RAG Pipeline Architectures — Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) pipeline design: vector databases, embedding models, chunking strategies, and context management to augment LLMs with enterprise data.
What is RAG?
Retrieval Augmented Generation (RAG) is an architectural approach that connects large language models (LLMs) to external data sources, providing access to information the model was not trained on. It combines retrieval and generation stages.
How Does It Work?
- Document Loading: Source documents (PDF, HTML, databases) are loaded into the system
- Chunking: Documents are split into meaningful pieces (100-1000 tokens)
- Embedding: Each piece is converted to a vector and stored in a vector database
- User Query: The question is converted to an embedding
- Retrieval: Most relevant pieces are fetched from the vector database
- Prompt Construction: Context + original question sent to the LLM
- Generation: LLM generates response using the context
Chunking Strategies
Fixed-Size Chunking: Fixed-length pieces. Simple but may have boundary issues. Recursive Chunking: Splitting by Markdown headings. Preserves structure. Semantic Chunking: Splitting by semantic similarity. Smarter segmentation. Document-Specific Chunking: Custom strategy based on document type.
Vector Databases
Pinecone: Cloud-native, scalable vector DB. pgvector: PostgreSQL extension, integrates with existing infrastructure. Chroma: Open source, ideal for rapid prototyping. Weaviate: Supports hybrid graph + vector search.
Embedding Models
- OpenAI text-embedding-3: High quality, paid
- Cohere Embed v3: Multi-language support
- BGE-M3: Open source, TR/EN support
- Jina Embeddings v3: Advanced fine-tuning
Advanced Techniques
Reranking: Re-rank initial retrieval results using cross-encoders. Query Expansion: Extend user query to multiple variants. Hybrid Search: Sparse + Dense search combination. Multi-hop Retrieval: Multi-step information retrieval. Self-RAG: Model evaluates its own retrieval quality.
Conclusion
RAG is the most effective method for connecting LLMs to enterprise knowledge. With the right chunking strategy, embedding model, and retrieval technique, 90%+ accuracy rates can be achieved.