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課程簡介

Introduction to LlamaIndex

  • Understanding LlamaIndex and its role in LLMs
  • Setting up LlamaIndex: environment and prerequisites
  • The basics of indexing custom data

LlamaIndex in Action

  • Querying with LlamaIndex: techniques and best practices
  • Building query and chat engines with LlamaIndex
  • Creating intuitive Streamlit interfaces for LLM applications

Advanced LlamaIndex Features

  • Employing retrieval-augmented generation (RAG) for enhanced data retrieval
  • Leveraging vectorstores for efficient data management
  • Designing and implementing LlamaIndex agents

Application Development with LlamaIndex

  • Prompt engineering: chain of thought, ReAct, few-shot prompting
  • Developing a documentation helper: a real-world LLM application
  • Debugging and testing LLM applications

Deployment and Scaling

  • Deploying LlamaIndex-based applications
  • Scaling LLM applications for high performance
  • Monitoring and optimizing LLM applications

Ethical and Practical Considerations

  • Navigating ethical implications in LLM applications
  • Ensuring privacy and data security with LlamaIndex
  • Preparing for future developments in LLM technology

Summary and Next Steps

最低要求

  • 具備 Python 編程基礎和基本機器學習概念知識
  • 擁有 API 及應用開發經驗
  • 熟悉自然語言處理者優選,但非必須

受眾

  • 開發人員
  • 數據科學家
 42 小時

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