Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does.
By angela shi
What an ID column, a date string, and a missing value taught me. The post My Machine Learning Pipeline Cheated, and Cross-Validation Never Noticed appeared first on Towards Data Science .
By Himanshu Sharma
The article describes how the author built an incremental data pipeline using dbt snapshots to monitor the job market they are attempting to enter. It also discusses the challenges faced with an API that provided misleading information during the process. The post was published on Towards Data Science.
By Ibrahim Salami
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post One RAG Pipeline, Four Very Different PDFs: Same Four Bricks, Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi
Enterprise Document Intelligence [Vol. 1 #9B] - One call wires the four upgraded bricks together, run on a paper, a NIST standard, and a report with a broken TOC The post A Production RAG Pipeline in Action: Every Answer Typed and Cited appeared first on Towards Data Science .
By angela shi
The article "When Labels Say No, But Embeddings Say Yes" discusses how conventional retrieval metrics can overlook meaningful similarities between items. It highlights the limitations of relying solely on labels for evaluation and suggests that embeddings can capture nuanced relationships that labels miss. The piece underscores the importance of considering alternative metrics to better assess similarity in data retrieval tasks.
By Theophano Mitsa