FILE-STORAGE::/, '')
-                        const fileData = await getFileFromStorage(fileName, options.orgId, options.chatflowid, options.chatId)
+                        const fileData = await getFileFromStorage(upload.name, options.orgId, options.chatflowid, options.chatId)
                         // as the image is stored in the server, read the file and convert it to base64
                         const bf = 'data:' + upload.mime + ';base64,' + fileData.toString('base64')
 
@@ -460,437 +456,6 @@ export const getPastChatHistoryImageMessages = async (
     }
 }
 
-/**
- * Gets MIME type from filename extension
- */
-export const getMimeTypeFromFilename = (filename: string): string => {
-    const extension = filename.toLowerCase().split('.').pop()
-    const mimeTypes: { [key: string]: string } = {
-        png: 'image/png',
-        jpg: 'image/jpeg',
-        jpeg: 'image/jpeg',
-        gif: 'image/gif',
-        pdf: 'application/pdf',
-        txt: 'text/plain',
-        csv: 'text/csv',
-        json: 'application/json',
-        html: 'text/html',
-        xml: 'application/xml'
-    }
-    return mimeTypes[extension || ''] || 'application/octet-stream'
-}
-
-/**
- * Gets artifact type from filename extension for UI rendering
- */
-export const getArtifactTypeFromFilename = (filename: string): string => {
-    const extension = filename.toLowerCase().split('.').pop()
-    const artifactTypes: { [key: string]: string } = {
-        png: 'png',
-        jpg: 'jpeg',
-        jpeg: 'jpeg',
-        html: 'html',
-        htm: 'html',
-        md: 'markdown',
-        markdown: 'markdown',
-        json: 'json',
-        js: 'javascript',
-        javascript: 'javascript',
-        tex: 'latex',
-        latex: 'latex',
-        txt: 'text',
-        csv: 'text',
-        pdf: 'text'
-    }
-    return artifactTypes[extension || ''] || 'text'
-}
-
-/**
- * Saves base64 image data to storage and returns file information
- */
-export const saveBase64Image = async (
-    outputItem: any,
-    options: ICommonObject
-): Promise<{ filePath: string; fileName: string; totalSize: number } | null> => {
-    try {
-        if (!outputItem.result) {
-            return null
-        }
-
-        // Extract base64 data and create buffer
-        const base64Data = outputItem.result
-        const imageBuffer = Buffer.from(base64Data, 'base64')
-
-        // Determine file extension and MIME type
-        const outputFormat = outputItem.output_format || 'png'
-        const fileName = `generated_image_${outputItem.id || Date.now()}.${outputFormat}`
-        const mimeType = outputFormat === 'png' ? 'image/png' : 'image/jpeg'
-
-        // Save the image using the existing storage utility
-        const { path, totalSize } = await addSingleFileToStorage(
-            mimeType,
-            imageBuffer,
-            fileName,
-            options.orgId,
-            options.chatflowid,
-            options.chatId
-        )
-
-        return { filePath: path, fileName, totalSize }
-    } catch (error) {
-        console.error('Error saving base64 image:', error)
-        return null
-    }
-}
-
-/**
- * Saves Gemini inline image data to storage and returns file information
- */
-export const saveGeminiInlineImage = async (
-    inlineItem: any,
-    options: ICommonObject
-): Promise<{ filePath: string; fileName: string; totalSize: number } | null> => {
-    try {
-        if (!inlineItem.data || !inlineItem.mimeType) {
-            return null
-        }
-
-        // Extract base64 data and create buffer
-        const base64Data = inlineItem.data
-        const imageBuffer = Buffer.from(base64Data, 'base64')
-
-        // Determine file extension from MIME type
-        const mimeType = inlineItem.mimeType
-        let extension = 'png'
-        if (mimeType.includes('jpeg') || mimeType.includes('jpg')) {
-            extension = 'jpg'
-        } else if (mimeType.includes('png')) {
-            extension = 'png'
-        } else if (mimeType.includes('gif')) {
-            extension = 'gif'
-        } else if (mimeType.includes('webp')) {
-            extension = 'webp'
-        }
-
-        const fileName = `gemini_generated_image_${Date.now()}.${extension}`
-
-        // Save the image using the existing storage utility
-        const { path, totalSize } = await addSingleFileToStorage(
-            mimeType,
-            imageBuffer,
-            fileName,
-            options.orgId,
-            options.chatflowid,
-            options.chatId
-        )
-
-        return { filePath: path, fileName, totalSize }
-    } catch (error) {
-        console.error('Error saving Gemini inline image:', error)
-        return null
-    }
-}
-
-/**
- * Downloads file content from container file citation
- */
-export const downloadContainerFile = async (
-    containerId: string,
-    fileId: string,
-    filename: string,
-    modelNodeData: INodeData,
-    options: ICommonObject
-): Promise<{ filePath: string; totalSize: number } | null> => {
-    try {
-        const credentialData = await getCredentialData(modelNodeData.credential ?? '', options)
-        const openAIApiKey = getCredentialParam('openAIApiKey', credentialData, modelNodeData)
-
-        if (!openAIApiKey) {
-            console.warn('No OpenAI API key available for downloading container file')
-            return null
-        }
-
-        // Download the file using OpenAI Container API
-        const response = await fetch(`https://api.openai.com/v1/containers/${containerId}/files/${fileId}/content`, {
-            method: 'GET',
-            headers: {
-                Accept: '*/*',
-                Authorization: `Bearer ${openAIApiKey}`
-            }
-        })
-
-        if (!response.ok) {
-            console.warn(
-                `Failed to download container file ${fileId} from container ${containerId}: ${response.status} ${response.statusText}`
-            )
-            return null
-        }
-
-        // Extract the binary data from the Response object
-        const data = await response.arrayBuffer()
-        const dataBuffer = Buffer.from(data)
-        const mimeType = getMimeTypeFromFilename(filename)
-
-        // Store the file using the same storage utility as OpenAIAssistant
-        const { path, totalSize } = await addSingleFileToStorage(
-            mimeType,
-            dataBuffer,
-            filename,
-            options.orgId,
-            options.chatflowid,
-            options.chatId
-        )
-
-        return { filePath: path, totalSize }
-    } catch (error) {
-        console.error('Error downloading container file:', error)
-        return null
-    }
-}
-
-/**
- * Replace inlineData base64 with file references in the response content
- */
-export const replaceInlineDataWithFileReferences = (
-    response: AIMessageChunk,
-    savedInlineImages: Array<{ filePath: string; fileName: string; mimeType: string }>
-): void => {
-    // Check if content is an array
-    if (!Array.isArray(response.content)) {
-        return
-    }
-
-    // Replace base64 data with file references in response content
-    let savedImageIndex = 0
-    for (let i = 0; i < response.content.length; i++) {
-        const contentItem = response.content[i]
-        if (
-            typeof contentItem === 'object' &&
-            contentItem.type === 'inlineData' &&
-            contentItem.inlineData &&
-            savedImageIndex < savedInlineImages.length
-        ) {
-            const savedImage = savedInlineImages[savedImageIndex]
-            // Replace with file reference
-            response.content[i] = {
-                type: 'stored-file',
-                name: savedImage.fileName,
-                mime: savedImage.mimeType,
-                path: savedImage.filePath
-            }
-            savedImageIndex++
-        }
-    }
-
-    // Clear the inlineData from response_metadata to avoid duplication
-    if (response.response_metadata?.inlineData) {
-        delete response.response_metadata.inlineData
-    }
-}
-
-/**
- * Extracts artifacts from response metadata (both annotations and built-in tools)
- */
-export const extractArtifactsFromResponse = async (
-    responseMetadata: any,
-    modelNodeData: INodeData,
-    options: ICommonObject
-): Promise<{
-    artifacts: any[]
-    fileAnnotations: any[]
-    savedInlineImages?: Array<{ filePath: string; fileName: string; mimeType: string }>
-}> => {
-    const artifacts: any[] = []
-    const fileAnnotations: any[] = []
-    const savedInlineImages: Array<{ filePath: string; fileName: string; mimeType: string }> = []
-
-    // Handle Gemini inline data (image generation)
-    if (responseMetadata?.inlineData && Array.isArray(responseMetadata.inlineData)) {
-        for (const inlineItem of responseMetadata.inlineData) {
-            if (inlineItem.type === 'gemini_inline_data' && inlineItem.data && inlineItem.mimeType) {
-                try {
-                    const savedImageResult = await saveGeminiInlineImage(inlineItem, options)
-                    if (savedImageResult) {
-                        // Create artifact in the same format as other image artifacts
-                        const fileType = getArtifactTypeFromFilename(savedImageResult.fileName)
-                        artifacts.push({
-                            type: fileType,
-                            data: savedImageResult.filePath
-                        })
-
-                        // Track saved image for replacing base64 data in content
-                        savedInlineImages.push({
-                            filePath: savedImageResult.filePath,
-                            fileName: savedImageResult.fileName,
-                            mimeType: inlineItem.mimeType
-                        })
-                    }
-                } catch (error) {
-                    console.error('Error processing Gemini inline image artifact:', error)
-                }
-            }
-        }
-    }
-
-    if (!responseMetadata?.output || !Array.isArray(responseMetadata.output)) {
-        return { artifacts, fileAnnotations, savedInlineImages: savedInlineImages.length > 0 ? savedInlineImages : undefined }
-    }
-
-    for (const outputItem of responseMetadata.output) {
-        // Handle container file citations from annotations
-        if (outputItem.type === 'message' && outputItem.content && Array.isArray(outputItem.content)) {
-            for (const contentItem of outputItem.content) {
-                if (contentItem.annotations && Array.isArray(contentItem.annotations)) {
-                    for (const annotation of contentItem.annotations) {
-                        if (annotation.type === 'container_file_citation' && annotation.file_id && annotation.filename) {
-                            try {
-                                // Download and store the file content
-                                const downloadResult = await downloadContainerFile(
-                                    annotation.container_id,
-                                    annotation.file_id,
-                                    annotation.filename,
-                                    modelNodeData,
-                                    options
-                                )
-
-                                if (downloadResult) {
-                                    const fileType = getArtifactTypeFromFilename(annotation.filename)
-
-                                    if (fileType === 'png' || fileType === 'jpeg' || fileType === 'jpg') {
-                                        const artifact = {
-                                            type: fileType,
-                                            data: downloadResult.filePath
-                                        }
-
-                                        artifacts.push(artifact)
-                                    } else {
-                                        fileAnnotations.push({
-                                            filePath: downloadResult.filePath,
-                                            fileName: annotation.filename
-                                        })
-                                    }
-                                }
-                            } catch (error) {
-                                console.error('Error processing annotation:', error)
-                            }
-                        }
-                    }
-                }
-            }
-        }
-
-        // Handle built-in tool artifacts (like image generation)
-        if (outputItem.type === 'image_generation_call' && outputItem.result) {
-            try {
-                const savedImageResult = await saveBase64Image(outputItem, options)
-                if (savedImageResult) {
-                    // Replace the base64 result with the file path in the response metadata
-                    outputItem.result = savedImageResult.filePath
-
-                    // Create artifact in the same format as other image artifacts
-                    const fileType = getArtifactTypeFromFilename(savedImageResult.fileName)
-                    artifacts.push({
-                        type: fileType,
-                        data: savedImageResult.filePath
-                    })
-                }
-            } catch (error) {
-                console.error('Error processing image generation artifact:', error)
-            }
-        }
-    }
-
-    return { artifacts, fileAnnotations, savedInlineImages: savedInlineImages.length > 0 ? savedInlineImages : undefined }
-}
-
-/**
- * Add image artifacts from previous assistant messages as user messages
- * This allows the LLM to see and reference the generated images in the conversation
- * Messages are marked with a special flag for later removal
- */
-export const addImageArtifactsToMessages = async (messages: BaseMessageLike[], options: ICommonObject): Promise<void> => {
-    const imageExtensions = ['png', 'jpg', 'jpeg', 'gif', 'webp']
-    const messagesToInsert: Array<{ index: number; message: any }> = []
-
-    // Iterate through messages to find assistant messages with image artifacts
-    for (let i = 0; i < messages.length; i++) {
-        const message = messages[i] as any
-
-        // Check if this is an assistant message with artifacts
-        if (
-            (message.role === 'assistant' || message.role === 'ai') &&
-            message.additional_kwargs?.artifacts &&
-            Array.isArray(message.additional_kwargs.artifacts)
-        ) {
-            const artifacts = message.additional_kwargs.artifacts
-            const imageArtifacts: Array<{ type: string; name: string; mime: string }> = []
-
-            // Extract image artifacts
-            for (const artifact of artifacts) {
-                if (artifact.type && artifact.data) {
-                    // Check if this is an image artifact by file type
-                    if (imageExtensions.includes(artifact.type.toLowerCase())) {
-                        // Extract filename from the file path
-                        const fileName = artifact.data.split('/').pop() || artifact.data
-                        const mimeType = `image/${artifact.type.toLowerCase()}`
-
-                        imageArtifacts.push({
-                            type: 'stored-file',
-                            name: fileName,
-                            mime: mimeType
-                        })
-                    }
-                }
-            }
-
-            // If we found image artifacts, prepare to insert a user message after this assistant message
-            if (imageArtifacts.length > 0) {
-                // Check if the next message already contains these image artifacts to avoid duplicates
-                const nextMessage = messages[i + 1] as any
-                const shouldInsert =
-                    !nextMessage ||
-                    nextMessage.role !== 'user' ||
-                    !Array.isArray(nextMessage.content) ||
-                    !nextMessage.content.some(
-                        (item: any) =>
-                            (item.type === 'stored-file' || item.type === 'image_url') &&
-                            imageArtifacts.some((artifact) => {
-                                // Compare with and without FILE-STORAGE:: prefix
-                                const artifactName = artifact.name.replace('FILE-STORAGE::', '')
-                                const itemName = item.name?.replace('FILE-STORAGE::', '') || ''
-                                return artifactName === itemName
-                            })
-                    )
-
-                if (shouldInsert) {
-                    messagesToInsert.push({
-                        index: i + 1,
-                        message: {
-                            role: 'user',
-                            content: imageArtifacts,
-                            _isTemporaryImageMessage: true // Mark for later removal
-                        }
-                    })
-                }
-            }
-        }
-    }
-
-    // Insert messages in reverse order to maintain correct indices
-    for (let i = messagesToInsert.length - 1; i >= 0; i--) {
-        const { index, message } = messagesToInsert[i]
-        messages.splice(index, 0, message)
-    }
-
-    // Convert stored-file references to base64 image_url format
-    if (messagesToInsert.length > 0) {
-        const { updatedMessages } = await processMessagesWithImages(messages, options)
-        // Replace the messages array content with the updated messages
-        messages.length = 0
-        messages.push(...updatedMessages)
-    }
-}
-
 /**
  * Updates the flow state with new values
  */

packages/components/nodes/agents/AirtableAgent/AirtableAgent.ts

@@ -183,7 +183,7 @@ json.dumps(my_dict)`
                 // TODO: get print console output
                 finalResult = await pyodide.runPythonAsync(code)
             } catch (error) {
-                throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using following code: "${pythonCode}"`)
+                throw new Error(`Sorry, I'm unable to find answer for question: "${input}" using follwoing code: "${pythonCode}"`)
             }
         }
 

packages/components/nodes/agents/ConversationalRetrievalToolAgent/ConversationalRetrievalToolAgent.ts

@@ -5,7 +5,7 @@ import { RunnableSequence } from '@langchain/core/runnables'
 import { BaseChatModel } from '@langchain/core/language_models/chat_models'
 import { ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate, PromptTemplate } from '@langchain/core/prompts'
 import { formatToOpenAIToolMessages } from 'langchain/agents/format_scratchpad/openai_tools'
-import { getBaseClasses, transformBracesWithColon, convertChatHistoryToText, convertBaseMessagetoIMessage } from '../../../src/utils'
+import { getBaseClasses, transformBracesWithColon } from '../../../src/utils'
 import { type ToolsAgentStep } from 'langchain/agents/openai/output_parser'
 import {
     FlowiseMemory,
@@ -23,10 +23,8 @@ import { Moderation, checkInputs, streamResponse } from '../../moderation/Modera
 import { formatResponse } from '../../outputparsers/OutputParserHelpers'
 import type { Document } from '@langchain/core/documents'
 import { BaseRetriever } from '@langchain/core/retrievers'
-import { RESPONSE_TEMPLATE, REPHRASE_TEMPLATE } from '../../chains/ConversationalRetrievalQAChain/prompts'
+import { RESPONSE_TEMPLATE } from '../../chains/ConversationalRetrievalQAChain/prompts'
 import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
-import { StringOutputParser } from '@langchain/core/output_parsers'
-import { Tool } from '@langchain/core/tools'
 
 class ConversationalRetrievalToolAgent_Agents implements INode {
     label: string
@@ -44,7 +42,7 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
     constructor(fields?: { sessionId?: string }) {
         this.label = 'Conversational Retrieval Tool Agent'
         this.name = 'conversationalRetrievalToolAgent'
-        this.author = 'niztal(falkor) and nikitas-novatix'
+        this.author = 'niztal(falkor)'
         this.version = 1.0
         this.type = 'AgentExecutor'
         this.category = 'Agents'
@@ -81,26 +79,6 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
                 optional: true,
                 default: RESPONSE_TEMPLATE
             },
-            {
-                label: 'Rephrase Prompt',
-                name: 'rephrasePrompt',
-                type: 'string',
-                description: 'Using previous chat history, rephrase question into a standalone question',
-                warning: 'Prompt must include input variables: {chat_history} and {question}',
-                rows: 4,
-                additionalParams: true,
-                optional: true,
-                default: REPHRASE_TEMPLATE
-            },
-            {
-                label: 'Rephrase Model',
-                name: 'rephraseModel',
-                type: 'BaseChatModel',
-                description:
-                    'Optional: Use a different (faster/cheaper) model for rephrasing. If not specified, uses the main Tool Calling Chat Model.',
-                optional: true,
-                additionalParams: true
-            },
             {
                 label: 'Input Moderation',
                 description: 'Detect text that could generate harmful output and prevent it from being sent to the language model',
@@ -125,9 +103,8 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
         this.sessionId = fields?.sessionId
     }
 
