milvus/docs/developer_guides/chap02_schema.md

2. Schema

2.1 Collection Schema

type CollectionSchema struct {
  Name string
  Description string
  AutoId bool
  Fields []FieldSchema
}

2.2 Field Schema

type FieldSchema struct {
  Name string
  Description string
  DataType DataType 
  TypeParams map[string]string
  IndexParams map[string]string
}

2.2.1 Data Types

DataType

enum DataType {
    NONE = 0;
    BOOL = 1;
    INT8 = 2;
    INT16 = 3;
    INT32 = 4;
    INT64 = 5;

    FLOAT = 10;
    DOUBLE = 11;

    STRING = 20;

    VECTOR_BINARY = 100;
    VECTOR_FLOAT = 101;
}

2.2.2 Type Params

2.2.3 Index Params

Intro to Index

For more detailed information about indexes, please refer to Milvus documentation index chapter.

To learn how to choose an appropriate index for your application scenarios, please read How to Select an Index in Milvus.

To learn how to choose an appropriate index for a metric, see Distance Metrics.

Different index types use different index params in construction and query. All index params are represented by the structure of map. This doc shows the map code in python.

IVF_FLAT BIN_IVF_FLAT IVF_PQ IVF_SQ8 IVF_SQ8_HYBRID ANNOY HNSW RHNSW_PQ RHNSW_SQ NSG

IVF_FLAT

IVF (Inverted File) is an index type based on quantization. It divides the points in space into nlist units by clustering method. During searching vectors, it compares the distances between the target vector and the center of all the units, and then select the nprobe nearest unit. Then, it compares all the vectors in these selected cells to get the final result.

IVF_FLAT is the most basic IVF index, and the encoded data stored in each unit is consistent with the original data.

• building parameters:

  nlist: Number of cluster units.

# IVF_FLAT
{
    "index_type": "IVF_FLAT",
    "metric_type": "L2",  # one of L2, IP
  
    #Special for IVF_FLAT
    "nlist": 100     # int. 1~65536
}

• search parameters:

  nprobe: Number of inverted file cell to probe.

# IVF_FLAT
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
  
    #Special for IVF_FLAT
    "nprobe": 8       # int. 1~nlist(cpu), 1~min[2048, nlist](gpu)
}

BIN_IVF_FLAT

BIN_IVF_FLAT is a binary variant of IVF_FLAT.

• building parameters:

  nlist: Number of cluster units.

# BIN_IVF_FLAT
{
    "index_type": "BIN_IVF_FLAT",
    "metric_type": "jaccard",  # one of jaccard, hamming, tanimoto
  
    #Special for BIN_IVF_FLAT
    "nlist": 100           # int. 1~65536
}

• search parameters:

  nprobe: Number of inverted file cell to probe.

# BIN_IVF_FLAT
{
    "topk": top_k,
    "query": queries,
  
  	#Special for BIN_IVF_FLAT
    "metric_type": "jaccard",  # one of jaccard, hamming, tanimoto
    "nprobe": 8            # int. 1~nlist(cpu), 1~min[2048, nlist](gpu)
}

IVF_PQ

PQ (Product Quantization) uniformly decomposes the original high-dimensional vector space into Cartesian products of m low-dimensional vector spaces, and then quantizes the decomposed low-dimensional vector spaces. Instead of calculating the distances between the target vector and the center of all the units, product quantization enables the calculation of distances between the target vector and the clustering center of each low-dimensional space and greatly reduces the time complexity and space complexity of the algorithm.

IVF_PQ performs IVF index clustering, and then quantizes the product of vectors. Its index file is even smaller than IVF_SQ8, but it also causes a loss of accuracy during searching.

• building parameters:

  nlist: Number of cluster units.

  m: Number of factors of product quantization. CPU-only Milvus: m ≡ dim (mod m); GPU-enabled Milvus: m ∈ {1, 2, 3, 4, 8, 12, 16, 20, 24, 28, 32, 40, 48, 56, 64, 96}, and (dim / m) ∈ {1, 2, 3, 4, 6, 8, 10, 12, 16, 20, 24, 28, 32}. (m x 1024) ≥ MaxSharedMemPerBlock of your graphics card.

