Learn how to overcome them using knowledge graphs and structured tools
Everyone loves text embedding models, and for good reason: They excel at encoding unstructured text, making it easier to discover semantically similar content. It’s no surprise that they form the backbone of most RAG applications, especially with the current emphasis on encoding and retrieving relevant information from documents and other textual resources. However, there are clear examples of questions one might ask wher...
Read it: The Limitations of Text Embeddings in RAG Applications