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Prashanth Rao discusses trade-offs and exploration of vector databases, including indices, hosting options, and query optimization. The podcast covers the evolution of vector databases in AI workflows and combining them with large language models for natural language querying. The hosts also explore future possibilities of vector databases and integration of graph databases for enhanced knowledge retrieval.
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Quick takeaways
- Using purpose-built vector databases vs existing solutions offer trade-offs in scalability and efficiency.
- Vector databases offer different options for managing embedding pipelines.
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Purpose-built vs. Existing Solutions
There is a trade-off between using purpose-built vector databases and existing solutions like Elasticsearch or Postgres. Purpose-built databases are more scalable and efficient for managing vectors at scale, while existing solutions may not be optimized for vector storage and retrieval.
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