-    // The agent will be prepared in run() with the correct user message - it needs the actual runtime input for rephrasing
-    async init(_nodeData: INodeData, _input: string, _options: ICommonObject): Promise<any> {
-        return null
+    async init(nodeData: INodeData, input: string, options: ICommonObject): Promise<any> {
+        return prepareAgent(nodeData, options, { sessionId: this.sessionId, chatId: options.chatId, input })
     }
 
     async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string | ICommonObject> {
@@ -171,23 +148,6 @@ class ConversationalRetrievalToolAgent_Agents implements INode {
                 sseStreamer.streamUsedToolsEvent(chatId, res.usedTools)
                 usedTools = res.usedTools
             }
-
-            // If the tool is set to returnDirect, stream the output to the client
-            if (res.usedTools && res.usedTools.length) {
-                let inputTools = nodeData.inputs?.tools
-                inputTools = flatten(inputTools)
-                for (const tool of res.usedTools) {
-                    const inputTool = inputTools.find((inputTool: Tool) => inputTool.name === tool.tool)
-                    if (inputTool && (inputTool as any).returnDirect && shouldStreamResponse) {
-                        sseStreamer.streamTokenEvent(chatId, tool.toolOutput)
-                        // Prevent CustomChainHandler from streaming the same output again
-                        if (res.output === tool.toolOutput) {
-                            res.output = ''
-                        }
-                    }
-                }
-            }
-            // The CustomChainHandler will send the stream end event
         } else {
             res = await executor.invoke({ input }, { callbacks: [loggerHandler, ...callbacks] })
             if (res.sourceDocuments) {
@@ -250,11 +210,9 @@ const prepareAgent = async (
     flowObj: { sessionId?: string; chatId?: string; input?: string }
 ) => {
     const model = nodeData.inputs?.model as BaseChatModel
-    const rephraseModel = (nodeData.inputs?.rephraseModel as BaseChatModel) || model // Use main model if not specified
     const maxIterations = nodeData.inputs?.maxIterations as string
     const memory = nodeData.inputs?.memory as FlowiseMemory
     let systemMessage = nodeData.inputs?.systemMessage as string
-    let rephrasePrompt = nodeData.inputs?.rephrasePrompt as string
     let tools = nodeData.inputs?.tools
     tools = flatten(tools)
     const memoryKey = memory.memoryKey ? memory.memoryKey : 'chat_history'
@@ -262,9 +220,6 @@ const prepareAgent = async (
     const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever as BaseRetriever
 
     systemMessage = transformBracesWithColon(systemMessage)
-    if (rephrasePrompt) {
-        rephrasePrompt = transformBracesWithColon(rephrasePrompt)
-    }
 
     const prompt = ChatPromptTemplate.fromMessages([
         ['system', systemMessage ? systemMessage : `You are a helpful AI assistant.`],
@@ -308,37 +263,6 @@ const prepareAgent = async (
 
     const modelWithTools = model.bindTools(tools)
 
-    // Function to get standalone question (either rephrased or original)
-    const getStandaloneQuestion = async (input: string): Promise<string> => {
-        // If no rephrase prompt, return the original input
-        if (!rephrasePrompt) {
-            return input
-        }
-
-        // Get chat history (use empty string if none)
-        const messages = (await memory.getChatMessages(flowObj?.sessionId, true)) as BaseMessage[]
-        const iMessages = convertBaseMessagetoIMessage(messages)
-        const chatHistoryString = convertChatHistoryToText(iMessages)
-
-        // Always rephrase to normalize/expand user queries for better retrieval
-        try {
-            const CONDENSE_QUESTION_PROMPT = PromptTemplate.fromTemplate(rephrasePrompt)
-            const condenseQuestionChain = RunnableSequence.from([CONDENSE_QUESTION_PROMPT, rephraseModel, new StringOutputParser()])
-            const res = await condenseQuestionChain.invoke({
-                question: input,
-                chat_history: chatHistoryString
-            })
-            return res
-        } catch (error) {
-            console.error('Error rephrasing question:', error)
-            // On error, fall back to original input
-            return input
-        }
-    }
-
-    // Get standalone question before creating runnable
-    const standaloneQuestion = await getStandaloneQuestion(flowObj?.input || '')
-
     const runnableAgent = RunnableSequence.from([
         {
             [inputKey]: (i: { input: string; steps: ToolsAgentStep[] }) => i.input,
@@ -348,9 +272,7 @@ const prepareAgent = async (
                 return messages ?? []
             },
             context: async (i: { input: string; chatHistory?: string }) => {
-                // Use the standalone question (rephrased or original) for retrieval
-                const retrievalQuery = standaloneQuestion || i.input
-                const relevantDocs = await vectorStoreRetriever.invoke(retrievalQuery)
+                const relevantDocs = await vectorStoreRetriever.invoke(i.input)
                 const formattedDocs = formatDocs(relevantDocs)
                 return formattedDocs
             }
@@ -373,6 +295,4 @@ const prepareAgent = async (
     return executor
 }
 
-module.exports = {
-    nodeClass: ConversationalRetrievalToolAgent_Agents
-}
+module.exports = { nodeClass: ConversationalRetrievalToolAgent_Agents }

packages/components/nodes/agents/OpenAIAssistant/OpenAIAssistant.ts

@@ -578,7 +578,7 @@ class OpenAIAssistant_Agents implements INode {
                                         toolOutput
                                     })
                                 } catch (e) {
-                                    await analyticHandlers.onToolError(toolIds, e)
+                                    await analyticHandlers.onToolEnd(toolIds, e)
                                     console.error('Error executing tool', e)
                                     throw new Error(
                                         `Error executing tool. Tool: ${tool.name}. Thread ID: ${threadId}. Run ID: ${runThreadId}`
@@ -703,7 +703,7 @@ class OpenAIAssistant_Agents implements INode {
                                                 toolOutput
                                             })
                                         } catch (e) {
-                                            await analyticHandlers.onToolError(toolIds, e)
+                                            await analyticHandlers.onToolEnd(toolIds, e)
                                             console.error('Error executing tool', e)
                                             clearInterval(timeout)
                                             reject(
@@ -1096,7 +1096,7 @@ async function handleToolSubmission(params: ToolSubmissionParams): Promise<ToolS
                                 toolOutput
                             })
                         } catch (e) {
-                            await analyticHandlers.onToolError(toolIds, e)
+                            await analyticHandlers.onToolEnd(toolIds, e)
                             console.error('Error executing tool', e)
                             throw new Error(`Error executing tool. Tool: ${tool.name}. Thread ID: ${threadId}. Run ID: ${runThreadId}`)
                         }

packages/components/nodes/chatmodels/ChatAnthropic/ChatAnthropic.ts

@@ -91,7 +91,7 @@ class ChatAnthropic_ChatModels implements INode {
                 label: 'Extended Thinking',
                 name: 'extendedThinking',
                 type: 'boolean',
-                description: 'Enable extended thinking for reasoning model such as Claude Sonnet 3.7 and Claude 4',
+                description: 'Enable extended thinking for reasoning model such as Claude Sonnet 3.7',
                 optional: true,
                 additionalParams: true
             },

packages/components/nodes/chatmodels/ChatGoogleGenerativeAI/ChatGoogleGenerativeAI.ts

@@ -174,18 +174,6 @@ class GoogleGenerativeAI_ChatModels implements INode {
                 optional: true,
                 additionalParams: true
             },
-            {
-                label: 'Thinking Budget',
-                name: 'thinkingBudget',
-                type: 'number',
-                description: 'Guides the number of thinking tokens. -1 for dynamic, 0 to disable, or positive integer (Gemini 2.5 models).',
-                step: 1,
-                optional: true,
-                additionalParams: true,
-                show: {
-                    modelName: ['gemini-2.5-pro', 'gemini-2.5-flash', 'gemini-2.5-flash-lite']
-                }
-            },
             {
                 label: 'Base URL',
                 name: 'baseUrl',
@@ -228,7 +216,6 @@ class GoogleGenerativeAI_ChatModels implements INode {
         const cache = nodeData.inputs?.cache as BaseCache
         const streaming = nodeData.inputs?.streaming as boolean
         const baseUrl = nodeData.inputs?.baseUrl as string | undefined
-        const thinkingBudget = nodeData.inputs?.thinkingBudget as string
 
         const allowImageUploads = nodeData.inputs?.allowImageUploads as boolean
 
@@ -248,7 +235,6 @@ class GoogleGenerativeAI_ChatModels implements INode {
         if (cache) obj.cache = cache
         if (temperature) obj.temperature = parseFloat(temperature)
         if (baseUrl) obj.baseUrl = baseUrl
-        if (thinkingBudget) obj.thinkingBudget = parseInt(thinkingBudget, 10)
 
         let safetySettings: SafetySetting[] = []
         if (_safetySettings) {

packages/components/nodes/chatmodels/ChatGoogleGenerativeAI/FlowiseChatGoogleGenerativeAI.ts

@@ -174,9 +174,6 @@ export interface GoogleGenerativeAIChatInput extends BaseChatModelParams, Pick<G
      * - Gemini 1.0 Pro version gemini-1.0-pro-002
      */
     convertSystemMessageToHumanContent?: boolean | undefined
-
-    /** Thinking budget for Gemini 2.5 thinking models. Supports -1 (dynamic), 0 (off), or positive integers. */
-    thinkingBudget?: number
 }
 
 /**
@@ -602,17 +599,10 @@ export class LangchainChatGoogleGenerativeAI
 
     convertSystemMessageToHumanContent: boolean | undefined
 
-    thinkingBudget?: number
-
     private client: GenerativeModel
 
     get _isMultimodalModel() {
-        return (
-            this.model.includes('vision') ||
-            this.model.startsWith('gemini-1.5') ||
-            this.model.startsWith('gemini-2') ||
-            this.model.startsWith('gemini-3')
-        )
+        return this.model.includes('vision') || this.model.startsWith('gemini-1.5') || this.model.startsWith('gemini-2')
     }
 
     constructor(fields: GoogleGenerativeAIChatInput) {
@@ -667,7 +657,6 @@ export class LangchainChatGoogleGenerativeAI
 
         this.streaming = fields.streaming ?? this.streaming
         this.json = fields.json
-        this.thinkingBudget = fields.thinkingBudget
 
         this.client = new GenerativeAI(this.apiKey).getGenerativeModel(
             {
@@ -687,22 +676,12 @@ export class LangchainChatGoogleGenerativeAI
                 baseUrl: fields.baseUrl
             }
         )
-        if (this.thinkingBudget !== undefined) {
-            ;(this.client.generationConfig as any).thinkingConfig = {
-                ...(this.thinkingBudget !== undefined ? { thinkingBudget: this.thinkingBudget } : {})
-            }
-        }
         this.streamUsage = fields.streamUsage ?? this.streamUsage
     }
 
     useCachedContent(cachedContent: CachedContent, modelParams?: ModelParams, requestOptions?: RequestOptions): void {
         if (!this.apiKey) return
         this.client = new GenerativeAI(this.apiKey).getGenerativeModelFromCachedContent(cachedContent, modelParams, requestOptions)
-        if (this.thinkingBudget !== undefined) {
-            ;(this.client.generationConfig as any).thinkingConfig = {
-                ...(this.thinkingBudget !== undefined ? { thinkingBudget: this.thinkingBudget } : {})
-            }
-        }
     }
 
     get useSystemInstruction(): boolean {

packages/components/nodes/chatmodels/ChatGoogleGenerativeAI/utils/common.ts

@@ -48,8 +48,6 @@ export function getMessageAuthor(message: BaseMessage) {
 }
 
 /**
- * !!! IMPORTANT: Must return 'user' as default instead of throwing error
- * https://github.com/FlowiseAI/Flowise/issues/4743
  * Maps a message type to a Google Generative AI chat author.
  * @param message The message to map.
  * @param model The model to use for mapping.
@@ -452,7 +450,6 @@ export function mapGenerateContentResultToChatResult(
     const [candidate] = response.candidates
     const { content: candidateContent, ...generationInfo } = candidate
     let content: MessageContent | undefined
-    const inlineDataItems: any[] = []
 
     if (Array.isArray(candidateContent?.parts) && candidateContent.parts.length === 1 && candidateContent.parts[0].text) {
         content = candidateContent.parts[0].text
@@ -473,18 +470,6 @@ export function mapGenerateContentResultToChatResult(
                     type: 'codeExecutionResult',
                     codeExecutionResult: p.codeExecutionResult
                 }
-            } else if ('inlineData' in p && p.inlineData) {
-                // Extract inline image data for processing by Agent
-                inlineDataItems.push({
-                    type: 'gemini_inline_data',
-                    mimeType: p.inlineData.mimeType,
-                    data: p.inlineData.data
-                })
-                // Return the inline data as part of the content structure
-                return {
-                    type: 'inlineData',
-                    inlineData: p.inlineData
-                }
             }
             return p
         })
@@ -501,12 +486,6 @@ export function mapGenerateContentResultToChatResult(
         text = block?.text ?? text
     }
 
-    // Build response_metadata with inline data if present
-    const response_metadata: any = {}
-    if (inlineDataItems.length > 0) {
-        response_metadata.inlineData = inlineDataItems
-    }
-
     const generation: ChatGeneration = {
         text,
         message: new AIMessage({
@@ -521,8 +500,7 @@ export function mapGenerateContentResultToChatResult(
             additional_kwargs: {
                 ...generationInfo
             },
-            usage_metadata: extra?.usageMetadata,
-            response_metadata: Object.keys(response_metadata).length > 0 ? response_metadata : undefined
+            usage_metadata: extra?.usageMetadata
         }),
         generationInfo
     }
@@ -553,8 +531,6 @@ export function convertResponseContentToChatGenerationChunk(
     const [candidate] = response.candidates
     const { content: candidateContent, ...generationInfo } = candidate
     let content: MessageContent | undefined
-    const inlineDataItems: any[] = []
-
     // Checks if some parts do not have text. If false, it means that the content is a string.
     if (Array.isArray(candidateContent?.parts) && candidateContent.parts.every((p) => 'text' in p)) {
         content = candidateContent.parts.map((p) => p.text).join('')
@@ -575,18 +551,6 @@ export function convertResponseContentToChatGenerationChunk(
                     type: 'codeExecutionResult',
                     codeExecutionResult: p.codeExecutionResult
                 }
-            } else if ('inlineData' in p && p.inlineData) {
-                // Extract inline image data for processing by Agent
-                inlineDataItems.push({
-                    type: 'gemini_inline_data',
-                    mimeType: p.inlineData.mimeType,
-                    data: p.inlineData.data
-                })
-                // Return the inline data as part of the content structure
-                return {
-                    type: 'inlineData',
-                    inlineData: p.inlineData
-                }
             }
             return p
         })
@@ -616,12 +580,6 @@ export function convertResponseContentToChatGenerationChunk(
         )
     }
 
-    // Build response_metadata with inline data if present
-    const response_metadata: any = {}
-    if (inlineDataItems.length > 0) {
-        response_metadata.inlineData = inlineDataItems
-    }
-
     return new ChatGenerationChunk({
         text,
         message: new AIMessageChunk({
@@ -631,8 +589,7 @@ export function convertResponseContentToChatGenerationChunk(
             // Each chunk can have unique "generationInfo", and merging strategy is unclear,
             // so leave blank for now.
             additional_kwargs: {},
-            usage_metadata: extra.usageMetadata,
-            response_metadata: Object.keys(response_metadata).length > 0 ? response_metadata : undefined
+            usage_metadata: extra.usageMetadata
         }),
         generationInfo
     })

packages/components/nodes/chatmodels/ChatHuggingFace/ChatHuggingFace.ts

@@ -41,17 +41,15 @@ class ChatHuggingFace_ChatModels implements INode {
                 label: 'Model',
                 name: 'model',
                 type: 'string',
-                description:
-                    'Model name (e.g., deepseek-ai/DeepSeek-V3.2-Exp:novita). If model includes provider (:) or using router endpoint, leave Endpoint blank.',
-                placeholder: 'deepseek-ai/DeepSeek-V3.2-Exp:novita'
+                description: 'If using own inference endpoint, leave this blank',
+                placeholder: 'gpt2'
             },
             {
                 label: 'Endpoint',
                 name: 'endpoint',
                 type: 'string',
                 placeholder: 'https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2',
-                description:
-                    'Custom inference endpoint (optional). Not needed for models with providers (:) or router endpoints. Leave blank to use Inference Providers.',
+                description: 'Using your own inference endpoint',
                 optional: true
             },
             {
@@ -105,7 +103,7 @@ class ChatHuggingFace_ChatModels implements INode {
                 type: 'string',
                 rows: 4,
                 placeholder: 'AI assistant:',
-                description: 'Sets the stop sequences to use. Use comma to separate different sequences.',
+                description: 'Sets the stop sequences to use. Use comma to seperate different sequences.',
                 optional: true,
                 additionalParams: true
             }
@@ -126,15 +124,6 @@ class ChatHuggingFace_ChatModels implements INode {
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const huggingFaceApiKey = getCredentialParam('huggingFaceApiKey', credentialData, nodeData)
 
-        if (!huggingFaceApiKey) {
-            console.error('[ChatHuggingFace] API key validation failed: No API key found')
-            throw new Error('HuggingFace API key is required. Please configure it in the credential settings.')
-        }
-
-        if (!huggingFaceApiKey.startsWith('hf_')) {
-            console.warn('[ChatHuggingFace] API key format warning: Key does not start with "hf_"')
-        }
-
         const obj: Partial<HFInput> = {
             model,
             apiKey: huggingFaceApiKey

packages/components/nodes/chatmodels/ChatHuggingFace/core.ts

@@ -56,9 +56,9 @@ export class HuggingFaceInference extends LLM implements HFInput {
         this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
         this.endpointUrl = fields?.endpointUrl
         this.includeCredentials = fields?.includeCredentials
-        if (!this.apiKey || this.apiKey.trim() === '') {
+        if (!this.apiKey) {
             throw new Error(
-                'Please set an API key for HuggingFace Hub. Either configure it in the credential settings in the UI, or set the environment variable HUGGINGFACEHUB_API_KEY.'
+                'Please set an API key for HuggingFace Hub in the environment variable HUGGINGFACEHUB_API_KEY or in the apiKey field of the HuggingFaceInference constructor.'
             )
         }
     }
@@ -68,21 +68,19 @@ export class HuggingFaceInference extends LLM implements HFInput {
     }
 
     invocationParams(options?: this['ParsedCallOptions']) {
-        // Return parameters compatible with chatCompletion API (OpenAI-compatible format)
-        const params: any = {
-            temperature: this.temperature,
-            max_tokens: this.maxTokens,
-            stop: options?.stop ?? this.stopSequences,
-            top_p: this.topP
+        return {
+            model: this.model,
+            parameters: {
+                // make it behave similar to openai, returning only the generated text
+                return_full_text: false,
+                temperature: this.temperature,
+                max_new_tokens: this.maxTokens,
+                stop: options?.stop ?? this.stopSequences,
+                top_p: this.topP,
+                top_k: this.topK,
+                repetition_penalty: this.frequencyPenalty
+            }
         }
-        // Include optional parameters if they are defined
-        if (this.topK !== undefined) {
-            params.top_k = this.topK
-        }
-        if (this.frequencyPenalty !== undefined) {
-            params.frequency_penalty = this.frequencyPenalty
-        }
-        return params
     }
 
     async *_streamResponseChunks(
@@ -90,109 +88,51 @@ export class HuggingFaceInference extends LLM implements HFInput {
         options: this['ParsedCallOptions'],
         runManager?: CallbackManagerForLLMRun
     ): AsyncGenerator<GenerationChunk> {
-        try {
-            const client = await this._prepareHFInference()
-            const stream = await this.caller.call(async () =>
-                client.chatCompletionStream({
-                    model: this.model,
-                    messages: [{ role: 'user', content: prompt }],
-                    ...this.invocationParams(options)
+        const hfi = await this._prepareHFInference()
+        const stream = await this.caller.call(async () =>
+            hfi.textGenerationStream({
+                ...this.invocationParams(options),
+                inputs: prompt
+            })
+        )
+        for await (const chunk of stream) {
+            const token = chunk.token.text
+            yield new GenerationChunk({ text: token, generationInfo: chunk })
+            await runManager?.handleLLMNewToken(token ?? '')
+
+            // stream is done
+            if (chunk.generated_text)
+                yield new GenerationChunk({
+                    text: '',
+                    generationInfo: { finished: true }
                 })
-            )
-            for await (const chunk of stream) {
-                const token = chunk.choices[0]?.delta?.content || ''
-                if (token) {
-                    yield new GenerationChunk({ text: token, generationInfo: chunk })
-                    await runManager?.handleLLMNewToken(token)
-                }
-                // stream is done when finish_reason is set
-                if (chunk.choices[0]?.finish_reason) {
-                    yield new GenerationChunk({
-                        text: '',
-                        generationInfo: { finished: true }
-                    })
-                    break
-                }
-            }
-        } catch (error: any) {
-            console.error('[ChatHuggingFace] Error in _streamResponseChunks:', error)
-            // Provide more helpful error messages
-            if (error?.message?.includes('endpointUrl') || error?.message?.includes('third-party provider')) {
-                throw new Error(
-                    `Cannot use custom endpoint with model "${this.model}" that includes a provider. Please leave the Endpoint field blank in the UI. Original error: ${error.message}`
-                )
-            }
-            throw error
         }
     }
 
