When working with thousands of documents in a vector database for RAG, a few optimizations can make a big difference in managing the data efficiently. Semantic search, for example, can become computationally intensive with a large dataset. To enhance performance, consider optimized algorithms like HNSW or even hybrid search methods that combine keyword search with embeddings. These are just starting points, and we can certainly explore additional ways to optimize based on your specific use case. Feel free to join our community for a deeper discussion community.phidata.com/ |discord.gg/Ye8rQbaT
When working with thousands of documents in a vector database for RAG, a few optimizations can make a big difference in managing the data efficiently. Semantic search, for example, can become computationally intensive with a large dataset. To enhance performance, consider optimized algorithms like HNSW (Hierarchical Navigable Small World) or even hybrid search methods that combine keyword search with embeddings. These are just starting points, and we can certainly explore additional ways to optimize based on your specific use case.
what happens if you vector thousands of documents for rag
When working with thousands of documents in a vector database for RAG, a few optimizations can make a big difference in managing the data efficiently. Semantic search, for example, can become computationally intensive with a large dataset. To enhance performance, consider optimized algorithms like HNSW or even hybrid search methods that combine keyword search with embeddings. These are just starting points, and we can certainly explore additional ways to optimize based on your specific use case.
Feel free to join our community for a deeper discussion community.phidata.com/ |discord.gg/Ye8rQbaT
When working with thousands of documents in a vector database for RAG, a few optimizations can make a big difference in managing the data efficiently. Semantic search, for example, can become computationally intensive with a large dataset. To enhance performance, consider optimized algorithms like HNSW (Hierarchical Navigable Small World) or even hybrid search methods that combine keyword search with embeddings. These are just starting points, and we can certainly explore additional ways to optimize based on your specific use case.