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To enable rapid deployment of AI chatbots and integrate multiple LLM models, follow these steps:
- environmental preparation: Install Docker and Python 3.8+, it is recommended to use a virtual environment to isolate dependencies
- Deployment of core services: One-click deployment of TaskingAI Community Edition via Docker Compose (docker-compose -p taskingai up -d)
- Model Configuration: add API keys for models like OpenAI/Claude in the console, or configure a local Ollama model
- Agent Creation: Create AI agent and bind model via Python SDK (taskingai.assistant.create())
- Multi-model switching: Specify different models by model_id parameter when calling API to realize dynamic switching.
Advanced solution: use task queues for model load balancing and enhance answer accuracy through RAG system.
This answer comes from the articleTaskingAI: An Open Source Platform for Developing AI Native ApplicationsThe
May not be reproduced without permission:AI productivity tools " How to achieve rapid deployment of an AI chatbot with integration of multiple large language models?
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