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31 lines
2.6 KiB
Markdown
31 lines
2.6 KiB
Markdown
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# Introduction to Vector Databases
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Vector databases are specialized systems designed to store, retrieve, and search high-dimensional vector embeddings efficiently. These databases are crucial for applications that require similarity searches, such as recommendation engines, image recognition, and natural language processing. Unlike traditional databases, vector databases handle complex relationships within data by focusing on vector proximity or similarity rather than exact matches[1][5].
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### Examples of Vector Databases
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1. **[FAISS (Facebook AI Similarity Search)](https://github.com/facebookresearch/faiss)**
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- FAISS is a high-performance library optimized for dense vector similarity search and clustering. It uses techniques like quantization and partitioning to enhance search efficiency[1].
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2. **[ChromaDB](https://www.trychroma.com/)**
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- Chroma is an open-source embedding database that facilitates the creation of large language model (LLM) applications by allowing easy management of text documents and similarity searches[2].
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3. **[Pinecone](https://www.pinecone.io/)**
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- Pinecone is a managed vector database platform designed for high-dimensional data. It offers features like real-time data ingestion and low-latency search, making it suitable for large-scale machine learning applications[2][4].
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4. **[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search)**
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- MongoDB Atlas integrates vector search capabilities with its core database, allowing for semantic search and generative AI applications. It provides a specialized vector index that can operate independently of the main database infrastructure[4][5].
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5. **[Weaviate](https://weaviate.io/)**
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- Weaviate is an open-source vector database that supports various AI applications, offering features like faceted search and integration with existing infrastructures[3].
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6. **[Qdrant](https://qdrant.tech/)**
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- Qdrant is a simple vector database known for its ease of use and a free-tier option. It is designed to handle vector data efficiently[3].
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7. **[Milvus](https://milvus.io/)**
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- Milvus is an open-source vector database capable of handling large-scale vector data with low latency, making it suitable for production environments[3].
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These databases provide the infrastructure needed to support advanced AI and machine learning applications by enabling efficient vector storage and retrieval.
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I have several examples of vector databases, RAG, RAG Fusion, RAPTOR, as well as an overview of Searchable Encryption, Homomorphic Encryption, and Multiparty Computation in AI implementations in my blog at https://becomingahacker.org
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