Vector search is a method used in information retrieval and machine learning to find similar items based on their mathematical representations as vectors. In this approach, each item is represented as a high-dimensional vector, with each dimension corresponding to a feature or characteristic of the item. Vector search algorithms then compare these vectors to find similar items, such as having similar features or being close together in the vector space. Read more here.
This is an attempt to run a vector search demo completely in IRIS There are no external tools and all you need is a Terminal / Console and the management portal. Special thanks to Alvin Ryanputra as his package iris-vector-search that was the base of inspiration and the source for test data.
Translated from the Spanish Community Article Contest.
Following the latest programming contest on OEX I had some surprising observation. There were almost exclusive applications based on AI in combination with pre-cooked Py modules. But digging deeper, all examples used the same technical pieces of IRIS.
Seen from point of view of IRIS it was pretty much the same whether searching for text or searching for images or other pattern. It ended in almost exchangeable methods.
Current triage systems often rely on the experience of admitting physicians. This can lead to delays in care for some patients, especially when faced with inexperienced residents or non-critical symptoms. Additionally, it can result in unnecessary hospital admissions, straining resources and increasing healthcare costs.
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model as a psychological framework to craft empathetic replies. This article elaborates on the backend architecture and its components, focusing on how InterSystems IRIS supports the system's functionality.
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.
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.
DNA Similarity and Classification was developed as a REST API utilizing InterSystems Vector Search technology to investigate genetic similarities and efficiently classify DNA sequences. This is an application that utilizes artificial intelligence techniques, such as machine learning, enhanced by vector search capabilities, to classify genetic families and identify known similar DNAs from an unknown input DNA.
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.
Our project was designed to optimize patient clinical outcomes by reducing hospitalization time and supporting the development of resident and novice physicians. Additionally, it contributes to lowering financial waste in the healthcare system by improving the monitoring of pregnant patients, thereby decreasing risks and enhancing their safety.
Using the most accessible tool, the smartphone, was the obvious choice to make patients' lives easier.
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The introduction of InterSystems' "Vector Search" marks a paradigm shift in data processing. This cutting-edge technology employs an embedding model to transform unstructured data, such as text, into structured vectors, resulting in significantly enhanced search capabilities. Inspired by this breakthrough, we've developed a specialized search engine tailored to companies.
Here is a brief walkthrough on the capabilities of IRIS AI Studio platform. It covers one complete flow from loading data into IRIS DB as vector embeddings and retrieving information through 4 different channels (search, chat, recommender and similarity). In the latest release, added docker support for local installation and live version to explore.
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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.
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.
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
We have more exciting news! The new InterSystems online programming contest dedicated to Generative AI, Vector Search and Machine Learning is starting very soon!
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 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.
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.
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.
With the advent of Embedded Python, a myriad of use cases are now possible from within IRIS directly using Python libraries for more complex operations. One such operation is the use of natural language processing tools such as textual similarity comparison.
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As you have seen in the latest community publications, InterSystems IRIS has included since version 2024.1 the possibility of including vector data types in its database and based on this type of data vector searches have been implemented. Well, these new features reminded me of the article I published a while ago that was based on facial recognition using Embedded Python.
Using VECTOR_COSINE() in SQL query to perform a text similarity search on existing embeddings in a %VECTOR column.
Code is below.
Commented out sql query returns this error: SQLCODE: -29 Field 'NEW_EMBEDDING_STR' not found in the applicable tables^ SELECT TOP ? maxID , activity , outcome FROMMain .AITest ORDER BY VECTOR_COSINE ( new_embedding_str ,
Sql query as written returns ERROR #5002: ObjectScript error: <PYTHON EXCEPTION> *<class 'OSError'>: isc_stdout_write: PyArg_ParseTuple failed!
As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation. But despite its advanced capabilities, ChatGPT is not able to access your personal data. So we need to build a custom ChatGPT AI by using LangChain Framework:
Below are the steps to build a custom ChatGPT:
Step 1: Load the document
Step 2: Splitting the document into chunks
Step 3: Use Embedding against Chunks Data and convert to vectors
Step 4: Save data to the Vector database
Step 5: Take data (question) from the user and get the embedding
Step 6: Connect to VectorDB and do a semantic search
Step 7: Retrieve relevant responses based on user queries and send them to LLM(ChatGPT)
Step 8: Get an answer from LLM and send it back to the user
InterSystems announces its fourth preview, as part of the developer preview program for the 2024.1 release. This release will include InterSystems IRIS®, InterSystems IRIS® for HealthTM, and HealthShare® Health Connect.
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