From 1b8c2901aff39eded5b776fcae3b0ad0c9fddcd4 Mon Sep 17 00:00:00 2001 From: sykp241095 Date: Sun, 16 Jun 2024 23:02:47 +0800 Subject: [PATCH] readme: add more demos to readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 3d85430..4cd55f8 100644 --- a/README.md +++ b/README.md @@ -245,5 +245,7 @@ There are some examples to show how to use the tidb-vector-python to interact wi - [Chat with URL](./llamaindex-tidb-vector/README.md): use LlamaIndex to build an [RAG(Retrieval-Augmented Generation)](https://docs.llamaindex.ai/en/latest/getting_started/concepts/) application that can chat with a URL. - [GraphRAG](./examples/graphrag-demo/README.md): 20 lines code of using TiDB Serverless to build a Knowledge Graph based RAG application. - [GraphRAG Step by Step Tutorial](./examples/graphrag-step-by-step-tutorial/README.md): 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](https://colab.research.google.com/drive/1LuJn4mtKsjr3lHbzMa2RM-oroUvpy83y?usp=sharing): use [SQLAlchemy](https://www.sqlalchemy.org/) to interact with TiDB Serverless: connect db, index&store data and then search vectors. +- [Build RAG with Jina AI Embeddings](./examples/jina-ai-embeddings-demo/README.md): use Jina AI to generate embeddings for text data, store the embeddings in TiDB Vector Storage, and search for similar embeddings. for more examples, see the [examples](./examples) directory.