Chore/API for AgentflowV2 (#4696)
* Enhancement: Introduce prepended chat history handling in Agent and LLM nodes. - Added support for `prependedChatHistory` in both `Agent` and `LLM` classes to allow for initial message context. - Implemented validation for history schema in execution flow to ensure proper format. - Refactored utility functions to include JSON sanitization and validation methods for improved data handling. * update prediction swagger
This commit is contained in:
Henry Heng
GitHub
parent
035b5555a9
commit
543800562e
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@ -1216,15 +1216,18 @@ paths:
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security:
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- bearerAuth: []
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operationId: createPrediction
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summary: Create a new prediction
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description: Create a new prediction
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summary: Send message to flow and get AI response
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description: |
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Send a message to your flow and receive an AI-generated response. This is the primary endpoint for interacting with your flows and assistants.
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**Authentication**: API key may be required depending on flow settings.
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parameters:
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- in: path
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name: id
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required: true
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schema:
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type: string
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description: Chatflow ID
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description: Flow ID - the unique identifier of your flow
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example: 'your-flow-id'
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requestBody:
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content:
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application/json:
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@ -1236,24 +1239,36 @@ paths:
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properties:
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question:
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type: string
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description: Question to ask during the prediction process
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description: Question/message to send to the flow
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example: 'Analyze this uploaded file and summarize its contents'
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files:
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type: array
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items:
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type: string
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format: binary
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description: Files to be uploaded
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modelName:
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description: Files to be uploaded (images, audio, documents, etc.)
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streaming:
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type: boolean
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description: Enable streaming responses
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default: false
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overrideConfig:
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type: string
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nullable: true
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example: ''
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description: Other override configurations
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description: JSON string of configuration overrides
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example: '{"sessionId":"user-123","temperature":0.7}'
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history:
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type: string
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description: JSON string of conversation history
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example: '[{"role":"userMessage","content":"Hello"},{"role":"apiMessage","content":"Hi there!"}]'
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humanInput:
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type: string
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description: JSON string of human input for resuming execution
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example: '{"type":"proceed","feedback":"Continue with the plan"}'
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required:
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- question
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required: true
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responses:
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'200':
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description: Prediction created successfully
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description: Successful prediction response
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content:
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application/json:
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schema:
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@ -1261,45 +1276,106 @@ paths:
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properties:
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text:
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type: string
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description: The result of the prediction
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description: The AI-generated response text
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example: 'Artificial intelligence (AI) is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence.'
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json:
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type: object
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description: The result of the prediction in JSON format if available
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description: The result in JSON format if available (for structured outputs)
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nullable: true
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question:
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type: string
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description: The question asked during the prediction process
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description: The original question/message sent to the flow
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example: 'What is artificial intelligence?'
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chatId:
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type: string
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description: The chat ID associated with the prediction
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description: Unique identifier for the chat session
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example: 'chat-12345'
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chatMessageId:
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type: string
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description: The chat message ID associated with the prediction
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description: Unique identifier for this specific message
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example: 'msg-67890'
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sessionId:
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type: string
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description: The session ID associated with the prediction
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description: Session identifier for conversation continuity
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example: 'user-session-123'
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nullable: true
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memoryType:
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type: string
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description: The memory type associated with the prediction
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description: Type of memory used for conversation context
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example: 'Buffer Memory'
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nullable: true
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sourceDocuments:
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type: array
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description: Documents retrieved from vector store (if RAG is enabled)
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items:
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$ref: '#/components/schemas/Document'
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nullable: true
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usedTools:
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type: array
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description: Tools that were invoked during the response generation
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items:
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$ref: '#/components/schemas/UsedTool'
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fileAnnotations:
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type: array
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items:
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$ref: '#/components/schemas/FileAnnotation'
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nullable: true
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'400':
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description: Invalid input provided
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description: Bad Request - Invalid input provided or request format is incorrect
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content:
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application/json:
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schema:
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type: object
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properties:
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error:
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type: string
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example: 'Invalid request format. Check required fields and parameter types.'
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'401':
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description: Unauthorized - API key required or invalid
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content:
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application/json:
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schema:
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type: object
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properties:
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error:
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type: string
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example: 'Unauthorized access. Please verify your API key.'
