Retrieval-Augmented Generation combines search with AI generation.
Build powerful RAG systems with DeepSeek models.
What is RAG?
RAG retrieves relevant documents and uses them as context for LLM responses.
RAG Architecture
1. Document ingestion
2. Embedding generation
3. Vector storage
4. Similarity search
5. Context assembly
6. LLM generation
Tools for RAG
✅ LangChain
✅ LlamaIndex
✅ Pinecone
✅ ChromaDB
Code Example
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)
DeepSeek for RAG
Use DeepSeek as the generation model in your RAG pipeline.
Performance Tips
✅ Use chunking strategies
✅ Optimize retrieval
✅ Cache embeddings
Conclusion
RAG systems enable powerful document-based AI applications!