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AI agents do not need a dedicated vector database to retrieve useful evidence. For text-heavy collections and exact terms, start with BM25 or full-text search; for retrieval alongside structured application data, assess your existing relational database; and for questions about connected entities, consider graph retrieval. Agentic search and reranking can add useful capabilities, but they are approaches to designing retrieval—not universal replacements for a particular database. The right choice depends on your queries, data, freshness needs, latency budget, and operating constraints.
At a glance
| Name | Best for | Key spec | Platforms | Checked |
|---|---|---|---|---|
| BM25 and full-text search | Exact terms, names, and identifiers in text-heavy collections | Strong first-stage lexical baseline | Search-engine platforms; Haystack integrations | Nov 2026 |
| Relational database retrieval | Keeping application data and retrieval in a familiar database environment | PostgreSQL with pgvector; assess workload and index limits | PostgreSQL; Aurora PostgreSQL | Nov 2026 |
| Graph retrieval | Entity relationships and questions requiring connected facts | Graph queries can complement vector similarity | Neptune Analytics; graph-plus-search designs | Nov 2026 |
| Agentic search | Questions that need iterative searching or query decomposition | Agent selects and refines searches as it gathers evidence | Azure AI Search retrieval guidance; custom tool workflows | Nov 2026 |
| Reranking | Improving the ordering of a noisy first-stage result set | Second stage after BM25 or hybrid retrieval | Azure AI Search retrieval guidance | Nov 2026 |
What are the alternatives to a vector database?
The main alternatives are lexical search (such as BM25), retrieval using a relational database, graph queries, and agent-directed searches across one or more sources. Reranking is a related technique: it improves candidate ordering after an initial retrieval step, rather than replacing the underlying store.
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The distinction is between how evidence is found and where it is stored. BM25 is a lexical ranking method; a relational database is a data-management system; a graph represents entities and links; agentic retrieval is a control pattern in which an agent chooses and iterates searches. These approaches can be combined. For example, a system can use keyword search to retrieve candidates, a graph to resolve relationships, and a reranker to prioritize the most relevant passages.
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When is BM25 or full-text search a better starting point?
Start with BM25 or full-text search when the query contains words that should match the documents directly—especially names, product codes, error messages, legal terms, or identifiers. BM25 is a strong first-stage baseline for agent retrieval, according to a 2025 VLDB workshop paper; search-engine platforms also have mature full-text capabilities, as described in Haystack’s document-store guidance.
Lexical search can be a better fit than semantic similarity when one exact token changes the answer. If an agent must locate a specific invoice ID, version string, or named policy, a semantically similar passage that omits the exact identifier may be a bad result. Full-text systems can also support language-aware matching and, depending on the platform and setup, combine text search with vector retrieval.
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A practical first experiment is to build a small representative query set that includes exact names and identifiers, paraphrases, and questions with no answer in the corpus. Compare whether the first-stage results contain the evidence the agent needs. If BM25 misses mostly paraphrases, evaluate a hybrid search path; if it already finds reliable evidence, a vector database may add complexity without solving a demonstrated problem.
Can an existing relational database handle agent retrieval?
Often, yes—particularly when the application already stores relevant records in a relational system and the team wants retrieval close to that data. PostgreSQL with pgvector is one documented option; AWS also describes Aurora PostgreSQL with pgvector as a custom retrieval path for teams that already have PostgreSQL vector infrastructure. See Haystack’s store options and AWS’s RAG architecture guidance.
The potential advantage is architectural fit: application records and retrieval may remain in a familiar database environment, and relational filters or joins can matter when an agent must honor structured constraints. This can reduce the need to introduce another system, but it does not make retrieval free or automatically scalable. The team still has to assess index performance, query patterns, data freshness, workload size, and its database’s operational limits.
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Before choosing this route, ask whether retrieval traffic will compete with transactional workloads, how often indexed content changes, and whether the database can meet the required latency under representative load. Examine the data model too: a relational query is a natural fit for records and defined relationships, but it may be awkward if the retrieval problem is primarily unstructured text discovery or deep relationship traversal.