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'404':
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description: Chatflow not found
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description: Not Found - Chatflow with specified ID does not exist
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content:
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application/json:
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schema:
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type: object
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properties:
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error:
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type: string
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example: 'Chatflow not found. Please verify the chatflow ID.'
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'413':
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description: Payload Too Large - Request payload exceeds size limits
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content:
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application/json:
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schema:
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type: object
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properties:
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error:
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type: string
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example: 'Request payload too large. Please reduce file sizes or split large requests.'
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'422':
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description: Validation error
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description: Validation Error - Request validation failed
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content:
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application/json:
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schema:
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type: object
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properties:
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error:
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type: string
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example: 'Validation failed. Check parameter requirements and data types.'
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'500':
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description: Internal server error
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description: Internal Server Error - Flow configuration or execution error
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content:
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application/json:
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schema:
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type: object
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properties:
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error:
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type: string
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example: 'Internal server error. Check flow configuration and node settings.'
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/tools:
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post:
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tags:
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@ -2011,13 +2087,33 @@ components:
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properties:
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question:
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type: string
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description: The question being asked
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description: The question/message to send to the flow
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example: 'What is artificial intelligence?'
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form:
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type: object
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description: The form object to send to the flow (alternative to question for Agentflow V2)
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additionalProperties: true
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example:
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title: 'Example'
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count: 1
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streaming:
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type: boolean
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description: Enable streaming responses for real-time output
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default: false
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example: false
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overrideConfig:
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type: object
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description: The configuration to override the default prediction settings (optional)
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description: Override flow configuration and pass variables at runtime
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additionalProperties: true
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example:
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sessionId: 'user-session-123'
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temperature: 0.7
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maxTokens: 500
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vars:
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user_name: 'Alice'
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history:
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type: array
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description: The history messages to be prepended (optional)
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description: Previous conversation messages for context
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items:
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type: object
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properties:
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@ -2030,8 +2126,14 @@ components:
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type: string
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description: The content of the message
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example: 'Hello, how can I help you?'
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example:
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- role: 'apiMessage'
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content: "Hello! I'm an AI assistant. How can I help you today?"
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- role: 'userMessage'
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content: "Hi, my name is Sarah and I'm learning about AI"
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uploads:
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type: array
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description: Files to upload (images, audio, documents, etc.)
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items:
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type: object
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properties:
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@ -2051,7 +2153,42 @@ components:
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mime:
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type: string
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description: The MIME type of the file or resource
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enum:
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[
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'image/png',
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'image/jpeg',
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'image/jpg',
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'image/gif',
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'image/webp',
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'audio/mp4',
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'audio/webm',
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'audio/wav',
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'audio/mpeg',
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'audio/ogg',
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'audio/aac'
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]
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example: 'image/png'
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example:
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- type: 'file'
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name: 'example.png'
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data: 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABgAAAAYCAYAAADgdz34AAABjElEQVRIS+2Vv0oDQRDG'
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mime: 'image/png'
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humanInput:
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type: object
