model selection cookbook with image updates

examples/partners/model_selection_guide/model_selection_guide.ipynb

@@ -61,7 +61,9 @@
     "\n",
     "### OpenAI Model Evolution \n",
     "\n",
-    "![OpenAI Model Evolution](./images/2.2_model_evolution.png)\n",
+
+    "![OpenAI Model Evolution](../../../images/2.2_model_evolution.png)\n",
+
     "\n",
     "### Key Characteristics\n",
     "\n",
@@ -79,7 +81,9 @@
     "\n",
     "## 3A. Use Case: Long-Context RAG for Legal Q&A\n",
     "\n",
-    "![Long-Context RAG for Legal Q&A](images/3A_rag_task_card.png)\n",
+
+    "![Long-Context RAG for Legal Q&A](../../../images/3A_rag_task_card.png)\n",
+
     "## 🗂️ TL;DR Matrix\n",
     "\n",
     "This table summarizes the core technology choices and their rationale for **this specific Long-Context Agentic RAG implementation**.\n",
@@ -133,7 +137,9 @@
    "id": "db9bad1b",
    "metadata": {},
    "source": [
-    "![Hierarchical Router](images/3A_rag_hierarchical_router.png)\n",
+
+    "![Hierarchical Router](../../../images/3A_rag_hierarchical_router.png)\n",
+
     "\n",
     "\n",
     "## Agentic RAG System: Model Usage\n",
@@ -1815,7 +1821,9 @@
     "================================================================================\n",
     "\n",
     "## 3B. Use Case: AI Co-Scientist for Pharma R&D\n",
-    "![AI Co-Scientist for Pharma R&D](images/3B_reasoning_task_card.png)\n",
+
+    "![AI Co-Scientist for Pharma R&D](../../../images/3B_reasoning_task_card.png)\n",
+
     "\n",
     "This section details how to build an AI system that functions as a \"co-scientist\" to accelerate experimental design in pharmaceutical R&D, focusing on optimizing a drug synthesis process under specific constraints.\n",
     "\n",
@@ -1855,7 +1863,9 @@
     "\n",
     "The system employs a multi-agent architecture that emulates a high-performing scientific team. Different AI components, acting in specialized roles (such as ideation, critique, and learning from outcomes), collaborate using various models and tools to execute the workflow.\n",
     "\n",
-    "![AI Co-Scientist Architecture](images/3B_coscientist_architecture.png)\n",
+
+    "![AI Co-Scientist Architecture](../../../images/3B_coscientist_architecture.png)\n",
+
     "\n",
     "### 2.1. **Scientist Input & Constraints:** \n",
     "The process starts with the scientist defining the goal, target compound, and constraints."
@@ -2463,7 +2473,9 @@
     "\n",
     "## 3C. Use Case: Insurance Claim Processing\n",
     "\n",
-    "![](./images/3C_insurance_task_card.png)\n",
+
+    "![](../../../images/3C_insurance_task_card.png)\n",
+
     "\n",
     "Many businesses are faced with the task of digitizing hand filled forms. In this section, we will demonstrate how OpenAI can be used to digitize and validate a hand filled insurance form. While this is a common problem for insurance, the same techniques can be applied to a variety of other industries and forms, for example tax forms, invoices, and more.\n",
     "\n",
@@ -2493,7 +2505,9 @@
     "\n",
     "The high level basic architecture of the solution is shown below.\n",
     "\n",
-    "![](./images/3C_insurance_architecture.png)\n",
+
+    "![](../../../images/3C_insurance_architecture.png)\n",
+
     "\n",
     "This task is complex and requires a wide variety of model capabilities, including vision, function calling, reasoning, and structured output. While `o3` is capable of doing all of these at once, we found during experimentation that `o4-mini` alone was not sufficient to achieve the necessary performance. Due to the higher relative costs of `o3`, we instead opted for a two-stage approach.\n",
     "\n",
@@ -2503,7 +2517,9 @@
     "\n",
     "To demonstrate concretely how this works, let's look at a sample image of an insurance form.\n",
     "\n",
-    "![](./images/3C_insurance_form.png)\n",
+
+    "![](../../../images/3C_insurance_form.png)\n",
+
     "\n",
     "While the form itself is fairly straightforward, there is missing data and ambiguous information that will be difficult for a traditional OCR system to fill out correctly. First, notice that the zip code and county have been omitted. Second, the email address of the user is ambiguous \\-- it could be `[email protected]` or `[email protected]`. In the following sections, we will walk through how a well-designed solution can handle these ambiguities and return the correct form results.\n",
     "\n",
@@ -3186,7 +3202,10 @@
     "\n",
     "## Adaptation Decision Tree\n",
     "\n",
-    "![Model Selection Decision Tree](images/3D_model_selection_flowchart.png)\n",
+
+    "![Model Selection Decision Tree](../../../images/3D_model_selection_flowchart.png)\n",
+
+
     "\n",
     "## Communicating Model Selection to Non-Technical Stakeholders\n",
     "\n",
@@ -3286,10 +3305,12 @@
     "\n",
     "## Contributors\n",
     "\n",
-    "- Kashyap Coimbatore Murali\n",
-    "- Nate Harada \n",
-    "- Sai Prashanth Soundararaj \n",
-    "- Shikhar Kwatra "
+
+    "- [Kashyap Coimbatore Murali](https://www.linkedin.com/in/kashyap-murali/)\n",
+    "- [Nate Harada](https://www.linkedin.com/in/nate-harada/) \n",
+    "- [Sai Prashanth Soundararaj](https://www.linkedin.com/in/saiprashanths/)\n",
+    "- [Shikhar Kwatra](https://www.linkedin.com/in/shikharkwatra/)"
+
    ]
   }
  ],