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Error: Operation not permitted #14770

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opened 2026-02-21 19:18:41 -05:00 by yindo · 5 comments
yindo commented 2026-02-21 19:18:41 -05:00
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Originally created by @gaigenticai on GitHub (Jun 20, 2025).

Self Checks

  • This is only for bug report, if you would like to ask a question, please head to Discussions.
  • I have searched for existing issues search for existing issues, including closed ones.
  • I confirm that I am using English to submit this report (我已阅读并同意 Language Policy).
  • [FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:)
  • Please do not modify this template :) and fill in all the required fields.

Dify version

1.4.3

Cloud or Self Hosted

Self Hosted (Docker)

Steps to reproduce

Yml I am using:
app:
description: ''
icon: 🤖
icon_background: '#FFEAD5'
mode: workflow
name: Cash reconciliation
use_icon_as_answer_icon: false
dependencies: []
kind: app
version: 0.3.0
workflow:
conversation_variables: []
environment_variables: []
features:
file_upload:
allowed_file_extensions:
- .JPG
- .JPEG
- .PNG
- .GIF
- .WEBP
- .SVG
- .xlsx # Added .xlsx
- .csv # Added .csv (optional, but good for data files)
allowed_file_types:
- image
- document # Ensure 'document' type is allowed here
allowed_file_upload_methods:
- local_file
- remote_url
enabled: true # Changed from false to true
fileUploadConfig:
audio_file_size_limit: 50
batch_count_limit: 5
file_size_limit: 15
image_file_size_limit: 10
video_file_size_limit: 100
workflow_file_upload_limit: 10
image:
enabled: false
number_limits: 3
transfer_methods:
- local_file
- remote_url
number_limits: 3
opening_statement: ''
retriever_resource:
enabled: true
sensitive_word_avoidance:
enabled: false
speech_to_text:
enabled: false
suggested_questions: []
suggested_questions_after_answer:
enabled: false
text_to_speech:
enabled: false
language: ''
voice: ''
graph:
edges:
- data:
isInIteration: false
isInLoop: false
sourceType: start
targetType: code
id: 1750334177528-source-1750334593451-target
source: '1750334177528'
sourceHandle: source
target: '1750334593451'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: code
targetType: code
id: 1750334593451--1750335806532-target
source: '1750334593451'
sourceHandle: source
target: '1750335806532'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: code
targetType: code
id: 1750335806532-source-1750337824506-target
source: '1750335806532'
sourceHandle: source
target: '1750337824506'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: code
targetType: code
id: 1750337824506-source-1750338014098-target
source: '1750337824506'
sourceHandle: source
target: '1750338014098'
targetHandle: target
type: custom
zIndex: 0
- data:
isInIteration: false
isInLoop: false
sourceType: code
targetType: end
id: 1750338014098-source-1750338195454-target
source: '1750338014098'
sourceHandle: source
target: '1750338195454'
targetHandle: target
type: custom
zIndex: 0
nodes:
- data:
desc: ''
selected: false
title: Start
type: start
variables:
- allowed_file_extensions:
- .xlsx # Added .xlsx here
- .csv # Added .csv here (optional, if you support CSV)
allowed_file_types:
- image
- document
allowed_file_upload_methods:
- local_file
- remote_url
label: Upload Bank Transaction File(s)
max_length: 5
options: []
required: true
type: file-list
variable: bank_transactions
- label: Forecast Period (in Days)
max_length: 48
options: []
required: true
type: number
variable: forecast_period
- label: Enable Action Recommendations?
max_length: 48
options:
- 'Yes'
- 'No'
required: false
type: select
variable: enable_action_suggestions
height: 142
id: '1750334177528'
position:
x: 80
y: 282
positionAbsolute:
x: 80
y: 282
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 244
- data:
code: |
# pip install pandas scikit-learn numpy
import pandas as pd
from sklearn.ensemble import RandomForestClassifier

      from sklearn.pipeline import Pipeline
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.ensemble import RandomForestClassifier

      # Load uploaded file correctly from Dify's 'files' context
      file = files['bank_transactions'][0]
      df = pd.read_excel(file) if file.name.endswith('.xlsx') else pd.read_csv(file)

