Replace dynamic imports with static imports in marketplace atom tests.
Convert type-only and not-toThrow assertions into proper state-change
verifications. Add comprehensive test suite for model-provider-page
atoms covering all four hooks, cross-hook interaction, selectAtom
granularity, and Provider isolation.
Replace 2 write-only derived atoms with primitive atom's built-in
updater functions. The selectAtom on the read side already prevents
unnecessary re-renders, making the manual guard logic redundant.
- Replace useEffect state sync with derived state pattern in useSystemDefaultModelAndModelList
- Use useCallback instead of useMemo for function memoization in useProviderCredentialsAndLoadBalancing
- Add memo() to ProviderAddedCard and CredentialPanel to prevent unnecessary re-renders
- Switch to useProviderContextSelector for precise context subscription in ProviderAddedCard
- Stabilize activate callback ref in useActivateCredential via supportedModelTypes ref
- Add usage priority tooltip with i18n support
Extract credential switching logic from dropdown-content into a dedicated
useActivateCredential hook with optimistic updates and proper data flow
separation. Credential items now stay visible in the popover after clicking
(no auto-close), show cursor-pointer, and disable during activation.
Migrate credential-item from legacy Tooltip and remixicon imports to
base-ui Tooltip and CSS icon classes, pruning stale ESLint suppressions.
Replace legacy PortalToFollowElem and Confirm with Dialog/AlertDialog
primitives. Remove manual ESC handler and backdrop div — now handled
natively by base-ui. Add backdropProps={{ forceRender: true }} for
correct nested overlay rendering.
- remove provider-page lifecycle reset effect and handle reset in explicit tab/close actions
- switch account setting tab state to controlled/uncontrolled pattern without sync effect
- use provider-scoped model list queryKey with exact invalidation in credential and model toggle mutations
- update related tests and mocks for new behavior
- add atom-based provider expansion state with reset/prune helpers
- remove event-emitter dependency from model provider refresh flow
- invalidate exact provider model-list query key on refresh
- reset expansion state on model provider page mount/unmount
- update and extend tests for external expansion and query invalidation
- update eslint suppressions to match current code
Replace i-ri-error-warning-fill (circle exclamation) with the
Warning component (triangle) for api-fallback and credits-fallback
variants to match Figma design.
When API Key is selected but unavailable/unconfigured and credits are
available, the card now shows "AI credits in use" with a warning icon
instead of "API key required". When both credits are exhausted and no
API key exists, it shows "No available usage" (destructive).
New deriveVariant logic for priority=apiKey:
- !exhausted + !authorized → credits-fallback (was api-required-*)
- exhausted + no credential → no-usage (was api-required-add)
- exhausted + named unauthorized → api-unavailable (unchanged)
Change mx-1 (4px) to mx-2 (8px) on CreditsFallbackAlert and
CreditsExhaustedAlert to match ApiKeySection's p-2 (8px) padding,
consistent with Figma design where both sections are 8px from the
dropdown edge.
Move the border from UsagePrioritySection (always visible) to
ApiKeySection's list variant (only when credentials exist). This
removes the unwanted divider line above the "No API Keys" empty
state card when on the AI Credits tab with no keys configured.
Replace broken HTML string interpolation with Trans component and
useModalContextSelector so "upgrade your plan" opens the pricing modal.
Add custom credits-coin SVG icon to replace the generic ri-coin-line.
Remove UPDATE_MODEL_PROVIDER_CUSTOM_MODEL_LIST event emission from
changePriority onSuccess. This event was designed for custom model
add/edit/delete scenarios where the card should expand, but firing
it on priority switch caused ProviderAddedCard to unexpectedly
expand via refreshModelList → setCollapsed(false).
- Expose totalCredits from useTrialCredits hook instead of hardcoding 10,000
- Align CreditsExhaustedAlert with Figma: dynamic progress bar, correct
design tokens (components-progress-error-bg/progress), sm-medium/xs-regular
typography
- Align CreditsFallbackAlert typography to sm-medium/xs-regular
- Fix ApiKeySection empty state: horizontal gradient, sm-medium title,
Figma-aligned padding (pl-7 for API KEYS label)
- Hoist empty credentials array constant to stabilize memo (rerender-memo-with-default-value)
- Remove redundant useCallback wrapper in ApiKeySection
- Replace nested ternary with Record lookup in TextLabel
- Remove dead || 0 guard in useTrialCredits
- Update all test mocks with totalCredits field
- Add changePreferredProviderType contract in model-providers.ts
- Register in consoleRouterContract
- Replace raw async changeModelProviderPriority with useMutation
- Use Toast.notify (static API) instead of useToastContext hook
- Pass isPending as isChangingPriority to disable buttons during switch
- Add disabled prop to UsagePrioritySection
- Fix pre-existing test assertions for api-unavailable variant
- Update all specs with isChangingPriority prop and oRPC mock pattern
Rewrite credential-panel.spec.tsx to match the new discriminated union
state model and variant-driven rendering. Add new test files for
useCredentialPanelState hook, SystemQuotaCard Label enhancement,
and all ModelAuthDropdown sub-components.
- Extract useCredentialPanelState hook with discriminated union CardVariant type replacing scattered boolean conditions
- Create ModelAuthDropdown compound component (Popover-based) with UsagePrioritySection, CreditsExhaustedAlert, and ApiKeySection
- Enhance SystemQuotaCard.Label to accept className override for flexible styling
- Add i18n keys for new card states and dropdown content (en-US, zh-Hans)
Extract trial credits calculation into a shared useTrialCredits hook to prevent
logic drift between QuotaPanel and CredentialPanel. Add SystemQuotaCard compound
component with explicit default/destructive variants for the system quota UI
state in provider cards, replacing inline conditional styling with composable
Label and Actions slots. Remove unnecessary useMemo for simple derived values.
- Replace deprecated Confirm mock with real AlertDialog role-based queries
- Add useInvalidateCheckInstalled mock for QueryClient dependency
- Wrap model-list-item renders in QueryClientProvider
- Migrate PluginVersionPicker from PortalToFollowElem to Popover
- Migrate UpdatePluginModal from Modal to Dialog
- Update version picker offset props (sideOffset/alignOffset)
- Migrate OperationDropdown from legacy portal-to-follow-elem to base UI DropdownMenu primitives
- Add placement, sideOffset, alignOffset, popupClassName props for flexible positioning
- Fix version badge font size: system-2xs-medium-uppercase (10px) → system-xs-medium-uppercase (12px)
- Set provider card dropdown to bottom-start placement with 192px width per Figma spec
- Fix PluginVersionPicker toggle: clicking badge now opens and closes the picker
- Add max-h-[224px] overflow scroll to version list
- Replace Remix icon imports with Tailwind CSS icon classes
- Prune stale eslint suppressions for migrated files
Integrate plugin version management into model provider cards by
reusing existing plugin detail panel hooks and components. Batch
query installed plugins at list level to avoid N+1 requests.
