The original fix seems correct on its own. However, for chatflows with multiple answer nodes, the `message_replace` command only preserves the output of the last executed answer node.
- 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: "Trigger when the user requests a review of frontend files (e.g., `.tsx`, `.ts`, `.js`). Support both pending-change reviews and focused file reviews while applying the checklist rules."
---
# Frontend Code Review
## Intent
Use this skill whenever the user asks to review frontend code (especially `.tsx`, `.ts`, or `.js` files). Support two review modes:
1. **Pending-change review**– inspect staged/working-tree files slated for commit and flag checklist violations before submission.
2. **File-targeted review**– review the specific file(s) the user names and report the relevant checklist findings.
Stick to the checklist below for every applicable file and mode.
## Checklist
See [references/code-quality.md](references/code-quality.md), [references/performance.md](references/performance.md), [references/business-logic.md](references/business-logic.md) for the living checklist split by category—treat it as the canonical set of rules to follow.
Flag each rule violation with urgency metadata so future reviewers can prioritize fixes.
## Review Process
1. Open the relevant component/module. Gather lines that relate to class names, React Flow hooks, prop memoization, and styling.
2. For each rule in the review point, note where the code deviates and capture a representative snippet.
3. Compose the review section per the template below. Group violations first by **Urgent** flag, then by category order (Code Quality, Performance, Business Logic).
## Required output
When invoked, the response must exactly follow one of the two templates:
### Template A (any findings)
```
# Code review
Found <N> urgent issues need to be fixed:
## 1 <briefdescriptionofbug>
FilePath: <path> line <line>
<relevantcodesnippetorpointer>
### Suggested fix
<briefdescriptionofsuggestedfix>
---
... (repeat for each urgent issue) ...
Found <M> suggestions for improvement:
## 1 <briefdescriptionofsuggestion>
FilePath: <path> line <line>
<relevantcodesnippetorpointer>
### Suggested fix
<briefdescriptionofsuggestedfix>
---
... (repeat for each suggestion) ...
```
If there are no urgent issues, omit that section. If there are no suggestions, omit that section.
If the issue number is more than 10, summarize as "10+ urgent issues" or "10+ suggestions" and just output the first 10 issues.
Don't compress the blank lines between sections; keep them as-is for readability.
If you use Template A (i.e., there are issues to fix) and at least one issue requires code changes, append a brief follow-up question after the structured output asking whether the user wants you to apply the suggested fix(es). For example: "Would you like me to use the Suggested fix section to address these issues?"
File path pattern of node components: `web/app/components/workflow/nodes/[nodeName]/node.tsx`
Node components are also used when creating a RAG Pipe from a template, but in that context there is no workflowStore Provider, which results in a blank screen. [This Issue](https://github.com/langgenius/dify/issues/29168) was caused by exactly this reason.
### Suggested Fix
Use `import { useNodes } from 'reactflow'` instead of `import useNodes from '@/app/components/workflow/store/workflow/use-nodes'`.
Ensure conditional CSS is handled via the shared `classNames` instead of custom ternaries, string concatenation, or template strings. Centralizing class logic keeps components consistent and easier to maintain.
Favor Tailwind CSS utility classes instead of adding new `.module.css` files unless a Tailwind combination cannot achieve the required styling. Keeping styles in Tailwind improves consistency and reduces maintenance overhead.
Update this file when adding, editing, or removing Code Quality rules so the catalog remains accurate.
## Classname ordering for easy overrides
### Description
When writing components, always place the incoming `className` prop after the component’s own class values so that downstream consumers can override or extend the styling. This keeps your component’s defaults but still lets external callers change or remove specific styles.
When rendering React Flow, prefer `useNodes`/`useEdges` for UI consumption and rely on `useStoreApi` inside callbacks that mutate or read node/edge state. Avoid manually pulling Flow data outside of these hooks.
## Complex prop memoization
IsUrgent: True
Category: Performance
### Description
Wrap complex prop values (objects, arrays, maps) in `useMemo` prior to passing them into child components to guarantee stable references and prevent unnecessary renders.
Update this file when adding, editing, or removing Performance rules so the catalog remains accurate.
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.
description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
---
# Skill Creator
This skill provides guidance for creating effective skills.
## About Skills
Skills are modular, self-contained packages that extend Claude's capabilities by providing
specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific
domains or tasks—they transform Claude from a general-purpose agent into a specialized agent
equipped with procedural knowledge that no model can fully possess.
### What Skills Provide
1. Specialized workflows - Multi-step procedures for specific domains
2. Tool integrations - Instructions for working with specific file formats or APIs
3. Domain expertise - Company-specific knowledge, schemas, business logic
4. Bundled resources - Scripts, references, and assets for complex and repetitive tasks
## Core Principles
### Concise is Key
The context window is a public good. Skills share the context window with everything else Claude needs: system prompt, conversation history, other Skills' metadata, and the actual user request.
**Default assumption: Claude is already very smart.** Only add context Claude doesn't already have. Challenge each piece of information: "Does Claude really need this explanation?" and "Does this paragraph justify its token cost?"
Prefer concise examples over verbose explanations.
### Set Appropriate Degrees of Freedom
Match the level of specificity to the task's fragility and variability:
**High freedom (text-based instructions)**: Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
**Medium freedom (pseudocode or scripts with parameters)**: Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
**Low freedom (specific scripts, few parameters)**: Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.
Think of Claude as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).
### Anatomy of a Skill
Every skill consists of a required SKILL.md file and optional bundled resources:
```
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
```
#### SKILL.md (required)
Every SKILL.md consists of:
- **Frontmatter** (YAML): Contains `name` and `description` fields. These are the only fields that Claude reads to determine when the skill gets used, thus it is very important to be clear and comprehensive in describing what the skill is, and when it should be used.
- **Body** (Markdown): Instructions and guidance for using the skill. Only loaded AFTER the skill triggers (if at all).
#### Bundled Resources (optional)
##### Scripts (`scripts/`)
Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
- **When to include**: When the same code is being rewritten repeatedly or deterministic reliability is needed
- **Example**: `scripts/rotate_pdf.py` for PDF rotation tasks
- **Benefits**: Token efficient, deterministic, may be executed without loading into context
- **Note**: Scripts may still need to be read by Claude for patching or environment-specific adjustments
##### References (`references/`)
Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.
- **When to include**: For documentation that Claude should reference while working
- **Examples**: `references/finance.md` for financial schemas, `references/mnda.md` for company NDA template, `references/policies.md` for company policies, `references/api_docs.md` for API specifications
- **Use cases**: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
- **Benefits**: Keeps SKILL.md lean, loaded only when Claude determines it's needed
- **Best practice**: If files are large (>10k words), include grep search patterns in SKILL.md
- **Avoid duplication**: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.
##### Assets (`assets/`)
Files not intended to be loaded into context, but rather used within the output Claude produces.
- **When to include**: When the skill needs files that will be used in the final output
- **Examples**: `assets/logo.png` for brand assets, `assets/slides.pptx` for PowerPoint templates, `assets/frontend-template/` for HTML/React boilerplate, `assets/font.ttf` for typography
- **Use cases**: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
- **Benefits**: Separates output resources from documentation, enables Claude to use files without loading them into context
#### What to Not Include in a Skill
A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
- README.md
- INSTALLATION_GUIDE.md
- QUICK_REFERENCE.md
- CHANGELOG.md
- etc.
