fix image paths

Author: lara-openai

examples/mcp/databricks_mcp_cookbook.ipynb

@@ -19,7 +19,7 @@
     "\n",
     "In supply-chain operations, an agent can resolve questions that directly affect service levels and revenue: Do we have the inventory and capacity to satisfy current demand? Where will manufacturing delays occur, and how will those delays propagate downstream? Which workflow adjustments will minimise disruption? \n",
     "\n",
-    "![Databricks MCP UI](../../../images/databricks_mcp_ui.png)\n",
+    "![Databricks MCP UI](../../images/databricks_mcp_ui.png)\n",
     "\n",
     "This cookbook outlines the process for building a supply-chain copilot with the OpenAI Agent SDK and Databricks Managed MCP. MCP enables the agent to tap structured and unstructured enterprise data, such as inventory, sales, supplier feeds, local events, and more, for real-time visibility, early detection of material shortages, and proactive recommendations. An orchestration layer underpins the system, unifying:\n",
     "- Queries against structured inventory, demand, and supplier data\n",
@@ -48,7 +48,7 @@
     "\n",
     "The result is a single, near-real-time conversational interface that delivers fine-grained forecasts, dynamic inventory recommendations, and data-driven decisions across the supply chain. The architecture yields an agent layer that harnesses your existing enterprise data (structured and unstructured), classical ML models, and graph-analytics capabilities.\n",
     "\n",
-    "![Databricks MCP Architecture](../../../images/databricks_mcp_architecture.png)"
+    "![Databricks MCP Architecture](../../images/databricks_mcp_architecture.png)"
    ]
   },
   {
@@ -137,7 +137,7 @@
     "The sample data mirrors a realistic pharma network: three plants manufacture 30 products, ship them to five distribution centres, and each distribution center serves 30-60 wholesalers. The repo ships time-series demand for every product-wholesaler pair, a distribution center-to-wholesaler mapping, a plant-to-distribution center cost matrix, plant output caps, and an e-mail archive flagging shipment delays.\n",
     "\n",
     "\n",
-    "![Pharma Network](../../../images/pharma_network.png)\n",
+    "![Pharma Network](../../images/pharma_network.png)\n",
     "\n",
     "The notebooks turn these raw feeds into governed, callable artefacts:\n",
     "- Demand forecasting & aggregation ([notebook 2](https://github.com/lararachidi/agent-supply-chain/blob/main/02_Fine_Grained_Demand_Forecasting.py)): Generates one-week-ahead SKU demand for every wholesaler and distribution center with a Holt-Winters seasonal model (or any preferred time-series approach). It leverages Spark’s parallelisation for large-scale forecasting tasks by using Pandas UDFs (taking your single node data science code and distributing it across multiple nodes). Forecasts are then rolled up to DC-level totals for each product. The output is a table  product_demand_forecasted with aggregate forecasts at the distribution center level.\n",
@@ -483,7 +483,7 @@
     "\n",
     "In the OpenAI API dashboard you can open the Traces view to see every function the agent invoked. In the example below, the agent first calls raw_from_product to fetch the material linked to a specific product, and then calls revenue_risk to estimate the revenue impact of a shortage.\n",
     "\n",
-    "![Tracing Dashboard](../../../tracing_dashboard_databricks_mcp.png)"
+    "![Tracing Dashboard](../../images/tracing_dashboard_databricks_mcp.png)"
    ]
   },
   {
@@ -550,4 +550,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}