How to Use Local Embedding Models for RAG Instead of Cloud Embeddings
Local embedding models compute document and query vectors on your own hardware, so text never leaves your machine before retrieval. LlamaIndex supports a fully local stack using…
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APIs, RAG & Development
Developer patterns for API integration, embeddings, retrieval, and debugging.
26 published articlesHow to Build a RAG Pipeline with LangChain and OpenAI
A LangChain RAG pipeline has three stages: index documents, retrieve the passages relevant to a query, and pass those passages to a chat model. This guide walks…
Metadata Filtering in RAG: Permissions, Dates, and Multi-Tenant Retrieval
Metadata filtering is the retrieval-layer control that turns user identity, permissions, and dates into vector query constraints. In multi-tenant RAG, the orchestrator should apply tenant, ACL, and…
Why a RAG System Retrieves the Wrong Documents
When a RAG system returns the wrong document, the language model is rarely the cause. The failure usually sits in chunking, retrieval, or ranking. This guide explains…
Retrieval-Augmented Generation: A Practical Guide to RAG Systems
Retrieval-Augmented Generation lets a language model answer from evidence in your own documents instead of relying only on its training data. This guide explains the four stages…
Common AI API Errors: Authentication, Rate Limits, and Timeouts
AI API failures usually fall into authentication, rate limit or quota, and timeout classes. Reading the HTTP status and provider error code first prevents wasted retries and…
How to Store AI API Credentials in a WordPress Integration
WordPress 7.0's Connectors API standardizes how AI provider keys are configured, but a custom integration still has to decide where the secret physically lives. This guide compares…
What Are Tokens in AI? A Practical Explanation
A token is the unit of text an AI model actually reads and writes — it can be a single character or a whole word, depending on…
How to Log AI Integration Errors Without Exposing Sensitive Data
Log AI integration errors for the operator, not for the caller or the model. The workable rule is to sanitize at the adapter boundary, return a stable…
AI APIs Explained: Authentication, Tokens, Costs, and Error Handling
AI APIs are metered HTTP interfaces: a credential authorizes the spend, tokens are the billing unit in both directions, and failures arrive as structured status codes rather…
How to Estimate the Cost of an AI API Workflow
Estimating an AI API workflow means pricing every billable step, not just the final model call. Map the pipeline into steps, count input and output tokens including…
How to Test an AI API Connection from WordPress
Testing an AI connection in WordPress has two distinct layers: whether the site can talk to an AI provider, and whether the plugin's REST endpoints answer correctly…