6 posts tagged with "data-indexing"

CocoIndex is the world's first open-source engine that supports both custom transformation logic and incremental processing specialized for data indexing. So, what is custom transformation logic?

When building data processing and indexing systems, one of the key challenges is handling system updates gracefully. These systems maintain state across multiple components (like Pinecone, PostgreSQL, etc.) and need to evolve over time. Let's explore the challenges and potential solutions.

When building data indexing pipelines, handling large files efficiently presents unique challenges. For example, patent XML files from the USPTO can contain hundreds of patents in a single file, with each file being over 1GB in size. Processing such large files requires careful consideration of processing granularity and resource management.

An indexing pipeline builds indexes derived from source data. The index should always be converging to the current version of source data. In other words, once a new version of source data is processed by the pipeline, all data derived from previous versions should no longer exist in the target index storage. This is called data consistency requirement for an indexing pipeline.

At its core, data indexing is the process of transforming raw data into a format that's optimized for retrieval. Unlike an arbitrary application that may generate new source-of-truth data, indexing pipelines process existing data in various ways while maintaining trackability back to the original source. This intrinsic nature - being a derivative rather than source of truth - creates unique challenges and requirements.

High-quality data tailored for specific use cases is essential for successful AI applications in production. The old adage "garbage in, garbage out" rings especially true for modern AI systems - when a RAG pipeline or agent workflow is built on poorly processed, inconsistent, or irrelevant data, no amount of prompt engineering or model sophistication can fully compensate. Even the most advanced AI models can't magically make sense of low-quality or improperly structured data.