This skill helps developers design and implement production-grade LLM applications using LangChain framework patterns for agents, chains, memory, and tool integration.
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正在使用「langchain-architecture」。 How do I choose the right memory type for my LangChain agent?
預期結果:
For short conversations under 10 messages, use ConversationBufferMemory to store all messages. For longer conversations, use ConversationSummaryMemory to summarize old messages. For tracking specific entities, use ConversationEntityMemory. For semantic retrieval of relevant history, use VectorStoreRetrieverMemory.
正在使用「langchain-architecture」。 Create a multi-step chain that extracts entities, analyzes them, and generates a summary.
預期結果:
Use SequentialChain with three LLMChain components: extract_prompt extracts entities, analyze_prompt analyzes them, and summary_prompt generates the final summary. Set output_variables to capture all intermediate and final outputs.
安全審計
安全All 27 static findings are false positives. The skill contains Python code examples in markdown documentation. The scanner incorrectly identified 'external_commands' (markdown code blocks), 'weak cryptographic algorithm' (agent type constants containing 'DESCRIPTION'), and 'network reconnaissance' (use case descriptions). This is educational documentation about LangChain with no executable code, network requests, or cryptographic operations.
低風險問題 (3)
品質評分
你能建構什麼
AI Application Developer
Build applications that use LLM agents with tool access to automate multi-step workflows
Data Engineer
Create RAG pipelines that combine internal documents with LLM capabilities for intelligent querying
ML Engineer
Design production-ready LLM systems with proper memory management, error handling, and observability
試試這些提示
Create a simple LangChain agent that can use a search tool and calculator. Show how to initialize the agent, add tools, and run a query.
Show me how to build a retrieval-augmented generation system using LangChain. Include document loading, text splitting, vector store creation, and the QA chain.
Explain the different memory types in LangChain and show code for implementing ConversationBufferMemory, ConversationSummaryMemory, and ConversationEntityMemory.
What are the best practices for deploying LangChain applications to production? Include error handling, callbacks for monitoring, rate limiting, and testing strategies.
最佳實務
- Always validate and sanitize user inputs before passing to LLM chains to prevent prompt injection
- Implement proper error handling with try-catch blocks around agent and chain executions
- Use callbacks for logging and monitoring token usage, latency, and errors in production
避免
- Do not exceed context window limits - implement proper memory truncation or summarization
- Avoid poorly defined tool descriptions - agents rely on descriptions to select the right tool
- Do not skip input validation - validate all user inputs before using them in chains or prompts