1. Faiss
Title: Faiss – Efficient Vector Search
Description: Faiss is a library for efficient similarity search and clustering of dense vectors. It provides implementations of state-of-the-art indexing algorithms, including inverted file indices, IVFADC (Inverted File with Approximate Distance Calculation), and more.
Features:
– Supports large-scale vector databases with billions of entries.
– Provides highly optimized GPU-accelerated search algorithms.
– Supports both exact and approximate nearest neighbor search.
Sample code for indexing and searching with Faiss:
```python import faiss # Create an index index = faiss.IndexFlatL2(embedding_dim) # Index your vectors index.add(vectors) # Search for nearest neighbors D, I = index.search(query_vector, k) ```
2. Milvus
Title: Milvus – An Open-Source Vector Database for AI Applications
Description: Milvus is an open-source vector database designed to power AI and machine learning applications. It provides efficient storage, indexing, and retrieval of high-dimensional vectors, making it suitable for similarity search, recommendation systems, and content-based image retrieval.
Features:
– Supports various vector similarity metrics, including L2 distance, inner product, and Hamming distance.
– Offers distributed storage and computing capabilities for scalability.
– Integrates with popular machine learning frameworks such as TensorFlow and PyTorch.
Sample code for indexing and searching with Milvus:
```python
from milvus import Milvus, MetricType, IndexType
# Connect to Milvus server
milvus = Milvus()
# Create a collection
collection_param = {
"fields": [
{"name": "embedding", "type": DataType.FLOAT_VECTOR, "dim": embedding_dim}
],
"segment_row_limit": 100000,
"auto_id": True
}
milvus.create_collection(collection_param)
# Insert vectors
milvus.insert(collection_name, vectors)
# Search for nearest neighbors
query_param = {
"collection_name": collection_name,
"query": {
"bool": {
"must": [
{"term": {"embedding": query_vector.tolist()}}
]
}
},
"params": {
"metric_type": MetricType.L2,
"top_k": k
}
}
results = milvus.search(query_param)
```3. Annoy
Title: Annoy – Approximate Nearest Neighbors on Disk
Description: Annoy is a C++ library with Python bindings that allows for fast approximate nearest neighbor search in large high-dimensional datasets. It uses random projection trees to build an index that can be efficiently stored on disk.
Features:
– Supports both static and dynamic indexes.
– Enables efficient retrieval of approximate nearest neighbors.
– Provides flexible parameters for trade-offs between search accuracy and speed.
Sample code for indexing and searching with Annoy:
```python from annoy import AnnoyIndex # Create an index index = AnnoyIndex(embedding_dim) # Add vectors to the index for i, vector in enumerate(vectors): index.add_item(i, vector) # Build the index index.build(n_trees) # Search for nearest neighbors results = index.get_nns_by_vector(query_vector, k) ```
4. Hnswlib
Title: Hnswlib – Hierarchical Navigable Small World graphs
Description: Hnswlib is a library for approximate nearest neighbor search based on hierarchical navigable small world (HNSW) graphs. It provides a memory-efficient solution for fast
retrieval of nearest neighbors in large-scale datasets.
Features:
– Supports both cosine similarity and L2 distance metrics.
– Offers efficient memory usage and low index build time.
– Provides flexibility in choosing the trade-off between search accuracy and speed.
Sample code for indexing and searching with Hnswlib:
```python from hnswlib import Index # Create an index index = Index(space='l2', dim=embedding_dim) # Add items to the index index.add_items(vectors) # Build the index index.build(n_threads=4) # Set ef parameter for search speed-accuracy trade-off index.set_ef(ef) # Search for nearest neighbors labels, distances = index.knn_query(query_vector, k) ```
5. Faiss-annoy
Title: Faiss-annoy – Integration of Faiss and Annoy
Description: Faiss-annoy is a combination of Faiss and Annoy libraries, leveraging the indexing efficiency of Faiss and the approximate search capabilities of Annoy. It allows for efficient indexing and approximate nearest neighbor search in large-scale vector databases.
Features:
– Provides a seamless integration of Faiss and Annoy libraries.
– Enables fast approximate nearest neighbor search with Faiss indexing structures.
– Supports various similarity metrics and trade-offs between search accuracy and speed.