      # Prepare text input
      df['text'] = (
          df["Description 1"].fillna("") + " " +
          df["Description 2"].fillna("") + " " +
          df["Description 3"].fillna("") + " " +
          df["Counterparty Name"].fillna("") + " " +
          df["Payment Reference"].fillna("")
      )

      # Quick example classifier (you can replace with better samples if needed)
      sample_data = pd.DataFrame({
          "text": [
              "salary monthly payout",
              "client invoice payment",
              "office rent",
              "VAT tax",
              "supplier purchase"
          ],
          "label": [
              "Salary",
              "Client Revenue",
              "Office Rent",
              "Taxes",
              "Supplier Payment"
          ]
      })

      # Train a mini classifier
      pipeline = Pipeline([
          ("tfidf", TfidfVectorizer(max_features=1000)),
          ("clf", RandomForestClassifier(n_estimators=10, random_state=42))
      ])
      pipeline.fit(sample_data["text"], sample_data["label"])

      # Predict categories
      df["Predicted Category"] = pipeline.predict(df["text"])

      # Outputs
      categorized_data = df.to_dict(orient="records")
      detected_categories_summary = df["Predicted Category"].value_counts().to_json()
    code_language: python3
    desc: Transaction Categorisation
    outputs:
      categorized_data:
        children: null
        type: array[object]
      detected_categories_summary:
        children: null
        type: string
    selected: true
    title: Transaction Categorisation
    type: code
    variables:
    - value_selector:
      - '1750334177528'
      - bank_transactions
      variable: bank_transactions
  height: 82
  id: '1750334593451'
  position:
    x: 360.1402250565834
    y: 77.35654567761921
  positionAbsolute:
    x: 360.1402250565834
    y: 77.35654567761921
  selected: true
  sourcePosition: right
  targetPosition: left
  type: custom
  width: 244
- data:
    code: |
      import pandas as pd
      import json
      from sklearn.linear_model import LinearRegression
      import numpy as np
      # Load data from previous node (Dify context)
      df = pd.DataFrame(categorized_data)

      # Convert to datetime
      df["Date"] = pd.to_datetime(df["Date"])

      # Add Year-Month column
      df["Month"] = df["Date"].dt.to_period("M").astype(str)

      # Monthly total per category
      monthly = (
          df.groupby(["Month", "Predicted Category"])["Amount"]
            .sum()
            .reset_index()
            .rename(columns={"Amount": "TotalAmount"})
      )

      # Pivot for trend analysis
      pivot = (
          monthly.pivot(index="Month", columns="Predicted Category", values="TotalAmount")
                 .fillna(0)
                 .reset_index()
      )

      # Output: Array[Object] for trend chart
      trend_data = pivot.to_dict(orient="records")

      # Text summary: top 5 categories by spend
      top_spend = (
          df.groupby("Predicted Category")["Amount"]
            .sum()
            .sort_values(ascending=False)
            .head(5)
      )

      trend_summary = (
          "Top expense categories overall:\n" +
          "\n".join(f"{cat}: €{amt:,.0f}" for cat, amt in top_spend.items())
      )
    code_language: python3
    desc: Pattern Trend Detection
    outputs:
      trend_data:
        children: null
        type: array[object]
      trend_summary:
        children: null
        type: string
    selected: false
    title: Pattern Trend Detection
    type: code
    variables:
    - value_selector:
      - '1750334593451'
      - categorized_data
      variable: "categorized_data\t"
  height: 82
  id: '1750335806532'
  position:
    x: 391.0204216397667
    y: 309.7097276475967
  positionAbsolute:
    x: 391.0204216397667
    y: 309.7097276475967
  selected: false
  sourcePosition: right
  targetPosition: left
  type: custom
  width: 244
- data:
    code: |
      import pandas as pd
      import numpy as np
      from datetime import timedelta
      from sklearn.linear_model import LinearRegression

      # Load and prepare data
      df = pd.DataFrame(categorized_data)
      df["Date"] = pd.to_datetime(df["Date"])
      df = df.sort_values("Date")

