AdalFlow: The library to build & auto-optimize LLM applications.
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Updated
Aug 5, 2025 - Python
AdalFlow: The library to build & auto-optimize LLM applications.
Financial Domain Question Answering with pre-trained BERT Language Model
Korean Sentence Embedding Model Performance Benchmark for RAG
VariantRetriever is a minimalist package for feature flagging
A Flutter application consisting of IPv4 Subnet Scanner, mDNS Scanner, TCP Port Scanner, Route Tracer, Pinger, File Hash Calculator, String Hash Calculator, Base Encoder, Morse Code Translator, QR Code Generator, Open Graph Protocol Data Extractor, Series URI Crawler, DNS Record Retriever, WHOIS Retriever, and Wi-Fi Information Viewer.
InfoSage is a Question and Answering (Q&A) model using the Retriever-Reader approach. The application is built using the Streamlit framework and utilizes several modules and functionalities along with a database to store user information and feedback.
BookGrabber - utility for obtaining audiobook files from the site `Книга в ухе`.
Domain adaptation in open domain question answering is tackled through theme specific rankers. We also propose a novel resource allocation algorithm to select the number of paragraph to be examined for extracting the answering. Finished 1st among participating IITs in Inter IIT tech Meet 11.0
An R Package To Read, Process, and Visualize Retriever's Output.
Writing down a agentic rag for very first time
'Gabo' is a RAG (Retrieval-Augmented Generation) system designed to enhance the capabilities of LLMs (Large Language Models). This project honors Colombian author Gabriel García Márquez by marking the tenth anniversary of his death.
This project demonstrates the power and versatility of LangChain tools and agents. It also showcases how Large Language Models (LLMs) can interact with various external data sources and perform complex tasks using a combination of natural language processing and specialized tools.
Final project to the Java Pro course emulate Internet Banking system
Retrieve the right extension for a file. Using an xml database containing signatures.
A lightweight and modular Retrieval-Augmented Generation (RAG) agent built with LangGraph and OpenAI. It supports document indexing with FAISS, and structured tool use. Ideal for prototyping question-answering systems over custom documents.
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