The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxilary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.
### Progressive Disclosure Design Principle
Skills use a three-level loading system to manage context efficiently:
2. **SKILL.md body** - When skill triggers (<5kwords)
3. **Bundled resources** - As needed by Claude (Unlimited because scripts can be executed without reading into context window)
#### Progressive Disclosure Patterns
Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the skill knows they exist and when to use them.
**Key principle:** When a skill supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details (patterns, examples, configuration) into separate reference files.
**Pattern 1: High-level guide with references**
```markdown
# PDF Processing
## Quick start
Extract text with pdfplumber:
[code example]
## Advanced features
- **Form filling**: See [FORMS.md](FORMS.md) for complete guide
- **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
- **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
```
Claude loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.
**Pattern 2: Domain-specific organization**
For Skills with multiple domains, organize content by domain to avoid loading irrelevant context:
```
bigquery-skill/
├── SKILL.md (overview and navigation)
└── reference/
├── finance.md (revenue, billing metrics)
├── sales.md (opportunities, pipeline)
├── product.md (API usage, features)
└── marketing.md (campaigns, attribution)
```
When a user asks about sales metrics, Claude only reads sales.md.
Similarly, for skills supporting multiple frameworks or variants, organize by variant:
```
cloud-deploy/
├── SKILL.md (workflow + provider selection)
└── references/
├── aws.md (AWS deployment patterns)
├── gcp.md (GCP deployment patterns)
└── azure.md (Azure deployment patterns)
```
When the user chooses AWS, Claude only reads aws.md.
**Pattern 3: Conditional details**
Show basic content, link to advanced content:
```markdown
# DOCX Processing
## Creating documents
Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).
## Editing documents
For simple edits, modify the XML directly.
**For tracked changes**: See [REDLINING.md](REDLINING.md)
**For OOXML details**: See [OOXML.md](OOXML.md)
```
Claude reads REDLINING.md or OOXML.md only when the user needs those features.
**Important guidelines:**
- **Avoid deeply nested references** - Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md.
- **Structure longer reference files** - For files longer than 100 lines, include a table of contents at the top so Claude can see the full scope when previewing.
## Skill Creation Process
Skill creation involves these steps:
1. Understand the skill with concrete examples
2. Plan reusable skill contents (scripts, references, assets)
3. Initialize the skill (run init_skill.py)
4. Edit the skill (implement resources and write SKILL.md)
5. Package the skill (run package_skill.py)
6. Iterate based on real usage
Follow these steps in order, skipping only if there is a clear reason why they are not applicable.
### Step 1: Understanding the Skill with Concrete Examples
Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.
To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
For example, when building an image-editor skill, relevant questions include:
- "What functionality should the image-editor skill support? Editing, rotating, anything else?"
- "Can you give some examples of how this skill would be used?"
- "I can imagine users asking for things like 'Remove the red-eye from this image' or 'Rotate this image'. Are there other ways you imagine this skill being used?"
- "What would a user say that should trigger this skill?"
To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
Conclude this step when there is a clear sense of the functionality the skill should support.
### Step 2: Planning the Reusable Skill Contents
To turn concrete examples into an effective skill, analyze each example by:
1. Considering how to execute on the example from scratch
2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly
Example: When building a `pdf-editor` skill to handle queries like "Help me rotate this PDF," the analysis shows:
1. Rotating a PDF requires re-writing the same code each time
2. A `scripts/rotate_pdf.py` script would be helpful to store in the skill
Example: When designing a `frontend-webapp-builder` skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
1. Writing a frontend webapp requires the same boilerplate HTML/React each time
2. An `assets/hello-world/` template containing the boilerplate HTML/React project files would be helpful to store in the skill
Example: When building a `big-query` skill to handle queries like "How many users have logged in today?" the analysis shows:
1. Querying BigQuery requires re-discovering the table schemas and relationships each time
2. A `references/schema.md` file documenting the table schemas would be helpful to store in the skill
To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
### Step 3: Initializing the Skill
At this point, it is time to actually create the skill.
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new skill from scratch, always run the `init_skill.py` script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
- Creates the skill directory at the specified path
- Generates a SKILL.md template with proper frontmatter and TODO placeholders
- Creates example resource directories: `scripts/`, `references/`, and `assets/`
- Adds example files in each directory that can be customized or deleted
After initialization, customize or remove the generated SKILL.md and example files as needed.
### Step 4: Edit the Skill
When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Claude to use. Include information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude instance execute these tasks more effectively.
#### Learn Proven Design Patterns
Consult these helpful guides based on your skill's needs:
- **Multi-step processes**: See references/workflows.md for sequential workflows and conditional logic
- **Specific output formats or quality standards**: See references/output-patterns.md for template and example patterns
These files contain established best practices for effective skill design.
#### Start with Reusable Skill Contents
To begin implementation, start with the reusable resources identified above: `scripts/`, `references/`, and `assets/` files. Note that this step may require user input. For example, when implementing a `brand-guidelines` skill, the user may need to provide brand assets or templates to store in `assets/`, or documentation to store in `references/`.
Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
Any example files and directories not needed for the skill should be deleted. The initialization script creates example files in `scripts/`, `references/`, and `assets/` to demonstrate structure, but most skills won't need all of them.
#### Update SKILL.md
**Writing Guidelines:** Always use imperative/infinitive form.
##### Frontmatter
Write the YAML frontmatter with `name` and `description`:
- `name`: The skill name
- `description`: This is the primary triggering mechanism for your skill, and helps Claude understand when to use the skill.
- Include both what the Skill does and specific triggers/contexts for when to use it.
- Include all "when to use" information here - Not in the body. The body is only loaded after triggering, so "When to Use This Skill" sections in the body are not helpful to Claude.
- Example description for a `docx` skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
Do not include any other fields in YAML frontmatter.
##### Body
Write instructions for using the skill and its bundled resources.
### Step 5: Packaging a Skill
Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:
1. **Validate** the skill automatically, checking:
- YAML frontmatter format and required fields
- Skill naming conventions and directory structure
- Description completeness and quality
- File organization and resource references
2. **Package** the skill if validation passes, creating a .skill file named after the skill (e.g., `my-skill.skill`) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
### Step 6: Iterate
After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
**Iteration workflow:**
1. Use the skill on real tasks
2. Notice struggles or inefficiencies
3. Identify how SKILL.md or bundled resources should be updated
For complex tasks, break operations into clear, sequential steps. It is often helpful to give Claude an overview of the process towards the beginning of SKILL.md:
```markdown
Filling a PDF form involves these steps:
1. Analyze the form (run analyze_form.py)
2. Create field mapping (edit fields.json)
3. Validate mapping (run validate_fields.py)
4. Fill the form (run fill_form.py)
5. Verify output (run verify_output.py)
```
## Conditional Workflows
For tasks with branching logic, guide Claude through decision points:
```markdown
1. Determine the modification type:
**Creating new content?** → Follow "Creation workflow" below
This project includes a devcontainer configuration that allows you to open the project in a container with a fully configured development environment.
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.
Both frontend and backend environments are initialized when the container is started.
