New post

#Vector Search

2

Vector search is a method used in information retrieval and machine learning to find similar items based on their mathematical representations as vectors. In this approach, each item is represented as a high-dimensional vector, with each dimension corresponding to a feature or characteristic of the item. Vector search algorithms then compare these vectors to find similar items, such as having similar features or being close together in the vector space. Read more here.

This article presents a potential solution for semantic code search in TrakCare using IRIS Vector Search.

Here's a brief overview of results from the TrakCare Semantic code search for the query: "Validation before database object save".

  • Code Embedding model

There are numerous embedding models designed for sentences and paragraphs, but they are not ideal for code specific embeddings.

3 0
0 80

Hi all!

I want to create an %Embedding.Config to use with an %Embedding property. I followed the documentation for %Embedding.OpenAI, and it works fine after setting sslConfig, modelName, and apiKey.

However, I need to use AzureOpenAI. While the embedding process is similar to OpenAI's, Azure requires additional connection parameters, like an endpoint.
My question is: is it possible to configure these extra parameters with %Embedding.Config, and if so, how?

0 2
0 28

I am testing vectorsearch, while doing so I am trying to paginate my resultset for a "next page" function to give me the first, second, third 15 entries within a table.

For this I have two embedding classes. One with a HNSW Index (vectornomicembedtextlatest) , and one without (vectornomicembedtexttest).

Calling SELECT ID,PRIMKEY FROM SQLUser.vectornomicembedtexttest LIMIT 5 OFFSET 1 works fine with the first entry having the rowID of 486448. (I deleted old entries in the beginning and reused the table)

0 0
0 20