Day 15 — RAG Chain Architecture

Why Chains

⛓️

From Manual Steps to an Elegant Pipeline

The manual approach — invoke retriever, format context, build prompt, call LLM, parse output — works but is tedious and fragile. LangChain chains replace all of that with a single pipeline you define once and invoke with one call.

💡 Chain = A pipeline where each component's output automatically becomes the next component's input.

Parallel Chain

⚡

Preparing Context and Question Simultaneously

RAG prompts need two inputs: context from the retriever and the original question. RunnableParallel prepares both at the same time — context is retrieved while the question is passed through unchanged.

·context key — retriever | format_docs — fetches documents and converts them to text

·question key — RunnablePassthrough — passes the user query through as-is

💡 RunnablePassthrough is a component that takes its input and returns it unchanged — useful for threading data through parallel branches.

Full Chain

🔗

Complete RAG Pipeline in One Expression

The parallel chain feeds into a prompt template, which feeds into the LLM, which feeds into the output parser. The pipe operator connects everything.

·chain = parallel_chain | prompt | llm | StrOutputParser()

·One invoke call runs the entire pipeline end to end

·Easy to modify — swap any component without rewriting the rest

Benefits

✨

Why Chains Are Production Grade

✓Clean, readable code — entire pipeline visible in one expression

✓Composable — combine chains to build more complex systems

✓Easy to modify — swap any component without rewriting everything else

✓Async support built in — fast and scalable in production

✦

LangChain chains turn your RAG code from a series of manual steps into an elegant, production-grade pipeline. Parallel chain prepares context and question simultaneously. Prompt template structures the LLM input. One invoke call does everything. Clean, composable, and easy to maintain at scale.

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