Sample code for indexing and searching with Faiss-annoy:
```python from faiss_annoy import AnnoyIndex # Create an index index = AnnoyIndex(embedding_dim) # Add vectors to the index for i, vector in enumerate(vectors): index.add_item(i, vector) # Build the index index.build(n_trees) # Search for nearest neighbors D, I = index.search(query_vector, k) ```
6. NMSLIB
Title: NMSLIB – Non-Metric Space Library
Description: NMSLIB is a library for similarity search in non-metric spaces. It provides a collection of indexing and searching algorithms, including hierarchical navigable small world (HNSW), VP-tree, and more. NMSLIB supports various distance metrics and allows for approximate nearest neighbor search.
Features:
– Supports a wide range of distance metrics, including Euclidean, cosine, and Jaccard.
– Offers multiple indexing algorithms for efficient search in high-dimensional spaces.
– Provides the ability to perform approximate nearest neighbor search.
Sample code for indexing and searching with NMSLIB:
```python
import nmslib
# Create an index
index = nmslib.init(method='hnsw', space='l2')
# Set index parameters
index_param = {
'M': 16,
'efConstruction': 200,
'post': 2
}
index.set_index_parameters(index_param)
# Build the index
index.addDataPointBatch(vectors)
index.createIndex({'post': 2})
# Search for nearest neighbors
ids, distances = index.knnQuery(query_vector, k)
```7. ScaNN
Title: ScaNN – Scalable Nearest Neighbor Search
Description: ScaNN is a library for scalable approximate nearest neighbor search. It provides a distributed system for indexing and searching high-dimensional vectors. ScaNN is built on TensorFlow and supports various similarity metrics and search configurations.
Features:
– Enables scalable approximate nearest neighbor search in large-scale datasets.
– Provides distributed indexing and searching capabilities.
– Supports both cosine similarity and dot product as similarity metrics.
Sample code for indexing and searching with ScaNN:
```python import scann # Create a searcher object searcher = scann.scann_ops_pybind.builder(vectors, num_neighbors=k).tree(num_leaves=2000).score_ah(2, anisotropic_quantization _threshold=0.2).reorder(100).build() # Search for nearest neighbors neighbors, distances = searcher.search(query_vector) ```
8. HNSW
Title: HNSW – Hierarchical Navigable Small World
Description: HNSW is an indexing structure for efficient approximate nearest neighbor search. It constructs a hierarchical graph where each node is connected to a set of nearest neighbors. HNSW provides fast search and supports high-dimensional vectors.
Features:
– Enables efficient approximate nearest neighbor search in high-dimensional spaces.
– Offers fast query time and supports indexing of large datasets.
– Provides a hierarchical graph structure for navigation during the search.
Sample code for indexing and searching with HNSW:
```python import hnswlib # Create an index index = hnswlib.Index(space='l2', dim=embedding_dim) # Set index parameters index.init_index(max_elements=len(vectors), ef_construction=200, M=16) # Add vectors to the index index.add_items(vectors) # Search for nearest neighbors labels, distances = index.knn_query(query_vector, k) ```
9. SPTAG
Title: SPTAG – Space Partition Tree and Graph
Description: SPTAG is a library for efficient approximate nearest neighbor search. It utilizes space partition tree and graph techniques to construct an index that enables fast retrieval of nearest neighbors. SPTAG supports both single-machine and distributed environments.
Features:
– Supports various distance metrics, including L2, cosine, and Jaccard.
– Offers parallel indexing and searching capabilities.
– Provides an intuitive interface for index building and querying.
Sample code for indexing and searching with SPTAG:
```python
import SPTAG
# Create an index
index = SPTAG.AnnIndex(algorithm='BKT', metric='L2', dim=embedding_dim)
# Set index parameters
index.setBuildParam("MaxItemCount", len(vectors))
# Build the index
index.build(vectors)
# Search for nearest neighbors
results = index.search(query_vector, k)
```10. PQk-means
Title: PQk-means – Product Quantization with k-means
Description: PQk-means is a technique for approximate nearest neighbor search that combines product quantization with k-means clustering. It provides a trade-off between search accuracy and computational efficiency by quantizing high-dimensional vectors into low-dimensional subvectors.
Features:
– Enables efficient indexing and search in high-dimensional spaces.
– Reduces memory consumption and speeds up query time through product quantization.
– Supports various distance metrics, including Euclidean and Manhattan.
Sample code for indexing and searching with PQk-means:
```python import faiss # Create an index index = faiss.IndexPQ(embedding_dim, subvector_dim, n_subvectors, n_bits_per_subvector) # Train the index index.train(vectors) # Add vectors to the index index.add(vectors) # Search for nearest neighbors D, I = index.search(query_vector, k) ```
Please note that the provided code snippets are simplified examples, and the actual implementation may require additional configuration and handling of data structures specific to each library.