## GitHub Codespaces
## GitHub Codespaces
[](https://codespaces.new/langgenius/dify)
[](https://codespaces.new/langgenius/dify)
you can simply click the button above to open this project in GitHub Codespaces.
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).
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
## VS Code Dev Containers
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langgenius/dify)
[](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.
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).
You can learn more in the [Dev Containers documentation](https://code.visualstudio.com/docs/devcontainers/containers).
## Pros of Devcontainer
## 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.
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.
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.
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
## Cons of Devcontainer
Learning Curve: For developers unfamiliar with Docker and VS Code, using devcontainers may be somewhat complex.
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.
Performance Impact: While usually minimal, programs running inside a devcontainer may be slightly slower than those running directly on the host.
## Troubleshooting
## Troubleshooting
if you see such error message when you open this project in codespaces:
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
Examples of behavior that contributes to a positive environment for our
community include:
community include:
* Demonstrating empathy and kindness toward other people
- Demonstrating empathy and kindness toward other people
* Being respectful of differing opinions, viewpoints, and experiences
- Being respectful of differing opinions, viewpoints, and experiences
* Giving and gracefully accepting constructive feedback
- Giving and gracefully accepting constructive feedback
* Accepting responsibility and apologizing to those affected by our mistakes,
- Accepting responsibility and apologizing to those affected by our mistakes,
and learning from the experience
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
overall community
Examples of unacceptable behavior include:
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
advances of any kind
* Trolling, insulting or derogatory comments, and personal or political attacks
- Trolling, insulting or derogatory comments, and personal or political attacks
* Public or private harassment
- Public or private harassment
* Publishing others' private information, such as a physical or email
- Publishing others' private information, such as a physical or email
address, without their explicit permission
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
professional setting
## Language Policy
## 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.
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 :)"
description:"To make sure we get to you in time, please check the following :)"
options:
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).
- 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
required:true
- label:I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
- label:I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
required:true
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
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
required:true
- label:"Please do not modify this template :) and fill in all the required fields."
- label:"Please do not modify this template :) and fill in all the required fields."
required:true
required:true
@ -42,20 +44,22 @@ body:
attributes:
attributes:
label:Steps to reproduce
label:Steps to reproduce
description:We highly suggest including screenshots and a bug report log. Please use the right markdown syntax for code blocks.
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:
validations:
required:true
required:true
- type:textarea
- type:textarea
attributes:
attributes:
label:✔️ Expected Behavior
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:
validations:
required:false
required:true
- type:textarea
- type:textarea
attributes:
attributes:
label:❌ Actual Behavior
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 :)"
description:"To make sure we get to you in time, please check the following :)"
options:
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.
- label:I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
required:true
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!:)"
required:true
required:true
- label:"Please do not modify this template :) and fill in all the required fields."
- 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]
<!-- 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. -->
> 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.
## Screenshots
# Screenshots
| Before | After |
| Before | After |
|--------|-------|
|--------|-------|
| ... | ... |
| ... | ... |
# Checklist
## Checklist
> [!IMPORTANT]
> Please review the checklist below before submitting your pull request.
- [ ] This change requires a documentation update, included: [Dify Document](https://github.com/langgenius/dify-docs)
- [ ] 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 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 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've updated the documentation accordingly.
- [x] I ran `dev/reformat`(backend) and `cd web && npx lint-staged`(frontend) to appease the lint gods
- [x] I ran `make lint` and `make type-check` (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.
| High-Priority Features as being labeled by a team member | High Priority |
| 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 |
| 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 |
| Non-core features and minor enhancements | Low Priority |
| Valuable but not immediate | Future-Feature |
| Valuable but not immediate | Future-Feature |
## Submitting your PR
## Submitting your PR
### Pull Request Process
### Pull Request Process
1. Fork the repository
1. Fork the repository
2. Before you draft a PR, please create an issue to discuss the changes you want to make
1. Before you draft a PR, please create an issue to discuss the changes you want to make
3. Create a new branch for your changes
1. Create a new branch for your changes
4. Please add tests for your changes accordingly
1. Please add tests for your changes accordingly
5. Ensure your code passes the existing tests
1. Ensure your code passes the existing tests
6. Please link the issue in the PR description, `fixes #<issue_number>`
1. Please link the issue in the PR description, `fixes #<issue_number>`
7. Get merrged!
1. Get merged!
### Setup the project
### Setup the project
#### Frontend
#### 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.
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
#### 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.
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.
@ -82,12 +86,14 @@ For setting up the backend service, kindly refer to our detailed [instructions](
#### Other things to note
#### Other things to note
We recommend reviewing this document carefully before proceeding with the setup, as it contains essential information about:
We recommend reviewing this document carefully before proceeding with the setup, as it contains essential information about:
- Prerequisites and dependencies
- Prerequisites and dependencies
- Installation steps
- Installation steps
- Configuration details
- Configuration details
- Common troubleshooting tips
- Common troubleshooting tips
Feel free to reach out if you encounter any issues during the setup process.
Feel free to reach out if you encounter any issues during the setup process.
## Getting Help
## 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.
Sie möchten also zu Dify beitragen - das ist großartig, wir können es kaum erwarten zu sehen, was Sie entwickeln. Als Startup mit begrenztem Personal und Finanzierung haben wir große Ambitionen, den intuitivsten Workflow für die Entwicklung und Verwaltung von LLM-Anwendungen zu gestalten. Jede Hilfe aus der Community zählt wirklich.
Wir müssen wendig sein und schnell liefern, aber wir möchten auch sicherstellen, dass Mitwirkende wie Sie eine möglichst reibungslose Erfahrung beim Beitragen haben. Wir haben diesen Leitfaden zusammengestellt, damit Sie sich schnell mit der Codebasis und unserer Arbeitsweise mit Mitwirkenden vertraut machen können.
Dieser Leitfaden ist, wie Dify selbst, in ständiger Entwicklung. Wir sind dankbar für Ihr Verständnis, falls er manchmal hinter dem eigentlichen Projekt zurückbleibt, und begrüßen jedes Feedback zur Verbesserung.
Bitte nehmen Sie sich einen Moment Zeit, um unsere [Lizenz- und Mitwirkungsvereinbarung](./LICENSE) zu lesen. Die Community hält sich außerdem an den [Verhaltenskodex](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md).
## Bevor Sie loslegen
Suchen Sie nach einer Aufgabe? Durchstöbern Sie unsere [Einsteiger-Issues](https://github.com/langgenius/dify/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22) und wählen Sie eines zum Einstieg!
Haben Sie eine neue Modell-Runtime oder ein Tool hinzuzufügen? Öffnen Sie einen PR in unserem [Plugin-Repository](https://github.com/langgenius/dify-plugins).
Möchten Sie eine bestehende Modell-Runtime oder ein Tool aktualisieren oder Bugs beheben? Besuchen Sie unser [offizielles Plugin-Repository](https://github.com/langgenius/dify-official-plugins)!
Vergessen Sie nicht, in der PR-Beschreibung ein bestehendes Issue zu verlinken oder ein neues zu erstellen.