     /** @ignore */
     async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
-        try {
-            const client = await this._prepareHFInference()
-            // Use chatCompletion for chat models (v4 supports conversational models via Inference Providers)
-            const args = {
-                model: this.model,
-                messages: [{ role: 'user', content: prompt }],
-                ...this.invocationParams(options)
-            }
-            const res = await this.caller.callWithOptions({ signal: options.signal }, client.chatCompletion.bind(client), args)
-            const content = res.choices[0]?.message?.content || ''
-            if (!content) {
-                console.error('[ChatHuggingFace] No content in response:', JSON.stringify(res))
-                throw new Error(`No content received from HuggingFace API. Response: ${JSON.stringify(res)}`)
-            }
-            return content
-        } catch (error: any) {
-            console.error('[ChatHuggingFace] Error in _call:', error.message)
-            // Provide more helpful error messages
-            if (error?.message?.includes('endpointUrl') || error?.message?.includes('third-party provider')) {
-                throw new Error(
-                    `Cannot use custom endpoint with model "${this.model}" that includes a provider. Please leave the Endpoint field blank in the UI. Original error: ${error.message}`
-                )
-            }
-            if (error?.message?.includes('Invalid username or password') || error?.message?.includes('authentication')) {
-                throw new Error(
-                    `HuggingFace API authentication failed. Please verify your API key is correct and starts with "hf_". Original error: ${error.message}`
-                )
-            }
-            throw error
-        }
+        const hfi = await this._prepareHFInference()
+        const args = { ...this.invocationParams(options), inputs: prompt }
+        const res = await this.caller.callWithOptions({ signal: options.signal }, hfi.textGeneration.bind(hfi), args)
+        return res.generated_text
     }
 
     /** @ignore */
     private async _prepareHFInference() {
-        if (!this.apiKey || this.apiKey.trim() === '') {
-            console.error('[ChatHuggingFace] API key validation failed: Empty or undefined')
-            throw new Error('HuggingFace API key is required. Please configure it in the credential settings.')
-        }
-
-        const { InferenceClient } = await HuggingFaceInference.imports()
-        // Use InferenceClient for chat models (works better with Inference Providers)
-        const client = new InferenceClient(this.apiKey)
-
-        // Don't override endpoint if model uses a provider (contains ':') or if endpoint is router-based
-        // When using Inference Providers, endpoint should be left blank - InferenceClient handles routing automatically
-        if (
-            this.endpointUrl &&
-            !this.model.includes(':') &&
-            !this.endpointUrl.includes('/v1/chat/completions') &&
-            !this.endpointUrl.includes('router.huggingface.co')
-        ) {
-            return client.endpoint(this.endpointUrl)
-        }
-
-        // Return client without endpoint override - InferenceClient will use Inference Providers automatically
-        return client
+        const { HfInference } = await HuggingFaceInference.imports()
+        const hfi = new HfInference(this.apiKey, {
+            includeCredentials: this.includeCredentials
+        })
+        return this.endpointUrl ? hfi.endpoint(this.endpointUrl) : hfi
     }
 
     /** @ignore */
     static async imports(): Promise<{
-        InferenceClient: typeof import('@huggingface/inference').InferenceClient
+        HfInference: typeof import('@huggingface/inference').HfInference
     }> {
         try {
-            const { InferenceClient } = await import('@huggingface/inference')
-            return { InferenceClient }
+            const { HfInference } = await import('@huggingface/inference')
+            return { HfInference }
         } catch (e) {
             throw new Error('Please install huggingface as a dependency with, e.g. `pnpm install @huggingface/inference`')
         }

packages/components/nodes/chatmodels/ChatOpenRouter/ChatOpenRouter.ts

@@ -1,8 +1,7 @@
-import { ChatOpenAI as LangchainChatOpenAI, ChatOpenAIFields } from '@langchain/openai'
+import { ChatOpenAI, ChatOpenAIFields } from '@langchain/openai'
 import { BaseCache } from '@langchain/core/caches'
-import { ICommonObject, IMultiModalOption, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
 import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
-import { ChatOpenRouter } from './FlowiseChatOpenRouter'
 
 class ChatOpenRouter_ChatModels implements INode {
     label: string
@@ -24,7 +23,7 @@ class ChatOpenRouter_ChatModels implements INode {
         this.icon = 'openRouter.svg'
         this.category = 'Chat Models'
         this.description = 'Wrapper around Open Router Inference API'
-        this.baseClasses = [this.type, ...getBaseClasses(LangchainChatOpenAI)]
+        this.baseClasses = [this.type, ...getBaseClasses(ChatOpenAI)]
         this.credential = {
             label: 'Connect Credential',
             name: 'credential',
@@ -115,40 +114,6 @@ class ChatOpenRouter_ChatModels implements INode {
                 type: 'json',
                 optional: true,
                 additionalParams: true
-            },
-            {
-                label: 'Allow Image Uploads',
-                name: 'allowImageUploads',
-                type: 'boolean',
-                description:
-                    'Allow image input. Refer to the <a href="https://docs.flowiseai.com/using-flowise/uploads#image" target="_blank">docs</a> for more details.',
-                default: false,
-                optional: true
-            },
-            {
-                label: 'Image Resolution',
-                description: 'This parameter controls the resolution in which the model views the image.',
-                name: 'imageResolution',
-                type: 'options',
-                options: [
-                    {
-                        label: 'Low',
-                        name: 'low'
-                    },
-                    {
-                        label: 'High',
-                        name: 'high'
-                    },
-                    {
-                        label: 'Auto',
-                        name: 'auto'
-                    }
-                ],
-                default: 'low',
-                optional: false,
-                show: {
-                    allowImageUploads: true
-                }
             }
         ]
     }
@@ -165,8 +130,6 @@ class ChatOpenRouter_ChatModels implements INode {
         const basePath = (nodeData.inputs?.basepath as string) || 'https://openrouter.ai/api/v1'
         const baseOptions = nodeData.inputs?.baseOptions
         const cache = nodeData.inputs?.cache as BaseCache
-        const allowImageUploads = nodeData.inputs?.allowImageUploads as boolean
-        const imageResolution = nodeData.inputs?.imageResolution as string
 
         const credentialData = await getCredentialData(nodeData.credential ?? '', options)
         const openRouterApiKey = getCredentialParam('openRouterApiKey', credentialData, nodeData)
@@ -192,7 +155,7 @@ class ChatOpenRouter_ChatModels implements INode {
             try {
                 parsedBaseOptions = typeof baseOptions === 'object' ? baseOptions : JSON.parse(baseOptions)
             } catch (exception) {
-                throw new Error("Invalid JSON in the ChatOpenRouter's BaseOptions: " + exception)
+                throw new Error("Invalid JSON in the ChatCerebras's BaseOptions: " + exception)
             }
         }
 
@@ -203,15 +166,7 @@ class ChatOpenRouter_ChatModels implements INode {
             }
         }
 
-        const multiModalOption: IMultiModalOption = {
-            image: {
-                allowImageUploads: allowImageUploads ?? false,
-                imageResolution
-            }
-        }
-
-        const model = new ChatOpenRouter(nodeData.id, obj)
-        model.setMultiModalOption(multiModalOption)
+        const model = new ChatOpenAI(obj)
         return model
     }
 }

packages/components/nodes/chatmodels/ChatOpenRouter/FlowiseChatOpenRouter.ts

@@ -1,29 +0,0 @@
-import { ChatOpenAI as LangchainChatOpenAI, ChatOpenAIFields } from '@langchain/openai'
-import { IMultiModalOption, IVisionChatModal } from '../../../src'
-
-export class ChatOpenRouter extends LangchainChatOpenAI implements IVisionChatModal {
-    configuredModel: string
-    configuredMaxToken?: number
-    multiModalOption: IMultiModalOption
-    id: string
-
-    constructor(id: string, fields?: ChatOpenAIFields) {
-        super(fields)
-        this.id = id
-        this.configuredModel = fields?.modelName ?? ''
-        this.configuredMaxToken = fields?.maxTokens
-    }
-
-    revertToOriginalModel(): void {
-        this.model = this.configuredModel
-        this.maxTokens = this.configuredMaxToken
-    }
-
-    setMultiModalOption(multiModalOption: IMultiModalOption): void {
-        this.multiModalOption = multiModalOption
-    }
-
-    setVisionModel(): void {
-        // pass - OpenRouter models don't need model switching
-    }
-}

packages/components/nodes/documentloaders/API/APILoader.ts

@@ -95,7 +95,7 @@ class API_DocumentLoaders implements INode {
                 type: 'string',
                 rows: 4,
                 description:
-                    'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, separated by comma. Use * to omit all metadata keys except the ones you specify in the Additional Metadata field',
+                    'Each document loader comes with a default set of metadata keys that are extracted from the document. You can use this field to omit some of the default metadata keys. The value should be a list of keys, seperated by comma. Use * to omit all metadata keys execept the ones you specify in the Additional Metadata field',
                 placeholder: 'key1, key2, key3.nestedKey1',
                 optional: true,
                 additionalParams: true

packages/components/nodes/documentloaders/Csv/CsvLoader.ts

@@ -2,7 +2,7 @@ import { TextLoader } from 'langchain/document_loaders/fs/text'
 import Papa from 'papaparse'
 
 type CSVLoaderOptions = {
-    // Return specific column from key (string) or index (integer)
+    // Return specifific column from key (string) or index (integer)
     column?: string | number
     // Force separator (default: auto detect)
     separator?: string

packages/components/nodes/documentloaders/Json/Json.ts

@@ -47,7 +47,7 @@ class Json_DocumentLoaders implements INode {
     constructor() {
         this.label = 'Json File'
         this.name = 'jsonFile'
-        this.version = 3.1
+        this.version = 3.0
         this.type = 'Document'
         this.icon = 'json.svg'
         this.category = 'Document Loaders'
@@ -66,14 +66,6 @@ class Json_DocumentLoaders implements INode {
                 type: 'TextSplitter',
                 optional: true
             },
-            {
-                label: 'Separate by JSON Object (JSON Array)',
-                name: 'separateByObject',
-                type: 'boolean',
-                description: 'If enabled and the file is a JSON Array, each JSON object will be extracted as a chunk',
-                optional: true,
-                additionalParams: true
-            },
             {
                 label: 'Pointers Extraction (separated by commas)',
                 name: 'pointersName',
@@ -81,10 +73,7 @@ class Json_DocumentLoaders implements INode {
                 description:
                     'Ex: { "key": "value" }, Pointer Extraction = "key", "value" will be extracted as pageContent of the chunk. Use comma to separate multiple pointers',
                 placeholder: 'key1, key2',
-                optional: true,
-                hide: {
-                    separateByObject: true
-                }
+                optional: true
             },
             {
                 label: 'Additional Metadata',
@@ -133,7 +122,6 @@ class Json_DocumentLoaders implements INode {
         const pointersName = nodeData.inputs?.pointersName as string
         const metadata = nodeData.inputs?.metadata
         const _omitMetadataKeys = nodeData.inputs?.omitMetadataKeys as string
-        const separateByObject = nodeData.inputs?.separateByObject as boolean
         const output = nodeData.outputs?.output as string
 
         let omitMetadataKeys: string[] = []
@@ -165,7 +153,7 @@ class Json_DocumentLoaders implements INode {
                 if (!file) continue
                 const fileData = await getFileFromStorage(file, orgId, chatflowid)
                 const blob = new Blob([fileData])
-                const loader = new JSONLoader(blob, pointers.length != 0 ? pointers : undefined, metadata, separateByObject)
+                const loader = new JSONLoader(blob, pointers.length != 0 ? pointers : undefined, metadata)
 
                 if (textSplitter) {
                     let splittedDocs = await loader.load()
@@ -188,7 +176,7 @@ class Json_DocumentLoaders implements INode {
                 splitDataURI.pop()
                 const bf = Buffer.from(splitDataURI.pop() || '', 'base64')
                 const blob = new Blob([bf])
-                const loader = new JSONLoader(blob, pointers.length != 0 ? pointers : undefined, metadata, separateByObject)
+                const loader = new JSONLoader(blob, pointers.length != 0 ? pointers : undefined, metadata)
 
                 if (textSplitter) {
                     let splittedDocs = await loader.load()
@@ -318,20 +306,13 @@ class TextLoader extends BaseDocumentLoader {
 class JSONLoader extends TextLoader {
     public pointers: string[]
     private metadataMapping: Record<string, string>
-    private separateByObject: boolean
 
-    constructor(
-        filePathOrBlob: string | Blob,
-        pointers: string | string[] = [],
-        metadataMapping: Record<string, string> = {},
-        separateByObject: boolean = false
-    ) {
+    constructor(filePathOrBlob: string | Blob, pointers: string | string[] = [], metadataMapping: Record<string, string> = {}) {
         super(filePathOrBlob)
         this.pointers = Array.isArray(pointers) ? pointers : [pointers]
         if (metadataMapping) {
             this.metadataMapping = typeof metadataMapping === 'object' ? metadataMapping : JSON.parse(metadataMapping)
         }
-        this.separateByObject = separateByObject
     }
 
     protected async parse(raw: string): Promise<Document[]> {
@@ -342,24 +323,14 @@ class JSONLoader extends TextLoader {
         const jsonArray = Array.isArray(json) ? json : [json]
 
         for (const item of jsonArray) {
-            if (this.separateByObject) {
-                if (typeof item === 'object' && item !== null && !Array.isArray(item)) {
-                    const metadata = this.extractMetadata(item)
-                    const pageContent = this.formatObjectAsKeyValue(item)
-                    documents.push({
-                        pageContent,
-                        metadata
-                    })
-                }
-            } else {
-                const content = this.extractContent(item)
-                const metadata = this.extractMetadata(item)
-                for (const pageContent of content) {
-                    documents.push({
-                        pageContent,
-                        metadata
-                    })
-                }
+            const content = this.extractContent(item)
+            const metadata = this.extractMetadata(item)
+
+            for (const pageContent of content) {
+                documents.push({
+                    pageContent,
+                    metadata
+                })
             }
         }
 
@@ -399,30 +370,6 @@ class JSONLoader extends TextLoader {
         return metadata
     }
 
-    /**
-     * Formats a JSON object as readable key-value pairs
-     */
-    private formatObjectAsKeyValue(obj: any, prefix: string = ''): string {
-        const lines: string[] = []
-
-        for (const [key, value] of Object.entries(obj)) {
-            const fullKey = prefix ? `${prefix}.${key}` : key
-
-            if (value === null || value === undefined) {
-                lines.push(`${fullKey}: ${value}`)
-            } else if (Array.isArray(value)) {
-                lines.push(`${fullKey}: ${JSON.stringify(value)}`)
-            } else if (typeof value === 'object') {
-                // Recursively format nested objects
-                lines.push(this.formatObjectAsKeyValue(value, fullKey))
-            } else {
-                lines.push(`${fullKey}: ${value}`)
-            }
-        }
-
-        return lines.join('\n')
-    }
-
     /**
      * If JSON pointers are specified, return all strings below any of them
      * and exclude all other nodes expect if they match a JSON pointer.

packages/components/nodes/documentloaders/Playwright/Playwright.ts

@@ -190,14 +190,11 @@ class Playwright_DocumentLoaders implements INode {
         async function playwrightLoader(url: string): Promise<Document[] | undefined> {
             try {
                 let docs = []
-
-                const executablePath = process.env.PLAYWRIGHT_EXECUTABLE_PATH
-
                 const config: PlaywrightWebBaseLoaderOptions = {
                     launchOptions: {
                         args: ['--no-sandbox'],
                         headless: true,
-                        executablePath: executablePath
+                        executablePath: process.env.PLAYWRIGHT_EXECUTABLE_FILE_PATH
                     }
                 }
                 if (waitUntilGoToOption) {

packages/components/nodes/documentloaders/Puppeteer/Puppeteer.ts

@@ -181,14 +181,11 @@ class Puppeteer_DocumentLoaders implements INode {
         async function puppeteerLoader(url: string): Promise<Document[] | undefined> {
             try {
                 let docs: Document[] = []
-
-                const executablePath = process.env.PUPPETEER_EXECUTABLE_PATH
-
                 const config: PuppeteerWebBaseLoaderOptions = {
                     launchOptions: {
                         args: ['--no-sandbox'],
                         headless: 'new',
-                        executablePath: executablePath
+                        executablePath: process.env.PUPPETEER_EXECUTABLE_FILE_PATH
                     }
                 }
                 if (waitUntilGoToOption) {

packages/components/nodes/documentloaders/Unstructured/Unstructured.ts

@@ -27,6 +27,8 @@ type Element = {
 }
 
 export class UnstructuredLoader extends BaseDocumentLoader {
+    public filePath: string
+
     private apiUrl = process.env.UNSTRUCTURED_API_URL || 'https://api.unstructuredapp.io/general/v0/general'
 
     private apiKey: string | undefined = process.env.UNSTRUCTURED_API_KEY
@@ -136,7 +138,7 @@ export class UnstructuredLoader extends BaseDocumentLoader {
         })
 
         if (!response.ok) {
-            throw new Error(`Failed to partition file with error ${response.status} and message ${await response.text()}`)
+            throw new Error(`Failed to partition file ${this.filePath} with error ${response.status} and message ${await response.text()}`)
         }
 
         const elements = await response.json()

packages/components/nodes/documentloaders/Unstructured/UnstructuredFile.ts

@@ -4,11 +4,15 @@ import {
     UnstructuredLoaderOptions,
     UnstructuredLoaderStrategy,
     SkipInferTableTypes,
-    HiResModelName
+    HiResModelName,
+    UnstructuredLoader as LCUnstructuredLoader
 } from '@langchain/community/document_loaders/fs/unstructured'
 import { getCredentialData, getCredentialParam, handleEscapeCharacters } from '../../../src/utils'
 import { getFileFromStorage, INodeOutputsValue } from '../../../src'
 import { UnstructuredLoader } from './Unstructured'
+import { isPathTraversal } from '../../../src/validator'
+import sanitize from 'sanitize-filename'
+import path from 'path'
 
