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app.py
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@@ -7,11 +7,8 @@ It exposes the following tools:
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"""
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import os
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from dotenv import load_dotenv
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load_dotenv() # Load environment variables
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import gradio as gr
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import requests
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def search_knowledge_base_for_context(query: str) -> str:
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"""
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# π MCP Powered RAG and Research Topic π
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I present to you an MCP-powered RAG and Research Topic.
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The Tools are hosted and executed on **Modal Labs
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RAG Tool uses the **GroundX** storage by **eyelevel.ai** to fetch the knowledge base. The knowledge base is a document that contains information about the SU-35 aircraft, including its features, capabilities, and specifications. Please check [this PDF](https://airgroup2000.com/gallery/albums/userpics/32438/SU-35_TM_eng.pdf) to formulate queries on Sukhoi.
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The Research Tool is implemented using a
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<br>
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The Agents use **Nebius** provided LLM.
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"""
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import os
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import requests
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import gradio as gr
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def search_knowledge_base_for_context(query: str) -> str:
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"""
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# π MCP Powered RAG and Research Topic π
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I present to you an MCP-powered RAG and Research Topic.
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The Tools are hosted and executed on **Modal Labs** platform.
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RAG Tool uses the **GroundX** storage by **eyelevel.ai** to fetch the knowledge base. The knowledge base is a document that contains information about the SU-35 aircraft, including its features, capabilities, and specifications. Please check [this PDF](https://airgroup2000.com/gallery/albums/userpics/32438/SU-35_TM_eng.pdf) to formulate queries on Sukhoi.
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The Research Tool is implemented using a Multi-Agent Workflow using **LlamaIndex**.
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<br>
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The Agents use **Nebius** provided LLM.
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