Using large language models to generate search query answers

A non-transitory computer-readable storage medium storing executable computer program instructions, the computer program instructions when executed by one or more processors of a system causing the system to:

• receive a user query associated with a plurality of content items stored in a content repository maintained by a content management system;
• retrieve a set of text chunks corresponding to the plurality of content items, wherein each text chunk has an associated text embedding;
• identify a subset of relevant text chunks from the set of text chunks based on the associated text embeddings, wherein identifying the subset of relevant text chunks comprises:
• generating an embedding similarity score between a query embedding corresponding to the user query and the text embedding associated with each text chunk in the set of text chunks;
• and
• selecting at least one relevant text chunk from the set of text chunks based on the embedding similarity score for the relevant text chunk being greater than a similarity threshold;
• send at least a portion of the subset of relevant text chunks to a large language model (LLM) to cause the LLM to generate an answer description for the user query;
• and
• display the answer description for the user query at a user interface.

A data processing system, comprising:

• one or more processors;
• and
• one or more non-transitory computer-readable storage media storing executable computer program instructions, the computer program instructions when executed by the one or more processors cause the data processing system to:
• receive a user query associated with a plurality of content items stored in a content repository maintained by a content management system;
• retrieve a set of text chunks corresponding to the plurality of content items, wherein each text chunk has an associated text embedding;
• identify a subset of relevant text chunks from the set of text chunks based on the associated text embeddings, wherein identifying the subset of relevant text chunks comprises:
• generating an embedding similarity score between a query embedding corresponding to the user query and the text embedding associated with each text chunk in the set of text chunks;
• and
• selecting at least one relevant text chunk from the set of text chunks based on the embedding similarity score for the relevant text chunk being greater than a similarity threshold;
• send at least a portion of the subset of relevant text chunks to a large language model (LLM) to cause the LLM to generate an answer description for the user query;
• and
• display the answer description for the user query at a user interface.