A number is Esthetic if, in any base from base2 up to base10, the absolute difference between every pair of its adjacent digits is constantly equal to 1.
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.
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.
In the previous article, we saw different modules in IRIS AI Studio and how it could help explore GenAI capabilities out of IRIS DB seamlessly, even for a non-technical stakeholder. In this article, we will deep dive into "Connectors" module, the one that enables users to seamlessly load data from local or cloud sources (AWS S3, Airtable, Azure Blob) into IRIS DB as vector embeddings, by also configuring embedding settings like model and dimensions.
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.
This article introduces using the langchain framework supported by IRIS for implementing a Q&A chatbot, focusing on Retrieval Augmented Generation (RAG). It explores how IRIS Vector Search within langchain-iris facilitates storage, retrieval, and semantic search of data, enabling precise and up-to-date responses to user queries. Through seamless integration and processes like indexing and retrieval/generation, RAG applications powered by IRIS enable the capabilities of GenAI systems for InterSystems developers.
Do you resonate with this - A capability and impact of a technology being truly discovered when it's packaged in a right way to it's audience. Finest example would be, how the Generative AI took off when ChatGPT was put in the public for easy access and not when Transformers/RAG's capabilities were identified. At least a much higher usage came in, when the audience were empowered to explore the possibilities.
If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package. The rating reflects the experience of the reviewer with the status found at the time of review. It is kind of a snapshot and might have changed meanwhile. Reviews by other members of the community are marked by * in the last column.
I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix. Some were accepted and merged, and some were just ignored. So if you made a major change and expect a changed review just let me know.
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
Principle: After dividing the article uploaded by the user into sentences using Python, the embedded value is obtained and stored in the Iris database. Then, the similarity between sentences is compared through Iris vector search, and finally displayed on the front-end page.
The traditional use of an IRIS production is for an inbound adapter to receive input from an external source, send that input to an IRIS service, then have that service send that input through the production.
In our previous article, we explored how to send emails through Microsoft 365 using the Graph API. Since then, an anonymous client reached out to me about setting up some other methods of notifications through that API.
In recent versions of IRIS, a powerful new data loading command has been introduced to SQL: LOAD DATA. This feature has been highly optimized to import data into IRIS extremely fast, allowing hundreds of gigabytes of data to be inserted in seconds instead of hours or days.
This is a very exciting improvement. However, a big problem in the data loading experience still exists. Namely, the time and hassle it takes to:
The basic idea is to use Vectors in the mathematical sense. I used geographic coordinates. These are of course only 2-dimensional but much easier to follow as vectors in text analysis with >200 dimensions.
The OpenAPI Specification (OAS) defines a standard, language-agnostic interface to HTTP APIs which allows both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection. When properly defined, a consumer can understand and interact with the remote service with a minimal amount of implementation logic. While for SOAP based APIs there is a special wizard in InterSystems IRIS that cuts down orchestrations development time, not all APIs used in integrations are SOAP. That's why @Jaime Lerga suggested to add a wizard similar to the SOAP wizard to generate a REST client from OpenAPI specification. Implementation of this idea cuts down the development time of the REST API orchestrations with InterSystems IRIS. This idea is one of most popular ideas on the InterSystems ideas. This article, the third in the "Implemented Ideas" series, focuses on the OpenAPI Suite solution developed by @Lorenzo Scalese.
Most examples I've seen so far in OEX or DC left the impression that VECTORs are just something available with SQL with the 3 Functions especially around VECTOR_Search. * TO_VECTOR() * VECTOR_DOT_PRODUCT () * VECTOR_COSINE ()
There is a very useful summary hidden in iris-vector-search demo package. From there you find everything you need over several links and corners.
Accessing Amazon S3 (Simple Storage Service) buckets programmatically is a common requirement for many applications. However, setting up and managing AWS accounts is daunting and expensive, especially for small-scale projects or local development environments. In this article, we'll explore how to overcome this hurdle by using Localstack to simulate AWS services. Localstack mimics most AWS services, meaning one can develop and test applications without incurring any costs or relying on an internet connection, which can be incredibly useful for rapid development and debugging. We used ObjectScript with embedded Python to communicate with Intersystems IRIS and AWS simultaneously.Before beginning, ensure you have Python and Docker installed on your system. When Localstack is set up and running, the bucket can be created and used.
Did you know that you can get JSON data directly from your SQL tables?
Let me introduce you to 2 useful SQL functions that are used to retrieve JSON data from SQL queries - JSON_ARRAY and JSON_OBJECT. You can use those functions in the SELECT statement with other types of select items, and they can be specified in other locations where an SQL function can be used, such as in a WHERE clause
The JSON_ARRAY function takes a comma-separated list of expressions and returns a JSON array containing those values.
If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package. The rating reflects the experience of the reviewer with the status found at the time of review. It is kind of a snapshot and might have changed meanwhile. Reviews by other members of the community are marked by * in the last column.
I also placed a bunch of Pull Requests on GitHub when I found a problem I could fix. Some were accepted and merged, and some were just ignored. So if you made a major change and expect a changed review just let me know.
In our previous article we presented the general concepts as well as the problem that we wanted to solve by using the task engine integrated in InterSystems IRIS, in today's article we will see how we configure an interoperability production to provide a solution.
Workflow Engine Configuration
First we are going to define the roles of the tasks that we are going to manage, in our example we are going to define two types:
BPL stands for Business Process Language. This is an XML format for describing complex information orchestration interactions between systems. InterSystems Integration engine has for two decades, provided a visual designer to build, configure, and maintain, BPL using a graphical interface. Think of it like drawing a process flow diagram that can be compiled and deployed.
The bottom line: MAXLEN is relevant mostly for odbc/jdbc connections and you need to specify an appropriate value within your tables (classes), otherwise the data might be truncated when you query it, or even fail when you try to insert data.
Hi - Recently I have been investigating an annoying situation whilst editing ObjectScript classes or routines in VSCode.
What was happening to me was, as I was typing in lines of code into my class (for example, adding a new Method, or changing the Class signature, or a block of code), this would quickly get syntax checked, re-formatted, and compiled - inevitably, (since I would be mid-way through my typing), this would generate compilation errors.
InterSystems worked closely with the Red Hat Insights team to implement a curated set of recommendations for system administrators to ensure the best experience running InterSystems IRIS on Red Hat Enterprise Linux (RHEL).
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