Keep the decision tied to the workload rather than to the fact that a database is already present. If the existing system handles the query mix and operational requirements, consolidating may be sensible. If retrieval needs create unacceptable contention, latency, or maintenance, evaluate a separate search or graph system instead. Vendor documentation can establish supported capabilities, but it is not a comparative benchmark for your production workload.
When should an AI agent use graph retrieval?
Consider graph retrieval when the answer depends on explicit connections among entities—such as which organization owns a service, which components depend on it, or how a person, project, and policy are linked. A graph stores relationships as first-class structure, so queries can traverse connections instead of relying only on text similarity.
Graph retrieval is not necessarily a substitute for semantic search. A 2025 VLDB workshop paper describes hybrid storage in which vectors support semantic similarity while a graph stores structured relationships. AWS names Neptune Analytics for combining vector search with graph queries. This combination can suit questions that need both a relevant passage and a path through connected facts; see the VLDB workshop discussion and AWS’s retrieval architecture guidance.
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The trade-off is that relationship data must be represented and maintained. If the source material is mostly disconnected prose and questions rarely require multi-hop reasoning, a graph layer may be unnecessary. If links change often, consider how updates propagate and how the agent will distinguish authoritative, current relationships from stale ones.
Try a graph-oriented design when a representative question cannot be answered reliably by retrieving isolated passages—for instance, when it asks how several entities relate through intermediate links. Compare the result with a simpler search baseline. A graph is valuable when those explicit paths improve the evidence available to the agent, not merely because the question sounds complex.
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What is agentic search, and when is it worth the extra calls?
Agentic retrieval lets an agent choose sources, refine or decompose a query, inspect intermediate results, and search again until it has enough context. Microsoft describes this iterative source selection as agentic RAG in its information-retrieval guidance.
This can help when a question is ambiguous, requires several sources, or cannot be resolved by one fixed retrieval query. An agent might identify a key entity, search for it, use the result to form a more specific follow-up query, and then gather supporting evidence from another source. The approach shifts some retrieval decisions from a fixed pipeline to an agent’s control logic.
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That flexibility brings costs and failure modes. More tool calls can increase end-to-end latency and operating cost; a poor intermediate result can send later searches in the wrong direction; and the agent may stop too early or keep searching without improving the answer. Measure the whole process—including query expansion, repeated calls, and any reranking—not just the speed of an individual search request. Microsoft recommends evaluating relevance and latency on the target workload before production.
A 2026 arXiv preprint on agentic keyword search reports that its implementation achieved over 90% of performance metrics compared with traditional RAG in the authors’ evaluation. Treat that figure as a result from that specific study, not a general guarantee that agentic keyword search matches or beats vector-based systems across tasks. The paper is available at arXiv; its finding is a reason to evaluate the approach on similar workloads, not a substitute for doing so.
What does reranking add if search already returns results?
Reranking gives a second-stage model or ranking process a chance to reorder a smaller set of candidates from a faster first-stage search. It is useful when initial retrieval is broad enough to find relevant evidence but noisy enough that the best passages do not reliably appear near the top.
A common pattern is BM25 or hybrid retrieval first, followed by reranking of the candidate set. Microsoft describes semantic ranking as a second stage and advises measuring relevance and latency for the target workload; the VLDB workshop paper also describes two-stage retrieval as a common pattern. See Microsoft’s retrieval guidance and the VLDB workshop paper.
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Reranking cannot recover evidence that the first stage never retrieved. It may also add latency and cost. Evaluate it by checking whether the useful evidence rises in the candidate ordering and whether that improvement matters to the final answer. If retrieval misses are caused by poor recall rather than ordering, improve the first-stage search or query formulation before adding a reranker.
How should you choose an approach for your agent?
Choose by evaluating the evidence your agent can retrieve on realistic questions—not by assuming one category wins for every workload. A useful comparison includes these five dimensions.
