Distributed vector search for AI-native applications
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Updated
May 16, 2024 - Go
Distributed vector search for AI-native applications
The open source Firebase alternative.
Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://codellama.h2o.ai/
Retrieval and Retrieval-augmented LLMs
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
Provide best practices for LMOps, as well as elegant and convenient access to the features of the Qianfan MaaS Platform. (提供大模型工具链最佳实践,以及优雅且便捷地访问千帆大模型平台)
It allows users to upload PDFs and ask questions about the content within these documents.
A FastAPI service for semantic text search using precomputed embeddings and advanced similarity measures, with built-in support for various file types through textract.
The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Exploring categorical features with various encodings and models
the AI-native open-source embedding database
This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models
Documentation for Google's Gen AI site - including the Gemini API and Gemma
Magick is a cutting-edge toolkit for a new kind of AI builder. Make Magick with us!
Chat with your notes & see links to related content with AI embeddings. Use local models or 100+ via APIs like Claude, Gemini, ChatGPT & Llama 3
Java version of LangChain
The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.
Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
Supabase Toolkit to perform vector similarity search on your knowledge base embeddings.
Add a description, image, and links to the embeddings topic page so that developers can more easily learn about it.
To associate your repository with the embeddings topic, visit your repo's landing page and select "manage topics."