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Short answer: use vector search to find code that is semantically similar to a question, and use a knowledge graph to follow explicit relationships such as calls, imports, implementations, inheritance, and database access. For most legacy-code assistants, a hybrid design is the strongest starting hypothesis: vector retrieval finds a likely entry point, then graph traversal gathers the connected context. It is not a universal winner, so validate it against your repository, languages, refresh cadence, latency target, and real developer questions.
What the two approaches actually retrieve
Vector databases retrieve semantic similarity
A vector database stores numerical embeddings for chunks of source code, symbols, comments, documentation, configuration, or other repository artifacts. At query time, it embeds the developer’s question and ranks stored vectors by similarity. A question such as “Where is the retry policy configured?” can retrieve methods, comments, and configuration snippets that discuss retries even when they do not share the exact words in the question.
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This approach is strongest when the answer is local or mainly expressed in natural language: the purpose of a class, the implementation of a validation rule, a comment explaining a workaround, or a method that resembles an example in a ticket. Chunking and metadata filters can keep results within a service, package, branch, language, or commit.
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Knowledge graphs retrieve typed relationships
A knowledge graph represents entities as nodes and their relationships as typed edges. A code-oriented schema might include files, packages, classes, interfaces, functions, methods, database tables, message topics, and configuration keys. Edges can represent imports, calls, implements, extends, reads, writes, publishes, subscribes, or depends-on relationships.
Graph traversal answers path and neighborhood questions. Starting at a deprecated method, a traversal can find its callers, the interfaces those callers use, implementations behind those interfaces, and the database tables reached downstream. A graph can therefore gather evidence spread across files and modules instead of relying on text similarity between the question and every relevant snippet.
The graph is only as useful as its construction. Missing files, unresolved symbols, incorrect language parsing, generated code that was skipped, or edges that do not match your team’s questions can make a traversal look authoritative while omitting the dependency that matters.
What “GraphRAG” means in this context
GraphRAG combines graph construction, retrieval, and language-model generation. Microsoft describes its general pipeline as extracting text information, creating a network of entities and relationships, analyzing or clustering that network, and using summaries or retrieved context for generation. That description concerns text corpora; it is not proof that a generic GraphRAG implementation automatically parses source code into reliable symbols and dependencies. Code requires a language-aware indexing strategy and a schema designed for software relationships.
Which one fits the question your developer is asking?
| Question shape | Vector retrieval | Graph retrieval |
|---|---|---|
| “What does this method do?” | Often effective when the method, comments, tests, or nearby documentation are indexed. | Useful for adding callers, callees, interfaces, and data touched by the method. |
| “Find code that validates an account number.” | Natural-language similarity can locate differently named implementations. | Useful after a candidate symbol is found, but not a replacement for semantic discovery. |
| “What calls this legacy function?” | May return mentions or similar functions without proving a call relationship. | Directly traverses call edges when symbol resolution and indexing are complete. |
| “Which classes implement this interface?” | Can find declarations that use similar wording, with possible omissions. | Traverses implements edges to enumerate known implementations. |
| “What parts of the system depend on this table?” | Can retrieve SQL and documentation that mention the table. | Can follow reads, writes, repository methods, services, and callers across files. |
| “Where is the workaround for this production error documented?” | Usually a good fit for comments, tickets, runbooks, and incident notes. | Helpful if the workaround is tied to a component or configuration node. |
These are complementary retrieval needs, not competing versions of the same index. A useful system lets intent determine whether it should search for meaning, follow a known relationship, or do both.
Why legacy code makes the distinction important
Legacy repositories often contain inconsistent naming, duplicated logic, stale comments, generated sources, reflection, dynamic dispatch, multiple build systems, and dependencies that cross package or repository boundaries. A vector index can make the vocabulary problem less painful by matching intent rather than exact identifiers. It can also surface documentation that a parser would not connect to a symbol.
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At the same time, the questions developers ask during maintenance are frequently relational: what invokes this batch job, which implementation is active in production, where does this value originate, and which tables are affected by a migration? Those answers require a connected set of evidence. Returning five individually similar snippets is not equivalent to showing a verified path through the system.
Neither method automatically solves dynamic behavior. Reflection, dependency injection configured outside source files, runtime-generated SQL, plugins, and external services can leave relationships unresolved. Mark inferred edges, preserve source locations, and show confidence or provenance so an assistant does not present an incomplete static view as a runtime fact.
A practical hybrid design
A hybrid pipeline uses each index for the task it is best at. The following sequence is a reasonable starting architecture, not a guarantee of better answers for every repository.