- Retrieval quality: Include exact identifiers, names, paraphrases, ambiguous wording, and multi-hop questions. Check whether retrieved passages actually support a correct answer, not just whether they look related.
- Latency and cost: Measure the end-to-end path. Include query expansion, multiple tool calls, reranking, and index maintenance where relevant; an agentic flow can have a different cost profile from one fixed search.
- Freshness: Determine how quickly changed or newly added knowledge must become searchable. Frequent updates may influence the design, but the right answer depends on the system’s actual update path and workload.
- Structure and access rules: Identify required metadata filters, permissions, joins, and entity relationships. A system that finds relevant content but cannot enforce required access boundaries is not suitable.
- Operations and integration: Account for infrastructure already operated, deployment model, governance, customization, and the team’s ability to maintain indexes and workflows.
Use a representative evaluation set and hold the questions constant across candidate designs. Include questions that should retrieve something and questions that should not. Inspect retrieved evidence as well as final answers: an answer can sound plausible even when its supporting passage is missing or irrelevant. Record latency and cost alongside relevance, then repeat the evaluation when the corpus, agent tools, or query mix changes.
For hybrid queries that combine attributes with similarity, also examine filtering behavior. A 2024 survey of vector database management systems identifies hybrid attribute-and-vector queries as a design challenge; its broad survey is useful context, not a benchmark of current products. See the survey. The practical question is whether your proposed system can apply the filters and permissions your agent needs without undermining result quality or response time.
Which retrieval path fits common scenarios?
Start with the simplest approach that can satisfy the agent’s evidence and operational requirements, then add complexity only to address observed gaps.
- Exact terminology dominates: Begin with BM25 or full-text search. Add semantic or hybrid retrieval only if representative queries reveal important paraphrase misses.
- Retrieval sits beside structured application records: Evaluate the relational database already in use, including its retrieval capabilities and workload limits.
- Questions depend on explicit connections: Evaluate graph retrieval, or a graph-plus-search design if the agent also needs semantic discovery.
- Questions require iterative source selection: Evaluate agentic retrieval and account for the extra calls in latency, cost, and error handling.
- Initial results contain the answer but rank it poorly: Try reranking and compare the relevance gain with its added latency and cost.
These are starting points, not mutually exclusive choices. A search engine may offer both full-text and vector capabilities; a relational system may coexist with a graph; an agent may call several retrieval tools; and a reranker may sit after any suitable first-stage retriever. The architecture should follow the information needs and constraints of the agent.
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How we picked
This is a comparison of retrieval approaches, not a ranking of products. The order prioritizes practical starting points: lexical search for exact language, relational retrieval for teams with relevant structured data, graph retrieval for connected facts, agentic search for iterative questions, and reranking as a complementary quality stage. The criteria come from the cited documentation and research: retrieval quality, latency and cost, freshness, structure and filtering, and operational fit. No approach is presented as a universal winner, and the cited vendor materials describe capabilities and guidance rather than independent head-to-head performance.
Frequently Asked Questions
Can an AI agent retrieve documents without generating embeddings?
Yes. A lexical search system can retrieve by matching document terms without a vector index, and an agent can call that search as a tool. Whether that is sufficient depends on how often users phrase questions differently from the source material.
Does hybrid search mean I still need a vector database?
Not necessarily. “Hybrid” describes combining retrieval signals, such as lexical and semantic search; the components may be offered within one search platform or implemented across systems. Check the capabilities and constraints of the specific architecture rather than treating the label as a storage requirement.
How often should I reevaluate my agent’s retrieval setup?
Reevaluate when the corpus, update frequency, permissions, query mix, or agent workflow changes. A change can affect both which evidence is available and the latency or cost of retrieving it, so keep a stable set of representative questions for comparison.
Can reranking fix a search system that returns no relevant passages?
Usually not: reranking reorders candidates it receives and cannot surface evidence that the first-stage retriever omitted. If relevant passages are absent from the candidate set, investigate query construction, indexing, filters, and first-stage recall.
