What is RAG

RAG (Retrieval-Augmented Generation) is a technical architecture that combines information retrieval with text generation. It enables large language models to cite relevant information from external knowledge bases when generating answers, thereby addressing the model's "hallucination" problem and knowledge cutoff limitations.

Core RAG Pipeline

A standard RAG system includes the following steps:

  1. Document Loading: Load documents from PDFs, web pages, databases, etc.
  2. Text Splitting: Split long documents into appropriately sized text chunks.
  3. Vector Embedding: Use an embedding model to convert text chunks into vector representations.
  4. Vector Storage: Store vectors in a vector database.
  5. Retrieval: Retrieve the most relevant text chunks based on the user query.
  6. Generation: Use the retrieved results as context for the LLM to generate the final answer.

RAG Tech Stack

Building a RAG system involves the following technical components:

  • LLM: Large language models such as DeepSeek, GPT-4, Claude, etc.
  • Embedding Models: text-embedding-3, bge-large-zh, etc.
  • Vector Databases: Chroma, Pinecone, Milvus, Weaviate
  • Document Processing: LangChain document loaders, Unstructured, etc.
  • Orchestration Frameworks: LangChain, LlamaIndex, Haystack

LangChain RAG Implementation

Below is a complete example of building a RAG application using LangChain:

from langchain.document_loaders import TextLoader, PDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import DeepSeek

# 1. Load documents
loader = TextLoader("knowledge_base.txt")
documents = loader.load()

# 2. Split text
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=500,
    chunk_overlap=50
)
chunks = text_splitter.split_documents(documents)

# 3. Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(
    documents=chunks,
    embedding=embeddings,
    persist_directory="./chroma_db"
)

# 4. Create retrieval chain
retriever = vectorstore.as_retriever(
    search_kwargs={"k": 4}
)
qa_chain = RetrievalQA.from_chain_type(
    llm=DeepSeek(),
    chain_type="stuff",
    retriever=retriever
)

# 5. Query
result = qa_chain.run("What is RAG?")
print(result)

RAG Challenges and Optimization

  • Retrieval Quality: Use hybrid retrieval (keyword + semantic) to improve recall.
  • Context Window: Carefully design chunk size to avoid exceeding model context limits.
  • Answer Accuracy: Add citation tracing to allow users to verify answer sources.
  • Latency Optimization: Use caching, parallel retrieval, and other techniques to reduce response time.

Conclusion

RAG is currently the best way to make AI "grounded." Starting with simple document Q&A and gradually adding advanced features, you can build truly useful AI applications.