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No—not for similarity search. DynamoDB’s native vector search compares a query vector with vectors stored in a vector index. Those vectors do not have to be generated by an embedding service, but the search still requires suitable vector representations. DynamoDB can host the vectors and index without a separate vector database; it cannot search raw text semantically on its own.
What “without embeddings” means in DynamoDB
A vector index stores vector representations on DynamoDB items, and the SearchVectors operation compares a supplied query vector with those indexed vectors. AWS describes these indexes as enabling similarity search on vector embeddings stored in table items, and documents approximate nearest-neighbor (ANN) search for uses such as semantic search, retrieval-augmented generation (RAG), recommendations, agent memory, and anomaly or fraud detection. AWS DynamoDB vector index guide
“Embedding” is often used to mean a vector generated from text by an embedding model. You can create or obtain vectors in different ways, but DynamoDB’s search operation still needs a query vector, and the index needs vectors to compare against it. Passing the original text alone does not make native vector search semantic.
What a DynamoDB vector search request needs
The SearchVectors API request identifies the table and active vector index, supplies a search vector, and specifies how many results to return. The query vector’s dimensionality must match the vector index’s configured dimensionality. AWS’s API definition allows a supplied vector with 1–4096 elements and a TopK value from 1 to 100; these are API bounds, not a guarantee that any index accepts every vector size. Vector elements are 32-bit IEEE-754 floating-point numbers. AWS SearchVectors API reference
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For text-based semantic retrieval, an embedding model commonly converts both stored text and a user’s query into vectors in the same compatible vector space. AWS’s LangChain example uses DynamoDBVectorStore with a BedrockEmbeddings function; that is one documented integration, not a requirement to use Bedrock or LangChain. AWS LangChain integration guide
Choose the right retrieval method
| Reader’s need | Approach | What it does |
|---|---|---|
| Find items similar to a query by semantic or other vector representation | DynamoDB vector index with SearchVectors |
Performs nearest-neighbor retrieval using vectors; a separate vector store is not required for this native DynamoDB feature. AWS vector index guide AWS DynamoDB and OpenSearch announcement |
| Retrieve records by exact key or key range | DynamoDB secondary index with Query or Scan, as appropriate |
Supports key-based access patterns; it does not rank records by vector similarity. AWS secondary indexes guide |
| Add full-text search, analytics, or hybrid retrieval alongside vector search | Evaluate DynamoDB’s Zero-ETL integration with OpenSearch | Connects DynamoDB data with OpenSearch capabilities. AWS documents it as an option for broader search needs, not as a universal replacement for native vector search. AWS DynamoDB and OpenSearch announcement |
Interpret results and filters carefully
Distance scores depend on the configured metric
There is no universal “similarity percentage” in a SearchVectors result. With cosine distance, AWS documents scores from 0 for identical vectors to 2 for opposite vectors; lower scores indicate closer matches. Euclidean distance also uses lower scores for closer matches. With dot product, higher scores indicate closer matches. Interpret scores using the index’s configured distance function. AWS SearchVectors API reference
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Search conditions have schema constraints
A search condition can filter on fields in the vector index search schema, but the API reference limits HASH and INLINE_FILTER schema attributes to equality conditions. Only top-level attributes in that search schema can be referenced. Design filters around those supported fields rather than assuming any table attribute can be used in a vector search condition. AWS SearchVectors API reference
Plan for indexing behavior, storage, and service limits
Results may not include a document immediately
AWS’s LangChain integration documentation says the vector index is eventually consistent: a document written moments ago may not appear in a search immediately. The integration also documents a maximum of 100 results. Account for that behavior when designing write-then-search flows or result handling. AWS LangChain integration guide
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Dimensions and projected attributes affect index storage
AWS says vector-index storage depends on vector dimensionality, projected attributes, and the number of indexed items. Its storage guidance estimates that a 1,536-dimension vector uses roughly four times the vector storage of a 384-dimension vector, all else equal. This is a comparison of vector storage, not a total-cost estimate. AWS recommends selecting the smallest dimension count that meets relevance needs and projecting only the attributes the application reads directly from search results. AWS vector index storage considerations
Check current limits and availability before deployment
AWS’s current vector index guide lists up to five vector indexes per table and support for on-demand capacity mode. Service limits, pricing, and regional availability can change; the cited guide does not establish regional exceptions. Check AWS’s current service and pricing information for your intended Region before production planning. AWS DynamoDB vector index guide
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Does DynamoDB need a separate vector database?
No. For its native vector-search feature, DynamoDB can store operational records and their vectors together and perform similarity retrieval through its vector index. That can avoid maintaining a separate vector-store replication pipeline. It does not remove the need to generate or obtain compatible vectors, supply a query vector, or account for the index’s search and consistency behavior.
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