Official code for ACL2025 "🔍 Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models"
- Updated
Dec 22, 2025 - JavaScript
Official code for ACL2025 "🔍 Retrieval Models Aren’t Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models"
🐸 KERMIT - A lightweight library to encode and interpret Universal Syntactic Embeddings
A CLI chatbot that uses RAG architecture for improving and adapting LLM to specific context. It allows users to ask questions and get response directly from open-source LLMs(OpenAI, MistralAI etc.) or from the information on a website which is provided as context using the RAG architecture.
The goal of this application is to generate suggestions based on the given resume of the candidate, store the candidate profile in Pinecone database, and shortlist candidates accroding to the skills matched with match score.
This project implements a Retrieval-Augmented Generation (RAG) chatbot that can answer medical questions—especially focused on Anatomy and Forensics—based on uploaded PDF documents. It uses Hugging Face models for embedding and language generation, FAISS for vector storage, and a simple Streamlit frontend for an interactive chat interface.
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