- Normalize and parse the repository. Record repository, branch or commit, file path, language, package, symbol name, line range, and access-control metadata. Parse supported languages with deterministic syntax or static-analysis tooling where possible.
- Create embeddings for searchable units. Embed functions, classes, configuration blocks, documentation, tests, and carefully chosen chunks. Keep symbol identifiers and source locations as metadata so a semantic hit can be mapped back to the graph.
- Build typed graph nodes and edges. Add imports, calls, inheritance, interface implementation, reads, writes, publishes, and other relationships that correspond to questions your team actually asks. Store the source file and line range that justified each edge.
- Classify or route the query. A local intent such as “explain this class” can begin with vector search. A path intent such as “who calls this?” should begin with a graph lookup. Ambiguous questions can run both retrievers.
- Use vector search to find entry points. Retrieve the top candidate symbols or documents, applying filters for repository version, language, service, and permissions. Preserve several candidates when names are overloaded.
- Expand through the graph. From each candidate, traverse callers, callees, implementers, parents, dependencies, or data-access edges to a bounded depth. Record the path rather than returning an unstructured list.
- Merge, rerank, and deduplicate. Combine semantic scores with relationship relevance, recency, symbol confidence, and path length. Avoid flooding the language model with repeated copies of the same file.
- Generate an answer with citations to code locations. Include file paths, symbols, and line ranges from the indexed commit. Clearly distinguish parsed relationships from inferred or text-only evidence.
Two ways to combine retrieval
You can run vector and graph retrieval independently and merge their results, or use one to seed the other. Vector-to-graph expansion is useful when the user describes behavior without knowing a symbol name. Graph-to-vector expansion is useful when a known method has many connected files and you need semantic ranking to choose the most relevant neighborhood. Intent routing can reduce latency when a query clearly belongs to one path.
How to construct the graph: deterministic ASTs versus language-model extraction
For source code, an abstract syntax tree (AST) or compiler-oriented parser can provide deterministic declarations, references, imports, calls, inheritance, and interface relationships. Tree-sitter is one parser technology used for this style of indexing. Bidirectional traversal lets a retriever move both upstream to callers and downstream to dependencies.
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Whichever method you choose, test symbol resolution against known examples. A graph with fewer, well-supported edges is often safer than a larger graph whose provenance is unclear. Keep parser versions and extraction prompts under version control so a refresh can be compared with the previous index.
What the January 2026 code study found
The preprint Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs compared three pipelines on Java repositories: vector-only retrieval-augmented generation, a language-model-generated knowledge graph, and a deterministic AST-derived graph built with Tree-sitter and bidirectional traversal. The projects included Shopizer, with additional runs on ThingsBoard and OpenMRS Core.
Each repository was tested with 15 architecture and code-tracing questions. The study measured indexing time, query latency, corpus coverage, cost, and answer correctness. In the reported setup, deterministic graph construction took seconds, while language-model-mediated graph generation took longer and omitted files. On the Shopizer question suite, the deterministic graph had the highest correctness score among the tested pipelines.
The result was workload-dependent. Vector-only retrieval performed well on localized questions and was competitive on some repositories. The authors also report that coupling between graph construction and the embedding pipeline affected coverage. These findings describe that implementation and test suite, not a general performance rate for all graph or vector products. The benchmark used Java repositories and a small set of questions; it should not be generalized automatically to other languages, repository sizes, or runtime environments.
| Pipeline in the study | Construction and retrieval characteristics reported | How to interpret the result |
|---|---|---|
| Vector-only RAG | Embedding-based retrieval; strong on localized questions in the reported workloads. | A credible baseline for semantic lookup, not a proof that relationships are captured. |
| LLM-generated knowledge graph | Longer graph-generation process and omitted files in the reported setup. | Extraction quality and cost must be measured rather than assumed. |
| Deterministic AST-derived graph | Built with Tree-sitter and bidirectional traversal; construction took seconds in the reported setup and achieved the highest Shopizer correctness score. | Promising for the tested Java questions, but still limited to the study’s repositories and implementation. |
How to decide for your repository
Start with the questions that consume the most engineering time, not with a preferred database category. Collect representative examples from incident response, onboarding, refactoring, dependency audits, and migration work. Label each question as semantic, relational, or mixed, then measure whether the retrieved context supports a correct answer.
