GraphRAG in Practice: How Semantica Combines Vector Search and Knowledge Graphs for Multi-Hop Questions

RAG has a problem: documents have relationships. Most RAG pipelines turn a knowledge base into chunks, embed them, and retrieve the closest ones. That works well for simple questions. But ask something like "Which customer decision was influenced by the findings from last quarter?" and suddenly similarity search starts to struggle. The answer may depend on several entities and relationships spread across different documents. That's where GraphRAG gets interesting. Semantica combines vector search with knowledge graphs, entity extraction, relationship extraction, temporal context, and agent memory. Instead of treating every chunk as an isolated piece of information, it builds a connected representation of the knowledge. The really interesting part is multi-agent systems. Multiple agents can work against the same context graph. A researcher can discover something, write it into shared context, and an analyst can immediately use it without passing the entire result through another agent. And this isn't just conceptual. Their benchmarks on a 118K-node graph report reducing node search from 24 ms to 0.004 ms, along with significant improvements in semantic deduplication and candidate generation. I think the interesting shift is this: RAG is moving from "find me some relevant text" to "help me navigate a knowledge system." That's a much more powerful primitive for AI agents.

Confirmed accurate:

• Semantica is a real, open-source Python library (Hawksight-AI) combining vector search, knowledge graphs, entity/relationship extraction, and agent memory — matches its own docs closely. Open-source Python library for AI memory, context graphs, and decision provenance. Add GraphRAG and causal reasoning to LangGraph, CrewAI, or LlamaIndex. MIT licensed, free forever. Semantica
• The exact benchmark cited — 118K nodes, 24ms → 0.004ms — is a real, documented figure from the project's own changelog, not a fabricated stat. O(log n) Indexed Search — inverted index with exact/token/prefix tiers; 118k nodes: 24ms → 0.004ms GitHub
• Temporal context support is real and specifically documented (Allen Interval Algebra, point-in-time queries, bi-temporal provenance). Temporal GraphRAG — retrieve knowledge as it existed at any past point; zero LLM calls GitHub
• Multi-agent shared context graph — where one agent writes a fact/decision and another can query it directly — matches the library's actual API design (AgentContext, record_decision, find_precedents). Record a decision with full causal chain... Search past decisions before making a new one Semantica
• The general GraphRAG premise (vector similarity alone struggles with multi-hop, relationship-dependent questions) is a well-established critique in the RAG literature, not an exaggeration. The core idea behind employing knowledge graphs is to address semantic search challenges by organizing data according to intrinsic relationships arxiv

Fluff / context missing:

• The "24ms → 0.004ms" figure is specifically an indexed node-lookup benchmark (an inverted-index optimization), not a measure of full end-to-end GraphRAG query latency. The post presents it as if it reflects the whole system's retrieval speed — that's a narrower, cherry-picked number being generalized.
• The post reads as unpaid promotion for a specific product (styled as a personal insight/opinion piece) rather than a neutral technical explainer — worth noting as the underlying motivation, even though the technical claims check out.

Net: unlike the Floci post, the specific numbers and product capabilities here are traceable to the actual project's own documentation and hold up — the only meaningful fluff is stretching a narrow indexing benchmark into a broader performance claim.