From 0412fe94d2e4b09f67c44ca0cb76c30667f7eacc Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:26:55 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 2c55b43..bf06f6a 100644 --- a/README.md +++ b/README.md @@ -61,3 +61,16 @@ The operator load the documentation, then split incoming the text and return chu String data with the text. + + +# More Resources + +- [Experiment with 5 Chunking Strategies via LangChain for LLM - Zilliz blog](https://zilliz.com/blog/experimenting-with-different-chunking-strategies-via-langchain): Explore the complexities of text chunking in retrieval augmented generation applications and learn how different chunking strategies impact the same piece of data. +- [Massive Text Embedding Benchmark (MTEB)](https://zilliz.com/glossary/massive-text-embedding-benchmark-(mteb)): A standardized way to evaluate text embedding models across a range of tasks and languages, leading to better text embedding models for your app +- [ChatGPT retrieval plugin with Zilliz and Milvus - Zilliz blog](https://zilliz.com/blog/chatgpt-retrieval-plugin-zilliz-milvus): Milvus and Zilliz are one of the preferred vector databases to store these embeddings that can be accessed with the ChatGPT retrieval plugin. +- [Tutorial: Diving into Text Embedding Models | Zilliz Webinar](https://zilliz.com/event/tutorial-text-embedding-models): Register for a free webinar diving into text embedding models in a presentation and tutorial +- [Tutorial: Diving into Text Embedding Models | Zilliz Webinar](https://zilliz.com/event/tutorial-text-embedding-models/success): Register for a free webinar diving into text embedding models in a presentation and tutorial +- [An LLM Powered Text to Image Prompt Generation with Milvus - Zilliz blog](https://zilliz.com/blog/llm-powered-text-to-image-prompt-generation-with-milvus): An interesting LLM project powered by the Milvus vector database for generating more efficient text-to-image prompts. +- [A Beginner's Guide to Website Chunking and Embedding for Your RAG Applications - Zilliz blog](https://zilliz.com/learn/beginner-guide-to-website-chunking-and-embedding-for-your-genai-applications): This post explains how to extract content from a website and use it as context for LLMs in a RAG application. However, before doing so, we need to understand website fundamentals. +- [Text as Data, From Anywhere to Anywhere - Zilliz blog](https://zilliz.com/blog/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. +- [From Text to Image: Fundamentals of CLIP - Zilliz blog](https://zilliz.com/blog/fundamentals-of-clip): Search algorithms rely on semantic similarity to retrieve the most relevant results. With the CLIP model, the semantics of texts and images can be connected in a high-dimensional vector space. Read this simple introduction to see how CLIP can help you build a powerful text-to-image service. \ No newline at end of file