Docarray

The skimm allows users to represent and manipulate multimodal data to build AI applications such as neural search and generative AI, docarray. As you have seen in the previous sectiondocarray, the fundamental building block of DocArray is the BaseDoc class which represents a single document, a docarray datapoint. However, in machine learning we often need to work with an array of documents, and an docarray of data points. This name of this library -- DocArray -- is derived from this concept and is short for DocumentArray.

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

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:. In v2, the Document Store has been renamed DocIndex and can be used for fast retrieval using vector similarity. DocArray v2 DocIndex supports:. 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 v2 Release. Engineering Group.

Jina scales things up and uplifts prototypes into services in production. This refactoring served as the foundation docarray the later DocArray. The first difference is that you don't need to call np, 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.

DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever , and build awesome Langchain apps! This notebook is split into two sections. The first section offers an introduction to all five supported document index backends. It provides guidance on setting up and indexing each backend and also instructs you on how to build a DocArrayRetriever for finding relevant documents. This determines what fields your documents will have and what type of data each field will hold. It is a great starting point for small datasets, where you may not want to launch a database server.

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.

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What to read next. Use DocList when you want to be able to rearrange or re-rank your data. We will go into the difference between DocList and DocVec in the next section, but let's first focus on what they have in common. 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. Learn more I accept. What this means concretely is you can access your data at the Array level in just the same way you would access your data at the document level. Dismiss alert. Vector Search Basics Qdrant vs. 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. DocVec is a columnar data structure. This similarity is measured by a similarity metric , which can be cosine similarity , Euclidean distance , or any other metric that you can think of. That's where DocArray steps in!

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

DocArray's Document Index concept achieves this by providing a unified interface to a number of vector databases. On the other hand, Jina itself had to remain stable and robust as it served as infrastructure. What does ChatGPT mean for the open source community? The data structure for multimodal data. That's where DocArray steps in! For DocVec it is a bit different. It's a growing community, and one that's open to everyone. 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. It is a BaseDoc instance but with a different way to access the data. Both are user-friendly and are best suited to small to medium-sized datasets. We tackled this by decoupling jina. The first section offers an introduction to all five supported document index backends.