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Data Ingestion and Vectorization at Scale

1 post · May 2026 – Jun 2026

What this trail covers

This post introduces robust strategies for importing and vectorizing large datasets into Weaviate. It details server-side batching for dynamic rate control, comprehensive error handling with retry-with-checkpoint mechanisms, and the `blobHash` data type for efficient storage of media embeddings. It also highlights Weaviate Embeddings for multimodal PDF ingestion without OCR. The MCP server is positioned for conversational, small-batch writes, distinct from bulk ingestion pipelines. The post also details building a coding assistant using the MCP server for RAG over code and docs, leveraging hybrid search and agentic workflows.

All Weaviate trails
  1. Import & Vectorize Data with Weaviate at Scale | WeaviateIntroduces server-side batching for streaming imports with dynamic rate control and backpressure. Details a retry-with-checkpoint pattern for robust error handling, including deterministic UUID generation using `generate_uuid5` to prevent duplicate work. Explains the `blobHash` data type for efficient storage of media embeddings by storing only a SHA-256 hash instead of the full payload, and its benefit in skipping re-vectorization on hash matches. Highlights Weaviate Embeddings for multimodal PDF ingestion without an OCR pipeline. Differentiates the MCP server for conversational, small-batch writes from bulk ingestion pipelines. Emphasizes careful selection of data types like `text` with appropriate tokenization, `uuid` for foreign keys, `int` vs. `number`, and reference types for efficient querying.
  2. Build a Coding Assistant with Weaviate MCP: RAG over Code & Docs | WeaviateThis post details the integration of Weaviate's built-in Model Context Protocol (MCP) server for building a coding assistant. It explains how to enable MCP, design schemas for code and documentation chunks, and ingest code using AST-aware chunking. The post highlights the benefits of hybrid search (BM25 + vector) for code retrieval and the operational simplicity of having the MCP server within Weaviate. It also touches on agentic workflows and the use of LLM clients like Claude Code, Cursor, and VS Code with Weaviate.