faiss-vector-database

There are 186 repositories under faiss-vector-database topic.

  • jordan-jakisa/blog_post_writer

    An AI agent that writes SEO-optimised blog posts and outputs a properly formatted markdown document.

    Language:Python471021
  • hrishi-008/SummarAI

    A tool for summarizing search results and website content using FAISS, LLMs, and the Retrieval-Augmented Generation (RAG) technique.

    Language:Python30106
  • chakka-guna-sekhar-venkata-chennaiah/Mutli-Modal-RAG-ChaBot

    Building Essence Towards Personalized Knowledge Model - PKM

    Language:Jupyter Notebook28209
  • Hemanthkumar2112/Reward-Modeling-RLHF-Finetune-and-RAG

    Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform

    Language:Jupyter Notebook22207
  • sagnik-datta-02/ChatwithPDF

    This is a RAG project to chat with your uploaded PDF , made using Langchain and Anthropic Claude 3 used as LLM , hosted using Streamlit

    Language:Python15102
  • aryashah2k/AryaGPT

    Webapp to answer questions about my resume leveraging Langchain, OpenAI, Streamlit

    Language:Python14100
  • yaacoo/graphRagSqlator

    LLM graph-RAG SQL generator for large databases with poor documentation

    Language:Python14215
  • sergio11/nemesys

    Nemesys is an ethical cybersecurity tool designed to automate exploitation and post-exploitation tasks using Metasploit. It enhances target attacks, privilege escalation, and system analysis while providing intelligent reporting through cloud-based large language models (LLMs). 🚀📊

    Language:Python11201
  • VuBacktracking/bert-faiss-qa-system

    Q&A System using BERT and Faiss Vector Database

    Language:Python9100
  • BjornMelin/polyagent-research-intelligence

    A modular, multi-agent AI research and report generation platform. Enter any topic, and PolyAgent Research Intelligence orchestrates multiple AI agents to retrieve literature, analyze data, and generate a polished report. Built for researchers and AI/ML engineers, leveraging LangChain, FastAPI, PostgreSQL, advanced LLMs, and a Next.js front-end.

    Language:TypeScript7102
  • BrunoTanabe/chatpdf-ai-powered-document-interaction

    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.

    Language:Python6101
  • dsba6010-llm-applications/baemax_tc

    LLM App to demystify and summarize Terms and Conditions agreements

    Language:Jupyter Notebook62
  • AnasAber/RAG_in_CPU

    This repo is for advanced RAG systems, each branch will represent a project based on RAG.

    Language:Python5110
  • Gaurav-Van/Chat-With-PDF

    It allows users to upload PDFs and ask questions about the content within these documents.

    Language:Python5112
  • Kdotseth7/advanced-rag

    Advanced RAG pipeline using Re-Ranking after initial retrieval

    Language:Python5100
  • 25mb-git/pdfchat

    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.

    Language:Python40
  • arkapatra31/LangChain

    Implementing LangChain concepts and building meaningful stuffs

    Language:Python4110
  • LuisHBeck/genAI-article-research

    Generative AI projetc using LangChain for similarity search. Input 3 articles urls and ask something about the topic

    Language:Python4200
  • Omar-Karimov/BankLLM

    BankLLM is an AI-driven recommendation engine for banking, using OpenAI's models to analyze customer data and generate personalized product suggestions. It integrates LangChain, FAISS, and LangServe, with a FastAPI backend and Streamlit frontend, following an LLMOps approach for scalable deployment.

    Language:Python4101
  • dsatyam09/DocSpot

    DocSpot - Connecting Students Together

    Language:JavaScript3100
  • Harshita1195/Chatbot-using-Langchain-with-Google-Gemini-Pro-API

    The project is a LangChain-based demo application integrated with Google's Gemini API, built using the Streamlit framework. It allows users to input a query or search topic through a text box, which is processed by a language model to generate helpful responses.

    Language:Jupyter Notebook3100
  • jojocoder28/Mutual_Fund_Chatbot

    This is a basic RAG chatbot and report generator made using LangChain, Streamlit, FAISS, Cohere's embed-english-v3.0 and Cohere's command-r

    Language:Python3103
  • k-arthik-r/ai_powered_log_parsing_tool

    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.

    Language:Python3200
  • maylad31/vector_sqlite

    Faiss with sqlite

    Language:Python3102
  • mohd-faizy/RAG-DeepSeek

    Efficiently search and retrieve information from PDF documents using a Retrieval-Augmented Generation (RAG) approach. This project leverages DeepSeek-R1 (1.5B) for advanced language understanding, FAISS for high-speed vector search, and Hugging Face’s ecosystem for enhanced NLP capabilities. With an intuitive Streamlit interface and Ollama for mode

    Language:Python30
  • NebeyouMusie/Chat-with-Multiple-PDF-Documents

    In this project I have built an app that can answer questions from your multiple PDFs using Google's gemini-1.5-flash model.

    Language:Python3103
  • NebeyouMusie/End-To-End-Advanced-RAG-Project-using-Open-Source-LLM-Models-And-Groq-Inferencing

    In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.

    Language:Python3101
  • abari111/abariBot

    abaritbot: AI assistant using RAG and openAI API.

    Language:Python2100
  • apsinghAnalytics/FinRAGify_App

    An LLM app leveraging RAG with LangChain and GPT-4 mini to analyze earnings call transcripts, assess company performance, using natural language queries (NLP), FAISS (vector database), and Hugging Face re-ranking models.

    Language:Jupyter Notebook2100
  • Ayeshaaaaaaaaa/Enhanced-Contextual-Insights-RAG-Based-GPT-Integration-for-Question-Answering-with-BBC-News-Data

    This project integrates RAG techniques with GPT-2 for advanced question-answering using BBC news articles. It employs FAISS for efficient document retrieval and SentenceTransformer for embeddings, providing detailed and contextually accurate answers by combining article content with publication dates.

    Language:Jupyter Notebook2100
  • Bushra-Butt-17/BudgetBuddy-Finance-Chatbot

    Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.

    Language:Python2110
  • Chaimaaorg/Medical-Chatbot-for-FAQ

    This project develops a chatbot using LangChain, GoogleGenerative AI , Hugging Face, Streamlit to handle FAQs with intelligent conversations.

    Language:Jupyter Notebook210
  • manishkolla/GenAI_University_Chatbot

    Imagine a website where users can skip complex navigation and get instant answers with just a question. This project explores how a Retrieval-Augmented Generation (RAG) chatbot reduces server load and network congestion by streamlining interactions-enhancing both efficiency and user experience in a way traditional navigation can't.

    Language:Python21
  • Srikanth-Banda/PDF-Data-Analyzer

    This application can intelligently answer all your questions regarding your PDFs.

    Language:Python2110
  • vinerya/faiss_vector_aggregator

    This Python library provides a suite of advanced methods for aggregating multiple embeddings associated with a single document or entity into a single representative embedding.

    Language:Python2100