Deep Dive: Keyword Search
Conventional keyword search matches user query words to document words using an inverted index data structure for efficient matching and ranking by relevancy.
Building RAG: All things retrieval
Vector searches have proven to be useful for handling free-text queries, as opposed to the traditional keyword-based search. However, developing a vector search based on word embeddings from a pre-trained model has limitations when it comes to adapting to custom domains. While keyword search can adapt to new domains they are inherently unsuitable for free-text query. How can we combine both these and implement an hybrid search?
Revolutionizing Question-and-Answer Systems
LLMs revolutionize question-and-answer systems with exceptional language understanding and creative writing skills. Lossy compression during training may make retrieving information challenging. Leveraging LLMs’ language expertise transforms building question-and-answer systems into reading comprehension using their ability to comprehend text, forming the basis for RAG systems that shift question answering to efficient knowledge base searches.