You built RAG by hand so you understand every part. Now see how LangChain and LlamaIndex collapse it into a few lines — and when the raw SDK is still the better choice.
Why: frameworks bundle loaders, splitters, stores, and retrievers so you write less glue — but they add abstraction, dependencies, and their own quirks. When: use the raw SDK when you want full control and a small dependency footprint; reach for a framework when you want batteries-included loaders and integrations. Where: you now understand what these frameworks do under the hood, which is exactly what makes them safe to use.
RAW SDK you wire load/chunk/embed/store/retrieve/generate yourself
-> full control, minimal deps, you own every decision
FRAMEWORK load(...).split(...).embed(...).as_retriever() in a few lines
-> fast to start, many integrations, more magic to debug
Build it once by hand (you did). Then choose deliberately.Why: LlamaIndex is built specifically for RAG, so ingestion and querying collapse into a handful of calls. When: use it when your job is "answer questions over a folder of documents" and you want sensible defaults. Where: it still does load → chunk → embed → store → retrieve → generate — the lessons you already built.
# pip install llama-index
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("./knowledge").load_data() # load + parse
index = VectorStoreIndex.from_documents(docs) # chunk + embed + store
engine = index.as_query_engine() # retrieve + generate
print(engine.query("How long do refunds take?"))Why: LangChain composes retrieval and generation as a chain, and is strongest when RAG is one step inside a larger app with tools and agents. When: choose it when you need its broad ecosystem of integrations or are already using it elsewhere. Where: notice the retriever is exactly the vector search you built, wrapped in an interface.
# pip install langchain langchain-community
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
emb = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
store = FAISS.from_texts(
["Refunds within 30 days.", "Downloads are non-refundable once accessed."],
embedding=emb,
)
retriever = store.as_retriever(search_kwargs={"k": 3})
for doc in retriever.invoke("can I return a download?"):
print(doc.page_content) # feed these into your grounded generation prompt