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#SQL

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SQL is a standard language for storing, manipulating and retrieving data in relational databases.

Hi Developers!

Here're the technology bonuses for the InterSystems Vector Search, GenAI, and ML contest 2024 that will give you extra points in the voting:

  • Vector Search usage - 5
  • IntegratedML usage - 3
  • Embedded Python - 3
  • LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
  • Questionnaire - 2
  • Docker container usage - 2
  • ZPM Package deployment - 2
  • Online Demo - 2
  • Implement InterSystems Community Idea - 4
  • Find a bug in Vector Search, or Integrated ML, or Embedded Python - 2
  • First Article on Developer Community - 2
  • Second Article On DC - 1
  • First Time Contribution - 3
  • Video on YouTube - 3
  • Suggest a new idea - 1

See the details below.<--break->

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Article
· Apr 26 3m read
Geo Vector Search #2

Technical surprises using VECTORs
>>> UPDATED

Building my tech. example provided me with a bunch of findings htt I want to share.
The first vectors I touched appeared with text analysis and more than 200 dimensions.
I have to confess that I feel well with Einstein's 4 dimensional world.
7 to 15 dimensions populating the String Theory are somewhat across the border.
But 200 and more is definitely far beyond my mathematical horizon.

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The InterSystems IRIS has a series of facilitators to capture, persist, interoperate, and generate analytical information from data in XML format. This article will demonstrate how to do the following:

  1. Capture XML (via a file in our example);
  2. Process the data captured in interoperability;
  3. Persist XML in persistent entities/tables;
  4. Create analytical views for the captured XML data.

Capture XML data

The InterSystems IRIS has many built-in adapters to capture data, including the next ones:

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Hi everyone.

I have a function that may end up being called from a number of transformations at the same time, and within the function there's some Embedded SQL to first check if a local table has an entry, and then adds the entry if it doesn't exist.

To prevent a race condition where the function is called by two transformations and they both end up attempting to insert the same value, I'm looking to use the table hint "WITH TABLOCK" on the insert, but this seems to be failing the syntax checks within vscode.

Are table hints supported with embedded sql?

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You need to install the application first. If not installed, please refer to the previous article

Application demonstration

After successfully running the iris image vector search application, some data needs to be stored to support image retrieval as it is not initialized in the library.

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Hello Community,

I'm executing the same query with same column name but in different case. An unique cached query generated while query executed first time. The query preparser only normalize the keywords and send to the SQL engine generates the Hash. Eventually use the cached query next use.

Now my question, The hash values are same for both of the queries. Then why it creates two cached queries.

Query1: select * from MyLearn.Test where Name['Kev1'

Query2: select * from MyLearn.Test where NamE['Kev1'

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Hi Community,

In this article, I will introduce my application iris-VectorLab along with step by step guide to performing vector operations.

IRIS-VectorLab is a web application that demonstrates the functionality of Vector Search with the help of embedded python. It leverages the functionality of the Python framework SentenceTransformers for state-of-the-art sentence embeddings.

Application Features

  • Text to Embeddings Translation.
  • VECTOR-typed Data Insertion.
  • View Vector Data
  • Perform Vector Search by using VECTOR_DOT_PRODUCT and VECTOR_COSINE functions.
  • Demonstrate the difference between normal and vector search
  • HuggingFace Text generation with the help of GPT2 LLM (Large Language Model) model and Hugging Face pipeline

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Hi Community,

In this article, I will introduce my application iris-image-vector-search.
The image vector retrieval demo uses IRIS Embedded Python and OpenAI CLIP model to convert images into 512 dimensional vector data. Through the new feature of Vector Search, VECTOR-COSINE is used to calculate similarity and display high similarity images.

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