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**Data type is binary
**This step ensures that the data chunks that are created in vector store have some overlap and hence less chance of hallucination
**Chunk size and chunk overlap are 2 variables to manage this
**Connect the model with google gemini model. You will need your own api key for this
**Make note of the embedding model also since the same embedding model has to be selected in Step 2
**Make note of the vector store name since it is same vector store you will have to use in Step 2
##Using Vector store nodes provided by n8n is the best way to get started to test out the workflow before you switch to more enterprise grade vector store nodes
**The name of vector store should match from Step 1, the embedding rule should match step 1
** Uses the system prompt (“You are a cricket expert… If info is missing, say 'Sorry I don't know'”). to prompt the model
** Has access to the memory (2.2) and the RAG tool (2.3).
** Generates the final response with Google Gemini, strictly limited to the retrieved IPL cricket rules data.
##Using simple memory store nodes provided by n8n is the best way to get started to test out the workflow before you switch to more enterprise grade vector store nodes