Building production-grade LLM applications requires understanding complex architectural patterns. This skill provides proven LangChain patterns for agents, chains, memory management, and tool integration.
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正在使用「langchain-architecture」。 Set up a basic conversational chain with memory
預期結果:
- 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
預期結果:
- 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
安全審計
安全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.
品質評分
你能建構什麼
Customer Support Chatbot
Build an intelligent customer support agent that can search knowledge bases, maintain conversation context, and escalate complex issues to human agents when needed.
Document Analysis Pipeline
Create a system that processes large document collections, extracts key information, and answers questions based on document content using retrieval augmented generation.
Multi-Tool AI Assistant
Develop an autonomous agent that can select and use multiple tools including search APIs, calculators, and databases to complete complex tasks.
試試這些提示
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].
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.
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.
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].
最佳實務
- 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