Embedding Techniques for RAG

Duration: 5 min

This module delves into the essential embedding techniques used in Retrieval-Augmented Generation (RAG) systems. Understanding these techniques is crucial for effectively integrating external knowledge into language models, enhancing their accuracy and relevance in generating responses.

Understanding Embeddings

Embeddings are vector representations of words, phrases, or documents that capture semantic meaning. In the context of RAG systems, embeddings are used to convert text into a format that can be efficiently stored and retrieved from vector databases. These embeddings allow for semantic search, enabling the system to find relevant information based on meaning rather than exact keyword matches.

import torch
from transformers import BertModel, BertTokenizer

# Load pre-trained BERT model and tokenizer
model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize and encode a sample text
text = 'The quick brown fox jumps over the lazy dog.'
inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)

# Get embeddings from BERT
with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state

# Extract embeddings for the first token (usually [CLS])
embedding = embeddings[:, 0, :].squeeze()

print(embedding)

Try it in Google Colab:

tensor([-0.0532,  1.0559,  0.3373, ..., -0.1563, -0.1439,  0.1099], grad_fn=<SelectBackward>)

Chunking and Embedding Documents

Chunking involves breaking down large documents into smaller, manageable pieces called chunks. Each chunk is then embedded individually. This process allows for more granular and efficient retrieval of information. Embedding these chunks enables the RAG system to match queries with relevant sections of documents, improving the precision of retrieved results.

import torch
from transformers import BertModel, BertTokenizer

# Load pre-trained BERT model and tokenizer
model = BertModel.from_pretrained('bert-base-uncased')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Sample document
document = 'The quick brown fox jumps over the lazy dog. This is a test document for chunking and embedding.'

# Define chunk size
chunk_size = 10

# Split document into chunks
chunks = [document[i:i+chunk_size] for i in range(0, len(document), chunk_size)]

# Embed each chunk
chunk_embeddings = []
for chunk in chunks:
    inputs = tokenizer(chunk, return_tensors='pt', padding=True, truncation=True)
    with torch.no_grad():
        outputs = model(**inputs)
        embedding = outputs.last_hidden_state[:, 0, :].squeeze()
    chunk_embeddings.append(embedding)

print(chunk_embeddings)

💡 Tip: When chunking documents, ensure that the chunk size is appropriate for the context. Too small chunks may lose contextual meaning, while too large chunks may become inefficient to process.

Key Concepts

Check Your Understanding