- Ensure `EventManager._notify_layers` logs exceptions instead of silently swallowing them
so GraphEngine layer failures surface for debugging
- Introduce unit tests to assert the logger captures the runtime error when collecting events
- Enable the `S110` lint rule to catch `try-except-pass` patterns
- Add proper error logging for existing `try-except-pass` blocks.
This commit:
1. Convert `pause_reason` to `pause_reasons` in `GraphExecution` and relevant classes. Change the field from a scalar value to a list that can contain multiple `PauseReason` objects, ensuring all pause events are properly captured.
2. Introduce a new `WorkflowPauseReason` model to record reasons associated with a specific `WorkflowPause`.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This PR changes the default value of `WORKFLOW_LOG_CLEANUP_ENABLED` from `true` to `false` across all configuration files.
## Motivation
Setting the default to `false` provides safer default behavior by:
- Preventing unintended data loss for new installations
- Giving users explicit control over when to enable log cleanup
- Following the opt-in principle for data deletion features
Users who need automatic cleanup can enable it by setting `WORKFLOW_LOG_CLEANUP_ENABLED=true` in their configuration.
Certain metadata (including but not limited to `InvokeFrom`, `call_depth`, and `streaming`) is required when resuming a paused workflow. However, these fields are not part of `GraphRuntimeState` and were not saved in the previous
implementation of `PauseStatePersistenceLayer`.
This commit addresses this limitation by introducing a `WorkflowResumptionContext` model that wraps both the `*GenerateEntity` and `GraphRuntimeState`. This approach provides:
- A structured container for all necessary resumption data
- Better separation of concerns between execution state and persistence
- Enhanced extensibility for future metadata additions
- Clearer naming that distinguishes from `GraphRuntimeState`
The `WorkflowResumptionContext` model makes extending the pause state easier while maintaining backward compatibility and proper version management for the entire execution state ecosystem.
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
This PR introduces a `BroadcastChannel` abstraction with broadcasting and at-most once delivery semantics, serving as the communication component between celery worker and API server.
It also includes a reference implementation backed by Redis PubSub.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Disable SSE events truncation for service api invocations to ensure backward compatibility.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Fixed the issue that filename will be 'inline' if response header contains `Content-Disposition: inline` while retrieving file by url.
Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Fixes#25619
The regex patterns for file-preview and image-preview contained an unescaped `?`,
which caused incorrect matches such as `file-previe` or `image-previw`.
This led to malformed URLs being incorrectly re-signed.
Changes:
- Escape `?` in both file-preview and image-preview regex patterns.
- Ensure only valid URLs are re-signed.
Added unit tests to cover:
- Valid file-preview and image-preview URLs (correctly re-signed).
- Misspelled file/image preview URLs (no longer incorrectly matched).
Other:
- Fix a deprecated function `datetime.utcnow()`
Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Asuka Minato <i@asukaminato.eu.org>
Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>
This PR refactors the handling of the default end user session ID by centralizing it as an enum in the models module where the `EndUser` model is defined. This improves code organization and makes the relationship between the constant and the model clearer.
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: crazywoola <100913391+crazywoola@users.noreply.github.com>
The `ChatMessageApi` (`POST /console/api/apps/{app_id}/chat-messages`) and
`ModelConfigResource` (`POST /console/api/apps/{app_id}/model-config`)
endpoints do not properly validate user permissions, allowing users without `editor`
permission to access restricted functionality.
This PR addresses this issue by adding proper permission check.
This PR fixes Alembic offline mode (`--sql` flag) by ensuring data migration functions only execute in online mode. When running in offline mode, these functions now skip data operations and output informational comments to the generated SQL.
The `Account._current_tenant` object is loaded by a database session (typically `db.session`) whose lifetime
is not aligned with the Account model instance. This misalignment causes a `DetachedInstanceError` to be raised
when accessing attributes of `Account._current_tenant` after the original session has been closed.
To resolve this issue, we now reload the tenant object with `expire_on_commit=False`, ensuring the tenant remains
accessible even after the session is closed.
* fix(oraclevector): SQL Injection
Signed-off-by: -LAN- <laipz8200@outlook.com>
* fix(oraclevector): Remove bind variables from FETCH FIRST clause
Oracle doesn't support bind variables in the FETCH FIRST clause.
Fixed by using validated integers directly in the SQL string while
maintaining proper input validation to prevent SQL injection.
- Updated search_by_vector method to use validated top_k directly
- Updated search_by_full_text method to use validated top_k directly
- Adjusted parameter numbering for document_ids_filter placeholders
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: Claude <noreply@anthropic.com>
Fix regression introduced in PR #22580 where admin users encountered
"Message not exists" errors when creating feedback on messages created
by other users.
The issue was caused by `MessageService.create_feedback()` incorrectly
filtering messages by the current user's ID, preventing admins from
accessing messages created by end users.
Reverts: #22580
This commit introduces a background task that automatically deletes `WorkflowDraftVariable` records when
their associated workflow apps are deleted.
Additionally, it adds a new cleanup script
`cleanup-orphaned-draft-variables` to remove existing orphaned draft variables from the database.
Fix flaky test
`TestWorkflowDraftVariableService.test_list_variables_without_values_success`
caused by low entropy in test data generation that led to
duplicate values violating unique constraints.
Also improve data generation in other tests within
`TestWorkflowDraftVariableService` to reduce the likelihood of
generating duplicate test data.
This PR introduces UUIDv7 implementations in both Python and SQL to establish the foundation for migrating from UUIDv4 to UUIDv7 as proposed in #19754.
ID generation algorithm of existing models are not changed, and new models should use UUIDv7 for ID generation.
Close#19754.
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)
This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization.
Key changes:
- Introduce distinct `value_type` tags for Integer and Float segments/variables
- Add `VariableUnion` and `SegmentUnion` types for proper type discrimination
- Leverage Pydantic's discriminated union feature for seamless serialization/deserialization
- Enable accurate serialization of data structures containing these types
Closes#22024.
The `BaseSession` class in the `core/mcp/session` package uses `ThreadPoolExecutor`
to run the receive loop but fails to properly clean up the executor and receiver
future, leading to potential thread leaks.
This PR addresses this issue by:
- Initializing `_executor` and `_receiver_future` attributes to `None` for proper cleanup checks
- Adding graceful shutdown with a 5-second timeout in the `__exit__` method
- Ensuring the ThreadPoolExecutor is properly shut down to prevent resource leaks
This fix prevents memory leaks and hanging threads in long-running scenarios where
multiple MCP sessions are created and destroyed.
Signed-off-by: neatguycoding <15627489+NeatGuyCoding@users.noreply.github.com>
Co-authored-by: QuantumGhost <obelisk.reg+git@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
as API module's version code refactored into pyproject.toml file in refactor: define the Dify project version in pyproject.toml #20910, the deprecated CURRENT_VERSION is no longger used and should be removed.
This PR fix the issue that `ObjectSegment` are not recursively added to the draft variable pool while loading draft variables from database. It also fixes an issue about loading variables with more than two elements in the its selector.
Enhances #19735.
Closes#21477.
This PR addresses issue #21441 by implementing explicit `version` method definitions for all `BaseNode` subclasses to improve code maintainability.
### Changes
Added explicit `version` method definitions for all `BaseNode` subclasses:
- `QuestionClassifierNode`
- `KnowledgeRetrievalNode`
- `AgentNode`
Added comprehensive test suite to validate:
1. All subclasses of `BaseNode` have explicitly defined `version` method
2. All subclasses have required `_node_type` property
3. The `(node_type, node_version)` combination is unique across all subclasses
This pull request introduces a feature aimed at improving the debugging experience during workflow editing. With the addition of variable persistence, the system will automatically retain the output variables from previously executed nodes. These persisted variables can then be reused when debugging subsequent nodes, eliminating the need for repetitive manual input.
By streamlining this aspect of the workflow, the feature minimizes user errors and significantly reduces debugging effort, offering a smoother and more efficient experience.
Key highlights of this change:
- Automatic persistence of output variables for executed nodes.
- Reuse of persisted variables to simplify input steps for nodes requiring them (e.g., `code`, `template`, `variable_assigner`).
- Enhanced debugging experience with reduced friction.
Closes#19735.
- Extract methods used by `ParameterExtractorNode` from `LLMNode` into a separate file.
- Convert `ParameterExtractorNode` into a subclass of `BaseNode`.
- Refactor code referencing the extracted methods to ensure functionality and clarity.
- Fixes the issue that `ParameterExtractorNode` returns error when executed.
- Fix relevant test cases.
Closes#20840.
The `QuestionClassifierNode` class extends `LLMNode`, meaning that, per the Liskov Substitution Principle, `QuestionClassifierNodeData` **SHOULD** be compatible in contexts where `LLMNodeData` is expected.
However, the absence of the `structured_output_enabled` attribute violates this principle, causing `QuestionClassifierNode` to fail during execution.
This commit implements a quick and temporary workaround. A proper resolution would involve refactoring to decouple `QuestionClassifierNode` from `LLMNode` to address the underlying design issue.
Fixes#20725.
- Add `node_execution_id` column to `WorkflowDraftVariable`, allowing efficient implementation of
the "Reset to last run value" feature.
- Add additional index for `WorkflowNodeExecutionModel` to improve the performance of last run lookup.
Closes#20745.
Currently, `WorkflowNodeExecution.execution_metadata_dict` returns `None` when metadata is absent in the database. This requires all callers to perform `None` checks when processing metadata, leading to more complex caller-side logic.
This pull request updates the `execution_metadata_dict` method to return an empty dictionary instead of `None` when metadata is absent. This change would simplify the caller logic, as it removes the need for explicit `None` checks and provides a more consistent data structure to work with.
- Introduce `WorkflowDraftVariable` model and the corresponding migration.
- Implement `EnumText`, a custom column type for SQLAlchemy designed
to work seamlessly with enumeration classes based on `StrEnum`.
Alembic's offline mode generates SQL from SQLAlchemy migration operations,
providing developers with a clear view of database schema changes without
requiring an active database connection.
However, some migration versions (specifically bbadea11becb and d7999dfa4aae)
were performing database schema introspection, which fails in offline mode
since it requires an actual database connection.
This commit:
- Adds offline mode support by detecting context.is_offline_mode()
- Skips introspection steps when in offline mode
- Adds warning messages in SQL output to inform users that assumptions were made
- Prompts users to review the generated SQL for accuracy
These changes ensure migrations work consistently in both online and offline modes.
Close#19284.
Enhance `LLMNode` with multimodal capability, introducing support for
image outputs.
This implementation extracts base64-encoded images from LLM responses,
saves them to the storage service, and records the file metadata in the
`ToolFile` table. In conversations, these images are rendered as
markdown-based inline images.
Additionally, the images are included in the LLMNode's output as
file variables, enabling subsequent nodes in the workflow to utilize them.
To integrate file outputs into workflows, adjustments to the frontend code
are necessary.
For multimodal output functionality, updates to related model configurations
are required. Currently, this capability has been applied exclusively to
Google's Gemini models.
Close#15814.
Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: -LAN- <laipz8200@outlook.com>
The `validators.url` method from the `validators==0.21.0` library enforces a
URL length limit of less than 90 characters, which led to failures in external
knowledge API requests for long URLs.
This PR addresses the issue by replacing `validators.url` with
`urllib.parse.urlparse`, effectively removing the restrictive URL length check.
Additionally, the unused `validators` dependency has been removed.
Fixes#18981.
Signed-off-by: kenwoodjw <blackxin55+@gmail.com>
When generating JSON schema using an LLM in the structured output feature,
models may occasionally return invalid JSON, which prevents clients from correctly
parsing the response and can lead to UI breakage.
This commit addresses the issue by introducing `json_repair` to automatically
fix invalid JSON strings returned by the LLM, ensuring smoother functionality
and better client-side handling of structured outputs.
Co-authored-by: lizb <lizb@sugon.com>
description: Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component --json` shows complexity > 50 or lineCount > 300, when the user asks for code splitting, hook extraction, or complexity reduction, or when `pnpm analyze-component` warns to refactor before testing; avoid for simple/well-structured components, third-party wrappers, or when the user explicitly wants testing without refactoring.
---
# Dify Component Refactoring Skill
Refactor high-complexity React components in the Dify frontend codebase with the patterns and workflow below.
> **Complexity Threshold**: Components with complexity > 50 (measured by `pnpm analyze-component`) should be refactored before testing.
## Quick Reference
### Commands (run from `web/`)
Use paths relative to `web/` (e.g., `app/components/...`).
Use `refactor-component` for refactoring prompts and `analyze-component` for testing prompts and metrics.
description: Generate Vitest + React Testing Library tests for Dify frontend components, hooks, and utilities. Triggers on testing, spec files, coverage, Vitest, RTL, unit tests, integration tests, or write/review test requests.
---
# Dify Frontend Testing Skill
This skill enables Claude to generate high-quality, comprehensive frontend tests for the Dify project following established conventions and best practices.
> **⚠️ Authoritative Source**: This skill is derived from `web/testing/testing.md`. Use Vitest mock/timer APIs (`vi.*`).
## When to Apply This Skill
Apply this skill when the user:
- Asks to **write tests** for a component, hook, or utility
- Asks to **review existing tests** for completeness
- Mentions **Vitest**, **React Testing Library**, **RTL**, or **spec files**
- Requests **test coverage** improvement
- Uses `pnpm analyze-component` output as context
- Mentions **testing**, **unit tests**, or **integration tests** for frontend code
- Wants to understand **testing patterns** in the Dify codebase
**Do NOT apply** when:
- User is asking about backend/API tests (Python/pytest)
- User is asking about E2E tests (Playwright/Cypress)
- User is only asking conceptual questions without code context
- Modules are not mocked automatically. Global mocks live in `web/vitest.setup.ts` (for example `react-i18next`, `next/image`); mock other modules like `ky` or `mime` locally in test files.
This guide defines the workflow for generating tests, especially for complex components or directories with multiple files.
## Scope Clarification
This guide addresses **multi-file workflow** (how to process multiple test files). For coverage requirements within a single test file, see `web/testing/testing.md` § Coverage Goals.
| Scope | Rule |
|-------|------|
| **Single file** | Complete coverage in one generation (100% function, >95% branch) |
| **Multi-file directory** | Process one file at a time, verify each before proceeding |
## ⚠️ Critical Rule: Incremental Approach for Multi-File Testing
When testing a **directory with multiple files**, **NEVER generate all test files at once.** Use an incremental, verify-as-you-go approach.
This project includes a devcontainer configuration that allows you to open the project in a container with a fully configured development environment.
Both frontend and backend environments are initialized when the container is started.
## GitHub Codespaces
[](https://codespaces.new/langgenius/dify)
you can simply click the button above to open this project in GitHub Codespaces.
For more info, check out the [GitHub documentation](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace#creating-a-codespace).
## VS Code Dev Containers
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langgenius/dify)
if you have VS Code installed, you can click the button above to open this project in VS Code Dev Containers.
You can learn more in the [Dev Containers documentation](https://code.visualstudio.com/docs/devcontainers/containers).
## Pros of Devcontainer
Unified Development Environment: By using devcontainers, you can ensure that all developers are developing in the same environment, reducing the occurrence of "it works on my machine" type of issues.
Quick Start: New developers can set up their development environment in a few simple steps, without spending a lot of time on environment configuration.
@ -25,11 +28,13 @@ Quick Start: New developers can set up their development environment in a few si
Isolation: Devcontainers isolate your project from your host operating system, reducing the chance of OS updates or other application installations impacting the development environment.
## Cons of Devcontainer
Learning Curve: For developers unfamiliar with Docker and VS Code, using devcontainers may be somewhat complex.
Performance Impact: While usually minimal, programs running inside a devcontainer may be slightly slower than those running directly on the host.
## Troubleshooting
if you see such error message when you open this project in codespaces:
# This file defines code ownership for the Dify project.
# Each line is a file pattern followed by one or more owners.
# Owners can be @username, @org/team-name, or email addresses.
# For more information, see: https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-owners
* @crazywoola @laipz8200 @Yeuoly
# CODEOWNERS file
/.github/CODEOWNERS @laipz8200 @crazywoola
# Docs
/docs/ @crazywoola
# Backend (default owner, more specific rules below will override)
@ -17,27 +17,25 @@ diverse, inclusive, and healthy community.
