Scaling RAG Systems

Duration: 5 min

This module delves into the intricacies of scaling Retrieval-Augmented Generation (RAG) systems, focusing on vector databases, embeddings, chunking, reranking, LangChain, and hybrid search. Understanding these components is crucial for optimizing RAG systems to handle large datasets efficiently and deliver high-quality responses.

Vector Databases and Embeddings

Vector databases store high-dimensional vectors derived from text embeddings. These embeddings capture semantic meaning, enabling efficient similarity searches. Using vector databases like Faiss or Pinecone allows for fast retrieval of relevant documents, which is essential for scaling RAG systems.

import faiss
from sentence_transformers import SentenceTransformer

# Load pre-trained model
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# Sample documents
documents = ['This is the first document.', 'This document is the second document.']

# Generate embeddings
embeddings = model.encode(documents)

# Initialize Faiss index
d = embeddings.shape[1]
index = faiss.IndexFlatL2(d)

# Add embeddings to the index
index.add(embeddings)

# Query the index
query = model.encode(['This is a query document.'])
D, I = index.search(query, k=2)

print('Distances:', D)
print('Indices:', I)

Try it in Google Colab:

Distances: [[0.023 0.034]]
Indices: [[0 1]]

Chunking and Reranking

Chunking involves breaking down large documents into smaller, manageable pieces to facilitate efficient processing and retrieval. Reranking refines the initial set of retrieved documents based on relevance, using techniques like BM25 or transformer-based models to improve the quality of results.

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Sample documents
documents = ['This is the first document.', 'This document is the second document.']

# Chunk documents
chunks = [' '.join(doc.split(' ')[:len(doc.split(' '))/2]) for doc in documents]

# Initialize TF-IDF Vectorizer
vectorizer = TfidfVectorizer()

# Generate TF-IDF matrix
tfidf_matrix = vectorizer.fit_transform(chunks)

# Query
query = 'This is a query document.'
query_vec = vectorizer.transform([query])

# Calculate cosine similarity
similarities = cosine_similarity(query_vec, tfidf_matrix)

# Rerank based on similarity
ranked_indices = similarities.argsort()[0][::-1]

print('Reranked Indices:', ranked_indices)

💡 Tip: When scaling RAG systems, ensure that your vector database can handle the increased load by optimizing index structures and considering distributed architectures.

Key Concepts

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