11 Application(s) results for #Embeddings
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Application NameDeveloperMade withRatingLast updatedViewsInstalls

iris-vector-search

Quick and easy ways to use iris vector search with Python.

F
Fan Ji
Docker
AI
ML
ML
4.0 (2)21 Apr, 2025 1.5k

iris-data-analysis

Implementing data query and analysis

l
lando miller
Docker
Python
IPM
AI
4.0 (1)01 Apr, 2025 130

InterSystems Ideas Waiting to be Implemented

AI extensibility Prompt keyword for Class and Method implementation. Also Prompt macro generator.

To accelerate capability of growing code generation. This proposal suggests new extensibility facilities and hooks that can be democratized to community and / or fulfilled by commercial partners. To add Training metadata to Refine a Large Language Model for code, a "Prompt Input" is associated with an expected "Code Output", as part of a class definition. This provide structured keywords to describe: * The expected output * And / Or Chain-of-thought to generate the correct output | /// The following Prompt describes the full implementation of the class Class alwo.Calculator [Abstract, Prompt = "Provides methods to Add, Subtract, Multiply and divide given numbers." ] { /// The following Prompt describes the full implementation of the method ClassMethod Add(arg1 As %Float, arg2 As %Float) As %Float [ Prompt ="Add numeric arguments and return result." ] { return arg1 + arg2 } ClassMethod Subtract(arg1 as %Float, arg2 As %Float) { &Prompt("Subtract numeric arguments and return result") ) } | The Prompt macro generates code based on the context of the method it is within. Once resolved, it automatically comments out the processed macro. | ClassMethod Subtract(arg1 as %Float, arg2 As %Float) { //&Prompt("Subtract arguments and return the result") return arg1 - arg2 //&Prompt("Model alwogen-objectscript-7.1.3") ) | The generator leveraged at compilation time could be configured in a similar way to how source control is configured for a namespace. Configuration could lock / exclude packages from being processed in this way. A "\prompt" compilation flag could be used to control the default environment behavior and editor compilation behavior. For example to force reprocessing of previously resolved prompts due to a newer more capable version of code Large Language Model, then a "\prompt=2" could be applied. Different models or third-party services could be applied depending the language of the given method. When redacting source code by "deployment", the existing "deploy" facility could be extended to also ensure removal of "Prompt" metadata from code.

A
by Alex Woodhead

3

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ollama-ai-iris

Using Ollama LLM (as an alternative to OpenAI) with IRIS

R
Rodolfo Pscheidt
Python
AI
0.0 (0)12 Mar, 2025 263

bas_labs

Connecting companies with climate actions.

A
Alice Heiman
Python
AI
ML
ML
0.0 (0)01 Mar, 2025 55

EduVerse

Accessible Learning Assistant

R
Rolano Rebelo
Python
AI
0.0 (0)11 Nov, 2024 50

VectorSearchOnPatientSimilarity

A Patient similarity comparison demo running on IRIS for Health

L
Lin Zhu
Docker
4.5 (1)23 Sep, 2024 110

IRIS AI Studio

AI Studio to load and retrieve vector embeddings from your files

Ikram Shah
Python
AI
0.0 (0)16 May, 2024 553

iris-VectorLab

The application demonstrates the functionality of Vector Search.

Muhammad Waseem
Docker
Python
IPM
AI
5.0 (1)15 May, 2024 291 19

HackUPC24_Klìnic

Symptoms Clinical Trial Search Tool using Knowledge Graphs

T
Tanguy Vansnick
AI
ML
ML
0.0 (0)15 May, 2024 230

iris-image-vector-search

Using IRIS vector search to achieve image retrieval

s
shan yue
Docker
Python
IPM
4.5 (1)15 May, 2024 227 1

Hackupc24_inter

Text-to-video application based on user photos.

J
Jonathan Rodríguez Barja
Python
AI
0.0 (0)05 May, 2024 177