sahithi-66
commented
1 month ago After analyzing vector databases like Pinecone and Milvus, it is evident that both offer efficient solutions for storing and retrieving high-dimensional embeddings. Their scalability and optimized support for similarity searches make them well-suited for handling large embedding datasets. Each database has unique strengths in terms of integration and performance.
For detailed insights, please refer to the research document.
Closing this issue.
Description: Analyze vector databases, such as Pinecone and Milvus, for their ability to store and retrieve high-dimensional embeddings efficiently. Focus on their support for similarity searches, scalability, and ease of integration with the project.
Outcome: An evaluation of vector databases, detailing their strengths and limitations, particularly in relation to storing and querying embedding vectors for image retrieval tasks.