I recently wrote a .NET client for the Qdrant vector database and ended up contributing it to the official Qdrant .NET SDK. A question came up from a user asking how to adapt an OpenAI cookbook article written in Python using the qdrant python client, to C# using the .NET SDK. This is the topic of today's post.
Elasticsearch allows indexing multiple values into a field, and to query on that field to find documents with matching values. This post explores some different ways of querying values, including approaches for matching values in the last position.
I recently came across a question on Stack Overflow asking about Boosting elasticsearch results with NEST when a secondary field is a specific value. I thought the question was interesting enough to warrant a blog post, the first I've written in a while!
In this blog post I’m going to show you how to get started with geospatial search with Elasticsearch, using the official and fantastic .NET client for Elasticsearch, NEST. An example like this is best served with real data, so given this post was written from Australia, we’ll use the State Suburbs (SSC) from 2006 provided by the Australian Bureau of Statistics as the data of interest; it's provided in ESRI Shapefile format and contains a collection of all the Australian Suburbs, each with a name, code and geometry; We’ll need to extract each suburb from the Shapefile and serialize them to a format that can be persisted to Elasticsearch and so that we can query them.
I’ve put together a demo application to illustrate geospatial search using Elasticsearch and NEST..