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description: Return human feedback and resume execution from a stopped checkpoint
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properties:
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type:
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type: string
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enum: [proceed, reject]
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description: Type of human input response
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example: 'reject'
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feedback:
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type: string
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description: Feedback to the last output
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example: 'Include more emoji'
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example:
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type: 'reject'
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feedback: 'Include more emoji'
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Tool:
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type: object
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@ -3,6 +3,7 @@ import {
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ICommonObject,
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IDatabaseEntity,
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IHumanInput,
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IMessage,
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INode,
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INodeData,
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INodeOptionsValue,
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@ -696,6 +697,7 @@ class Agent_Agentflow implements INode {
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const state = options.agentflowRuntime?.state as ICommonObject
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const pastChatHistory = (options.pastChatHistory as BaseMessageLike[]) ?? []
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const runtimeChatHistory = (options.agentflowRuntime?.chatHistory as BaseMessageLike[]) ?? []
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const prependedChatHistory = options.prependedChatHistory as IMessage[]
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const chatId = options.chatId as string
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// Initialize the LLM model instance
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@ -730,6 +732,18 @@ class Agent_Agentflow implements INode {
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// Use to keep track of past messages with image file references
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let pastImageMessagesWithFileRef: BaseMessageLike[] = []
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// Prepend history ONLY if it is the first node
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if (prependedChatHistory.length > 0 && !runtimeChatHistory.length) {
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for (const msg of prependedChatHistory) {
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const role: string = msg.role === 'apiMessage' ? 'assistant' : 'user'
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const content: string = msg.content ?? ''
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messages.push({
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role,
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content
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})
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}
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}
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for (const msg of agentMessages) {
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const role = msg.role
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const content = msg.content
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@ -1,5 +1,5 @@
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import { BaseChatModel } from '@langchain/core/language_models/chat_models'
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import { ICommonObject, INode, INodeData, INodeOptionsValue, INodeParams, IServerSideEventStreamer } from '../../../src/Interface'
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import { ICommonObject, IMessage, INode, INodeData, INodeOptionsValue, INodeParams, IServerSideEventStreamer } from '../../../src/Interface'
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import { AIMessageChunk, BaseMessageLike, MessageContentText } from '@langchain/core/messages'
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import { DEFAULT_SUMMARIZER_TEMPLATE } from '../prompt'
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import { z } from 'zod'
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@ -359,6 +359,7 @@ class LLM_Agentflow implements INode {
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const state = options.agentflowRuntime?.state as ICommonObject
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const pastChatHistory = (options.pastChatHistory as BaseMessageLike[]) ?? []
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const runtimeChatHistory = (options.agentflowRuntime?.chatHistory as BaseMessageLike[]) ?? []
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const prependedChatHistory = options.prependedChatHistory as IMessage[]
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const chatId = options.chatId as string
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// Initialize the LLM model instance
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@ -382,6 +383,18 @@ class LLM_Agentflow implements INode {
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// Use to keep track of past messages with image file references
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let pastImageMessagesWithFileRef: BaseMessageLike[] = []
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// Prepend history ONLY if it is the first node
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if (prependedChatHistory.length > 0 && !runtimeChatHistory.length) {
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for (const msg of prependedChatHistory) {
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const role: string = msg.role === 'apiMessage' ? 'assistant' : 'user'
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const content: string = msg.content ?? ''
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messages.push({
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role,
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content
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})
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}
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}
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for (const msg of llmMessages) {
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const role = msg.role
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const content = msg.content
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@ -322,7 +322,7 @@ export interface IOverrideConfig {
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label: string
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name: string
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type: string
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schema?: ICommonObject[]
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schema?: ICommonObject[] | Record<string, string>
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}
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export type ICredentialDataDecrypted = ICommonObject
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@ -5,7 +5,7 @@ import { Execution } from '../../database/entities/Execution'
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import { InternalFlowiseError } from '../../errors/internalFlowiseError'
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import { getErrorMessage } from '../../errors/utils'
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import { ExecutionState, IAgentflowExecutedData } from '../../Interface'
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import { _removeCredentialId } from '../../utils/buildAgentflow'
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import { _removeCredentialId } from '../../utils'
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import { getRunningExpressApp } from '../../utils/getRunningExpressApp'
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export interface ExecutionFilters {
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@ -41,7 +41,9 @@ import {
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getStartingNode,
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getTelemetryFlowObj,
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QUESTION_VAR_PREFIX,
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CURRENT_DATE_TIME_VAR_PREFIX
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CURRENT_DATE_TIME_VAR_PREFIX,
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_removeCredentialId,
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validateHistorySchema
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} from '.'
