Case Studies in RAG Applications

Duration: 5 min

This module delves into practical applications of Retrieval-Augmented Generation (RAG) systems, focusing on real-world case studies that demonstrate the integration of vector databases, embeddings, chunking, reranking, LangChain, and hybrid search. Understanding these applications is crucial for leveraging RAG systems effectively in various domains such as customer support, content generation, and data analysis.

Vector Databases and Embeddings

Vector databases store high-dimensional vectors derived from text embeddings, enabling efficient similarity search. Embeddings convert text into dense vector representations, capturing semantic meaning. Together, they form the backbone of RAG systems by allowing quick retrieval of relevant information.

import numpy as np
from sentence_transformers import SentenceTransformer

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

# Sample text
texts = ['This is a sample sentence.', 'Another example sentence.']

# Generate embeddings
embeddings = model.encode(texts)

print(embeddings)

Try it in Google Colab:

[[ 0.12345678 -0.23456789  0.3456789 ]
 [-0.456789   0.56789    0.678901   ]]

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 their relevance to the query, improving the quality of results.

from transformers import pipeline

# Load pre-trained model
reranker = pipeline('text-classification', model='distilbert-base-uncased')

# Sample chunks and query
chunks = ['This is chunk one.', 'This is chunk two.']
query ='relevant information'

# Generate embeddings for chunks and query
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
chunk_embeddings = model.encode(chunks)
query_embedding = model.encode([query])[0]

# Calculate similarity scores
scores = [np.dot(chunk_embedding, query_embedding) for chunk_embedding in chunk_embeddings]

# Rerank chunks based on scores
ranked_chunks = [chunk for _, chunk in sorted(zip(scores, chunks), key=lambda pair: pair[0], reverse=True)]

print(ranked_chunks)

💡 Tip: Ensure that the chunk size is optimal for your specific use case to balance between retrieval efficiency and context preservation.

Key Concepts

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