# IVF_PQ
{
    "index_type": "IVF_PQ",
    "metric_type": "L2",  # one of L2, IP
  
		#Special for IVF_PQ
    "nlist": 100,     # int. 1~65536
    "m": 8
}

• search parameters:

  nprobe: Number of inverted file cell to probe.

# IVF_PQ
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
  
    #Special for IVF_PQ
    "nprobe": 8       # int. 1~nlist(cpu), 1~min[2048, nlist](gpu)
}

IVF_SQ8

IVF_SQ8 does scalar quantization for each vector placed in the unit based on IVF. Scalar quantization converts each dimension of the original vector from a 4-byte floating-point number to a 1-byte unsigned integer, so the IVF_SQ8 index file occupies much less space than the IVF_FLAT index file. However, scalar quantization results in a loss of accuracy during searching vectors.

• building parameters:

  nlist: Number of cluster units.

# IVF_SQ8
{
    "index_type": "IVF_SQ8",
    "metric_type": "L2",  # one of L2, IP
  
  	#Special for IVF_SQ8
    "nlist": 100      # int. 1~65536
}

• search parameters:

  nprobe: Number of inverted file cell to probe.

# IVF_SQ8
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
            
    #Special for IVF_SQ8
    "nprobe": 8       # int. 1~nlist(cpu), 1~min[2048, nlist](gpu)
}

IVF_SQ8_HYBRID

Optimized version of IVF_SQ8 that requires both CPU and GPU to work. Unlike IVF_SQ8, IVF_SQ8H uses a GPU-based coarse quantizer, which greatly reduces time to quantize.

IVF_SQ8H is an IVF_SQ8 index that optimizes query execution.

The query method is as follows:

• If nq ≥ gpu_search_threshold, GPU handles the entire query task.

• If nq < gpu_search_threshold, GPU handles the task of retrieving the nprobe nearest unit in the IVF index file, and CPU handles the rest.

• building parameters:

  nlist: Number of cluster units.

# IVF_SQ8_HYBRID
{
    "index_type": "IVF_SQ8_HYBRID",
    "metric_type": "L2",  # one of L2, IP
    
  	#Special for IVF_SQ8_HYBRID
    "nlist": 100      # int. 1~65536
}

• search parameters:

  nprobe: Number of inverted file cell to probe.

# IVF_SQ8_HYBRID
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
  
    #Special for IVF_SQ8_HYBRID
    "nprobe": 8       # int. 1~nlist(cpu), 1~min[2048, nlist](gpu)
}

ANNOY

ANNOY (Approximate Nearest Neighbors Oh Yeah) is an index that uses a hyperplane to divide a high-dimensional space into multiple subspaces, and then stores them in a tree structure.

When searching for vectors, ANNOY follows the tree structure to find subspaces closer to the target vector, and then compares all the vectors in these subspaces (The number of vectors being compared should not be less than search_k) to obtain the final result. Obviously, when the target vector is close to the edge of a certain subspace, sometimes it is necessary to greatly increase the number of searched subspaces to obtain a high recall rate. Therefore, ANNOY uses n_trees different methods to divide the whole space, and searches all the dividing methods simultaneously to reduce the probability that the target vector is always at the edge of the subspace.

• building parameters:

  n_trees: The number of methods of space division.

# ANNOY
{
    "index_type": "ANNOY",
    "metric_type": "L2",  # one of L2, IP
  
    #Special for ANNOY
    "n_trees": 8      # int. 1~1024
}

• search parameters:

  search_k: The number of nodes to search. -1 means 5% of the whole data.

# ANNOY
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
    
  	#Special for ANNOY
    "search_k": -1    # int. {-1} U [top_k, n*n_trees], n represents vectors count.
}

HNSW

HNSW (Hierarchical Navigable Small World Graph) is a graph-based indexing algorithm. It builds a multi-layer navigation structure for an image according to certain rules. In this structure, the upper layers are more sparse and the distances between nodes are farther; the lower layers are denser and he distances between nodes are closer. The search starts from the uppermost layer, finds the node closest to the target in this layer, and then enters the next layer to begin another search. After multiple iterations, it can quickly approach the target position.