Examples of behavior that contributes to a positive environment for our
community include:
* Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
- Demonstrating empathy and kindness toward other people
- Being respectful of differing opinions, viewpoints, and experiences
- Giving and gracefully accepting constructive feedback
- Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
* Focusing on what is best not just for us as individuals, but for the
- Focusing on what is best not just for us as individuals, but for the
overall community
Examples of unacceptable behavior include:
* The use of sexualized language or imagery, and sexual attention or
- The use of sexualized language or imagery, and sexual attention or
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or email
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or email
address, without their explicit permission
* Other conduct which could reasonably be considered inappropriate in a
- Other conduct which could reasonably be considered inappropriate in a
professional setting
## Language Policy
To facilitate clear and effective communication, all discussions, comments, documentation, and pull requests in this project should be conducted in English. This ensures that all contributors can participate and collaborate effectively.
description:"To make sure we get to you in time, please check the following :)"
options:
- label:I have read the [Contributing Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) and [Language Policy](https://github.com/langgenius/dify/issues/1542).
required:true
- label: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).
required:true
- label:I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
required:true
- label: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)).
- label:I confirm that I am using English to submit this report, otherwise it will be closed.
required:true
- label:"[FOR CHINESE USERS] 请务必使用英文提交 Issue,否则会被关闭。谢谢!:)"
- label:【中文用户 & Non English User】请使用英语提交,否则会被关闭 :)
required:true
- label:"Please do not modify this template :) and fill in all the required fields."
required:true
@ -42,20 +44,22 @@ body:
attributes:
label:Steps to reproduce
description:We highly suggest including screenshots and a bug report log. Please use the right markdown syntax for code blocks.
placeholder:Having detailed steps helps us reproduce the bug.
placeholder:Having detailed steps helps us reproduce the bug. If you have logs, please use fenced code blocks (triple backticks ```) to format them.
validations:
required:true
- type:textarea
attributes:
label:✔️ Expected Behavior
placeholder:What were you expecting?
description:Describe what you expected to happen.
placeholder:What were you expecting? Please do not copy and paste the steps to reproduce here.
validations:
required:false
required:true
- type:textarea
attributes:
label:❌ Actual Behavior
placeholder:What happened instead?
description:Describe what actually happened.
placeholder:What happened instead? Please do not copy and paste the steps to reproduce here.
about:Report security vulnerabilities through GitHub Security Advisories to ensure responsible disclosure. 💡 Please do not report security vulnerabilities in public issues.
about:Report issues with the documentation, such as typos, outdated information, or missing content. Please provide the specific section and details of the issue.
description:"To make sure we get to you in time, please check the following :)"
options:
- label:I have read the [Contributing Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) and [Language Policy](https://github.com/langgenius/dify/issues/1542).
required:true
- label:I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
required:true
- label: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)).
required:true
- label:"[FOR CHINESE USERS] 请务必使用英文提交 Issue,否则会被关闭。谢谢!:)"
- label:I confirm that I am using English to submit this report, otherwise it will be closed.
required:true
- label:"Please do not modify this template :) and fill in all the required fields."
description:Refactor existing code or perform maintenance chores to improve readability and reliability.
title:"[Refactor/Chore] "
body:
- type:checkboxes
attributes:
label:Self Checks
description:"To make sure we get to you in time, please check the following :)"
options:
- label:I have read the [Contributing Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) and [Language Policy](https://github.com/langgenius/dify/issues/1542).
required:true
- label:This is only for refactors or chores; if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general).
required:true
- label:I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
required:true
- label:I confirm that I am using English to submit this report, otherwise it will be closed.
required:true
- label:【中文用户 & Non English User】请使用英语提交,否则会被关闭 :)
required:true
- label:"Please do not modify this template :) and fill in all the required fields."
required:true
- type:textarea
id:description
attributes:
label:Description
placeholder:"Describe the refactor or chore you are proposing."
validations:
required:true
- type:textarea
id:motivation
attributes:
label:Motivation
placeholder:"Explain why this refactor or chore is necessary."
validations:
required:false
- type:textarea
id:additional-context
attributes:
label:Additional Context
placeholder:"Add any other context or screenshots about the request here."
> 1. Make sure you have read our [contribution guidelines](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)
> 1. Ensure there is an associated issue and you have been assigned to it
> 1. Use the correct syntax to link this PR: `Fixes #<issue number>`.
Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
## Summary
> [!Tip]
> Close issue syntax: `Fixes #<issue number>` or `Resolves #<issue number>`, see [documentation](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) for more details.
<!-- Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change. -->
# Screenshots
## Screenshots
| Before | After |
|--------|-------|
| ... | ... |
# Checklist
> [!IMPORTANT]
> Please review the checklist below before submitting your pull request.
## Checklist
- [ ] This change requires a documentation update, included: [Dify Document](https://github.com/langgenius/dify-docs)
- [x] I understand that this PR may be closed in case there was no previous discussion or issues. (This doesn't apply to typos!)
- [x] I've added a test for each change that was introduced, and I tried as much as possible to make a single atomic change.
- [x] I've updated the documentation accordingly.
- [x] I ran `dev/reformat`(backend) and `cd web && npx lint-staged`(frontend) to appease the lint gods
This `launch.json.template` file provides various debug configurations for the Dify project within VS Code / Cursor. To use these configurations, you should copy the contents of this file into a new file named `launch.json` in the same `.vscode` directory.
## How to Use
1. **Create `launch.json`**: If you don't have one, create a file named `launch.json` inside the `.vscode` directory.
1. **Copy Content**: Copy the entire content from `launch.json.template` into your newly created `launch.json` file.
1. **Select Debug Configuration**: Go to the Run and Debug view in VS Code / Cursor (Ctrl+Shift+D or Cmd+Shift+D).
1. **Start Debugging**: Select the desired configuration from the dropdown menu and click the green play button.
## Tips
- If you need to debug with Edge browser instead of Chrome, modify the `serverReadyAction` configuration in the "Next.js: debug full stack" section, change `"debugWithChrome"` to `"debugWithEdge"` to use Microsoft Edge for debugging.
Dify is an open-source platform for developing LLM applications with an intuitive interface combining agentic AI workflows, RAG pipelines, agent capabilities, and model management.
- Run backend CLI commands through `uv run --project api <command>`.
- Before submission, all backend modifications must pass local checks: `make lint`, `make type-check`, and `uv run --project api --dev dev/pytest/pytest_unit_tests.sh`.
- Use Makefile targets for linting and formatting; `make lint` and `make type-check` cover the required checks.
- Integration tests are CI-only and are not expected to run in the local environment.
## Frontend Workflow
```bash
cd web
pnpm lint:fix
pnpm type-check:tsgo
pnpm test
```
## Testing & Quality Practices
- Follow TDD: red → green → refactor.
- Use `pytest` for backend tests with Arrange-Act-Assert structure.
- Enforce strong typing; avoid `Any` and prefer explicit type annotations.
- Write self-documenting code; only add comments that explain intent.
## Language Style
- **Python**: Keep type hints on functions and attributes, and implement relevant special methods (e.g., `__repr__`, `__str__`).
- **TypeScript**: Use the strict config, rely on ESLint (`pnpm lint:fix` preferred) plus `pnpm type-check:tsgo`, and avoid `any` types.
## General Practices
- Prefer editing existing files; add new documentation only when requested.
- Inject dependencies through constructors and preserve clean architecture boundaries.
- Handle errors with domain-specific exceptions at the correct layer.
## Project Conventions
- Backend architecture adheres to DDD and Clean Architecture principles.
- Async work runs through Celery with Redis as the broker.
- Frontend user-facing strings must use `web/i18n/en-US/`; avoid hardcoded text.
@ -10,148 +10,90 @@ In terms of licensing, please take a minute to read our short [License and Contr
## Before you jump in
[Find](https://github.com/langgenius/dify/issues?q=is:issue+is:open) an existing issue, or [open](https://github.com/langgenius/dify/issues/new/choose) a new one. We categorize issues into 2 types:
Looking for something to tackle? Browse our [good first issues](https://github.com/langgenius/dify/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22) and pick one to get started!
Got a cool new model runtime or tool to add? Open a PR in our [plugin repo](https://github.com/langgenius/dify-plugins) and show us what you've built.
Need to update an existing model runtime, tool, or squash some bugs? Head over to our [official plugin repo](https://github.com/langgenius/dify-official-plugins) and make your magic happen!
Join the fun, contribute, and let's build something awesome together! 💡✨
Don't forget to link an existing issue or open a new issue in the PR's description.
### Bug reports
> [!IMPORTANT]
> Please make sure to include the following information when submitting a bug report:
- A clear and descriptive title
- A detailed description of the bug, including any error messages
- Steps to reproduce the bug
- Expected behavior
- **Logs**, if available, for backend issues, this is really important, you can find them in docker-compose logs
| Bugs in core functions (cloud service, cannot login, applications not working, security loopholes) | Critical |
| Non-critical bugs, performance boosts | Medium Priority |
| Minor fixes (typos, confusing but working UI) | Low Priority |
### Feature requests
* If you're opening a new feature request, we'd like you to explain what the proposed feature achieves, and include as much context as possible. [@perzeusss](https://github.com/perzeuss) has made a solid [Feature Request Copilot](https://udify.app/chat/MK2kVSnw1gakVwMX) that helps you draft out your needs. Feel free to give it a try.
> [!NOTE]
> Please make sure to include the following information when submitting a feature request:
* If you want to pick one up from the existing issues, simply drop a comment below it saying so.
- A clear and descriptive title
- A detailed description of the feature
- A use case for the feature
- Any other context or screenshots about the feature request
A team member working in the related direction will be looped in. If all looks good, they will give the go-ahead for you to start coding. We ask that you hold off working on the feature until then, so none of your work goes to waste should we propose changes.
How we prioritize:
Depending on whichever area the proposed feature falls under, you might talk to different team members. Here's rundown of the areas each our team members are working on at the moment:
| High-Priority Features as being labeled by a team member | High Priority |
| Popular feature requests from our [community feedback board](https://github.com/langgenius/dify/discussions/categories/feedbacks) | Medium Priority |
| Non-core features and minor enhancements | Low Priority |
* [npm](https://www.npmjs.com/) version 8.x.x or [Yarn](https://yarnpkg.com/)
* [Python](https://www.python.org/) version 3.11.x or 3.12.x
### 4. Installations
Dify is composed of a backend and a frontend. Navigate to the backend directory by `cd api/`, then follow the [Backend README](api/README.md) to install it. In a separate terminal, navigate to the frontend directory by `cd web/`, then follow the [Frontend README](web/README.md) to install.
Check the [installation FAQ](https://docs.dify.ai/learn-more/faq/install-faq) for a list of common issues and steps to troubleshoot.
### 5. Visit dify in your browser
To validate your set up, head over to [http://localhost:3000](http://localhost:3000) (the default, or your self-configured URL and port) in your browser. You should now see Dify up and running.
## Developing
If you are adding a model provider, [this guide](https://github.com/langgenius/dify/blob/main/api/core/model_runtime/README.md) is for you.
If you are adding a tool provider to Agent or Workflow, [this guide](./api/core/tools/README.md) is for you.
To help you quickly navigate where your contribution fits, a brief, annotated outline of Dify's backend & frontend is as follows:
### Backend
Dify’s backend is written in Python using [Flask](https://flask.palletsprojects.com/en/3.0.x/). It uses [SQLAlchemy](https://www.sqlalchemy.org/) for ORM and [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html) for task queueing. Authorization logic goes via Flask-login.
```text
[api/]
├── constants // Constant settings used throughout code base.
├── controllers // API route definitions and request handling logic.
├── core // Core application orchestration, model integrations, and tools.
├── docker // Docker & containerization related configurations.
├── events // Event handling and processing
├── extensions // Extensions with 3rd party frameworks/platforms.
├── fields // field definitions for serialization/marshalling.
├── tasks // Handling of async tasks and background jobs.
└── tests
```
### Frontend
The website is bootstrapped on [Next.js](https://nextjs.org/) boilerplate in Typescript and uses [Tailwind CSS](https://tailwindcss.com/) for styling. [React-i18next](https://react.i18next.com/) is used for internationalization.
```text
[web/]
├── app // layouts, pages, and components
│ ├── (commonLayout) // common layout used throughout the app
│ ├── (shareLayout) // layouts specifically shared across token-specific sessions
│ ├── activate // activate page
│ ├── components // shared by pages and layouts
│ ├── install // install page
│ ├── signin // signin page
│ └── styles // globally shared styles
├── assets // Static assets
├── bin // scripts ran at build step
├── config // adjustable settings and options
├── context // shared contexts used by different portions of the app
| High-Priority Features as being labeled by a team member | High Priority |
| Popular feature requests from our [community feedback board](https://github.com/langgenius/dify/discussions/categories/feedbacks) | Medium Priority |
| Non-core features and minor enhancements | Low Priority |
| Valuable but not immediate | Future-Feature |
## Submitting your PR
At last, time to open a pull request (PR) to our repo. For major features, we first merge them into the `deploy/dev` branch for testing, before they go into the `main` branch. If you run into issues like merge conflicts or don't know how to open a pull request, check out [GitHub's pull request tutorial](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests).
### Pull Request Process
And that's it! Once your PR is merged, you will be featured as a contributor in our [README](https://github.com/langgenius/dify/blob/main/README.md).
1. Fork the repository
1. Before you draft a PR, please create an issue to discuss the changes you want to make
1. Create a new branch for your changes
1. Please add tests for your changes accordingly
1. Ensure your code passes the existing tests
1. Please link the issue in the PR description, `fixes #<issue_number>`
1. Get merged!
### Setup the project
#### Frontend
For setting up the frontend service, please refer to our comprehensive [guide](https://github.com/langgenius/dify/blob/main/web/README.md) in the `web/README.md` file. This document provides detailed instructions to help you set up the frontend environment properly.
**Testing**: All React components must have comprehensive test coverage. See [web/testing/testing.md](https://github.com/langgenius/dify/blob/main/web/testing/testing.md) for the canonical frontend testing guidelines and follow every requirement described there.
#### Backend
For setting up the backend service, kindly refer to our detailed [instructions](https://github.com/langgenius/dify/blob/main/api/README.md) in the `api/README.md` file. This document contains step-by-step guidance to help you get the backend up and running smoothly.
#### Other things to note
We recommend reviewing this document carefully before proceeding with the setup, as it contains essential information about:
- Prerequisites and dependencies
- Installation steps
- Configuration details
- Common troubleshooting tips
Feel free to reach out if you encounter any issues during the setup process.
## Getting Help
If you ever get stuck or got a burning question while contributing, simply shoot your queries our way via the related GitHub issue, or hop onto our [Discord](https://discord.gg/8Tpq4AcN9c) for a quick chat.
If you ever get stuck or get a burning question while contributing, simply shoot your queries our way via the related GitHub issue, or hop onto our [Discord](https://discord.gg/8Tpq4AcN9c) for a quick chat.
Thật tuyệt vời khi bạn muốn đóng góp cho Dify! Chúng tôi rất mong chờ được thấy những gì bạn sẽ làm. Là một startup với nguồn nhân lực và tài chính hạn chế, chúng tôi có tham vọng lớn là thiết kế quy trình trực quan nhất để xây dựng và quản lý các ứng dụng LLM. Mọi sự giúp đỡ từ cộng đồng đều rất quý giá đối với chúng tôi.
Chúng tôi cần linh hoạt và làm việc nhanh chóng, nhưng đồng thời cũng muốn đảm bảo các cộng tác viên như bạn có trải nghiệm đóng góp thuận lợi nhất có thể. Chúng tôi đã tạo ra hướng dẫn đóng góp này nhằm giúp bạn làm quen với codebase và cách chúng tôi làm việc với các cộng tác viên, để bạn có thể nhanh chóng bắt tay vào phần thú vị.
Hướng dẫn này, cũng như bản thân Dify, đang trong quá trình cải tiến liên tục. Chúng tôi rất cảm kích sự thông cảm của bạn nếu đôi khi nó không theo kịp dự án thực tế, và chúng tôi luôn hoan nghênh mọi phản hồi để cải thiện.
Về vấn đề cấp phép, xin vui lòng dành chút thời gian đọc qua [Thỏa thuận Cấp phép và Đóng góp](./LICENSE) ngắn gọn của chúng tôi. Cộng đồng cũng tuân thủ [quy tắc ứng xử](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md).
## Trước khi bắt đầu
[Tìm kiếm](https://github.com/langgenius/dify/issues?q=is:issue+is:open) một vấn đề hiện có, hoặc [tạo mới](https://github.com/langgenius/dify/issues/new/choose) một vấn đề. Chúng tôi phân loại các vấn đề thành 2 loại:
### Yêu cầu tính năng:
* Nếu bạn đang tạo một yêu cầu tính năng mới, chúng tôi muốn bạn giải thích tính năng đề xuất sẽ đạt được điều gì và cung cấp càng nhiều thông tin chi tiết càng tốt. [@perzeusss](https://github.com/perzeuss) đã tạo một [Trợ lý Yêu cầu Tính năng](https://udify.app/chat/MK2kVSnw1gakVwMX) rất hữu ích để giúp bạn soạn thảo nhu cầu của mình. Hãy thử dùng nó nhé.
