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# This is a simple standalone implementation showing rag pipeline using Nvidia AI Foundational models.
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# It uses a simple Streamlit UI and one file implementation of a minimalistic RAG pipeline.
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- ############################################
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- # Component #1 - Document Loader
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- ############################################
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-
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import streamlit as st
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import os
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from langchain_nvidia_ai_endpoints import ChatNVIDIA , NVIDIAEmbeddings
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# Component #2 - Embedding Model and LLM
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llm = ChatNVIDIA (model = "meta/llama3-70b-instruct" )
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- document_embedder = NVIDIAEmbeddings (model = "NV-Embed-QA" , model_type = "passage" )
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- #query_embedder = NVIDIAEmbeddings(model="NV-Embed-QA", model_type="query")
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+ document_embedder = NVIDIAEmbeddings (model = "nvidia/nv-embedqa-e5-v5" , model_type = "passage" )
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# Component #3 - Vector Database Store
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with st .sidebar :
You can’t perform that action at this time.
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