In order to improve performance, HNSW limits the maximum degree of nodes on each layer of the graph to M. In addition, you can use efConstruction (when building index) or ef (when searching targets) to specify a search range.

• building parameters:

  M: Maximum degree of the node.

  efConstruction: Take the effect in stage of index construction.

# HNSW
{
    "index_type": "HNSW",
    "metric_type": "L2",      # one of L2, IP
  
    #Special for HNSW
    "M": 16,              # int. 4~64
    "efConstruction": 40  # int. 8~512
}

• search parameters:

  ef: Take the effect in stage of search scope, should be larger than top_k.

# HNSW

{
        "topk": top_k,
        "query": queries,
        "metric_type": "L2",  # one of L2, IP
        
  			#Special for HNSW
        "ef": 64          # int. top_k~32768
}

RHNSW_PQ

RHNSW_PQ is a variant index type combining PQ and HNSW. It first uses PQ to quantize the vector, then uses HNSW to quantize the PQ quantization result to get the index.

• building parameters:

  M: Maximum degree of the node.

  efConstruction: Take effect in stage of index construction.

  PQM: m for PQ.

# RHNSW_PQ
{
    "index_type": "RHNSW_PQ",
    "metric_type": "L2",
  
    #Special for RHNSW_PQ
    "M": 16,               # int. 4~64
    "efConstruction": 40,  # int. 8~512
    "PQM": 8,              # int. CPU only. PQM = dim (mod m)
}

• search parameters:

  ef: Take the effect in stage of search scope, should be larger than top_k.

# RHNSW_PQ
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
        
      
  	#Special for RHNSW_PQ
    "ef": 64          # int. top_k~32768
}

RHNSW_SQ

RHNSW_SQ is a variant index type combining SQ and HNSW. It first uses SQ to quantize the vector, then uses HNSW to quantize the SQ quantization result to get the index.

• building parameters:

  M: Maximum degree of the node.

  efConstruction: Take effect in stage of index construction, search scope.

# RHNSW_SQ
{
    "index_type": "RHNSW_SQ",
    "metric_type": "L2",      # one of L2, IP
  
    #Special for RHNSW_SQ
    "M": 16,              # int. 4~64
    "efConstruction": 40  # int. 8~512
}

• search parameters:

  ef: Take the effect in stage of search scope, should be larger than top_k.

# RHNSW_SQ
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",  # one of L2, IP
  
    #Special for RHNSW_SQ
    "ef": 64          # int. top_k~32768
}

NSG

NSG (Refined Navigating Spreading-out Graph) is a graph-based indexing algorithm. It sets the center position of the whole image as a navigation point, and then uses a specific edge selection strategy to control the out-degree of each point (less than or equal to out_degree). Therefore, it can reduce memory usage and quickly locate the target position nearby during searching vectors.

The graph construction process of NSG is as follows:

1. Find knng nearest neighbors for each point.
2. Iterate at least search_length times based on knng nearest neighbor nodes to select candidate_pool_size possible nearest neighbor nodes.
3. Construct the out-edge of each point in the selected candidate_pool_size nodes according to the edge selection strategy.

The query process is similar to the graph building process. It starts from the navigation point and iterates at least search_length times to get the final result.

• building parameters:

  search_length: Number of query iterations.

  out_degree: Maximum out-degree of the node.

  candidate_pool_size: Candidate pool size of the node.

  knng: Number of nearest neighbors

# NSG
{
    "index_type": "NSG",
    "metric_type": "L2",
  
    #Special for RHNSW_SQ
    "search_length": 60,         # int. 10~300
    "out_degree": 30,            # int. 5~300
    "candidate_pool_size": 300,  # int. 50~1000
    "knng": 50                   # int. 5~300
}

• search parameters:

  search_length: Number of query iterations

# NSG
{
    "topk": top_k,
    "query": queries,
    "metric_type": "L2",      # one of L2, IP
        
    #Special for RHNSW_SQ
    "search_length": 100  # int. 10~300
}