### Fehlermeldungen
> [!WICHTIG]
> Bitte stellen Sie sicher, dass Sie folgende Informationen bei der Einreichung eines Fehlerberichts angeben:
- Ein klarer und beschreibender Titel
- Eine detaillierte Beschreibung des Fehlers, einschließlich Fehlermeldungen
- Schritte zur Reproduktion des Fehlers
- Erwartetes Verhalten
- **Logs** bei Backend-Problemen (sehr wichtig, zu finden in docker-compose logs)
| Hochprioritäre Features (durch Teammitglied gekennzeichnet) | Hohe Priorität |
| Beliebte Feature-Anfragen aus unserem [Community-Feedback-Board](https://github.com/langgenius/dify/discussions/categories/feedbacks) | Mittlere Priorität |
| Nicht-Kernfunktionen und kleinere Verbesserungen | Niedrige Priorität |
| Wertvoll, aber nicht dringend | Zukunfts-Feature |
## Einreichen Ihres PRs
### Pull-Request-Prozess
1. Repository forken
2. Vor dem Erstellen eines PRs bitte ein Issue zur Diskussion der Änderungen erstellen
3. Einen neuen Branch für Ihre Änderungen erstellen
4. Tests für Ihre Änderungen hinzufügen
5. Sicherstellen, dass Ihr Code die bestehenden Tests besteht
6. Issue in der PR-Beschreibung verlinken (`fixes #<issue_number>`)
7. Auf den Merge warten!
### Projekt einrichten
#### Frontend
Für die Einrichtung des Frontend-Service folgen Sie bitte unserer ausführlichen [Anleitung](https://github.com/langgenius/dify/blob/main/web/README.md) in der Datei `web/README.md`.
#### Backend
Für die Einrichtung des Backend-Service folgen Sie bitte unseren detaillierten [Anweisungen](https://github.com/langgenius/dify/blob/main/api/README.md) in der Datei `api/README.md`.
#### Weitere Hinweise
Wir empfehlen, dieses Dokument sorgfältig zu lesen, da es wichtige Informationen enthält über:
- Voraussetzungen und Abhängigkeiten
- Installationsschritte
- Konfigurationsdetails
- Häufige Problemlösungen
Bei Problemen während der Einrichtung können Sie sich gerne an uns wenden.
## Hilfe bekommen
Wenn Sie beim Mitwirken Fragen haben oder nicht weiterkommen, stellen Sie Ihre Fragen einfach im entsprechenden GitHub Issue oder besuchen Sie unseren [Discord](https://discord.gg/8Tpq4AcN9c) für einen schnellen Austausch.
Bạn đang muốn đóng góp cho Dify - thật tuyệt vời, chúng tôi rất mong 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 trong việc 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 có ý nghĩa.
Chúng tôi cần phải nhanh nhẹn và triển khai nhanh chóng, nhưng cũng muốn đảm bảo những người đóng góp như bạn có trải nghiệm đóng góp thuận lợi nhất có thể. Chúng tôi đã tạo hướng dẫn đóng góp này nhằm giúp bạn làm quen với codebase và cách chúng tôi làm việc với người đóng góp, để bạn có thể nhanh chóng bắt đầu phần thú vị.
Hướng dẫn này, giống như Dify, đang được phát triển liên tục. Chúng tôi rất cảm kích sự thông cảm của bạn nếu đôi khi nó chưa theo kịp dự án thực tế, và hoan nghênh mọi phản hồi để cải thiện.
Về giấy phép, vui lòng dành chút thời gian đọc [Thỏa thuận Cấp phép và Người đóng góp](./LICENSE) ngắn gọn của chúng tôi. Cộng đồng cũng tuân theo [quy tắc ứng xử](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md).
## Trước khi bắt đầu
Đang tìm việc để thực hiện? Hãy xem qua [các issue dành cho người mới](https://github.com/langgenius/dify/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22) và chọn một để bắt đầu!
Bạn có một model runtime hoặc công cụ mới thú vị để thêm vào? Mở PR trong [repo plugin](https://github.com/langgenius/dify-plugins) của chúng tôi và cho chúng tôi thấy những gì bạn đã xây dựng.
Cần cập nhật model runtime, công cụ hiện có hoặc sửa lỗi? Ghé thăm [repo plugin chính thức](https://github.com/langgenius/dify-official-plugins) và thực hiện phép màu của bạn!
Hãy tham gia, đóng góp và cùng nhau xây dựng điều tuyệt vời! 💡✨
Đừng quên liên kết đến issue hiện có hoặc mở issue mới trong mô tả PR.
### Báo cáo lỗi
> [!QUAN TRỌNG]
> Vui lòng đảm bảo cung cấp các thông tin sau khi gửi báo cáo lỗi:
- Tiêu đề rõ ràng và mô tả
- Mô tả chi tiết về lỗi, bao gồm các thông báo lỗi
- Các bước để tái hiện lỗi
- Hành vi mong đợi
- **Log**, nếu có, cho các vấn đề backend, điều này rất quan trọng, bạn có thể tìm thấy chúng trong docker-compose logs
- Ảnh chụp màn hình hoặc video, nếu có thể
Cách chúng tôi ưu tiên:
| Loại vấn đề | Mức độ ưu tiên |
| ----------- | -------------- |
| Lỗi trong các chức năng cốt lõi (dịch vụ đám mây, không thể đăng nhập, ứng dụng không hoạt động, lỗ hổng bảo mật) | Quan trọng |
| Lỗi không nghiêm trọng, cải thiện hiệu suất | Ưu tiên trung bình |
| Sửa lỗi nhỏ (lỗi chính tả, UI gây nhầm lẫn nhưng vẫn hoạt động) | Ưu tiên thấp |
### Yêu cầu tính năng
> [!LƯU Ý]
> Vui lòng đảm bảo cung cấp các thông tin sau khi gửi yêu cầu tính năng:
- Tiêu đề rõ ràng và mô tả
- Mô tả chi tiết về tính năng
- Trường hợp sử dụng cho tính năng
- Bất kỳ ngữ cảnh hoặc ảnh chụp màn hình nào về yêu cầu tính năng
Cách chúng tôi ưu tiên:
| Loại tính năng | Mức độ ưu tiên |
| -------------- | -------------- |
| Tính năng ưu tiên cao được gắn nhãn bởi thành viên nhóm | Ưu tiên cao |
| Yêu cầu tính năng phổ biến từ [bảng phản hồi cộng đồng](https://github.com/langgenius/dify/discussions/categories/feedbacks) | Ưu tiên trung bình |
| Tính năng không cốt lõi và cải tiến nhỏ | Ưu tiên thấp |
| Có giá trị nhưng không cấp bách | Tính năng tương lai |
## Gửi PR của bạn
### Quy trình tạo Pull Request
1. Fork repository
2. Trước khi soạn PR, vui lòng tạo issue để thảo luận về các thay đổi bạn muốn thực hiện
3. Tạo nhánh mới cho các thay đổi của bạn
4. Vui lòng thêm test cho các thay đổi tương ứng
5. Đảm bảo code của bạn vượt qua các test hiện có
6. Vui lòng liên kết issue trong mô tả PR, `fixes #<số_issue>`
7. Được merge!
### Thiết lập dự án
#### Frontend
Để thiết lập dịch vụ frontend, vui lòng tham khảo [hướng dẫn](https://github.com/langgenius/dify/blob/main/web/README.md) chi tiết của chúng tôi trong file `web/README.md`. Tài liệu này cung cấp hướng dẫn chi tiết để giúp bạn thiết lập môi trường frontend một cách đúng đắn.
