Drahten is an open-source project utilizing a microservices architecture written in .NET Core 8.0.

The platform for building AI from enterprise data

A curated list of Generative AI tools, works, models, and references

Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.

🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search.

All-in-One: Text Embedding, Retrieval, Reranking and RAG

🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.

Tool to detect duplicate Reddit posts in subreddits using semantic search

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

🍁 Sycamore is an LLM-powered search and analytics platform for unstructured data.

MTEB: Massive Text Embedding Benchmark

Code for Embeddings, VectorStore, SemanticSearch, and RAG using Azure OpenAI

Your AI Powered Enterprise Knowledge Partner. Designed to be used at scale from ingesting large amounts of documents formats such as pdfs, docx, xlsx, png, jpgs, tiff, mp3, mp4, jpeg. Integrates with s3, Windows Shares, Google Drive and more.

Semantic embedding-based system for question answering from PDFs with visual analysis tools.

Hybrid search engine, combining best features of text and semantic search worlds

SemanticFinder - frontend-only live semantic search with transformers.js

GPT-powered chat for documentation, chat with your documents

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

A collection of Jupyter notebook experiments and applications centered around Generative AI with LLMs.

A compute framework for turning complex data into vectors. Build multimodal vectors with ease and define weights at query time so you don't need a custom reranking algorithm to optimise results. Go straight from notebook to production with the same SDK.