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”.
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Reviews (1)
👍 2026-02-12
I don’t want to downvote this add-on because I feel like, based on the description, it has a great deal of potential to be very useful; however, I couldn’t get it working after ~1 hr of troubleshoot. A more user friendly experience is needed!
