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Short answer: vector databases are usually the fastest way to find semantically similar legacy code, while knowledge graphs are better for tracing explicit relationships such as callers, implementations, imports, and data dependencies. If engineers need both kinds of answers, a hybrid workflow—vector retrieval to find an entry point, followed by graph traversal to expand the surrounding context—is the strongest starting architecture. It is a hypothesis to test on your repository, not a universal winner.
What each technology contributes to legacy-code understanding
Legacy systems rarely fail because a developer cannot find a file. They fail because the meaning of a change is distributed across old names, implicit conventions, generated code, database tables, configuration, and call chains that cross many modules. Search technology therefore needs to answer two different questions:
- “Which code looks relevant to this description?” This is a semantic-similarity problem.
- “How is this symbol connected to the rest of the system?” This is a relationship and path-tracing problem.
A vector database and a knowledge graph optimize for those different questions. Treating either as a complete replacement for the other creates predictable blind spots.
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Vector databases: similarity-first retrieval
A vector pipeline splits a repository into chunks or symbols, converts each chunk into an embedding, and stores the resulting vectors with metadata such as file path, language, branch, and symbol name. A natural-language query is embedded in the same space. The database then ranks code whose embeddings are closest to the query.
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This works well when intent is expressed in prose or when the relevant implementation is locally contained. Queries such as “where is invoice retry logic handled?”, “find code that validates an expired session,” or “show the parser for the old flat-file format” can surface useful functions even when names and comments use different wording. Comments, documentation, tests, and issue descriptions can also be indexed alongside source code.
Similarity is not the same as dependency. A vector result can mention the same business concept as a query without being the function that calls it, implements an interface, reads a particular table, or controls a production path. Baseline vector retrieval can therefore miss facts that are spread across records or files. Microsoft’s GraphRAG material describes this limitation in the broader context of text-corpus retrieval; the same distinction is relevant when code evidence is distributed across a repository.
Knowledge graphs: relationship-first retrieval
A knowledge graph stores entities as nodes and typed relationships as edges. A code-oriented schema might include files, packages, classes, functions, interfaces, database tables, message topics, and configuration keys. Edges could represent imports, calls, implements, extends, reads, writes, or publishes.
With that model, a query can follow a path instead of merely ranking similar text. “What calls this legacy method?” can traverse incoming call edges. “Which implementations sit behind this interface?” can follow implements relationships. “What parts of the system depend on this table?” can expand from a table to data-access methods, services, controllers, jobs, and tests.
The graph is only as useful as its construction. Missing files, unresolved symbols, incorrect language parsing, stale references, or an edge schema that omits the relationships engineers actually investigate will produce incomplete answers with an appearance of precision. A graph is not automatically a source of truth just because it uses explicit edges.
Should you use a graph database or vector database for code search?
Choose according to the shape of the questions your team asks most often. The following comparison is a practical starting point; latency, coverage, and cost must be measured against your own repository.
| Decision axis | Vector database | Knowledge graph | What to verify locally |
|---|---|---|---|
| Best question shape | Semantic lookup, intent, comments, documentation, and locally contained implementation details | Dependency tracing, callers, implementers, inheritance, data lineage, and multi-hop context | Sample real questions from incidents, onboarding, and change reviews |
| Primary evidence | Nearest embedded chunks or symbols | Nodes and typed paths between entities | Whether returned context contains the source locations needed to verify an answer |
| Cross-file reasoning | Possible only when relevant chunks are retrieved together; relationships are implicit | Native to the model when edges are complete and correctly resolved | Coverage of imports, calls, interfaces, generated code, and reflection |
| Build work | Chunking, embedding, metadata design, and vector indexing | Parsing or static analysis, symbol resolution, schema design, and edge loading | Parser availability, build integration, and incremental-update complexity |
| Refresh behavior | Re-embed changed chunks and remove stale vectors | Update affected nodes and edges while preserving repository-version consistency | How quickly an index reflects a commit, branch, or release |
| Failure mode | Semantically plausible but structurally wrong results | Confident paths built on missing or incorrect edges | Whether the interface exposes provenance and uncertainty |
| Operational concerns | Embedding-model changes, chunk drift, metadata filters, and vector storage | Schema evolution, graph growth, symbol resolution, access control, and stale edges | Ownership, backups, permissions, and repository-version tracking |
For a team that mainly asks “where is the code for this behavior?”, start with vector retrieval. For a team that asks “what breaks if I change this?” or “trace this request to the database,” a graph deserves early investment. If both categories are common, test a hybrid design before committing to one retrieval method.
