☁️ Build multimodal AI applications with cloud-native stack
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Updated
Aug 9, 2024 - Python
☁️ Build multimodal AI applications with cloud-native stack
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
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.
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Represent, send, store and search multimodal data
🌊 A Human-in-the-Loop workflow for creating HD images from text
MTEB: Massive Text Embedding Benchmark
🎯 Task-oriented embedding tuning for BERT, CLIP, etc.
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
Epsilla is a high performance Vector Database Management System. Try out hosted Epsilla at https://cloud.epsilla.com/
SGPT: GPT Sentence Embeddings for Semantic Search
PostgreSQL vector database extension for building AI applications
The prime repository for state-of-the-art Multilingual Question Answering research and development.
A Python vector database you just need - no more, no less.
Jina examples and demos to help you get started
An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.
Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity search using exact and approximate algorithms.
Neural Search
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