0.9
The big new feature in this release is support for embeddings.
Embedding models take a piece of text - a word, sentence, paragraph or even a whole article, and convert that into an array of floating point numbers. #185
This embedding vector can be thought of as representing a position in many-dimensional-space, where the distance between two vectors represents how semantically similar they are to each other within the content of a language model.
Embeddings can be used to find related documents, and also to implement semantic search - where a user can search for a phrase and get back results that are semantically similar to that phrase even if they do not share any exact keywords.
LLM now provides both CLI and Python APIs for working with embeddings. Embedding models are defined by plugins, so you can install additional models using the plugins mechanism.
The first two embedding models supported by LLM are:
- OpenAI's ada-002 embedding model, available via an inexpensive API if you set an OpenAI key using
llm keys set openai. - The sentence-transformers family of models, available via the new llm-sentence-transformers plugin.
See Embedding with the CLI for detailed instructions on working with embeddings using LLM.
The new commands for working with embeddings are:
- llm embed - calculate embeddings for content and return them to the console or store them in a SQLite database.
- llm embed-multi - run bulk embeddings for multiple strings, using input from a CSV, TSV or JSON file, data from a SQLite database or data found by scanning the filesystem. #215
- llm similar - run similarity searches against your stored embeddings - starting with a search phrase or finding content related to a previously stored vector. #190
- llm embed-models - list available embedding models.
- llm embed-db - commands for inspecting and working with the default embeddings SQLite database.
There's also a new llm.Collection class for creating and searching collections of embedding from Python code, and a llm.get_embedding_model() interface for embedding strings directly. #191