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Can vector search find how a legacy function is connected to the rest of the codebase?
It can sometimes retrieve adjacent evidence, but similarity alone cannot guarantee a complete connection map. A caller may have a short, generic name and no comments that resemble the query. An interface implementation may be selected by configuration or reflection rather than by text similarity. A database dependency may appear only in an XML mapping, migration, stored procedure, or generated class.
You can improve vector-only results by indexing symbols rather than arbitrary token windows, attaching file and module metadata, retrieving neighboring chunks, and applying filters for language, branch, or package. These techniques increase the chance that related code appears together, but they still do not encode an explicit path. If the answer requires proving a chain of relationships, use a graph or a static-analysis tool to validate the chain.
When should you combine GraphRAG and vector search for code?
A hybrid design is useful when the query contains both an ambiguous concept and a structural follow-up. The vector stage discovers likely entry points from natural language; the graph stage expands those points through typed relationships. The final prompt or result view can show the original snippets, traversed symbols, file paths, and edge types so an engineer can verify the reasoning.
Pattern 1: vector entry point, then graph expansion
- Embed the question and retrieve the highest-ranked functions, classes, documents, and tests.
- Resolve each candidate to a stable symbol identifier and repository revision.
- Traverse selected edges, such as callers, callees, implementers, imports, reads, and writes.
- Apply limits for hop count, module boundaries, and result size so a highly connected utility does not overwhelm the context.
- Present the source snippets and the path that links them, rather than only a generated summary.
For example, “where is customer deletion handled and what does it affect?” can retrieve code mentioning customer deletion, then expand to callers, authorization checks, event publishers, and data-access methods.
Pattern 2: intent-based routing
Not every query needs both systems. A lightweight classifier or ruleset can route “find code that…” questions to vector search, “who calls…” and “what depends on…” questions to graph traversal, and mixed questions to both. Routing reduces unnecessary graph work and makes latency easier to control. Keep a fallback path: ambiguous wording should be allowed to run both retrieval methods rather than being forced into a low-confidence route.
Pattern 3: parallel retrieval and reranking
Run vector and graph retrieval independently, normalize their evidence, and rerank results using query intent, symbol centrality, path length, recency, and repository version. This can be more robust when the graph is incomplete, because vector evidence may recover files that have not yet been connected. It also makes disagreement visible: a semantically strong result with no structural path should be treated differently from a well-connected result with weak textual relevance.
How to build a code graph that is useful rather than decorative
Begin with the questions the graph must answer, then design only the nodes and edges needed for those questions. A broad schema built without a workload often creates expensive noise.
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1. Define entities and stable identifiers
Use identifiers that survive formatting changes and can be mapped back to source. A function node might include repository, commit, language, package, fully qualified name, file path, and source range. Distinguish overloaded methods and generated artifacts. Store the commit or release associated with every node and edge so an answer does not mix versions.
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Start with relationships that static analysis can establish reliably:
- file-to-file
importsor module dependencies; - function-to-function
callsandcalled_by; - class-to-interface
implementsand class inheritance; - symbol-to-table or topic
reads,writes,publishes, andsubscribes; - test-to-symbol coverage or test-target relationships;
- configuration-to-component links where the build or runtime system can resolve them.