 class UnstructuredFile_DocumentLoaders implements INode {
     label: string
@@ -40,6 +44,17 @@ class UnstructuredFile_DocumentLoaders implements INode {
             optional: true
         }
         this.inputs = [
+            /** Deprecated
+            {
+                label: 'File Path',
+                name: 'filePath',
+                type: 'string',
+                placeholder: '',
+                optional: true,
+                warning:
+                    'Use the File Upload instead of File path. If file is uploaded, this path is ignored. Path will be deprecated in future releases.'
+            },
+             */
             {
                 label: 'Files Upload',
                 name: 'fileObject',
@@ -440,6 +455,7 @@ class UnstructuredFile_DocumentLoaders implements INode {
     }
 
     async init(nodeData: INodeData, _: string, options: ICommonObject): Promise<any> {
+        const filePath = nodeData.inputs?.filePath as string
         const unstructuredAPIUrl = nodeData.inputs?.unstructuredAPIUrl as string
         const strategy = nodeData.inputs?.strategy as UnstructuredLoaderStrategy
         const encoding = nodeData.inputs?.encoding as string
@@ -544,8 +560,37 @@ class UnstructuredFile_DocumentLoaders implements INode {
                     docs.push(...loaderDocs)
                 }
             }
+        } else if (filePath) {
+            if (!filePath || typeof filePath !== 'string') {
+                throw new Error('Invalid file path format')
+            }
+
+            if (isPathTraversal(filePath)) {
+                throw new Error('Invalid path characters detected in filePath - path traversal not allowed')
+            }
+
+            const parsedPath = path.parse(filePath)
+            const sanitizedFilename = sanitize(parsedPath.base)
+
+            if (!sanitizedFilename || sanitizedFilename.trim() === '') {
+                throw new Error('Invalid filename after sanitization')
+            }
+
+            const sanitizedFilePath = path.join(parsedPath.dir, sanitizedFilename)
+
+            if (!path.isAbsolute(sanitizedFilePath)) {
+                throw new Error('File path must be absolute')
+            }
+
+            if (sanitizedFilePath.includes('..')) {
+                throw new Error('Invalid file path - directory traversal not allowed')
+            }
+
+            const loader = new LCUnstructuredLoader(sanitizedFilePath, obj)
+            const loaderDocs = await loader.load()
+            docs.push(...loaderDocs)
         } else {
-            throw new Error('File upload is required')
+            throw new Error('File path or File upload is required')
         }
 
         if (metadata) {

packages/components/nodes/documentloaders/Unstructured/UnstructuredFolder.ts

@@ -1,6 +1,3 @@
-/*
-* Uncomment this if you want to use the UnstructuredFolder to load a folder from the file system
-
 import { omit } from 'lodash'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams } from '../../../src/Interface'
 import {
@@ -519,4 +516,3 @@ class UnstructuredFolder_DocumentLoaders implements INode {
 }
 
 module.exports = { nodeClass: UnstructuredFolder_DocumentLoaders }
-*/

packages/components/nodes/embeddings/AWSBedrockEmbedding/AWSBedrockEmbedding.ts

@@ -96,7 +96,7 @@ class AWSBedrockEmbedding_Embeddings implements INode {
             {
                 label: 'Max AWS API retries',
                 name: 'maxRetries',
-                description: 'This will limit the number of AWS API for Titan model embeddings call retries. Used to avoid throttling.',
+                description: 'This will limit the nubmer of AWS API for Titan model embeddings call retries. Used to avoid throttling.',
                 type: 'number',
                 optional: true,
                 default: 5,

packages/components/nodes/embeddings/HuggingFaceInferenceEmbedding/core.ts

@@ -23,22 +23,24 @@ export class HuggingFaceInferenceEmbeddings extends Embeddings implements Huggin
         this.model = fields?.model ?? 'sentence-transformers/distilbert-base-nli-mean-tokens'
         this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
         this.endpoint = fields?.endpoint ?? ''
-        const hf = new HfInference(this.apiKey)
-        // v4 uses Inference Providers by default; only override if custom endpoint provided
-        this.client = this.endpoint ? hf.endpoint(this.endpoint) : hf
+        this.client = new HfInference(this.apiKey)
+        if (this.endpoint) this.client.endpoint(this.endpoint)
     }
 
     async _embed(texts: string[]): Promise<number[][]> {
         // replace newlines, which can negatively affect performance.
         const clean = texts.map((text) => text.replace(/\n/g, ' '))
+        const hf = new HfInference(this.apiKey)
         const obj: any = {
             inputs: clean
         }
-        if (!this.endpoint) {
+        if (this.endpoint) {
+            hf.endpoint(this.endpoint)
+        } else {
             obj.model = this.model
         }
 
-        const res = await this.caller.callWithOptions({}, this.client.featureExtraction.bind(this.client), obj)
+        const res = await this.caller.callWithOptions({}, hf.featureExtraction.bind(hf), obj)
         return res as number[][]
     }
 

packages/components/nodes/engine/SubQuestionQueryEngine/SubQuestionQueryEngine.ts

@@ -39,7 +39,7 @@ class SubQuestionQueryEngine_LlamaIndex implements INode {
         this.icon = 'subQueryEngine.svg'
         this.category = 'Engine'
         this.description =
-            'Breaks complex query into sub questions for each relevant data source, then gather all the intermediate responses and synthesizes a final response'
+            'Breaks complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response'
         this.baseClasses = [this.type, 'BaseQueryEngine']
         this.tags = ['LlamaIndex']
         this.inputs = [

packages/components/nodes/llms/HuggingFaceInference/core.ts

@@ -78,8 +78,6 @@ export class HuggingFaceInference extends LLM implements HFInput {
     async _call(prompt: string, options: this['ParsedCallOptions']): Promise<string> {
         const { HfInference } = await HuggingFaceInference.imports()
         const hf = new HfInference(this.apiKey)
-        // v4 uses Inference Providers by default; only override if custom endpoint provided
-        const hfClient = this.endpoint ? hf.endpoint(this.endpoint) : hf
         const obj: any = {
             parameters: {
                 // make it behave similar to openai, returning only the generated text
@@ -92,10 +90,12 @@ export class HuggingFaceInference extends LLM implements HFInput {
             },
             inputs: prompt
         }
-        if (!this.endpoint) {
+        if (this.endpoint) {
+            hf.endpoint(this.endpoint)
+        } else {
             obj.model = this.model
         }
-        const res = await this.caller.callWithOptions({ signal: options.signal }, hfClient.textGeneration.bind(hfClient), obj)
+        const res = await this.caller.callWithOptions({ signal: options.signal }, hf.textGeneration.bind(hf), obj)
         return res.generated_text
     }
 

packages/components/nodes/multiagents/Supervisor/Supervisor.ts

@@ -21,7 +21,6 @@ import { ChatOpenAI } from '../../chatmodels/ChatOpenAI/FlowiseChatOpenAI'
 import { ChatAnthropic } from '../../chatmodels/ChatAnthropic/FlowiseChatAnthropic'
 import { addImagesToMessages, llmSupportsVision } from '../../../src/multiModalUtils'
 import { ChatGoogleGenerativeAI } from '../../chatmodels/ChatGoogleGenerativeAI/FlowiseChatGoogleGenerativeAI'
-import { AzureChatOpenAI } from '../../chatmodels/AzureChatOpenAI/FlowiseAzureChatOpenAI'
 
 const sysPrompt = `You are a supervisor tasked with managing a conversation between the following workers: {team_members}.
 Given the following user request, respond with the worker to act next.
@@ -243,7 +242,7 @@ class Supervisor_MultiAgents implements INode {
                             }
                         }
                     })
-            } else if (llm instanceof ChatOpenAI || llm instanceof AzureChatOpenAI) {
+            } else if (llm instanceof ChatOpenAI) {
                 let prompt = ChatPromptTemplate.fromMessages([
                     ['system', systemPrompt],
                     new MessagesPlaceholder('messages'),

packages/components/nodes/prompts/ChatPromptTemplate/ChatPromptTemplate.ts

@@ -11,7 +11,7 @@ return [
         tool_calls: [
         {
             id: "12345",
-            name: "calculator",
+            name: "calulator",
             args: {
                 number1: 333382,
                 number2: 1932,

packages/components/nodes/recordmanager/MySQLRecordManager/MySQLrecordManager.ts

@@ -62,6 +62,7 @@ class MySQLRecordManager_RecordManager implements INode {
                 label: 'Namespace',
                 name: 'namespace',
                 type: 'string',
+                description: 'If not specified, chatflowid will be used',
                 additionalParams: true,
                 optional: true
             },
@@ -204,8 +205,8 @@ class MySQLRecordManager implements RecordManagerInterface {
     }
 
     async createSchema(): Promise<void> {
-        const dataSource = await this.getDataSource()
         try {
+            const dataSource = await this.getDataSource()
             const queryRunner = dataSource.createQueryRunner()
             const tableName = this.sanitizeTableName(this.tableName)
 
@@ -218,16 +219,7 @@ class MySQLRecordManager implements RecordManagerInterface {
                 unique key \`unique_key_namespace\` (\`key\`,
 \`namespace\`));`)
 
-            // Add doc_id column if it doesn't exist (migration for existing tables)
-            const checkColumn = await queryRunner.manager.query(
-                `SELECT COUNT(1) ColumnExists FROM INFORMATION_SCHEMA.COLUMNS 
-                    WHERE table_schema=DATABASE() AND table_name='${tableName}' AND column_name='doc_id';`
-            )
-            if (checkColumn[0].ColumnExists === 0) {
-                await queryRunner.manager.query(`ALTER TABLE \`${tableName}\` ADD COLUMN \`doc_id\` longtext;`)
-            }
-
-            const columns = [`updated_at`, `key`, `namespace`, `group_id`, `doc_id`]
+            const columns = [`updated_at`, `key`, `namespace`, `group_id`]
             for (const column of columns) {
                 // MySQL does not support 'IF NOT EXISTS' function for Index
                 const Check = await queryRunner.manager.query(
@@ -249,8 +241,6 @@ class MySQLRecordManager implements RecordManagerInterface {
                 return
             }
             throw e
-        } finally {
-            await dataSource.destroy()
         }
     }
 
@@ -269,7 +259,7 @@ class MySQLRecordManager implements RecordManagerInterface {
         }
     }
 
-    async update(keys: Array<{ uid: string; docId: string }> | string[], updateOptions?: UpdateOptions): Promise<void> {
+    async update(keys: string[], updateOptions?: UpdateOptions): Promise<void> {
         if (keys.length === 0) {
             return
         }
@@ -285,23 +275,23 @@ class MySQLRecordManager implements RecordManagerInterface {
             throw new Error(`Time sync issue with database ${updatedAt} < ${timeAtLeast}`)
         }
 
-        // Handle both new format (objects with uid and docId) and old format (strings)
-        const isNewFormat = keys.length > 0 && typeof keys[0] === 'object' && 'uid' in keys[0]
-        const keyStrings = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.uid) : (keys as string[])
-        const docIds = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.docId) : keys.map(() => null)
+        const groupIds = _groupIds ?? keys.map(() => null)
 
-        const groupIds = _groupIds ?? keyStrings.map(() => null)
-
-        if (groupIds.length !== keyStrings.length) {
-            throw new Error(`Number of keys (${keyStrings.length}) does not match number of group_ids (${groupIds.length})`)
+        if (groupIds.length !== keys.length) {
+            throw new Error(`Number of keys (${keys.length}) does not match number of group_ids (${groupIds.length})`)
         }
 
-        const recordsToUpsert = keyStrings.map((key, i) => [key, this.namespace, updatedAt, groupIds[i] ?? null, docIds[i] ?? null])
+        const recordsToUpsert = keys.map((key, i) => [
+            key,
+            this.namespace,
+            updatedAt,
+            groupIds[i] ?? null // Ensure groupIds[i] is null if undefined
+        ])
 
         const query = `
-            INSERT INTO \`${tableName}\` (\`key\`, \`namespace\`, \`updated_at\`, \`group_id\`, \`doc_id\`)
-            VALUES (?, ?, ?, ?, ?)
-            ON DUPLICATE KEY UPDATE \`updated_at\` = VALUES(\`updated_at\`), \`doc_id\` = VALUES(\`doc_id\`)`
+            INSERT INTO \`${tableName}\` (\`key\`, \`namespace\`, \`updated_at\`, \`group_id\`)
+            VALUES (?, ?, ?, ?)
+            ON DUPLICATE KEY UPDATE \`updated_at\` = VALUES(\`updated_at\`)`
 
         // To handle multiple files upsert
         try {
@@ -357,13 +347,13 @@ class MySQLRecordManager implements RecordManagerInterface {
         }
     }
 
-    async listKeys(options?: ListKeyOptions & { docId?: string }): Promise<string[]> {
+    async listKeys(options?: ListKeyOptions): Promise<string[]> {
         const dataSource = await this.getDataSource()
         const queryRunner = dataSource.createQueryRunner()
         const tableName = this.sanitizeTableName(this.tableName)
 
         try {
-            const { before, after, limit, groupIds, docId } = options ?? {}
+            const { before, after, limit, groupIds } = options ?? {}
             let query = `SELECT \`key\` FROM \`${tableName}\` WHERE \`namespace\` = ?`
             const values: (string | number | string[])[] = [this.namespace]
 
@@ -390,11 +380,6 @@ class MySQLRecordManager implements RecordManagerInterface {
                 values.push(...groupIds.filter((gid): gid is string => gid !== null))
             }
 
-            if (docId) {
-                query += ` AND \`doc_id\` = ?`
-                values.push(docId)
-            }
-
             query += ';'
 
             // Directly using try/catch with async/await for cleaner flow

packages/components/nodes/recordmanager/PostgresRecordManager/PostgresRecordManager.ts

@@ -78,6 +78,7 @@ class PostgresRecordManager_RecordManager implements INode {
                 label: 'Namespace',
                 name: 'namespace',
                 type: 'string',
+                description: 'If not specified, chatflowid will be used',
                 additionalParams: true,
                 optional: true
             },
@@ -221,8 +222,8 @@ class PostgresRecordManager implements RecordManagerInterface {
     }
 
     async createSchema(): Promise<void> {
-        const dataSource = await this.getDataSource()
         try {
+            const dataSource = await this.getDataSource()
             const queryRunner = dataSource.createQueryRunner()
             const tableName = this.sanitizeTableName(this.tableName)
 
@@ -240,19 +241,6 @@ class PostgresRecordManager implements RecordManagerInterface {
   CREATE INDEX IF NOT EXISTS namespace_index ON "${tableName}" (namespace);
   CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
 
-            // Add doc_id column if it doesn't exist (migration for existing tables)
-            await queryRunner.manager.query(`
-  DO $$
-  BEGIN
-    IF NOT EXISTS (
-      SELECT 1 FROM information_schema.columns 
-      WHERE table_name = '${tableName}' AND column_name = 'doc_id'
-    ) THEN
-      ALTER TABLE "${tableName}" ADD COLUMN doc_id TEXT;
-      CREATE INDEX IF NOT EXISTS doc_id_index ON "${tableName}" (doc_id);
-    END IF;
-  END $$;`)
-
             await queryRunner.release()
         } catch (e: any) {
             // This error indicates that the table already exists
@@ -263,8 +251,6 @@ class PostgresRecordManager implements RecordManagerInterface {
                 return
             }
             throw e
-        } finally {
-            await dataSource.destroy()
         }
     }
 
@@ -298,7 +284,7 @@ class PostgresRecordManager implements RecordManagerInterface {
         return `(${placeholders.join(', ')})`
     }
 
-    async update(keys: Array<{ uid: string; docId: string }> | string[], updateOptions?: UpdateOptions): Promise<void> {
+    async update(keys: string[], updateOptions?: UpdateOptions): Promise<void> {
         if (keys.length === 0) {
             return
         }
@@ -314,22 +300,17 @@ class PostgresRecordManager implements RecordManagerInterface {
             throw new Error(`Time sync issue with database ${updatedAt} < ${timeAtLeast}`)
         }
 
-        // Handle both new format (objects with uid and docId) and old format (strings)
-        const isNewFormat = keys.length > 0 && typeof keys[0] === 'object' && 'uid' in keys[0]
-        const keyStrings = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.uid) : (keys as string[])
-        const docIds = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.docId) : keys.map(() => null)
+        const groupIds = _groupIds ?? keys.map(() => null)
 
-        const groupIds = _groupIds ?? keyStrings.map(() => null)
-
-        if (groupIds.length !== keyStrings.length) {
-            throw new Error(`Number of keys (${keyStrings.length}) does not match number of group_ids ${groupIds.length})`)
+        if (groupIds.length !== keys.length) {
+            throw new Error(`Number of keys (${keys.length}) does not match number of group_ids ${groupIds.length})`)
         }
 
-        const recordsToUpsert = keyStrings.map((key, i) => [key, this.namespace, updatedAt, groupIds[i], docIds[i]])
+        const recordsToUpsert = keys.map((key, i) => [key, this.namespace, updatedAt, groupIds[i]])
 
         const valuesPlaceholders = recordsToUpsert.map((_, j) => this.generatePlaceholderForRowAt(j, recordsToUpsert[0].length)).join(', ')
 
-        const query = `INSERT INTO "${tableName}" (key, namespace, updated_at, group_id, doc_id) VALUES ${valuesPlaceholders} ON CONFLICT (key, namespace) DO UPDATE SET updated_at = EXCLUDED.updated_at, doc_id = EXCLUDED.doc_id;`
+        const query = `INSERT INTO "${tableName}" (key, namespace, updated_at, group_id) VALUES ${valuesPlaceholders} ON CONFLICT (key, namespace) DO UPDATE SET updated_at = EXCLUDED.updated_at;`
         try {
             await queryRunner.manager.query(query, recordsToUpsert.flat())
             await queryRunner.release()
@@ -368,8 +349,8 @@ class PostgresRecordManager implements RecordManagerInterface {
         }
     }
 
-    async listKeys(options?: ListKeyOptions & { docId?: string }): Promise<string[]> {
-        const { before, after, limit, groupIds, docId } = options ?? {}
+    async listKeys(options?: ListKeyOptions): Promise<string[]> {
+        const { before, after, limit, groupIds } = options ?? {}
         const tableName = this.sanitizeTableName(this.tableName)
 
         let query = `SELECT key FROM "${tableName}" WHERE namespace = $1`
@@ -400,12 +381,6 @@ class PostgresRecordManager implements RecordManagerInterface {
             index += 1
         }
 
-        if (docId) {
-            values.push(docId)
-            query += ` AND doc_id = $${index}`
-            index += 1
-        }
-
         query += ';'
 
         const dataSource = await this.getDataSource()

packages/components/nodes/recordmanager/SQLiteRecordManager/SQLiteRecordManager.ts

@@ -51,6 +51,7 @@ class SQLiteRecordManager_RecordManager implements INode {
                 label: 'Namespace',
                 name: 'namespace',
                 type: 'string',
+                description: 'If not specified, chatflowid will be used',
                 additionalParams: true,
                 optional: true
             },
@@ -178,8 +179,8 @@ class SQLiteRecordManager implements RecordManagerInterface {
     }
 
     async createSchema(): Promise<void> {
-        const dataSource = await this.getDataSource()
         try {
+            const dataSource = await this.getDataSource()
             const queryRunner = dataSource.createQueryRunner()
             const tableName = this.sanitizeTableName(this.tableName)
 