      # Aggregate to daily net cash flow
      daily = df.groupby("Date")["Amount"].sum().reset_index()
      daily.columns = ["ds", "y"]
      daily["day_index"] = (daily["ds"] - daily["ds"].min()).dt.days

      # Train a linear regression model
      X = daily[["day_index"]]
      y = daily["y"]
      model = LinearRegression()
      model.fit(X, y)

      # Forecast future days
      future_days = int(forecast_period)
      last_day = daily["ds"].max()
      future_dates = [last_day + timedelta(days=i) for i in range(1, future_days + 1)]
      base_date = daily["ds"].min()
      future_X = np.array([(d - base_date).days for d in future_dates]).reshape(-1, 1)
      future_y = model.predict(future_X)

      # Format output for Dify (Array[Object])
      forecast_data = [
          {"Date": d.strftime("%Y-%m-%d"), "Predicted Cash Flow": float(v)}
          for d, v in zip(future_dates, future_y)
      ]
    code_language: python3
    desc: Cash Flow Forecasting
    outputs:
      forecast_data:
        children: null
        type: array[object]
    selected: false
    title: Cash Flow Forecasting
    type: code
    variables:
    - value_selector:
      - '1750334593451'
      - categorized_data
      variable: categorized_data
    - value_selector:
      - '1750334177528'
      - forecast_period
      variable: "forecast_period\t"
  height: 82
  id: '1750337824506'
  position:
    x: 695.0204216397667
    y: 309.7097276475967
  positionAbsolute:
    x: 695.0204216397667
    y: 309.7097276475967
  selected: false
  sourcePosition: right
  targetPosition: left
  type: custom
  width: 244
- data:
    code: |
      from datetime import datetime
      import numpy as np

      # Threshold configuration
      surplus_threshold = 10000  # EUR
      deficit_threshold = -5000  # EUR

      # Prepare data
      future = forecast_data
      values = [entry["Predicted Cash Flow"] for entry in future]

      total_forecasted_cash = sum(values)
      max_surplus = max(values)
      min_deficit = min(values)

      suggestions = []

      if enable_action_suggestions.lower() == "yes":

          # Surplus handling
          if max_surplus > surplus_threshold:
              duration = len([v for v in values if v > surplus_threshold])
              suggestions.append(
                  f"\U0001F4B0 Consider placing excess cash in a term deposit "
                  f"for {duration} day(s) (max daily surplus: €{int(max_surplus):,})."
              )

          # Deficit handling
          if min_deficit < deficit_threshold:
              deficit_days = len([v for v in values if v < 0])
              loan_amount = abs(int(min_deficit))
              suggestions.append(
                  f"\U0001F4B8 Forecast shows {deficit_days} day(s) with negative cash flow. "
                  f"Consider applying for a short-term loan of ~€{loan_amount:,}."
              )

          # FX alert (if relevant)
          if "USD" in str(forecast_data).upper():
              suggestions.append(
                  "\U0001F4B1 Detected possible FX flow (USD). Consider hedging strategies."
              )

      else:
          suggestions.append("✅ Action recommendations disabled by user toggle.")

      # Output variable for Dify
      next_best_action = "\n\n".join(suggestions)
    code_language: python3
    desc: Next-Best-Action
    outputs:
      next_best_action:
        children: null
        type: string
    selected: false
    title: Next-Best-Action
    type: code
    variables:
    - value_selector:
      - '1750337824506'
      - forecast_data
      variable: "forecast_data\t"
    - value_selector:
      - '1750334177528'
      - enable_action_suggestions
      variable: enable_action_suggestions
  height: 82
  id: '1750338014098'
  position:
    x: 999.0204216397667
    y: 309.7097276475967
  positionAbsolute:
    x: 999.0204216397667
    y: 309.7097276475967
  selected: false
  sourcePosition: right
  targetPosition: left
  type: custom
  width: 244
- data:
    desc: Final Output
    outputs:
    - value_selector:
      - '1750334593451'
      - categorized_data
      variable: "Categorized Transactions\t"
    - value_selector:
      - '1750335806532'
      - trend_data
      variable: "Cash Flow Trends\t"
    - value_selector:
      - '1750335806532'
      - trend_summary
      variable: "Trend Summary\t"
    - value_selector:
      - '1750337824506'
      - forecast_data
      variable: "Forecast Results\t"
    - value_selector:
      - '1750338014098'
      - next_best_action
      variable: Next Best Action
    selected: false
    title: Final Output
    type: end
  height: 222
  id: '1750338195454'
  position:
    x: 1303.0204216397667
    y: 309.7097276475967
  positionAbsolute:
    x: 1303.0204216397667
    y: 309.7097276475967
  sourcePosition: right
  targetPosition: left
  type: custom
  width: 244
viewport:
  x: 149.54607814746032
  y: 305.48889091476565
  zoom: 1.0224285306099221

