{"v":1,"id":"model:hf:kalle07/embedder_collection","slug":"model-kalle07-embedder-collection","kind":"model","category":"embedding","title":"embedder_collection","summary":"A practical introduction to embedding models and local RAG","source":{"provider":"hf","ref":"kalle07/embedder_collection","url":"https://huggingface.co/kalle07/embedder_collection","rev":"ae08f42f1840011c09f063917d257aebf21fb498","fetchedAt":"2026-10-02T21:00:46.450Z","etag":"W/\"417a-uRo2MOGd6p0nsJa4t8PQ+bbleHY\""},"author":{"name":"kalle07","url":"https://huggingface.co/kalle07"},"license":{"spdx":null,"raw":null,"open":null,"note":"the source did not name a license"},"metrics":{"downloads":15119,"downloadsWeek":327838,"likes":30,"takenAt":"2026-10-02T21:00:46.450Z"},"tags":["sentence-transformers","gguf","sentence-similarity","feature-extraction","embedder","embedding","models","GGUF","Bert","Nomic","Gist","Granite","BGE","Jina","gemma","Snowflake","Qwen","text-embeddings-inference","RAG","Rerank","similarity","PDF","Parsing","Parser","en","de","endpoints_compatible","deploy:azure"],"pipeline":"sentence-similarity","links":{"github":"ggml-org/llama.cpp","npm":"node-llama-cpp"},"updatedAt":"2026-09-28T09:18:52.000Z","collectedAt":"2026-10-02T21:00:46.450Z","review":{"numbers":["15,119 downloads on Hugging Face","30 likes","license not named","0.6 GB for Qwen3-Embedding-0.6B-Q8_0.gguf","327,838 npm downloads a week for node-llama-cpp","latest node-llama-cpp@3.22.1"],"log":null},"trust":"unlabeled","health":{"status":"alive","checkedAt":"2026-10-02T21:00:46.450Z","http":200},"description":"# A practical introduction to embedding models and local RAG\n\nThis repository contains more than 25 different types of embedding models, together with practical information about embeddings, document retrieval, chunking, context length, and local RAG (Retrieval-Augmented Generation).\n\nThe most important point is this:\n\nAn embedding model is only one part of a good RAG system.\n\nA good result depends on several things working together:\n\n* the quality and structure of your source documents\n* how the documents are converted to text\n* how the text is split into chunks\n* the embedding model\n* the vector database and retrieval settings\n* optional keyword or reranking steps\n* the context given to the main LLM\n* the main LLM itself\n* the system prompt\n\nChanging only the embedding model therefore does not automatically make a RAG system better.\n\nAt the end of the file list on this repo, click the button shown below to see all files:\n\n# Table of Contents\n\n* [Testing and software compatibility](#testing-and-software-compatibility)\n* [My short practical impression](#my-short-practical-impression)\n* [German and more complex documents](#german-and-more-complex-documents)\n* [Toxic-content models](#toxic-content-models)\n* [Practical starting settings for a local RAG system](#practical-starting-settings-for-a-local-rag-system)\n* [What do these numbers actually mean?](#what-do-these-numbers-actually-mean)\n* [Characters, words and tokens](#characters-words-and-tokens)\n* [VRAM and context length](#vram-and-context-length)\n* [Vector size (dimensions)](#vector-size-dimensions)\n* [Vector count](#vector-count)\n* [Chunk length](#chunk-length)\n* [How embedding and retrieval actually work](#how-embedding-and-retrieval-actually-work)\n* [Embedding search is not the same as keyword search](#embedding-search-is-not-the-same-as-keyword-search)\n* [Why the most relevant snippets matter](#why-the-most-relevant-snippets-matter)\n* [Chunk size…\n\nSource: 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