Vector Database Framework
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A Vector Database Framework is a database framework designed for creating and managing vector databases and instances.
- Context:
- It can (typically) provide the underlying infrastructure and tools to develop and manage vector databases.
- It can (often) support functionalities like vector similarity search, indexing, and data retrieval.
- It can (often) use specialized indexing and search algorithms to handle high-dimensional vector data efficiently.
- It can support operations such as similarity search, nearest neighbor search, and vector space transformations.
- It can range from being a Lightweight Library-based Vector Database Framework to being a Vector Database Platform.
- It can range from being an Open-Source Vector Database Library to being a Closed-Source Vector Database Framework.
- It can range from being a Self-Hosted VDBMS to being a Fully-Managed VDBMS.
- It can integrate with various machine learning and AI frameworks to enhance data processing and analysis.
- It can be used to build applications requiring high-dimensional data handling, such as recommendation systems and image retrieval systems.
- It can support various data formats and provide interfaces for different programming languages.
- It can offer scalability and performance optimizations for handling large volumes of vector data.
- It can enable hybrid search capabilities, combining vector search with traditional full-text search.
- It can include features like GPU acceleration, zero-copy data access, and automatic versioning for efficient data management.
- ...
- Example(s):
- Lightweight Library-based Vector Database Frameworks, such as:
- Faiss - Developed by Facebook AI, Faiss is a library for efficient similarity search and clustering of dense vectors. It supports various algorithms and is optimized for large-scale data, such as image search and document retrieval.
- Vector Database Platforms, such as:
- Pinecone - A fully managed, cloud-native vector database designed for high-performance machine learning applications. It offers seamless API integration, high scalability, and supports various machine learning algorithms.
- Open-Source Vector Database Frameworks, such as:
- Milvus - An open-source platform designed for managing vector data and similarity search. It supports large-scale data handling and integrates with multiple AI frameworks.
- Weaviate - An open-source vector search engine that combines vector search with structured filtering, supporting various data types like text and images.
- Chroma - Tailored for AI-native embedding, Chroma simplifies the creation of applications powered by large language models and supports feature-rich querying and filtering.
- Closed-Source Vector Database Frameworks, such as:
- Pinecone - Despite its open-source components, the core Pinecone service is closed-source but offers extensive features for managing and querying vector data in AI applications.
- LanceDB Database Platform, which supports multimodal AI applications and offers serverless vector search.
- Milvus, known for its robust performance and scalability in handling large-scale vector data.
- Qdrant, focusing on high-performance, low-latency vector search capabilities.
- Weaviate, combining vector search with structured filtering and fault tolerance.
- Elasticsearch DBMS supports clustering and high availability.
- Vespa DBMS is known for its fast data writes and vector search operators.
- Managed VDBMS Service, such as: Pinecone DBMS.
- Open-Source VDMS, such as: Chroma DBMS.
- ...
- Lightweight Library-based Vector Database Frameworks, such as:
- Counter-Example(s):
- Traditional Relational Database Framework, which is optimized for structured data rather than vector or high-dimensional data.
- File-Based Storage Systems, which lack advanced querying capabilities and performance optimizations needed for vector data.
- Relational DBMS, such as: Maria DBMS or PostgreSQL.
- NoSQL databases like Cassandra that do not primarily focus on vector data.
- See: Vector Database, Multimodal AI, Machine Learning Framework, Recommendation Systems, Content Moderation, Vector Space Model, Nearest Neighbor Search, High-Dimensional Data Management.
References
2024
- GPT-4
| Name | Open Source | Key Features |
|---|---|---|
| Elasticsearch | Yes | Clustering, High Availability, Automatic Node Recovery, Horizontal Scalability, Cross-Cluster Replication |
| Vespa | Yes | Fast Data Writes, Configurable Data Redundancy, Structured Filters, Text Search Operators, Vector Search Operators |
| Vald | Yes | Automatic Backups, Distributed Vector Indexes, Index Replication, Multi-Language Support |
| ScaNN | Yes | Search Space Trimming, Quantization for Maximum Inner Product Search, Euclidean Distance Support |
| Pgvector | Yes | Nearest Neighbor Search, L2 Distance, Inner Product, Cosine Distance, PostgreSQL Client Compatibility |
| Chroma | Yes | Queries, Filtering, Density Estimates, LangChain Support, Scalable API |
| Pinecone | No | Fully Managed Service, Scalability, Real-time Data Ingestion, Low-Latency Search, LangChain Integration |
| Weaviate | Yes | Fast Search, Flexibility, Modules Integration with OpenAI, Cohere |
| Faiss | Yes | Similarity Search, Clustering of Dense Vectors, Various Indexing and Search Algorithms, Large-Scale Dataset Optimization |
| Annoy | Yes | Memory Efficiency, Tree-Based Search, Euclidean/Cosine Distance Metrics |
| Milvus | Yes | Scalable Storage and Search, Metric Indexing, Multiple Programming Languages Support |
| Hnswlib | Yes | Memory Efficiency, Small-World Graph Search, Euclidean/Cosine Distance Metrics |
| FaunaDB | Not Specified | Cloud-Native, Serverless, k-d Tree Algorithm, ACID Transactions |
| Amazon Neptune | Not Specified | Fully Managed Graph Database, Gremlin and SPARQL Support, Scalable Infrastructure |
2023
- (Pan, Wang et al., 2023) ⇒ James Jie Pan, Jianguo Wang, and Guoliang Li. (2023). “Survey of Vector Database Management Systems.” doi:10.48550/arXiv.2310.14021
- NOTES:
- It thoroughly evaluates over 20 commercial Vector Database Management Systems (VDBMSs) that have emerged in recent years, focusing on the obstacles in managing vector data.
- It details the process of query processing in VDBMSs, discussing aspects like similarity scores, query types, and interfaces, along with the complexities of basic search query operators.
- It outlines various storage and indexing strategies used in VDBMSs, including partitioning techniques (like randomization and learned partitioning) and different types of indexes such as tree-based, table-based, and graph-based.
- It delves into the optimization and execution aspects of VDBMSs, explaining plan enumeration, selection, hybrid operators for predicated queries, and the utilization of hardware acceleration and distributed search techniques.
- It classifies current VDBMSs into categories such as native, extended, and search engines/libraries, analyzing their design and runtime characteristics to highlight each type's strengths.
- It acknowledges the importance of benchmarks in evaluating VDBMSs, but it doesn't provide an in-depth analysis of specific benchmarks, suggesting an area for future exploration.
- It analyzes EuclidesDB VDBMS (2018), Vearch VDBMS (2018), Pinecone VDBMS (2019), Vald (2020), Chroma (2022), Weaviate (2019), Milvus (2021), NucliaDB (2021), Qdrant (2021), Manu (2022), Marqo (2022), Vespa (2020), Cosmos DB (2023), MongoDB DBMS (2023), Neo4j DBMS (2023), Redis (2023), AnalyticDB-V (2020), PASE+PG (2020), pgvector+PG (2021), SingleStoreDB (2022), ClickHouse (2023), MyScale (2023).
- NOTES: