RAG combines document retrieval with LLM generation.
Build powerful document-based Q&A systems.
RAG Components
1. Document Loader
2. Text Splitter
3. Embeddings
4. Vector Store
5. Retriever
Implementation
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
loader = TextLoader(“document.txt”)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)
retriever = db.as_retriever()
Conclusion
RAG enables knowledge-based AI applications!