Proposal: Lightweight Graph Capabilities (for GraphRAG)

[YBazanPro]

Hi Michel!

First off, massive kudos on v1.2. The HNSW implementation is flying 🚀. Having a native .NET embedded DB that handles both structured data and vectors this fast is a game-changer for the ecosystem.

I’ve been thinking about the next logical step, especially for those of us building AI Agents and Local LLM tools (like my current project).

Missing Piece: GraphRAG
Vector search (HNSW) is perfect for "fuzzy" semantic retrieval, but it lacks structural precision. For code analysis or complex knowledge bases, we often need to answer:
"Find chunks semantically similar to this query, BUT only if they are connected to Class X within 2 hops."

Doing 3+ hop traversals with standard SQL JOINs on B-Trees gets expensive fast.

Idea
I'm not suggesting a full Cypher implementation, but rather structural optimizations to support graph-like traversals efficiently:

1. Direct Record Links (Index-Free Adjacency): Instead of a Foreign Key that requires an Index Seek (O(log n)), a column type that stores the direct physical file pointer/RowID to the related record (O(1)).
2. Adjacency Lists: Optimizing storage to keep "neighbor IDs" collocated with the node data (maybe piggybacking on the FILESTREAM or array support?).

Inspiration / Competitors to watch
You don’t need to be Neo4j. I’d look at the "Embedded Graph" players:

• KùzuDB: Basically the "DuckDB of Graphs." They use columnar storage + vectorized execution for graph traversals. Very similar DNA to SharpCoreDB.
• SurrealDB: They handle this beautifully with "Record Links." You can treat a relationship like a pointer without needing a strict graph schema.

Why this matters
If SharpCoreDB can handle Vectors + Relations (Graph) in a single embedded, zero-dependency DLL, it essentially becomes the ultimate memory backend for .NET AI Agents, significantly outperforming a SQLite + Extensions stack.

Would love to hear your thoughts on if/how this could fit into the roadmap!

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[MPCoreDeveloper]

I wil create implementation docs and start working on this as it seems not a to big effort to do this for sharpcoredb (80% already fits)