Augment LLMs with external knowledge through retrieval-augmented generation techniques
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RAG (Retrieval-Augmented Generation) enhances LLMs by retrieving relevant external knowledge from documents, databases, or knowledge bases before generating responses, grounding answers in verifiable facts.
LLMs have knowledge cutoff dates and can hallucinate. RAG provides up-to-date, domain-specific information with citations, reducing hallucinations and enabling access to proprietary knowledge.
Use RAG when you need current information, domain-specific knowledge, or verifiable sources. Combine with semantic search and proper chunking for optimal retrieval quality.
RAG (Retrieval-Augmented Generation) is a technique that enhances LLMs by giving them access to external knowledge. Instead of relying only on training data, the model can "look up" relevant information from documents, databases, or knowledge bases before generating a response.
Access current data beyond the model's training cutoff date
Ground responses in verifiable facts from your knowledge base
Use proprietary company docs, manuals, and internal wikis
Provide citations showing exactly where information came from
User asks a question: "What is our company's remote work policy?"
Convert the question into a vector (numerical representation)
Search vector database for most similar document chunks
Add retrieved chunks to the original prompt
Model generates answer based on retrieved context
Convert text to numerical vectors that capture semantic meaning
Find relevant documents based on meaning, not just keywords
Add retrieved knowledge to prompts for grounded generation
Generate responses with citations and source attribution
Numerical representations of text that capture semantic meaning. Similar concepts have similar vectors.
Finding documents based on meaning, not just keywords. Understands "furry feline" means "cat".
Specialized databases optimized for storing and searching embeddings at scale.
Breaking large documents into smaller pieces for efficient retrieval and processing.
Let's build a simple RAG system for company documentation:
# 1. Load and chunk documents
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
loader = TextLoader('company_policies.txt')
documents = loader.load()
splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(documents)
# 2. Create embeddings and store in vector DB
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)
# 3. Create retriever
retriever = vectorstore.as_retriever()
# 4. Build RAG chain
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(),
retriever=retriever
)
# 5. Ask questions!
answer = qa_chain.run("What is the vacation policy?")
print(answer)Agentic RAG adds a reasoning layer that actively evaluates, validates, and refines retrieved information: