AI Quick Reference

Looking for fast answers or a quick refresher on AI-related topics? The AI Quick Reference has everything you need—straightforward explanations, practical solutions, and insights on the latest trends like LLMs, vector databases, RAG, and more to supercharge your AI projects!

• What role does transfer learning play in improving video search models?
• What challenges arise when indexing and searching short-form video content?
• How are real-time indexing and search updates implemented for video content?
• How can cloud services enhance the scalability of video search applications?
• How is “precision” defined for nearest neighbor search results, and in what cases is precision@K a more appropriate metric than recall@K for judging search quality?
• When using Annoy, how does the number of trees in the forest and the search “k” parameter impact the accuracy and speed of queries, and how do you decide on their values?
• How do licensing and community support differ among FAISS (MIT licensed library), Annoy (open-source library), Milvus and Weaviate (open source databases), and Pinecone (closed-source service)?
• What is Mean Reciprocal Rank (MRR) in the context of retrieval evaluation, and how can it be applied to gauge how well a RAG system’s retriever finds relevant documents?
• How is query throughput (QPS, queries per second) measured for vector search, and what factors most directly impact achieving a high QPS in a vector database?
• What is the purpose of indexing in a vector database, and how does having an index affect search performance and accuracy?
• What is meant by “approximate” nearest neighbor search, and why is it necessary for high-dimensional vector data?
• What does the retrieval metric “precision@K” tell us about the top-K documents returned, and why might a high precision@3 be critical for the subsequent generation step?
• What is “recall” in the context of vector search results, and how is recall typically calculated when evaluating an ANN algorithm against ground-truth neighbors?
• What is a Hierarchical Navigable Small World (HNSW) graph index, and how does it organize vectors to enable efficient approximate nearest neighbor search?
• What does a recall@10 = 95% signify in practical terms for a vector search system, and how might a user determine if that level of recall is sufficient for their needs?
• What are some signs that your vector database configuration is suboptimal (for example, high CPU usage but low throughput, or memory usage far below capacity) and how would you go about addressing them?
• How do ANN benchmark datasets and evaluations account for different distance metrics? (Do they typically assume Euclidean distance, or do they evaluate algorithms under multiple metrics?)
• What role do tools like ANN-Benchmark (for algorithm-level comparison) and VectorDBBench (for full database benchmarking) play, and how does each assist in evaluating different aspects of performance?
• Why might an application prioritize precision over recall (or vice versa) in its vector search results? Can you give examples of use cases where one metric is more critical than the other?
• How does Annoy (Approximate Nearest Neighbors Oh Yeah) structure its index (using multiple trees) and in what situations is Annoy a preferred choice over other ANN libraries?
• What data structures or algorithmic strategies allow Annoy to quickly find neighbors (e.g., multiple random projection trees), and how do these contribute to its query performance?
• How does applying boolean filters or metadata-based pre-filtering alongside vector similarity search influence the overall query performance?
• How can approximate algorithms maintain efficiency at very large scales? For instance, do parameters need to be retuned as the dataset size increases to maintain the same recall?
• How might the quality of nearest neighbors retrieval change as the dataset grows much larger? (Consider phenomena like increased probability of finding very close impostor points in a big dataset.)
• At large scale, how do failure and recovery scenarios play out (for example, if a node holding part of a huge index goes down, how is that portion of the data recovered or reconstructed)?
• How might you use automated hyperparameter optimization techniques to find optimal index configurations, and what metrics would you optimize for (e.g., maximizing recall at fixed latency)?
• How can precision and recall metrics for retrieval be balanced when tuning a retriever for RAG — for example, what happens to the final output if we retrieve many documents vs. few highly relevant ones?
• How does batching multiple queries together affect latency and throughput? In what scenarios is batch querying beneficial or detrimental for vector search?
• Why should benchmark tests include both cold-start scenarios (first query, empty cache) and warm cache scenarios, especially for measuring latency in vector searches?
• How does using a binary embedding (e.g., sign of components only, or learned binary codes) drastically cut down storage, and what kind of search algorithms support such binary vectors?
• What are the engineering considerations for building an index on a very large dataset (for example, needing distributed computing or chunking the build process to avoid running out of memory)?
• In what ways can caching improve vector search performance (for example, caching frequently accessed vectors or the results of recent searches)?
• How does using a different distance metric affect the internal behavior of indexes like HNSW or IVF? (For example, does changing the metric require rebuilding the index, or affect performance?)