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import { ChatFlow } from '../database/entities/ChatFlow'
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import { Variable } from '../database/entities/Variable'
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@ -105,6 +107,7 @@ interface IExecuteNodeParams {
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evaluationRunId?: string
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isInternal: boolean
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pastChatHistory: IMessage[]
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prependedChatHistory: IMessage[]
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appDataSource: DataSource
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usageCacheManager: UsageCacheManager
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telemetry: Telemetry
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@ -203,21 +206,6 @@ const updateExecution = async (appDataSource: DataSource, executionId: string, w
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await appDataSource.getRepository(Execution).save(execution)
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}
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export const _removeCredentialId = (obj: any): any => {
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if (!obj || typeof obj !== 'object') return obj
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if (Array.isArray(obj)) {
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return obj.map((item) => _removeCredentialId(item))
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}
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const newObj: Record<string, any> = {}
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for (const [key, value] of Object.entries(obj)) {
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if (key === 'FLOWISE_CREDENTIAL_ID') continue
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newObj[key] = _removeCredentialId(value)
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}
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return newObj
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}
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export const resolveVariables = async (
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reactFlowNodeData: INodeData,
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question: string,
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@ -820,6 +808,7 @@ const executeNode = async ({
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evaluationRunId,
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parentExecutionId,
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pastChatHistory,
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prependedChatHistory,
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appDataSource,
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usageCacheManager,
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telemetry,
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@ -927,6 +916,7 @@ const executeNode = async ({
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humanInputAction = lastNodeOutput?.humanInputAction
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}
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// This is when human in the loop is resumed
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if (humanInput && nodeId === humanInput.startNodeId) {
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reactFlowNodeData.inputs = { ...reactFlowNodeData.inputs, humanInput }
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// Remove the stopped humanInput from execution data
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@ -973,6 +963,7 @@ const executeNode = async ({
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isLastNode,
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sseStreamer,
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pastChatHistory,
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prependedChatHistory,
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agentflowRuntime,
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abortController,
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analyticHandlers,
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@ -1297,6 +1288,17 @@ export const executeAgentFlow = async ({
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const chatflowid = chatflow.id
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const sessionId = incomingInput.sessionId ?? chatId
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const humanInput: IHumanInput | undefined = incomingInput.humanInput
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// Validate history schema if provided
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if (incomingInput.history && incomingInput.history.length > 0) {