* Nếu bạn muốn chọn một vấn đề từ danh sách hiện có, chỉ cần để lại bình luận dưới vấn đề đó nói rằng bạn sẽ làm.
Một thành viên trong nhóm làm việc trong lĩnh vực liên quan sẽ được thông báo. Nếu mọi thứ ổn, họ sẽ cho phép bạn bắt đầu code. Chúng tôi yêu cầu bạn chờ đợi cho đến lúc đó trước khi bắt tay vào làm tính năng, để không lãng phí công sức của bạn nếu chúng tôi đề xuất thay đổi.
Tùy thuộc vào lĩnh vực mà tính năng đề xuất thuộc về, bạn có thể nói chuyện với các thành viên khác nhau trong nhóm. Dưới đây là danh sách các lĩnh vực mà các thành viên trong nhóm chúng tôi đang làm việc hiện tại:
| [@yeuoly](https://github.com/Yeuoly) | Thiết kế kiến trúc Agents |
| [@jyong](https://github.com/JohnJyong) | Thiết kế quy trình RAG |
| [@GarfieldDai](https://github.com/GarfieldDai) | Xây dựng quy trình làm việc |
| [@iamjoel](https://github.com/iamjoel) & [@zxhlyh](https://github.com/zxhlyh) | Làm cho giao diện người dùng dễ sử dụng |
| [@guchenhe](https://github.com/guchenhe) & [@crazywoola](https://github.com/crazywoola) | Trải nghiệm nhà phát triển, đầu mối liên hệ cho mọi vấn đề |
| [@takatost](https://github.com/takatost) | Định hướng và kiến trúc tổng thể sản phẩm |
| Tính năng ưu tiên cao được gắn nhãn bởi thành viên trong nhóm | Ưu tiên cao |
| Yêu cầu tính năng phổ biến từ [bảng phản hồi cộng đồng](https://github.com/langgenius/dify/discussions/categories/feedbacks) của chúng tôi | Ưu tiên trung bình |
| Tính năng không quan trọng và cải tiến nhỏ | Ưu tiên thấp |
| Có giá trị nhưng không cấp bách | Tính năng tương lai |
### Những vấn đề khác (ví dụ: báo cáo lỗi, tối ưu hiệu suất, sửa lỗi chính tả):
- [npm](https://www.npmjs.com/) phiên bản 8.x.x hoặc [Yarn](https://yarnpkg.com/)
- [Python](https://www.python.org/) phiên bản 3.11.x hoặc 3.12.x
### 4. Cài đặt
Dify bao gồm một backend và một frontend. Đi đến thư mục backend bằng lệnh `cd api/`, sau đó làm theo hướng dẫn trong [README của Backend](api/README.md) để cài đặt. Trong một terminal khác, đi đến thư mục frontend bằng lệnh `cd web/`, sau đó làm theo hướng dẫn trong [README của Frontend](web/README.md) để cài đặt.
Kiểm tra [FAQ về cài đặt](https://docs.dify.ai/learn-more/faq/install-faq) để xem danh sách các vấn đề thường gặp và các bước khắc phục.
### 5. Truy cập Dify trong trình duyệt của bạn
Để xác nhận cài đặt của bạn, hãy truy cập [http://localhost:3000](http://localhost:3000) (địa chỉ mặc định, hoặc URL và cổng bạn đã cấu hình) trong trình duyệt. Bạn sẽ thấy Dify đang chạy.
## Phát triển
Nếu bạn đang thêm một nhà cung cấp mô hình, [hướng dẫn này](https://github.com/langgenius/dify/blob/main/api/core/model_runtime/README.md) dành cho bạn.
Nếu bạn đang thêm một nhà cung cấp công cụ cho Agent hoặc Workflow, [hướng dẫn này](./api/core/tools/README.md) dành cho bạn.
Để giúp bạn nhanh chóng định hướng phần đóng góp của mình, dưới đây là một bản phác thảo ngắn gọn về cấu trúc backend & frontend của Dify:
### Backend
Backend của Dify được viết bằng Python sử dụng [Flask](https://flask.palletsprojects.com/en/3.0.x/). Nó sử dụng [SQLAlchemy](https://www.sqlalchemy.org/) cho ORM và [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html) cho hàng đợi tác vụ. Logic xác thực được thực hiện thông qua Flask-login.
```
[api/]
├── constants // Các cài đặt hằng số được sử dụng trong toàn bộ codebase.
├── controllers // Định nghĩa các route API và logic xử lý yêu cầu.
├── core // Điều phối ứng dụng cốt lõi, tích hợp mô hình và công cụ.
├── docker // Cấu hình liên quan đến Docker & containerization.
├── events // Xử lý và xử lý sự kiện
├── extensions // Mở rộng với các framework/nền tảng bên thứ 3.
├── fields // Định nghĩa trường cho serialization/marshalling.
├── libs // Thư viện và tiện ích có thể tái sử dụng.
├── migrations // Script cho việc di chuyển cơ sở dữ liệu.
├── models // Mô hình cơ sở dữ liệu & định nghĩa schema.
├── services // Xác định logic nghiệp vụ.
├── storage // Lưu trữ khóa riêng tư.
├── tasks // Xử lý các tác vụ bất đồng bộ và công việc nền.
└── tests
```
### Frontend
Website được khởi tạo trên boilerplate [Next.js](https://nextjs.org/) bằng Typescript và sử dụng [Tailwind CSS](https://tailwindcss.com/) cho styling. [React-i18next](https://react.i18next.com/) được sử dụng cho việc quốc tế hóa.
```
[web/]
├── app // layouts, pages và components
│ ├── (commonLayout) // layout chung được sử dụng trong toàn bộ ứng dụng
│ ├── (shareLayout) // layouts được chia sẻ cụ thể cho các phiên dựa trên token
│ ├── activate // trang kích hoạt
│ ├── components // được chia sẻ bởi các trang và layouts
│ ├── install // trang cài đặt
│ ├── signin // trang đăng nhập
│ └── styles // styles được chia sẻ toàn cục
├── assets // Tài nguyên tĩnh
├── bin // scripts chạy ở bước build
├── config // cài đặt và tùy chọn có thể điều chỉnh
├── context // contexts được chia sẻ bởi các phần khác nhau của ứng dụng
├── dictionaries // File dịch cho từng ngôn ngữ
├── docker // cấu hình container
├── hooks // Hooks có thể tái sử dụng
├── i18n // Cấu hình quốc tế hóa
├── models // mô tả các mô hình dữ liệu & hình dạng của phản hồi API
├── public // tài nguyên meta như favicon
├── service // xác định hình dạng của các hành động API
├── test
├── types // mô tả các tham số hàm và giá trị trả về
└── utils // Các hàm tiện ích được chia sẻ
```
## Gửi PR của bạn
Cuối cùng, đã đến lúc mở một pull request (PR) đến repository của chúng tôi. Đối với các tính năng lớn, chúng tôi sẽ merge chúng vào nhánh `deploy/dev` để kiểm tra trước khi đưa vào nhánh `main`. Nếu bạn gặp vấn đề như xung đột merge hoặc không biết cách mở pull request, hãy xem [hướng dẫn về pull request của GitHub](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests).
Và thế là xong! Khi PR của bạn được merge, bạn sẽ được giới thiệu là một người đóng góp trong [README](https://github.com/langgenius/dify/blob/main/README.md) của chúng tôi.
## Nhận trợ giúp
Nếu bạn gặp khó khăn hoặc có câu hỏi cấp bách trong quá trình đóng góp, hãy đặt câu hỏi của bạn trong vấn đề GitHub liên quan, hoặc tham gia [Discord](https://discord.gg/8Tpq4AcN9c) của chúng tôi để trò chuyện nhanh chóng.
Dify is licensed under the Apache License 2.0, with the following additional conditions:
Dify is licensed under a modified version of the Apache License 2.0, with the following additional conditions:
1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer:
@ -10,8 +10,6 @@ a. Multi-tenant service: Unless explicitly authorized by Dify in writing, you ma
b. LOGO and copyright information: In the process of using Dify's frontend, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend.
- Frontend Definition: For the purposes of this license, the "frontend" of Dify includes all components located in the `web/` directory when running Dify from the raw source code, or the "web" image when running Dify with Docker.
Please contact business@dify.ai by email to inquire about licensing matters.
2. As a contributor, you should agree that:
a. The producer can adjust the open-source agreement to be more strict or relaxed as deemed necessary.
@ -21,19 +19,4 @@ Apart from the specific conditions mentioned above, all other rights and restric
The interactive design of this product is protected by appearance patent.
<ahref="./docs/vi-VN/README.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
<ahref="./docs/de-DE/README.md"><imgalt="README in Deutsch"src="https://img.shields.io/badge/German-d9d9d9"></a>
<ahref="./docs/bn-BD/README.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
Dify is an open-source LLM app development platform. Its intuitive interface combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
Dify is an open-source platform for developing LLM applications. Its intuitive interface combines agentic AI workflows, RAG pipelines, agent capabilities, model management, observability features, and more—allowing you to quickly move from prototype to production.
## Quick start
> Before installing Dify, make sure your machine meets the following minimum system requirements:
>
>- CPU >= 2 Core
>- RAM >= 4 GiB
>- CPU >= 2 Core
>- RAM >= 4 GiB
</br>
<br/>
The easiest way to start the Dify server is through [docker compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
The easiest way to start the Dify server is through [Docker Compose](docker/docker-compose.yaml). Before running Dify with the following commands, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
```bash
cd dify
@ -71,54 +83,49 @@ docker compose up -d
After running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization process.
#### Seeking help
Please refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) if you encounter problems setting up Dify. Reach out to [the community and us](#community--contact) if you are still having issues.
> If you'd like to contribute to Dify or do additional development, refer to our [guide to deploying from source code](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
## Key features
**1. Workflow**:
Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
**2. Comprehensive model support**:
Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
**4. RAG Pipeline**:
Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
**5. Agent capabilities**:
You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
**6. LLMOps**:
Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
**7. Backend-as-a-Service**:
All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
## Using Dify
- **Cloud </br>**
We host a [Dify Cloud](https://dify.ai) service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan.
- **Cloud <br/>**
We host a [Dify Cloud](https://dify.ai) service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan.
- **Self-hosting Dify Community Edition</br>**
Quickly get Dify running in your environment with this [starter guide](#quick-start).
Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
- **Self-hosting Dify Community Edition<br/>**
Quickly get Dify running in your environment with this [starter guide](#quick-start).
Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
- **Dify for enterprise / organizations</br>**
We provide additional enterprise-centric features. [Log your questions for us through this chatbot](https://udify.app/chat/22L1zSxg6yW1cWQg) or [send us an email](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs. </br>
> For startups and small businesses using AWS, check out [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one-click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
- **Dify for enterprise / organizations<br/>**
We provide additional enterprise-centric features. [Send us an email](mailto:business@dify.ai?subject=%5BGitHub%5DBusiness%20License%20Inquiry) to discuss your enterprise needs. <br/>
> For startups and small businesses using AWS, check out [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
## Staying ahead
@ -126,25 +133,52 @@ Star Dify on GitHub and be instantly notified of new releases.
If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
#### Customizing Suggested Questions
You can now customize the "Suggested Questions After Answer" feature to better fit your use case. For example, to generate longer, more technical questions:
```bash
# In your .env file
SUGGESTED_QUESTIONS_PROMPT='Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: ["question1","question2","question3","question4","question5"]'
SUGGESTED_QUESTIONS_MAX_TOKENS=512
SUGGESTED_QUESTIONS_TEMPERATURE=0.3
```
See the [Suggested Questions Configuration Guide](docs/suggested-questions-configuration.md) for detailed examples and usage instructions.
### Metrics Monitoring with Grafana
Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.
- [Grafana Dashboard by @bowenliang123](https://github.com/bowenliang123/dify-grafana-dashboard)
### Deployment with Kubernetes
If you'd like to configure a highly-available setup, there are community-contributed [Helm Charts](https://helm.sh/) and YAML files which allow Dify to be deployed on Kubernetes.
- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
- [🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym](https://github.com/Zhoneym/DifyAI-Kubernetes)
#### Using Terraform for Deployment
Deploy Dify to Cloud Platform with a single click using [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### Using AWS CDK for Deployment
@ -152,22 +186,35 @@ Deploy Dify to Cloud Platform with a single click using [terraform](https://www.
Deploy Dify to AWS with [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
- [AWS CDK by @KevinZhao (EKS based)](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
- [AWS CDK by @tmokmss (ECS based)](https://github.com/aws-samples/dify-self-hosted-on-aws)
#### Using Alibaba Cloud Computing Nest
Quickly deploy Dify to Alibaba cloud with [Alibaba Cloud Computing Nest](https://computenest.console.aliyun.com/service/instance/create/default?type=user&ServiceName=Dify%E7%A4%BE%E5%8C%BA%E7%89%88)
#### Using Alibaba Cloud Data Management
One-Click deploy Dify to Alibaba Cloud with [Alibaba Cloud Data Management](https://www.alibabacloud.com/help/en/dms/dify-in-invitational-preview/)
#### Deploy to AKS with Azure Devops Pipeline
One-Click deploy Dify to AKS with [Azure Devops Pipeline Helm Chart by @LeoZhang](https://github.com/Ruiruiz30/Dify-helm-chart-AKS)
## Contributing
For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.
> We are looking for contributors to help with translating Dify to languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/8Tpq4AcN9c).
> We are looking for contributors to help translate Dify into languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n-config/README.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/8Tpq4AcN9c).
## Community & contact
* [Github Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
* [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
* [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
- [GitHub Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
- [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
- [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
- [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
**Contributors**
@ -179,12 +226,10 @@ At the same time, please consider supporting Dify by sharing it on social media
[](https://star-history.com/#langgenius/dify&Date)
## Security disclosure
To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.
To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to security@dify.ai, and our team will respond with detailed answer.
## License
This repository is available under the [Dify Open Source License](LICENSE), which is essentially Apache 2.0 with a few additional restrictions.
This repository is licensed under the [Dify Open Source License](LICENSE), based on Apache 2.0 with additional conditions.
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
</p>
<divstyle="text-align: right;">
مشروع Dify هو منصة تطوير تطبيقات الذكاء الصناعي مفتوحة المصدر. تجمع واجهته البديهية بين سير العمل الذكي بالذكاء الاصطناعي وخط أنابيب RAG وقدرات الوكيل وإدارة النماذج وميزات الملاحظة وأكثر من ذلك، مما يتيح لك الانتقال بسرعة من المرحلة التجريبية إلى الإنتاج. إليك قائمة بالميزات الأساسية:
</br></br>
**1. سير العمل**: قم ببناء واختبار سير عمل الذكاء الاصطناعي القوي على قماش بصري، مستفيدًا من جميع الميزات التالية وأكثر.
**2. الدعم الشامل للنماذج**: تكامل سلس مع مئات من LLMs الخاصة / مفتوحة المصدر من عشرات من موفري التحليل والحلول المستضافة ذاتيًا، مما يغطي GPT و Mistral و Llama3 وأي نماذج متوافقة مع واجهة OpenAI API. يمكن العثور على قائمة كاملة بمزودي النموذج المدعومين [هنا](https://docs.dify.ai/getting-started/readme/model-providers).
**3. بيئة التطوير للأوامر**: واجهة بيئة التطوير المبتكرة لصياغة الأمر ومقارنة أداء النموذج، وإضافة ميزات إضافية مثل تحويل النص إلى كلام إلى تطبيق قائم على الدردشة.
**4. خط أنابيب RAG**: قدرات RAG الواسعة التي تغطي كل شيء من استيعاب الوثائق إلى الاسترجاع، مع الدعم الفوري لاستخراج النص من ملفات PDF و PPT وتنسيقات الوثائق الشائعة الأخرى.
**5. قدرات الوكيل**: يمكنك تعريف الوكلاء بناءً على أمر وظيفة LLM أو ReAct، وإضافة أدوات مدمجة أو مخصصة للوكيل. توفر Dify أكثر من 50 أداة مدمجة لوكلاء الذكاء الاصطناعي، مثل البحث في Google و DALL·E وStable Diffusion و WolframAlpha.
**6. الـ LLMOps**: راقب وتحلل سجلات التطبيق والأداء على مر الزمن. يمكنك تحسين الأوامر والبيانات والنماذج باستمرار استنادًا إلى البيانات الإنتاجية والتعليقات.
**7.الواجهة الخلفية (Backend) كخدمة**: تأتي جميع عروض Dify مع APIs مطابقة، حتى يمكنك دمج Dify بسهولة في منطق أعمالك الخاص.
