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Database / Weaviate Vector database Interview questions

What is a Flat index in Weaviate?

A Flat index performs exact, brute-force nearest neighbor search, comparing a query vector against every stored vector directly, with no graph or clustering structure involved. It trades the speed advantage of an approximate index for perfect recall and much lower memory overhead per vector.

vector_index_config=Configure.VectorIndex.flat()

Flat indexes are the right choice specifically for small collections, and for multi-tenant setups where each individual tenant's data is small even if the total collection is large, since brute-force search over a small number of vectors is fast enough in absolute terms that the overhead of building and maintaining an HNSW graph isn't worthwhile. Flat indexes also support RQ and BQ compression, letting a brute-force search scan a compressed representation faster than it could scan full-precision vectors.

A Flat index performs search by:
Flat indexes are particularly well suited for:

More Related questions...

What is Weaviate? What is a Collection in Weaviate? What is a vectorizer module in Weaviate? What is hybrid search in Weaviate? What is the HNSW index in Weaviate? What is a Flat index in Weaviate? What is the Dynamic index in Weaviate? What is a cross-reference in Weaviate? What are named vectors in Weaviate? What is Weaviate Cloud? What is quantization/compression in Weaviate, and why is it used? What is Rotational Quantization (RQ) in Weaviate? What is generative search (RAG) in Weaviate? What is multi-tenancy in Weaviate? What is Object TTL in Weaviate? What is the Weaviate Query Agent? What is a UUID's role for objects in Weaviate? What are properties in a Weaviate collection? What is BM25 in the context of Weaviate? What is the alpha parameter in Weaviate hybrid search? What are the main deployment options for Weaviate? What is replication in Weaviate? What is sharding in Weaviate? What are the main use cases for Weaviate? What is the difference between bringing your own vectors and using a vectorizer module? Explain the data flow of an object being vectorized and indexed in Weaviate? Why does Weaviate combine BM25 and vector search instead of using vector search alone? How does Weaviate differ from Milvus? What is the difference between HNSW and the HFresh index in Weaviate? How do you choose between PQ, BQ, SQ, and RQ quantization? When should you use the Dynamic index instead of always using HNSW? How do you troubleshoot poor recall after enabling quantization in Weaviate? What is the difference between rescoring and raw compressed-vector search? How does Weaviate's multi-tenancy isolate tenant data on disk? Explain the internal working of Weaviate's rescoring mechanism for quantized vectors? What is the difference between Weaviate and Pinecone? How do you implement RAG using Weaviate's generative search module? Why use cross-references sparingly, according to Weaviate's own guidance? What is the difference between rankedFusion and relativeScoreFusion in hybrid search? How does the Weaviate Query Agent route natural-language questions across collections? When would you choose multi-vector (ColBERT-style) embeddings over single-vector embeddings? How do you configure replication factor for high availability in Weaviate? What is the difference between Weaviate's REST/GraphQL API and its gRPC API? Explain the lifecycle of a hybrid search query in Weaviate? How do you optimize Weaviate for memory efficiency at large scale? What is the difference between PQ and RQ quantization internally? How does Weaviate decide which shard(s) to query for a given request? Why should you avoid excessive cross-reference traversal in a single query? What is the difference between Weaviate Database (self-hosted) and Weaviate Cloud? How do you troubleshoot a Weaviate collection running out of memory at scale?
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