What are common chain composition patterns?

Beyond simple prompt | model | parser pipes, a handful of patterns appear repeatedly in production LangChain applications:

• RAG pattern — retrieve relevant documents, inject them into a prompt, generate an answer. The retriever and passthrough run in parallel so both context and question reach the prompt: {context: retriever, question: RunnablePassthrough()} | rag_prompt | llm | parser
• Router / conditional branch — use RunnableBranch or a lambda to route different inputs to different sub-chains. Useful for multi-intent chatbots where a general question goes to one chain and a SQL query goes to another.
• Map-reduce — split a long document into chunks, process each chunk in parallel with .batch(), then reduce the results with a combine chain. Standard pattern for summarising books or analysing large codebases.
• Refine — process chunks sequentially, passing the previous summary into the next iteration to progressively build a better answer. More accurate than map-reduce for certain summarisation tasks.
• Fallback chain — primary chain with a backup: gpt4_chain.with_fallbacks([gpt35_chain]). If the primary raises an exception, the fallback is tried automatically.
• Branching + merge — run parallel branches (e.g. extract entities, summarise, classify sentiment) and merge their outputs into a final dict for downstream use.

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