Cortex vs
Traditional RAG
Traditional RAG (Retrieval-Augmented Generation) retrieves text chunks via vector similarity search. Cortex goes further — it builds a knowledge graph from your documents, extracting entities and relationships, then combines graph traversal with vector and keyword search for retrieval. The result: AI agents get context that understands connections between concepts, not just semantically similar text fragments.
How Traditional RAG Works
A standard RAG pipeline follows four steps: chunk your documents into passages, generate vector embeddings for each chunk, store them in a vector database, then retrieve the most similar chunks at query time and pass them as context to an LLM.
Chunk
Split documents into fixed-size text passages
Embed
Generate vector embeddings for each chunk
Retrieve
Find chunks with highest cosine similarity to the query
Generate
Pass retrieved chunks as context to the LLM
This works for simple question-answering, but it treats every document as isolated text. There is no understanding of entities, relationships, or how information connects across documents.
How Cortex Enhances RAG
Cortex adds a knowledge graph layer on top of vector search. Documents are not just chunked and embedded — they are analyzed for entities, relationships, and community structures that enable deeper reasoning.
Entity Extraction
Cortex automatically identifies people, organizations, concepts, and other entities from your documents using NER and LLM-powered extraction.
Relationship Mapping
Extracted entities are connected by typed relationships — who reports to whom, which projects relate to which teams, how concepts depend on each other.
GraphRAG & Community Detection
The knowledge graph is analyzed for communities and clusters, enabling multi-hop reasoning and context that spans document boundaries.
Hybrid Search
Every query combines vector similarity, BM25 keyword matching, and graph traversal. Results are merged and re-ranked with a cross-encoder for maximum relevance.
Side-by-Side Comparison
| Capability | Traditional RAG | Cortex |
|---|---|---|
| Retrieval Method | Vector similarity search | Hybrid: vector + keyword + graph traversal |
| Context Quality | Semantically similar chunks | Entity-aware, relationship-rich context |
| Entity Awareness | None — treats text as opaque chunks | Automatic entity extraction and linking |
| Cross-Document Reasoning | Limited to chunk overlap | Graph traversal across document boundaries |
| Relationship Discovery | Not supported | Automatic relationship mapping and communities |
| Hallucination Risk | Higher — loose semantic matching | Lower — grounded in entities and citations |
| Setup Complexity | Moderate — requires chunking, embedding, vector DB | Low — single API handles full pipeline |
| Maintenance | Manual pipeline management | Managed platform with automatic updates |
When to Use What
Traditional RAG is fine when
- You have a single document collection with simple Q&A needs
- Documents are self-contained and don't reference each other
- Speed matters more than context depth
- You need a quick prototype or proof of concept
Cortex is better when
- Multiple documents relate to each other and share entities
- You need entity-level precision, not just passage similarity
- AI agents need organizational context — people, projects, processes
- Accuracy and citation support matter more than raw speed
- You want a managed platform instead of maintaining a pipeline
Frequently Asked Questions
Common questions about Cortex vs traditional RAG.
Yes. Cortex provides a complete retrieval pipeline out of the box — document ingestion, chunking, embedding, entity extraction, knowledge graph construction, and hybrid search. You can replace a traditional RAG stack with a single Cortex API integration.
Go beyond traditional RAG
See how Cortex's knowledge graph retrieval compares in practice. Try the live demo or join the waitlist.