## مقارنة الميزات
<tablestyle="width: 100%;">
<tr>
<thalign="center">الميزة</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">OpenAI Assistants API</th>
</tr>
<tr>
<tdalign="center">نهج البرمجة</td>
<tdalign="center">موجّه لـ تطبيق + واجهة برمجة تطبيق (API)</td>
<tdalign="center">برمجة Python</td>
<tdalign="center">موجه لتطبيق</td>
<tdalign="center">واجهة برمجة تطبيق (API)</td>
</tr>
<tr>
<tdalign="center">LLMs المدعومة</td>
<tdalign="center">تنوع غني</td>
<tdalign="center">تنوع غني</td>
<tdalign="center">تنوع غني</td>
<tdalign="center">فقط OpenAI</td>
</tr>
<tr>
<tdalign="center">محرك RAG</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">الوكيل</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">سير العمل</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">الملاحظة</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">ميزات الشركات (SSO / مراقبة الوصول)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">نشر محلي</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## استخدام Dify
- **سحابة </br>**
نحن نستضيف [خدمة Dify Cloud](https://dify.ai) لأي شخص لتجربتها بدون أي إعدادات. توفر كل قدرات النسخة التي تمت استضافتها ذاتيًا، وتتضمن 200 أمر GPT-4 مجانًا في خطة الصندوق الرملي.
- **استضافة ذاتية لنسخة المجتمع Dify</br>**
ابدأ سريعًا في تشغيل Dify في بيئتك باستخدام [دليل البدء السريع](#البدء السريع).
استخدم [توثيقنا](https://docs.dify.ai) للمزيد من المراجع والتعليمات الأعمق.
- **مشروع Dify للشركات / المؤسسات</br>**
نحن نوفر ميزات إضافية مركزة على الشركات. [جدول اجتماع معنا](https://cal.com/guchenhe/30min) أو [أرسل لنا بريدًا إلكترونيًا](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) لمناقشة احتياجات الشركات. </br>
> بالنسبة للشركات الناشئة والشركات الصغيرة التي تستخدم خدمات AWS، تحقق من [Dify Premium على AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) ونشرها في شبكتك الخاصة على AWS VPC بنقرة واحدة. إنها عرض AMI بأسعار معقولة مع خيار إنشاء تطبيقات بشعار وعلامة تجارية مخصصة.
## البقاء قدمًا
قم بإضافة نجمة إلى Dify على GitHub وتلق تنبيهًا فوريًا بالإصدارات الجديدة.
> قبل تثبيت Dify، تأكد من أن جهازك يلبي الحد الأدنى من متطلبات النظام التالية:
>
>- معالج >= 2 نواة
>- ذاكرة وصول عشوائي (RAM) >= 4 جيجابايت
</br>
أسهل طريقة لبدء تشغيل خادم Dify هي تشغيل ملف [docker-compose.yml](docker/docker-compose.yaml) الخاص بنا. قبل تشغيل أمر التثبيت، تأكد من تثبيت [Docker](https://docs.docker.com/get-docker/) و [Docker Compose](https://docs.docker.com/compose/install/) على جهازك:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
بعد التشغيل، يمكنك الوصول إلى لوحة تحكم Dify في متصفحك على [http://localhost/install](http://localhost/install) وبدء عملية التهيئة.
> إذا كنت ترغب في المساهمة في Dify أو القيام بتطوير إضافي، فانظر إلى [دليلنا للنشر من الشفرة (code) المصدرية](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
## الخطوات التالية
إذا كنت بحاجة إلى تخصيص الإعدادات، فيرجى الرجوع إلى التعليقات في ملف [.env.example](docker/.env.example) وتحديث القيم المقابلة في ملف `.env`. بالإضافة إلى ذلك، قد تحتاج إلى إجراء تعديلات على ملف `docker-compose.yaml` نفسه، مثل تغيير إصدارات الصور أو تعيينات المنافذ أو نقاط تحميل وحدات التخزين، بناءً على بيئة النشر ومتطلباتك الخاصة. بعد إجراء أي تغييرات، يرجى إعادة تشغيل `docker-compose up -d`. يمكنك العثور على قائمة كاملة بمتغيرات البيئة المتاحة [هنا](https://docs.dify.ai/getting-started/install-self-hosted/environments).
يوجد مجتمع خاص بـ [Helm Charts](https://helm.sh/) وملفات YAML التي تسمح بتنفيذ Dify على Kubernetes للنظام من الإيجابيات العلوية.
- [رسم بياني Helm من قبل @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [رسم بياني Helm من قبل @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [ملف YAML من قبل @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### استخدام Terraform للتوزيع
انشر Dify إلى منصة السحابة بنقرة واحدة باستخدام [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform بواسطة @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform بواسطة @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### استخدام AWS CDK للنشر
انشر Dify على AWS باستخدام [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK بواسطة @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## المساهمة
لأولئك الذين يرغبون في المساهمة، انظر إلى [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) لدينا.
في الوقت نفسه، يرجى النظر في دعم Dify عن طريق مشاركته على وسائل التواصل الاجتماعي وفي الفعاليات والمؤتمرات.
> نحن نبحث عن مساهمين لمساعدة في ترجمة Dify إلى لغات أخرى غير اللغة الصينية المندرين أو الإنجليزية. إذا كنت مهتمًا بالمساعدة، يرجى الاطلاع على [README للترجمة](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) لمزيد من المعلومات، واترك لنا تعليقًا في قناة `global-users` على [خادم المجتمع على Discord](https://discord.gg/8Tpq4AcN9c).
* [المشكلات على GitHub](https://github.com/langgenius/dify/issues). الأفضل لـ: الأخطاء التي تواجهها في استخدام Dify.AI، واقتراحات الميزات. انظر [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
* [تويتر](https://twitter.com/dify_ai). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
## تاريخ النجمة
[](https://star-history.com/#langgenius/dify&Date)
## الكشف عن الأمان
لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى security@dify.ai وسنقدم لك إجابة أكثر تفصيلاً.
## الرخصة
هذا المستودع متاح تحت [رخصة البرنامج الحر Dify](LICENSE)، والتي تعتبر بشكل أساسي Apache 2.0 مع بعض القيود الإضافية.
## الكشف عن الأمان
لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى security@dify.ai وسنقدم لك إجابة أكثر تفصيلاً.
## الرخصة
هذا المستودع متاح تحت [رخصة البرنامج الحر Dify](LICENSE)، والتي تعتبر بشكل أساسي Apache 2.0 مع بعض القيود الإضافية.
<imgalt="Actividad de Commits el último mes"src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
Dify es una plataforma de desarrollo de aplicaciones de LLM de código abierto. Su interfaz intuitiva combina flujo de trabajo de IA, pipeline RAG, capacidades de agente, gestión de modelos, características de observabilidad y más, lo que le permite pasar rápidamente de un prototipo a producción. Aquí hay una lista de las características principales:
</br></br>
**1. Flujo de trabajo**:
Construye y prueba potentes flujos de trabajo de IA en un lienzo visual, aprovechando todas las siguientes características y más.
Integración perfecta con cientos de LLMs propietarios / de código abierto de docenas de proveedores de inferencia y soluciones auto-alojadas, que cubren GPT, Mistral, Llama3 y cualquier modelo compatible con la API de OpenAI. Se puede encontrar una lista completa de proveedores de modelos admitidos [aquí](https://docs.dify.ai/getting-started/readme/model-providers).
Interfaz intuitiva para crear prompts, comparar el rendimiento del modelo y agregar características adicionales como texto a voz a una aplicación basada en chat.
**4. Pipeline RAG**:
Amplias capacidades de RAG que cubren todo, desde la ingestión de documentos hasta la recuperación, con soporte listo para usar para la extracción de texto de PDF, PPT y otros formatos de documento comunes.
**5. Capacidades de agente**:
Puedes definir agent
es basados en LLM Function Calling o ReAct, y agregar herramientas preconstruidas o personalizadas para el agente. Dify proporciona más de 50 herramientas integradas para agentes de IA, como Búsqueda de Google, DALL·E, Difusión Estable y WolframAlpha.
**6. LLMOps**:
Supervisa y analiza registros de aplicaciones y rendimiento a lo largo del tiempo. Podrías mejorar continuamente prompts, conjuntos de datos y modelos basados en datos de producción y anotaciones.
**7. Backend como servicio**:
Todas las ofertas de Dify vienen con APIs correspondientes, por lo que podrías integrar Dify sin esfuerzo en tu propia lógica empresarial.
## Comparación de características
<tablestyle="width: 100%;">
<tr>
<thalign="center">Característica</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">API de Asistentes de OpenAI</th>
</tr>
<tr>
<tdalign="center">Enfoque de programación</td>
<tdalign="center">API + orientado a la aplicación</td>
<tdalign="center">Código Python</td>
<tdalign="center">Orientado a la aplicación</td>
<tdalign="center">Orientado a la API</td>
</tr>
<tr>
<tdalign="center">LLMs admitidos</td>
<tdalign="center">Gran variedad</td>
<tdalign="center">Gran variedad</td>
<tdalign="center">Gran variedad</td>
<tdalign="center">Solo OpenAI</td>
</tr>
<tr>
<tdalign="center">Motor RAG</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Agente</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Flujo de trabajo</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Observabilidad</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Característica empresarial (SSO/Control de acceso)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Implementación local</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## Usando Dify
- **Nube </br>**
Hospedamos un servicio [Dify Cloud](https://dify.ai) para que cualquiera lo pruebe sin configuración. Proporciona todas las capacidades de la versión autoimplementada e incluye 200 llamadas gratuitas a GPT-4 en el plan sandbox.
- **Auto-alojamiento de Dify Community Edition</br>**
Pon rápidamente Dify en funcionamiento en tu entorno con esta [guía de inicio rápido](#quick-start).
Usa nuestra [documentación](https://docs.dify.ai) para más referencias e instrucciones más detalladas.
- **Dify para Empresas / Organizaciones</br>**
Proporcionamos características adicionales centradas en la empresa. [Envíanos un correo electrónico](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) para discutir las necesidades empresariales. </br>
> Para startups y pequeñas empresas que utilizan AWS, echa un vistazo a [Dify Premium en AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) e impleméntalo en tu propio VPC de AWS con un clic. Es una AMI asequible que ofrece la opción de crear aplicaciones con logotipo y marca personalizados.
## Manteniéndote al tanto
Dale estrella a Dify en GitHub y serás notificado instantáneamente de las nuevas versiones.
> Antes de instalar Dify, asegúrate de que tu máquina cumpla con los siguientes requisitos mínimos del sistema:
>
>- CPU >= 2 núcleos
>- RAM >= 4GB
</br>
La forma más fácil de iniciar el servidor de Dify es ejecutar nuestro archivo [docker-compose.yml](docker/docker-compose.yaml). Antes de ejecutar el comando de instalación, asegúrate de que [Docker](https://docs.docker.com/get-docker/) y [Docker Compose](https://docs.docker.com/compose/install/) estén instalados en tu máquina:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
Después de ejecutarlo, puedes acceder al panel de control de Dify en tu navegador en [http://localhost/install](http://localhost/install) y comenzar el proceso de inicialización.
> Si deseas contribuir a Dify o realizar desarrollo adicional, consulta nuestra [guía para implementar desde el código fuente](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
## Próximos pasos
Si necesita personalizar la configuración, consulte los comentarios en nuestro archivo [.env.example](docker/.env.example) y actualice los valores correspondientes en su archivo `.env`. Además, es posible que deba realizar ajustes en el propio archivo `docker-compose.yaml`, como cambiar las versiones de las imágenes, las asignaciones de puertos o los montajes de volúmenes, según su entorno de implementación y requisitos específicos. Después de realizar cualquier cambio, vuelva a ejecutar `docker-compose up -d`. Puede encontrar la lista completa de variables de entorno disponibles [aquí](https://docs.dify.ai/getting-started/install-self-hosted/environments).
. Después de realizar los cambios, ejecuta `docker-compose up -d` nuevamente. Puedes ver la lista completa de variables de entorno [aquí](https://docs.dify.ai/getting-started/install-self-hosted/environments).
Si desea configurar una configuración de alta disponibilidad, la comunidad proporciona [Gráficos Helm](https://helm.sh/) y archivos YAML, a través de los cuales puede desplegar Dify en Kubernetes.
- [Gráfico Helm por @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Gráfico Helm por @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [Ficheros YAML por @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### Uso de Terraform para el despliegue
Despliega Dify en una plataforma en la nube con un solo clic utilizando [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform por @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform por @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### Usando AWS CDK para el Despliegue
Despliegue Dify en AWS usando [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK por @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Contribuir
Para aquellos que deseen contribuir con código, consulten nuestra [Guía de contribución](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
Al mismo tiempo, considera apoyar a Dify compartiéndolo en redes sociales y en eventos y conferencias.
> Estamos buscando colaboradores para ayudar con la traducción de Dify a idiomas que no sean el mandarín o el inglés. Si estás interesado en ayudar, consulta el [README de i18n](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) para obtener más información y déjanos un comentario en el canal `global-users` de nuestro [Servidor de Comunidad en Discord](https://discord.gg/8Tpq4AcN9c).
* [Discusión en GitHub](https://github.com/langgenius/dify/discussions). Lo mejor para: compartir comentarios y hacer preguntas.
* [Reporte de problemas en GitHub](https://github.com/langgenius/dify/issues). Lo mejor para: errores que encuentres usando Dify.AI y propuestas de características. Consulta nuestra [Guía de contribución](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.
* [X(Twitter)](https://twitter.com/dify_ai). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.
## Historial de Estrellas
[](https://star-history.com/#langgenius/dify&Date)
## Divulgación de Seguridad
Para proteger tu privacidad, evita publicar problemas de seguridad en GitHub. En su lugar, envía tus preguntas a security@dify.ai y te proporcionaremos una respuesta más detallada.
## Licencia
Este repositorio está disponible bajo la [Licencia de Código Abierto de Dify](LICENSE), que es esencialmente Apache 2.0 con algunas restricciones adicionales.
## Divulgación de Seguridad
Para proteger tu privacidad, evita publicar problemas de seguridad en GitHub. En su lugar, envía tus preguntas a security@dify.ai y te proporcionaremos una respuesta más detallada.
## Licencia
Este repositorio está disponible bajo la [Licencia de Código Abierto de Dify](LICENSE), que es esencialmente Apache 2.0 con algunas restricciones adicionales.
Dify est une plateforme de développement d'applications LLM open source. Son interface intuitive combine un flux de travail d'IA, un pipeline RAG, des capacités d'agent, une gestion de modèles, des fonctionnalités d'observabilité, et plus encore, vous permettant de passer rapidement du prototype à la production. Voici une liste des fonctionnalités principales:
</br></br>
**1. Flux de travail**:
Construisez et testez des flux de travail d'IA puissants sur un canevas visuel, en utilisant toutes les fonctionnalités suivantes et plus encore.
Intégration transparente avec des centaines de LLM propriétaires / open source provenant de dizaines de fournisseurs d'inférence et de solutions auto-hébergées, couvrant GPT, Mistral, Llama3, et tous les modèles compatibles avec l'API OpenAI. Une liste complète des fournisseurs de modèles pris en charge se trouve [ici](https://docs.dify.ai/getting-started/readme/model-providers).
Interface intuitive pour créer des prompts, comparer les performances des modèles et ajouter des fonctionnalités supplémentaires telles que la synthèse vocale à une application basée sur des chats.
**4. Pipeline RAG**:
Des capacités RAG étendues qui couvrent tout, de l'ingestion de documents à la récupération, avec un support prêt à l'emploi pour l'extraction de texte à partir de PDF, PPT et autres formats de document courants.
**5. Capac
ités d'agent**:
Vous pouvez définir des agents basés sur l'appel de fonction LLM ou ReAct, et ajouter des outils pré-construits ou personnalisés pour l'agent. Dify fournit plus de 50 outils intégrés pour les agents d'IA, tels que la recherche Google, DALL·E, Stable Diffusion et WolframAlpha.
**6. LLMOps**:
Surveillez et analysez les journaux d'application et les performances au fil du temps. Vous pouvez continuellement améliorer les prompts, les ensembles de données et les modèles en fonction des données de production et des annotations.
**7. Backend-as-a-Service**:
Toutes les offres de Dify sont accompagnées d'API correspondantes, vous permettant d'intégrer facilement Dify dans votre propre logique métier.
Nous hébergeons un service [Dify Cloud](https://dify.ai) pour que tout le monde puisse l'essayer sans aucune configuration. Il fournit toutes les capacités de la version auto-hébergée et comprend 200 appels GPT-4 gratuits dans le plan bac à sable.
- **Auto-hébergement Dify Community Edition</br>**
Lancez rapidement Dify dans votre environnement avec ce [guide de démarrage](#quick-start).