- Rename title from "Quota" to "AI Credits" and update tooltip copy
(Message Credits → AI Credits, free → Trial)
- Show "Credits exhausted" in destructive text when credits reach zero
instead of displaying the number "0"
- Migrate from deprecated Tooltip to base UI Tooltip compound component
- Add 4px grid background with radial fade mask via CSS module
- Simplify provider icon tooltip text for uninstalled state
- Update i18n keys for both en-US and zh-Hans
- remove mount-time workspace invalidate in model provider page
- read quota with useCurrentWorkspace and keep loading only for initial empty fetch
- reuse existing useSystemFeaturesQuery for marketplace and trial models
- update model provider and quota panel tests for new query/loading behavior
Replace the no-provider test assertion from checking a nonexistent i18n
key to verifying actual warning keys are absent. Add unit tests for
useInvalidateDefaultModel following the useUpdateModelList pattern.
The empty state card below already prompts users to install a provider,
so the top warning bar is redundant for the no-provider case. Remove
the unused noProviderInstalled i18n key and replace the lookup map with
a ternary to preserve i18n literal types without assertions.
After saving system models, only the model list cache was invalidated
but not the default model cache, causing stale config status in the UI.
Add useInvalidateDefaultModel hook and call it for all 5 model types
after a successful save.
Replace the single `defaultModelNotConfigured` boolean with a derived
`systemModelConfigStatus` that distinguishes between no-provider,
none-configured, partially-configured, and fully-configured states,
each showing a context-appropriate warning message. Also updates the
button label from "System Model Settings" to "Default Model Settings"
and migrates remixicon imports to Tailwind CSS icon classes.
The `/api/system-features` is required for the web app initialization.
Authentication would create circular dependency (can't authenticate without web app loading).
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
The `/console/api/system-features` is required for the dashboard initialization. Authentication would create circular dependency (can't login without dashboard loading).
ref: CVE-2025-63387
Related: #31368
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.
yyhGitHubClaude Opus 4.5autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
zyssyz123GitHubautofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>Joel-LAN-
- 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.
YeuolyGitHubStreamlyzno1zhsamaHarrylyzno1yesseniahjlarrygemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>WTW0313Copilot
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>
Boris PolonskyGitHubgemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
description:Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.
---
# Backend Code Review
## When to use this skill
Use this skill whenever the user asks to **review, analyze, or improve** backend code (e.g., `.py`) under the `api/` directory. Supports the following review modes:
- **Pending-change review**: when the user asks to review current changes (inspect staged/working-tree files slated for commit to get the changes).
- **Code snippets review**: when the user pastes code snippets (e.g., a function/class/module excerpt) into the chat and asks for a review.
- **File-focused review**: when the user points to specific files and asks for a review of those files (one file or a small, explicit set of files, e.g., `api/...`, `api/app.py`).
Do NOT use this skill when:
- The request is about frontend code or UI (e.g., `.tsx`, `.ts`, `.js`, `web/`).
- The user is not asking for a review/analysis/improvement of backend code.
- The scope is not under `api/` (unless the user explicitly asks to review backend-related changes outside `api/`).
## How to use this skill
Follow these steps when using this skill:
1.**Identify the review mode** (pending-change vs snippet vs file-focused) based on the user’s input. Keep the scope tight: review only what the user provided or explicitly referenced.
2. Follow the rules defined in **Checklist** to perform the review. If no Checklist rule matches, apply **General Review Rules** as a fallback to perform the best-effort review.
3. Compose the final output strictly follow the **Required Output Format**.
Notes when using this skill:
- Always include actionable fixes or suggestions (including possible code snippets).
- Use best-effort `File:Line` references when a file path and line numbers are available; otherwise, use the most specific identifier you can.
## Checklist
- db schema design: if the review scope includes code/files under `api/models/` or `api/migrations/`, follow [references/db-schema-rule.md](references/db-schema-rule.md) to perform the review
- architecture: if the review scope involves controller/service/core-domain/libs/model layering, dependency direction, or moving responsibilities across modules, follow [references/architecture-rule.md](references/architecture-rule.md) to perform the review
- repositories abstraction: if the review scope contains table/model operations (e.g., `select(...)`, `session.execute(...)`, joins, CRUD) and is not under `api/repositories`, `api/core/repositories`, or `api/extensions/*/repositories/`, follow [references/repositories-rule.md](references/repositories-rule.md) to perform the review
- sqlalchemy patterns: if the review scope involves SQLAlchemy session/query usage, db transaction/crud usage, or raw SQL usage, follow [references/sqlalchemy-rule.md](references/sqlalchemy-rule.md) to perform the review
## General Review Rules
### 1. Security Review
Check for:
- SQL injection vulnerabilities
- Server-Side Request Forgery (SSRF)
- Command injection
- Insecure deserialization
- Hardcoded secrets/credentials
- Improper authentication/authorization
- Insecure direct object references
### 2. Performance Review
Check for:
- N+1 queries
- Missing database indexes
- Memory leaks
- Blocking operations in async code
- Missing caching opportunities
### 3. Code Quality Review
Check for:
- Code forward compatibility
- Code duplication (DRY violations)
- Functions doing too much (SRP violations)
- Deep nesting / complex conditionals
- Magic numbers/strings
- Poor naming
- Missing error handling
- Incomplete type coverage
### 4. Testing Review
Check for:
- Missing test coverage for new code
- Tests that don't test behavior
- Flaky test patterns
- Missing edge cases
## Required Output Format
When this skill invoked, the response must exactly follow one of the two templates:
### Template A (any findings)
```markdown
# Code Review Summary
Found <X> critical issues need to be fixed:
## 🔴 Critical (Must Fix)
### 1. <brief description of the issue>
FilePath: <path> line <line>
<relevant code snippet or pointer>
#### Explanation
<detailed explanation and references of the issue>
#### Suggested Fix
1. <brief description of suggested fix>
2. <code example> (optional, omit if not applicable)
---
... (repeat for each critical issue) ...
