AdalFlow: The library to build & auto-optimize LLM applications.
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Updated
Nov 10, 2024 - 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
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.
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
BookGrabber - utility for obtaining audiobook files from the site `Книга в ухе`.
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.
Retrieve the right extension for a file. Using an xml database containing signatures.
'Gabo' is a RAG (Retrieval-Augmented Generation) system designed to enhance the capabilities of LLMs (Large Language Models) such as 'Llama 3.2' or 'Phi 3.5'. This project honors Colombian author Gabriel García Márquez by marking the tenth anniversary of his death.
A Java-program which retrieves the full-texts or datasets from the Publication-Web-Pages.
This RAG Application used techniques like Similarity Search, Chain and Retriever to chat with our files using OllamaEmbeddings, LLAMA3, Chromadb, FaissDB.
Retrieve coordinates for each cities in ETS2 and ATS. List all companies in each cities
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