Utilisez notre [documentation](https://docs.dify.ai) pour plus de références et des instructions plus détaillées.
- **Dify pour les entreprises / organisations</br>**
Nous proposons des fonctionnalités supplémentaires adaptées aux entreprises. [Envoyez-nous un e-mail](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) pour discuter des besoins de l'entreprise. </br>
> Pour les startups et les petites entreprises utilisant AWS, consultez [Dify Premium sur AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) et déployez-le dans votre propre VPC AWS en un clic. C'est une offre AMI abordable avec la possibilité de créer des applications avec un logo et une marque personnalisés.
## Rester en avance
Mettez une étoile à Dify sur GitHub et soyez instantanément informé des nouvelles versions.
> Avant d'installer Dify, assurez-vous que votre machine répond aux exigences système minimales suivantes:
>
>- CPU >= 2 cœurs
>- RAM >= 4 Go
</br>
La manière la plus simple de démarrer le serveur Dify est d'exécuter notre fichier [docker-compose.yml](docker/docker-compose.yaml). Avant d'exécuter la commande d'installation, assurez-vous que [Docker](https://docs.docker.com/get-docker/) et [Docker Compose](https://docs.docker.com/compose/install/) sont installés sur votre machine:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
Après l'exécution, vous pouvez accéder au tableau de bord Dify dans votre navigateur à [http://localhost/install](http://localhost/install) et commencer le processus d'initialisation.
> Si vous souhaitez contribuer à Dify ou effectuer un développement supplémentaire, consultez notre [guide de déploiement à partir du code source](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
## Prochaines étapes
Si vous devez personnaliser la configuration, veuillez vous référer aux commentaires dans notre fichier [.env.example](docker/.env.example) et mettre à jour les valeurs correspondantes dans votre fichier `.env`. De plus, vous devrez peut-être apporter des modifications au fichier `docker-compose.yaml` lui-même, comme changer les versions d'image, les mappages de ports ou les montages de volumes, en fonction de votre environnement de déploiement et de vos exigences spécifiques. Après avoir effectué des modifications, veuillez réexécuter `docker-compose up -d`. Vous pouvez trouver la liste complète des variables d'environnement disponibles [ici](https://docs.dify.ai/getting-started/install-self-hosted/environments).
Si vous souhaitez configurer une configuration haute disponibilité, la communauté fournit des [Helm Charts](https://helm.sh/) et des fichiers YAML, à travers lesquels vous pouvez déployer Dify sur Kubernetes.
- [Helm Chart par @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart par @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [Fichier YAML par @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### Utilisation de Terraform pour le déploiement
Déployez Dify sur une plateforme cloud en un clic en utilisant [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform par @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform par @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### Utilisation d'AWS CDK pour le déploiement
Déployez Dify sur AWS en utilisant [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK par @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Contribuer
Pour ceux qui souhaitent contribuer du code, consultez notre [Guide de contribution](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
Dans le même temps, veuillez envisager de soutenir Dify en le partageant sur les réseaux sociaux et lors d'événements et de conférences.
> Nous recherchons des contributeurs pour aider à traduire Dify dans des langues autres que le mandarin ou l'anglais. Si vous êtes intéressé à aider, veuillez consulter le [README i18n](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) pour plus d'informations, et laissez-nous un commentaire dans le canal `global-users` de notre [Serveur communautaire Discord](https://discord.gg/8Tpq4AcN9c).
* [Discussion GitHub](https://github.com/langgenius/dify/discussions). Meilleur pour: partager des commentaires et poser des questions.
* [Problèmes GitHub](https://github.com/langgenius/dify/issues). Meilleur pour: les bogues que vous rencontrez en utilisant Dify.AI et les propositions de fonctionnalités. Consultez notre [Guide de contribution](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Meilleur pour: partager vos applications et passer du temps avec la communauté.
* [X(Twitter)](https://twitter.com/dify_ai). Meilleur pour: partager vos applications et passer du temps avec la communauté.
## Historique des étoiles
[](https://star-history.com/#langgenius/dify&Date)
## Divulgation de sécurité
Pour protéger votre vie privée, veuillez éviter de publier des problèmes de sécurité sur GitHub. Au lieu de cela, envoyez vos questions à security@dify.ai et nous vous fournirons une réponse plus détaillée.
## Licence
Ce référentiel est disponible sous la [Licence open source Dify](LICENSE), qui est essentiellement l'Apache 2.0 avec quelques restrictions supplémentaires.
## Divulgation de sécurité
Pour protéger votre vie privée, veuillez éviter de publier des problèmes de sécurité sur GitHub. Au lieu de cela, envoyez vos questions à security@dify.ai et nous vous fournirons une réponse plus détaillée.
## Licence
Ce référentiel est disponible sous la [Licence open source Dify](LICENSE), qui est essentiellement l'Apache 2.0 avec quelques restrictions supplémentaires.
LLM Function CallingやReActに基づくエージェントの定義が可能で、AIエージェント用のプリビルトまたはカスタムツールを追加できます。Difyには、Google検索、DALL·E、Stable Diffusion、WolframAlphaなどのAIエージェント用の50以上の組み込みツールが提供します。
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production. Here's a list of the core features:
</br></br>
**1. Workflow**:
Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
**4. RAG Pipeline**:
Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
**5. Agent capabilities**:
You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
**6. LLMOps**:
Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
**7. Backend-as-a-Service**:
All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
We host a [Dify Cloud](https://dify.ai) service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan.
- **Self-hosting Dify Community Edition</br>**
Quickly get Dify running in your environment with this [starter guide](#quick-start).
Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
- **Dify for Enterprise / Organizations</br>**
We provide additional enterprise-centric features. [Send us an email](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs. </br>
> For startups and small businesses using AWS, check out [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one-click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
## Staying ahead
Star Dify on GitHub and be instantly notified of new releases.
> Before installing Dify, make sure your machine meets the following minimum system requirements:
>
>- CPU >= 2 Core
>- RAM >= 4GB
</br>
The easiest way to start the Dify server is to run our [docker-compose.yml](docker/docker-compose.yaml) file. Before running the installation command, make sure that [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/) are installed on your machine:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
After running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization process.
> If you'd like to contribute to Dify or do additional development, refer to our [guide to deploying from source code](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
## Next steps
If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
If you'd like to configure a highly-available setup, there are community-contributed [Helm Charts](https://helm.sh/) and YAML files which allow Dify to be deployed on Kubernetes.
- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.
> We are looking for contributors to help with translating Dify to languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/8Tpq4AcN9c).
). Best for: sharing feedback and asking questions.
* [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
* [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
## Star History
[](https://star-history.com/#langgenius/dify&Date)
## Security Disclosure
To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.
## License
This repository is available under the [Dify Open Source License](LICENSE), which is essentially Apache 2.0 with a few additional restrictions.
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
</p>
Dify는 오픈 소스 LLM 앱 개발 플랫폼입니다. 직관적인 인터페이스를 통해 AI 워크플로우, RAG 파이프라인, 에이전트 기능, 모델 관리, 관찰 기능 등을 결합하여 프로토타입에서 프로덕션까지 빠르게 전환할 수 있습니다. 주요 기능 목록은 다음과 같습니다:</br></br>
**1. 워크플로우**:
다음 기능들을 비롯한 다양한 기능을 활용하여 시각적 캔버스에서 강력한 AI 워크플로우를 구축하고 테스트하세요.
수십 개의 추론 제공업체와 자체 호스팅 솔루션에서 제공하는 수백 개의 독점 및 오픈 소스 LLM과 원활하게 통합되며, GPT, Mistral, Llama3 및 모든 OpenAI API 호환 모델을 포함합니다. 지원되는 모델 제공업체의 전체 목록은 [여기](https://docs.dify.ai/getting-started/readme/model-providers)에서 확인할 수 있습니다.
프롬프트를 작성하고, 모델 성능을 비교하며, 텍스트-음성 변환과 같은 추가 기능을 채팅 기반 앱에 추가할 수 있는 직관적인 인터페이스를 제공합니다.
**4. RAG 파이프라인**:
문서 수집부터 검색까지 모든 것을 다루며, PDF, PPT 및 기타 일반적인 문서 형식에서 텍스트 추출을 위한 기본 지원이 포함되어 있는 광범위한 RAG 기능을 제공합니다.
**5. 에이전트 기능**:
LLM 함수 호출 또는 ReAct를 기반으로 에이전트를 정의하고 에이전트에 대해 사전 구축된 도구나 사용자 정의 도구를 추가할 수 있습니다. Dify는 Google Search, DALL·E, Stable Diffusion, WolframAlpha 등 AI 에이전트를 위한 50개 이상의 내장 도구를 제공합니다.
**6. LLMOps**:
시간 경과에 따른 애플리케이션 로그와 성능을 모니터링하고 분석합니다. 생산 데이터와 주석을 기반으로 프롬프트, 데이터세트, 모델을 지속적으로 개선할 수 있습니다.
**7. Backend-as-a-Service**:
Dify의 모든 제품에는 해당 API가 함께 제공되므로 Dify를 자신의 비즈니스 로직에 쉽게 통합할 수 있습니다.
## 기능 비교
<tablestyle="width: 100%;">
<tr>
<thalign="center">기능</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">OpenAI Assistants API</th>
</tr>
<tr>
<tdalign="center">프로그래밍 접근 방식</td>
<tdalign="center">API + 앱 중심</td>
<tdalign="center">Python 코드</td>
<tdalign="center">앱 중심</td>
<tdalign="center">API 중심</td>
</tr>
<tr>
<tdalign="center">지원되는 LLMs</td>
<tdalign="center">다양한 종류</td>
<tdalign="center">다양한 종류</td>
<tdalign="center">다양한 종류</td>
<tdalign="center">OpenAI 전용</td>
</tr>
<tr>
<tdalign="center">RAG 엔진</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">에이전트</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">워크플로우</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">가시성</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">기업용 기능 (SSO/접근 제어)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">로컬 배포</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## Dify 사용하기
- **클라우드 </br>**
우리는 누구나 설정이 필요 없이 사용해 볼 수 있도록 [Dify 클라우드](https://dify.ai) 서비스를 호스팅합니다. 이는 자체 배포 버전의 모든 기능을 제공하며, 샌드박스 플랜에서 무료로 200회의 GPT-4 호출을 포함합니다.
- **셀프-호스팅 Dify 커뮤니티 에디션</br>**
환경에서 Dify를 빠르게 실행하려면 이 [스타터 가이드를](#quick-start) 참조하세요.
추가 참조 및 더 심층적인 지침은 [문서](https://docs.dify.ai)를 사용하세요.
- **기업 / 조직을 위한 Dify</br>**
우리는 추가적인 기업 중심 기능을 제공합니다. 잡거나 [이메일 보내기](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry)를 통해 기업 요구 사항을 논의하십시오. </br>
> AWS를 사용하는 스타트업 및 중소기업의 경우 [AWS Marketplace에서 Dify Premium](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6)을 확인하고 한 번의 클릭으로 자체 AWS VPC에 배포하십시오. 맞춤형 로고와 브랜딩이 포함된 앱을 생성할 수 있는 옵션이 포함된 저렴한 AMI 제품입니다.
>Dify를 설치하기 전에 컴퓨터가 다음과 같은 최소 시스템 요구 사항을 충족하는지 확인하세요 :
>- CPU >= 2 Core
>- RAM >= 4GB
</br>
Dify 서버를 시작하는 가장 쉬운 방법은 [docker-compose.yml](docker/docker-compose.yaml) 파일을 실행하는 것입니다. 설치 명령을 실행하기 전에 [Docker](https://docs.docker.com/get-docker/) 및 [Docker Compose](https://docs.docker.com/compose/install/)가 머신에 설치되어 있는지 확인하세요.
```bash
cd docker
cp .env.example .env
docker compose up -d
```
실행 후 브라우저의 [http://localhost/install](http://localhost/install) 에서 Dify 대시보드에 액세스하고 초기화 프로세스를 시작할 수 있습니다.
> Dify에 기여하거나 추가 개발을 하고 싶다면 소스 코드에서 [배포에 대한 가이드](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)를 참조하세요.
## 다음 단계
구성을 사용자 정의해야 하는 경우 [.env.example](docker/.env.example) 파일의 주석을 참조하고 `.env` 파일에서 해당 값을 업데이트하십시오. 또한 특정 배포 환경 및 요구 사항에 따라 `docker-compose.yaml` 파일 자체를 조정해야 할 수도 있습니다. 예를 들어 이미지 버전, 포트 매핑 또는 볼륨 마운트를 변경합니다. 변경 한 후 `docker-compose up -d`를 다시 실행하십시오. 사용 가능한 환경 변수의 전체 목록은 [여기](https://docs.dify.ai/getting-started/install-self-hosted/environments)에서 찾을 수 있습니다.
Dify를 Kubernetes에 배포하고 프리미엄 스케일링 설정을 구성했다는 커뮤니티가 제공하는 [Helm Charts](https://helm.sh/)와 YAML 파일이 존재합니다.
- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### Terraform을 사용한 배포
[terraform](https://www.terraform.io/)을 사용하여 단 한 번의 클릭으로 Dify를 클라우드 플랫폼에 배포하십시오
코드에 기여하고 싶은 분들은 [기여 가이드](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)를 참조하세요.
동시에 Dify를 소셜 미디어와 행사 및 컨퍼런스에 공유하여 지원하는 것을 고려해 주시기 바랍니다.
> 우리는 Dify를 중국어나 영어 이외의 언어로 번역하는 데 도움을 줄 수 있는 기여자를 찾고 있습니다. 도움을 주고 싶으시다면 [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md)에서 더 많은 정보를 확인하시고 [Discord 커뮤니티 서버](https://discord.gg/8Tpq4AcN9c)의 `global-users` 채널에 댓글을 남겨주세요.
* [Github 토론](https://github.com/langgenius/dify/discussions). 피드백 공유 및 질문하기에 적합합니다.
* [GitHub 이슈](https://github.com/langgenius/dify/issues). Dify.AI 사용 중 발견한 버그와 기능 제안에 적합합니다. [기여 가이드](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)를 참조하세요.
* [디스코드](https://discord.gg/FngNHpbcY7). 애플리케이션 공유 및 커뮤니티와 소통하기에 적합합니다.
* [트위터](https://twitter.com/dify_ai). 애플리케이션 공유 및 커뮤니티와 소통하기에 적합합니다.
## Star 히스토리
[](https://star-history.com/#langgenius/dify&Date)
## 보안 공개
개인정보 보호를 위해 보안 문제를 GitHub에 게시하지 마십시오. 대신 security@dify.ai로 질문을 보내주시면 더 자세한 답변을 드리겠습니다.
## 라이선스
이 저장소는 기본적으로 몇 가지 추가 제한 사항이 있는 Apache 2.0인 [Dify 오픈 소스 라이선스](LICENSE)에 따라 사용할 수 있습니다.
📌 <ahref="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">Introduzindo o Dify Workflow com Upload de Arquivo: Recrie o Podcast Google NotebookLM</a>
<ahref="./README_KR.md"><imgalt="README em Coreano"src="https://img.shields.io/badge/한국어-d9d9d9"></a>
<ahref="./README_AR.md"><imgalt="README em Árabe"src="https://img.shields.io/badge/العربية-d9d9d9"></a>
<ahref="./README_TR.md"><imgalt="README em Turco"src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
<ahref="./README_VI.md"><imgalt="README em Vietnamita"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
<ahref="./README_PT.md"><imgalt="README em Português - BR"src="https://img.shields.io/badge/Portugu%C3%AAs-BR?style=flat&label=BR&color=d9d9d9"></a>
</p>
Dify é uma plataforma de desenvolvimento de aplicativos LLM de código aberto. Sua interface intuitiva combina workflow de IA, pipeline RAG, capacidades de agente, gerenciamento de modelos, recursos de observabilidade e muito mais, permitindo que você vá rapidamente do protótipo à produção. Aqui está uma lista das principais funcionalidades:
</br></br>
**1. Workflow**:
Construa e teste workflows poderosos de IA em uma interface visual, aproveitando todos os recursos a seguir e muito mais.
Integração perfeita com centenas de LLMs proprietários e de código aberto de diversas provedoras e soluções auto-hospedadas, abrangendo GPT, Mistral, Llama3 e qualquer modelo compatível com a API da OpenAI. A lista completa de provedores suportados pode ser encontrada [aqui](https://docs.dify.ai/getting-started/readme/model-providers).
Interface intuitiva para criação de prompts, comparação de desempenho de modelos e adição de recursos como conversão de texto para fala em um aplicativo baseado em chat.
**4. Pipeline RAG**:
Extensas capacidades de RAG que cobrem desde a ingestão de documentos até a recuperação, com suporte nativo para extração de texto de PDFs, PPTs e outros formatos de documentos comuns.
