<< back to Guides

Bonus: Retrieval-Augmented Generation (RAG) Architecture

RAG (Retrieval-Augmented Generation) combines information retrieval and language generation to produce more accurate, context-aware, and up-to-date outputs. It enhances LLMs by grounding their answers in external documents.

🧠 Why RAG?

Problem with LLMs	How RAG Helps
Hallucinations	Uses real documents as context
Limited context window	Retrieves only the most relevant pieces
Outdated knowledge	Can pull in current, dynamic data sources
Inefficient fine-tuning	Uses search instead of retraining the model

🔁 High-Level RAG Flow

User Query →
Retriever: Finds relevant docs from a knowledge base (vector DB, etc)
Reader / Generator: LLM uses both query + retrieved docs to generate a grounded answer

[User Query]
     ↓
[Retriever] ←→ [Vector DB / Corpus]
     ↓
[Context + Query]
     ↓
[LLM Generator (e.g. GPT, LLaMA)]
     ↓
[Answer]

🛠️ Core Components

1. Document Ingestion Pipeline

Load raw docs (PDF, HTML, Notion, DB)
Chunk + clean + embed them
Store in vector database (e.g., FAISS, Pinecone)

from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS

docs = WebBaseLoader("https://example.com/blog").load()
db = FAISS.from_documents(docs, OpenAIEmbeddings())

2. Retriever

Converts query to embedding
Finds top-k relevant documents

query = "What is RAG architecture?"
results = db.similarity_search(query, k=3)

3. Prompt + Generation

Combine retrieved documents and user query into a single prompt for the LLM.

from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=db.as_retriever()
)

response = qa.run("Explain Retrieval-Augmented Generation")

🧱 RAG Stack Example

Layer	Tool Example
Embedding	OpenAI, Cohere, SentenceTransformers
Storage	FAISS, Weaviate, Pinecone, Qdrant
LLM	GPT-4, LLaMA, Claude, Mistral
Framework	LangChain, LlamaIndex, Haystack
Frontend	Streamlit, Next.js, Gradio

🔍 Use Cases

Chatbots with knowledge base grounding
Legal, financial, or healthcare assistants
R&D document search
Internal Q&A for company wikis
Search-augmented creative writing tools

⚠️ Challenges & Tips

Challenge	Solution
Bad retrieval results	Improve chunking, embedding model, metadata filtering
Context too large	Use summarization or reranking
Slow retrieval	Use pre-computed, indexed vector DB
Privacy / access	Add RBAC or hybrid RAG with permission-aware sources

🧠 Advanced RAG Patterns

✳️ Multi-hop RAG

Chain multiple queries for reasoning across multiple docs.

🧵 RAG with Memory

Add chat history/context to RAG pipeline.

🤖 Agentic RAG

Use agents to run tools or fetch external resources beyond vector DB.

🧪 Evaluation of RAG

Groundedness: Does the answer align with source docs?
Faithfulness: No hallucinated claims?
Latency: Time from query to generation
Retrieval quality: Precision/recall of relevant passages

📚 Further Resources

<< back to Guides