Automated Chatbot with Mistral-7B-Instruct via Hugging Face
detail.loadingPreview
Build intelligent chatbots powered by open-source Large Language Models like Mistral-7B-Instruct, seamlessly integrated with n8n. This workflow leverages Hugging Face's inference capabilities to deliver dynamic and polite AI responses.
About This Workflow
This n8n workflow demonstrates a foundational LLM chain, specifically designed to interact with the Mistral-7B-Instruct-v0.1 model hosted on Hugging Face. Upon receiving a chat message, the workflow constructs a prompt that guides the LLM to act as a polite and helpful assistant. The Mistral model then generates a response, which can be further processed or displayed. This provides a robust starting point for creating custom AI-powered conversational agents without the need for complex local setups, utilizing the power of open-source models and a user-friendly automation platform.
Key Features
- Open-Source LLM Integration: Connects directly to powerful open-source models like Mistral-7B-Instruct-v0.1.
- Hugging Face Inference: Utilizes Hugging Face's inference API for easy access to pre-trained models.
- Customizable Prompting: Define specific instructions and context for your AI assistant.
- Event-Driven Automation: Trigger AI responses based on incoming chat messages.
- No-Code/Low-Code Setup: Build AI-powered workflows visually within n8n.
How To Use
- Trigger: Configure the 'When chat message received' node to listen for incoming chat events.
- LLM Chain Setup: In the 'Basic LLM Chain' node, define the initial system prompt to guide the AI's behavior. Ensure the prompt format aligns with the expected input for the chosen LLM.
- Model Configuration: Connect the 'Basic LLM Chain' to the 'Hugging Face Inference Model' node. Select the desired model (e.g.,
mistralai/Mistral-7B-Instruct-v0.1) and optionally adjust inference parameters likemaxTokensandtemperature. - Credentials: Ensure your Hugging Face API credentials are set up and selected in the 'Hugging Face Inference Model' node.
- Connections: Verify that the 'When chat message received' node's output is connected to the 'Basic LLM Chain' and that the LLM Chain is connected to the 'Hugging Face Inference Model' for language model inference.
Apps Used
Workflow JSON
{
"id": "c59ed58f-ba42-4bcf-a610-83461746790e",
"name": "Automated Chatbot with Mistral-7B-Instruct via Hugging Face",
"nodes": 11,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: c59ed58f-ba42...
About the Author
N8N_Community_Pick
Curator
Hand-picked high quality workflows from the global community.
Statistics
Related Workflows
Discover more workflows you might like
WhatsApp AI Assistant: LLaMA 4 & Google Search for Real-Time Insights
Instantly deploy a smart AI assistant on WhatsApp, powered by Groq's lightning-fast LLaMA 4 model. This workflow enables real-time conversations, remembers context, and provides up-to-date answers by integrating live Google Search results.
AI-Powered On-Page SEO Audit & Report Automation
Instantly generate comprehensive on-page SEO technical and content audits for any website URL. This AI-powered workflow automates the entire process, from scraping the page to delivering a detailed report directly to your inbox, empowering you to optimize for better search rankings and user engagement.
Automate LinkedIn Content Promotion for Your Ghost Blog with AI
Effortlessly promote your latest Ghost blog posts on LinkedIn. This workflow leverages AI to generate engaging, professional LinkedIn messages based on your article content and saves them, along with article metadata, directly to a Google Sheet.