**5. Capacidades de agente**:
Você pode definir agentes com base em LLM Function Calling ou ReAct e adicionar ferramentas pré-construídas ou personalizadas para o agente. O Dify oferece mais de 50 ferramentas integradas para agentes de IA, como Google Search, DALL·E, Stable Diffusion e WolframAlpha.
**6. LLMOps**:
Monitore e analise os registros e o desempenho do aplicativo ao longo do tempo. É possível melhorar continuamente prompts, conjuntos de dados e modelos com base nos dados de produção e anotações.
**7. Backend como Serviço**:
Todas os recursos do Dify vêm com APIs correspondentes, permitindo que você integre o Dify sem esforço na lógica de negócios da sua empresa.
## Comparação de recursos
<tablestyle="width: 100%;">
<tr>
<thalign="center">Recurso</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">OpenAI Assistants API</th>
</tr>
<tr>
<tdalign="center">Abordagem de Programação</td>
<tdalign="center">Orientada a API + Aplicativo</td>
<tdalign="center">Código Python</td>
<tdalign="center">Orientada a Aplicativo</td>
<tdalign="center">Orientada a API</td>
</tr>
<tr>
<tdalign="center">LLMs Suportados</td>
<tdalign="center">Variedade Rica</td>
<tdalign="center">Variedade Rica</td>
<tdalign="center">Variedade Rica</td>
<tdalign="center">Apenas OpenAI</td>
</tr>
<tr>
<tdalign="center">RAG Engine</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Agente</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Workflow</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Observabilidade</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Recursos Empresariais (SSO/Controle de Acesso)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Implantação Local</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## Usando o Dify
- **Nuvem </br>**
Oferecemos o serviço [Dify Cloud](https://dify.ai) para qualquer pessoa experimentar sem nenhuma configuração. Ele fornece todas as funcionalidades da versão auto-hospedada, incluindo 200 chamadas GPT-4 gratuitas no plano sandbox.
- **Auto-hospedagem do Dify Community Edition</br>**
Configure rapidamente o Dify no seu ambiente com este [guia inicial](#quick-start).
Use nossa [documentação](https://docs.dify.ai) para referências adicionais e instruções mais detalhadas.
- **Dify para empresas/organizações</br>**
Oferecemos recursos adicionais voltados para empresas. [Envie suas perguntas através deste chatbot](https://udify.app/chat/22L1zSxg6yW1cWQg) ou [envie-nos um e-mail](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) para discutir necessidades empresariais. </br>
> Para startups e pequenas empresas que utilizam AWS, confira o [Dify Premium no AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) e implemente no seu próprio AWS VPC com um clique. É uma oferta AMI acessível com a opção de criar aplicativos com logotipo e marca personalizados.
## Mantendo-se atualizado
Dê uma estrela no Dify no GitHub e seja notificado imediatamente sobre novos lançamentos.
> Antes de instalar o Dify, certifique-se de que sua máquina atenda aos seguintes requisitos mínimos de sistema:
>
>- CPU >= 2 Núcleos
>- RAM >= 4 GiB
</br>
A maneira mais fácil de iniciar o servidor Dify é executar nosso arquivo [docker-compose.yml](docker/docker-compose.yaml). Antes de rodar o comando de instalação, certifique-se de que o [Docker](https://docs.docker.com/get-docker/) e o [Docker Compose](https://docs.docker.com/compose/install/) estão instalados na sua máquina:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
Após a execução, você pode acessar o painel do Dify no navegador em [http://localhost/install](http://localhost/install) e iniciar o processo de inicialização.
> Se você deseja contribuir com o Dify ou fazer desenvolvimento adicional, consulte nosso [guia para implantar a partir do código fonte](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code).
## Próximos passos
Se precisar personalizar a configuração, consulte os comentários no nosso arquivo [.env.example](docker/.env.example) e atualize os valores correspondentes no seu arquivo `.env`. Além disso, talvez seja necessário fazer ajustes no próprio arquivo `docker-compose.yaml`, como alterar versões de imagem, mapeamentos de portas ou montagens de volumes, com base no seu ambiente de implantação específico e nas suas necessidades. Após fazer quaisquer alterações, execute novamente `docker-compose up -d`. Você pode encontrar a lista completa de variáveis de ambiente disponíveis [aqui](https://docs.dify.ai/getting-started/install-self-hosted/environments).
Se deseja configurar uma instalação de alta disponibilidade, há [Helm Charts](https://helm.sh/) e arquivos YAML contribuídos pela comunidade que permitem a implantação do Dify no Kubernetes.
- [Helm Chart de @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart de @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [Arquivo YAML de @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### Usando o Terraform para Implantação
Implante o Dify na Plataforma Cloud com um único clique usando [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform por @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform por @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### Usando AWS CDK para Implantação
Implante o Dify na AWS usando [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK por @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Contribuindo
Para aqueles que desejam contribuir com código, veja nosso [Guia de Contribuição](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
Ao mesmo tempo, considere apoiar o Dify compartilhando-o nas redes sociais e em eventos e conferências.
> Estamos buscando contribuidores para ajudar na tradução do Dify para idiomas além de Mandarim e Inglês. Se você tiver interesse em ajudar, consulte o [README i18n](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) para mais informações e deixe-nos um comentário no canal `global-users` em nosso [Servidor da Comunidade no Discord](https://discord.gg/8Tpq4AcN9c).
* [Discussões no GitHub](https://github.com/langgenius/dify/discussions). Melhor para: compartilhar feedback e fazer perguntas.
* [Problemas no GitHub](https://github.com/langgenius/dify/issues). Melhor para: relatar bugs encontrados no Dify.AI e propor novos recursos. Veja nosso [Guia de Contribuição](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Melhor para: compartilhar suas aplicações e interagir com a comunidade.
* [X(Twitter)](https://twitter.com/dify_ai). Melhor para: compartilhar suas aplicações e interagir com a comunidade.
## Histórico de estrelas
[](https://star-history.com/#langgenius/dify&Date)
## Divulgação de segurança
Para proteger sua privacidade, evite postar problemas de segurança no GitHub. Em vez disso, envie suas perguntas para security@dify.ai e forneceremos uma resposta mais detalhada.
## Licença
Este repositório está disponível sob a [Licença de Código Aberto Dify](LICENSE), que é essencialmente Apache 2.0 com algumas restrições adicionais.
Dify je odprtokodna platforma za razvoj aplikacij LLM. Njegov intuitivni vmesnik združuje agentski potek dela z umetno inteligenco, cevovod RAG, zmogljivosti agentov, upravljanje modelov, funkcije opazovanja in več, kar vam omogoča hiter prehod od prototipa do proizvodnje.
## Hitri začetek
> Preden namestite Dify, se prepričajte, da vaša naprava izpolnjuje naslednje minimalne sistemske zahteve:
>
>- CPU >= 2 Core
>- RAM >= 4 GiB
</br>
Najlažji način za zagon strežnika Dify je prek docker compose . Preden zaženete Dify z naslednjimi ukazi, se prepričajte, da sta Docker in Docker Compose nameščena na vašem računalniku:
```bash
cd dify
cd docker
cp .env.example .env
docker compose up -d
```
Po zagonu lahko dostopate do nadzorne plošče Dify v brskalniku na [http://localhost/install](http://localhost/install) in začnete postopek inicializacije.
#### Iskanje pomoči
Prosimo, glejte naša pogosta vprašanja [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) če naletite na težave pri nastavitvi Dify. Če imate še vedno težave, se obrnite na [skupnost ali nas](#community--contact).
> Če želite prispevati k Difyju ali narediti dodaten razvoj, glejte naš vodnik za [uvajanje iz izvorne kode](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
## Ključne značilnosti
**1. Potek dela**:
Zgradite in preizkusite zmogljive poteke dela AI na vizualnem platnu, pri čemer izkoristite vse naslednje funkcije in več.
Brezhibna integracija s stotinami lastniških/odprtokodnih LLM-jev ducatov ponudnikov sklepanja in samostojnih rešitev, ki pokrivajo GPT, Mistral, Llama3 in vse modele, združljive z API-jem OpenAI. Celoten seznam podprtih ponudnikov modelov najdete [tukaj](https://docs.dify.ai/getting-started/readme/model-providers).
intuitivni vmesnik za ustvarjanje pozivov, primerjavo zmogljivosti modela in dodajanje dodatnih funkcij, kot je pretvorba besedila v govor, aplikaciji, ki temelji na klepetu.
**4. RAG Pipeline**:
E Obsežne zmogljivosti RAG, ki pokrivajo vse od vnosa dokumenta do priklica, s podporo za ekstrakcijo besedila iz datotek PDF, PPT in drugih običajnih formatov dokumentov.
**5. Agent capabilities**:
definirate lahko agente, ki temeljijo na klicanju funkcij LLM ali ReAct, in dodate vnaprej izdelana orodja ali orodja po meri za agenta. Dify ponuja več kot 50 vgrajenih orodij za agente AI, kot so Google Search, DALL·E, Stable Diffusion in WolframAlpha.
**6. LLMOps**:
Spremljajte in analizirajte dnevnike aplikacij in učinkovitost skozi čas. Pozive, nabore podatkov in modele lahko nenehno izboljšujete na podlagi proizvodnih podatkov in opomb.
**7. Backend-as-a-Service**:
AVse ponudbe Difyja so opremljene z ustreznimi API-ji, tako da lahko Dify brez težav integrirate v svojo poslovno logiko.
## Uporaba Dify
- **Cloud </br>**
Gostimo storitev Dify Cloud za vsakogar, ki jo lahko preizkusite brez nastavitev. Zagotavlja vse zmožnosti različice za samostojno namestitev in vključuje 200 brezplačnih klicev GPT-4 v načrtu peskovnika.
- **Self-hosting Dify Community Edition</br>**
Hitro zaženite Dify v svojem okolju s tem [začetnim vodnikom](#quick-start) . Za dodatne reference in podrobnejša navodila uporabite našo [dokumentacijo](https://docs.dify.ai) .
- **Dify za podjetja/organizacije</br>**
Ponujamo dodatne funkcije, osredotočene na podjetja. Zabeležite svoja vprašanja prek tega klepetalnega robota ali nam pošljite e-pošto, da se pogovorimo o potrebah podjetja. </br>
> Za novoustanovljena podjetja in mala podjetja, ki uporabljajo AWS, si oglejte Dify Premium na AWS Marketplace in ga z enim klikom uvedite v svoj AWS VPC. To je cenovno ugodna ponudba AMI z možnostjo ustvarjanja aplikacij z logotipom in blagovno znamko po meri.
## Staying ahead
Star Dify on GitHub and be instantly notified of new releases.
Če morate prilagoditi konfiguracijo, si oglejte komentarje v naši datoteki .env.example in posodobite ustrezne vrednosti v svoji .env datoteki. Poleg tega boste morda morali prilagoditi docker-compose.yamlsamo datoteko, na primer spremeniti različice slike, preslikave vrat ali namestitve nosilca, glede na vaše specifično okolje in zahteve za uvajanje. Po kakršnih koli spremembah ponovno zaženite docker-compose up -d. Celoten seznam razpoložljivih spremenljivk okolja najdete tukaj .
Če želite konfigurirati visoko razpoložljivo nastavitev, so na voljo Helm Charts in datoteke YAML, ki jih prispeva skupnost, ki omogočajo uvedbo Difyja v Kubernetes.
- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### Uporaba Terraform za uvajanje
namestite Dify v Cloud Platform z enim klikom z uporabo [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### Uporaba AWS CDK za uvajanje
Uvedite Dify v AWS z uporabo [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Prispevam
Za tiste, ki bi radi prispevali kodo, si oglejte naš vodnik za prispevke . Hkrati vas prosimo, da podprete Dify tako, da ga delite na družbenih medijih ter na dogodkih in konferencah.
> Iščemo sodelavce za pomoč pri prevajanju Difyja v jezike, ki niso mandarinščina ali angleščina. Če želite pomagati, si oglejte i18n README za več informacij in nam pustite komentar v global-userskanalu našega strežnika skupnosti Discord .
* [GitHub Issues](https://github.com/langgenius/dify/issues). Najboljše za: hrošče, na katere naletite pri uporabi Dify.AI, in predloge funkcij. Oglejte si naš [vodnik za prispevke](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Najboljše za: deljenje vaših aplikacij in druženje s skupnostjo.
* [X(Twitter)](https://twitter.com/dify_ai). Najboljše za: deljenje vaših aplikacij in druženje s skupnostjo.
[](https://star-history.com/#langgenius/dify&Date)
## Varnostno razkritje
Zaradi zaščite vaše zasebnosti se izogibajte objavljanju varnostnih vprašanj na GitHub. Namesto tega pošljite vprašanja na security@dify.ai in zagotovili vam bomo podrobnejši odgovor.
## Licenca
To skladišče je na voljo pod [odprtokodno licenco Dify](LICENSE) , ki je v bistvu Apache 2.0 z nekaj dodatnimi omejitvami.
<imgalt="Geçen ay yapılan commitler"src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
</p>
Dify, açık kaynaklı bir LLM uygulama geliştirme platformudur. Sezgisel arayüzü, AI iş akışı, RAG pipeline'ı, ajan yetenekleri, model yönetimi, gözlemlenebilirlik özellikleri ve daha fazlasını birleştirerek, prototipten üretime hızlıca geçmenizi sağlar. İşte temel özelliklerin bir listesi:
</br></br>
**1. Workflow**:
Görsel bir arayüz üzerinde güçlü AI iş akışları oluşturun ve test edin, aşağıdaki tüm özellikleri ve daha fazlasını kullanarak.
Çok sayıda çıkarım sağlayıcısı ve kendi kendine barındırılan çözümlerden yüzlerce özel / açık kaynaklı LLM ile sorunsuz entegrasyon sağlar. GPT, Mistral, Llama3 ve OpenAI API uyumlu tüm modelleri kapsar. Desteklenen model sağlayıcılarının tam listesine [buradan](https://docs.dify.ai/getting-started/readme/model-providers) ulaşabilirsiniz.
Özür dilerim, haklısınız. Daha anlamlı ve akıcı bir çeviri yapmaya çalışayım. İşte güncellenmiş çeviri:
**3. Prompt IDE**:
Komut istemlerini oluşturmak, model performansını karşılaştırmak ve sohbet tabanlı uygulamalara metin-konuşma gibi ek özellikler eklemek için kullanıcı dostu bir arayüz.
**4. RAG Pipeline**:
Belge alımından bilgi çekmeye kadar geniş kapsamlı RAG yetenekleri. PDF'ler, PPT'ler ve diğer yaygın belge formatlarından metin çıkarma için hazır destek sunar.
**5. Ajan yetenekleri**:
LLM Fonksiyon Çağırma veya ReAct'a dayalı ajanlar tanımlayabilir ve bu ajanlara önceden hazırlanmış veya özel araçlar ekleyebilirsiniz. Dify, AI ajanları için Google Arama, DALL·E, Stable Diffusion ve WolframAlpha gibi 50'den fazla yerleşik araç sağlar.
**6. LLMOps**:
Uygulama loglarını ve performans metriklerini zaman içinde izleme ve analiz etme imkanı. Üretim ortamından elde edilen verilere ve kullanıcı geri bildirimlerine dayanarak, prompt'ları, veri setlerini ve modelleri sürekli olarak optimize edebilirsiniz. Bu sayede, AI uygulamanızın performansını ve doğruluğunu sürekli olarak artırabilirsiniz.
**7. Hizmet Olarak Backend**:
Dify'ın tüm özellikleri ilgili API'lerle birlikte gelir, böylece Dify'ı kendi iş mantığınıza kolayca entegre edebilirsiniz.
## Özellik karşılaştırması
<tablestyle="width: 100%;">
<tr>
<thalign="center">Özellik</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">OpenAI Assistants API</th>
</tr>
<tr>
<tdalign="center">Programlama Yaklaşımı</td>
<tdalign="center">API + Uygulama odaklı</td>
<tdalign="center">Python Kodu</td>
<tdalign="center">Uygulama odaklı</td>
<tdalign="center">API odaklı</td>
</tr>
<tr>
<tdalign="center">Desteklenen LLM'ler</td>
<tdalign="center">Zengin Çeşitlilik</td>
<tdalign="center">Zengin Çeşitlilik</td>
<tdalign="center">Zengin Çeşitlilik</td>
<tdalign="center">Yalnızca OpenAI</td>
</tr>
<tr>
<tdalign="center">RAG Motoru</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Ajan</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">İş Akışı</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Gözlemlenebilirlik</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Kurumsal Özellikler (SSO/Erişim kontrolü)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Yerel Dağıtım</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## Dify'ı Kullanma
- **Cloud </br>**
İşte verdiğiniz metnin Türkçe çevirisi, kod bloğu içinde:
-
Herkesin sıfır kurulumla denemesi için bir [Dify Cloud](https://dify.ai) hizmeti sunuyoruz. Bu hizmet, kendi kendine dağıtılan versiyonun tüm yeteneklerini sağlar ve sandbox planında 200 ücretsiz GPT-4 çağrısı içerir.
