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https://github.com/Nikhil-Doye/workflow-builder.git
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Nikhil-Doye
ba52bd6eeb
- Updated workflowStore to implement variable substitution for node outputs, allowing dynamic input handling across different node types. - Modified processing functions for LLM, data output, web scraping, embedding, similarity search, and structured output nodes to utilize the new variable substitution mechanism. - Adjusted demo-workflows.json to reflect changes in node configurations, including format updates for web scraping and model changes for LLM tasks.
146 lines
3.9 KiB
Markdown
146 lines
3.9 KiB
Markdown
# Variable Substitution Example
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This document demonstrates how the `{{input-1.output}}` variable substitution works in the workflow system.
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## How It Works
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### 1. **Data Flow Process**
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```
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Testing Panel Input → Data Input Node → Web Scraping Node → LLM Node → Data Output Node
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```
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### 2. **Variable Substitution Examples**
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#### Example 1: Web Scraping Node
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```json
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{
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"config": {
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"url": "{{input-1.output}}",
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"formats": ["markdown", "html"]
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}
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}
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```
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**What happens:**
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1. User enters `"https://github.com"` in testing panel
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2. Data Input Node stores: `{ output: "https://github.com" }`
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3. Web Scraping Node processes: `"{{input-1.output}}"` → `"https://github.com"`
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4. Firecrawl scrapes the actual GitHub URL
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#### Example 2: LLM Task Node
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```json
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{
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"config": {
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"prompt": "Analyze this content: {{scraper-1.output}}",
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"model": "deepseek-chat"
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}
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}
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```
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**What happens:**
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1. Web Scraping Node outputs: `{ output: "[scraped content]" }`
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2. LLM Node processes: `"{{scraper-1.output}}"` → `"[scraped content]"`
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3. AI analyzes the actual scraped content
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### 3. **Supported Variable Patterns**
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| Pattern | Description | Example |
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| ------------------- | ------------------------- | --------------------- |
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| `{{nodeId}}` | Entire output of the node | `{{input-1}}` |
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| `{{nodeId.output}}` | Output property | `{{input-1.output}}` |
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| `{{nodeId.data}}` | Data property | `{{scraper-1.data}}` |
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| `{{nodeId.status}}` | Status property | `{{llm-1.status}}` |
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| `{{nodeId.error}}` | Error property | `{{scraper-1.error}}` |
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### 4. **Complete Workflow Example**
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```json
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{
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"nodes": [
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{
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"id": "input-1",
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"type": "dataInput",
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"config": {
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"dataType": "url",
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"defaultValue": "https://example.com"
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}
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},
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{
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"id": "scraper-1",
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"type": "webScraping",
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"config": {
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"url": "{{input-1.output}}",
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"formats": ["markdown", "html"]
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}
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},
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{
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"id": "llm-1",
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"type": "llmTask",
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"config": {
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"prompt": "Summarize: {{scraper-1.output}}",
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"model": "deepseek-chat"
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}
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},
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{
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"id": "output-1",
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"type": "dataOutput",
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"config": {
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"format": "json",
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"filename": "analysis.json"
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}
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}
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]
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}
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```
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### 5. **Execution Flow**
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1. **Testing Panel**: User enters `"https://github.com"`
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2. **Data Input Node** (`input-1`):
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- Receives: `"https://github.com"`
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- Stores: `{ output: "https://github.com" }`
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- Status: `"success"`
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3. **Web Scraping Node** (`scraper-1`):
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- Config URL: `"{{input-1.output}}"`
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- Variable substitution: `"{{input-1.output}}"` → `"https://github.com"`
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- Calls Firecrawl with: `"https://github.com"`
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- Returns: `{ output: "[GitHub page content]", markdown: "...", html: "..." }`
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4. **LLM Task Node** (`llm-1`):
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- Config prompt: `"Summarize: {{scraper-1.output}}"`
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- Variable substitution: `"{{scraper-1.output}}"` → `"[GitHub page content]"`
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- Calls AI with: `"Summarize: [GitHub page content]"`
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- Returns: `{ output: "[AI summary of GitHub page]" }`
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5. **Data Output Node** (`output-1`):
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- Receives: `"[AI summary of GitHub page]"`
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- Formats as JSON and saves to `analysis.json`
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### 6. **Error Handling**
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If a variable reference doesn't exist:
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- `{{nonexistent-node.output}}` → `{{nonexistent-node.output}}` (unchanged)
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- Warning logged to console
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- Node continues with original string
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### 7. **Testing the Feature**
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1. Create a new workflow
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2. Add a Data Input node with URL as default value
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3. Add a Web Scraping node with URL set to `{{input-1.output}}`
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4. Add an LLM Task node with prompt containing `{{scraper-1.output}}`
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5. Run the workflow with a test URL
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6. Observe how variables are substituted with actual data
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This variable substitution system enables powerful data flow between nodes, making workflows dynamic and flexible!
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