FlagEmbedding holds a whole curriculum for retrieval, embedding models, RAG, etc. This section is currently being actively updated, suggestions are very welcome. No matter you are new to NLP or a veteran, we hope you can find something helpful!
If you are new to embedding and retrieval, check out the 5 minute [quick start](./quick_start.ipynb)!
Although not included in the quick start, indexing is a very important part in practical cases. This module shows how to use popular libraries like Faiss and Milvus to do indexing.
To balance accuracy and efficiency tradeoff, many retrieval system use a more efficient retriever to quickly narrow down the candidates. Then use more accurate models do reranking for the final results.
RAG is one of the most popular approach to enchance the capabilities of LLMs by integrating information retrieval with them. In this module, we will cover the implementation, popular tools and libraries, and more advanced techniques.
