节点与集成
Ollama Chat Model
The Ollama Chat Model node allows you use local Llama 2 models with conversational agents1.
On this page, you'll find the node parameters for the Ollama Chat Model node, and links to more resources.
Node parameters
- Model: Select the model that generates the completion. Choose from:
- Llama2
- Llama2 13B
- Llama2 70B
- Llama2 Uncensored
Refer to the Ollama Models Library documentation for more information about available models.
Node options
- Sampling Temperature: Use this option to control the randomness of the sampling process. A higher temperature creates more diverse sampling, but increases the risk of hallucinations.
- Top K: Enter the number of token choices the model uses to generate the next token.
- Top P: Use this option to set the probability the completion should use. Use a lower value to ignore less probable options.
Templates and examples
Browse n8n-nodes-langchain.lmchatollama integration templates or search all templates
Related resources
Refer to LangChains's Ollama Chat Model documentation for more information about the service.
View n8n's Advanced AI documentation.
Common issues
For common questions or issues and suggested solutions, refer to Common issues.
New to working with AI and using self-hosted n8n? Try n8n's self-hosted AI Starter Kit to get started with a proof-of-concept or demo playground using Ollama, Qdrant, and PostgreSQL.
Footnotes
- Back
AI agents are artificial intelligence systems capable of responding to requests, making decisions, and performing real-world tasks for users. They use large language models (LLMs) to interpret user input and make decisions about how to best process requests using the information and resources they have available.
官方原文和授权
本页来自 N8N 英文官方网站固定快照，并转换成 xueai 静态页面。内容以 N8N 持续更新的官方页面为准。