• How do cloud-based solutions manage very large indexes behind the scenes? For instance, does Zilliz Cloud automatically handle sharding when the vector count is extremely high?
• In practice, what steps are involved in constructing an index (like training quantizers or building graph connections), and how do these steps scale with the size of the dataset?
• How do delete operations or updates in a vector database affect storage usage over time? For example, is there a compaction process to reclaim space from removed vectors?
• How can dimensionality reduction techniques (such as PCA) be applied before indexing to reduce storage needs, and what are the potential downsides of doing so?
• What is the concept of a DiskANN algorithm, and how does it facilitate ANN search on datasets that are too large to fit entirely in memory?
• How do enterprise vector databases ensure durability of stored vectors and indexes (e.g., write-ahead logs, replication), and what is the storage cost of these reliability features?
• What specific challenges do extremely large datasets (say, hundreds of millions or billions of vectors) introduce to vector search that might not appear at smaller scale?
• What are the key capabilities of FAISS (Facebook AI Similarity Search) and how has it become a standard library for implementing vector similarity search?
• What optimizations do libraries like FAISS implement to maintain high throughput for vector search on CPUs, and how do these differ when utilizing GPU acceleration?
• Which of these tools (FAISS, Annoy, Milvus, Weaviate) allow tuning of index parameters (like HNSW M or Annoy tree count), and how does that flexibility impact performance tuning?
• How do false positives and false negatives manifest in ANN search results, and how do they relate to the concepts of precision and recall respectively in a vector search evaluation?
• For a given application requiring real-time updates (inserting new vectors frequently), which vector databases or libraries are better suited and why?
• What techniques can be used to generate a realistic query workload for testing (e.g., sampling queries from logs, using a mix of easy and hard queries, setting concurrency levels)?
• How can hardware-specific configurations (like enabling AVX2/AVX512 instructions for distance computations, or tuning GPU memory usage) influence the performance of a vector search system?
• Why are high recall values important when benchmarking approximate nearest neighbor searches, and how do vector databases typically trade off recall for speed?
• What does it mean for a vector database to scale horizontally, and how do systems achieve this (for example, through sharding the vector index across multiple nodes or partitions)?
• What does it indicate if a RAG system’s retriever achieves high recall@5, but the end-to-end question answering accuracy remains low?
• What techniques can be used to increase recall if initial tests show that the vector search is missing many true neighbors (e.g., adjusting index parameters or using re-ranking with exact search)?
• In a RAG pipeline, why is a high recall from the retriever often considered more important than high precision, and what are the trade-offs between these two in practice?
• In a deployed service, why might some queries be significantly slower than others, and what steps can be taken to ensure more consistent query latency?
• In a distributed vector database, how is the search query executed across multiple machines, and how are partial results merged to produce the final nearest neighbors list?
• Why might one incorporate a re-ranking step (exact distance calculation on a shortlist of candidates) after an approximate search, and how does this affect precision?
• How does increasing the number of concurrent queries affect a system’s scalability and what techniques (like connection pooling or query scheduling) help manage high concurrency at scale?
• How does incremental indexing or periodic batch indexing help in handling continuously growing large datasets, and what are the limitations of these approaches?
• How much memory overhead is typically introduced by indexes like HNSW or IVF for a given number of vectors, and how can this overhead be managed or configured?
• How should one interpret latency vs. throughput trade-offs in benchmarks (e.g., a system might achieve low latency at low QPS, but latency rises under higher QPS)?
• How can logging and profiling during a benchmark help identify bottlenecks (like if most time is spent in distance computation vs data transfer vs index traversal)?
• Are there cases where Manhattan distance or Hamming distance are useful for vector search, and how do these metrics differ in computational cost or index support compared to Euclidean/Cosine?
• How can Mean Average Precision (MAP) or F1-score be used in evaluating retrieval results for RAG, and in what scenarios would these be insightful?
• What is mean average precision (mAP) or average precision in the context of similarity search, and how can it be applied to measure the quality of ranked retrieval results from a vector database?
• How do memory access patterns and cache misses influence the latency and throughput of vector search algorithms, especially in graph-based vs. flat indexes?
• How easy or difficult is it to migrate from one vector database solution to another (for instance, exporting data from Pinecone to Milvus)? What standards or formats help in this process?
• How do Milvus and Weaviate approach distributed deployment differently (for example, Milvus using a cluster of service components, Weaviate using sharding and replicas), and what does that mean for a user?
