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}
)
)

Online course in

Financial Planning and Applied Finance

Online course in

Financial Planning and Applied Finance