We are all familiar with AI Embeddings. They are the base in RAG (Retrieval Augmented Generation) for AI. This library converts SQLite and every database into a vector database. The embeddings of the chunked text must be saved as text (the json list toCompactString) in SQLite and the other non vector DBs. This means that you can do RAG without accessing external databases (or using in B4J also other non vector databases) directly in your iOS, Android, Window/Linux/Mac app. It contains all four ways of calculation of embeddings distance because different AI models use different embeddings approach.
The object is AIEmbeddings and when you define the object, then you start writting for example:
and you get a code suggestion that you can copy to start directly.
You will notice that you have to define in the AIQuery method an acceptable distance (MinimumOrMaximumDistance). For two of the distance calculations this is the minimum acceptable similarity and for the other two distance calculations it is the maximum acceptable distance. You will notice that it is extremely fast as it uses approaches used in arithmetical methods to return the result. The advantage of using the library instead of Python is that you can run the embeddings relevance in B4i, B4A also (just get the embeddings vector for a query from OpenAI or Ollama and run the relevance check with the data you have on your device - no need to be stored in a Python accessible location - and that you can use any database for vector search and not just vector databases). The price is 49.99EUR and you can send me a private message if you want to acquire this b4xlib.
In post#4 there is a video showcasing everything.
In post#20 there is a video showcasing library compatibility with Ollama.
The object is AIEmbeddings and when you define the object, then you start writting for example:
B4X:
Dim aiemb As AIEmbeddings
aiemb.Instructionsand you get a code suggestion that you can copy to start directly.
You will notice that you have to define in the AIQuery method an acceptable distance (MinimumOrMaximumDistance). For two of the distance calculations this is the minimum acceptable similarity and for the other two distance calculations it is the maximum acceptable distance. You will notice that it is extremely fast as it uses approaches used in arithmetical methods to return the result. The advantage of using the library instead of Python is that you can run the embeddings relevance in B4i, B4A also (just get the embeddings vector for a query from OpenAI or Ollama and run the relevance check with the data you have on your device - no need to be stored in a Python accessible location - and that you can use any database for vector search and not just vector databases). The price is 49.99EUR and you can send me a private message if you want to acquire this b4xlib.
In post#4 there is a video showcasing everything.
In post#20 there is a video showcasing library compatibility with Ollama.
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