Rag and . Graph ragretrieval augmented generation rag or vector rag can enhance the user query to an . Llm the prompt by searching business data and adding relevant information to the user query. . can be converted to a vector and used to . Search business data in a database. The user query enhanced with results from the vector . Search in the database the enhanced prompt can be input to the llm which can . Then output useful results in human language even though it was not trained on that .
Data. Graph rag adds a new dimension to enhancing the prompt argentina phone number data by adding information on . How entities in a user query are connected to other data entities a graph. Graphs . Capture information on how data is related, which is difficult to capture with other data . Models. Some examples of the type of information a graph can capture are:• connections in . A social media network • flow of money in a financial network• paths in a . Supply chainand so on.Graph rag in actionconsider this example: a streaming company called moviestream wants .
To make movie recommendations to increase customer usage of their service. An llm can find . Movies of a specific genre, or with a specific plot, or from a specific era. . But for adriana, a target customer, the llm does not have adriana’s prior movie-watching history, . Or adriana’s social network in the context of movie watching: whom has she watched movies . With, what types of movies has she watched with the same set of friends, what . Types of movies has she watched with her family, and so on.
A user query in human language
-
- Posts: 1
- Joined: Thu Dec 12, 2024 6:35 am