Found <Y> suggestions for improvement:
## 🟡 Suggestions (Should Consider)
### 1. <brief description of the suggestion>
FilePath: <path> line <line>
<relevant code snippet or pointer>
#### Explanation
<detailed explanation and references of the suggestion>
#### Suggested Fix
1. <brief description of suggested fix>
2. <code example> (optional, omit if not applicable)
---
... (repeat for each suggestion) ...
Found <Z> optional nits:
## 🟢 Nits (Optional)
### 1. <brief description of the nit>
FilePath: <path> line <line>
<relevant code snippet or pointer>
#### Explanation
<explanation and references of the optional nit>
#### Suggested Fix
- <minor suggestions>
---
... (repeat for each nits) ...
## ✅ What's Good
- <Positive feedback on good patterns>
```
- If there are no critical issues or suggestions or option nits or good points, just omit that section.
- If the issue number is more than 10, summarize as "Found 10+ critical issues/suggestions/optional nits" and only output the first 10 items.
- Don't compress the blank lines between sections; keep them as-is for readability.
- If there is any issue requires code changes, append a brief follow-up question to ask whether the user wants to apply the fix(es) after the structured output. For example: "Would you like me to use the Suggested fix(es) to address these issues?"
- Description: Controllers should parse input, call services, and return serialized responses. Business decisions inside controllers make behavior hard to reuse and test.
- Suggested fix: Move domain/business logic into the service or core/domain layer. Keep controller handlers thin and orchestration-focused.
- Example:
- Bad:
```python
@bp.post("/apps/<app_id>/publish")
def publish_app(app_id: str):
payload = request.get_json() or {}
if payload.get("force") and current_user.role != "admin":
raise ValueError("only admin can force publish")
app = App.query.get(app_id)
app.status = "published"
db.session.commit()
return {"result": "ok"}
```
- Good:
```python
@bp.post("/apps/<app_id>/publish")
def publish_app(app_id: str):
payload = PublishRequest.model_validate(request.get_json() or {})
- Description: Controllers may depend on services, and services may depend on core/domain abstractions. Reversing this direction (for example, core importing controller/web modules) creates cycles and leaks transport concerns into domain code.
- Suggested fix: Extract shared contracts into core/domain or service-level modules and make upper layers depend on lower, not the reverse.
- Example:
- Bad:
```python
# core/policy/publish_policy.py
from controllers.console.app import request_context
def can_publish() -> bool:
return request_context.current_user.is_admin
```
- Good:
```python
# core/policy/publish_policy.py
def can_publish(role: str) -> bool:
return role == "admin"
# service layer adapts web/user context to domain input
allowed = can_publish(role=current_user.role)
```
### Keep libs business-agnostic
- Category: maintainability
- Severity: critical
- Description: Modules under `api/libs/` should remain reusable, business-agnostic building blocks. They must not encode product/domain-specific rules, workflow orchestration, or business decisions.
- Suggested fix:
- If business logic appears in `api/libs/`, extract it into the appropriate `services/` or `core/` module and keep `libs` focused on generic, cross-cutting helpers.
- Covers: model/base inheritance, schema boundaries in model properties, tenant-aware schema design, index redundancy checks, dialect portability in models, and cross-database compatibility in migrations.
- Does NOT cover: session lifecycle, transaction boundaries, and query execution patterns (handled by `sqlalchemy-rule.md`).
## Rules
### Do not query other tables inside `@property`
- Category: [maintainability, performance]
- Severity: critical
- Description: A model `@property` must not open sessions or query other tables. This hides dependencies across models, tightly couples schema objects to data access, and can cause N+1 query explosions when iterating collections.
- Suggested fix:
- Keep model properties pure and local to already-loaded fields.
- Move cross-table data fetching to service/repository methods.
- For list/batch reads, fetch required related data explicitly (join/preload/bulk query) before rendering derived values.
- Example:
- Bad:
```python
class Conversation(TypeBase):
__tablename__ = "conversations"
@property
def app_name(self) -> str:
with Session(db.engine, expire_on_commit=False) as session:
# Service/repository layer performs explicit batch fetch for related App rows.
```
### Prefer including `tenant_id` in model definitions
- Category: maintainability
- Severity: suggestion
- Description: In multi-tenant domains, include `tenant_id` in schema definitions whenever the entity belongs to tenant-owned data. This improves data isolation safety and keeps future partitioning/sharding strategies practical as data volume grows.
- Suggested fix:
- Add a `tenant_id` column and ensure related unique/index constraints include tenant dimension when applicable.
- Propagate `tenant_id` through service/repository contracts to keep access paths tenant-aware.
- Exception: if a table is explicitly designed as non-tenant-scoped global metadata, document that design decision clearly.
- Description: Review index definitions for leftmost-prefix redundancy. For example, index `(a, b, c)` can safely cover most lookups for `(a, b)`. Keeping both may increase write overhead and can mislead the optimizer into suboptimal execution plans.
- Suggested fix:
- Before adding an index, compare against existing composite indexes by leftmost-prefix rules.
- Drop or avoid creating redundant prefixes unless there is a proven query-pattern need.
- Apply the same review standard in both model `__table_args__` and migration index DDL.
### Avoid PostgreSQL-only dialect usage in models; wrap in `models.types`
- Category: maintainability
- Severity: critical
- Description: Model/schema definitions should avoid PostgreSQL-only constructs directly in business models. When database-specific behavior is required, encapsulate it in `api/models/types.py` using both PostgreSQL and MySQL dialect implementations, then consume that abstraction from model code.
- Suggested fix:
- Do not directly place dialect-only types/operators in model columns when a portable wrapper can be used.
- Add or extend wrappers in `models.types` (for example, `AdjustedJSON`, `LongText`, `BinaryData`) to normalize behavior across PostgreSQL and MySQL.
### Guard migration incompatibilities with dialect checks and shared types
- Category: maintainability
- Severity: critical
- Description: Migration scripts under `api/migrations/versions/` must account for PostgreSQL/MySQL incompatibilities explicitly. For dialect-sensitive DDL or defaults, branch on the active dialect (for example, `conn.dialect.name == "postgresql"`), and prefer reusable compatibility abstractions from `models.types` where applicable.
- Suggested fix:
- In migration upgrades/downgrades, bind connection and branch by dialect for incompatible SQL fragments.
- Reuse `models.types` wrappers in column definitions when that keeps behavior aligned with runtime models.
- Avoid one-dialect-only migration logic unless there is a documented, deliberate compatibility exception.
