使用Cohere的 Command R自托管RAG应用程序
Cohere的Command R在检索增强生成(RAG)和工具使用任务方面拥有高精度。它提供低延迟和高吞吐量,具有长的128k令牌上下文长度。此外,它还展示了10种关键语言的强大多语能力。
在这个工作室里,我们正在构建一个完全自主托管的“与您的文档聊天”RAG应用程序,使用:
- -Cohere的“R”在当地使用Ollama服务。
- -Qdrant矢量数据库(自托管)
- -用于生成嵌入的Fastembed
下面是我们正在构建的内容的快速演示:
https://youtu.be/aLLw3iCPhtM
亚马逊Bedrock的智能体
Bedrock演示的代理
亚马逊Bedrock的代理商提高了运营效率、客户服务和决策,同时降低了成本并实现了创新
了解如何使用AWS SageMaker JumpStart Foundation Models使用LLM代理构建和部署工具
Large language model (LLM) agents are programs that extend the capabilities of standalone LLMs with 1) access to external tools (APIs, functions, webhooks, plugins, and so on), and 2) the ability to plan and execute tasks in a self-directed fashion. Often, LLMs need to interact with other software, databases, or APIs to accomplish complex tasks. For example, an administrative chatbot that schedules meetings would require access to employees’ calendars and email. With access to tools, LLM agents can become more powerful—at the cost of additional complexity.