Project: Building a Simple RAG System

Duration: 5 min

This module will guide you through the process of building a simple Retrieval-Augmented Generation (RAG) system. You will learn about vector databases, embeddings, chunking, reranking, LangChain, and hybrid search. Understanding these concepts is crucial for developing advanced natural language processing applications.

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, phrases, or documents that capture semantic meaning. By using embeddings, we can perform semantic search, allowing us to find documents similar in meaning to a query, even if the exact words do not match.

import numpy as np

# Example embeddings for words
embeddings = {
    'apple': np.array([0.1, 0.2, 0.3]),
    'orange': np.array([0.4, 0.5, 0.6]),
    'banana': np.array([0.7, 0.8, 0.9])
}

# Function to compute cosine similarity
def cosine_similarity(vec1, vec2):
    return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))

# Query embedding
query = 'apple'
query_embedding = embeddings[query]

# Compute similarity with all embeddings
similarities = {word: cosine_similarity(query_embedding, embedding) for word, embedding in embeddings.items()}
print(similarities)

Try it in Google Colab:

{'apple': 1.0, 'orange': 0.9746318461861854, 'banana': 0.960767399869895}

Chunking and Reranking

Chunking involves breaking down large documents into smaller, manageable pieces called chunks. This allows for more efficient processing and retrieval. Reranking is the process of reordering the retrieved chunks based on their relevance to the query, often using additional models or algorithms to improve the quality of the results.

import random

# Example chunks from a document
chunks = [
    'The quick brown fox jumps over the lazy dog.',
    'A journey of a thousand miles begins with a single step.',
    'To be or not to be, that is the question.',
    'All that glitters is not gold.'
]

# Simple reranking function based on random scores
def rerank_chunks(chunks):
    scores = {chunk: random.random() for chunk in chunks}
    ranked_chunks = sorted(chunks, key=lambda chunk: scores[chunk], reverse=True)
    return ranked_chunks

# Rerank the chunks
ranked_chunks = rerank_chunks(chunks)
print(ranked_chunks)

💡 Tip: When implementing chunking, ensure that the chunks are semantically coherent to maintain the context and meaning of the original document.

Key Concepts

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