reorder eval docs (#11738)

cc @leo-gan

docs/docs/guides/evaluation/comparison/custom.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "5046d96f-d578-4d5b-9a7e-43b28cafe61d",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 2\n",
+    "title: Custom pairwise evaluator\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "657d2c8c-54b4-42a3-9f02-bdefa0ed6728",
    "metadata": {},
    "source": [
-                "# Custom Pairwise Evaluator\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/custom.ipynb)\n",
     "\n",
     "You can make your own pairwise string evaluators by inheriting from `PairwiseStringEvaluator` class and overwriting the `_evaluate_string_pairs` method (and the `_aevaluate_string_pairs` method if you want to use the evaluator asynchronously).\n",
@@ -273,7 +283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-            "version": "3.11.2"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb

@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 1\n",
+    "title: Pairwise embedding distance\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -7,7 +17,6 @@
     "tags": []
    },
    "source": [
-                "# Pairwise Embedding Distance \n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
     "\n",
     "One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
@@ -225,7 +234,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-            "version": "3.11.2"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/guides/evaluation/comparison/pairwise_string.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "dcfcf124-78fe-4d67-85a4-cfd3409a1ff6",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 0\n",
+    "title: Pairwise string comparison\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2da95378",
    "metadata": {},
    "source": [
-                "# Pairwise String Comparison\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
     "\n",
     "Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The `StringComparison` evaluators facilitate this so you can answer questions like:\n",
@@ -374,7 +384,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-            "version": "3.11.2"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,