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· 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 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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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,

This is a detailed, candid walkthrough of the IRIS AI Studio platform. I speak out loud on my thoughts while trying different examples, some of which fail to deliver expected results - which I believe is a need for such a platform to explore different models, configurations and limitations. This will be helpful if you're interested in how to build 'Chat with PDF' or data recommendation systems using IRIS DB and LLM models.

https://www.youtube.com/embed/bcu1gt0BDhY
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In the previous article, we saw in detail about Connectors, that let user upload their file and get it converted into embeddings and store it to IRIS DB. In this article, we'll explore different retrieval options that IRIS AI Studio offers - Semantic Search, Chat, Recommender and Similarity.

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