Step-by-Step Guide: Building a Production RAG System with LangChain, Qdrant & LLaMA 3.3

Retrieval-Augmented Generation Tutorial: Build an end-to-end RAG architecture with vector embedding chunking, Qdrant similarity search, and ground-truth verification.

Step 1: Document Chunking & Vector Embeddings

from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceEmbeddings

splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64)
chunks = splitter.split_documents(raw_documents)

embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")

Step 2: Index Chunks into Qdrant Vector DB

from langchain_community.vectorstores import Qdrant

vector_store = Qdrant.from_documents(
    chunks,
    embeddings,
    location=":memory:",
    collection_name="technical_docs"
)

Step 3: Query & Synthesize Answer with RAG Prompt

Combine top-K retrieved context chunks into LLM prompt template to generate factual responses.