your favorite ML models, and scale seamlessly into billions of data objects.What is Weaviate?Weaviate is an open-source ‚Äãdatabase of the type ‚Äãvector search engine.Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space.Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities.Weaviate has a GraphQL-API to access your data easily.We aim to bring your vector search set up to production to query in mere milliseconds (check our open-source benchmarks to see if Weaviate fits your use case).Get to know Weaviate in the basics getting started guide in under five minutes.Weaviate in detail:Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages.Installation and Setup‚ÄãInstall the Python SDK:pip install weaviate-clientVector Store‚ÄãThere exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import WeaviateFor a more detailed walkthrough of the Weaviate wrapper, see this notebookPreviousWeatherNextWhatsAppInstallation and SetupVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from ->: your favorite ML models, and scale seamlessly into billions of data objects.What is Weaviate?Weaviate is an open-source ‚Äãdatabase of the type ‚Äãvector search engine.Weaviate allows you to store JSON documents in a class property-like fashion while attaching machine learning vectors to these documents to represent them in vector space.Weaviate can be used stand-alone (aka bring your vectors) or with a variety of modules that can do the vectorization for you and extend the core capabilities.Weaviate has a GraphQL-API to access your data easily.We aim to bring your vector search set up to production to query in mere milliseconds (check our open-source benchmarks to see if Weaviate fits your use case).Get to know Weaviate in the basics getting started guide in under five minutes.Weaviate in detail:Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), etc. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering and the fault tolerance of a cloud-native database. It is all accessible through GraphQL, REST, and various client-side programming languages.Installation and Setup‚ÄãInstall the Python SDK:pip install weaviate-clientVector Store‚ÄãThere exists a wrapper around Weaviate indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import WeaviateFor a more detailed walkthrough of the Weaviate wrapper, see this notebookPreviousWeatherNextWhatsAppInstallation and SetupVector StoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.