#### Backend
Để thiết lập dịch vụ backend, vui lòng tham khảo [hướng dẫn](https://github.com/langgenius/dify/blob/main/api/README.md) chi tiết của chúng tôi trong file `api/README.md`. Tài liệu này chứa hướng dẫn từng bước để giúp bạn khởi chạy backend một cách suôn sẻ.
#### Các điểm cần lưu ý khác
Chúng tôi khuyến nghị xem xét kỹ tài liệu này trước khi tiến hành thiết lập, vì nó chứa thông tin thiết yếu về:
- Điều kiện tiên quyết và dependencies
- Các bước cài đặt
- Chi tiết cấu hình
- Các mẹo xử lý sự cố phổ biến
Đừng ngần ngại liên hệ nếu bạn gặp bất kỳ vấn đề nào trong quá trình thiết lập.
## Nhận trợ giúp
Nếu bạn bị mắc kẹt hoặc có câu hỏi cấp bách trong quá trình đóng góp, chỉ cần gửi câu hỏi của bạn thông qua issue GitHub liên quan, hoặc tham gia [Discord](https://discord.gg/8Tpq4AcN9c) của chúng tôi để trò chuyện nhanh.
@ -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.
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.
- 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:
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.
a. The producer can adjust the open-source agreement to be more strict or relaxed as deemed necessary.
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
<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="./README_DE.md"><imgalt="README in Deutsch"src="https://img.shields.io/badge/German-d9d9d9"></a>
<ahref="./docs/de-DE/README.md"><imgalt="README in Deutsch"src="https://img.shields.io/badge/German-d9d9d9"></a>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
<ahref="./docs/bn-BD/README.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
</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
## Quick start
@ -63,9 +69,9 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
> - CPU >= 2 Core
> - CPU >= 2 Core
> - RAM >= 4 GiB
> - 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
```bash
cd dify
cd dify
@ -87,8 +93,6 @@ Please refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-host
**1. Workflow**:
**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.
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).
@ -109,86 +113,19 @@ Monitor and analyze application logs and performance over time. You could contin
**7. Backend-as-a-Service**:
**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.
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.
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>**
- **Self-hosting Dify Community Edition<br/>**
Quickly get Dify running in your environment with this [starter guide](#quick-start).
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.
Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
- **Dify for enterprise / organizations</br>**
- **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>
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.
> 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
## Staying ahead
@ -198,8 +135,31 @@ Star Dify on GitHub and be instantly notified of new releases.
## Advanced Setup
## Advanced Setup
### Custom configurations
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 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.
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 @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
@ -207,6 +167,7 @@ If you'd like to configure a highly-available setup, there are community-contrib
- [Helm Chart by @magicsong](https://github.com/magicsong/ai-charts)
- [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 @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
- [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
#### Using Terraform for Deployment
@ -226,18 +187,31 @@ Deploy Dify to AWS with [CDK](https://aws.amazon.com/cdk/)
##### AWS
##### 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
## Contributing
For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
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.
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
## Community & contact
- [Github Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
- [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).
- [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.
- [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.
- [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
@ -254,8 +228,8 @@ At the same time, please consider supporting Dify by sharing it on social media
## Security disclosure
## 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
## 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>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-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)
- [رسم بياني Helm من قبل @magicsong](https://github.com/magicsong/ai-charts)
- [ملف YAML من قبل @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [ملف YAML من قبل @wyy-holding](https://github.com/wyy-holding/dify-k8s)
#### 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.
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
<ahref="./README_DE.md"><imgalt="README in Deutsch"src="https://img.shields.io/badge/German-d9d9d9"></a>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
ডিফাই একটি ওপেন-সোর্স LLM অ্যাপ ডেভেলপমেন্ট প্ল্যাটফর্ম। এটি ইন্টুইটিভ ইন্টারফেস, এজেন্টিক AI ওয়ার্কফ্লো, RAG পাইপলাইন, এজেন্ট ক্যাপাবিলিটি, মডেল ম্যানেজমেন্ট, মনিটরিং সুবিধা এবং আরও অনেক কিছু একত্রিত করে, যা দ্রুত প্রোটোটাইপ থেকে প্রোডাকশন পর্যন্ত নিয়ে যেতে সহায়তা করে।
## কুইক স্টার্ট
>
> ডিফাই ইনস্টল করার আগে, নিশ্চিত করুন যে আপনার মেশিন নিম্নলিখিত ন্যূনতম কনফিগারেশনের প্রয়োজনীয়তা পূরন করে :
>
>- সিপিউ >= 2 কোর
>- র্যাম >= 4 জিবি
</br>
ডিফাই সার্ভার চালু করার সবচেয়ে সহজ উপায় [docker compose](docker/docker-compose.yaml) মাধ্যমে। নিম্নলিখিত কমান্ডগুলো ব্যবহার করে ডিফাই চালানোর আগে, নিশ্চিত করুন যে আপনার মেশিনে [Docker](https://docs.docker.com/get-docker/) এবং [Docker Compose](https://docs.docker.com/compose/install/) ইনস্টল করা আছে :
```bash
cd dify
cd docker
cp .env.example .env
docker compose up -d
```
চালানোর পর, আপনি আপনার ব্রাউজারে [http://localhost/install](http://localhost/install)-এ ডিফাই ড্যাশবোর্ডে অ্যাক্সেস করতে পারেন এবং ইনিশিয়ালাইজেশন প্রক্রিয়া শুরু করতে পারেন।
#### সাহায্যের খোঁজে
ডিফাই সেট আপ করতে সমস্যা হলে দয়া করে আমাদের [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) দেখুন। যদি তবুও সমস্যা থেকে থাকে, তাহলে [কমিউনিটি এবং আমাদের](#community--contact) সাথে যোগাযোগ করুন।