@@ -197,15 +198,6 @@ CREATE INDEX IF NOT EXISTS key_index ON "${tableName}" (key);
 CREATE INDEX IF NOT EXISTS namespace_index ON "${tableName}" (namespace);
 CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
 
-            // Add doc_id column if it doesn't exist (migration for existing tables)
-            const checkColumn = await queryRunner.manager.query(
-                `SELECT COUNT(*) as count FROM pragma_table_info('${tableName}') WHERE name='doc_id';`
-            )
-            if (checkColumn[0].count === 0) {
-                await queryRunner.manager.query(`ALTER TABLE "${tableName}" ADD COLUMN doc_id TEXT;`)
-                await queryRunner.manager.query(`CREATE INDEX IF NOT EXISTS doc_id_index ON "${tableName}" (doc_id);`)
-            }
-
             await queryRunner.release()
         } catch (e: any) {
             // This error indicates that the table already exists
@@ -216,8 +208,6 @@ CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
                 return
             }
             throw e
-        } finally {
-            await dataSource.destroy()
         }
     }
 
@@ -236,7 +226,7 @@ CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
         }
     }
 
-    async update(keys: Array<{ uid: string; docId: string }> | string[], updateOptions?: UpdateOptions): Promise<void> {
+    async update(keys: string[], updateOptions?: UpdateOptions): Promise<void> {
         if (keys.length === 0) {
             return
         }
@@ -251,23 +241,23 @@ CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
             throw new Error(`Time sync issue with database ${updatedAt} < ${timeAtLeast}`)
         }
 
-        // Handle both new format (objects with uid and docId) and old format (strings)
-        const isNewFormat = keys.length > 0 && typeof keys[0] === 'object' && 'uid' in keys[0]
-        const keyStrings = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.uid) : (keys as string[])
-        const docIds = isNewFormat ? (keys as Array<{ uid: string; docId: string }>).map((k) => k.docId) : keys.map(() => null)
+        const groupIds = _groupIds ?? keys.map(() => null)
 
-        const groupIds = _groupIds ?? keyStrings.map(() => null)
-
-        if (groupIds.length !== keyStrings.length) {
-            throw new Error(`Number of keys (${keyStrings.length}) does not match number of group_ids (${groupIds.length})`)
+        if (groupIds.length !== keys.length) {
+            throw new Error(`Number of keys (${keys.length}) does not match number of group_ids (${groupIds.length})`)
         }
 
-        const recordsToUpsert = keyStrings.map((key, i) => [key, this.namespace, updatedAt, groupIds[i] ?? null, docIds[i] ?? null])
+        const recordsToUpsert = keys.map((key, i) => [
+            key,
+            this.namespace,
+            updatedAt,
+            groupIds[i] ?? null // Ensure groupIds[i] is null if undefined
+        ])
 
         const query = `
-        INSERT INTO "${tableName}" (key, namespace, updated_at, group_id, doc_id)
-        VALUES (?, ?, ?, ?, ?)
-        ON CONFLICT (key, namespace) DO UPDATE SET updated_at = excluded.updated_at, doc_id = excluded.doc_id`
+        INSERT INTO "${tableName}" (key, namespace, updated_at, group_id)
+        VALUES (?, ?, ?, ?)
+        ON CONFLICT (key, namespace) DO UPDATE SET updated_at = excluded.updated_at`
 
         try {
             // To handle multiple files upsert
@@ -322,8 +312,8 @@ CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
         }
     }
 
-    async listKeys(options?: ListKeyOptions & { docId?: string }): Promise<string[]> {
-        const { before, after, limit, groupIds, docId } = options ?? {}
+    async listKeys(options?: ListKeyOptions): Promise<string[]> {
+        const { before, after, limit, groupIds } = options ?? {}
         const tableName = this.sanitizeTableName(this.tableName)
 
         let query = `SELECT key FROM "${tableName}" WHERE namespace = ?`
@@ -352,11 +342,6 @@ CREATE INDEX IF NOT EXISTS group_id_index ON "${tableName}" (group_id);`)
             values.push(...groupIds.filter((gid): gid is string => gid !== null))
         }
 
-        if (docId) {
-            query += ` AND doc_id = ?`
-            values.push(docId)
-        }
-
         query += ';'
 
         const dataSource = await this.getDataSource()

packages/components/nodes/sequentialagents/commonUtils.ts

@@ -238,7 +238,7 @@ export function filterConversationHistory(
 export const restructureMessages = (llm: BaseChatModel, state: ISeqAgentsState) => {
     const messages: BaseMessage[] = []
     for (const message of state.messages as unknown as BaseMessage[]) {
-        // Sometimes Anthropic can return a message with content types of array, ignore that EXCEPT when tool calls are present
+        // Sometimes Anthropic can return a message with content types of array, ignore that EXECEPT when tool calls are present
         if ((message as any).tool_calls?.length && message.content !== '') {
             message.content = JSON.stringify(message.content)
         }

packages/components/nodes/tools/AgentAsTool/AgentAsTool.ts

@@ -4,13 +4,7 @@ import { RunnableConfig } from '@langchain/core/runnables'
 import { CallbackManagerForToolRun, Callbacks, CallbackManager, parseCallbackConfigArg } from '@langchain/core/callbacks/manager'
 import { StructuredTool } from '@langchain/core/tools'
 import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
-import {
-    getCredentialData,
-    getCredentialParam,
-    executeJavaScriptCode,
-    createCodeExecutionSandbox,
-    parseWithTypeConversion
-} from '../../../src/utils'
+import { getCredentialData, getCredentialParam, executeJavaScriptCode, createCodeExecutionSandbox } from '../../../src/utils'
 import { isValidUUID, isValidURL } from '../../../src/validator'
 import { v4 as uuidv4 } from 'uuid'
 
@@ -279,7 +273,7 @@ class AgentflowTool extends StructuredTool {
         }
         let parsed
         try {
-            parsed = await parseWithTypeConversion(this.schema, arg)
+            parsed = await this.schema.parseAsync(arg)
         } catch (e) {
             throw new Error(`Received tool input did not match expected schema: ${JSON.stringify(arg)}`)
         }

packages/components/nodes/tools/ChatflowTool/ChatflowTool.ts

@@ -4,13 +4,7 @@ import { RunnableConfig } from '@langchain/core/runnables'
 import { CallbackManagerForToolRun, Callbacks, CallbackManager, parseCallbackConfigArg } from '@langchain/core/callbacks/manager'
 import { StructuredTool } from '@langchain/core/tools'
 import { ICommonObject, IDatabaseEntity, INode, INodeData, INodeOptionsValue, INodeParams } from '../../../src/Interface'
-import {
-    getCredentialData,
-    getCredentialParam,
-    executeJavaScriptCode,
-    createCodeExecutionSandbox,
-    parseWithTypeConversion
-} from '../../../src/utils'
+import { getCredentialData, getCredentialParam, executeJavaScriptCode, createCodeExecutionSandbox } from '../../../src/utils'
 import { isValidUUID, isValidURL } from '../../../src/validator'
 import { v4 as uuidv4 } from 'uuid'
 
@@ -287,7 +281,7 @@ class ChatflowTool extends StructuredTool {
         }
         let parsed
         try {
-            parsed = await parseWithTypeConversion(this.schema, arg)
+            parsed = await this.schema.parseAsync(arg)
         } catch (e) {
             throw new Error(`Received tool input did not match expected schema: ${JSON.stringify(arg)}`)
         }

packages/components/nodes/tools/CodeInterpreterE2B/CodeInterpreterE2B.ts

@@ -1,5 +1,5 @@
 import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseWithTypeConversion } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { StructuredTool, ToolInputParsingException, ToolParams } from '@langchain/core/tools'
 import { Sandbox } from '@e2b/code-interpreter'
 import { z } from 'zod'
@@ -159,7 +159,7 @@ export class E2BTool extends StructuredTool {
         }
         let parsed
         try {
-            parsed = await parseWithTypeConversion(this.schema, arg)
+            parsed = await this.schema.parseAsync(arg)
         } catch (e) {
             throw new ToolInputParsingException(`Received tool input did not match expected schema`, JSON.stringify(arg))
         }

packages/components/nodes/tools/CustomTool/core.ts

@@ -2,7 +2,7 @@ import { z } from 'zod'
 import { RunnableConfig } from '@langchain/core/runnables'
 import { StructuredTool, ToolParams } from '@langchain/core/tools'
 import { CallbackManagerForToolRun, Callbacks, CallbackManager, parseCallbackConfigArg } from '@langchain/core/callbacks/manager'
-import { executeJavaScriptCode, createCodeExecutionSandbox, parseWithTypeConversion } from '../../../src/utils'
+import { executeJavaScriptCode, createCodeExecutionSandbox } from '../../../src/utils'
 import { ICommonObject } from '../../../src/Interface'
 
 class ToolInputParsingException extends Error {
@@ -68,7 +68,7 @@ export class DynamicStructuredTool<
         }
         let parsed
         try {
-            parsed = await parseWithTypeConversion(this.schema, arg)
+            parsed = await this.schema.parseAsync(arg)
         } catch (e) {
             throw new ToolInputParsingException(`Received tool input did not match expected schema`, JSON.stringify(arg))
         }

packages/components/nodes/tools/MCP/CustomMCP/CustomMCP.ts

@@ -136,17 +136,17 @@ class Custom_MCP implements INode {
         }
 
         let sandbox: ICommonObject = {}
-        const workspaceId = options?.searchOptions?.workspaceId?._value || options?.workspaceId
 
         if (mcpServerConfig.includes('$vars')) {
             const appDataSource = options.appDataSource as DataSource
             const databaseEntities = options.databaseEntities as IDatabaseEntity
-            // If options.workspaceId is not set, create a new options object with the workspaceId for getVars.
-            const optionsWithWorkspaceId = options.workspaceId ? options : { ...options, workspaceId }
-            const variables = await getVars(appDataSource, databaseEntities, nodeData, optionsWithWorkspaceId)
+
+            const variables = await getVars(appDataSource, databaseEntities, nodeData, options)
             sandbox['$vars'] = prepareSandboxVars(variables)
         }
 
+        const workspaceId = options?.searchOptions?.workspaceId?._value || options?.workspaceId
+
         let canonicalConfig
         try {
             canonicalConfig = JSON.parse(mcpServerConfig)

packages/components/nodes/tools/MCP/core.ts

@@ -114,7 +114,7 @@ export class MCPToolkit extends BaseToolkit {
         const res = await Promise.allSettled(toolsPromises)
         const errors = res.filter((r) => r.status === 'rejected')
         if (errors.length !== 0) {
-            console.error('MCP Tools failed to be resolved', errors)
+            console.error('MCP Tools falied to be resolved', errors)
         }
         const successes = res.filter((r) => r.status === 'fulfilled').map((r) => r.value)
         return successes

packages/components/nodes/tools/OpenAPIToolkit/core.ts

@@ -3,7 +3,7 @@ import { RequestInit } from 'node-fetch'
 import { RunnableConfig } from '@langchain/core/runnables'
 import { StructuredTool, ToolParams } from '@langchain/core/tools'
 import { CallbackManagerForToolRun, Callbacks, CallbackManager, parseCallbackConfigArg } from '@langchain/core/callbacks/manager'
-import { executeJavaScriptCode, createCodeExecutionSandbox, parseWithTypeConversion } from '../../../src/utils'
+import { executeJavaScriptCode, createCodeExecutionSandbox } from '../../../src/utils'
 import { ICommonObject } from '../../../src/Interface'
 
 const removeNulls = (obj: Record<string, any>) => {
@@ -174,7 +174,7 @@ export class DynamicStructuredTool<
         }
         let parsed
         try {
-            parsed = await parseWithTypeConversion(this.schema, arg)
+            parsed = await this.schema.parseAsync(arg)
         } catch (e) {
             throw new ToolInputParsingException(`Received tool input did not match expected schema ${e}`, JSON.stringify(arg))
         }

packages/components/nodes/tools/ReadFile/ReadFile.ts

@@ -0,0 +1,85 @@
+import { z } from 'zod'
+import { StructuredTool, ToolParams } from '@langchain/core/tools'
+import { Serializable } from '@langchain/core/load/serializable'
+import { NodeFileStore } from 'langchain/stores/file/node'
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+
+abstract class BaseFileStore extends Serializable {
+    abstract readFile(path: string): Promise<string>
+    abstract writeFile(path: string, contents: string): Promise<void>
+}
+
+class ReadFile_Tools implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Read File'
+        this.name = 'readFile'
+        this.version = 1.0
+        this.type = 'ReadFile'
+        this.icon = 'readfile.svg'
+        this.category = 'Tools'
+        this.description = 'Read file from disk'
+        this.baseClasses = [this.type, 'Tool', ...getBaseClasses(ReadFileTool)]
+        this.inputs = [
+            {
+                label: 'Base Path',
+                name: 'basePath',
+                placeholder: `C:\\Users\\User\\Desktop`,
+                type: 'string',
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const basePath = nodeData.inputs?.basePath as string
+        const store = basePath ? new NodeFileStore(basePath) : new NodeFileStore()
+        return new ReadFileTool({ store })
+    }
+}
+
+interface ReadFileParams extends ToolParams {
+    store: BaseFileStore
+}
+
+/**
+ * Class for reading files from the disk. Extends the StructuredTool
+ * class.
+ */
+export class ReadFileTool extends StructuredTool {
+    static lc_name() {
+        return 'ReadFileTool'
+    }
+
+    schema = z.object({
+        file_path: z.string().describe('name of file')
+    }) as any
+
+    name = 'read_file'
+
+    description = 'Read file from disk'
+
+    store: BaseFileStore
+
+    constructor({ store }: ReadFileParams) {
+        super(...arguments)
+
+        this.store = store
+    }
+
+    async _call({ file_path }: z.infer<typeof this.schema>) {
+        return await this.store.readFile(file_path)
+    }
+}
+
+module.exports = { nodeClass: ReadFile_Tools }

packages/components/nodes/tools/ReadFile/readfile.svg

@@ -0,0 +1,4 @@
+<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M18 5H9C7.89543 5 7 5.89543 7 7V25C7 26.1046 7.89543 27 9 27H12M18 5L25 12M18 5V12H25M25 12V25C25 26.1046 24.1046 27 23 27H20" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M16 17V29M16 17L13 20.1361M16 17L19 20.1361" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

packages/components/nodes/tools/RetrieverTool/RetrieverTool.ts

@@ -3,7 +3,7 @@ import { CallbackManager, CallbackManagerForToolRun, Callbacks, parseCallbackCon
 import { BaseDynamicToolInput, DynamicTool, StructuredTool, ToolInputParsingException } from '@langchain/core/tools'
 import { BaseRetriever } from '@langchain/core/retrievers'
 import { ICommonObject, INode, INodeData, INodeParams } from '../../../src/Interface'
-import { getBaseClasses, resolveFlowObjValue, parseWithTypeConversion } from '../../../src/utils'
+import { getBaseClasses, resolveFlowObjValue } from '../../../src/utils'
 import { SOURCE_DOCUMENTS_PREFIX } from '../../../src/agents'
 import { RunnableConfig } from '@langchain/core/runnables'
 import { VectorStoreRetriever } from '@langchain/core/vectorstores'
@@ -58,7 +58,7 @@ class DynamicStructuredTool<T extends z.ZodObject<any, any, any, any> = z.ZodObj
         }
         let parsed
         try {
-            parsed = await parseWithTypeConversion(this.schema, arg)
+            parsed = await this.schema.parseAsync(arg)
         } catch (e) {
             throw new ToolInputParsingException(`Received tool input did not match expected schema`, JSON.stringify(arg))
         }

packages/components/nodes/tools/WriteFile/WriteFile.ts

@@ -0,0 +1,87 @@
+import { z } from 'zod'
+import { StructuredTool, ToolParams } from '@langchain/core/tools'
+import { Serializable } from '@langchain/core/load/serializable'
+import { NodeFileStore } from 'langchain/stores/file/node'
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+
+abstract class BaseFileStore extends Serializable {
+    abstract readFile(path: string): Promise<string>
+    abstract writeFile(path: string, contents: string): Promise<void>
+}
+
+class WriteFile_Tools implements INode {
+    label: string
+    name: string
+    version: number
+    description: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Write File'
+        this.name = 'writeFile'
+        this.version = 1.0
+        this.type = 'WriteFile'
+        this.icon = 'writefile.svg'
+        this.category = 'Tools'
+        this.description = 'Write file to disk'
+        this.baseClasses = [this.type, 'Tool', ...getBaseClasses(WriteFileTool)]
+        this.inputs = [
+            {
+                label: 'Base Path',
+                name: 'basePath',
+                placeholder: `C:\\Users\\User\\Desktop`,
+                type: 'string',
+                optional: true
+            }
+        ]
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const basePath = nodeData.inputs?.basePath as string
+        const store = basePath ? new NodeFileStore(basePath) : new NodeFileStore()
+        return new WriteFileTool({ store })
+    }
+}
+
+interface WriteFileParams extends ToolParams {
+    store: BaseFileStore
+}
+
+/**
+ * Class for writing data to files on the disk. Extends the StructuredTool
+ * class.
+ */
+export class WriteFileTool extends StructuredTool {
+    static lc_name() {
+        return 'WriteFileTool'
+    }
+
+    schema = z.object({
+        file_path: z.string().describe('name of file'),
+        text: z.string().describe('text to write to file')
+    }) as any
+
+    name = 'write_file'
+
+    description = 'Write file from disk'
+
+    store: BaseFileStore
+
+    constructor({ store, ...rest }: WriteFileParams) {
+        super(rest)
+
+        this.store = store
+    }
+
+    async _call({ file_path, text }: z.infer<typeof this.schema>) {
+        await this.store.writeFile(file_path, text)
+        return 'File written to successfully.'
+    }
+}
+
+module.exports = { nodeClass: WriteFile_Tools }

packages/components/nodes/tools/WriteFile/writefile.svg

@@ -0,0 +1,4 @@
+<svg width="32" height="32" viewBox="0 0 32 32" fill="none" xmlns="http://www.w3.org/2000/svg">
+<path d="M25 18V25C25 26.1046 24.1046 27 23 27H9C7.89543 27 7 26.1046 7 25V7C7 5.89543 7.89543 5 9 5H18L19 6" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M12 19.3284V22H14.6716C15.202 22 15.7107 21.7893 16.0858 21.4142L24.5858 12.9142C25.3668 12.1332 25.3668 10.8668 24.5858 10.0858L23.9142 9.41421C23.1332 8.63316 21.8668 8.63317 21.0858 9.41421L12.5858 17.9142C12.2107 18.2893 12 18.798 12 19.3284Z" stroke="black" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"/>
+</svg>

packages/components/nodes/utilities/CustomFunction/CustomFunction.ts

@@ -84,16 +84,11 @@ class CustomFunction_Utilities implements INode {
 
         const variables = await getVars(appDataSource, databaseEntities, nodeData, options)
         const flow = {
-            input,
             chatflowId: options.chatflowid,
             sessionId: options.sessionId,
             chatId: options.chatId,
-            rawOutput: options.postProcessing?.rawOutput || '',
-            chatHistory: options.postProcessing?.chatHistory || [],
-            sourceDocuments: options.postProcessing?.sourceDocuments,
-            usedTools: options.postProcessing?.usedTools,
-            artifacts: options.postProcessing?.artifacts,
-            fileAnnotations: options.postProcessing?.fileAnnotations
+            rawOutput: options.rawOutput || '',
+            input
         }
 
         let inputVars: ICommonObject = {}

packages/components/nodes/vectorstores/Chroma/Chroma.ts

@@ -3,7 +3,7 @@ import { Chroma } from '@langchain/community/vectorstores/chroma'
 import { Embeddings } from '@langchain/core/embeddings'
 import { Document } from '@langchain/core/documents'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { ChromaExtended } from './core'
 import { index } from '../../../src/indexing'
 
