DM GDMBASE V4.0: HyperRAG, Long‑Term Memory & NL Agents for Graph‑Vector AI

From Stitching to Fusion: A Fundamental Architecture Shift

Enterprises building AI knowledge bases currently juggle relational, vector, graph, and big‑data stores, incurring heavy data movement, cleaning, and hardware costs. Vector retrieval excels at semantic similarity but cannot capture complex business logic, leading to inaccurate results.

GDMBASE V4.0 solves this by natively integrating vectors as attributes of graph nodes and edges. Vectors up to 4096 dimensions are supported, with optimised paging for half‑precision, full‑precision, int8, and int4 quantisation. Indexing uses a custom‑extended Hierarchical Navigable Small World (HNSW) structure, allowing indexing, lookup and similarity computation within a single execution engine while preserving transactional consistency for graph‑vector updates.

Three AI‑Native Capabilities

HyperRAG: Global Retrieval that Eliminates Hallucinations

HyperRAG is DM’s self‑developed graph‑vector execution engine that tightly couples multi‑hop graph reasoning, vector semantic search, secondary‑index acceleration, and full‑text search. By scheduling graph traversal and vector lookup in a unified plan, it achieves stable performance, supports three‑hop reasoning, and improves mixed execution efficiency by four times.

Example: When asked “Is it better to drive or walk to a gas station 50 m away?”, a pure LLM might answer “walk” because it lacks the fact that a car needs fuel. HyperRAG traverses the graph “user → owns car → needs fuel → gas station distance” and combines vector‑matched location data to give the correct recommendation, illustrating the “vector for breadth, graph for depth” principle.

Long‑Term Memory: Turning AI from a Goldfish into a Business Assistant

DM builds a graph‑enhanced memory system that stores dialogue history as structured memory sub‑graphs. With one million semantic sub‑graphs, retrieval latency stays below 100 ms and memory‑linked reasoning accuracy exceeds 98 %.

This enables AI to recall past quarterly risk mentions and infer long‑term impacts of current decisions, moving from a fleeting chat tool to a persistent business aide.

Natural‑Language Intelligent Agent: Breaking the Tech‑Business Barrier

Traditional graph query languages (e.g., Cypher) are hard for business users. DM’s Text2Cypher engine converts natural language to graph queries with over 95 % accuracy across 50 industry templates. Users can simply ask “Find the top five account clusters with highest correlation in abnormal transactions over the last three months,” and the agent handles intent understanding, graph traversal, multi‑hop reasoning, and result aggregation.

The agent is split into technical and business variants: the technical side handles language conversion for developers, while the business side offers pre‑packaged task templates, allowing non‑technical staff to operate via plain language.

Open Ecosystem: Avoiding Vendor Lock‑In

GDMBASE V4.0 natively supports the MCP protocol, enabling zero‑code integration with mainstream AI agents. DM participates in the national graph‑database standard and contributes to the ISO GQL draft, which is 70 % based on Cypher, ensuring future portability.

The platform is compatible with domestic and international large models (Llama, ChatGLM, GPT series) and integrates with Hadoop‑style ecosystems. Developers can invoke GDMBASE capabilities through familiar frameworks such as LangChain or LlamaIndex without learning new APIs.

Industry Practice: From Pilot to Scale

Government agencies use the combined large‑model + knowledge‑graph system for policy Q&A, document verification, and intelligent writing. In public security, the solution upgrades from simple relationship calculations to intelligent crime‑pattern analysis, allowing officers to discover gang structures without mastering complex query languages.

An audit department of a provincial agency integrated cross‑departmental multidimensional data via GDMBASE, dramatically improving anomaly detection efficiency and meeting stringent “fact‑check + abnormal‑pattern” requirements.

Future Outlook

Official data shows HyperRAG can retrieve from a mixed dataset of one billion graph‑vector points in under 500 ms, boosting recall by 30 % and cutting hallucinations by 60 %. GDMBASE V4.0 scales to over a hundred billion graph nodes, reduces AI deployment cycles by 60 % and cuts overall costs by 40 %.

While the graph‑vector fusion approach may not suit every scenario and the NL agent’s accuracy in complex domains remains to be proven, the trend toward structured, associative, and inferable data is clear. Graph databases, with their innate relational expressiveness, are positioned at the centre of this AI‑driven transformation.