UNDER THE HOOD

How a Production RAG System Is Actually Built

No hand-waving. We break down the real architecture — from document ingestion and chunking strategy to retrieval tuning and response generation.

01

Ingest & Chunk

Load documents, split with semantic awareness, handle overlap for context continuity.

02

Embed & Index

Generate embeddings, store in a vector DB, configure HNSW params for speed vs. recall.

03

Retrieve & Rerank

Hybrid search, MMR diversity, cross-encoder reranking before the context window.

04

Generate & Stream

Chain to LLM with citation tracking, output parsing, and streaming to UI.

rag_pipeline.py

            
              from 
            
              langchain_community.vectorstores 
            
              import 
            
              Chroma
            
              from 
            
              langchain.text_splitter 
            
              import 
            
              RecursiveCharacterTextSplitter
            
              from 
            
              langchain_openai 
            
              import 
            
              OpenAIEmbeddings, ChatOpenAI
            
              from 
            
              langchain.chains 
            
              import 
            
              RetrievalQA
            
            # Chunk documents
          
              splitter 
            
              = RecursiveCharacterTextSplitter(
            
              chunk_size 
            
              =512, 
            
              chunk_overlap 
            
              =64
            
              )
            
            # Build retriever
          
              vectorstore 
            
              = Chroma.from_documents(
            
              docs 
            
              , OpenAIEmbeddings()
            
              )
            
              qa 
            
              = RetrievalQA.from_chain_type(
            
              llm 
            
              =ChatOpenAI(model= 
            
              "gpt-4o" 
            
              ),
            
              retriever 
            
              =vectorstore.as_retriever(
            
              search_kwargs 
            
              ={ 
            
              "k" 
            
              : 6}
            
              )
            
              )

Langchain Certification Online to Build Apps & Agents

What’s the learning curve for developers new to LLMs?

Do I need prior experience with LangChain to start building?

Langchain Certification Online to Build Apps & Agents

What’s the learning curve for developers new to LLMs?

Do I need prior experience with LangChain to start building?