Retrieval-Augmented Generation (RAG) is a technique that improves AI answers by doing a “similarity search” on company data. In order to perform a similarity search, the source data needs to be converted into embeddings (vectors) first. Peliqan provides a RAG Manager app to create embeddings from source data (e.g. Google Drive files, Notion pages, Github files etc.) and to perform RAG searches. These RAG searches are then used in e.g. an AI Chatbot or an MCP Server.
The RAG Manager app in Peliqan allows you to configure the automatic scheduled creation of embeddings (vectors) for RAG. The Peliqan data warehouse is used as vector store (using pgvector).
Install the app from the “Rag Manager” tile:
In the app, add one or more source tables under Settings:
Go to Processing and use the Process button to create embeddings.
Add a schedule to the app with e.g. a daily interval, to automatically create embeddings for new and updated rows in the source tables.
RAG can be used on text sources. Here are examples of Peliqan connectors that are typically used as a source for RAG embeddings:
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For some connectors, you need to enable a custom pipeline script to fetch the actual content for each file or item.
E.g. for Notion you need a custom pipeline to fetch the contents of each page. For Google Drive and Github you need a custom pipeline to fetch the text content of files.
Click on the above connector links for more information.
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