- **Dify Topluluk Sürümünü Kendi Sunucunuzda Barındırma</br>**
Bu [başlangıç kılavuzu](#quick-start) ile Dify'ı kendi ortamınızda hızlıca çalıştırın.
Daha fazla referans ve detaylı talimatlar için [dokümantasyonumuzu](https://docs.dify.ai) kullanın.
- **Kurumlar / organizasyonlar için Dify</br>**
Ek kurumsal odaklı özellikler sunuyoruz. Kurumsal ihtiyaçları görüşmek için [bize bir e-posta gönderin](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry). </br>
> AWS kullanan startuplar ve küçük işletmeler için, [AWS Marketplace'deki Dify Premium'a](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) göz atın ve tek tıklamayla kendi AWS VPC'nize dağıtın. Bu, özel logo ve marka ile uygulamalar oluşturma seçeneğine sahip uygun fiyatlı bir AMI teklifdir.
## Güncel Kalma
GitHub'da Dify'a yıldız verin ve yeni sürümlerden anında haberdar olun.
> Dify'ı kurmadan önce, makinenizin aşağıdaki minimum sistem gereksinimlerini karşıladığından emin olun:
>
>- CPU >= 2 Çekirdek
>- RAM >= 4GB
</br>
İşte verdiğiniz metnin Türkçe çevirisi, kod bloğu içinde:
Dify sunucusunu başlatmanın en kolay yolu, [docker-compose.yml](docker/docker-compose.yaml) dosyamızı çalıştırmaktır. Kurulum komutunu çalıştırmadan önce, makinenizde [Docker](https://docs.docker.com/get-docker/) ve [Docker Compose](https://docs.docker.com/compose/install/)'un kurulu olduğundan emin olun:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
Çalıştırdıktan sonra, tarayıcınızda [http://localhost/install](http://localhost/install) adresinden Dify kontrol paneline erişebilir ve başlangıç ayarları sürecini başlatabilirsiniz.
> Eğer Dify'a katkıda bulunmak veya ek geliştirmeler yapmak isterseniz, [kaynak koddan dağıtım kılavuzumuza](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code) başvurun.
## Sonraki adımlar
Yapılandırmayı özelleştirmeniz gerekiyorsa, lütfen [.env.example](docker/.env.example) dosyamızdaki yorumlara bakın ve `.env` dosyanızdaki ilgili değerleri güncelleyin. Ayrıca, spesifik dağıtım ortamınıza ve gereksinimlerinize bağlı olarak `docker-compose.yaml` dosyasının kendisinde de, imaj sürümlerini, port eşlemelerini veya hacim bağlantılarını değiştirmek gibi ayarlamalar yapmanız gerekebilir. Herhangi bir değişiklik yaptıktan sonra, lütfen `docker-compose up -d` komutunu tekrar çalıştırın. Kullanılabilir tüm ortam değişkenlerinin tam listesini [burada](https://docs.dify.ai/getting-started/install-self-hosted/environments) bulabilirsiniz.
Yüksek kullanılabilirliğe sahip bir kurulum yapılandırmak isterseniz, Dify'ın Kubernetes üzerine dağıtılmasına olanak tanıyan topluluk katkılı [Helm Charts](https://helm.sh/) ve YAML dosyaları mevcuttur.
- [@LeoQuote tarafından Helm Chart](https://github.com/douban/charts/tree/master/charts/dify)
- [@BorisPolonsky tarafından Helm Chart](https://github.com/BorisPolonsky/dify-helm)
- [@Winson-030 tarafından YAML dosyası](https://github.com/Winson-030/dify-kubernetes)
#### Dağıtım için Terraform Kullanımı
Dify'ı bulut platformuna tek tıklamayla dağıtın [terraform](https://www.terraform.io/) kullanarak
##### Azure Global
- [Azure Terraform tarafından @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform tarafından @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### AWS CDK ile Dağıtım
[CDK](https://aws.amazon.com/cdk/) kullanarak Dify'ı AWS'ye dağıtın
##### AWS
- [AWS CDK tarafından @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Katkıda Bulunma
Kod katkısında bulunmak isteyenler için [Katkı Kılavuzumuza](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) bakabilirsiniz.
Aynı zamanda, lütfen Dify'ı sosyal medyada, etkinliklerde ve konferanslarda paylaşarak desteklemeyi düşünün.
> Dify'ı Mandarin veya İngilizce dışındaki dillere çevirmemize yardımcı olacak katkıda bulunanlara ihtiyacımız var. Yardımcı olmakla ilgileniyorsanız, lütfen daha fazla bilgi için [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) dosyasına bakın ve [Discord Topluluk Sunucumuzdaki](https://discord.gg/8Tpq4AcN9c) `global-users` kanalında bize bir yorum bırakın.
* [Github Tartışmaları](https://github.com/langgenius/dify/discussions). En uygun: geri bildirim paylaşmak ve soru sormak için.
* [GitHub Sorunları](https://github.com/langgenius/dify/issues). En uygun: Dify.AI kullanırken karşılaştığınız hatalar ve özellik önerileri için. [Katkı Kılavuzumuza](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) bakın.
* [Discord](https://discord.gg/FngNHpbcY7). En uygun: uygulamalarınızı paylaşmak ve toplulukla vakit geçirmek için.
* [X(Twitter)](https://twitter.com/dify_ai). En uygun: uygulamalarınızı paylaşmak ve toplulukla vakit geçirmek için.
## Star history
[](https://star-history.com/#langgenius/dify&Date)
## Güvenlik açıklaması
Gizliliğinizi korumak için, lütfen güvenlik sorunlarını GitHub'da paylaşmaktan kaçının. Bunun yerine, sorularınızı security@dify.ai adresine gönderin ve size daha detaylı bir cevap vereceğiz.
## Lisans
Bu depo, temel olarak Apache 2.0 lisansı ve birkaç ek kısıtlama içeren [Dify Açık Kaynak Lisansı](LICENSE) altında kullanıma sunulmuştur.
<imgalt="Vấn đề đã đóng"src="https://img.shields.io/github/issues-search?query=repo%3Alanggenius%2Fdify%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a>
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
</p>
Dify là một nền tảng phát triển ứng dụng LLM mã nguồn mở. Giao diện trực quan kết hợp quy trình làm việc AI, mô hình RAG, khả năng tác nhân, quản lý mô hình, tính năng quan sát và hơn thế nữa, cho phép bạn nhanh chóng chuyển từ nguyên mẫu sang sản phẩm. Đây là danh sách các tính năng cốt lõi:
</br></br>
**1. Quy trình làm việc**:
Xây dựng và kiểm tra các quy trình làm việc AI mạnh mẽ trên một canvas trực quan, tận dụng tất cả các tính năng sau đây và hơn thế nữa.
Tích hợp liền mạch với hàng trăm mô hình LLM độc quyền / mã nguồn mở từ hàng chục nhà cung cấp suy luận và giải pháp tự lưu trữ, bao gồm GPT, Mistral, Llama3, và bất kỳ mô hình tương thích API OpenAI nào. Danh sách đầy đủ các nhà cung cấp mô hình được hỗ trợ có thể được tìm thấy [tại đây](https://docs.dify.ai/getting-started/readme/model-providers).
Giao diện trực quan để tạo prompt, so sánh hiệu suất mô hình và thêm các tính năng bổ sung như chuyển văn bản thành giọng nói cho một ứng dụng dựa trên trò chuyện.
**4. Mô hình RAG**:
Khả năng RAG mở rộng bao gồm mọi thứ từ nhập tài liệu đến truy xuất, với hỗ trợ sẵn có cho việc trích xuất văn bản từ PDF, PPT và các định dạng tài liệu phổ biến khác.
**5. Khả năng tác nhân**:
Bạn có thể định nghĩa các tác nhân dựa trên LLM Function Calling hoặc ReAct, và thêm các công cụ được xây dựng sẵn hoặc tùy chỉnh cho tác nhân. Dify cung cấp hơn 50 công cụ tích hợp sẵn cho các tác nhân AI, như Google Search, DALL·E, Stable Diffusion và WolframAlpha.
**6. LLMOps**:
Giám sát và phân tích nhật ký và hiệu suất ứng dụng theo thời gian. Bạn có thể liên tục cải thiện prompt, bộ dữ liệu và mô hình dựa trên dữ liệu sản xuất và chú thích.
**7. Backend-as-a-Service**:
Tất cả các dịch vụ của Dify đều đi kèm với các API tương ứng, vì vậy bạn có thể dễ dàng tích hợp Dify vào logic kinh doanh của riêng mình.
## So sánh tính năng
<tablestyle="width: 100%;">
<tr>
<thalign="center">Tính năng</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">OpenAI Assistants API</th>
</tr>
<tr>
<tdalign="center">Phương pháp lập trình</td>
<tdalign="center">Hướng API + Ứng dụng</td>
<tdalign="center">Mã Python</td>
<tdalign="center">Hướng ứng dụng</td>
<tdalign="center">Hướng API</td>
</tr>
<tr>
<tdalign="center">LLMs được hỗ trợ</td>
<tdalign="center">Đa dạng phong phú</td>
<tdalign="center">Đa dạng phong phú</td>
<tdalign="center">Đa dạng phong phú</td>
<tdalign="center">Chỉ OpenAI</td>
</tr>
<tr>
<tdalign="center">RAG Engine</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Agent</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
</tr>
<tr>
<tdalign="center">Quy trình làm việc</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Khả năng quan sát</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Tính năng doanh nghiệp (SSO/Kiểm soát truy cập)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Triển khai cục bộ</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## Sử dụng Dify
- **Cloud </br>**
Chúng tôi lưu trữ dịch vụ [Dify Cloud](https://dify.ai) cho bất kỳ ai muốn thử mà không cần cài đặt. Nó cung cấp tất cả các khả năng của phiên bản tự triển khai và bao gồm 200 lượt gọi GPT-4 miễn phí trong gói sandbox.
- **Tự triển khai Dify Community Edition</br>**
Nhanh chóng chạy Dify trong môi trường của bạn với [hướng dẫn bắt đầu](#quick-start) này.
Sử dụng [tài liệu](https://docs.dify.ai) của chúng tôi để tham khảo thêm và nhận hướng dẫn chi tiết hơn.
- **Dify cho doanh nghiệp / tổ chức</br>**
Chúng tôi cung cấp các tính năng bổ sung tập trung vào doanh nghiệp. [Ghi lại câu hỏi của bạn cho chúng tôi thông qua chatbot này](https://udify.app/chat/22L1zSxg6yW1cWQg) hoặc [gửi email cho chúng tôi](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) để thảo luận về nhu cầu doanh nghiệp. </br>
> Đối với các công ty khởi nghiệp và doanh nghiệp nhỏ sử dụng AWS, hãy xem [Dify Premium trên AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) và triển khai nó vào AWS VPC của riêng bạn chỉ với một cú nhấp chuột. Đây là một AMI giá cả phải chăng với tùy chọn tạo ứng dụng với logo và thương hiệu tùy chỉnh.
## Luôn cập nhật
Yêu thích Dify trên GitHub và được thông báo ngay lập tức về các bản phát hành mới.
> Trước khi cài đặt Dify, hãy đảm bảo máy của bạn đáp ứng các yêu cầu hệ thống tối thiểu sau:
>
>- CPU >= 2 Core
>- RAM >= 4GB
</br>
Cách dễ nhất để khởi động máy chủ Dify là chạy tệp [docker-compose.yml](docker/docker-compose.yaml) của chúng tôi. Trước khi chạy lệnh cài đặt, hãy đảm bảo rằng [Docker](https://docs.docker.com/get-docker/) và [Docker Compose](https://docs.docker.com/compose/install/) đã được cài đặt trên máy của bạn:
```bash
cd docker
cp .env.example .env
docker compose up -d
```
Sau khi chạy, bạn có thể truy cập bảng điều khiển Dify trong trình duyệt của bạn tại [http://localhost/install](http://localhost/install) và bắt đầu quá trình khởi tạo.
> Nếu bạn muốn đóng góp cho Dify hoặc phát triển thêm, hãy tham khảo [hướng dẫn triển khai từ mã nguồn](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code) của chúng tôi
## Các bước tiếp theo
Nếu bạn cần tùy chỉnh cấu hình, vui lòng tham khảo các nhận xét trong tệp [.env.example](docker/.env.example) của chúng tôi và cập nhật các giá trị tương ứng trong tệp `.env` của bạn. Ngoài ra, bạn có thể cần điều chỉnh tệp `docker-compose.yaml`, chẳng hạn như thay đổi phiên bản hình ảnh, ánh xạ cổng hoặc gắn kết khối lượng, dựa trên môi trường triển khai cụ thể và yêu cầu của bạn. Sau khi thực hiện bất kỳ thay đổi nào, vui lòng chạy lại `docker-compose up -d`. Bạn có thể tìm thấy danh sách đầy đủ các biến môi trường có sẵn [tại đây](https://docs.dify.ai/getting-started/install-self-hosted/environments).
Nếu bạn muốn cấu hình một cài đặt có độ sẵn sàng cao, có các [Helm Charts](https://helm.sh/) và tệp YAML do cộng đồng đóng góp cho phép Dify được triển khai trên Kubernetes.
- [Helm Chart bởi @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
- [Helm Chart bởi @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
- [Tệp YAML bởi @Winson-030](https://github.com/Winson-030/dify-kubernetes)
#### Sử dụng Terraform để Triển khai
Triển khai Dify lên nền tảng đám mây với một cú nhấp chuột bằng cách sử dụng [terraform](https://www.terraform.io/)
##### Azure Global
- [Azure Terraform bởi @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### Google Cloud
- [Google Cloud Terraform bởi @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### Sử dụng AWS CDK để Triển khai
Triển khai Dify trên AWS bằng [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK bởi @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Đóng góp
Đối với những người muốn đóng góp mã, xem [Hướng dẫn Đóng góp](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) của chúng tôi.
Đồng thời, vui lòng xem xét hỗ trợ Dify bằng cách chia sẻ nó trên mạng xã hội và tại các sự kiện và hội nghị.
> Chúng tôi đang tìm kiếm người đóng góp để giúp dịch Dify sang các ngôn ngữ khác ngoài tiếng Trung hoặc tiếng Anh. Nếu bạn quan tâm đến việc giúp đỡ, vui lòng xem [README i18n](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) để biết thêm thông tin và để lại bình luận cho chúng tôi trong kênh `global-users` của [Máy chủ Cộng đồng Discord](https://discord.gg/8Tpq4AcN9c) của chúng tôi.
* [Thảo luận GitHub](https://github.com/langgenius/dify/discussions). Tốt nhất cho: chia sẻ phản hồi và đặt câu hỏi.
* [Vấn đề GitHub](https://github.com/langgenius/dify/issues). Tốt nhất cho: lỗi bạn gặp phải khi sử dụng Dify.AI và đề xuất tính năng. Xem [Hướng dẫn Đóng góp](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) của chúng tôi.
* [Discord](https://discord.gg/FngNHpbcY7). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.
* [X(Twitter)](https://twitter.com/dify_ai). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.
## Lịch sử Yêu thích
[](https://star-history.com/#langgenius/dify&Date)
## Tiết lộ bảo mật
Để bảo vệ quyền riêng tư của bạn, vui lòng tránh đăng các vấn đề bảo mật trên GitHub. Thay vào đó, hãy gửi câu hỏi của bạn đến security@dify.ai và chúng tôi sẽ cung cấp cho bạn câu trả lời chi tiết hơn.
## Giấy phép
Kho lưu trữ này có sẵn theo [Giấy phép Mã nguồn Mở Dify](LICENSE), về cơ bản là Apache 2.0 với một vài hạn chế bổ sung.
