Docarray

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

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:.

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. To perform similarity search, you take your input query and encode it in the same way as the data in your database. Then, the database will search through the stored vectors and return those that are most similar to your query. This similarity is measured by a similarity metric , which can be cosine similarity , Euclidean distance , or any other metric that you can think of. If you store a lot of data, performing this similarity computation for every data point in your database is expensive. Therefore, vector databases usually perform approximate nearest neighbor ANN search. There are various algorithms for doing this, such as HNSW , but in a nutshell, they allow you to search through a large database of vectors very quickly, at the expense of a small loss in accuracy. DocArray's Document Index concept achieves this by providing a unified interface to a number of vector databases. This doesn't require a database server - rather, it saves your data locally.

DocVec is always an array of homogeneous Documents. Plus, docarray, it gets even better - you can utilize your DocArray document index to create a DocArrayRetrieverdocarray build awesome Langchain apps! One flaw of DocList is that none of the data is contiguous docarray memory, so you cannot leverage functions that require contiguous data without first copying the data in a continuous array.

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?

Docarray

Released: Dec 22, View statistics for this project via Libraries. Tags docarray, deep-learning, data-structures cross-modal multi-modal, unstructured-data, nested-data, neural-search. 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.

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DocArray is a library for representing, sending and storing multi-modal data, perfect for Machine Learning applications. But this concept only works if and only if all of the BaseDoc s in the AnyDocArray have the same schema. Tailored for the development of multimodal AI applications, its design guarantees seamless integration with the extensive Python and machine learning ecosystems. That's why you can easily collect multiple Documents :. In the following sections, DocArray maintainers Sami Jaghouar and Johannes Messner give you a taste of the next release. DocArray gives you the freedom to establish flexible document schemas and choose from different backends for document storage. This allows you to reason about your data using DocArray's abstractions deep inside of nn. It is similar to doing:. Security policy. Notifications Fork Star 2.

You should start by reading the Representing data section, and then the Sending data and Storing data sections can be read in any order.

Skip to content. Document Indexes let you index your Documents in a vector database for efficient similarity-based retrieval. These comments are closed. GitHub docarray. More about me. Therefore, vector databases usually perform approximate nearest neighbor ANN search. Skip to content. DocArray 0. Skip to content. An introduction to DocArray, an open source AI library. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice. One of the other main differences between both of them is how you can access documents inside them. If you're building a machine learning application that deals with multimodal data, then DocArray is the way to go.