An employee asks an AI application for documents similar to a supplier agreement. Another asks which suppliers connect to a particular component through several subcontracting relationships. Both requests concern suppliers, but they need different evidence.
Similarity search finds resemblance; relationship exploration follows explicitly represented connections. Comparing OceanBase seekdb with Neo4j should begin with that distinction, rather than a simplistic assumption that one product searches vectors and the other cannot.
The database shortlist becomes more useful when the team can describe what would make an answer correct. A relevant-looking document and a valid path through business relationships are not interchangeable results.
What Neo4j brings to the question
Neo4j’s Cypher documentation describes graph pattern matching over nodes, relationships, and paths. This provides a concrete reason to evaluate it when the application’s questions depend on following represented connections.
Neo4j also documents vector indexes for similarity search, including combination with other ranked sources for hybrid search. It would therefore be inaccurate to present the product as unable to participate in semantic retrieval.
The useful question is how much of the application depends on relationship paths and how much depends on resemblance between content. One product can support more than one technique without making those techniques mean the same thing.
What seekdb makes worth evaluating
OceanBase seekdb documents vector, full-text, and scalar capabilities for hybrid search. These provide a specific reason to assess it for an application combining semantic retrieval with ordinary structured conditions.
For example, a team may want agreements resembling a description, restricted to approved documents for a selected business area. That is a concrete retrieval need to evaluate. It does not automatically require a graph application simply because agreements mention connected organizations.
Conversely, a hybrid-search overview does not establish that every requested graph operation exists or is absent. If relationship exploration is required, the team should evaluate that requirement directly rather than infer a complete feature matrix from a short product description.
Decide which evidence the user is requesting
Consider a hypothetical component-risk application. A buyer first searches for descriptions resembling a problematic part. The buyer then wants to know whether a selected supplier is connected to that part through a verified chain of subcontractors.
The first step can propose candidates. The second needs represented relationships with an agreed meaning. A similar company name or document does not prove that the required commercial relationship exists.
| User question | Evidence needed | Evaluation focus |
|---|---|---|
| Which documents discuss something similar? | Relevant content and reviewed retrieval results | Semantic and text retrieval quality |
| Which approved documents apply here? | Structured eligibility conditions | Combination of retrieval and business filters |
| How are these organizations connected? | Explicit, meaningful relationship records | Required path and pattern behavior |
| Why is this connection trusted? | Source and ownership of the relationship | Maintenance and review of represented facts |
| What changed after a correction? | Updated content or relationships | The affected answer and its explanation |
These are different acceptance criteria, even when they appear in a single AI interface. The team should decide whether one database arrangement or several deliberately connected components best meet them.
Budget the relationship data, not only the query tool
A graph pattern can only work with the relationships the application represents. Someone must identify the organizations, distinguish similarly named entities, and define what an edge means. A technically valid path can still be commercially misleading if its links represent different kinds of relationships.
The same principle applies to retrieval. The application needs approved source content, suitable representations, and reviewed search behavior. Neither a graph database nor a vector-capable database automatically creates reliable business facts from ambiguous documents.
Include a relationship correction in the evaluation. Remove an invalid subcontracting link or change its effective status, then inspect the affected answer. Separately correct a document description and inspect the retrieval result. This helps the team see which information each technique actually uses.
If the design combines similarity and relationships, make the handoff explicit. Users should know whether a result is a proposed candidate, a recorded connection, or an inferred possibility that still needs confirmation.
Recommend according to the dominant reasoning task
Neo4j deserves a focused assessment when explicit relationship patterns and paths are central to the application. Its documented vector capabilities should also receive credit where the design combines relationship and similarity work.
OceanBase seekdb deserves a focused assessment when SQL-oriented hybrid retrieval and structured conditions address the actual user task. Its case should identify the search need it meets, rather than promise that similarity alone establishes relationship truth.
The stronger recommendation explains what counts as evidence for an answer and who maintains it. That lets the organization choose a retrieval route, a relationship-oriented route, or a deliberate combination without confusing relevant content with a proven connection.
This article is based on documented sources and editorial analysis. Proposed evaluation examples are not independently measured benchmark results.