# When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). Leading dots are optional.
# You can configure multiple ones, separated by commas. eg: test1@dify.ai,test2@dify.ai
QUEUE_MONITOR_ALERT_EMAILS=
# Monitor interval in minutes, default is 30 minutes
QUEUE_MONITOR_INTERVAL=30
# Swagger UI configuration
SWAGGER_UI_ENABLED=true
SWAGGER_UI_PATH=/swagger-ui.html
# Whether to encrypt dataset IDs when exporting DSL files (default: true)
# Set to false to export dataset IDs as plain text for easier cross-environment import
DSL_EXPORT_ENCRYPT_DATASET_ID=true
# Suggested Questions After Answer Configuration
# These environment variables allow customization of the suggested questions feature
#
# Custom prompt for generating suggested questions (optional)
# If not set, uses the default prompt that generates 3 questions under 20 characters each
# Example: "Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: [\"question1\",\"question2\",\"question3\",\"question4\",\"question5\"]"
# SUGGESTED_QUESTIONS_PROMPT=
# Maximum number of tokens for suggested questions generation (default: 256)
# Adjust this value for longer questions or more questions
# SUGGESTED_QUESTIONS_MAX_TOKENS=256
# Temperature for suggested questions generation (default: 0.0)
# Higher values (0.5-1.0) produce more creative questions, lower values (0.0-0.3) produce more focused questions
# SUGGESTED_QUESTIONS_TEMPERATURE=0
# Tenant isolated task queue configuration
TENANT_ISOLATED_TASK_CONCURRENCY=1
# Maximum number of segments for dataset segments API (0 for unlimited)
DATASET_MAX_SEGMENTS_PER_REQUEST=0
# Multimodal knowledgebase limit
SINGLE_CHUNK_ATTACHMENT_LIMIT=10
ATTACHMENT_IMAGE_FILE_SIZE_LIMIT=2
ATTACHMENT_IMAGE_DOWNLOAD_TIMEOUT=60
IMAGE_FILE_BATCH_LIMIT=10
# Maximum allowed CSV file size for annotation import in megabytes
ANNOTATION_IMPORT_FILE_SIZE_LIMIT=2
#Maximum number of annotation records allowed in a single import
ANNOTATION_IMPORT_MAX_RECORDS=10000
# Minimum number of annotation records required in a single import
ANNOTATION_IMPORT_MIN_RECORDS=1
ANNOTATION_IMPORT_RATE_LIMIT_PER_MINUTE=5
ANNOTATION_IMPORT_RATE_LIMIT_PER_HOUR=20
# Maximum number of concurrent annotation import tasks per tenant
Start with the section that best matches your need. Each entry lists the problems it solves plus key files/concepts so you know what to expect before opening it.
- You’re building or debugging a marketplace plugin.
- You need to know how manifests, providers, daemons, and migrations fit together.\
What it covers: plugin manifests (`core/plugin/entities/plugin.py`), installation/upgrade flows (`services/plugin/plugin_service.py`, CLI commands), runtime adapters (`core/plugin/impl/*` for tool/model/datasource/trigger/endpoint/agent), daemon coordination (`core/plugin/entities/plugin_daemon.py`), and how provider registries surface capabilities to the rest of the platform.
- All skill docs assume you follow the coding style guide—run Ruff/BasedPyright/tests listed there before submitting changes.
- When you cannot find an answer in these briefs, search the codebase using the paths referenced (e.g., `core/plugin/impl/tool.py`, `services/dataset_service.py`).
- If you run into cross-cutting concerns (tenancy, configuration, storage), check the infrastructure guide first; it links to most supporting modules.
- Keep multi-tenancy and configuration central: everything flows through `configs.dify_config` and `tenant_id`.
- When touching plugins or triggers, consult both the system overview and the specialised doc to ensure you adjust lifecycle, storage, and observability consistently.
> When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). The frontend and backend must be under the same top-level domain in order to share authentication cookies.
1. Generate a `SECRET_KEY` in the `.env` file.
bash for Linux
```bash for Linux
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
```
bash for Mac
```bash for Mac
secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env
```
4. Create environment.
1. Create environment.
Dify API service uses [Poetry](https://python-poetry.org/docs/) to manage dependencies. You can execute `poetry shell` to activate the environment.
5. Install dependencies
Dify API service uses [UV](https://docs.astral.sh/uv/) to manage dependencies.
First, you need to add the uv package manager, if you don't have it already.
```bash
poetry env use 3.12
poetry install
pip install uv
# Or on macOS
brew install uv
```
6. Run migrate
1. Install dependencies
```bash
uv sync --dev
```
1. Run migrate
Before the first launch, migrate the database to the latest version.
```bash
poetry run python -m flask db upgrade
uv run flask db upgrade
```
7. Start backend
1. Start backend
```bash
poetry run python -m flask run --host 0.0.0.0 --port=5001 --debug
uv run flask run --host 0.0.0.0 --port=5001 --debug
```
8. Start Dify [web](../web) service.
9. Setup your application by visiting `http://localhost:3000`...
10. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
1. Start Dify [web](../web) service.
```bash
poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion
```
1. Setup your application by visiting `http://localhost:3000`.
1. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
```bash
uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
```
Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:
```bash
uv run celery -A app.celery beat
```
## Testing
1. Install dependencies for both the backend and the test environment
```bash
poetry install -C api --with dev
uv sync --dev
```
2. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`
1. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`, more can check [Claude.md](../CLAUDE.md)
```bash
poetry run -C api bash dev/pytest/pytest_all_tests.sh
uv run pytest # Run all tests
uv run pytest tests/unit_tests/ # Unit tests only
uv run pytest tests/integration_tests/ # Integration tests
- Import `configs.dify_config` for every runtime toggle. Do not read environment variables directly.
- Add new settings to the proper mixin inside `configs/` (deployment, feature, middleware, etc.) so they load through `DifyConfig`.
- Remote overrides come from the optional providers in `configs/remote_settings_sources`; keep defaults in code safe when the value is missing.
- Example: logging pulls targets from `extensions/ext_logging.py`, and model provider URLs are assembled in `services/entities/model_provider_entities.py`.
## Dependencies
- Runtime dependencies live in `[project].dependencies` inside `pyproject.toml`. Optional clients go into the `storage`, `tools`, or `vdb` groups under `[dependency-groups]`.
- Always pin versions and keep the list alphabetised. Shared tooling (lint, typing, pytest) belongs in the `dev` group.
- When code needs a new package, explain why in the PR and run `uv lock` so the lockfile stays current.
## Storage & Files
- Use `extensions.ext_storage.storage` for all blob IO; it already respects the configured backend.
- Convert files for workflows with helpers in `core/file/file_manager.py`; they handle signed URLs and multimodal payloads.
- When writing controller logic, delegate upload quotas and metadata to `services/file_service.py` instead of touching storage directly.
- All outbound HTTP fetches (webhooks, remote files) must go through the SSRF-safe client in `core/helper/ssrf_proxy.py`; it wraps `httpx` with the allow/deny rules configured for the platform.
## Redis & Shared State
- Access Redis through `extensions.ext_redis.redis_client`. For locking, reuse `redis_client.lock`.
- Prefer higher-level helpers when available: rate limits use `libs.helper.RateLimiter`, provider metadata uses caches in `core/helper/provider_cache.py`.
## Models
- SQLAlchemy models sit in `models/` and inherit from the shared declarative `Base` defined in `models/base.py` (metadata configured via `models/engine.py`).
- `models/__init__.py` exposes grouped aggregates: account/tenant models, app and conversation tables, datasets, providers, workflow runs, triggers, etc. Import from there to avoid deep path churn.
- Follow the DDD boundary: persistence objects live in `models/`, repositories under `repositories/` translate them into domain entities, and services consume those repositories.
- When adding a table, create the model class, register it in `models/__init__.py`, wire a repository if needed, and generate an Alembic migration as described below.
## Vector Stores
- Vector client implementations live in `core/rag/datasource/vdb/<provider>`, with a common factory in `core/rag/datasource/vdb/vector_factory.py` and enums in `core/rag/datasource/vdb/vector_type.py`.
- Retrieval pipelines call these providers through `core/rag/datasource/retrieval_service.py` and dataset ingestion flows in `services/dataset_service.py`.
- The CLI helper `flask vdb-migrate` orchestrates bulk migrations using routines in `commands.py`; reuse that pattern when adding new backend transitions.
- To add another store, mirror the provider layout, register it with the factory, and include any schema changes in Alembic migrations.
## Observability & OTEL
- OpenTelemetry settings live under the observability mixin in `configs/observability`. Toggle exporters and sampling via `dify_config`, not ad-hoc env reads.
- HTTP, Celery, Redis, SQLAlchemy, and httpx instrumentation is initialised in `extensions/ext_app_metrics.py` and `extensions/ext_request_logging.py`; reuse these hooks when adding new workers or entrypoints.
- When creating background tasks or external calls, propagate tracing context with helpers in the existing instrumented clients (e.g. use the shared `httpx` session from `core/helper/http_client_pooling.py`).
- If you add a new external integration, ensure spans and metrics are emitted by wiring the appropriate OTEL instrumentation package in `pyproject.toml` and configuring it in `extensions/`.
## Ops Integrations
- Langfuse support and other tracing bridges live under `core/ops/opik_trace`. Config toggles sit in `configs/observability`, while exporters are initialised in the OTEL extensions mentioned above.
- External monitoring services should follow this pattern: keep client code in `core/ops`, expose switches via `dify_config`, and hook initialisation in `extensions/ext_app_metrics.py` or sibling modules.
- Before instrumenting new code paths, check whether existing context helpers (e.g. `extensions/ext_request_logging.py`) already capture the necessary metadata.
## Controllers, Services, Core
- Controllers only parse HTTP input and call a service method. Keep business rules in `services/`.
- Services enforce tenant rules, quotas, and orchestration, then call into `core/` engines (workflow execution, tools, LLMs).
- When adding a new endpoint, search for an existing service to extend before introducing a new layer. Example: workflow APIs pipe through `services/workflow_service.py` into `core/workflow`.
## Plugins, Tools, Providers
- In Dify a plugin is a tenant-installable bundle that declares one or more providers (tool, model, datasource, trigger, endpoint, agent strategy) plus its resource needs and version metadata. The manifest (`core/plugin/entities/plugin.py`) mirrors what you see in the marketplace documentation.
- Installation, upgrades, and migrations are orchestrated by `services/plugin/plugin_service.py` together with helpers such as `services/plugin/plugin_migration.py`.
- Runtime loading happens through the implementations under `core/plugin/impl/*` (tool/model/datasource/trigger/endpoint/agent). These modules normalise plugin providers so that downstream systems (`core/tools/tool_manager.py`, `services/model_provider_service.py`, `services/trigger/*`) can treat builtin and plugin capabilities the same way.
- For remote execution, plugin daemons (`core/plugin/entities/plugin_daemon.py`, `core/plugin/impl/plugin.py`) manage lifecycle hooks, credential forwarding, and background workers that keep plugin processes in sync with the main application.
- Acquire tool implementations through `core/tools/tool_manager.py`; it resolves builtin, plugin, and workflow-as-tool providers uniformly, injecting the right context (tenant, credentials, runtime config).
- To add a new plugin capability, extend the relevant `core/plugin/entities` schema and register the implementation in the matching `core/plugin/impl` module rather than importing the provider directly.
## Async Workloads
see `agent_skills/trigger.md` for more detailed documentation.
- Enqueue background work through `services/async_workflow_service.py`. It routes jobs to the tiered Celery queues defined in `tasks/`.
- Workers boot from `celery_entrypoint.py` and execute functions in `tasks/workflow_execution_tasks.py`, `tasks/trigger_processing_tasks.py`, etc.
- Scheduled workflows poll from `schedule/workflow_schedule_tasks.py`. Follow the same pattern if you need new periodic jobs.
## Database & Migrations
- SQLAlchemy models live under `models/` and map directly to migration files in `migrations/versions`.
- Generate migrations with `uv run --project api flask db revision --autogenerate -m "<summary>"`, then review the diff; never hand-edit the database outside Alembic.
- Apply migrations locally using `uv run --project api flask db upgrade`; production deploys expect the same history.
- If you add tenant-scoped data, confirm the upgrade includes tenant filters or defaults consistent with the service logic touching those tables.
## CLI Commands
- Maintenance commands from `commands.py` are registered on the Flask CLI. Run them via `uv run --project api flask <command>`.
- Use the built-in `db` commands from Flask-Migrate for schema operations (`flask db upgrade`, `flask db stamp`, etc.). Only fall back to custom helpers if you need their extra behaviour.
- Custom entries such as `flask reset-password`, `flask reset-email`, and `flask vdb-migrate` handle self-hosted account recovery and vector database migrations.
- Before adding a new command, check whether an existing service can be reused and ensure the command guards edition-specific behaviour (many enforce `SELF_HOSTED`). Document any additions in the PR.
- Ruff helpers are run directly with `uv`: `uv run --project api --dev ruff format ./api` for formatting and `uv run --project api --dev ruff check ./api` (add `--fix` if you want automatic fixes).
## When You Add Features
- Check for an existing helper or service before writing a new util.
- Uphold tenancy: every service method should receive the tenant ID from controller wrappers such as `controllers/console/wraps.py`.
- Update or create tests alongside behaviour changes (`tests/unit_tests` for fast coverage, `tests/integration_tests` when touching orchestrations).
- Run `uv run --project api --dev ruff check ./api`, `uv run --directory api --dev basedpyright`, and `uv run --project api --dev dev/pytest/pytest_unit_tests.sh` before submitting changes.
Trigger is a collection of nodes that we called `Start` nodes, also, the concept of `Start` is the same as `RootNode` in the workflow engine `core/workflow/graph_engine`, On the other hand, `Start` node is the entry point of workflows, every workflow run always starts from a `Start` node.
## Trigger nodes
- `UserInput`
- `Trigger Webhook`
- `Trigger Schedule`
- `Trigger Plugin`
### UserInput
Before `Trigger` concept is introduced, it's what we called `Start` node, but now, to avoid confusion, it was renamed to `UserInput` node, has a strong relation with `ServiceAPI` in `controllers/service_api/app`
1. `UserInput` node introduces a list of arguments that need to be provided by the user, finally it will be converted into variables in the workflow variable pool.
1. `ServiceAPI` accept those arguments, and pass through them into `UserInput` node.
1. For its detailed implementation, please refer to `core/workflow/nodes/start`
### Trigger Webhook
Inside Webhook Node, Dify provided a UI panel that allows user define a HTTP manifest `core/workflow/nodes/trigger_webhook/entities.py`.`WebhookData`, also, Dify generates a random webhook id for each `Trigger Webhook` node, the implementation was implemented in `core/trigger/utils/endpoint.py`, as you can see, `webhook-debug` is a debug mode for webhook, you may find it in `controllers/trigger/webhook.py`.
Finally, requests to `webhook` endpoint will be converted into variables in workflow variable pool during workflow execution.
### Trigger Schedule
`Trigger Schedule` node is a node that allows user define a schedule to trigger the workflow, detailed manifest is here `core/workflow/nodes/trigger_schedule/entities.py`, we have a poller and executor to handle millions of schedules, see `docker/entrypoint.sh` / `schedule/workflow_schedule_task.py` for help.
To Achieve this, a `WorkflowSchedulePlan` model was introduced in `models/trigger.py`, and a `events/event_handlers/sync_workflow_schedule_when_app_published.py` was used to sync workflow schedule plans when app is published.
### Trigger Plugin
`Trigger Plugin` node allows user define there own distributed trigger plugin, whenever a request was received, Dify forwards it to the plugin and wait for parsed variables from it.
1. Requests were saved in storage by `services/trigger/trigger_request_service.py`, referenced by `services/trigger/trigger_service.py`.`TriggerService`.`process_endpoint`
1. Plugins accept those requests and parse variables from it, see `core/plugin/impl/trigger.py` for details.
A `subscription` concept was out here by Dify, it means an endpoint address from Dify was bound to thirdparty webhook service like `Github``Slack``Linear``GoogleDrive``Gmail` etc. Once a subscription was created, Dify continually receives requests from the platforms and handle them one by one.
## Worker Pool / Async Task
All the events that triggered a new workflow run is always in async mode, a unified entrypoint can be found here `services/async_workflow_service.py`.`AsyncWorkflowService`.`trigger_workflow_async`.
The infrastructure we used is `celery`, we've already configured it in `docker/entrypoint.sh`, and the consumers are in `tasks/async_workflow_tasks.py`, 3 queues were used to handle different tiers of users, `PROFESSIONAL_QUEUE``TEAM_QUEUE``SANDBOX_QUEUE`.
## Debug Strategy
Dify divided users into 2 groups: builders / end users.
Builders are the users who create workflows, in this stage, debugging a workflow becomes a critical part of the workflow development process, as the start node in workflows, trigger nodes can `listen` to the events from `WebhookDebug``Schedule``Plugin`, debugging process was created in `controllers/console/app/workflow.py`.`DraftWorkflowTriggerNodeApi`.
A polling process can be considered as combine of few single `poll` operations, each `poll` operation fetches events cached in `Redis`, returns `None` if no event was found, more detailed implemented: `core/trigger/debug/event_bus.py` was used to handle the polling process, and `core/trigger/debug/event_selectors.py` was used to select the event poller based on the trigger type.