Materials on AI, Python, Git, SQL, Web/API, SAP/ERP, statistics and time series: explanations, examples, practical tips, sources and applicability limits.
A guide to setting up multimodal search in Azure AI Search using vectorization of text and images. Describes steps for creating an index, selecting models (e.g., CLIP), methods for generating embeddings, and executing hybrid queries to improve relevance.
A guide to setting up multimodal search in Azure AI Search using vectorization of text and images. Describes steps for creating an index, selecting models (e.g., CLIP), methods for generating embeddings, and executing hybrid queries to improve relevance.
A practical guide to choosing precision-oriented metrics, threshold analysis, and confusion matrix decomposition in scikit-learn for minimizing false positive rates in binary classification tasks.
This guide explains how LIMIT and OFFSET work in PostgreSQL, their use cases for pagination, why large OFFSET values cause performance issues, and when to switch to cursor-based pagination. It includes syntax examples, mandatory ORDER BY requirements, common pitfalls, and alternative methods aligned with PostgreSQL and GitHub REST API standards.
This document outlines how to configure document splitting within SAP S/4HANA to enable balance sheets for multiple business segments, leveraging the Universal Journal. It details the benefits of this approach, including the elimination of reconciliation efforts and the creation of granular financial reports. We cover the key organizational elements - Chart of Accounts and Ledger Unit - and how they interact with segment reporting.
Explore the principles of semantic search, its reliance on embeddings, and the reasons why text that appears similar might not provide the desired answer. Learn about vector search, hybrid search, and their applications in information retrieval.
Learn how to effectively evaluate the precision and recall of AI models, especially when working with small question datasets. This guide covers the concepts, calculation methods, and practical considerations using scikit-learn.
Step-by-step guide to creating an index, generating embeddings, and executing hybrid queries that combine full-text and vector search using Azure AI Search.
A practical guide to selecting a decision threshold by weighting false‑positive and false‑negative costs, using a held‑out validation set and scikit‑learn confusion matrices.
Understand the differences between Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), how to choose the right metric for your prediction task, and the impact of large errors on each.
Distinguish storage, validation and shared caching. Three response policies show when to use public max-age, private no-cache and no-store.
Separate a request timeout from the total retry budget, interpret Retry-After, and avoid duplicating writes when the server outcome is unknown.
Define the caller, payload, atomic claim and replay policy so a retry has a predictable application result.
Understand the differences between offset-based and cursor-based pagination and when to use each, especially when dealing with frequently changing datasets.
Compare RESTRICT, CASCADE and SET NULL using a small isolated example, then check the actual constraint and dependent rows before changing real data.
Select one whole event per account with an explicit tie-breaker and a deliberate policy for missing timestamps.
Use ON CONFLICT to atomically insert or update rows based on unique constraints. Specify the exact conflict target, handle duplicate input rows, and understand that excluded values replace, not accumulate.
Prediction intervals aim to contain future individual observations, while confidence intervals target a mean or other parameter, and the two are not interchangeable. Empirical coverage is the held-out fraction inside the interval and width is the distance between quantile limits; a constructed five-value example gives 80 percent coverage and mean width 1.8.
Distinguish missing and empty settings, parse ports and booleans explicitly, and report configuration errors without exposing secrets.
Use ISO timestamp strings and decimal strings, then reconstruct the fields explicitly. A complete standard-library example shows timezone checks, precision and predictable failures.
Learn how to parse JSON data in Python from both string literals and files, and how to effectively handle potential decoding errors using the built-in json module.
Use a named logger, configure the application once, and attach safe context without duplicating every error.
A practical guide to using pathlib for file existence checks, extension manipulation, directory navigation, and path resolution without introducing platform-specific bugs on Windows or Unix systems.
This article explains how to choose effective chunk boundaries and overlap in retrieval-augmented generation (RAG) systems, comparing fixed-size and document-aware strategies. Using a hypothetical support note example, it demonstrates the risks of splitting across semantic units and shows how metadata and overlap can preserve context. The guidance is based on Microsoft Azure documentation for chunking in vector search and RAG workflows.
A citation identifier only proves a source was retrieved, not that it supports the claim. This article shows how to decompose answers, locate supporting passages, and separate retrieval checks from groundedness checks using a hypothetical policy example.