feat: AI Workflow Builder backend (no-changelog) (#14837)

packages/@n8n/ai-workflow-builder/src/chains/planner.ts

@@ -0,0 +1,94 @@
+import type { BaseChatModel } from '@langchain/core/language_models/chat_models';
+import type { AIMessageChunk } from '@langchain/core/messages';
+import { SystemMessage } from '@langchain/core/messages';
+import { ChatPromptTemplate, HumanMessagePromptTemplate } from '@langchain/core/prompts';
+import { DynamicStructuredTool } from '@langchain/core/tools';
+import { OperationalError } from 'n8n-workflow';
+import { z } from 'zod';
+
+export const plannerPrompt = new SystemMessage(
+	`You are a Workflow Planner for n8n, a platform that helps users automate processes across different services and APIs.
+
+## Your Task
+Convert user requests into clear, sequential workflow steps that can be implemented with n8n nodes.
+
+## Guidelines
+1. Analyze the user request to understand their end goal and required process
+2. Break down the automation into logical steps based on complexity - simpler workflows need fewer steps, complex ones may need more
+3. Focus on actions (fetch data, transform, filter, send notification, etc.)
+4. Create steps that can be mapped to n8n nodes later
+5. Order steps sequentially from trigger to final action
+6. Be specific about data transformations needed
+7. Include error handling steps when appropriate
+8. Only recommend raw HTTP requests if you think there isn't a suitable n8n node
+
+## Output Format
+Return ONLY a JSON object with this structure:
+\`\`\`json
+{
+  "steps": [
+    "[Brief action-oriented description]",
+    "[Brief action-oriented description]",
+    ...
+  ]
+}
+\`\`\`
+
+## Examples of Good Step Descriptions
+- "Trigger when a new email arrives in Gmail inbox"
+- "Filter emails to only include those with attachments"
+- "Extract data from CSV attachments"
+- "Transform data to required format for the API"
+- "Send HTTP request to external API with extracted data"
+- "Post success message to Slack channel"
+
+IMPORTANT: Do not include HTML tags, markdown formatting, or explanations outside the JSON.`,
+);
+
+const planSchema = z.object({
+	steps: z
+		.array(
+			z
+				.string()
+				.describe(
+					'A clear, action-oriented description of a single workflow step. Do not include "Step N" or similar, just the action',
+				),
+		)
+		.min(1)
+		.describe(
+			'An ordered list of workflow steps that, when implemented, will fulfill the user request. Each step should be concise, action-oriented, and implementable with n8n nodes.',
+		),
+});
+
+const generatePlanTool = new DynamicStructuredTool({
+	name: 'generate_plan',
+	description:
+		'Convert a user workflow request into a logical sequence of clear, achievable steps that can be implemented with n8n nodes.',
+	schema: planSchema,
+	func: async (input) => {
+		return { steps: input.steps };
+	},
+});
+
+const humanTemplate = '{prompt}';
+const chatPrompt = ChatPromptTemplate.fromMessages([
+	plannerPrompt,
+	HumanMessagePromptTemplate.fromTemplate(humanTemplate),
+]);
+
+export const plannerChain = (llm: BaseChatModel) => {
+	if (!llm.bindTools) {
+		throw new OperationalError("LLM doesn't support binding tools");
+	}
+
+	return chatPrompt
+		.pipe(
+			llm.bindTools([generatePlanTool], {
+				tool_choice: generatePlanTool.name,
+			}),
+		)
+		.pipe((x: AIMessageChunk) => {
+			const toolCall = x.tool_calls?.[0];
+			return (toolCall?.args as z.infer<typeof planSchema>).steps;
+		});
+};