agentic-design-patterns-chinese

# Agentic Design Patterns (Chinese Translation) > Skill by [ara.so](https://ara.so) — AI Agent Skills collection. This project is a comprehensive Chinese translation of Google's "Agentic Design Patterns" book, covering 21 core patterns for building intelligent AI agent systems, plus 7 appendices and additional resources. ## Overview The book systematically introduces AI agent design patterns from basic to advanced: - **Basic Patterns**: Prompt Chaining, Routing, Parallelization - **Intermediate Patterns**: Reflection, Tool Use, Planning - **Advanced Patterns**: Multi-Agent Collaboration, Memory Management, RAG - **Practical Patterns**: Safety/Guardrails, Evaluation & Monitoring ## Project Structure ``` agentic-design-patterns/ ├── chapters/ # Translated chapters (32 files) │ ├── Chapter 1_ Prompt Chaining.md │ ├── Chapter 2_ Routing.md │ └── ... ├── original/ # Original English chapters ├── images/ # Image assets organized by chapter ├── glossary.md # Terminology reference ├── progress.md # Translation progress tracker └── translation-guide.md # Translation standards ``` ## Accessing the Content ### Online Reading Visit the deployed GitHub Pages site: ``` https://adp.xindoo.xyz/ ``` ### Local Setup 1. Clone the repository: ```bash git clone https://github.com/xindoo/agentic-design-patterns.git cd agentic-design-patterns ``` 2. For local Jekyll server (GitHub Pages style): ```bash bundle install bundle exec jekyll serve # Visit http://localhost:4000 ``` 3. For GitBook format: ```bash npm install -g gitbook-cli gitbook install gitbook serve # Visit http://localhost:4001 ``` ## Key Chapters & Patterns ### Chapter 1: Prompt Chaining (提示链) Breaking complex tasks into sequential prompts. ```python # Example: Document analysis chain def analyze_document(doc): # Step 1: Extract key points summary_prompt = f"Summarize key points from: {doc}" summary = llm.generate(summary_prompt) # Step 2: Analyze sentiment sentiment_prompt = f"Analyze sentiment of: {summary}" sentiment = llm.generate(sentiment_prompt) # Step 3: Generate recommendations rec_prompt = f"Based on sentiment {sentiment}, provide recommendations" recommendations = llm.generate(rec_prompt) return recommendations ``` ### Chapter 2: Routing (路由) Directing requests to specialized agents or models. ```python # Example: Intent-based routing def route_query(user_query): classifier_prompt = f"Classify intent: {user_query}\nOptions: technical, billing, general" intent = llm.generate(classifier_prompt) routes = { "technical": technical_agent, "billing": billing_agent, "general": general_agent } agent = routes.get(intent, general_agent) return agent.process(user_query) ``` ### Chapter 5: Tool Use (工具使用) Enabling agents to call external tools and APIs. ```python # Example: Function calling pattern tools = [ { "name": "search_database", "description": "Search product database", "parameters": {"query": "string"} }, { "name": "calculate_price", "description": "Calculate final price with discount", "parameters": {"base_price": "float", "discount": "float"} } ] def agent_with_tools(user_request): # Agent decides which tool to use response = llm.generate( prompt=user_request, tools=tools, tool_choice="auto" ) if response.tool_calls: for tool_call in response.tool_calls: result = execute_tool(tool_call.name, tool_call.arguments) # Feed result back to agent final_response = llm.generate( context=[user_request, result] ) return final_response ``` ### Chapter 7: Multi-Agent Collaboration (多智能体协作) Coordinating multiple specialized agents. ```python # Example: Research team pattern class ResearchTeam: def __init__(self): self.researcher = Agent("researcher", "Find information") self.analyst = Agent("analyst", "Analyze data") self.writer = Agent("writer", "Write report") def collaborate(self, topic): # Stage 1: Research research_data = self.researcher.execute( f"Research topic: {topic}" ) # Stage 2: Analysis analysis = self.analyst.execute( f"Analyze research: {research_data}" ) # Stage 3: Writing report = self.writer.execute( f"Write report based on: {analysis}" ) return report ``` ### Chapter 14: Knowledge Retrieval (RAG) Retrieval-Augmented Generation pattern. ```python # Example: RAG implementation from sentence_transformers import SentenceTransformer import faiss class RAGAgent: def __init__(self, knowledge_base): self.encoder = SentenceTransformer('all-MiniLM-L6-v2') self.index = self._build_index(knowledge_base) self.documents = knowledge_base def _build_index(self, documents): embeddings = self.encoder.encode([doc['text'] for doc in documents]) index = faiss.IndexFlatL2(embeddings.shape[1]) index.add(embeddings) return index def query(self, question, top_k=3): # Retrieve relevant documents query_embedding = self.encoder.encode([question]) distances, indices = self.index.search(query_embedding, top_k) context = "\n".join([ self.documents[i]['text'] for i in indices[0] ]) # Generate answer with context prompt = f"Context: {context}\n\nQuestion: {question}\nAnswer:" answer = llm.generate(prompt) return answer ``` ### Chapter 8: Memory Management (记忆管理) Managing short-term and long-term memory. ```python # Example: Conversational memory class