Welcome to the Generative AI category! This space is dedicated to discussions, questions, and insights around the fascinating world of Generative AI and its integration with vector databases. Whether you’re a seasoned data scientist, a curious developer, or someone just starting out, this is the place to explore the cutting-edge technologies that power AI creativity and innovation.
Topics here include, but are not limited to:
- Model Integration: How to effectively integrate generative models with our vector database for optimal performance.
- Use Cases: Share and discover real-world applications of Generative AI in various industries, powered by our vector database.
- Technical Challenges: Discuss challenges and solutions related to data storage, retrieval, and management in the context of Generative AI.
- Performance Optimization: Tips and best practices for tuning both generative models and vector databases to work seamlessly together.
- New Developments: Stay updated with the latest advancements in Generative AI and how they can be leveraged with vector databases.
- Community Projects: Collaborate on projects, share your work, and get feedback from fellow enthusiasts.
Join us in exploring how Generative AI can be enhanced and empowered by the unique capabilities of our vector database. Share your experiences, ask questions, and connect with a community passionate about the future of AI.
Quick Start
There are some examples to show how to use the tidb-vector-python to interact with TiDB Vector in different scenarios.
- OpenAI Embedding: use the OpenAI embedding model to generate vectors for text data, store them in TiDB Vector, and search for similar text.
- Image Search: use the OpenAI CLIP model to generate vectors for image and text, store them in TiDB Vector, and search for similar images.
- LlamaIndex RAG with UI: use the LlamaIndex to build an RAG(Retrieval-Augmented Generation) application.
- Chat with URL: use LlamaIndex to build an RAG(Retrieval-Augmented Generation) application that can chat with a URL.
- GraphRAG: 20 lines code of using TiDB Serverless to build a Knowledge Graph based RAG application.
- GraphRAG Step by Step Tutorial: Step by step tutorial to build a Knowledge Graph based RAG application with Colab notebook. In this tutorial, you will learn how to extract knowledge from a text corpus, build a Knowledge Graph, store the Knowledge Graph in TiDB Serverless, and search from the Knowledge Graph.
- Vector Search Notebook with SQLAlchemy: use SQLAlchemy to interact with TiDB Serverless: connect db, index&store data and then search vectors.
- Build RAG with Jina AI Embeddings: use Jina AI to generate embeddings for text data, store the embeddings in TiDB Vector Storage, and search for similar embeddings.
- Semantic Cache: build a semantic cache with Jina AI and TiDB Vector.
for more examples, see the examples directory.