- Example:
- Bad:
```python
with op.batch_alter_table("dataset_keyword_tables") as batch_op:
- Covers: when to reuse existing repository abstractions, when to introduce new repositories, and how to preserve dependency direction between service/core and infrastructure implementations.
- Does NOT cover: SQLAlchemy session lifecycle and query-shape specifics (handled by `sqlalchemy-rule.md`), and table schema/migration design (handled by `db-schema-rule.md`).
## Rules
### Introduce repositories abstraction
- Category: maintainability
- Severity: suggestion
- Description: If a table/model already has a repository abstraction, all reads/writes/queries for that table should use the existing repository. If no repository exists, introduce one only when complexity justifies it, such as large/high-volume tables, repeated complex query logic, or likely storage-strategy variation.
- Suggested fix:
- First check `api/repositories`, `api/core/repositories`, and `api/extensions/*/repositories/` to verify whether the table/model already has a repository abstraction. If it exists, route all operations through it and add missing repository methods instead of bypassing it with ad-hoc SQLAlchemy access.
- If no repository exists, add one only when complexity warrants it (for example, repeated complex queries, large data domains, or multiple storage strategies), while preserving dependency direction (service/core depends on abstraction; infra provides implementation).
- Example:
- Bad:
```python
# Existing repository is ignored and service uses ad-hoc table queries.
- Covers: SQLAlchemy session and transaction lifecycle, query construction, tenant scoping, raw SQL boundaries, and write-path concurrency safeguards.
- Does NOT cover: table/model schema and migration design details (handled by `db-schema-rule.md`).
## Rules
### Use Session context manager with explicit transaction control behavior
- Category: best practices
- Severity: critical
- Description: Session and transaction lifecycle must be explicit and bounded on write paths. Missing commits can silently drop intended updates, while ad-hoc or long-lived transactions increase contention, lock duration, and deadlock risk.
- Suggested fix:
- Use **explicit `session.commit()`** after completing a related write unit.
- Or use **`session.begin()` context manager** for automatic commit/rollback on a scoped block.
- Keep transaction windows short: avoid network I/O, heavy computation, or unrelated work inside the transaction.
- Example:
- Bad:
```python
# Missing commit: write may never be persisted.
with Session(db.engine, expire_on_commit=False) as session:
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
# Long transaction: external I/O inside a DB transaction.
with Session(db.engine, expire_on_commit=False) as session, session.begin():
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
call_external_api()
```
- Good:
```python
# Option 1: explicit commit.
with Session(db.engine, expire_on_commit=False) as session:
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
session.commit()
# Option 2: scoped transaction with automatic commit/rollback.
with Session(db.engine, expire_on_commit=False) as session, session.begin():
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
# Keep non-DB work outside transaction scope.
call_external_api()
```
### Enforce tenant_id scoping on shared-resource queries
- Category: security
- Severity: critical
- Description: Reads and writes against shared tables must be scoped by `tenant_id` to prevent cross-tenant data leakage or corruption.
- Suggested fix: Add `tenant_id` predicate to all tenant-owned entity queries and propagate tenant context through service/repository interfaces.
### Prefer SQLAlchemy expressions over raw SQL by default
- Category: maintainability
- Severity: suggestion
- Description: Raw SQL should be exceptional. ORM/Core expressions are easier to evolve, safer to compose, and more consistent with the codebase.
- Suggested fix: Rewrite straightforward raw SQL into SQLAlchemy `select/update/delete` expressions; keep raw SQL only when required by clear technical constraints.
- Example:
- Bad:
```python
row = session.execute(
text("SELECT * FROM workflows WHERE id = :id AND tenant_id = :tenant_id"),
{"id": workflow_id, "tenant_id": tenant_id},
).first()
```
- Good:
```python
stmt = select(Workflow).where(
Workflow.id == workflow_id,
Workflow.tenant_id == tenant_id,
)
row = session.execute(stmt).scalar_one_or_none()
```
### Protect write paths with concurrency safeguards
- Category: quality
- Severity: critical
- Description: Multi-writer paths without explicit concurrency control can silently overwrite data. Choose the safeguard based on contention level, lock scope, and throughput cost instead of defaulting to one strategy.
- Suggested fix:
- **Optimistic locking**: Use when contention is usually low and retries are acceptable. Add a version (or updated_at) guard in `WHERE` and treat `rowcount == 0` as a conflict.
- **Redis distributed lock**: Use when the critical section spans multiple steps/processes (or includes non-DB side effects) and you need cross-worker mutual exclusion.
- **SELECT ... FOR UPDATE**: Use when contention is high on the same rows and strict in-transaction serialization is required. Keep transactions short to reduce lock wait/deadlock risk.
- In all cases, scope by `tenant_id` and verify affected row counts for conditional writes.
- Example:
- Bad:
```python
# No tenant scope, no conflict detection, and no lock on a contested write path.
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 <brief description of bug>
FilePath: <path> line <line>
<relevant code snippet or pointer>
### Suggested fix
<brief description of suggested fix>
---
... (repeat for each urgent issue) ...
Found <M> suggestions for improvement:
## 1 <brief description of suggestion>
FilePath: <path> line <line>
<relevant code snippet or pointer>
### Suggested fix
<brief description of suggested fix>
---
... (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/docs/test.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.
See [Zustand Store Testing](#zustand-store-testing) section for full details.
## Mock Placement
| Location | Purpose |
|----------|---------|
| `web/vitest.setup.ts` | Global mocks shared by all tests (`react-i18next`, `next/image`, `zustand`) |
| `web/__mocks__/zustand.ts` | Zustand mock implementation (auto-resets stores after each test) |
| `web/__mocks__/` | Reusable mock factories shared across multiple test files |
| Test file | Test-specific mocks, inline with `vi.mock()` |
Modules are not mocked automatically. Use `vi.mock` in test files, or add global mocks in `web/vitest.setup.ts`.
**Note**: Zustand is special - it's globally mocked but you should NOT mock store modules manually. See [Zustand Store Testing](#zustand-store-testing).
## Essential Mocks
### 1. i18n (Auto-loaded via Global Mock)
A global mock is defined in `web/vitest.setup.ts` and is auto-loaded by Vitest setup.
The global mock provides:
-`useTranslation` - returns translation keys with namespace prefix
-`Trans` component - renders i18nKey and components
1.**Use real base components** - Import from `@/app/components/base/` directly
1.**Use real project components** - Prefer importing over mocking
1.**Use real Zustand stores** - Set test state via `store.setState()`
1.**Reset mocks in `beforeEach`**, not `afterEach`
1.**Match actual component behavior** in mocks (when mocking is necessary)
1.**Use factory functions** for complex mock data
1.**Import actual types** for type safety
1.**Reset shared mock state** in `beforeEach`
### ❌ DON'T
1.**Don't mock base components** (`Loading`, `Button`, `Tooltip`, etc.)
