ppb是什么单位

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A vector database, vector store or vector search engine is a database that uses the vector space model to store vectors (fixed-length lists of numbers) along with other data items. Vector databases typically implement one or more approximate nearest neighbor algorithms, so that one can search the database with a query vector to retrieve the closest matching database records.

Vectors are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, with the number of dimensions ranging from a few hundred to tens of thousands, depending on the complexity of the data being represented. A vector's position in this space represents its characteristics. Words, phrases, or entire documents, as well as images, audio, and other types of data, can all be vectorized.

These feature vectors may be computed from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically similar data items receive feature vectors close to each other.

Vector databases can be used for similarity search, semantic search, multi-modal search, recommendations engines, large language models (LLMs), object detection, etc.

Vector databases are also often used to implement retrieval-augmented generation (RAG), a method to improve domain-specific responses of large language models. The retrieval component of a RAG can be any search system, but is most often implemented as a vector database. Text documents describing the domain of interest are collected, and for each document or document section, a feature vector (known as an "embedding") is computed, typically using a deep learning network, and stored in a vector database. Given a user prompt, the feature vector of the prompt is computed, and the database is queried to retrieve the most relevant documents. These are then automatically added into the context window of the large language model, and the large language model proceeds to create a response to the prompt given this context.

Techniques

The most important techniques for similarity search on high-dimensional vectors include:

• Hierarchical Navigable Small World (HNSW) graphs
• Locality-sensitive Hashing (LSH) and Sketching
• Product Quantization (PQ)
• Inverted Files

and combinations of these techniques.

In recent benchmarks, HNSW-based implementations have been among the best performers. Conferences such as the International Conference on Similarity Search and Applications, SISAP and the Conference on Neural Information Processing Systems (NeurIPS) host competitions on vector search in large databases.

Implementations

This is a dynamic list and may never be able to satisfy particular standards for completeness. You can help by adding missing items with reliable sources.

Name	License
Aerospike	Proprietary
AllegroGraph	Proprietary (Managed Service)
AlloyDB AI	Proprietary (Managed Service)
Apache Cassandra	Apache License 2.0
Azure Cosmos DB	Proprietary (Managed Service)
Chroma	Apache License 2.0
ClickHouse	Apache License 2.0
Couchbase	BSL 1.1
CrateDB	Apache License 2.0
DataStax	Proprietary (Managed Service)
Elasticsearch	Server Side Public License, Elastic License
HAKES	Apache License 2.0
HDF5 Query Indexing	BSD 3-Clause
JaguarDB	Proprietary
LanceDB	Apache License 2.0
Lantern	BSL 1.1
LlamaIndex	MIT License
MariaDB	GPL v2
Marqo	Apache License 2.0
Meilisearch	MIT License
Milvus	Apache License 2.0
MongoDB Atlas	Server Side Public License (Managed service)
Neo4j	GPL v3 (Community Edition)
ObjectBox	Apache License 2.0
OpenSearch	Apache License 2.0
Oracle Database	Proprietary (Managed Service or License)
Pinecone	Proprietary (Managed Service)
Pixeltable (Incremental Embedding)	Apache License 2.0
Postgres with pgvector	PostgreSQL License
Qdrant	Apache License 2.0
Redis Stack	Redis Source Available License
Snowflake	Proprietary (Managed Service)
SurrealDB	BSL 1.1
TiDB	Apache License 2.0
Typesense	GPL v3 (Community Edition)
VectorXDB.ai	Proprietary (Managed Service or License)
Vespa	Apache License 2.0
Weaviate	BSD 3-Clause
YDB	Apache License 2.0

See also

• Curse of dimensionality?– Difficulties arising when analyzing data with many aspects ("dimensions")
• Machine learning?– Study of algorithms that improve automatically through experience
• Nearest neighbor search?– Optimization problem in computer science
• Recommender system?– System to predict users' preferences

External links

• Sawers, Paul (2025-08-14). "Why vector databases are having a moment as the AI hype cycle peaks". TechCrunch. Retrieved 2025-08-14.