Library / Chatbots & Messaging Bots
Chatbots & Messaging Bots
Chat with DeepSeek V3 and R1 Reasoning Models
When a chat message arrives, an agent with memory answers using DeepSeek V3 or the R1 reasoning model, or a local Ollama DeepSeek.
What it does
# 🐋DeepSeek V3 Chat & R1 Reasoning Quick Start ## Overview Automated workflow: 🐋DeepSeek V3 Chat & R1 Reasoning Quick Start. This workflow integrates 9 different services: stickyNote, httpRequest, agent, chainLlm, stopAndError. It contains 20 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 20 - Node Types: 9 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - When chat message received: chatTrigger - AI Agent: agent - DeepSeek: lmChatOpenAi - Window Buffer Memory: memoryBufferWindow - Basic LLM Chain2: chainLlm - Sticky Note: stickyNote - Sticky Note1: stickyNote - Ollama DeepSeek: lmChatOllama - Sticky Note2: stickyNote - Sticky Note3: stickyNote - ... and 10 more nodes ## Usage Instructions 1. Configure Credentials: Set up all required API keys and credentials 2. Update Variables: Replace any placeholder values with actual data 3. Test Workflow: Run in test mode to verify functionality 4. Deploy: Activate the workflow for production use ## Security Notes - All sensitive data has been removed or replaced with placeholders - Error handling is implemented for reliability - Follow security best practices when configuring credentials ## Troubleshooting - Check error logs if workflow fails - Verify all credentials are properly configured - Ensure all required services are accessible
The steps
- Manual Trigger
- When chat message received
- AI Agent
- DeepSeek
- Window Buffer Memory
- Basic LLM Chain2
- Ollama DeepSeek
- DeepSeek JSON Body
- DeepSeek Raw Body
- Error Handler
- Stopanderror 1
- Error Handler for 07a8c74c
- Error Handler for ec46acef
- Error Handler for e5295120
29 steps in total.
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