ConversationMemory: def __init__(self, max_history=10): self.short_term = [] # Recent messages self.long_term = {} # Summary of topics self.max_history = max_history def add_message(self, role, content): self.short_term.append({"role": role, "content": content}) # Summarize if history too long if len(self.short_term) > self.max_history: summary = self._summarize_old_messages() self._store_to_long_term(summary) self.short_term = self.short_term[-self.max_history:] def get_context(self): # Combine long-term summary with recent history context = [] if self.long_term: context.append({"role": "system", "content": f"Previous context: {self.long_term}"}) context.extend(self.short_term) return context ``` ## Translation Workflow ### Contributing to Translation 1. **Check translation progress**: ```bash cat progress.md # View current status ``` 2. **Select a chapter** (currently all are in review status): ```markdown # Update progress.md - [x] 已翻译 Chapter X - [ ] 已审核 Chapter X ``` 3. **Follow translation guide**: ```bash cat translation-guide.md # Review standards cat glossary.md # Check terminology ``` 4. **Key translation principles**: - Use glossary for consistent terminology - Keep code examples in original language - Preserve markdown structure - Maintain image paths relative to `images/` ### Terminology Reference Common AI agent terms (from `glossary.md`): ```markdown | English | 中文 | Notes | |---------|------|-------| | Agent | 智能体 / 代理 | Context-dependent | | Prompt Chaining | 提示链 | | | Routing | 路由 | | | Tool Use | 工具使用 | | | RAG | 检索增强生成 | Keep acronym | | Multi-Agent | 多智能体 | | | Guardrails | 护栏 / 安全防护 | | | Human-in-the-Loop | 人机协同 | | ``` ## Configuration ### GitHub Pages (_config.yml) ```yaml title: Agentic Design Patterns 中文翻译 description: AI Agent 系统设计模式完整中文指南 url: "https://adp.xindoo.xyz" baseurl: "" markdown: kramdown theme: jekyll-theme-minimal ``` ### GitBook (SUMMARY.md) The book structure is defined in `SUMMARY.md`: ```markdown # Summary * [简介](README.md) * [核心章节](chapters/README.md) * [第1章：提示链](chapters/Chapter 1_ Prompt Chaining.md) * [第2章：路由](chapters/Chapter 2_ Routing.md) ... * [附录](chapters/README.md) * [附录A：高级提示技术](chapters/Appendix A_ Advanced Prompting Techniques.md) ... ``` ## Common Patterns & Use Cases ### Pattern 1: Sequential Processing (Prompt Chaining) Use when: Breaking down complex analysis into steps ```python result = chain_step1() → chain_step2() → chain_step3() ``` ### Pattern 2: Parallel Processing (Parallelization) Use when: Independent subtasks can run concurrently ```python results = await asyncio.gather( task1(), task2(), task3() ) ``` ### Pattern 3: Self-Improvement (Reflection) Use when: Output quality needs iterative refinement ```python output = generate() critique = reflect(output) improved = regenerate(critique) ``` ### Pattern 4: Dynamic Routing Use when: Different inputs need different handling ```python handler = router.select(input_type) result = handler.process(input) ``` ## Troubleshooting ### Issue: Images not displaying **Problem**: Image paths broken after translation ```markdown  # Wrong ``` **Solution**: Use correct relative paths ```markdown  # Correct from chapters/ ``` ### Issue: Inconsistent terminology **Problem**: Same English term translated differently ``` Agent → 智能体 (Chapter 1) Agent → 代理 (Chapter 2) # Inconsistent ``` **Solution**: Always check glossary.md first ```bash grep "Agent" glossary.md # Use: 智能体 (preferred) or 代理 (context-specific) ``` ### Issue: Jekyll build fails **Problem**: ``` Liquid Exception: Invalid Date ``` **Solution**: Check frontmatter dates in markdown files ```yaml --- # Remove or fix invalid date fields updated_at: "2026-05-17" # Future date might cause issues --- ``` ### Issue: Missing dependencies **Problem**: `bundle exec jekyll serve` fails **Solution**: Install dependencies ```bash gem install bundler bundle install # Or for GitBook: npm install -g gitbook-cli gitbook install ``` ## Advanced Usage ### Searching the Content Use grep for term searches: ```bash # Find all mentions of "RAG" grep -r "RAG" chapters/ # Find specific pattern implementations grep -r "def.*agent" chapters/ # Search in Chinese grep -r "多智能体" chapters/ ``` ### Extracting Code Examples ```bash # Extract all Python code blocks from a chapter sed -n '/```python/,/```/p' chapters/Chapter\ 5_\ Tool\ Use.md ``` ### Generating PDF/EPUB ```bash # Using GitBook gitbook pdf ./ ./agentic-patterns-zh.pdf gitbook epub ./ ./agentic-patterns-zh.epub ``` ## Resources - **Online Book**: https://adp.xindoo.xyz/ - **GitHub Repo**: https://github.com/xindoo/agentic-design-patterns - **Author**: xindoo (https://zxs.io) - **Translation Guide**: `translation-guide.md` - **Glossary**: `glossary.md` - **Progress Tracker**: `progress.md` ## Contributing ```bash # Fork and clone git clone https://github.com/YOUR_USERNAME/agentic-design-patterns.git # Create feature branch git checkout -b review/chapter-1-improvements # Make changes and commit git add chapters/Chapter\ 1_\ Prompt\ Chaining.md git commit -m "Review and improve Chapter 1 translation" # Push and create PR git push origin review/chapter-1-improvements ``` Follow `CONTRIBUTING.md` for detailed guidelines.