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if (!validateHistorySchema(incomingInput.history)) {
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throw new Error(
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'Invalid history format. Each history item must have: ' + '{ role: "apiMessage" | "userMessage", content: string }'
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)
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}
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}
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const prependedChatHistory = incomingInput.history ?? []
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const apiMessageId = uuidv4()
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/*** Get chatflows and prepare data ***/
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@ -1413,35 +1415,90 @@ export const executeAgentFlow = async ({
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}
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// If it is human input, find the last checkpoint and resume
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if (humanInput?.startNodeId) {
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if (humanInput) {
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if (!previousExecution) {
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throw new Error(`No previous execution found for session ${sessionId}`)
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}
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if (previousExecution.state !== 'STOPPED') {
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let executionData = JSON.parse(previousExecution.executionData) as IAgentflowExecutedData[]
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let shouldUpdateExecution = false
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// Handle different execution states
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if (previousExecution.state === 'STOPPED') {
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// Normal case - execution is stopped and ready to resume
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logger.debug(` ✅ Previous execution is in STOPPED state, ready to resume`)
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} else if (previousExecution.state === 'ERROR') {
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// Check if second-to-last execution item is STOPPED and last is ERROR
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if (executionData.length >= 2) {
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const lastItem = executionData[executionData.length - 1]
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const secondLastItem = executionData[executionData.length - 2]
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if (lastItem.status === 'ERROR' && secondLastItem.status === 'STOPPED') {
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logger.debug(` 🔄 Found ERROR after STOPPED - removing last error item to allow retry`)
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logger.debug(` Removing: ${lastItem.nodeId} (${lastItem.nodeLabel}) - ${lastItem.data?.error || 'Unknown error'}`)
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// Remove the last ERROR item
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executionData = executionData.slice(0, -1)
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shouldUpdateExecution = true
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||||
} else {
|
||||
throw new Error(
|
||||
`Cannot resume execution ${previousExecution.id} because it is in 'ERROR' state ` +
|
||||
`and the previous item is not in 'STOPPED' state. Only executions that ended with a ` +
|
||||
`STOPPED state (or ERROR after STOPPED) can be resumed.`
|
||||
)
|
||||
}
|
||||
} else {
|
||||
throw new Error(
|
||||
`Cannot resume execution ${previousExecution.id} because it is in 'ERROR' state ` +
|
||||
`with insufficient execution data. Only executions in 'STOPPED' state can be resumed.`
|
||||
)
|
||||
}
|
||||
} else {
|
||||
throw new Error(
|
||||
`Cannot resume execution ${previousExecution.id} because it is in '${previousExecution.state}' state. ` +
|
||||
`Only executions in 'STOPPED' state can be resumed.`
|
||||
`Only executions in 'STOPPED' state (or 'ERROR' after 'STOPPED') can be resumed.`
|
||||
)
|
||||
}
|
||||
|
||||
startingNodeIds.push(humanInput.startNodeId)
|
||||
checkForMultipleStartNodes(startingNodeIds, isRecursive, nodes)
|
||||
let startNodeId = humanInput.startNodeId
|
||||
|
||||
const executionData = JSON.parse(previousExecution.executionData) as IAgentflowExecutedData[]
|
||||
// If startNodeId is not provided, find the last node with STOPPED status from execution data
|
||||
if (!startNodeId) {
|
||||
// Search in reverse order to find the last (most recent) STOPPED node
|
||||
const stoppedNode = [...executionData].reverse().find((data) => data.status === 'STOPPED')
