RAG

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).

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.

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.

Cross-modal embeddings represent a breakthrough in artificial intelligence, enabling understanding and reasoning across different data types within a unified representation space.

Retrieval-Augmented Generation (RAG) has evolved far beyond simple vector similarity search. LongRAG, Self-RAG, and GraphRAG represent the cutting edge of these capabilities.

Since standard Ollama doesn’t have a direct rerank API, you’ll need to implement reranking using Qwen3 Reranker in GO by generating embeddings for query-document pairs and scoring them.

This little Reranking Go code example is calling Ollama to generate embeddings for the query and for eache candidate document, then sorting descending by cosine similarity.

The Qwen3 Embedding and Reranker models are the latest releases in the Qwen family, specifically designed for advanced text embedding, retrieval, and reranking tasks.

• Search is best for quick, straightforward information retrieval using keywords.
• Deep Search excels at understanding context and intent, delivering more relevant and comprehensive results for complex queries.

Reranking is a second step in Retrieval Augmented Generation (RAG) systems, right between Retrieving and Generating.