✔️ Expected Behavior

Workflow runs without error and displays the output

❌ Actual Behavior

Error: operation not permitted. Please help!!!

Originally created by @gaigenticai on GitHub (Jun 20, 2025). ### Self Checks - [x] This is only for bug report, if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general). - [x] I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones. - [x] I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)). - [x] [FOR CHINESE USERS] 请务必使用英文提交 Issue，否则会被关闭。谢谢！:) - [x] Please do not modify this template :) and fill in all the required fields. ### Dify version 1.4.3 ### Cloud or Self Hosted Self Hosted (Docker) ### Steps to reproduce Yml I am using: app: description: '' icon: 🤖 icon_background: '#FFEAD5' mode: workflow name: Cash reconciliation use_icon_as_answer_icon: false dependencies: [] kind: app version: 0.3.0 workflow: conversation_variables: [] environment_variables: [] features: file_upload: allowed_file_extensions: - .JPG - .JPEG - .PNG - .GIF - .WEBP - .SVG - .xlsx # Added .xlsx - .csv # Added .csv (optional, but good for data files) allowed_file_types: - image - document # Ensure 'document' type is allowed here allowed_file_upload_methods: - local_file - remote_url enabled: true # Changed from false to true fileUploadConfig: audio_file_size_limit: 50 batch_count_limit: 5 file_size_limit: 15 image_file_size_limit: 10 video_file_size_limit: 100 workflow_file_upload_limit: 10 image: enabled: false number_limits: 3 transfer_methods: - local_file - remote_url number_limits: 3 opening_statement: '' retriever_resource: enabled: true sensitive_word_avoidance: enabled: false speech_to_text: enabled: false suggested_questions: [] suggested_questions_after_answer: enabled: false text_to_speech: enabled: false language: '' voice: '' graph: edges: - data: isInIteration: false isInLoop: false sourceType: start targetType: code id: 1750334177528-source-1750334593451-target source: '1750334177528' sourceHandle: source target: '1750334593451' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: code targetType: code id: 1750334593451--1750335806532-target source: '1750334593451' sourceHandle: source target: '1750335806532' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: code targetType: code id: 1750335806532-source-1750337824506-target source: '1750335806532' sourceHandle: source target: '1750337824506' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: code targetType: code id: 1750337824506-source-1750338014098-target source: '1750337824506' sourceHandle: source target: '1750338014098' targetHandle: target type: custom zIndex: 0 - data: isInIteration: false isInLoop: false sourceType: code targetType: end id: 1750338014098-source-1750338195454-target source: '1750338014098' sourceHandle: source target: '1750338195454' targetHandle: target type: custom zIndex: 0 nodes: - data: desc: '' selected: false title: Start type: start variables: - allowed_file_extensions: - .xlsx # Added .xlsx here - .csv # Added .csv here (optional, if you support CSV) allowed_file_types: - image - document allowed_file_upload_methods: - local_file - remote_url label: Upload Bank Transaction File(s) max_length: 5 options: [] required: true type: file-list variable: bank_transactions - label: Forecast Period (in Days) max_length: 48 options: [] required: true type: number variable: forecast_period - label: Enable Action Recommendations? max_length: 48 options: - 'Yes' - 'No' required: false type: select variable: enable_action_suggestions height: 142 id: '1750334177528' position: x: 80 y: 282 positionAbsolute: x: 80 y: 282 selected: false sourcePosition: right targetPosition: left type: custom width: 244 - data: code: | # pip install pandas scikit-learn numpy import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.ensemble import RandomForestClassifier # Load uploaded file correctly from Dify's 'files' context