IndexIVFFlat y IndexIVFPQ

다음은 IndexIVFFlat 및 IndexIVFPQ 인덱스 간의 비교와 사용에 대한 몇 가지 대안입니다.

비교: IndexIVFFlat 대 IndexIVFPQ

특성

Characteristic	IndexIVFFlat	IndexIVFPQ
Storage Type	Stores vectors in their original form.	Utilizes product quantization (PQ) to compress vectors.
Precision	High precision, as it performs exact searches within cells.	May sacrifice some precision for compression, but still provides good results.
Search Speed	Slower on large datasets due to exhaustive search.	Faster, especially on large sets, thanks to reduced search space.
Memory Usage	Consumes more memory as it stores all vectors without compression.	Consumes significantly less memory due to compression (up to 97% less).
Configuration	Simpler, only requires defining the number of cells (nlist).	Requires defining both the number of cells (nlist) and code size (code_size).
Training	Needs to be trained to create cells before adding data.	Also requires training, but the process is more complex due to quantization.

색인IVFFlat 색인IVFPQ 저장 유형 벡터를 원래 형식으로 저장합니다. 곱 양자화(PQ)를 활용하여 벡터를 압축합니다. 정밀도 셀 내에서 정확한 검색을 수행하므로 정밀도가 높습니다. 압축을 위해 일부 정밀도가 희생될 수 있지만 여전히 좋은 결과를 제공합니다. 검색 속도 철저한 검색으로 인해 대규모 데이터 세트에서는 속도가 느려집니다. 검색 공간이 줄어들어 특히 대규모 세트에서 더 빠릅니다. 메모리 사용량 모든 벡터를 압축하지 않고 저장하므로 더 많은 메모리를 소비합니다. 압축으로 인해 훨씬 ​​적은 메모리를 소비합니다(최대 97% 감소). 구성 더 간단합니다. 셀 수(nlist)만 정의하면 됩니다. 셀 수(nlist)와 코드 크기(code_size)를 모두 정의해야 합니다. 훈련 데이터를 추가하기 전에 셀을 생성하도록 교육이 필요합니다. 또한 교육이 필요하지만 양자화로 인해 프로세스가 더 복잡합니다.

优点和缺点

IndexIVFFlat 的优点

• 精度：在每个单元格内搜索时提供准确的结果。
• 简单：易于理解和配置。

IndexIVFFlat 的缺点

• 速度：处理大量数据时可能会非常慢。
• 内存使用：不优化内存使用，这对于大型数据集可能会出现问题。

IndexIVFPQ 的优点

• 速度：由于搜索空间减少，搜索速度更快。
• 内存效率：显着减少内存使用量，从而可以处理更大的数据集。

IndexIVFPQ 的缺点

• 精度：由于压缩，精度可能会略有损失。
• 复杂性：配置和训练比 IndexIVFFlat 更复杂。

替代方案

1. IndexFlatL2

   • 在不压缩的情况下执行详尽的搜索。非常适合需要最大精度的小型数据集。

2. IndexPQ

   • 仅使用乘积量化而不使用聚类。当需要速度和精度之间的平衡但不需要聚类时很有用。

3. IndexIVFScalarQuantizer

   • 将倒排索引与标量量化相结合，提供了一种不同的方法来减少内存使用并提高速度。

4. 索引IVFPQR

   • 将 IVF 和 PQ 与基于代码的重新排名相结合的变体，在速度和提高的精度之间取得平衡。

5. 综合索引

   • 使用index_factory创建组合索引，结合多种技术（例如OPQ IVF PQ）来进一步优化性能。

这些替代方案允许根据要解决的具体情况使解决方案适应精度、速度和内存使用方面的不同需求。

引用：
[1] https://github.com/facebookresearch/faiss/wiki/Faiss-indexes/9df19586b3a75e4cb1c2fb915f2c695755a599b8
[2] https://ai.plainenglish.io/speeding-up-similarity-search-in-recommender-systems-with-faiss-advanced-concepts-part-ii-95e796a7db74?gi=ce57aff1a0c4
[3] https://www.pinecone.io/learn/series/faiss/faiss-tutorial/
[4] https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexIVFFlat.html
[5] https://unfoldai.com/effortless-large-scale-image-retrieval-with-faiss-a-hands-on-tutorial/
[6] https://www.pinecone.io/learn/series/faiss/product-quantization/
[7] https://www.pinecone.io/learn/series/faiss/composite-indexes/
[8] https://github.com/facebookresearch/faiss/issues/1113

En Español，Soy Español，pero por respeto a la comunidad，pongo primero la traduccion al inglés。

Aquí tienes una comparación entre los índices IndexIVFFlat e IndexIVFPQ, junto con algunas alternativas para su uso:

