MTEB: Massive Text Embedding Benchmark
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Updated
Jun 16, 2024 - Python
MTEB: Massive Text Embedding Benchmark
compilation of the most important books, scraped from twitter with advanced search and filtering functionality
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.
RAG architecture: index and query any data using LLM and natural language, track sources, show citations, asynchronous memory patterns.
🔍 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.
🔮 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.
Open Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
All-in-One: Text Embedding, Retrieval, Rerank and RAG
Your AI second brain. Get answers to your questions, whether they be online or in your own notes. Use online AI models (e.g gpt4) or private, local LLMs (e.g llama3). Self-host locally or use our cloud instance. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.
Tool to detect duplicate Reddit posts in subreddits using semantic search
18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
🍁 Sycamore is an LLM-powered search and analytics platform for unstructured data.
A simple web application to generate vector embeddings for PDF document, store them in a vector database, and enable semantic search and information retrieval using OpenAI's language models.
The codebase for the book "AI-Powered Search" (Manning Publications, 2024)
GPT-powered chat for documentation, chat with your documents
Jupyter Notebooks to help you get hands-on with Pinecone vector databases
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS
The official TypeScript/Node client for the Pinecone vector database
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