4 Application(s) results for #langchain
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Application NameDeveloperMade withRatingLast updatedViewsInstalls

iris-RAG-Gen

RAG Personal ChatGPT app leverages IRIS Vector Search func.

Muhammad Waseem
Docker
Python
IPM
AI
5.0 (1)31 Jul, 2024 366 1

iris-medicopilot

MediCopilot uses AI to assist healthcare professionals

Henry Pereira
Docker
Python
IPM
AI
4.5 (1)19 May, 2024 454 7

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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HackUPC24_Klìnic

Symptoms Clinical Trial Search Tool using Knowledge Graphs

T
Tanguy Vansnick
AI
ML
ML
0.0 (0)15 May, 2024 214

WALL-M

A Platform for Retrieval Augmented Generation (RAG) for Question-Answering of E-Mails

S
Somesh Mehra
Python
AI
ML
ML
0.0 (0)13 May, 2024 270