file = files['bank_transactions'][0] df = pd.read_excel(file) if file.name.endswith('.xlsx') else pd.read_csv(file) # Prepare text input df['text'] = ( df["Description 1"].fillna("") + " " + df["Description 2"].fillna("") + " " + df["Description 3"].fillna("") + " " + df["Counterparty Name"].fillna("") + " " + df["Payment Reference"].fillna("") ) # Quick example classifier (you can replace with better samples if needed) sample_data = pd.DataFrame({ "text": [ "salary monthly payout", "client invoice payment", "office rent", "VAT tax", "supplier purchase" ], "label": [ "Salary", "Client Revenue", "Office Rent", "Taxes", "Supplier Payment" ] }) # Train a mini classifier pipeline = Pipeline([ ("tfidf", TfidfVectorizer(max_features=1000)), ("clf", RandomForestClassifier(n_estimators=10, random_state=42)) ]) pipeline.fit(sample_data["text"], sample_data["label"]) # Predict categories df["Predicted Category"] = pipeline.predict(df["text"]) # Outputs categorized_data = df.to_dict(orient="records") detected_categories_summary = df["Predicted Category"].value_counts().to_json() code_language: python3 desc: Transaction Categorisation outputs: categorized_data: children: null type: array[object] detected_categories_summary: children: null type: string selected: true title: Transaction Categorisation type: code variables: - value_selector: - '1750334177528' - bank_transactions variable: bank_transactions height: 82 id: '1750334593451' position: x: 360.1402250565834 y: 77.35654567761921 positionAbsolute: x: 360.1402250565834 y: 77.35654567761921 selected: true sourcePosition: right targetPosition: left type: custom width: 244 - data: code: | import pandas as pd import json from sklearn.linear_model import LinearRegression import numpy as np # Load data from previous node (Dify context) df = pd.DataFrame(categorized_data) # Convert to datetime df["Date"] = pd.to_datetime(df["Date"]) # Add Year-Month column df["Month"] = df["Date"].dt.to_period("M").astype(str) # Monthly total per category monthly = ( df.groupby(["Month", "Predicted Category"])["Amount"] .sum() .reset_index() .rename(columns={"Amount": "TotalAmount"}) ) # Pivot for trend analysis pivot = ( monthly.pivot(index="Month", columns="Predicted Category", values="TotalAmount") .fillna(0) .reset_index() ) # Output: Array[Object] for trend chart trend_data = pivot.to_dict(orient="records") # Text summary: top 5 categories by spend top_spend = ( df.groupby("Predicted Category")["Amount"] .sum() .sort_values(ascending=False) .head(5) ) trend_summary = ( "Top expense categories overall:\n" + "\n".join(f"{cat}: €{amt:,.0f}" for cat, amt in top_spend.items()) ) code_language: python3 desc: Pattern Trend Detection outputs: trend_data: children: null type: array[object] trend_summary: children: null type: string selected: false title: Pattern Trend Detection type: code variables: - value_selector: - '1750334593451' - categorized_data variable: "categorized_data\t" height: 82 id: '1750335806532' position: x: 391.0204216397667 y: 309.7097276475967 positionAbsolute: x: 391.0204216397667 y: 309.7097276475967 selected: false sourcePosition: right targetPosition: left type: custom width: 244 - data: code: | import pandas as pd import numpy as np from datetime import timedelta from sklearn.linear_model import LinearRegression # Load and prepare data df = pd.DataFrame(categorized_data) df["Date"] = pd.to_datetime(df["Date"]) df = df.sort_values("Date") # Aggregate to daily net cash flow daily = df.groupby("Date")["Amount"].sum().reset_index() daily.columns = ["ds", "y"] daily["day_index"] = (daily["ds"] - daily["ds"].min()).dt.days # Train a linear regression model X = daily[["day_index"]] y = daily["y"] model = LinearRegression() model.fit(X, y) # Forecast future days future_days = int(forecast_period) last_day = daily["ds"].max() future_dates = [last_day + timedelta(days=i) for i in range(1, future_days + 1)] base_date = daily["ds"].min() future_X = np.array([(d - base_date).days for d in future_dates]).reshape(-1, 1) future_y = model.predict(future_X) # Format output for Dify (Array[Object]) forecast_data = [ {"Date": d.strftime("%Y-%m-%d"), "Predicted Cash Flow": float(v)} for d, v in zip(future_dates, future_y) ] code_language: python3 desc: Cash Flow Forecasting outputs: forecast_data: children: null type: array[object] selected: false title: Cash Flow Forecasting type: code variables: - value_selector: - '1750334593451' - categorized_data variable: categorized_data - value_selector: - '1750334177528' - forecast_period variable: "forecast_period\t" height: 82 id: '1750337824506' position: x: 695.0204216397667 y: 309.7097276475967 positionAbsolute: x: 695.0204216397667 y: 309.7097276475967 selected: false sourcePosition: right targetPosition: left type: custom width: 244 - data: code: | from datetime import datetime import numpy as np # Threshold configuration surplus_threshold = 10000 # EUR deficit_threshold = -5000 # EUR # Prepare data future = forecast_data values = [entry["Predicted Cash Flow"] for entry in future] total_forecasted_cash = sum(values) max_surplus = max(values) min_deficit = min(values) suggestions = [] if enable_action_suggestions.lower() == "yes": # Surplus handling if max_surplus > surplus_threshold: duration = len([v for v in values if v > surplus_threshold]) suggestions.append( f"\U0001F4B0 Consider placing excess cash in a term deposit " f"for {duration} day(s) (max daily surplus: €{int(max_surplus):,})." ) # Deficit handling if min_deficit < deficit_threshold: deficit_days = len([v for v in values if v < 0]) loan_amount = abs(int(min_deficit)) suggestions.append( f"\U0001F4B8 Forecast shows {deficit_days} day(s) with negative cash flow. " f"Consider applying for a short-term loan of ~€{loan_amount:,}." ) # FX alert (if relevant) if "USD" in str(forecast_data).upper(): suggestions.append( "\U0001F4B1 Detected possible FX flow (USD). Consider hedging strategies." ) else: suggestions.append("✅ Action recommendations disabled by user toggle.") # Output variable for Dify next_best_action = "\n\n".join(suggestions) code_language: python3 desc: Next-Best-Action outputs: next_best_action: children: null type: string selected: false title: Next-Best-Action type: code variables: - value_selector: - '1750337824506' - forecast_data variable: "forecast_data\t" - value_selector: - '1750334177528' - enable_action_suggestions variable: enable_action_suggestions height: 82 id: '1750338014098' position: x: 999.0204216397667 y: 309.7097276475967 positionAbsolute: x: 999.0204216397667 y: 309.7097276475967 selected: false sourcePosition: right targetPosition: left type: custom width: 244 - data: desc: Final Output outputs: - value_selector: - '1750334593451' - categorized_data variable: "Categorized Transactions\t" - value_selector: - '1750335806532' - trend_data variable: "Cash Flow Trends\t" - value_selector: - '1750335806532' - trend_summary variable: "Trend Summary\t" - value_selector: - '1750337824506' - forecast_data variable: "Forecast Results\t" - value_selector: - '1750338014098' - next_best_action variable: Next Best Action selected: false title: Final Output type: end height: 222 id: '1750338195454' position: x: 1303.0204216397667 y: 309.7097276475967 positionAbsolute: x: 1303.0204216397667 y: 309.7097276475967 sourcePosition: right targetPosition: left type: custom width: 244 viewport: x: 149.54607814746032 y: 305.48889091476565 zoom: 1.0224285306099221 ### ✔️ Expected Behavior Workflow runs without error and displays the output ### ❌ Actual Behavior Error: operation not permitted. Please help!!!
yindo closed this issue 2026-02-21 19:18:41 -05:00
yindo commented 2026-02-21 19:18:42 -05:00
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@dosubot[bot] commented on GitHub (Jun 20, 2025):