> যদি আপনি ডিফাইতে অবদান রাখতে বা অতিরিক্ত উন্নয়ন করতে চান, আমাদের [সোর্স কোড থেকে ডিপ্লয়মেন্টের গাইড](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code) দেখুন।
## প্রধান ফিচারসমূহ
**১. ওয়ার্কফ্লো**:
ভিজ্যুয়াল ক্যানভাসে AI ওয়ার্কফ্লো তৈরি এবং পরীক্ষা করুন, নিম্নলিখিত সব ফিচার এবং তার বাইরেও আরও অনেক কিছু ব্যবহার করে।
GPT, Mistral, Llama3, এবং যেকোনো OpenAI API-সামঞ্জস্যপূর্ণ মডেলসহ, কয়েক ডজন ইনফারেন্স প্রদানকারী এবং সেল্ফ-হোস্টেড সমাধান থেকে শুরু করে প্রোপ্রাইটরি/ওপেন-সোর্স LLM-এর সাথে সহজে ইন্টিগ্রেশন। সমর্থিত মডেল প্রদানকারীদের একটি সম্পূর্ণ তালিকা পাওয়া যাবে [এখানে](https://docs.dify.ai/getting-started/readme/model-providers)।
প্রম্পট তৈরি, মডেলের পারফরম্যান্স তুলনা এবং চ্যাট-বেজড অ্যাপে টেক্সট-টু-স্পিচের মতো বৈশিষ্ট্য যুক্ত করার জন্য ইন্টুইটিভ ইন্টারফেস।
**4. RAG পাইপলাইন**:
ডকুমেন্ট ইনজেশন থেকে শুরু করে রিট্রিভ পর্যন্ত সবকিছুই বিস্তৃত RAG ক্যাপাবিলিটির আওতাভুক্ত। PDF, PPT এবং অন্যান্য সাধারণ ডকুমেন্ট ফর্ম্যাট থেকে টেক্সট এক্সট্রাকশনের জন্য আউট-অফ-বক্স সাপোর্ট।
**5. এজেন্ট ক্যাপাবিলিটি**:
LLM ফাংশন কলিং বা ReAct উপর ভিত্তি করে এজেন্ট ডিফাইন করতে পারেন এবং এজেন্টের জন্য পূর্ব-নির্মিত বা কাস্টম টুলস যুক্ত করতে পারেন। Dify AI এজেন্টদের জন্য 50+ বিল্ট-ইন টুলস সরবরাহ করে, যেমন Google Search, DALL·E, Stable Diffusion এবং WolframAlpha।
**6. এলএলএম-অপ্স**:
সময়ের সাথে সাথে অ্যাপ্লিকেশন লগ এবং পারফরম্যান্স মনিটর এবং বিশ্লেষণ করুন। প্রডাকশন ডেটা এবং annotation এর উপর ভিত্তি করে প্রম্পট, ডেটাসেট এবং মডেলগুলিকে ক্রমাগত উন্নত করতে পারেন।
**7. ব্যাকএন্ড-অ্যাজ-এ-সার্ভিস**:
ডিফাই-এর সমস্ত অফার সংশ্লিষ্ট API-সহ আছে, যাতে আপনি অনায়াসে ডিফাইকে আপনার নিজস্ব বিজনেস লজিকে ইন্টেগ্রেট করতে পারেন।
জিরো সেটাপে ব্যবহার করতে আমাদের [Dify Cloud](https://dify.ai) সার্ভিসটি ব্যবহার করতে পারেন। এখানে সেল্ফহোস্টিং-এর সকল ফিচার ও ক্যাপাবিলিটিসহ স্যান্ডবক্সে ২০০ জিপিটি-৪ কল ফ্রি পাবেন।
- **সেল্ফহোস্টিং ডিফাই কমিউনিটি সংস্করণ</br>**
সেল্ফহোস্ট করতে এই [স্টার্টার গাইড](#quick-start) ব্যবহার করে দ্রুত আপনার এনভায়রনমেন্টে ডিফাই চালান।
আরো ইন-ডেপথ রেফারেন্সের জন্য [ডকুমেন্টেশন](https://docs.dify.ai) দেখেন।
- **এন্টারপ্রাইজ / প্রতিষ্ঠানের জন্য Dify</br>**
আমরা এন্টারপ্রাইজ/প্রতিষ্ঠান-কেন্দ্রিক সেবা প্রদান করে থাকি । [এই চ্যাটবটের মাধ্যমে আপনার প্রশ্নগুলি আমাদের জন্য লগ করুন।](https://udify.app/chat/22L1zSxg6yW1cWQg) অথবা [আমাদের ইমেল পাঠান](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) আপনার চাহিদা সম্পর্কে আলোচনা করার জন্য। </br>
> AWS ব্যবহারকারী স্টার্টআপ এবং ছোট ব্যবসার জন্য, [AWS মার্কেটপ্লেসে Dify Premium](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) দেখুন এবং এক-ক্লিকের মাধ্যমে এটি আপনার নিজস্ব AWS VPC-তে ডিপ্লয় করুন। এটি একটি সাশ্রয়ী মূল্যের AMI অফার, যাতে কাস্টম লোগো এবং ব্র্যান্ডিং সহ অ্যাপ তৈরির সুবিধা আছে।
## এগিয়ে থাকুন
GitHub-এ ডিফাইকে স্টার দিয়ে রাখুন এবং নতুন রিলিজের খবর তাৎক্ষণিকভাবে পান।
যদি আপনার কনফিগারেশনটি কাস্টমাইজ করার প্রয়োজন হয়, তাহলে অনুগ্রহ করে আমাদের [.env.example](docker/.env.example) ফাইল দেখুন এবং আপনার `.env` ফাইলে সংশ্লিষ্ট মানগুলি আপডেট করুন। এছাড়াও, আপনার নির্দিষ্ট এনভায়রনমেন্ট এবং প্রয়োজনীয়তার উপর ভিত্তি করে আপনাকে `docker-compose.yaml` ফাইলে সমন্বয় করতে হতে পারে, যেমন ইমেজ ভার্সন পরিবর্তন করা, পোর্ট ম্যাপিং করা, অথবা ভলিউম মাউন্ট করা।
যেকোনো পরিবর্তন করার পর, অনুগ্রহ করে `docker-compose up -d` পুনরায় চালান। ভেরিয়েবলের সম্পূর্ণ তালিকা [এখানে] (https://docs.dify.ai/getting-started/install-self-hosted/environments) খুঁজে পেতে পারেন।
যদি আপনি একটি হাইলি এভেইলেবল সেটআপ কনফিগার করতে চান, তাহলে কমিউনিটি [Helm Charts](https://helm.sh/) এবং YAML ফাইল রয়েছে যা Dify কে Kubernetes-এ ডিপ্লয় করার প্রক্রিয়া বর্ণনা করে।
- [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)
#### টেরাফর্ম ব্যবহার করে ডিপ্লয়
[terraform](https://www.terraform.io/) ব্যবহার করে এক ক্লিকেই ক্লাউড প্ল্যাটফর্মে Dify ডিপ্লয় করুন।
##### অ্যাজুর গ্লোবাল
- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
##### গুগল ক্লাউড
- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
#### AWS CDK ব্যবহার করে ডিপ্লয়
[CDK](https://aws.amazon.com/cdk/) দিয়ে AWS-এ Dify ডিপ্লয় করুন
##### AWS
- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Contributing
যারা কোড অবদান রাখতে চান, তাদের জন্য আমাদের [অবদান নির্দেশিকা] দেখুন (https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)।
একই সাথে, সোশ্যাল মিডিয়া এবং ইভেন্ট এবং কনফারেন্সে এটি শেয়ার করে Dify কে সমর্থন করুন।
> আমরা ম্যান্ডারিন বা ইংরেজি ছাড়া অন্য ভাষায় Dify অনুবাদ করতে সাহায্য করার জন্য অবদানকারীদের খুঁজছি। আপনি যদি সাহায্য করতে আগ্রহী হন, তাহলে আরও তথ্যের জন্য [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) দেখুন এবং আমাদের [ডিসকর্ড কমিউনিটি সার্ভার](https://discord.gg/8Tpq4AcN9c) এর `গ্লোবাল-ইউজারস` চ্যানেলে আমাদের একটি মন্তব্য করুন।
## কমিউনিটি এবং যোগাযোগ
- [Github Discussion](https://github.com/langgenius/dify/discussions) ফিডব্যাক এবং প্রতিক্রিয়া জানানোর মাধ্যম।
- [GitHub Issues](https://github.com/langgenius/dify/issues). Dify.AI ব্যবহার করে আপনি যেসব বাগের সম্মুখীন হন এবং ফিচার প্রস্তাবনা। আমাদের [অবদান নির্দেশিকা](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) দেখুন।
- [Discord](https://discord.gg/FngNHpbcY7) আপনার এপ্লিকেশন শেয়ার এবং কমিউনিটি আড্ডার মাধ্যম।
- [X(Twitter)](https://twitter.com/dify_ai) আপনার এপ্লিকেশন শেয়ার এবং কমিউনিটি আড্ডার মাধ্যম।
[](https://star-history.com/#langgenius/dify&Date)
## নিরাপত্তা বিষয়ক
আপনার গোপনীয়তা রক্ষা করতে, অনুগ্রহ করে GitHub-এ নিরাপত্তা সংক্রান্ত সমস্যা পোস্ট করা এড়িয়ে চলুন। পরিবর্তে, আপনার প্রশ্নগুলি <security@dify.ai> ঠিকানায় পাঠান এবং আমরা আপনাকে আরও বিস্তারিত উত্তর প্রদান করব।
## লাইসেন্স
এই রিপোজিটরিটি [ডিফাই ওপেন সোর্স লাইসেন্স](LICENSE) এর অধিনে , যা মূলত অ্যাপাচি ২.০, তবে কিছু অতিরিক্ত বিধিনিষেধ রয়েছে।
<ahref="./README_VI.md"><imgalt="README Tiếng Việt"src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
<ahref="./README_DE.md"><imgalt="README in Deutsch"src="https://img.shields.io/badge/German-d9d9d9"></a>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
Dify ist eine Open-Source-Plattform zur Entwicklung von LLM-Anwendungen. Ihre intuitive Benutzeroberfläche vereint agentenbasierte KI-Workflows, RAG-Pipelines, Agentenfunktionen, Modellverwaltung, Überwachungsfunktionen und mehr, sodass Sie schnell von einem Prototyp in die Produktion übergehen können.
