Docarray

DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, docarray, video, 3D mesh, and so on. It allows deep-learning docarray 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.

This is useful if you want to store a bunch of data, and at a later point retrieve documents that are similar to some query that you provide. Relevant concrete examples are neural search applications, augmenting LLMs and chatbots with domain knowledge Retrieval-Augmented Generation , or recommender systems. You represent every data point that you have in our case, a document as a vector , or embedding. This vector should represent as much semantic information about your data as possible: Similar data points should be represented by similar vectors. These vectors embeddings are usually obtained by passing the data through a suitable neural network that has been trained to produce such semantic representations - this is the encoding step. Once you have your vectors that represent your data, you can store them, for example in a vector database.

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. That's why you can easily collect multiple Documents :.

WeaviateDocumentIndex is docarray document index that is built upon Weaviate vector database. Be sure to check the documentation to prepare your migration, docarray.

Announcing the brand new rewrite of DocArray. If you're building a machine learning application that deals with multimodal data, then DocArray is the way to go. If you have been using recent versions of DocArray, you will already be familiar with its dataclass API. DocArray v2 is that idea, taken seriously. Every Document is created through a dataclass-like interface, courtesy of Pydantic. You may also be familiar with our old Document Store for vector database integration. They are now called Document Indexes and offer the following improvements:.

You should start by reading the Representing data section, and then the Sending data and Storing data sections can be read in any order. This will install the main dependencies of DocArray and will work with all the supported data modalities. To install a very light version of DocArray with only the core dependencies, you can use the following command:. Depending on your usage you might want to use DocArray with only a couple of specific modalities and their dependencies. For instance, if you only want to work with images, you can install DocArray using the following command:. Skip to content. Table of contents Install DocArray.

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?

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AnyDocArray AnyDocArray is an abstract class that represents an array of BaseDoc s which is not meant to be used directly, but to be subclassed. They are now called Document Indexes and offer the following improvements see here for the new API :. Module , and provides a FastAPI-compatible schema that eases the transition between model training and model serving. This is useful if you want to store a bunch of data, and at a later point retrieve documents that are similar to some query that you provide. The code snippets above just scratch the surface of what a Document Index can do. Go to file. Of course, serialization is not all you need. As with other document stores, you can easily instantiate a DocumentArray with Milvus storage:. Instead of creating a DocumentArray instance and setting the storage parameter to a vector database of your choice, in v2 you can initialize a DocIndex object of your choice, such as:. DocArray has allied with open source partners like Weaviate, Qdrant, Redis, FastAPI, pydantic, and Jupyter for integration and most importantly for seeking a common standard. DocArray v2 is that idea, taken seriously. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. We chose the name DocArray because we want to make something as fundamental and widely-used as NumPy's ndarray.

DocArray is a versatile, open-source tool for managing your multi-modal data.

This is just the same way that you would do it with BaseDoc :. Therefore, vector databases usually perform approximate nearest neighbor ANN search. You can use Qdrant natively in DocArray, where Qdrant serves as a high-performance document store to enable scalable vector search. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Array of documents DocArray allows users to represent and manipulate multimodal data to build AI applications such as neural search and generative AI. This version of DocArrray is a complete rewrite, therefore it includes several more than breaking changes. Latest AI and machine learning articles. In the context of open source software, "open governance" means the project is governed openly and transparently, and anyone is welcome to participate in that governance. Notifications Fork Star 2. This really matters when you need to handle multimodal data that you will feed into an algorithm that requires contiguous data, like matrix multiplication which is at the heart of Machine Learning, especially in Deep Learning. Latest commit. On the other hand, Jina itself had to remain stable and robust as it served as infrastructure. The second difference is that you just get the column and don't need to create it at each call. If you come from PyTorch, you can see DocArray mainly as a way of organizing your data as it flows through your model.