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Explora todas las tecnologías del grafo por tipo y categoría.
Por tipo
Por categoría
- Algoritmos 30
- Modelos 16
- Modelos de embedding 8
- Técnicas 35
- Prompting 10
- Frameworks 12
- Frameworks de LLM 6
- Bases de datos 3
- Bases de datos vectoriales 8
- Motores de búsqueda 4
- Infraestructura 15
- MLOps 4
- Evaluación 6
- Hardware 11
- Seguridad y alineación 6
- Aplicaciones 7
- Multimodal 5
- Agentes de IA 4
- Benchmarks 11
- Conjuntos de datos 7
13 technologies shown
- Qdrant database Open-source vector database written in Rust, with payload filtering.
- Pinecone database Fully managed proprietary vector database with no self-hosted option.
- Milvus database Open-source distributed vector database designed for billion-scale collections.
- Weaviate database Open-source vector database with hybrid keyword and vector search built in.
- Elasticsearch database Distributed search engine with vector support alongside full-text search.
- PostgreSQL database General-purpose relational database, and a vector store through an extension.
- SQLite database Embedded relational database, used for local-first vector search.
- Redis database In-memory data store whose vector search runs in the same place as the cache.
- Chroma database Embedded vector database designed to be added to a Python application in minutes.
- OpenSearch database Open-source fork of Elasticsearch, with the same hybrid retrieval model.
- Typesense database Typo-tolerant search engine with vector support, aimed at developer experience.
- Vespa database Search platform combining vector and structured ranking in one query.
- LanceDB database Embedded vector database built on a columnar format for multimodal data.