## Schnellstart
> Bevor Sie Dify installieren, stellen Sie sicher, dass Ihr System die folgenden Mindestanforderungen erfüllt:
>
>- CPU >= 2 Core
>- RAM >= 4 GiB
</br>
Der einfachste Weg, den Dify-Server zu starten, ist über [docker compose](docker/docker-compose.yaml). Stellen Sie vor dem Ausführen von Dify mit den folgenden Befehlen sicher, dass [Docker](https://docs.docker.com/get-docker/) und [Docker Compose](https://docs.docker.com/compose/install/) auf Ihrem System installiert sind:
```bash
cd dify
cd docker
cp .env.example .env
docker compose up -d
```
Nachdem Sie den Server gestartet haben, können Sie über Ihren Browser auf das Dify Dashboard unter [http://localhost/install](http://localhost/install) zugreifen und den Initialisierungsprozess starten.
#### Hilfe suchen
Bitte beachten Sie unsere [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs), wenn Sie Probleme bei der Einrichtung von Dify haben. Wenden Sie sich an [die Community und uns](#community--contact), falls weiterhin Schwierigkeiten auftreten.
> Wenn Sie zu Dify beitragen oder zusätzliche Entwicklungen durchführen möchten, lesen Sie bitte unseren [Leitfaden zur Bereitstellung aus dem Quellcode](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code).
## Wesentliche Merkmale
**1. Workflow**:
Erstellen und testen Sie leistungsstarke KI-Workflows auf einer visuellen Oberfläche, wobei Sie alle der folgenden Funktionen und darüber hinaus nutzen können.
Nahtlose Integration mit Hunderten von proprietären und Open-Source-LLMs von Dutzenden Inferenzanbietern und selbstgehosteten Lösungen, die GPT, Mistral, Llama3 und alle mit der OpenAI API kompatiblen Modelle abdecken. Eine vollständige Liste der unterstützten Modellanbieter finden Sie [hier](https://docs.dify.ai/getting-started/readme/model-providers).
Intuitive Benutzeroberfläche zum Erstellen von Prompts, zum Vergleichen der Modellleistung und zum Hinzufügen zusätzlicher Funktionen wie Text-to-Speech in einer chatbasierten Anwendung.
**4. RAG Pipeline**:
Umfassende RAG-Funktionalitäten, die alles von der Dokumenteneinlesung bis zur -abfrage abdecken, mit sofort einsatzbereiter Unterstützung für die Textextraktion aus PDFs, PPTs und anderen gängigen Dokumentformaten.
**5. Fähigkeiten des Agenten**:
Sie können Agenten basierend auf LLM Function Calling oder ReAct definieren und vorgefertigte oder benutzerdefinierte Tools für den Agenten hinzufügen. Dify stellt über 50 integrierte Tools für KI-Agenten bereit, wie zum Beispiel Google Search, DALL·E, Stable Diffusion und WolframAlpha.
**6. LLMOps**:
Überwachen und analysieren Sie Anwendungsprotokolle und die Leistung im Laufe der Zeit. Sie können kontinuierlich Prompts, Datensätze und Modelle basierend auf Produktionsdaten und Annotationen verbessern.
**7. Backend-as-a-Service**:
Alle Dify-Angebote kommen mit entsprechenden APIs, sodass Sie Dify mühelos in Ihre eigene Geschäftslogik integrieren können.
Wir hosten einen [Dify Cloud](https://dify.ai)-Service, den jeder ohne Einrichtung ausprobieren kann. Er bietet alle Funktionen der selbstgehosteten Version und beinhaltet 200 kostenlose GPT-4-Aufrufe im Sandbox-Plan.
- **Selbstgehostete Dify Community Edition</br>**
Starten Sie Dify schnell in Ihrer Umgebung mit diesem [Schnellstart-Leitfaden](#quick-start). Nutzen Sie unsere [Dokumentation](https://docs.dify.ai) für weiterführende Informationen und detaillierte Anweisungen.
- **Dify für Unternehmen / Organisationen</br>**
Wir bieten zusätzliche, unternehmensspezifische Funktionen. [Über diesen Chatbot können Sie uns Ihre Fragen mitteilen](https://udify.app/chat/22L1zSxg6yW1cWQg) oder [senden Sie uns eine E-Mail](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry), um Ihre unternehmerischen Bedürfnisse zu besprechen. </br>
> Für Startups und kleine Unternehmen, die AWS nutzen, schauen Sie sich [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) an und stellen Sie es mit nur einem Klick in Ihrer eigenen AWS VPC bereit. Es handelt sich um ein erschwingliches AMI-Angebot mit der Option, Apps mit individuellem Logo und Branding zu erstellen.
## Immer einen Schritt voraus
Star Dify auf GitHub und lassen Sie sich sofort über neue Releases benachrichtigen.
Falls Sie die Konfiguration anpassen müssen, lesen Sie bitte die Kommentare in unserer [.env.example](docker/.env.example)-Datei und aktualisieren Sie die entsprechenden Werte in Ihrer `.env`-Datei. Zusätzlich müssen Sie eventuell Anpassungen an der `docker-compose.yaml`-Datei vornehmen, wie zum Beispiel das Ändern von Image-Versionen, Portzuordnungen oder Volumen-Mounts, je nach Ihrer spezifischen Einsatzumgebung und Ihren Anforderungen. Nachdem Sie Änderungen vorgenommen haben, starten Sie `docker-compose up -d` erneut. Eine vollständige Liste der verfügbaren Umgebungsvariablen finden Sie [hier](https://docs.dify.ai/getting-started/install-self-hosted/environments).
