🌌 A complete search engine and RAG pipeline in your browser, server or edge network with support for full-text, vector, and hybrid search in less than 2kb.

The universal tool suite for vector database management. Manage Pinecone, Chroma, Qdrant, Weaviate and more vector databases with ease.

A Python vector database you just need - no more, no less.

A Python library to chunk/group your texts based on semantic similarity.

S3 vector database for LLM Agents and RAG.

Embed anything.

V3CTRON | Vector Embeddings Data Retrieval | ChatGPT Plugin

Examples of vector DB indexing and query with various vector databases.

Machine Learning, LLM and other Jupyter Notebooks and resources

Scalable API extension for advanced vector database functions. Enhance machine learning, search, and analytics applications with an API that supports efficient embedding storage and similarity searches.

Experimenting with Pinecone as vector data continues to take center stage in AI-native systems. The purpose of this project is to explore the core capabilities, benchmark performance across different embedding models, and better understand what is possible with vector search in production environments.

Create a ChatGPT-like experience with your data.

End-to-End Research Bot for Summarizing and Extracting Insights from Multiple URLs using advanced text processing, FAISS vector storage, and OpenAI services for accurate and concise responses.

A web app that uses Retrieval-Augmented Generation (RAG) to create an AI expert over a codebase. The app allows users to interact with a codebase via chat, retrieving relevant code snippets from a Pinecone vector database and generating responses using LLMs.

임베딩(vector) 스토어를 위한 캐시 매니저

This repository contains source code which encompasses usage of the Langchain framework to extract information from distinct types of documents and subsequently perform Retrieval Augmented Generation(RAG) on these documents as well.

Feature Store implementation for storing product information and text/visual embeddings for further use in datascience projects.

An easy way to understand vector store working and creation.