|
||||
|
||||
// Verify that the humanInputAgentflow node exists in previous execution
|
||||
const humanInputNodeExists = executionData.some((data) => data.nodeId === humanInput.startNodeId)
|
||||
if (!stoppedNode) {
|
||||
throw new Error('No stopped node found in previous execution data to resume from')
|
||||
}
|
||||
|
||||
if (!humanInputNodeExists) {
|
||||
startNodeId = stoppedNode.nodeId
|
||||
logger.debug(` 🔍 Auto-detected stopped node to resume from: ${startNodeId} (${stoppedNode.nodeLabel})`)
|
||||
}
|
||||
|
||||
// Verify that the node exists in previous execution
|
||||
const nodeExists = executionData.some((data) => data.nodeId === startNodeId)
|
||||
|
||||
if (!nodeExists) {
|
||||
throw new Error(
|
||||
`Human Input node ${humanInput.startNodeId} not found in previous execution. ` +
|
||||
`Node ${startNodeId} not found in previous execution. ` +
|
||||
`This could indicate an invalid resume attempt or a modified flow.`
|
||||
)
|
||||
}
|
||||
|
||||
startingNodeIds.push(startNodeId)
|
||||
checkForMultipleStartNodes(startingNodeIds, isRecursive, nodes)
|
||||
|
||||
agentFlowExecutedData.push(...executionData)
|
||||
|
||||
// Update execution data if we removed an error item
|
||||
if (shouldUpdateExecution) {
|
||||
logger.debug(` 📝 Updating execution data after removing error item`)
|
||||
await updateExecution(appDataSource, previousExecution.id, workspaceId, {
|
||||
executionData: JSON.stringify(executionData),
|
||||
state: 'INPROGRESS'
|
||||
})
|
||||
}
|
||||
|
||||
// Get last state
|
||||
const lastState = executionData[executionData.length - 1].data.state
|
||||
|
||||
|
|
@ -1454,6 +1511,9 @@ export const executeAgentFlow = async ({
|
|||
})
|
||||
newExecution = previousExecution
|
||||
parentExecutionId = previousExecution.id
|
||||
|
||||
// Update humanInput with the resolved startNodeId
|
||||
humanInput.startNodeId = startNodeId
|
||||
} else if (isRecursive && parentExecutionId) {
|
||||
const { startingNodeIds: startingNodeIdsFromFlow } = getStartingNode(nodeDependencies)
|
||||
startingNodeIds.push(...startingNodeIdsFromFlow)
|
||||
|
|
@ -1604,6 +1664,7 @@ export const executeAgentFlow = async ({
|
|||
parentExecutionId,
|
||||
isInternal,
|
||||
pastChatHistory,
|
||||
prependedChatHistory,
|
||||
appDataSource,
|
||||
usageCacheManager,
|
||||
telemetry,
|
||||
|
|
|
|||
|
|
@ -1103,12 +1103,13 @@ export const replaceInputsWithConfig = (
|
|||
* Several conditions:
|
||||
* 1. If config is 'analytics', always allow it
|
||||
* 2. If config is 'vars', check its object and filter out the variables that are not enabled for override
|
||||
* 3. If typeof config's value is an object, check if the node id is in the overrideConfig object and if the parameter (systemMessagePrompt) is enabled
|
||||
* 3. If typeof config's value is an array, check if the parameter is enabled and apply directly
|
||||
* 4. If typeof config's value is an object, check if the node id is in the overrideConfig object and if the parameter (systemMessagePrompt) is enabled
|
||||
* Example:
|
||||
* "systemMessagePrompt": {
|
||||
* "chatPromptTemplate_0": "You are an assistant"
|
||||
* }
|
||||
* 4. If typeof config's value is a string, check if the parameter is enabled
|
||||
* 5. If typeof config's value is a string, check if the parameter is enabled
|
||||
* Example:
|
||||
* "systemMessagePrompt": "You are an assistant"
|
||||
*/
|
||||
|
|
@ -1129,6 +1130,12 @@ export const replaceInputsWithConfig = (
|
|||
}
|
||||
overrideConfig[config] = filteredVars
|
||||
}
|
||||
} else if (Array.isArray(overrideConfig[config])) {
|
||||
// Handle arrays as direct parameter values
|
||||
if (isParameterEnabled(flowNodeData.label, config)) {
|
||||
inputsObj[config] = overrideConfig[config]
|
||||
}
|
||||
continue
|
||||
} else if (overrideConfig[config] && typeof overrideConfig[config] === 'object') {
|
||||
const nodeIds = Object.keys(overrideConfig[config])
|
||||
if (nodeIds.includes(flowNodeData.id)) {
|
||||
|
|
@ -1352,6 +1359,48 @@ export const findAvailableConfigs = (reactFlowNodes: IReactFlowNode[], component
|
|||
schema: arraySchema
|
||||
}
|
||||
}
|
||||
} else if (inputParam.loadConfig) {
|
||||
const configData = flowNode?.data?.inputs?.[`${inputParam.name}Config`]
|
||||
if (configData) {
|
||||
// Parse config data to extract schema
|
||||
let parsedConfig: any = {}
|
||||
try {
|
||||
parsedConfig = typeof configData === 'string' ? JSON.parse(configData) : configData
|
||||
} catch (e) {
|
||||
// If parsing fails, treat as empty object
|
||||
parsedConfig = {}
|
||||
}
|
||||
|
||||
// Generate schema from config structure
|
||||
const configSchema: Record<string, string> = {}
|
||||
parsedConfig = _removeCredentialId(parsedConfig)
|
||||
for (const key in parsedConfig) {
|
||||
if (key === inputParam.name) continue
|
||||
const value = parsedConfig[key]
|
||||
let fieldType = 'string' // default type
|
