MTEB: Massive Text Embedding Benchmark
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Updated
Jun 12, 2024 - Python
MTEB: Massive Text Embedding Benchmark
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.
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
👑 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.
PostgreSQL vector database extension for building AI applications
Represent, send, store and search multimodal data
OpenSearch Neural Search example. Load BERT to OpenSearch and create embeddings as data is indexed. Use the embedding to preform vector search
Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity search using exact and approximate algorithms.
Neural Search
Epsilla is a high performance Vector Database Management System. Try out hosted Epsilla at https://cloud.epsilla.com/
Official Weaviate TypeScript Client
Sample Integration of OpenSearch Neural Search with Alfresco
Neural Search
☁️ Build multimodal AI applications with cloud-native stack
UI widget for adding semantic search to your React UI in just a few lines of code
Official Python SDK for Kern AI refinery.
The prime repository for state-of-the-art Multilingual Question Answering research and development.
An easy to use Neural Search Engine. Index latent vectors along with JSON metadata and do efficient k-NN search.
An Open-Source Package for Information Retrieval
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