1.**Don't mock Zustand store modules** - Use real stores with `setState()`
1. Don't mock components you can import directly
1. Don't create overly simplified mocks that miss conditional logic
1. Don't forget to clean up nock after each test
1. Don't use `any` types in mocks without necessity
### Mock Decision Tree
```
Need to use a component in test?
│
├─ Is it from @/app/components/base/*?
│ └─ YES → Import real component, DO NOT mock
│
├─ Is it a project component?
│ └─ YES → Prefer importing real component
│ Only mock if setup is extremely complex
│
├─ Is it an API service (@/service/*)?
│ └─ YES → Mock it
│
├─ Is it a third-party lib with side effects?
│ └─ YES → Mock it (next/navigation, external SDKs)
│
├─ Is it a Zustand store?
│ └─ YES → DO NOT mock the module!
│ Use real store + setState() to set test state
│ (Global mock handles auto-reset)
│
└─ Is it i18n?
└─ YES → Uses shared mock (auto-loaded). Override only for custom translations
```
## Zustand Store Testing
### Global Zustand Mock (Auto-loaded)
Zustand is globally mocked in `web/vitest.setup.ts` following the [official Zustand testing guide](https://zustand.docs.pmnd.rs/guides/testing). The mock in `web/__mocks__/zustand.ts` provides:
- Real store behavior with `getState()`, `setState()`, `subscribe()` methods
- Automatic store reset after each test via `afterEach`
- Proper test isolation between tests
### ✅ Recommended: Use Real Stores (Official Best Practice)
**DO NOT mock store modules manually.** Import and use the real store, then use `setState()` to set test state:
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/docs/test.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 implementing oRPC contract-first API patterns in Dify frontend. Trigger when creating or updating contracts in web/contract, wiring router composition, integrating TanStack Query with typed contracts, migrating legacy service calls to oRPC, or deciding whether to call queryOptions directly vs extracting a helper or use-* hook in web/service.
---
# oRPC Contract-First Development
## Intent
- Keep contract as single source of truth in `web/contract/*`.
- Default query usage: call-site `useQuery(consoleQuery|marketplaceQuery.xxx.queryOptions(...))` when endpoint behavior maps 1:1 to the contract.
- Keep abstractions minimal and preserve TypeScript inference.
## Minimal Structure
```text
web/contract/
├── base.ts
├── router.ts
├── marketplace.ts
└── console/
├── billing.ts
└── ...other domains
web/service/client.ts
```
## Core Workflow
1. Define contract in `web/contract/console/{domain}.ts` or `web/contract/marketplace.ts`
- Use `base.route({...}).output(type<...>())` as baseline.
- Add `.input(type<...>())` only when request has `params/query/body`.
- For `GET` without input, omit `.input(...)` (do not use `.input(type<unknown>())`).
2. Register contract in `web/contract/router.ts`
- Import directly from domain files and nest by API prefix.
3. Consume from UI call sites via oRPC query utils.
1. Default: call mutation helpers from `consoleQuery` / `marketplaceQuery`, for example `useMutation(consoleQuery.billing.bindPartnerStack.mutationOptions(...))`.
2. If mutation flow is heavily custom, use oRPC clients as `mutationFn` (for example `consoleClient.xxx` / `marketplaceClient.xxx`), instead of generic handwritten non-oRPC mutation logic.
## Key API Guide (`.key` vs `.queryKey` vs `.mutationKey`)
-`.key(...)`:
- Use for partial matching operations (recommended for invalidation/refetch/cancel patterns).
- Use for a specific query's full key (exact query identity / direct cache addressing).
-`.mutationKey(...)`:
- Use for a specific mutation's full key.
- Typical use cases: mutation defaults registration, mutation-status filtering (`useIsMutating`, `queryClient.isMutating`), or explicit devtools grouping.
## Anti-Patterns
- Do not wrap `useQuery` with `options?: Partial<UseQueryOptions>`.
- Do not split local `queryKey/queryFn` when oRPC `queryOptions` already exists and fits the use case.
- Do not create thin `use-*` passthrough hooks for a single endpoint.
- Reason: these patterns can degrade inference (`data` may become `unknown`, especially around `throwOnError`/`select`) and add unnecessary indirection.
## Contract Rules
- **Input structure**: Always use `{ params, query?, body? }` format
- **No-input GET**: Omit `.input(...)`; do not use `.input(type<unknown>())`
- **Path params**: Use `{paramName}` in path, match in `params` object
- **Router nesting**: Group by API prefix (e.g., `/billing/*` -> `billing: {}`)
- **No barrel files**: Import directly from specific files
- **Types**: Import from `@/types/`, use `type<T>()` helper
- **Mutations**: Prefer `mutationOptions`; use explicit `mutationKey` mainly for defaults/filtering/devtools
# 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
# Agents
/.agents/skills/ @hyoban
# Docs
/docs/ @crazywoola
# Backend (default owner, more specific rules below will override)
description:Refactor existing code for improved readability and maintainability.
title:"[Chore/Refactor] "
labels:
- refactor
name:"✨ Refactor or Chore"
description:Refactor existing code or perform maintenance chores to improve readability and reliability.
title:"[Refactor/Chore] "
body:
- type:checkboxes
attributes:
@@ -11,7 +9,7 @@ body:
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 refactoring, 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 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
@@ -25,14 +23,14 @@ body:
id:description
attributes:
label:Description
placeholder:"Describe the refactor you are proposing."
placeholder:"Describe the refactor or chore you are proposing."
validations:
required:true
- type:textarea
id:motivation
attributes:
label:Motivation
placeholder:"Explain why this refactor is necessary."
placeholder:"Explain why this refactor or chore is necessary."
- 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
pnpm lint:fix
pnpm test
```
- Read `web/AGENTS.md` for details
## Testing & Quality Practices
@@ -38,8 +29,8 @@ pnpm test
## Language Style
- **Python**: Keep type hints on functions and attributes, and implement relevant special methods (e.g., `__repr__`, `__str__`).
- **TypeScript**: Use the strict config, lean on ESLint + Prettier workflows, and avoid `any` types.
