Future plans for LLM include indexing and chunking. Indexing will speed up the process of calculating a cosine difference between an input vector and every other embedding in the collection. Chunking will improve the process of building an embeddings-based search engine by being smarter about what to embed. The potential scope of the LLM project is vast, and the developers are encouraging users to get involved by testing plugins, building new ones, and sharing their creations.
Key takeaways:
- LLM is a Python library and command-line tool for working with language models, and its latest version, LLM 0.9, has new features for working with embeddings.
- Embeddings are a way to convert text into an array of floating point numbers, or an embedding vector, which can be used to measure semantic similarity between texts.
- LLM 0.9 introduces the concept of a collection of embeddings, where all embeddings in a collection are generated by the same model to ensure comparability.
- LLM also provides command-line tools and a Python API for working with embeddings, and has a new plugin, llm-cluster, for clustering content using embeddings.