Building Essence Towards Personalized Knowledge Model - PKM
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Updated
Oct 20, 2024 - Jupyter Notebook
Building Essence Towards Personalized Knowledge Model - PKM
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
An AI agent that writes SEO-optimised blog posts and outputs a properly formatted markdown document.
Webapp to answer questions about my resume leveraging Langchain, OpenAI, Streamlit
Q&A System using BERT and Faiss Vector Database
This is a RAG project to chat with your uploaded PDF , made using Langchain and Anthropic Claude 3 used as LLM , hosted using Streamlit
LLM App to demystify and summarize Terms and Conditions agreements
This repo is for advanced RAG systems, each branch will represent a project based on RAG.
It allows users to upload PDFs and ask questions about the content within these documents.
Generative AI projetc using LangChain for similarity search. Input 3 articles urls and ask something about the topic
Advanced RAG pipeline using Re-Ranking after initial retrieval
ChatPDF leverages Retrieval Augmented Generation (RAG) to let users chat with their PDF documents using natural language. Simply upload a PDF, and interactively query its content with ease. Perfect for extracting information, summarizing text, and enhancing document accessibility.
LLM graph-RAG SQL generator for large databases with poor documentation
DocSpot - Connecting Students Together
RAG-based Local PDF Chatbot: Supports multiple PDFs and concurrent users. Powered by Mistral 7B LLM, LangChain, Ollama, FAISS vector store, and Streamlit for an interactive experience.
Faiss with sqlite
In this project I have built an app that can answer questions from your multiple PDFs using Google's gemini-1.5-flash model.
Implementing LangChain concepts and building meaningful stuffs
An advanced AI-powered solution enhances network diagnostics by leveraging large language models (LLMs). It parses various logs to identify patterns and anomalies, providing actionable insights for diagnosing and resolving network issues efficiently. This simplifies analysis, enabling quicker and more accurate problem detection and resolution.
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