Import & Vectorize Data with Weaviate at Scale

Many vector database pilots encounter issues at the data ingestion stage rather than during search, with challenges often arising when scaling from small datasets to much larger ones. This guide provides insights into overcoming common problems such as rate limits, partial batch failures, and memory issues, emphasizing the importance of server-side batching, error handling, and data type decisions when using Weaviate for data import. It discusses the use of server-side batching that dynamically adjusts based on server workload, and highlights strategies for error handling and retries to ensure successful data import. The guide also covers the benefits of using the blobHash data type for media to reduce storage requirements and enable efficient re-vectorization checks. Additionally, it explores options for multimodal data ingestion, particularly for image-based document retrieval without needing an OCR pipeline, and offers practical advice on making schema decisions to avoid costly post-import fixes. A Weaviate Cloud trial with Weaviate Embeddings is recommended for users to experiment with these features in a low-friction environment.

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