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In the context of Profound AI's Knowledge Documents, semantic search enables the AI agents to sift through extensive documentation and find the most relevant sections that answer or relate to the user's query. The approach used is typically referred to as RAG, or Retrieval-Augmented Generation. This approach ensures more accurate, context-aware responses and a better understanding of user needs, compared to traditional keyword-based search methods.
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Finally, test searching the knowledge documents by typing a query in the Agent Preview section of the IDE.
RAG Options
A model can optionally be configured to use specific RAG, or Retrieval-Augmented Generation, options. See Model Configuration for additional details. When no RAG configuration is provided, default settings are used.
A RAG configuration consists of the following:
Provider - this points to the service or library of code that provides RAG capabilities. Current options include “openai” and “llamaindex”.
Embedding Model - this specifies the model that translates text into a representation of meaning in the form of embeddings, which are also knows as vectors. If not specified, a default embedding model from OpenAI is used.
Vector Database - this specifies the database that holds indexed documents. The documents are broken up into chunks, assigned embeddings/vectors using the Embedding Model, and then placed into a database.
The default vector database for the “openai” provider is the built-in cloud database used by OpenAI.
For “llamaindex”, the default is to use local .json files, which are always loaded in memory. To scale beyond this, an external vector database should be provisioned.