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RAG (Retrieval Augmented Generation)

pattern
Adopt

Retrieval Augmented Generation (RAG) is a critical pattern for building knowledge-aware AI agents that can access and reason over large document collections and business data in real-time.

Why RAG is fundamental:

  • Current Knowledge: Agents access up-to-date information beyond training data cutoffs
  • Business Context: Integrate proprietary documents, policies, and business knowledge
  • Accuracy: Reduce hallucinations by grounding responses in retrieved facts
  • Transparency: Clear citations and sources for agent recommendations
  • Cost Effective: More efficient than fine-tuning for domain-specific knowledge

RAG architecture components:

  1. Document Processing: Chunking, embedding, and indexing business documents
  2. Vector Storage: Efficient similarity search over embedded knowledge
  3. Retrieval: Finding relevant context for agent queries
  4. Generation: LLM generates responses grounded in retrieved information
  5. Citation: Tracking and presenting sources for transparency

Implementation at Redefynd:

  • Vector Databases: Evaluating Pinecone, Weaviate, and pgvector for different use cases
  • Embedding Models: Using OpenAI embeddings with fallback to open-source alternatives
  • Chunking Strategies: Semantic chunking for better context preservation
  • Hybrid Search: Combining vector similarity with keyword search for accuracy
  • Metadata Filtering: Access control and filtering based on user permissions

Advanced RAG patterns:

  • Hierarchical RAG: Multi-level document organization for complex knowledge bases
  • Agent RAG: Agents that can dynamically choose what to retrieve and when
  • Multi-Modal RAG: Combining text, images, and structured data retrieval
  • Temporal RAG: Time-aware retrieval for business processes and historical context

Integration with our platform:

  • Works seamlessly with our Knative serverless infrastructure
  • Supports event-driven knowledge updates and real-time document processing
  • Compatible with our monitoring stack for tracking retrieval performance
  • Enables secure, role-based access to business knowledge