Sticky Note Automate

For Sticky Note, this automated workflow streamlines the process of generating responses using AI, allowing users to easily input queries and receive instant answers. It integrates with LangChain to enhance interaction, making it ideal for quick information retrieval and engaging conversations.

7/8/2025
10 nodes
Medium
manualmediumsticky notelangchain
Categories:
Manual TriggeredMedium Workflow
Integrations:
Sticky NoteLangChain

Target Audience

Target Audience


- Developers: Those looking to implement automated workflows with AI integration.
- Product Managers: Individuals seeking to streamline processes and improve efficiency with automation.
- Data Scientists: Professionals who want to leverage AI models for data analysis and insights.
- Business Analysts: Users who need to automate repetitive tasks and enhance productivity.
- Educators: Teachers aiming to utilize AI tools for educational purposes and student engagement.

Problem Solved

Problem Solved


This workflow addresses the challenge of automating interactions with AI models and integrating them seamlessly into daily tasks. It allows users to:
- Efficiently generate responses: By automating queries to AI models, users can obtain quick and relevant answers.
- Reduce manual effort: Automating the process of asking questions and receiving answers minimizes the time spent on repetitive tasks.
- Enhance productivity: Users can focus on more critical activities while the workflow handles routine queries.

Workflow Steps

Workflow Steps


1. Manual Trigger: The workflow starts when the user clicks "Execute Workflow".
2. Set Input Values: Two separate inputs are set:
- First input: "Tell me a joke".
- Second input: "What year was Einstein born?".
3. LLM Chain Node: The first input is processed through a custom LLM chain node, which utilizes a prompt template to generate a response from the AI model.
4. OpenAI Chat Model: The response from the LLM chain is generated using the OpenAI chat model, specifically the gpt-4o-mini model.
5. AI Agent: The second input is processed through an AI agent that can utilize various tools, including a custom Wikipedia tool.
6. Wikipedia Tool: This tool queries Wikipedia for relevant information based on the second input, returning concise results.
7. Output Generation: The responses from both the LLM chain and Wikipedia tool are made available for further use or display.

Customization Guide

Customization Guide


- Change Input Queries: Modify the values assigned in the Set2 and Set3 nodes to customize the questions asked to the AI model.
- Adjust AI Model: In the OpenAI Chat Model and OpenAI Chat Model1 nodes, change the model type or parameters to suit specific needs or preferences.
- Enhance Tool Functionality: Customize the Custom - Wikipedia1 node by altering the jsCode to include different tools or modify the parameters for the Wikipedia query.
- Add More Steps: Users can add additional nodes to the workflow for further processing or integration with other tools as required.