Jina AI
Vector databases and embedding models are key tools for building good search systems and AI applications that can understand and answer questions.
Use this integration for FreeAbout Jina Ai
Jina AI is a Neural Search Company that provides cloud-native neural search powered by advanced AI and deep learning. The company's mission is to offer an open-source neural search ecosystem for businesses and developers, enabling efficient information search across various data types with high availability and scalability.
Jina AI's journey in the field of neural search began with fine-tuning existing models like BERT. These fine-tuned models showed significant improvements over their pre-trained counterparts, as evidenced by performance comparisons. However, despite these technical achievements, the industry's reception was lukewarm. At the time, the search industry was only beginning to explore vector-based approaches and was not yet ready for fine-tuned embedding models. Recognizing this gap between technological capability and market readiness, Jina AI took a bold step. Instead of continuing with incremental improvements through fine-tuning, the company decided to develop its own embedding model from scratch. This ambitious move was driven by the belief that a homegrown solution could push the boundaries of what was possible in neural search.
Today, Jina AI offers the Search Foundation, which comprises Embeddings, Rerankers, Prompt Ops, and Infra. These components work together to transform how data is searched and understood, leading to enhanced search experiences, increased user trust, direct sales boosts, and new opportunities for business growth.
Jina AI and Milvus Vector Database Working Together
Vector databases and embedding models are key tools for building good search systems and AI applications that can understand and answer questions. These tools often work together to find similar information quickly.
Milvus is a free, open-source vector database and Zilliz Cloud is the managed version of Milvus. They are both good at storing and finding billions of vector embeddings, which are special number lists that represent information. Recently, Jina AI's embeddings have been added to the PyMilvus model library. This makes it easier for developers to build AI applications because they don't need to add extra embedding tools.
Learn
The best way to start is with a hands-on tutorial. This tutorial will walk you through how to build Semantic Search with Jina & Milvus. https://milvus.io/docs/integrate_with_jina.md#Semantic-Search-with-Jina--Milvus
And here are a few more resources
- Blog | Training Text Embeddings with Jina AI
- Video | Beyond 512 Tokens
- Popular Models | Jina AI / jina-embeddings-v2-base-en
- Generate vector embeddings via PyMilvus and insert them into Zilliz Cloud for semantic search
- Generate vector embeddings via SentenceTransformer and insert them into Zilliz Cloud for semantic search