Retriever

A retriever is a crucial part of retrieval-augmented generation (RAG) systems. It converts queries and documents into vector representations to find the most relevant information. This process involves embedding documents and queries into a high-dimensional vector space, where the distance between vectors indicates relevance . The retriever's role is to capture complex semantic relationships between the query and documents, ensuring accurate retrieval results.

Understanding retrievers is important because they enhance the accuracy and contextual relevance of AI responses by linking generative models with external knowledge sources. This integration allows for more precise and authoritative answers, especially in tasks requiring specific factual knowledge.

To use a retriever, you first need to convert the input query into a vector representation. Then, this vector is matched against a vector database containing document embeddings. The most similar documents are retrieved and passed to a large language model for response generation. The number of documents retrieved can be adjusted based on the task's requirements.

Consider a chatbot designed to answer employee benefits questions. When an employee asks about their annual leave policy, the retriever fetches relevant policy documents and the employee's past leave records. These documents are then used by a large language model to generate a precise and personalized response regarding the employee's annual leave entitlement.