- **Python**: Keep type hints on functions and attributes, and implement relevant special methods (e.g., `__repr__`, `__str__`). Prefer `TypedDict` over `dict` or `Mapping` for type safety and better code documentation.
- **TypeScript**: Use the strict config, rely on ESLint (`pnpm lint:fix` preferred) plus `pnpm type-check:tsgo`, and avoid `any` types.
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/docs/test.md](https://github.com/langgenius/dify/blob/main/web/docs/test.md) for the canonical frontend testing guidelines and follow every requirement described there.
#### Backend
For setting up the backend service, kindly refer to our detailed [instructions](https://github.com/langgenius/dify/blob/main/api/README.md) in the `api/README.md` file. This document contains step-by-step guidance to help you get the backend up and running smoothly.
<img alt="LFX Active Contributors" src="https://insights.linuxfoundation.org/api/badge/active-contributors?project=langgenius-dify"></a>
</p>
<p align="center">
@@ -117,7 +119,7 @@ All of Dify's offerings come with corresponding APIs, so you could effortlessly
Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
- **Dify for enterprise / organizations<br/>**
We provide additional enterprise-centric features. [Log your questions for us through this chatbot](https://udify.app/chat/22L1zSxg6yW1cWQg) or [send us an email](mailto:business@dify.ai?subject=%5BGitHub%5DBusiness%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.
@@ -133,6 +135,19 @@ Star Dify on GitHub and be instantly notified of new releases.
If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
#### Customizing Suggested Questions
You can now customize the "Suggested Questions After Answer" feature to better fit your use case. For example, to generate longer, more technical questions:
```bash
# In your .env file
SUGGESTED_QUESTIONS_PROMPT='Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: ["question1","question2","question3","question4","question5"]'
SUGGESTED_QUESTIONS_MAX_TOKENS=512
SUGGESTED_QUESTIONS_TEMPERATURE=0.3
```
See the [Suggested Questions Configuration Guide](docs/suggested-questions-configuration.md) for detailed examples and usage instructions.
### Metrics Monitoring with Grafana
Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.
# Set COOKIE_DOMAIN when the console frontend and API are on different subdomains.
# Provide the registrable domain (e.g. example.com); leading dots are optional.
# 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.
# 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.
Before changing any backend code under `api/`, you MUST read the surrounding docstrings and comments. These notes contain required context (invariants, edge cases, trade-offs) and are treated as part of the spec.
In this section, “notes” means module/class/function docstrings plus any relevant paragraph/block comments.
## Plugin & Extension Development
- **Before working**
- Read the notes in the area you’ll touch; treat them as part of the spec.
- If a docstring or comment conflicts with the current code, treat the **code as the single source of truth** and update the docstring or comment to match reality.
- If important intent/invariants/edge cases are missing, add them in the closest docstring or comment (module for overall scope, function for behaviour).
- **During working**
- Keep the notes in sync as you discover constraints, make decisions, or change approach.
- If you move/rename responsibilities across modules/classes, update the affected docstrings and comments so readers can still find the “why” and the invariants.
- Record non-obvious edge cases, trade-offs, and the test/verification plan in the nearest docstring or comment that will stay correct.
- Keep the notes **coherent**: integrate new findings into the relevant docstrings and comments; avoid append-only “recent fix” / changelog-style additions.
- **When finishing**
- Update the notes to reflect what changed, why, and any new edge cases/tests.
- Remove or rewrite any comments that could be mistaken as current guidance but no longer apply.
- Keep docstrings and comments concise and accurate; they are meant to prevent repeated rediscovery.
- **[Plugin Systems](agent_skills/plugin.md)**\
When to read this:
## Coding Style
- 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.
This is the default standard for backend code in this repo. Follow it for new code and use it as the checklist when reviewing changes.
- Code should usually include type annotations that match the repo’s current Python version (avoid untyped public APIs and “mystery” values).
- Prefer modern typing forms (e.g. `list[str]`, `dict[str, int]`) and avoid `Any` unless there’s a strong reason.
- For classes, declare member variables at the top of the class body (before `__init__`) so the class shape is obvious at a glance:
## Additional Notes for Agents
```python
fromdatetimeimportdatetime
- 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.
> [`uv`](https://docs.astral.sh/uv/) as the package manager
> for Dify API backend service.
1. Start the docker-compose stack
`uv` and `pnpm` are required to run the setup and development commands below.
The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
### Using scripts (recommended)
The scripts resolve paths relative to their location, so you can run them from anywhere.
1. Run setup (copies env files and installs dependencies).
```bash
cd ../docker
cp middleware.env.example middleware.env
# change the profile to other vector database if you are not using weaviate
docker compose -f docker-compose.middleware.yaml --profile weaviate -p dify up -d
cd ../api
./dev/setup
```
1. Copy `.env.example` to `.env`
1. Review `api/.env`, `web/.env.local`, and `docker/middleware.env` values (see the `SECRET_KEY` note below).
```cli
cp .env.example .env
```
1. Generate a `SECRET_KEY` in the `.env` file.
bash for Linux
```bash for Linux
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
```
bash for Mac
```bash for Mac
secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env
```
1. Create environment.
Dify API service uses [UV](https://docs.astral.sh/uv/) to manage dependencies.
First, you need to add the uv package manager, if you don't have it already.
1. Start middleware (PostgreSQL/Redis/Weaviate).
```bash
pip install uv
# Or on macOS
brew install uv
./dev/start-docker-compose
```
1. Install dependencies
1. Start backend (runs migrations first).
```bash
uv sync --dev
```
1. Run migrate
Before the first launch, migrate the database to the latest version.
```bash
uv run flask db upgrade
```
1. Start backend
```bash
uv run flask run --host 0.0.0.0 --port=5001 --debug
./dev/start-api
```
1. Start Dify [web](../web) service.
1. Setup your application by visiting `http://localhost:3000`.
```bash
./dev/start-web
```
1. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
1. Set up your application by visiting `http://localhost:3000`.
```bash
uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,priority_pipeline,pipeline
```
1. Start the worker service (async and scheduler tasks, runs from `api`).
Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:
```bash
./dev/start-worker
```
```bash
uv run celery -A app.celery beat
```
1. Optional: start Celery Beat (scheduled tasks).
```bash
./dev/start-beat
```
### Environment notes
> [!IMPORTANT]
>
> When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). The frontend and backend must be under the same top-level domain in order to share authentication cookies.
