chromadb

There are 705 repositories under chromadb topic.

  • Josh-XT/AGiXT

    AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.

    Language:Python3.1k71415429

  • athina-ai/rag-cookbooks

    This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.

    Language:Jupyter Notebook2.2k255272

  • NotJoeMartinez/yt-fts

    YouTube Full Text Search - Search all of YouTube from the command line

    Language:Python1.7k109194

  • onlyphantom/llm-python

    Large Language Models (LLMs) tutorials & sample scripts, ft. langchain, openai, llamaindex, gpt, chromadb & pinecone

    Language:Jupyter Notebook8791411312

  • philippgille/chromem-go

    Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. In-memory with optional persistence.

    Language:Go7147950

  • Denis2054/RAG-Driven-Generative-AI

    This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.

    Language:Jupyter Notebook502102162

  • DonTizi/ReMind

    Your Local Artificial Memory on your Device.

    Language:TypeScript492121028

  • alphasecio/langchain-examples

    A collection of apps powered by the LangChain LLM framework.

    Language:Python45443134
  • langchain-chatbot

    Haste171/langchain-chatbot

    AI Chatbot for analyzing/extracting information from data in conversational format.

    Language:Python43791994
  • ThinkRAG

    wzdavid/ThinkRAG

    A LLM RAG system runs on your laptop. 大模型检索增强生成系统，可以轻松部署在笔记本电脑上，实现本地知识库智能问答。

    Language:Python272102438
  • vector-io

    AI-Northstar-Tech/vector-io

    Comprehensive Vector Data Tooling. The universal interface for all vector database, datasets and RAG platforms. Easily export, import, backup, re-embed (using any model) or access your vector data from any vector databases or repository.

    Language:Jupyter Notebook25864027

  • CNTRLAI/Notate

    Notate is a desktop chat application that takes AI conversations to the next level. It combines the simplicity of chat with advanced features like document analysis, vector search, and multi-model AI support - all while keeping your data private.

    Language:TypeScript249101320

  • umbertogriffo/rag-chatbot

    RAG (Retrieval-augmented generation) ChatBot that provides answers based on contextual information extracted from a collection of Markdown files.

    Language:Python2423457

  • flanker/chromadb-admin

    Admin UI for Chroma embedding database built with Next.js

    Language:TypeScript1795843

  • lawglance/lawglance

    A free open source RAG based AI legal assistant.

    Language:Jupyter Notebook1754224

  • Wilson-ZheLin/SearchGPT

    GPT Enhanced with Real-Time Web Browsing 🔗

    Language:Python1623231

  • ChuloAI/BrainChulo

    Harnessing the Memory Power of the Camelids

    Language:Python14672510

  • GURPREETKAURJETHRA/RAG-using-Llama3-Langchain-and-ChromaDB

    RAG using Llama3, Langchain and ChromaDB

    Language:Jupyter Notebook1111032

  • amikos-tech/chroma-go

    The Go client for Chroma vector database

    Language:Go10829723

  • petermartens98/GPT4-LangChain-Agents-Research-Web-App

    Python Streamlit web app utilizing OpenAI (GPT4) and LangChain LLM tools with access to Wikipedia, DuckDuckgo Search, and a ChromaDB with previous research embeddings. Ultimately delivering a research report for a user-specified input, including an introduction, quantitative facts, as well as relevant publications, books, and youtube links.

    Language:Python725115

  • amikos-tech/chromadb-java-client

    A thin client for Chroma Vector DB implemented in Java

    Language:Java614458

  • amikos-tech/chroma-cookbook

    Learn how to use ChromaDB

    Language:Jupyter Notebook552277

  • davideuler/gpt4-pdf-chatbot-langchain-chromadb

    GPT4 & LangChain Chatbot for large PDF, docx, pptx, csv, txt, html docs, powered by ChromaDB and ChatGPT.

    Language:TypeScript552410

  • teilomillet/raggo

    A lightweight, production-ready RAG (Retrieval Augmented Generation) library in Go.

    Language:Go55113

  • AkiRusProd/llm-agent

    LLM using long-term memory through vector database

    Language:Python52209

  • harsh-vardhhan/ai-agent-flight-scanner

    AI agent to search Google Flights data

    Language:Python50206
  • VectorVerse

    abhishek-ch/VectorVerse

    Explore Multiple Vector Databases and chat with documents on Multiple LLM models, private LLM models

    Language:Python485410

  • Bangla-RAG/PoRAG

    Fully Configurable RAG Pipeline for Bengali Language RAG Applications. Supports both Local and Huggingface Models, Built with Langchain.

    Language:Python46257

  • amikos-tech/chromadb-chart

    Chart for deploying ChromaDB in Kubernetes

    Language:Smarty4524522

  • alexdphan/babyagi-chroma-agent

    AI-powered task management system template w/ free storage use

    Language:Python43229

  • BlueBash/BlogIQ

    Clone of writesonic.com & copy.ai - BlogIQ is an innovative app powered by OpenAI and Langchain, designed to streamline the content creation process for bloggers.

    Language:Python42002

  • Azure-Samples/aks-openai-chainlit-terraform

    This sample shows how to create two AKS-hosted chat applications that use OpenAI, LangChain, ChromaDB, and Chainlit using Python and deploy them to an AKS environment built in Terraform.

    Language:HCL4018114

  • ushakrishnan/SearchWithOpenAI

    Quick start. Index multiple documents in a repository using HuggingFace embeddings. Save them in Chroma and / or FAISS for recall. Choose OpenAI or Azure OpenAI APIs to get answers to your questions - Q&A with OpenAI and Azure OpenAI.

    Language:Python40249

  • neo-con/chromadb-tutorial

    This repo is a beginner's guide to using Chroma. It covers all the major features including adding data, querying collections, updating and deleting data, and using different embedding functions. Each topic has its own dedicated folder with a detailed README and corresponding Python scripts for a practical understanding.

    Language:Python391212

  • AIAnytime/Zephyr-7B-beta-RAG-Demo

    Zephyr 7B beta RAG Demo inside a Gradio app powered by BGE Embeddings, ChromaDB, and Zephyr 7B Beta LLM.

    Language:Python353321

  • Anush008/chromadb-rs

    Rust client library for ChromaDB

    Language:Rust352710