Docarray

You can use Qdrant natively in DocArray, docarray, where Qdrant serves as a high-performance document store to enable scalable docarray search.

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.

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 :.

Moduledocarray, and provides a FastAPI-compatible schema that eases the transition between model training and model serving. That's docarray DocArray steps in! DocArray v2 Release.

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

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! ImageBind is a new embedding model from Meta that is capable of learning a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data inertial measurement units, which track motion and position. The true power of ImageBind lies in its ability to surpass specialist models that are trained for one specific modality, demonstrating its capability to analyze a multitude of data forms in unison. This holistic approach enables ImageBind to relate objects in an image to their corresponding sounds, 3D shapes, temperatures, and motion patterns, mirroring our human ability to process a complex blend of sensory information. Moreover, ImageBind demonstrates that it's feasible to create a shared embedding space across multiple modalities without the need for training on data encompassing every possible combination of these modalities.

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What to read next. Module , and provides a FastAPI-compatible schema that eases the transition between model training and model serving. Once you've defined a Banner , you can use it as a building block to represent more complicated data:. Branches Tags. If you come from Pydantic, you can see DocArray documents as juiced up Pydantic models, and DocArray as a collection of goodies around them. You switched accounts on another tab or window. If you're interested in open source AI, Python, or big data, then you're invited to follow along with the DocArray project as it develops. Every Document is created through a dataclass-like interface, courtesy of Pydantic. DocArray is a library for representing, sending and storing multi-modal data, perfect for Machine Learning applications. New to DocArray? Custom properties. Learn more I accept. It is a BaseDoc instance but with a different way to access the data. First, you should know that a Document is a pydantic model with a random ID and the Protobuf interface:. Both are user-friendly and are best suited to small to medium-sized datasets.

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

All rights reserved. The usage of a heterogeneous DocList is similar to a normal Python list but still offers DocArray functionality like serialization and sending over the wire. What does ChatGPT mean for the open source community? You may also be familiar with our old Document Stores for vector DB integration. View all files. The code below shows a minimum working example with a running Milvus server on localhost:. It was then that DocArray's mission crystallized for the team: to provide a data structure for AI engineers to easily represent, store, transmit, and embed multimodal data. This means that when you access the attribute of a BaseDoc at the Array level, we don't collect the data under the hood from all the documents like DocList before giving it back to you. DocArray v2 DocIndex supports:. What to read next. Security policy. Be sure to check the documentation to prepare your migration. Not very easy on the eyes if you ask us.