Architecture Breakdown

Dense Vector RAG vs Topological Graph RAG

An engineering breakdown comparing traditional vector embedding distance with Memora's Graph RAG architecture.

Rag Vs Graphrag
Traditional Paradigm
  • Manual typing & wiki upkeep
  • Rapid documentation decay (stale in weeks)
  • Keyword or flat vector similarity
  • Context lost when senior devs depart
VS
● Memora
Living Knowledge Graph
  • Automated background stream ingestion
  • Real-time sync with code diffs & chats
  • Hybrid Graph RAG with verifiable citations
  • Zero institutional amnesia during departures

Traditional Retrieval-Augmented Generation (RAG) relies on converting text chunks into high-dimensional vector embeddings and storing them in vector databases (e.g., Pinecone, Milvus, Qdrant). When a user queries the system, cosine similarity finds the closest text chunks.

However, flat vector search fails in enterprise software environments due to Multi-Hop Disconnection:

CODE
Query: "Why did Alex Chen approve the payment gateway refactor?"
Vector RAG Search Result: Finds chunks about "Alex Chen" and "payment gateway", but misses the Slack discussion thread explaining WHY the change was requested.

How Memora’s Graph RAG Solves Multi-Hop Queries

Memora’s Graph RAG engine builds an explicit entity-relationship network across Slack, GitHub, Jira, and Google Drive. It retrieves context using a 3-step hybrid algorithm:

  1. Entity Extraction: Identifies services, pull requests, developers, and issue IDs via Pydantic schemas.
  2. Topological Graph Traversal: Traverses localized subgraph neighbors up to N-hops out (NetworkX).
  3. Reciprocal Rank Fusion (RRF): Merges dense vector similarity scores with graph topological distance ranks using the RRF algorithm:
CODE
RRF_Score(d) = Sum( 1 / (k + rank_m(d)) )

This guarantees 0% hallucinated answers with 100% traceable source evidence links.

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