How does the retrieval phase of RAG ensure that the relevant text is fetched based on user queries?

The retrieval phase of RAG ensures relevant text is fetched by transforming the user query into a vector embedding and then retrieving documents from the vector store based on the smallest distance between the query vector and stored vectors. This process allows the model to receive contextually relevant information to generate accurate answers.

During the retrieval phase, the user query is first converted into a vector embedding. The system then searches the vector store for documents that are closest to this query vector, effectively identifying the most relevant text. This retrieved text is then combined with the original user prompt, providing the model with the necessary context to formulate a precise response.