@@ -186,11 +186,7 @@ class Chroma_VectorStores implements INode {
                     const vectorStoreName = collectionName
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     const chromaStore = new ChromaExtended(embeddings, obj)
 
@@ -233,7 +229,7 @@ class Chroma_VectorStores implements INode {
         if (chromaTenant) obj.chromaTenant = chromaTenant
         if (chromaDatabase) obj.chromaDatabase = chromaDatabase
         if (chromaMetadataFilter) {
-            const metadatafilter = typeof chromaMetadataFilter === 'object' ? chromaMetadataFilter : parseJsonBody(chromaMetadataFilter)
+            const metadatafilter = typeof chromaMetadataFilter === 'object' ? chromaMetadataFilter : JSON.parse(chromaMetadataFilter)
             obj.filter = metadatafilter
         }
 

packages/components/nodes/vectorstores/Couchbase/Couchbase.ts

@@ -4,7 +4,7 @@ import { Document } from '@langchain/core/documents'
 import { CouchbaseVectorStore, CouchbaseVectorStoreArgs } from '@langchain/community/vectorstores/couchbase'
 import { Cluster } from 'couchbase'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Couchbase_VectorStores implements INode {
@@ -215,8 +215,7 @@ class Couchbase_VectorStores implements INode {
             if (!embeddingKey || embeddingKey === '') couchbaseConfig.embeddingKey = 'embedding'
 
             if (couchbaseMetadataFilter) {
-                metadatafilter =
-                    typeof couchbaseMetadataFilter === 'object' ? couchbaseMetadataFilter : parseJsonBody(couchbaseMetadataFilter)
+                metadatafilter = typeof couchbaseMetadataFilter === 'object' ? couchbaseMetadataFilter : JSON.parse(couchbaseMetadataFilter)
             }
 
             const vectorStore = await CouchbaseVectorStore.initialize(embeddings, couchbaseConfig)

packages/components/nodes/vectorstores/Elasticsearch/Elasticsearch.ts

@@ -198,11 +198,7 @@ class Elasticsearch_VectorStores implements INode {
                     const vectorStoreName = indexName
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await vectorStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)

packages/components/nodes/vectorstores/Kendra/Kendra.ts

@@ -3,7 +3,7 @@ import { AmazonKendraRetriever } from '@langchain/aws'
 import { KendraClient, BatchPutDocumentCommand, BatchDeleteDocumentCommand } from '@aws-sdk/client-kendra'
 import { Document } from '@langchain/core/documents'
 import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { FLOWISE_CHATID, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { FLOWISE_CHATID, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { howToUseFileUpload } from '../VectorStoreUtils'
 import { MODEL_TYPE, getRegions } from '../../../src/modelLoader'
 
@@ -248,7 +248,7 @@ class Kendra_VectorStores implements INode {
 
         let filter = undefined
         if (attributeFilter) {
-            filter = typeof attributeFilter === 'object' ? attributeFilter : parseJsonBody(attributeFilter)
+            filter = typeof attributeFilter === 'object' ? attributeFilter : JSON.parse(attributeFilter)
         }
 
         // Add chat-specific filtering if file upload is enabled

packages/components/nodes/vectorstores/Meilisearch/Meilisearch.ts

@@ -79,7 +79,7 @@ class MeilisearchRetriever_node implements INode {
                 label: 'Semantic Ratio',
                 name: 'semanticRatio',
                 type: 'number',
-                description: 'percentage of semantic reasoning in meilisearch hybrid search, default is 0.75',
+                description: 'percentage of sematic reasoning in meilisearch hybrid search, default is 0.75',
                 additionalParams: true,
                 optional: true
             },
@@ -162,7 +162,7 @@ class MeilisearchRetriever_node implements INode {
                     }
                 } catch (error) {
                     console.error(error)
-                    console.warn('Error occurred when deleting your index, if it did not exist, we will create one for you... ')
+                    console.warn('Error occured when deleting your index, if it did not exist, we will create one for you... ')
                 }
             }
 

packages/components/nodes/vectorstores/MongoDBAtlas/MongoDBAtlas.ts

@@ -2,7 +2,7 @@ import { flatten } from 'lodash'
 import { Embeddings } from '@langchain/core/embeddings'
 import { Document } from '@langchain/core/documents'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 import { MongoDBAtlasVectorSearch } from './core'
 
@@ -187,7 +187,7 @@ class MongoDBAtlas_VectorStores implements INode {
             })
 
             if (mongoMetadataFilter) {
-                const metadataFilter = typeof mongoMetadataFilter === 'object' ? mongoMetadataFilter : parseJsonBody(mongoMetadataFilter)
+                const metadataFilter = typeof mongoMetadataFilter === 'object' ? mongoMetadataFilter : JSON.parse(mongoMetadataFilter)
 
                 for (const key in metadataFilter) {
                     mongoDbFilter.preFilter = {

packages/components/nodes/vectorstores/Pinecone/Pinecone.ts

@@ -5,7 +5,7 @@ import { Embeddings } from '@langchain/core/embeddings'
 import { Document } from '@langchain/core/documents'
 import { VectorStore } from '@langchain/core/vectorstores'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { FLOWISE_CHATID, getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { FLOWISE_CHATID, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { addMMRInputParams, howToUseFileUpload, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 import { index } from '../../../src/indexing'
 
@@ -212,11 +212,7 @@ class Pinecone_VectorStores implements INode {
                     const vectorStoreName = pineconeNamespace
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await pineconeStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)
@@ -252,8 +248,7 @@ class Pinecone_VectorStores implements INode {
 
         if (pineconeNamespace) obj.namespace = pineconeNamespace
         if (pineconeMetadataFilter) {
-            const metadatafilter =
-                typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : parseJsonBody(pineconeMetadataFilter)
+            const metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : JSON.parse(pineconeMetadataFilter)
             obj.filter = metadatafilter
         }
         if (isFileUploadEnabled && options.chatId) {

packages/components/nodes/vectorstores/Pinecone/Pinecone_LlamaIndex.ts

@@ -16,7 +16,7 @@ import { FetchResponse, Index, Pinecone, ScoredPineconeRecord } from '@pinecone-
 import { flatten } from 'lodash'
 import { Document as LCDocument } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { flattenObject, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { flattenObject, getCredentialData, getCredentialParam } from '../../../src/utils'
 
 class PineconeLlamaIndex_VectorStores implements INode {
     label: string
@@ -176,7 +176,7 @@ class PineconeLlamaIndex_VectorStores implements INode {
 
         let metadatafilter = {}
         if (pineconeMetadataFilter) {
-            metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : parseJsonBody(pineconeMetadataFilter)
+            metadatafilter = typeof pineconeMetadataFilter === 'object' ? pineconeMetadataFilter : JSON.parse(pineconeMetadataFilter)
             obj.queryFilter = metadatafilter
         }
 

packages/components/nodes/vectorstores/Postgres/Postgres.ts

@@ -1,7 +1,7 @@
 import { flatten } from 'lodash'
 import { Document } from '@langchain/core/documents'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { FLOWISE_CHATID, getBaseClasses, parseJsonBody } from '../../../src/utils'
+import { FLOWISE_CHATID, getBaseClasses } from '../../../src/utils'
 import { index } from '../../../src/indexing'
 import { howToUseFileUpload } from '../VectorStoreUtils'
 import { VectorStore } from '@langchain/core/vectorstores'
@@ -49,7 +49,7 @@ class Postgres_VectorStores implements INode {
     constructor() {
         this.label = 'Postgres'
         this.name = 'postgres'
-        this.version = 7.1
+        this.version = 7.0
         this.type = 'Postgres'
         this.icon = 'postgres.svg'
         this.category = 'Vector Stores'
@@ -173,15 +173,6 @@ class Postgres_VectorStores implements INode {
                 additionalParams: true,
                 optional: true
             },
-            {
-                label: 'Upsert Batch Size',
-                name: 'batchSize',
-                type: 'number',
-                step: 1,
-                description: 'Upsert in batches of size N',
-                additionalParams: true,
-                optional: true
-            },
             {
                 label: 'Additional Configuration',
                 name: 'additionalConfig',
@@ -241,7 +232,6 @@ class Postgres_VectorStores implements INode {
             const docs = nodeData.inputs?.document as Document[]
             const recordManager = nodeData.inputs?.recordManager
             const isFileUploadEnabled = nodeData.inputs?.fileUpload as boolean
-            const _batchSize = nodeData.inputs?.batchSize
             const vectorStoreDriver: VectorStoreDriver = Postgres_VectorStores.getDriverFromConfig(nodeData, options)
 
             const flattenDocs = docs && docs.length ? flatten(docs) : []
@@ -275,15 +265,7 @@ class Postgres_VectorStores implements INode {
 
                     return res
                 } else {
-                    if (_batchSize) {
-                        const batchSize = parseInt(_batchSize, 10)
-                        for (let i = 0; i < finalDocs.length; i += batchSize) {
-                            const batch = finalDocs.slice(i, i + batchSize)
-                            await vectorStoreDriver.fromDocuments(batch)
-                        }
-                    } else {
-                        await vectorStoreDriver.fromDocuments(finalDocs)
-                    }
+                    await vectorStoreDriver.fromDocuments(finalDocs)
 
                     return { numAdded: finalDocs.length, addedDocs: finalDocs }
                 }
@@ -303,11 +285,7 @@ class Postgres_VectorStores implements INode {
                     const vectorStoreName = tableName
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await vectorStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)
@@ -330,7 +308,7 @@ class Postgres_VectorStores implements INode {
 
         let pgMetadataFilter: any
         if (_pgMetadataFilter) {
-            pgMetadataFilter = typeof _pgMetadataFilter === 'object' ? _pgMetadataFilter : parseJsonBody(_pgMetadataFilter)
+            pgMetadataFilter = typeof _pgMetadataFilter === 'object' ? _pgMetadataFilter : JSON.parse(_pgMetadataFilter)
         }
         if (isFileUploadEnabled && options.chatId) {
             pgMetadataFilter = {

packages/components/nodes/vectorstores/Postgres/driver/TypeORM.ts

@@ -5,11 +5,6 @@ import { TypeORMVectorStore, TypeORMVectorStoreArgs, TypeORMVectorStoreDocument
 import { VectorStore } from '@langchain/core/vectorstores'
 import { Document } from '@langchain/core/documents'
 import { Pool } from 'pg'
-import { v4 as uuid } from 'uuid'
-
-type TypeORMAddDocumentOptions = {
-    ids?: string[]
-}
 
 export class TypeORMDriver extends VectorStoreDriver {
     protected _postgresConnectionOptions: DataSourceOptions
@@ -100,45 +95,15 @@ export class TypeORMDriver extends VectorStoreDriver {
                 try {
                     instance.appDataSource.getRepository(instance.documentEntity).delete(ids)
                 } catch (e) {
-                    console.error('Failed to delete', e)
+                    console.error('Failed to delete')
                 }
             }
         }
 
-        instance.addVectors = async (
-            vectors: number[][],
-            documents: Document[],
-            documentOptions?: TypeORMAddDocumentOptions
-        ): Promise<void> => {
-            const rows = vectors.map((embedding, idx) => {
-                const embeddingString = `[${embedding.join(',')}]`
-                const documentRow = {
-                    id: documentOptions?.ids?.length ? documentOptions.ids[idx] : uuid(),
-                    pageContent: documents[idx].pageContent,
-                    embedding: embeddingString,
-                    metadata: documents[idx].metadata
-                }
-                return documentRow
-            })
+        const baseAddVectorsFn = instance.addVectors.bind(instance)
 
-            const documentRepository = instance.appDataSource.getRepository(instance.documentEntity)
-            const _batchSize = this.nodeData.inputs?.batchSize
-            const chunkSize = _batchSize ? parseInt(_batchSize, 10) : 500
-
-            for (let i = 0; i < rows.length; i += chunkSize) {
-                const chunk = rows.slice(i, i + chunkSize)
-                try {
-                    await documentRepository.save(chunk)
-                } catch (e) {
-                    console.error(e)
-                    throw new Error(`Error inserting: ${chunk[0].pageContent}`)
-                }
-            }
-        }
-
-        instance.addDocuments = async (documents: Document[], options?: { ids?: string[] }): Promise<void> => {
-            const texts = documents.map(({ pageContent }) => pageContent)
-            return (instance.addVectors as any)(await this.getEmbeddings().embedDocuments(texts), documents, options)
+        instance.addVectors = async (vectors, documents) => {
+            return baseAddVectorsFn(vectors, this.sanitizeDocuments(documents))
         }
 
         return instance

packages/components/nodes/vectorstores/Qdrant/Qdrant.ts

@@ -6,7 +6,7 @@ import { Document } from '@langchain/core/documents'
 import { QdrantVectorStore, QdrantLibArgs } from '@langchain/qdrant'
 import { Embeddings } from '@langchain/core/embeddings'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { FLOWISE_CHATID, getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { FLOWISE_CHATID, getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { index } from '../../../src/indexing'
 import { howToUseFileUpload } from '../VectorStoreUtils'
 
@@ -77,8 +77,7 @@ class Qdrant_VectorStores implements INode {
             {
                 label: 'Qdrant Collection Name',
                 name: 'qdrantCollection',
-                type: 'string',
-                acceptVariable: true
+                type: 'string'
             },
             {
                 label: 'File Upload',
@@ -385,11 +384,7 @@ class Qdrant_VectorStores implements INode {
                     const vectorStoreName = collectionName
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await vectorStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)
@@ -444,7 +439,7 @@ class Qdrant_VectorStores implements INode {
             qdrantCollectionConfiguration =
                 typeof qdrantCollectionConfiguration === 'object'
                     ? qdrantCollectionConfiguration
-                    : parseJsonBody(qdrantCollectionConfiguration)
+                    : JSON.parse(qdrantCollectionConfiguration)
             dbConfig.collectionConfig = {
                 ...qdrantCollectionConfiguration,
                 vectors: {
@@ -456,7 +451,7 @@ class Qdrant_VectorStores implements INode {
         }
 
         if (queryFilter) {
-            retrieverConfig.filter = typeof queryFilter === 'object' ? queryFilter : parseJsonBody(queryFilter)
+            retrieverConfig.filter = typeof queryFilter === 'object' ? queryFilter : JSON.parse(queryFilter)
         }
         if (isFileUploadEnabled && options.chatId) {
             retrieverConfig.filter = retrieverConfig.filter || {}

packages/components/nodes/vectorstores/Supabase/Supabase.ts

@@ -5,7 +5,7 @@ import { Document } from '@langchain/core/documents'
 import { Embeddings } from '@langchain/core/embeddings'
 import { SupabaseVectorStore, SupabaseLibArgs } from '@langchain/community/vectorstores/supabase'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 import { index } from '../../../src/indexing'
 import { FilterParser } from './filterParser'
@@ -197,11 +197,7 @@ class Supabase_VectorStores implements INode {
                     const vectorStoreName = tableName + '_' + queryName
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await supabaseStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)
@@ -234,8 +230,7 @@ class Supabase_VectorStores implements INode {
         }
 
         if (supabaseMetadataFilter) {
-            const metadatafilter =
-                typeof supabaseMetadataFilter === 'object' ? supabaseMetadataFilter : parseJsonBody(supabaseMetadataFilter)
+            const metadatafilter = typeof supabaseMetadataFilter === 'object' ? supabaseMetadataFilter : JSON.parse(supabaseMetadataFilter)
             obj.filter = metadatafilter
         }
 

packages/components/nodes/vectorstores/Upstash/Upstash.ts

@@ -187,11 +187,7 @@ class Upstash_VectorStores implements INode {
                     const vectorStoreName = UPSTASH_VECTOR_REST_URL
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await upstashStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)

packages/components/nodes/vectorstores/Weaviate/Weaviate.ts

@@ -4,7 +4,7 @@ import { WeaviateLibArgs, WeaviateStore } from '@langchain/weaviate'
 import { Document } from '@langchain/core/documents'
 import { Embeddings } from '@langchain/core/embeddings'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, normalizeKeysRecursively, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam, normalizeKeysRecursively } from '../../../src/utils'
 import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 import { index } from '../../../src/indexing'
 import { VectorStore } from '@langchain/core/vectorstores'
@@ -252,11 +252,7 @@ class Weaviate_VectorStores implements INode {
                     const vectorStoreName = weaviateTextKey ? weaviateIndex + '_' + weaviateTextKey : weaviateIndex
                     await recordManager.createSchema()
                     ;(recordManager as any).namespace = (recordManager as any).namespace + '_' + vectorStoreName
-                    const filterKeys: ICommonObject = {}
-                    if (options.docId) {
-                        filterKeys.docId = options.docId
-                    }
-                    const keys: string[] = await recordManager.listKeys(filterKeys)
+                    const keys: string[] = await recordManager.listKeys({})
 
                     await weaviateStore.delete({ ids: keys })
                     await recordManager.deleteKeys(keys)
@@ -298,7 +294,7 @@ class Weaviate_VectorStores implements INode {
         if (weaviateTextKey) obj.textKey = weaviateTextKey
         if (weaviateMetadataKeys) obj.metadataKeys = JSON.parse(weaviateMetadataKeys.replace(/\s/g, ''))
         if (weaviateFilter) {
-            weaviateFilter = typeof weaviateFilter === 'object' ? weaviateFilter : parseJsonBody(weaviateFilter)
+            weaviateFilter = typeof weaviateFilter === 'object' ? weaviateFilter : JSON.parse(weaviateFilter)
         }
 
         const vectorStore = (await WeaviateStore.fromExistingIndex(embeddings, obj)) as unknown as VectorStore

packages/components/nodes/vectorstores/Zep/Zep.ts

@@ -4,7 +4,7 @@ import { ZepVectorStore, IZepConfig } from '@langchain/community/vectorstores/ze
 import { Embeddings } from '@langchain/core/embeddings'
 import { Document } from '@langchain/core/documents'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 
 class Zep_VectorStores implements INode {
@@ -159,7 +159,7 @@ class Zep_VectorStores implements INode {
         }
         if (apiKey) zepConfig.apiKey = apiKey
         if (zepMetadataFilter) {
-            const metadatafilter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : parseJsonBody(zepMetadataFilter)
+            const metadatafilter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : JSON.parse(zepMetadataFilter)
             zepConfig.filter = metadatafilter
         }
 

packages/components/nodes/vectorstores/ZepCloud/ZepCloud.ts

@@ -3,7 +3,7 @@ import { ZepClient } from '@getzep/zep-cloud'
 import { IZepConfig, ZepVectorStore } from '@getzep/zep-cloud/langchain'
 import { Document } from 'langchain/document'
 import { ICommonObject, INode, INodeData, INodeOutputsValue, INodeParams, IndexingResult } from '../../../src/Interface'
-import { getBaseClasses, getCredentialData, getCredentialParam, parseJsonBody } from '../../../src/utils'
+import { getBaseClasses, getCredentialData, getCredentialParam } from '../../../src/utils'
 import { addMMRInputParams, resolveVectorStoreOrRetriever } from '../VectorStoreUtils'
 import { FakeEmbeddings } from 'langchain/embeddings/fake'
 import { Embeddings } from '@langchain/core/embeddings'
@@ -129,7 +129,7 @@ class Zep_CloudVectorStores implements INode {
             collectionName: zepCollection
         }
         if (zepMetadataFilter) {
-            zepConfig.filter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : parseJsonBody(zepMetadataFilter)
+            zepConfig.filter = typeof zepMetadataFilter === 'object' ? zepMetadataFilter : JSON.parse(zepMetadataFilter)
         }
         zepConfig.client = new ZepClient({
             apiKey: apiKey

packages/components/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise-components",
-    "version": "3.0.11",
+    "version": "3.0.7",
     "description": "Flowiseai Components",
     "main": "dist/src/index",
     "types": "dist/src/index.d.ts",
@@ -42,9 +42,8 @@
         "@google-ai/generativelanguage": "^2.5.0",
         "@google-cloud/storage": "^7.15.2",
         "@google/generative-ai": "^0.24.0",
-        "@grpc/grpc-js": "^1.10.10",
-        "@huggingface/inference": "^4.13.2",
-        "@langchain/anthropic": "0.3.33",
+        "@huggingface/inference": "^2.6.1",
+        "@langchain/anthropic": "0.3.29",
         "@langchain/aws": "^0.1.11",
         "@langchain/baidu-qianfan": "^0.1.0",
         "@langchain/cohere": "^0.0.7",
@@ -74,20 +73,6 @@
         "@modelcontextprotocol/server-slack": "^2025.1.17",
         "@notionhq/client": "^2.2.8",
         "@opensearch-project/opensearch": "^1.2.0",
-        "@opentelemetry/api": "1.9.0",
-        "@opentelemetry/auto-instrumentations-node": "^0.52.0",
-        "@opentelemetry/core": "1.27.0",
-        "@opentelemetry/exporter-metrics-otlp-grpc": "0.54.0",
-        "@opentelemetry/exporter-metrics-otlp-http": "0.54.0",
-        "@opentelemetry/exporter-metrics-otlp-proto": "0.54.0",
-        "@opentelemetry/exporter-trace-otlp-grpc": "0.54.0",
-        "@opentelemetry/exporter-trace-otlp-http": "0.54.0",
-        "@opentelemetry/exporter-trace-otlp-proto": "0.54.0",
-        "@opentelemetry/resources": "1.27.0",
-        "@opentelemetry/sdk-metrics": "1.27.0",
-        "@opentelemetry/sdk-node": "^0.54.0",
-        "@opentelemetry/sdk-trace-base": "1.27.0",
-        "@opentelemetry/semantic-conventions": "1.27.0",
         "@pinecone-database/pinecone": "4.0.0",
         "@qdrant/js-client-rest": "^1.9.0",
         "@stripe/agent-toolkit": "^0.1.20",

packages/components/src/Interface.ts

@@ -134,7 +134,6 @@ export interface INodeProperties {
     documentation?: string
     color?: string
     hint?: string
-    warning?: string
 }
 
 export interface INode extends INodeProperties {

packages/components/src/agents.ts

@@ -1021,7 +1021,7 @@ export class JsonOutputToolsParser extends BaseLLMOutputParser<ParsedToolCall[]>
         const parsedToolCalls = []
 
         if (!toolCalls) {
-            // @ts-expect-error name and arguments are defined by Object.defineProperty
+            // @ts-expect-error name and arguemnts are defined by Object.defineProperty
             const parsedToolCall: ParsedToolCall = {
                 type: 'undefined',
                 args: {}
@@ -1047,7 +1047,7 @@ export class JsonOutputToolsParser extends BaseLLMOutputParser<ParsedToolCall[]>
         const clonedToolCalls = JSON.parse(JSON.stringify(toolCalls))
         for (const toolCall of clonedToolCalls) {
             if (toolCall.function !== undefined) {
-                // @ts-expect-error name and arguments are defined by Object.defineProperty
+                // @ts-expect-error name and arguemnts are defined by Object.defineProperty
                 const parsedToolCall: ParsedToolCall = {
                     type: toolCall.function.name,
                     args: JSON.parse(toolCall.function.arguments)

packages/components/src/handler.ts

@@ -1774,7 +1774,7 @@ export class AnalyticHandler {
         }
 
         if (Object.prototype.hasOwnProperty.call(this.handlers, 'lunary')) {
-            const toolEventId: string = this.handlers['lunary'].toolEvent[returnIds['lunary'].toolEvent]
+            const toolEventId: string = this.handlers['lunary'].llmEvent[returnIds['lunary'].toolEvent]
             const monitor = this.handlers['lunary'].client
 
             if (monitor && toolEventId) {

packages/components/src/indexing.ts

@@ -8,10 +8,6 @@ import { IndexingResult } from './Interface'
 
 type Metadata = Record<string, unknown>
 
-export interface ExtendedRecordManagerInterface extends RecordManagerInterface {
-    update(keys: Array<{ uid: string; docId: string }> | string[], updateOptions?: Record<string, any>): Promise<void>
-}
-
 type StringOrDocFunc = string | ((doc: DocumentInterface) => string)
 
 export interface HashedDocumentInterface extends DocumentInterface {
@@ -211,7 +207,7 @@ export const _isBaseDocumentLoader = (arg: any): arg is BaseDocumentLoader => {
 
 interface IndexArgs {
     docsSource: BaseDocumentLoader | DocumentInterface[]
-    recordManager: ExtendedRecordManagerInterface
+    recordManager: RecordManagerInterface
     vectorStore: VectorStore
     options?: IndexOptions
 }
@@ -279,7 +275,7 @@ export async function index(args: IndexArgs): Promise<IndexingResult> {
 
         const uids: string[] = []
         const docsToIndex: DocumentInterface[] = []
-        const docsToUpdate: Array<{ uid: string; docId: string }> = []
+        const docsToUpdate: string[] = []
         const seenDocs = new Set<string>()
         hashedDocs.forEach((hashedDoc, i) => {
             const docExists = batchExists[i]
@@ -287,7 +283,7 @@ export async function index(args: IndexArgs): Promise<IndexingResult> {
                 if (forceUpdate) {
                     seenDocs.add(hashedDoc.uid)
                 } else {
-                    docsToUpdate.push({ uid: hashedDoc.uid, docId: hashedDoc.metadata.docId as string })
+                    docsToUpdate.push(hashedDoc.uid)
                     return
                 }
             }
@@ -312,7 +308,7 @@ export async function index(args: IndexArgs): Promise<IndexingResult> {
         }
 
         await recordManager.update(
-            hashedDocs.map((doc) => ({ uid: doc.uid, docId: doc.metadata.docId as string })),
+            hashedDocs.map((doc) => doc.uid),
             { timeAtLeast: indexStartDt, groupIds: sourceIds }
         )
 

packages/components/src/utils.ts

@@ -4,12 +4,11 @@ import * as fs from 'fs'
 import * as path from 'path'
 import { JSDOM } from 'jsdom'
 import { z } from 'zod'
-import { cloneDeep, omit, get } from 'lodash'
 import TurndownService from 'turndown'
 import { DataSource, Equal } from 'typeorm'
 import { ICommonObject, IDatabaseEntity, IFileUpload, IMessage, INodeData, IVariable, MessageContentImageUrl } from './Interface'
-import { BaseChatModel } from '@langchain/core/language_models/chat_models'
 import { AES, enc } from 'crypto-js'
+import { omit, get } from 'lodash'
 import { AIMessage, HumanMessage, BaseMessage } from '@langchain/core/messages'
 import { Document } from '@langchain/core/documents'
 import { getFileFromStorage } from './storageUtils'
@@ -1501,29 +1500,9 @@ export const executeJavaScriptCode = async (
 
             const sbx = await Sandbox.create({ apiKey: process.env.E2B_APIKEY, timeoutMs })
 
-            // Determine which libraries to install
-            const librariesToInstall = new Set<string>(libraries)
-
-            // Auto-detect required libraries from code
-            // Extract required modules from import/require statements
-            const importRegex = /(?:import\s+.*?\s+from\s+['"]([^'"]+)['"]|require\s*\(\s*['"]([^'"]+)['"]\s*\))/g
-            let match
-            while ((match = importRegex.exec(code)) !== null) {
-                const moduleName = match[1] || match[2]
-                // Extract base module name (e.g., 'typeorm' from 'typeorm/something')
-                const baseModuleName = moduleName.split('/')[0]
-                librariesToInstall.add(baseModuleName)
-            }
-
             // Install libraries
-            for (const library of librariesToInstall) {
-                // Validate library name to prevent command injection.
-                const validPackageNameRegex = /^(@[a-z0-9-~][a-z0-9-._~]*\/)?[a-z0-9-~][a-z0-9-._~]*$/
-                if (validPackageNameRegex.test(library)) {
-                    await sbx.commands.run(`npm install ${library}`)
-                } else {
-                    console.warn(`[Sandbox] Skipping installation of invalid module: ${library}`)
-                }
+            for (const library of libraries) {
+                await sbx.commands.run(`npm install ${library}`)
             }
 
             // Separate imports from the rest of the code for proper ES6 module structure
@@ -1781,321 +1760,3 @@ export const parseJsonBody = (body: string): any => {
         }
     }
 }
-
-/**
- * Parse a value against a Zod schema with automatic type conversion for common type mismatches
- * @param schema - The Zod schema to parse against
- * @param arg - The value to parse
- * @param maxDepth - Maximum recursion depth to prevent infinite loops (default: 10)
- * @returns The parsed value
- * @throws Error if parsing fails after attempting type conversions
- */
-export async function parseWithTypeConversion<T extends z.ZodTypeAny>(schema: T, arg: unknown, maxDepth: number = 10): Promise<z.infer<T>> {
-    // Safety check: prevent infinite recursion
-    if (maxDepth <= 0) {
-        throw new Error('Maximum recursion depth reached in parseWithTypeConversion')
-    }
-
-    try {
-        return await schema.parseAsync(arg)
-    } catch (e) {
-        // Check if it's a ZodError and try to fix type mismatches
-        if (z.ZodError && e instanceof z.ZodError) {
-            const zodError = e as z.ZodError
-            // Deep clone the arg to avoid mutating the original
-            const modifiedArg = typeof arg === 'object' && arg !== null ? cloneDeep(arg) : arg
-            let hasModification = false
-
-            // Helper function to set a value at a nested path
-            const setValueAtPath = (obj: any, path: (string | number)[], value: any): void => {
-                let current = obj
-                for (let i = 0; i < path.length - 1; i++) {
-                    const key = path[i]
-                    if (current && typeof current === 'object' && key in current) {
-                        current = current[key]
-                    } else {
-                        return // Path doesn't exist
-                    }
-                }
-                if (current !== undefined && current !== null) {
-                    const finalKey = path[path.length - 1]
-                    current[finalKey] = value
-                }
-            }
-
-            // Helper function to get a value at a nested path
-            const getValueAtPath = (obj: any, path: (string | number)[]): any => {
-                let current = obj
-                for (const key of path) {
-                    if (current && typeof current === 'object' && key in current) {
-                        current = current[key]
-                    } else {
-                        return undefined
-                    }
-                }
-                return current
-            }
-
-            // Helper function to convert value to expected type
-            const convertValue = (value: any, expected: string, received: string): any => {
-                // Expected string
-                if (expected === 'string') {
-                    if (received === 'object' || received === 'array') {
-                        return JSON.stringify(value)
-                    }
-                    if (received === 'number' || received === 'boolean') {
-                        return String(value)
-                    }
-                }
-                // Expected number
-                else if (expected === 'number') {
-                    if (received === 'string') {
-                        const parsed = parseFloat(value)
-                        if (!isNaN(parsed)) {
-                            return parsed
-                        }
-                    }
-                    if (received === 'boolean') {
-                        return value ? 1 : 0
-                    }
-                }
-                // Expected boolean
-                else if (expected === 'boolean') {
-                    if (received === 'string') {
-                        const lower = String(value).toLowerCase().trim()
-                        if (lower === 'true' || lower === '1' || lower === 'yes') {
-                            return true
-                        }
-                        if (lower === 'false' || lower === '0' || lower === 'no') {
-                            return false
-                        }
-                    }
-                    if (received === 'number') {
-                        return value !== 0
-                    }
-                }
-                // Expected object
-                else if (expected === 'object') {
-                    if (received === 'string') {
-                        try {
-                            const parsed = JSON.parse(value)
-                            if (typeof parsed === 'object' && parsed !== null && !Array.isArray(parsed)) {
-                                return parsed
-                            }
-                        } catch {
-                            // Invalid JSON, return undefined to skip conversion
-                        }
-                    }
-                }
-                // Expected array
-                else if (expected === 'array') {
-                    if (received === 'string') {
-                        try {
-                            const parsed = JSON.parse(value)
-                            if (Array.isArray(parsed)) {
-                                return parsed
-                            }
-                        } catch {
-                            // Invalid JSON, return undefined to skip conversion
-                        }
-                    }
-                    if (received === 'object' && value !== null) {
-                        // Convert object to array (e.g., {0: 'a', 1: 'b'} -> ['a', 'b'])
-                        // Only if it looks like an array-like object
-                        const keys = Object.keys(value)
-                        const numericKeys = keys.filter((k) => /^\d+$/.test(k))
-                        if (numericKeys.length === keys.length) {
-                            return numericKeys.map((k) => value[k])
-                        }
-                    }
-                }
-                return undefined // No conversion possible
-            }
-
-            // Process each issue in the error
-            for (const issue of zodError.issues) {
-                // Handle invalid_type errors (type mismatches)
-                if (issue.code === 'invalid_type' && issue.path.length > 0) {
-                    try {
-                        const valueAtPath = getValueAtPath(modifiedArg, issue.path)
-                        if (valueAtPath !== undefined) {
-                            const convertedValue = convertValue(valueAtPath, issue.expected, issue.received)
-                            if (convertedValue !== undefined) {
-                                setValueAtPath(modifiedArg, issue.path, convertedValue)
-                                hasModification = true
-                            }
-                        }
-                    } catch (pathError) {
-                        console.error('Error processing path in Zod error', pathError)
-                    }
-                }
-            }
-
-            // If we modified the arg, recursively call parseWithTypeConversion
-            // This allows newly surfaced nested errors to also get conversion treatment
-            // Decrement maxDepth to prevent infinite recursion
-            if (hasModification) {
-                return await parseWithTypeConversion(schema, modifiedArg, maxDepth - 1)
-            }
-        }
-        // Re-throw the original error if not a ZodError or no conversion possible
-        throw e
-    }
-}
-
-/**
- * Configures structured output for the LLM using Zod schema
- * @param {BaseChatModel} llmNodeInstance - The LLM instance to configure
- * @param {any[]} structuredOutput - Array of structured output schema definitions
- * @returns {BaseChatModel} - The configured LLM instance
- */
-export const configureStructuredOutput = (llmNodeInstance: BaseChatModel, structuredOutput: any[]): BaseChatModel => {
-    try {
-        const zodObj: ICommonObject = {}
-        for (const sch of structuredOutput) {
-            if (sch.type === 'string') {
-                zodObj[sch.key] = z.string().describe(sch.description || '')
-            } else if (sch.type === 'stringArray') {
-                zodObj[sch.key] = z.array(z.string()).describe(sch.description || '')
-            } else if (sch.type === 'number') {
-                zodObj[sch.key] = z.number().describe(sch.description || '')
-            } else if (sch.type === 'boolean') {
-                zodObj[sch.key] = z.boolean().describe(sch.description || '')
-            } else if (sch.type === 'enum') {
-                const enumValues = sch.enumValues?.split(',').map((item: string) => item.trim()) || []
-                zodObj[sch.key] = z
-                    .enum(enumValues.length ? (enumValues as [string, ...string[]]) : ['default'])
-                    .describe(sch.description || '')
-            } else if (sch.type === 'jsonArray') {
-                const jsonSchema = sch.jsonSchema
-                if (jsonSchema) {
-                    try {
-                        // Parse the JSON schema
-                        const schemaObj = JSON.parse(jsonSchema)
-
-                        // Create a Zod schema from the JSON schema
-                        const itemSchema = createZodSchemaFromJSON(schemaObj)
-
-                        // Create an array schema of the item schema
-                        zodObj[sch.key] = z.array(itemSchema).describe(sch.description || '')
-                    } catch (err) {
-                        console.error(`Error parsing JSON schema for ${sch.key}:`, err)
-                        // Fallback to generic array of records
-                        zodObj[sch.key] = z.array(z.record(z.any())).describe(sch.description || '')
-                    }
-                } else {
-                    // If no schema provided, use generic array of records
-                    zodObj[sch.key] = z.array(z.record(z.any())).describe(sch.description || '')
-                }
-            }
-        }
-        const structuredOutputSchema = z.object(zodObj)
-
-        // @ts-ignore
-        return llmNodeInstance.withStructuredOutput(structuredOutputSchema)
-    } catch (exception) {
-        console.error(exception)
-        return llmNodeInstance
-    }
-}
-
-/**
- * Creates a Zod schema from a JSON schema object
- * @param {any} jsonSchema - The JSON schema object
- * @returns {z.ZodTypeAny} - A Zod schema
- */
-export const createZodSchemaFromJSON = (jsonSchema: any): z.ZodTypeAny => {
-    // If the schema is an object with properties, create an object schema
-    if (typeof jsonSchema === 'object' && jsonSchema !== null) {
-        const schemaObj: Record<string, z.ZodTypeAny> = {}
-
-        // Process each property in the schema
-        for (const [key, value] of Object.entries(jsonSchema)) {
-            if (value === null) {
-                // Handle null values
-                schemaObj[key] = z.null()
-            } else if (typeof value === 'object' && !Array.isArray(value)) {
-                // Check if the property has a type definition
-                if ('type' in value) {
-                    const type = value.type as string
-                    const description = ('description' in value ? (value.description as string) : '') || ''
-
-                    // Create the appropriate Zod type based on the type property
-                    if (type === 'string') {
-                        schemaObj[key] = z.string().describe(description)
-                    } else if (type === 'number') {
-                        schemaObj[key] = z.number().describe(description)
-                    } else if (type === 'boolean') {
-                        schemaObj[key] = z.boolean().describe(description)
-                    } else if (type === 'array') {
-                        // If it's an array type, check if items is defined
-                        if ('items' in value && value.items) {
-                            const itemSchema = createZodSchemaFromJSON(value.items)
-                            schemaObj[key] = z.array(itemSchema).describe(description)
-                        } else {
-                            // Default to array of any if items not specified
-                            schemaObj[key] = z.array(z.any()).describe(description)
-                        }
-                    } else if (type === 'object') {
-                        // If it's an object type, check if properties is defined
-                        if ('properties' in value && value.properties) {
-                            const nestedSchema = createZodSchemaFromJSON(value.properties)
-                            schemaObj[key] = nestedSchema.describe(description)
-                        } else {
-                            // Default to record of any if properties not specified
-                            schemaObj[key] = z.record(z.any()).describe(description)
-                        }
-                    } else {
-                        // Default to any for unknown types
-                        schemaObj[key] = z.any().describe(description)
-                    }
-
-                    // Check if the property is optional
-                    if ('optional' in value && value.optional === true) {
-                        schemaObj[key] = schemaObj[key].optional()
-                    }
-                } else if (Array.isArray(value)) {
-                    // Array values without a type property
-                    if (value.length > 0) {
-                        // If the array has items, recursively create a schema for the first item
-                        const itemSchema = createZodSchemaFromJSON(value[0])
-                        schemaObj[key] = z.array(itemSchema)
-                    } else {
-                        // Empty array, allow any array
-                        schemaObj[key] = z.array(z.any())
-                    }
-                } else {
-                    // It's a nested object without a type property, recursively create schema
-                    schemaObj[key] = createZodSchemaFromJSON(value)
-                }
-            } else if (Array.isArray(value)) {
-                // Array values
-                if (value.length > 0) {
-                    // If the array has items, recursively create a schema for the first item
-                    const itemSchema = createZodSchemaFromJSON(value[0])
-                    schemaObj[key] = z.array(itemSchema)
-                } else {
-                    // Empty array, allow any array
-                    schemaObj[key] = z.array(z.any())
-                }
-            } else {
-                // For primitive values (which shouldn't be in the schema directly)
-                // Use the corresponding Zod type
-                if (typeof value === 'string') {
-                    schemaObj[key] = z.string()
-                } else if (typeof value === 'number') {
-                    schemaObj[key] = z.number()
-                } else if (typeof value === 'boolean') {
-                    schemaObj[key] = z.boolean()
-                } else {
-                    schemaObj[key] = z.any()
-                }
-            }
-        }
-
-        return z.object(schemaObj)
-    }
-
-    // Fallback to any for unknown types
-    return z.any()
-}

