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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsNo: a retrieval-augmented generation (RAG) pipeline does not inherently need a separate vector database. It may use full-text search, vector search inside a database you already run, a vector-search library, or a combination of text and vector retrieval. The useful distinction is between needing vector search and needing another database product: those are not the same requirement.
Does RAG need a vector database?
RAG retrieves relevant material from a corpus and supplies it to a language model as context. Vector search is one way to find that material: it can surface passages with similar meaning even when the query uses different wording. But retrieval can also use lexical, or full-text, search, which matches words and terms. Some applications need one method; others benefit from both.
A separate vector database is an architectural choice, not a defining requirement of RAG. You can keep retrieval within an existing database, use a library in your application, or choose a managed search service. The right option depends on the corpus, the questions people ask, the relevance you need, and the operational trade-offs.
When is vector search useful, and when can text search be enough?
Vector retrieval: meaning and wording variation
Vector search is useful when relevant material may express the same concept in different words. A question phrased differently from the source passage may still be semantically similar and worth retrieving.
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Lexical retrieval: exact terms and identifiers
Full-text search is often valuable for exact terminology: names, dates, codes, product identifiers, and specialist jargon. It can be a strong starting point when users’ questions contain terms that also appear in the relevant documents. Its limitation is that a relevant passage may be missed when it uses different words from the query.
These approaches address different matching problems. If exact terms are essential, vector search alone may not be sufficient; if users routinely paraphrase concepts, lexical search alone may miss useful passages. Which matters more should be established with representative questions from the intended application.
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What are the main alternatives to a separate vector database?
| Approach | When to consider it | Important trade-off |
|---|---|---|
| Full-text search | Exact terminology, names, dates, identifiers, or specialist vocabulary are important. | May miss relevant passages expressed with different words. PostgreSQL supports indexed full-text search using GIN indexes; see PostgreSQL’s GIN documentation. |
| PostgreSQL with pgvector | Your application already uses PostgreSQL and you want vectors alongside application data. | pgvector uses exact nearest-neighbor search by default and also offers optional approximate HNSW and IVFFlat indexes. Approximate indexes trade recall for speed, so test their results for your workload. See the pgvector documentation. |
| FAISS library | You want application-controlled vector similarity search without adopting a managed vector database. | FAISS is a vector-search library; its overview does not establish that it provides every database or hosted-service feature. Data integration and operational responsibilities remain part of your design. See the FAISS README. |
| Hybrid search | Both conceptual similarity and exact-term matching matter. | Combines text and vector result lists, but fusion, filtering, and reranking add work that can affect resource use and latency. Microsoft documents this approach for Azure AI Search in its hybrid search overview. |
| Managed hybrid search | You want a service that integrates full-text and vector retrieval. | Service cost and operational fit still need to be evaluated for your workload. Azure AI Search documents hybrid queries, filters, Reciprocal Rank Fusion, and semantic ranking; see Microsoft’s hybrid-query guidance. |
How does hybrid retrieval combine results?
Hybrid search runs text and vector queries and merges their result lists. The rankings differ: text search can use BM25, while vector search can use methods such as HNSW or exhaustive nearest-neighbor search. Reciprocal Rank Fusion (RRF) is one method for combining rankings from those distinct result sets. Microsoft describes the approach in its Azure AI Search hybrid search documentation.
Hybrid retrieval can help when a query needs both semantic matching and exact-term coverage. It is not automatically better for every corpus or query: the merged ranking, filters, and any subsequent reranking still need to work well for the application.
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How should you choose for your RAG workload?
- Build a representative evaluation set. Use real or realistic questions, including paraphrases and queries containing exact names, dates, codes, or domain terms. Identify the documents or passages that should be retrieved for each question.
- Compare retrieval approaches against the same questions. Evaluate full-text search, vector search, and hybrid retrieval where relevant. Check whether the expected passages appear and whether the ranking is useful; do not infer quality from the architecture label alone.
- Test filters and exact-match behavior. Check that metadata constraints behave as required and that important identifiers are retrievable. A method that performs well on broad semantic questions may still be a poor fit for exact lookups.
- Measure latency, throughput, and operational cost. Consider corpus size and growth as well as the work of operating and integrating the chosen system. For approximate vector indexes, compare speed with recall rather than assuming approximate results are equivalent to exact search.
- Keep the simplest approach that meets the requirements. Add a separate service or more complex retrieval stages when evaluation shows a concrete need, not merely because a RAG design is expected to include them.
There is no workload-independent winner established by the cited documentation. The choice should follow the retrieval quality and operating behavior you observe with your own representative questions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can make hybrid search costly?
Combining retrieval methods adds query work, and semantic reranking can add more. Microsoft’s Azure AI Search guidance warns that increasing lexical candidate contribution alongside expensive vector settings and semantic reranking can increase CPU and memory pressure, latency, and the risk of throttling. Tune those settings against your workload and monitor the service rather than assuming that more candidates or more ranking stages are free.
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