RAG

Most LLM “structured output” tutorials are unserious. They teach you to ask for JSON politely and then hope the model behaves. That is not validation. That is optimism with braces.

If you are working through retrieval-augmented generation (RAG), this section walks through text embeddings in plain terms — what they are, how they fit search and retrieval, and how to call two common local setups from Python using Ollama or an OpenAI-compatible HTTP API (as many llama.cpp-based servers expose).

Neo4j is what you reach for when the relationships are the data. If your domain looks like a whiteboard of circles and arrows, forcing it into tables is painful.

Most local AI setups start with a model and a runtime.

OpenClaw is a self-hosted AI assistant designed to run with local LLM runtimes like Ollama or with cloud-based models such as Claude Sonnet.

Most local AI setups start the same way: a model, a runtime, and a chat interface.

Chunking is the most under-estimated hyperparameter in Retrieval ‑ Augmented Generation (RAG): it silently determines what your LLM “sees”, how expensive ingestion becomes, and how much of the LLM’s context window you burn per answer.

Production-focused guide to building RAG systems: chunking, vector stores, hybrid retrieval, reranking, evaluation, and when to choose RAG over fine-tuning.

Self-hosting LLMs keeps data, models, and inference under your control-a practical path to AI sovereignty for teams, enterprises, nations.

The Python ecosystem this month is dominated by Claude Skills and AI agent tooling. This overview analyzes the top trending Python repositories on GitHub.

The Go ecosystem continues to thrive with innovative projects spanning AI tooling, self-hosted applications, and developer infrastructure. This overview analyzes the top trending Go repositories on GitHub this month.

Cognee is a Python framework for building knowledge graphs from documents using LLMs. But does it work with self-hosted models?

Related guides for persistent knowledge layers — agent memory plugins, graph tooling, and stack context — live under the AI Systems Memory hub.

Choosing the Best LLM for Cognee demands balancing graph-building quality, hallucination rates, and hardware constraints. Cognee excels with larger, low-hallucination models (32B+) via Ollama but mid-size options work for lighter setups.

Ollama’s Python library now includes native OLlama web search capabilities. With just a few lines of code, you can augment your local LLMs with real-time information from the web, reducing hallucinations and improving accuracy.

Choosing the right vector store can make or break your RAG application’s performance, cost, and scalability. This comprehensive comparison covers the most popular options in 2024-2025.

Ollama’s Web Search API lets you augment local LLMs with real-time web information. This guide shows you how to implement web search capabilities in Go, from simple API calls to full-featured search agents.