Getting Started with RAG

Introduction to Retrieval-Augmented Generation (RAG) fundamentals, architecture, and basic implementation.

Overview

Retrieval-Augmented Generation (RAG) is a technique that enhances Large Language Models (LLMs) by providing them with external, up-to-date, or proprietary information. Instead of relying solely on their training data, RAG systems "retrieve" relevant context from a knowledge base and "augment" the prompt before "generating" a response.

Why RAG?

LLMs like GPT-4 are powerful but have limitations:

  • Hallucinations: They can confidently invent facts.
  • Outdated Knowledge: Their training data has a cut-off date.
  • No Private Knowledge: They don't know your company's internal documents.

RAG solves these by grounding the model in retrieved evidence.

Core Architecture

A typical RAG pipeline has three main stages:

  1. Ingestion (Offline):

    • Load: Import documents (PDFs, HTML, Text).
    • Chunk: Split text into smaller, manageable pieces.
    • Embed: Convert chunks into vector representations using an embedding model.
    • Store: Save vectors and metadata in a Vector Database.

  2. Retrieval (Online):

    • Query Embedding: Convert user query into a vector.
    • Similarity Search: Find the most similar chunks in the Vector DB.
    • Re-ranking (Optional): Refine results for better relevance.

  3. Generation (Online):

    • Context Construction: Combine retrieved chunks with the user query.
    • Prompting: Send the augmented prompt to the LLM.
    • Response: The LLM generates an answer based on the provided context.

Basic Implementation

Here is a minimal example using Python, sentence-transformers, and lancedb.

Prerequisites

pip install sentence-transformers lancedb openai

1. Ingestion

import lancedb
from sentence_transformers import SentenceTransformer

# Initialize model and database
model = SentenceTransformer('all-MiniLM-L6-v2')
db = lancedb.connect("./rag-db")

# Sample documents
documents = [
    {"text": "RAG stands for Retrieval-Augmented Generation.", "id": 1},
    {"text": "Embeddings convert text into vector numbers.", "id": 2},
    {"text": "Vector databases store embeddings for fast search.", "id": 3},
]

# Create embeddings
data = []
for doc in documents:
    embedding = model.encode(doc['text']).tolist()
    data.append({
        "vector": embedding,
        "text": doc['text'],
        "id": doc['id']
    })

# Store in LanceDB
table = db.create_table("knowledge_base", data, mode="overwrite")

2. Retrieval

query = "What is RAG?"
query_vector = model.encode(query).tolist()

# Search for top 2 similar documents
results = table.search(query_vector).limit(2).to_list()

context = "\n".join([r['text'] for r in results])
print(f"Retrieved Context:\n{context}")

3. Generation

from openai import OpenAI

client = OpenAI(api_key="your-api-key")

prompt = f"""
Answer the question based ONLY on the context below.

Context:
{context}

Question: {query}
"""

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": prompt}]
)

print(f"Answer: {response.choices[0].message.content}")

Key Components

Embedding Models

The "translator" that turns text into numbers.

Vector Databases

Specialized databases for storing and searching high-dimensional vectors.

  • Local: LanceDB, Chroma, FAISS.
  • Cloud: Pinecone, Weaviate, Qdrant.

LLMs

The reasoning engine.

  • Proprietary: GPT-4, Claude 3.5 Sonnet.
  • Open Source: Llama 3, Mistral.

Common Challenges

  1. Bad Retrieval: The system finds irrelevant documents.
    • Fix: Improve chunking, use better embeddings, or add re-ranking.
  2. Lost in Middle: LLM ignores context in the middle of a long prompt.
    • Fix: Re-order documents so most relevant are at start/end.
  3. Hallucination: LLM ignores context and uses training data.
    • Fix: Prompt engineering ("Answer only using the context").

Next Steps