Python SDK for Milvus Vector Database
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Updated
May 26, 2025 - Python
Python SDK for Milvus Vector Database
A Model Context Protocol (MCP) server that enables AI assistants to interact with HubSpot CRM data, providing built-in vector storage and caching mechanisms help overcome HubSpot API limitations while improving response times.
An AI agent that writes SEO-optimised blog posts and outputs a properly formatted markdown document.
A tool for summarizing search results and website content using FAISS, LLMs, and the Retrieval-Augmented Generation (RAG) technique.
Building Essence Towards Personalized Knowledge Model - PKM
Gemma2(9B), Llama3-8B-Finetune-and-RAG, code base for sample, implemented in Kaggle platform
Webapp to answer questions about my resume leveraging Langchain, OpenAI, Streamlit
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 graph-RAG SQL generator for large databases with poor documentation
Q&A System using BERT and Faiss Vector Database
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.
AI Lawyer is an intelligent reasoning legal assistant powered by DeepSeek , Ollama RAG and LangChain, designed to streamline legal research and document analysis. By leveraging retrieval-augmented generation (RAG), it provides precise legal insights, and contract summarization. With an intuitive Streamlit-based UI, analyze legal documents.
It allows users to upload PDFs and ask questions about the content within these documents.
LLM App to demystify and summarize Terms and Conditions agreements
Advanced RAG pipeline using Re-Ranking after initial retrieval
This repo is for advanced RAG systems, each branch will represent a project based on RAG.
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.
Implementing LangChain concepts and building meaningful stuffs
Generative AI projetc using LangChain for similarity search. Input 3 articles urls and ask something about the topic
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