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课程大纲
LangGraph and Agent Patterns: A Practical Primer
- Graphs vs. linear chains: when and why
- Agents, tools, and planner-executor loops
- Hello workflow: a minimal agentic graph
State, Memory, and Context Passing
- Designing graph state and node interfaces
- Short-term memory vs. persisted memory
- Context windows, summarization, and rehydration
Branching Logic and Control Flow
- Conditional routing and multi-path decisions
- Retries, timeouts, and circuit breakers
- Fallbacks, dead-ends, and recovery nodes
Tool Use and External Integrations
- Function/tool calling from nodes and agents
- Consuming REST APIs and databases from the graph
- Structured output parsing and validation
Retrieval-Augmented Agent Workflows
- Document ingestion and chunking strategies
- Embeddings and vector stores with ChromaDB
- Grounded responses with citations and safeguards
Evaluation, Debugging, and Observability
- Tracing paths and inspecting node interactions
- Golden sets, evaluations, and regression tests
- Quality, safety, and cost/latency monitoring
Packaging and Delivery
- FastAPI serving and dependency management
- Versioning graphs and rollback strategies
- Operational playbooks and incident response
Summary and Next Steps
要求
- Working knowledge of Python
- Experience building LLM applications or prompt chains
- Familiarity with REST APIs and JSON
Audience
- AI engineers
- Product managers
- Developers building interactive LLM-driven systems
14 小时
