# Cursor Chat: ai-agent-book

## Metadata
- **Project**: ai-agent-book
- **Path**: `/Users/boj`
- **Date**: 2025-09-25 17:11:08
- **Session ID**: `94ca162b-2b0f-47f0-869c-a54071bb424a`

## Conversation

### 👤 You

Update README in projects/README.md to reflect week3 projects. Read chapter3.md to understand what they are about.

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### 🤖 Cursor Assistant

I'll help you update the projects/README.md to reflect the week3 projects. Let me first read chapter3.md to understand what the week3 projects are about, and then check the current README structure.

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### 🤖 Cursor Assistant

Now I'll update the README to include the week3 projects based on chapter3.md. Let me first check what projects actually exist in the week3 directory to ensure accuracy.

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### 🤖 Cursor Assistant

Now I'll update the README.md to include the Week 3 projects based on chapter3.md content:

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### 🤖 Cursor Assistant

Let me update the learning suggestions to reflect that we now have three weeks of content:

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### 🤖 Cursor Assistant

Perfect! I've successfully updated the `projects/README.md` to include all Week 3 projects. The update adds:

## Summary of Changes

1. **Added Week 3 Section** - "知识库与学习机制" (Knowledge Base and Learning Mechanisms)

2. **Included 12 Week 3 Projects**:
   - **dense-embedding**: Vector similarity search with ANNOY vs HNSW comparison
   - **sparse-embedding**: BM25 search engine implementation from scratch  
   - **retrieval-pipeline**: Hybrid retrieval with neural reranking
   - **multimodal-agent**: Comparison of multimodal processing strategies
   - **structured-index**: RAPTOR and GraphRAG structured indexing
   - **agentic-rag**: Agentic vs Non-Agentic RAG comparison
   - **agentic-rag-for-user-memory**: Applying Agentic RAG to user memory
   - **contextual-retrieval**: Anthropic's contextual retrieval technique
   - **contextual-retrieval-for-user-memory**: Enhanced user memory with context
   - **structured-knowledge-extraction**: Knowledge discovery from datasets
   - **gaia-experience**: Learning from successful task trajectories
   - **browser-use-rpa**: Workflow recording and replay automation

3. **Updated Learning Suggestions** to reflect three weeks of content progression

Each project description includes:
- Clear explanation of what the project demonstrates
- Key technical concepts and methods explored
- Educational value and learning outcomes
- Consistent formatting with existing Week 1 and Week 2 entries

The descriptions are directly based on the content from chapter3.md, ensuring they accurately represent what each project teaches about RAG techniques, knowledge organization, and Agent learning mechanisms.

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*Exported from [Cursor View](https://github.com/saharmor/cursor-view)*