比较：IndexIVFFlat 与 IndexIVFPQ

特征

Característica	IndexIVFFlat	IndexIVFPQ
Tipo de Almacenamiento	Almacena vectores en su forma original.	Utiliza cuantización de producto (PQ) para comprimir vectores.
Precisión	Alta precisión, ya que realiza búsquedas exactas dentro de las celdas.	Puede sacrificar algo de precisión por la compresión, pero aún proporciona buenos resultados.
Velocidad de Búsqueda	Más lento en grandes conjuntos de datos debido a la búsqueda exhaustiva.	Más rápido, especialmente en grandes conjuntos, gracias a la reducción del espacio de búsqueda.
Uso de Memoria	Consume más memoria porque almacena todos los vectores sin compresión.	Consume significativamente menos memoria debido a la compresión (hasta 97% menos).
Configuración	Más simple, solo requiere definir el número de celdas (nlist).	Requiere definir tanto el número de celdas (nlist) como el tamaño del código (code_size).
Entrenamiento	Necesita ser entrenado para crear las celdas antes de añadir datos.	También necesita entrenamiento, pero el proceso es más complejo debido a la cuantización.

IndexIVFFlat IndexIVFPQ 标题> Tipo de Almacenamiento 阿尔马塞纳矢量 en su forma 原始。 利用comprimir向量的产品优化（PQ）。 精确 高度精确，您可以实现精确的 las celdas 速度。 为了压缩而牺牲算法，但结果却是成比例的。 Búsqueda 速度 Más lento en grandes conjuntos de datos debido a la búsqueda exhausiva. 速度越来越快，尤其是大型联合，感谢 a la reducción del espacio de búsqueda。 Uso de Memoria 消耗更多的记忆来完成所有的罪恶向量。 消耗有意义的menos memoria debido a la compresión（hasta 97% menos）。 配置 非常简单，需要单独定义 el número de celdas (nlist)。 需要定义 tanto el número de celdas (nlist) 和 tamaño del código (code_size)。 娱乐 Necesita ser entrenado para crear las celdas antes de añadir datos。 También necesita entrenamiento, pero el proceso es más complejo debido a la cuantización. 表>

Pros and Cons

Pros of IndexIVFFlat

• Precision: Provides exact results when searching each cell.
• Simplicity: Easy to understand and configure.

Cons of IndexIVFFlat

• Speed: Can be very slow with large volumes of data.
• Memory Usage: Does not optimize memory usage, which can be a problem with large data sets.

Pros of IndexIVFPQ

• Speed: Much faster in searches due to the reduction of the search space.
• Memory Efficiency: Significantly reduces memory usage, allowing larger data sets to be handled.

Cons of IndexIVFPQ

• Accuracy: There may be a slight loss in accuracy due to compression.
• Complexity: The configuration and training are more complex than in IndexIVFFlat.

Alternatives

1. IndexFlatL2

   • Performs an exhaustive search without compression. Ideal for small data sets where maximum precision is required.

2. IndexPQ

   • Use only product quantization without grouping. It is useful when a balance between speed and precision is needed, but grouping is not required.

3. IndexIVFScalarQuantizer

   • It combines inverted index with scalar quantization, offering a different approach to reduce memory usage and improve speed.

4. IndexIVFPQR

   • A variant that combines IVF and PQ with code-based re-ranking, offering a balance between speed and improved accuracy.

5. Composite Indexes

   • Use index_factory to create composite indexes that combine multiple techniques (e.g. OPQ IVF PQ) to further optimize performance.

These alternatives allow you to adapt the solution to different needs in terms of precision, speed and memory usage depending on the specific case you are addressing.

Citations:
[1] https://www.pinecone.io/learn/series/faiss/faiss-tutorial/
[2] https://www.pinecone.io/learn/series/faiss/product-quantization/
[3] https://www.pinecone.io/learn/series/faiss/composite-indexes/
[4] https://github.com/facebookresearch/faiss/wiki/Faiss-indexes/9df19586b3a75e4cb1c2fb915f2c695755a599b8
[5] https://faiss.ai/cpp_api/struct/structfaiss_1_1IndexIVFFlat.html
[6] https://pub.towardsai.net/unlocking-the-power-of-efficient-vector-search-in-rag-applications-c2e3a0c551d5?gi=71a82e3ea10e
[7] https://www.pingcap.com/article/mastering-faiss-vector-database-a-beginners-handbook/
[8] https://wangzwhu.github.io/home/file/acmmm-t-part3-ann.pdf
[9] https://github.com/alonsoir/ubiquitous-carnival/blob/main/contextual-data-faiss-IndexIVFPQ.py
[10] https://github.com/alonsoir/ubiquitous-carnival/blob/main/contextual-data-faiss-indexivfflat.py

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