Anki add-on: AnkiVec

AnkiVec - Vector Search for Anki This anki addon creates vector embeddings for your cards, allowing you to search using natural language queries rather than keywords. Features Vector Embeddings : Generate embeddings for all cards using local Ollama models Semantic Search : Find cards by meaning, not just keywords Fast Local Processing : Uses lightweight embedding models (nomic-embed-text by default) Persistent Storage : ChromaDB stores embeddings for quick retrieval How to Use It When you restart Anki after installing the add-on, a vector-database will be initialized for your deck. This process may take a few minute- you should see a loading dialog with a progress bar. From this point on, any changes to your deck will be automatically indexed. To search the vector database, use the form "vec: [your query here]" in Anki's usual search bar. You can combine keyword searches with vector searches by putting the "vec" section at the end of the query: e.g. "keyword1 keyword2 vec: [natural language description]". Prerequisites Anki (version 2.1.45+) Ollama installed and running locally Install Ollama Download from ollama.ai and install. Pull the embedding model: ollama pull nomic-embed-text Configuration There are three parameters that govern how AnkiVec operates, available in the add-on's configuration dialog: model_name : The name of the Ollama model to use for generating embeddings. The default is "nomic-embed-text", but you can specify any model supported by Ollama (for example, "kronos483/MedEmbed-large-v0.1" for a specialized medical model). search_results_limit : The maximum number of search results to return. Default is 20. ollama_host : The URL of the Ollama server. Default is "http://localhost:11434".

GitHub repository: samanklesaria/ankivec
Repository description: Vector embeddings for Anki using sqlite_vec
Languages: Python, Shell