- Generate a `SECRET_KEY` in the `.env` file.
bash for Linux
```bash
sed -i "/^SECRET_KEY=/c\\SECRET_KEY=$(openssl rand -base64 42)" .env
```
bash for Mac
```bash
secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env
```
## Testing
1. Install dependencies for both the backend and the test environment
```bash
uv sync --dev
cd api
uv sync --group dev
```
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
cd api
uv run pytest # Run all tests
uv run pytest tests/unit_tests/ # Unit tests only
uv run pytest tests/integration_tests/ # Integration tests
- Import `configs.dify_config` for every runtime toggle. Do not read environment variables directly.
- Add new settings to the proper mixin inside `configs/` (deployment, feature, middleware, etc.) so they load through `DifyConfig`.
- Remote overrides come from the optional providers in `configs/remote_settings_sources`; keep defaults in code safe when the value is missing.
- Example: logging pulls targets from `extensions/ext_logging.py`, and model provider URLs are assembled in `services/entities/model_provider_entities.py`.
## Dependencies
- Runtime dependencies live in `[project].dependencies` inside `pyproject.toml`. Optional clients go into the `storage`, `tools`, or `vdb` groups under `[dependency-groups]`.
- Always pin versions and keep the list alphabetised. Shared tooling (lint, typing, pytest) belongs in the `dev` group.
- When code needs a new package, explain why in the PR and run `uv lock` so the lockfile stays current.
## Storage & Files
- Use `extensions.ext_storage.storage` for all blob IO; it already respects the configured backend.
- Convert files for workflows with helpers in `core/file/file_manager.py`; they handle signed URLs and multimodal payloads.
- When writing controller logic, delegate upload quotas and metadata to `services/file_service.py` instead of touching storage directly.
- All outbound HTTP fetches (webhooks, remote files) must go through the SSRF-safe client in `core/helper/ssrf_proxy.py`; it wraps `httpx` with the allow/deny rules configured for the platform.
## Redis & Shared State
- Access Redis through `extensions.ext_redis.redis_client`. For locking, reuse `redis_client.lock`.
- Prefer higher-level helpers when available: rate limits use `libs.helper.RateLimiter`, provider metadata uses caches in `core/helper/provider_cache.py`.
## Models
- SQLAlchemy models sit in `models/` and inherit from the shared declarative `Base` defined in `models/base.py` (metadata configured via `models/engine.py`).
-`models/__init__.py` exposes grouped aggregates: account/tenant models, app and conversation tables, datasets, providers, workflow runs, triggers, etc. Import from there to avoid deep path churn.
- Follow the DDD boundary: persistence objects live in `models/`, repositories under `repositories/` translate them into domain entities, and services consume those repositories.
- When adding a table, create the model class, register it in `models/__init__.py`, wire a repository if needed, and generate an Alembic migration as described below.
## Vector Stores
- Vector client implementations live in `core/rag/datasource/vdb/<provider>`, with a common factory in `core/rag/datasource/vdb/vector_factory.py` and enums in `core/rag/datasource/vdb/vector_type.py`.
- Retrieval pipelines call these providers through `core/rag/datasource/retrieval_service.py` and dataset ingestion flows in `services/dataset_service.py`.
- The CLI helper `flask vdb-migrate` orchestrates bulk migrations using routines in `commands.py`; reuse that pattern when adding new backend transitions.
- To add another store, mirror the provider layout, register it with the factory, and include any schema changes in Alembic migrations.
## Observability & OTEL
- OpenTelemetry settings live under the observability mixin in `configs/observability`. Toggle exporters and sampling via `dify_config`, not ad-hoc env reads.
- HTTP, Celery, Redis, SQLAlchemy, and httpx instrumentation is initialised in `extensions/ext_app_metrics.py` and `extensions/ext_request_logging.py`; reuse these hooks when adding new workers or entrypoints.
- When creating background tasks or external calls, propagate tracing context with helpers in the existing instrumented clients (e.g. use the shared `httpx` session from `core/helper/http_client_pooling.py`).
- If you add a new external integration, ensure spans and metrics are emitted by wiring the appropriate OTEL instrumentation package in `pyproject.toml` and configuring it in `extensions/`.
## Ops Integrations
- Langfuse support and other tracing bridges live under `core/ops/opik_trace`. Config toggles sit in `configs/observability`, while exporters are initialised in the OTEL extensions mentioned above.
- External monitoring services should follow this pattern: keep client code in `core/ops`, expose switches via `dify_config`, and hook initialisation in `extensions/ext_app_metrics.py` or sibling modules.
- Before instrumenting new code paths, check whether existing context helpers (e.g. `extensions/ext_request_logging.py`) already capture the necessary metadata.
## Controllers, Services, Core
- Controllers only parse HTTP input and call a service method. Keep business rules in `services/`.
- Services enforce tenant rules, quotas, and orchestration, then call into `core/` engines (workflow execution, tools, LLMs).
- When adding a new endpoint, search for an existing service to extend before introducing a new layer. Example: workflow APIs pipe through `services/workflow_service.py` into `core/workflow`.
## Plugins, Tools, Providers
- In Dify a plugin is a tenant-installable bundle that declares one or more providers (tool, model, datasource, trigger, endpoint, agent strategy) plus its resource needs and version metadata. The manifest (`core/plugin/entities/plugin.py`) mirrors what you see in the marketplace documentation.
- Installation, upgrades, and migrations are orchestrated by `services/plugin/plugin_service.py` together with helpers such as `services/plugin/plugin_migration.py`.
- Runtime loading happens through the implementations under `core/plugin/impl/*` (tool/model/datasource/trigger/endpoint/agent). These modules normalise plugin providers so that downstream systems (`core/tools/tool_manager.py`, `services/model_provider_service.py`, `services/trigger/*`) can treat builtin and plugin capabilities the same way.
- For remote execution, plugin daemons (`core/plugin/entities/plugin_daemon.py`, `core/plugin/impl/plugin.py`) manage lifecycle hooks, credential forwarding, and background workers that keep plugin processes in sync with the main application.
- Acquire tool implementations through `core/tools/tool_manager.py`; it resolves builtin, plugin, and workflow-as-tool providers uniformly, injecting the right context (tenant, credentials, runtime config).
- To add a new plugin capability, extend the relevant `core/plugin/entities` schema and register the implementation in the matching `core/plugin/impl` module rather than importing the provider directly.
## Async Workloads
see `agent_skills/trigger.md` for more detailed documentation.
- Enqueue background work through `services/async_workflow_service.py`. It routes jobs to the tiered Celery queues defined in `tasks/`.