• In what ways does Milvus serve as a full-fledged vector database (beyond just an ANN library), and what features does it offer for scalability and manageability of vector data?
• What is the role of monitoring in configuration tuning (i.e., how do metrics from production use guide further tuning adjustments over time)?
• How can multi-stage or hybrid indexing (for example, coarse quantization followed by finer search) improve search efficiency without significantly sacrificing recall?
• What is the role of multi-tenancy in scalability considerations for vector databases, and how might resource isolation be handled when multiple applications share the same infrastructure?
• How does parallelization (using multiple CPU cores or GPUs) enhance the search efficiency of vector databases, and what libraries or frameworks take advantage of hardware acceleration?
• How does the parameter for candidate set size (for example, nprobe in IVF or efSearch in HNSW) affect search efficiency and result quality in ANN searches?
• How do precision and recall complement each other in evaluating a vector database’s performance, and why might one consider both for a comprehensive assessment?
• How does product quantization (PQ) reduce the memory footprint of a vector index, and what impact does this compression have on search recall and precision?
• How is query latency defined and measured in the context of vector databases (e.g., average latency vs. 95th or 99th percentile latency)?
• In evaluating vector search, what are the differences between Recall@1 vs. Recall@100 (or precision@1 vs precision@10), and what do those differences reveal about a system’s behavior?
• What are the benefits and drawbacks of reducing precision for stored vectors (for instance, using 8-bit integers or float16 instead of 32-bit floats) in terms of both storage and retrieval quality?
• What are the typical bottlenecks when scaling a vector database to very large data volumes (such as network communication, disk I/O, CPU, memory), and how can each be mitigated?
• What is the difference between storing raw vectors versus only storing compressed representations or references to vectors, in terms of retrieval speed and storage savings?
• Why is tail latency (p95/p99) often more important than average latency for evaluating the performance of a vector search in user-facing applications?
• Why is it important to test vector database performance on datasets that mimic your actual use case (for example, testing on the same embedding model outputs or same text/image domain)?
• What impact does the metric have on performance? For instance, is computing cosine similarity generally more or less efficient than Euclidean, or is it roughly the same after transformations?
• What factors influence the choice of an indexing technique for a given application (e.g., data size, dimensionality, required query latency, update frequency)?
• How does the choice of distance metric (Euclidean distance vs. cosine similarity vs. dot product) influence the results of a vector search in terms of which neighbors are considered “nearest”?
• How does the choice of index type (e.g., flat brute-force vs HNSW vs IVF) influence the distribution of query latencies experienced?
• How does the concept of the “curse of dimensionality” influence the design of indexing techniques for vector search?
• How does the dimensionality of vectors impact search efficiency, and what challenges do extremely high-dimensional spaces pose for ANN algorithms?
• How important is the distribution of data (like clusterability or presence of duplicates) in determining whether a method will scale well to very large datasets?
• Why might one choose dot product as a similarity metric for certain applications (such as embeddings that are not normalized), and how does it relate to cosine similarity mathematically?
• What are the key configuration parameters for an HNSW index (such as M and efConstruction/efSearch), and how does each influence the trade-off between index size, build time, query speed, and recall?
• What are the main contributors to query latency in a vector search pipeline (consider embedding generation time, network overhead, index traversal time, etc.)?
• How can the performance of a vector DB be affected by the hardware it runs on, and what role do things like CPU cache sizes, RAM speed, or presence of GPU acceleration play in benchmark outcomes?
• How does the quality (relevance) of retrieved documents impact the final answer accuracy in RAG, and what metrics could highlight this impact?
• What does the trade-off curve between recall and query latency or throughput typically look like, and how can this curve inform decisions about index parameters?
• What are the trade-offs between an in-memory index (fast access, higher cost) and a disk-based index (slower access, lower cost) for large-scale deployment?
• What strategies can an application use to hide or tolerate latency in vector retrieval (for example, asynchronous queries, prefetching likely results, or using smaller indexes for quick preliminary filtering)?
• What strategies can be used to compress or quantize not just the vectors but also the index metadata (such as storing pointers or graph links more compactly) to save space?
• In scenarios where memory is limited, how can one configure a vector database to spill over to disk effectively (e.g., setting up hybrid memory/disk indexes or using external storage for bulk data)?
• How should one design a benchmark test to evaluate a vector database under conditions similar to a real production environment (considering data distribution, query patterns, etc.)?