Mark inferred edges separately from compiler- or parser-confirmed edges. An inferred relationship can be useful for discovery but should not be presented with the same confidence as a resolved call.
3. Resolve language-specific cases
Legacy repositories often mix languages, generated sources, macros, reflection, dependency injection, and dynamic dispatch. Record unresolved references instead of silently dropping them. Include build-system metadata and generated-source provenance where possible. If a framework wires implementations through annotations or configuration, model that mechanism explicitly or label the resulting edge as inferred.
4. Preserve provenance
Every edge should point to the file, line or source range, parser rule, and repository revision that produced it. Provenance allows an engineer to challenge a graph result and lets the indexer repair only the affected records after a change.
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The construction method affects coverage, refresh time, cost, and trust. Deterministic parsing or AST analysis is generally preferable for relationships the language toolchain can resolve. LLM extraction can add semantic links that are difficult to derive mechanically, but it introduces latency, cost, nondeterminism, and the risk of omitted files or invented relationships.
| Construction approach | Strengths | Risks and limits | Good use |
|---|---|---|---|
| Deterministic AST or parser analysis | Repeatable output, source locations, fast incremental updates, and strong coverage for supported language constructs | Requires parser and build integration; struggles with reflection, dynamic loading, and missing dependencies | Calls, imports, inheritance, interfaces, symbols, and other compiler-visible relationships |
| LLM-mediated extraction | Can interpret prose, naming conventions, configuration, and undocumented semantic relationships | Higher indexing cost and latency; may omit files or produce inconsistent edges; requires validation | Supplementary semantic links, migration notes, business concepts, and unresolved legacy conventions |
| Combined pipeline | Uses deterministic structure as a reliable base and semantic extraction as an annotated supplement | More components, conflict resolution, and provenance rules | Repositories where structural and business-level questions are both important |
What the January 2026 code study actually shows
The most directly relevant evidence is the January 2026 arXiv preprint Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs. It compares three pipelines on Java repositories: vector-only retrieval-augmented generation, an LLM-generated knowledge graph, and a deterministic AST-derived graph built with Tree-sitter and bidirectional traversal.
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The evaluated projects include Shopizer, with additional runs on ThingsBoard and OpenMRS Core. The researchers used 15 architecture and code-tracing queries per repository and measured indexing time, query latency, corpus coverage, cost, and answer correctness. In their setup, deterministic graph construction took seconds, while LLM-mediated graph generation took longer and omitted files. On the Shopizer question suite, the deterministic graph achieved the highest correctness score reported in that experiment.
The result was not uniform across workloads. Vector-only retrieval performed well on localized questions and was competitive on some repositories. The paper also reports that coupling between graph construction and the embedding pipeline affected coverage. These are findings from one implementation, a small Java-oriented benchmark, and a limited query set—not a general performance rate or proof that every AST graph will beat every vector system. Do not generalize the results to other languages, repository sizes, or production workloads without testing them.
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Build an evaluation set from real work rather than synthetic questions. Include incidents, code-review questions, onboarding tasks, and planned refactors. Ask maintainers to record the expected files, symbols, relationships, and acceptable alternative answers.
Measure answer quality
- Correctness: Does the answer identify the right symbol, dependency, or path?
- Coverage: Does it include all material files and relationships, or only the first plausible result?
- Traceability: Can a reviewer open the cited source range and verify each claim?
- Failure severity: Is a missing low-risk helper different from omitting an authorization check or data write?
Measure system behavior
- Indexing and refresh cost: Include parser time, embedding time, storage, model calls, and the cost of updating a typical commit.
- Latency: Record retrieval, graph expansion, reranking, and generation separately at the repository’s actual scale.
- Coverage: Count indexed files, resolved symbols, unresolved references, and valid edges by language and module.
- Version freshness: Measure how long it takes for a merged change to become searchable and whether old branches remain isolated.
- Operational effort: Track schema migrations, parser upgrades, access-control changes, and repair work for stale records.