Hi @gaigenticai! I'm Dosu and I’m helping the dify team.

The "Operation not permitted" error in your Docker-based Dify setup is almost always caused by a permissions issue with host directories mounted into the container, especially for file uploads or storage. In your case, Dify mounts ./volumes/app/storage from your host to /app/api/storage in the container for user files and uploads. If the container user doesn't have write permissions on this host directory, you'll get this error when trying to upload or process files reference.

Here’s what you should check:

  1. On your host, make sure the ./volumes/app/storage directory exists and is writable by the user running the Docker container. You can run:

    sudo chown -R 1000:1000 ./volumes/app/storage
sudo chmod -R 775 ./volumes/app/storage

    (Replace 1000:1000 with the UID:GID used inside your container if different.)

  2. If you’re running Docker as a non-root user or with user remapping, ensure the mapped user has the correct permissions on the mounted directory.

  3. If you’re on SELinux or AppArmor-enabled systems, check that your security policies aren’t blocking file access.

  4. If you recently changed Docker Compose files or upgraded Dify, double-check that your volume mounts and environment variables are still correct, as recent changes have affected storage and vector database configs reference.

If you’re still stuck, try running a shell inside the container (docker exec -it <container> /bin/bash) and attempt to write to /app/api/storage to see the exact error.

Let me know if you need help with any of these steps or if you can share more details about where in the workflow the error appears!

To reply, just mention @dosu.

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@dosubot[bot] commented on GitHub (Jun 20, 2025): <!-- Greeting --> Hi @gaigenticai! I'm [Dosu](https://go.dosu.dev/dosubot) and I’m helping the dify team. <!-- Answer --> The "Operation not permitted" error in your Docker-based Dify setup is almost always caused by a permissions issue with host directories mounted into the container, especially for file uploads or storage. In your case, Dify mounts `./volumes/app/storage` from your host to `/app/api/storage` in the container for user files and uploads. If the container user doesn't have write permissions on this host directory, you'll get this error when trying to upload or process files [reference](https://github.com/langgenius/dify/blob/223448af186456199446ecbcbf6ba4261a68215d/docker/docker-compose.yaml). Here’s what you should check: 1. On your host, make sure the `./volumes/app/storage` directory exists and is writable by the user running the Docker container. You can run: ``` sudo chown -R 1000:1000 ./volumes/app/storage sudo chmod -R 775 ./volumes/app/storage ``` (Replace `1000:1000` with the UID:GID used inside your container if different.) 2. If you’re running Docker as a non-root user or with user remapping, ensure the mapped user has the correct permissions on the mounted directory. 3. If you’re on SELinux or AppArmor-enabled systems, check that your security policies aren’t blocking file access. 4. If you recently changed Docker Compose files or upgraded Dify, double-check that your volume mounts and environment variables are still correct, as recent changes have affected storage and vector database configs [reference](https://github.com/langgenius/dify/pull/20714). If you’re still stuck, try running a shell inside the container (`docker exec -it <container> /bin/bash`) and attempt to write to `/app/api/storage` to see the exact error. Let me know if you need help with any of these steps or if you can share more details about where in the workflow the error appears! <!-- Dosu Comment Footer --> *To reply, just mention [@dosu](https://go.dosu.dev/dosubot).* --- <sup>How did I do? [Good](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=great_response) | [Irrelevant](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=irrelevant_answer) | [Incorrect](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=incorrect_sources) | [Verbose](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=too_verbose) | [Hallucination](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=hallucination) | [Report 🐛](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=bug_report) | [Other](https://app.dosu.dev/response-feedback/5e34a268-7e20-4806-b9bc-b6e43f60cfb3?feedback_type=other)</sup>&nbsp;&nbsp;[![Join Discord](https://img.shields.io/badge/join-5865F2?logo=discord&logoColor=white&label=)](https://go.dosu.dev/discord-bot)&nbsp;[![Share on X](https://img.shields.io/badge/X-share-black)](https://twitter.com/intent/tweet?text=%40dosu_ai%20helped%20me%20solve%20this%20issue!&url=https%3A//github.com/langgenius/dify/issues/21296)
yindo commented 2026-02-21 19:18:42 -05:00
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@gaigenticai commented on GitHub (Jun 20, 2025):

I have followed the ssaid instructions, still I get the same issue. Is there a way where I can install dify on my local outside a docker?

