Docarray

DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, and so on. Docarray allows deep-learning engineers to efficiently process, embed, search, store, docarray, recommend, and transfer multi-modal data with a Pythonic API. This is the start of a new day for DocArray. Today, DocArray powers docarray of multimodal AI applications.

You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search. DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer the data with a Pythonic API. Subscribe to our e-mail newsletter if you want to be updated on new features and news regarding Qdrant. Like what we are doing? We use cookies to learn more about you. At any time you can delete or block cookies through your browser settings.

Docarray

The data structure for multimodal data. Refer to its codebase , documentation , and its hot-fixes branch for more information. DocArray is a Python library expertly crafted for the representation , transmission , storage , and retrieval of multimodal data. Tailored for the development of multimodal AI applications, its design guarantees seamless integration with the extensive Python and machine learning ecosystems. New to DocArray? Depending on your use case and background, there are multiple ways to learn about DocArray:. DocArray empowers you to represent your data in a manner that is inherently attuned to machine learning. You'll be pleased to learn that DocArray is not only constructed atop Pydantic but also maintains complete compatibility with it! Furthermore, we have a specific section dedicated to your needs! In essence, DocArray facilitates data representation in a way that mirrors Python dataclasses, with machine learning being an integral component:. So not only can you define the types of your data, you can even specify the shape of your tensors! You rarely work with a single data point at a time, especially in machine learning applications.

It docarray actually at the heart of DocArray, but we'll come back to it later and continue with this example for now, docarray.

DocArray allows users to represent and manipulate multimodal data to build AI applications such as neural search and generative AI. As you have seen in the previous section , the fundamental building block of DocArray is the BaseDoc class which represents a single document, a single datapoint. However, in machine learning we often need to work with an array of documents, and an array of data points. This name of this library -- DocArray -- is derived from this concept and is short for DocumentArray. AnyDocArray is an abstract class that represents an array of BaseDoc s which is not meant to be used directly, but to be subclassed. We provide two concrete implementations of AnyDocArray :.

You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search. DocArray is a library from Jina AI for nested, unstructured data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer the data with a Pythonic API. Subscribe to our e-mail newsletter if you want to be updated on new features and news regarding Qdrant. Like what we are doing? We use cookies to learn more about you. At any time you can delete or block cookies through your browser settings. Docs Menu.

Docarray

The data structure for multimodal data. Refer to its codebase , documentation , and its hot-fixes branch for more information. DocArray is a Python library expertly crafted for the representation , transmission , storage , and retrieval of multimodal data. Tailored for the development of multimodal AI applications, its design guarantees seamless integration with the extensive Python and machine learning ecosystems. New to DocArray? Depending on your use case and background, there are multiple ways to learn about DocArray:. DocArray empowers you to represent your data in a manner that is inherently attuned to machine learning. Familiar with Pydantic? You'll be pleased to learn that DocArray is not only constructed atop Pydantic but also maintains complete compatibility with it! Furthermore, we have a specific section dedicated to your needs!

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Looks much better, doesn't it? Tensor , this is not the case for the TensorFlowTensor : Due to some technical limitations of tf. So not only can you define the types of your data, you can even specify the shape of your tensors! Community links. This gives your multimodal BlogPost four embedding representations: title , excerpt , content , and banner. If you come from Pydantic, you can see DocArray documents as juiced up Pydantic models, and DocArray as a collection of goodies around them. QdrantDocumentIndex is a document index that is built upon Qdrant vector database. We use cookies to learn more about you. On the one hand, jina. This determines what fields your documents will have and what type of data each field will hold. By a "nested optional field" we mean a document that is contained within another document, and declared as Optional :. After creating your document index, you can connect it to your Langchain app using DocArrayRetriever. Furthermore, we have a specific section dedicated to your needs! We chose the name DocArray because we want to make something as fundamental and widely-used as NumPy's ndarray.

This time they embrace further multimodal AI with a focus on embeddings with the new ImageBind Model. We gave it a try and in this blog post, we will show how you can use this cool model along with DocArray to implement a cross-modal search system!

What is edge machine learning? Dec 22, Tailored for the development of multimodal AI applications, its design guarantees seamless integration with the extensive Python and machine learning ecosystems. Used by 3. It is a BaseDoc instance but with a different way to access the data. This similarity is measured by a similarity metric , which can be cosine similarity , Euclidean distance , or any other metric that you can think of. Let's delve into how DocArray streamlines this process:. You rarely work with a single data point at a time, especially in machine learning applications. While jina. In Jina 2. If one of your BaseDoc s has an attribute that the others don't, you will get an error if you try to access it at the Array level. Comments are closed.