Evaluation dimensions
| Dimension | What to measure |
|---|---|
| Answer correctness | Whether the cited code actually answers the question and whether the stated relationship exists. |
| Coverage | Indexed files and symbols, resolved references, graph-edge completeness, generated code, and cross-repository dependencies. |
| Latency | Embedding search time, graph expansion time, reranking time, and end-to-end response latency at your repository’s scale. |
| Build and refresh cost | Parser or static-analysis work, embedding generation, language-model extraction, storage, and re-indexing after commits. |
| Traceability | Whether every returned claim has a file path, symbol, line range, commit, and relationship provenance. |
| Language and tool support | Parser availability, build-system integration, generated-source handling, and compatibility with your existing stack. |
| Operations | Schema changes, stale indexes, access control, branch isolation, rollback, and synchronization with repository versions. |
Build a labeled test set
Include local questions such as explaining a method, cross-file questions such as finding all callers, and multi-hop questions such as tracing a request from an API endpoint to a table. Record the expected files or paths with help from maintainers. Evaluate exact evidence and not merely whether a language model produced fluent prose. A system that sounds plausible while citing an unrelated implementation should fail the test.
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Implementation patterns and an example stack
A graph database that also supports vector indexes is one way to keep semantic candidates and relationship traversal close together. Neo4j’s GraphRAG package for Python documents vector indexes, graph retrieval, and integrations with external vector systems including Weaviate, Pinecone, and Qdrant. Package compatibility and integration details change, so verify the current documentation, supported Python versions, and database version before adopting it.
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Suggested code-oriented schema
- Nodes: repository, commit, file, package, class, interface, function, method, table, queue, configuration key, test, and documentation page.
- Edges: contains, imports, calls, implements, extends, overrides, reads, writes, publishes, subscribes, configures, tests, and documents.
- Metadata: language, branch, commit, line range, parser version, generated-source flag, visibility, and confidence.
- Vector metadata: node identifier, chunk boundaries, embedding model version, tokenization settings, and document type.
Keep commit identity on every node or index partition. Without version-aware retrieval, an answer can combine a current caller with a stale implementation and produce a path that never existed in one revision.
Indexing, refresh, and maintenance costs
Vector indexes require chunking decisions, embedding generation, storage, and refresh when code or documentation changes. Large commits can trigger substantial re-embedding unless the pipeline supports incremental updates. Chunk boundaries also affect recall: a function split from its signature or surrounding type information may be harder to interpret.
Graphs require parser maintenance, symbol resolution, edge validation, and schema evolution. A new language feature, build plugin, generated source directory, or repository split can reduce coverage without causing an obvious error. Track indexed-file counts and unresolved-reference counts on every refresh, and alert when they change unexpectedly.
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Language-model extraction adds token and inference costs and may need repeated runs to improve coverage. Deterministic parsing generally offers more repeatable refreshes for relationships it can resolve. A mixed pipeline should store which extractor produced each edge so operators can reprocess only the affected layer.
Common failure modes and fixes
The vector result is relevant but not the answer
Symptom: the assistant returns similar methods but misses the actual caller chain. Fix: use the top semantic hits as graph seeds, traverse call or dependency edges, and require the final response to cite the path.
The graph misses an obvious dependency
Symptom: a known caller or implementation is absent. Fix: inspect parser support, generated sources, build configuration, reflection, dynamic dispatch, and repository boundaries. Mark unresolved edges instead of silently treating the graph as complete.
Answers mix revisions
Symptom: a cited path combines files from different commits. Fix: partition or filter both vector and graph retrieval by commit or branch, and display the revision in the answer.
Graph expansion overwhelms the context window
Symptom: a broad traversal returns hundreds of nodes. Fix: set hop and node limits, prioritize relationship types relevant to the intent, collapse repeated files, and rerank with semantic similarity before generation.
Latency rises after adding graph retrieval
Symptom: answers become too slow for interactive use. Fix: route clearly local questions to vector search, cache stable neighborhoods, precompute high-value traversals, and measure each stage separately rather than assuming the database is the bottleneck.
Should you use a graph database or a vector database for code search?
Choose a vector-first design when your dominant workload is semantic discovery across code, comments, tests, and documentation, and when relationships are either unnecessary or already supplied by a separate tool. Choose a graph-first design when developers repeatedly need callers, implementers, dependency paths, ownership boundaries, or data-lineage answers and you can build reliable edges.
Choose hybrid retrieval when both workloads matter, which is common in legacy modernization. Keep the architecture modular so you can compare vector-only, graph-only, and hybrid answers on the same labeled questions. The decision should follow measured correctness, coverage, latency, and refresh cost for your repository rather than a generic claim that one category is superior.
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