Falls Sie eine hochverfügbare Konfiguration einrichten möchten, gibt es von der Community bereitgestellte [Helm Charts](https://helm.sh/) und YAML-Dateien, die es ermöglichen, Dify auf Kubernetes bereitzustellen.
- [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)
#### Terraform für die Bereitstellung verwenden
Stellen Sie Dify mit nur einem Klick mithilfe von [terraform](https://www.terraform.io/) auf einer Cloud-Plattform bereit.
##### 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)
#### Verwendung von AWS CDK für die Bereitstellung
Bereitstellung von Dify auf AWS mit [CDK](https://aws.amazon.com/cdk/)
##### AWS
- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
## Contributing
Falls Sie Code beitragen möchten, lesen Sie bitte unseren [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). Gleichzeitig bitten wir Sie, Dify zu unterstützen, indem Sie es in den sozialen Medien teilen und auf Veranstaltungen und Konferenzen präsentieren.
> Wir suchen Mitwirkende, die dabei helfen, Dify in weitere Sprachen zu übersetzen – außer Mandarin oder Englisch. Wenn Sie Interesse an einer Mitarbeit haben, lesen Sie bitte die [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) für weitere Informationen und hinterlassen Sie einen Kommentar im `global-users`-Kanal unseres [Discord Community Servers](https://discord.gg/8Tpq4AcN9c).
## Gemeinschaft & Kontakt
* [Github Discussion](https://github.com/langgenius/dify/discussions). Am besten geeignet für: den Austausch von Feedback und das Stellen von Fragen.
* [GitHub Issues](https://github.com/langgenius/dify/issues). Am besten für: Fehler, auf die Sie bei der Verwendung von Dify.AI stoßen, und Funktionsvorschläge. Siehe unseren [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
* [Discord](https://discord.gg/FngNHpbcY7). Am besten geeignet für: den Austausch von Bewerbungen und den Austausch mit der Community.
* [X(Twitter)](https://twitter.com/dify_ai). Am besten geeignet für: den Austausch von Bewerbungen und den Austausch mit der Community.
[](https://star-history.com/#langgenius/dify&Date)
## Offenlegung der Sicherheit
Um Ihre Privatsphäre zu schützen, vermeiden Sie es bitte, Sicherheitsprobleme auf GitHub zu posten. Schicken Sie Ihre Fragen stattdessen an security@dify.ai und wir werden Ihnen eine ausführlichere Antwort geben.
## Lizenz
Dieses Repository steht unter der [Dify Open Source License](LICENSE), die im Wesentlichen Apache 2.0 mit einigen zusätzlichen Einschränkungen ist.
<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 agentes 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)
- [Gráfico Helm por @magicsong](https://github.com/magicsong/ai-charts)
- [Ficheros YAML por @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [Ficheros YAML por @wyy-holding](https://github.com/wyy-holding/dify-k8s)
#### 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. Capacités d'agent** :
Vous pouvez définir des agents basés sur l'appel de fonction LLM ou ReAct, et ajouter des outils pré-construits ou personnalisés pour l'agent. Dify fournit plus de 50 outils intégrés pour les agents d'IA, tels que la recherche Google, DALL·E, Stable Diffusion et WolframAlpha.
**6. LLMOps** :
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)
- [Helm Chart par @magicsong](https://github.com/magicsong/ai-charts)
- [Fichier YAML par @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [Fichier YAML par @wyy-holding](https://github.com/wyy-holding/dify-k8s)
#### 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)
- [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)
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>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-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)
- [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)
#### 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>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-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)
- [Helm Chart de @magicsong](https://github.com/magicsong/ai-charts)
- [Arquivo YAML por @Winson-030](https://github.com/Winson-030/dify-kubernetes)
- [Arquivo YAML por @wyy-holding](https://github.com/wyy-holding/dify-k8s)
#### 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.
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
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.
## Primerjava Funkcij
<tablestyle="width: 100%;">
<tr>
<thalign="center">Funkcija</th>
<thalign="center">Dify.AI</th>
<thalign="center">LangChain</th>
<thalign="center">Flowise</th>
<thalign="center">OpenAI Assistants API</th>
</tr>
<tr>
<tdalign="center">Programski pristop</td>
<tdalign="center">API + usmerjeno v aplikacije</td>
<tdalign="center">Python koda</td>
<tdalign="center">Usmerjeno v aplikacije</td>
<tdalign="center">Usmerjeno v API</td>
</tr>
<tr>
<tdalign="center">Podprti LLM-ji</td>
<tdalign="center">Bogata izbira</td>
<tdalign="center">Bogata izbira</td>
<tdalign="center">Bogata izbira</td>
<tdalign="center">Samo OpenAI</td>
</tr>
<tr>
<tdalign="center">RAG pogon</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">Potek dela</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Spremljanje</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Funkcija za podjetja (SSO/nadzor dostopa)</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
<tdalign="center">❌</td>
</tr>
<tr>
<tdalign="center">Lokalna namestitev</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">✅</td>
<tdalign="center">❌</td>
</tr>
</table>
## 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)
- [YAML file by @wyy-holding](https://github.com/wyy-holding/dify-k8s)
#### 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>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-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.
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>**
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>
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)
- [@wyy-holding tarafından YAML dosyası](https://github.com/wyy-holding/dify-k8s)
#### 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>
<ahref="./README_BN.md"><imgalt="README in বাংলা"src="https://img.shields.io/badge/বাংলা-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)
- [Tệp YAML bởi @wyy-holding](https://github.com/wyy-holding/dify-k8s)
#### 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
```bash for Linux
```bash for Linux
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
```
```
bash for Mac
bash for Mac
```bash for Mac
```bash for Mac
secret_key=$(openssl rand -base64 42)
secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env
SECRET_KEY=${secret_key}" .env
```
```
4. Create environment.
1. Create environment.
Dify API service uses [Poetry](https://python-poetry.org/docs/) to manage dependencies. First, you need to add the poetry shell plugin, if you don't have it already, in order to run in a virtual environment. [Note: Poetry shell is no longer a native command so you need to install the poetry plugin beforehand]
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
```bash
poetry self add poetry-plugin-shell
pip install uv
# Or on macOS
brew install uv
```
```
Then, You can execute `poetry shell` to activate the environment.
1. Install dependencies
5. Install dependencies
```bash
```bash
poetry env use 3.12
uv sync --dev
poetry install
```
```
6. Run migrate
1. Run migrate
Before the first launch, migrate the database to the latest version.
Before the first launch, migrate the database to the latest version.
```bash
```bash
poetry run python -m flask db upgrade
uv run flask db upgrade
```
```
7. Start backend
1. Start backend
```bash
```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.
1. 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.
```bash
1. Setup your application by visiting `http://localhost:3000`.
poetry run python -m celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail,ops_trace,app_deletion
```
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
## Testing
1. Install dependencies for both the backend and the test environment
1. Install dependencies for both the backend and the test environment
```bash
```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
```bash
poetry run -P 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
# Code quality
../dev/reformat # Run all formatters and linters
uv run ruff check --fix ./ # Fix linting issues
uv run ruff format ./ # Format code
uv run basedpyright . # Type checking
```
```
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