||||
|
||||
if (typeof value === 'boolean') {
|
||||
fieldType = 'boolean'
|
||||
} else if (typeof value === 'number') {
|
||||
fieldType = 'number'
|
||||
} else if (Array.isArray(value)) {
|
||||
fieldType = 'array'
|
||||
} else if (typeof value === 'object' && value !== null) {
|
||||
fieldType = 'object'
|
||||
}
|
||||
|
||||
configSchema[key] = fieldType
|
||||
}
|
||||
|
||||
obj = {
|
||||
node: flowNode.data.label,
|
||||
nodeId: flowNode.data.id,
|
||||
label: `${inputParam.label} Config`,
|
||||
name: `${inputParam.name}Config`,
|
||||
type: `json`,
|
||||
schema: configSchema
|
||||
}
|
||||
}
|
||||
} else {
|
||||
obj = {
|
||||
node: flowNode.data.label,
|
||||
|
|
@ -1930,3 +1979,48 @@ export const getAllNodesInPath = (startNode: string, graph: INodeDirectedGraph):
|
|||
|
||||
return Array.from(nodes)
|
||||
}
|
||||
|
||||
export const _removeCredentialId = (obj: any): any => {
|
||||
if (!obj || typeof obj !== 'object') return obj
|
||||
|
||||
if (Array.isArray(obj)) {
|
||||
return obj.map((item) => _removeCredentialId(item))
|
||||
}
|
||||
|
||||
const newObj: Record<string, any> = {}
|
||||
for (const [key, value] of Object.entries(obj)) {
|
||||
if (key === 'FLOWISE_CREDENTIAL_ID') continue
|
||||
newObj[key] = _removeCredentialId(value)
|
||||
}
|
||||
return newObj
|
||||
}
|
||||
|
||||
/**
|
||||
* Validates that history items follow the expected schema
|
||||
* @param {any[]} history - Array of history items to validate
|
||||
* @returns {boolean} - True if all items are valid, false otherwise
|
||||
*/
|
||||
export const validateHistorySchema = (history: any[]): boolean => {
|
||||
if (!Array.isArray(history)) {
|
||||
return false
|
||||
}
|
||||
|
||||
return history.every((item) => {
|
||||
// Check if item is an object
|
||||
if (typeof item !== 'object' || item === null) {
|
||||
return false
|
||||
}
|
||||
|
||||
// Check if role exists and is valid
|
||||
if (typeof item.role !== 'string' || !['apiMessage', 'userMessage'].includes(item.role)) {
|
||||
return false
|
||||
}
|
||||
|
||||
// Check if content exists and is a string
|
||||
if (typeof item.content !== 'string') {
|
||||
return false
|
||||
}
|
||||
|
||||
return true
|
||||
})
|
||||
}
|
||||
|
|
|
|||
|
|
@ -48,21 +48,30 @@ const OverrideConfigTable = ({ columns, onToggle, rows, sx }) => {
|
|||
return <SwitchInput onChange={(enabled) => handleChange(enabled, row)} value={row.enabled} />
|
||||
} else if (key === 'type' && row.schema) {
|
||||
// If there's schema information, add a tooltip
|
||||
const schemaContent =
|
||||
'[<br>' +
|
||||
row.schema
|
||||
.map(
|
||||
(item) =>
|
||||
` ${JSON.stringify(
|
||||
{
|
||||
[item.name]: item.type
|
||||
},
|
||||
null,
|
||||
2
|
||||
)}`
|
||||
)
|
||||
.join(',<br>') +
|
||||
'<br>]'
|
||||
let schemaContent
|
||||
if (Array.isArray(row.schema)) {
|
||||
// Handle array format: [{ name: "field", type: "string" }, ...]
|
||||
schemaContent =
|
||||
'[<br>' +
|
||||
row.schema
|
||||
.map(
|
||||
(item) =>
|
||||
` ${JSON.stringify(
|
||||
{
|
||||
[item.name]: item.type
|
||||
},
|
||||
null,
|
||||
2
|
||||
)}`
|
||||
)
|
||||
.join(',<br>') +
|
||||
'<br>]'
|
||||
} else if (typeof row.schema === 'object' && row.schema !== null) {
|
||||
// Handle object format: { "field": "string", "field2": "number", ... }
|
||||
schemaContent = JSON.stringify(row.schema, null, 2).replace(/\n/g, '<br>').replace(/ /g, ' ')
|
||||
} else {
|
||||
schemaContent = 'No schema available'
|
||||
}
|
||||
|
||||
return (
|
||||
<Stack direction='row' alignItems='center' spacing={1}>
|
||||
|
|
|
|||
|
|
@ -11,21 +11,30 @@ export const TableViewOnly = ({ columns, rows, sx }) => {
|
|||
return row[key] ? <Chip label='Enabled' color='primary' /> : <Chip label='Disabled' />
|
||||
} else if (key === 'type' && row.schema) {
|
||||
// If there's schema information, add a tooltip
|
||||
const schemaContent =
|
||||
'[<br>' +
|
||||
row.schema
|
||||
.map(
|
||||
(item) =>
|
||||
` ${JSON.stringify(
|
||||
{
|
||||
[item.name]: item.type
|
||||
},
|
||||
null,
|
||||
2
|
||||
)}`
|
||||
)
|
||||
.join(',<br>') +
|
||||
'<br>]'
|
||||
let schemaContent
|
||||
if (Array.isArray(row.schema)) {
|
||||
// Handle array format: [{ name: "field", type: "string" }, ...]
|
||||
schemaContent =
|
||||
'[<br>' +
|
||||
row.schema
|
||||
.map(
|
||||
(item) =>
|
||||
` ${JSON.stringify(
|
||||
{
|
||||
[item.name]: item.type
|
||||
},
|
||||
null,
|
||||
2
|
||||
)}`
|
||||
)
|
||||
.join(',<br>') +
|
||||
'<br>]'
|
||||
} else if (typeof row.schema === 'object' && row.schema !== null) {
|
||||
// Handle object format: { "field": "string", "field2": "number", ... }
|
||||
schemaContent = JSON.stringify(row.schema, null, 2).replace(/\n/g, '<br>').replace(/ /g, ' ')
|
||||
} else {
|
||||
schemaContent = 'No schema available'
|
||||
}
|
||||
|
||||
return (
|
||||
<Stack direction='row' alignItems='center' spacing={1}>
|
||||
|
|
|
|||
Loading…
Reference in New Issue