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Update vector_databases.md
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@ -4,26 +4,13 @@ Vector databases are specialized systems designed to store, retrieve, and search
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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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- **[FAISS (Facebook AI Similarity Search)](https://github.com/facebookresearch/faiss)**
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- **[ChromaDB](https://www.trychroma.com/)**
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- **[Pinecone](https://www.pinecone.io/)**
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- **[MongoDB Atlas Vector Search](https://www.mongodb.com/products/platform/atlas-vector-search)**
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- **[Weaviate](https://weaviate.io/)**
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- **[Qdrant](https://qdrant.tech/)**
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- **[Milvus](https://milvus.io/)**
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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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