packages/components/src/validator.ts

@@ -41,31 +41,3 @@ export const isPathTraversal = (path: string): boolean => {
 
     return dangerousPatterns.some((pattern) => path.toLowerCase().includes(pattern))
 }
-
-/**
- * Enhanced path validation for workspace-scoped file operations
- * @param {string} filePath The file path to validate
- * @returns {boolean} True if path traversal detected, false otherwise
- */
-export const isUnsafeFilePath = (filePath: string): boolean => {
-    if (!filePath || typeof filePath !== 'string') {
-        return true
-    }
-
-    // Check for path traversal patterns
-    const dangerousPatterns = [
-        /\.\./, // Directory traversal (..)
-        /%2e%2e/i, // URL encoded ..
-        /%2f/i, // URL encoded /
-        /%5c/i, // URL encoded \
-        /\0/, // Null bytes
-        // eslint-disable-next-line no-control-regex
-        /[\x00-\x1f]/, // Control characters
-        /^\/[^/]/, // Absolute Unix paths (starting with /)
-        /^[a-zA-Z]:\\/, // Absolute Windows paths (C:\)
-        /^\\\\[^\\]/, // UNC paths (\\server\)
-        /^\\\\\?\\/ // Extended-length paths (\\?\)
-    ]
-
-    return dangerousPatterns.some((pattern) => pattern.test(filePath))
-}

packages/server/.env.example

@@ -38,8 +38,6 @@ PORT=3000
 # DEBUG=true
 # LOG_PATH=/your_log_path/.flowise/logs
 # LOG_LEVEL=info #(error | warn | info | verbose | debug)
-# LOG_SANITIZE_BODY_FIELDS=password,pwd,pass,secret,token,apikey,api_key,accesstoken,access_token,refreshtoken,refresh_token,clientsecret,client_secret,privatekey,private_key,secretkey,secret_key,auth,authorization,credential,credentials
-# LOG_SANITIZE_HEADER_FIELDS=authorization,x-api-key,x-auth-token,cookie
 # TOOL_FUNCTION_BUILTIN_DEP=crypto,fs
 # TOOL_FUNCTION_EXTERNAL_DEP=moment,lodash
 # ALLOW_BUILTIN_DEP=false

packages/server/README.md

@@ -41,7 +41,7 @@ cd Flowise/packages/server
 pnpm install
 ./node_modules/.bin/cypress install
 pnpm build
-#Only for writing new tests on local dev -> pnpm run cypress:open
+#Only for writting new tests on local dev -> pnpm run cypress:open
 pnpm run e2e
 ```
 

packages/server/marketplaces/agentflowsv2/SQL Agent.json

@@ -284,7 +284,7 @@
                 "inputAnchors": [],
                 "inputs": {
                     "customFunctionInputVariables": "",
-                    "customFunctionJavascriptFunction": "const { DataSource } = require('typeorm');\nconst { Pool } = require('pg');\n\nconst HOST = 'localhost';\nconst USER = 'testuser';\nconst PASSWORD = 'testpwd';\nconst DATABASE = 'abudhabi';\nconst PORT = 5555;\n\nlet sqlSchemaPrompt = '';\n\nconst AppDataSource = new DataSource({\n  type: 'postgres',\n  host: HOST,\n  port: PORT,\n  username: USER,\n  password: PASSWORD,\n  database: DATABASE,\n  synchronize: false,\n  logging: false,\n});\n\nasync function getSQLPrompt() {\n  try {\n    await AppDataSource.initialize();\n    const queryRunner = AppDataSource.createQueryRunner();\n\n    // Get all user-defined tables (excluding system tables)\n    const tablesResult = await queryRunner.query(`\n      SELECT table_name\n      FROM information_schema.tables\n      WHERE table_schema = 'public' AND table_type = 'BASE TABLE'\n    `);\n\n    for (const tableRow of tablesResult) {\n      const tableName = tableRow.table_name;\n\n      const schemaInfo = await queryRunner.query(`\n        SELECT column_name, data_type, is_nullable\n        FROM information_schema.columns\n        WHERE table_name = '${tableName}'\n      `);\n\n      const createColumns = [];\n      const columnNames = [];\n\n      for (const column of schemaInfo) {\n        const name = column.column_name;\n        const type = column.data_type.toUpperCase();\n        const notNull = column.is_nullable === 'NO' ? 'NOT NULL' : '';\n        columnNames.push(name);\n        createColumns.push(`${name} ${type} ${notNull}`);\n      }\n\n      const sqlCreateTableQuery = `CREATE TABLE ${tableName} (${createColumns.join(', ')})`;\n      const sqlSelectTableQuery = `SELECT * FROM ${tableName} LIMIT 3`;\n\n      let allValues = [];\n      try {\n        const rows = await queryRunner.query(sqlSelectTableQuery);\n\n        allValues = rows.map(row =>\n          columnNames.map(col => row[col]).join(' ')\n        );\n      } catch (err) {\n        allValues.push('[ERROR FETCHING ROWS]');\n      }\n\n      sqlSchemaPrompt +=\n        sqlCreateTableQuery +\n        '\\n' +\n        sqlSelectTableQuery +\n        '\\n' +\n        columnNames.join(' ') +\n        '\\n' +\n        allValues.join('\\n') +\n        '\\n\\n';\n    }\n\n    await queryRunner.release();\n  } catch (err) {\n    console.error(err);\n    throw err;\n  }\n}\n\nasync function main() {\n  await getSQLPrompt();\n}\n\nawait main();\n\nreturn sqlSchemaPrompt;\n",
+                    "customFunctionJavascriptFunction": "const { DataSource } = require('typeorm');\n\nconst HOST = 'localhost';\nconst USER = 'testuser';\nconst PASSWORD = 'testpwd';\nconst DATABASE = 'abudhabi';\nconst PORT = 5555;\n\nlet sqlSchemaPrompt = '';\n\nconst AppDataSource = new DataSource({\n  type: 'postgres',\n  host: HOST,\n  port: PORT,\n  username: USER,\n  password: PASSWORD,\n  database: DATABASE,\n  synchronize: false,\n  logging: false,\n});\n\nasync function getSQLPrompt() {\n  try {\n    await AppDataSource.initialize();\n    const queryRunner = AppDataSource.createQueryRunner();\n\n    // Get all user-defined tables (excluding system tables)\n    const tablesResult = await queryRunner.query(`\n      SELECT table_name\n      FROM information_schema.tables\n      WHERE table_schema = 'public' AND table_type = 'BASE TABLE'\n    `);\n\n    for (const tableRow of tablesResult) {\n      const tableName = tableRow.table_name;\n\n      const schemaInfo = await queryRunner.query(`\n        SELECT column_name, data_type, is_nullable\n        FROM information_schema.columns\n        WHERE table_name = '${tableName}'\n      `);\n\n      const createColumns = [];\n      const columnNames = [];\n\n      for (const column of schemaInfo) {\n        const name = column.column_name;\n        const type = column.data_type.toUpperCase();\n        const notNull = column.is_nullable === 'NO' ? 'NOT NULL' : '';\n        columnNames.push(name);\n        createColumns.push(`${name} ${type} ${notNull}`);\n      }\n\n      const sqlCreateTableQuery = `CREATE TABLE ${tableName} (${createColumns.join(', ')})`;\n      const sqlSelectTableQuery = `SELECT * FROM ${tableName} LIMIT 3`;\n\n      let allValues = [];\n      try {\n        const rows = await queryRunner.query(sqlSelectTableQuery);\n\n        allValues = rows.map(row =>\n          columnNames.map(col => row[col]).join(' ')\n        );\n      } catch (err) {\n        allValues.push('[ERROR FETCHING ROWS]');\n      }\n\n      sqlSchemaPrompt +=\n        sqlCreateTableQuery +\n        '\\n' +\n        sqlSelectTableQuery +\n        '\\n' +\n        columnNames.join(' ') +\n        '\\n' +\n        allValues.join('\\n') +\n        '\\n\\n';\n    }\n\n    await queryRunner.release();\n  } catch (err) {\n    console.error(err);\n    throw err;\n  }\n}\n\nasync function main() {\n  await getSQLPrompt();\n}\n\nawait main();\n\nreturn sqlSchemaPrompt;\n",
                     "customFunctionUpdateState": ""
                 },
                 "outputAnchors": [
@@ -913,7 +913,7 @@
                             "variableValue": "<p><span class=\"variable\" data-type=\"mention\" data-id=\"$flow.state.sqlQuery\" data-label=\"$flow.state.sqlQuery\">{{ $flow.state.sqlQuery }}</span> </p>"
                         }
                     ],
-                    "customFunctionJavascriptFunction": "const { DataSource } = require('typeorm');\nconst { Pool } = require('pg');\n\n// Configuration\nconst HOST = 'localhost';\nconst USER = 'testuser';\nconst PASSWORD = 'testpwd';\nconst DATABASE = 'abudhabi';\nconst PORT = 5555;\n\nconst sqlQuery = $sqlQuery;\n\nconst AppDataSource = new DataSource({\n  type: 'postgres',\n  host: HOST,\n  port: PORT,\n  username: USER,\n  password: PASSWORD,\n  database: DATABASE,\n  synchronize: false,\n  logging: false,\n});\n\nlet formattedResult = '';\n\nasync function runSQLQuery(query) {\n  try {\n    await AppDataSource.initialize();\n    const queryRunner = AppDataSource.createQueryRunner();\n\n    const rows = await queryRunner.query(query);\n    console.log('rows =', rows);\n\n    if (rows.length === 0) {\n      formattedResult = '[No results returned]';\n    } else {\n      const columnNames = Object.keys(rows[0]);\n      const header = columnNames.join(' ');\n      const values = rows.map(row =>\n        columnNames.map(col => row[col]).join(' ')\n      );\n\n      formattedResult = query + '\\n' + header + '\\n' + values.join('\\n');\n    }\n\n    await queryRunner.release();\n  } catch (err) {\n    console.error('[ERROR]', err);\n    formattedResult = `[Error executing query]: ${err}`;\n  }\n\n  return formattedResult;\n}\n\nasync function main() {\n  formattedResult = await runSQLQuery(sqlQuery);\n}\n\nawait main();\n\nreturn formattedResult;\n",
+                    "customFunctionJavascriptFunction": "const { DataSource } = require('typeorm');\n\n// Configuration\nconst HOST = 'localhost';\nconst USER = 'testuser';\nconst PASSWORD = 'testpwd';\nconst DATABASE = 'abudhabi';\nconst PORT = 5555;\n\nconst sqlQuery = $sqlQuery;\n\nconst AppDataSource = new DataSource({\n  type: 'postgres',\n  host: HOST,\n  port: PORT,\n  username: USER,\n  password: PASSWORD,\n  database: DATABASE,\n  synchronize: false,\n  logging: false,\n});\n\nlet formattedResult = '';\n\nasync function runSQLQuery(query) {\n  try {\n    await AppDataSource.initialize();\n    const queryRunner = AppDataSource.createQueryRunner();\n\n    const rows = await queryRunner.query(query);\n    console.log('rows =', rows);\n\n    if (rows.length === 0) {\n      formattedResult = '[No results returned]';\n    } else {\n      const columnNames = Object.keys(rows[0]);\n      const header = columnNames.join(' ');\n      const values = rows.map(row =>\n        columnNames.map(col => row[col]).join(' ')\n      );\n\n      formattedResult = query + '\\n' + header + '\\n' + values.join('\\n');\n    }\n\n    await queryRunner.release();\n  } catch (err) {\n    console.error('[ERROR]', err);\n    formattedResult = `[Error executing query]: ${err}`;\n  }\n\n  return formattedResult;\n}\n\nasync function main() {\n  formattedResult = await runSQLQuery(sqlQuery);\n}\n\nawait main();\n\nreturn formattedResult;\n",
                     "customFunctionUpdateState": ""
                 },
                 "outputAnchors": [

packages/server/package.json

@@ -1,6 +1,6 @@
 {
     "name": "flowise",
-    "version": "3.0.11",
+    "version": "3.0.7",
     "description": "Flowiseai Server",
     "main": "dist/index",
     "types": "dist/index.d.ts",
@@ -66,7 +66,7 @@
         "@google-cloud/logging-winston": "^6.0.0",
         "@keyv/redis": "^4.2.0",
         "@oclif/core": "4.0.7",
-        "@opentelemetry/api": "1.9.0",
+        "@opentelemetry/api": "^1.3.0",
         "@opentelemetry/auto-instrumentations-node": "^0.52.0",
         "@opentelemetry/core": "1.27.0",
         "@opentelemetry/exporter-metrics-otlp-grpc": "0.54.0",
@@ -119,12 +119,12 @@
         "lodash": "^4.17.21",
         "moment": "^2.29.3",
         "moment-timezone": "^0.5.34",
-        "multer": "^2.0.2",
+        "multer": "^1.4.5-lts.1",
         "multer-cloud-storage": "^4.0.0",
         "multer-s3": "^3.0.1",
         "mysql2": "^3.11.3",
         "nanoid": "3",
-        "nodemailer": "^7.0.7",
+        "nodemailer": "^6.9.14",
         "openai": "^4.96.0",
         "passport": "^0.7.0",
         "passport-auth0": "^1.4.4",

packages/server/src/Interface.ts

@@ -70,7 +70,7 @@ export interface IChatFlow {
     apiConfig?: string
     category?: string
     type?: ChatflowType
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface IChatMessage {
@@ -115,7 +115,7 @@ export interface ITool {
     func?: string
     updatedDate: Date
     createdDate: Date
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface IAssistant {
@@ -125,7 +125,7 @@ export interface IAssistant {
     iconSrc?: string
     updatedDate: Date
     createdDate: Date
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface ICredential {
@@ -135,7 +135,7 @@ export interface ICredential {
     encryptedData: string
     updatedDate: Date
     createdDate: Date
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface IVariable {
@@ -145,7 +145,7 @@ export interface IVariable {
     type: string
     updatedDate: Date
     createdDate: Date
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface ILead {
@@ -177,7 +177,7 @@ export interface IExecution {
     createdDate: Date
     updatedDate: Date
     stoppedDate: Date
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface IComponentNodes {
@@ -333,7 +333,7 @@ export interface ICredentialReqBody {
     name: string
     credentialName: string
     plainDataObj: ICredentialDataDecrypted
-    workspaceId: string
+    workspaceId?: string
 }
 
 // Decrypted credential object sent back to client
@@ -352,7 +352,7 @@ export interface IApiKey {
     apiKey: string
     apiSecret: string
     updatedDate: Date
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface ICustomTemplate {
@@ -366,7 +366,7 @@ export interface ICustomTemplate {
     badge?: string
     framework?: string
     usecases?: string
-    workspaceId: string
+    workspaceId?: string
 }
 
 export interface IFlowConfig {

packages/server/src/StripeManager.ts

@@ -38,10 +38,6 @@ export class StripeManager {
     }
 
     public async getProductIdFromSubscription(subscriptionId: string) {
-        if (!subscriptionId || subscriptionId.trim() === '') {
-            return ''
-        }
-
         if (!this.stripe) {
             throw new Error('Stripe is not initialized')
         }
@@ -66,7 +62,8 @@ export class StripeManager {
 
             return productId
         } catch (error) {
-            return ''
+            console.error('Error getting product ID from subscription:', error)
+            throw error
         }
     }
 

packages/server/src/UsageCacheManager.ts

@@ -37,19 +37,7 @@ export class UsageCacheManager {
         if (process.env.MODE === MODE.QUEUE) {
             let redisConfig: string | Record<string, any>
             if (process.env.REDIS_URL) {
-                redisConfig = {
-                    url: process.env.REDIS_URL,
-                    socket: {
-                        keepAlive:
-                            process.env.REDIS_KEEP_ALIVE && !isNaN(parseInt(process.env.REDIS_KEEP_ALIVE, 10))
-                                ? parseInt(process.env.REDIS_KEEP_ALIVE, 10)
-                                : undefined
-                    },
-                    pingInterval:
-                        process.env.REDIS_KEEP_ALIVE && !isNaN(parseInt(process.env.REDIS_KEEP_ALIVE, 10))
-                            ? parseInt(process.env.REDIS_KEEP_ALIVE, 10)
-                            : undefined
-                }
+                redisConfig = process.env.REDIS_URL
             } else {
                 redisConfig = {
                     username: process.env.REDIS_USERNAME || undefined,
@@ -60,16 +48,8 @@ export class UsageCacheManager {
                         tls: process.env.REDIS_TLS === 'true',
                         cert: process.env.REDIS_CERT ? Buffer.from(process.env.REDIS_CERT, 'base64') : undefined,
                         key: process.env.REDIS_KEY ? Buffer.from(process.env.REDIS_KEY, 'base64') : undefined,
-                        ca: process.env.REDIS_CA ? Buffer.from(process.env.REDIS_CA, 'base64') : undefined,
-                        keepAlive:
-                            process.env.REDIS_KEEP_ALIVE && !isNaN(parseInt(process.env.REDIS_KEEP_ALIVE, 10))
-                                ? parseInt(process.env.REDIS_KEEP_ALIVE, 10)
-                                : undefined
-                    },
-                    pingInterval:
-                        process.env.REDIS_KEEP_ALIVE && !isNaN(parseInt(process.env.REDIS_KEEP_ALIVE, 10))
-                            ? parseInt(process.env.REDIS_KEEP_ALIVE, 10)
-                            : undefined
+                        ca: process.env.REDIS_CA ? Buffer.from(process.env.REDIS_CA, 'base64') : undefined
+                    }
                 }
             }
             this.cache = createCache({