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Indexing Data for RAG pipelines can be more complex than people think! With vector databases, we can perform hybrid search combined with keyword filtering, so it is a matter of extracting the right data! First of all, it is critical to understand the data to chunk it using the natural structure of the data. Schema-aware chunking cuts your corpus along meaningful structural boundaries defined by a schema (tables/fields, JSON keys, headings/sections, code AST nodes, graph entities/edges) instead of arbitrary fixed-size windows. Each chunk should carry rich breadcrumbs (schema path + IDs) so retrieval can filter and rank precisely. This is a real data engineering endeavor; don't underestimate it! When encoding the data, semantic embeddings are not the only way! Lexical information captured by encodings like BM25 still carries a lot of retrieval power! It is just a matter of finding the middle point in how the different vector representations will contribute to the retrieval score. Also, for the semantic encoding, it is often useful to project the document by compiling a summary or a set of meaningful keywords that will capture a more refined semantic information of the original document. Along with semantic and lexical matching, there are a lot of opportunities to capture additional metadata beyond the text value itself: The source of the document, the age, the author, ..., which allows for further filtering on keyword metadata! If you want to see how to do it in practice, come join me for my upcoming Agentic-RAG course: https://lnkd.in/gmfDb2Gx
🤖💡 Brilliant breakdown! RAG pipelines aren’t just about chunking data—they’re about schema-aware structure, hybrid retrieval, and rich metadata 🔍⚡ Love how you highlight the balance between semantic + lexical approaches 🙌🚀
How fascinating is the interplay between semantic and lexical encodings in data retrieval? Understanding that balance can indeed transform how we extract insights. #DataEngineering
Agreed... hybrid search is underrated. Now, balancing lexical and semantic signals is the art.