Report results by question type. A single average score can hide the fact that vector retrieval excels at intent search while graph traversal excels at multi-hop tracing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implementation options for a hybrid system
You can keep vectors and graph data in separate systems or use a graph platform that also supports vector indexes. Neo4j’s GraphRAG package for Python documents vector indexes, graph retrieval, and integrations with external vector databases such as Weaviate, Pinecone, and Qdrant. Package compatibility and APIs change, so verify the current documentation against your language version and deployment model before standardizing on an implementation.
A minimal production architecture usually contains:
- an ingestion worker that watches commits or release artifacts;
- language parsers and static-analysis adapters;
- a symbol and edge store with repository-version metadata;
- an embedding pipeline for code, comments, documentation, and tests;
- a query planner that chooses vector, graph, or hybrid retrieval;
- a provenance layer that returns paths and source ranges;
- evaluation and observability data for latency, coverage, and errors.
Keep ingestion idempotent. Reprocessing the same commit should not duplicate nodes or edges. Use tombstones or transactional deletes for removed files, and isolate branches or snapshots so a query cannot combine incompatible versions.
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Is GraphRAG worth the extra indexing and maintenance work?
It is worth considering when the cost of missing a relationship is high or when engineers repeatedly reconstruct dependency paths by hand. Examples include safety-critical changes, large monoliths with unclear ownership, migrations spanning many services, and incident response where callers and data flows matter more than textual similarity.
The investment is harder to justify when the repository is small, questions are mostly local, the language tooling cannot resolve important constructs, or the code changes faster than the graph can be refreshed. In those cases, a well-designed vector index with symbol-aware chunking and strong provenance may deliver more value with less operational burden.
Use a staged rollout:
- Establish a vector baseline for the most common natural-language questions.
- Add a deterministic graph for one high-value language and a narrow edge set.
- Compare hybrid answers on real dependency-tracing tasks.
- Expand the schema only when an observed question cannot be answered reliably with the current model.
- Keep a searchable audit trail of retrieval evidence so users can report incorrect edges and stale results.
Common pitfalls and how to correct them
Assuming a graph is complete because it returns a path
Check parser coverage, unresolved symbols, generated code, and dynamic dispatch. Display confidence and provenance, and treat an absent edge as “not established,” not proof that no dependency exists.
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Chunks that split a function from its signature, class, or imports make both retrieval and citation harder. Prefer symbol-aware units, retain neighboring context as metadata, and avoid mixing unrelated functions in one embedding.
Ignoring repository versions
A vector created from one commit and a graph edge from another can produce a plausible but false answer. Store revision identifiers with every artifact and reject mixed-version joins unless the user explicitly requests historical comparison.
Letting high-degree nodes dominate traversal
Utility classes, central interfaces, and shared tables can connect thousands of nodes. Set hop and fan-out limits, prioritize edge types relevant to the query, and expose truncation so users know when the context was bounded.
Using LLM extraction as the only structural source
Validate generated edges against parsers, compilers, tests, or targeted human review. Keep extracted relationships labeled as inferred until they pass a deterministic check.
Optimizing latency before measuring usefulness
A fast answer that omits the caller or data write is worse than a slower answer that shows the complete path. Measure correctness and coverage first, then tune hop limits, caching, parallel retrieval, and reranking.
A practical decision framework
- Choose vector-first when your dominant tasks are intent search, unfamiliar terminology, comments, documentation, and local implementation lookup.
- Choose graph-first when your dominant tasks are callers, implementers, inheritance, imports, data lineage, and impact analysis.
- Choose hybrid when users begin with ambiguous natural language and then need a defensible multi-file explanation.
- Delay a broad graph rollout when you cannot maintain symbol resolution, repository-version isolation, or provenance.
The right architecture is the one that answers your highest-risk questions with verifiable evidence at an acceptable refresh cost. Start with a measured baseline, add explicit relationships where they remove recurring uncertainty, and keep the two retrieval modes available when their strengths are complementary.
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