mirror of
https://github.com/james-m-jordan/openai-cookbook.git
synced 2025-05-09 19:32:38 +00:00
34 lines
1.6 KiB
Markdown
34 lines
1.6 KiB
Markdown
# Kusto as a Vector database
|
||
|
||
|
||
|
||
[Azure Data Explorer aka Kusto](https://azure.microsoft.com/en-us/products/data-explorer) is a cloud-based data analytics service that enables users to perform advanced analytics on large datasets in real-time. It is particularly well-suited for handling large volumes of data, making it an excellent choice for storing and searching vectors.
|
||
|
||
Kusto supports a special data type called dynamic, which can store unstructured data such as arrays and properties bag. [Dynamic data type](https://learn.microsoft.com/en-us/azure/data-explorer/kusto/query/scalar-data-types/dynamic) is perfect for storing vector values. You can further augment the vector value by storing metadata related to the original object as separate columns in your table.
|
||
Kusto also supports in-built function [series_cosine_similarity_fl](https://learn.microsoft.com/en-us/azure/data-explorer/kusto/functions-library/series-cosine-similarity-fl) to perform vector similarity searches.
|
||
|
||
[Get started](https://aka.ms/kustofree) with Kusto for free.
|
||
|
||
|
||
|
||
|
||
|
||
## Getting started with Kusto and Open AI embedding
|
||
|
||
### Demo Scenario
|
||
|
||
|
||
|
||
|
||
|
||
If you’d like to try this demo, please follow the instructions in the [Notebook](Getting_started_with_kusto_and_openai_embeddings.ipynb).
|
||
|
||
It will allow you to -
|
||
|
||
1. Use precomputed embeddings created by OpenAI API.
|
||
|
||
2. Store the embeddings in Kusto.
|
||
|
||
3. Convert raw text query to an embedding with OpenAI API.
|
||
|
||
4. Use Kusto to perform cosine similarity search in the stored embeddings. |