Engineering
Training Text Embeddings with Jina AI
In a recent talk by Bo Wang, he discussed the creation of Jina text embeddings for modern vector search and RAG systems. He also shared methodologies for training embedding models that effectively encode extensive information, along with guidance o
Community
Unstructured Data Processing from Cloud to Edge
Edge computing brings data processing closer to the source on small devices; vectorDBs empower them to handle the growing unstructured data in real-time.
Community
A Different Angle: Retrieval Optimized Embedding Models
This blog will demonstrate how GCL can be integrated with Milvus, a leading vector database, to create optimized Retrieval-Augmented Generation (RAG) systems.
Case Study
Generative AI for Creative Applications Using Storia Lab
This post discusses how Storia AI generates and edits images through simple text prompts or clicks and how we can leverage Storia AI and Milvus to build multimodal RAG.
Community
Build Better Multimodal RAG Pipelines with FiftyOne, LlamaIndex, and Milvus
Enhance the capabilities of multimodal systems by efficiently leveraging text and visual data for improved data retrieval and context-rich responses.
Community
Build RAG with LangChain, Milvus, and Strapi
A step-by-step guide to building an AI-powered FAQ system using Milvus as the vector database, LangChain for workflow coordination, and Strapi for content management
Community
How Vector Databases are Revolutionizing Unstructured Data Search in AI Applications
Learn how vector databases have emerged as a transformative technology in the field of AI and machine learning, particularly for handling unstructured data. Their applications extend far beyond simple retrieval-augmented generation (RAG) systems, revolutionizing various domains including customer support, recommendation systems, drug discovery, and multimodal search.
Engineering
Text as Data, From Anywhere to Anywhere
Whether you prefer a no-code or minimal-code approach, Airbyte and PyAirbyte offer robust solutions for integrating both structured and unstructured data. AJ Steers' painted a good picture of the potential of these tools in revolutionizing data workflows.
Engineering
Copilot Workspace: What It Is, How It Works, Why It Matters
The presentation by Idan Gazit and Cole Bemis illuminates the potential of the GitHub Copilot Workspace. This dev environment represents a significant step in streamlining complicated software development like RAG, enhancing productivity by allowing for task-to-code development workflow using generative AI.