Excelize is a library written in pure Go providing a set of functions that allow you to write to and read from XLSX / XLSM / XLTM / XLTX files. Supports reading and writing spreadsheet documents generated by Microsoft Excel™ 2007 and later. Supports complex components by high compatibility, and provided streaming API for generating or reading data from a worksheet with huge amounts of data.
 
With Excelize chart generation and management is as easy as a few lines of code. You can build charts based off data in your worksheet or generate charts without any data in your worksheet at all.

Excelize is a library written in pure Go providing a set of functions that allow you to write to and read from XLSX / XLSM / XLTM / XLTX files. Supports reading and writing spreadsheet documents generated by Microsoft Excel™ 2007 and later. Supports complex components by high compatibility, and provided streaming API for generating or reading data from a worksheet with huge amounts of data

Excelize - Golang library for reading and writing Microsoft Excel (XLSX) files

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.
Weaviate 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.
Weaviate provides out-of-the-box modules for NLP / semantic search, automatic classification, and image similarity search. It has a modular setup that allows you to use your ML models inside Weaviate. You can also use out-of-the-box ML models (e.g., SBERT, ResNet, fasttext, etc.). It typically performs nearest neighbor (NN) searches of millions of objects in considerably less than 100ms.&nbsp;
Weaviate allows for efficient, combined vector and scalar searches. For example, "articles related to the COVID-19 pandemic published within the past 7 days." Weaviate stores both objects and vectors and ensures the retrieval of both is always efficient. There is no need for a third-party object storage. Within Weaviate, all individual data objects are based on a class property structure where a vector represents each data object. You can connect data objects (like in a traditional graph) and search for data objects in the vector space.

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.

Weaviate - Vector database that is robust, scalable, cloud-native, and fast



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Excelize - Golang library for reading and writing Microsoft Excel (XLSX) files

Weaviate - Vector database that is robust, scalable, cloud-native, and fast

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Excelize - Golang library for reading and writing Microsoft Excel (XLSX) files

Weaviate - Vector database that is robust, scalable, cloud-native, and fast

TechStack

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