@gaigenticai commented on GitHub (Jun 20, 2025): I have followed the ssaid instructions, still I get the same issue. Is there a way where I can install dify on my local outside a docker?
yindo commented 2026-02-21 19:18:42 -05:00
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@dosubot[bot] commented on GitHub (Jun 20, 2025):

There isn't official documentation for installing Dify locally outside of Docker, but it is possible by manually setting up the required dependencies: PostgreSQL, Redis, Weaviate, SSRF Proxy (Squid), and DifySandbox. You'll need to install and configure these services yourself, following their native setup guides. For the backend, you can use Python virtual environments and pip to install dependencies (see requirements.txt and setup-dev.sh in the repo). For the frontend, you can run it locally with Node.js and pnpm—just follow the instructions in web/README.md to install dependencies, set up your .env.local, and start the dev server with pnpm run dev. More details and a starting point are in the main README, which links to a guide for deploying from source code: README.md and web/README.md.

Just note: you’ll need to manually provision and configure all the backing services that Docker Compose would normally handle for you, and there may be some trial and error since this isn’t the officially supported path.

To reply, just mention @dosu.

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@dosubot[bot] commented on GitHub (Jun 20, 2025): <!-- Answer --> There isn't official documentation for installing Dify locally outside of Docker, but it is possible by manually setting up the required dependencies: PostgreSQL, Redis, Weaviate, SSRF Proxy (Squid), and DifySandbox. You'll need to install and configure these services yourself, following their native setup guides. For the backend, you can use Python virtual environments and pip to install dependencies (see requirements.txt and setup-dev.sh in the repo). For the frontend, you can run it locally with Node.js and pnpm—just follow the instructions in web/README.md to install dependencies, set up your .env.local, and start the dev server with pnpm run dev. More details and a starting point are in the main README, which links to a guide for deploying from source code: [README.md](https://github.com/langgenius/dify/blob/223448af186456199446ecbcbf6ba4261a68215d/README.md) and [web/README.md](https://github.com/langgenius/dify/blob/223448af186456199446ecbcbf6ba4261a68215d/web/README.md). Just note: you’ll need to manually provision and configure all the backing services that Docker Compose would normally handle for you, and there may be some trial and error since this isn’t the officially supported path. <!-- Dosu Comment Footer --> *To reply, just mention [@dosu](https://go.dosu.dev/dosubot).* --- <sup>How did I do? [Good](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=great_response) | [Irrelevant](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=irrelevant_answer) | [Incorrect](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=incorrect_sources) | [Verbose](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=too_verbose) | [Hallucination](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=hallucination) | [Report 🐛](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=bug_report) | [Other](https://app.dosu.dev/response-feedback/ab8903a8-28f9-4232-8726-48511252269e?feedback_type=other)</sup>&nbsp;&nbsp;[![Join Discord](https://img.shields.io/badge/join-5865F2?logo=discord&logoColor=white&label=)](https://go.dosu.dev/discord-bot)&nbsp;[![Share on X](https://img.shields.io/badge/X-share-black)](https://twitter.com/intent/tweet?text=%40dosu_ai%20helped%20me%20solve%20this%20issue!&url=https%3A//github.com/langgenius/dify/issues/21296)
yindo commented 2026-02-21 19:18:42 -05:00
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@IthacaDream commented on GitHub (Jun 20, 2025):

@gaigenticai I think you need to read this: my-python-code-returns-an-operation-not-permitted-error

@IthacaDream commented on GitHub (Jun 20, 2025): @gaigenticai I think you need to read this: [my-python-code-returns-an-operation-not-permitted-error](https://github.com/langgenius/dify-sandbox/blob/main/FAQ.md#2-my-python-code-returns-an-operation-not-permitted-error)
yindo commented 2026-02-21 19:18:42 -05:00
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@gaigenticai commented on GitHub (Jun 20, 2025):

Thank you very much! Let me read this.

@gaigenticai commented on GitHub (Jun 20, 2025): Thank you very much! Let me read this.
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