Update notebooks with images.

Author: xhagrg

images/astro_langchain.png

@@ -6,7 +6,9 @@
    "source": [
     "# Astro Query  ReACT Agent\n",
     "\n",
-    "#### Using Langchain Agent Library"
+    "#### Using Langchain Agent Library\n",
+    "\n",
+    "![Astro langchain](../images/astro_langchain.png)"
    ]
   },
   {
@@ -757,7 +759,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.12 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -771,7 +773,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.1"
+   "version": "3.9.12"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "8987afb44532b2110e1a5e1b229dd281f8440b44477d285826a54acdd52d8797"
+   }
   }
  },
  "nbformat": 4,

images/cmr_langchain.png

@@ -8,7 +8,9 @@
     "\n",
     "#### Using Langchain Agent Library\n",
     "\n",
-    "This Notebook demonstrates how to use the Langchain Agent Library to query the CMR API. It uses the ReACT prompt pattern that language model leverages to chain multiple external tools (e.g. geocoding) together to geenrate a valid CMR query from natural language."
+    "This Notebook demonstrates how to use the Langchain Agent Library to query the CMR API. It uses the ReACT prompt pattern that language model leverages to chain multiple external tools (e.g. geocoding) together to geenrate a valid CMR query from natural language.\n",
+    "\n",
+    "![CMR langchain](../images/cmr_langchain.png)"
    ]
   },
   {