Capstone Project: Comprehensive RAG Application

Duration: 5 min

This module focuses on building a comprehensive Retrieval-Augmented Generation (RAG) application using vector databases, embeddings, chunking, reranking, and LangChain. You will learn how to integrate these components to create a robust system capable of retrieving relevant information and generating coherent responses. Understanding these concepts is crucial for developing advanced AI applications that can handle complex queries and provide accurate, context-aware answers.

Vector Databases and Embeddings

Vector databases store data in a high-dimensional space where each data point is represented as a vector. Embeddings are vector representations of words, sentences, or documents that capture semantic meaning. By using embeddings, we can perform semantic search, allowing the system to retrieve documents based on their meaning rather than exact keyword matches. This enhances the relevance of retrieved information.

import numpy as np
from sentence_transformers import SentenceTransformer

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

# Sample documents
documents = ['The quick brown fox jumps over the lazy dog.', 'A journey of a thousand miles begins with a single step.']

# Generate embeddings
embeddings = model.encode(documents)

print(embeddings)

Try it in Google Colab:

[[ 0.1234  0.5678 -0.9876...], [-0.4321  0.8765  0.2345...]]

Chunking and Reranking

Chunking involves breaking down large documents into smaller, manageable pieces called chunks. This makes it easier to process and retrieve relevant sections. Reranking is the process of reordering retrieved chunks based on their relevance to the query. This ensures that the most relevant information is presented first, improving the overall quality of the response.

from transformers import pipeline

# Load pre-trained model for chunking and reranking
chunker = pipeline('feature-extraction', model='distilbert-base-uncased')

# Sample document
document = 'The quick brown fox jumps over the lazy dog. A journey of a thousand miles begins with a single step.'

# Chunk the document
chunks = [document[i:i+10] for i in range(0, len(document), 10)]

# Generate features for each chunk
features = chunker(chunks)

print(features)

💡 Tip: Ensure that the chunk size is appropriate for your application. Too small chunks may lose context, while too large chunks may be difficult to process.

Key Concepts

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