Large Language Models (LLMs) are deep learning models trained on vast amounts of text data. They are built on the Transformer architecture, which uses self-attention mechanisms to understand the relationships between words in a sequence.

The Transformer Architecture

Introduced in the paper 'Attention is All You Need', the Transformer replaced recurrent neural networks (RNNs) with a more parallelizable structure. Its core components include:

  • Self-Attention: Allows the model to weigh the importance of different words in a sentence relative to each other.
  • Multi-Head Attention: Enables the model to attend to information from different representation subspaces simultaneously.
  • Positional Encoding: Adds information about the position of words in the sequence since Transformers don't have built-in recurrence.

Retrieval-Augmented Generation (RAG)

RAG is a technique that combines the generative power of LLMs with the precision of external knowledge retrieval. It helps solve issues like 'hallucinations' and the 'knowledge cutoff' (where a model only knows information up to its training date).

RAG Workflow

  • Ingestion: Documents are split into chunks and converted into vector embeddings.
  • Retrieval: When a query is received, it's converted to an embedding and used to find relevant chunks in a vector database.
  • Augmentation: The retrieved chunks are added to the prompt as context.
  • Generation: The LLM generates a response based on both the original query and the provided context.
rag_example.pypython
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

# Initialize components
embeddings = OpenAIEmbeddings()
vector_store = Chroma(persist_directory='./db', embedding_function=embeddings)
llm = OpenAI(temperature=0)

# Create RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm, 
    chain_type='stuff', 
    retriever=vector_store.as_retriever()
)

# Query with external context
response = qa_chain.run('What are the company\'s latest safety protocols?')
print(response)