- Workers boot from `celery_entrypoint.py` and execute functions in `tasks/workflow_execution_tasks.py`, `tasks/trigger_processing_tasks.py`, etc.
- Scheduled workflows poll from `schedule/workflow_schedule_tasks.py`. Follow the same pattern if you need new periodic jobs.
## Database & Migrations
- SQLAlchemy models live under `models/` and map directly to migration files in `migrations/versions`.
- Generate migrations with `uv run --project api flask db revision --autogenerate -m "<summary>"`, then review the diff; never hand-edit the database outside Alembic.
- Apply migrations locally using `uv run --project api flask db upgrade`; production deploys expect the same history.
- If you add tenant-scoped data, confirm the upgrade includes tenant filters or defaults consistent with the service logic touching those tables.
## CLI Commands
- Maintenance commands from `commands.py` are registered on the Flask CLI. Run them via `uv run --project api flask <command>`.
- Use the built-in `db` commands from Flask-Migrate for schema operations (`flask db upgrade`, `flask db stamp`, etc.). Only fall back to custom helpers if you need their extra behaviour.
- Custom entries such as `flask reset-password`, `flask reset-email`, and `flask vdb-migrate` handle self-hosted account recovery and vector database migrations.
- Before adding a new command, check whether an existing service can be reused and ensure the command guards edition-specific behaviour (many enforce `SELF_HOSTED`). Document any additions in the PR.
- Ruff helpers are run directly with `uv`: `uv run --project api --dev ruff format ./api` for formatting and `uv run --project api --dev ruff check ./api` (add `--fix` if you want automatic fixes).
## When You Add Features
- Check for an existing helper or service before writing a new util.
- Uphold tenancy: every service method should receive the tenant ID from controller wrappers such as `controllers/console/wraps.py`.
- Update or create tests alongside behaviour changes (`tests/unit_tests` for fast coverage, `tests/integration_tests` when touching orchestrations).
- Run `uv run --project api --dev ruff check ./api`, `uv run --directory api --dev basedpyright`, and `uv run --project api --dev dev/pytest/pytest_unit_tests.sh` before submitting changes.
Trigger is a collection of nodes that we called `Start` nodes, also, the concept of `Start` is the same as `RootNode` in the workflow engine `core/workflow/graph_engine`, On the other hand, `Start` node is the entry point of workflows, every workflow run always starts from a `Start` node.
## Trigger nodes
-`UserInput`
-`Trigger Webhook`
-`Trigger Schedule`
-`Trigger Plugin`
### UserInput
Before `Trigger` concept is introduced, it's what we called `Start` node, but now, to avoid confusion, it was renamed to `UserInput` node, has a strong relation with `ServiceAPI` in `controllers/service_api/app`
1.`UserInput` node introduces a list of arguments that need to be provided by the user, finally it will be converted into variables in the workflow variable pool.
1.`ServiceAPI` accept those arguments, and pass through them into `UserInput` node.
1. For its detailed implementation, please refer to `core/workflow/nodes/start`
### Trigger Webhook
Inside Webhook Node, Dify provided a UI panel that allows user define a HTTP manifest `core/workflow/nodes/trigger_webhook/entities.py`.`WebhookData`, also, Dify generates a random webhook id for each `Trigger Webhook` node, the implementation was implemented in `core/trigger/utils/endpoint.py`, as you can see, `webhook-debug` is a debug mode for webhook, you may find it in `controllers/trigger/webhook.py`.
Finally, requests to `webhook` endpoint will be converted into variables in workflow variable pool during workflow execution.
### Trigger Schedule
`Trigger Schedule` node is a node that allows user define a schedule to trigger the workflow, detailed manifest is here `core/workflow/nodes/trigger_schedule/entities.py`, we have a poller and executor to handle millions of schedules, see `docker/entrypoint.sh` / `schedule/workflow_schedule_task.py` for help.
To Achieve this, a `WorkflowSchedulePlan` model was introduced in `models/trigger.py`, and a `events/event_handlers/sync_workflow_schedule_when_app_published.py` was used to sync workflow schedule plans when app is published.
### Trigger Plugin
`Trigger Plugin` node allows user define there own distributed trigger plugin, whenever a request was received, Dify forwards it to the plugin and wait for parsed variables from it.
1. Requests were saved in storage by `services/trigger/trigger_request_service.py`, referenced by `services/trigger/trigger_service.py`.`TriggerService`.`process_endpoint`
1. Plugins accept those requests and parse variables from it, see `core/plugin/impl/trigger.py` for details.
A `subscription` concept was out here by Dify, it means an endpoint address from Dify was bound to thirdparty webhook service like `Github``Slack``Linear``GoogleDrive``Gmail` etc. Once a subscription was created, Dify continually receives requests from the platforms and handle them one by one.
## Worker Pool / Async Task
All the events that triggered a new workflow run is always in async mode, a unified entrypoint can be found here `services/async_workflow_service.py`.`AsyncWorkflowService`.`trigger_workflow_async`.
The infrastructure we used is `celery`, we've already configured it in `docker/entrypoint.sh`, and the consumers are in `tasks/async_workflow_tasks.py`, 3 queues were used to handle different tiers of users, `PROFESSIONAL_QUEUE``TEAM_QUEUE``SANDBOX_QUEUE`.
## Debug Strategy
Dify divided users into 2 groups: builders / end users.
Builders are the users who create workflows, in this stage, debugging a workflow becomes a critical part of the workflow development process, as the start node in workflows, trigger nodes can `listen` to the events from `WebhookDebug``Schedule``Plugin`, debugging process was created in `controllers/console/app/workflow.py`.`DraftWorkflowTriggerNodeApi`.
A polling process can be considered as combine of few single `poll` operations, each `poll` operation fetches events cached in `Redis`, returns `None` if no event was found, more detailed implemented: `core/trigger/debug/event_bus.py` was used to handle the polling process, and `core/trigger/debug/event_selectors.py` was used to select the event poller based on the trigger type.
@@ -22,7 +22,17 @@ class WeaviateConfig(BaseSettings):
default=True,
)
WEAVIATE_GRPC_ENDPOINT:str|None=Field(
description="URL of the Weaviate gRPC server (e.g., 'grpc://localhost:50051' or 'grpcs://weaviate.example.com:443')",
default=None,
)
WEAVIATE_BATCH_SIZE:PositiveInt=Field(
description="Number of objects to be processed in a single batch operation (default is 100)",
default=100,
)
WEAVIATE_TOKENIZATION:str|None=Field(
description="Tokenization for Weaviate (default is word)",
default="word",
)
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