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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →You need retrieval for a RAG chatbot, but you do not automatically need a separately operated vector database service. For a first prototype, a managed retrieval API or PostgreSQL with pgvector can provide a practical starting point. Choose by testing your documents and questions, then weighing data handling, operating effort, integration, and cost—not by relying on a universal size or speed threshold.
What “no vector database” actually means
Retrieval-augmented generation (RAG) uses retrieved source material to inform a model’s response. Retrieval is essential to that design; the component that stores and finds relevant content can vary.
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In this article, “no vector database” means no separate, dedicated vector-database service. It does not mean no retrieval index or no vector search. A PostgreSQL setup using pgvector still stores embeddings and performs vector search, even if it lives in a database your application already uses.
What retrieval contributes to a RAG chatbot
Semantic search can find content that is conceptually relevant even when it shares few words with the query. OpenAI’s Retrieval API documentation describes this as surfacing semantically similar results “even when they match few or no keywords.” Its retrieval options also include keyword similarity and hybrid-search controls, which combine semantic and lexical signals. Whether those controls help your chatbot depends on its documents and questions, so evaluate them rather than assuming they improve every result.
#1 Best Overall
Three ways to start
| Starting path | What it provides | What to evaluate |
|---|---|---|
| Managed retrieval API | A provider-managed vector-store workflow. OpenAI documents automatic chunking, embedding, and indexing of files added to a store. | Retrieval quality on your examples; API and storage costs; provider limits; data-handling requirements; and portability. |
| PostgreSQL with pgvector | Embeddings and vector retrieval in PostgreSQL, potentially alongside application data. AWS and Google document PostgreSQL-based RAG architectures using this approach. | Your existing PostgreSQL skills; extension and version support; indexing and query behavior; performance; backup and restore; and the extra database work involved. |
| Dedicated vector database service | A separate service specialized for vector retrieval, which may be worth evaluating when measured requirements are not met by your other options. | Retrieval quality, workload scale, latency, filtering, hybrid search, integration, operational effort, reliability, and total cost. |
These are options to test, not a ranking. The cited documentation does not establish a neutral head-to-head winner for a typical first chatbot or a universal point at which a dedicated service becomes necessary.
How to choose a first implementation
- Start with your current stack and constraints. If you already use PostgreSQL, pgvector may let you keep retrieval in a database you know. If you want to avoid operating retrieval infrastructure directly, a managed API may be a simpler prototype—provided its data handling, limits, and pricing fit.
- Build a small representative evaluation set. Include questions that depend on semantic matching, questions with exact names or identifiers, and questions whose answers are absent from your documents.
- Inspect the retrieved passages, not just the final answer. Record whether the passages actually support the response. A fluent answer can still be unsupported if retrieval returns irrelevant or incomplete material.
- Compare operational and product requirements. Consider access controls, data integration, backup and recovery, cost, latency, filtering, and the work required to keep the system reliable. Measure these against your workload instead of choosing from a presumed threshold.
- Revisit the architecture when evidence calls for it. If your tests reveal unmet needs, compare a dedicated service with improvements to the managed or PostgreSQL path. The sources do not support a fixed document count, request rate, or latency number for making that change.
Costs and provider-specific details
OpenAI’s Retrieval documentation listed storage of up to 1 GB across vector stores as free, with storage beyond 1 GB priced at $0.10 per GB per day, when checked on October 7, 2026. This is an OpenAI product price, not a general market rate; check the current documentation before estimating costs.
Cloud architecture examples are not necessarily turnkey beginner instructions. AWS documents RAG approaches using Aurora PostgreSQL with pgvector, including a Bedrock knowledge-base workflow that chunks source text from S3, converts it to vectors, stores it in PostgreSQL, and retrieves it to augment model responses. AWS’s high-speed chatbot guidance also notes that Cloud9 is no longer available to new customers, so do not rely on that dependency when adapting its example.
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A separate vector database is a reasonable option to assess when your measurements or constraints show that your existing database or managed retrieval path is not meeting your needs. Compare the options using the same documents and evaluation questions, and include operational burden and total cost alongside retrieval quality and latency. There is no source-backed universal threshold that makes a dedicated service mandatory for a first RAG chatbot.
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