技能 langchain-architecture
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构建生产级 LLM 应用程序需要理解复杂的架构模式。本技能提供经过验证的 LangChain 模式，包括代理、链、内存管理和工具集成。
支持: Claude Codex Code(CC)
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下载技能 ZIP
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在 Claude 中上传
前往 设置 → 功能 → 技能 → 上传技能
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开启并开始使用
测试它
正在使用“langchain-architecture”。 Set up a basic conversational chain with memory
预期结果:
- Initialized ConversationBufferMemory for chat history
- Created LLMChain with conversation prompt template
- Configured memory to store input/output pairs
- Chain ready for multi-turn conversation with context retention
正在使用“langchain-architecture”。 Build an agent with search and calculator tools
预期结果:
- Loaded serpapi tool for web search queries
- Loaded llm-math tool for mathematical calculations
- Initialized agent with ReAct reasoning pattern
- Agent successfully answered: 'What's the weather in SF? Then calculate 25 * 4' by searching weather data and computing the result
安全审计
安全v1 • 2/25/2026
All 27 static analysis findings were evaluated and determined to be false positives. The external_commands detections (20 locations) incorrectly identified Markdown code block backticks as Ruby/shell execution. The blocker findings for weak cryptography and network reconnaissance were pattern mismatches on documentation text. This is a legitimate LangChain tutorial and architecture guide with no security concerns.
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已扫描文件
353
分析行数
0
发现项
1
审计总数
未发现安全问题
审计者: claude
质量评分
38
架构
100
可维护性
87
内容
50
社区
100
安全
100
规范符合性
你能构建什么
客户支持聊天机器人
构建一个智能客户支持代理，可以搜索知识库、保持对话上下文，并在需要时将复杂问题升级给人工代理。
文档分析管道
创建一个处理大型文档集合、提取关键信息并基于文档内容使用检索增强生成回答问题的系统。
多工具 AI 助手
开发一个能够选择和使用多个工具（包括搜索 API、计算器和数据库）来完成复杂任务的自主代理。
试试这些提示
基础 LangChain 设置
I want to build a simple LangChain application. Help me set up the basic components including an LLM, a prompt template, and a chain. My use case is: [describe your use case].
RAG 实现
I need to build a retrieval augmented generation system for my documents. Guide me through loading documents from [source], splitting them appropriately, creating embeddings, and setting up a retrieval chain for question answering.
自定义代理与工具
Create a LangChain agent that can use these custom tools: [list your tools]. The agent should reason about which tool to use based on user requests. Include proper error handling and verbose logging for debugging.
生产就绪架构
Review my LangChain application architecture for production deployment. Consider: memory management for long conversations, caching strategies for cost optimization, callback handlers for observability, and error handling for reliability. My current setup is: [describe your architecture].
最佳实践
- Choose memory type based on conversation length: use buffer memory for short interactions, summary memory for long conversations, and vector store memory for semantic retrieval of relevant history
- Provide clear, descriptive tool definitions to help agents select the right tool for each task
- Implement callback handlers early for observability, logging token usage, latency, and errors from the start
避免
- Storing entire conversation history without limits, leading to context window overflow and increased costs
- Using generic tool descriptions that confuse the agent about when to use each tool
- Skipping error handling for agent execution, causing failures when agents cannot complete tasks
常见问题
What is LangChain and why should I use it?
LangChain is a framework for developing applications powered by language models. It provides modular components for chains, agents, memory, and tool integration that simplify building complex LLM applications.
How do I choose the right memory type for my application?
Use ConversationBufferMemory for short conversations under 10 messages. Use ConversationSummaryMemory for longer conversations to avoid token limits. Use VectorStoreMemory when you need semantic search of conversation history.
What are agents in LangChain and how do they work?
Agents are autonomous systems that use an LLM to decide which actions to take. They reason through problems step by step, selecting and using tools until they reach a solution. Common types include ReAct, OpenAI Functions, and Structured Chat agents.
How can I optimize LangChain application performance?
Enable caching to avoid redundant LLM calls, use batch processing for document operations, implement streaming for faster responses, and choose appropriate chunk sizes for your documents to balance retrieval quality and speed.
Can I use LangChain with AI assistants like Claude or Claude Code?
Yes, LangChain integrates with multiple LLM providers. You can configure it to work with Anthropic's Claude models, and use AI coding assistants like Claude Code to help develop and debug your LangChain applications.
What is RAG and how do I implement it with LangChain?
RAG (Retrieval Augmented Generation) combines document retrieval with LLM generation. In LangChain, load documents using DocumentLoaders, split them with TextSplitters, store embeddings in a VectorStore, and use RetrievalQA chains to answer questions based on your documents.