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Creation

1

Choose your Vector Database and corresponding RAG metrics

RAG metrics include the embedding model, Vector Space, Similarity Metric, and metadata values to be included
2

Connect your Data Sources and click submit

Once Complete the OCE will build all the infrastructure you will need to leverage this RAG pipeline within Onyx

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Usage

The RAG node will be responsible for Embedding incoming queries and performing simalirty search to retrieve relevant documenation. This can be passed to an LLM node for further processing/inference.
1

Choose your Knowledge Base

From the dropdown select the appropriate KB
2

Input Query

This can be paramaterized (for example if you want to pass in a query from your front end:)
  1. Denote Variable: < ?input_query? >
  2. Input Query will be embedded and semantic search will take place
  3. Relevant documents returned
3

Pass to LLM Node

Pipe doc information to LLM node (ensure you are reffering the RAG Node ID: < ?rag_node_id? >)
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Rag Implementation